53
1 Eddy covariance measurement of ammonia fluxes: 1 comparison of high frequency correction 2 methodologies 3 R. M. FERRARA a,1 , B. LOUBET b , P. DI TOMMASI c , T. BERTOLINI c , V. MAGLIULO c , P. CELLIER b , 4 W. EUGSTER e , G. RANA d 5 a Agricultural Research Council Centre for Research for agrobiology and pedology (CRA 6 ABP), Piazza Massimo D’Azeglio,30 50121 Firenze, Italy, phone +39 055 2491245, fax 7 +39 055 241485, [email protected] 8 b INRA, UMR 1091 Environnement et Grandes Cultures, F- 78850 Thiverval-Grignon, 9 France 10 c CNR ISAFoM, Via Patacca 85, Ercolano (Na), Italy 11 d Agricultural Research Council - Research Unit for cropping systems in dry environments, 12 via C. Ulpiani, 5 70125 Bari, Italy 13 e ETH Zurich, Institute of Agricultural Sciences, Universitätsstr. 2, 8092 Zurich, Switzerland 14 15 Key words: transfer function, closed-path analyser, ogive, inductance, QC-TILDAS 16 17 Summary 18 Several methods are available for evaluating and correcting the underestimation of trace gas 19 fluxes measured by eddy covariance (EC) method and due to the design and the setup of the 20 employed equipment. Different frequency correction factors (CF) to apply to the measured 21 EC ammonia (NH 3 ) fluxes have been applied using the following approaches: (i) the spectral 22 theoretical transfer function (CF Theor ) with and without phase shift; (ii) the in-situ ogive 23 method (CF O ); and (iii) the inductance correction method (CF L ). The NH 3 fluxes were 24 measured in an experimental field located in south Italy, above a sorghum crop submitted to 25 Mediterranean semi-arid climate and fertilized with urea. A fast analyser based on Tunable 26 1 Corresponding author, present address: Agricultural Research Council - Research Unit for cropping systems in dry environments, via C. Ulpiani, 5 70125 Bari, Italy. Phone :+390805475007 Fax :+390805475023

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Page 1: Eddy covariance measurement of ammonia fluxes: comparison of

1

Eddy covariance measurement of ammonia fluxes 1

comparison of high frequency correction 2

methodologies 3

R M FERRARAa1

B LOUBETb P DI TOMMASI

c T BERTOLINI

c V MAGLIULO

c P CELLIER

b 4

W EUGSTERe G RANA

d 5

a Agricultural Research Council ndash Centre for Research for agrobiology and pedology (CRA ndash 6

ABP) Piazza Massimo DrsquoAzeglio30 ndash 50121 Firenze Italy phone +39 055 2491245 fax 7

+39 055 241485 rossanaferraraentecrait 8

b INRA UMR 1091 Environnement et Grandes Cultures F- 78850 Thiverval-Grignon 9

France 10

c CNR ndash ISAFoM Via Patacca 85 Ercolano (Na) Italy 11

d Agricultural Research Council - Research Unit for cropping systems in dry environments 12

via C Ulpiani 5 70125 Bari Italy 13

e ETH Zurich Institute of Agricultural Sciences Universitaumltsstr 2 8092 Zurich Switzerland

14

15

Key words transfer function closed-path analyser ogive inductance QC-TILDAS 16

17

Summary 18

Several methods are available for evaluating and correcting the underestimation of trace gas 19

fluxes measured by eddy covariance (EC) method and due to the design and the setup of the 20

employed equipment Different frequency correction factors (CF) to apply to the measured 21

EC ammonia (NH3) fluxes have been applied using the following approaches (i) the spectral 22

theoretical transfer function (CFTheor) with and without phase shift (ii) the in-situ ogive 23

method (CFO) and (iii) the inductance correction method (CFL) The NH3 fluxes were 24

measured in an experimental field located in south Italy above a sorghum crop submitted to 25

Mediterranean semi-arid climate and fertilized with urea A fast analyser based on Tunable 26

1 Corresponding author present address Agricultural Research Council - Research Unit for cropping systems in

dry environments via C Ulpiani 5 70125 Bari Italy Phone +390805475007 Fax +390805475023

2

Infrared Laser Differential Absorption Spectroscopy (TILDAS) coupled with a quantum 27

cascade (QC) laser was used to measure NH3 concentration at high frequency The results 28

showed that correction of the values of NH3 EC fluxes over natural surfaces are necessary for 29

taking into account the dumping of high frequency contribution due to the coupling of 30

ammonia fast analyser by QC-TILDAS and sonic anemometer In particular the calculated 31

flux losses ranged between -23 (inductance method with the CF threshold selected to 25) 32

and -43 (experimental-ogive method) while the two theoretical transfer function 33

approaches gave comparable loss estimates ie -30 and -31 for the methods with time lag 34

and phase shift respectively 35

36

3

INTRODUCTION 37

Ammonia (NH3) is naturally present in the troposphere with ambient concentrations varying 38

from 5 ppt in remote regions (Sutton et al 2001) to 500 ppb near sources (Krupa 2003) 39

Actually NH3 plays a key role in the environment as the main base neutralizing atmospheric 40

acids and as a precursor of particulate matter which causes human health hazards visibility 41

problems and modifies the radiative forcing of the climate system (Asman et al 1998 IPCC 42

2007) Due to its negative environmental impacts (Galloway et al 2003 Sutton et al 2009) 43

NH3 has become the object of many investigations by the scientific community in 1999 the 44

UN Convention on Long-range Transboundary Air Pollution to Abate Acidification 45

Eutrophication and Ground-level Ozone was extended by the Gothenburg Protocol to include 46

NH3 (httpwwwuneceorgenvlrtap) and its emissions started to be more regulated and 47

monitored 48

Ammonia is mainly emitted by anthropogenic sources It is estimated that the majority of NH3 49

emissions arises from agricultural activities in central Europe agriculture is responsible for 50

about 90 of NH3 release (Erisman et al 2008) However large uncertainty exists on NH3 51

dynamics in the soil-plant-atmosphere continuum due to the scarcity of reliable direct flux 52

measurements This can mainly be attributed to difficulties in measuring fluxes of such a 53

reactive compound (Harper 2005) In particular the first issue is the presence of NH3 in 54

atmosphere in gaseous particulate (eg (NH4)2SO4 and NH4NO3) and liquid (ie NH4OH in 55

cloud and fog droplets) forms where the partition of these three phases is highly variable 56

(Fehsenfeld 1995) Another difficulty is the tendency of NH3 to form strong hydrogen bonds 57

with water and thus to be readily adsorbed on any material exposed to ambient air producing 58

memory effects in the measurements These troubles ndash in addition to the contamination due to 59

NH3 produced by people (Sutton et al 2000) ndash have delayed progress in the development of 60

NH3 measurement techniques with respect to other trace gases 61

Considering its reactive nature and the dependence of NH3 exchanges by environmental 62

factors the micrometeorological methods are the preferred ones due to their non intrusive 63

character (Denmead 1983 Kaimal and Finnigan 1994) The aerodynamic gradient method 64

coupled to wet chemical analysis is the most widely used (Wyers et al 1993 Neftel et al 65

1998 Erisman et al 2005 Kruit et al 2007 Milford et al 2009 Spirig et al 2010) but it is 66

very time consuming Moreover the lack of fast sensors for NH3 has delayed the application 67

of the eddy covariance (EC) method which is considered the most direct and least error-prone 68

approach (Denmead 2008) The evolution of techniques for NH3 monitoring starts with the 69

4

passive filters and denuders developed by Ferm (1979) nowadays the most widely used 70

method for its simplicity and inexpensiveness However the drawbacks of this approach are 71

the relatively low time resolution (minute to hours) and the laboratory intensive labour 72

required for preparing them and for manual off-line sample analysis Coupled to the chemical 73

analytical methods progress in optical methods have been achieved with the development of 74

spectroscopic devices characterized by high time resolution and sensitivity to NH3 but which 75

are still expensive Von Bobrutzki et al (2010) recently provided inter-comparisons for NH3 76

measurement techniques in field highlighting that many challenges still have to be faced for 77

measuring NH3 accurately Nevertheless Tunable Infrared Laser Differential Absorption 78

Spectroscopy (TILDAS) using a quantum cascade (QC) laser are now available for 79

monitoring NH3 concentration with a sufficient sensitivity and robustness to be employed in 80

the field However the application of the EC method for monitoring NH3 is considered a 81

challenge by the scientific community as demonstrated by the scarce literature available on 82

this topic (Shaw et al 1998 Famulari et al 2005 Whitehead et al 2008 Brouder et al 83

2009 Sintermann et al 2011) in which are highlighted the difficulties to its routine 84

applicability In particular the employment of a setup involving a fast closed-path analyser 85

could introduce considerable damping of high frequency fluctuations and consequent 86

underestimation of the fluxes that have to be corrected for (eg Massman 2000) 87

Several methods are available for evaluating and correcting these underestimations when EC 88

equipment is used for trace gas fluxes (Massman and Clement 2004 for CO2 for example) In 89

particular Whitehead et al (2008) evidenced that the correction for EC fluxes of NH3 using 90

TILDAS is fundamental to accurately estimate the real fluxes at different time scales 91

However up to now no attempt has been made to evaluate using different methodologies 92

the correction of the underestimation of EC ammonia fluxes when a QC-TILDAS is used for 93

measuring NH3 fluctuations in the cropped field Furthermore no EC data of NH3 turbulent 94

fluxes are available for crop grown in arid environments 95

In this study we report EC measurements of NH3 fluxes performed employing a fast closed-96

path analyser for NH3 concentration the QC-TILDAS-76 SN002-U developed by Aerodyne 97

Research Inc (ARI Billerica MA USA) (Zahniser et al 2005) under custom specifications 98

The trial was carried out over a field in an intensive agricultural area of south Italy in a semi-99

arid to arid Mediterranean climate The plot was fertilized with urea pellets to stimulate NH3 100

emission and to test the device over a wide range of concentrations The aim was to assess the 101

performance of the QC-TILDAS used for the first time in an EC system for measuring NH3 102

fluxes over an irrigated cropland in Mediterranean environment The challenge was to verify 103

5

the adequacy of the system in terms of setup and quality of the data In order to reach a robust 104

assessment of the needed correction three possible methods for correcting the flux losses due 105

to the design and the setup of the employed equipment were considered in this analysis In 106

particular we compare different frequency correction factors (CF) to apply to the measured 107

EC NH3 fluxes obtained by the following approaches (i) the spectral theoretical transfer 108

function (CFTheor) (ii) the in-situ ogive method (CFO) (Massman and Clement 2004) and 109

(iii) the inductance correction model (CFL) developed by Eugster and Senn (1995) 110

1 MATERIAL AND METHOD 111

11 Field site 112

The experiment was carried out from 17 to 30 July 2008 (DOYs 199 to 211) in Rutigliano 113

(41degN 17deg54 E 122 m asl) near Bari in southern Italy All experimental activities were 114

performed in a 2 hectares field dimensions were 200 m x 100 m The soil type is classified as 115

loamy clay (Clay 41 Silt 44 Sand 15 ) and the terrain is flat The climate is semi-arid 116

Mediterranean with hot and dry summer and short and temperate winter The mean annual 117

temperature is 157 degC with a mean annual precipitation of 600 mm the main wind direction 118

is north during the summer time In this study most of the analysis will focus on the period 119

22-29 July 2008 (DOYs 204-211) where the NH3 fluxes were the largest 120

12 Crop and urea spreading 121

During the experimental campaign the field was cultivated with Sorghum vulgare (cv Hay 122

Day) sowed on 10th

June 2008 and irrigated by sprinkler irrigation A commercial urea 123

product (46 N content) in granular form was applied The total N supply was around 240 124

kg N ha-1

divided into three applications 30 90 and 120 kg N ha-1

on 1st 16

th and 22

nd July 125

2008 respectively The first application was carried out at crop emergence Before the second 126

application the field was irrigated with 79 mm water while further irrigation (93 mm) was 127

applied before and after the third application The amount of urea was slightly higher than 128

current agronomic practices in the area since the dry conditions (high temperature no rain 129

and no irrigation applied) between the second and the last urea spreading inhibited NH3 130

volatilization so that no fluxes were detected The last urea application combined with 131

irrigation was necessary in order to increase the probability to detect NH3 fluxes 132

6

13 NH3 fluxes by the eddy covariance method 133

The fluxes of NH3 were measured with the eddy covariance method (eg Kaimal amp Finnigan 134

1994 Lee et al 2004 Mahrt 2010) The EC method is based on estimating the transport of a 135

scalar by turbulent motions of upward and downward moving air contained in the atmosphere 136

The vertical flux (F) of scalar at the measurement height is given by 137

wwF

(1) 138

where w is the component of the wind speed normal to the surface and β is the scalar 139

concentration and the usual Reynolds decomposition is used where prime denotes fluctuating 140

quantities w is the covariance between and w With the usual assumption of steady state 141

horizontally homogeneous flow there is no vertical flux of dry air ie 0wcd where cd is 142

the dry air concentration Since the dry air concentration fluctuates due to heat and water 143

vapour fluxes w is not necessarily equal to zero The Webb Pearmann Leuning (WPL) 144

correction (Webb et al 1980 Leuning 2007) gives the expression of w The impact of this 145

term depends on the concentration of the gas and the typical magnitude of the flux (Denmead 146

1983 Liebethal and Foken 2004) for trace gas with small concentration and large fluxes 147

such as NH3 over a source these corrections are negligible 148

The turbulent flux w as a time-averaged quantity can also be viewed as a frequency 149

averaged quantity ie the integral of the co-spectral density (Cowβ) (Kaimal and Finnigan 150

1994 Aubinet et al 2000 Massman 2000) 151

0

)( dffCow w

(2)

152

To catch all relevant turbulent fluctuations β and w should be measured with a sampling 153

frequency much larger than the frequency of the eddies carrying the energy of the signal (fx) 154

(Horst 1997 Massman 2000) Usually a sampling frequency of 10 Hz is considered 155

sufficient above a vegetated surface However flux loss is inevitable with any EC system 156

especially employing closed-path analysers to which the air sample is transported into the 157

measuring cell by means of a tube The mixing effects in the tube and additional possible 158

chemical or micro-physical processes occurring in the system produce low-pass filtering 159

effects on the measured covariances (Ibrom et al 2007) Nowadays a variety of methods are 160

available to correct these underestimated measured fluxes by means of frequency response 161

correction factors (eg Ammann et al 2006 Shimizu 2007 Aubinet et al 2000 Massman 162

2000 Eugster and Senn 1995) However in this work since the adopted closed-path analyser 163

7

for NH3 concentration measurements is relatively new and it is the first time it is being used 164

under semi-arid climates we compare a variety of methods to correct the NH3 fluxes 165

measured over an agricultural plot 166

14 NH3 concentration measurements 167

141 The QC-TILDAS analyser 168

1411 The instrument 169

The QC-TILDAS employed during this research is the compact QC-TILDAS-76 SN002-U 170

developed by Aerodyne Research Inc (ARI Billerica MA USA) (Zahniser et al 2005) 171

This spectrometer combines QC laser designed for pulsed operation at near room temperature 172

(Alpes Lasers Neuchacirctel Switzerland) an optical system together with a computer-controlled 173

system that incorporates the electronics for driving the QC laser along with signal generation 174

and a data logger Molecular transition selection for given laser is achieved by temperature 175

tuning typically within a small range around 40 ordmC using a two-stage Peltier element The 176

laser frequency is swept across the full molecular transition at 96734 cm-1

which means that 177

the scan is done over a narrow range of wave numbers chosen to contain an absorption feature 178

of the trace gas of interest The optical system collects light from the QC laser and directs it 179

through an astigmatic Herriott type multi-pass absorption cell (76 m effective path length) 180

onto a TE-cooled photovoltaic detector (Vigo Systems) A detailed description of the device 181

can be found in Zahniser et al (2005) 182

1412 The inlet design and the sampling 183

Ellis et al (2010) report the characterization of the QC-TILDAS by laboratory tests In 184

particular they reported a detailed analysis on the performance of the QC-TILDAS in 185

function of the inlet design To achieve accurate atmospheric measurements of NH3 the 186

sampling setup has to reduce gas interactions with surfaces then it is crucial the configuration 187

of the devicersquos inlet through which the air is drawn into the optical cell The inlet used is a 188

short glass tube containing a critical orifice needed to keep the air flow rate constant and to 189

reduce the pressure in the optical cell to approximately 540 kPa a compromise between 190

minimising line broadening and adsorption effects maximising time response and achieving 191

good sensitivity (Warland et al 2001) The setup of the inlet is done to remove particles in 192

the air sample relying on inertia to remove more than 50 of particles larger than 300 nm in 193

8

particular the flow is split into two branches 90 of the flow makes a sharp turn and is 194

pulled into the optical cell by a pump (VARIAN TriScroll 600 Series) the other 10 is 195

pulled by a second air pump (VARIAN mod SH110) This inlet design is optimized to 196

eliminate the need of filters and any interference they may cause The operation of the 197

spectrometer is fully automated and computer-controlled through a Windows-based 198

proprietary software (TDL Wintel) developed at ARI Concentrations are real-time 199

determined from the light absorption spectra through a non-linear least-squares fitting 200

algorithm (Levenberg-Marquardt) that uses spectral parameters from the HITRAN (high-201

resolution transmission) molecular absorption database (Rothman et al 2003) The pressure 202

and temperature in the multi-pass cell are continuously monitored to supply information 203

needed for spectral fitting The software calculates absolute concentrations so no calibration 204

was performed in the field However laboratory tests demonstrate the need to carry out a 205

post-proceeding calibration in order to overcome the failure of the analyzer to reproduce the 206

values of the ammonia standard Moreover we would justify the calibration factor found for 207

the QC-TILDAS using ethilene standard which has absorbing frequency close to ammonia 208

one but it is not affected by stickiness Part of the problem with absolute spectroscopic values 209

for NH3 in our instrument was due to using pulsed-QCLs rather than CW-QCLs with the 210

laser linewidth considerably greater than the molecular linewidth the spectral analysis 211

required much effort to get absolute values Thus a post-proceeding calibration was done and 212

zeroing was routinely carried out in order to optimize the calculated spectra following 213

Whitehead et al (2008) 214

1413 The response time 215

The decreasing of the mixing ratio of the ammonia was well represented by two exponential 216

decay functions which in Ellis et al (2010) is written as 217

2

02

1

010 expexp

ttA

ttAyy (3) 218

where A1 and A2 are constants and their sum is equal to the stable mixing ratio of NH3 prior to 219

the calibration flow being switched off y0 is equal to the NH3 mixing ratio reached at the end 220

of the decay t is time and t0 is the time at the start of the fit τ1 and τ2 are time decay 221

constants the first of which is fast and corresponds to the gas exchange time of the system 222

(04 s in our case) and the second being much slower and corresponding to interactions of 223

NH3 with surfaces on the interior surfaces of the inlet sampling lines and absorption cell (15 224

9

s) In this case the response time was checked in laboratory and it is controlled by pumping 225

speed and by adsorptiondesorption on the walls In particular it is affected by the flow rate 226

the length and the heating of the sampling tubes Therefore acting on these parameters would 227

allow optimizing the instrument for measuring the fluxes by EC technique 228

Moreover for a mixing ratio of 350 ppb the amount of noise (1) in a 10 Hz measurement 229

obtained by Ellis et al (2010) by an Allan variance analysis was 315 ppb (22 microg NH3 m-3

) 230

Based on statistics obtained for sensible heat CO2 and temperature w w was estimated 231

to be around 023 plusmn 013 (mean plusmn SE) and taking ~ 2 microg NH3 m-3

and w ~ 125 u would 232

mean that the flux detection limit of the setup would be around 25 u (in microg NH3 m-2

s-1

) 233

hence typically around 750 ng NH3 m-2

s-1

However most of the turbulent signal is 234

transported by eddies with a frequency around 02 Hz (evaluated as the co-spectral peak 235

frequency for air temperature (Ta) CO2 and water vapour (H2O)) At this frequency the Allan 236

variance of the QC-TILDAS found by Ellis et al (2010) is around 02 microg NH3 m-3

and hence 237

the detection limit should be on the order of 025 u (typically 75 ng NH3 m-2

s-1

) As reported 238

by Ellis et al (2010) there is an increase in the relative importance of NH3 surface 239

interactions at mixing ratios lower than 100 ppb which further increased when sampling 240

humid air as opposed to dry nitrogen Then the heating in the sampling line to prevent 241

condensation is crucial to perform accurate NH3 measurements There was no line heating 242

used in our study since we assumed that this was not needed since the air temperature 243

encountered in the field was very high (up to 33degC) On the other hand under such 244

conditions it was required the use of an air conditioner for the box containing the QC-245

TILDAS to limit instrumental drift induced by changes in the optical alignment as well as in 246

the laser power beam width and temperature 247

142 The ALPHA badges and ROSAA system 248

The NH3 concentration measured with the QC-TILDAS were compared against passive 249

diffusion samples (ALPHA badges Tang et al 2001) These ALPHA badges were 250

positioned at the same height as the QC-TILDAS inlet and changed twice a day to get daily 251

and nightly concentrations Three replicates were used to evaluate the uncertainty in the 252

measurements The background concentration bgd was estimated with three sets of badges put 253

at three locations at more than 100 100 and 300 m away from the field and changed weekly 254

The badges were prepared with filters coated with citric acid The filters were extracted in 3 255

ml double deionised water and analysed with a FLORRIA analyser (Mechatronics NL) In 256

10

addition during the trial NH3 concentration was monitored by means of the new device 257

ROSAA developed at INRA (Loubet et al 2010 2011) It relies on the coupling of NH3 258

capturing by means of an acid stripping solution (wet-effluent denuders) to convert NH3 in 259

NH4+ and the analysis of the NH4

+ concentration by means of a detector based on a semi-260

permeable membrane and a unit to perform temperature controlled conductivity 261

measurements (ECN Netherlands) In the ROSAA system the three denuders were sampling 262

at three heights (at z = 05 07 and 14 m) above ground and the averaged concentration was 263

measure every 30 minutes 264

15 The eddy covariance system description of the equipment 265

The EC system was equipped with the QC-TILDAS and a three axis ultrasonic anemometer 266

(Gill R2 Gill Instruments Ltd UK) The analogue signals from the QC-TILDAS were passed 267

to the sonic anemometer which uses an analogue-to-digital converter to digitise the signal at 268

10 Hz The sonic anemometer acquired data at 168 Hz internally and reported average values 269

at 208 Hz The digitised signals from the QC-TILDAS were then combined with the wind 270

velocity information and sent to the serial port of a personal computer NH3 fluxes were 271

computed offline with a time step of half an hour The software package EddySoft (Kolle and 272

Rebmann 2007) was used for the data acquisition The same software package was used for 273

post processing EC data and to obtain the cospectra useful for applying the TF method while 274

custom Matlabreg and R scripts were used to process and analyse data for the ogive and the 275

inductance methods respectively Trace gas and heat fluxes were calculated as well as other 276

micrometeorological parameters The components of the wind velocity were rotated every 30 277

minutes to set w and v to zero No detrending was applied and block averaging was instead 278

used (Leuning 2007) 279

The sonic anemometer was mounted on a 15 m tall mast so that the height of the EC sensors 280

was about 07 m above the top of the crop and adjusted to follow crop development Ambient 281

air close to the sonic anemometer head was continuously sampled through a 25 m long inlet 282

line (PFA 38rdquo outer diameter) leading to the QC-TILDAS The flow of air through the tube 283

was measured before and after the T connection used for splitting the flow the flow rate 284

towards the sampling cell was about 9 litresminute resulting in a laminar flow regime with a 285

Reynolds number (Re) of about 2000 below the turbulence limit (for pipe flow Re=2300 286

Shimizu 2007) which was the primary cause of the high frequency loss in the measuring 287

system (Eugster and Senn 1995) The limits in the power supply lines in our experimental 288

11

site have prevented the use of pumps able to provide a turbulent regime into the sampling 289

tube 290

The eddy covariance system together with ROSAA and ALPHA samples equipments were 291

located close to the centre of the field which provided an acceptable upwind fetch (around 292

120 m) in the prevalent wind direction during the trial anyway the footprint of EC 293

measurements was found to be always inside the experimental field 294

16 Spectral correction methods for low-pass filtering in a closed-path EC system 295

As mentioned before all eddy covariance systems tend to underestimate the true atmospheric 296

fluxes due to physical limitations of the flux measurement instrumentation which causes 297

losses of eddy flux contributions at low and high frequencies These losses can be quantified 298

and corrected for by multiplying the measured covariance (subscript m) with a frequency 299

response correction factor (CF ge1) 300

mwCFw (4) 301

The correction factor can be estimated by means of spectral correction methods that can be 302

divided into two families the spectral theoretical transfer function (TF) methods and the in-303

situ methods (Massman and Clement 2004) The general approach consists of three steps 1) 304

the identification of a spectral transfer function (theoretical or experimental) 2) its use to 305

calculate a correction factor and 3) the parameterisation of the correction factor with 306

meteorological variables mainly the wind speed which is directly linked to the turbulence 307

Here three methods have been employed in order to estimate CFs to apply at our measured 308

EC NH3 fluxes (i) Theoretical TF analysis CFTheor (ii) A variant of In situ experimental TF 309

method the Ogive method CFO (iii) the inductance method CFL These methodologies are 310

the most analysed in the literature for evaluating the most accurate CFs for gas trace fluxes 311

over natural surfaces 312

Common to all approaches is the requirement of a measured or evaluated reference 313

cospectrum for scalar as well as a measured cospectrum and the estimate of the flux loss as a 314

function of frequency In any case an important aspect relative to any EC system is the time 315

delay between the instant open-path w measurement and the closed-path scalar concentration 316

measurement This time lag is due to the transport time in the tubing system and the phase 317

shift due to low-pass filtering (Ibrom et al 2007) It depends on the tube length and the flow 318

rate it may change slightly due to changing flow rates in the tube caused by pumping 319

variation or density changes Usually this delay can be calculated empirically by determining 320

12

the maximum of the covariance function between wrsquo and rsquo (Moncrieff et al 1997) which 321

includes the time lag due to the longitudinal (streamwise) separation distance from the air 322

sample inlet to the sonic anemometer depending on wind speed and direction The 323

individually determined time delays for each 30 minutes set of data can be used for 324

calculating the cospectra in this case the phase shift due to low-pass filtering and longitudinal 325

sensor separation has already been corrected for (Ibrom et al 2007) So two subcategories 326

can be identified in the theoretical transfer function analysis with (CFTheor_time_lag) and without 327

the time lag (CFTheor_phase_shift) applied to computation of the covariances and cospectra 328

329

161 Theoretical TF analysis 330

Following Moore (1986) theoretical transfer functions characterizing the measurement 331

process have been developed The correction factor in this case is summarized by Massman 332

and Clement (2004) using the following equation 333

dffCofHfT

fT

dffCo

w

wCF

w

b

b

w

m

TF

)()(sin

1

)(

0

2

2

0

(5)

334

where mw is the measured covariance and w is the true flux H(f) is the product of all 335

the appropriate transfer functions associated with HF losses (

n

i

iTFfH1

)( ) )( fCow is the 336

one-side reference cospectrum Tb is the block averaging period 22sin1 bb fTfT is the 337

TF for the low frequency spectral attenuation The reference cospectrum is either taken from 338

literature (Kaimal and Finnigan 1994) or a site specific reference cospectrum usually derived 339

from sensible heat flux measurements The requirements to apply this method are the TFs 340

and a model of the cospectra which is the weakness of this approach since smooth cospectra 341

are not typical of the atmospheric surface layer (Massman and Clement 2004) In particular 342

in this work we used the reference cospectral models (eq 6-9) given by Moncrieff et al 343

(1997) where udzffk )( is normalized frequency f is the natural frequency u is 344

horizontal wind speed z-d is height above the zero-plane displacement d For stable 345

atmospheric condition the reference cospectrum is 346

12)(

kww

kw

fBA

fffCo

(6)

347

where 348

13

750

4612840

L

dzAw (7)

349

and 350

11342 ww AB (8)

351

While for unstable atmospheric conditions the reference cospectrum is 352

540

7261

9212)(

3751

k

k

kw ffor

f

fffCo (9a)

353

540

831

3784)(

42

k

k

kw ffor

f

fffCo (9b)

354

Moore (1986) developed the first theoretical TFs for EC systems taking into account the 355

attenuation due to the dynamic frequency response of the sensors the scalar path averaging 356

the sensor separation the sensor response mismatch while Moncrieff et al (1997) gave a 357

complete description of TFs of EC systems with closed-path analysers Then considering the 358

wide literature on the used theoretical TFs (see Shimizu 2007 for a review) and the weight of 359

each TF on the total one for our equipment set-up here we report only details about TFs 360

related to (i) the lateral separation (ie perpendicular to the mean wind direction) between 361

the sonic anemometer and QC-TILDAS inlet (TFs Moore 1986) (ii) the tube damping effect 362

(TFtube Lenschow and Raupach 1991) (iii) the volume averaging in a measurement cell 363

(TFvol Massman 2004) (iv) the total phase shift (TFphase_shift Massman 2000) In detail 364

(i) Following Moore (1986) the effect of laterallongitudinal separation s between two 365

sensors can be represented by the following cospectral transfer function 366

)99exp(99 51nTFs (10)

367

where n=fsu As suggested by Massman (2000) the horizontal separation between the two 368

sensors has been set to 030 m 369

(ii) The function TFtube describing the attenuation of the fluctuation concentrations down the 370

sampling tube in which there is a laminar flow for covariance calculation (Lenschow and 371

Raupach 1991 Aubinet et al 2000) is 372

ts

t

tubeUD

LfrTF t

12exp

222 (11)

373

Here rt is the tube radius and Ds the molecular diffusivity of the scalar around 02 cm2s

-1 for 374

NH3 (Massman 1998) Moreover the attenuation may vary with time Leuning and Judd 375

(1996) showed that the attenuation to water vapour by aged and dirty tubes is much greater 376

than that by new and clear tube In the case of laminar flow through the tube the theoretical 377

14

cut off frequency fco_t at which the TF equals 2-12

is given by 378

tt

sttco

Lr

DUf

2_ 6490 (12)

379

about 14 Hz in our EC system To minimizing HF losses due to nonuniform velocity profiles 380

turbulent flow would be preferred (Lenschow and Raupach 1991) This requires a powerful 381

pump with high power consumption and a tremendous start-up current (13 A at 230 V AC for 382

an Edwards Tri-Scroll pump for example pers comm W Eugster) that was unrealistic at our 383

field site Hence we had to use a less powerful pump and thus need to account for the extra 384

loss in frequency response due to laminar flow in the intake hose (Moncrieff et al 1997) The 385

possible tube damping correction does not correct potential absorptiondesorption of moisture 386

by hygroscopic dirt particles in the tube however a strong pressure drop along the intake 387

hose reduces risk of condensation on the walls of the tube On the other hand HF due to wall 388

interaction in the sample tube could be reduced by minimizing lengthrsquos tube and maintaining 389

high flow rate In general the line loss is a function of tubing type and how dirty the tube is 390

(Sukyer and Verma 1993) 391

(iii) As reported by Massman (2004) in a closed-path detector the flow velocity within the 392

detection chamber can be very different than the wind speed of the ambient atmosphere near 393

the tube inlet so that the transfer functions need to be expressed in terms of the volume 394

flushing time constant of the detection chamber τvol 395

2

2sin 2

vol

volvolTF

(13)

396

where f 2 and τvol is equal to the time needed to fill the chamber and its minimum value 397

is given by the ratio between the volume of the chamber (05 l) and the maximum flow rate 398

(iv) However if covariances and the cospectra are calculated without the time lag that means 399

without the method of maximum covariance function or if there is no clear time lag due to 400

lag depending on the frequency of the eddies (sensor separation in the wind direction) or if 401

there are complex adsorption and desorption processes it is necessary to take into account the 402

phase shift (Massman 2000) The TF associated with the total phase shift (Massman 2000) 403

is 404

t

tlon

t

tlonshiftphase

U

L

u

l

U

L

u

lTF sincos_ (14)

405

where llon is the longitudinal sensor separation (function of wind direction) and β is the 406

intrinsic time constant of the scalar sensor This latter was estimated by using the Boylersquos law 407

15

applied to the measurement chamber working at 33 kPa and using the measured flow rate it 408

is about 01 s and moreover it means that the sampling frequency of 10 Hz is adequate for 409

the employed QC-TILDAS in the EC system 410

For the theoretical TF approach cospectra of the vertical wind speed and T and NH3 411

respectively were calculated using the post processing EddySpec package of Eddysoft 412

software using the following setup number of frequencies 4096 for calculating the cospectra 413

for half-hourly runs multiplied by frequency tapering the time series with a Hamming 414

window (Kaimal and Finnigan 1994) and averaging into 20 logarithmically spaced bins 415

Either the time lag or the phase shift was used in the functions of the two approaches 416

CFTheor_time_lag and CFTheor_phase_shift respectively 417

418

162 In situ ogive method 419

The limitation of the theoretical TF approach is that it could miss additional chemical or 420

micro-physical process occurring in the EC system a restriction that is expected to be 421

overcome by the in situ experimental methods Moreover the experimental approach is based 422

on the assumption of cospectral similarity between scalar fluxes and unlike the theoretical TF 423

approach these methods do not require smooth models of atmospheric cospectra (Massman 424

and Clement 2004) The correction factor can be estimated as the ratio between the 425

attenuated flux and a reference flux (usually the sensible heat flux) Aubinet et al (2000) give 426

the procedure to estimate this experimental frequency transfer function but since the 427

cospectra derived from sensible heat fluxes are often not well-defined then direct application 428

of this approach can produce implausible values 429

Starting from the hypothesis of similarity between all scalars cospectra Ammann et al (2006) 430

developed a method for estimating HF losses Applied to EC measurement of NH3 fluxes the 431

method consists in fitting the ogive for NH3 OgwNH3 (the ogive is the integrated cospectra 432

CowNH3 from low to high frequencies) against the ogive of air temperature OgwTa The fitting 433

was made with an iterative linear least square method in which the points being too far away 434

from the regression line were eliminated progressively (robustfit function under Matlabreg) 435

The high-frequency damping correction (CFO) is simply evaluated as the value of the fitted 436

NH3 ogive (OgwNH3) at the highest frequency The frequency band over which the linear 437

regression is performed is determined as the frequency for which OgwT larger than 01 and 438

OgwNH3 is smaller than 08 This choice was arbitrary and may not be appropriate if the 439

damping of the NH3 signal was below the range chosen The ogives were calculated using 440

detrended signals 441

16

442

163 The inductance method 443

Eugster and Senn (1995) propose a correction model for EC data affected by systematic error 444

introduced by the combination of all sources of damping which also includes chemical 445

reaction (in the case of NO2) besides the possible damping due to laminar flow in the 446

sampling tube and sensor separation This damping loss is described by an analogy to 447

inductance L in an alternating current circuit which can be used for taking into account the 448

reduction of spectral density in the inertial subrange This model follows the approach of 449

Moore (1986) using electronic components in a current circuit instead of a gain function 450

As reported by Eugster and Senn (1995) the covariance of the measured (ie damped) scalar 451

flux is given by 452

02220

)(41

1)(

0dffCo

LfdffCow

LL wwL

(15)

453

where )(0 fCo

Lw is the undamped cospectrum The value of L is modified in order to fit the 454

measured cospectrum The independent variables in this correction model are the measuring 455

height above zero-plane displacement z-d the mean horizontal wind speed u the Monin-456

Obukhov stability parameter ζ the natural frequency f and the inductance L The value for 457

L can be derived from spectral and cospectral analysis and is assumed not vary significantly 458

as long as the measuring system is not altered 459

The damped cospectra can be derived combining equation (14) with the empirical formula of 460

Kaimal et al (1972) for stable unstable and neutral stratification as modified by Eugster and 461

Senn (1995) (equations 29 and 30 in their paper) The approximated solutions of Eugster and 462

Senn (1995) for CF range between 10 (no damping at all) and infin (no signal at all) but they 463

suggest to restrict the fit to the range between CF=10 and CF=25 that means all 464

measurements which represent less than 40 of the real fluxes were discarded 465

17

2 RESULTS AND DISCUSSION 466

21 Micrometeorological conditions 467

During the trial the air temperature ranged from 16degC to 335degC and averaged 247degC while 468

the soil surface temperature reached more than 546degC at hourly scale The sky was clear 469

most of the period apart from the 23-24072008 where there was little rain (82 mm) The 470

global solar radiation was following the fair weather diurnal course throughout the period and 471

reached 950 W m-2

at maximum The daily average relative humidity was around 60 with 472

variations ranging between 25 and 93 473

The prevalent wind direction during the experimental period (84 of the runs) was NW the 474

wind speed ranged from 05 to 89 ms-1

and averaged 34 ms-1

while the friction velocity u 475

ranged between 001 and 099 ms-1

with an average of 030 ms-1

(Figure 1a) The energy 476

balance for the period of the trial is reported in Figure 2b at hourly scale In particular it is 477

clear the direct impact of the net radiation (Rn) on the sensible heat flux (H) and on the latent 478

heat flux (LE) The pattern of LE were strongly correlated with the available energy Rn with 479

distinct peaks immediately following irrigation which provided water to evapotranspirate 480

from plant and soil 481

22 Ammonia concentrations 482

The field NH3 concentrations measured by the diffusive samplers (ALPHA badges) over 483

several hours ranged from 3 to 118 microg NH3 m-3

while the 30 minutes ROSAA concentrations 484

varied from around 0 to around 90 microg NH3 m-3

The ROSAA concentrations averaged over 485

the same intervals as the ALPHA badges slightly underestimated the badges concentration by 486

8 on average (ROSAA = 092 ALPHA R2 = 082) The uncalibrated QC-TILDAS 487

concentrations strongly underestimated the ALPHA badges concentrations by 67 but 488

without any offset (QC-TILDAS = 034 ALPHA R2 = 090) This underestimation is larger than 489

what Ellis et al (2010) found with the same instrument in a laboratory calibration Since the 490

correlation was very good the regression against the ALPHA badges was used to calibrate the 491

QC-TILDAS concentration data The correction factor was found to be 286 plusmn 009 The 492

calibrated QC-TILDAS concentration was in quite good agreement with the ROSAA 493

concentrations both in terms of magnitude and dynamics (Figure 2) In Figure 3 a detailed 494

comparison between calibrated values of QC-TILDAS NH3 concentrations and ROSAA 495

values is shown Since we did not find a significant offset in the regression of QC-TILDAS 496

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 2: Eddy covariance measurement of ammonia fluxes: comparison of

2

Infrared Laser Differential Absorption Spectroscopy (TILDAS) coupled with a quantum 27

cascade (QC) laser was used to measure NH3 concentration at high frequency The results 28

showed that correction of the values of NH3 EC fluxes over natural surfaces are necessary for 29

taking into account the dumping of high frequency contribution due to the coupling of 30

ammonia fast analyser by QC-TILDAS and sonic anemometer In particular the calculated 31

flux losses ranged between -23 (inductance method with the CF threshold selected to 25) 32

and -43 (experimental-ogive method) while the two theoretical transfer function 33

approaches gave comparable loss estimates ie -30 and -31 for the methods with time lag 34

and phase shift respectively 35

36

3

INTRODUCTION 37

Ammonia (NH3) is naturally present in the troposphere with ambient concentrations varying 38

from 5 ppt in remote regions (Sutton et al 2001) to 500 ppb near sources (Krupa 2003) 39

Actually NH3 plays a key role in the environment as the main base neutralizing atmospheric 40

acids and as a precursor of particulate matter which causes human health hazards visibility 41

problems and modifies the radiative forcing of the climate system (Asman et al 1998 IPCC 42

2007) Due to its negative environmental impacts (Galloway et al 2003 Sutton et al 2009) 43

NH3 has become the object of many investigations by the scientific community in 1999 the 44

UN Convention on Long-range Transboundary Air Pollution to Abate Acidification 45

Eutrophication and Ground-level Ozone was extended by the Gothenburg Protocol to include 46

NH3 (httpwwwuneceorgenvlrtap) and its emissions started to be more regulated and 47

monitored 48

Ammonia is mainly emitted by anthropogenic sources It is estimated that the majority of NH3 49

emissions arises from agricultural activities in central Europe agriculture is responsible for 50

about 90 of NH3 release (Erisman et al 2008) However large uncertainty exists on NH3 51

dynamics in the soil-plant-atmosphere continuum due to the scarcity of reliable direct flux 52

measurements This can mainly be attributed to difficulties in measuring fluxes of such a 53

reactive compound (Harper 2005) In particular the first issue is the presence of NH3 in 54

atmosphere in gaseous particulate (eg (NH4)2SO4 and NH4NO3) and liquid (ie NH4OH in 55

cloud and fog droplets) forms where the partition of these three phases is highly variable 56

(Fehsenfeld 1995) Another difficulty is the tendency of NH3 to form strong hydrogen bonds 57

with water and thus to be readily adsorbed on any material exposed to ambient air producing 58

memory effects in the measurements These troubles ndash in addition to the contamination due to 59

NH3 produced by people (Sutton et al 2000) ndash have delayed progress in the development of 60

NH3 measurement techniques with respect to other trace gases 61

Considering its reactive nature and the dependence of NH3 exchanges by environmental 62

factors the micrometeorological methods are the preferred ones due to their non intrusive 63

character (Denmead 1983 Kaimal and Finnigan 1994) The aerodynamic gradient method 64

coupled to wet chemical analysis is the most widely used (Wyers et al 1993 Neftel et al 65

1998 Erisman et al 2005 Kruit et al 2007 Milford et al 2009 Spirig et al 2010) but it is 66

very time consuming Moreover the lack of fast sensors for NH3 has delayed the application 67

of the eddy covariance (EC) method which is considered the most direct and least error-prone 68

approach (Denmead 2008) The evolution of techniques for NH3 monitoring starts with the 69

4

passive filters and denuders developed by Ferm (1979) nowadays the most widely used 70

method for its simplicity and inexpensiveness However the drawbacks of this approach are 71

the relatively low time resolution (minute to hours) and the laboratory intensive labour 72

required for preparing them and for manual off-line sample analysis Coupled to the chemical 73

analytical methods progress in optical methods have been achieved with the development of 74

spectroscopic devices characterized by high time resolution and sensitivity to NH3 but which 75

are still expensive Von Bobrutzki et al (2010) recently provided inter-comparisons for NH3 76

measurement techniques in field highlighting that many challenges still have to be faced for 77

measuring NH3 accurately Nevertheless Tunable Infrared Laser Differential Absorption 78

Spectroscopy (TILDAS) using a quantum cascade (QC) laser are now available for 79

monitoring NH3 concentration with a sufficient sensitivity and robustness to be employed in 80

the field However the application of the EC method for monitoring NH3 is considered a 81

challenge by the scientific community as demonstrated by the scarce literature available on 82

this topic (Shaw et al 1998 Famulari et al 2005 Whitehead et al 2008 Brouder et al 83

2009 Sintermann et al 2011) in which are highlighted the difficulties to its routine 84

applicability In particular the employment of a setup involving a fast closed-path analyser 85

could introduce considerable damping of high frequency fluctuations and consequent 86

underestimation of the fluxes that have to be corrected for (eg Massman 2000) 87

Several methods are available for evaluating and correcting these underestimations when EC 88

equipment is used for trace gas fluxes (Massman and Clement 2004 for CO2 for example) In 89

particular Whitehead et al (2008) evidenced that the correction for EC fluxes of NH3 using 90

TILDAS is fundamental to accurately estimate the real fluxes at different time scales 91

However up to now no attempt has been made to evaluate using different methodologies 92

the correction of the underestimation of EC ammonia fluxes when a QC-TILDAS is used for 93

measuring NH3 fluctuations in the cropped field Furthermore no EC data of NH3 turbulent 94

fluxes are available for crop grown in arid environments 95

In this study we report EC measurements of NH3 fluxes performed employing a fast closed-96

path analyser for NH3 concentration the QC-TILDAS-76 SN002-U developed by Aerodyne 97

Research Inc (ARI Billerica MA USA) (Zahniser et al 2005) under custom specifications 98

The trial was carried out over a field in an intensive agricultural area of south Italy in a semi-99

arid to arid Mediterranean climate The plot was fertilized with urea pellets to stimulate NH3 100

emission and to test the device over a wide range of concentrations The aim was to assess the 101

performance of the QC-TILDAS used for the first time in an EC system for measuring NH3 102

fluxes over an irrigated cropland in Mediterranean environment The challenge was to verify 103

5

the adequacy of the system in terms of setup and quality of the data In order to reach a robust 104

assessment of the needed correction three possible methods for correcting the flux losses due 105

to the design and the setup of the employed equipment were considered in this analysis In 106

particular we compare different frequency correction factors (CF) to apply to the measured 107

EC NH3 fluxes obtained by the following approaches (i) the spectral theoretical transfer 108

function (CFTheor) (ii) the in-situ ogive method (CFO) (Massman and Clement 2004) and 109

(iii) the inductance correction model (CFL) developed by Eugster and Senn (1995) 110

1 MATERIAL AND METHOD 111

11 Field site 112

The experiment was carried out from 17 to 30 July 2008 (DOYs 199 to 211) in Rutigliano 113

(41degN 17deg54 E 122 m asl) near Bari in southern Italy All experimental activities were 114

performed in a 2 hectares field dimensions were 200 m x 100 m The soil type is classified as 115

loamy clay (Clay 41 Silt 44 Sand 15 ) and the terrain is flat The climate is semi-arid 116

Mediterranean with hot and dry summer and short and temperate winter The mean annual 117

temperature is 157 degC with a mean annual precipitation of 600 mm the main wind direction 118

is north during the summer time In this study most of the analysis will focus on the period 119

22-29 July 2008 (DOYs 204-211) where the NH3 fluxes were the largest 120

12 Crop and urea spreading 121

During the experimental campaign the field was cultivated with Sorghum vulgare (cv Hay 122

Day) sowed on 10th

June 2008 and irrigated by sprinkler irrigation A commercial urea 123

product (46 N content) in granular form was applied The total N supply was around 240 124

kg N ha-1

divided into three applications 30 90 and 120 kg N ha-1

on 1st 16

th and 22

nd July 125

2008 respectively The first application was carried out at crop emergence Before the second 126

application the field was irrigated with 79 mm water while further irrigation (93 mm) was 127

applied before and after the third application The amount of urea was slightly higher than 128

current agronomic practices in the area since the dry conditions (high temperature no rain 129

and no irrigation applied) between the second and the last urea spreading inhibited NH3 130

volatilization so that no fluxes were detected The last urea application combined with 131

irrigation was necessary in order to increase the probability to detect NH3 fluxes 132

6

13 NH3 fluxes by the eddy covariance method 133

The fluxes of NH3 were measured with the eddy covariance method (eg Kaimal amp Finnigan 134

1994 Lee et al 2004 Mahrt 2010) The EC method is based on estimating the transport of a 135

scalar by turbulent motions of upward and downward moving air contained in the atmosphere 136

The vertical flux (F) of scalar at the measurement height is given by 137

wwF

(1) 138

where w is the component of the wind speed normal to the surface and β is the scalar 139

concentration and the usual Reynolds decomposition is used where prime denotes fluctuating 140

quantities w is the covariance between and w With the usual assumption of steady state 141

horizontally homogeneous flow there is no vertical flux of dry air ie 0wcd where cd is 142

the dry air concentration Since the dry air concentration fluctuates due to heat and water 143

vapour fluxes w is not necessarily equal to zero The Webb Pearmann Leuning (WPL) 144

correction (Webb et al 1980 Leuning 2007) gives the expression of w The impact of this 145

term depends on the concentration of the gas and the typical magnitude of the flux (Denmead 146

1983 Liebethal and Foken 2004) for trace gas with small concentration and large fluxes 147

such as NH3 over a source these corrections are negligible 148

The turbulent flux w as a time-averaged quantity can also be viewed as a frequency 149

averaged quantity ie the integral of the co-spectral density (Cowβ) (Kaimal and Finnigan 150

1994 Aubinet et al 2000 Massman 2000) 151

0

)( dffCow w

(2)

152

To catch all relevant turbulent fluctuations β and w should be measured with a sampling 153

frequency much larger than the frequency of the eddies carrying the energy of the signal (fx) 154

(Horst 1997 Massman 2000) Usually a sampling frequency of 10 Hz is considered 155

sufficient above a vegetated surface However flux loss is inevitable with any EC system 156

especially employing closed-path analysers to which the air sample is transported into the 157

measuring cell by means of a tube The mixing effects in the tube and additional possible 158

chemical or micro-physical processes occurring in the system produce low-pass filtering 159

effects on the measured covariances (Ibrom et al 2007) Nowadays a variety of methods are 160

available to correct these underestimated measured fluxes by means of frequency response 161

correction factors (eg Ammann et al 2006 Shimizu 2007 Aubinet et al 2000 Massman 162

2000 Eugster and Senn 1995) However in this work since the adopted closed-path analyser 163

7

for NH3 concentration measurements is relatively new and it is the first time it is being used 164

under semi-arid climates we compare a variety of methods to correct the NH3 fluxes 165

measured over an agricultural plot 166

14 NH3 concentration measurements 167

141 The QC-TILDAS analyser 168

1411 The instrument 169

The QC-TILDAS employed during this research is the compact QC-TILDAS-76 SN002-U 170

developed by Aerodyne Research Inc (ARI Billerica MA USA) (Zahniser et al 2005) 171

This spectrometer combines QC laser designed for pulsed operation at near room temperature 172

(Alpes Lasers Neuchacirctel Switzerland) an optical system together with a computer-controlled 173

system that incorporates the electronics for driving the QC laser along with signal generation 174

and a data logger Molecular transition selection for given laser is achieved by temperature 175

tuning typically within a small range around 40 ordmC using a two-stage Peltier element The 176

laser frequency is swept across the full molecular transition at 96734 cm-1

which means that 177

the scan is done over a narrow range of wave numbers chosen to contain an absorption feature 178

of the trace gas of interest The optical system collects light from the QC laser and directs it 179

through an astigmatic Herriott type multi-pass absorption cell (76 m effective path length) 180

onto a TE-cooled photovoltaic detector (Vigo Systems) A detailed description of the device 181

can be found in Zahniser et al (2005) 182

1412 The inlet design and the sampling 183

Ellis et al (2010) report the characterization of the QC-TILDAS by laboratory tests In 184

particular they reported a detailed analysis on the performance of the QC-TILDAS in 185

function of the inlet design To achieve accurate atmospheric measurements of NH3 the 186

sampling setup has to reduce gas interactions with surfaces then it is crucial the configuration 187

of the devicersquos inlet through which the air is drawn into the optical cell The inlet used is a 188

short glass tube containing a critical orifice needed to keep the air flow rate constant and to 189

reduce the pressure in the optical cell to approximately 540 kPa a compromise between 190

minimising line broadening and adsorption effects maximising time response and achieving 191

good sensitivity (Warland et al 2001) The setup of the inlet is done to remove particles in 192

the air sample relying on inertia to remove more than 50 of particles larger than 300 nm in 193

8

particular the flow is split into two branches 90 of the flow makes a sharp turn and is 194

pulled into the optical cell by a pump (VARIAN TriScroll 600 Series) the other 10 is 195

pulled by a second air pump (VARIAN mod SH110) This inlet design is optimized to 196

eliminate the need of filters and any interference they may cause The operation of the 197

spectrometer is fully automated and computer-controlled through a Windows-based 198

proprietary software (TDL Wintel) developed at ARI Concentrations are real-time 199

determined from the light absorption spectra through a non-linear least-squares fitting 200

algorithm (Levenberg-Marquardt) that uses spectral parameters from the HITRAN (high-201

resolution transmission) molecular absorption database (Rothman et al 2003) The pressure 202

and temperature in the multi-pass cell are continuously monitored to supply information 203

needed for spectral fitting The software calculates absolute concentrations so no calibration 204

was performed in the field However laboratory tests demonstrate the need to carry out a 205

post-proceeding calibration in order to overcome the failure of the analyzer to reproduce the 206

values of the ammonia standard Moreover we would justify the calibration factor found for 207

the QC-TILDAS using ethilene standard which has absorbing frequency close to ammonia 208

one but it is not affected by stickiness Part of the problem with absolute spectroscopic values 209

for NH3 in our instrument was due to using pulsed-QCLs rather than CW-QCLs with the 210

laser linewidth considerably greater than the molecular linewidth the spectral analysis 211

required much effort to get absolute values Thus a post-proceeding calibration was done and 212

zeroing was routinely carried out in order to optimize the calculated spectra following 213

Whitehead et al (2008) 214

1413 The response time 215

The decreasing of the mixing ratio of the ammonia was well represented by two exponential 216

decay functions which in Ellis et al (2010) is written as 217

2

02

1

010 expexp

ttA

ttAyy (3) 218

where A1 and A2 are constants and their sum is equal to the stable mixing ratio of NH3 prior to 219

the calibration flow being switched off y0 is equal to the NH3 mixing ratio reached at the end 220

of the decay t is time and t0 is the time at the start of the fit τ1 and τ2 are time decay 221

constants the first of which is fast and corresponds to the gas exchange time of the system 222

(04 s in our case) and the second being much slower and corresponding to interactions of 223

NH3 with surfaces on the interior surfaces of the inlet sampling lines and absorption cell (15 224

9

s) In this case the response time was checked in laboratory and it is controlled by pumping 225

speed and by adsorptiondesorption on the walls In particular it is affected by the flow rate 226

the length and the heating of the sampling tubes Therefore acting on these parameters would 227

allow optimizing the instrument for measuring the fluxes by EC technique 228

Moreover for a mixing ratio of 350 ppb the amount of noise (1) in a 10 Hz measurement 229

obtained by Ellis et al (2010) by an Allan variance analysis was 315 ppb (22 microg NH3 m-3

) 230

Based on statistics obtained for sensible heat CO2 and temperature w w was estimated 231

to be around 023 plusmn 013 (mean plusmn SE) and taking ~ 2 microg NH3 m-3

and w ~ 125 u would 232

mean that the flux detection limit of the setup would be around 25 u (in microg NH3 m-2

s-1

) 233

hence typically around 750 ng NH3 m-2

s-1

However most of the turbulent signal is 234

transported by eddies with a frequency around 02 Hz (evaluated as the co-spectral peak 235

frequency for air temperature (Ta) CO2 and water vapour (H2O)) At this frequency the Allan 236

variance of the QC-TILDAS found by Ellis et al (2010) is around 02 microg NH3 m-3

and hence 237

the detection limit should be on the order of 025 u (typically 75 ng NH3 m-2

s-1

) As reported 238

by Ellis et al (2010) there is an increase in the relative importance of NH3 surface 239

interactions at mixing ratios lower than 100 ppb which further increased when sampling 240

humid air as opposed to dry nitrogen Then the heating in the sampling line to prevent 241

condensation is crucial to perform accurate NH3 measurements There was no line heating 242

used in our study since we assumed that this was not needed since the air temperature 243

encountered in the field was very high (up to 33degC) On the other hand under such 244

conditions it was required the use of an air conditioner for the box containing the QC-245

TILDAS to limit instrumental drift induced by changes in the optical alignment as well as in 246

the laser power beam width and temperature 247

142 The ALPHA badges and ROSAA system 248

The NH3 concentration measured with the QC-TILDAS were compared against passive 249

diffusion samples (ALPHA badges Tang et al 2001) These ALPHA badges were 250

positioned at the same height as the QC-TILDAS inlet and changed twice a day to get daily 251

and nightly concentrations Three replicates were used to evaluate the uncertainty in the 252

measurements The background concentration bgd was estimated with three sets of badges put 253

at three locations at more than 100 100 and 300 m away from the field and changed weekly 254

The badges were prepared with filters coated with citric acid The filters were extracted in 3 255

ml double deionised water and analysed with a FLORRIA analyser (Mechatronics NL) In 256

10

addition during the trial NH3 concentration was monitored by means of the new device 257

ROSAA developed at INRA (Loubet et al 2010 2011) It relies on the coupling of NH3 258

capturing by means of an acid stripping solution (wet-effluent denuders) to convert NH3 in 259

NH4+ and the analysis of the NH4

+ concentration by means of a detector based on a semi-260

permeable membrane and a unit to perform temperature controlled conductivity 261

measurements (ECN Netherlands) In the ROSAA system the three denuders were sampling 262

at three heights (at z = 05 07 and 14 m) above ground and the averaged concentration was 263

measure every 30 minutes 264

15 The eddy covariance system description of the equipment 265

The EC system was equipped with the QC-TILDAS and a three axis ultrasonic anemometer 266

(Gill R2 Gill Instruments Ltd UK) The analogue signals from the QC-TILDAS were passed 267

to the sonic anemometer which uses an analogue-to-digital converter to digitise the signal at 268

10 Hz The sonic anemometer acquired data at 168 Hz internally and reported average values 269

at 208 Hz The digitised signals from the QC-TILDAS were then combined with the wind 270

velocity information and sent to the serial port of a personal computer NH3 fluxes were 271

computed offline with a time step of half an hour The software package EddySoft (Kolle and 272

Rebmann 2007) was used for the data acquisition The same software package was used for 273

post processing EC data and to obtain the cospectra useful for applying the TF method while 274

custom Matlabreg and R scripts were used to process and analyse data for the ogive and the 275

inductance methods respectively Trace gas and heat fluxes were calculated as well as other 276

micrometeorological parameters The components of the wind velocity were rotated every 30 277

minutes to set w and v to zero No detrending was applied and block averaging was instead 278

used (Leuning 2007) 279

The sonic anemometer was mounted on a 15 m tall mast so that the height of the EC sensors 280

was about 07 m above the top of the crop and adjusted to follow crop development Ambient 281

air close to the sonic anemometer head was continuously sampled through a 25 m long inlet 282

line (PFA 38rdquo outer diameter) leading to the QC-TILDAS The flow of air through the tube 283

was measured before and after the T connection used for splitting the flow the flow rate 284

towards the sampling cell was about 9 litresminute resulting in a laminar flow regime with a 285

Reynolds number (Re) of about 2000 below the turbulence limit (for pipe flow Re=2300 286

Shimizu 2007) which was the primary cause of the high frequency loss in the measuring 287

system (Eugster and Senn 1995) The limits in the power supply lines in our experimental 288

11

site have prevented the use of pumps able to provide a turbulent regime into the sampling 289

tube 290

The eddy covariance system together with ROSAA and ALPHA samples equipments were 291

located close to the centre of the field which provided an acceptable upwind fetch (around 292

120 m) in the prevalent wind direction during the trial anyway the footprint of EC 293

measurements was found to be always inside the experimental field 294

16 Spectral correction methods for low-pass filtering in a closed-path EC system 295

As mentioned before all eddy covariance systems tend to underestimate the true atmospheric 296

fluxes due to physical limitations of the flux measurement instrumentation which causes 297

losses of eddy flux contributions at low and high frequencies These losses can be quantified 298

and corrected for by multiplying the measured covariance (subscript m) with a frequency 299

response correction factor (CF ge1) 300

mwCFw (4) 301

The correction factor can be estimated by means of spectral correction methods that can be 302

divided into two families the spectral theoretical transfer function (TF) methods and the in-303

situ methods (Massman and Clement 2004) The general approach consists of three steps 1) 304

the identification of a spectral transfer function (theoretical or experimental) 2) its use to 305

calculate a correction factor and 3) the parameterisation of the correction factor with 306

meteorological variables mainly the wind speed which is directly linked to the turbulence 307

Here three methods have been employed in order to estimate CFs to apply at our measured 308

EC NH3 fluxes (i) Theoretical TF analysis CFTheor (ii) A variant of In situ experimental TF 309

method the Ogive method CFO (iii) the inductance method CFL These methodologies are 310

the most analysed in the literature for evaluating the most accurate CFs for gas trace fluxes 311

over natural surfaces 312

Common to all approaches is the requirement of a measured or evaluated reference 313

cospectrum for scalar as well as a measured cospectrum and the estimate of the flux loss as a 314

function of frequency In any case an important aspect relative to any EC system is the time 315

delay between the instant open-path w measurement and the closed-path scalar concentration 316

measurement This time lag is due to the transport time in the tubing system and the phase 317

shift due to low-pass filtering (Ibrom et al 2007) It depends on the tube length and the flow 318

rate it may change slightly due to changing flow rates in the tube caused by pumping 319

variation or density changes Usually this delay can be calculated empirically by determining 320

12

the maximum of the covariance function between wrsquo and rsquo (Moncrieff et al 1997) which 321

includes the time lag due to the longitudinal (streamwise) separation distance from the air 322

sample inlet to the sonic anemometer depending on wind speed and direction The 323

individually determined time delays for each 30 minutes set of data can be used for 324

calculating the cospectra in this case the phase shift due to low-pass filtering and longitudinal 325

sensor separation has already been corrected for (Ibrom et al 2007) So two subcategories 326

can be identified in the theoretical transfer function analysis with (CFTheor_time_lag) and without 327

the time lag (CFTheor_phase_shift) applied to computation of the covariances and cospectra 328

329

161 Theoretical TF analysis 330

Following Moore (1986) theoretical transfer functions characterizing the measurement 331

process have been developed The correction factor in this case is summarized by Massman 332

and Clement (2004) using the following equation 333

dffCofHfT

fT

dffCo

w

wCF

w

b

b

w

m

TF

)()(sin

1

)(

0

2

2

0

(5)

334

where mw is the measured covariance and w is the true flux H(f) is the product of all 335

the appropriate transfer functions associated with HF losses (

n

i

iTFfH1

)( ) )( fCow is the 336

one-side reference cospectrum Tb is the block averaging period 22sin1 bb fTfT is the 337

TF for the low frequency spectral attenuation The reference cospectrum is either taken from 338

literature (Kaimal and Finnigan 1994) or a site specific reference cospectrum usually derived 339

from sensible heat flux measurements The requirements to apply this method are the TFs 340

and a model of the cospectra which is the weakness of this approach since smooth cospectra 341

are not typical of the atmospheric surface layer (Massman and Clement 2004) In particular 342

in this work we used the reference cospectral models (eq 6-9) given by Moncrieff et al 343

(1997) where udzffk )( is normalized frequency f is the natural frequency u is 344

horizontal wind speed z-d is height above the zero-plane displacement d For stable 345

atmospheric condition the reference cospectrum is 346

12)(

kww

kw

fBA

fffCo

(6)

347

where 348

13

750

4612840

L

dzAw (7)

349

and 350

11342 ww AB (8)

351

While for unstable atmospheric conditions the reference cospectrum is 352

540

7261

9212)(

3751

k

k

kw ffor

f

fffCo (9a)

353

540

831

3784)(

42

k

k

kw ffor

f

fffCo (9b)

354

Moore (1986) developed the first theoretical TFs for EC systems taking into account the 355

attenuation due to the dynamic frequency response of the sensors the scalar path averaging 356

the sensor separation the sensor response mismatch while Moncrieff et al (1997) gave a 357

complete description of TFs of EC systems with closed-path analysers Then considering the 358

wide literature on the used theoretical TFs (see Shimizu 2007 for a review) and the weight of 359

each TF on the total one for our equipment set-up here we report only details about TFs 360

related to (i) the lateral separation (ie perpendicular to the mean wind direction) between 361

the sonic anemometer and QC-TILDAS inlet (TFs Moore 1986) (ii) the tube damping effect 362

(TFtube Lenschow and Raupach 1991) (iii) the volume averaging in a measurement cell 363

(TFvol Massman 2004) (iv) the total phase shift (TFphase_shift Massman 2000) In detail 364

(i) Following Moore (1986) the effect of laterallongitudinal separation s between two 365

sensors can be represented by the following cospectral transfer function 366

)99exp(99 51nTFs (10)

367

where n=fsu As suggested by Massman (2000) the horizontal separation between the two 368

sensors has been set to 030 m 369

(ii) The function TFtube describing the attenuation of the fluctuation concentrations down the 370

sampling tube in which there is a laminar flow for covariance calculation (Lenschow and 371

Raupach 1991 Aubinet et al 2000) is 372

ts

t

tubeUD

LfrTF t

12exp

222 (11)

373

Here rt is the tube radius and Ds the molecular diffusivity of the scalar around 02 cm2s

-1 for 374

NH3 (Massman 1998) Moreover the attenuation may vary with time Leuning and Judd 375

(1996) showed that the attenuation to water vapour by aged and dirty tubes is much greater 376

than that by new and clear tube In the case of laminar flow through the tube the theoretical 377

14

cut off frequency fco_t at which the TF equals 2-12

is given by 378

tt

sttco

Lr

DUf

2_ 6490 (12)

379

about 14 Hz in our EC system To minimizing HF losses due to nonuniform velocity profiles 380

turbulent flow would be preferred (Lenschow and Raupach 1991) This requires a powerful 381

pump with high power consumption and a tremendous start-up current (13 A at 230 V AC for 382

an Edwards Tri-Scroll pump for example pers comm W Eugster) that was unrealistic at our 383

field site Hence we had to use a less powerful pump and thus need to account for the extra 384

loss in frequency response due to laminar flow in the intake hose (Moncrieff et al 1997) The 385

possible tube damping correction does not correct potential absorptiondesorption of moisture 386

by hygroscopic dirt particles in the tube however a strong pressure drop along the intake 387

hose reduces risk of condensation on the walls of the tube On the other hand HF due to wall 388

interaction in the sample tube could be reduced by minimizing lengthrsquos tube and maintaining 389

high flow rate In general the line loss is a function of tubing type and how dirty the tube is 390

(Sukyer and Verma 1993) 391

(iii) As reported by Massman (2004) in a closed-path detector the flow velocity within the 392

detection chamber can be very different than the wind speed of the ambient atmosphere near 393

the tube inlet so that the transfer functions need to be expressed in terms of the volume 394

flushing time constant of the detection chamber τvol 395

2

2sin 2

vol

volvolTF

(13)

396

where f 2 and τvol is equal to the time needed to fill the chamber and its minimum value 397

is given by the ratio between the volume of the chamber (05 l) and the maximum flow rate 398

(iv) However if covariances and the cospectra are calculated without the time lag that means 399

without the method of maximum covariance function or if there is no clear time lag due to 400

lag depending on the frequency of the eddies (sensor separation in the wind direction) or if 401

there are complex adsorption and desorption processes it is necessary to take into account the 402

phase shift (Massman 2000) The TF associated with the total phase shift (Massman 2000) 403

is 404

t

tlon

t

tlonshiftphase

U

L

u

l

U

L

u

lTF sincos_ (14)

405

where llon is the longitudinal sensor separation (function of wind direction) and β is the 406

intrinsic time constant of the scalar sensor This latter was estimated by using the Boylersquos law 407

15

applied to the measurement chamber working at 33 kPa and using the measured flow rate it 408

is about 01 s and moreover it means that the sampling frequency of 10 Hz is adequate for 409

the employed QC-TILDAS in the EC system 410

For the theoretical TF approach cospectra of the vertical wind speed and T and NH3 411

respectively were calculated using the post processing EddySpec package of Eddysoft 412

software using the following setup number of frequencies 4096 for calculating the cospectra 413

for half-hourly runs multiplied by frequency tapering the time series with a Hamming 414

window (Kaimal and Finnigan 1994) and averaging into 20 logarithmically spaced bins 415

Either the time lag or the phase shift was used in the functions of the two approaches 416

CFTheor_time_lag and CFTheor_phase_shift respectively 417

418

162 In situ ogive method 419

The limitation of the theoretical TF approach is that it could miss additional chemical or 420

micro-physical process occurring in the EC system a restriction that is expected to be 421

overcome by the in situ experimental methods Moreover the experimental approach is based 422

on the assumption of cospectral similarity between scalar fluxes and unlike the theoretical TF 423

approach these methods do not require smooth models of atmospheric cospectra (Massman 424

and Clement 2004) The correction factor can be estimated as the ratio between the 425

attenuated flux and a reference flux (usually the sensible heat flux) Aubinet et al (2000) give 426

the procedure to estimate this experimental frequency transfer function but since the 427

cospectra derived from sensible heat fluxes are often not well-defined then direct application 428

of this approach can produce implausible values 429

Starting from the hypothesis of similarity between all scalars cospectra Ammann et al (2006) 430

developed a method for estimating HF losses Applied to EC measurement of NH3 fluxes the 431

method consists in fitting the ogive for NH3 OgwNH3 (the ogive is the integrated cospectra 432

CowNH3 from low to high frequencies) against the ogive of air temperature OgwTa The fitting 433

was made with an iterative linear least square method in which the points being too far away 434

from the regression line were eliminated progressively (robustfit function under Matlabreg) 435

The high-frequency damping correction (CFO) is simply evaluated as the value of the fitted 436

NH3 ogive (OgwNH3) at the highest frequency The frequency band over which the linear 437

regression is performed is determined as the frequency for which OgwT larger than 01 and 438

OgwNH3 is smaller than 08 This choice was arbitrary and may not be appropriate if the 439

damping of the NH3 signal was below the range chosen The ogives were calculated using 440

detrended signals 441

16

442

163 The inductance method 443

Eugster and Senn (1995) propose a correction model for EC data affected by systematic error 444

introduced by the combination of all sources of damping which also includes chemical 445

reaction (in the case of NO2) besides the possible damping due to laminar flow in the 446

sampling tube and sensor separation This damping loss is described by an analogy to 447

inductance L in an alternating current circuit which can be used for taking into account the 448

reduction of spectral density in the inertial subrange This model follows the approach of 449

Moore (1986) using electronic components in a current circuit instead of a gain function 450

As reported by Eugster and Senn (1995) the covariance of the measured (ie damped) scalar 451

flux is given by 452

02220

)(41

1)(

0dffCo

LfdffCow

LL wwL

(15)

453

where )(0 fCo

Lw is the undamped cospectrum The value of L is modified in order to fit the 454

measured cospectrum The independent variables in this correction model are the measuring 455

height above zero-plane displacement z-d the mean horizontal wind speed u the Monin-456

Obukhov stability parameter ζ the natural frequency f and the inductance L The value for 457

L can be derived from spectral and cospectral analysis and is assumed not vary significantly 458

as long as the measuring system is not altered 459

The damped cospectra can be derived combining equation (14) with the empirical formula of 460

Kaimal et al (1972) for stable unstable and neutral stratification as modified by Eugster and 461

Senn (1995) (equations 29 and 30 in their paper) The approximated solutions of Eugster and 462

Senn (1995) for CF range between 10 (no damping at all) and infin (no signal at all) but they 463

suggest to restrict the fit to the range between CF=10 and CF=25 that means all 464

measurements which represent less than 40 of the real fluxes were discarded 465

17

2 RESULTS AND DISCUSSION 466

21 Micrometeorological conditions 467

During the trial the air temperature ranged from 16degC to 335degC and averaged 247degC while 468

the soil surface temperature reached more than 546degC at hourly scale The sky was clear 469

most of the period apart from the 23-24072008 where there was little rain (82 mm) The 470

global solar radiation was following the fair weather diurnal course throughout the period and 471

reached 950 W m-2

at maximum The daily average relative humidity was around 60 with 472

variations ranging between 25 and 93 473

The prevalent wind direction during the experimental period (84 of the runs) was NW the 474

wind speed ranged from 05 to 89 ms-1

and averaged 34 ms-1

while the friction velocity u 475

ranged between 001 and 099 ms-1

with an average of 030 ms-1

(Figure 1a) The energy 476

balance for the period of the trial is reported in Figure 2b at hourly scale In particular it is 477

clear the direct impact of the net radiation (Rn) on the sensible heat flux (H) and on the latent 478

heat flux (LE) The pattern of LE were strongly correlated with the available energy Rn with 479

distinct peaks immediately following irrigation which provided water to evapotranspirate 480

from plant and soil 481

22 Ammonia concentrations 482

The field NH3 concentrations measured by the diffusive samplers (ALPHA badges) over 483

several hours ranged from 3 to 118 microg NH3 m-3

while the 30 minutes ROSAA concentrations 484

varied from around 0 to around 90 microg NH3 m-3

The ROSAA concentrations averaged over 485

the same intervals as the ALPHA badges slightly underestimated the badges concentration by 486

8 on average (ROSAA = 092 ALPHA R2 = 082) The uncalibrated QC-TILDAS 487

concentrations strongly underestimated the ALPHA badges concentrations by 67 but 488

without any offset (QC-TILDAS = 034 ALPHA R2 = 090) This underestimation is larger than 489

what Ellis et al (2010) found with the same instrument in a laboratory calibration Since the 490

correlation was very good the regression against the ALPHA badges was used to calibrate the 491

QC-TILDAS concentration data The correction factor was found to be 286 plusmn 009 The 492

calibrated QC-TILDAS concentration was in quite good agreement with the ROSAA 493

concentrations both in terms of magnitude and dynamics (Figure 2) In Figure 3 a detailed 494

comparison between calibrated values of QC-TILDAS NH3 concentrations and ROSAA 495

values is shown Since we did not find a significant offset in the regression of QC-TILDAS 496

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 3: Eddy covariance measurement of ammonia fluxes: comparison of

3

INTRODUCTION 37

Ammonia (NH3) is naturally present in the troposphere with ambient concentrations varying 38

from 5 ppt in remote regions (Sutton et al 2001) to 500 ppb near sources (Krupa 2003) 39

Actually NH3 plays a key role in the environment as the main base neutralizing atmospheric 40

acids and as a precursor of particulate matter which causes human health hazards visibility 41

problems and modifies the radiative forcing of the climate system (Asman et al 1998 IPCC 42

2007) Due to its negative environmental impacts (Galloway et al 2003 Sutton et al 2009) 43

NH3 has become the object of many investigations by the scientific community in 1999 the 44

UN Convention on Long-range Transboundary Air Pollution to Abate Acidification 45

Eutrophication and Ground-level Ozone was extended by the Gothenburg Protocol to include 46

NH3 (httpwwwuneceorgenvlrtap) and its emissions started to be more regulated and 47

monitored 48

Ammonia is mainly emitted by anthropogenic sources It is estimated that the majority of NH3 49

emissions arises from agricultural activities in central Europe agriculture is responsible for 50

about 90 of NH3 release (Erisman et al 2008) However large uncertainty exists on NH3 51

dynamics in the soil-plant-atmosphere continuum due to the scarcity of reliable direct flux 52

measurements This can mainly be attributed to difficulties in measuring fluxes of such a 53

reactive compound (Harper 2005) In particular the first issue is the presence of NH3 in 54

atmosphere in gaseous particulate (eg (NH4)2SO4 and NH4NO3) and liquid (ie NH4OH in 55

cloud and fog droplets) forms where the partition of these three phases is highly variable 56

(Fehsenfeld 1995) Another difficulty is the tendency of NH3 to form strong hydrogen bonds 57

with water and thus to be readily adsorbed on any material exposed to ambient air producing 58

memory effects in the measurements These troubles ndash in addition to the contamination due to 59

NH3 produced by people (Sutton et al 2000) ndash have delayed progress in the development of 60

NH3 measurement techniques with respect to other trace gases 61

Considering its reactive nature and the dependence of NH3 exchanges by environmental 62

factors the micrometeorological methods are the preferred ones due to their non intrusive 63

character (Denmead 1983 Kaimal and Finnigan 1994) The aerodynamic gradient method 64

coupled to wet chemical analysis is the most widely used (Wyers et al 1993 Neftel et al 65

1998 Erisman et al 2005 Kruit et al 2007 Milford et al 2009 Spirig et al 2010) but it is 66

very time consuming Moreover the lack of fast sensors for NH3 has delayed the application 67

of the eddy covariance (EC) method which is considered the most direct and least error-prone 68

approach (Denmead 2008) The evolution of techniques for NH3 monitoring starts with the 69

4

passive filters and denuders developed by Ferm (1979) nowadays the most widely used 70

method for its simplicity and inexpensiveness However the drawbacks of this approach are 71

the relatively low time resolution (minute to hours) and the laboratory intensive labour 72

required for preparing them and for manual off-line sample analysis Coupled to the chemical 73

analytical methods progress in optical methods have been achieved with the development of 74

spectroscopic devices characterized by high time resolution and sensitivity to NH3 but which 75

are still expensive Von Bobrutzki et al (2010) recently provided inter-comparisons for NH3 76

measurement techniques in field highlighting that many challenges still have to be faced for 77

measuring NH3 accurately Nevertheless Tunable Infrared Laser Differential Absorption 78

Spectroscopy (TILDAS) using a quantum cascade (QC) laser are now available for 79

monitoring NH3 concentration with a sufficient sensitivity and robustness to be employed in 80

the field However the application of the EC method for monitoring NH3 is considered a 81

challenge by the scientific community as demonstrated by the scarce literature available on 82

this topic (Shaw et al 1998 Famulari et al 2005 Whitehead et al 2008 Brouder et al 83

2009 Sintermann et al 2011) in which are highlighted the difficulties to its routine 84

applicability In particular the employment of a setup involving a fast closed-path analyser 85

could introduce considerable damping of high frequency fluctuations and consequent 86

underestimation of the fluxes that have to be corrected for (eg Massman 2000) 87

Several methods are available for evaluating and correcting these underestimations when EC 88

equipment is used for trace gas fluxes (Massman and Clement 2004 for CO2 for example) In 89

particular Whitehead et al (2008) evidenced that the correction for EC fluxes of NH3 using 90

TILDAS is fundamental to accurately estimate the real fluxes at different time scales 91

However up to now no attempt has been made to evaluate using different methodologies 92

the correction of the underestimation of EC ammonia fluxes when a QC-TILDAS is used for 93

measuring NH3 fluctuations in the cropped field Furthermore no EC data of NH3 turbulent 94

fluxes are available for crop grown in arid environments 95

In this study we report EC measurements of NH3 fluxes performed employing a fast closed-96

path analyser for NH3 concentration the QC-TILDAS-76 SN002-U developed by Aerodyne 97

Research Inc (ARI Billerica MA USA) (Zahniser et al 2005) under custom specifications 98

The trial was carried out over a field in an intensive agricultural area of south Italy in a semi-99

arid to arid Mediterranean climate The plot was fertilized with urea pellets to stimulate NH3 100

emission and to test the device over a wide range of concentrations The aim was to assess the 101

performance of the QC-TILDAS used for the first time in an EC system for measuring NH3 102

fluxes over an irrigated cropland in Mediterranean environment The challenge was to verify 103

5

the adequacy of the system in terms of setup and quality of the data In order to reach a robust 104

assessment of the needed correction three possible methods for correcting the flux losses due 105

to the design and the setup of the employed equipment were considered in this analysis In 106

particular we compare different frequency correction factors (CF) to apply to the measured 107

EC NH3 fluxes obtained by the following approaches (i) the spectral theoretical transfer 108

function (CFTheor) (ii) the in-situ ogive method (CFO) (Massman and Clement 2004) and 109

(iii) the inductance correction model (CFL) developed by Eugster and Senn (1995) 110

1 MATERIAL AND METHOD 111

11 Field site 112

The experiment was carried out from 17 to 30 July 2008 (DOYs 199 to 211) in Rutigliano 113

(41degN 17deg54 E 122 m asl) near Bari in southern Italy All experimental activities were 114

performed in a 2 hectares field dimensions were 200 m x 100 m The soil type is classified as 115

loamy clay (Clay 41 Silt 44 Sand 15 ) and the terrain is flat The climate is semi-arid 116

Mediterranean with hot and dry summer and short and temperate winter The mean annual 117

temperature is 157 degC with a mean annual precipitation of 600 mm the main wind direction 118

is north during the summer time In this study most of the analysis will focus on the period 119

22-29 July 2008 (DOYs 204-211) where the NH3 fluxes were the largest 120

12 Crop and urea spreading 121

During the experimental campaign the field was cultivated with Sorghum vulgare (cv Hay 122

Day) sowed on 10th

June 2008 and irrigated by sprinkler irrigation A commercial urea 123

product (46 N content) in granular form was applied The total N supply was around 240 124

kg N ha-1

divided into three applications 30 90 and 120 kg N ha-1

on 1st 16

th and 22

nd July 125

2008 respectively The first application was carried out at crop emergence Before the second 126

application the field was irrigated with 79 mm water while further irrigation (93 mm) was 127

applied before and after the third application The amount of urea was slightly higher than 128

current agronomic practices in the area since the dry conditions (high temperature no rain 129

and no irrigation applied) between the second and the last urea spreading inhibited NH3 130

volatilization so that no fluxes were detected The last urea application combined with 131

irrigation was necessary in order to increase the probability to detect NH3 fluxes 132

6

13 NH3 fluxes by the eddy covariance method 133

The fluxes of NH3 were measured with the eddy covariance method (eg Kaimal amp Finnigan 134

1994 Lee et al 2004 Mahrt 2010) The EC method is based on estimating the transport of a 135

scalar by turbulent motions of upward and downward moving air contained in the atmosphere 136

The vertical flux (F) of scalar at the measurement height is given by 137

wwF

(1) 138

where w is the component of the wind speed normal to the surface and β is the scalar 139

concentration and the usual Reynolds decomposition is used where prime denotes fluctuating 140

quantities w is the covariance between and w With the usual assumption of steady state 141

horizontally homogeneous flow there is no vertical flux of dry air ie 0wcd where cd is 142

the dry air concentration Since the dry air concentration fluctuates due to heat and water 143

vapour fluxes w is not necessarily equal to zero The Webb Pearmann Leuning (WPL) 144

correction (Webb et al 1980 Leuning 2007) gives the expression of w The impact of this 145

term depends on the concentration of the gas and the typical magnitude of the flux (Denmead 146

1983 Liebethal and Foken 2004) for trace gas with small concentration and large fluxes 147

such as NH3 over a source these corrections are negligible 148

The turbulent flux w as a time-averaged quantity can also be viewed as a frequency 149

averaged quantity ie the integral of the co-spectral density (Cowβ) (Kaimal and Finnigan 150

1994 Aubinet et al 2000 Massman 2000) 151

0

)( dffCow w

(2)

152

To catch all relevant turbulent fluctuations β and w should be measured with a sampling 153

frequency much larger than the frequency of the eddies carrying the energy of the signal (fx) 154

(Horst 1997 Massman 2000) Usually a sampling frequency of 10 Hz is considered 155

sufficient above a vegetated surface However flux loss is inevitable with any EC system 156

especially employing closed-path analysers to which the air sample is transported into the 157

measuring cell by means of a tube The mixing effects in the tube and additional possible 158

chemical or micro-physical processes occurring in the system produce low-pass filtering 159

effects on the measured covariances (Ibrom et al 2007) Nowadays a variety of methods are 160

available to correct these underestimated measured fluxes by means of frequency response 161

correction factors (eg Ammann et al 2006 Shimizu 2007 Aubinet et al 2000 Massman 162

2000 Eugster and Senn 1995) However in this work since the adopted closed-path analyser 163

7

for NH3 concentration measurements is relatively new and it is the first time it is being used 164

under semi-arid climates we compare a variety of methods to correct the NH3 fluxes 165

measured over an agricultural plot 166

14 NH3 concentration measurements 167

141 The QC-TILDAS analyser 168

1411 The instrument 169

The QC-TILDAS employed during this research is the compact QC-TILDAS-76 SN002-U 170

developed by Aerodyne Research Inc (ARI Billerica MA USA) (Zahniser et al 2005) 171

This spectrometer combines QC laser designed for pulsed operation at near room temperature 172

(Alpes Lasers Neuchacirctel Switzerland) an optical system together with a computer-controlled 173

system that incorporates the electronics for driving the QC laser along with signal generation 174

and a data logger Molecular transition selection for given laser is achieved by temperature 175

tuning typically within a small range around 40 ordmC using a two-stage Peltier element The 176

laser frequency is swept across the full molecular transition at 96734 cm-1

which means that 177

the scan is done over a narrow range of wave numbers chosen to contain an absorption feature 178

of the trace gas of interest The optical system collects light from the QC laser and directs it 179

through an astigmatic Herriott type multi-pass absorption cell (76 m effective path length) 180

onto a TE-cooled photovoltaic detector (Vigo Systems) A detailed description of the device 181

can be found in Zahniser et al (2005) 182

1412 The inlet design and the sampling 183

Ellis et al (2010) report the characterization of the QC-TILDAS by laboratory tests In 184

particular they reported a detailed analysis on the performance of the QC-TILDAS in 185

function of the inlet design To achieve accurate atmospheric measurements of NH3 the 186

sampling setup has to reduce gas interactions with surfaces then it is crucial the configuration 187

of the devicersquos inlet through which the air is drawn into the optical cell The inlet used is a 188

short glass tube containing a critical orifice needed to keep the air flow rate constant and to 189

reduce the pressure in the optical cell to approximately 540 kPa a compromise between 190

minimising line broadening and adsorption effects maximising time response and achieving 191

good sensitivity (Warland et al 2001) The setup of the inlet is done to remove particles in 192

the air sample relying on inertia to remove more than 50 of particles larger than 300 nm in 193

8

particular the flow is split into two branches 90 of the flow makes a sharp turn and is 194

pulled into the optical cell by a pump (VARIAN TriScroll 600 Series) the other 10 is 195

pulled by a second air pump (VARIAN mod SH110) This inlet design is optimized to 196

eliminate the need of filters and any interference they may cause The operation of the 197

spectrometer is fully automated and computer-controlled through a Windows-based 198

proprietary software (TDL Wintel) developed at ARI Concentrations are real-time 199

determined from the light absorption spectra through a non-linear least-squares fitting 200

algorithm (Levenberg-Marquardt) that uses spectral parameters from the HITRAN (high-201

resolution transmission) molecular absorption database (Rothman et al 2003) The pressure 202

and temperature in the multi-pass cell are continuously monitored to supply information 203

needed for spectral fitting The software calculates absolute concentrations so no calibration 204

was performed in the field However laboratory tests demonstrate the need to carry out a 205

post-proceeding calibration in order to overcome the failure of the analyzer to reproduce the 206

values of the ammonia standard Moreover we would justify the calibration factor found for 207

the QC-TILDAS using ethilene standard which has absorbing frequency close to ammonia 208

one but it is not affected by stickiness Part of the problem with absolute spectroscopic values 209

for NH3 in our instrument was due to using pulsed-QCLs rather than CW-QCLs with the 210

laser linewidth considerably greater than the molecular linewidth the spectral analysis 211

required much effort to get absolute values Thus a post-proceeding calibration was done and 212

zeroing was routinely carried out in order to optimize the calculated spectra following 213

Whitehead et al (2008) 214

1413 The response time 215

The decreasing of the mixing ratio of the ammonia was well represented by two exponential 216

decay functions which in Ellis et al (2010) is written as 217

2

02

1

010 expexp

ttA

ttAyy (3) 218

where A1 and A2 are constants and their sum is equal to the stable mixing ratio of NH3 prior to 219

the calibration flow being switched off y0 is equal to the NH3 mixing ratio reached at the end 220

of the decay t is time and t0 is the time at the start of the fit τ1 and τ2 are time decay 221

constants the first of which is fast and corresponds to the gas exchange time of the system 222

(04 s in our case) and the second being much slower and corresponding to interactions of 223

NH3 with surfaces on the interior surfaces of the inlet sampling lines and absorption cell (15 224

9

s) In this case the response time was checked in laboratory and it is controlled by pumping 225

speed and by adsorptiondesorption on the walls In particular it is affected by the flow rate 226

the length and the heating of the sampling tubes Therefore acting on these parameters would 227

allow optimizing the instrument for measuring the fluxes by EC technique 228

Moreover for a mixing ratio of 350 ppb the amount of noise (1) in a 10 Hz measurement 229

obtained by Ellis et al (2010) by an Allan variance analysis was 315 ppb (22 microg NH3 m-3

) 230

Based on statistics obtained for sensible heat CO2 and temperature w w was estimated 231

to be around 023 plusmn 013 (mean plusmn SE) and taking ~ 2 microg NH3 m-3

and w ~ 125 u would 232

mean that the flux detection limit of the setup would be around 25 u (in microg NH3 m-2

s-1

) 233

hence typically around 750 ng NH3 m-2

s-1

However most of the turbulent signal is 234

transported by eddies with a frequency around 02 Hz (evaluated as the co-spectral peak 235

frequency for air temperature (Ta) CO2 and water vapour (H2O)) At this frequency the Allan 236

variance of the QC-TILDAS found by Ellis et al (2010) is around 02 microg NH3 m-3

and hence 237

the detection limit should be on the order of 025 u (typically 75 ng NH3 m-2

s-1

) As reported 238

by Ellis et al (2010) there is an increase in the relative importance of NH3 surface 239

interactions at mixing ratios lower than 100 ppb which further increased when sampling 240

humid air as opposed to dry nitrogen Then the heating in the sampling line to prevent 241

condensation is crucial to perform accurate NH3 measurements There was no line heating 242

used in our study since we assumed that this was not needed since the air temperature 243

encountered in the field was very high (up to 33degC) On the other hand under such 244

conditions it was required the use of an air conditioner for the box containing the QC-245

TILDAS to limit instrumental drift induced by changes in the optical alignment as well as in 246

the laser power beam width and temperature 247

142 The ALPHA badges and ROSAA system 248

The NH3 concentration measured with the QC-TILDAS were compared against passive 249

diffusion samples (ALPHA badges Tang et al 2001) These ALPHA badges were 250

positioned at the same height as the QC-TILDAS inlet and changed twice a day to get daily 251

and nightly concentrations Three replicates were used to evaluate the uncertainty in the 252

measurements The background concentration bgd was estimated with three sets of badges put 253

at three locations at more than 100 100 and 300 m away from the field and changed weekly 254

The badges were prepared with filters coated with citric acid The filters were extracted in 3 255

ml double deionised water and analysed with a FLORRIA analyser (Mechatronics NL) In 256

10

addition during the trial NH3 concentration was monitored by means of the new device 257

ROSAA developed at INRA (Loubet et al 2010 2011) It relies on the coupling of NH3 258

capturing by means of an acid stripping solution (wet-effluent denuders) to convert NH3 in 259

NH4+ and the analysis of the NH4

+ concentration by means of a detector based on a semi-260

permeable membrane and a unit to perform temperature controlled conductivity 261

measurements (ECN Netherlands) In the ROSAA system the three denuders were sampling 262

at three heights (at z = 05 07 and 14 m) above ground and the averaged concentration was 263

measure every 30 minutes 264

15 The eddy covariance system description of the equipment 265

The EC system was equipped with the QC-TILDAS and a three axis ultrasonic anemometer 266

(Gill R2 Gill Instruments Ltd UK) The analogue signals from the QC-TILDAS were passed 267

to the sonic anemometer which uses an analogue-to-digital converter to digitise the signal at 268

10 Hz The sonic anemometer acquired data at 168 Hz internally and reported average values 269

at 208 Hz The digitised signals from the QC-TILDAS were then combined with the wind 270

velocity information and sent to the serial port of a personal computer NH3 fluxes were 271

computed offline with a time step of half an hour The software package EddySoft (Kolle and 272

Rebmann 2007) was used for the data acquisition The same software package was used for 273

post processing EC data and to obtain the cospectra useful for applying the TF method while 274

custom Matlabreg and R scripts were used to process and analyse data for the ogive and the 275

inductance methods respectively Trace gas and heat fluxes were calculated as well as other 276

micrometeorological parameters The components of the wind velocity were rotated every 30 277

minutes to set w and v to zero No detrending was applied and block averaging was instead 278

used (Leuning 2007) 279

The sonic anemometer was mounted on a 15 m tall mast so that the height of the EC sensors 280

was about 07 m above the top of the crop and adjusted to follow crop development Ambient 281

air close to the sonic anemometer head was continuously sampled through a 25 m long inlet 282

line (PFA 38rdquo outer diameter) leading to the QC-TILDAS The flow of air through the tube 283

was measured before and after the T connection used for splitting the flow the flow rate 284

towards the sampling cell was about 9 litresminute resulting in a laminar flow regime with a 285

Reynolds number (Re) of about 2000 below the turbulence limit (for pipe flow Re=2300 286

Shimizu 2007) which was the primary cause of the high frequency loss in the measuring 287

system (Eugster and Senn 1995) The limits in the power supply lines in our experimental 288

11

site have prevented the use of pumps able to provide a turbulent regime into the sampling 289

tube 290

The eddy covariance system together with ROSAA and ALPHA samples equipments were 291

located close to the centre of the field which provided an acceptable upwind fetch (around 292

120 m) in the prevalent wind direction during the trial anyway the footprint of EC 293

measurements was found to be always inside the experimental field 294

16 Spectral correction methods for low-pass filtering in a closed-path EC system 295

As mentioned before all eddy covariance systems tend to underestimate the true atmospheric 296

fluxes due to physical limitations of the flux measurement instrumentation which causes 297

losses of eddy flux contributions at low and high frequencies These losses can be quantified 298

and corrected for by multiplying the measured covariance (subscript m) with a frequency 299

response correction factor (CF ge1) 300

mwCFw (4) 301

The correction factor can be estimated by means of spectral correction methods that can be 302

divided into two families the spectral theoretical transfer function (TF) methods and the in-303

situ methods (Massman and Clement 2004) The general approach consists of three steps 1) 304

the identification of a spectral transfer function (theoretical or experimental) 2) its use to 305

calculate a correction factor and 3) the parameterisation of the correction factor with 306

meteorological variables mainly the wind speed which is directly linked to the turbulence 307

Here three methods have been employed in order to estimate CFs to apply at our measured 308

EC NH3 fluxes (i) Theoretical TF analysis CFTheor (ii) A variant of In situ experimental TF 309

method the Ogive method CFO (iii) the inductance method CFL These methodologies are 310

the most analysed in the literature for evaluating the most accurate CFs for gas trace fluxes 311

over natural surfaces 312

Common to all approaches is the requirement of a measured or evaluated reference 313

cospectrum for scalar as well as a measured cospectrum and the estimate of the flux loss as a 314

function of frequency In any case an important aspect relative to any EC system is the time 315

delay between the instant open-path w measurement and the closed-path scalar concentration 316

measurement This time lag is due to the transport time in the tubing system and the phase 317

shift due to low-pass filtering (Ibrom et al 2007) It depends on the tube length and the flow 318

rate it may change slightly due to changing flow rates in the tube caused by pumping 319

variation or density changes Usually this delay can be calculated empirically by determining 320

12

the maximum of the covariance function between wrsquo and rsquo (Moncrieff et al 1997) which 321

includes the time lag due to the longitudinal (streamwise) separation distance from the air 322

sample inlet to the sonic anemometer depending on wind speed and direction The 323

individually determined time delays for each 30 minutes set of data can be used for 324

calculating the cospectra in this case the phase shift due to low-pass filtering and longitudinal 325

sensor separation has already been corrected for (Ibrom et al 2007) So two subcategories 326

can be identified in the theoretical transfer function analysis with (CFTheor_time_lag) and without 327

the time lag (CFTheor_phase_shift) applied to computation of the covariances and cospectra 328

329

161 Theoretical TF analysis 330

Following Moore (1986) theoretical transfer functions characterizing the measurement 331

process have been developed The correction factor in this case is summarized by Massman 332

and Clement (2004) using the following equation 333

dffCofHfT

fT

dffCo

w

wCF

w

b

b

w

m

TF

)()(sin

1

)(

0

2

2

0

(5)

334

where mw is the measured covariance and w is the true flux H(f) is the product of all 335

the appropriate transfer functions associated with HF losses (

n

i

iTFfH1

)( ) )( fCow is the 336

one-side reference cospectrum Tb is the block averaging period 22sin1 bb fTfT is the 337

TF for the low frequency spectral attenuation The reference cospectrum is either taken from 338

literature (Kaimal and Finnigan 1994) or a site specific reference cospectrum usually derived 339

from sensible heat flux measurements The requirements to apply this method are the TFs 340

and a model of the cospectra which is the weakness of this approach since smooth cospectra 341

are not typical of the atmospheric surface layer (Massman and Clement 2004) In particular 342

in this work we used the reference cospectral models (eq 6-9) given by Moncrieff et al 343

(1997) where udzffk )( is normalized frequency f is the natural frequency u is 344

horizontal wind speed z-d is height above the zero-plane displacement d For stable 345

atmospheric condition the reference cospectrum is 346

12)(

kww

kw

fBA

fffCo

(6)

347

where 348

13

750

4612840

L

dzAw (7)

349

and 350

11342 ww AB (8)

351

While for unstable atmospheric conditions the reference cospectrum is 352

540

7261

9212)(

3751

k

k

kw ffor

f

fffCo (9a)

353

540

831

3784)(

42

k

k

kw ffor

f

fffCo (9b)

354

Moore (1986) developed the first theoretical TFs for EC systems taking into account the 355

attenuation due to the dynamic frequency response of the sensors the scalar path averaging 356

the sensor separation the sensor response mismatch while Moncrieff et al (1997) gave a 357

complete description of TFs of EC systems with closed-path analysers Then considering the 358

wide literature on the used theoretical TFs (see Shimizu 2007 for a review) and the weight of 359

each TF on the total one for our equipment set-up here we report only details about TFs 360

related to (i) the lateral separation (ie perpendicular to the mean wind direction) between 361

the sonic anemometer and QC-TILDAS inlet (TFs Moore 1986) (ii) the tube damping effect 362

(TFtube Lenschow and Raupach 1991) (iii) the volume averaging in a measurement cell 363

(TFvol Massman 2004) (iv) the total phase shift (TFphase_shift Massman 2000) In detail 364

(i) Following Moore (1986) the effect of laterallongitudinal separation s between two 365

sensors can be represented by the following cospectral transfer function 366

)99exp(99 51nTFs (10)

367

where n=fsu As suggested by Massman (2000) the horizontal separation between the two 368

sensors has been set to 030 m 369

(ii) The function TFtube describing the attenuation of the fluctuation concentrations down the 370

sampling tube in which there is a laminar flow for covariance calculation (Lenschow and 371

Raupach 1991 Aubinet et al 2000) is 372

ts

t

tubeUD

LfrTF t

12exp

222 (11)

373

Here rt is the tube radius and Ds the molecular diffusivity of the scalar around 02 cm2s

-1 for 374

NH3 (Massman 1998) Moreover the attenuation may vary with time Leuning and Judd 375

(1996) showed that the attenuation to water vapour by aged and dirty tubes is much greater 376

than that by new and clear tube In the case of laminar flow through the tube the theoretical 377

14

cut off frequency fco_t at which the TF equals 2-12

is given by 378

tt

sttco

Lr

DUf

2_ 6490 (12)

379

about 14 Hz in our EC system To minimizing HF losses due to nonuniform velocity profiles 380

turbulent flow would be preferred (Lenschow and Raupach 1991) This requires a powerful 381

pump with high power consumption and a tremendous start-up current (13 A at 230 V AC for 382

an Edwards Tri-Scroll pump for example pers comm W Eugster) that was unrealistic at our 383

field site Hence we had to use a less powerful pump and thus need to account for the extra 384

loss in frequency response due to laminar flow in the intake hose (Moncrieff et al 1997) The 385

possible tube damping correction does not correct potential absorptiondesorption of moisture 386

by hygroscopic dirt particles in the tube however a strong pressure drop along the intake 387

hose reduces risk of condensation on the walls of the tube On the other hand HF due to wall 388

interaction in the sample tube could be reduced by minimizing lengthrsquos tube and maintaining 389

high flow rate In general the line loss is a function of tubing type and how dirty the tube is 390

(Sukyer and Verma 1993) 391

(iii) As reported by Massman (2004) in a closed-path detector the flow velocity within the 392

detection chamber can be very different than the wind speed of the ambient atmosphere near 393

the tube inlet so that the transfer functions need to be expressed in terms of the volume 394

flushing time constant of the detection chamber τvol 395

2

2sin 2

vol

volvolTF

(13)

396

where f 2 and τvol is equal to the time needed to fill the chamber and its minimum value 397

is given by the ratio between the volume of the chamber (05 l) and the maximum flow rate 398

(iv) However if covariances and the cospectra are calculated without the time lag that means 399

without the method of maximum covariance function or if there is no clear time lag due to 400

lag depending on the frequency of the eddies (sensor separation in the wind direction) or if 401

there are complex adsorption and desorption processes it is necessary to take into account the 402

phase shift (Massman 2000) The TF associated with the total phase shift (Massman 2000) 403

is 404

t

tlon

t

tlonshiftphase

U

L

u

l

U

L

u

lTF sincos_ (14)

405

where llon is the longitudinal sensor separation (function of wind direction) and β is the 406

intrinsic time constant of the scalar sensor This latter was estimated by using the Boylersquos law 407

15

applied to the measurement chamber working at 33 kPa and using the measured flow rate it 408

is about 01 s and moreover it means that the sampling frequency of 10 Hz is adequate for 409

the employed QC-TILDAS in the EC system 410

For the theoretical TF approach cospectra of the vertical wind speed and T and NH3 411

respectively were calculated using the post processing EddySpec package of Eddysoft 412

software using the following setup number of frequencies 4096 for calculating the cospectra 413

for half-hourly runs multiplied by frequency tapering the time series with a Hamming 414

window (Kaimal and Finnigan 1994) and averaging into 20 logarithmically spaced bins 415

Either the time lag or the phase shift was used in the functions of the two approaches 416

CFTheor_time_lag and CFTheor_phase_shift respectively 417

418

162 In situ ogive method 419

The limitation of the theoretical TF approach is that it could miss additional chemical or 420

micro-physical process occurring in the EC system a restriction that is expected to be 421

overcome by the in situ experimental methods Moreover the experimental approach is based 422

on the assumption of cospectral similarity between scalar fluxes and unlike the theoretical TF 423

approach these methods do not require smooth models of atmospheric cospectra (Massman 424

and Clement 2004) The correction factor can be estimated as the ratio between the 425

attenuated flux and a reference flux (usually the sensible heat flux) Aubinet et al (2000) give 426

the procedure to estimate this experimental frequency transfer function but since the 427

cospectra derived from sensible heat fluxes are often not well-defined then direct application 428

of this approach can produce implausible values 429

Starting from the hypothesis of similarity between all scalars cospectra Ammann et al (2006) 430

developed a method for estimating HF losses Applied to EC measurement of NH3 fluxes the 431

method consists in fitting the ogive for NH3 OgwNH3 (the ogive is the integrated cospectra 432

CowNH3 from low to high frequencies) against the ogive of air temperature OgwTa The fitting 433

was made with an iterative linear least square method in which the points being too far away 434

from the regression line were eliminated progressively (robustfit function under Matlabreg) 435

The high-frequency damping correction (CFO) is simply evaluated as the value of the fitted 436

NH3 ogive (OgwNH3) at the highest frequency The frequency band over which the linear 437

regression is performed is determined as the frequency for which OgwT larger than 01 and 438

OgwNH3 is smaller than 08 This choice was arbitrary and may not be appropriate if the 439

damping of the NH3 signal was below the range chosen The ogives were calculated using 440

detrended signals 441

16

442

163 The inductance method 443

Eugster and Senn (1995) propose a correction model for EC data affected by systematic error 444

introduced by the combination of all sources of damping which also includes chemical 445

reaction (in the case of NO2) besides the possible damping due to laminar flow in the 446

sampling tube and sensor separation This damping loss is described by an analogy to 447

inductance L in an alternating current circuit which can be used for taking into account the 448

reduction of spectral density in the inertial subrange This model follows the approach of 449

Moore (1986) using electronic components in a current circuit instead of a gain function 450

As reported by Eugster and Senn (1995) the covariance of the measured (ie damped) scalar 451

flux is given by 452

02220

)(41

1)(

0dffCo

LfdffCow

LL wwL

(15)

453

where )(0 fCo

Lw is the undamped cospectrum The value of L is modified in order to fit the 454

measured cospectrum The independent variables in this correction model are the measuring 455

height above zero-plane displacement z-d the mean horizontal wind speed u the Monin-456

Obukhov stability parameter ζ the natural frequency f and the inductance L The value for 457

L can be derived from spectral and cospectral analysis and is assumed not vary significantly 458

as long as the measuring system is not altered 459

The damped cospectra can be derived combining equation (14) with the empirical formula of 460

Kaimal et al (1972) for stable unstable and neutral stratification as modified by Eugster and 461

Senn (1995) (equations 29 and 30 in their paper) The approximated solutions of Eugster and 462

Senn (1995) for CF range between 10 (no damping at all) and infin (no signal at all) but they 463

suggest to restrict the fit to the range between CF=10 and CF=25 that means all 464

measurements which represent less than 40 of the real fluxes were discarded 465

17

2 RESULTS AND DISCUSSION 466

21 Micrometeorological conditions 467

During the trial the air temperature ranged from 16degC to 335degC and averaged 247degC while 468

the soil surface temperature reached more than 546degC at hourly scale The sky was clear 469

most of the period apart from the 23-24072008 where there was little rain (82 mm) The 470

global solar radiation was following the fair weather diurnal course throughout the period and 471

reached 950 W m-2

at maximum The daily average relative humidity was around 60 with 472

variations ranging between 25 and 93 473

The prevalent wind direction during the experimental period (84 of the runs) was NW the 474

wind speed ranged from 05 to 89 ms-1

and averaged 34 ms-1

while the friction velocity u 475

ranged between 001 and 099 ms-1

with an average of 030 ms-1

(Figure 1a) The energy 476

balance for the period of the trial is reported in Figure 2b at hourly scale In particular it is 477

clear the direct impact of the net radiation (Rn) on the sensible heat flux (H) and on the latent 478

heat flux (LE) The pattern of LE were strongly correlated with the available energy Rn with 479

distinct peaks immediately following irrigation which provided water to evapotranspirate 480

from plant and soil 481

22 Ammonia concentrations 482

The field NH3 concentrations measured by the diffusive samplers (ALPHA badges) over 483

several hours ranged from 3 to 118 microg NH3 m-3

while the 30 minutes ROSAA concentrations 484

varied from around 0 to around 90 microg NH3 m-3

The ROSAA concentrations averaged over 485

the same intervals as the ALPHA badges slightly underestimated the badges concentration by 486

8 on average (ROSAA = 092 ALPHA R2 = 082) The uncalibrated QC-TILDAS 487

concentrations strongly underestimated the ALPHA badges concentrations by 67 but 488

without any offset (QC-TILDAS = 034 ALPHA R2 = 090) This underestimation is larger than 489

what Ellis et al (2010) found with the same instrument in a laboratory calibration Since the 490

correlation was very good the regression against the ALPHA badges was used to calibrate the 491

QC-TILDAS concentration data The correction factor was found to be 286 plusmn 009 The 492

calibrated QC-TILDAS concentration was in quite good agreement with the ROSAA 493

concentrations both in terms of magnitude and dynamics (Figure 2) In Figure 3 a detailed 494

comparison between calibrated values of QC-TILDAS NH3 concentrations and ROSAA 495

values is shown Since we did not find a significant offset in the regression of QC-TILDAS 496

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 4: Eddy covariance measurement of ammonia fluxes: comparison of

4

passive filters and denuders developed by Ferm (1979) nowadays the most widely used 70

method for its simplicity and inexpensiveness However the drawbacks of this approach are 71

the relatively low time resolution (minute to hours) and the laboratory intensive labour 72

required for preparing them and for manual off-line sample analysis Coupled to the chemical 73

analytical methods progress in optical methods have been achieved with the development of 74

spectroscopic devices characterized by high time resolution and sensitivity to NH3 but which 75

are still expensive Von Bobrutzki et al (2010) recently provided inter-comparisons for NH3 76

measurement techniques in field highlighting that many challenges still have to be faced for 77

measuring NH3 accurately Nevertheless Tunable Infrared Laser Differential Absorption 78

Spectroscopy (TILDAS) using a quantum cascade (QC) laser are now available for 79

monitoring NH3 concentration with a sufficient sensitivity and robustness to be employed in 80

the field However the application of the EC method for monitoring NH3 is considered a 81

challenge by the scientific community as demonstrated by the scarce literature available on 82

this topic (Shaw et al 1998 Famulari et al 2005 Whitehead et al 2008 Brouder et al 83

2009 Sintermann et al 2011) in which are highlighted the difficulties to its routine 84

applicability In particular the employment of a setup involving a fast closed-path analyser 85

could introduce considerable damping of high frequency fluctuations and consequent 86

underestimation of the fluxes that have to be corrected for (eg Massman 2000) 87

Several methods are available for evaluating and correcting these underestimations when EC 88

equipment is used for trace gas fluxes (Massman and Clement 2004 for CO2 for example) In 89

particular Whitehead et al (2008) evidenced that the correction for EC fluxes of NH3 using 90

TILDAS is fundamental to accurately estimate the real fluxes at different time scales 91

However up to now no attempt has been made to evaluate using different methodologies 92

the correction of the underestimation of EC ammonia fluxes when a QC-TILDAS is used for 93

measuring NH3 fluctuations in the cropped field Furthermore no EC data of NH3 turbulent 94

fluxes are available for crop grown in arid environments 95

In this study we report EC measurements of NH3 fluxes performed employing a fast closed-96

path analyser for NH3 concentration the QC-TILDAS-76 SN002-U developed by Aerodyne 97

Research Inc (ARI Billerica MA USA) (Zahniser et al 2005) under custom specifications 98

The trial was carried out over a field in an intensive agricultural area of south Italy in a semi-99

arid to arid Mediterranean climate The plot was fertilized with urea pellets to stimulate NH3 100

emission and to test the device over a wide range of concentrations The aim was to assess the 101

performance of the QC-TILDAS used for the first time in an EC system for measuring NH3 102

fluxes over an irrigated cropland in Mediterranean environment The challenge was to verify 103

5

the adequacy of the system in terms of setup and quality of the data In order to reach a robust 104

assessment of the needed correction three possible methods for correcting the flux losses due 105

to the design and the setup of the employed equipment were considered in this analysis In 106

particular we compare different frequency correction factors (CF) to apply to the measured 107

EC NH3 fluxes obtained by the following approaches (i) the spectral theoretical transfer 108

function (CFTheor) (ii) the in-situ ogive method (CFO) (Massman and Clement 2004) and 109

(iii) the inductance correction model (CFL) developed by Eugster and Senn (1995) 110

1 MATERIAL AND METHOD 111

11 Field site 112

The experiment was carried out from 17 to 30 July 2008 (DOYs 199 to 211) in Rutigliano 113

(41degN 17deg54 E 122 m asl) near Bari in southern Italy All experimental activities were 114

performed in a 2 hectares field dimensions were 200 m x 100 m The soil type is classified as 115

loamy clay (Clay 41 Silt 44 Sand 15 ) and the terrain is flat The climate is semi-arid 116

Mediterranean with hot and dry summer and short and temperate winter The mean annual 117

temperature is 157 degC with a mean annual precipitation of 600 mm the main wind direction 118

is north during the summer time In this study most of the analysis will focus on the period 119

22-29 July 2008 (DOYs 204-211) where the NH3 fluxes were the largest 120

12 Crop and urea spreading 121

During the experimental campaign the field was cultivated with Sorghum vulgare (cv Hay 122

Day) sowed on 10th

June 2008 and irrigated by sprinkler irrigation A commercial urea 123

product (46 N content) in granular form was applied The total N supply was around 240 124

kg N ha-1

divided into three applications 30 90 and 120 kg N ha-1

on 1st 16

th and 22

nd July 125

2008 respectively The first application was carried out at crop emergence Before the second 126

application the field was irrigated with 79 mm water while further irrigation (93 mm) was 127

applied before and after the third application The amount of urea was slightly higher than 128

current agronomic practices in the area since the dry conditions (high temperature no rain 129

and no irrigation applied) between the second and the last urea spreading inhibited NH3 130

volatilization so that no fluxes were detected The last urea application combined with 131

irrigation was necessary in order to increase the probability to detect NH3 fluxes 132

6

13 NH3 fluxes by the eddy covariance method 133

The fluxes of NH3 were measured with the eddy covariance method (eg Kaimal amp Finnigan 134

1994 Lee et al 2004 Mahrt 2010) The EC method is based on estimating the transport of a 135

scalar by turbulent motions of upward and downward moving air contained in the atmosphere 136

The vertical flux (F) of scalar at the measurement height is given by 137

wwF

(1) 138

where w is the component of the wind speed normal to the surface and β is the scalar 139

concentration and the usual Reynolds decomposition is used where prime denotes fluctuating 140

quantities w is the covariance between and w With the usual assumption of steady state 141

horizontally homogeneous flow there is no vertical flux of dry air ie 0wcd where cd is 142

the dry air concentration Since the dry air concentration fluctuates due to heat and water 143

vapour fluxes w is not necessarily equal to zero The Webb Pearmann Leuning (WPL) 144

correction (Webb et al 1980 Leuning 2007) gives the expression of w The impact of this 145

term depends on the concentration of the gas and the typical magnitude of the flux (Denmead 146

1983 Liebethal and Foken 2004) for trace gas with small concentration and large fluxes 147

such as NH3 over a source these corrections are negligible 148

The turbulent flux w as a time-averaged quantity can also be viewed as a frequency 149

averaged quantity ie the integral of the co-spectral density (Cowβ) (Kaimal and Finnigan 150

1994 Aubinet et al 2000 Massman 2000) 151

0

)( dffCow w

(2)

152

To catch all relevant turbulent fluctuations β and w should be measured with a sampling 153

frequency much larger than the frequency of the eddies carrying the energy of the signal (fx) 154

(Horst 1997 Massman 2000) Usually a sampling frequency of 10 Hz is considered 155

sufficient above a vegetated surface However flux loss is inevitable with any EC system 156

especially employing closed-path analysers to which the air sample is transported into the 157

measuring cell by means of a tube The mixing effects in the tube and additional possible 158

chemical or micro-physical processes occurring in the system produce low-pass filtering 159

effects on the measured covariances (Ibrom et al 2007) Nowadays a variety of methods are 160

available to correct these underestimated measured fluxes by means of frequency response 161

correction factors (eg Ammann et al 2006 Shimizu 2007 Aubinet et al 2000 Massman 162

2000 Eugster and Senn 1995) However in this work since the adopted closed-path analyser 163

7

for NH3 concentration measurements is relatively new and it is the first time it is being used 164

under semi-arid climates we compare a variety of methods to correct the NH3 fluxes 165

measured over an agricultural plot 166

14 NH3 concentration measurements 167

141 The QC-TILDAS analyser 168

1411 The instrument 169

The QC-TILDAS employed during this research is the compact QC-TILDAS-76 SN002-U 170

developed by Aerodyne Research Inc (ARI Billerica MA USA) (Zahniser et al 2005) 171

This spectrometer combines QC laser designed for pulsed operation at near room temperature 172

(Alpes Lasers Neuchacirctel Switzerland) an optical system together with a computer-controlled 173

system that incorporates the electronics for driving the QC laser along with signal generation 174

and a data logger Molecular transition selection for given laser is achieved by temperature 175

tuning typically within a small range around 40 ordmC using a two-stage Peltier element The 176

laser frequency is swept across the full molecular transition at 96734 cm-1

which means that 177

the scan is done over a narrow range of wave numbers chosen to contain an absorption feature 178

of the trace gas of interest The optical system collects light from the QC laser and directs it 179

through an astigmatic Herriott type multi-pass absorption cell (76 m effective path length) 180

onto a TE-cooled photovoltaic detector (Vigo Systems) A detailed description of the device 181

can be found in Zahniser et al (2005) 182

1412 The inlet design and the sampling 183

Ellis et al (2010) report the characterization of the QC-TILDAS by laboratory tests In 184

particular they reported a detailed analysis on the performance of the QC-TILDAS in 185

function of the inlet design To achieve accurate atmospheric measurements of NH3 the 186

sampling setup has to reduce gas interactions with surfaces then it is crucial the configuration 187

of the devicersquos inlet through which the air is drawn into the optical cell The inlet used is a 188

short glass tube containing a critical orifice needed to keep the air flow rate constant and to 189

reduce the pressure in the optical cell to approximately 540 kPa a compromise between 190

minimising line broadening and adsorption effects maximising time response and achieving 191

good sensitivity (Warland et al 2001) The setup of the inlet is done to remove particles in 192

the air sample relying on inertia to remove more than 50 of particles larger than 300 nm in 193

8

particular the flow is split into two branches 90 of the flow makes a sharp turn and is 194

pulled into the optical cell by a pump (VARIAN TriScroll 600 Series) the other 10 is 195

pulled by a second air pump (VARIAN mod SH110) This inlet design is optimized to 196

eliminate the need of filters and any interference they may cause The operation of the 197

spectrometer is fully automated and computer-controlled through a Windows-based 198

proprietary software (TDL Wintel) developed at ARI Concentrations are real-time 199

determined from the light absorption spectra through a non-linear least-squares fitting 200

algorithm (Levenberg-Marquardt) that uses spectral parameters from the HITRAN (high-201

resolution transmission) molecular absorption database (Rothman et al 2003) The pressure 202

and temperature in the multi-pass cell are continuously monitored to supply information 203

needed for spectral fitting The software calculates absolute concentrations so no calibration 204

was performed in the field However laboratory tests demonstrate the need to carry out a 205

post-proceeding calibration in order to overcome the failure of the analyzer to reproduce the 206

values of the ammonia standard Moreover we would justify the calibration factor found for 207

the QC-TILDAS using ethilene standard which has absorbing frequency close to ammonia 208

one but it is not affected by stickiness Part of the problem with absolute spectroscopic values 209

for NH3 in our instrument was due to using pulsed-QCLs rather than CW-QCLs with the 210

laser linewidth considerably greater than the molecular linewidth the spectral analysis 211

required much effort to get absolute values Thus a post-proceeding calibration was done and 212

zeroing was routinely carried out in order to optimize the calculated spectra following 213

Whitehead et al (2008) 214

1413 The response time 215

The decreasing of the mixing ratio of the ammonia was well represented by two exponential 216

decay functions which in Ellis et al (2010) is written as 217

2

02

1

010 expexp

ttA

ttAyy (3) 218

where A1 and A2 are constants and their sum is equal to the stable mixing ratio of NH3 prior to 219

the calibration flow being switched off y0 is equal to the NH3 mixing ratio reached at the end 220

of the decay t is time and t0 is the time at the start of the fit τ1 and τ2 are time decay 221

constants the first of which is fast and corresponds to the gas exchange time of the system 222

(04 s in our case) and the second being much slower and corresponding to interactions of 223

NH3 with surfaces on the interior surfaces of the inlet sampling lines and absorption cell (15 224

9

s) In this case the response time was checked in laboratory and it is controlled by pumping 225

speed and by adsorptiondesorption on the walls In particular it is affected by the flow rate 226

the length and the heating of the sampling tubes Therefore acting on these parameters would 227

allow optimizing the instrument for measuring the fluxes by EC technique 228

Moreover for a mixing ratio of 350 ppb the amount of noise (1) in a 10 Hz measurement 229

obtained by Ellis et al (2010) by an Allan variance analysis was 315 ppb (22 microg NH3 m-3

) 230

Based on statistics obtained for sensible heat CO2 and temperature w w was estimated 231

to be around 023 plusmn 013 (mean plusmn SE) and taking ~ 2 microg NH3 m-3

and w ~ 125 u would 232

mean that the flux detection limit of the setup would be around 25 u (in microg NH3 m-2

s-1

) 233

hence typically around 750 ng NH3 m-2

s-1

However most of the turbulent signal is 234

transported by eddies with a frequency around 02 Hz (evaluated as the co-spectral peak 235

frequency for air temperature (Ta) CO2 and water vapour (H2O)) At this frequency the Allan 236

variance of the QC-TILDAS found by Ellis et al (2010) is around 02 microg NH3 m-3

and hence 237

the detection limit should be on the order of 025 u (typically 75 ng NH3 m-2

s-1

) As reported 238

by Ellis et al (2010) there is an increase in the relative importance of NH3 surface 239

interactions at mixing ratios lower than 100 ppb which further increased when sampling 240

humid air as opposed to dry nitrogen Then the heating in the sampling line to prevent 241

condensation is crucial to perform accurate NH3 measurements There was no line heating 242

used in our study since we assumed that this was not needed since the air temperature 243

encountered in the field was very high (up to 33degC) On the other hand under such 244

conditions it was required the use of an air conditioner for the box containing the QC-245

TILDAS to limit instrumental drift induced by changes in the optical alignment as well as in 246

the laser power beam width and temperature 247

142 The ALPHA badges and ROSAA system 248

The NH3 concentration measured with the QC-TILDAS were compared against passive 249

diffusion samples (ALPHA badges Tang et al 2001) These ALPHA badges were 250

positioned at the same height as the QC-TILDAS inlet and changed twice a day to get daily 251

and nightly concentrations Three replicates were used to evaluate the uncertainty in the 252

measurements The background concentration bgd was estimated with three sets of badges put 253

at three locations at more than 100 100 and 300 m away from the field and changed weekly 254

The badges were prepared with filters coated with citric acid The filters were extracted in 3 255

ml double deionised water and analysed with a FLORRIA analyser (Mechatronics NL) In 256

10

addition during the trial NH3 concentration was monitored by means of the new device 257

ROSAA developed at INRA (Loubet et al 2010 2011) It relies on the coupling of NH3 258

capturing by means of an acid stripping solution (wet-effluent denuders) to convert NH3 in 259

NH4+ and the analysis of the NH4

+ concentration by means of a detector based on a semi-260

permeable membrane and a unit to perform temperature controlled conductivity 261

measurements (ECN Netherlands) In the ROSAA system the three denuders were sampling 262

at three heights (at z = 05 07 and 14 m) above ground and the averaged concentration was 263

measure every 30 minutes 264

15 The eddy covariance system description of the equipment 265

The EC system was equipped with the QC-TILDAS and a three axis ultrasonic anemometer 266

(Gill R2 Gill Instruments Ltd UK) The analogue signals from the QC-TILDAS were passed 267

to the sonic anemometer which uses an analogue-to-digital converter to digitise the signal at 268

10 Hz The sonic anemometer acquired data at 168 Hz internally and reported average values 269

at 208 Hz The digitised signals from the QC-TILDAS were then combined with the wind 270

velocity information and sent to the serial port of a personal computer NH3 fluxes were 271

computed offline with a time step of half an hour The software package EddySoft (Kolle and 272

Rebmann 2007) was used for the data acquisition The same software package was used for 273

post processing EC data and to obtain the cospectra useful for applying the TF method while 274

custom Matlabreg and R scripts were used to process and analyse data for the ogive and the 275

inductance methods respectively Trace gas and heat fluxes were calculated as well as other 276

micrometeorological parameters The components of the wind velocity were rotated every 30 277

minutes to set w and v to zero No detrending was applied and block averaging was instead 278

used (Leuning 2007) 279

The sonic anemometer was mounted on a 15 m tall mast so that the height of the EC sensors 280

was about 07 m above the top of the crop and adjusted to follow crop development Ambient 281

air close to the sonic anemometer head was continuously sampled through a 25 m long inlet 282

line (PFA 38rdquo outer diameter) leading to the QC-TILDAS The flow of air through the tube 283

was measured before and after the T connection used for splitting the flow the flow rate 284

towards the sampling cell was about 9 litresminute resulting in a laminar flow regime with a 285

Reynolds number (Re) of about 2000 below the turbulence limit (for pipe flow Re=2300 286

Shimizu 2007) which was the primary cause of the high frequency loss in the measuring 287

system (Eugster and Senn 1995) The limits in the power supply lines in our experimental 288

11

site have prevented the use of pumps able to provide a turbulent regime into the sampling 289

tube 290

The eddy covariance system together with ROSAA and ALPHA samples equipments were 291

located close to the centre of the field which provided an acceptable upwind fetch (around 292

120 m) in the prevalent wind direction during the trial anyway the footprint of EC 293

measurements was found to be always inside the experimental field 294

16 Spectral correction methods for low-pass filtering in a closed-path EC system 295

As mentioned before all eddy covariance systems tend to underestimate the true atmospheric 296

fluxes due to physical limitations of the flux measurement instrumentation which causes 297

losses of eddy flux contributions at low and high frequencies These losses can be quantified 298

and corrected for by multiplying the measured covariance (subscript m) with a frequency 299

response correction factor (CF ge1) 300

mwCFw (4) 301

The correction factor can be estimated by means of spectral correction methods that can be 302

divided into two families the spectral theoretical transfer function (TF) methods and the in-303

situ methods (Massman and Clement 2004) The general approach consists of three steps 1) 304

the identification of a spectral transfer function (theoretical or experimental) 2) its use to 305

calculate a correction factor and 3) the parameterisation of the correction factor with 306

meteorological variables mainly the wind speed which is directly linked to the turbulence 307

Here three methods have been employed in order to estimate CFs to apply at our measured 308

EC NH3 fluxes (i) Theoretical TF analysis CFTheor (ii) A variant of In situ experimental TF 309

method the Ogive method CFO (iii) the inductance method CFL These methodologies are 310

the most analysed in the literature for evaluating the most accurate CFs for gas trace fluxes 311

over natural surfaces 312

Common to all approaches is the requirement of a measured or evaluated reference 313

cospectrum for scalar as well as a measured cospectrum and the estimate of the flux loss as a 314

function of frequency In any case an important aspect relative to any EC system is the time 315

delay between the instant open-path w measurement and the closed-path scalar concentration 316

measurement This time lag is due to the transport time in the tubing system and the phase 317

shift due to low-pass filtering (Ibrom et al 2007) It depends on the tube length and the flow 318

rate it may change slightly due to changing flow rates in the tube caused by pumping 319

variation or density changes Usually this delay can be calculated empirically by determining 320

12

the maximum of the covariance function between wrsquo and rsquo (Moncrieff et al 1997) which 321

includes the time lag due to the longitudinal (streamwise) separation distance from the air 322

sample inlet to the sonic anemometer depending on wind speed and direction The 323

individually determined time delays for each 30 minutes set of data can be used for 324

calculating the cospectra in this case the phase shift due to low-pass filtering and longitudinal 325

sensor separation has already been corrected for (Ibrom et al 2007) So two subcategories 326

can be identified in the theoretical transfer function analysis with (CFTheor_time_lag) and without 327

the time lag (CFTheor_phase_shift) applied to computation of the covariances and cospectra 328

329

161 Theoretical TF analysis 330

Following Moore (1986) theoretical transfer functions characterizing the measurement 331

process have been developed The correction factor in this case is summarized by Massman 332

and Clement (2004) using the following equation 333

dffCofHfT

fT

dffCo

w

wCF

w

b

b

w

m

TF

)()(sin

1

)(

0

2

2

0

(5)

334

where mw is the measured covariance and w is the true flux H(f) is the product of all 335

the appropriate transfer functions associated with HF losses (

n

i

iTFfH1

)( ) )( fCow is the 336

one-side reference cospectrum Tb is the block averaging period 22sin1 bb fTfT is the 337

TF for the low frequency spectral attenuation The reference cospectrum is either taken from 338

literature (Kaimal and Finnigan 1994) or a site specific reference cospectrum usually derived 339

from sensible heat flux measurements The requirements to apply this method are the TFs 340

and a model of the cospectra which is the weakness of this approach since smooth cospectra 341

are not typical of the atmospheric surface layer (Massman and Clement 2004) In particular 342

in this work we used the reference cospectral models (eq 6-9) given by Moncrieff et al 343

(1997) where udzffk )( is normalized frequency f is the natural frequency u is 344

horizontal wind speed z-d is height above the zero-plane displacement d For stable 345

atmospheric condition the reference cospectrum is 346

12)(

kww

kw

fBA

fffCo

(6)

347

where 348

13

750

4612840

L

dzAw (7)

349

and 350

11342 ww AB (8)

351

While for unstable atmospheric conditions the reference cospectrum is 352

540

7261

9212)(

3751

k

k

kw ffor

f

fffCo (9a)

353

540

831

3784)(

42

k

k

kw ffor

f

fffCo (9b)

354

Moore (1986) developed the first theoretical TFs for EC systems taking into account the 355

attenuation due to the dynamic frequency response of the sensors the scalar path averaging 356

the sensor separation the sensor response mismatch while Moncrieff et al (1997) gave a 357

complete description of TFs of EC systems with closed-path analysers Then considering the 358

wide literature on the used theoretical TFs (see Shimizu 2007 for a review) and the weight of 359

each TF on the total one for our equipment set-up here we report only details about TFs 360

related to (i) the lateral separation (ie perpendicular to the mean wind direction) between 361

the sonic anemometer and QC-TILDAS inlet (TFs Moore 1986) (ii) the tube damping effect 362

(TFtube Lenschow and Raupach 1991) (iii) the volume averaging in a measurement cell 363

(TFvol Massman 2004) (iv) the total phase shift (TFphase_shift Massman 2000) In detail 364

(i) Following Moore (1986) the effect of laterallongitudinal separation s between two 365

sensors can be represented by the following cospectral transfer function 366

)99exp(99 51nTFs (10)

367

where n=fsu As suggested by Massman (2000) the horizontal separation between the two 368

sensors has been set to 030 m 369

(ii) The function TFtube describing the attenuation of the fluctuation concentrations down the 370

sampling tube in which there is a laminar flow for covariance calculation (Lenschow and 371

Raupach 1991 Aubinet et al 2000) is 372

ts

t

tubeUD

LfrTF t

12exp

222 (11)

373

Here rt is the tube radius and Ds the molecular diffusivity of the scalar around 02 cm2s

-1 for 374

NH3 (Massman 1998) Moreover the attenuation may vary with time Leuning and Judd 375

(1996) showed that the attenuation to water vapour by aged and dirty tubes is much greater 376

than that by new and clear tube In the case of laminar flow through the tube the theoretical 377

14

cut off frequency fco_t at which the TF equals 2-12

is given by 378

tt

sttco

Lr

DUf

2_ 6490 (12)

379

about 14 Hz in our EC system To minimizing HF losses due to nonuniform velocity profiles 380

turbulent flow would be preferred (Lenschow and Raupach 1991) This requires a powerful 381

pump with high power consumption and a tremendous start-up current (13 A at 230 V AC for 382

an Edwards Tri-Scroll pump for example pers comm W Eugster) that was unrealistic at our 383

field site Hence we had to use a less powerful pump and thus need to account for the extra 384

loss in frequency response due to laminar flow in the intake hose (Moncrieff et al 1997) The 385

possible tube damping correction does not correct potential absorptiondesorption of moisture 386

by hygroscopic dirt particles in the tube however a strong pressure drop along the intake 387

hose reduces risk of condensation on the walls of the tube On the other hand HF due to wall 388

interaction in the sample tube could be reduced by minimizing lengthrsquos tube and maintaining 389

high flow rate In general the line loss is a function of tubing type and how dirty the tube is 390

(Sukyer and Verma 1993) 391

(iii) As reported by Massman (2004) in a closed-path detector the flow velocity within the 392

detection chamber can be very different than the wind speed of the ambient atmosphere near 393

the tube inlet so that the transfer functions need to be expressed in terms of the volume 394

flushing time constant of the detection chamber τvol 395

2

2sin 2

vol

volvolTF

(13)

396

where f 2 and τvol is equal to the time needed to fill the chamber and its minimum value 397

is given by the ratio between the volume of the chamber (05 l) and the maximum flow rate 398

(iv) However if covariances and the cospectra are calculated without the time lag that means 399

without the method of maximum covariance function or if there is no clear time lag due to 400

lag depending on the frequency of the eddies (sensor separation in the wind direction) or if 401

there are complex adsorption and desorption processes it is necessary to take into account the 402

phase shift (Massman 2000) The TF associated with the total phase shift (Massman 2000) 403

is 404

t

tlon

t

tlonshiftphase

U

L

u

l

U

L

u

lTF sincos_ (14)

405

where llon is the longitudinal sensor separation (function of wind direction) and β is the 406

intrinsic time constant of the scalar sensor This latter was estimated by using the Boylersquos law 407

15

applied to the measurement chamber working at 33 kPa and using the measured flow rate it 408

is about 01 s and moreover it means that the sampling frequency of 10 Hz is adequate for 409

the employed QC-TILDAS in the EC system 410

For the theoretical TF approach cospectra of the vertical wind speed and T and NH3 411

respectively were calculated using the post processing EddySpec package of Eddysoft 412

software using the following setup number of frequencies 4096 for calculating the cospectra 413

for half-hourly runs multiplied by frequency tapering the time series with a Hamming 414

window (Kaimal and Finnigan 1994) and averaging into 20 logarithmically spaced bins 415

Either the time lag or the phase shift was used in the functions of the two approaches 416

CFTheor_time_lag and CFTheor_phase_shift respectively 417

418

162 In situ ogive method 419

The limitation of the theoretical TF approach is that it could miss additional chemical or 420

micro-physical process occurring in the EC system a restriction that is expected to be 421

overcome by the in situ experimental methods Moreover the experimental approach is based 422

on the assumption of cospectral similarity between scalar fluxes and unlike the theoretical TF 423

approach these methods do not require smooth models of atmospheric cospectra (Massman 424

and Clement 2004) The correction factor can be estimated as the ratio between the 425

attenuated flux and a reference flux (usually the sensible heat flux) Aubinet et al (2000) give 426

the procedure to estimate this experimental frequency transfer function but since the 427

cospectra derived from sensible heat fluxes are often not well-defined then direct application 428

of this approach can produce implausible values 429

Starting from the hypothesis of similarity between all scalars cospectra Ammann et al (2006) 430

developed a method for estimating HF losses Applied to EC measurement of NH3 fluxes the 431

method consists in fitting the ogive for NH3 OgwNH3 (the ogive is the integrated cospectra 432

CowNH3 from low to high frequencies) against the ogive of air temperature OgwTa The fitting 433

was made with an iterative linear least square method in which the points being too far away 434

from the regression line were eliminated progressively (robustfit function under Matlabreg) 435

The high-frequency damping correction (CFO) is simply evaluated as the value of the fitted 436

NH3 ogive (OgwNH3) at the highest frequency The frequency band over which the linear 437

regression is performed is determined as the frequency for which OgwT larger than 01 and 438

OgwNH3 is smaller than 08 This choice was arbitrary and may not be appropriate if the 439

damping of the NH3 signal was below the range chosen The ogives were calculated using 440

detrended signals 441

16

442

163 The inductance method 443

Eugster and Senn (1995) propose a correction model for EC data affected by systematic error 444

introduced by the combination of all sources of damping which also includes chemical 445

reaction (in the case of NO2) besides the possible damping due to laminar flow in the 446

sampling tube and sensor separation This damping loss is described by an analogy to 447

inductance L in an alternating current circuit which can be used for taking into account the 448

reduction of spectral density in the inertial subrange This model follows the approach of 449

Moore (1986) using electronic components in a current circuit instead of a gain function 450

As reported by Eugster and Senn (1995) the covariance of the measured (ie damped) scalar 451

flux is given by 452

02220

)(41

1)(

0dffCo

LfdffCow

LL wwL

(15)

453

where )(0 fCo

Lw is the undamped cospectrum The value of L is modified in order to fit the 454

measured cospectrum The independent variables in this correction model are the measuring 455

height above zero-plane displacement z-d the mean horizontal wind speed u the Monin-456

Obukhov stability parameter ζ the natural frequency f and the inductance L The value for 457

L can be derived from spectral and cospectral analysis and is assumed not vary significantly 458

as long as the measuring system is not altered 459

The damped cospectra can be derived combining equation (14) with the empirical formula of 460

Kaimal et al (1972) for stable unstable and neutral stratification as modified by Eugster and 461

Senn (1995) (equations 29 and 30 in their paper) The approximated solutions of Eugster and 462

Senn (1995) for CF range between 10 (no damping at all) and infin (no signal at all) but they 463

suggest to restrict the fit to the range between CF=10 and CF=25 that means all 464

measurements which represent less than 40 of the real fluxes were discarded 465

17

2 RESULTS AND DISCUSSION 466

21 Micrometeorological conditions 467

During the trial the air temperature ranged from 16degC to 335degC and averaged 247degC while 468

the soil surface temperature reached more than 546degC at hourly scale The sky was clear 469

most of the period apart from the 23-24072008 where there was little rain (82 mm) The 470

global solar radiation was following the fair weather diurnal course throughout the period and 471

reached 950 W m-2

at maximum The daily average relative humidity was around 60 with 472

variations ranging between 25 and 93 473

The prevalent wind direction during the experimental period (84 of the runs) was NW the 474

wind speed ranged from 05 to 89 ms-1

and averaged 34 ms-1

while the friction velocity u 475

ranged between 001 and 099 ms-1

with an average of 030 ms-1

(Figure 1a) The energy 476

balance for the period of the trial is reported in Figure 2b at hourly scale In particular it is 477

clear the direct impact of the net radiation (Rn) on the sensible heat flux (H) and on the latent 478

heat flux (LE) The pattern of LE were strongly correlated with the available energy Rn with 479

distinct peaks immediately following irrigation which provided water to evapotranspirate 480

from plant and soil 481

22 Ammonia concentrations 482

The field NH3 concentrations measured by the diffusive samplers (ALPHA badges) over 483

several hours ranged from 3 to 118 microg NH3 m-3

while the 30 minutes ROSAA concentrations 484

varied from around 0 to around 90 microg NH3 m-3

The ROSAA concentrations averaged over 485

the same intervals as the ALPHA badges slightly underestimated the badges concentration by 486

8 on average (ROSAA = 092 ALPHA R2 = 082) The uncalibrated QC-TILDAS 487

concentrations strongly underestimated the ALPHA badges concentrations by 67 but 488

without any offset (QC-TILDAS = 034 ALPHA R2 = 090) This underestimation is larger than 489

what Ellis et al (2010) found with the same instrument in a laboratory calibration Since the 490

correlation was very good the regression against the ALPHA badges was used to calibrate the 491

QC-TILDAS concentration data The correction factor was found to be 286 plusmn 009 The 492

calibrated QC-TILDAS concentration was in quite good agreement with the ROSAA 493

concentrations both in terms of magnitude and dynamics (Figure 2) In Figure 3 a detailed 494

comparison between calibrated values of QC-TILDAS NH3 concentrations and ROSAA 495

values is shown Since we did not find a significant offset in the regression of QC-TILDAS 496

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 5: Eddy covariance measurement of ammonia fluxes: comparison of

5

the adequacy of the system in terms of setup and quality of the data In order to reach a robust 104

assessment of the needed correction three possible methods for correcting the flux losses due 105

to the design and the setup of the employed equipment were considered in this analysis In 106

particular we compare different frequency correction factors (CF) to apply to the measured 107

EC NH3 fluxes obtained by the following approaches (i) the spectral theoretical transfer 108

function (CFTheor) (ii) the in-situ ogive method (CFO) (Massman and Clement 2004) and 109

(iii) the inductance correction model (CFL) developed by Eugster and Senn (1995) 110

1 MATERIAL AND METHOD 111

11 Field site 112

The experiment was carried out from 17 to 30 July 2008 (DOYs 199 to 211) in Rutigliano 113

(41degN 17deg54 E 122 m asl) near Bari in southern Italy All experimental activities were 114

performed in a 2 hectares field dimensions were 200 m x 100 m The soil type is classified as 115

loamy clay (Clay 41 Silt 44 Sand 15 ) and the terrain is flat The climate is semi-arid 116

Mediterranean with hot and dry summer and short and temperate winter The mean annual 117

temperature is 157 degC with a mean annual precipitation of 600 mm the main wind direction 118

is north during the summer time In this study most of the analysis will focus on the period 119

22-29 July 2008 (DOYs 204-211) where the NH3 fluxes were the largest 120

12 Crop and urea spreading 121

During the experimental campaign the field was cultivated with Sorghum vulgare (cv Hay 122

Day) sowed on 10th

June 2008 and irrigated by sprinkler irrigation A commercial urea 123

product (46 N content) in granular form was applied The total N supply was around 240 124

kg N ha-1

divided into three applications 30 90 and 120 kg N ha-1

on 1st 16

th and 22

nd July 125

2008 respectively The first application was carried out at crop emergence Before the second 126

application the field was irrigated with 79 mm water while further irrigation (93 mm) was 127

applied before and after the third application The amount of urea was slightly higher than 128

current agronomic practices in the area since the dry conditions (high temperature no rain 129

and no irrigation applied) between the second and the last urea spreading inhibited NH3 130

volatilization so that no fluxes were detected The last urea application combined with 131

irrigation was necessary in order to increase the probability to detect NH3 fluxes 132

6

13 NH3 fluxes by the eddy covariance method 133

The fluxes of NH3 were measured with the eddy covariance method (eg Kaimal amp Finnigan 134

1994 Lee et al 2004 Mahrt 2010) The EC method is based on estimating the transport of a 135

scalar by turbulent motions of upward and downward moving air contained in the atmosphere 136

The vertical flux (F) of scalar at the measurement height is given by 137

wwF

(1) 138

where w is the component of the wind speed normal to the surface and β is the scalar 139

concentration and the usual Reynolds decomposition is used where prime denotes fluctuating 140

quantities w is the covariance between and w With the usual assumption of steady state 141

horizontally homogeneous flow there is no vertical flux of dry air ie 0wcd where cd is 142

the dry air concentration Since the dry air concentration fluctuates due to heat and water 143

vapour fluxes w is not necessarily equal to zero The Webb Pearmann Leuning (WPL) 144

correction (Webb et al 1980 Leuning 2007) gives the expression of w The impact of this 145

term depends on the concentration of the gas and the typical magnitude of the flux (Denmead 146

1983 Liebethal and Foken 2004) for trace gas with small concentration and large fluxes 147

such as NH3 over a source these corrections are negligible 148

The turbulent flux w as a time-averaged quantity can also be viewed as a frequency 149

averaged quantity ie the integral of the co-spectral density (Cowβ) (Kaimal and Finnigan 150

1994 Aubinet et al 2000 Massman 2000) 151

0

)( dffCow w

(2)

152

To catch all relevant turbulent fluctuations β and w should be measured with a sampling 153

frequency much larger than the frequency of the eddies carrying the energy of the signal (fx) 154

(Horst 1997 Massman 2000) Usually a sampling frequency of 10 Hz is considered 155

sufficient above a vegetated surface However flux loss is inevitable with any EC system 156

especially employing closed-path analysers to which the air sample is transported into the 157

measuring cell by means of a tube The mixing effects in the tube and additional possible 158

chemical or micro-physical processes occurring in the system produce low-pass filtering 159

effects on the measured covariances (Ibrom et al 2007) Nowadays a variety of methods are 160

available to correct these underestimated measured fluxes by means of frequency response 161

correction factors (eg Ammann et al 2006 Shimizu 2007 Aubinet et al 2000 Massman 162

2000 Eugster and Senn 1995) However in this work since the adopted closed-path analyser 163

7

for NH3 concentration measurements is relatively new and it is the first time it is being used 164

under semi-arid climates we compare a variety of methods to correct the NH3 fluxes 165

measured over an agricultural plot 166

14 NH3 concentration measurements 167

141 The QC-TILDAS analyser 168

1411 The instrument 169

The QC-TILDAS employed during this research is the compact QC-TILDAS-76 SN002-U 170

developed by Aerodyne Research Inc (ARI Billerica MA USA) (Zahniser et al 2005) 171

This spectrometer combines QC laser designed for pulsed operation at near room temperature 172

(Alpes Lasers Neuchacirctel Switzerland) an optical system together with a computer-controlled 173

system that incorporates the electronics for driving the QC laser along with signal generation 174

and a data logger Molecular transition selection for given laser is achieved by temperature 175

tuning typically within a small range around 40 ordmC using a two-stage Peltier element The 176

laser frequency is swept across the full molecular transition at 96734 cm-1

which means that 177

the scan is done over a narrow range of wave numbers chosen to contain an absorption feature 178

of the trace gas of interest The optical system collects light from the QC laser and directs it 179

through an astigmatic Herriott type multi-pass absorption cell (76 m effective path length) 180

onto a TE-cooled photovoltaic detector (Vigo Systems) A detailed description of the device 181

can be found in Zahniser et al (2005) 182

1412 The inlet design and the sampling 183

Ellis et al (2010) report the characterization of the QC-TILDAS by laboratory tests In 184

particular they reported a detailed analysis on the performance of the QC-TILDAS in 185

function of the inlet design To achieve accurate atmospheric measurements of NH3 the 186

sampling setup has to reduce gas interactions with surfaces then it is crucial the configuration 187

of the devicersquos inlet through which the air is drawn into the optical cell The inlet used is a 188

short glass tube containing a critical orifice needed to keep the air flow rate constant and to 189

reduce the pressure in the optical cell to approximately 540 kPa a compromise between 190

minimising line broadening and adsorption effects maximising time response and achieving 191

good sensitivity (Warland et al 2001) The setup of the inlet is done to remove particles in 192

the air sample relying on inertia to remove more than 50 of particles larger than 300 nm in 193

8

particular the flow is split into two branches 90 of the flow makes a sharp turn and is 194

pulled into the optical cell by a pump (VARIAN TriScroll 600 Series) the other 10 is 195

pulled by a second air pump (VARIAN mod SH110) This inlet design is optimized to 196

eliminate the need of filters and any interference they may cause The operation of the 197

spectrometer is fully automated and computer-controlled through a Windows-based 198

proprietary software (TDL Wintel) developed at ARI Concentrations are real-time 199

determined from the light absorption spectra through a non-linear least-squares fitting 200

algorithm (Levenberg-Marquardt) that uses spectral parameters from the HITRAN (high-201

resolution transmission) molecular absorption database (Rothman et al 2003) The pressure 202

and temperature in the multi-pass cell are continuously monitored to supply information 203

needed for spectral fitting The software calculates absolute concentrations so no calibration 204

was performed in the field However laboratory tests demonstrate the need to carry out a 205

post-proceeding calibration in order to overcome the failure of the analyzer to reproduce the 206

values of the ammonia standard Moreover we would justify the calibration factor found for 207

the QC-TILDAS using ethilene standard which has absorbing frequency close to ammonia 208

one but it is not affected by stickiness Part of the problem with absolute spectroscopic values 209

for NH3 in our instrument was due to using pulsed-QCLs rather than CW-QCLs with the 210

laser linewidth considerably greater than the molecular linewidth the spectral analysis 211

required much effort to get absolute values Thus a post-proceeding calibration was done and 212

zeroing was routinely carried out in order to optimize the calculated spectra following 213

Whitehead et al (2008) 214

1413 The response time 215

The decreasing of the mixing ratio of the ammonia was well represented by two exponential 216

decay functions which in Ellis et al (2010) is written as 217

2

02

1

010 expexp

ttA

ttAyy (3) 218

where A1 and A2 are constants and their sum is equal to the stable mixing ratio of NH3 prior to 219

the calibration flow being switched off y0 is equal to the NH3 mixing ratio reached at the end 220

of the decay t is time and t0 is the time at the start of the fit τ1 and τ2 are time decay 221

constants the first of which is fast and corresponds to the gas exchange time of the system 222

(04 s in our case) and the second being much slower and corresponding to interactions of 223

NH3 with surfaces on the interior surfaces of the inlet sampling lines and absorption cell (15 224

9

s) In this case the response time was checked in laboratory and it is controlled by pumping 225

speed and by adsorptiondesorption on the walls In particular it is affected by the flow rate 226

the length and the heating of the sampling tubes Therefore acting on these parameters would 227

allow optimizing the instrument for measuring the fluxes by EC technique 228

Moreover for a mixing ratio of 350 ppb the amount of noise (1) in a 10 Hz measurement 229

obtained by Ellis et al (2010) by an Allan variance analysis was 315 ppb (22 microg NH3 m-3

) 230

Based on statistics obtained for sensible heat CO2 and temperature w w was estimated 231

to be around 023 plusmn 013 (mean plusmn SE) and taking ~ 2 microg NH3 m-3

and w ~ 125 u would 232

mean that the flux detection limit of the setup would be around 25 u (in microg NH3 m-2

s-1

) 233

hence typically around 750 ng NH3 m-2

s-1

However most of the turbulent signal is 234

transported by eddies with a frequency around 02 Hz (evaluated as the co-spectral peak 235

frequency for air temperature (Ta) CO2 and water vapour (H2O)) At this frequency the Allan 236

variance of the QC-TILDAS found by Ellis et al (2010) is around 02 microg NH3 m-3

and hence 237

the detection limit should be on the order of 025 u (typically 75 ng NH3 m-2

s-1

) As reported 238

by Ellis et al (2010) there is an increase in the relative importance of NH3 surface 239

interactions at mixing ratios lower than 100 ppb which further increased when sampling 240

humid air as opposed to dry nitrogen Then the heating in the sampling line to prevent 241

condensation is crucial to perform accurate NH3 measurements There was no line heating 242

used in our study since we assumed that this was not needed since the air temperature 243

encountered in the field was very high (up to 33degC) On the other hand under such 244

conditions it was required the use of an air conditioner for the box containing the QC-245

TILDAS to limit instrumental drift induced by changes in the optical alignment as well as in 246

the laser power beam width and temperature 247

142 The ALPHA badges and ROSAA system 248

The NH3 concentration measured with the QC-TILDAS were compared against passive 249

diffusion samples (ALPHA badges Tang et al 2001) These ALPHA badges were 250

positioned at the same height as the QC-TILDAS inlet and changed twice a day to get daily 251

and nightly concentrations Three replicates were used to evaluate the uncertainty in the 252

measurements The background concentration bgd was estimated with three sets of badges put 253

at three locations at more than 100 100 and 300 m away from the field and changed weekly 254

The badges were prepared with filters coated with citric acid The filters were extracted in 3 255

ml double deionised water and analysed with a FLORRIA analyser (Mechatronics NL) In 256

10

addition during the trial NH3 concentration was monitored by means of the new device 257

ROSAA developed at INRA (Loubet et al 2010 2011) It relies on the coupling of NH3 258

capturing by means of an acid stripping solution (wet-effluent denuders) to convert NH3 in 259

NH4+ and the analysis of the NH4

+ concentration by means of a detector based on a semi-260

permeable membrane and a unit to perform temperature controlled conductivity 261

measurements (ECN Netherlands) In the ROSAA system the three denuders were sampling 262

at three heights (at z = 05 07 and 14 m) above ground and the averaged concentration was 263

measure every 30 minutes 264

15 The eddy covariance system description of the equipment 265

The EC system was equipped with the QC-TILDAS and a three axis ultrasonic anemometer 266

(Gill R2 Gill Instruments Ltd UK) The analogue signals from the QC-TILDAS were passed 267

to the sonic anemometer which uses an analogue-to-digital converter to digitise the signal at 268

10 Hz The sonic anemometer acquired data at 168 Hz internally and reported average values 269

at 208 Hz The digitised signals from the QC-TILDAS were then combined with the wind 270

velocity information and sent to the serial port of a personal computer NH3 fluxes were 271

computed offline with a time step of half an hour The software package EddySoft (Kolle and 272

Rebmann 2007) was used for the data acquisition The same software package was used for 273

post processing EC data and to obtain the cospectra useful for applying the TF method while 274

custom Matlabreg and R scripts were used to process and analyse data for the ogive and the 275

inductance methods respectively Trace gas and heat fluxes were calculated as well as other 276

micrometeorological parameters The components of the wind velocity were rotated every 30 277

minutes to set w and v to zero No detrending was applied and block averaging was instead 278

used (Leuning 2007) 279

The sonic anemometer was mounted on a 15 m tall mast so that the height of the EC sensors 280

was about 07 m above the top of the crop and adjusted to follow crop development Ambient 281

air close to the sonic anemometer head was continuously sampled through a 25 m long inlet 282

line (PFA 38rdquo outer diameter) leading to the QC-TILDAS The flow of air through the tube 283

was measured before and after the T connection used for splitting the flow the flow rate 284

towards the sampling cell was about 9 litresminute resulting in a laminar flow regime with a 285

Reynolds number (Re) of about 2000 below the turbulence limit (for pipe flow Re=2300 286

Shimizu 2007) which was the primary cause of the high frequency loss in the measuring 287

system (Eugster and Senn 1995) The limits in the power supply lines in our experimental 288

11

site have prevented the use of pumps able to provide a turbulent regime into the sampling 289

tube 290

The eddy covariance system together with ROSAA and ALPHA samples equipments were 291

located close to the centre of the field which provided an acceptable upwind fetch (around 292

120 m) in the prevalent wind direction during the trial anyway the footprint of EC 293

measurements was found to be always inside the experimental field 294

16 Spectral correction methods for low-pass filtering in a closed-path EC system 295

As mentioned before all eddy covariance systems tend to underestimate the true atmospheric 296

fluxes due to physical limitations of the flux measurement instrumentation which causes 297

losses of eddy flux contributions at low and high frequencies These losses can be quantified 298

and corrected for by multiplying the measured covariance (subscript m) with a frequency 299

response correction factor (CF ge1) 300

mwCFw (4) 301

The correction factor can be estimated by means of spectral correction methods that can be 302

divided into two families the spectral theoretical transfer function (TF) methods and the in-303

situ methods (Massman and Clement 2004) The general approach consists of three steps 1) 304

the identification of a spectral transfer function (theoretical or experimental) 2) its use to 305

calculate a correction factor and 3) the parameterisation of the correction factor with 306

meteorological variables mainly the wind speed which is directly linked to the turbulence 307

Here three methods have been employed in order to estimate CFs to apply at our measured 308

EC NH3 fluxes (i) Theoretical TF analysis CFTheor (ii) A variant of In situ experimental TF 309

method the Ogive method CFO (iii) the inductance method CFL These methodologies are 310

the most analysed in the literature for evaluating the most accurate CFs for gas trace fluxes 311

over natural surfaces 312

Common to all approaches is the requirement of a measured or evaluated reference 313

cospectrum for scalar as well as a measured cospectrum and the estimate of the flux loss as a 314

function of frequency In any case an important aspect relative to any EC system is the time 315

delay between the instant open-path w measurement and the closed-path scalar concentration 316

measurement This time lag is due to the transport time in the tubing system and the phase 317

shift due to low-pass filtering (Ibrom et al 2007) It depends on the tube length and the flow 318

rate it may change slightly due to changing flow rates in the tube caused by pumping 319

variation or density changes Usually this delay can be calculated empirically by determining 320

12

the maximum of the covariance function between wrsquo and rsquo (Moncrieff et al 1997) which 321

includes the time lag due to the longitudinal (streamwise) separation distance from the air 322

sample inlet to the sonic anemometer depending on wind speed and direction The 323

individually determined time delays for each 30 minutes set of data can be used for 324

calculating the cospectra in this case the phase shift due to low-pass filtering and longitudinal 325

sensor separation has already been corrected for (Ibrom et al 2007) So two subcategories 326

can be identified in the theoretical transfer function analysis with (CFTheor_time_lag) and without 327

the time lag (CFTheor_phase_shift) applied to computation of the covariances and cospectra 328

329

161 Theoretical TF analysis 330

Following Moore (1986) theoretical transfer functions characterizing the measurement 331

process have been developed The correction factor in this case is summarized by Massman 332

and Clement (2004) using the following equation 333

dffCofHfT

fT

dffCo

w

wCF

w

b

b

w

m

TF

)()(sin

1

)(

0

2

2

0

(5)

334

where mw is the measured covariance and w is the true flux H(f) is the product of all 335

the appropriate transfer functions associated with HF losses (

n

i

iTFfH1

)( ) )( fCow is the 336

one-side reference cospectrum Tb is the block averaging period 22sin1 bb fTfT is the 337

TF for the low frequency spectral attenuation The reference cospectrum is either taken from 338

literature (Kaimal and Finnigan 1994) or a site specific reference cospectrum usually derived 339

from sensible heat flux measurements The requirements to apply this method are the TFs 340

and a model of the cospectra which is the weakness of this approach since smooth cospectra 341

are not typical of the atmospheric surface layer (Massman and Clement 2004) In particular 342

in this work we used the reference cospectral models (eq 6-9) given by Moncrieff et al 343

(1997) where udzffk )( is normalized frequency f is the natural frequency u is 344

horizontal wind speed z-d is height above the zero-plane displacement d For stable 345

atmospheric condition the reference cospectrum is 346

12)(

kww

kw

fBA

fffCo

(6)

347

where 348

13

750

4612840

L

dzAw (7)

349

and 350

11342 ww AB (8)

351

While for unstable atmospheric conditions the reference cospectrum is 352

540

7261

9212)(

3751

k

k

kw ffor

f

fffCo (9a)

353

540

831

3784)(

42

k

k

kw ffor

f

fffCo (9b)

354

Moore (1986) developed the first theoretical TFs for EC systems taking into account the 355

attenuation due to the dynamic frequency response of the sensors the scalar path averaging 356

the sensor separation the sensor response mismatch while Moncrieff et al (1997) gave a 357

complete description of TFs of EC systems with closed-path analysers Then considering the 358

wide literature on the used theoretical TFs (see Shimizu 2007 for a review) and the weight of 359

each TF on the total one for our equipment set-up here we report only details about TFs 360

related to (i) the lateral separation (ie perpendicular to the mean wind direction) between 361

the sonic anemometer and QC-TILDAS inlet (TFs Moore 1986) (ii) the tube damping effect 362

(TFtube Lenschow and Raupach 1991) (iii) the volume averaging in a measurement cell 363

(TFvol Massman 2004) (iv) the total phase shift (TFphase_shift Massman 2000) In detail 364

(i) Following Moore (1986) the effect of laterallongitudinal separation s between two 365

sensors can be represented by the following cospectral transfer function 366

)99exp(99 51nTFs (10)

367

where n=fsu As suggested by Massman (2000) the horizontal separation between the two 368

sensors has been set to 030 m 369

(ii) The function TFtube describing the attenuation of the fluctuation concentrations down the 370

sampling tube in which there is a laminar flow for covariance calculation (Lenschow and 371

Raupach 1991 Aubinet et al 2000) is 372

ts

t

tubeUD

LfrTF t

12exp

222 (11)

373

Here rt is the tube radius and Ds the molecular diffusivity of the scalar around 02 cm2s

-1 for 374

NH3 (Massman 1998) Moreover the attenuation may vary with time Leuning and Judd 375

(1996) showed that the attenuation to water vapour by aged and dirty tubes is much greater 376

than that by new and clear tube In the case of laminar flow through the tube the theoretical 377

14

cut off frequency fco_t at which the TF equals 2-12

is given by 378

tt

sttco

Lr

DUf

2_ 6490 (12)

379

about 14 Hz in our EC system To minimizing HF losses due to nonuniform velocity profiles 380

turbulent flow would be preferred (Lenschow and Raupach 1991) This requires a powerful 381

pump with high power consumption and a tremendous start-up current (13 A at 230 V AC for 382

an Edwards Tri-Scroll pump for example pers comm W Eugster) that was unrealistic at our 383

field site Hence we had to use a less powerful pump and thus need to account for the extra 384

loss in frequency response due to laminar flow in the intake hose (Moncrieff et al 1997) The 385

possible tube damping correction does not correct potential absorptiondesorption of moisture 386

by hygroscopic dirt particles in the tube however a strong pressure drop along the intake 387

hose reduces risk of condensation on the walls of the tube On the other hand HF due to wall 388

interaction in the sample tube could be reduced by minimizing lengthrsquos tube and maintaining 389

high flow rate In general the line loss is a function of tubing type and how dirty the tube is 390

(Sukyer and Verma 1993) 391

(iii) As reported by Massman (2004) in a closed-path detector the flow velocity within the 392

detection chamber can be very different than the wind speed of the ambient atmosphere near 393

the tube inlet so that the transfer functions need to be expressed in terms of the volume 394

flushing time constant of the detection chamber τvol 395

2

2sin 2

vol

volvolTF

(13)

396

where f 2 and τvol is equal to the time needed to fill the chamber and its minimum value 397

is given by the ratio between the volume of the chamber (05 l) and the maximum flow rate 398

(iv) However if covariances and the cospectra are calculated without the time lag that means 399

without the method of maximum covariance function or if there is no clear time lag due to 400

lag depending on the frequency of the eddies (sensor separation in the wind direction) or if 401

there are complex adsorption and desorption processes it is necessary to take into account the 402

phase shift (Massman 2000) The TF associated with the total phase shift (Massman 2000) 403

is 404

t

tlon

t

tlonshiftphase

U

L

u

l

U

L

u

lTF sincos_ (14)

405

where llon is the longitudinal sensor separation (function of wind direction) and β is the 406

intrinsic time constant of the scalar sensor This latter was estimated by using the Boylersquos law 407

15

applied to the measurement chamber working at 33 kPa and using the measured flow rate it 408

is about 01 s and moreover it means that the sampling frequency of 10 Hz is adequate for 409

the employed QC-TILDAS in the EC system 410

For the theoretical TF approach cospectra of the vertical wind speed and T and NH3 411

respectively were calculated using the post processing EddySpec package of Eddysoft 412

software using the following setup number of frequencies 4096 for calculating the cospectra 413

for half-hourly runs multiplied by frequency tapering the time series with a Hamming 414

window (Kaimal and Finnigan 1994) and averaging into 20 logarithmically spaced bins 415

Either the time lag or the phase shift was used in the functions of the two approaches 416

CFTheor_time_lag and CFTheor_phase_shift respectively 417

418

162 In situ ogive method 419

The limitation of the theoretical TF approach is that it could miss additional chemical or 420

micro-physical process occurring in the EC system a restriction that is expected to be 421

overcome by the in situ experimental methods Moreover the experimental approach is based 422

on the assumption of cospectral similarity between scalar fluxes and unlike the theoretical TF 423

approach these methods do not require smooth models of atmospheric cospectra (Massman 424

and Clement 2004) The correction factor can be estimated as the ratio between the 425

attenuated flux and a reference flux (usually the sensible heat flux) Aubinet et al (2000) give 426

the procedure to estimate this experimental frequency transfer function but since the 427

cospectra derived from sensible heat fluxes are often not well-defined then direct application 428

of this approach can produce implausible values 429

Starting from the hypothesis of similarity between all scalars cospectra Ammann et al (2006) 430

developed a method for estimating HF losses Applied to EC measurement of NH3 fluxes the 431

method consists in fitting the ogive for NH3 OgwNH3 (the ogive is the integrated cospectra 432

CowNH3 from low to high frequencies) against the ogive of air temperature OgwTa The fitting 433

was made with an iterative linear least square method in which the points being too far away 434

from the regression line were eliminated progressively (robustfit function under Matlabreg) 435

The high-frequency damping correction (CFO) is simply evaluated as the value of the fitted 436

NH3 ogive (OgwNH3) at the highest frequency The frequency band over which the linear 437

regression is performed is determined as the frequency for which OgwT larger than 01 and 438

OgwNH3 is smaller than 08 This choice was arbitrary and may not be appropriate if the 439

damping of the NH3 signal was below the range chosen The ogives were calculated using 440

detrended signals 441

16

442

163 The inductance method 443

Eugster and Senn (1995) propose a correction model for EC data affected by systematic error 444

introduced by the combination of all sources of damping which also includes chemical 445

reaction (in the case of NO2) besides the possible damping due to laminar flow in the 446

sampling tube and sensor separation This damping loss is described by an analogy to 447

inductance L in an alternating current circuit which can be used for taking into account the 448

reduction of spectral density in the inertial subrange This model follows the approach of 449

Moore (1986) using electronic components in a current circuit instead of a gain function 450

As reported by Eugster and Senn (1995) the covariance of the measured (ie damped) scalar 451

flux is given by 452

02220

)(41

1)(

0dffCo

LfdffCow

LL wwL

(15)

453

where )(0 fCo

Lw is the undamped cospectrum The value of L is modified in order to fit the 454

measured cospectrum The independent variables in this correction model are the measuring 455

height above zero-plane displacement z-d the mean horizontal wind speed u the Monin-456

Obukhov stability parameter ζ the natural frequency f and the inductance L The value for 457

L can be derived from spectral and cospectral analysis and is assumed not vary significantly 458

as long as the measuring system is not altered 459

The damped cospectra can be derived combining equation (14) with the empirical formula of 460

Kaimal et al (1972) for stable unstable and neutral stratification as modified by Eugster and 461

Senn (1995) (equations 29 and 30 in their paper) The approximated solutions of Eugster and 462

Senn (1995) for CF range between 10 (no damping at all) and infin (no signal at all) but they 463

suggest to restrict the fit to the range between CF=10 and CF=25 that means all 464

measurements which represent less than 40 of the real fluxes were discarded 465

17

2 RESULTS AND DISCUSSION 466

21 Micrometeorological conditions 467

During the trial the air temperature ranged from 16degC to 335degC and averaged 247degC while 468

the soil surface temperature reached more than 546degC at hourly scale The sky was clear 469

most of the period apart from the 23-24072008 where there was little rain (82 mm) The 470

global solar radiation was following the fair weather diurnal course throughout the period and 471

reached 950 W m-2

at maximum The daily average relative humidity was around 60 with 472

variations ranging between 25 and 93 473

The prevalent wind direction during the experimental period (84 of the runs) was NW the 474

wind speed ranged from 05 to 89 ms-1

and averaged 34 ms-1

while the friction velocity u 475

ranged between 001 and 099 ms-1

with an average of 030 ms-1

(Figure 1a) The energy 476

balance for the period of the trial is reported in Figure 2b at hourly scale In particular it is 477

clear the direct impact of the net radiation (Rn) on the sensible heat flux (H) and on the latent 478

heat flux (LE) The pattern of LE were strongly correlated with the available energy Rn with 479

distinct peaks immediately following irrigation which provided water to evapotranspirate 480

from plant and soil 481

22 Ammonia concentrations 482

The field NH3 concentrations measured by the diffusive samplers (ALPHA badges) over 483

several hours ranged from 3 to 118 microg NH3 m-3

while the 30 minutes ROSAA concentrations 484

varied from around 0 to around 90 microg NH3 m-3

The ROSAA concentrations averaged over 485

the same intervals as the ALPHA badges slightly underestimated the badges concentration by 486

8 on average (ROSAA = 092 ALPHA R2 = 082) The uncalibrated QC-TILDAS 487

concentrations strongly underestimated the ALPHA badges concentrations by 67 but 488

without any offset (QC-TILDAS = 034 ALPHA R2 = 090) This underestimation is larger than 489

what Ellis et al (2010) found with the same instrument in a laboratory calibration Since the 490

correlation was very good the regression against the ALPHA badges was used to calibrate the 491

QC-TILDAS concentration data The correction factor was found to be 286 plusmn 009 The 492

calibrated QC-TILDAS concentration was in quite good agreement with the ROSAA 493

concentrations both in terms of magnitude and dynamics (Figure 2) In Figure 3 a detailed 494

comparison between calibrated values of QC-TILDAS NH3 concentrations and ROSAA 495

values is shown Since we did not find a significant offset in the regression of QC-TILDAS 496

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 6: Eddy covariance measurement of ammonia fluxes: comparison of

6

13 NH3 fluxes by the eddy covariance method 133

The fluxes of NH3 were measured with the eddy covariance method (eg Kaimal amp Finnigan 134

1994 Lee et al 2004 Mahrt 2010) The EC method is based on estimating the transport of a 135

scalar by turbulent motions of upward and downward moving air contained in the atmosphere 136

The vertical flux (F) of scalar at the measurement height is given by 137

wwF

(1) 138

where w is the component of the wind speed normal to the surface and β is the scalar 139

concentration and the usual Reynolds decomposition is used where prime denotes fluctuating 140

quantities w is the covariance between and w With the usual assumption of steady state 141

horizontally homogeneous flow there is no vertical flux of dry air ie 0wcd where cd is 142

the dry air concentration Since the dry air concentration fluctuates due to heat and water 143

vapour fluxes w is not necessarily equal to zero The Webb Pearmann Leuning (WPL) 144

correction (Webb et al 1980 Leuning 2007) gives the expression of w The impact of this 145

term depends on the concentration of the gas and the typical magnitude of the flux (Denmead 146

1983 Liebethal and Foken 2004) for trace gas with small concentration and large fluxes 147

such as NH3 over a source these corrections are negligible 148

The turbulent flux w as a time-averaged quantity can also be viewed as a frequency 149

averaged quantity ie the integral of the co-spectral density (Cowβ) (Kaimal and Finnigan 150

1994 Aubinet et al 2000 Massman 2000) 151

0

)( dffCow w

(2)

152

To catch all relevant turbulent fluctuations β and w should be measured with a sampling 153

frequency much larger than the frequency of the eddies carrying the energy of the signal (fx) 154

(Horst 1997 Massman 2000) Usually a sampling frequency of 10 Hz is considered 155

sufficient above a vegetated surface However flux loss is inevitable with any EC system 156

especially employing closed-path analysers to which the air sample is transported into the 157

measuring cell by means of a tube The mixing effects in the tube and additional possible 158

chemical or micro-physical processes occurring in the system produce low-pass filtering 159

effects on the measured covariances (Ibrom et al 2007) Nowadays a variety of methods are 160

available to correct these underestimated measured fluxes by means of frequency response 161

correction factors (eg Ammann et al 2006 Shimizu 2007 Aubinet et al 2000 Massman 162

2000 Eugster and Senn 1995) However in this work since the adopted closed-path analyser 163

7

for NH3 concentration measurements is relatively new and it is the first time it is being used 164

under semi-arid climates we compare a variety of methods to correct the NH3 fluxes 165

measured over an agricultural plot 166

14 NH3 concentration measurements 167

141 The QC-TILDAS analyser 168

1411 The instrument 169

The QC-TILDAS employed during this research is the compact QC-TILDAS-76 SN002-U 170

developed by Aerodyne Research Inc (ARI Billerica MA USA) (Zahniser et al 2005) 171

This spectrometer combines QC laser designed for pulsed operation at near room temperature 172

(Alpes Lasers Neuchacirctel Switzerland) an optical system together with a computer-controlled 173

system that incorporates the electronics for driving the QC laser along with signal generation 174

and a data logger Molecular transition selection for given laser is achieved by temperature 175

tuning typically within a small range around 40 ordmC using a two-stage Peltier element The 176

laser frequency is swept across the full molecular transition at 96734 cm-1

which means that 177

the scan is done over a narrow range of wave numbers chosen to contain an absorption feature 178

of the trace gas of interest The optical system collects light from the QC laser and directs it 179

through an astigmatic Herriott type multi-pass absorption cell (76 m effective path length) 180

onto a TE-cooled photovoltaic detector (Vigo Systems) A detailed description of the device 181

can be found in Zahniser et al (2005) 182

1412 The inlet design and the sampling 183

Ellis et al (2010) report the characterization of the QC-TILDAS by laboratory tests In 184

particular they reported a detailed analysis on the performance of the QC-TILDAS in 185

function of the inlet design To achieve accurate atmospheric measurements of NH3 the 186

sampling setup has to reduce gas interactions with surfaces then it is crucial the configuration 187

of the devicersquos inlet through which the air is drawn into the optical cell The inlet used is a 188

short glass tube containing a critical orifice needed to keep the air flow rate constant and to 189

reduce the pressure in the optical cell to approximately 540 kPa a compromise between 190

minimising line broadening and adsorption effects maximising time response and achieving 191

good sensitivity (Warland et al 2001) The setup of the inlet is done to remove particles in 192

the air sample relying on inertia to remove more than 50 of particles larger than 300 nm in 193

8

particular the flow is split into two branches 90 of the flow makes a sharp turn and is 194

pulled into the optical cell by a pump (VARIAN TriScroll 600 Series) the other 10 is 195

pulled by a second air pump (VARIAN mod SH110) This inlet design is optimized to 196

eliminate the need of filters and any interference they may cause The operation of the 197

spectrometer is fully automated and computer-controlled through a Windows-based 198

proprietary software (TDL Wintel) developed at ARI Concentrations are real-time 199

determined from the light absorption spectra through a non-linear least-squares fitting 200

algorithm (Levenberg-Marquardt) that uses spectral parameters from the HITRAN (high-201

resolution transmission) molecular absorption database (Rothman et al 2003) The pressure 202

and temperature in the multi-pass cell are continuously monitored to supply information 203

needed for spectral fitting The software calculates absolute concentrations so no calibration 204

was performed in the field However laboratory tests demonstrate the need to carry out a 205

post-proceeding calibration in order to overcome the failure of the analyzer to reproduce the 206

values of the ammonia standard Moreover we would justify the calibration factor found for 207

the QC-TILDAS using ethilene standard which has absorbing frequency close to ammonia 208

one but it is not affected by stickiness Part of the problem with absolute spectroscopic values 209

for NH3 in our instrument was due to using pulsed-QCLs rather than CW-QCLs with the 210

laser linewidth considerably greater than the molecular linewidth the spectral analysis 211

required much effort to get absolute values Thus a post-proceeding calibration was done and 212

zeroing was routinely carried out in order to optimize the calculated spectra following 213

Whitehead et al (2008) 214

1413 The response time 215

The decreasing of the mixing ratio of the ammonia was well represented by two exponential 216

decay functions which in Ellis et al (2010) is written as 217

2

02

1

010 expexp

ttA

ttAyy (3) 218

where A1 and A2 are constants and their sum is equal to the stable mixing ratio of NH3 prior to 219

the calibration flow being switched off y0 is equal to the NH3 mixing ratio reached at the end 220

of the decay t is time and t0 is the time at the start of the fit τ1 and τ2 are time decay 221

constants the first of which is fast and corresponds to the gas exchange time of the system 222

(04 s in our case) and the second being much slower and corresponding to interactions of 223

NH3 with surfaces on the interior surfaces of the inlet sampling lines and absorption cell (15 224

9

s) In this case the response time was checked in laboratory and it is controlled by pumping 225

speed and by adsorptiondesorption on the walls In particular it is affected by the flow rate 226

the length and the heating of the sampling tubes Therefore acting on these parameters would 227

allow optimizing the instrument for measuring the fluxes by EC technique 228

Moreover for a mixing ratio of 350 ppb the amount of noise (1) in a 10 Hz measurement 229

obtained by Ellis et al (2010) by an Allan variance analysis was 315 ppb (22 microg NH3 m-3

) 230

Based on statistics obtained for sensible heat CO2 and temperature w w was estimated 231

to be around 023 plusmn 013 (mean plusmn SE) and taking ~ 2 microg NH3 m-3

and w ~ 125 u would 232

mean that the flux detection limit of the setup would be around 25 u (in microg NH3 m-2

s-1

) 233

hence typically around 750 ng NH3 m-2

s-1

However most of the turbulent signal is 234

transported by eddies with a frequency around 02 Hz (evaluated as the co-spectral peak 235

frequency for air temperature (Ta) CO2 and water vapour (H2O)) At this frequency the Allan 236

variance of the QC-TILDAS found by Ellis et al (2010) is around 02 microg NH3 m-3

and hence 237

the detection limit should be on the order of 025 u (typically 75 ng NH3 m-2

s-1

) As reported 238

by Ellis et al (2010) there is an increase in the relative importance of NH3 surface 239

interactions at mixing ratios lower than 100 ppb which further increased when sampling 240

humid air as opposed to dry nitrogen Then the heating in the sampling line to prevent 241

condensation is crucial to perform accurate NH3 measurements There was no line heating 242

used in our study since we assumed that this was not needed since the air temperature 243

encountered in the field was very high (up to 33degC) On the other hand under such 244

conditions it was required the use of an air conditioner for the box containing the QC-245

TILDAS to limit instrumental drift induced by changes in the optical alignment as well as in 246

the laser power beam width and temperature 247

142 The ALPHA badges and ROSAA system 248

The NH3 concentration measured with the QC-TILDAS were compared against passive 249

diffusion samples (ALPHA badges Tang et al 2001) These ALPHA badges were 250

positioned at the same height as the QC-TILDAS inlet and changed twice a day to get daily 251

and nightly concentrations Three replicates were used to evaluate the uncertainty in the 252

measurements The background concentration bgd was estimated with three sets of badges put 253

at three locations at more than 100 100 and 300 m away from the field and changed weekly 254

The badges were prepared with filters coated with citric acid The filters were extracted in 3 255

ml double deionised water and analysed with a FLORRIA analyser (Mechatronics NL) In 256

10

addition during the trial NH3 concentration was monitored by means of the new device 257

ROSAA developed at INRA (Loubet et al 2010 2011) It relies on the coupling of NH3 258

capturing by means of an acid stripping solution (wet-effluent denuders) to convert NH3 in 259

NH4+ and the analysis of the NH4

+ concentration by means of a detector based on a semi-260

permeable membrane and a unit to perform temperature controlled conductivity 261

measurements (ECN Netherlands) In the ROSAA system the three denuders were sampling 262

at three heights (at z = 05 07 and 14 m) above ground and the averaged concentration was 263

measure every 30 minutes 264

15 The eddy covariance system description of the equipment 265

The EC system was equipped with the QC-TILDAS and a three axis ultrasonic anemometer 266

(Gill R2 Gill Instruments Ltd UK) The analogue signals from the QC-TILDAS were passed 267

to the sonic anemometer which uses an analogue-to-digital converter to digitise the signal at 268

10 Hz The sonic anemometer acquired data at 168 Hz internally and reported average values 269

at 208 Hz The digitised signals from the QC-TILDAS were then combined with the wind 270

velocity information and sent to the serial port of a personal computer NH3 fluxes were 271

computed offline with a time step of half an hour The software package EddySoft (Kolle and 272

Rebmann 2007) was used for the data acquisition The same software package was used for 273

post processing EC data and to obtain the cospectra useful for applying the TF method while 274

custom Matlabreg and R scripts were used to process and analyse data for the ogive and the 275

inductance methods respectively Trace gas and heat fluxes were calculated as well as other 276

micrometeorological parameters The components of the wind velocity were rotated every 30 277

minutes to set w and v to zero No detrending was applied and block averaging was instead 278

used (Leuning 2007) 279

The sonic anemometer was mounted on a 15 m tall mast so that the height of the EC sensors 280

was about 07 m above the top of the crop and adjusted to follow crop development Ambient 281

air close to the sonic anemometer head was continuously sampled through a 25 m long inlet 282

line (PFA 38rdquo outer diameter) leading to the QC-TILDAS The flow of air through the tube 283

was measured before and after the T connection used for splitting the flow the flow rate 284

towards the sampling cell was about 9 litresminute resulting in a laminar flow regime with a 285

Reynolds number (Re) of about 2000 below the turbulence limit (for pipe flow Re=2300 286

Shimizu 2007) which was the primary cause of the high frequency loss in the measuring 287

system (Eugster and Senn 1995) The limits in the power supply lines in our experimental 288

11

site have prevented the use of pumps able to provide a turbulent regime into the sampling 289

tube 290

The eddy covariance system together with ROSAA and ALPHA samples equipments were 291

located close to the centre of the field which provided an acceptable upwind fetch (around 292

120 m) in the prevalent wind direction during the trial anyway the footprint of EC 293

measurements was found to be always inside the experimental field 294

16 Spectral correction methods for low-pass filtering in a closed-path EC system 295

As mentioned before all eddy covariance systems tend to underestimate the true atmospheric 296

fluxes due to physical limitations of the flux measurement instrumentation which causes 297

losses of eddy flux contributions at low and high frequencies These losses can be quantified 298

and corrected for by multiplying the measured covariance (subscript m) with a frequency 299

response correction factor (CF ge1) 300

mwCFw (4) 301

The correction factor can be estimated by means of spectral correction methods that can be 302

divided into two families the spectral theoretical transfer function (TF) methods and the in-303

situ methods (Massman and Clement 2004) The general approach consists of three steps 1) 304

the identification of a spectral transfer function (theoretical or experimental) 2) its use to 305

calculate a correction factor and 3) the parameterisation of the correction factor with 306

meteorological variables mainly the wind speed which is directly linked to the turbulence 307

Here three methods have been employed in order to estimate CFs to apply at our measured 308

EC NH3 fluxes (i) Theoretical TF analysis CFTheor (ii) A variant of In situ experimental TF 309

method the Ogive method CFO (iii) the inductance method CFL These methodologies are 310

the most analysed in the literature for evaluating the most accurate CFs for gas trace fluxes 311

over natural surfaces 312

Common to all approaches is the requirement of a measured or evaluated reference 313

cospectrum for scalar as well as a measured cospectrum and the estimate of the flux loss as a 314

function of frequency In any case an important aspect relative to any EC system is the time 315

delay between the instant open-path w measurement and the closed-path scalar concentration 316

measurement This time lag is due to the transport time in the tubing system and the phase 317

shift due to low-pass filtering (Ibrom et al 2007) It depends on the tube length and the flow 318

rate it may change slightly due to changing flow rates in the tube caused by pumping 319

variation or density changes Usually this delay can be calculated empirically by determining 320

12

the maximum of the covariance function between wrsquo and rsquo (Moncrieff et al 1997) which 321

includes the time lag due to the longitudinal (streamwise) separation distance from the air 322

sample inlet to the sonic anemometer depending on wind speed and direction The 323

individually determined time delays for each 30 minutes set of data can be used for 324

calculating the cospectra in this case the phase shift due to low-pass filtering and longitudinal 325

sensor separation has already been corrected for (Ibrom et al 2007) So two subcategories 326

can be identified in the theoretical transfer function analysis with (CFTheor_time_lag) and without 327

the time lag (CFTheor_phase_shift) applied to computation of the covariances and cospectra 328

329

161 Theoretical TF analysis 330

Following Moore (1986) theoretical transfer functions characterizing the measurement 331

process have been developed The correction factor in this case is summarized by Massman 332

and Clement (2004) using the following equation 333

dffCofHfT

fT

dffCo

w

wCF

w

b

b

w

m

TF

)()(sin

1

)(

0

2

2

0

(5)

334

where mw is the measured covariance and w is the true flux H(f) is the product of all 335

the appropriate transfer functions associated with HF losses (

n

i

iTFfH1

)( ) )( fCow is the 336

one-side reference cospectrum Tb is the block averaging period 22sin1 bb fTfT is the 337

TF for the low frequency spectral attenuation The reference cospectrum is either taken from 338

literature (Kaimal and Finnigan 1994) or a site specific reference cospectrum usually derived 339

from sensible heat flux measurements The requirements to apply this method are the TFs 340

and a model of the cospectra which is the weakness of this approach since smooth cospectra 341

are not typical of the atmospheric surface layer (Massman and Clement 2004) In particular 342

in this work we used the reference cospectral models (eq 6-9) given by Moncrieff et al 343

(1997) where udzffk )( is normalized frequency f is the natural frequency u is 344

horizontal wind speed z-d is height above the zero-plane displacement d For stable 345

atmospheric condition the reference cospectrum is 346

12)(

kww

kw

fBA

fffCo

(6)

347

where 348

13

750

4612840

L

dzAw (7)

349

and 350

11342 ww AB (8)

351

While for unstable atmospheric conditions the reference cospectrum is 352

540

7261

9212)(

3751

k

k

kw ffor

f

fffCo (9a)

353

540

831

3784)(

42

k

k

kw ffor

f

fffCo (9b)

354

Moore (1986) developed the first theoretical TFs for EC systems taking into account the 355

attenuation due to the dynamic frequency response of the sensors the scalar path averaging 356

the sensor separation the sensor response mismatch while Moncrieff et al (1997) gave a 357

complete description of TFs of EC systems with closed-path analysers Then considering the 358

wide literature on the used theoretical TFs (see Shimizu 2007 for a review) and the weight of 359

each TF on the total one for our equipment set-up here we report only details about TFs 360

related to (i) the lateral separation (ie perpendicular to the mean wind direction) between 361

the sonic anemometer and QC-TILDAS inlet (TFs Moore 1986) (ii) the tube damping effect 362

(TFtube Lenschow and Raupach 1991) (iii) the volume averaging in a measurement cell 363

(TFvol Massman 2004) (iv) the total phase shift (TFphase_shift Massman 2000) In detail 364

(i) Following Moore (1986) the effect of laterallongitudinal separation s between two 365

sensors can be represented by the following cospectral transfer function 366

)99exp(99 51nTFs (10)

367

where n=fsu As suggested by Massman (2000) the horizontal separation between the two 368

sensors has been set to 030 m 369

(ii) The function TFtube describing the attenuation of the fluctuation concentrations down the 370

sampling tube in which there is a laminar flow for covariance calculation (Lenschow and 371

Raupach 1991 Aubinet et al 2000) is 372

ts

t

tubeUD

LfrTF t

12exp

222 (11)

373

Here rt is the tube radius and Ds the molecular diffusivity of the scalar around 02 cm2s

-1 for 374

NH3 (Massman 1998) Moreover the attenuation may vary with time Leuning and Judd 375

(1996) showed that the attenuation to water vapour by aged and dirty tubes is much greater 376

than that by new and clear tube In the case of laminar flow through the tube the theoretical 377

14

cut off frequency fco_t at which the TF equals 2-12

is given by 378

tt

sttco

Lr

DUf

2_ 6490 (12)

379

about 14 Hz in our EC system To minimizing HF losses due to nonuniform velocity profiles 380

turbulent flow would be preferred (Lenschow and Raupach 1991) This requires a powerful 381

pump with high power consumption and a tremendous start-up current (13 A at 230 V AC for 382

an Edwards Tri-Scroll pump for example pers comm W Eugster) that was unrealistic at our 383

field site Hence we had to use a less powerful pump and thus need to account for the extra 384

loss in frequency response due to laminar flow in the intake hose (Moncrieff et al 1997) The 385

possible tube damping correction does not correct potential absorptiondesorption of moisture 386

by hygroscopic dirt particles in the tube however a strong pressure drop along the intake 387

hose reduces risk of condensation on the walls of the tube On the other hand HF due to wall 388

interaction in the sample tube could be reduced by minimizing lengthrsquos tube and maintaining 389

high flow rate In general the line loss is a function of tubing type and how dirty the tube is 390

(Sukyer and Verma 1993) 391

(iii) As reported by Massman (2004) in a closed-path detector the flow velocity within the 392

detection chamber can be very different than the wind speed of the ambient atmosphere near 393

the tube inlet so that the transfer functions need to be expressed in terms of the volume 394

flushing time constant of the detection chamber τvol 395

2

2sin 2

vol

volvolTF

(13)

396

where f 2 and τvol is equal to the time needed to fill the chamber and its minimum value 397

is given by the ratio between the volume of the chamber (05 l) and the maximum flow rate 398

(iv) However if covariances and the cospectra are calculated without the time lag that means 399

without the method of maximum covariance function or if there is no clear time lag due to 400

lag depending on the frequency of the eddies (sensor separation in the wind direction) or if 401

there are complex adsorption and desorption processes it is necessary to take into account the 402

phase shift (Massman 2000) The TF associated with the total phase shift (Massman 2000) 403

is 404

t

tlon

t

tlonshiftphase

U

L

u

l

U

L

u

lTF sincos_ (14)

405

where llon is the longitudinal sensor separation (function of wind direction) and β is the 406

intrinsic time constant of the scalar sensor This latter was estimated by using the Boylersquos law 407

15

applied to the measurement chamber working at 33 kPa and using the measured flow rate it 408

is about 01 s and moreover it means that the sampling frequency of 10 Hz is adequate for 409

the employed QC-TILDAS in the EC system 410

For the theoretical TF approach cospectra of the vertical wind speed and T and NH3 411

respectively were calculated using the post processing EddySpec package of Eddysoft 412

software using the following setup number of frequencies 4096 for calculating the cospectra 413

for half-hourly runs multiplied by frequency tapering the time series with a Hamming 414

window (Kaimal and Finnigan 1994) and averaging into 20 logarithmically spaced bins 415

Either the time lag or the phase shift was used in the functions of the two approaches 416

CFTheor_time_lag and CFTheor_phase_shift respectively 417

418

162 In situ ogive method 419

The limitation of the theoretical TF approach is that it could miss additional chemical or 420

micro-physical process occurring in the EC system a restriction that is expected to be 421

overcome by the in situ experimental methods Moreover the experimental approach is based 422

on the assumption of cospectral similarity between scalar fluxes and unlike the theoretical TF 423

approach these methods do not require smooth models of atmospheric cospectra (Massman 424

and Clement 2004) The correction factor can be estimated as the ratio between the 425

attenuated flux and a reference flux (usually the sensible heat flux) Aubinet et al (2000) give 426

the procedure to estimate this experimental frequency transfer function but since the 427

cospectra derived from sensible heat fluxes are often not well-defined then direct application 428

of this approach can produce implausible values 429

Starting from the hypothesis of similarity between all scalars cospectra Ammann et al (2006) 430

developed a method for estimating HF losses Applied to EC measurement of NH3 fluxes the 431

method consists in fitting the ogive for NH3 OgwNH3 (the ogive is the integrated cospectra 432

CowNH3 from low to high frequencies) against the ogive of air temperature OgwTa The fitting 433

was made with an iterative linear least square method in which the points being too far away 434

from the regression line were eliminated progressively (robustfit function under Matlabreg) 435

The high-frequency damping correction (CFO) is simply evaluated as the value of the fitted 436

NH3 ogive (OgwNH3) at the highest frequency The frequency band over which the linear 437

regression is performed is determined as the frequency for which OgwT larger than 01 and 438

OgwNH3 is smaller than 08 This choice was arbitrary and may not be appropriate if the 439

damping of the NH3 signal was below the range chosen The ogives were calculated using 440

detrended signals 441

16

442

163 The inductance method 443

Eugster and Senn (1995) propose a correction model for EC data affected by systematic error 444

introduced by the combination of all sources of damping which also includes chemical 445

reaction (in the case of NO2) besides the possible damping due to laminar flow in the 446

sampling tube and sensor separation This damping loss is described by an analogy to 447

inductance L in an alternating current circuit which can be used for taking into account the 448

reduction of spectral density in the inertial subrange This model follows the approach of 449

Moore (1986) using electronic components in a current circuit instead of a gain function 450

As reported by Eugster and Senn (1995) the covariance of the measured (ie damped) scalar 451

flux is given by 452

02220

)(41

1)(

0dffCo

LfdffCow

LL wwL

(15)

453

where )(0 fCo

Lw is the undamped cospectrum The value of L is modified in order to fit the 454

measured cospectrum The independent variables in this correction model are the measuring 455

height above zero-plane displacement z-d the mean horizontal wind speed u the Monin-456

Obukhov stability parameter ζ the natural frequency f and the inductance L The value for 457

L can be derived from spectral and cospectral analysis and is assumed not vary significantly 458

as long as the measuring system is not altered 459

The damped cospectra can be derived combining equation (14) with the empirical formula of 460

Kaimal et al (1972) for stable unstable and neutral stratification as modified by Eugster and 461

Senn (1995) (equations 29 and 30 in their paper) The approximated solutions of Eugster and 462

Senn (1995) for CF range between 10 (no damping at all) and infin (no signal at all) but they 463

suggest to restrict the fit to the range between CF=10 and CF=25 that means all 464

measurements which represent less than 40 of the real fluxes were discarded 465

17

2 RESULTS AND DISCUSSION 466

21 Micrometeorological conditions 467

During the trial the air temperature ranged from 16degC to 335degC and averaged 247degC while 468

the soil surface temperature reached more than 546degC at hourly scale The sky was clear 469

most of the period apart from the 23-24072008 where there was little rain (82 mm) The 470

global solar radiation was following the fair weather diurnal course throughout the period and 471

reached 950 W m-2

at maximum The daily average relative humidity was around 60 with 472

variations ranging between 25 and 93 473

The prevalent wind direction during the experimental period (84 of the runs) was NW the 474

wind speed ranged from 05 to 89 ms-1

and averaged 34 ms-1

while the friction velocity u 475

ranged between 001 and 099 ms-1

with an average of 030 ms-1

(Figure 1a) The energy 476

balance for the period of the trial is reported in Figure 2b at hourly scale In particular it is 477

clear the direct impact of the net radiation (Rn) on the sensible heat flux (H) and on the latent 478

heat flux (LE) The pattern of LE were strongly correlated with the available energy Rn with 479

distinct peaks immediately following irrigation which provided water to evapotranspirate 480

from plant and soil 481

22 Ammonia concentrations 482

The field NH3 concentrations measured by the diffusive samplers (ALPHA badges) over 483

several hours ranged from 3 to 118 microg NH3 m-3

while the 30 minutes ROSAA concentrations 484

varied from around 0 to around 90 microg NH3 m-3

The ROSAA concentrations averaged over 485

the same intervals as the ALPHA badges slightly underestimated the badges concentration by 486

8 on average (ROSAA = 092 ALPHA R2 = 082) The uncalibrated QC-TILDAS 487

concentrations strongly underestimated the ALPHA badges concentrations by 67 but 488

without any offset (QC-TILDAS = 034 ALPHA R2 = 090) This underestimation is larger than 489

what Ellis et al (2010) found with the same instrument in a laboratory calibration Since the 490

correlation was very good the regression against the ALPHA badges was used to calibrate the 491

QC-TILDAS concentration data The correction factor was found to be 286 plusmn 009 The 492

calibrated QC-TILDAS concentration was in quite good agreement with the ROSAA 493

concentrations both in terms of magnitude and dynamics (Figure 2) In Figure 3 a detailed 494

comparison between calibrated values of QC-TILDAS NH3 concentrations and ROSAA 495

values is shown Since we did not find a significant offset in the regression of QC-TILDAS 496

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 7: Eddy covariance measurement of ammonia fluxes: comparison of

7

for NH3 concentration measurements is relatively new and it is the first time it is being used 164

under semi-arid climates we compare a variety of methods to correct the NH3 fluxes 165

measured over an agricultural plot 166

14 NH3 concentration measurements 167

141 The QC-TILDAS analyser 168

1411 The instrument 169

The QC-TILDAS employed during this research is the compact QC-TILDAS-76 SN002-U 170

developed by Aerodyne Research Inc (ARI Billerica MA USA) (Zahniser et al 2005) 171

This spectrometer combines QC laser designed for pulsed operation at near room temperature 172

(Alpes Lasers Neuchacirctel Switzerland) an optical system together with a computer-controlled 173

system that incorporates the electronics for driving the QC laser along with signal generation 174

and a data logger Molecular transition selection for given laser is achieved by temperature 175

tuning typically within a small range around 40 ordmC using a two-stage Peltier element The 176

laser frequency is swept across the full molecular transition at 96734 cm-1

which means that 177

the scan is done over a narrow range of wave numbers chosen to contain an absorption feature 178

of the trace gas of interest The optical system collects light from the QC laser and directs it 179

through an astigmatic Herriott type multi-pass absorption cell (76 m effective path length) 180

onto a TE-cooled photovoltaic detector (Vigo Systems) A detailed description of the device 181

can be found in Zahniser et al (2005) 182

1412 The inlet design and the sampling 183

Ellis et al (2010) report the characterization of the QC-TILDAS by laboratory tests In 184

particular they reported a detailed analysis on the performance of the QC-TILDAS in 185

function of the inlet design To achieve accurate atmospheric measurements of NH3 the 186

sampling setup has to reduce gas interactions with surfaces then it is crucial the configuration 187

of the devicersquos inlet through which the air is drawn into the optical cell The inlet used is a 188

short glass tube containing a critical orifice needed to keep the air flow rate constant and to 189

reduce the pressure in the optical cell to approximately 540 kPa a compromise between 190

minimising line broadening and adsorption effects maximising time response and achieving 191

good sensitivity (Warland et al 2001) The setup of the inlet is done to remove particles in 192

the air sample relying on inertia to remove more than 50 of particles larger than 300 nm in 193

8

particular the flow is split into two branches 90 of the flow makes a sharp turn and is 194

pulled into the optical cell by a pump (VARIAN TriScroll 600 Series) the other 10 is 195

pulled by a second air pump (VARIAN mod SH110) This inlet design is optimized to 196

eliminate the need of filters and any interference they may cause The operation of the 197

spectrometer is fully automated and computer-controlled through a Windows-based 198

proprietary software (TDL Wintel) developed at ARI Concentrations are real-time 199

determined from the light absorption spectra through a non-linear least-squares fitting 200

algorithm (Levenberg-Marquardt) that uses spectral parameters from the HITRAN (high-201

resolution transmission) molecular absorption database (Rothman et al 2003) The pressure 202

and temperature in the multi-pass cell are continuously monitored to supply information 203

needed for spectral fitting The software calculates absolute concentrations so no calibration 204

was performed in the field However laboratory tests demonstrate the need to carry out a 205

post-proceeding calibration in order to overcome the failure of the analyzer to reproduce the 206

values of the ammonia standard Moreover we would justify the calibration factor found for 207

the QC-TILDAS using ethilene standard which has absorbing frequency close to ammonia 208

one but it is not affected by stickiness Part of the problem with absolute spectroscopic values 209

for NH3 in our instrument was due to using pulsed-QCLs rather than CW-QCLs with the 210

laser linewidth considerably greater than the molecular linewidth the spectral analysis 211

required much effort to get absolute values Thus a post-proceeding calibration was done and 212

zeroing was routinely carried out in order to optimize the calculated spectra following 213

Whitehead et al (2008) 214

1413 The response time 215

The decreasing of the mixing ratio of the ammonia was well represented by two exponential 216

decay functions which in Ellis et al (2010) is written as 217

2

02

1

010 expexp

ttA

ttAyy (3) 218

where A1 and A2 are constants and their sum is equal to the stable mixing ratio of NH3 prior to 219

the calibration flow being switched off y0 is equal to the NH3 mixing ratio reached at the end 220

of the decay t is time and t0 is the time at the start of the fit τ1 and τ2 are time decay 221

constants the first of which is fast and corresponds to the gas exchange time of the system 222

(04 s in our case) and the second being much slower and corresponding to interactions of 223

NH3 with surfaces on the interior surfaces of the inlet sampling lines and absorption cell (15 224

9

s) In this case the response time was checked in laboratory and it is controlled by pumping 225

speed and by adsorptiondesorption on the walls In particular it is affected by the flow rate 226

the length and the heating of the sampling tubes Therefore acting on these parameters would 227

allow optimizing the instrument for measuring the fluxes by EC technique 228

Moreover for a mixing ratio of 350 ppb the amount of noise (1) in a 10 Hz measurement 229

obtained by Ellis et al (2010) by an Allan variance analysis was 315 ppb (22 microg NH3 m-3

) 230

Based on statistics obtained for sensible heat CO2 and temperature w w was estimated 231

to be around 023 plusmn 013 (mean plusmn SE) and taking ~ 2 microg NH3 m-3

and w ~ 125 u would 232

mean that the flux detection limit of the setup would be around 25 u (in microg NH3 m-2

s-1

) 233

hence typically around 750 ng NH3 m-2

s-1

However most of the turbulent signal is 234

transported by eddies with a frequency around 02 Hz (evaluated as the co-spectral peak 235

frequency for air temperature (Ta) CO2 and water vapour (H2O)) At this frequency the Allan 236

variance of the QC-TILDAS found by Ellis et al (2010) is around 02 microg NH3 m-3

and hence 237

the detection limit should be on the order of 025 u (typically 75 ng NH3 m-2

s-1

) As reported 238

by Ellis et al (2010) there is an increase in the relative importance of NH3 surface 239

interactions at mixing ratios lower than 100 ppb which further increased when sampling 240

humid air as opposed to dry nitrogen Then the heating in the sampling line to prevent 241

condensation is crucial to perform accurate NH3 measurements There was no line heating 242

used in our study since we assumed that this was not needed since the air temperature 243

encountered in the field was very high (up to 33degC) On the other hand under such 244

conditions it was required the use of an air conditioner for the box containing the QC-245

TILDAS to limit instrumental drift induced by changes in the optical alignment as well as in 246

the laser power beam width and temperature 247

142 The ALPHA badges and ROSAA system 248

The NH3 concentration measured with the QC-TILDAS were compared against passive 249

diffusion samples (ALPHA badges Tang et al 2001) These ALPHA badges were 250

positioned at the same height as the QC-TILDAS inlet and changed twice a day to get daily 251

and nightly concentrations Three replicates were used to evaluate the uncertainty in the 252

measurements The background concentration bgd was estimated with three sets of badges put 253

at three locations at more than 100 100 and 300 m away from the field and changed weekly 254

The badges were prepared with filters coated with citric acid The filters were extracted in 3 255

ml double deionised water and analysed with a FLORRIA analyser (Mechatronics NL) In 256

10

addition during the trial NH3 concentration was monitored by means of the new device 257

ROSAA developed at INRA (Loubet et al 2010 2011) It relies on the coupling of NH3 258

capturing by means of an acid stripping solution (wet-effluent denuders) to convert NH3 in 259

NH4+ and the analysis of the NH4

+ concentration by means of a detector based on a semi-260

permeable membrane and a unit to perform temperature controlled conductivity 261

measurements (ECN Netherlands) In the ROSAA system the three denuders were sampling 262

at three heights (at z = 05 07 and 14 m) above ground and the averaged concentration was 263

measure every 30 minutes 264

15 The eddy covariance system description of the equipment 265

The EC system was equipped with the QC-TILDAS and a three axis ultrasonic anemometer 266

(Gill R2 Gill Instruments Ltd UK) The analogue signals from the QC-TILDAS were passed 267

to the sonic anemometer which uses an analogue-to-digital converter to digitise the signal at 268

10 Hz The sonic anemometer acquired data at 168 Hz internally and reported average values 269

at 208 Hz The digitised signals from the QC-TILDAS were then combined with the wind 270

velocity information and sent to the serial port of a personal computer NH3 fluxes were 271

computed offline with a time step of half an hour The software package EddySoft (Kolle and 272

Rebmann 2007) was used for the data acquisition The same software package was used for 273

post processing EC data and to obtain the cospectra useful for applying the TF method while 274

custom Matlabreg and R scripts were used to process and analyse data for the ogive and the 275

inductance methods respectively Trace gas and heat fluxes were calculated as well as other 276

micrometeorological parameters The components of the wind velocity were rotated every 30 277

minutes to set w and v to zero No detrending was applied and block averaging was instead 278

used (Leuning 2007) 279

The sonic anemometer was mounted on a 15 m tall mast so that the height of the EC sensors 280

was about 07 m above the top of the crop and adjusted to follow crop development Ambient 281

air close to the sonic anemometer head was continuously sampled through a 25 m long inlet 282

line (PFA 38rdquo outer diameter) leading to the QC-TILDAS The flow of air through the tube 283

was measured before and after the T connection used for splitting the flow the flow rate 284

towards the sampling cell was about 9 litresminute resulting in a laminar flow regime with a 285

Reynolds number (Re) of about 2000 below the turbulence limit (for pipe flow Re=2300 286

Shimizu 2007) which was the primary cause of the high frequency loss in the measuring 287

system (Eugster and Senn 1995) The limits in the power supply lines in our experimental 288

11

site have prevented the use of pumps able to provide a turbulent regime into the sampling 289

tube 290

The eddy covariance system together with ROSAA and ALPHA samples equipments were 291

located close to the centre of the field which provided an acceptable upwind fetch (around 292

120 m) in the prevalent wind direction during the trial anyway the footprint of EC 293

measurements was found to be always inside the experimental field 294

16 Spectral correction methods for low-pass filtering in a closed-path EC system 295

As mentioned before all eddy covariance systems tend to underestimate the true atmospheric 296

fluxes due to physical limitations of the flux measurement instrumentation which causes 297

losses of eddy flux contributions at low and high frequencies These losses can be quantified 298

and corrected for by multiplying the measured covariance (subscript m) with a frequency 299

response correction factor (CF ge1) 300

mwCFw (4) 301

The correction factor can be estimated by means of spectral correction methods that can be 302

divided into two families the spectral theoretical transfer function (TF) methods and the in-303

situ methods (Massman and Clement 2004) The general approach consists of three steps 1) 304

the identification of a spectral transfer function (theoretical or experimental) 2) its use to 305

calculate a correction factor and 3) the parameterisation of the correction factor with 306

meteorological variables mainly the wind speed which is directly linked to the turbulence 307

Here three methods have been employed in order to estimate CFs to apply at our measured 308

EC NH3 fluxes (i) Theoretical TF analysis CFTheor (ii) A variant of In situ experimental TF 309

method the Ogive method CFO (iii) the inductance method CFL These methodologies are 310

the most analysed in the literature for evaluating the most accurate CFs for gas trace fluxes 311

over natural surfaces 312

Common to all approaches is the requirement of a measured or evaluated reference 313

cospectrum for scalar as well as a measured cospectrum and the estimate of the flux loss as a 314

function of frequency In any case an important aspect relative to any EC system is the time 315

delay between the instant open-path w measurement and the closed-path scalar concentration 316

measurement This time lag is due to the transport time in the tubing system and the phase 317

shift due to low-pass filtering (Ibrom et al 2007) It depends on the tube length and the flow 318

rate it may change slightly due to changing flow rates in the tube caused by pumping 319

variation or density changes Usually this delay can be calculated empirically by determining 320

12

the maximum of the covariance function between wrsquo and rsquo (Moncrieff et al 1997) which 321

includes the time lag due to the longitudinal (streamwise) separation distance from the air 322

sample inlet to the sonic anemometer depending on wind speed and direction The 323

individually determined time delays for each 30 minutes set of data can be used for 324

calculating the cospectra in this case the phase shift due to low-pass filtering and longitudinal 325

sensor separation has already been corrected for (Ibrom et al 2007) So two subcategories 326

can be identified in the theoretical transfer function analysis with (CFTheor_time_lag) and without 327

the time lag (CFTheor_phase_shift) applied to computation of the covariances and cospectra 328

329

161 Theoretical TF analysis 330

Following Moore (1986) theoretical transfer functions characterizing the measurement 331

process have been developed The correction factor in this case is summarized by Massman 332

and Clement (2004) using the following equation 333

dffCofHfT

fT

dffCo

w

wCF

w

b

b

w

m

TF

)()(sin

1

)(

0

2

2

0

(5)

334

where mw is the measured covariance and w is the true flux H(f) is the product of all 335

the appropriate transfer functions associated with HF losses (

n

i

iTFfH1

)( ) )( fCow is the 336

one-side reference cospectrum Tb is the block averaging period 22sin1 bb fTfT is the 337

TF for the low frequency spectral attenuation The reference cospectrum is either taken from 338

literature (Kaimal and Finnigan 1994) or a site specific reference cospectrum usually derived 339

from sensible heat flux measurements The requirements to apply this method are the TFs 340

and a model of the cospectra which is the weakness of this approach since smooth cospectra 341

are not typical of the atmospheric surface layer (Massman and Clement 2004) In particular 342

in this work we used the reference cospectral models (eq 6-9) given by Moncrieff et al 343

(1997) where udzffk )( is normalized frequency f is the natural frequency u is 344

horizontal wind speed z-d is height above the zero-plane displacement d For stable 345

atmospheric condition the reference cospectrum is 346

12)(

kww

kw

fBA

fffCo

(6)

347

where 348

13

750

4612840

L

dzAw (7)

349

and 350

11342 ww AB (8)

351

While for unstable atmospheric conditions the reference cospectrum is 352

540

7261

9212)(

3751

k

k

kw ffor

f

fffCo (9a)

353

540

831

3784)(

42

k

k

kw ffor

f

fffCo (9b)

354

Moore (1986) developed the first theoretical TFs for EC systems taking into account the 355

attenuation due to the dynamic frequency response of the sensors the scalar path averaging 356

the sensor separation the sensor response mismatch while Moncrieff et al (1997) gave a 357

complete description of TFs of EC systems with closed-path analysers Then considering the 358

wide literature on the used theoretical TFs (see Shimizu 2007 for a review) and the weight of 359

each TF on the total one for our equipment set-up here we report only details about TFs 360

related to (i) the lateral separation (ie perpendicular to the mean wind direction) between 361

the sonic anemometer and QC-TILDAS inlet (TFs Moore 1986) (ii) the tube damping effect 362

(TFtube Lenschow and Raupach 1991) (iii) the volume averaging in a measurement cell 363

(TFvol Massman 2004) (iv) the total phase shift (TFphase_shift Massman 2000) In detail 364

(i) Following Moore (1986) the effect of laterallongitudinal separation s between two 365

sensors can be represented by the following cospectral transfer function 366

)99exp(99 51nTFs (10)

367

where n=fsu As suggested by Massman (2000) the horizontal separation between the two 368

sensors has been set to 030 m 369

(ii) The function TFtube describing the attenuation of the fluctuation concentrations down the 370

sampling tube in which there is a laminar flow for covariance calculation (Lenschow and 371

Raupach 1991 Aubinet et al 2000) is 372

ts

t

tubeUD

LfrTF t

12exp

222 (11)

373

Here rt is the tube radius and Ds the molecular diffusivity of the scalar around 02 cm2s

-1 for 374

NH3 (Massman 1998) Moreover the attenuation may vary with time Leuning and Judd 375

(1996) showed that the attenuation to water vapour by aged and dirty tubes is much greater 376

than that by new and clear tube In the case of laminar flow through the tube the theoretical 377

14

cut off frequency fco_t at which the TF equals 2-12

is given by 378

tt

sttco

Lr

DUf

2_ 6490 (12)

379

about 14 Hz in our EC system To minimizing HF losses due to nonuniform velocity profiles 380

turbulent flow would be preferred (Lenschow and Raupach 1991) This requires a powerful 381

pump with high power consumption and a tremendous start-up current (13 A at 230 V AC for 382

an Edwards Tri-Scroll pump for example pers comm W Eugster) that was unrealistic at our 383

field site Hence we had to use a less powerful pump and thus need to account for the extra 384

loss in frequency response due to laminar flow in the intake hose (Moncrieff et al 1997) The 385

possible tube damping correction does not correct potential absorptiondesorption of moisture 386

by hygroscopic dirt particles in the tube however a strong pressure drop along the intake 387

hose reduces risk of condensation on the walls of the tube On the other hand HF due to wall 388

interaction in the sample tube could be reduced by minimizing lengthrsquos tube and maintaining 389

high flow rate In general the line loss is a function of tubing type and how dirty the tube is 390

(Sukyer and Verma 1993) 391

(iii) As reported by Massman (2004) in a closed-path detector the flow velocity within the 392

detection chamber can be very different than the wind speed of the ambient atmosphere near 393

the tube inlet so that the transfer functions need to be expressed in terms of the volume 394

flushing time constant of the detection chamber τvol 395

2

2sin 2

vol

volvolTF

(13)

396

where f 2 and τvol is equal to the time needed to fill the chamber and its minimum value 397

is given by the ratio between the volume of the chamber (05 l) and the maximum flow rate 398

(iv) However if covariances and the cospectra are calculated without the time lag that means 399

without the method of maximum covariance function or if there is no clear time lag due to 400

lag depending on the frequency of the eddies (sensor separation in the wind direction) or if 401

there are complex adsorption and desorption processes it is necessary to take into account the 402

phase shift (Massman 2000) The TF associated with the total phase shift (Massman 2000) 403

is 404

t

tlon

t

tlonshiftphase

U

L

u

l

U

L

u

lTF sincos_ (14)

405

where llon is the longitudinal sensor separation (function of wind direction) and β is the 406

intrinsic time constant of the scalar sensor This latter was estimated by using the Boylersquos law 407

15

applied to the measurement chamber working at 33 kPa and using the measured flow rate it 408

is about 01 s and moreover it means that the sampling frequency of 10 Hz is adequate for 409

the employed QC-TILDAS in the EC system 410

For the theoretical TF approach cospectra of the vertical wind speed and T and NH3 411

respectively were calculated using the post processing EddySpec package of Eddysoft 412

software using the following setup number of frequencies 4096 for calculating the cospectra 413

for half-hourly runs multiplied by frequency tapering the time series with a Hamming 414

window (Kaimal and Finnigan 1994) and averaging into 20 logarithmically spaced bins 415

Either the time lag or the phase shift was used in the functions of the two approaches 416

CFTheor_time_lag and CFTheor_phase_shift respectively 417

418

162 In situ ogive method 419

The limitation of the theoretical TF approach is that it could miss additional chemical or 420

micro-physical process occurring in the EC system a restriction that is expected to be 421

overcome by the in situ experimental methods Moreover the experimental approach is based 422

on the assumption of cospectral similarity between scalar fluxes and unlike the theoretical TF 423

approach these methods do not require smooth models of atmospheric cospectra (Massman 424

and Clement 2004) The correction factor can be estimated as the ratio between the 425

attenuated flux and a reference flux (usually the sensible heat flux) Aubinet et al (2000) give 426

the procedure to estimate this experimental frequency transfer function but since the 427

cospectra derived from sensible heat fluxes are often not well-defined then direct application 428

of this approach can produce implausible values 429

Starting from the hypothesis of similarity between all scalars cospectra Ammann et al (2006) 430

developed a method for estimating HF losses Applied to EC measurement of NH3 fluxes the 431

method consists in fitting the ogive for NH3 OgwNH3 (the ogive is the integrated cospectra 432

CowNH3 from low to high frequencies) against the ogive of air temperature OgwTa The fitting 433

was made with an iterative linear least square method in which the points being too far away 434

from the regression line were eliminated progressively (robustfit function under Matlabreg) 435

The high-frequency damping correction (CFO) is simply evaluated as the value of the fitted 436

NH3 ogive (OgwNH3) at the highest frequency The frequency band over which the linear 437

regression is performed is determined as the frequency for which OgwT larger than 01 and 438

OgwNH3 is smaller than 08 This choice was arbitrary and may not be appropriate if the 439

damping of the NH3 signal was below the range chosen The ogives were calculated using 440

detrended signals 441

16

442

163 The inductance method 443

Eugster and Senn (1995) propose a correction model for EC data affected by systematic error 444

introduced by the combination of all sources of damping which also includes chemical 445

reaction (in the case of NO2) besides the possible damping due to laminar flow in the 446

sampling tube and sensor separation This damping loss is described by an analogy to 447

inductance L in an alternating current circuit which can be used for taking into account the 448

reduction of spectral density in the inertial subrange This model follows the approach of 449

Moore (1986) using electronic components in a current circuit instead of a gain function 450

As reported by Eugster and Senn (1995) the covariance of the measured (ie damped) scalar 451

flux is given by 452

02220

)(41

1)(

0dffCo

LfdffCow

LL wwL

(15)

453

where )(0 fCo

Lw is the undamped cospectrum The value of L is modified in order to fit the 454

measured cospectrum The independent variables in this correction model are the measuring 455

height above zero-plane displacement z-d the mean horizontal wind speed u the Monin-456

Obukhov stability parameter ζ the natural frequency f and the inductance L The value for 457

L can be derived from spectral and cospectral analysis and is assumed not vary significantly 458

as long as the measuring system is not altered 459

The damped cospectra can be derived combining equation (14) with the empirical formula of 460

Kaimal et al (1972) for stable unstable and neutral stratification as modified by Eugster and 461

Senn (1995) (equations 29 and 30 in their paper) The approximated solutions of Eugster and 462

Senn (1995) for CF range between 10 (no damping at all) and infin (no signal at all) but they 463

suggest to restrict the fit to the range between CF=10 and CF=25 that means all 464

measurements which represent less than 40 of the real fluxes were discarded 465

17

2 RESULTS AND DISCUSSION 466

21 Micrometeorological conditions 467

During the trial the air temperature ranged from 16degC to 335degC and averaged 247degC while 468

the soil surface temperature reached more than 546degC at hourly scale The sky was clear 469

most of the period apart from the 23-24072008 where there was little rain (82 mm) The 470

global solar radiation was following the fair weather diurnal course throughout the period and 471

reached 950 W m-2

at maximum The daily average relative humidity was around 60 with 472

variations ranging between 25 and 93 473

The prevalent wind direction during the experimental period (84 of the runs) was NW the 474

wind speed ranged from 05 to 89 ms-1

and averaged 34 ms-1

while the friction velocity u 475

ranged between 001 and 099 ms-1

with an average of 030 ms-1

(Figure 1a) The energy 476

balance for the period of the trial is reported in Figure 2b at hourly scale In particular it is 477

clear the direct impact of the net radiation (Rn) on the sensible heat flux (H) and on the latent 478

heat flux (LE) The pattern of LE were strongly correlated with the available energy Rn with 479

distinct peaks immediately following irrigation which provided water to evapotranspirate 480

from plant and soil 481

22 Ammonia concentrations 482

The field NH3 concentrations measured by the diffusive samplers (ALPHA badges) over 483

several hours ranged from 3 to 118 microg NH3 m-3

while the 30 minutes ROSAA concentrations 484

varied from around 0 to around 90 microg NH3 m-3

The ROSAA concentrations averaged over 485

the same intervals as the ALPHA badges slightly underestimated the badges concentration by 486

8 on average (ROSAA = 092 ALPHA R2 = 082) The uncalibrated QC-TILDAS 487

concentrations strongly underestimated the ALPHA badges concentrations by 67 but 488

without any offset (QC-TILDAS = 034 ALPHA R2 = 090) This underestimation is larger than 489

what Ellis et al (2010) found with the same instrument in a laboratory calibration Since the 490

correlation was very good the regression against the ALPHA badges was used to calibrate the 491

QC-TILDAS concentration data The correction factor was found to be 286 plusmn 009 The 492

calibrated QC-TILDAS concentration was in quite good agreement with the ROSAA 493

concentrations both in terms of magnitude and dynamics (Figure 2) In Figure 3 a detailed 494

comparison between calibrated values of QC-TILDAS NH3 concentrations and ROSAA 495

values is shown Since we did not find a significant offset in the regression of QC-TILDAS 496

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 8: Eddy covariance measurement of ammonia fluxes: comparison of

8

particular the flow is split into two branches 90 of the flow makes a sharp turn and is 194

pulled into the optical cell by a pump (VARIAN TriScroll 600 Series) the other 10 is 195

pulled by a second air pump (VARIAN mod SH110) This inlet design is optimized to 196

eliminate the need of filters and any interference they may cause The operation of the 197

spectrometer is fully automated and computer-controlled through a Windows-based 198

proprietary software (TDL Wintel) developed at ARI Concentrations are real-time 199

determined from the light absorption spectra through a non-linear least-squares fitting 200

algorithm (Levenberg-Marquardt) that uses spectral parameters from the HITRAN (high-201

resolution transmission) molecular absorption database (Rothman et al 2003) The pressure 202

and temperature in the multi-pass cell are continuously monitored to supply information 203

needed for spectral fitting The software calculates absolute concentrations so no calibration 204

was performed in the field However laboratory tests demonstrate the need to carry out a 205

post-proceeding calibration in order to overcome the failure of the analyzer to reproduce the 206

values of the ammonia standard Moreover we would justify the calibration factor found for 207

the QC-TILDAS using ethilene standard which has absorbing frequency close to ammonia 208

one but it is not affected by stickiness Part of the problem with absolute spectroscopic values 209

for NH3 in our instrument was due to using pulsed-QCLs rather than CW-QCLs with the 210

laser linewidth considerably greater than the molecular linewidth the spectral analysis 211

required much effort to get absolute values Thus a post-proceeding calibration was done and 212

zeroing was routinely carried out in order to optimize the calculated spectra following 213

Whitehead et al (2008) 214

1413 The response time 215

The decreasing of the mixing ratio of the ammonia was well represented by two exponential 216

decay functions which in Ellis et al (2010) is written as 217

2

02

1

010 expexp

ttA

ttAyy (3) 218

where A1 and A2 are constants and their sum is equal to the stable mixing ratio of NH3 prior to 219

the calibration flow being switched off y0 is equal to the NH3 mixing ratio reached at the end 220

of the decay t is time and t0 is the time at the start of the fit τ1 and τ2 are time decay 221

constants the first of which is fast and corresponds to the gas exchange time of the system 222

(04 s in our case) and the second being much slower and corresponding to interactions of 223

NH3 with surfaces on the interior surfaces of the inlet sampling lines and absorption cell (15 224

9

s) In this case the response time was checked in laboratory and it is controlled by pumping 225

speed and by adsorptiondesorption on the walls In particular it is affected by the flow rate 226

the length and the heating of the sampling tubes Therefore acting on these parameters would 227

allow optimizing the instrument for measuring the fluxes by EC technique 228

Moreover for a mixing ratio of 350 ppb the amount of noise (1) in a 10 Hz measurement 229

obtained by Ellis et al (2010) by an Allan variance analysis was 315 ppb (22 microg NH3 m-3

) 230

Based on statistics obtained for sensible heat CO2 and temperature w w was estimated 231

to be around 023 plusmn 013 (mean plusmn SE) and taking ~ 2 microg NH3 m-3

and w ~ 125 u would 232

mean that the flux detection limit of the setup would be around 25 u (in microg NH3 m-2

s-1

) 233

hence typically around 750 ng NH3 m-2

s-1

However most of the turbulent signal is 234

transported by eddies with a frequency around 02 Hz (evaluated as the co-spectral peak 235

frequency for air temperature (Ta) CO2 and water vapour (H2O)) At this frequency the Allan 236

variance of the QC-TILDAS found by Ellis et al (2010) is around 02 microg NH3 m-3

and hence 237

the detection limit should be on the order of 025 u (typically 75 ng NH3 m-2

s-1

) As reported 238

by Ellis et al (2010) there is an increase in the relative importance of NH3 surface 239

interactions at mixing ratios lower than 100 ppb which further increased when sampling 240

humid air as opposed to dry nitrogen Then the heating in the sampling line to prevent 241

condensation is crucial to perform accurate NH3 measurements There was no line heating 242

used in our study since we assumed that this was not needed since the air temperature 243

encountered in the field was very high (up to 33degC) On the other hand under such 244

conditions it was required the use of an air conditioner for the box containing the QC-245

TILDAS to limit instrumental drift induced by changes in the optical alignment as well as in 246

the laser power beam width and temperature 247

142 The ALPHA badges and ROSAA system 248

The NH3 concentration measured with the QC-TILDAS were compared against passive 249

diffusion samples (ALPHA badges Tang et al 2001) These ALPHA badges were 250

positioned at the same height as the QC-TILDAS inlet and changed twice a day to get daily 251

and nightly concentrations Three replicates were used to evaluate the uncertainty in the 252

measurements The background concentration bgd was estimated with three sets of badges put 253

at three locations at more than 100 100 and 300 m away from the field and changed weekly 254

The badges were prepared with filters coated with citric acid The filters were extracted in 3 255

ml double deionised water and analysed with a FLORRIA analyser (Mechatronics NL) In 256

10

addition during the trial NH3 concentration was monitored by means of the new device 257

ROSAA developed at INRA (Loubet et al 2010 2011) It relies on the coupling of NH3 258

capturing by means of an acid stripping solution (wet-effluent denuders) to convert NH3 in 259

NH4+ and the analysis of the NH4

+ concentration by means of a detector based on a semi-260

permeable membrane and a unit to perform temperature controlled conductivity 261

measurements (ECN Netherlands) In the ROSAA system the three denuders were sampling 262

at three heights (at z = 05 07 and 14 m) above ground and the averaged concentration was 263

measure every 30 minutes 264

15 The eddy covariance system description of the equipment 265

The EC system was equipped with the QC-TILDAS and a three axis ultrasonic anemometer 266

(Gill R2 Gill Instruments Ltd UK) The analogue signals from the QC-TILDAS were passed 267

to the sonic anemometer which uses an analogue-to-digital converter to digitise the signal at 268

10 Hz The sonic anemometer acquired data at 168 Hz internally and reported average values 269

at 208 Hz The digitised signals from the QC-TILDAS were then combined with the wind 270

velocity information and sent to the serial port of a personal computer NH3 fluxes were 271

computed offline with a time step of half an hour The software package EddySoft (Kolle and 272

Rebmann 2007) was used for the data acquisition The same software package was used for 273

post processing EC data and to obtain the cospectra useful for applying the TF method while 274

custom Matlabreg and R scripts were used to process and analyse data for the ogive and the 275

inductance methods respectively Trace gas and heat fluxes were calculated as well as other 276

micrometeorological parameters The components of the wind velocity were rotated every 30 277

minutes to set w and v to zero No detrending was applied and block averaging was instead 278

used (Leuning 2007) 279

The sonic anemometer was mounted on a 15 m tall mast so that the height of the EC sensors 280

was about 07 m above the top of the crop and adjusted to follow crop development Ambient 281

air close to the sonic anemometer head was continuously sampled through a 25 m long inlet 282

line (PFA 38rdquo outer diameter) leading to the QC-TILDAS The flow of air through the tube 283

was measured before and after the T connection used for splitting the flow the flow rate 284

towards the sampling cell was about 9 litresminute resulting in a laminar flow regime with a 285

Reynolds number (Re) of about 2000 below the turbulence limit (for pipe flow Re=2300 286

Shimizu 2007) which was the primary cause of the high frequency loss in the measuring 287

system (Eugster and Senn 1995) The limits in the power supply lines in our experimental 288

11

site have prevented the use of pumps able to provide a turbulent regime into the sampling 289

tube 290

The eddy covariance system together with ROSAA and ALPHA samples equipments were 291

located close to the centre of the field which provided an acceptable upwind fetch (around 292

120 m) in the prevalent wind direction during the trial anyway the footprint of EC 293

measurements was found to be always inside the experimental field 294

16 Spectral correction methods for low-pass filtering in a closed-path EC system 295

As mentioned before all eddy covariance systems tend to underestimate the true atmospheric 296

fluxes due to physical limitations of the flux measurement instrumentation which causes 297

losses of eddy flux contributions at low and high frequencies These losses can be quantified 298

and corrected for by multiplying the measured covariance (subscript m) with a frequency 299

response correction factor (CF ge1) 300

mwCFw (4) 301

The correction factor can be estimated by means of spectral correction methods that can be 302

divided into two families the spectral theoretical transfer function (TF) methods and the in-303

situ methods (Massman and Clement 2004) The general approach consists of three steps 1) 304

the identification of a spectral transfer function (theoretical or experimental) 2) its use to 305

calculate a correction factor and 3) the parameterisation of the correction factor with 306

meteorological variables mainly the wind speed which is directly linked to the turbulence 307

Here three methods have been employed in order to estimate CFs to apply at our measured 308

EC NH3 fluxes (i) Theoretical TF analysis CFTheor (ii) A variant of In situ experimental TF 309

method the Ogive method CFO (iii) the inductance method CFL These methodologies are 310

the most analysed in the literature for evaluating the most accurate CFs for gas trace fluxes 311

over natural surfaces 312

Common to all approaches is the requirement of a measured or evaluated reference 313

cospectrum for scalar as well as a measured cospectrum and the estimate of the flux loss as a 314

function of frequency In any case an important aspect relative to any EC system is the time 315

delay between the instant open-path w measurement and the closed-path scalar concentration 316

measurement This time lag is due to the transport time in the tubing system and the phase 317

shift due to low-pass filtering (Ibrom et al 2007) It depends on the tube length and the flow 318

rate it may change slightly due to changing flow rates in the tube caused by pumping 319

variation or density changes Usually this delay can be calculated empirically by determining 320

12

the maximum of the covariance function between wrsquo and rsquo (Moncrieff et al 1997) which 321

includes the time lag due to the longitudinal (streamwise) separation distance from the air 322

sample inlet to the sonic anemometer depending on wind speed and direction The 323

individually determined time delays for each 30 minutes set of data can be used for 324

calculating the cospectra in this case the phase shift due to low-pass filtering and longitudinal 325

sensor separation has already been corrected for (Ibrom et al 2007) So two subcategories 326

can be identified in the theoretical transfer function analysis with (CFTheor_time_lag) and without 327

the time lag (CFTheor_phase_shift) applied to computation of the covariances and cospectra 328

329

161 Theoretical TF analysis 330

Following Moore (1986) theoretical transfer functions characterizing the measurement 331

process have been developed The correction factor in this case is summarized by Massman 332

and Clement (2004) using the following equation 333

dffCofHfT

fT

dffCo

w

wCF

w

b

b

w

m

TF

)()(sin

1

)(

0

2

2

0

(5)

334

where mw is the measured covariance and w is the true flux H(f) is the product of all 335

the appropriate transfer functions associated with HF losses (

n

i

iTFfH1

)( ) )( fCow is the 336

one-side reference cospectrum Tb is the block averaging period 22sin1 bb fTfT is the 337

TF for the low frequency spectral attenuation The reference cospectrum is either taken from 338

literature (Kaimal and Finnigan 1994) or a site specific reference cospectrum usually derived 339

from sensible heat flux measurements The requirements to apply this method are the TFs 340

and a model of the cospectra which is the weakness of this approach since smooth cospectra 341

are not typical of the atmospheric surface layer (Massman and Clement 2004) In particular 342

in this work we used the reference cospectral models (eq 6-9) given by Moncrieff et al 343

(1997) where udzffk )( is normalized frequency f is the natural frequency u is 344

horizontal wind speed z-d is height above the zero-plane displacement d For stable 345

atmospheric condition the reference cospectrum is 346

12)(

kww

kw

fBA

fffCo

(6)

347

where 348

13

750

4612840

L

dzAw (7)

349

and 350

11342 ww AB (8)

351

While for unstable atmospheric conditions the reference cospectrum is 352

540

7261

9212)(

3751

k

k

kw ffor

f

fffCo (9a)

353

540

831

3784)(

42

k

k

kw ffor

f

fffCo (9b)

354

Moore (1986) developed the first theoretical TFs for EC systems taking into account the 355

attenuation due to the dynamic frequency response of the sensors the scalar path averaging 356

the sensor separation the sensor response mismatch while Moncrieff et al (1997) gave a 357

complete description of TFs of EC systems with closed-path analysers Then considering the 358

wide literature on the used theoretical TFs (see Shimizu 2007 for a review) and the weight of 359

each TF on the total one for our equipment set-up here we report only details about TFs 360

related to (i) the lateral separation (ie perpendicular to the mean wind direction) between 361

the sonic anemometer and QC-TILDAS inlet (TFs Moore 1986) (ii) the tube damping effect 362

(TFtube Lenschow and Raupach 1991) (iii) the volume averaging in a measurement cell 363

(TFvol Massman 2004) (iv) the total phase shift (TFphase_shift Massman 2000) In detail 364

(i) Following Moore (1986) the effect of laterallongitudinal separation s between two 365

sensors can be represented by the following cospectral transfer function 366

)99exp(99 51nTFs (10)

367

where n=fsu As suggested by Massman (2000) the horizontal separation between the two 368

sensors has been set to 030 m 369

(ii) The function TFtube describing the attenuation of the fluctuation concentrations down the 370

sampling tube in which there is a laminar flow for covariance calculation (Lenschow and 371

Raupach 1991 Aubinet et al 2000) is 372

ts

t

tubeUD

LfrTF t

12exp

222 (11)

373

Here rt is the tube radius and Ds the molecular diffusivity of the scalar around 02 cm2s

-1 for 374

NH3 (Massman 1998) Moreover the attenuation may vary with time Leuning and Judd 375

(1996) showed that the attenuation to water vapour by aged and dirty tubes is much greater 376

than that by new and clear tube In the case of laminar flow through the tube the theoretical 377

14

cut off frequency fco_t at which the TF equals 2-12

is given by 378

tt

sttco

Lr

DUf

2_ 6490 (12)

379

about 14 Hz in our EC system To minimizing HF losses due to nonuniform velocity profiles 380

turbulent flow would be preferred (Lenschow and Raupach 1991) This requires a powerful 381

pump with high power consumption and a tremendous start-up current (13 A at 230 V AC for 382

an Edwards Tri-Scroll pump for example pers comm W Eugster) that was unrealistic at our 383

field site Hence we had to use a less powerful pump and thus need to account for the extra 384

loss in frequency response due to laminar flow in the intake hose (Moncrieff et al 1997) The 385

possible tube damping correction does not correct potential absorptiondesorption of moisture 386

by hygroscopic dirt particles in the tube however a strong pressure drop along the intake 387

hose reduces risk of condensation on the walls of the tube On the other hand HF due to wall 388

interaction in the sample tube could be reduced by minimizing lengthrsquos tube and maintaining 389

high flow rate In general the line loss is a function of tubing type and how dirty the tube is 390

(Sukyer and Verma 1993) 391

(iii) As reported by Massman (2004) in a closed-path detector the flow velocity within the 392

detection chamber can be very different than the wind speed of the ambient atmosphere near 393

the tube inlet so that the transfer functions need to be expressed in terms of the volume 394

flushing time constant of the detection chamber τvol 395

2

2sin 2

vol

volvolTF

(13)

396

where f 2 and τvol is equal to the time needed to fill the chamber and its minimum value 397

is given by the ratio between the volume of the chamber (05 l) and the maximum flow rate 398

(iv) However if covariances and the cospectra are calculated without the time lag that means 399

without the method of maximum covariance function or if there is no clear time lag due to 400

lag depending on the frequency of the eddies (sensor separation in the wind direction) or if 401

there are complex adsorption and desorption processes it is necessary to take into account the 402

phase shift (Massman 2000) The TF associated with the total phase shift (Massman 2000) 403

is 404

t

tlon

t

tlonshiftphase

U

L

u

l

U

L

u

lTF sincos_ (14)

405

where llon is the longitudinal sensor separation (function of wind direction) and β is the 406

intrinsic time constant of the scalar sensor This latter was estimated by using the Boylersquos law 407

15

applied to the measurement chamber working at 33 kPa and using the measured flow rate it 408

is about 01 s and moreover it means that the sampling frequency of 10 Hz is adequate for 409

the employed QC-TILDAS in the EC system 410

For the theoretical TF approach cospectra of the vertical wind speed and T and NH3 411

respectively were calculated using the post processing EddySpec package of Eddysoft 412

software using the following setup number of frequencies 4096 for calculating the cospectra 413

for half-hourly runs multiplied by frequency tapering the time series with a Hamming 414

window (Kaimal and Finnigan 1994) and averaging into 20 logarithmically spaced bins 415

Either the time lag or the phase shift was used in the functions of the two approaches 416

CFTheor_time_lag and CFTheor_phase_shift respectively 417

418

162 In situ ogive method 419

The limitation of the theoretical TF approach is that it could miss additional chemical or 420

micro-physical process occurring in the EC system a restriction that is expected to be 421

overcome by the in situ experimental methods Moreover the experimental approach is based 422

on the assumption of cospectral similarity between scalar fluxes and unlike the theoretical TF 423

approach these methods do not require smooth models of atmospheric cospectra (Massman 424

and Clement 2004) The correction factor can be estimated as the ratio between the 425

attenuated flux and a reference flux (usually the sensible heat flux) Aubinet et al (2000) give 426

the procedure to estimate this experimental frequency transfer function but since the 427

cospectra derived from sensible heat fluxes are often not well-defined then direct application 428

of this approach can produce implausible values 429

Starting from the hypothesis of similarity between all scalars cospectra Ammann et al (2006) 430

developed a method for estimating HF losses Applied to EC measurement of NH3 fluxes the 431

method consists in fitting the ogive for NH3 OgwNH3 (the ogive is the integrated cospectra 432

CowNH3 from low to high frequencies) against the ogive of air temperature OgwTa The fitting 433

was made with an iterative linear least square method in which the points being too far away 434

from the regression line were eliminated progressively (robustfit function under Matlabreg) 435

The high-frequency damping correction (CFO) is simply evaluated as the value of the fitted 436

NH3 ogive (OgwNH3) at the highest frequency The frequency band over which the linear 437

regression is performed is determined as the frequency for which OgwT larger than 01 and 438

OgwNH3 is smaller than 08 This choice was arbitrary and may not be appropriate if the 439

damping of the NH3 signal was below the range chosen The ogives were calculated using 440

detrended signals 441

16

442

163 The inductance method 443

Eugster and Senn (1995) propose a correction model for EC data affected by systematic error 444

introduced by the combination of all sources of damping which also includes chemical 445

reaction (in the case of NO2) besides the possible damping due to laminar flow in the 446

sampling tube and sensor separation This damping loss is described by an analogy to 447

inductance L in an alternating current circuit which can be used for taking into account the 448

reduction of spectral density in the inertial subrange This model follows the approach of 449

Moore (1986) using electronic components in a current circuit instead of a gain function 450

As reported by Eugster and Senn (1995) the covariance of the measured (ie damped) scalar 451

flux is given by 452

02220

)(41

1)(

0dffCo

LfdffCow

LL wwL

(15)

453

where )(0 fCo

Lw is the undamped cospectrum The value of L is modified in order to fit the 454

measured cospectrum The independent variables in this correction model are the measuring 455

height above zero-plane displacement z-d the mean horizontal wind speed u the Monin-456

Obukhov stability parameter ζ the natural frequency f and the inductance L The value for 457

L can be derived from spectral and cospectral analysis and is assumed not vary significantly 458

as long as the measuring system is not altered 459

The damped cospectra can be derived combining equation (14) with the empirical formula of 460

Kaimal et al (1972) for stable unstable and neutral stratification as modified by Eugster and 461

Senn (1995) (equations 29 and 30 in their paper) The approximated solutions of Eugster and 462

Senn (1995) for CF range between 10 (no damping at all) and infin (no signal at all) but they 463

suggest to restrict the fit to the range between CF=10 and CF=25 that means all 464

measurements which represent less than 40 of the real fluxes were discarded 465

17

2 RESULTS AND DISCUSSION 466

21 Micrometeorological conditions 467

During the trial the air temperature ranged from 16degC to 335degC and averaged 247degC while 468

the soil surface temperature reached more than 546degC at hourly scale The sky was clear 469

most of the period apart from the 23-24072008 where there was little rain (82 mm) The 470

global solar radiation was following the fair weather diurnal course throughout the period and 471

reached 950 W m-2

at maximum The daily average relative humidity was around 60 with 472

variations ranging between 25 and 93 473

The prevalent wind direction during the experimental period (84 of the runs) was NW the 474

wind speed ranged from 05 to 89 ms-1

and averaged 34 ms-1

while the friction velocity u 475

ranged between 001 and 099 ms-1

with an average of 030 ms-1

(Figure 1a) The energy 476

balance for the period of the trial is reported in Figure 2b at hourly scale In particular it is 477

clear the direct impact of the net radiation (Rn) on the sensible heat flux (H) and on the latent 478

heat flux (LE) The pattern of LE were strongly correlated with the available energy Rn with 479

distinct peaks immediately following irrigation which provided water to evapotranspirate 480

from plant and soil 481

22 Ammonia concentrations 482

The field NH3 concentrations measured by the diffusive samplers (ALPHA badges) over 483

several hours ranged from 3 to 118 microg NH3 m-3

while the 30 minutes ROSAA concentrations 484

varied from around 0 to around 90 microg NH3 m-3

The ROSAA concentrations averaged over 485

the same intervals as the ALPHA badges slightly underestimated the badges concentration by 486

8 on average (ROSAA = 092 ALPHA R2 = 082) The uncalibrated QC-TILDAS 487

concentrations strongly underestimated the ALPHA badges concentrations by 67 but 488

without any offset (QC-TILDAS = 034 ALPHA R2 = 090) This underestimation is larger than 489

what Ellis et al (2010) found with the same instrument in a laboratory calibration Since the 490

correlation was very good the regression against the ALPHA badges was used to calibrate the 491

QC-TILDAS concentration data The correction factor was found to be 286 plusmn 009 The 492

calibrated QC-TILDAS concentration was in quite good agreement with the ROSAA 493

concentrations both in terms of magnitude and dynamics (Figure 2) In Figure 3 a detailed 494

comparison between calibrated values of QC-TILDAS NH3 concentrations and ROSAA 495

values is shown Since we did not find a significant offset in the regression of QC-TILDAS 496

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 9: Eddy covariance measurement of ammonia fluxes: comparison of

9

s) In this case the response time was checked in laboratory and it is controlled by pumping 225

speed and by adsorptiondesorption on the walls In particular it is affected by the flow rate 226

the length and the heating of the sampling tubes Therefore acting on these parameters would 227

allow optimizing the instrument for measuring the fluxes by EC technique 228

Moreover for a mixing ratio of 350 ppb the amount of noise (1) in a 10 Hz measurement 229

obtained by Ellis et al (2010) by an Allan variance analysis was 315 ppb (22 microg NH3 m-3

) 230

Based on statistics obtained for sensible heat CO2 and temperature w w was estimated 231

to be around 023 plusmn 013 (mean plusmn SE) and taking ~ 2 microg NH3 m-3

and w ~ 125 u would 232

mean that the flux detection limit of the setup would be around 25 u (in microg NH3 m-2

s-1

) 233

hence typically around 750 ng NH3 m-2

s-1

However most of the turbulent signal is 234

transported by eddies with a frequency around 02 Hz (evaluated as the co-spectral peak 235

frequency for air temperature (Ta) CO2 and water vapour (H2O)) At this frequency the Allan 236

variance of the QC-TILDAS found by Ellis et al (2010) is around 02 microg NH3 m-3

and hence 237

the detection limit should be on the order of 025 u (typically 75 ng NH3 m-2

s-1

) As reported 238

by Ellis et al (2010) there is an increase in the relative importance of NH3 surface 239

interactions at mixing ratios lower than 100 ppb which further increased when sampling 240

humid air as opposed to dry nitrogen Then the heating in the sampling line to prevent 241

condensation is crucial to perform accurate NH3 measurements There was no line heating 242

used in our study since we assumed that this was not needed since the air temperature 243

encountered in the field was very high (up to 33degC) On the other hand under such 244

conditions it was required the use of an air conditioner for the box containing the QC-245

TILDAS to limit instrumental drift induced by changes in the optical alignment as well as in 246

the laser power beam width and temperature 247

142 The ALPHA badges and ROSAA system 248

The NH3 concentration measured with the QC-TILDAS were compared against passive 249

diffusion samples (ALPHA badges Tang et al 2001) These ALPHA badges were 250

positioned at the same height as the QC-TILDAS inlet and changed twice a day to get daily 251

and nightly concentrations Three replicates were used to evaluate the uncertainty in the 252

measurements The background concentration bgd was estimated with three sets of badges put 253

at three locations at more than 100 100 and 300 m away from the field and changed weekly 254

The badges were prepared with filters coated with citric acid The filters were extracted in 3 255

ml double deionised water and analysed with a FLORRIA analyser (Mechatronics NL) In 256

10

addition during the trial NH3 concentration was monitored by means of the new device 257

ROSAA developed at INRA (Loubet et al 2010 2011) It relies on the coupling of NH3 258

capturing by means of an acid stripping solution (wet-effluent denuders) to convert NH3 in 259

NH4+ and the analysis of the NH4

+ concentration by means of a detector based on a semi-260

permeable membrane and a unit to perform temperature controlled conductivity 261

measurements (ECN Netherlands) In the ROSAA system the three denuders were sampling 262

at three heights (at z = 05 07 and 14 m) above ground and the averaged concentration was 263

measure every 30 minutes 264

15 The eddy covariance system description of the equipment 265

The EC system was equipped with the QC-TILDAS and a three axis ultrasonic anemometer 266

(Gill R2 Gill Instruments Ltd UK) The analogue signals from the QC-TILDAS were passed 267

to the sonic anemometer which uses an analogue-to-digital converter to digitise the signal at 268

10 Hz The sonic anemometer acquired data at 168 Hz internally and reported average values 269

at 208 Hz The digitised signals from the QC-TILDAS were then combined with the wind 270

velocity information and sent to the serial port of a personal computer NH3 fluxes were 271

computed offline with a time step of half an hour The software package EddySoft (Kolle and 272

Rebmann 2007) was used for the data acquisition The same software package was used for 273

post processing EC data and to obtain the cospectra useful for applying the TF method while 274

custom Matlabreg and R scripts were used to process and analyse data for the ogive and the 275

inductance methods respectively Trace gas and heat fluxes were calculated as well as other 276

micrometeorological parameters The components of the wind velocity were rotated every 30 277

minutes to set w and v to zero No detrending was applied and block averaging was instead 278

used (Leuning 2007) 279

The sonic anemometer was mounted on a 15 m tall mast so that the height of the EC sensors 280

was about 07 m above the top of the crop and adjusted to follow crop development Ambient 281

air close to the sonic anemometer head was continuously sampled through a 25 m long inlet 282

line (PFA 38rdquo outer diameter) leading to the QC-TILDAS The flow of air through the tube 283

was measured before and after the T connection used for splitting the flow the flow rate 284

towards the sampling cell was about 9 litresminute resulting in a laminar flow regime with a 285

Reynolds number (Re) of about 2000 below the turbulence limit (for pipe flow Re=2300 286

Shimizu 2007) which was the primary cause of the high frequency loss in the measuring 287

system (Eugster and Senn 1995) The limits in the power supply lines in our experimental 288

11

site have prevented the use of pumps able to provide a turbulent regime into the sampling 289

tube 290

The eddy covariance system together with ROSAA and ALPHA samples equipments were 291

located close to the centre of the field which provided an acceptable upwind fetch (around 292

120 m) in the prevalent wind direction during the trial anyway the footprint of EC 293

measurements was found to be always inside the experimental field 294

16 Spectral correction methods for low-pass filtering in a closed-path EC system 295

As mentioned before all eddy covariance systems tend to underestimate the true atmospheric 296

fluxes due to physical limitations of the flux measurement instrumentation which causes 297

losses of eddy flux contributions at low and high frequencies These losses can be quantified 298

and corrected for by multiplying the measured covariance (subscript m) with a frequency 299

response correction factor (CF ge1) 300

mwCFw (4) 301

The correction factor can be estimated by means of spectral correction methods that can be 302

divided into two families the spectral theoretical transfer function (TF) methods and the in-303

situ methods (Massman and Clement 2004) The general approach consists of three steps 1) 304

the identification of a spectral transfer function (theoretical or experimental) 2) its use to 305

calculate a correction factor and 3) the parameterisation of the correction factor with 306

meteorological variables mainly the wind speed which is directly linked to the turbulence 307

Here three methods have been employed in order to estimate CFs to apply at our measured 308

EC NH3 fluxes (i) Theoretical TF analysis CFTheor (ii) A variant of In situ experimental TF 309

method the Ogive method CFO (iii) the inductance method CFL These methodologies are 310

the most analysed in the literature for evaluating the most accurate CFs for gas trace fluxes 311

over natural surfaces 312

Common to all approaches is the requirement of a measured or evaluated reference 313

cospectrum for scalar as well as a measured cospectrum and the estimate of the flux loss as a 314

function of frequency In any case an important aspect relative to any EC system is the time 315

delay between the instant open-path w measurement and the closed-path scalar concentration 316

measurement This time lag is due to the transport time in the tubing system and the phase 317

shift due to low-pass filtering (Ibrom et al 2007) It depends on the tube length and the flow 318

rate it may change slightly due to changing flow rates in the tube caused by pumping 319

variation or density changes Usually this delay can be calculated empirically by determining 320

12

the maximum of the covariance function between wrsquo and rsquo (Moncrieff et al 1997) which 321

includes the time lag due to the longitudinal (streamwise) separation distance from the air 322

sample inlet to the sonic anemometer depending on wind speed and direction The 323

individually determined time delays for each 30 minutes set of data can be used for 324

calculating the cospectra in this case the phase shift due to low-pass filtering and longitudinal 325

sensor separation has already been corrected for (Ibrom et al 2007) So two subcategories 326

can be identified in the theoretical transfer function analysis with (CFTheor_time_lag) and without 327

the time lag (CFTheor_phase_shift) applied to computation of the covariances and cospectra 328

329

161 Theoretical TF analysis 330

Following Moore (1986) theoretical transfer functions characterizing the measurement 331

process have been developed The correction factor in this case is summarized by Massman 332

and Clement (2004) using the following equation 333

dffCofHfT

fT

dffCo

w

wCF

w

b

b

w

m

TF

)()(sin

1

)(

0

2

2

0

(5)

334

where mw is the measured covariance and w is the true flux H(f) is the product of all 335

the appropriate transfer functions associated with HF losses (

n

i

iTFfH1

)( ) )( fCow is the 336

one-side reference cospectrum Tb is the block averaging period 22sin1 bb fTfT is the 337

TF for the low frequency spectral attenuation The reference cospectrum is either taken from 338

literature (Kaimal and Finnigan 1994) or a site specific reference cospectrum usually derived 339

from sensible heat flux measurements The requirements to apply this method are the TFs 340

and a model of the cospectra which is the weakness of this approach since smooth cospectra 341

are not typical of the atmospheric surface layer (Massman and Clement 2004) In particular 342

in this work we used the reference cospectral models (eq 6-9) given by Moncrieff et al 343

(1997) where udzffk )( is normalized frequency f is the natural frequency u is 344

horizontal wind speed z-d is height above the zero-plane displacement d For stable 345

atmospheric condition the reference cospectrum is 346

12)(

kww

kw

fBA

fffCo

(6)

347

where 348

13

750

4612840

L

dzAw (7)

349

and 350

11342 ww AB (8)

351

While for unstable atmospheric conditions the reference cospectrum is 352

540

7261

9212)(

3751

k

k

kw ffor

f

fffCo (9a)

353

540

831

3784)(

42

k

k

kw ffor

f

fffCo (9b)

354

Moore (1986) developed the first theoretical TFs for EC systems taking into account the 355

attenuation due to the dynamic frequency response of the sensors the scalar path averaging 356

the sensor separation the sensor response mismatch while Moncrieff et al (1997) gave a 357

complete description of TFs of EC systems with closed-path analysers Then considering the 358

wide literature on the used theoretical TFs (see Shimizu 2007 for a review) and the weight of 359

each TF on the total one for our equipment set-up here we report only details about TFs 360

related to (i) the lateral separation (ie perpendicular to the mean wind direction) between 361

the sonic anemometer and QC-TILDAS inlet (TFs Moore 1986) (ii) the tube damping effect 362

(TFtube Lenschow and Raupach 1991) (iii) the volume averaging in a measurement cell 363

(TFvol Massman 2004) (iv) the total phase shift (TFphase_shift Massman 2000) In detail 364

(i) Following Moore (1986) the effect of laterallongitudinal separation s between two 365

sensors can be represented by the following cospectral transfer function 366

)99exp(99 51nTFs (10)

367

where n=fsu As suggested by Massman (2000) the horizontal separation between the two 368

sensors has been set to 030 m 369

(ii) The function TFtube describing the attenuation of the fluctuation concentrations down the 370

sampling tube in which there is a laminar flow for covariance calculation (Lenschow and 371

Raupach 1991 Aubinet et al 2000) is 372

ts

t

tubeUD

LfrTF t

12exp

222 (11)

373

Here rt is the tube radius and Ds the molecular diffusivity of the scalar around 02 cm2s

-1 for 374

NH3 (Massman 1998) Moreover the attenuation may vary with time Leuning and Judd 375

(1996) showed that the attenuation to water vapour by aged and dirty tubes is much greater 376

than that by new and clear tube In the case of laminar flow through the tube the theoretical 377

14

cut off frequency fco_t at which the TF equals 2-12

is given by 378

tt

sttco

Lr

DUf

2_ 6490 (12)

379

about 14 Hz in our EC system To minimizing HF losses due to nonuniform velocity profiles 380

turbulent flow would be preferred (Lenschow and Raupach 1991) This requires a powerful 381

pump with high power consumption and a tremendous start-up current (13 A at 230 V AC for 382

an Edwards Tri-Scroll pump for example pers comm W Eugster) that was unrealistic at our 383

field site Hence we had to use a less powerful pump and thus need to account for the extra 384

loss in frequency response due to laminar flow in the intake hose (Moncrieff et al 1997) The 385

possible tube damping correction does not correct potential absorptiondesorption of moisture 386

by hygroscopic dirt particles in the tube however a strong pressure drop along the intake 387

hose reduces risk of condensation on the walls of the tube On the other hand HF due to wall 388

interaction in the sample tube could be reduced by minimizing lengthrsquos tube and maintaining 389

high flow rate In general the line loss is a function of tubing type and how dirty the tube is 390

(Sukyer and Verma 1993) 391

(iii) As reported by Massman (2004) in a closed-path detector the flow velocity within the 392

detection chamber can be very different than the wind speed of the ambient atmosphere near 393

the tube inlet so that the transfer functions need to be expressed in terms of the volume 394

flushing time constant of the detection chamber τvol 395

2

2sin 2

vol

volvolTF

(13)

396

where f 2 and τvol is equal to the time needed to fill the chamber and its minimum value 397

is given by the ratio between the volume of the chamber (05 l) and the maximum flow rate 398

(iv) However if covariances and the cospectra are calculated without the time lag that means 399

without the method of maximum covariance function or if there is no clear time lag due to 400

lag depending on the frequency of the eddies (sensor separation in the wind direction) or if 401

there are complex adsorption and desorption processes it is necessary to take into account the 402

phase shift (Massman 2000) The TF associated with the total phase shift (Massman 2000) 403

is 404

t

tlon

t

tlonshiftphase

U

L

u

l

U

L

u

lTF sincos_ (14)

405

where llon is the longitudinal sensor separation (function of wind direction) and β is the 406

intrinsic time constant of the scalar sensor This latter was estimated by using the Boylersquos law 407

15

applied to the measurement chamber working at 33 kPa and using the measured flow rate it 408

is about 01 s and moreover it means that the sampling frequency of 10 Hz is adequate for 409

the employed QC-TILDAS in the EC system 410

For the theoretical TF approach cospectra of the vertical wind speed and T and NH3 411

respectively were calculated using the post processing EddySpec package of Eddysoft 412

software using the following setup number of frequencies 4096 for calculating the cospectra 413

for half-hourly runs multiplied by frequency tapering the time series with a Hamming 414

window (Kaimal and Finnigan 1994) and averaging into 20 logarithmically spaced bins 415

Either the time lag or the phase shift was used in the functions of the two approaches 416

CFTheor_time_lag and CFTheor_phase_shift respectively 417

418

162 In situ ogive method 419

The limitation of the theoretical TF approach is that it could miss additional chemical or 420

micro-physical process occurring in the EC system a restriction that is expected to be 421

overcome by the in situ experimental methods Moreover the experimental approach is based 422

on the assumption of cospectral similarity between scalar fluxes and unlike the theoretical TF 423

approach these methods do not require smooth models of atmospheric cospectra (Massman 424

and Clement 2004) The correction factor can be estimated as the ratio between the 425

attenuated flux and a reference flux (usually the sensible heat flux) Aubinet et al (2000) give 426

the procedure to estimate this experimental frequency transfer function but since the 427

cospectra derived from sensible heat fluxes are often not well-defined then direct application 428

of this approach can produce implausible values 429

Starting from the hypothesis of similarity between all scalars cospectra Ammann et al (2006) 430

developed a method for estimating HF losses Applied to EC measurement of NH3 fluxes the 431

method consists in fitting the ogive for NH3 OgwNH3 (the ogive is the integrated cospectra 432

CowNH3 from low to high frequencies) against the ogive of air temperature OgwTa The fitting 433

was made with an iterative linear least square method in which the points being too far away 434

from the regression line were eliminated progressively (robustfit function under Matlabreg) 435

The high-frequency damping correction (CFO) is simply evaluated as the value of the fitted 436

NH3 ogive (OgwNH3) at the highest frequency The frequency band over which the linear 437

regression is performed is determined as the frequency for which OgwT larger than 01 and 438

OgwNH3 is smaller than 08 This choice was arbitrary and may not be appropriate if the 439

damping of the NH3 signal was below the range chosen The ogives were calculated using 440

detrended signals 441

16

442

163 The inductance method 443

Eugster and Senn (1995) propose a correction model for EC data affected by systematic error 444

introduced by the combination of all sources of damping which also includes chemical 445

reaction (in the case of NO2) besides the possible damping due to laminar flow in the 446

sampling tube and sensor separation This damping loss is described by an analogy to 447

inductance L in an alternating current circuit which can be used for taking into account the 448

reduction of spectral density in the inertial subrange This model follows the approach of 449

Moore (1986) using electronic components in a current circuit instead of a gain function 450

As reported by Eugster and Senn (1995) the covariance of the measured (ie damped) scalar 451

flux is given by 452

02220

)(41

1)(

0dffCo

LfdffCow

LL wwL

(15)

453

where )(0 fCo

Lw is the undamped cospectrum The value of L is modified in order to fit the 454

measured cospectrum The independent variables in this correction model are the measuring 455

height above zero-plane displacement z-d the mean horizontal wind speed u the Monin-456

Obukhov stability parameter ζ the natural frequency f and the inductance L The value for 457

L can be derived from spectral and cospectral analysis and is assumed not vary significantly 458

as long as the measuring system is not altered 459

The damped cospectra can be derived combining equation (14) with the empirical formula of 460

Kaimal et al (1972) for stable unstable and neutral stratification as modified by Eugster and 461

Senn (1995) (equations 29 and 30 in their paper) The approximated solutions of Eugster and 462

Senn (1995) for CF range between 10 (no damping at all) and infin (no signal at all) but they 463

suggest to restrict the fit to the range between CF=10 and CF=25 that means all 464

measurements which represent less than 40 of the real fluxes were discarded 465

17

2 RESULTS AND DISCUSSION 466

21 Micrometeorological conditions 467

During the trial the air temperature ranged from 16degC to 335degC and averaged 247degC while 468

the soil surface temperature reached more than 546degC at hourly scale The sky was clear 469

most of the period apart from the 23-24072008 where there was little rain (82 mm) The 470

global solar radiation was following the fair weather diurnal course throughout the period and 471

reached 950 W m-2

at maximum The daily average relative humidity was around 60 with 472

variations ranging between 25 and 93 473

The prevalent wind direction during the experimental period (84 of the runs) was NW the 474

wind speed ranged from 05 to 89 ms-1

and averaged 34 ms-1

while the friction velocity u 475

ranged between 001 and 099 ms-1

with an average of 030 ms-1

(Figure 1a) The energy 476

balance for the period of the trial is reported in Figure 2b at hourly scale In particular it is 477

clear the direct impact of the net radiation (Rn) on the sensible heat flux (H) and on the latent 478

heat flux (LE) The pattern of LE were strongly correlated with the available energy Rn with 479

distinct peaks immediately following irrigation which provided water to evapotranspirate 480

from plant and soil 481

22 Ammonia concentrations 482

The field NH3 concentrations measured by the diffusive samplers (ALPHA badges) over 483

several hours ranged from 3 to 118 microg NH3 m-3

while the 30 minutes ROSAA concentrations 484

varied from around 0 to around 90 microg NH3 m-3

The ROSAA concentrations averaged over 485

the same intervals as the ALPHA badges slightly underestimated the badges concentration by 486

8 on average (ROSAA = 092 ALPHA R2 = 082) The uncalibrated QC-TILDAS 487

concentrations strongly underestimated the ALPHA badges concentrations by 67 but 488

without any offset (QC-TILDAS = 034 ALPHA R2 = 090) This underestimation is larger than 489

what Ellis et al (2010) found with the same instrument in a laboratory calibration Since the 490

correlation was very good the regression against the ALPHA badges was used to calibrate the 491

QC-TILDAS concentration data The correction factor was found to be 286 plusmn 009 The 492

calibrated QC-TILDAS concentration was in quite good agreement with the ROSAA 493

concentrations both in terms of magnitude and dynamics (Figure 2) In Figure 3 a detailed 494

comparison between calibrated values of QC-TILDAS NH3 concentrations and ROSAA 495

values is shown Since we did not find a significant offset in the regression of QC-TILDAS 496

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 10: Eddy covariance measurement of ammonia fluxes: comparison of

10

addition during the trial NH3 concentration was monitored by means of the new device 257

ROSAA developed at INRA (Loubet et al 2010 2011) It relies on the coupling of NH3 258

capturing by means of an acid stripping solution (wet-effluent denuders) to convert NH3 in 259

NH4+ and the analysis of the NH4

+ concentration by means of a detector based on a semi-260

permeable membrane and a unit to perform temperature controlled conductivity 261

measurements (ECN Netherlands) In the ROSAA system the three denuders were sampling 262

at three heights (at z = 05 07 and 14 m) above ground and the averaged concentration was 263

measure every 30 minutes 264

15 The eddy covariance system description of the equipment 265

The EC system was equipped with the QC-TILDAS and a three axis ultrasonic anemometer 266

(Gill R2 Gill Instruments Ltd UK) The analogue signals from the QC-TILDAS were passed 267

to the sonic anemometer which uses an analogue-to-digital converter to digitise the signal at 268

10 Hz The sonic anemometer acquired data at 168 Hz internally and reported average values 269

at 208 Hz The digitised signals from the QC-TILDAS were then combined with the wind 270

velocity information and sent to the serial port of a personal computer NH3 fluxes were 271

computed offline with a time step of half an hour The software package EddySoft (Kolle and 272

Rebmann 2007) was used for the data acquisition The same software package was used for 273

post processing EC data and to obtain the cospectra useful for applying the TF method while 274

custom Matlabreg and R scripts were used to process and analyse data for the ogive and the 275

inductance methods respectively Trace gas and heat fluxes were calculated as well as other 276

micrometeorological parameters The components of the wind velocity were rotated every 30 277

minutes to set w and v to zero No detrending was applied and block averaging was instead 278

used (Leuning 2007) 279

The sonic anemometer was mounted on a 15 m tall mast so that the height of the EC sensors 280

was about 07 m above the top of the crop and adjusted to follow crop development Ambient 281

air close to the sonic anemometer head was continuously sampled through a 25 m long inlet 282

line (PFA 38rdquo outer diameter) leading to the QC-TILDAS The flow of air through the tube 283

was measured before and after the T connection used for splitting the flow the flow rate 284

towards the sampling cell was about 9 litresminute resulting in a laminar flow regime with a 285

Reynolds number (Re) of about 2000 below the turbulence limit (for pipe flow Re=2300 286

Shimizu 2007) which was the primary cause of the high frequency loss in the measuring 287

system (Eugster and Senn 1995) The limits in the power supply lines in our experimental 288

11

site have prevented the use of pumps able to provide a turbulent regime into the sampling 289

tube 290

The eddy covariance system together with ROSAA and ALPHA samples equipments were 291

located close to the centre of the field which provided an acceptable upwind fetch (around 292

120 m) in the prevalent wind direction during the trial anyway the footprint of EC 293

measurements was found to be always inside the experimental field 294

16 Spectral correction methods for low-pass filtering in a closed-path EC system 295

As mentioned before all eddy covariance systems tend to underestimate the true atmospheric 296

fluxes due to physical limitations of the flux measurement instrumentation which causes 297

losses of eddy flux contributions at low and high frequencies These losses can be quantified 298

and corrected for by multiplying the measured covariance (subscript m) with a frequency 299

response correction factor (CF ge1) 300

mwCFw (4) 301

The correction factor can be estimated by means of spectral correction methods that can be 302

divided into two families the spectral theoretical transfer function (TF) methods and the in-303

situ methods (Massman and Clement 2004) The general approach consists of three steps 1) 304

the identification of a spectral transfer function (theoretical or experimental) 2) its use to 305

calculate a correction factor and 3) the parameterisation of the correction factor with 306

meteorological variables mainly the wind speed which is directly linked to the turbulence 307

Here three methods have been employed in order to estimate CFs to apply at our measured 308

EC NH3 fluxes (i) Theoretical TF analysis CFTheor (ii) A variant of In situ experimental TF 309

method the Ogive method CFO (iii) the inductance method CFL These methodologies are 310

the most analysed in the literature for evaluating the most accurate CFs for gas trace fluxes 311

over natural surfaces 312

Common to all approaches is the requirement of a measured or evaluated reference 313

cospectrum for scalar as well as a measured cospectrum and the estimate of the flux loss as a 314

function of frequency In any case an important aspect relative to any EC system is the time 315

delay between the instant open-path w measurement and the closed-path scalar concentration 316

measurement This time lag is due to the transport time in the tubing system and the phase 317

shift due to low-pass filtering (Ibrom et al 2007) It depends on the tube length and the flow 318

rate it may change slightly due to changing flow rates in the tube caused by pumping 319

variation or density changes Usually this delay can be calculated empirically by determining 320

12

the maximum of the covariance function between wrsquo and rsquo (Moncrieff et al 1997) which 321

includes the time lag due to the longitudinal (streamwise) separation distance from the air 322

sample inlet to the sonic anemometer depending on wind speed and direction The 323

individually determined time delays for each 30 minutes set of data can be used for 324

calculating the cospectra in this case the phase shift due to low-pass filtering and longitudinal 325

sensor separation has already been corrected for (Ibrom et al 2007) So two subcategories 326

can be identified in the theoretical transfer function analysis with (CFTheor_time_lag) and without 327

the time lag (CFTheor_phase_shift) applied to computation of the covariances and cospectra 328

329

161 Theoretical TF analysis 330

Following Moore (1986) theoretical transfer functions characterizing the measurement 331

process have been developed The correction factor in this case is summarized by Massman 332

and Clement (2004) using the following equation 333

dffCofHfT

fT

dffCo

w

wCF

w

b

b

w

m

TF

)()(sin

1

)(

0

2

2

0

(5)

334

where mw is the measured covariance and w is the true flux H(f) is the product of all 335

the appropriate transfer functions associated with HF losses (

n

i

iTFfH1

)( ) )( fCow is the 336

one-side reference cospectrum Tb is the block averaging period 22sin1 bb fTfT is the 337

TF for the low frequency spectral attenuation The reference cospectrum is either taken from 338

literature (Kaimal and Finnigan 1994) or a site specific reference cospectrum usually derived 339

from sensible heat flux measurements The requirements to apply this method are the TFs 340

and a model of the cospectra which is the weakness of this approach since smooth cospectra 341

are not typical of the atmospheric surface layer (Massman and Clement 2004) In particular 342

in this work we used the reference cospectral models (eq 6-9) given by Moncrieff et al 343

(1997) where udzffk )( is normalized frequency f is the natural frequency u is 344

horizontal wind speed z-d is height above the zero-plane displacement d For stable 345

atmospheric condition the reference cospectrum is 346

12)(

kww

kw

fBA

fffCo

(6)

347

where 348

13

750

4612840

L

dzAw (7)

349

and 350

11342 ww AB (8)

351

While for unstable atmospheric conditions the reference cospectrum is 352

540

7261

9212)(

3751

k

k

kw ffor

f

fffCo (9a)

353

540

831

3784)(

42

k

k

kw ffor

f

fffCo (9b)

354

Moore (1986) developed the first theoretical TFs for EC systems taking into account the 355

attenuation due to the dynamic frequency response of the sensors the scalar path averaging 356

the sensor separation the sensor response mismatch while Moncrieff et al (1997) gave a 357

complete description of TFs of EC systems with closed-path analysers Then considering the 358

wide literature on the used theoretical TFs (see Shimizu 2007 for a review) and the weight of 359

each TF on the total one for our equipment set-up here we report only details about TFs 360

related to (i) the lateral separation (ie perpendicular to the mean wind direction) between 361

the sonic anemometer and QC-TILDAS inlet (TFs Moore 1986) (ii) the tube damping effect 362

(TFtube Lenschow and Raupach 1991) (iii) the volume averaging in a measurement cell 363

(TFvol Massman 2004) (iv) the total phase shift (TFphase_shift Massman 2000) In detail 364

(i) Following Moore (1986) the effect of laterallongitudinal separation s between two 365

sensors can be represented by the following cospectral transfer function 366

)99exp(99 51nTFs (10)

367

where n=fsu As suggested by Massman (2000) the horizontal separation between the two 368

sensors has been set to 030 m 369

(ii) The function TFtube describing the attenuation of the fluctuation concentrations down the 370

sampling tube in which there is a laminar flow for covariance calculation (Lenschow and 371

Raupach 1991 Aubinet et al 2000) is 372

ts

t

tubeUD

LfrTF t

12exp

222 (11)

373

Here rt is the tube radius and Ds the molecular diffusivity of the scalar around 02 cm2s

-1 for 374

NH3 (Massman 1998) Moreover the attenuation may vary with time Leuning and Judd 375

(1996) showed that the attenuation to water vapour by aged and dirty tubes is much greater 376

than that by new and clear tube In the case of laminar flow through the tube the theoretical 377

14

cut off frequency fco_t at which the TF equals 2-12

is given by 378

tt

sttco

Lr

DUf

2_ 6490 (12)

379

about 14 Hz in our EC system To minimizing HF losses due to nonuniform velocity profiles 380

turbulent flow would be preferred (Lenschow and Raupach 1991) This requires a powerful 381

pump with high power consumption and a tremendous start-up current (13 A at 230 V AC for 382

an Edwards Tri-Scroll pump for example pers comm W Eugster) that was unrealistic at our 383

field site Hence we had to use a less powerful pump and thus need to account for the extra 384

loss in frequency response due to laminar flow in the intake hose (Moncrieff et al 1997) The 385

possible tube damping correction does not correct potential absorptiondesorption of moisture 386

by hygroscopic dirt particles in the tube however a strong pressure drop along the intake 387

hose reduces risk of condensation on the walls of the tube On the other hand HF due to wall 388

interaction in the sample tube could be reduced by minimizing lengthrsquos tube and maintaining 389

high flow rate In general the line loss is a function of tubing type and how dirty the tube is 390

(Sukyer and Verma 1993) 391

(iii) As reported by Massman (2004) in a closed-path detector the flow velocity within the 392

detection chamber can be very different than the wind speed of the ambient atmosphere near 393

the tube inlet so that the transfer functions need to be expressed in terms of the volume 394

flushing time constant of the detection chamber τvol 395

2

2sin 2

vol

volvolTF

(13)

396

where f 2 and τvol is equal to the time needed to fill the chamber and its minimum value 397

is given by the ratio between the volume of the chamber (05 l) and the maximum flow rate 398

(iv) However if covariances and the cospectra are calculated without the time lag that means 399

without the method of maximum covariance function or if there is no clear time lag due to 400

lag depending on the frequency of the eddies (sensor separation in the wind direction) or if 401

there are complex adsorption and desorption processes it is necessary to take into account the 402

phase shift (Massman 2000) The TF associated with the total phase shift (Massman 2000) 403

is 404

t

tlon

t

tlonshiftphase

U

L

u

l

U

L

u

lTF sincos_ (14)

405

where llon is the longitudinal sensor separation (function of wind direction) and β is the 406

intrinsic time constant of the scalar sensor This latter was estimated by using the Boylersquos law 407

15

applied to the measurement chamber working at 33 kPa and using the measured flow rate it 408

is about 01 s and moreover it means that the sampling frequency of 10 Hz is adequate for 409

the employed QC-TILDAS in the EC system 410

For the theoretical TF approach cospectra of the vertical wind speed and T and NH3 411

respectively were calculated using the post processing EddySpec package of Eddysoft 412

software using the following setup number of frequencies 4096 for calculating the cospectra 413

for half-hourly runs multiplied by frequency tapering the time series with a Hamming 414

window (Kaimal and Finnigan 1994) and averaging into 20 logarithmically spaced bins 415

Either the time lag or the phase shift was used in the functions of the two approaches 416

CFTheor_time_lag and CFTheor_phase_shift respectively 417

418

162 In situ ogive method 419

The limitation of the theoretical TF approach is that it could miss additional chemical or 420

micro-physical process occurring in the EC system a restriction that is expected to be 421

overcome by the in situ experimental methods Moreover the experimental approach is based 422

on the assumption of cospectral similarity between scalar fluxes and unlike the theoretical TF 423

approach these methods do not require smooth models of atmospheric cospectra (Massman 424

and Clement 2004) The correction factor can be estimated as the ratio between the 425

attenuated flux and a reference flux (usually the sensible heat flux) Aubinet et al (2000) give 426

the procedure to estimate this experimental frequency transfer function but since the 427

cospectra derived from sensible heat fluxes are often not well-defined then direct application 428

of this approach can produce implausible values 429

Starting from the hypothesis of similarity between all scalars cospectra Ammann et al (2006) 430

developed a method for estimating HF losses Applied to EC measurement of NH3 fluxes the 431

method consists in fitting the ogive for NH3 OgwNH3 (the ogive is the integrated cospectra 432

CowNH3 from low to high frequencies) against the ogive of air temperature OgwTa The fitting 433

was made with an iterative linear least square method in which the points being too far away 434

from the regression line were eliminated progressively (robustfit function under Matlabreg) 435

The high-frequency damping correction (CFO) is simply evaluated as the value of the fitted 436

NH3 ogive (OgwNH3) at the highest frequency The frequency band over which the linear 437

regression is performed is determined as the frequency for which OgwT larger than 01 and 438

OgwNH3 is smaller than 08 This choice was arbitrary and may not be appropriate if the 439

damping of the NH3 signal was below the range chosen The ogives were calculated using 440

detrended signals 441

16

442

163 The inductance method 443

Eugster and Senn (1995) propose a correction model for EC data affected by systematic error 444

introduced by the combination of all sources of damping which also includes chemical 445

reaction (in the case of NO2) besides the possible damping due to laminar flow in the 446

sampling tube and sensor separation This damping loss is described by an analogy to 447

inductance L in an alternating current circuit which can be used for taking into account the 448

reduction of spectral density in the inertial subrange This model follows the approach of 449

Moore (1986) using electronic components in a current circuit instead of a gain function 450

As reported by Eugster and Senn (1995) the covariance of the measured (ie damped) scalar 451

flux is given by 452

02220

)(41

1)(

0dffCo

LfdffCow

LL wwL

(15)

453

where )(0 fCo

Lw is the undamped cospectrum The value of L is modified in order to fit the 454

measured cospectrum The independent variables in this correction model are the measuring 455

height above zero-plane displacement z-d the mean horizontal wind speed u the Monin-456

Obukhov stability parameter ζ the natural frequency f and the inductance L The value for 457

L can be derived from spectral and cospectral analysis and is assumed not vary significantly 458

as long as the measuring system is not altered 459

The damped cospectra can be derived combining equation (14) with the empirical formula of 460

Kaimal et al (1972) for stable unstable and neutral stratification as modified by Eugster and 461

Senn (1995) (equations 29 and 30 in their paper) The approximated solutions of Eugster and 462

Senn (1995) for CF range between 10 (no damping at all) and infin (no signal at all) but they 463

suggest to restrict the fit to the range between CF=10 and CF=25 that means all 464

measurements which represent less than 40 of the real fluxes were discarded 465

17

2 RESULTS AND DISCUSSION 466

21 Micrometeorological conditions 467

During the trial the air temperature ranged from 16degC to 335degC and averaged 247degC while 468

the soil surface temperature reached more than 546degC at hourly scale The sky was clear 469

most of the period apart from the 23-24072008 where there was little rain (82 mm) The 470

global solar radiation was following the fair weather diurnal course throughout the period and 471

reached 950 W m-2

at maximum The daily average relative humidity was around 60 with 472

variations ranging between 25 and 93 473

The prevalent wind direction during the experimental period (84 of the runs) was NW the 474

wind speed ranged from 05 to 89 ms-1

and averaged 34 ms-1

while the friction velocity u 475

ranged between 001 and 099 ms-1

with an average of 030 ms-1

(Figure 1a) The energy 476

balance for the period of the trial is reported in Figure 2b at hourly scale In particular it is 477

clear the direct impact of the net radiation (Rn) on the sensible heat flux (H) and on the latent 478

heat flux (LE) The pattern of LE were strongly correlated with the available energy Rn with 479

distinct peaks immediately following irrigation which provided water to evapotranspirate 480

from plant and soil 481

22 Ammonia concentrations 482

The field NH3 concentrations measured by the diffusive samplers (ALPHA badges) over 483

several hours ranged from 3 to 118 microg NH3 m-3

while the 30 minutes ROSAA concentrations 484

varied from around 0 to around 90 microg NH3 m-3

The ROSAA concentrations averaged over 485

the same intervals as the ALPHA badges slightly underestimated the badges concentration by 486

8 on average (ROSAA = 092 ALPHA R2 = 082) The uncalibrated QC-TILDAS 487

concentrations strongly underestimated the ALPHA badges concentrations by 67 but 488

without any offset (QC-TILDAS = 034 ALPHA R2 = 090) This underestimation is larger than 489

what Ellis et al (2010) found with the same instrument in a laboratory calibration Since the 490

correlation was very good the regression against the ALPHA badges was used to calibrate the 491

QC-TILDAS concentration data The correction factor was found to be 286 plusmn 009 The 492

calibrated QC-TILDAS concentration was in quite good agreement with the ROSAA 493

concentrations both in terms of magnitude and dynamics (Figure 2) In Figure 3 a detailed 494

comparison between calibrated values of QC-TILDAS NH3 concentrations and ROSAA 495

values is shown Since we did not find a significant offset in the regression of QC-TILDAS 496

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 11: Eddy covariance measurement of ammonia fluxes: comparison of

11

site have prevented the use of pumps able to provide a turbulent regime into the sampling 289

tube 290

The eddy covariance system together with ROSAA and ALPHA samples equipments were 291

located close to the centre of the field which provided an acceptable upwind fetch (around 292

120 m) in the prevalent wind direction during the trial anyway the footprint of EC 293

measurements was found to be always inside the experimental field 294

16 Spectral correction methods for low-pass filtering in a closed-path EC system 295

As mentioned before all eddy covariance systems tend to underestimate the true atmospheric 296

fluxes due to physical limitations of the flux measurement instrumentation which causes 297

losses of eddy flux contributions at low and high frequencies These losses can be quantified 298

and corrected for by multiplying the measured covariance (subscript m) with a frequency 299

response correction factor (CF ge1) 300

mwCFw (4) 301

The correction factor can be estimated by means of spectral correction methods that can be 302

divided into two families the spectral theoretical transfer function (TF) methods and the in-303

situ methods (Massman and Clement 2004) The general approach consists of three steps 1) 304

the identification of a spectral transfer function (theoretical or experimental) 2) its use to 305

calculate a correction factor and 3) the parameterisation of the correction factor with 306

meteorological variables mainly the wind speed which is directly linked to the turbulence 307

Here three methods have been employed in order to estimate CFs to apply at our measured 308

EC NH3 fluxes (i) Theoretical TF analysis CFTheor (ii) A variant of In situ experimental TF 309

method the Ogive method CFO (iii) the inductance method CFL These methodologies are 310

the most analysed in the literature for evaluating the most accurate CFs for gas trace fluxes 311

over natural surfaces 312

Common to all approaches is the requirement of a measured or evaluated reference 313

cospectrum for scalar as well as a measured cospectrum and the estimate of the flux loss as a 314

function of frequency In any case an important aspect relative to any EC system is the time 315

delay between the instant open-path w measurement and the closed-path scalar concentration 316

measurement This time lag is due to the transport time in the tubing system and the phase 317

shift due to low-pass filtering (Ibrom et al 2007) It depends on the tube length and the flow 318

rate it may change slightly due to changing flow rates in the tube caused by pumping 319

variation or density changes Usually this delay can be calculated empirically by determining 320

12

the maximum of the covariance function between wrsquo and rsquo (Moncrieff et al 1997) which 321

includes the time lag due to the longitudinal (streamwise) separation distance from the air 322

sample inlet to the sonic anemometer depending on wind speed and direction The 323

individually determined time delays for each 30 minutes set of data can be used for 324

calculating the cospectra in this case the phase shift due to low-pass filtering and longitudinal 325

sensor separation has already been corrected for (Ibrom et al 2007) So two subcategories 326

can be identified in the theoretical transfer function analysis with (CFTheor_time_lag) and without 327

the time lag (CFTheor_phase_shift) applied to computation of the covariances and cospectra 328

329

161 Theoretical TF analysis 330

Following Moore (1986) theoretical transfer functions characterizing the measurement 331

process have been developed The correction factor in this case is summarized by Massman 332

and Clement (2004) using the following equation 333

dffCofHfT

fT

dffCo

w

wCF

w

b

b

w

m

TF

)()(sin

1

)(

0

2

2

0

(5)

334

where mw is the measured covariance and w is the true flux H(f) is the product of all 335

the appropriate transfer functions associated with HF losses (

n

i

iTFfH1

)( ) )( fCow is the 336

one-side reference cospectrum Tb is the block averaging period 22sin1 bb fTfT is the 337

TF for the low frequency spectral attenuation The reference cospectrum is either taken from 338

literature (Kaimal and Finnigan 1994) or a site specific reference cospectrum usually derived 339

from sensible heat flux measurements The requirements to apply this method are the TFs 340

and a model of the cospectra which is the weakness of this approach since smooth cospectra 341

are not typical of the atmospheric surface layer (Massman and Clement 2004) In particular 342

in this work we used the reference cospectral models (eq 6-9) given by Moncrieff et al 343

(1997) where udzffk )( is normalized frequency f is the natural frequency u is 344

horizontal wind speed z-d is height above the zero-plane displacement d For stable 345

atmospheric condition the reference cospectrum is 346

12)(

kww

kw

fBA

fffCo

(6)

347

where 348

13

750

4612840

L

dzAw (7)

349

and 350

11342 ww AB (8)

351

While for unstable atmospheric conditions the reference cospectrum is 352

540

7261

9212)(

3751

k

k

kw ffor

f

fffCo (9a)

353

540

831

3784)(

42

k

k

kw ffor

f

fffCo (9b)

354

Moore (1986) developed the first theoretical TFs for EC systems taking into account the 355

attenuation due to the dynamic frequency response of the sensors the scalar path averaging 356

the sensor separation the sensor response mismatch while Moncrieff et al (1997) gave a 357

complete description of TFs of EC systems with closed-path analysers Then considering the 358

wide literature on the used theoretical TFs (see Shimizu 2007 for a review) and the weight of 359

each TF on the total one for our equipment set-up here we report only details about TFs 360

related to (i) the lateral separation (ie perpendicular to the mean wind direction) between 361

the sonic anemometer and QC-TILDAS inlet (TFs Moore 1986) (ii) the tube damping effect 362

(TFtube Lenschow and Raupach 1991) (iii) the volume averaging in a measurement cell 363

(TFvol Massman 2004) (iv) the total phase shift (TFphase_shift Massman 2000) In detail 364

(i) Following Moore (1986) the effect of laterallongitudinal separation s between two 365

sensors can be represented by the following cospectral transfer function 366

)99exp(99 51nTFs (10)

367

where n=fsu As suggested by Massman (2000) the horizontal separation between the two 368

sensors has been set to 030 m 369

(ii) The function TFtube describing the attenuation of the fluctuation concentrations down the 370

sampling tube in which there is a laminar flow for covariance calculation (Lenschow and 371

Raupach 1991 Aubinet et al 2000) is 372

ts

t

tubeUD

LfrTF t

12exp

222 (11)

373

Here rt is the tube radius and Ds the molecular diffusivity of the scalar around 02 cm2s

-1 for 374

NH3 (Massman 1998) Moreover the attenuation may vary with time Leuning and Judd 375

(1996) showed that the attenuation to water vapour by aged and dirty tubes is much greater 376

than that by new and clear tube In the case of laminar flow through the tube the theoretical 377

14

cut off frequency fco_t at which the TF equals 2-12

is given by 378

tt

sttco

Lr

DUf

2_ 6490 (12)

379

about 14 Hz in our EC system To minimizing HF losses due to nonuniform velocity profiles 380

turbulent flow would be preferred (Lenschow and Raupach 1991) This requires a powerful 381

pump with high power consumption and a tremendous start-up current (13 A at 230 V AC for 382

an Edwards Tri-Scroll pump for example pers comm W Eugster) that was unrealistic at our 383

field site Hence we had to use a less powerful pump and thus need to account for the extra 384

loss in frequency response due to laminar flow in the intake hose (Moncrieff et al 1997) The 385

possible tube damping correction does not correct potential absorptiondesorption of moisture 386

by hygroscopic dirt particles in the tube however a strong pressure drop along the intake 387

hose reduces risk of condensation on the walls of the tube On the other hand HF due to wall 388

interaction in the sample tube could be reduced by minimizing lengthrsquos tube and maintaining 389

high flow rate In general the line loss is a function of tubing type and how dirty the tube is 390

(Sukyer and Verma 1993) 391

(iii) As reported by Massman (2004) in a closed-path detector the flow velocity within the 392

detection chamber can be very different than the wind speed of the ambient atmosphere near 393

the tube inlet so that the transfer functions need to be expressed in terms of the volume 394

flushing time constant of the detection chamber τvol 395

2

2sin 2

vol

volvolTF

(13)

396

where f 2 and τvol is equal to the time needed to fill the chamber and its minimum value 397

is given by the ratio between the volume of the chamber (05 l) and the maximum flow rate 398

(iv) However if covariances and the cospectra are calculated without the time lag that means 399

without the method of maximum covariance function or if there is no clear time lag due to 400

lag depending on the frequency of the eddies (sensor separation in the wind direction) or if 401

there are complex adsorption and desorption processes it is necessary to take into account the 402

phase shift (Massman 2000) The TF associated with the total phase shift (Massman 2000) 403

is 404

t

tlon

t

tlonshiftphase

U

L

u

l

U

L

u

lTF sincos_ (14)

405

where llon is the longitudinal sensor separation (function of wind direction) and β is the 406

intrinsic time constant of the scalar sensor This latter was estimated by using the Boylersquos law 407

15

applied to the measurement chamber working at 33 kPa and using the measured flow rate it 408

is about 01 s and moreover it means that the sampling frequency of 10 Hz is adequate for 409

the employed QC-TILDAS in the EC system 410

For the theoretical TF approach cospectra of the vertical wind speed and T and NH3 411

respectively were calculated using the post processing EddySpec package of Eddysoft 412

software using the following setup number of frequencies 4096 for calculating the cospectra 413

for half-hourly runs multiplied by frequency tapering the time series with a Hamming 414

window (Kaimal and Finnigan 1994) and averaging into 20 logarithmically spaced bins 415

Either the time lag or the phase shift was used in the functions of the two approaches 416

CFTheor_time_lag and CFTheor_phase_shift respectively 417

418

162 In situ ogive method 419

The limitation of the theoretical TF approach is that it could miss additional chemical or 420

micro-physical process occurring in the EC system a restriction that is expected to be 421

overcome by the in situ experimental methods Moreover the experimental approach is based 422

on the assumption of cospectral similarity between scalar fluxes and unlike the theoretical TF 423

approach these methods do not require smooth models of atmospheric cospectra (Massman 424

and Clement 2004) The correction factor can be estimated as the ratio between the 425

attenuated flux and a reference flux (usually the sensible heat flux) Aubinet et al (2000) give 426

the procedure to estimate this experimental frequency transfer function but since the 427

cospectra derived from sensible heat fluxes are often not well-defined then direct application 428

of this approach can produce implausible values 429

Starting from the hypothesis of similarity between all scalars cospectra Ammann et al (2006) 430

developed a method for estimating HF losses Applied to EC measurement of NH3 fluxes the 431

method consists in fitting the ogive for NH3 OgwNH3 (the ogive is the integrated cospectra 432

CowNH3 from low to high frequencies) against the ogive of air temperature OgwTa The fitting 433

was made with an iterative linear least square method in which the points being too far away 434

from the regression line were eliminated progressively (robustfit function under Matlabreg) 435

The high-frequency damping correction (CFO) is simply evaluated as the value of the fitted 436

NH3 ogive (OgwNH3) at the highest frequency The frequency band over which the linear 437

regression is performed is determined as the frequency for which OgwT larger than 01 and 438

OgwNH3 is smaller than 08 This choice was arbitrary and may not be appropriate if the 439

damping of the NH3 signal was below the range chosen The ogives were calculated using 440

detrended signals 441

16

442

163 The inductance method 443

Eugster and Senn (1995) propose a correction model for EC data affected by systematic error 444

introduced by the combination of all sources of damping which also includes chemical 445

reaction (in the case of NO2) besides the possible damping due to laminar flow in the 446

sampling tube and sensor separation This damping loss is described by an analogy to 447

inductance L in an alternating current circuit which can be used for taking into account the 448

reduction of spectral density in the inertial subrange This model follows the approach of 449

Moore (1986) using electronic components in a current circuit instead of a gain function 450

As reported by Eugster and Senn (1995) the covariance of the measured (ie damped) scalar 451

flux is given by 452

02220

)(41

1)(

0dffCo

LfdffCow

LL wwL

(15)

453

where )(0 fCo

Lw is the undamped cospectrum The value of L is modified in order to fit the 454

measured cospectrum The independent variables in this correction model are the measuring 455

height above zero-plane displacement z-d the mean horizontal wind speed u the Monin-456

Obukhov stability parameter ζ the natural frequency f and the inductance L The value for 457

L can be derived from spectral and cospectral analysis and is assumed not vary significantly 458

as long as the measuring system is not altered 459

The damped cospectra can be derived combining equation (14) with the empirical formula of 460

Kaimal et al (1972) for stable unstable and neutral stratification as modified by Eugster and 461

Senn (1995) (equations 29 and 30 in their paper) The approximated solutions of Eugster and 462

Senn (1995) for CF range between 10 (no damping at all) and infin (no signal at all) but they 463

suggest to restrict the fit to the range between CF=10 and CF=25 that means all 464

measurements which represent less than 40 of the real fluxes were discarded 465

17

2 RESULTS AND DISCUSSION 466

21 Micrometeorological conditions 467

During the trial the air temperature ranged from 16degC to 335degC and averaged 247degC while 468

the soil surface temperature reached more than 546degC at hourly scale The sky was clear 469

most of the period apart from the 23-24072008 where there was little rain (82 mm) The 470

global solar radiation was following the fair weather diurnal course throughout the period and 471

reached 950 W m-2

at maximum The daily average relative humidity was around 60 with 472

variations ranging between 25 and 93 473

The prevalent wind direction during the experimental period (84 of the runs) was NW the 474

wind speed ranged from 05 to 89 ms-1

and averaged 34 ms-1

while the friction velocity u 475

ranged between 001 and 099 ms-1

with an average of 030 ms-1

(Figure 1a) The energy 476

balance for the period of the trial is reported in Figure 2b at hourly scale In particular it is 477

clear the direct impact of the net radiation (Rn) on the sensible heat flux (H) and on the latent 478

heat flux (LE) The pattern of LE were strongly correlated with the available energy Rn with 479

distinct peaks immediately following irrigation which provided water to evapotranspirate 480

from plant and soil 481

22 Ammonia concentrations 482

The field NH3 concentrations measured by the diffusive samplers (ALPHA badges) over 483

several hours ranged from 3 to 118 microg NH3 m-3

while the 30 minutes ROSAA concentrations 484

varied from around 0 to around 90 microg NH3 m-3

The ROSAA concentrations averaged over 485

the same intervals as the ALPHA badges slightly underestimated the badges concentration by 486

8 on average (ROSAA = 092 ALPHA R2 = 082) The uncalibrated QC-TILDAS 487

concentrations strongly underestimated the ALPHA badges concentrations by 67 but 488

without any offset (QC-TILDAS = 034 ALPHA R2 = 090) This underestimation is larger than 489

what Ellis et al (2010) found with the same instrument in a laboratory calibration Since the 490

correlation was very good the regression against the ALPHA badges was used to calibrate the 491

QC-TILDAS concentration data The correction factor was found to be 286 plusmn 009 The 492

calibrated QC-TILDAS concentration was in quite good agreement with the ROSAA 493

concentrations both in terms of magnitude and dynamics (Figure 2) In Figure 3 a detailed 494

comparison between calibrated values of QC-TILDAS NH3 concentrations and ROSAA 495

values is shown Since we did not find a significant offset in the regression of QC-TILDAS 496

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 12: Eddy covariance measurement of ammonia fluxes: comparison of

12

the maximum of the covariance function between wrsquo and rsquo (Moncrieff et al 1997) which 321

includes the time lag due to the longitudinal (streamwise) separation distance from the air 322

sample inlet to the sonic anemometer depending on wind speed and direction The 323

individually determined time delays for each 30 minutes set of data can be used for 324

calculating the cospectra in this case the phase shift due to low-pass filtering and longitudinal 325

sensor separation has already been corrected for (Ibrom et al 2007) So two subcategories 326

can be identified in the theoretical transfer function analysis with (CFTheor_time_lag) and without 327

the time lag (CFTheor_phase_shift) applied to computation of the covariances and cospectra 328

329

161 Theoretical TF analysis 330

Following Moore (1986) theoretical transfer functions characterizing the measurement 331

process have been developed The correction factor in this case is summarized by Massman 332

and Clement (2004) using the following equation 333

dffCofHfT

fT

dffCo

w

wCF

w

b

b

w

m

TF

)()(sin

1

)(

0

2

2

0

(5)

334

where mw is the measured covariance and w is the true flux H(f) is the product of all 335

the appropriate transfer functions associated with HF losses (

n

i

iTFfH1

)( ) )( fCow is the 336

one-side reference cospectrum Tb is the block averaging period 22sin1 bb fTfT is the 337

TF for the low frequency spectral attenuation The reference cospectrum is either taken from 338

literature (Kaimal and Finnigan 1994) or a site specific reference cospectrum usually derived 339

from sensible heat flux measurements The requirements to apply this method are the TFs 340

and a model of the cospectra which is the weakness of this approach since smooth cospectra 341

are not typical of the atmospheric surface layer (Massman and Clement 2004) In particular 342

in this work we used the reference cospectral models (eq 6-9) given by Moncrieff et al 343

(1997) where udzffk )( is normalized frequency f is the natural frequency u is 344

horizontal wind speed z-d is height above the zero-plane displacement d For stable 345

atmospheric condition the reference cospectrum is 346

12)(

kww

kw

fBA

fffCo

(6)

347

where 348

13

750

4612840

L

dzAw (7)

349

and 350

11342 ww AB (8)

351

While for unstable atmospheric conditions the reference cospectrum is 352

540

7261

9212)(

3751

k

k

kw ffor

f

fffCo (9a)

353

540

831

3784)(

42

k

k

kw ffor

f

fffCo (9b)

354

Moore (1986) developed the first theoretical TFs for EC systems taking into account the 355

attenuation due to the dynamic frequency response of the sensors the scalar path averaging 356

the sensor separation the sensor response mismatch while Moncrieff et al (1997) gave a 357

complete description of TFs of EC systems with closed-path analysers Then considering the 358

wide literature on the used theoretical TFs (see Shimizu 2007 for a review) and the weight of 359

each TF on the total one for our equipment set-up here we report only details about TFs 360

related to (i) the lateral separation (ie perpendicular to the mean wind direction) between 361

the sonic anemometer and QC-TILDAS inlet (TFs Moore 1986) (ii) the tube damping effect 362

(TFtube Lenschow and Raupach 1991) (iii) the volume averaging in a measurement cell 363

(TFvol Massman 2004) (iv) the total phase shift (TFphase_shift Massman 2000) In detail 364

(i) Following Moore (1986) the effect of laterallongitudinal separation s between two 365

sensors can be represented by the following cospectral transfer function 366

)99exp(99 51nTFs (10)

367

where n=fsu As suggested by Massman (2000) the horizontal separation between the two 368

sensors has been set to 030 m 369

(ii) The function TFtube describing the attenuation of the fluctuation concentrations down the 370

sampling tube in which there is a laminar flow for covariance calculation (Lenschow and 371

Raupach 1991 Aubinet et al 2000) is 372

ts

t

tubeUD

LfrTF t

12exp

222 (11)

373

Here rt is the tube radius and Ds the molecular diffusivity of the scalar around 02 cm2s

-1 for 374

NH3 (Massman 1998) Moreover the attenuation may vary with time Leuning and Judd 375

(1996) showed that the attenuation to water vapour by aged and dirty tubes is much greater 376

than that by new and clear tube In the case of laminar flow through the tube the theoretical 377

14

cut off frequency fco_t at which the TF equals 2-12

is given by 378

tt

sttco

Lr

DUf

2_ 6490 (12)

379

about 14 Hz in our EC system To minimizing HF losses due to nonuniform velocity profiles 380

turbulent flow would be preferred (Lenschow and Raupach 1991) This requires a powerful 381

pump with high power consumption and a tremendous start-up current (13 A at 230 V AC for 382

an Edwards Tri-Scroll pump for example pers comm W Eugster) that was unrealistic at our 383

field site Hence we had to use a less powerful pump and thus need to account for the extra 384

loss in frequency response due to laminar flow in the intake hose (Moncrieff et al 1997) The 385

possible tube damping correction does not correct potential absorptiondesorption of moisture 386

by hygroscopic dirt particles in the tube however a strong pressure drop along the intake 387

hose reduces risk of condensation on the walls of the tube On the other hand HF due to wall 388

interaction in the sample tube could be reduced by minimizing lengthrsquos tube and maintaining 389

high flow rate In general the line loss is a function of tubing type and how dirty the tube is 390

(Sukyer and Verma 1993) 391

(iii) As reported by Massman (2004) in a closed-path detector the flow velocity within the 392

detection chamber can be very different than the wind speed of the ambient atmosphere near 393

the tube inlet so that the transfer functions need to be expressed in terms of the volume 394

flushing time constant of the detection chamber τvol 395

2

2sin 2

vol

volvolTF

(13)

396

where f 2 and τvol is equal to the time needed to fill the chamber and its minimum value 397

is given by the ratio between the volume of the chamber (05 l) and the maximum flow rate 398

(iv) However if covariances and the cospectra are calculated without the time lag that means 399

without the method of maximum covariance function or if there is no clear time lag due to 400

lag depending on the frequency of the eddies (sensor separation in the wind direction) or if 401

there are complex adsorption and desorption processes it is necessary to take into account the 402

phase shift (Massman 2000) The TF associated with the total phase shift (Massman 2000) 403

is 404

t

tlon

t

tlonshiftphase

U

L

u

l

U

L

u

lTF sincos_ (14)

405

where llon is the longitudinal sensor separation (function of wind direction) and β is the 406

intrinsic time constant of the scalar sensor This latter was estimated by using the Boylersquos law 407

15

applied to the measurement chamber working at 33 kPa and using the measured flow rate it 408

is about 01 s and moreover it means that the sampling frequency of 10 Hz is adequate for 409

the employed QC-TILDAS in the EC system 410

For the theoretical TF approach cospectra of the vertical wind speed and T and NH3 411

respectively were calculated using the post processing EddySpec package of Eddysoft 412

software using the following setup number of frequencies 4096 for calculating the cospectra 413

for half-hourly runs multiplied by frequency tapering the time series with a Hamming 414

window (Kaimal and Finnigan 1994) and averaging into 20 logarithmically spaced bins 415

Either the time lag or the phase shift was used in the functions of the two approaches 416

CFTheor_time_lag and CFTheor_phase_shift respectively 417

418

162 In situ ogive method 419

The limitation of the theoretical TF approach is that it could miss additional chemical or 420

micro-physical process occurring in the EC system a restriction that is expected to be 421

overcome by the in situ experimental methods Moreover the experimental approach is based 422

on the assumption of cospectral similarity between scalar fluxes and unlike the theoretical TF 423

approach these methods do not require smooth models of atmospheric cospectra (Massman 424

and Clement 2004) The correction factor can be estimated as the ratio between the 425

attenuated flux and a reference flux (usually the sensible heat flux) Aubinet et al (2000) give 426

the procedure to estimate this experimental frequency transfer function but since the 427

cospectra derived from sensible heat fluxes are often not well-defined then direct application 428

of this approach can produce implausible values 429

Starting from the hypothesis of similarity between all scalars cospectra Ammann et al (2006) 430

developed a method for estimating HF losses Applied to EC measurement of NH3 fluxes the 431

method consists in fitting the ogive for NH3 OgwNH3 (the ogive is the integrated cospectra 432

CowNH3 from low to high frequencies) against the ogive of air temperature OgwTa The fitting 433

was made with an iterative linear least square method in which the points being too far away 434

from the regression line were eliminated progressively (robustfit function under Matlabreg) 435

The high-frequency damping correction (CFO) is simply evaluated as the value of the fitted 436

NH3 ogive (OgwNH3) at the highest frequency The frequency band over which the linear 437

regression is performed is determined as the frequency for which OgwT larger than 01 and 438

OgwNH3 is smaller than 08 This choice was arbitrary and may not be appropriate if the 439

damping of the NH3 signal was below the range chosen The ogives were calculated using 440

detrended signals 441

16

442

163 The inductance method 443

Eugster and Senn (1995) propose a correction model for EC data affected by systematic error 444

introduced by the combination of all sources of damping which also includes chemical 445

reaction (in the case of NO2) besides the possible damping due to laminar flow in the 446

sampling tube and sensor separation This damping loss is described by an analogy to 447

inductance L in an alternating current circuit which can be used for taking into account the 448

reduction of spectral density in the inertial subrange This model follows the approach of 449

Moore (1986) using electronic components in a current circuit instead of a gain function 450

As reported by Eugster and Senn (1995) the covariance of the measured (ie damped) scalar 451

flux is given by 452

02220

)(41

1)(

0dffCo

LfdffCow

LL wwL

(15)

453

where )(0 fCo

Lw is the undamped cospectrum The value of L is modified in order to fit the 454

measured cospectrum The independent variables in this correction model are the measuring 455

height above zero-plane displacement z-d the mean horizontal wind speed u the Monin-456

Obukhov stability parameter ζ the natural frequency f and the inductance L The value for 457

L can be derived from spectral and cospectral analysis and is assumed not vary significantly 458

as long as the measuring system is not altered 459

The damped cospectra can be derived combining equation (14) with the empirical formula of 460

Kaimal et al (1972) for stable unstable and neutral stratification as modified by Eugster and 461

Senn (1995) (equations 29 and 30 in their paper) The approximated solutions of Eugster and 462

Senn (1995) for CF range between 10 (no damping at all) and infin (no signal at all) but they 463

suggest to restrict the fit to the range between CF=10 and CF=25 that means all 464

measurements which represent less than 40 of the real fluxes were discarded 465

17

2 RESULTS AND DISCUSSION 466

21 Micrometeorological conditions 467

During the trial the air temperature ranged from 16degC to 335degC and averaged 247degC while 468

the soil surface temperature reached more than 546degC at hourly scale The sky was clear 469

most of the period apart from the 23-24072008 where there was little rain (82 mm) The 470

global solar radiation was following the fair weather diurnal course throughout the period and 471

reached 950 W m-2

at maximum The daily average relative humidity was around 60 with 472

variations ranging between 25 and 93 473

The prevalent wind direction during the experimental period (84 of the runs) was NW the 474

wind speed ranged from 05 to 89 ms-1

and averaged 34 ms-1

while the friction velocity u 475

ranged between 001 and 099 ms-1

with an average of 030 ms-1

(Figure 1a) The energy 476

balance for the period of the trial is reported in Figure 2b at hourly scale In particular it is 477

clear the direct impact of the net radiation (Rn) on the sensible heat flux (H) and on the latent 478

heat flux (LE) The pattern of LE were strongly correlated with the available energy Rn with 479

distinct peaks immediately following irrigation which provided water to evapotranspirate 480

from plant and soil 481

22 Ammonia concentrations 482

The field NH3 concentrations measured by the diffusive samplers (ALPHA badges) over 483

several hours ranged from 3 to 118 microg NH3 m-3

while the 30 minutes ROSAA concentrations 484

varied from around 0 to around 90 microg NH3 m-3

The ROSAA concentrations averaged over 485

the same intervals as the ALPHA badges slightly underestimated the badges concentration by 486

8 on average (ROSAA = 092 ALPHA R2 = 082) The uncalibrated QC-TILDAS 487

concentrations strongly underestimated the ALPHA badges concentrations by 67 but 488

without any offset (QC-TILDAS = 034 ALPHA R2 = 090) This underestimation is larger than 489

what Ellis et al (2010) found with the same instrument in a laboratory calibration Since the 490

correlation was very good the regression against the ALPHA badges was used to calibrate the 491

QC-TILDAS concentration data The correction factor was found to be 286 plusmn 009 The 492

calibrated QC-TILDAS concentration was in quite good agreement with the ROSAA 493

concentrations both in terms of magnitude and dynamics (Figure 2) In Figure 3 a detailed 494

comparison between calibrated values of QC-TILDAS NH3 concentrations and ROSAA 495

values is shown Since we did not find a significant offset in the regression of QC-TILDAS 496

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 13: Eddy covariance measurement of ammonia fluxes: comparison of

13

750

4612840

L

dzAw (7)

349

and 350

11342 ww AB (8)

351

While for unstable atmospheric conditions the reference cospectrum is 352

540

7261

9212)(

3751

k

k

kw ffor

f

fffCo (9a)

353

540

831

3784)(

42

k

k

kw ffor

f

fffCo (9b)

354

Moore (1986) developed the first theoretical TFs for EC systems taking into account the 355

attenuation due to the dynamic frequency response of the sensors the scalar path averaging 356

the sensor separation the sensor response mismatch while Moncrieff et al (1997) gave a 357

complete description of TFs of EC systems with closed-path analysers Then considering the 358

wide literature on the used theoretical TFs (see Shimizu 2007 for a review) and the weight of 359

each TF on the total one for our equipment set-up here we report only details about TFs 360

related to (i) the lateral separation (ie perpendicular to the mean wind direction) between 361

the sonic anemometer and QC-TILDAS inlet (TFs Moore 1986) (ii) the tube damping effect 362

(TFtube Lenschow and Raupach 1991) (iii) the volume averaging in a measurement cell 363

(TFvol Massman 2004) (iv) the total phase shift (TFphase_shift Massman 2000) In detail 364

(i) Following Moore (1986) the effect of laterallongitudinal separation s between two 365

sensors can be represented by the following cospectral transfer function 366

)99exp(99 51nTFs (10)

367

where n=fsu As suggested by Massman (2000) the horizontal separation between the two 368

sensors has been set to 030 m 369

(ii) The function TFtube describing the attenuation of the fluctuation concentrations down the 370

sampling tube in which there is a laminar flow for covariance calculation (Lenschow and 371

Raupach 1991 Aubinet et al 2000) is 372

ts

t

tubeUD

LfrTF t

12exp

222 (11)

373

Here rt is the tube radius and Ds the molecular diffusivity of the scalar around 02 cm2s

-1 for 374

NH3 (Massman 1998) Moreover the attenuation may vary with time Leuning and Judd 375

(1996) showed that the attenuation to water vapour by aged and dirty tubes is much greater 376

than that by new and clear tube In the case of laminar flow through the tube the theoretical 377

14

cut off frequency fco_t at which the TF equals 2-12

is given by 378

tt

sttco

Lr

DUf

2_ 6490 (12)

379

about 14 Hz in our EC system To minimizing HF losses due to nonuniform velocity profiles 380

turbulent flow would be preferred (Lenschow and Raupach 1991) This requires a powerful 381

pump with high power consumption and a tremendous start-up current (13 A at 230 V AC for 382

an Edwards Tri-Scroll pump for example pers comm W Eugster) that was unrealistic at our 383

field site Hence we had to use a less powerful pump and thus need to account for the extra 384

loss in frequency response due to laminar flow in the intake hose (Moncrieff et al 1997) The 385

possible tube damping correction does not correct potential absorptiondesorption of moisture 386

by hygroscopic dirt particles in the tube however a strong pressure drop along the intake 387

hose reduces risk of condensation on the walls of the tube On the other hand HF due to wall 388

interaction in the sample tube could be reduced by minimizing lengthrsquos tube and maintaining 389

high flow rate In general the line loss is a function of tubing type and how dirty the tube is 390

(Sukyer and Verma 1993) 391

(iii) As reported by Massman (2004) in a closed-path detector the flow velocity within the 392

detection chamber can be very different than the wind speed of the ambient atmosphere near 393

the tube inlet so that the transfer functions need to be expressed in terms of the volume 394

flushing time constant of the detection chamber τvol 395

2

2sin 2

vol

volvolTF

(13)

396

where f 2 and τvol is equal to the time needed to fill the chamber and its minimum value 397

is given by the ratio between the volume of the chamber (05 l) and the maximum flow rate 398

(iv) However if covariances and the cospectra are calculated without the time lag that means 399

without the method of maximum covariance function or if there is no clear time lag due to 400

lag depending on the frequency of the eddies (sensor separation in the wind direction) or if 401

there are complex adsorption and desorption processes it is necessary to take into account the 402

phase shift (Massman 2000) The TF associated with the total phase shift (Massman 2000) 403

is 404

t

tlon

t

tlonshiftphase

U

L

u

l

U

L

u

lTF sincos_ (14)

405

where llon is the longitudinal sensor separation (function of wind direction) and β is the 406

intrinsic time constant of the scalar sensor This latter was estimated by using the Boylersquos law 407

15

applied to the measurement chamber working at 33 kPa and using the measured flow rate it 408

is about 01 s and moreover it means that the sampling frequency of 10 Hz is adequate for 409

the employed QC-TILDAS in the EC system 410

For the theoretical TF approach cospectra of the vertical wind speed and T and NH3 411

respectively were calculated using the post processing EddySpec package of Eddysoft 412

software using the following setup number of frequencies 4096 for calculating the cospectra 413

for half-hourly runs multiplied by frequency tapering the time series with a Hamming 414

window (Kaimal and Finnigan 1994) and averaging into 20 logarithmically spaced bins 415

Either the time lag or the phase shift was used in the functions of the two approaches 416

CFTheor_time_lag and CFTheor_phase_shift respectively 417

418

162 In situ ogive method 419

The limitation of the theoretical TF approach is that it could miss additional chemical or 420

micro-physical process occurring in the EC system a restriction that is expected to be 421

overcome by the in situ experimental methods Moreover the experimental approach is based 422

on the assumption of cospectral similarity between scalar fluxes and unlike the theoretical TF 423

approach these methods do not require smooth models of atmospheric cospectra (Massman 424

and Clement 2004) The correction factor can be estimated as the ratio between the 425

attenuated flux and a reference flux (usually the sensible heat flux) Aubinet et al (2000) give 426

the procedure to estimate this experimental frequency transfer function but since the 427

cospectra derived from sensible heat fluxes are often not well-defined then direct application 428

of this approach can produce implausible values 429

Starting from the hypothesis of similarity between all scalars cospectra Ammann et al (2006) 430

developed a method for estimating HF losses Applied to EC measurement of NH3 fluxes the 431

method consists in fitting the ogive for NH3 OgwNH3 (the ogive is the integrated cospectra 432

CowNH3 from low to high frequencies) against the ogive of air temperature OgwTa The fitting 433

was made with an iterative linear least square method in which the points being too far away 434

from the regression line were eliminated progressively (robustfit function under Matlabreg) 435

The high-frequency damping correction (CFO) is simply evaluated as the value of the fitted 436

NH3 ogive (OgwNH3) at the highest frequency The frequency band over which the linear 437

regression is performed is determined as the frequency for which OgwT larger than 01 and 438

OgwNH3 is smaller than 08 This choice was arbitrary and may not be appropriate if the 439

damping of the NH3 signal was below the range chosen The ogives were calculated using 440

detrended signals 441

16

442

163 The inductance method 443

Eugster and Senn (1995) propose a correction model for EC data affected by systematic error 444

introduced by the combination of all sources of damping which also includes chemical 445

reaction (in the case of NO2) besides the possible damping due to laminar flow in the 446

sampling tube and sensor separation This damping loss is described by an analogy to 447

inductance L in an alternating current circuit which can be used for taking into account the 448

reduction of spectral density in the inertial subrange This model follows the approach of 449

Moore (1986) using electronic components in a current circuit instead of a gain function 450

As reported by Eugster and Senn (1995) the covariance of the measured (ie damped) scalar 451

flux is given by 452

02220

)(41

1)(

0dffCo

LfdffCow

LL wwL

(15)

453

where )(0 fCo

Lw is the undamped cospectrum The value of L is modified in order to fit the 454

measured cospectrum The independent variables in this correction model are the measuring 455

height above zero-plane displacement z-d the mean horizontal wind speed u the Monin-456

Obukhov stability parameter ζ the natural frequency f and the inductance L The value for 457

L can be derived from spectral and cospectral analysis and is assumed not vary significantly 458

as long as the measuring system is not altered 459

The damped cospectra can be derived combining equation (14) with the empirical formula of 460

Kaimal et al (1972) for stable unstable and neutral stratification as modified by Eugster and 461

Senn (1995) (equations 29 and 30 in their paper) The approximated solutions of Eugster and 462

Senn (1995) for CF range between 10 (no damping at all) and infin (no signal at all) but they 463

suggest to restrict the fit to the range between CF=10 and CF=25 that means all 464

measurements which represent less than 40 of the real fluxes were discarded 465

17

2 RESULTS AND DISCUSSION 466

21 Micrometeorological conditions 467

During the trial the air temperature ranged from 16degC to 335degC and averaged 247degC while 468

the soil surface temperature reached more than 546degC at hourly scale The sky was clear 469

most of the period apart from the 23-24072008 where there was little rain (82 mm) The 470

global solar radiation was following the fair weather diurnal course throughout the period and 471

reached 950 W m-2

at maximum The daily average relative humidity was around 60 with 472

variations ranging between 25 and 93 473

The prevalent wind direction during the experimental period (84 of the runs) was NW the 474

wind speed ranged from 05 to 89 ms-1

and averaged 34 ms-1

while the friction velocity u 475

ranged between 001 and 099 ms-1

with an average of 030 ms-1

(Figure 1a) The energy 476

balance for the period of the trial is reported in Figure 2b at hourly scale In particular it is 477

clear the direct impact of the net radiation (Rn) on the sensible heat flux (H) and on the latent 478

heat flux (LE) The pattern of LE were strongly correlated with the available energy Rn with 479

distinct peaks immediately following irrigation which provided water to evapotranspirate 480

from plant and soil 481

22 Ammonia concentrations 482

The field NH3 concentrations measured by the diffusive samplers (ALPHA badges) over 483

several hours ranged from 3 to 118 microg NH3 m-3

while the 30 minutes ROSAA concentrations 484

varied from around 0 to around 90 microg NH3 m-3

The ROSAA concentrations averaged over 485

the same intervals as the ALPHA badges slightly underestimated the badges concentration by 486

8 on average (ROSAA = 092 ALPHA R2 = 082) The uncalibrated QC-TILDAS 487

concentrations strongly underestimated the ALPHA badges concentrations by 67 but 488

without any offset (QC-TILDAS = 034 ALPHA R2 = 090) This underestimation is larger than 489

what Ellis et al (2010) found with the same instrument in a laboratory calibration Since the 490

correlation was very good the regression against the ALPHA badges was used to calibrate the 491

QC-TILDAS concentration data The correction factor was found to be 286 plusmn 009 The 492

calibrated QC-TILDAS concentration was in quite good agreement with the ROSAA 493

concentrations both in terms of magnitude and dynamics (Figure 2) In Figure 3 a detailed 494

comparison between calibrated values of QC-TILDAS NH3 concentrations and ROSAA 495

values is shown Since we did not find a significant offset in the regression of QC-TILDAS 496

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 14: Eddy covariance measurement of ammonia fluxes: comparison of

14

cut off frequency fco_t at which the TF equals 2-12

is given by 378

tt

sttco

Lr

DUf

2_ 6490 (12)

379

about 14 Hz in our EC system To minimizing HF losses due to nonuniform velocity profiles 380

turbulent flow would be preferred (Lenschow and Raupach 1991) This requires a powerful 381

pump with high power consumption and a tremendous start-up current (13 A at 230 V AC for 382

an Edwards Tri-Scroll pump for example pers comm W Eugster) that was unrealistic at our 383

field site Hence we had to use a less powerful pump and thus need to account for the extra 384

loss in frequency response due to laminar flow in the intake hose (Moncrieff et al 1997) The 385

possible tube damping correction does not correct potential absorptiondesorption of moisture 386

by hygroscopic dirt particles in the tube however a strong pressure drop along the intake 387

hose reduces risk of condensation on the walls of the tube On the other hand HF due to wall 388

interaction in the sample tube could be reduced by minimizing lengthrsquos tube and maintaining 389

high flow rate In general the line loss is a function of tubing type and how dirty the tube is 390

(Sukyer and Verma 1993) 391

(iii) As reported by Massman (2004) in a closed-path detector the flow velocity within the 392

detection chamber can be very different than the wind speed of the ambient atmosphere near 393

the tube inlet so that the transfer functions need to be expressed in terms of the volume 394

flushing time constant of the detection chamber τvol 395

2

2sin 2

vol

volvolTF

(13)

396

where f 2 and τvol is equal to the time needed to fill the chamber and its minimum value 397

is given by the ratio between the volume of the chamber (05 l) and the maximum flow rate 398

(iv) However if covariances and the cospectra are calculated without the time lag that means 399

without the method of maximum covariance function or if there is no clear time lag due to 400

lag depending on the frequency of the eddies (sensor separation in the wind direction) or if 401

there are complex adsorption and desorption processes it is necessary to take into account the 402

phase shift (Massman 2000) The TF associated with the total phase shift (Massman 2000) 403

is 404

t

tlon

t

tlonshiftphase

U

L

u

l

U

L

u

lTF sincos_ (14)

405

where llon is the longitudinal sensor separation (function of wind direction) and β is the 406

intrinsic time constant of the scalar sensor This latter was estimated by using the Boylersquos law 407

15

applied to the measurement chamber working at 33 kPa and using the measured flow rate it 408

is about 01 s and moreover it means that the sampling frequency of 10 Hz is adequate for 409

the employed QC-TILDAS in the EC system 410

For the theoretical TF approach cospectra of the vertical wind speed and T and NH3 411

respectively were calculated using the post processing EddySpec package of Eddysoft 412

software using the following setup number of frequencies 4096 for calculating the cospectra 413

for half-hourly runs multiplied by frequency tapering the time series with a Hamming 414

window (Kaimal and Finnigan 1994) and averaging into 20 logarithmically spaced bins 415

Either the time lag or the phase shift was used in the functions of the two approaches 416

CFTheor_time_lag and CFTheor_phase_shift respectively 417

418

162 In situ ogive method 419

The limitation of the theoretical TF approach is that it could miss additional chemical or 420

micro-physical process occurring in the EC system a restriction that is expected to be 421

overcome by the in situ experimental methods Moreover the experimental approach is based 422

on the assumption of cospectral similarity between scalar fluxes and unlike the theoretical TF 423

approach these methods do not require smooth models of atmospheric cospectra (Massman 424

and Clement 2004) The correction factor can be estimated as the ratio between the 425

attenuated flux and a reference flux (usually the sensible heat flux) Aubinet et al (2000) give 426

the procedure to estimate this experimental frequency transfer function but since the 427

cospectra derived from sensible heat fluxes are often not well-defined then direct application 428

of this approach can produce implausible values 429

Starting from the hypothesis of similarity between all scalars cospectra Ammann et al (2006) 430

developed a method for estimating HF losses Applied to EC measurement of NH3 fluxes the 431

method consists in fitting the ogive for NH3 OgwNH3 (the ogive is the integrated cospectra 432

CowNH3 from low to high frequencies) against the ogive of air temperature OgwTa The fitting 433

was made with an iterative linear least square method in which the points being too far away 434

from the regression line were eliminated progressively (robustfit function under Matlabreg) 435

The high-frequency damping correction (CFO) is simply evaluated as the value of the fitted 436

NH3 ogive (OgwNH3) at the highest frequency The frequency band over which the linear 437

regression is performed is determined as the frequency for which OgwT larger than 01 and 438

OgwNH3 is smaller than 08 This choice was arbitrary and may not be appropriate if the 439

damping of the NH3 signal was below the range chosen The ogives were calculated using 440

detrended signals 441

16

442

163 The inductance method 443

Eugster and Senn (1995) propose a correction model for EC data affected by systematic error 444

introduced by the combination of all sources of damping which also includes chemical 445

reaction (in the case of NO2) besides the possible damping due to laminar flow in the 446

sampling tube and sensor separation This damping loss is described by an analogy to 447

inductance L in an alternating current circuit which can be used for taking into account the 448

reduction of spectral density in the inertial subrange This model follows the approach of 449

Moore (1986) using electronic components in a current circuit instead of a gain function 450

As reported by Eugster and Senn (1995) the covariance of the measured (ie damped) scalar 451

flux is given by 452

02220

)(41

1)(

0dffCo

LfdffCow

LL wwL

(15)

453

where )(0 fCo

Lw is the undamped cospectrum The value of L is modified in order to fit the 454

measured cospectrum The independent variables in this correction model are the measuring 455

height above zero-plane displacement z-d the mean horizontal wind speed u the Monin-456

Obukhov stability parameter ζ the natural frequency f and the inductance L The value for 457

L can be derived from spectral and cospectral analysis and is assumed not vary significantly 458

as long as the measuring system is not altered 459

The damped cospectra can be derived combining equation (14) with the empirical formula of 460

Kaimal et al (1972) for stable unstable and neutral stratification as modified by Eugster and 461

Senn (1995) (equations 29 and 30 in their paper) The approximated solutions of Eugster and 462

Senn (1995) for CF range between 10 (no damping at all) and infin (no signal at all) but they 463

suggest to restrict the fit to the range between CF=10 and CF=25 that means all 464

measurements which represent less than 40 of the real fluxes were discarded 465

17

2 RESULTS AND DISCUSSION 466

21 Micrometeorological conditions 467

During the trial the air temperature ranged from 16degC to 335degC and averaged 247degC while 468

the soil surface temperature reached more than 546degC at hourly scale The sky was clear 469

most of the period apart from the 23-24072008 where there was little rain (82 mm) The 470

global solar radiation was following the fair weather diurnal course throughout the period and 471

reached 950 W m-2

at maximum The daily average relative humidity was around 60 with 472

variations ranging between 25 and 93 473

The prevalent wind direction during the experimental period (84 of the runs) was NW the 474

wind speed ranged from 05 to 89 ms-1

and averaged 34 ms-1

while the friction velocity u 475

ranged between 001 and 099 ms-1

with an average of 030 ms-1

(Figure 1a) The energy 476

balance for the period of the trial is reported in Figure 2b at hourly scale In particular it is 477

clear the direct impact of the net radiation (Rn) on the sensible heat flux (H) and on the latent 478

heat flux (LE) The pattern of LE were strongly correlated with the available energy Rn with 479

distinct peaks immediately following irrigation which provided water to evapotranspirate 480

from plant and soil 481

22 Ammonia concentrations 482

The field NH3 concentrations measured by the diffusive samplers (ALPHA badges) over 483

several hours ranged from 3 to 118 microg NH3 m-3

while the 30 minutes ROSAA concentrations 484

varied from around 0 to around 90 microg NH3 m-3

The ROSAA concentrations averaged over 485

the same intervals as the ALPHA badges slightly underestimated the badges concentration by 486

8 on average (ROSAA = 092 ALPHA R2 = 082) The uncalibrated QC-TILDAS 487

concentrations strongly underestimated the ALPHA badges concentrations by 67 but 488

without any offset (QC-TILDAS = 034 ALPHA R2 = 090) This underestimation is larger than 489

what Ellis et al (2010) found with the same instrument in a laboratory calibration Since the 490

correlation was very good the regression against the ALPHA badges was used to calibrate the 491

QC-TILDAS concentration data The correction factor was found to be 286 plusmn 009 The 492

calibrated QC-TILDAS concentration was in quite good agreement with the ROSAA 493

concentrations both in terms of magnitude and dynamics (Figure 2) In Figure 3 a detailed 494

comparison between calibrated values of QC-TILDAS NH3 concentrations and ROSAA 495

values is shown Since we did not find a significant offset in the regression of QC-TILDAS 496

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 15: Eddy covariance measurement of ammonia fluxes: comparison of

15

applied to the measurement chamber working at 33 kPa and using the measured flow rate it 408

is about 01 s and moreover it means that the sampling frequency of 10 Hz is adequate for 409

the employed QC-TILDAS in the EC system 410

For the theoretical TF approach cospectra of the vertical wind speed and T and NH3 411

respectively were calculated using the post processing EddySpec package of Eddysoft 412

software using the following setup number of frequencies 4096 for calculating the cospectra 413

for half-hourly runs multiplied by frequency tapering the time series with a Hamming 414

window (Kaimal and Finnigan 1994) and averaging into 20 logarithmically spaced bins 415

Either the time lag or the phase shift was used in the functions of the two approaches 416

CFTheor_time_lag and CFTheor_phase_shift respectively 417

418

162 In situ ogive method 419

The limitation of the theoretical TF approach is that it could miss additional chemical or 420

micro-physical process occurring in the EC system a restriction that is expected to be 421

overcome by the in situ experimental methods Moreover the experimental approach is based 422

on the assumption of cospectral similarity between scalar fluxes and unlike the theoretical TF 423

approach these methods do not require smooth models of atmospheric cospectra (Massman 424

and Clement 2004) The correction factor can be estimated as the ratio between the 425

attenuated flux and a reference flux (usually the sensible heat flux) Aubinet et al (2000) give 426

the procedure to estimate this experimental frequency transfer function but since the 427

cospectra derived from sensible heat fluxes are often not well-defined then direct application 428

of this approach can produce implausible values 429

Starting from the hypothesis of similarity between all scalars cospectra Ammann et al (2006) 430

developed a method for estimating HF losses Applied to EC measurement of NH3 fluxes the 431

method consists in fitting the ogive for NH3 OgwNH3 (the ogive is the integrated cospectra 432

CowNH3 from low to high frequencies) against the ogive of air temperature OgwTa The fitting 433

was made with an iterative linear least square method in which the points being too far away 434

from the regression line were eliminated progressively (robustfit function under Matlabreg) 435

The high-frequency damping correction (CFO) is simply evaluated as the value of the fitted 436

NH3 ogive (OgwNH3) at the highest frequency The frequency band over which the linear 437

regression is performed is determined as the frequency for which OgwT larger than 01 and 438

OgwNH3 is smaller than 08 This choice was arbitrary and may not be appropriate if the 439

damping of the NH3 signal was below the range chosen The ogives were calculated using 440

detrended signals 441

16

442

163 The inductance method 443

Eugster and Senn (1995) propose a correction model for EC data affected by systematic error 444

introduced by the combination of all sources of damping which also includes chemical 445

reaction (in the case of NO2) besides the possible damping due to laminar flow in the 446

sampling tube and sensor separation This damping loss is described by an analogy to 447

inductance L in an alternating current circuit which can be used for taking into account the 448

reduction of spectral density in the inertial subrange This model follows the approach of 449

Moore (1986) using electronic components in a current circuit instead of a gain function 450

As reported by Eugster and Senn (1995) the covariance of the measured (ie damped) scalar 451

flux is given by 452

02220

)(41

1)(

0dffCo

LfdffCow

LL wwL

(15)

453

where )(0 fCo

Lw is the undamped cospectrum The value of L is modified in order to fit the 454

measured cospectrum The independent variables in this correction model are the measuring 455

height above zero-plane displacement z-d the mean horizontal wind speed u the Monin-456

Obukhov stability parameter ζ the natural frequency f and the inductance L The value for 457

L can be derived from spectral and cospectral analysis and is assumed not vary significantly 458

as long as the measuring system is not altered 459

The damped cospectra can be derived combining equation (14) with the empirical formula of 460

Kaimal et al (1972) for stable unstable and neutral stratification as modified by Eugster and 461

Senn (1995) (equations 29 and 30 in their paper) The approximated solutions of Eugster and 462

Senn (1995) for CF range between 10 (no damping at all) and infin (no signal at all) but they 463

suggest to restrict the fit to the range between CF=10 and CF=25 that means all 464

measurements which represent less than 40 of the real fluxes were discarded 465

17

2 RESULTS AND DISCUSSION 466

21 Micrometeorological conditions 467

During the trial the air temperature ranged from 16degC to 335degC and averaged 247degC while 468

the soil surface temperature reached more than 546degC at hourly scale The sky was clear 469

most of the period apart from the 23-24072008 where there was little rain (82 mm) The 470

global solar radiation was following the fair weather diurnal course throughout the period and 471

reached 950 W m-2

at maximum The daily average relative humidity was around 60 with 472

variations ranging between 25 and 93 473

The prevalent wind direction during the experimental period (84 of the runs) was NW the 474

wind speed ranged from 05 to 89 ms-1

and averaged 34 ms-1

while the friction velocity u 475

ranged between 001 and 099 ms-1

with an average of 030 ms-1

(Figure 1a) The energy 476

balance for the period of the trial is reported in Figure 2b at hourly scale In particular it is 477

clear the direct impact of the net radiation (Rn) on the sensible heat flux (H) and on the latent 478

heat flux (LE) The pattern of LE were strongly correlated with the available energy Rn with 479

distinct peaks immediately following irrigation which provided water to evapotranspirate 480

from plant and soil 481

22 Ammonia concentrations 482

The field NH3 concentrations measured by the diffusive samplers (ALPHA badges) over 483

several hours ranged from 3 to 118 microg NH3 m-3

while the 30 minutes ROSAA concentrations 484

varied from around 0 to around 90 microg NH3 m-3

The ROSAA concentrations averaged over 485

the same intervals as the ALPHA badges slightly underestimated the badges concentration by 486

8 on average (ROSAA = 092 ALPHA R2 = 082) The uncalibrated QC-TILDAS 487

concentrations strongly underestimated the ALPHA badges concentrations by 67 but 488

without any offset (QC-TILDAS = 034 ALPHA R2 = 090) This underestimation is larger than 489

what Ellis et al (2010) found with the same instrument in a laboratory calibration Since the 490

correlation was very good the regression against the ALPHA badges was used to calibrate the 491

QC-TILDAS concentration data The correction factor was found to be 286 plusmn 009 The 492

calibrated QC-TILDAS concentration was in quite good agreement with the ROSAA 493

concentrations both in terms of magnitude and dynamics (Figure 2) In Figure 3 a detailed 494

comparison between calibrated values of QC-TILDAS NH3 concentrations and ROSAA 495

values is shown Since we did not find a significant offset in the regression of QC-TILDAS 496

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 16: Eddy covariance measurement of ammonia fluxes: comparison of

16

442

163 The inductance method 443

Eugster and Senn (1995) propose a correction model for EC data affected by systematic error 444

introduced by the combination of all sources of damping which also includes chemical 445

reaction (in the case of NO2) besides the possible damping due to laminar flow in the 446

sampling tube and sensor separation This damping loss is described by an analogy to 447

inductance L in an alternating current circuit which can be used for taking into account the 448

reduction of spectral density in the inertial subrange This model follows the approach of 449

Moore (1986) using electronic components in a current circuit instead of a gain function 450

As reported by Eugster and Senn (1995) the covariance of the measured (ie damped) scalar 451

flux is given by 452

02220

)(41

1)(

0dffCo

LfdffCow

LL wwL

(15)

453

where )(0 fCo

Lw is the undamped cospectrum The value of L is modified in order to fit the 454

measured cospectrum The independent variables in this correction model are the measuring 455

height above zero-plane displacement z-d the mean horizontal wind speed u the Monin-456

Obukhov stability parameter ζ the natural frequency f and the inductance L The value for 457

L can be derived from spectral and cospectral analysis and is assumed not vary significantly 458

as long as the measuring system is not altered 459

The damped cospectra can be derived combining equation (14) with the empirical formula of 460

Kaimal et al (1972) for stable unstable and neutral stratification as modified by Eugster and 461

Senn (1995) (equations 29 and 30 in their paper) The approximated solutions of Eugster and 462

Senn (1995) for CF range between 10 (no damping at all) and infin (no signal at all) but they 463

suggest to restrict the fit to the range between CF=10 and CF=25 that means all 464

measurements which represent less than 40 of the real fluxes were discarded 465

17

2 RESULTS AND DISCUSSION 466

21 Micrometeorological conditions 467

During the trial the air temperature ranged from 16degC to 335degC and averaged 247degC while 468

the soil surface temperature reached more than 546degC at hourly scale The sky was clear 469

most of the period apart from the 23-24072008 where there was little rain (82 mm) The 470

global solar radiation was following the fair weather diurnal course throughout the period and 471

reached 950 W m-2

at maximum The daily average relative humidity was around 60 with 472

variations ranging between 25 and 93 473

The prevalent wind direction during the experimental period (84 of the runs) was NW the 474

wind speed ranged from 05 to 89 ms-1

and averaged 34 ms-1

while the friction velocity u 475

ranged between 001 and 099 ms-1

with an average of 030 ms-1

(Figure 1a) The energy 476

balance for the period of the trial is reported in Figure 2b at hourly scale In particular it is 477

clear the direct impact of the net radiation (Rn) on the sensible heat flux (H) and on the latent 478

heat flux (LE) The pattern of LE were strongly correlated with the available energy Rn with 479

distinct peaks immediately following irrigation which provided water to evapotranspirate 480

from plant and soil 481

22 Ammonia concentrations 482

The field NH3 concentrations measured by the diffusive samplers (ALPHA badges) over 483

several hours ranged from 3 to 118 microg NH3 m-3

while the 30 minutes ROSAA concentrations 484

varied from around 0 to around 90 microg NH3 m-3

The ROSAA concentrations averaged over 485

the same intervals as the ALPHA badges slightly underestimated the badges concentration by 486

8 on average (ROSAA = 092 ALPHA R2 = 082) The uncalibrated QC-TILDAS 487

concentrations strongly underestimated the ALPHA badges concentrations by 67 but 488

without any offset (QC-TILDAS = 034 ALPHA R2 = 090) This underestimation is larger than 489

what Ellis et al (2010) found with the same instrument in a laboratory calibration Since the 490

correlation was very good the regression against the ALPHA badges was used to calibrate the 491

QC-TILDAS concentration data The correction factor was found to be 286 plusmn 009 The 492

calibrated QC-TILDAS concentration was in quite good agreement with the ROSAA 493

concentrations both in terms of magnitude and dynamics (Figure 2) In Figure 3 a detailed 494

comparison between calibrated values of QC-TILDAS NH3 concentrations and ROSAA 495

values is shown Since we did not find a significant offset in the regression of QC-TILDAS 496

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 17: Eddy covariance measurement of ammonia fluxes: comparison of

17

2 RESULTS AND DISCUSSION 466

21 Micrometeorological conditions 467

During the trial the air temperature ranged from 16degC to 335degC and averaged 247degC while 468

the soil surface temperature reached more than 546degC at hourly scale The sky was clear 469

most of the period apart from the 23-24072008 where there was little rain (82 mm) The 470

global solar radiation was following the fair weather diurnal course throughout the period and 471

reached 950 W m-2

at maximum The daily average relative humidity was around 60 with 472

variations ranging between 25 and 93 473

The prevalent wind direction during the experimental period (84 of the runs) was NW the 474

wind speed ranged from 05 to 89 ms-1

and averaged 34 ms-1

while the friction velocity u 475

ranged between 001 and 099 ms-1

with an average of 030 ms-1

(Figure 1a) The energy 476

balance for the period of the trial is reported in Figure 2b at hourly scale In particular it is 477

clear the direct impact of the net radiation (Rn) on the sensible heat flux (H) and on the latent 478

heat flux (LE) The pattern of LE were strongly correlated with the available energy Rn with 479

distinct peaks immediately following irrigation which provided water to evapotranspirate 480

from plant and soil 481

22 Ammonia concentrations 482

The field NH3 concentrations measured by the diffusive samplers (ALPHA badges) over 483

several hours ranged from 3 to 118 microg NH3 m-3

while the 30 minutes ROSAA concentrations 484

varied from around 0 to around 90 microg NH3 m-3

The ROSAA concentrations averaged over 485

the same intervals as the ALPHA badges slightly underestimated the badges concentration by 486

8 on average (ROSAA = 092 ALPHA R2 = 082) The uncalibrated QC-TILDAS 487

concentrations strongly underestimated the ALPHA badges concentrations by 67 but 488

without any offset (QC-TILDAS = 034 ALPHA R2 = 090) This underestimation is larger than 489

what Ellis et al (2010) found with the same instrument in a laboratory calibration Since the 490

correlation was very good the regression against the ALPHA badges was used to calibrate the 491

QC-TILDAS concentration data The correction factor was found to be 286 plusmn 009 The 492

calibrated QC-TILDAS concentration was in quite good agreement with the ROSAA 493

concentrations both in terms of magnitude and dynamics (Figure 2) In Figure 3 a detailed 494

comparison between calibrated values of QC-TILDAS NH3 concentrations and ROSAA 495

values is shown Since we did not find a significant offset in the regression of QC-TILDAS 496

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 18: Eddy covariance measurement of ammonia fluxes: comparison of

18

measurements with respect to the ALPHA badges the same correction factor (286 plusmn 009) 497

could be used for EC fluxes for which only the span calibration is relevant 498

The background concentration measured with ALPHA badges over several days varied from 499

19 plusmn 03 to 7 plusmn 15 microg NH3 m-3

and averaged 27 plusmn 12 microg NH3 m-3

over the whole 500

experimental period The background was therefore very small compared to the NH3 501

concentration in the field during the period 22 to 30 July 2008 (see Figure 2) 502

503

23 Quality of the eddy covariance signal 504

231 Integral Turbulence Test 505

In order to test the validity of the eddy covariance measurements and to test the development 506

of the turbulent conditions the integral turbulence characteristic the so-called flux variance 507

similarity was analyzed as first proposed by Foken and Wichura (1996) The ratio of the 508

standard deviation of vertical wind component and sonic temperature and their respective 509

turbulent scaling variable (u and T respective) are shown in Figure 4 as a function of the 510

Monin-Obukhov stability parameter ζ (defined by zLMO where LMO is the Monin-Obukhov 511

length) The experimental data are compared to the theoretical model under unstable and near 512

neutral conditions (Kaimal and Finnigan 1994) 513

2

1

c

MO

w

L

zc

u

(16)

514

2

1

c

MO

T

L

zc

T

(17)

515

where the constant c1 and c2 are reported by Foken et al (2004) Because of the relative large 516

errors on integral turbulence characteristic in near neutral condition temperature data were 517

discarded when sensible heat flux density was less than 100 W m-2

The experimental data 518

nicely follow the theoretical behaviour for the vertical wind component whereas values 519

slightly higher than the model are observed for the sonic temperature variance This additional 520

turbulence was likely due to the high surface heterogeneity of a not completely closed canopy 521

and to the low measurement height 522

523

232 Cross-correlations wrsquorsquo 524

Before the analysis of the fluxes we looked at both the time and frequency response of the 525

signal An example of the cross-correlation functions between wrsquo rsquo (CO2 and NH3) is given 526

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 19: Eddy covariance measurement of ammonia fluxes: comparison of

19

in Figure 5 The delay time (or lag time) can clearly be identified by the location of the 527

maximum of the cross-correlation function between wrsquo and rsquo It is close to zero for 528

temperature and CO2 (measured by an open path analyzer Li-7500 Licor Biosciences USA) 529

A positive lag is also clearly seen for the QC-TILDAS NH3 signal The difficulty for 530

estimating the time delay from the cross-correlation function can be seen from the long tail 531

observed for NH3 This tail can be interpreted as NH3 adsorbed and subsequently released 532

from the lines Nevertheless the lag times estimated with the cross-correlation method for 533

CO2 H2O and NH3 were 020plusmn004 s 020 plusmn003 s and 690 plusmn 014 s respectively In the case 534

of NH3 however we have to take into account that this delay is basically the sum of two 535

terms the time delay of the DAC conversion (6 s) and the gas residence time in the tube and 536

device 537

24 Cospectral density of NH3 and other scalars 538

An example of averaged and normalized scalar cospectra for one day of good data is given in 539

Figure 6 for the runs from 800 to 1600 in a clear day We did not use the dimensionless 540

frequency to average our cospectra because wind speed variation within the selection of runs 541

was small The cospectrum of sensible heat CowT compares very well to the theoretical one 542

while the cospectrum of NH3 CowNH3 obtained taking in to account the time lag falls off 543

faster than the theoretical one for frequencies higher than the cut-off frequency of fx ~ 03 Hz 544

Moreover for higher frequencies the NH3 cospectrum is also more noisy with values 545

sometimes being negative we hence show the absolute values as suggested by Mammarella 546

et al (2010) This allows inspecting the fall-off in the inertial subrange which is the result of 547

all high frequency losses In the following we analyse the correction of these HF losses by the 548

considered approaches Moreover Figure 7 is a clear example of the data with an 549

underestimation of all the measured scalar cospectra during the trial in particular both the 550

cospectra relative to the CO2 and the sensible heat show a loss at high frequency like the NH3 551

cospectra 552

241 High Frequency loss corrections 553

Figure 8 shows the trends of the theoretical TF due to the dynamic response time reported and 554

all the TF expressed in equations from 9 to13 (TFs TFtube TFvol TFphase_shift) moreover their 555

product to give the total theoretical transfer function to apply at cospectra is reported In 556

particular two total theoretical TFs are plotted the TF_tot=TFdyn_responseTFsTFtubeTFvol to 557

apply at data elaborated taking into account the maximum correlation function method for the 558

computation of the time lag and the TF_tot_phase_shift=TFdyn_responseTFsTFtube 559

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 20: Eddy covariance measurement of ammonia fluxes: comparison of

20

TFvolTFphase_shift where the total phase shift is involved For the latter the TFphase_shift is 560

responsible for negative values of the overall transfer function (Massman 2000) The 561

individual cut-off frequencies can be estimated for the two theoretical approaches 562

considering the frequency at which the total TF equals 2-12

these values are around 06 and 563

02 Hz for TF_tot and TF_tot_phase_shift respectively (see Figure 8) Then the resulting 564

CFTheor_phase_shift are higher than the CFTheor_time_lag ones (around 15) with a wider range 565

between 16 and 45 566

On average the correction factor found by the ogive method was 136 plusmn 037 Figure 9 shows 567

an example of temperature CO2 and NH3 ogive as well as the fitted NH3 ogive and the 568

corresponding estimation of the high-frequency attenuation for NH3 It shows a typical 569

situation where the CO2 and H2O HF loss was around 15 while the NH3 HF loss was around 570

20 The corresponding HF correction factor for NH3 for this particular example would be 571

around 108 572

The damping computed using the inductance method CFL has a wide range starting from 1 573

however following suggestion by Eugster and Senn (1995) a threshold of 25 has been used 574

in order to reject data where we have not measured at least 40 of the expected flux An 575

example of the application of the method is reported in Figure 10 where measured NH3 576

cospectrum is plotted with the idealized empirical one given by Kaimal et al (1972) and the 577

damped cospectrum obtained using in equation (14) the value of L which gives the best fit of 578

the measured cospectrum 579

For invariant set up of instruments the frequency correction factors should depend on (i) the 580

lag time which affects the shape of the measured spectra (ii) the atmospheric stability which 581

affects the shape of the reference cospectra and (iii) the wind speed which affects the 582

distribution of cospectra density across the frequency range In order to compare the above-583

described correction methodologies applied to our set of data relationships between the 584

correction factors and the wind speed have been searched separating stable and unstable 585

conditions (Table 1) The relationships for the theoretical TF have been obtained using all the 586

data while for the inductance method because of the relatively large scatter of the individual 587

data they were grouped in wind speed classes of 1 m s-1

width for which the median were 588

determined and fitted describing the overall dependence of the damping factor on wind speed 589

under stable and unstable condition using only damping factor less than 25 as suggested by 590

Eugster and Senn (1995) For stable conditions the relationships in Table 1 show lower r2 591

than the ones under unstable conditions due to the lower number of data By means of these 592

relationships the EC NH3 fluxes have been corrected and the ones relative to 5 days of the 593

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 21: Eddy covariance measurement of ammonia fluxes: comparison of

21

second week of the trial (24-29 July) are plotted in Figure 11 594

Table 1 Frequency correction factors (CF) in function of wind speed (u) under stable and unstable atmospheric 595 conditions 596

Unstable Stable

Theoretical TF with

time lag

950

211080

2

__

r

uCF lagtimetheor

320

441080

2

__

r

uCF lagtimetheor

Theoretical TF with

phase shift

960

381550

2

__

r

uCF shiftphasetheor

850

300303

2

__

r

uCF shiftphasetheor

Experimental-Ogive

990

501110

2

r

uCFO

470

2310250

2

r

uCFO

Inductance

810

171300

2

r

uCFL

760

810210

2

r

uCFL

597

598

599

In general the correction applied following the different approaches agree quite well among 600

them except where the inductance method gives larger correction (CFL gt 25) with respect to 601

the theoretical ones 602

To evaluate the overall effects of spectral losses on eddy covariance measurements the 603

cumulated values of the NH3 fluxes in the experimental period were calculated taking into 604

account the correction factors following the methods studied in this work The results are 605

reported in Table 2 as percentage of the losses estimated by the applied corrections 606

607

Table 2 Flux losses estimated by the four correction methods described in the section 26 608

Methods Losses ()

Theoretical TF with time lag -31

Theoretical TF with phase shift -30

Experimental-Ogive -43

Inductance -23

609

Calculated flux losses ranged between -23 (inductance method with the CFL threshold 610

selected to 25) and -43 (experimental-ogive method) while the two theoretical TF 611

approaches gave comparable loss estimates However it should be underlined that the 612

inductance method only corrects for high-frequency losses whereas the ogive method also 613

corrects low-frequency differences There is no doubt that the experimental approach takes 614

into account damping effects due to absorption and desorption of ammonia which are not 615

considered by the theoretical ones 616

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 22: Eddy covariance measurement of ammonia fluxes: comparison of

22

It is of interest to compare these results with the other few works where the fluxes are 617

measured with equipment based on EC technique with NH3 fast analysers Actually the 618

values of flux underestimation are in agreement with the work of Whitehead et al (2008) 619

who found -41 using a QCLAS and -37 using a TDLAS during a specific experimental 620

period and applying the cospectral similarity between the ammonia and heat fluxes On the 621

other hand Shaw et al (1998) found a greater underestimation of -48 using a tandem mass 622

spectrometer and a TF approach Sintermann et al (2011) found high frequency attenuation 623

losses from 20 to 40 using a chemical ionisation mass spectrometer 624

625

4 Conclusions 626

In this work we evaluated the correction to be applied to the NH3 eddy covariance fluxes from 627

a crop for taking into account the damping of the high frequencies contribution due to the set-628

up of the equipment when a Tunable Infrared Laser Differential Absorption Spectrometer 629

with a quantum cascade laser source is used This instrument has only recently become 630

available for routine measurements of NH3 concentrations at high frequency as is needed for 631

direct eddy covariance flux measurements Here three methodologies were analysed in detail 632

for determining the correction factor for the NH3 fluxes (1a) the spectral theoretical transfer 633

function with time lag (1b) the spectral theoretical transfer function with total phase shift (2) 634

the ogive method which is a variant of the in-situ spectral experimental transfer function 635

technique and (3) the inductance correction method 636

A comparison between passive diffusion samples (ALPHA badges) and QC-TILDAS clearly 637

showed that this last equipment underestimated the NH3 concentration values therefore they 638

should be corrected before the calculation of EC fluxes (the correction factor was found to be 639

around 3) When this correction factor is applied the concentration values of ammonia 640

measured by the QC-TILDAS are quite similar to that measured by an independent system 641

(ROSAA) based on wet-effluent denuders 642

Secondly the correction factors of the EC fluxes all follow linear functions of the wind speed 643

(CF=a u+b u wind speed) for all the methodologies above mentioned However for 644

unstable atmospheric conditions the most common encountered during the experiment the 645

wind speed dependency of CF is very low for the spectral theoretical TF with time lag 646

(a=008 and b=121) and the ogive method (a=011 and b=150) whereas it is similar for the 647

remaining two methodologies (a=055 030 b=138 117 for the TF with phase shift and 648

inductance respectively) Overall when the calculated CFs are applied to EC NH3 fluxes 649

measured in our experimental field the values of the fluxes obtained are comparable and 650

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 23: Eddy covariance measurement of ammonia fluxes: comparison of

23

basically reflected the conceptual differences in assumptions that must be made under absence 651

of an undisputed reference flux measurement 652

In conclusion correction of the values of NH3 concentration and EC fluxes over natural 653

surfaces are necessary for taking into account the dumping of high frequency contribution due 654

to the coupling of ammonia fast analyser by QC-TILDAS and sonic anemometer 655

Considering that we made measurements just above the crop the contribution of the high 656

frequencies to the whole flux of ammonia is comparatively large therefore the 657

underestimation of the EC fluxes has to be corrected for 658

Moreover considering that we lack an undisputed reference NH3 flux against which the 659

methods could be tested and hence the differences are basically the conceptual differences of 660

the approaches it would not be possible to firmly establish that one is better than the other 661

however there are pro and contra for each of them Hence at present either method could 662

provide realistic results and therefore future research should also focus on the question of 663

how realistic it is to use sensible heat flux for similarity with NH3 fluxes namely on fields 664

with finite extent where fertilizer was applied and from which NH3 fluxes can originate 665

whereas sensible heat flux is also determined by surrounding However in case the sensible 666

heat cospectra of the experimental site are not available the theoretical TF approach or the 667

inductance method could be used to determine the percentage of the flux that is lost by the 668

measurement system 669

670

Acknowledgements 671

This study has been supported by Italian Projects FISR ldquoCLIMESCOrdquo (DD 285ric 222006) 672

and AQUATER (DM n 209730305) European Project NitroEurope supported the 673

experimental field work First author has been also funded by NinE - ESF Research 674

Networking Programme ldquoNitrogen in Europerdquo and ACCENT - Biaflux (Network of 675

Excellence funded by EC FP6 PRIORITY 1163 Global Change and Ecosystems 676

subproject Biosphere-Atmosphere Exchange of Pollutants) The authors thank Ceacuteline Decuq 677

Pasquale Introna Nicola Martinelli and Sylvie Masson for them helping in conducting the 678

experimental work in the field and Dr Domenico Vitale for artwork 679

680

References 681

682

Ammann C Brunner A Spirig C and Neftel A 2006 Technical note Water vapour 683

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 24: Eddy covariance measurement of ammonia fluxes: comparison of

24

concentration and flux measurements with PTR-MS Atmos Chem Phys 6 4643-4651 684

Asman W A H Sutton M A and Schjorring J K 1998 Ammonia emission 685

atmospheric transport and deposition New Phytol 139 27ndash48 686

Aubinet M Grelle A Ibrom A Rannik U Moncrieff J Foken T Kowalski AS 687

Martin PH Berbigier P Bernhofer C Clement R Elbers J Granier A Grunwald 688

T Morgenstern K Pilegaard K Rebmann C Snijders W Valentini R Vesala T 689

2000 Estimates of the annual net carbon and water exchange of forests the EUROFLUX 690

methodology Adv Ecol Res 30 113-175 691

Brodeur JJ Warland JS Staebler RM amp Wagner-Riddle C 2009 Technical note 692

Laboratory evaluation of a tunable diode laser system for eddy covariance measurements 693

of ammonia flux Agric For Meteorol 149 385-391 694

Denmead OT 1983 Micrometeorological methods for measuring gaseous losses of 695

nitrogen in the field in Freney JR Simpson JR (Eds) Gaseous loss of nitrogen from 696

plant-soil systems Martinus NjhoffDr W Junk The Hague pp 133-157 697

Denmead OT 2008 Approaches to measuring fluxes of methane and nitrous oxide between 698

landscapes and the atmosphere Plant Soil 309 5ndash24 699

Ellis R A Murphy J G Pattey E van Haarlem R OBrien J M Herndon S C 2010 700

Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption 701

Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia Atmos Meas 702

Tech 3 397-406 703

Erisman J W Bleeker A Hensen A Vermeulen A 2008 Agricultural air quality in 704

Europe and the future perspectives Atmos Environ 42 3209ndash3217 705

Erisman JW Vermeulen A Hensen A Flechard C Dammgen U Fowler D Sutton 706

M Grunhage L Tuovinen JP 2005 Monitoring and modelling of 707

biosphereatmosphere exchange of gases and aerosols in Europe Environ Pollut 133(3) 708

403-413 709

Eugster W Senn W 1995 A cospectral correction model for measurement of turbulent 710

NO2 flux Boundary-Layer Meteor 74(4) 321-340 711

Famulari D Fowler D Hargreaves K Milford C Nemitz E Sutton M Weston K 712

2005 Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption 713

spectroscopy Water Air Soil Pollut Focus 4 (6) 151-158 714

Fehsenfeld F 1995 Measurement of chemically reactive gases at ambient concentrations in 715

Matson PA Harris RC (Eds) Biogenic trace gases measuring emission from soil 716

and water Blackwell Science Cambridge England pp 206-258 717

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 25: Eddy covariance measurement of ammonia fluxes: comparison of

25

Ferm M 1979 Method for determination for atmospheric ammonia Atmos Environ 13 718

1385-1393 719

Foken T Wichura B 1996 Tools for quality assessment of surface-based flux 720

measurements Agric For Meteorol 78 83-105 721

Foken T Gӧ cked M Mauder M Mahrt L Amiro B Munger W 2004 Post-field data 722

quality control in Lee X Massman W J Law B (Eds) Handbook of 723

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 724

Publishers pp 181-208 725

Galloway JN Aber JD Erisman JW Seitzinger SP Howarth RW Cowling EB 726

Cosby BJ 2003 The Nitrogen Cascade BioScience 53 (4) 341-356 727

Harper LA 2005 Ammonia measurement issues in Hatfield JL Baker JM Viney 728

MK (Eds) Micrometeorology in Agricultural systems Agronomy Monograph 47 729

ASA CSSA amp SSSA Madison Wisconsin USA pp 345-379 730

Horst TW 1997 A simple formula for attenuation of eddy fluxes measured with first-order 731

response scalar sensors Boundary-Layer Meteorol 82 219ndash233 732

Ibrom A Dellwik E Flyvbjerg H Jensen N O and Pilegaard K 2007 Strong low-733

pass filtering effects on water vapour flux measurements with closed-path eddy 734

correlation systems Agric For Meteorol 147 140ndash156 735

IPCC 2007 The Physical Science basis of Climate Change A Report of Working Group I of 736

the Intergovernmental Panel on Climate Change Fourth Assessment Report Summary 737

for Policymakers httpipcc-wg1ucareduwg1wg1-reporthtml 738

Kaimal JCWyngaard JC Izumi Y Cote OR 1972 Deriving power spectra from a 739

three-component sonic anemometer J Appl Meteorol 7 827ndash837 740

Kaimal JC amp Finnigan JJ 1994 Atmospheric boundary layer flows ndash their structure and 741

measurements Oxford University Press Oxford 742

Kolle O Rebmann C 2007 EddySoft documentation of a software package to acquire and 743

process eddy covariance data Max Planck Institute for Biochemistry Jena Germany 744

Kruit RJW van Pul WAJ Otjes RP Hofschreuder P Jacobs AFG and Holtslag 745

AAM 2007 Ammonia fluxes and derived canopy compensation points over non-746

fertilized agricultural grassland in The Netherlands using the new gradient ammonia - 747

high accuracy - monitor (GRAHAM) Atmos Environ 41(6) 1275-1287 748

Krupa S V 2003 Effects of atmospheric ammonia (NH3) on terrestrial vegetation a 749

review Environ Pollut 124 179ndash221 750

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 26: Eddy covariance measurement of ammonia fluxes: comparison of

26

Lee X Massman W Law BE 2004 Handbook of micrometeorology a guide for surface 751

flux measurement and analysis Atmospheric and Oceanographic Sciences Library 752

Kluwer Academic Publishers Dordrecht The Netherlands 753

Lenschow DH Raupach MR 1991 The attenuation of fluctuations in scalar concentration 754

through sampling tubes J Geophys Res 96(D8) 15259ndash15268 755

Leuning R 2007 The correct form of the Webb Pearman and Leuning equation for eddy 756

fluxes of trace gases in steady and non-steady state horizontally homogeneous flows 757

Boundary-Layer Meteorol 123(2) 263-267 758

Leuning R Judd MJ 1996 The relative merits of open- and closed-path analysers for 759

measurement of eddy fluxes Global Change Biol 2 241-253 760

Liebethal C and Foken T 2004 On the significance of the Webb correction to fluxes (vol 761

109 pg 99 2004) Boundary-Layer Meteorol 113(2) 301-301 762

Loubet B Decuq C Personne E Massad RS Fleacutechard C Fanucci O Mascher N 763

Gueudet J-C Masson S Durand B Geacutenermont S Fauvel Y Cellier P 2011 764

Investigating the stomatal cuticular and soil ammonia fluxes over a growing tritical crop 765

under high acidic loads Biogeosciences Discussions 8 10317-10350 766

Loubet B Geacutenermont S Ferrara R Bedos C Decuq C Personne E Fanucci O 767

Durand B Rana G Cellier P 2010 An inverse model to estimate ammonia 768

emissions from fields Eur J Soil Sci 61(5) 793-805 769

Mahrt L 2010 Computing turbulent fluxes near the surface Needed improvements Agric 770

For Meteorol 150(4) 501-509 771

Mammarella I Werle P Pihlatie M Eugster W Haapanala S Kiese R Markkanen T 772

Rannik Uuml Vesala T 2010 A case study of eddy covariance flux of N2O measured 773

within forest ecosystems quality control and flux error analysis Biogeosciences 7 427-774

440 775

Massman 1998 A review of the molecular diffusivities of H2O CO2 CH4 CO O3 SO2 776

NH3 N2O NO and NO2 in air O2 and N2 near STP Atmos Environ 777

32(6) 1111-1127 778

Massman W 2004 Concerning the measurement of atmospheric trace gas fluxes with open 779

and closed-path eddy covariance system the WPL terms and spectral attenuation in Lee 780

X Massman W J Law B (Eds) Handbook of micrometeorology Atmospheric and 781

Oceanographic Sciences Library Kluwer Academic Publishers pp 133-160 782

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 27: Eddy covariance measurement of ammonia fluxes: comparison of

27

Massman W Clement R 2004 Uncertainty in eddy covariance flux estimates resulting 783

from spectral attenuation in Lee X Massman W J Law B (Eds) Handbook of 784

micrometeorology Atmospheric and Oceanographic Sciences Library Kluwer Academic 785

Publishers pp 67 -100 786

Massman WJ 2000 A simple method for estimating frequency response corrections for 787

eddy covariance systems Agric For Meteorol 104 (3) 185-198 788

Milford C Theobald MR Nemitz E Hargreaves KJ Horvath L Raso J Dammgen 789

U Neftel A Jones SK Hensen A Loubet B Cellier P Sutton MA 2009 790

Ammonia fluxes in relation to cutting and fertilization of an intensively managed 791

grassland derived from an inter-comparison of gradient measurements Biogeosciences 792

6(5) 819-834 793

Moncrieff JB Massheder JM de Bruin H Elbers J Friborg T Heusinkveld B 794

Kabat P Scott S Soegaard H Verhoef A 1997 A system to measure surface fluxes 795

of momentum sensible heat water vapour and carbon dioxide J Hydrol 188-189 589-796

611 797

Moore CJ 1986 Frequency response corrections for eddy correlation systems Boundary-798

Layer Meteorol 3717ndash35 799

Neftel A Blatter A Gut A Houmlgger D Meixner F Ammann C Nathaus F J 1998 800

NH3 soil and soil surface gas measurements in a Triticale wheat field Atmos Environ 801

32 499ndash505 802

Rothman LS Barbe A Chris Benner D Brown LR Camy-Peyret C Carleer 803

KCMR Clerbaux C Dana V Devi VM Fayt A Flaud RRG J-M Goldman 804

A Jacquemart D Jucks KW Lafferty J-YM WJ Massie ST Nemtchinov V 805

Newnham DA Perrin CPR A Schroeder J Smith KM Smith MAH Tang K 806

Toth JVA RA Varanasi P Yoshino K 2003 The HITRAN molecular 807

spectroscopic database Edition of 2000 including updates through 2001 J Quant 808

Spectrosc Radiat Transfer 82 5-44 809

Shaw WJ Spicer CW amp Kenny DV 1998 Eddy correlation fluxes of trace gases using a 810

tandem mass spectrometer Atmos Environ 32 (17) 2887-2898 811

Shimizu T 2007 Practical applicability of high frequency correction theories to CO2 flux 812

measured by a closed-path system Boundary-Layer Meteorol 122 417ndash438 813

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 28: Eddy covariance measurement of ammonia fluxes: comparison of

28

Sintermann J Spirig C Jordan A Kuhn U Ammann C and Neftel A 2011 Eddy 814

covariance flux measurements of ammonia by high temperature chemical ionisation mass 815

spectrometry Atmos Meas Tech 4(3) 599-616 816

Spirig C Flechard C R Ammann C Neftel A 2010 The annual ammonia budget of 817

fertilised cut grassland ndash Part 1 Micrometeorological flux measurements and emissions 818

after slurry application Biogeosciences 7 521ndash536 819

Sutton M A Dragosits U Tang Y S and Fowler D 2000 Ammonia emissions from non 820

agricultural sources in the UK Atmos Environ 34 855ndash869 821

Sutton M A Reis S and Baker S M H 2009 Atmospheric ammonia detecting emission 822

changes and environmental impacts Springer 823

Sutton M A Tang Y S Dragosits U Fournier N Dore T Smith R I Weston K J 824

and Fowler D 2001 A spatial analysis of atmospheric ammonia and ammonium in the 825

UK The Scientific World 1 (S2) 275ndash286 826

Suyker Ae Verma SB 1993 Eddy correlation measurement of CO2 flux using a closed 827

path sensor theory and field tests against an open path sensor Boundary-Layer Meteorol 828

64 391-407 829

Tang YS Cape JN Sutton MA 2001 Development and types of passive samplers for 830

NH3 and NOx International Symposium on Passive Sampling of Gaseous Pollutants in 831

Ecol Res 1 513-529 832

von Bobrutzki K Braban C F Famulari D Jones S K Blackall T Smith T E L 833

Blom M Coe H Gallagher M Ghalaieny M McGillen M R Percival C J 834

Whitehead J D Ellis R Murphy J Mohacsi A Pogany A Junninen H Rantanen 835

S Sutton M A Nemitz E 2010 Field inter-comparison of eleven atmospheric 836

ammonia measurement techniques Atmos Meas Tech 3 91ndash112 837

Warland JS Dias GM Thurtell GW 2001 A tunable diode laser system for ammonia 838

flux measurements over multiple plot Environ Pollut 114 215-221 839

Webb EK Pearman G Leuning R 1980 Correction of flux measurements for density 840

effects due to heat and water vapour transfer Quart J Roy Meteorol Soc 106 85-100 841

Whitehead J Twigg M Famulari D Nemitz E Sutton MA Gallagher MW Fowler 842

D 2008 Evaluation of Laser Absorption Spectroscopic Techniques for Eddy covariance 843

Flux Measurements of Ammonia Environ Sci Technol 42 2041-2046 844

Wyers GP Otjes RP and Slanina J 1993 A continuous-flow denuder for the 845

measurement of ambient concentrations and surface-exchange fluxes of ammonia 846

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 29: Eddy covariance measurement of ammonia fluxes: comparison of

29

Atmos Environ 27A 2085-2090 847

Zahniser MS Nelson DD McManus JB Shorter JH Herndon S Jimenez R 2005 848

Development of a Quantum Cascade Laser-Based Detector for Ammonia and Nitric 849

Acid Final Report US Department of Energy SBIR Phase II Grant No DE-FG02-850

01ER83139 851

852

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 30: Eddy covariance measurement of ammonia fluxes: comparison of

30

Figure captions 853

854

Figure 1 Trends of (a) friction velocity u and (b) energy balance components during the 855

trial Net radiation (Rn) latent heat flux (LE) sensible heat flux (H) and soil heat flux (G) are 856

shown Moments of irrigation are shown with blue triangles 857

Figure 2 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) the 858

ROSAA analyser (calibrated) and the ALPHA diffusion samplers The QC-TILDAS 859

concentrations were calibrated as explained in the section 32 860

Figure 3 Ammonia (NH3) concentration measured with the QC-TILDAS (calibrated) against 861

the values measured by the ROSAA analyser The linear regression gives 862

ConcentrationQCL = ConcentrationROSAA times 09958 [09356 -10560] where the 95 863

confidence interval is given in brackets 864

Figure 4 Normalized standard deviation for vertical wind speed σwu and temperature σTT 865

in function of the Monin Obukhov stability parameter (|zLMO|) Continuous lines are relative 866

to the model reported by Foken and Wichura (1996) 867

Figure 5 Typical cross-correlation function for (wrsquo Ta

rsquo) (w

rsquo βCO2

rsquo) and (w

rsquo βNH3

rsquo) for the 868

DOY 207 at 11h30 UT and DOY 2009 at 7h30 UT determined over 60 minutes Here w 869

vertical component of the wind speed Ta the sonic temperature βCO2 the CO2 concentration 870

and βNH3 the NH3 concentration while a prime denote a fluctuating quantity determined by 871

linear detrending 872

Figure 6 Normalized averaged cospectra (ratio of cospectra over covariance) of NH3 (CowNH3) 873

and sensible heat fluxes (CowT) over a 8 h period (from 800 am to 4 pm with U = 2 m s-1

874

and zLMO = -002) The negative values of NH3 cospectrum (CowNH3neg) have been inverted 875

to be represented on the logarithmic scale 876

877

Figure 7 Normalized averaged cospectra of NH3(CoSpwnh3) temperature (CoSpwt) water 878

(CoSpwq) and CO2 (CoSpwc) over a 4 h period (DOY 208 from 800 am to 1200 am with 879

U = 21 m s-1

and zLMO = -002 A high pass filter has been applied at f = 000139 Hz (12 880

min) 881

882

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 31: Eddy covariance measurement of ammonia fluxes: comparison of

31

883

Figure 8 Trend of the single theoretical transfer functions (TF) and the total ones TF_total 884

and TF_total_phase shift to apply to cospectra obtained taking into account the maximum 885

cross-correlation function and the total phase shift respectively (see the section 261 for 886

details about each theoretical TF) The data are relative to a half an hour with U=21 ms-1

and 887

zLMO=-004 888

889

Figure 9 Example ogives for heat CO2 H2O and NH3 fluxes and corresponding estimation of 890

the high frequency damping by ogive fitting (DOY 208 from 0800 am to 1200 am) T 891

stands for temperature ogive init is the initial ogive and corr is the corrected ogive (fit to 892

the temperature ogive as explained in section 262) The High Frequency loss damping is 893

simply one minus the value of the corrected ogive at the higher frequency Here the CO2 894

H2O and NH3 high frequency dampings are 017 = 1 ndash 083 018 = 1 ndash 082 and 037 = 1 ndash 895

063 A high pass filter has been applied at f = 000139 Hz (12 min) 896

897

Figure 10 An example of the inductance method applied to half an hour dataset (DOY 207 898

1000 am) The measured (black dots and lines) the Kaimal et al (1972) (blue) and the 899

damped (red) cospectra are plotted The values of inductance L wind speed u flux FNH3 and 900

time lag are reported 901

Figure 11 Ammonia (NH3) fluxes corrected by means of the correction factors (CF) 902

calculated using the approaches explained in the section 26 (i) theoretical transfer function 903

with time lag (CF_theor_time_lag) and total phase shift (CF_theor_phase_shift) (ii) in situ Ogive 904

method (CFO) (iii) inductance method (CFL) 905

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 32: Eddy covariance measurement of ammonia fluxes: comparison of

32

906

FIGURE 1907

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 33: Eddy covariance measurement of ammonia fluxes: comparison of

33

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

908

909

FIGURE 2 910

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

TF

dyn

amic

freq

uenc

y re

spon

seT

F la

tera

l sep

arat

ion

TF

tube

lam

inal

flow

TF

vol

ume

aver

agin

gT

F p

hase

shi

ftT

F to

tal

TF

tota

l pha

se s

hift

Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

56+

minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 34: Eddy covariance measurement of ammonia fluxes: comparison of

34

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

911

912

FIGURE 3 913

914

915

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

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Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

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100

101

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0

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5

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05

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Nat

ural

freq

uenc

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amic

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Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

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2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

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Dam

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L=

025

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0917

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000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

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time

lag

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shift

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CF

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Figure

Page 35: Eddy covariance measurement of ammonia fluxes: comparison of

35

916

917 FIGURE 4 918

919

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

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FIGURE 5

Figure

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3

10minus3

10minus2

10minus1

100

101

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10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

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100

101

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0

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Nat

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Figure

FigureClick here to download high resolution image

Nat

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lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

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2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

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Dam

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L=

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minus0

0917

(p=

000

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u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

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CF

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Figure

Page 36: Eddy covariance measurement of ammonia fluxes: comparison of

36

920 FIGURE 5 921

922

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

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amic

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spon

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F la

tera

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FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

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Dam

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L=

025

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minus0

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000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

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(28

6)C

F th

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time

lag

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r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 37: Eddy covariance measurement of ammonia fluxes: comparison of

37

923

924

FIGURE6 925

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

TF(minus)

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amic

freq

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spon

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tera

l sep

arat

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FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

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L=

025

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minus0

0917

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000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

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ibra

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(28

6)C

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lag

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ase

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Figure

Page 38: Eddy covariance measurement of ammonia fluxes: comparison of

38

926

FIGURE 7 927

928

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

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QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

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3 m

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Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

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10minus2

10minus1

100

01

1010

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Figure

FIGURE 5

Figure

minus4

3

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10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

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NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

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10minus1

100

101

minus1

0

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00

05

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ural

freq

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FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

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10minus2

10minus1

100

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minus0

4

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2

00

02

04

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Lag=

68

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Idea

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000

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257

2m

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NH

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146

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Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

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time

lag

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CF

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Figure

Page 39: Eddy covariance measurement of ammonia fluxes: comparison of

39

929

930

FIGURE 8 931

932

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

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QCL (calibrated)

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Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

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on

(microg

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3 m

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Figure

10minus5

10minus3

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101

01

1010

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MO|

σwu

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10minus2

10minus1

100

01

1010

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FIGURE 5

Figure

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3

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10minus2

10minus1

100

101

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10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

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10minus1

100

101

minus1

0

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5

00

05

10

Nat

ural

freq

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amic

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FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

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000

66)

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257

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sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

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EC

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ibra

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(28

6)C

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time

lag

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theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 40: Eddy covariance measurement of ammonia fluxes: comparison of

40

933

FIGURE 9 934

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

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10minus2

10minus1

100

01

1010

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Figure

FIGURE 5

Figure

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3

10minus3

10minus2

10minus1

100

101

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10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

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(Hz)

fsdot(ScalarCospectra)

Co w

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Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

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co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

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5

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freq

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Nat

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lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

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10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

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Dam

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L=

025

56+

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0917

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000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

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6)C

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lag

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shift

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CF

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Figure

Page 41: Eddy covariance measurement of ammonia fluxes: comparison of

41

935

FIGURE 10 936

937

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

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0

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5

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Nat

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freq

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Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

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Dam

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L=

025

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minus0

0917

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000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 42: Eddy covariance measurement of ammonia fluxes: comparison of

42

938

939

FIGURE 11 940

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

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Nat

ural

freq

uenc

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amic

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tal

TF

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Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

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minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

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ibra

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(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

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CF

O

Figure

Page 43: Eddy covariance measurement of ammonia fluxes: comparison of

0

20

40

60

80

100

120

140

170708 190708 210708 230708 250708 270708 290708

Co

nc

(micro

g N

H3 m

-3)

ROSAA (calibrated)

QCL (calibrated)

alpha Badges

Figure

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

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FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

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10minus2

10minus1

100

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minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

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L=

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000

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257

2m

sminus1F

NH

3=

146

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bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

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ibra

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(28

6)C

F th

eor

time

lag

CF

theo

r ph

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shift

CF

Llt

25

CF

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Figure

Page 44: Eddy covariance measurement of ammonia fluxes: comparison of

00

02

04

06

08

10

u(msminus1

)(a

)

Energyfluxes(Wmminus2

)

170

708

190

708

210

708

230

708

250

708

270

708

290

708

minus20

00

200

400

600

800

(b)

Rn

HLE

GIr

rigat

ion

Figure

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

|zL

MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

y(H

z)

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amic

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arat

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Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

101

minus0

4

minus0

2

00

02

04

06

Lag=

68

6 s

NH

3

Idea

lized

Dam

ped

L=

025

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minus0

0917

(p=

000

66)

u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

ted

(28

6)C

F th

eor

time

lag

CF

theo

r ph

ase

shift

CF

Llt

25

CF

O

Figure

Page 45: Eddy covariance measurement of ammonia fluxes: comparison of

0

20

40

60

80

100

0 20 40 60 80 100

ROSAA concentration

(microg NH3 m-3

)

QC

L c

alib

rate

d c

on

ce

ntr

ati

on

(microg

NH

3 m

-3)

Figure

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

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MO|

σTT

Figure

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

minus1

0

minus0

5

00

05

10

Nat

ural

freq

uenc

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Figure

FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

10minus3

10minus2

10minus1

100

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minus0

4

minus0

2

00

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04

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Lag=

68

6 s

NH

3

Idea

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Dam

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L=

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0917

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000

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u=

257

2m

sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

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F th

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time

lag

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Figure

Page 46: Eddy covariance measurement of ammonia fluxes: comparison of

10minus5

10minus3

10minus1

101

01

1010

|zL

MO|

σwu

10minus3

10minus2

10minus1

100

01

1010

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FIGURE 5

Figure

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3

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10minus2

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100

101

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10minus2

10minus1

100

Nat

ura

lfre

qu

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(Hz)

fsdot(ScalarCospectra)

Co w

TC

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3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

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100

101

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0

minus0

5

00

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Nat

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freq

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FigureClick here to download high resolution image

Nat

ura

lfre

qu

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(Hz)

fCowNH3(f)wrsquoNH3rsquo

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100

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4

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2

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Lag=

68

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NH

3

Idea

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257

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sminus1F

NH

3=

146

26pp

bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

)

EC

cal

ibra

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(28

6)C

F th

eor

time

lag

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theo

r ph

ase

shift

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Llt

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O

Figure

Page 47: Eddy covariance measurement of ammonia fluxes: comparison of

FIGURE 5

Figure

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

ency

(Hz)

fsdot(ScalarCospectra)

Co w

TC

o wN

H3

Co w

NH

3neg

Figure

10minus2

10minus1

100

101

005

01

015

02

025

fC

oSp

co

var)

20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

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100

101

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0

minus0

5

00

05

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Nat

ural

freq

uenc

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FigureClick here to download high resolution image

Nat

ura

lfre

qu

ency

(Hz)

fCowNH3(f)wrsquoNH3rsquo

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100

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minus0

4

minus0

2

00

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Lag=

68

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NH

3

Idea

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257

2m

sminus1F

NH

3=

146

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bm

sminus1

Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

sminus1

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6)C

F th

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Llt

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Figure

Page 48: Eddy covariance measurement of ammonia fluxes: comparison of

minus4

3

10minus3

10minus2

10minus1

100

101

10minus4

10minus3

10minus2

10minus1

100

Nat

ura

lfre

qu

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(Hz)

fsdot(ScalarCospectra)

Co w

TC

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H3

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Figure

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100

101

005

01

015

02

025

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co

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20082080800

freq (Hz)

CoSpwtCoSpwcCoSpwqCoSpwnh3KaimalminusFinnigan 036 09

3

Figure

10minus3

10minus2

10minus1

100

101

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0

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5

00

05

10

Nat

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freq

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Nat

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230

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240

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250

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260

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270

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280

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290

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0

500

1000

1500

2000

2500

3000

Dat

e(U

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NH3flux(ngmminus2

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Page 49: Eddy covariance measurement of ammonia fluxes: comparison of

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Nat

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fCowNH3(f)wrsquoNH3rsquo

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Figure

230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

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NH3flux(ngmminus2

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Page 50: Eddy covariance measurement of ammonia fluxes: comparison of

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fCowNH3(f)wrsquoNH3rsquo

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230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

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EC

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(28

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F th

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lag

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Page 51: Eddy covariance measurement of ammonia fluxes: comparison of

FigureClick here to download high resolution image

Nat

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(Hz)

fCowNH3(f)wrsquoNH3rsquo

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230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

T)

NH3flux(ngmminus2

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EC

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(28

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Page 52: Eddy covariance measurement of ammonia fluxes: comparison of

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fCowNH3(f)wrsquoNH3rsquo

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230

708

240

708

250

708

260

708

270

708

280

708

290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

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NH3flux(ngmminus2

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Figure

Page 53: Eddy covariance measurement of ammonia fluxes: comparison of

230

708

240

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250

708

260

708

270

708

280

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290

708

0

500

1000

1500

2000

2500

3000

Dat

e(U

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NH3flux(ngmminus2

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