Development and testing of a new variable scale air pollution model—ACDEP

Pergamon Atmospheric Environment Vol. 29, No. 11, pp. 1267-1290, 1995 Copyright 0 1995 Elsevier Sciiaa Ltd

FYinted in Great Britain. All rights reserved 1352~2310/95 S9.50 + 0.00

13522310(95)ooo67-4

DEVELOPMENT AND TESTING OF A NEW VARIABLE SCALE AIR POLLUTION MODEL-ACDEP

OLE HERTEL, JESPER CHRISTENSEN, ERIK H. RUNGE, WILLEM A. H. ASMAN, RUWIM BERKOWICZ and MADS F. HOVMAND

National Environmental Research Institute, Frederiksborgvej 399,4OOO Roskilde, Denmark

and

OYSTEIN HOV Geophysical Institute, University of Bergen, Allegaten 70, N-5007 Bergen, Norway

(First received 10 August 1994 and in final form 23 January 1995)

Abstract-A comprehensive trajectory model, Atmospheric Chemistry and Deposition model (ACDEP), has been developed to calculate the nitrogen deposition to the Danish sea waters. The model is constructed with the ability of taking into account spatially detailed emissions and land use data for Denmark and on a more coarse grid for the rest of Europe. In the ACDEP-model a one-dimensional column is advected along 96 h back-trajectories. The chemical mechanism in the model is a slightly extended version of the Carbon-Bond Mechanism IV (CBM-IV). The model describes the dry deposition processes with special emphasis on the conditions at sea. For the wet deposition processes both in-cloud and below-cloud scavenging are taken into account. The model results are tested versus one years measurements from six Danish and one Swedish monitoring station. Additional tests are performed for six selected stations from the European Monitoring and Evaluation Programme (EMEP) networks. The model is capable of reproducing both air concentrations and wet depositions of the nitrogen compounds in land and sea areas.

Key word index: Variable scale model, trajectory model, nitrogen compounds, nitrogen deposition, deposition to sea.

1. l.NTRODUCTION

Large-scale air pollution models are usually used for calculations of concentrations and depositions of pollutants on regional or even continental scale. An example of such a m.odel is the EMEP-model (Elias- sen and Saltbones, 1983; Iversen et al., 1990; Sandnes, 1993) which, in different versions, has been used for calculations of sulphur and nitrogen budgets for European countries, as well as for analyses of photochemical episodes. The EMEP-model provides results on a regular 150 x 150 km2 grid covering the Euro- pean continent. Often there is, however, a need for calculations of, e.g., depositions over an area covering only a few hundred kilometres, but with much higher geographical resolution than it is possible with the EMEP-model. This will especially be the case in areas with a high spatial variation in emission density and landuse for which large-scale air pollution models cannot produce maps, which are detailed enough to estimate depositions to complex ecosystems. Because the influence of remote sources on concentrations of pollutants in a certain area might be quite significant,

it is not possible to limit the calculations to only local sources. A way to handle this problem is to use horizontal concentration fluxes calculated by a large-scale model as boundary conditions for a mesoscale model. Such an approach was used in the PHOXA-project (Photochemical Oxidants and Acid Deposition Model; Pankrath et aZ., 1988). The boundary conditions for the PHOXA-model, which was applied for Northern Europe (an area of about 4000 x 800 km2) with the geographical resolution of approxim- ately 30 x 30 km’, were provided by the EMEP- model. Another possible solution is to use a nested grid method, i.e. to refine the grid system in the area where calculations with higher geographical resolution are required. An example of such a model is the RADM model (Pleim et al., 1991). Both the PHOXA and the RADM models are Eulerian models. Applica- tion of the nested grid method in Eulerian models requires use of special techniques for solution of the transport-diffusion equations which usually is connected with large demands to computer power.

A Lagrangian atmospheric transport model-Atmo- spheric Chemistry and Deposition model (ACDEP) is

1267

1268 0. HERTEL et al.

developed under the framework of the Danish Marine Research Programme at the Danish National Envir- onmental Research Institute (NERI). The purpose of the model is to provide calculations of atmospheric depositions of nitrogen compounds to the Danish sea water-an area of less than 100 km in dimension. Preliminary results in this project were provided by the TREND-model (Asman and van Jaarsveld, 1992). Emissions occur mainly on land. For this reason large horizontal gradients of primary compounds from land to sea can be expected. Moreover, the dry deposition velocity and the amount of precipitation are different for land and sea areas. For this reason high resolution is required. The pollution load to the area of interest is, however, significantly affected by contribution from long-range transport on the European scale. The model area covered by ACDEP is therefore the whole European continent, but in the present case with a refinement of the emission and computational grid for Denmark and the neighbouring areas. As the model is formulated in a Lagrangian framework, this refinement could be achieved much easier than in Eulerian models.

In the ACDEP-model advantage was taken from the experience gained from the EMEP-model, as well as from another long-range transport model developed at NERI-the Danish Eulerian model (Zlatev et al., 1985; Zlatev and Christensen, 1989; Zlatev et al., 1992). The meteorological preprocessor and the parametrization of the vertical diffusion is based on the work with the 1 DIM-model (Hertel et al., 1994), developed for the description of the chemical degrada- tion of DMS in the marine atmosphere.

Regarding parametrization of the physical processes in the model, main emphasis is put on the description of the deposition processes. For the wet deposition both in-cloud and below-cloud scavenging are taken into account. For the dry deposition, emphasis is put on the description of the special conditions over the sea.

A description of the model and some test results are provided in the present paper. In Hertel et al. (1995) the influence of horizontal and vertical resolution on model results is discussed. Detailed description of the parametrization of the physical and chemical processes in the ACDEP-model is given in the reports in the series from the Danish Marine Research Pro- gramme (Asman and Jensen, 1993; Asman et al., 1993, 1994a,b; Hertel et al., 1993a). Results of the model calculations of the atmospheric nitrogen deposition to Kattegat are presented in Asman et al. (1994b), while a summary of the atmospheric part of the Danish Marine Research Programme is given in Asman et al. (1995).

2. DFSCRIF’TION OF THE MODEL

The ACDEP-model is a trajectory model, where transport, chemical transformations and depositions

are computed by following an air parcel along a trajectory to a given receptor point. Each trajectory is traced 96 h back in time, which means that air masses at the receptor point may contain air pollution that is up to 96 h old (or older for the initial concentrations). The same concept is used in the EMEP-model (Elias- sen and Saltbones, 1983; Iversen et al., 1990, Sandnes, 1993), but as an innovation, a vertical resolution is introduced in the model. The air parcel is represented in the model by a one-dimensional column divided into 10 layers. The distance between layers increases logarithmically towards the top of the model domain. The lowest layer is at 2 m and the top of the model domain is at 2 km. The remaining vertical grids are defined at: 25,138,343,591,858,1136,1420 and 1708 m. The fine resolution close to the ground is necessary for a proper description of the dry deposition processes and for a good representation of low- level emission sources.

During the horizontal transport, the concentrations of the chemical species are computed by taking into account:

??emissions of gases at the place where the air parcel is situated to a given time; ??vertical dispersion of the chemical species; ??chemical transformation of gases to other gases

and/or particles; ??removal of species (gases or particles) from the air

by dry and wet deposition.

Emissions in the model area are provided on a grid with a variable resolution. For most of Europe, the emissions are provided on 150 x 150 km2 square grid system identical with the EMEP grid (except for NHs that was provided on 75 x 75 km2 grid), while for Denmark and the surrounding areas, the resolution of the emissions is 15 x 15 km2. For practical reasons, emission fields for the whole model domain are sub- divided into 15 x 15 km2 grid squares. The position of the column of air along a trajectory is calculated with an advection time step of 15 min. Input of pollutants by emissions, chemical transformations, vertical mlx- ing and removal processes are calculated for each advection time step. Due to its Lagrangian nature, the model can easily be run with a variable time step, which is adapted to the resolution of the emission fields at the actual position of the advected column of air. This feature is, however, not yet implemented in the model.

2.1. Meteorological data

The meteorological data used presently in the model are provided by the routine Norwegian Weather Prediction Model and are listed in Table 1. The same data are used for calculations with the EMEP-model. The data are given on the EMEP grid (150 x 150 km*) with 6 h intervals (mixing heights although with 12 h intervals). The special treatment of precipitation data is discussed in Chap. 3.

A new variable scale air pollution model 1269

Table 1. Meteorological parameters used in the model

Parameter Remarks

Mixed-layer wind field height ca. 750 m; used for calculation of trajectories Surface-layer wind field height 10 m; used for calculation of dry deposition velocities Mixing height used for determination of vertical dispersion Cloud cover used for calculation of photodissociation coefficients Relative humidity used for estimation of deliquescence point and some reaction coefficients Precipitation accumulated over 6 h; used for calculation of wet deposition Surface temperature height 2 m; used for calculation of reaction coefficients Mixed-layer temperature height ca 750 m; used for calculation of reaction coefficients Surface heat flux used for calculation of vertical diffusion Surface momentum flux used for calculation of vertical diffusion and dry deposition velocities

2.2. Horizontal transport

Ninety-six hour baok-trajectories are computed using a method developed at the Norwegian Meteoro- logical Institute (DNMI). Trajectories are calculated

receptor

using a-level 0.925 wind fields. It is assumed that these wind fields represent transport in the middle of the boundary-layer. The same procedure is used in the EMEP model (Iversen et al., 1990).

Fig. 1. The emissions inputted to the one-dimensional column of air are averaged over a square, where the sides of the square are defined as l/lOth of the distance along the traject-

ory to the receptor point. Trajectories are computed with arrival times at the

receptor points every 6 h (0000,0600,1200,1800 GMT). Coordinates for points along trajectories are stored with 2 h intervals for further calculations with the ACDEP-model. In the model, the position of the advected air column b computed using a linear inter- polation between the 2 h intervals.

2.3. Horizontal dispersion

The air concentrations calculated at the receptor points result from emissions collected by the one- dimensional model column as it moves along the particular trajectory. Trajectories describe the mean motions of air parcels, but due to horizontal dispersion, diffusion of the air masses takes place. Consider- ing time averaged concentrations, the arriving air masses will actually originate not only from point emissions along a particular trajectory, but from re- gions whose area increases with distance from the receptor point. This effect is simulated in the ACDEP-model by averaging emissions in the advected air column over an area that increases with the distance from the receptor point (see Fig. 1).

Assuming that the width of a plume is 10% of the travel distance, an average is taken over a square, where the sides in the square are l/lOth of the distance along the trajectory to the receptor point. This is clearly a simplification, as the horizontal diffusion depends in genera1 on the meteorological conditions and not only on travel distance.

Averaging of emissions reduces to some extent the influence of the inacc:uracy in computations of the

2.4. Vertical dispersion

Exchange of species (gases and particles) between the layers is described by the diffusion equation using the first-order approximation (K-theory),

-=- Kac ac a at aZ ( ) 2 aZ 9

where C is the concentration of the species, t is the time, z is the height above the surface and K, is the eddy diffusivity coefficient. The variation of the pressure with height is disregarded.

The eddy diffusivity coefficient is computed using the Monin-Obukhov similarity theory with a simple extrapolation to the whole boundary-layer,

K,(Z) =K11.Z 1-k , ( >

(2) @“(;I

where rc is the von Karman constant ( = 0.4), u* is the friction velocity, L is the Monin-Obukhov length and ZmiX is the height of the mixing layer.

The similarity functions for heat are given by (Sein- feld, 1986)

Z - l/2

L *“i; &) for t < 0,

@h ; = 1 + 4.7; for t d 0. (3)

trajectories. The effect. of the vertical wind shear on Equation (1) is solved numerically using a semi-impli- the horizontal transport can also, at least to a certain tit method (the &method; Lambert, 1991). Two diffu- degree, be represented by horizontal dispersion. sion time steps are performed for each advection time


step to ensure numerical stability of the vertical diffu- WH,NO,l sion algorithm. dt = - UK,, - CHNWCNHJ)> (8)

The dry deposition flux is implied as the lower boundary condition for equation (1). where K,, (ppbv’) is the equilibrium product of

[NH,] and [HN03] in the air. This product is a function of relative humidity and temperature (Stelson et al., 1979; Stelson and Seinfeld, 1982). k (ppbv- ’ s- ‘) is the reaction coefficient for the reaction between NH3 and HN03 (k = 5.6 x 10m4 ppbv-’ s-l). PHN~~ and PNH3 (ppbv s- ‘) are the chemical production terms (excluding the dissociation of NH4N03, but including emission) for HN03 and NH3, respectively LHNo3 and LNH3 are the chemical loss terms (excluding the reaction between HN03 and NH3) for HN03 and NH3, respectively.

K,$=v,C forz=2m.

Parametrization of the deposition velocities vd is discussed in Section 4.

2.5. Computation of concentrations and depositions at the receptor points

Air concentrations at the receptor points are computed for each arrival time of a trajectory, i.e. with 6 h intervals. The depositions (both dry and wet) in the receptor point are estimated as an accumulated deposition over the 6 h interval. For this purpose the concentrations in air are linearly interpolated in time. We found this procedure to give the most reasonable results compared to measurements. However, Asman and Janssen (1987) argue that an exponential interpo- lation procedure is more appropriate considering mass conservation.

The wet deposition at the receptor point is calculated by

w&p = R,C Qj AZj At 9

where Ej is the time-averaged (assuming linear inter- polation) concentration in layer j, 1, is the scavenging coefficient in layer j, AZ, is thickness of the layer j and At is the time interval for which the deposition is calculated (in this case At = 6 h). Summation is over all model layers. Rr is a coefficient that is used in order to account for subgrid-scale effects in the precipitation fields, This correction and parametrization of scavenging coefficients for the chemical compounds included in the model is discussed in Section 3.

2.6. Chemistry

The description of the chemical reactions in the ACDEP-model is based on the Carbon-Bond Mecha- nism IV (CBM-IV) (Gery et al., 1989a, b). CBM-IV is originally developed for smog episodes with ozone, but contains also a rather detailed description of the nitrogen chemistry. A new method for treatment of the ammonia/ammonium chemistry is introduced in the ACDEP-model. Reaction between NH3 and HNOs and the formation of ammonium nitrate (NH,NOs) is given by

dCHr31 = k(K,, - [HN03]mH3])

+ hN03 - LHN03 X CHNW 3 (6)

F = k(K, - [HN03]mH3])

+PNH3 -hi,x~H,l, (7)

Equations (6)-(8) describe kinetics of the NH3/ HN03/NH4N03 system, taking also into account presence of other reactants and emissions. As discussed by, e.g., Harrison and Mackenzie (1990), the details of the system kinetics are still not well under- stood. The formulation proposed here has the advantage that it is possible to avoid the assumption about instantaneous equilibrium between particulate NH4N03 and gaseous NH3/HN03. It is easy to show that the steady state solution of equations (6)-(8), in the absence of other reactants and emissions, is K, = mH3][HN03], but how fast this equilibrium is achieved is determined by the value of the reaction coefficient k. The higher the value of k, the faster the system reaches equilibrium. To our best knowledge, there exist no literature data on the value of this reaction coefficient and the value of k used in the model is only a tentative estimate. With the present choice and ambient concentrations of NH3/HN03 equal to 1 ppbv, the equilibrium is achieved in about half an hour.

The chemical mechanism used in the model is presented in the Appendix.

A new numerical method, Euler Backward Iterative (EBI) method, has been developed to solve the system of the chemical equations in the model (Hertel et al., 1993b). Tests of the EBI method implemented on the CBM-IV mechanism showed that the method is accurate and efficient. Efficiency of the computational method used for solving the chemical reaction system is very important, as about 90% of the computing time in the ACDEP-model is used on the chemical part of the model.

2.7. Emissions In the ACDEP-model emissions for the year 1990

are specified for ammonia (NH,), nitrogen oxides (NO,), sulphur dioxide (SO*) and Non-methane Vol- atile Organic Compounds (NMVOC). Furthermore, emissions of isoprenes are computed as a function of temperature using an empirical relation by Liibkert and Schapp (1989).

The European emissions on the 150 x 150 km* grid are from Sandnes (1993) and Asman et al. (1993), and NH3 emissions on a 75 x 75 km2 grid are from Asman


(1992). For coastal areas the emissions are redistributed with 15 x 15 km2 precision, so that emissions take place over land surfaces only.

The more detailed emission inventory for Denmark has been prepared on the basis of a 1 x 1 km2 landruse map of the country (Runge and Asman, 1989). Tbe 15 x 15 km2 emission net was constructed using this basis inventory (Asman et al., 1993).

All emissions are separated in low and high sources. Emissions from high sources are distributed uniformly from 81 m to 467 m, and emissions from low sources uniformly from 2 to 8 1 m. The relatively large thickness of the low source emission layer is due to limitations implied by the numerical scheme used in the model. As the emission-chemistry part and vertical diffusion are treated separately (splitting method), unrealistic high concentrations result when low level sources are redistributed in a significantly shallower layer.

The seasonal variation in emissions of SO2 is given by (Sandnes, 1993)

fso, = 1 +o.35cos(2(y-~)). (9)

The seasonal variation of NH3 emissions is as in Asman (1992),

&nJ = 1 +0.38sin(2xe-::)) (10)

where f&,, and fNHJ are the respective seasonal variation factors with respect to the annual average emissions. TIME is the time from beginning of the year and T,,,, is the length of the year in seconds. No diurnal variation is assumed for these species.

For NO, and NMVOC a 3 times higher emission is assumed for daytime than for night, but with a constant emission through the year.

2.8. Initial concentrations

The initial concentrations are the concentrations at the beginning of the 96 h trajectory. The influence of the initialisation on the concentrations at the receptor point depends on the life time of the particular com- pound and its reaction product compared to the transport time along the trajectory. If the life time is considerably shorter than the transport time along the trajectory, the initialisation has very little influence on the concentrations at the receptor point. This is the case for most gaseous compounds in the

ACDEP-model, ozone is one exception. In cases where the air mass passes over areas with low emissions, the concentration of ozone at the receptor point is significantly influenced by the initial concentration. An estimate of average concentrations in “clean” background areas (Seinfeld, 1986) are therefore used as initialisation in the ACDEP-model. The seasonal variation of the initial concentrations of ozone is given in Table 2.

Tbe initial concentration of CO is set to 150 ppbv, while for all the remaining gaseous compounds it is set to zero.

Because the only effective removal mechanism for particulate compounds (aerosols) is washout by rain, their lifetime can be much longer than the four days travel time along a trajectory. This is especially the case for situations with only little or no precipitation. In the ACDEP-model, initial concentrations of ammonium nitrate and ammonium sulphate aerosols are set to 1 ppbv.

3. PARAMETRIZATION OF WET DEPOSITION

The wet deposition is calculated in the ACDEP- model taking into account both in-cloud and below- cloud scavenging (Asman and Jensen, 1993).

When the cloud droplets evaporate without precipitation, the species taken up are released back to the surrounding air. Therefore, it is a good approximation to assume that species are effectively removed from the atmosphere during rain events only. In-cloud oxidation reactions are not taken explicitly into account in the model, but they are included implicitly in the gas-phase chemistry as simple first-order reactions.

In order to simplify the calculation procedure, the parametrization used to describe these processes is rather crude but still based on general principles of physics and chemistry of clouds.

It is assumed that m-cloud scavenging takes place in model layers between 250m and 2 km, while below-cloud scavenging takes place in layers below 250 m. The anticipated height of cloud base reflects conditions typical for precipitation events and was estimated from Danish and Dutch synoptic data.

The in-cloud scavenging coefficient 1 (s-l) is given by

&sir i Hi’

Table 2. The applied seasonal variation of the initial concentrations of ozone (ppbv) (based on Seinfeld, 1986)

Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec.

22 30 40 54 50 42 32 25 22 17 18 20


where Si is the in-cloud scavenging ratio (ratio between concentration in precipitation and concentration in air), I is the precipitation rate (here in m s- ‘) and Hi is the height over which in-cloud scavenging takes place (m)

Sr for gases is calculated by

1 si = (I-cl) ’

ULJW + Cl

where cl is the liquid water content of the cloud (m’ water per m3 cloud), Heff is the Henry’s law constant for gases (Matm-‘); for gases that can dissociate, dissociation is taken into account (Asman and Jensen, 1993; Asman et al., 1993), R is the gas constant (8.2057 x lo-* atm M-’ K-i) and T is the temperature (K).

Sr for particles is calculated by

si =fT,

where fnuc is the fraction of aerosols that is activated, i.e. act as condensation nuclei (dimensionless). In the model fnUE is a given a value of 0.9.

This parametrization is based on the assumption that the interstitial air is in equilibrium with concentrations in cloud water and no entrainment takes place.

The liquid water content in clouds is related to rainfall rates. The higher the liquid water content, the higher the rainfall rate. The exact relationship is not well known, but based on data reported in the literature the following empirical expression has been de- rived and applied in the model:

cl = 2 x lo-71O.36,

where I is in mm h-i

(14)

For slightly soluble gases the below-cloud scavenging is very inefficient and therefore the below-cloud scavenging coefficient is set to zero for these gases. For highly soluble gases and for aerosols, the following parametrization of the below-cloud scavenging coefficient & (s-i) is used:

Lb = azb, (15)

where b is here given a constant value ( = 0.62) and a is a specie specific coefficient (Asman, 1994).

The values of a are ranging from 4 x 10e5 for NrOs to 9.5 x lo-’ for NH3 depending on the diffusivity of the gas. For all aerosols u = 5 x 10e6. This result in a below-cloud scavenging coefficient for aerosols

which is about 10 times smaller than for highly soluble gases.

The contribution from in-cloud scavenging is in most cases larger than below-cloud scavenging, since the depth of the cloud layer is much larger than the depth of the below-cloud layer and the short residence time of the rain droplets. Only when the concentrations in air at cloud level are very low compared to the concentrations in the lower atmosphere, can below- cloud scavenging dominate over the in-cloud scavenging, and this is rarely the case. The below-cloud scavenging parametrization is really only valid for rain events, but for simplicity the same procedure is assumed to hold for below-cloud scavenging by snow as well.

Values of the scavenging coefficients for all the chemical species in the model, calculated for a precipitation rate of 1 mm h- ’ and mixing height of 1000 m are presented in the Appendix.

The rain data used in the model are given as 6 h accumulated precipitation for the EMEP grid with 150 x 150 km2 grid cells. A precipitation episode will normally not fall on the whole grid cell, neither will the rain intensity be constant during the 6 h period. To take this into account, an empirical relation between rain intensity and the 6 h accumulated precipitation amount, proposed by Sandnes (1993), is introduced in the ACDEP-model. According to this relationship, the effective rain intensity is given by

I = WG, (16)

where I6 is the average rain intensity for the 6 h period and Rf is an empirical factor. It is assumed that the fraction of the area that is covered by rain is also equal to Rf. It is furthermore assumed, that the precipitating and non-precipitating air masses are mixed every 900 s. Rf as function of the 6 h accumulated precipitation is shown in Table 3.

Removal of species from air by wet deposition during the transport along a trajectory is thus calculated by,

AC = c(1 - emAAt)Rc (17)

where AC is the decrease in concentration due to wet removal and the scavenging coefficient 1 is calculated using the effective rain intensity. The amount of wet deposition at a receptor point is calculated using equation (5).

3.1. Local correction of precipitation data

Examination of the precipitation data revealed that there was significant disagreement between the aver-

Table 3. The factor RI as function of the 6-hourly precipitation amount (Sandnes, 1993)

6 h precipitation amount (mm)

&

3 6 12 20 50 90 150

0.31 0.48 0.66 0.72 0.80 0.85 0.91


age precipitation data on the 150 x 150 km2 grid cells speed at 10 m height of 5 m s- ’ are presented in the and actual precipitati’on amounts for the area cover- Appendix. ing Kattegat. In the model calculations presented The dry deposition velocities over the sea area are, here, measurements of precipitation from Anholt, an for most of the species, significantly smaller than over island in Kattegat, were used for the whole Kattegat the land area. This is due to the fact that the aerody- area instead of the original data. namic resistance over sea is, as a rule, much larger

than over the land. It is important to take this difference into account when deposition to sea is calculated.

4 PARAMETRIZATION IOF DRY DEPOSITION VELOCITIFS

The dry deposition velocity is specie specific and depends on the meteorological conditions. A detailed 5. ADVANTAGES AND LIMITATIONS OF THE MODEL

description of the parametrization of the dry deposition is given in Asman et al. (1994a). Only a short

Considering the usefulness of the ACDEP-model as

summary will be given here. a tool for scientific and policy investigations one

In the present version of the ACDEP-model no should take into account that the model was de-

difference is made in (dry deposition velocities to dif- veloped with the special aim to calculate air concen-

ferent land surfaces. The model concept allow a dis- trations and deposition of nitrogen compounds to the

tinction in deposition velocity between different land Danish sea waters.

surfaces, but in the present version of the model, The main advantage of the model is that the com-

a distinction is made between land and sea surfaces putations can be made for a limited region with high

only. All land surfaces are considered as mixed vegeta- geographical resolution, but still taking into account

tion of grass and forest. contributions from both remote and local sources.

The dry deposition in the ACDEP-model is com- Regarding this, it appears that a detailed emission

puted using the resistatnce method, as given in Wesely inventory for the region considered is of crucial im-

and Hicks (1977): portance. Furthermore, the model contains improved parametrization of processes that are significant for

vd = (r, + rb + r,)- ‘, (18) the particular region, in this case-dry and wet depos-

where r, is the aerod,ynamic resistance calculated in ition to water surfaces. The vertical structure of the

ACDEP for the reference height Zrcf = 2 m, i.e. the model is especially adapted to the requirements of

lowest layer in the model, rb is the laminar boundary a better description of the deposition processes.

layer resistance and r; is the surface resistance. The Lagrangian concept makes the model suitable

The aerodynamic resistance is calculated using for quick assessment studies and thorough analyses of

standard method based on the relationship between air pollution episodes. Moreover, the model can be

wind speed, stability and the friction velocity u+ (Arya, used for investigation of parametrization of different

1988). processes. The ACDEP-model suffers, however, from

The laminar boundary layer resistance is given by the limitations that are common to trajectory models. The most important limitations are:

rL =: Iln (z,,\. ”

KU* \zom/’ ??representation of the horizontal transport by

a wind field at only one height, disregarding vertical

where IC is von Karmans constant ( = 0.4), II+ is the wind shear;

friction velocity (m s- ‘), z, is the surface roughness ??uncertainties in determination of initial concen-

parameter for the species (m) and z, is the surface trations.

roughness parameter for momentum (m). The other limitations and uncertainties are connec- For the land surface, a constant value z,, = 0.3 m is

assumed. The sea surface roughness is calculated us- ted with quality of the input data, especially regarding

ing a slightly modified Chamock’s formula (Lindfors the meteorological and emission data. The time and

et al., 1991; Asman er al., 1994b), so the interdepen- geographical resolution of wind fields used presently

dence between u, and the sea surface roughness is for calculations of trajectories is very crude. The very

taken into account. simplified procedure used for simulation of the time

The roughness parameter for gaseous compounds variation of emissions can be questioned.

(z,) is computed using formulae proposed by Brut- The quality of the model, in its present formulation,

saert (1982), while for particles is used a method based can be judged when model results and measurements

on Slinn and Slinn (1980). are compared.

The surface resistance over the sea is modelled taking into account the solubility and reactivity of 6. TESTS OF THE ACDEP-MODEL species in water (Asman et al., 1994b).

The values of deposition velocities for all the species The ACDEP-model is tested on measurements included in the ACDEP-model, calculated for wind from 6 Danish monitoring stations under the Nation-


Fig. 2. Location of the Danish and Swedish monitoring stations used for the model test.

wide Monitoring Programme on Atmospheric Back- ground Levels (Hovmand et al., 1993): Anholt, Frederiksborg, Keldsnor, Lindet, Tange and Ulborg. Furthermore, measurements from the Swedish monitoring station in Riirvik were used for the model test (Naturviirdsverket, 1991). Location of these stations are shown in Fig. 2.

The monitoring sites are chosen so that local emissions from roads, industries, cities and heavy agricultural activity are avoided or minimized. As nearly 70% of the Danish land area is farmland, it is not possible completely to avoid local influence from agricultural manure spreading. All stations are placed more than 0.5 km from arable land, in forests or natural resorts and in two cases close to the coast line. A short description of the surroundings of the stations is given below.

The Ulborg station is located in a conifer planta- tion located in mixed forest and heath land, there are small arable areas 1 km to the north and west of the station.

The Tange station lies in an area of mixed grassland and deciduous trees, surrounded by wet lands and a lake in southern direction, farmland is found in a narrow sector north of the station.

Anholt is a small island with 200 inhabitants and an area of 20 km2 with no agricultural fields. The island is situated in the middle of Kattegat more than 50 km from the continent.

The Frederiksborg station is situated in a mixed deciduous forest with low agricultural activity outside

the forest. The forest is within the densely populated area of Greater Copenhagen with the city centre and power plants situated 50 km to the south and west of the site.

The Keldsnor station is placed at the coast line of the Langeland island. Agricultural activity is found close to the station in the western direction.

The Lindet station is situated in a conifer forest but with heavy agricultural activity as close as 0.5 km south of the station.

Riirvik station is placed in the southern part of Sweden at the coast, and about 40 km south of Goteborg.

Additionally, model results are compared with measurements from 6 European EMEP-stations, which are all placed in rural areas: Langenbriigge in Germany, which is placed very close to the former boarder between East and West Germany, and about 20 km North of Braunschweig and about 30 km east of Hannover. Deuselbach also in Germany is placed 480 m o.s.1. in the southwest part of Germany, close to the Belgium and French boarder. Witteveen is placed in the northern part of The Netherlands. Hoburg in Sweden, is placed on the southern part of Gotland island in the Baltic sea. Birkenes is placed at the coast in the southern part of Norway. Virolahti is placed in the southern Finland, close to the Russian boarder in the Botnic bay.

As nitrogen compounds had the prime interest in the Danish Marine Research Programme, main emphasis has been laid on tests for these compounds.


Selected results are presented here, with the aim to dry deposition accounts for no more than 10% of the illustrate the general aspects of the model. wet deposition (Sjerberg et al., 1991).

6.1. Measuring methods

Sulphur dioxide and ammonia plus the aerosols are sampled by a “Filter pack sampler”. The filter pack is a holder mounted with a sandwich of four filters in series. The first filter collects aerosols and the three succeeding filters are impregnated to collect nitric acid, sulphur dioxide and ammonia, respectively (Fuglsang, 1986). The air inlet for the filter packs is placed 2 m above the ground; at the forest sites Ulborg and Fredensborg the samplers are placed at a scaffold 6 m above the ground in order to place the air inlet above the forest canopy. Each filter pack is individually connected to a solenoid valve. The flow is regulated by a flow sensor and a motor valve, which ensures a constant ilow throughout the sampling pe!iod, despite an increasing pressure above the particle filter as particles are collected. The flow is about 40 emin-’ (at VC) and for 24 h sampling this gives a total air volume of about 58 m3. The actual flow is recorded with a continuous print and the integrated flow is read from a meter (Hovmand and Grundahl, 1991). Detection limits for 24 h samplings are: 0.2 ppbv for SOz, 0.1 ppbv for NH,, 0.2 ppbv for NH: and 0.1 ppbv for NO; (Hovmand et al., 1993).

6.2. Comparisons with diurnal means of air concentrations

A detailed validation of the model is performed by comparing the computed and measured concentrations in air on a daily basis. For the model results, the diurnal means are calculated as the arithmetic mean of the computed concentrations at the lowest grid with 6 h intervals at trajectory arrival times. As an example of such a comparison, results for March 1990 are shown in Figs 3-6. Here, the measured diurnal means of gas concentrations of NH3 and NOz, and particulate NH: and NO; are compared with model predictions for some selected stations. NO; is here defined as the sum of HN03 in gas phase and NO; in aerosols (total NO;). HN03 is not separately determined in routine measurements in the Danish monitoring network. Campaign wise denuder measurements show average concentrations of HN03 around 0.3 ppbv in summer month and 0.2 ppbv in winter (Hovmand et al., 1993).

The first filter is a cellulose based (MF-Millipore, 1.2 pm) particle filter. The three other filters are What- man 41 filters; impregnated with sodium fluoride for nitric acid sampling, with potassium hydroxide for sulphur dioxide sampling and with oxalic acid for sampling ammonia.

It should be noted that the filter pack method originally was developed for determination of NH, (sum of NH3 and NH:) and using the method for separate measurements of NH3 can be connected with significant uncertainties. Comparison with denuder measurements show that NH3 concentrations can be underestimated by as much as 50%, especially in cases when NH, concentrations are much lower than NH: concentrations (Andersen and Hovmand, 1994).

Results for NH3, presented in Fig. 3, show large variations of concentrations in time and also between the different monitoring stations. This variation is qualitatively reproduced by the model, but the agreement between measured and modelled values is not always good. Concentrations of NH3 are known to be highly influenced by contributions from very local sources (range of few kilometres), while the modelled concentrations are more representative for the average concentrations in the grid square (15 x 15 km2). Since the monitoring sites are in forests and natural resorts, the measured NH3 concentrations will in general be lower than the model computed average values for a grid square.

Nitrogen dioxide is sampled by glass filters impregnated with KI. The glass filter is placed in a glass vessel with pipe stubs in each end. The sampling device is designed and constructed at the National Environmental Research Institute. The flow is about 0.7lmin-‘, which gives a total volume of about 1 m3 of air for a 24 h sample. The flow is regulated by a critical orifice and registered by a flow meter, which relates to a meter for each channel. The detection limit is 0.3 ppbv for 24 h sampling (Hilbert, 1993).

Concentrations of NH:, shown in Fig. 4, do not exhibit large differences between the measuring locations, due to dominance from long-range transport contributions. This behaviour is well reproduced by the model. Especially it should be pointed out that the episode observed at nearly all stations in the period 15-20 March, is also predicted by the model, even though, the levels are somewhat underestimated. In general, there is a good agreement between computed and observed concentrations. Results for other stations, not presented here, show the same tendency.

Wet deposition is sampled with bulk samplers of NILU (Norwegian Institute of Air Research) design with a sampling area of 314 cm’. The accumulated sampling period is half a month. Because the samplers are exposed to gases and particulates also during dry periods, this can result in some overestimation of concentrations. However, bulk samplers are in this case located outside high emission areas. In such areas

Results for NOz, including the selected EMEP- stations, are presented in Fig. 5. They show fine agreement between measured and modelled values, especially considering periods with elevated concentrations. Model results for the station Langenbrugge, show, however significant overestimation compared to measurements. Langenbrugge is placed close to border between two EMEP-grids. The grid in which the station is situated has 3 times higher emission than the neighbouring grid, that may be more representative for the station than the grid in which it is placed. NOz concentrations are more dominated by contri-

1276 0. HERTEL er al.

Anhon Ulboq

10 15 20 25 30

Day in month

0 5 10 15 20 25 30 0 5 10 15 20 25 30

Day in month Day in month

0 5 10 15 20 25 30

Day in month

0 5 10 15 20 25 30

Day in month

0 5 10 15 20 25 30

Day in month

lo-

0 5 10 15 20 25 30

Dayinnmmth

Fig. 3. Comparison of modelled (dashed line) and measured (solid tine) concentrations of NH, for March 1990.


Anholl

0 5 10 15 20 25 30 0 5 10 15 20 25 30 Day in monlh Day in month

+-T---m ’ ’ ’ I ’ ’ ’ ’ I ’ ’ ’ ’ I ’ ’ ’ ’ I 1 0 5 10 15 20 25 30

Day in monlh

0 5 10 15 20 25 30

Day in month

o-1 1 ’ ’ I ’ ’ ’ ’ I ’ ’ ’ ’ I ’ ’ ’ ’ I ’ 0 5 10 15 20 25 30

Dayinmonth

:-: I : j i

15-

3! 1 .e lo-

P I

P.J

5- :

0 , , , , , , , , /,,,I 1 , , , , , , , , , , ( , , , ( , , (

Day in month

0 5 10 15 20 25 30

Day in monih

lb 1;

Day in month

Fig. 4. Comparison of modelled (dashed line) and measured (solid line) concentrations of NHf for March 1990.


Virdahti Bifkenes

3”: fs =4-

.s .E --I j p4- d -

z ii

2- 2-

_I. ,_;-*&fi ._I

O,,‘,,,,‘,,,,1’1,111r,“~~,~‘~~l’ 0 ,IIJ,II,I,IIII,IIII, IIIII~III~ 0 5 10 15 20 25 30 0 5 10 15 20 25 30


Fig. 5. Comparison of modelled (dashed line) and measured (solid line) concentrations of NO, for March 1990.

AtlhOH Kaldsnor


0 5 10 15 20 25 30 0 5 10 15 20 25 30 Day in month Day in month

Wtiw90ll

8-

Fig. 6. Comparison of modelled (dashed line) and measured (solid line) concentrations of NO; for March 1990.


butions from near sources (10-100 km) than by long- range transport. The highest concentrations usually occur during low wind speed conditions. This is also the case for the elevated concentrations in the period 12th-17th March.

The results for NO; concentrations presented in Fig. 6 (incl. the EMEP-station Witteveen) are similar to the results for NH: shown in Fig. 4. The NHf concentrations are about 50% higher than the NO; concentrations. This means that NH,N03 is the dominating fraction of both NH: and NO;. This effect is well reproduced by the model, although the levels are underestimated during the aforementioned long-range transport episode.

Results for sulphur compounds, SOz and SO:-, are shown in Figs ‘7 and 8 only for two selected stations, Keldsnor and Tange. Results for the other stations, not shown here, are similar. It is observed that the agreement between modelled and observed concentrations is in general good, but concentrations are overestimated during the long-range transport episode.

6.3. Comparisons with monthly means of air concentrations

To investigate how the model reproduces the observed seasonal variations, comparisons are performed between observed and computed monthly means for the 6 Danish stations and Riirvik. The seasonal variation in concentrations depends mainly on variation of emissions and chemical transformation rates. Results for NH3, presented in Fig. 9, indi- cate that the seasonal variation in ammonia emissions, at least locally, is not always well described, as explained in Section 6.2. This is most evident for Tange and Ulborg, which may be due to location of these stations. In the situations where the monitoring point is in an area with mixed land use, as, e.g., Lindet and Keldsnor, the observed and modelled results are in better agreement. The use of the filter pack method, as mentioned in Section 6.1, may lead to underestimation of NH3 concentrations in low level areas, and this can be the case for Anholt and Fredensborg stations.

Results obtained for NH: (Fig. lo), for which the local emissions are less important, are in general in

0 5 10 15 20 25 30 0 5 10 15 20 25 30

Dayinmonth Dayinmonth

Fig. 7. Comparison of modelled (dashed line) and measured (solid line) concentrations of SO, for March 1990.

I3 ii E 4-

I--

ji- 2-

0 5 10 15 20 25 30

Dayinmonth

Fig. 8. Comparison of modelled (dashed line) and measured (solid line) concentrations of SOi- for March 1990.


hhdl

6-

%‘+- .E

”

P 2-

__-- _.-_-----._-

0 < , ( , , , , I , , .I F M A M J.J A S 0 N D

6

y _E’_x_k (

JFMAMJJASOND

6

-I

KY .m, JFMAMJJASOND

01 , , , , ( ( ( , , , , (

JFMAMJJA 9 0 N D

OI JFMAMJJA S 0 N D

I I I, I, I I I I I1 J FMAMJJASONO

Fig. 9. Comparison of modelled (dashed line) and measured (solid line) monthly concentrations of NH, for 1990.

much better agreement with the measurements than what was found for NH3. Good agreement is obtained for NOz (Fig. 11) but the model tends to underesti- mate in the summer period. This may be due to an overestimation of the oxidation rate of NOz. The seasonal variation of NO; (Fig. 12) is very similar to what is obtained for NH:. This is evident for both measurements and model results.

Monthly means for SOz and SOi- for Keldsnor and Tange are shown in Figs 13 and 14. The agreement is in general good, but with a slight tendency to overestimation of SOz concentrations in the winter period. This may be due to some overestimation of emissions from Danish and perhaps German sources.

6.4. Comparisons of monthly means of wet depositions

Comparisons of model results with observations of the wet depositions are shown in Figs 15 and 16 for NH: and NO;, respectively. The accuracy of the estimation of wet depositions is determined by the accuracy in the precipitation data used in the model. This explains why the calculations and measurements for Anholt in general agree much better than for the other locations. As mentioned in Section 3.1, the monthly precipitation amounts for the sea station Anholt are adjusted according to observations. No such adjustment is made for the other stations. Large difkrences between modelled and measured wet depositions can occur as a result of ditferences between


O’,’ I I ’ ’ ’ ’ ’ ’ AMJJASOND

01 , , , ( , , , , , , , ,

JFYAMJJA S 0 N D

, ( , ( , , I , , ( , (

JFMAMJJASOND

00 JFMAMJJA S 0 N D

JFYAMJJA S 0 N D JFMAMJJASOND

Fig. 10. Comparison of modelled (dashed line) and measured (solid line) monthly concentrations of NH: for 1990.

the model precipitation and the actual precipitation. The significant overestimation of wet deposition in July for Frederiksborg can be explained by the precipitation used in the model being twice the measured precipitation. Comparison between observed and model precipitation data is shown in Fig. 17.

Just like for the air concentrations, there is a large similarity in the seasonal variation of wet depositions of nitrate and ammonium. This is well reproduced by the model.

7: DISCUSSION

The presented test results show how the model calculations of air concentrations and wet depositions

of several compounds compare with measurements. The best results are achieved for reaction products such as ammonium, nitrate and sulphate, for which concentrations are mainly determined by long-range transport. Results for Not, which are influenced both by local and remote sources, are also fair. There is, however, a general underestimation of the NO2 concentrations during summer period. This is in accord- ance with the recent findings by Hov et al. (1994). They presented calculations for August-October 1985 using a version of the EMEP model with special emphasis on the transport and deposition of nitrogen compounds. Their results for 6 EMEP sites showed in general an underestimation of a factor of 2, and they propose the chemical formulation to be responsible for this underestimation.


01 , , , , I , I 1 I I 1 I JFMAMJJI S 0 N D

FrederikSbOrg Tenge

o] , , , , , ( , I I 1 1 1

JFMAMJJASOND

Keldmor Ulborg

01 ( , 1 , I , , I , I I1 0; , ) , , 1 , I , I I I I

J FMAMJJA S 0 N D JFMAMJJASOND

01 , , I , , , , 1 , , 1 I JFMAMJJASOND

01 , , , ( , , I I I , , I

JFMAMJJASOND

Fig. 11. Comparison of modelled (dashed line) and measured (solid line) monthly concentrations of NO, for 1990.

For species with significant influence from local sources, espexklly NH3, there are periods with large differences between observedsand computed concentrations. This is probably due to inadequate description of local emissions in the model. For species like ammonia more accurate calculation of the concentrations at receptors close to emission areas is only possible with a detailed description of the emission variation in space and time. A geographical resolution, which is even better than the 15 x 15 km’ grid that is used in model, is required. Refinement of the vertical resolution might also be necessary, as NH3 is actually emitted mostly from ground level sources.

Uncertainties in computations of wet depositions depend on uncertainties in the representation of rain events in the input data to the model. The interpola- tion procedure for air concentrations used at the receptor point for calculation of wet deposition can also influence the accuracy. Correction of rain data for Kattegat was necessary in order to get a better agreement with observations. It must be assumed that a similar correction would be necessary for other sea areas.

Monthly means of wet deposition are more uncer- tain when the wet deposition is highly episodic. A more detailed description of rain events is then


Rik

%7---’ ’ ’ I ’ ’ ’ ’ ’ MAMJJASOND oi , , ( , , ,

I , , I 1 JFMAMJJASOND

x!,&$y/ ;;w .E “2

p ./ i : ??**; : ,.-

_.-- It ‘., ,,,’ ,,C--- :

v ,,

_.-’ ,/:

: _,-- ‘a, / :

,A

l- : l- ,’

: ‘. : \,

:_.--- *,:’ 9, ..__‘”

MJJASOND

Fig. 12. Comparison of modelled (dashed line) and measured (solid line) monthly concentrations of NO; for 1990.

0 ,,,,‘I’,,,‘( 0 ,,,,,,,,,,,, JFMAMJJASOND JFMAMJJASOND

Fig. 13. Comparison of modelled (dashed line) and measured (solid line) nionthly concentrations of SO, for 1990.


JFMAMJJASOND JFMAMJJASOND

Fig. 14. Comparison of modelled (dashed line) and measured (solid line) monthly concentrations of SOi- for 1990.

150-

-f loo- s 2

a 5D-

0; ( , , , , , , I I , I I

JFMAMJJA S 0 N D

Frederiksborg

Linde!

01 , ( , ( , , , ( , , ( ,

JFMAMJJA S 0 N D

Tange

ol , ( , , , I 1 , , I , 1

JFMAMJJASOND

h ;: : : ;

5 Ii; _____& 0 ,,,,,,,,,,,1

JFMAMJJASOND

01 , , , , , , , ( , , I 1

JFMAMJJA S 0 N D

ullxxg

l!io-

? loo- 1 z P

SO-

o] ( , I , , , , , ( , , (

JFMAMJJASOND

Fig. 15. Comparison of modelled (dashed tine) and measured (solid line) monthly wet depositions of NH: for 1990.

A new variable scale air pollution model

0 JFMAMJJASOND

1285

Linda

M J J A S 0 N D

JFMAMJJA S 0 N D

iI& . ‘., / --._ i’

---.

0 ,,,,,,,I,,,(

JFMAMJJA S 0 N D

: : ::-A *./ .,_.-”

0 ,,,,,,,,,,,, JFMAMJJASOND

01 , , , , ( ( ( , , , , (

JFMAMJJA S 0 N D

Fig. 16. Comparison of medelled (dashed line) and measured (solid line) monthly wet deposition of NO; for 1990.

necessary to obtain better agreement with observations. Computations over longer periods are, however, considered to be more reliable, since episodicity then makes a smaller contribution to the total wet deposition.

8. CONCLUSIONS AND RECOMMENDATIONS

With the ACDEP-model the computations of concentrations and depositions can be made for a limited area and with high geographical resolution. The model accounts for the vertical structure in the atmo-

spheric dispersion, which is especially important for

calculation of dry deposition and better representation of low-level emission sources.

Several improvements of the model are planned. Better resolution of meteorological data is required for more precise determination of trajectories close to receptor points. Data from higher resolution weather prediction models can be suitable for this purpose.

The vertical dispersion is presently represented in the ACDEP-model by the first order K-theory approximation. Limitations of the local K-theory, especially for fine scale atmospheric dispersion, are well known. A more physical description can be achieved based on the concept of non-local closure as, e.g. Berkowitz and Prahm (1984), Stull (1991) and


Lindel

01 , , , , , , , , I I , ,

JFMAMJJASOND

01 , ( , ( , 1 , I I , n I JFMAMJJASOND

K8ldWlO~

01 , , , , , , , , ( ( , (

JFMAMJJASOND

01 , , , , , , , , , , , (

JFMAMJJASOND

ullmq

200-

0 ,,,,,,,,,I,, 0 ,,,,,,,,,,,, JFMAMJJASOND JFMAMJJASOND

Fig 17. Comparison of measured (solid line) precipitation at the site and the precipitation used in the model (dashed line). For Anholt the observed precipitation is used in the model.

Romanoff (1989). Presently, the model is operated with a constant time step for chemistry and advection. In order to fully utilise the advantage of fine geographical resolution the time step should be adjusted to the local resolution of the emission data.

Acknowledgements--This work is partially funded by the Danish Environmental Research Programme, the Nordic Couucil of Ministers and the OtRce of Naval Research Washington D.C., U.S.A. The Danish Research Academy is acknowledged for their financial support of one of the authors (0. Hertel) Ph.D. study, which the present work is a part of. Meteorological data for the trajectory calculations were obtained from Helge Styve at EMEP MSC/W at the Norwegian Meteorological Institute in Oslo, Norway. Karin Kindbom at fVL, Giiteborg, Sweden has provided measured

gas phase concentrations from Rdrvik. Niels A. Kilde from Rise National Laboratory was helpful with estimation of the emission data for Denmark. Data from EMEP measuring network were provided by EMEP Chemical Coordination Centre (CCC) at NfLU, Kjeller, Norway.

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Stelson A. W., Friedlander S. K. and Seinfeld J. H. (1979) Zlatev Z., Christensen J. and Hov 0. (1992). A Eulerian air A note on the equilibrium relationship between ammonia pollution model for Europe with non-linear chemistry. J. and nitric acid and particulate ammonium nitrate. Atmo- ntmos. Chem. 15, l-37.

APPENDIX

Table A.l. List of the chemical species in the ACDEP-model

Species name Representation

1. 2. 3. 4. 5. 6. 7. 8. 9.

10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35.

Nitric oxide Nitrogen dioxide Ozone Hydroxyl radical Hydroperoxy radical Formaldehyde High molecular weight ald. (RCHO,R > H) Peroxyacyl radical (CH,C(O)OO.) Peroxyacyl nitrate (CH,qO)OONO,) NO to NO, operator Ammonia Oxygen atom (triplet) Ammonium nitrate Sulphur dioxide Nitrogen trioxide (nitrate radical) Dinitrogen pentoxide Nitric acid Carbon monoxide Nitrous acid Hydrogen peroxide Peroxynitric acid (H0,N02) Methylglyoxal (CH,C(O)C(O)H Paraffin carbon bond (C-C) NO to nitrate operator ParafEn removal operator Olefinic carbon bond (C-C) Ethene (CH1 =CH2) Toluene (CeHI-CHJ) Aromatic phenols Methylphenoxy radical Organic nitrate Xylene (C,H,_(CH&) Sulphuric acid Ammonium bisulphate Ammonium sulphate

NO NOz 03 OH HOz

HCHO ALD CZO, PAN XO, NH3 O(“P)

NH&NO3 SO1 NO, NZOS HNO,

co HN02 HzOz HNO, MGLY

PAR X02N

RXPAR OLE ETH TOL

PHEN PHO ONIT XYL

H,SO, NHdHSO. (NH&SO*

A new variable scale air pollution model

Table A.2. The chemical kinetic mechanism in the ACDEP-model

1289

Reaction Reaction rate’

1 NO, + hv - > NO + O(“P) 2 NO+Os - > NO1 3 HOr+NO - >NO,+OH 4 HCHO + hv - >2HO,+CO 5 HCHO + hv _ >co 6 HCHO + OH - >HO*+CO 7 ALD+hv - > X0* + 2.HOr + CO + HCHO 8 ALD+OH - > GO3

9 C203 + NO - > NO, + XOI + HCHO + HOz 10 Cz03 + NO2 - > PAN 11 PAN - > C203 + NOr 12 XOz + NO - > NO, 14 NO2 + OH - > HNO, 15 qsP) - > 03

16 0, + hv - > 0.205.0H + 0.8970.0(3P) 17 0s + hv - > O(3P) 18 SOs + OH - > H,SO, + HO2 19 NO3 + hv - > 0.15.NO + 0.85.N02 + 0.85*q3P) 20 NOz + 0s - >NO, 21 NOB + NO;: - >NO+NOz 22 NO3 + NO;! - ’ N,O, 23 N20s - >N03+N0, 24 NzOS - > 2.HN03 25 NO+NO - > 2.NOs 26 OH+CO - > HO2 27 O(“P) + NOz - >NO 28 O(“P) + NO1 - > NO3 29 O(“P) + NO - > NOr 30 0, + OH - > HO2 31 Ox+HOz - >OH 32 Nb, + NG - > 2.N02 33 NO + NO, - > 2.HN0, 34 HNG, + HNO, 35 HNO, + hv 36 NO+OH 37 OH + HNOz 38 HO, + HO> 39 Hz02 + hv 40 H202 + OH 41 HCHO + q3P) 42 HCHO + NO, 43 ALD + O(“P) 44 ALD + Nb:,’ 45 ALD + HO;! 46 C203 + C,O, 47 C,O, + HO, 48 MGLY + ht 49 MGLY + OH 50 OH 51 PAR + OH

52 q”P) + 0L:E

53 OH + OLE

54 0, + OLE

55 NOB + OL6

56 0(3P) + ETH 57 OH + ETH 58 0s + ETH 59 TOL + OH

60 PHEN + NOs - > PHO + HN03 61 PHO + NO:, - > ONIT

- >NO+iO, - >NO+OH - > HNOz - > NOz - ’ HzOz - > 2.0H - > HO2 - >OH+HOr+CO - >HO,+CO+HNO, - >C,O,+OH - > C203 + HNO, - > XOr 0 + 2.HCHO + HO2 - > 2.X0, + 2.HCHO + 2.H0,

1 c:O, + HO2 + CO - > x02 + c203

- > XOz + HCHO + HO2 - > 0.49.X0, + 0.067.XOrN + 0.93.H0,

+ 0.45 .ALD + 0.75.RXPAR - > 0.95.ALD + 0.35.HOr + 0.20.X03 +

0.15CO + O.OS.HCHO + 0.05C303 + 0.35.RXPAR

- >HCHO+ALD+XOz+H02+ RXPAR

- > O.S.ALD + 0.66.HCHO + 0.212CO + 0.28.HOa + 0.144.XOs + 0.08.OH + RXPAR

- > 0.91.X02 + 0.91.H02 + 0.09.X02N + ONIT

- > HCH?O + XOs + CO + 2.HOs - > XOz + 2*HCHO + HO2 - > HCHO + 0.37CO + 0.13.H02 - > HO2 + 0.64.X02 + 1.13.HCHO +

0.56.MGLY + 0.36.PHEN + 0.36.PAR + 1.13CO

0.5833 x lo-’ = Rl 0.4433 x 10-j 0.2050 0.1434 x lo+ 0.1367 x 1O-4 0.2500 0.2167 x lo-’ 0.4000 0.2750 0.1500 0.3670 x lo-” 0.2ooo 0.2717 O.StKlO x lo+’ 0.5833 x 10-s 0.042.Rl 0.2067 x 10 - 1 30.6.Rl 0.7900 x 1o-6 0.9800 x 10-s 0.2967 x 10-r 0.5200 x 10-r 0.4750 x 1o-3 0.2533 x 1O-8 0.6667 x lo-’ 0.2300 0.3867 x 10-r 0.5200 x 10-l 0.1667 x lo-* 0.5ooo x 1o-4 0.4683 0.4ooox lo-” 0.2500 x lo-’ 0.18.Rl 0.1628 0.1628 0.1236 0.4141 x 1o-5 0.5015 x 10-r 0.3950 x 1o-2 0.1550 x 10-4 0.3950 x 10-r 0.6170 x 1O-4 0.8330 x 1O-4 0.6167 x 10-i 0.1600 0.02.Rl 0.4333 0.3500

0.1917 x 10-r

0.9867 x 10-r

0.7ooo

0.3ooo x 1o-6

0.1900 x 10-s 0.1800 x 10-l 0.2otm 0.4500 x lo-’

0.1625 0.2333 0.3333


Table A.2. (Continued)

Reaction Reaction rate’

62 XYL + OH

63 RXPAR + PAR 64 XOzN + NO 65 XOt + HO2 66 x0* + c203 67 HOz + NOz 68 HN04 69 HN04 + OH

- > HO, + 0.72.XOr + 0.67CO + 1.33.MGLY + 0.28.PHEN + 0.67.HCHO + 0.56.PAR

-> - > ONIT

I = XOr + HO* + HCHO - > HNO., - >HOz+NO, - > NOz

0.6000 0.2000 x lo+’ 0.1667 x 10-r 0.8333 x 10-i 0.4000 x 10-i 0.3926 x 10-i 0.8536 x 10-i 0.123364

’ Reaction rates for first-order reactions are in units of s- ‘. Reaction rates for second-order reactions are in units of s-r ppbv- l. Values shown in the table are for temperature of 298 K, solar zenith angle of 60” and water vapour contents of 1.5%. Ammonia/ammonium chemistry is described in Section 2.

Table A.3. In-lcoud and below-cloud scavenging coefficients” and dry deposition velocities for land and sea areasb

Species

Scavenging coefficient I (s- ‘)

In-cloud Below-cloud

Dry deposition velocity V,, (cm s- ‘)

Sea Land”

NO NO2 03

OH

ZIO ALD C*G3 PAN

$S)

NOZ, N205

HN03 co HN02 H202

HN04 MGLY PAR OLE ETH TOL PHEN PHO ONIT XYL ISOP Particles

1.9 x lo-” 1.4 x lo-‘0 1.4 x lo-‘0 1.4 x 1o-3 1.4 x 1o-3 2.6 x 1O-4 9.3 x 10-s 1.3 x lo-’

;.:; ;;I’ 3

1.4 x 10-a 1.5 x 1o-5

;.;; it&” 3

1.4 x 10-j

2; ;;‘” 5 9.9 x 1o-4 1.4x 1o-3 6.2x lo-” 6.2x 10-l’ 6.2x lo-” 3.1 x lo-” 2;; W:’

6:2x lo-’ 1.2 x 10-s 1.3 x 1o-g

0.0 0.0 0.0 9.0x 1o-5 8.0x lo-’ 0.0 0.0 0.0 0.0 9.5 x 1o-5 9.0 x 10-S 9.5 x 10-e 6.0 x 1O-5 4.0x 10-S 6.2 x 1O-5 0.0 0.0 8.0x 1O-5

zxlo-5 0:o 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 x 10-e

3.5 x 10-s 2.2 x 1o-4 4.5 x lo-* 7.8 x 10-l 7.2 x 10-l 5.9 x 10-l 1.6 x 10-l 1.1 x 10-l 1.0 x 10-i 7.6 x lo- 1 7.8 x lo- 1 6.6 x 10-l 6.6 x lo- 1 6.1 x 10-l 6.4 x lo- 1 1.7 x 10-S 6.9 x 10-l 7.2x 10-l 6.4x 10-l 1.4 x 1o-3 1.4 x 10-d 1.4 x 1o-4 6.9 x 1O-5 9.3 x 10-e 1.4 x 10-Z 1.4 x 10-z 2.7 x 1O-2 2.9 x 1O-3 1.4 x 1o-4 0.2 x 10-l

1.0x 10-l 6.1 x 10-‘/2.7x 10-l 6.1 x 10-‘/2.7x 10-l 6.5 x 10-l 6.5 x 10-l 3.8 x 10-l 1.9x 10-l 1.9x 10-l 1.9 x 10-i 2.2 6.5 lJ6.1 x 10-l 6.5 6.5 6.5 5x 10-2

E” lo-’ 6.5 5.0 x lo-* 5.0 x 10-r 5.0 x 10-r 5.0 x lo-* 5.0x lo-* 5.0 x 10-r 5.0 x 10-Z 1.2 x 10-i 5.0 x 10-Z 5.0 x 1o-2 1.2x 10-l

‘Calculated for precipitation intensity of 1 mm h- ’ and mixing height of 1000 m. r’Calculated for a wind speed of 5 ms-‘. ‘When more than one value is given, the first refers to daytime and the second to night-time conditions.

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Development and testing of a new variable scale air pollution model—ACDEP