Air-surface Exchange of Persistent Organic Pollutants in ......This thesis examines the air-soil and air-water gas exchange of persistent organic pollutants (POPs) with emphasis on

Air-surface Exchange of Persistent Organic Pollutants in North America

by

Fiona Wong

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Graduate Department of Chemistry University of Toronto

© Copyright by Fiona Wong 2010

ii

Fiona Wong

Doctor of Philosophy Department of Chemistry

University of Toronto 2010

Abstract

This thesis examines the air-soil and air-water gas exchange of persistent organic pollutants

(POPs) with emphasis on organochlorine pesticides (OCPs). The current status of net exchange,

factors which influence the exchange process, and different approaches used to estimate the

surface exchange were explored. The net exchange of chemicals was evaluated using the

fugacity approach, with the aid of chemical tracers (congener profiles of complex mixtures and

enantiomer proportions of chiral chemicals) to infer current use vs. legacy sources to the

atmosphere. DDT in southern Mexico was undergoing net deposition from air to soil.

Occurrence of fresher DDT residues in the south was indicated by a higher proportion of p,p’-

DDT relative to p,p’-DDE and racemic o,p’-DDT in air and soils. Congener profiles of

toxaphene suggested soil emissions as the source to air. The influence of chemical aging on soil-

air exchange and bioaccessibility was studied in a high organic soil. The use of nonexhaustive

extraction with hydroxypropyl-β-cyclodextrin (HPCD) to predict bioaccessibility was optimized

for OCPs and polychlorinated biphenyls (PCBs). Reduced volatility of spiked chemicals

correlated with reduced HPCD extractability for soil that had been aged under indoor and

outdoor conditions for 730 d and infers volatility could be used as a surrogate for

bioaccessibility. Measured soil-air partition coefficients (KSA) were lower than those predicted

from the Karickhoff relationship, which considers octanol as a surrogate for soil organic matter.

The role of soil moisture, organic carbon, temperature, depth of soil surface horizon and

dissolved organic carbon in the fate of organic contaminants in soil were assessed using chemical

iii

partitioning space maps. These maps allow instant visual prediction of the phase distribution and

transport process of a chemical among the three major phases in soil; i.e., air, water and solid.

Net volatilization of α-hexachlorocyclohexane from water to air was found in the southern

Beaufort Sea using fugacity calculations and flux measurements. The influence of ice cover on

volatilization was indicated by a winter-summer shift from racemic to nonracemic α-HCH in

boundary layer air.

iv

Acknowledgments

My supervisors: Terry Bidleman and Frank Wania, for their guidance and support over the years

that I have worked with them. I am grateful to Terry for giving me opportunities to participate in

the Mexico and ArcticNet studies. I have learned a lot from him and thank you for his patience.

Supervising committee members: Jon Abbatt, Myrna Simpson for their advice throughout my

thesis.

Liisa Jantunen for her guidance, especially during the ArcticNet study.

Henry Alegria and all the Mexican collaborators who helped with the passive air sampling and

soil collection.

Crew members and fellow scientists from the Canadian Coast Guard Ship - Amundsen.

Members of the Hazardous Pollutants Lab at Environment Canada and the Wania lab group:

Perihan Kurt-Karakus, Tom Harner, Mahiba Shoeib, Sum Chi Lee, Yushan Su, Hang Xiao, Susie

Genualdi, Suba Singham, Hayley Hung, Jessica Karpowicz, Martina Kloblizkova, Anne

Motelay, Karla Pozo, Todd Gouin, Baby Gs, Virginia, Andrea, Binnur, Mavis, Frank, Oscar.

My parents, family and friends: BC, BW2, CL, LC, DT, DW3.

v

Table of Contents Acknowledgments iv

Table of Contents v

List of Tables ix

List of Figures x

List of Appendices xiv

1 INTRODUCTION 1

1.1 BACKGROUND INFORMATION AND MOTIVATION 2 1.1.1 Air-soil exchange of persistent organic pollutants (POPs) 2 1.1.2 Air-water gas exchange of persistent organic pollutants (POPs) 12

1.2 REFERENCES 14

2 PASSIVE AIR SAMPLING OF ORGANOCHLORINE PESTICIDES IN MEXICO 25

2.1 ABSTRACT 27

2.2 INTRODUCTION 28

2.3 METHODS 29 2.3.1 Air sampling 29 2.3.2 Extraction and analysis 31 2.3.3 Quality control 32

2.4 RESULTS AND DISCUSSION 32 2.4.1 Air concentrations of OCPs 32 2.4.2 Seasonal variation of OCPs 43

2.5 ACKNOWLEDGMENTS 44

2.6 REFERENCES 44

3 ORGANOCHLORINE PESTICIDES IN SOILS OF MEXICO AND THE POTENTIAL FOR SOIL-AIR EXCHANGE 50

3.1 ABSTRACT 51

3.2 INTRODUCTION 51

3.3 METHODS 52 3.3.1 Sample collection and analysis 52 3.3.2 Quality control 54

3.4 RESULTS AND DISCUSSION 55 3.4.1 Organochlorine pesticide concentrations 55 3.4.2 Soil-air exchange 65

vi

3.5 CONCLUSION 68

3.6 ACKNOWLEDGEMENTS 68

3.7 REFERENCES 69

4 HYDROXYPROPYL-β-CYCLODEXTRIN AS NON-EXHAUSTIVE EXTRACTANT FOR ORGANOCHLORINE PESTICIDES AND POLYCHLORINATED BIPHENYLS IN MUCK SOIL 72

4.1 ABSTRACT 73

4.2 INTRODUCTION 73

4.3 MATERIALS AND METHODS 74 4.3.1 Chemicals 74 4.3.2 Muck soil 75 4.3.3 Soil preparation 75 4.3.4 Optimization of the HPCD extraction procedure 75 4.3.5 Exhaustive extraction 77 4.3.6 Soil aging experiment 78 4.3.7 Bacterial activity 78 4.3.8 Instrumental analysis 78 4.3.9 Quality control 79

4.4 RESULTS AND DISCUSSION 80 4.4.1 Optimization of HPCD extraction method 80 4.4.2 Concentrations of chemicals in the soil over 550 d of Aging 83 4.4.3 Effect of aging on HPCD extractability 85 4.4.5 Effect of physical-chemical properties on HPCD extractability 88 4.4.6 Comparison of HPCD extractability with other studies 91

4.5 CONCLUSION 91


4.7 REFERENCES 92

5 AGING OF ORGANOCHLORINE PESTICIDES AND POLYCHLORINATED BIPHENYLS IN MUCK SOIL: VOLATILIZATION, BIOACCESSIBILITY AND DEGRADATION 95

5.1 ABSTRACT 96

5.2 INTRODUCTION 96

5.3 EXPERIMENTAL METHOD 9898 5.3.1 Soil preparation 98 5.3.2 Aging 99 5.3.3 Volatilization measurements 99 5.3.4 Bioaccessibility and bacterial activity 100 5.3.5 Quantitative analysis 101 5.3.6 Quality control 101

vii

5.4 RESULTS 102 5.4.1 Effect of aging on volatility 102 5.4.2 Effect of aging on bioaccessibility and correlation with KSA 105 5.4.3 Role of microbial activity and sterilization of the soil 107 5.4.4 Comparison with the Karickhoff model 107 5.4.5 Degradation of chemicals in soils 109 5.4.6 Enantioselective degradation of 13C6-α-HCH 110 5.4.7 Enantioselective volatilization 113


5.6 REFERENCES 114

6 VISUALISING THE EQUILIBRIUM DISTRIBUTION AND MOBILITY OF ORGANIC CONTAMINANTS IN SOIL USING THE CHEMICAL PARTITIONING SPACE 119

6.1 ABSTRACT 120

6.2 INTRODUCTION 120

6.3 METHODS 122 6.3.1 Calculating organic chemical phase distribution in soil at equilibrium 122 6.3.2 Calculating the relative importance of chemical transport processes in soil 123 6.3.3 Placing chemicals onto the chemical space maps 124

6.4 RESULTS 126 6.4.1 Equilibrium phase partitioning and mobility of organic chemicals in a typical temperate

soil 126 6.4.2 Placing the chemicals onto the space maps 129 6.4.3 Comparing different soils: role of the depth of the surface soil horizon, the amount and

type of soil organic matter, dissolved organic carbon 130 6.4.4 Rapid changes in phase distribution and mobility: the role of soil moisture 135 6.4.5 Discussion 137


6.6 REFERENCES 138

7 AIR-WATER EXCHANGE OF ANTHROPOGENIC AND NATURAL ORGANOHALOGENS ON INTERNATIONAL POLAR YEAR (IPY) EXPEDITIONS IN THE CANADIAN ARCTIC 142

7.1 ABSTRACT 143

7.2 INTRODUCTION 143

7.3 EXPERIMENTAL METHOD 145 7.3.1 Air and water sampling, extraction and analysis 145 7.3.2 Micrometeorological measurements 147

7.4 RESULTS AND DISCUSSION 148 7.4.1 Air and water concentrations 148

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7.4.2 Air-water gas exchange 151 7.4.3 Enantiomers as tracers of α-HCH volatilization 153 7.4.4 Fluxes from micrometeorological measurements vs. Whitman two-film model 157


7.6 REFERENCES 163

8 CONCLUSIONS AND RECOMMENDATIONS 163

8.1 CONCLUSIONS 163

8.2 RECOMMENDATIONS 167

8.3 REFERENCES 170

ix

List of Tables Table 2.1 Enantiomer fraction of trans-chlordane (TC) and cis-chlordane (CC). TP, MT, VC

and TB = Data obtained from the 2002-2004 sampling campaign. Nd = not detected. N = number of samples. 42

Table 3.1 Summary of OCPs in rural, urban and agricultural soils of Mexico (ng g-1, dry weight) 57 Table 4.1 Soil concentrations of native and spiked OCPs and PCBs after 2 d of spiking 76 Table 5.1 Half-lives of 13C6-α-HCH, endosulfans and PCB 8, 18, 28, 32 for indoor and outdoor

soils. ns = No significant degradation or degradation does not follow first order kinetics. 110

Table 6.1 Equations and parameters used to derive mass fractions of chemicals in soil. 123 Table 6.2 Equations and parameters used to derive relative importance of chemical transport

processes in soil. 125 Table 7.1 Summary of air (pg m-3) and water (pg L-1) concentrations for α-HCH, γ-HCH, HCB,

DBA and TBA. 150

x

List of Figures Figure 1.1 Phase distribution maps for a A) dry soil (water content, WC = 5%) and B)

waterlogged soil (WC = 49%). The organic carbon content of both soils is 5%.

Chemical X sorbs to the organic solid regardless of the moisture content.

Chemical Y prefers in the gas phase of a dry soil but it is most likely found in

the water phase of a waterlogged soil. 10

Figure 2.1 Map of air sampling sites in Mexico during 2005-2006 (this study) and 2002-

2004 (25). BAJ = Baja California, CHI = Chihuahua, CEL = Celestun, COL =

Colima, COR = Cordoba, CUE = Cuernavaca, MAZ = Mazatlan, MEX =

Mexico City, MON = Monterrey, SLP = San Luis Potosi, TUX = Tuxpan, TB

=Tabasco, MT = Chiapas mountain, TP = Tapachula, VC = Veracruz. 30

Figure 2.2 Box-whisker plot of organochlorine pesticides (pg m-3) in this study and the

2002-2004 sampling campaign (25). The top end of the box represents the 75th

percentile of the data, and the bottom box represented 25th percentile. The

horizontal line between the boxes is the median, the circle is the geometric

mean, and the asterisk is the arithmetic mean. The whiskers on the top and

bottom of the boxes indicate the maximum and minimum (1/2 LOD in some

cases) values of the data set. ΣHCH = α-HCH + γ-HCH. ΣCHL = TC+CC+TN.

ΣENDO = ENDO I + ENDO II + ESUL. ΣDDT = p,p'-DDT + o,p'-DDT +

p,p'-DDE + o,p'-DDE + p,p'-DDD + o,p'-DDD. ΣTOX = quantified as technical

toxaphene. 33

Figure 2.3 A) Spatial distribution of ΣDDT in air (pg m-3) and DDT used for public health

purposes between 1989-1999 (44). B) FDDTe and FDDTo vs. DDT used. C) FDDTe

and FDDTo vs. latitude. FDDTe is significantly positively correlated with DDT

used for malaria control (r2 = 0.45, p = 0.01) and negatively with latitude (r2 =

0.57, p = 0.001). FDDTo is not significantly correlated with either usage or

latitude. Tech Vap = technical vapour. These figures included data from

Alegria et al. (25). 35

xi

Figure 2.4 Correlation of DEVrac of o,p-DDT with DDT used and latitude. 38

Figure 2.5 Proportions of toxaphene congeners in air and technical standard. Amounts

normalized to Parlar 40+41. This includes data from Wong et al. (7). 40

Figure 3.1 Box-whisker plot of OCPs in soils of Mexico (ng g-1, dry weight). The top end

of the box represents the 75th percentile of the data, and the bottom box

represented 25th percentile. The horizontal line between the boxes is the

median, the square is the geometric mean, and the asterisk is the arithmetic

mean. The whiskers on the top and bottom of the boxes indicate 10th and 90th

percentile. Data fell outside this range are plotted as circle with station

numbers. ΣHCH = α-HCH + γ-HCH. ΣCHL = TC+CC+TN. ΣENDO =

ENDOI + ENDOII + ESUL. ΣDDT = p,p'-DDT + o,p'-DDT + p,p'-DDE +

o,p'-DDE + p,p'-DDD + o,p'-DDD. ΣTOX = quantified as technical toxaphene. 56

Figure 3.2 Plots of FDDTe vs. A) latitude; B) DDT used; FDDTo vs.C) latitude; D) DDT used. 60

Figure 3.3 Proportion of toxaphene congeners in soils, air and technical toxaphene standards

normalized to the amount of Parlar 40+41. Regression statistics for average log

Q vs. log liquid vapour pressure (PL/Pa) for toxaphenes. Q = CSOIL/CAIR. CAIR

was obtained from Wong et al. (2009a). 62

Figure 3.4 Fugacity fractions (ff) of OCPs in Mexico. ff = fS/(fS+fA), where fS = fugacity of

soil; fA = fugacity of air. ff = 0.5 indicates soil-air equilibrium. ff > 0.5 indicates

net volatilization from soil to air. ff< 0.5 indicates net deposition from air to soils.

The top end of the box represents the 75th percentile of the data, and the bottom

box represented 25th percentile. The horizontal line between the boxes is the

median, the square is the geometric mean, and the asterisk is the arithmetic

mean. The whiskers on the top and bottom of the boxes indicate 10th and 90th

percentile. Data fell outside this range are plotted as circle. Dashed line

indicates the limits over which ff may not be significantly different from

equilibrium (Daly et al., 2007a). 66

Figure 4.1 Effect of HPCD concentration on the extractability of selected native and

spiked OCPs (A to C) and spiked PCBs (D to F) 81

Figure 4.2 Degradation of 13C6-α-HCH, PCB 8, PCB 28, Endo I, II and ESUL in soils

over 550 d of aging. 84

xii

Figure 4.3 Effect of aging on the HPCD extractability of spiked and native OCPs, and

spiked PCBs. 86

Figure 4.4 Relative HPCD Extractability of spiked to native OCPs ratio over 550 days of

aging. 87

Figure 4.5 Log KCD-Soil of spiked OCPs and PCBs vs. Log KOW at Day 2, 90, 255 and 550

of aging. Regression is performed on PCBs only. 90

Figure 5.1 Changes in log KSA over the aging time for selected OCPs and PCBs in Indoor

(IN), Outdoor (OUT) and Sterile (ST) soils. 104

Figure 5.2 Changes in HPCD extractability % and KSA over the aging time for spiked

OCPs and PCBs in the Indoor soils. 106

Figure 5.3 Plateau log KSA of native OCPs, spiked OCPs and PCBs vs. log KOA for Indoor,

Outdoor and Sterile soils. Plateau log KSA of Indoor soils equals the mean log

KSA from Day 195 to 550; Outdoor - from Day 390 to 730; Sterile - from Day

210 to 550. Solid line = log KSA predicted by the modified Karickhoff model. 108

Figure 5.4 Changes in the enantiomer fractions (EF) of 13C6-α-HCH in the Indoor,

Outdoor and Sterile soils over time (A), and ln CSOIL of the (+) and (−)

enantiomer in the Indoor (B) and Outdoor Soils (C). Day 60 to 230 and Day

390 to 620 are the two winter periods for the Outdoor soils. 112

Figure 5.5 Enantiomer fractions (EF) of 13C6-α-HCH in air, HPCD and soils that have

been aged under Indoor, Outdoor and Sterile conditions. 113

Figure 6.1 Phase distribution (A) and mobility (B) of selected organic chemicals in a

typical temperate soil (OC 5%, WC 25%). Each chemical corresponds to a

short diagonal line, which indicates the temperature dependence of its

partitioning properties. Herbicides are shown in orange, volatile chemicals are

in white. PCBs are in black and PPCP as dotted yellow lines. 128

Figure 6.2 Phase distribution (top) and transport process (bottom) of selected chemicals at

15 °C in soils that differ in the amount of organic matter (different panels: low

organic carbon left, typical middle, peat soil right) and in the type of humic

substance (five different markers for one chemical, black marker indicates the

Leonardite humic acid). 132

xiii

Figure 6.3 The influence of surface soil horizon depth (he) on the mobility of chemicals in

a typical temperate soil. The numbering of chemicals is the same as in Figure

6.1 133

Figure 6.4 Influence of 25 mg/L of dissolved organic carbon (DOC) on the mobility of

organic chemicals in a typical temperate soil. 134

Figure 6.5 Phase distribution of selected organic chemicals in a soil at wilting point (WC

= 5%), field capacity (WC = 25%) and waterlogged conditions (WC = 49%)

soils. Each chemical corresponds to a short diagonal line, which indicates the

temperature dependence of its partitioning properties. Herbicides are shown in

orange, volatile chemicals are in white, PCBs are in black and PPCP as dotted

yellow lines. 136

Figure 7.1 Map of the cruise track during Legs 1a and 1b, and sampling area in the

southern Beaufort Sea during Legs 5 – 9 (see insert). LV Air15 and LV Air1 =

low volume air sampling taken at 15 m and 1 m above water; HV Air = high

volume air sampling taken at deck level. Star denotes LV Air and Water

sampling events during Legs 1b, 8 and 9. 146

Figure 7.2 Air-water gas exchange of α-HCH, γ-HCH, HCB, DBA and TBA. The dash

lines are the equilibrium window for α-HCH, γ-HCH, DBA and TBA (0.40-

0.64). For HCB, the equilibrium window was 0.37-0.73. 152

Figure 7.3 Concentration and EF of α-HCH in air of the southern Beaufort Sea from Legs

5 to 9, January to July 2008. 154

Figure 7.4 Flux of α-HCH in the southern Beaufort Sea during Leg 9, sampling events #

10 to 17 (Table A7.6). FM = flux determined from meteorological approach,

FTF = flux determined from the two-film model. Vertical lines indicate

propagated standard deviations. Positive and negative fluxes indicate

volatilization and deposition, respectively. FM was not estimated for event 13

because of non-neutral atmospheric stability. 156

xiv

List of Appendices 1 INTRODUCTION

A1.1 Alegria, H. A.; Wong, F.; Jantunen, L. M.; Bidleman, T. F.; Salvador-Figueroa, M.; Gold-Bouchot, G.; Moreno Ceja, V.; Waliszewski, S. M.; Infanzon, R. Organochlorine pesticides and PCBs in air of southern Mexico (2002-2004). Atmos. Environ. 2008, 42, 8810-8818.

172

A1.2 Wong, F.; Alegria, H. A.; Jantunen, L. M.; Bidleman, T. F.; Salvador Figueroa, M.; Gold Bouchot G.; Waliszewski, S.; Moreno Ceja, V.; Infanzon, R. Organochlorine pesticides in soils and air of southern Mexico: Chemical profiles and potential for soil emissions. Atmos. Environ. 2008, 42, 7737-7745.

181

2 PASSIVE AIR SAMPLING OF ORGANOCHLORINE PESTICIDES IN MEXICO

A2.1 Determining the sampling rates for passive samplers. 191

Table A2.1 Description of sampling sites and schedule. 193

Table A2.2 Sampling rates for each site at each sampling period. 195

Table A2.3 Organochlorine pesticides in Mexico air – annual arithmetic mean (pg m-

3). 196

Table A2.4 Enantiomer fraction (EF) and its deviation from racemic (DEVrac) of o,p-DDT. TP, MT, VC and TB = Data obtained from the 2002-2004 sampling campaign. Nd = not detected. N = number of samples. DEVrac = Deviation from racemic: absolute value of (EF – 0.5)

197

Figures A2.1.1- 2.1.10

Three-day back trajectory airshed maps and seasonality of OCP concentrations at each site.

198

3 ORGANOCHLORINE PESTICIDES IN SOILS OF MEXICO AND THE POTENTIAL FOR SOIL-AIR EXCHANGE

Figure A3.1 Map of the sampling area in Mexico. Green = urban sites; yellow = agricultural sites; red = rural sites. Data for Sites 19–29 were published in Wong et al., 2008.

210

Figure A3.2 Plots of deviation from racemic (DEVrac) of o,p’-DDT vs. (A) DDT used and (B) latitude.

211

Figure A3.3 Deviation from Racemic values (DEVRac) of A) o’p-DDT, B) trans-chlordane (TC) and C) cis-chlordane (CC) in soils and air in Mexico.

212

xv

Table A3.1 Description of soil sampling sites. 213

Table A3.2 OCP concentration in Mexico soils (ng g -1, dry weight). 215

Table A3.3 Enantiomer fraction of o,p’-DDT, trans-chlordane (TC) and cis-chlordane (CC) in Mexico soils.

216

Table A3.4 Fugacity fractions (ff) of OCPs in Mexico. ff = fS/(fS+fA), where fS = fugacity of soil; fA = fugacity of air.

217

4 HYDROXYPROPYL-β-CYCLODEXTRIN AS NON-EXHAUSTIVE EXTRACTANT FOR ORGANOCHLORINE PESTICIDES AND POLYCHLORINATED BIPHENYLS IN MUCK SOIL

Figure A4.1 HPCD extractability of OCPs over a range of soil concentration. Soil concentration (ng g-1) for Level 1, 2 and 3 are shown in the table below.

219

Figure A4.2 HPCD extractability (%) of native OCPs from soils for 20 vs. 40 hrs. 220

Figure A4.3 Sequential HPCD extraction of trans-chlordane (TC) and p,p’-DDT and from soil.

221

Figure A4.4 HPCD extractability vs. molecular volume of PCBs. Molecular volume = LeBas molar volume (nm3 mol-1) divided by Avogadro’s number (molecules mol-1).

222

Table A4.1 Sequential Soxhlet extraction of soils using DCM (F1), acetone/hexane (F2) and methanol (F3). Data are normalized to F1. Soils have been aged for 2, 90, 135, 195, 255, 390 and 550 d.

223

Table A4.2 Optimization of OCPs and PCBs using increasing strength of HPCD solution.

224

Table A4.3 Sorption capacities (SC), extraction capacities (EC) and maximum extraction fraction (MEF), and HPCD extractability measured at day 2 of this study. SC = QsoilfOCKOC. EC = QCDKCD. MEF = EC/(EC+SC). Qsoil = mass of soil. QCD = mass of HPCD.

225

Table A4.4 Soil concentration of OCPs and PCBs over 550 days of aging (ng g-1). 226

Table A4.5 HPCD extraction of OCPs and PCBs from sand. 227

Table A4.6 Fitted parameters for eq [4.1] and experimental data at day 2. Y0 = the percent of chemical remain extractable overtime, Y0 + A = the initial fraction that is available, k = rate constant.

228

Table A4.7 HPCD extractability of OCPs and PCBs over 550 days of aging.

229

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5 AGING OF ORGANOCHLORINE PESTICIDES AND POLYCHLORINATED BIPHENYLS IN MUCK SOIL: VOLATILIZATION, BIOACCESSIBILITY AND DEGRADATION

Figure A5.1 Air concentration (ng m-3) as a function of flow rate (L min-1) for muck soil.

231

Figure A5.2 Plateau log KSA of spiked OCPs and selected PCBs. Plateau Log KSA for Indoor (IN) ranged from Day 195 to 550, Outdoor (OUT) Day 230 to 730 and Sterile (ST) Day 210 to 550.

232

Figure A5.3 HPCD extractability of selected OCPs and PCBs for Indoor (IN), Outdoor (OUT) and Sterile (ST) soils.

233

Figure A5.4 Ln CSOIL of 13C6-α-HCH, ENDO I, PCB 8 and 28 over the aging time for Indoor, Outdoor and Sterile soils. Day 60 to 230, 390 to 620 are the winter periods for the Outdoor soils.

234

Table A5.1 Mean blanks and limits of detection (LOD) of PCBs in air. 235

Table A5.2 Soil bacteria colony forming units (CFU) for the Indoor, Outdoor and Sterile soils.

236

Table A5.3 Log KSA for the Indoor soils. Plateau is mean log KSA from 195 to 550d. 237

Table A5.4 Log KSA for the Outdoor soils. Plateau is mean log KSA from 390 to 730 d.

238

Table A5.5 Log KSA for the Sterile soils. Plateau is mean log KSA from 210 to 550 d. 239

Table A5.6 Relative KSA of OCPs (Spiked/Native). 240

Table A5.7 Indoor plateau log KSA for native OCPs compared to literature values obtained from Meijer et al., (2003). Soil from the same farm was used in the Meijer study.

241

Table A5.8 HPCD extractability% for the Indoor soils. 242

Table A5.9 HPCD extractability% for the Outdoor soils. 243

Table A5.10 HPCD extractability% for the Sterile soils.

244

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7 AIR-WATER EXCHANGE OF ANTHROPOGENIC AND NATURAL ORGANOHALOGENS ON INTERNATIONAL POLAR YEAR (IPY) EXPEDITIONS IN THE CANADIAN ARCTIC

A7.1 Sample collection, extraction, analysis and quality control 246

A7.2 Air-water gas exchange 250

A7.3 Micrometeorological measurements 252 A7.4 Estimation of fluxes using micrometeorology 253

A7.5 Whitman two-film model 257

A7.6 Error analysis for the micrometeorological flux (FM) and two-film flux (FTF)

258

Table A7.1 Details of low volume air (LV Air) sampling at ~1 m and 15 m above surface during Legs 1b, 8 and 9.

259

Table A7.2 Concentrations (pg m-3) of α-HCH, γ-HCH, HCB, DBA and TBA in low volume air samples (LV Air) collected on Legs 1b, 8 and 9

260

Table A7.3 Concentrations of α-HCH, γ-HCH, (pg m-3) in high volume air samples (HV Air) collected on Legs 1a, 1b, 8 and 9.

261

Table A7.4 Concentrations of α-HCH, γ-HCH, DBA and TBA (pg L-1) in low volume water samples (LV Water) collected on Legs 1a, 1b, 8 and 9

262

Table A7.5 Concentrations of α-HCH, γ-HCH and HCB (pg L-1) in high volume water samples (HV Water) collected on Legs 1a, 1b, 8 and 9

263

Table A7.6 Flux of α-HCH at the southern Beaufort Sea determined using micrometeorological method (FM) and the two-film model (FTF) for Leg 9 and one event on Leg 8. u* = friction velocity. U10 = wind speed at 10 m height. Ri = Richardson number. φ = stability parameter. Positive and negative flux indicate volatilization and deposition respectively.

264

Figure A7.1 Ratio of LV Air collected at 1 m to 15 m (C1/C15) above the surface for events 1-17 (Table A7.2). Legs 1b and 9 samples were collected over water. Leg 8 samples were collected over ice, except sampling event #8.

265

1

1 INTRODUCTION Persistent organic pollutants (POPs) are toxic, stable, bioccumulative and subject to long range

transport. The production and use of POPs has been banned or severely restricted under several

international protocols, such as the United Nations Environmental Program (UNEP) Stockholm

Convention, North America Regional Action Plans (NARAPs) of the North America

Commission for Environmental Cooperation (NACEC) and UN Economic Commission for

Europe Convention on Long-Range Trans-boundary Air Pollution (UN-ECE-CLRTAP). Due to

the great persistence of POPs, they can remain in the environment for decades after emission.

Environmental media such as soil, water, vegetation, ice or snow are sinks for POPs during

periods of primary emission and as the release of POPs declines, these media become a source to

the atmosphere through re-volatilization. The air-surface exchange of POPs has crucial

implications for global distribution of POPs (Bidleman, 1999; Ockenden et al., 2003) and such

process facilitates the “grasshopper effect”, where chemicals can be transported from temperate

to polar regions through a series of deposition and volatilization events (Gouin and Wania, 2007;

Semeena and Lammel, 2005; von Waldow et al., 2010; Wania and Mackay, 1996). They also

regulate the atmospheric levels of POPs, particularly those that are banned.

This primary objective of this thesis is to investigate the air-soil exchange of POPs with

emphasis on the organochlorine pesticides (OCPs). Secondarily, air-water gas exchange of POPs

is also examined. Factors which influence the air-surface exchange processes, the current net

exchange status in the environment, and the different approaches that are used to estimate the net

surface exchange were explored by means of laboratory, field and modelling studies. Chapter 1

provides the background information, motivation and structure of the thesis. Chapters 2 to 7

are papers that have been published or will be submitted for publication in peer-reviewed

journals. Chapter 8 provides a summary of the major findings of the thesis, along with future

recommendations.

2

1.1 BACKGROUND INFORMATION AND MOTIVATION

1.1.1 Air-soil exchange of persistent organic pollutants (POPs)

Over the years, there have been numerous studies carried out to measure the air-soil exchange of

POPs. Most studies are focus on the temperate regions of U.S. (Bidleman et al., 2004a, b;

Harner et al., 2001; Spencer et al., 1996), Canada (Bidleman et al., 2006; Kurt-Karakus et al.,

2006; Meijer et al., 2003a), Europe (Backe et al., 2004; Cousins and Jones, 1998; Růžičková et

al., 2008) and China (Li et al., 2009; Tao et al., 2008), where organochlorine pesticides (OCPs)

have been mostly banned for decades. Only one study was performed in the tropical region, e.g.

Costa Rica (Daly et al., 2007a). The air-soil exchange of POPs in tropic countries, such as

Mexico, where the use of DDTs and other OCPs were only recently deregistered, is not known.

Tropics could be an important secondary source of POPs since dissipation rates of OCPs from

tropical soils are often faster than in temperate climates, due to the warmer temperature and thus

a higher volatility (Khan, 1994). Lalah et al. (2001) reported that DDT half-lives in tropical soils

of Kenya ranged from 65-250 d, compared to years to decades in California (Spencer et al.,

1996), the Netherlands (Martijn et al., 1993) and New Zealand (Boul et al., 1994). Half lives for

p,p'-DDE due to volatilization alone from tilled and untilled soil ranged from 11 to 80 y in

southern U.S. soils (Scholtz and Bidleman, 2007) and 200 y was estimated for the volatilization

half life of ΣDDT from a high organic matter soil in Ontario (Kurt-Karakus et al., 2006).

The fugacity approach developed by Mackay (2001) is commonly used to evaluate air-soil

exchange of chemicals. A chemical’s fugacity to escape from air (fA, Pa) and soil (fS, Pa) are

defined in eqs [1.1] and [1.2],

SOILOAOCASS KRTCf ρφ411.0/= Eq [1.1]

AAA RTCf = Eq [1.2]

3

where CS and CA are soil and air concentrations (mol m-3), R = 8.314 Pa m3 mol-1K-1 is the gas

constant, TA is air temperature (K), φOC is fraction of organic carbon, KOA is the octanol-air

partition coefficient and ρSOIL is soil density (kg L-1) . The factor 0.411 (Karickhoff, 1981) is

used to improve the correlation between the octanol-water, and organic carbon-water coefficient.

As noted by Kobližková et al. (2009), it has units of L kg-1.

Later, this empirical relationship was applied by Hippelein and McLachlan (1998) to relate KOA

and the dimensionless soil-air partition coefficient (KSA), defined as CS/CA:

SOILOAOCSA KK ρφ411.0= Eq [1.3]

Fugacities are expressed as fractions which defined as,

]/[ ASS fffff += Eq [1.4]

A ff = 0.50 indicates equilibrium and no net exchange between air and soil; ff >0.50 indicates net

volatilization and ff < 0.50 indicates net deposition. It is assumed here that the fugacity capacity

of soil is entirely due to the soil organic carbon fraction, which is valid for the high organic

carbon soil investigated in this thesis (Chapter 5).

KSA is an indicator for a chemical’s volatility and a key parameter used in soil emission models.

Over the years, there have been many studies carried out to directly measure KSA as well as to

study the effects of temperature, humidity and soil moisture on KSA (Cousins et al., 1998; He et

al., 2009a, b; Hippelein and McLachlan 1998, 2000; Kobližková et al., 2009; Meijer et al.,

2003ab; Niederer et al., 2006, 2007; Wolters et al., 2008). Many of them have found conflicting

results when comparing measured and predicted KSA. For example, Hippelein and McLachlan

(1998) found that the modified Karickhoff relationship under predicts the KSA of PCBs and

chlorobenzenes by a factor of 1.5. He et al. (2009a, b) reported that the modeled KSA was greater

than the measured KSA by a factor of 2.7 for PCBs, 3 for PAHs, 18 for trans-nonachlor and less

than an order of magnitude for other OCPs (α- and γ-HCH, trans- and cis-chlordane, o,p’-DDE).

A soil-air flux chamber study showed that fugacity calculations based on the modified

4

Karickhoff model underestimated the volatilization of PCBs and OCPs, particularly for the high

molecular weight chemicals and in high organic carbon soils (Kobližková et al., 2009).

Niederer et al. (2007) reported that the humic acid-water sorption coefficient (KHW) of organic

chemicals to ten humic and fulvic acids varied by more than one order of magnitude, depending

on the origin of the sorbent. Terrestrial humic acids showed higher sorptive capacities with

higher KHW. The large variability is likely due to the number of available sorption sites per mass

of sorbent rather than the types of intermolecular interactions between the chemical and the soil

organic matter. The authors suggested that a single partition coefficient (e.g. KOA) may not be

suitable to describe the sorption behaviour of a chemical in all soil types, and recommended use

of polyparameter linear free energy relationships (pp-LFERs), which take into account multiple

physical and chemical interactions.

Despite there is a wealth of literature in examining the process of air-soil exchange of POPs in

the environment, gaps remain in the knowledge about the current status of air-soil exchange (e.g.

tropical region) and the factors which influence the process.

• What is the air-soil exchange condition in tropical regions, particularly in areas where use

of legacy pesticides has just been banned?

• How does chemical aging in soil affect KSA?

• Is there a relationship between KSA and bioaccessibility of a chemical in soil?

• How well does the Karickhoff model predict KSA for a high organic matter soil?

• Can enantiomer proportions of chiral chemicals or congener profiles of complex mixtures

be used as tracers for chemical volatilization from soils?

• What is the role of soil moisture, temperature, organic carbon and other soil properties on

the phase distribution and mobility of organic chemicals in soils?

5

To address these questions, the following projects were undertaken in this thesis: a) Air-soil

exchange of OCPs in Mexico; b) Effect of aging on volatility and bioaccessibility of POPs; c)

Understanding the fate of organic chemicals in soil using chemical partitioning space maps;

Background information, motivation and specific objectives of each project are described below.

Air-soil exchange of organochlorine pesticides (OCPs) in Mexico

Mexico is known for its long history of OCP usage for malaria control and agriculture. During

1974 to 1991, 60,000 tons of OCPs were applied, and DDT accounted for ~10% of these

products (Lopez-Carrillo et al., 1996). Among the Latin America countries, Mexico had the

highest OCP consumption (Lopez-Carrillo et al., 1996). Usage of DDT, chlordane and lindane

has been eliminated or severely restricted under NARAPs. In 1990, DDT use was limited to

public sanitation campaigns and reduction by 80% was targeted by 2002 (NACEC, 1997a).

Mexico exceeded this target and applications for malaria control were stopped by 2000

(NACEC, 2003). Chlordane was controlled under a 1997 NARAP (NACEC, 1997b), and it has

been discontinued since 2003 (Moody, 2003). Mexico is phasing out lindane for agricultural and

veterinary use (NACEC, 2006). Use of toxaphene in the Mexico-Central America region was

stopped in the 1990s (Carvalho et al., 2003; Castillo et al., 1997). OCP levels in the blood of the

Mexican population are high; e.g. DDT (sum of p,p’-DDT and p,p’-DDE) in Mexicans was 3-18

times higher than the populations in Sweden or Greenland (Waliszewski et al., 2004a) and this

may be due to ongoing usage as well as exposure to old residues.

The objective of this study is to assess whether atmospheric OCPs in Mexico originate from

recent usage or soil re-emission. In order to do so, one must know the OCP levels in soil and air

of Mexico. However, there is very little information about the OCPs levels in the abiotic

environment of Mexico (UNEP, 2002). Most of the studies on OCPs in Mexico have been

focused on human blood or tissues (Herrera-Portugal et al., 2005; Lopez-Carrillo et al., 1996;

Rivero-Rodriguez et al., 1997; Waliszewski et al., 2001 and 2004a), sediment (Galindo Reyes et

al., 1999; Gutierrez-Galindo et al., 1998; Leyva-Cardoso et al., 2003; Norena-Barroso et al.,

2007; Robledo-Marenco et al., 2006), coastal waters (Galindo Reyes et al., 1999; Hernández-

Romero et al., 2004; Osuna-Flores et al., 2002), marine biota (Gold-Bouchot et al., 1993 and

6

2006), and food (Waliszewski 2003 and 2004b). The few studies in southern Mexico reported

that DDT and toxaphene concentrations in air were 1-2 orders of magnitude above levels in the

Laurentian Great Lakes and arctic regions (Alegria et al., 2006 and 2008; Shen et al., 2005).

Atmospheric levels of OCPs in southern Mexico were generally higher than those in central

Mexico (Bohlin et al. 2008), Costa Rica (Daly et al., 2007a, b) and Cuba (Pozo et al., 2009), and

comparable to those in Belize (Alegria et al., 2000). Herrera-Portugal et al. (2005) reported that

soils from a DDT-contaminated area in Chiapas contained μg g-1 concentrations of DDTs and the

mean blood DDT concentration of the children living in this area was ~ 7 times greater than in

those living in an area with low DDT exposure, where DDT levels in soils were ~ 20 times

lower.

Studies reported in Chapters 2 and 3 document the occurrence of OCPs in air and soil of Mexico

and assess the potential of Mexican soil to act as a source of OCPs to the atmosphere. This is an

extension to an earlier effort by Alegria et al. (2008) and Wong et al. (2008) who investigated

airborne OCPs and soil-air exchange at four sampling sites located in southern Mexico during

2002-2004. These papers are included in this thesis as appendices (A) for reference (A1.1,

A1.2). Collectively, these investigations were the first large-scale survey of OCPs across

Mexico, and provided baseline data to Mexican authorities for planning an air monitoring

program.

Chapter 2 presents findings from a survey of OCPs in the air of Mexico using passive samplers.

This chapter focuses on the spatial distribution of OCPs in air from the south to north of Mexico,

which is later used to investigate the air-soil exchange. The paper presents a description of the

passive air sampling technique, monthly and annual OCP concentrations over 15 monitoring

sites in Mexico, correlation with latitude and DDT usage pattern, and chemical profiles as source

indicators: DDT isomers and metabolites, toxaphene congeners, and the enantiomers of chiral

o,p'-DDT and chlordanes. Back air trajectory analysis was performed in an attempt to identify

the source regions of atmospheric OCPs.

7

Chapter 3 examines the air-soil exchange of OCPs in Mexico. Results of OCPs measured in

soils collected near the air sampling sites are presented. Coupled with the air data presented in

Chapter 2, the potential of soil to act as a net source or sink of OCPs to the atmosphere of

Mexico was investigated using the fugacity approach. In addition, the profiles of DDT and

toxaphene compounds, and o, p’-DDT enantiomers were compared between the air and soil to

seek evidence of soil emission.

Effect of aging on volatility and bioaccessibility of persistent organic pollutants (POPs) in

soils

It is perceived that chemicals residing in soils are always available for volatilization and their

ability to volatilize does not vary over time. After chemicals enter the soil, they can be relocated

within the soil matrix to areas where microorganisms and plants cannot access, thereby

decreasing their bioavailability (Alexander 2000; Barriuso et al., 2008; Gevao et al., 2005; Luthy

et al., 1997; Morrison et al., 2000). Analogous to bioavailability, it is hypothesized that the

volatility of a chemical from soils may also decrease as the chemical ages. If only a fraction of

the total extractable chemical is available for volatilization, this may lead to over-estimation of

chemical outgassing in soil-air exchange models.

To test the hypothesis, the change of a chemical’s volatility and bioaccessibility in soil was

monitored over a long period of time. The soil-air partition coefficient (KSA) is used as an

indicator for volatility. Bioaccessibility was measured using non-exhaustive extraction by

aqueous hydroxypropyl-β-cyclodextrin (HPCD). HPCD is a macromolecule comprising a torus

of α-1, 4−linked glucose units, and it has shown to be a promising chemical agent to measure

bioaccessibility. Previous studies have demonstrated that HPCD extractability was able to

predict the bioaccessible fraction of phenanthrene in a wide range of soils (Allan et al., 2006;

Barthe and Pelletier, 2007; Cuypers et al., 2002; Doick et al., 2006; Hickman et al., 2008; Reid et

al. 2000; Stokes et al., 2005). In some cases, a 1:1 correlation between HPCD extractability and

microbial degradation rate was reported and a scaling factor was not needed (Allan et al., 2006;

Doick et al., 2006; Reid et al., 2000). Most work performed so far has been focused on PAHs

and no work has been done on organohalogens, such as the OCPs and polychlorinated biphenyls

8

(PCBs). Hartnik et al. (2008) developed a HPCD extraction method for the currently used

pesticides α-cypermethrin and chlorfenvinphos and found that HPCD extractability correlated

well with earthworm uptake.

Chapters 4 and 5 report the investigations of the effect of aging on the volatility and

bioaccessibility of OCPs and PCBs in a high organic muck soil taken from an agricultural region

in Ontario, Canada. Chapter 4 is a method development paper designed to estimate the

bioaccessible fraction of organic chemicals in soil using a mild extraction with HPCD. The

extraction method was optimized for HPCD concentration, extraction time, effect of multiple

extractions, and chemical concentration. Relationships between chemical extractability and KOW

and molecular size were explored. Results are discussed from an aging experiment which

monitored the changes in HPCD extractability of freshly spiked OCPs and PCBs, with

comparison to the HPCD extractability of the native OCPs in the muck soil.

Chapter 5 employed the HPCD extraction method established in Chapter 4 to monitor the

changes in the bioaccessibility (as estimated by HPCD extractability) of OCPs and PCBs in a

spiked muck soil that had been aged under indoor, outdoor and sterile condition over 730 d.

Changes in KSA were also measured as an indicator of volatility and were correlated with HPCD

extractability. Results were compared to native OCPs which have resided in the soils for many

years. Predicted KSA was estimated by the Karickhoff model and evaluated with the measured

KSA. The chapter concludes with an analysis of the preferential enantiomer degradation of α-

hexachlorocyclohexane (HCH) in soils and relationship to the enantiomer proportions in air and

HPCD extract.

Visualising the equilibrium distribution and mobility of organic contaminants in soil using the

chemical partitioning space

Soil is a complex medium which is composed of air, water, mineral and organic solids, and biota.

Its composition depends on the origin of the parent materials which vary geographically, and

diagenetic factors involved in soil formation (Brady and Weil, 1999). For example, desert soils

are low in organic carbon and dry. Soils in salt marshes are wet and organic rich. Moreover,

9

these components are dynamic which can change over the course of days, months and years in

response to climate (i.e. temperature, precipitation), topography, land use or management

practices.

The toxicity, bioaccumulation and persistence of organic contaminants which are applied to soil

have always been a concern (Harnly et al., 2005; Herrera-Portugal et al., 2005; Lock et al., 2002;

Welp et al., 1999). Despite numerous experimental studies that have been conducted to elucidate

chemical transport in soil, there are still a lot of uncertainties about the sorption kinetics and

transport processes and many chemical classes have not been investigated experimentally at all

(Bedos et al., 2002). This is primarily due to the many classes of chemicals involved and the

dynamic and heterogeneous nature of the soil (Grathwohl, 1990; Niederer et al., 2007; Prueger et

al., 2005; Reichman et al., 2000). The assessment of environmental fate would clearly benefit

from a simple way to quickly obtain information of the likely distribution and mobility of a

substance in soil.

The environmental distribution and transport of an organic chemical is largely depended on its

equilibrium phase partitioning coefficients. In an environment composed of three major phases,

two such coefficients are often sufficient to characterize chemical fate. This allows a graphical

representation of fate parameters as a function of a two dimensional coordinate system defined

by these two partitioning coefficients (Breivik et al., 2003). Such chemical partitioning space

maps have shown to be an effective tool for displaying the distribution and fate of neutral

organic chemicals in the atmosphere (Lei and Wania, 2004) and in a snow pack (Meyer and

Wania, 2008). Differences in fate of chemicals, the influence of changes in environmental phase

composition and phase volumes, the influence of temperature, and the sensitivity of fate to

uncertainties in the partitioning coefficients can all be displayed and assessed graphically using

the partitioning space.

For instance, a partitioning space map can be constructed for a soil which shows a chemical’s

phase distribution among the three phases (i.e. air-filled pore space, water-filled pore space and

organic solid). Figure 1.1A shows an example of a partitioning space map for a dry soil which

contains 5% organic carbon and 5% water content (WC). This space map is defined by the air-

10

water partition coefficient (KAW) and humic acid-water partition coefficient (KHW). Chemicals

which are primarily (>90%) sorbed to organic solids are located in the green region, those that

prefer the gas phase in the air-filled pores are found in the red region and those most likely

dissolved in water-filled pores are in the blue region. The light green, yellow and blue colours

are transition region where the chemicals are found in more than one phase by at least 50%. The

area of these region changes according to the characteristics of a soil, such as moisture and

organic carbon content. Figure 1.1B shows that in a waterlogged soil (i.e. WC = 49%), the blue

region becomes larger and expands to the right and upwards, indicating that a greater proportion

of the chemical is found in the water phase.

Furthermore, if the partitioning properties (e.g. KAW, KHW) of a compound are known or can be

estimated, they can be superimposed onto the chemical space maps which allow instant

estimation of the phase distribution of a chemical. As shown in Figure 1.1A, 50% of Chemical

Y appears in the gas phase in the dry soil. As water content increases and the organic carobn

remains the same, 90% of Chemical Y partitions into the water phase. On the other hand,

Chemical X is strongly sorbed to the organic solids regardless of the water content of the soil.

Figure 1.1 Phase distribution maps for a A) dry soil (water content, WC = 5%) and B)

waterlogged soil (WC = 49%). The organic carbon content of both soils is 5%. Chemical X

sorbs to the organic solid regardless of the moisture content. Chemical Y prefers in the gas phase

of a dry soil but it is most likely found in the water phase of a waterlogged soil.

-2 -1 0 1 2 3 4 5 6 7-7

-6

-5

-4

-3

-2

-1

0

1

2

-2 -1 0 1 2 3 4 5 6 7-7

-6

-5

-4

-3

-2

-1

0

1

2

-7

-6

-5

-4

-3

-2

-1

0

1

2

Y

X

Organic solidWater

Air Air

A. Dry Soil, WC = 5% B. Waterlogged Soil, WC = 49%

Log KHW Log KHW

Log K

AW

WaterOrganic solid

X

Y> 90 % in air-filled pore space> 50 % in air-filled pore space> 90 % in water-filled pore space> 50 % in water-filled pore space> 90 % sorbed to organic solids> 50 % sorbed to organic solids

-2 -1 0 1 2 3 4 5 6 7-7

-6

-5

-4

-3

-2

-1

0

1

2

-2 -1 0 1 2 3 4 5 6 7-7

-6

-5

-4

-3

-2

-1

0

1

2

-7

-6

-5

-4

-3

-2

-1

0

1

2

Y

X

Organic solidWater

Air Air

A. Dry Soil, WC = 5% B. Waterlogged Soil, WC = 49%

Log KHW Log KHW

Log K

AW

WaterOrganic solid

X

Y> 90 % in air-filled pore space> 50 % in air-filled pore space> 90 % in water-filled pore space> 50 % in water-filled pore space> 90 % sorbed to organic solids> 50 % sorbed to organic solids

> 90 % in air-filled pore space> 50 % in air-filled pore space> 90 % in water-filled pore space> 50 % in water-filled pore space> 90 % sorbed to organic solids> 50 % sorbed to organic solids

11

The partition properties of a chemical can be described by pp-LFER-based equations (Goss,

2001). These divide the free energy of a sorption process into various contributions of sorbate-

sorbent interactions. The basic form of these equations is:

Log Kixy = lxyLi + vxyVi + bxyBi + axyAi + sxySi + cxy Eq [1.5]

where Kixy is the equilibrium partition coefficient of a chemical i between phases x and y. Each

term on the right side of eq. [1.5] describe a type of interaction between the solute and the two

phases that contribute to the overall partitioning. The uppercase letters are the solute descriptors.

L is the log hexadecane-air partition coefficient. V refers to the McGowan volume of the solute.

Both L and V describe non-specific interactions which describes the dispersive van der Waals

force and cavity formation energy. B and A are measures of the solute’s hydrogen-bond basicity

and acidity, respectively. S is the solute’s dipolarity or polarizability. The lowercase letters are

the complementary phase (sorbent) descriptors and cxy is a system constant. Phase descriptors

have been developed for various environmental systems, soil organic matter-water (Niederer

2006, 2007; Endo et al., 2009); air-soot (Roth et al., 2005); air-water (Goss 2006); air-mineral

surface (Goss et al., 2003, 2004); gas-particle (Götz et al., 2007); water-nonaqueous phase

liquids (Endo and Schmidt, 2006). Solute descriptors are also known as Abraham descriptors

(Abraham 1993 and 2005), and are available for more than a thousand of chemicals covering a

wide range of chemical class.

The advantage of pp-LFERs is that they account for specific and non-specific molecular

interactions. Particularly, it has been demonstrated that pp-LFERs provide good prediction of

equilibrium partitioning of polar organic chemicals, which involves specific intermolecular

interactions (Goss and Schwarzenbach, 2001) that cannot be considered by the octanol-based

single parameter (sp)-LFER. On the other hand, Brown and Wania (2009) compared results

from a sp-LFER and pp-LFER based multimedia chemical fate model and reported that the

overall differences between the model results are small and that the dispersive van der Waals

force (described by the Li term in eq. [1.5]) has the greatest influence on the environmental

distribution of chemicals. The authors suggested that whether to use pp-LFERs or sp-LFERs

depends on the availability and quality of chemical input values. pp-LFERs are preferred if

12

reliable solute descriptors are available and that their quality is better than or as good as the

octanol-based partition coefficients.

In Chapter 6, two types of chemical space maps defined by the KAW and KHW are employed to

visually assess the environmental fate of a large suite of neutral organic chemicals in different

soils. They are: 1) equilibrium phase distribution maps which displays a chemical’s phase

distribution between the air-filled pores, the pore water and organic solids in soil; 2) mobility

map which illustrate the relative importance of the three transport process in soils, i.e. leaching,

evaporation and erosion. KAW and KHW of the chemicals are determined using pp-LFERs and

superimposed onto the space maps to provide instantaneous assessment of their fate. The roles

of temperature, water content, fraction of organic carbon, type of soil organic matter, and surface

soil depth are investigated.

1.1.2 Air-water gas exchange of persistent organic pollutants (POPs)

A further objective in this thesis is to examine the air-water exchange of POPs in the Arctic.

Large bodies of water such as oceans and lakes play an important role in the global processes

that distribute POPs, acting either as a sink or a source to the atmosphere. The Arctic is a pristine

area with few sources of local pollution. Most contaminants found in the Arctic are derived from

long-range transport via air and ocean currents. Since the 1980s, there has been a reduction in

primary emissions of the insecticide technical HCH with an accompanying decline in

atmospheric concentration of its main component, α-HCH (Becker et al., 2008; Ding et al.,

2007; Hung et al., 2010; Li and Macdonald, 2005; Su et al., 2006). As a result, a reversed

direction of air-water exchange from net deposition to equilibrium or net volatilization has been

observed in some arctic regions (Ding et al., 2007; Harner et al., 1999; Jantunen et al., 1995,

1996, 2008; Laskaschus et al., 2002; Lohmann et al., 2009; Sahsuvar et al., 2003; Su et al.,

2006). Τhe currently used insecticide endosulfan is undergoing net deposition to the Arctic

Ocean (Weber et al., 2010), while other organochlorines experience seasonal cycles of

deposition or volatilization depending on changing air and water concentrations and ice cover

(Hargrave et al., 1997).

13

With climate change, decreasing ice cover will open larger areas of the Arctic Ocean and its

regional seas to air-water gas exchange. It is expected that loss of ice cover will increase rates of

deposition and volatilization of chemicals as the system strives to achieve air-water equilibrium.

There is a need to understand the current status of air-water exchange in the Arctic in order to

predict the effects of ice cover loss on the system. Specific objectives are:

• Investigate seasonal variations of POPs in air and water of the Canadian Arctic;

• Determine net air-water exchange direction and flux using micrometeorological methods

and the two-film model;

• Explore the effect of ice cover loss on air-water exchange;

• Employ α-HCH enantiomers as tracers of local volatilization vs. long-range transport

sources.

Chapter 7 reports air-water gas exchange of HCH (α-, and γ-isomers), hexachlorobenzene

(HCB) and bromoanisoles in air and water samples collected from shipboard in the eastern

(Hudson Bay, Labrador Sea) and western (southern Beaufort Sea) Canadian Arctic during 2007-

2008. HCHs and HCB are anthropogenic chemicals, while di- and tribromoanisoles are largely

natural substances produced by marine macro algae. Levels of these chemicals in water and

overlying air were compared among the three regions and in the Beaufort Sea during open water

vs. ice-covered conditions. The potential for a chemical to volatilize from water to air was

examined using the fugacity approach, where fA is determined by eq. [1.2] and fugacity in water,

(fW) is described by,

HCf WW = Eq [1.6]

where CW is water concentrations (mol m-3) and the Henry’s law constant H (Pa m3 mol-1) is

corrected for water temperature. As for soil-air partitioning, fugacities were expressed as

fractions, where

]/[ AWw fffff += Eq [1.7]

14

A ff = 0.50 indicates equilibrium and no net exchange between air and water; ff >0.50 indicates

net volatilization and ff < 0.50 indicates net deposition. Changes in the proportion of α-HCH

enantiomers in air over winter, spring and summer were employed to infer increasing

volatilization from the ocean which accompanied ice cover loss. The chapter concludes by

presenting results from a novel experiment which coupled vertical gradient measurements of α-

HCH in air with micrometeorological data to directly estimate fluxes of α-HCH from the Arctic

Ocean. Fluxes determined from the micrometeorological approach (Lenschow, 1995) and

calculated from the classic Whitman two-film model (Bidleman and McConnell, 1995; Mackay

and Yeun 1983) were compared.

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2 PASSIVE AIR SAMPLING OF ORGANOCHLORINE PESTICIDES IN MEXICO

Fiona Wonga, b, Henry A. Alegriac, Terry F. Bidlemana , Víctor Alvaradod, Felipe Angelesd,

Alfredo Ávila Galarzae, Erick R. Bandalaf, Idolina de la Cerda Hinojosag, Ignacio Galindo

Estradah, Guillermo Galindo Reyesi, Gerardo Gold-Bouchotj, José Vinicio Macías Zamorak,

Joaquín Murguía-Gonzálezl, Elias Ramirez Espinozam

a Centre for Atmospheric Research Experiments, Science and Technology Branch, Environment

Canada, 6248 Eighth Line, Egbert, ON, L0L 1N0, Canada

b Department of Chemistry and Department of Physical and Environmental Sciences, University

of Toronto Scarborough, 1265 Military Trail, Toronto, ON, M1C 1A4, Canada

c Department of Environmental Science, Policy and Geography, University of South Florida St.

Petersburg, 140 7th Ave. S., St. Petersburg, FL 33701, U.S.A.

d Sustancias Tóxicas En Suelos y Residuos, Centro Nacional de Investigación y Capacitación

Ambiental, (CENICA) , Av. San Rafael Atlixco 186, Col. Vicentina, Delegación Iztapalapa, C.P.

09340, México, D.F., México

e Facultad de Ingeniería, Universidad Autónoma de San Luis Potosí, Facultad de Ingeniería, Av.

Manuel Nava No. 8, Zona Universitaria, C.P. 78290, San Luis Potosí, México

f Departamento de Ingeniería Civil y Ambiental, Universidad de Las Americas-Puebla,

Sta. Catarina Mártir. Cholula, C.P. 82720, Puebla, México

26

g Jefa del Sistema Integral de Monitoreo Ambiental Agencia de Protección al Medio Ambiente y

Recursos Naturales, Av. Alfonso Reyes No. 1000. Interior del Parque Niños Héroes. Col. Regina

Monterrey, C.P. 64290, Nuevo León México

h Centro Universitario de Investigaciones en Ciencias del Ambiente, Universidad de Colima,

Revolución No. 427, Col. Centro, C.P. 28000, Colima, México

i Laboratorio de Toxicología, Facultad de Ciencias del Mar, Univeridad Autónoma de Sinaloa,

Paseo Claussen s/n, C.P. 82000, Mazatlán, Sinaloa, México

j Centro de Investigación y de Estudios Avanzados del IPN, Unidad Merida, Km 6 Antigua

Carretera a Progeso, C.P. 97310, Mérida, Yucatán, México

k Instituto de Investigaciones Oceanológicas, Universidad Autónoma de Baja California, Km.

107 Carretera Tijuana – Ensenada, C.P. 22880, Ensenada, Baja California, Mexico

l Facultad de Ciencias Biológicas y Agropecuarias, Región Orizaba-Córdoba, Universidad

Veracruzana, Km. 1 Carretera Peñuela-Amatlan de Los Reyes, C.P. 94945, Veracruz, Mexico.

m Centro de Investigación en Materiales Avanzados, Chihuahua, S.C., Miguel de Cervantes 120,

Complejo Industrial Chihuahua, C.P. 31109, Chihuahua, Chihuahua, México

Contribution: F. Wong and H. Alegria deployed passive air samplers in Mexico. F. Wong

prepared all air sampling media, shipped, processed and analyzed all samples. Collaborators

from Mexico collected air samples. Manuscript was prepared by F. Wong under supervision of

Terry F. Bidleman with input from co-authors.

Reproduced with permission from Environmental Science and Technology, 2009, 43, 704−710.

Copyright 2009 American Chemical Society.

27

2.1 ABSTRACT

The spatial and temporal variation of organochlorine pesticides (OCPs) in air across Mexico was

investigated by deploying passive samplers at eleven stations across Mexico during 2005-2006.

Integrated samples were taken over three-month periods and quantified for DDT compounds,

endosulfans, toxaphenes, components of technical chlordane, hexachlorocyclohexanes (HCHs)

and dieldrin. Enantiomers of chiral chlordanes and o,p’-DDT were determined on chiral

stationary phase columns as an indicator of source and age. Results are discussed in combination

with pumped air samples taken at four other stations in southern Mexico during 2002-2004. DDT

and its metabolites, endosulfan and toxaphene were the most abundant OCPs detected in all

sampling sites. Atmospheric concentrations of ΣDDT (p,p'-DDT + o,p'-DDT + p,p'-DDE + o,p'-

DDE + p,p'-DDD + o,p'-DDD) ranged from 15 to 2360 pg m-3 with the highest concentrations

found in southern Mexico and the lowest in northern and central Mexico. A fresher DDT residue

was observed at sites with greater DDT use and in the southern part of the country, as shown

from the higher FDDTe = p,p’-DDT/( p,p’-DDT + p,p’-DDE) and nearly racemic o,p’-DDT.

This agrees with the former heavy use of DDT in the endemic malarious area of the country. A

local hotspot of endosulfan was identified at an agricultural area in Mazatlan, Sinaloa, with

annual mean concentration of ΣENDO (endosulfans I + II + endosulfan sulfate) = 26800 pg m-3.

At this site, higher concentrations of ΣENDO were recorded during the winter (November to

February) and spring (February to May) periods. From back trajectory analysis, this coincides

with a shift in the air mass coming from the Pacific Ocean (May to November) to the inland

agricultural area (November to May). The elevated ΣENDO observed is likely due to the local

agricultural usage. HCHs, chlordanes, trans-nonachlors and dieldrin were more evenly

distributed across the country likely due to them being aged residues and more diffuse in the

environment. In contrast, hotspots of endosulfans, DDTs and toxaphenes were observed as they

were heavily used in localized agricultural or malarious regions of Mexico.

28

2.2 INTRODUCTION

Mexico is known for its long history of organochlorine pesticides (OCPs) usage in malaria

control and agriculture. According to Li and MacDonald (1), Mexico ranked sixth in the world

for the overall use of dichlorodiphenyltrichloroethane (DDT). Approximately 250 kt of DDT

was used from 1947 to 2000. Under North American Regional Action Plans (NARAPs)

initiated by the North American Commission for Environmental Cooperation, Mexico is phasing

out usage of DDT, chlordane and lindane (γ-hexachlorocyclohexane) (2-4). Mexico exceeded

this target and was successful in halting DDT use by 2000 (5). Elimination of chlordane use was

achieved in 2003 (6) and Mexico has agreed to phase out lindane in the coming years (4).

Most of the studies on OCPs in Mexico have been focused on human blood or tissues (7-11),

sediment (12-16), coastal waters (16-18), marine biota (19-21) and food products (22, 23).

Information on the atmospheric levels of OCPs in Mexico is scarce. Previous studies in southern

Mexico found that DDT and toxaphene concentrations in air were 1-2 orders of magnitude above

levels in the Laurentian Great Lakes and arctic regions (24-26). Atmospheric levels in southern

Mexico were generally higher than those in central Mexico (27), Costa Rica (28, 29) and Cuba

(30), and comparable to those in Belize (31). Until now, no atmospheric data for OCPs have

been available for the larger area of Mexico, and establishing baseline data across the country

would be useful for developing a long-term air monitoring program. The current study is an

extension to an earlier effort by Alegria et al. (25) and Wong et al. (32) who investigated

airborne OCPs at four sampling sites located in southern Mexico during 2002-2004. Here we

report results of a passive air sampling campaign from the south to north of Mexico during 2005-

2006.

29

2.3 METHODS

2.3.1 Air sampling

Passive air samplers (PAS) were deployed at eleven sampling sites across Mexico during 2005-

2006. Locations of the sampling sites and four stations in southern Mexico where pumped and

passive samples were collected in 2002-2004 (25) are illustrated in Figure 2.1. Samples were

collected every three to four months during which about 350 m3 air was sampled. Duplicate

PAS were deployed at each site within 3 m. Details about each site and sampling schedule are

given in Table Appendix (A) A2.1.

PAS consisted of polyurethane foam (PUF) disks enclosed in a stainless steel housing. Design

and theory behind PAS are described in Shoeib and Harner (33). Similar PAS have been used in

air sampling campaigns worldwide (34-39). The PUF disks (PacWill Environmental, Stony

Creek, ON, Canada) were rinsed with tap water, Soxhlet extracted once with acetone for 22 h,

two times with petroleum ether for 22 h and dried in a vacuum desiccator.

In order to determine the sampling rate (m3d-1), 30 mL of a petroleum ether solution containing a

suite of depuration compounds (DCs) was spiked onto the PUF disks prior to field deployment.

DCs included 262 ng [2H6]-γ-hexachlorocyclohexane (HCH), 120 ng [13C12]-polychlorinated

biphenyl (PCB) 52, 8 ng PCB107 and 8 ng PCB198. Period 3 and Period 4 samples were also

spiked with 100 ng [13C12]-PCB9 and 100 ng [13C12]-PCB32. The procedure for deriving the

sampling rate using DCs can be found in Gouin et al. (37) and A2.1 and Table A2.2. Annual

mean sampling rates for all sites ranged from 3.0 to 7.7 m3d-1 and they are consistent with those

previously measured elsewhere (26, 30, 37).

30

Figure 2.1 Map of air sampling sites in Mexico during 2005-2006 (this study) and 2002-2004

(25). BAJ = Baja California, CHI = Chihuahua, CEL = Celestun, COL = Colima, COR =

Cordoba, CUE = Cuernavaca, MAZ = Mazatlan, MEX = Mexico City, MON = Monterrey, SLP

= San Luis Potosi, TUX = Tuxpan, TB =Tabasco, MT = Chiapas mountain, TP = Tapachula, VC

= Veracruz.

BAJ

CHI

MAZ SLP

MON

COLMEX

CUE

CORVC

CEL

MT

TP

TB

TUX

Passive air samples, 11 sites, 2005-2006 (this study)Pumped and passive air samples, 4 sites, 2002-2004 (25)

BAJ

CHI

MAZ SLP

MON

COLMEX

CUE

CORVC

CEL

MT

TP

TB

TUX

Passive air samples, 11 sites, 2005-2006 (this study)Pumped and passive air samples, 4 sites, 2002-2004 (25)

31

2.3.2 Extraction and analysis

Extraction was achieved by Soxhlet apparatus using 400 mL petroleum ether for 18 h. Prior to

extraction, samples were fortified with surrogate recovery compounds: 20 ng [2H6]-α- HCH, 20

ng [13C10]-heptachlor exo-epoxide (HEPX), 20 ng [13C10]-trans-nonachlor (TN), 20 ng [13C12]-

dieldrin (DIEL), 100 ng [2H8]-p,p’-DDT. Cleanup procedure followed those in Alegria et al.

(25). The final extract was concentrated to 500 µl in isooctane. The duplicate samples were

analyzed individually for most compounds. To improve detectability, extracts of duplicate

samples were combined, given an additional cleanup by shaking with 15% fuming sulfuric acid,

blown down to 150 μL, and quantitatively analyzed for toxaphenes. These combine extracts

were also used for chiral analysis.

OCPs were determined using capillary gas chromatography - electron capture negative ion mass

spectrometry (GC-ECNI-MS), on an Agilent 6890 GC – 5973 MSD with a 60-m DB-5 column

(0.25 mm i.d., 0.25 µm film, J&W Scientific, U.S.A.). Toxaphene was determined using GC-

ECNI-MS on a Hewlett-Packard 5890 GC – 5989B MS-Engine with a 60-m DB-5 column (0.25

mm i.d., 0.25 µm film, J&W Scientific, U.S.A.). The ΣTOX residues were quantified versus

technical toxaphene as the sum of 7-Cl, 8-Cl and 9-Cl homologs (40, 41). Eight individual peaks

were also quantified versus Parlar congeners 26, 39, 40, 41, 42, 44, 50 and 63. Details of the

operating condition, ions monitored and sources of chemical standards are found in Alegria et al.

(25).

Enantiomer separations of trans- chlordane (TC), cis-chlordane (CC) and o,p'-DDT were

performed as Kurt-Karakaus et al. (42). Chiral results were expressed as the enantiomer

fraction, EF = peak areas of the (+)/[(+) + (–)] enantiomers and also as deviation from racemic,

DEVrac = the absolute value of (0.500 - EF) (42). A racemic EF = 0.500 (DEVrac = 0) whereas

preferential degradation of the (+) or (–) enantiomer yields EFs <0.500 and >0.500, respectively

(DEVrac > 0 in both cases).

32

2.3.3 Quality control

Limits of detection (LOD) were defined as mean blank + 3 times the standard deviation. If a

chemical was not found in the blanks, LOD was defined as instrumental detection limit (IDL),

which were estimated by injecting low concentrations of target analytes until a small peak at

~3:1 signal: noise ratio was obtained. LODs are expressed in pg m-3 considering ~170 m3 of air

volume (350 m3 for ΣTOX) and a final sample volume of 500 µl (150 µl for ΣTOX). IDLs

ranged from 0.08-22 pg m-3 for individual OCPs and 5.1 pg m-3 for ΣTOX. Values are listed in

Table A2.3. Recovery percentages for the spiked surrogates were (n=112): [2H6]-α-HCH:

78±7%; [13C10]-HEPX: 101±14%; [13C10]-TN: 80±6%; [13C12]-DIEL: 95±11%; [2H8]-p,p'-DDT:

86±9%.

Duplicate samples agreed within 20% for three-quarters of the measurements and within 35% for

ninety percent and of the measurements. Decisions as to whether a particular sample contained

racemic or nonracemic residues were made by determining whether the EF for the compound in

question fell within or outside of the ± 95% confidence interval of the standards EFs. Air

concentrations were calculated from the mean of the duplicate sampler results, with the

exception of ΣTOX, in which case the combined extract from duplicates was used.

2.4 RESULTS AND DISCUSSION

2.4.1 Air concentrations of OCPs

Discussions in this paper include pumped air data collected in a 2002-2004 study where

sampling was conducted at four sites in southern Mexico: suburban Tapachula (TP) and a

mountain site (MT) in Chiapas, Veracruz City (VC), and rural Tabasco (TB) (25, 32). These

sites are also shown in Figure 2.1. In that study, results from co-located passive and pumped

samplers were compared for γ-HCH, chlordanes, endosulfans, p,p’-DDT, p,p’-DDE and o,p’-

DDT. Mean values of passive/pumped ratios ranged from 0.76 for p,p’-DDT to 1.5 for

endosulfans, with an overall average of 1.0 for all compounds (25). Figure 2.2 shows a box-

33

whisker plot summarizing the OCP concentrations in air at all sites as the sums within each

compound class. The most abundant OCPs were the ΣDDT = p,p'-DDT + o,p'-DDT + p,p'-DDE

+ o,p'-DDE + p,p'-DDD + o,p'-DDD, ΣENDO = endosulfan I (ENDO I) + endosulfan II (ENDO

II) + endosulfan sulfate (ESUL) and ΣTOX. Considering all sites and deployment periods,

detection frequencies of the OCP classes were: DDTs, chlordanes, HCHs, endosulfans,

toxaphenes – 100%, heptachlor –18%; heptachlor exo-epoxide – 8%, aldrin – 0%, dieldrin –

88%. Annual mean concentrations of the OCP species at individual sites are presented in Table

A2.3.

Figure 2.2 Box-whisker plot of organochlorine pesticides (pg m-3) in this study and the 2002-

2004 sampling campaign (25). The top end of the box represents the 75th percentile of the data,

and the bottom box represented 25th percentile. The horizontal line between the boxes is the

median, the circle is the geometric mean, and the asterisk is the arithmetic mean. The whiskers

on the top and bottom of the boxes indicate the maximum and minimum (1/2 LOD in some

cases) values of the data set. ΣHCH = α-HCH + γ-HCH. ΣCHL = TC+CC+TN. ΣENDO =

ENDO I + ENDO II + ESUL. ΣDDT = p,p'-DDT + o,p'-DDT + p,p'-DDE + o,p'-DDE + p,p'-

DDD + o,p'-DDD. ΣTOX = quantified as technical toxaphene.

0

1

10

100

1000

10000

100000

ΣHCH ΣCHL Dieldrin ΣENDO ΣDDT ΣTOX

Air C

once

ntrati

on (p

g m-3

)

0

1

10

100

1000

10000

100000

ΣHCH ΣCHL Dieldrin ΣENDO ΣDDT ΣTOX0

1

10

100

1000

10000

100000

ΣHCH ΣCHL Dieldrin ΣENDO ΣDDT ΣTOX

Air C

once

ntrati

on (p

g m-3

)

34

DDT The ΣDDT ranged from 15-1975 pg m-3 in this study, and 239-2360 pg m-3 at the

southern Mexico sites in 2002-2004 (25). The arithmetic and geometric mean (AM and GM)

concentrations for both studies were 558 and 200 pg m-3. The highest concentration was found

at MT and the lowest at MON. Figure 2.3A shows the spatial distribution of ΣDDT

measurement in both sampling campaigns. ΣDDT tended to be higher in the south (MT 2360

pg m-3, CEL 1975 pg m-3, VC 1200 pg m-3, TP 547 pg m-3, TB 239 pg m-3) and some central

sites (COL 750 pg m-3, CUE 500 pg m-3, COR 129 pg m-3). Most sampling sites located in the

central (SLP 21 pg m-3, MON 15 pg m-3, MEX 55 pg m-3, TUX 50 pg m-3, MAZ 76 pg m-3), and

northern (CHI 34 pg m-3) Mexico had lower ΣDDT concentrations. An exception was 338 pg m-3

at the most northern station, BAJ, which is located in an agricultural region of the Mexicali

valley where DDT may have been used in the past. BAJ is also close to southern California,

where ΣDDT in the hundreds to thousands of ng g-1 have been reported in agricultural soils (43).

DDT levels observed at MEX were consistent with those previously reported with mean of 110

pg m-3 at an urban site in Mexico city (27). Air concentration of ΣDDT at most Mexico sites

were 1-2 orders of magnitude greater than in Costa Rica, where ΣDDT ranged from 10-16 pg m-3

in samples collected between 2001-2004 (26, 28).

The endemic malarious areas in Mexico extended from the south (comprising the states of

Chiapas, Yucatan, Campeche, Veracruz and Tabasco) to the north along the Pacific coast

(Oaxaca, Guerrero, Jalisco and Sinaloa). These areas were heavily sprayed with DDT in past

sanitary campaigns (44). The total amount of DDT applied by the government from 1989 to1999

was the greatest in the south and decreased to the north (Figure 2.3A). This is consistent with

results showing that the sites in southern Chiapas (TP, MT), Yucatan (CEL) and Colima (COL)

had relatively high ΣDDT in air. It should be noted that Chiapas consumed 16% (i.e. 1527 tons)

of the total DDT used in Mexico during 1989-1999, more than any other state (44). However,

the correlation between DDT used in malaria campaigns and atmospheric concentration of

ΣDDT is not significant (r2 =0.13, p =0.19) and there is also no significant relationship with

latitude (r2 = 0.17, p = 0.13). Since these samples were collected in urban, rural and agricultural

sites, sources of DDT at each site would be different. Urban sites would likely be a reflection of

DDT used in sanitary campaigns, whereas the agricultural sites would be more related to soil

emissions from the former use of DDT in agriculture. DDT use in agriculture began to decline in

35

Figure 2.3 A) Spatial distribution of ΣDDT in air (pg m-3) and DDT used for public health

purposes between 1989-1999 (44). B) FDDTe and FDDTo vs. DDT used. C) FDDTe and FDDTo vs.

latitude. FDDTe is significantly positively correlated with DDT used for malaria control (r2 = 0.45,

p = 0.01) and negatively with latitude (r2 = 0.57, p = 0.001). FDDTo is not significantly correlated

with either usage or latitude. Tech Vap = technical vapour. These figures included data from

Alegria et al. (25). #

#

#

#

#

#

#

#

#

#

#

#

#

#

#

ΣDDT Air Conc

500 (pg m-3)

DDT used (Tons)0-3333-102102-212212-380380-976976-1527

#

#

#

#

#

#

#

#

#

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ΣDDT Air Conc

500 (pg m-3)

DDT used (Tons)0-3333-102102-212212-380380-976976-1527

DDT used (Tons)0-3333-102102-212212-380380-976976-1527

0-3333-102102-212212-380380-976976-1527

0.0

0.5

1.0

10 15 20 25 30 35

Latitude (degrees)

F DD

Teor

F DD

To

FDDTeFDDTo

Tech Vap FDDTe

Tech Vap FDDTo

0.0

0.5

1.0

0.1 1.0 10 100 1000

DDT used (tonnes)

F DD

Te

or F

DD

To

FDDTe

FDDTo

Tech Vap FDDTe

Tech Vap FDDTo

A

B

C

#

#

#

#

#

#

#

#

#

#

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#

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ΣDDT Air Conc

500 (pg m-3)

DDT used (Tons)0-3333-102102-212212-380380-976976-1527

#

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ΣDDT Air Conc

500 (pg m-3)

DDT used (Tons)0-3333-102102-212212-380380-976976-1527

DDT used (Tons)0-3333-102102-212212-380380-976976-1527

0-3333-102102-212212-380380-976976-1527

0.0

0.5

1.0

10 15 20 25 30 35

Latitude (degrees)

F DD

Teor

F DD

To

FDDTeFDDTo

Tech Vap FDDTe

Tech Vap FDDTo

0.0

0.5

1.0

10 15 20 25 30 35

Latitude (degrees)

F DD

Teor

F DD

To

FDDTeFDDTo

0.0

0.5

1.0

10 15 20 25 30 35

Latitude (degrees)

F DD

Teor

F DD

To

FDDTeFDDTo

Tech Vap FDDTe

Tech Vap FDDTo

0.0

0.5

1.0

0.1 1.0 10 100 1000

DDT used (tonnes)

F DD

Te

or F

DD

To

FDDTe

FDDTo

Tech Vap FDDTe

Tech Vap FDDTo

0.0

0.5

1.0

0.1 1.0 10 100 1000

DDT used (tonnes)

F DD

Te

or F

DD

To

FDDTe

FDDTo0.0

0.5

1.0

0.1 1.0 10 100 1000

DDT used (tonnes)

F DD

Te

or F

DD

To

FDDTe

FDDTo

Tech Vap FDDTe

Tech Vap FDDTo

A

B

C

36

the 1970s and by 1997 DDT was registered only in government-sponsored public health

campaigns (2). However the spatial distribution of DDT consumption in agriculture is not

available and thus cannot be accounted for in Figure 2.3A. Another possibility is atmospheric

transport from southern neighbouring countries. As shown in the air trajectory analysis by

Alegria et al. (25), southern Mexico is often impacted by air parcels that passed over Guatemala,

Honduras, and Belize. DDT levels in Belize air are similar to those in southern Mexico have

been reported (26, 31).

To gain further insight to sources and age of DDT residues, the relative amounts of p,p’-DDT,

p,p’-DDE and o,p’-DDT were expressed as fractional values, FDDTe = p,p'-DDT/( p,p'-DDT +

p,p'-DDE) and FDDTo = p,p'-DDT/( p,p'-DDT + o,p'-DDT) (45). FDDTe and FDDTo of technical

DDT are 0.95 and 0.84, respectively, assuming the World Health Organization (46) reported

composition for technical DDT: 77%, p,p'-DDT, 15% o,p'-DDT, 4 % p,p'-DDE. Fractionation

of these compounds during evaporation of technical DDT was estimated using liquid-phase

vapour pressures at 25 °C, resulting in FDDTe = 0.75 and FDDTo = 0.59 for technical DDT vapour.

Field experiments conducted by sampling air close to an agricultural soil indicate that FDDTe and

FDDTo in the overlying air can be predicted from those in the soil using relative vapour pressures

(45). FDDTe close to the vapour-phase technical DDT value suggests emissions from recent use

while a lower FDDTe suggests a greater proportion of the metabolite p,p’-DDE and an older

residue.

FDDTe in the current study ranged from 0.01-0.56, indicating the presence of residual p,p'-DDE at

all sites. FDDTe reported for the 2002-2004 campaign in southern Mexico ranged from 0.45-0.83.

Highest FDDTe in both sampling campaign were found at MT (0.83) and TB (0.74), suggestive of

“fresh” DDT. FDDTe in VC and TP were 0.45 and 0.55, respectively, and indicated more aged

residues (25, 32). When combined with the larger passive sampling data set from this study,

FDDTe shows a significant positive correlation with DDT used (r2 = 0.45 and p = 0.01) and

negative correlation with latitude (r2 = 0.57 and p = 0.001) (Figures 2.3B and C). This implies

fresher DDT residues in air are associated with a higher (and probably more recent) DDT

application in southern Mexico. Thus, it is suggested that southern Mexico is subjected to recent

DDT input either locally or from atmospheric transport from neighbouring countries.

37

FDDTo was not significantly related to DDT used with r2 = 0.08 and p = 0.30 nor with latitude

with r2 = 0.20, and p = 0.09 (Figure 2.3B and C). Including the four sites from the 2002-2004

sampling campaign (25), twelve out of fifteen sites exceeded the FDDTo of the WHO DDT

technical vapour of 0.59 and these sites were often associated with higher ΣDDT. However, the

relationship between FDDTo and the “freshness” of the DDT residue is not clear. Similar

degradation rates for p,p’-DDT and o,p’-DDT in soil would lead to residues with FDDTo

unchanged from the technical product. For example, FDDTo in an Ontario farm soil where the

last DDT application was 3 decades ago was 0.77, similar to FDDTo = 0.84 in technical DDT (45).

The variability of FDDTo can also due to the origin of the technical DDT, as it has been reported

that proportion of o,p'-DDT in technical DDT can be quite different depending on the

manufacturer (47). Mexico manufactured DDT, but the composition of its technical product is

not known.

The EFs of o,p’-DDT ranged from 0.480-0.510, showing a mix of racemic and non-racemic

signatures. Since degradation of o,p’-DDT in soils is ambivalent, with approximately equal

proportions of residues with EFs <0.5, = 0.5 and >0.5 (42), deviation from racemic (DEVrac) is

reported to indicate degradation regardless of which enantiomer is depleted (42). Plots showing

significant relationships of DEVrac versus DDT used (r2 = 0.33, p = 0.03) and latitude (r2 = 0.57,

p = 0.001) are shown in Figure 2.4 and values in Table A2.4. More nearly racemic o,p’-DDT at

southern Mexico sites agrees with the relative “freshness” indicated by the higher FDDTe in that

region, as mentioned earlier. This may also suggest the lack of enantioselective degradation of

o,p’-DDT in sites with higher DDT applications.

38

Figure 2.4 Correlation of DEVrac of o,p-DDT with DDT used and latitude.

Endosulfan An extremely high annual mean concentration (26800 pg m-3) of ΣENDO was

measured at Mazatlan (MAZ), which is located in the agricultural area of western Sinaloa.

Endosulfan is the only currently used pesticide that is reported here and it is commonly used

worldwide. It is one of the most frequently detected pesticides in the estuarine sediments and

waters of Ensenada del Pabellion and Bahia et Santa Maria which located in Sinaloa, Mexico

(16). High levels of ENDO had also been reported in the sediments of San Blas, Nayarit which

located ~300 km south of MAZ (15).

Excluding MAZ, ΣENDO ranged from 36-3730 pg m-3 in this study and 92-341 pg m -3 in the

2002-2004 sampling campaign (25). The AM and GM measured at all sites from both sampling

campaigns are 2400 and 368 pg m-3 respectively. High ΣENDO was not always associated with

agricultural sites. For example, a suburban site COL had the second highest ΣENDO (3730 pg

m-3) and moderate level was observed at another suburban site CUE (1280 pg m-3). ΣENDO at

23 sites in Costa Rica ranged from 5-17300 pg m-3 with AM and GM of 342 and 109 pg m-3 (29).

Isomers of parent endosulfan are expressed as FENDO, which defined as FENDO = ENDO I/

(ENDO I + ENDO II). ENDO II is often found to be more vulnerable to degradation (48),

hence a higher FENDO indicates an aged compound. Technical endosulfan has ENDOI/II of 2.3

(49) and the ratio of liquid-phase vapour pressures is 1.39 (50), leading to a vapour ratio of 3.2

for the technical product and gives a FENDO of 0.76. The range of FENDO from both sampling

p = 0.001r2 = 0.57

0.000

0.005

0.010

0.015

0.020

10 15 20 25 30 35Latitude (Degrees)

0.000

0.005

0.010

0.015

0.020

o,p’

-DD

T -D

EVra

c p = 0.03r2 = 0.33

0 50 100 150DDT used (tons)

p = 0.001r2 = 0.57

0.000

0.005

0.010

0.015

0.020


p = 0.001r2 = 0.57

0.000

0.005

0.010

0.015

0.020


0.000

0.005

0.010

0.015

0.020

o,p’

-DD

T -D

EVra

c p = 0.03r2 = 0.33


0.000

0.005

0.010

0.015

0.020

o,p’

-DD

T -D

EVra

c p = 0.03r2 = 0.33


p = 0.03r2 = 0.33


p = 0.03r2 = 0.33


p = 0.03r2 = 0.33

0 50 100 1500 50 100 150DDT used (tons)

39

campaigns is 0.66-0.88 with a mean of 0.82. This is close to the FENDO of technical endosulfan

which suggests recent application in the region.

Toxaphene Toxaphene is a complex pesticide containing hundreds of chlorobornane

congeners. The ΣTOX reported here is the sum of hepta-, octa- and nonachlorobornanes (Table

A2.3). The ΣTOX in this study ranged from 27-689 pg m-3, and 6.2-229 in the 2002-2004

sampling campaign. The AM and GM for both studies were 108 and 58 pg m-3. Highest ΣTOX

was found at BAJ (689 pg m-3), TP (229 pg m-3), and MAZ (150 pg m-3), all in or near cotton-

growing regions in northwest Mexico and Chiapas. Figure 2.5 summarizes the proportion of

toxaphene congeners in air collected in this study and the 2002-2004 sampling campaign (32).

This includes octachlorobornanes (Parlar numbers P26, P39, P42 and P44+) and

nonachlorobornanes (P50 and P63). The symbol 44+ is used to indicate that the

chromatographic peak at this retention time consists of P44 and other unidentified

octachlorobornane (51, 52). These proportions are normalized to coeluting P40+41. The samples

showed depletion in P39, P42 and enrichment of P26 and P44+. Since vapour pressure of P39,

P42 are similar (53), fractionation due to volatility difference is unlikely. Hence, the depletions

of P39 and P42 are likely reflecting the toxaphene congener pattern in soils (32). Enrichment of

P26 is due to its higher liquid phase vapour pressure (51) and hence it is preferentially volatilize

into the air. P50 showed a similar proportion as the technical TOX (0.91) at most sites except

that depletion was observed at BAJ (0.55), CHI (0.73), MAZ (0.54) and MON (0.72). Depletion

of P63 was observed in all the air samples. The congener pattern observed here is a typical soil

emission signature which has been reported in the southern U.S. (54).

40

Figure 2.5 Proportions of toxaphene congeners in air and technical standard. Amounts

normalized to Parlar 40+41. This includes data from Wong et al. (7).

Chlordane and related compounds Chlordane (CHL) was used in Mexico for agriculture and

as a termiticide until 1992, when it was restricted to termiticide use only. Between 1992-1996,

212 tons of chlordane was imported from the U.S. (3). The ΣCHL (TC + CC + TN)

concentrations in this campaign were low, ranging from 1.0 to 18 pg m-3. These levels were

consistent with those reported for southern Mexico in 2002-2004 (25), and the AM and GM for

both sampling campaign were 10 and 8.8 pg m-3 respectively. The ΣCHL concentrations were

significantly higher in urban-suburban areas (CHI, MEX, SLP, COL, COR, CUE, TP, VC; AM =

12 ± 3.4 pg m-3) than at rural and agricultural sites (BAJ, CEL, MAZ, MON, TUX, MT, TB; AM

= 7.3 ± 4.1 pg m-3) at p=0.006. Higher chlordanes in the urban area of Mexico City (11 pg m-3)

than its rural area (2.1 pg m-3) were similarly reported by Bohlin et al. (27). Chlordanes in air of

Mexico are far lower than in U.S. and Canadian cities where ΣCHL from ~70-600 pg m-3 are

reported (26, 35, 40, 55-58). The chlordane-related compounds heptachlor (HEPT) and

heptachlor exo-epoxide (HEPX) were low; only three out of fifteen sites had detectable levels in

both sampling campaigns. The range of positive values over both sampling campaigns was <1.0-

5.0 pg m-3 for HEPT and <1.3-5.9 pg m-3 for HEPX.

0

1

2

3

4

P26 P39 P42 P44+ P50 P63

Amou

nts

norm

aliz

ed to

P40

+41 Technical Standard Air

0

1

2

3

4

P26 P39 P42 P44+ P50 P63

Amou

nts

norm

aliz

ed to

P40

+41 Technical Standard Air

41

Isomers of chlordanes are expressed as fraction of TC (FTC), which defined as TC/(TC+CC).

Technical TC vapour has FTC of 0.62 (40). FTC averaged 0.52 ± 0.19 at agricultural-rural sites

and 0.62 ± 0.13 at urban-suburban sites but not statistically different with p = 0.11. Lower FTC at

nonurban sites was observed in the Great Lakes region (55). The average FTC in Costa Rican air

was 0.58 (28), which is similar to Mexico.

Enantiomeric fractions of trans-chlordane (TC) and cis-chlordane (CC) are presented in Table

2.1. EFs of TC and CC averaged 0.499 ± 0.009 and 0.507 ± 0.003 at agricultural-rural sites, and

0.492 ± 0.006 and 0.509 ± 0.003 at urban-suburban sites. The urban and nonurban means are not

significantly different (p >0.05) in each case. The average EFs for all sites, TC = 0.495 ± 0.008

and CC = 0.508 ± 0.004, were closer to racemic than for chlordanes in the Great Lakes region

(means: TC = 0.473, CC = 0.513), where emission of aged residues in soils is thought to be a

contributor to atmospheric levels (26, 55). EFs in Costa Rican air averaged 0.488 for TC and

0.518 for CC (28).

Lindane and HCH isomers There is no production of lindane (γ-HCH) in Mexico and about 20

tons per year is imported, mainly used for seed treatment for oats, barely and beans. Currently, it

is listed as a restricted pesticide that is authorized for parasite control on livestock and

pharmaceutical use (4). Lindane measured in this study ranged from 8.2-104 pg m-3 and 12-52

pg m-3 in the 2002-2004 sampling campaign (25). Combining data from both studies results in

AM and GM of 30 pg m-3 and 23 pg m-3 respectively. Highest concentration was observed at the

most northern site – BAJ (104 pg m-3). Other isomers in formerly used technical HCH were

sought. α-HCH was measured in both studies ranged from 1.9-20 pg m-3 with AM 10 pg m-3 and

GM 8.8 pg m-3) but β-HCH and δ-HCH were not found.

42

Table 2.1 Enantiomer fraction of trans-chlordane (TC) and cis-chlordane (CC). TP, MT, VC

and TB = Data obtained from the 2002-2004 sampling campaign. Nd = not detected. N =

number of samples.

Dieldrin Low levels of dieldrin were found, ranging from 1.6-7.8 pg m-3 in this study and

0.86-11 pg m-3 in the 2002-2004 sampling campaign (25). Concentrations were distributed fairly

evenly across the country, with slightly higher levels at MT (11 pg m-3). The AM and GM for

both sampling campaign were 3.8 and 3.2 pg m-3 respectively. Much higher levels of dieldrin,

averaging 1200 pg m-3 and accompanied by similarly high levels of aldrin, were found in

Belmopan, Belize during 1995-1996 (31), and 260 pg m-3 of dieldrin was reported in 2000-2001

(26). Levels of dieldrin in Costa Rica in 2004 ranged from 0.4-27 pg m-3 (28), which is

consistent with this study.

Mean Stdev N Mean Stdev NBAJ 0.505 0.007 2 0.507 0.004 2CEL 0.498 0.002 4 0.508 0.004 4CHI 0.498 0.002 4 0.512 0.001 4COL 0.488 0.002 4 0.511 0.007 3COR 0.489 0.004 4 0.507 0.008 4CUE 0.482 0.003 4 0.514 0.002 4MAZ 0.483 0.010 4 0.509 0.002 3MEX 0.491 0.002 4 0.508 0.003 3MON 0.508 0.003 4 0.511 0.003 4SLP 0.497 0.002 4 0.508 0.004 4TUX nd nd nd nd nd ndTP 0.498 0.003 14 0.502 0.004 10MT 0.500 0.002 19 0.503 0.004 18VC 0.497 0.002 20 0.504 0.002 20TB 0.498 0.003 14 0.503 0.003 13

TC CCMean Stdev N Mean Stdev N

BAJ 0.505 0.007 2 0.507 0.004 2CEL 0.498 0.002 4 0.508 0.004 4CHI 0.498 0.002 4 0.512 0.001 4COL 0.488 0.002 4 0.511 0.007 3COR 0.489 0.004 4 0.507 0.008 4CUE 0.482 0.003 4 0.514 0.002 4MAZ 0.483 0.010 4 0.509 0.002 3MEX 0.491 0.002 4 0.508 0.003 3MON 0.508 0.003 4 0.511 0.003 4SLP 0.497 0.002 4 0.508 0.004 4TUX nd nd nd nd nd ndTP 0.498 0.003 14 0.502 0.004 10MT 0.500 0.002 19 0.503 0.004 18VC 0.497 0.002 20 0.504 0.002 20TB 0.498 0.003 14 0.503 0.003 13

Mean Stdev N Mean Stdev NBAJ 0.505 0.007 2 0.507 0.004 2CEL 0.498 0.002 4 0.508 0.004 4CHI 0.498 0.002 4 0.512 0.001 4COL 0.488 0.002 4 0.511 0.007 3COR 0.489 0.004 4 0.507 0.008 4CUE 0.482 0.003 4 0.514 0.002 4MAZ 0.483 0.010 4 0.509 0.002 3MEX 0.491 0.002 4 0.508 0.003 3MON 0.508 0.003 4 0.511 0.003 4SLP 0.497 0.002 4 0.508 0.004 4TUX nd nd nd nd nd ndTP 0.498 0.003 14 0.502 0.004 10MT 0.500 0.002 19 0.503 0.004 18VC 0.497 0.002 20 0.504 0.002 20TB 0.498 0.003 14 0.503 0.003 13

TC CC

43

2.4.2 Seasonal variation of OCPs

Seasonal trends for selected OCPs in relation to 3-days back trajectory airshed maps are shown

for each site in Figures Appendix (A) 2.1.1 to 2.1.10. In general, there were not large differences

in airshed maps among the four sampling periods at a particular site. The strongest seasonality

observed was at Mazatlan, Sinaloa (MAZ). ENDO I showed a sharp increase at MAZ during

Period 3 (November to February) and 4 (February to May) and this may be explained by

examining the back trajectory airshed map shown in Figure A2.1.7. Methods of generating the

airshed maps are described in Gouin et al. (37). The airshed maps showed where the air most

frequently passed within three days before arriving at the sampling site. At MAZ, the air mostly

originated from the Pacific Ocean during Periods 1 (May to August) and 2 (August to

November), but flowed from inland during Periods 3 and 4. It is suggested that endosulfans

measured during the Periods 3 and 4 were due to local agricultural usage, while during Periods 1

and 2, the site was mainly impacted by the cleaner air mass that came from the ocean which led

to lower concentrations. Colima also showed strong seasonality for ENDO I with peak level

observed in Period 3 (November to February). Figure A2.1.4 showed that the air during this

period mainly comes from inland which could account for the source of ENDO I.

Robledo-Marenco et al. (15) indicated that highest concentration of pesticides in the San Blas,

Nayarit estuaries appeared during October to February, where it coincided the time in which

pesticides are applied in the agricultural areas in the region. Presumably the pesticide

application time is the same in Mazatlan and Colima; this would explain our results as our

sampling time during Periods 3 and 4 (November to May), corresponded to the pesticide

application period. In other cases, air pathways do not explain the concentrations observed.

Examples are BAJ (Figure A2.1.1) and Cuernavaca (Figure A2.1.5), which show similar airshed

maps for the sampling periods, yet large differences in seasonal concentrations for some

pesticides. It is cautioned that the back trajectory airshed analysis does not consider the

“quality” of the region through which the air has traveled. Even if the probability of air passing

through a particular region is low its impact may still be high if the emission source is strong.

Furthermore, back trajectory alone may not be sufficient to explain the seasonal difference in air

concentration. Seasonal variation is likely due to a combination of factors, such as current and

44

historical regional usage pattern, wind speed, precipitation, temperature and other meteorological

factors.

This study has provided some baseline data on the organochlorine pesticide levels in the

atmosphere of Mexico which could be useful for establishing long-term atmospheric monitoring

program in the country. This study has also demonstrated the cost-effectiveness of using passive

air sampling technique for mapping regional differences in air concentrations as has been

demonstrated in other studies worldwide (26-28, 30, 35-37, 39, 59). Although this study has

identified some hotspots, other areas of potentially high emissions have not been covered. These

include Oaxaca, where the state with the highest DDT consumption in the country during 1989-

1999 (44), inland agricultural areas of Sinaloa, and cotton-growing regions in Sonora.

2.5 ACKNOWLEDGMENTS

Funding for this study was provided through the North American Commission for Environmental

Cooperation (NACEC) and Environment Canada through the Research Affiliate Program. We

thank Noe Aguilar Rivera, Alejandro Sosa Martínez, Silvia Gelover, Victor Ceja-Moreno, Arturo

Keer Rendón, Juan Emilio García Cárdenas, Fernando Enciso Saracho for their assistance in

collecting the samples. We are grateful to Jacinthe Racine from the Canadian Meteorological

Centre for providing back trajectories data.

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extracts of background air samples (Point Petre, Ontario) using multidimensional gas chromatography-electron capture detection (MD-GC-ECD). Chemosphere, 2000, 40: 201–211.

(53) Bidleman, T.F.; Leone, A.D.; Falconer, R.L. Vapor pressures and enthalpies of

vaporization of toxaphene congeners. J. Chem. Eng. Data, 2003, 48: 1122-1127. (54) Bidleman, T.F.; Leone, A.D. Soil-air relationships for toxaphenes in the southern United

States. Environ. Toxicol. Chem., 2004, 23: 2337-2342. (55) Gouin, T.; Jantunen, L.; Harner, T.; Blanchard, P.; Bidleman, T. Spatial and temporal

trends of chiral organochlorine signatures in Great Lakes air using passive air samplers. 2007, Environ. Sci. Technol., 41: 3877-3883.

(56) Harner, T.; Shoeib, M.; Diamond, M.L.; Stern, G.; Rosenberg, B. Using passive air

samplers to assess urban-rural trends for persistent organic pollutants. 1. Polychlorinated biphenyls and organochlorine pesticides. Environ. Sci. Technol., 2004, 38: 4474-4483.

(57) Offenberg, J.H.; Nelson, E.D.; Gigliotti, C.L.; Eisenreich, S.J. Chlordanes in New Jersey

Air: 1997–1999. Environ. Sci. Technol., 2004, 38: 3488–3497. (58) Offenberg, J.H.; Naumova, Y.Y.; Turpin, B.J.; Eisenreich, S.J.; Morandi, M.T.; Stock, T.;

Colome, S.D., Winer, A.M.; Spektor, D.M.; Zhang, J.; Weisel, C.P. Chlordanes in the indoor and outdoor air of three U.S. cities. Environ. Sci. Technol., 2004, 38: 2760-2768.

(59) Klánová, J.; Kohouteka, J.; Hamplováa, L.; Urbanováa, P.; Holoubeka, I. Passive air

sampler as a tool for long-term air pollution monitoring: Part 1. Performance assessment for seasonal and spatial variations. Environ. Pollut., 2004, 144: 393-405.

50

3 ORGANOCHLORINE PESTICIDES IN SOILS OF MEXICO AND THE POTENTIAL FOR SOIL-AIR EXCHANGE

Fiona Wonga, b, Henry A. Alegriac, Terry F. Bidlemana


Canada, 6248 Eighth Line, Egbert, Ontario, L01 1N0, Canada


of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario, M1C 1A4, Canada

c Department of Environmental Science, Policy and Geography, University of South Florida St.

Petersburg, 140 7th Ave. S., St. Petersburg, Florida, 33701, U.S.

Contribution: F. Wong and H. Alegria collected soil samples in Mexico. F. Wong processed

and analyzed all soil samples. Manuscript was prepared by F. Wong under supervision of Terry

F. Bidleman.

Reproduced with permission from Environmental Pollution, 2010, 158, 749−755. Copyright

2010 Elsevier.

51

3.1 ABSTRACT

The spatial distribution of organochlorine pesticides (OCPs) in soils and their potential for soil-

air exchange was examined. The most prominent OCPs were the DDTs (Geometric Mean, GM

= 1.6 ng g-1), endosulfans (0.16 ng g-1), and toxaphenes (0.64 ng g-1). DDTs in soils of southern

Mexico showed fresher signatures with higher FDDTe = p,p’-DDT/(p,p’-DDT+ p,p’-DDE) and

more racemic o,p’-DDT, while the signatures in the central and northern part of Mexico were

more indicative of aged residues. Soil-air fugacity fractions showed that some soils are net

recipients of DDTs from the atmosphere, while other soils are net sources. Toxaphene profiles in

soils and air showed depletion of Parlar 39 and 42 which suggests that soil is the source to the

atmosphere. Endosulfan was undergoing net deposition at most sites as it is a currently used

pesticide. Other OCPs showed wide variability in fugacity, suggesting a mix of net deposition

and volatilization.

3.2 INTRODUCTION

Among the Latin American countries, Mexico was the largest consumer of organochlorine

pesticides (OCPs) for sanitary and agricultural purposes (Li and MacDonald, 2005; Lopez-

Carrillo et al., 1996). Even though the use of dichlorodiphenyltrichloroethane (DDT) was

stopped in 2000 under the North American Regional Action Plan (NACEC, 2003, 1997a, b), air

samplings conducted between 2002–2005 have shown that DDT concentrations in the air of

southern Mexico were two orders of magnitude above those in the North America Great Lakes

region (Wong et al., 2009a; Alegria et al., 2008 and 2006). Concentrations of DDT, ranging

from hundreds to thousands of ng g-1 dry weight, were reported in residential soils of a highly

exposed community in Chiapas, the southernmost state of Mexico as well as a region where

malaria has historically been endemic (Wong et al., 2008; Herrera-Portugal et al., 2005). As the

use of most OCPs has been restricted, the role of secondary emission from soils may become an

important source to the atmosphere. Our earlier studies have shown that soils in southern

Mexico serve as a source of toxaphene and DDT to the air in some areas and as a sink in others

(Wong et al., 2008). To our knowledge, OCP data have been published only for soils of

52

southern Mexico (Wong et al., 2008; Herrera-Portugal et al., 2005), and some agricultural soils

in central Mexico (Waliszewski et al., 2004). This study was conducted to examine the spatial

distribution of OCPs in rural, urban and agricultural soils of Mexico and to investigate the net

direction of soil−air exchange by coupling soil residue data with air concentrations from

colocated samplers (Wong et al., 2009a and 2008) using the fugacity approach.

3.3 METHODS

3.3.1 Sample collection and analysis

Soil samples were collected at 18 sites across 9 states of Mexico during 2005. The soils came

from various land use types, i.e. urban, agricultural and rural (no agricultural activity and away

from urban centres). Each sample was a composite of 3−6 individual soil cores collected at the

0−5 cm depth. Samples were sieved through a 2-mm mesh and stored at -20°C until analysis.

Figure Appendix (A) 3.1 shows a map of the sampling sites and description of each site is

detailed in Table Appendix (A) 3.1. Soil samples (30 g, wet weight) were fortified with

surrogates prior to extraction. The surrogates were 10 ng each of [2H6]-α-

hexachlorocyclohexane, [13C10]-heptachlor exo-epoxide, [13C10]-trans-nonachlor, [13C12]-

dieldrin, and 50 ng [2H8]-p,p’-DDT. The spiked soils were ground in a glass mortar, mixed with

15 g granular anhydrous sodium sulfate (baked at 450o), placed in precleaned ceramic thimbles

and extracted using Soxhlet apparatus with 400 mL of dichloromethane (DCM) for 18-22 h. The

extracts then were concentrated to 1 mL and solvent exchanged to isooctane, passed through a

column packed with 3 g neutral alumina (0.063–0.30 mm grain size, baked at 450oC and

deactivated by adding 6% deionized water, and eluted with 35 mL of 20% DCM in hexane. The

extracts were finally concentrated to 1 mL and solvent exchange to isooctane for analysis.

Target pesticides were measured using capillary gas chromatography-electron capture negative

ion mass spectrometry (GC-ECNI-MS), on an Agilent 6890 GC – 5973 MSD. Target OCPs

were hexachlorocyclohexanes (α-HCH, β-HCH, γ-HCH, δ-HCH), trans-chlordane (TC), cis-

chlordane (CC), trans-nonachlor (TN), aldrin, dieldrin (DIEL), heptachlor (HEPT), heptachlor

exo-epoxide (HEPX), endosulfans (ENDO I, ENDO II, endosulfan sulfate-ESUL), DDTs (p,p’-

53

DDE, o,p’-DDE, o,p’-DDD, p,p’-DDD, o,p’-DDT, p,p’-DDT) and toxaphenes. For toxaphene

analysis, extracts were further cleanup by vortex mixing with 1 mL of 15% fuming sulfuric acid

and 2 mL of petroleum ether, centrifuging and washing the solvent layer with deionized water.

The extracts were finally blown down to 150 μl. Toxaphenes were quantified as technical

toxaphenes, i.e. sum of 7-Cl, 8-Cl and 9-Cl homologs. Ten individual peaks were quantified

versus Parlar congeners 21, 26, 32, 39, 40, 41, 42, 44+, 50, 63 (44+ refers to Parlar 44 and

unidentified octachlorobornanes which co-elute with 44). Details of the instrumental operating

conditions, ions monitored and sources of chemical standards are reported in Alegria et al.,

(2008).

Chiral analysis was performed after the extracts had undergone sulfuric acid cleanup.

Enantiomer separations for TC and CC were done on a primary column, BetaDEX-120 (20%

permethylated β-cyclodextrin in polydimethylsiloxane), with confirmation using a secondary

column, BGB-172 (30% tert-butyldimethyl-silylated β-cyclodextrin). Details of analytical

procedures are given in Kurt-Karakus et al. (2005). Results of enantiomer separations were

expressed as enantiomer fraction (EF), defined as the peak areas of the (+)/[(+) + (–)]

enantiomers. EF = 0.500 indicates that the chemical is racemic, whereas EF ≠ 0.500 indicates

non-racemic. The average difference in EFs determined by the two chiral columns was 1.5% for

TC (n=24) and 1.1% for CC (n=6). Similar comparisons for a larger set of soil samples are

given by Kurt-Karakus et al. (2005). Only the BGB-172 column was used to separate

enantiomers of o,p’-DDT. Results were also expressed as deviation from racemic, where

DEVrac = the absolute value of (0.500–EF) (Kurt-Karakus et al., 2005).

A separate portion of each soil sample was dried at 105°C for 48 h to determine the moisture

content. Organic carbon content was determined by combustion and measurement of evolved

CO2 after acidification to remove carbonates (Chemisar Laboratories, Guelph, ON, Canada).

54

OCPs in air were measured at co-located stations using passive air samplers which had been

deployed for three months over one year. Annual mean air concentration was used to calculate

the soil-air fugacity. Soil samples were taken at the beginning of the passive air sampling

campaign. Description of the air sampling stations, methods and results are given in Wong et al.

(2009a).


Soxhlet thimbles containing sodium sulfate (15 g) were extracted as blanks (n=7) and the

extracts underwent the same procedures as the soil samples. Limit of detection (LOD) was

defined as mean blank + 3 times the standard deviation. If a chemical was not found in the

blanks, LOD was defined as instrumental detection limit, which was estimated by injecting low

concentrations of target analytes until a small peak at ~3:1 signal: noise ratio was obtained.

LODs were expressed in ng g-1 considering 30 g of soil extracted and a final sample volume of 1

mL, except for toxaphenes, for which the sample volume was 0.15 mL. LOD for individual

chemical are listed in Table A3.2 Half of LOD was used in statistical calculations when the

target chemical was below LOD. Recovery percentages for the fortified surrogates were (n=51):

[2H6]-α-HCH: 91±16%; [13C10]-HEPX: 104±36%; [13C10]-TN: 90±12%; [13C12]-DIEL: 99±28%;

[2H8]-p,p'-DDT: 82±15%. Results are based on the dry weight of the soils.

In chiral analysis, ion ratios for each enantiomer peak were required to fall within the 95%

confidence interval of standards for a satisfactory analysis; otherwise, the result was rejected.

Decisions as to whether a particular sample contained racemic or nonracemic residues were

made by determining whether its EF was significantly different from the mean EF of standards at

p <0.05. Standard EFs ± SD were: TC = 0.501 ± 0.004 (n=10), CC = 0.500 ± 0.002 (n=9), o,p'-

DDT = 0.501 ± 0.002 (n=7).

55


3.4.1 Organochlorine pesticide concentrations

Discussion in this paper includes data collected from southern Mexico (Wong et al., 2008).

Combined with the previous study, there are a total of 29 soil sampling sites covering 12 states of

Mexico, comprising rural (n=4), urban (n=9) and agricultural (n=16) sites. Soil concentrations at

each site are listed in Table A3.2 and a summary of the descriptive statistics is presented in

Table 3.1. Arithmetic means (AM) and geometric means (GM) were calculated with substitution

of 1/2 the LOD for concentrations less than the LOD. Detection frequencies of the OCP classes

were: DDTs 100%, toxaphenes 97%, endosulfans 93%, chlordanes 93%, HCHs 55%, DIEL

21%, HEPX 14% and HEPT 3%. No β-HCH, δ-HCH nor aldrin was detected. Figure 3.1 shows

the box-whisker plot for the most frequently detected OCPs. The arithmetic mean is greater than

the geometric mean which suggested the data are skewed. This is reflected in kurtosis and

skewness coefficients (K = kurtosis/standard error of kurtosis; S = skewness/standard error of

skewness) of frequency distributions (Table 3.1) in which most OCPs have K>10 and S>3.

Significant kurtosis and skewness is shown by K and S values >2; HCHs have the lowest K and

S, with both values < 2. These results indicate the wide variability of most OCPs in the soils and

therefore GM is probably the best measure of central tendency.

56

Figure 3.1 Box-whisker plot of OCPs in soils of Mexico (ng g-1, dry weight). The top end of the

box represents the 75th percentile of the data, and the bottom box represented 25th percentile.

The horizontal line between the boxes is the median, the square is the geometric mean, and the

asterisk is the arithmetic mean. The whiskers on the top and bottom of the boxes indicate 10th

and 90th percentile. Data fell outside this range are plotted as circle with station numbers.

ΣHCH = α-HCH + γ-HCH. ΣCHL = TC+CC+TN. ΣENDO = ENDOI + ENDOII + ESUL.

ΣDDT = p,p'-DDT + o,p'-DDT + p,p'-DDE + o,p'-DDE + p,p'-DDD + o,p'-DDD. ΣTOX =

quantified as technical toxaphene.

1915

21 1515

1

HCH CHL ENDO DDTΣ Σ Σ Σ ΣTOX

8

314

1529

22

7, 119

17, 1824

2914

1428

2629

18

1915

21 1515

0.001

0.01

0.1

10

100

1000

8

314

1529

22

7, 119

17, 1824

2914

1428

2629

18

Soil

conc

entra

tion

(ng

g -1

)

1915

21 1515

1

HCH CHL ENDO DDTΣ Σ Σ Σ ΣΣ Σ Σ Σ ΣTOX

8

314

1529

22

7, 119

17, 1824

2914

1428

2629

18

1915

21 1515

0.001

0.01

0.1

10

100

1000

8

314

1529

22

7, 119

17, 1824

2914

1428

2629

18

Soil

conc

entra

tion

(ng

g -1

)

57

Table 3.1 Summary of OCPs in rural, urban and agricultural soils of Mexico (ng g-1, dry weight)

Abbreviations: GM = geometric mean; AM = arithmetic mean; S.D. = standard deviation; MIN = minimum; MAX = maximum; nd = not detected. K = kurtosis/standard error of kurtosis; S = skewness/standard error of skewness; Kurtosis and skewness analysis was only performed for chemicals that were detected in more than 50% of the sites. ΣHCH = α−HCH +γ−HCH; ΣCHL = TC+CC+TN; ΣENDO = ENDO I + ENDO II + ESUL; ΣDDT = p,p'-DDT + o,p'-DDT + p,p'-DDE + o,p'-DDE + p,p'-DDD + o,p'-DDD; ΣTOX = quantified as technical toxaphene. FTC = TC/(TC +CC); FENDO = ENDO I/(ENDO I + ENDO II); FDDTe = p,p'-DDT/ (p,p'-DDT + p,p'-DDE); FDDTo = p,p'-DDT/ (p,p'-DDT + o,p'-DDT). Calculations were not performed when both species were not detected.

GM GM GM GM MIN MAX K

Σ HCH nd 0.032 0.043 ± 0.034 0.029 0.044 ± 0.044 0.027 0.039 ± 0.038 nd 0.14 1.8

HEPT nd nd nd nd nd 0.028 -HEPX nd nd nd 0.05 ± 0.15 nd 0.03 ± 0.11 nd 0.59 -

Σ CHL 0.013 0.033 ± 0.044 0.13 0.21 ± 0.20 0.036 0.36 ± 0.83 0.047 0.27 ± 0.63 nd 2.7 11F TC 0.47 0.47 ± 0.10 0.49 0.49 ± 0.09 0.58 0.60 ± 0.15 0.53 0.55 ± 0.14 0.40 0.85

Aldrin nd nd nd nd nd nd -Dieldrin nd nd 0.02 ± 0.02 0.01 0.22 ± 0.76 nd 0.13 ± 0.57 nd 3.1 -

Σ ENDO 0.041 0.14 ± 0.23 0.17 0.60 ± 1.3 0.22 57 ± 227 0.16 32 ± 169 0.011 909 28F ENDO 0.72 0.75 ± 0.22 0.59 0.66 ± 0.30 0.43 0.48 ± 0.24 0.51 0.57 ± 0.27 0.21 0.98

Σ DDT nd 0.17 ± 0.07 4.7 45 ± 118 1.5 10 ± 21 1.6 19 ± 67 0.016 360 25F DDTe 0.36 0.40 ± 0.19 0.15 0.31 ± 0.21 0.17 0.27 ± 0.21 0.18 0.30 ± 0.21 0.004 0.72F DDTo 0.77 0.77 0.43 0.62 ± 0.32 0.52 0.66 ± 0.30 0.49 0.65 ± 0.29 0.024 0.94

Σ TOX 0.20 0.24 ± 0.18 1.1 11 ± 22 0.65 23 ± 83 0.64 16 ± 63 nd 334 25

nd

nd

nd

nd

nd nd nd

nd

nd nd

ndnd

AM±S.D. Agricultural (n=16)

AM±S.D.All Sites (n = 29)

AM±S.D. Rural (n=4)

AM±S.D.Urban (n=9)

58

DDT The ΣDDT (sum of o,p'-DDE, p,p'-DDE, o,p'-DDD, p,p'-DDD, o,p'-DDT, p,p'-DDT) in

soils ranged from <LOD to 360 ng g-1, with GM =1.6 ng g-1 and AM =19 ± 67 ng g-1. The

urban soils had the highest ΣDDT (GM = 4.7 ng g-1, AM = 45±118 ng g-1) followed by the

agricultural soils (GM = 1.5 ng g-1, AM = 10±21 ng g-1) and the rural soils (GM = <LOD ng g-1,

AM = 0.17±0.07 ng g-1). There is no significant difference between the urban and agricultural-

rural soils (p>0.05) due to the large standard deviations of the urban soils. The high ΣDDT in

the urban soils is driven by one high value of 360 ng g-1 at a cemetery located in Tapachula,

Chiapas (Site 21). Chiapas is an endemic malarious region and it consumed the greatest amount

of DDT in Mexico during 1989−1999 in sanitary campaigns (Gallardo Diaz et al., 2000).

Herrera-Portugal et al. (2005) reported that outdoor soils from a community in Chiapas with high

DDT exposure contained ΣDDT levels ranging from 350–117 000 ng g-1, with AM = 4760 ng g-

1. Soils from the indoor environment contained even higher ΣDDT, ranging from 2000–683 000

and averaging 21 920 ng g-1. Outdoor soils from a less exposed community contained an

average of 190 ng g-1 ΣDDT. The AM ΣDDT levels in the farm soils sampled by us is five times

lower than in agricultural soils of central Mexico, sampled in 2003 (AM = 54±21 ng g-1)

(Waliszewski et al., 2004). DDT levels in our rural soils are similar to those in background soils

of Costa Rica which had a maximum concentration of 1.7 ng g-1 and most <0.04 ng g-1 (p,p'-

DDE + p,p’-DDD) (Daly et al., 2007a). A weak positive, but not significant correlation was

found between DDT used for malaria control during 1989−1999 and ΣDDT in soils (p = 0.07, r2

= 0.11).

Congeners of DDT were expressed as fractions: 1) FDDTe = p,p’-DDT/( p,p’-DDT + p,p’-DDE)

and 2) FDDTo = p,p’-DDT/( p,p’-DDT + o,p’-DDT). All soils contained detectable p,p’-DDE;

59% contained detectable o,p’-DDT, 48% p,p’-DDT, and 38% o,p’-DDE (Table A3.2). In

calculating the above fractions, 1/2 the LOD was assumed to replace one undetectable species,

while no fractional values were calculated in cases where both species were below the LOD.

Technical DDT has FDDTe = 0.95, and FDDTo = 0.84 assuming the World Health Organization

(WHO) reported composition for technical DDT: 77%, p,p'-DDT, 15% o,p'-DDT, 4 % p,p'-DDE

(WHO, 1989). FDDTe in soils ranged from 0.004−0.72 with AM = 0.30±0.21. DDTs at most sites

(24 out of 29 sites) were dominated by the degradation product, p,p’-DDE, indicative of old

DDT residues. On the other hand, there were 5 out of 29 sites with p,p’-DDT greater than p,p’-

59

DDE, which suggests recent DDT usage or slower degradation in these soils. In soils sampled

by Waliszewski et al. (2004), p,p’-DDT was detected in 100% of these soils, while p,p’-DDE

was detected in only 12%, despite the fact that DDT had not been applied for at least ten years.

FDDTe in air samples from Mexico showed similar values to the soils with mean of 0.34±0.26

(Wong et al., 2009a).

Plots of FDDTe vs. latitude and DDT usage for malaria campaigns are shown in Figure 3.2A and

2B. FDDTe was negatively correlated with latitude (p = 0.001) and positively correlated with DDT

used (p = 0.002). This suggests that fresher DDT is associated with greater DDT usage in

southern Mexico, attributed to the more recent use of DDT in the malaria endemic region, which

is concentrated in the southern part of the country. In this endemic region, increased spraying of

DDTs occurred, mainly in residential areas and in homes, and this is reflected in high ΣDDTs in

soils from the outdoor and indoor environments of highly exposed communities (Herrera-

Portugal et al., 2005). Moreover, there could be atmospheric transport of DDT from the southern

neighboring countries, e.g. Guatemala, or Belize. Alegria et al. (2000) reported that atmospheric

concentrations of DDTs in Belize were as high as those in southern Mexico. Similar

correlations between FDDTe and DDT used/latitude were found for the corresponding air samples


FDDTo in soils ranged from 0.024 to 0.94 with AM = 0.65±0.29 (n=18). A similar value (0.70)

was found in agricultural soils by Waliszewski et al. (2004). FDDTo in WHO technical DDT is

0.84. Interpretation of FDDTo is complicated, as the percentage of o,p’-DDT in the technical DDT

mixture can be considerably varied depending on the manufacturer, and the composition of the

technical DDT used in Mexico is unknown. Plots of FDDTo vs. latitude and DDT used are shown

in Figures 3.2C and D. Although these correlations are significant (p = 0.002 and 0.03,

respectively), examination of the plots suggest that FDDTo is not actually related to these factors.

60

Figure 3.2 Plots of FDDTe vs. A) latitude; B) DDT used; FDDTo vs.C) latitude; D) DDT used.

Most soils have FDDTo ~0.8 and higher, regardless of latitude or DDT use, while four soils have

FDDTo, ~0.2 and lower. In all four of these soils, p,p’-DDT was below detection and 1/2 the

LOD was used in calculating FDDTo. Another source of o,p’-DDT could be from dicofol, a

pesticide synthesized from technical DDT and containing o,p’-DDT as an impurity. According

to Qiu et al. (2005), the Chinese dicofol technical mixture has o,p’-DDT/p,p’-DDT ratio of 6.7,

i.e. FDDTo = 0.13. However, o,p’-DDE is also prevalent in technical dicofol at 44 g kg-1,

compared to 17 g kg-1 for p,p’-DDT, while p,p’-DDE is generally below detection. In this study,

o,p’-DDE were detected in 38% of the samples and they were all ~30-100 times lower than p,p’-

DDE. Given the generally high mean value of FDDTo observed here and the low levels of o,p’-

DDE, it is concluded that the DDT residues in Mexico are probably not related to dicofol usage.

y = 0.0025x + 0.17r2 = 0.30p = 0.002

0.0

0.2

0.4

0.6

0.8

1.0

0 50 100 150

DDT Used (tons)

F DD

Te

B

y = -0.031x + 0.92r2 = 0.33p = 0.001

0.0

0.2

0.4

0.6

0.8

1.0

14 16 18 20 22 24 26 28 30

Latitude

F DD

Te

A

NorthSouth

y = 0.0032x + 0.46r2 = 0.27p = 0.03

0.0

0.2

0.4

0.6

0.8

1.0

0 50 100 150

DDT Used (tons)

F DD

To

D

y = -0.048x + 1.6r2 = 0.45p = 0.002

0.0

0.2

0.4

0.6

0.8

1.0

14 16 18 20 22 24 26 28 30Latitude

F DD

To

C

NorthSouth

61

EF of o,p’-DDT were measurable at 19 out of 29 sites (Table A3.3). EFs in soils ranged from

0.456–0.647. The o,p'-DDT in four soils was racemic (EFs ranged from 0.497–0.500). Other

soils showed enantioselective degradation of either the (+) or the (–), with 7 soils showing EF

<0.500 and 8 soils EF >0.500. The ambivalent enantioselective degradation of o,p’-DDT in soils

is commonly reported worldwide (Wong et al., 2009b; Li et al., 2006; Kurt-Karakus et al., 2005;

Wiberg et al., 2001; Aigner et al., 1998). Accordingly, EFs are also expressed as deviation from

racemic (DEVrac) in order to analyse the degree of enantioselective degradation regardless of

which enantiomer is depleted (Kurt-Karakus et al., 2005). Figure A3.2 displays a positive

correlation between DEVrac of o,p’-DDT in soils and DDT used for malaria control (r2= 0.21, p

=0.05). This correlation is driven by Site 22, which has high DEVrac = 0.147, and removal of

this site resulted in non-significant correlation. No correlation between DEVrac and latitude (r2

= 0.03, p = 0.44) was found, with or without Site 22. DEVrac in Mexican air showed significant

positive correlation with latitude and negative correlation with DDT used, both reflecting

“fresher” DDT in the air of southern Mexico (Wong et al., 2009a). It is noted that the o,p'-DDT

in air was closer to racemic than in the soils as indicated by DEVrac values in air that were

generally smaller than those in soils, Figure A3.3). This suggests that, overall the air is largely

influenced by atmospheric transport of DDT rather than soil emission. There were only three

sites that showed DEVrac in air about the same or greater than in soils. Air and soil samples

were not collected at exactly the same locations and air samples were taken several meters above

ground. Therefore DEVrac of DDTs in air does not necessarily reflect the soil samples at that

site, but could be due to emissions from different soils in the vicinity. Concentrations and

chemical profiles in air reflect those in the soil immediately above the surface and change within

a few meters above the soil (Kurt-Karakus et al., 2006; Eitzer et al., 2003; Finizio et al., 1998).

Toxaphene Toxaphenes are reported as the sum of hepta-, octa- and nonachlorobornanes

(ΣTOX). The ΣTOX from all sampling sites ranged from <LOD to 334 ng g-1, with GM = 0.64

ng g-1 and AM = 16±63 ng g-1. The highest toxaphene concentration, 334 ng g-1, was found at a

farm in Mazatlan (Site 15). The rural soils (GM = 0.20, AM = 0.24±0.18 ng g-1) contained the

lowest ΣTOX. Higher toxaphenes were found in the agricultural (GM = 0.65, AM = 23±83 ng g-

1) and urban soils (GM = 1.1, AM = 11±22 ng g-1). The ΣTOX concentrations in soils of Mexico

are lower compared to those in southern U.S. soils, for which AM and GM of 690 and 92 ng g-1

62

were reported in Alabama, Louisiana and Texas (Bidleman and Leone, 2004a). AM and GM in

South Carolina were 277 and 72 ng g-1, while 83 and 55 ng g-1 were found in Georgia (Kannan

et al., 2003). Congener profiles were normalized to the total amount of the partly resolved Parlar

40+41. Figure 3.3 compares the congener profiles among the soils, air and technical toxaphene

standard. Compared to Parlar 40+41 in technical toxaphene, Parlar 26 is slightly depleted in

soils and 39 and 42 more so. The relative loss of 39 and 42 in soils of Mexico is similar to

profiles in southern U.S. soils and is likely due to microbial degradation of these labile congeners

(Bidleman and Leone, 2004a). Parlar 39 and 42 are also depleted in air, an indication that soil

residues are the main source rather than current toxaphene usage (Wong et al., 2009a; Bidleman

and Leone, 2004a). The enrichment of P44 in both air and soils may due to its formation as the

degradation product of P62 (Ruppe et al., 2004). The lower volatility Parlar 50 and 63 also

showed enrichment in the soils relative to the standard.

Figure 3.3 Proportion of toxaphene congeners in soils, air and technical toxaphene standards

normalized to the amount of Parlar 40+41. Regression statistics for average log Q vs. log liquid

vapour pressure (PL/Pa) for toxaphenes. Q = CSOIL/CAIR. CAIR was obtained from Wong et al.

(2009a).

0

1

2

3

4

5

P26 P39 P42 P44+ P50 P63

Am

ount

rela

tive

to P

arla

r 40+

41

Technical standard Soils Air

y = -0.67x + 5.75r2= 0.52p=0.04

7.2

7.6

8.0

8.4

-3.5 -3.0 -2.5 -2.0

Log PL

Log

Q

0

1

2

3

4

5

P26 P39 P42 P44+ P50 P63

Am

ount

rela

tive

to P

arla

r 40+

41


0

1

2

3

4

5

P26 P39 P42 P44+ P50 P63

Am

ount

rela

tive

to P

arla

r 40+

41


y = -0.67x + 5.75r2= 0.52p=0.04

7.2

7.6

8.0

8.4

-3.5 -3.0 -2.5 -2.0

Log PL

Log

Q

y = -0.67x + 5.75r2= 0.52p=0.04

7.2

7.6

8.0

8.4

-3.5 -3.0 -2.5 -2.0

Log PL

Log

Q

63

Log soil/air concentration ratio (log Q) was plotted against the log liquid-phase vapour pressure

(log PL/Pa) of ten congeners: Parlar 21, 26, 32, 39, 40, 41, 42, 44+, 50 and 63 at all sites. Parlar

21 and 32 were not determined in Chiapas, Veracruz and Tabasco soils collected in 2002-2004 in

the Mexico soil and air samples (Wong et al., 2008). Figure 3.3 shows a significant negative

correlation with r2 = 0.52, p = 0.04. This reveals that, regardless of whether toxaphene is

undergoing net deposition or volatilization, the lighter congeners accumulate in air whereas the

heavier ones accumulate in the soil.

Endosulfan Endosulfan is the only currently used pesticide reported here. The ΣENDO (sum

of ENDO I, ENDO II and ESUL) ranged from <LOD to 909 ng g-1, with overall GM =

0.16 ng g-1 and AM = 32±169 ng g-1. Agricultural soils had the highest ΣENDO, with GM =

0.22, AM = 57±227 ng g-1, followed by the urban soils (GM = 0.17, AM = 0.60±1.3 ng g-1), and

rural soils (GM = 0.041, AM = 0.14±0.23 ng g-1). The highest ΣENDO concentration was found

at a Mazatlan farm (Site 15). This is not surprising as Mazatlan is an intensive agricultural area

where the annual AM of ΣENDO in air was 26 800 pg m-3, which is the highest measured in

Mexico (Wong et al., 2009a). ΣENDO in soils of Costa Rican montane forests ranged from

0.020 to 3.2 ng g-1 (Daly et al., 2009b), which is similar to the levels in Mexican urban and rural

soils. FENDO (ENDO I/ENDOI +ENDO II) for all soils ranged from 0.20–0.98, with AM =

0.58±0.26. In air, FENDO averaged 0.82 (Wong et al., 2009a), which may reflect the higher

volatility of ENDO I (Shen and Wania, 2005). Endosulfan sulfate (ESUL) was detected in 93%

of the soil samples. It is a major degradation product of ENDO I and II, and ranged from 22-

69% (AM = 47%) of ΣENDO. There was no correlation between the percentage of ESUL and

ΣENDO.

Chlordane The ΣCHL (sum of TC, CC and TN) ranged from <0.0033 to 2.7 ng g-1, with GM

= 0.047 ng g-1 and AM = 0.27±0.63 ng g-1. Highest ΣCHL was found at a farm in Mazatlan (Site

15, the same farm where the highest ΣTOX and ΣENDO concentrations were found). The mean

ΣCHL in agricultural soils (GM = 0.036, AM = 0.36±0.83 ng g-1) was not significantly different

from that in the urban soils (GM = 0.13, AM = 0.21±0.20 ng g-1); both were an order of

magnitude greater than in rural soils (GM = 0.013, AM = 0.033±0.044 ng g-1). The AM

concentration in the Mexican agricultural soils was similar to the AM = 0.56 ng g-1 in southern

64

U.S. agricultural soils (Bidleman and Leone, 2004b). The GM concentration for all Mexican

soils was comparable to Costa Rica soils, GM = 0.036 ng g-1 (Daly et al., 2007a). The fraction

of TC to the sum of TC and CC was calculated for soils with 1 or 2 detectable species, as for the

DDTs, FTC = TC/(TC+CC). Technical chlordane has FTC = 0.54 (Jantunen et al., 2000). FTC of

all soil samples spanned a narrow range from 0.40–0.85 with AM = 0.55±0.14. There were no

significant differences among the FTC of urban, agricultural-rural soils (Table A3.3) and this is

similarly reported in the air of Mexico (Wong et al., 2009a).

EFs of chiral chlordanes were measurable at 18 sites for TC and 11 sites for CC (Table A3.3).

Enantioselective degradation of (+)TC and (–)CC were generally found. EFs of TC ranged from

0.380−0.499 with AM = 0.459±0.028. EFs of CC ranged from 0.498−0.588 with AM =

0.529±0.024. These are typical degradation patterns seen in agricultural (Eitzer et al., 2003;

Wiberg et al., 2001; Aigner et al., 1998; Falconer et al., 1997) and background (Wong et al.,

2009b; Daly et al., 2007a; Kurt-Karakus et al., 2005) soils. No significant difference was found

between EFs in the urban and agricultural-rural soils. Heavily contaminated soils near

foundations of houses in the U.S. that were treated with technical chlordane for termite control

contained fresher, racemic chlordane residues (Eitzer et al., 2003). The DEVrac of TC and CC

in soils were compared to the corresponding air. Both showed the same patterns of

enantioselective degradation, but the extent of such degradation was greater in soils than air

(Figure A3.3). Since the air was collected a few meters above ground, the EFs of chlordanes in

air are likely to be more influenced by regional atmospheric transport than soil emission at that

site. Also, the net direction of soil-air exchange appears to be closer to deposition than

volatilization which will be discussed later.

Other OCPs HCHs were above the LOD only in some urban and agricultural soils. The AM

and GM concentrations of ΣHCH (sum of α-HCH and γ-HCH) were 0.039±0.038 and 0.027 ng

g-1. No β-HCH or δ-HCH were detected in any of the samples. HCHs in Mexico soils are lower

than in those in background Costa Rican soils, for which GM = 0.14 ng g-1(Daly et al., 2007a).

Waliszewski et al. (2004) reported ΣHCH = 2.4±3.3 ng g-1 in agricultural soils of central Mexico

which is two orders of magnitude greater than in this study. DIEL was detected in only 6

samples. Five of these samples had concentrations ranging from 0.028–0.30 ng g-1 and one soil

65

with 3.1 ng g-1 was found at MAZ (Site 15). HEPT was detected in 1 sample, with very low

concentration of 0.028 ng g-1. HEPX was detected in 6 samples at <LOD–0.59 ng g-1 with the

maximum at Site 15. Mexican air also showed low concentrations of DIEL, HEPT, and HEPX


3.4.2 Soil-air exchange

Fugacities (f, Pa) in soil and air were estimated for OCPs using the method described in Daly et

al. (2007a). Octanol-air partition coefficients (KOA) as functions of temperature were taken from

Shoeib and Harner (2002), except for toxaphene, in which KOA was estimated from its octanol-

water partition coefficient (KOW) and Henry’s law constant (Bidleman and Leone, 2004a). The

dry solid density of soil was assumed to be 2650 kg m-3. Annual mean air concentrations at or

near soil sampling sites were obtained from Wong et al. (2009a). Results are expressed as

fugacity fraction, ff = fS/(fS + fA), where fS and fA is the fugacity of the chemical in soil and air.

ff = 0.500 indicates that the compound is at soil-air equilibrium, ff >0.500 indicates net

volatilization from soil to air, and <0.500 indicates net deposition from air to soil. However, a

sensitivity analysis performed by Daly et al. (2007a) indicated that a window of 0.50 ± 0.35 may

not represent a significant departure from equilibrium. Others have used a more narrow window

of 0.50±0.20 as an equilibrium condition (Růžičková et al., 2008; Meijer et al., 2003; Harner et

al., 2001).

Figure 3.4 presents the ff of selected OCPs across Mexico and values can be found in Table

A3.4. The ffs cover a wide range, indicating that some soils are net recipients of OCPs from the

atmosphere, while other soils are net sources. Similar wide ranges of ffs have been reported for

soils in eastern and southern Europe (Růžičková et al., 2008). In the latter study, a trend toward

lower median ffs in the cooler seasons was noted. Air concentrations in Mexico also varied

seasonally, although not always with temperature (Wong et al., 2009a). For this reason, only

annual mean concentrations were used to estimate ffs. Since the ffs of most OCPs showed great

variability, GM probably is the best representative of the overall situation at the studied sites and

is used in the following discussions.

66

Figure 3.4 Fugacity fractions (ff) of OCPs in Mexico. ff = fS/(fS+fA), where fS = fugacity of soil;

fA = fugacity of air. ff = 0.5 indicates soil-air equilibrium. ff > 0.5 indicates net volatilization from

soil to air. ff< 0.5 indicates net deposition from air to soils. The top end of the box represents the

75th percentile of the data, and the bottom box represented 25th percentile. The horizontal line

between the boxes is the median, the square is the geometric mean, and the asterisk is the

arithmetic mean. The whiskers on the top and bottom of the boxes indicate 10th and 90th

percentile. Data fell outside this range are plotted as circle. Dashed line indicates the limits over

which ff may not be significantly different from equilibrium (Daly et al., 2007a).

0.00

0.50

1.00

α-H

CH

γ-H

CH TC

EN

DO

I

p,p’

-DD

E

p,p’

-DD

D

o,p’

-DD

T

p,p’

-DD

T

ΣTO

X

Fuga

city

frac

tion

0.00

0.50

1.00

α-H

CH

γ-H

CH TC

EN

DO

I

p,p’

-DD

E

p,p’

-DD

D

o,p’

-DD

T

p,p’

-DD

T

ΣTO

X

Fuga

city

frac

tion

67

The GM ff of DDTs (p,p’-DDT, p,p’-DDE, o,p’-DDT, p,p’-DDD) ranged from 0.02-0.05, which

indicates net deposition. DDTs in air of Mexico are mostly originated from local use and/or

regional air transport rather than from revolatilization of old soil residues. This is supported by

less enantioselective degradation of o,p’-DDT in air than soils. Since the use of DDT stopped in

2000 respectively, it may take more time before they can achieve soil-air equilibrium. As noted

earlier, other studies have reported substantially higher concentrations of DDTs in agricultural

soils (Waliszewski et al., 2004) and in soils of communities where DDT was used for malaria

control (Herrera-Portugal et al., 2005). DDTs have not been measured in the air at these

locations, however when assessing the mean soil concentrations against the mean air

concentrations of DDTs from Wong et al. (2009a), these soils show ffs of >0.90, indicating a

strong potential for net volatilization. DDTs in Mexico is probably experiencing net deposition

in certain areas while others are under net volatilization, particularly in the malarious area where

DDTs were heavily used.

The GM ff of toxaphenes is 0.17. Although below the equilibrium ff = 0.5, the GM ff is within

the equilibrium range as suggested by Daly et al. (2007a). As noted above, depletion of Parlar

congeners 39 and 42 in both soil and air suggests that soil residues are the main source in air, and

the negative correlation of the soil/air concentration ratio with vapour pressure (Figure 3.3)

indicates a close association between the two compartments. The ff of α-HCH, γ-HCH and TC

averaged 0.57, 0.32 and 0.17 respectively which indicates air-soil near equilibrium. The ff of γ-

HCH and TC in Mexico is lower than those reported in Costa Rica (Daly et al., 2007a). The ff of

ENDO I was the lowest among all OCPs, with GM = 0.02. ffs of all sites except one falling

below 0.15, the lower range of the equilibrium boundary. This clearly indicates that ENDO I is

undergoing net deposition from air to soils, this is not surprising as ENDO I is a commonly used

pesticide nowadays.

68

3.5 CONCLUSION

Base on the results from this study, DDTs, endosulfan and toxaphenes are the most prominent

pesticides detected in air and soils of Mexico.We have demonstrated that the soils and air reflects

the DDT usage pattern of Mexico. It is apparent from the sites examined in this study that certain

‘‘hot spot’’ areas exist where soils may be a source of DDTs and other OCPs to the atmosphere

(Figure 3.4). A larger survey of Mexican soils, particularly covering the endemic regions, such

as Oaxaca, is needed to fully assess the contribution of soil emissions to atmospheric levels in

Mexico.

3.6 ACKNOWLEDGEMENTS

We acknowledge funding from the North American Commission for Environmental Cooperation

(NACEC) and Environment Canada through the Research Affiliate Program. We thank the

following colleagues for their assistance in soil sample collection: Víctor Alvarado (Sustancias

Tóxicas En Suelos Y Residuos, Centro Nacional de Investigación y Capacitación Ambiental),

Alfredo Ávila Galarza (Facultad de Ingeniería, Universidad Autónoma de San Luis Potosí),

Erick R. Bandala and Silvia Gelover (Departamento de Ingeniería Civil y Ambiental,

Universidad de Las Americas-Puebla), Juan Emilio García Cárdenas, Idolina de la Cerda

Hinojosa (Jefa del Sistema Integral de Monitoreo Ambiental Agencia de Protección al Medio

Ambiente y Recursos Naturales), Ignacio Galindo Estrada (Centro Universitario de

Investigaciones en Ciencias del Ambiente, Universidad de Colima), Guillermo Galindo Reyes,

Fernando Enciso Caracho (Laboratorio de Toxicología, Facultad de Ciencias del Mar,

Univeridad Autónoma de Sinaloa), Gerardo Gold-Bouchot (Centro de Investigación y de

Estudios Avanzados del IPN), Joaquín Murguía-González and Noe Aguilar Rivera (Facultad de

Ciencias Biológicas y Agropecuarias, Región Orizaba-Córdoba, Universidad Veracruzana), Elias

Ramirez Espinoza (Centro de Investigación en Materiales Avanzados, Chihuahua), Alejandro Sosa

Martínez (Universidad Veracruzana).

69

3.7 REFERENCES

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Alegria, H. A., Bidleman, T. F., Shaw, T. J., 2000. Organochlorine pesticides in the ambient air of Belize, Central America. Environ. Sci. Technol. 34, 1953−1958. Alegria, H. A., Bidleman, T. F., Salvador-Figueroa, M., 2006. Organochlorine pesticides in the ambient air of Chiapas, Mexico. Environ. Pollut. 140, 483−491. Alegria, H. A., Wong, F., Jantunen, L. M., Bidleman, T. F., Salvador-Figueroa, M., Gold-Bouchot, G., Moreno Ceja, V., Waliszewski, S. M., Infanzon, R., 2008. Organochlorine pesticides and PCBs in air of southern Mexico (2002-2004). Atmos. Environ. 42, 8810−8818. Bidleman, T. F., Leone, A. D., 2004a. Soil-air relationships for toxaphenes in the southern United States. Environ. Toxicol. Chem. 23, 2337−2342. Bidleman, T. F., Leone, A. D., 2004b. Soil-air exchange of organochlorine pesticides in the southern United States. Environ. Pollut. 128, 49−57. Daly, G., Lei, Y. D., Teixeira, C., Muir, D. C. G., Castillo, L. E., Jantunen, L. M., Wania, F., 2007a. Organochlorine pesticides in the soils and atmosphere of Costa Rica. Environ. Sci. Technol. 41, 1124−1130. Daly, G., Lei, Y. D., Teixeira, C., Muir, D. C. G., Castillo, L. E., Wania, F., 2007b. Accumulation of current-use pesticides in neotropical montane forest. Environ. Sci. Technol. 41, 1118−1123. Eitzer, B. D., Iannucci-Berger, W., Mattina, M. J. I., 2003. Volatilization of weathered chiral and achiral chlordane residues from soil. Environ. Sci. Technol. 37, 4887−4893. Falconer, R.L., Bidleman, T.F., Szeto, S.Y., 1997. Chiral pesticides in soils of the Fraser Valley, British Columbia. J. Agric. Food Chem. 45, 1946–1951. Finizio, A., Bidleman, T.F. and Szeto, S.Y., 1998. Emission of chiral pesticides from an agricultural soil in the Fraser Valley, British Columbia. Chemosphere 36, 345–355. Gallardo Diaz, E. G., Borja Aburto, V. H., Méndez Galván, J. F., Sánchez Tejeda, G., Olguin Bernal, H., Ramierez Hernández, J. A. Situacion actual de la malaria y el uso de DDT en México. Centro Nacional de Salud Ambiental. Centro de Vigilancia Epidemiologica. Report 01-0206-HEQ, 2000, Ministry of Health of Mexico. Harner, T., Bidleman, T. F., Jantunen, L. M., Mackay, D. 2001. Soil-air exchange model of persistent pesticides in the United States cotton belt. Environ. Toxicol. Chem. 20,1612−21.

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Harner, T., Wideman, J. L., Jantunen, L. M., Bidleman, T. F., Parkhurst, W. J., 1999. Residues of organochlorine pesticides in Alabama soils. Environ. Pollut. 106, 323−332. Herrera-Portugal, C., Ochoa, H., Franco-Sanchez, G., Yanez, L.; Diaz-Barriga, F., 2005. Environmental pathways of exposure to DDT for children living in a malarious area of Chiapas, Mexico. Environ. Research 99, 158−163. Jantunen, L.M., Bidleman, T.F., Harner, T., Parkhurst, W.J. 2000. Toxaphene and other organochlorine pesticides in Alabama air. Environ. Sci. Technol., 34, 5097-5101. Kannan, K., Battula, S., Loganathan, B. G., Hong, C. S., Lam, W. H., Villeneuve, D. L., Sajwan, K., Giesy, J. P., Aldous, K. M., 2003. Trace organic contaminants, including toxaphene and trifluralin, in cotton field soils from Georgia and South Carolina, USA. Arch. Environ. Contam. Toxicol. 45, 30–36. Kurt-Karakus, P.B., Bidleman, T.F., Staebler, R.M., Jones, K.C. 2006. Measurement of DDT fluxes from a historically treated agricultural soil in Canada. Environ. Sci. Technol. 40, 4578–4585. Kurt-Karakus, P. B., Bidleman, T. F., Jones, K. C., 2005. Chiral organochlorine pesticide signatures in global background soils. Environ. Sci. Technol. 39, 8671−8677. Li, J., Zhang, G., Qi, S., Li, X., Peng, X., 2006. Concentrations, enantiomeric compositions, and sources of HCH, DDT and chlordane in soils from the Pearl River Delta, South China. Sci. Total Environ. 372, 215−224. Li, Y. F., Macdonald, R.W., 2005. Sources and pathways of selected organochlorine pesticides in the Arctic and the effect of pathway divergence on HCH trends in biota: a review. Sci. Total Environ. 342, 87−106. Lopez-Carrillo, L., Torres-Arreola, L., Torres-Sanchez, L., Espinosa-Torres, F., Jimenez, C., Cebrian, M., Waliszewski, S., Saldate, O., 1996. Is DDT use a public health problem in Mexico? Environ. Health Perspect. 104, 584−588. Meijer, S.N., Shoeib, M., Jantunen, L.M.M., Jones, K.C., Harner, T. 2003. Air-soil exchange of organochlorine pesticides in agricultural soils. 1. Field measurements using a novel in situ soil sampling device. Environ. Sci. Technol., 33, 1292−1299. NACEC. DDT no longer used in North America. Fact Sheet. DDT. 04-2003. 2003. North American Commission for Environmental Cooperation, Sound Management of Chemicals, Montreal. NACEC. North American Regional Action Plan on Chlordane. 1997a. North American Commission for Environmental Cooperation, Sound Management of Chemicals, Montreal.

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NACEC. North American Regional Action Plan on DDT. 1997b. North American Commission for Environmental Cooperation, Sound Management of Chemicals, Montreal. Qiu, X.; Zhu, T.; Yao, B.; Hu, J.; Hu S., 2005. Contribution of dicofol to the current DDT pollution in China. Environ. Sci. Technol. 39, 4385−4390. Shen, L., Wania, F., 2005. Compilation, evaluation, and selection of physical-chemical property data for Organochlorine Pesticides. J. Chem. Eng. Data 50, 742−768. Shoeib, M., Harner, T., 2002. Using measured octanol-air partition coefficients to explain environmental partitioning of organochlorine pesticides. Environ. Toxicol. Chem. 21, 984−990. Waliszewski, S. M., Carvajal, O., Infanzon, R. M., Trujillo, P., Aguirre, A. A., Maxwell, M., 2004. Levels of organochlorine pesticides in soils and rye plant tissues in a field study. J. Agric. Food Chem. 52, 7045−7050. World Health Organization. Environmental Health Criteria for DDT and its Derivatives, Environmental Aspects. ISBN 92-4-154283-7. World Health Organization: Geneva, Switzerland, 1989. Wiberg, K., Harner, T., Wideman, J. L., Bidleman, T. F., 2001. Chiral analysis of organochlorine pesticides in Alabama soils. Chemosphere 45, 843−848. Wong, F., Alegria, H. A., Bidleman, T. F., Alvarado, V., Angeles, F., Ávila Galarza, A.; Bandala, E. R., de la Cerda Hinojosa, I., Galindo Estrada, I., Galindo Reyes, G., Gold-Bouchot, G., Vinicio Macías Zamora, J., Murguía-González, J., Ramirez Espinoza, E., 2009a. Passive air sampling of organochlorine pesticides in Mexico. Environ. Sci. Technol. 43, 704−710. Wong, F., Robson, M., Diamond, M. L., Harrad, S., Truong, J., 2009b. Concentrations and chiral signatures of POPs in soils and sediments: A comparative urban versus rural study in Canada and UK. Chemosphere 74, 404−411. Wong, F., Alegria, H. A., Jantunen, L. M., Bidleman, T. F., Salvador-Figueroa, M., Gold-Bouchot, G., Ceja-Moreno, V., Waliszewski, S. M., Infanzon, R., 2008. Organochlorine pesticides in soils and air of southern Mexico: chemical profiles and potential for soil emissions. Atmos. Environ. 42, 7737−7745. Ruppe, S., Neumann, A., Diekert, G., Vetter, W., 2004. Abiotic transformation of toxaphene by superreduced vitamin B12 and dicyanocobinamide. Environ. Sci. Technol. 38, 3063–3067. Růžičková, P., Klánová, J., Čupr, P., Lammel, G., Holoubek, I. 2008. An assessment of air-soil exchange of polychlorinated biphenyls and organochlorine pesticides across central and southern Europe. Environ. Sci. Technol. 42, 179-185.

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4 HYDROXYPROPYL-β-CYCLODEXTRIN AS NON-EXHAUSTIVE EXTRACTANT FOR ORGANOCHLORINE PESTICIDES AND POLYCHLORINATED BIPHENYLS IN MUCK SOIL

Fiona Wonga, b, Terry F. Bidlemana


Canada, 6248 Eighth Line, Egbert, ON, L0L 1N0, Canada



Contribution: Experiment designed by Terry F. Bidleman and F. Wong. F. Wong carried out

the experiments, analysis and prepared manuscript under supervision of Terry F. Bidleman.

Reproduced with permission from Environmental Pollution, 2010, 158, 1303−1310. Copyright

2010 Elsevier.

73

4.1 ABSTRACT

Hydroxypropyl-b-cyclodextrin (HPCD) was used as a non-exhaustive extractant for

organochlorine pesticides (OCs) and polychlorinated biphenyls (PCBs) in muck soil. An

optimized extraction method was developed which involved using a HPCD to soil mass ratio of

5.8 with a single extraction period of 20 h. An aging experiment was performed by spiking a

muck soil with 13C-labeled OCs and non-labeled PCBs. The soil was extracted with the

optimized HPCD method and Soxhlet apparatus with dichloromethane over 550 d periodically.

The HPCD extractability of the spiked OCPs was greater than of the native OCPs. A decreased

in HPCD extractability was observed for the spiked OCPs after 550 d of aging and their

extractability approached those of the natives. The partition coefficient between HPCD and soil

(log KCD-Soil) was negatively correlated with the octanol–water partition coefficient (log KOW)

with r2 = 0.67 and p < 0.05.

4.2 INTRODUCTION

Chemical concentration in soil has been used as a measure of soil contamination in risk

assessments. However, the reported concentration is highly dependent on the analytical method

and may not necessarily reflect the actual ecological risk or bioavailability. It is known that after

a chemical enters the soil matrix, it may relocate into the micropores or become irreversibly

bound to the soil, where it is not accessible for microbial breakdown or plant uptake. Hence, the

bioavailability of a chemical may decline over time, while the total concentration in soil remains

the same (Alexander 2000; Barriuso et al., 2008; Luthy et al., 1997). Consequently, risks based

on total concentration may be over-estimated.

Assessing bioavailability of a chemical in soil often involves exposing organisms to a

contaminated soil for a given time period and determining the chemical uptake. These

experiments are very specific to the organism, soil type and chemical properties. Methods

employed are usually costly and labor intensive. Instead of measuring bioavailability,

bioaccessibility has been suggested to be a more feasible measure of risk. According to Semple

74

et al. (2004 and 2007), the bioaccessible fraction of a chemical is freely available for uptake

regardless of its proximity to the organism, whereas the bioavailable fraction is available at a

specific time and location to cross the cell membrane of an organism.

Many studies have demonstrated that non-exhaustive extraction by aqueous hydroxypropyl-β-

cyclodextrin (HPCD, CAS #128446-35-5) was able to predict the bioaccessible fraction of

phenanthrene in a wide range of soils (Allan et al., 2006; Barthe and Pelletier, 2007; Cuypers et

al., 2002; Doick et al., 2006; Hickman et al., 2008; Reid et al. 2000; Stokes et al., 2005). In some

cases, a 1:1 correlation between HPCD extractability and microbial degradation rate was

reported and a scaling factor was not needed (Allan et al., 2006; Reid et al., 2000). Most work

performed so far has been focused on PAHs and little work has been done on organohalogens,

such as the organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs). Recently,

Hartnik et al. (2008) developed a HPCD extraction method for the currently used pesticides α-

cypermethrin and chlorfenvinphos and found that HPCD extractability correlated well with

earthworm uptake. Although the use of PCBs and most OCPs are banned worldwide under the

Stockholm Convention, these chemicals are still commonly found in soil due to their long

persistence and could still pose long term ecological risk. The aim of this study was to develop a

method for HPCD extraction of OCPs and PCBs from a soil of high organic matter content and

investigate changes in extractability with aging of the chemicals in the soil.

4.3 MATERIALS AND METHODS

4.3.1 Chemicals

HPCD (2-hydroxypropyl-β-cyclodextrin, MW 1460) was purchased from Sigma Aldrich

(Steinheim, Germany). Unlabeled OCP and PCB standards were purchased from AccuStandard

(New Haven, CT, USA). 13C–labeled standards were purchased from Cambridge Isotope

Laboratories, Inc. (Andover, MA, USA). Deuterated chemicals were obtained from CDN

Isotopes (Pointe-Claire, QC, Canada). Solvents were supplied by EMD Science (Gibbstown, NJ,

USA). Sodium sulfate, aluminum oxide (neutral), water (HPLC grade), Ringer’s tablets were

75

purchased from EMD Science (Gibbstown, NJ, USA) and R2A agar plates from Oxoid (Nepean,

ON, Canada).

4.3.2 Muck soil

Muck soil with an organic carbon content of 42% and pH of 5.5 (deionized water) was collected

from a farm in Ontario, Canada (Kurt-Karakus et al., 2006). The soil was highly contaminated

with OCPs with last known application about 25 – 40 years ago, except the currently used

pesticide endosulfan. Concentrations of the native OCPs are listed in 4.1. Briefly, the soil

contained 14400 ng g-1 total DDTs, 600 ng g-1 dieldrin, 54 ng g-1 chlordanes (trans- + cis-

chlordane + trans-nonachlor = TC, CC and TN), 1120 ng g-1 endosulfans (endosulfan I, II and

endosulfan sulfate = ENDO I, ENDO II, ESUL). PCBs (below ~ 0.4–2.0 ng g-1, depending on

the congener) and hexachlorocyclohexanes (HCHs, below 0.32 ng g-1) were not detected.

4.3.3 Soil preparation

About 2 kg of the muck soil was sieved with 2 mm mesh and stored at room temperature before

use. An aliquot of the soil (200 g) was spiked with 35 mL of an acetone: isooctane (50:50)

solution which contained isotopically 13C-labeled OCPs and non-labeled PCBs: 13C6-α- HCH, 13C10-TC, 13C10-TN, 13C12-dieldrin, 13C12-p,p’-DDT, and PCB-8, 18, 28, 32, 44, 52, 66, 77, 95,

101, 105, 118, 126, 128, 136, 138, 149, 153, 170, 180, 187, 195. Spiked soil was dried under the

fumehood to dissipate the residual solvent and mixed with the remaining soil by stirring for 30

min. The final concentrations of the spiked and native chemicals are shown in Table 4.1. All

experiments described below were performed in triplicate.

4.3.4 Optimization of the HPCD extraction procedure

i) Effect of HPCD strength on chemical extractability. The soil was used in this experiment after

2 d of spiking. HPCD solutions were prepared in a series of strength (10, 50, 100, 200, 400 and

500 mM) by dissolving the HPCD powder in water. The soil (1 g) was weighed into a 15 mL

glass test tube and 10 mL of HPCD solution was added. The mixture was shaken in the long

76

dimension for 20 h, centrifuged for 30 min at 2500 rpm, decanted and filtered with a glass fiber

filter with 1 μm pore size (Type A/E, Pall Corporation, Ann Arbor, MI, USA) into a 50 mL glass

test tube. Methanol (10 mL) was added to the HPCD solution, which was then fortified with a

suite of surrogate chemicals to monitor recovery during the transfer step from HPCD into

isooctane for analysis. These surrogates were 2H6-γ-HCH, 12C10-heptachlor exo-epoxide

(HEPX), 13C12-PCB-32, 77, 118 and 126. The HPCD–methanol mixture was sonicated for 1 h

and then settled for another 24 h to ensure that the chemicals were fully released from the HPCD.

The mixture was then extracted three times with 10 mL hexane. The combined extract was

blown down under a gentle stream of nitrogen to 1 mL, and solvent exchanged to isooctane.

HPCD solutions (n=8) with no soil were extracted as blanks.

Table 4.1 Soil concentrations of native and spiked OCPs and PCBs after 2 d of spiking

Native OCs ng g-1 Spiked PCBs ng g-1

TC 20 PCB-8 34CC 19 PCB-18 33TN 15 PCB-28 36Dieldrin 600 PCB-32 6.0Endo I 23 PCB-44 55Endo II 380 PCB-52 33ESUL 720 PCB-66 36o,p' -DDE 56 PCB-77 110p,p' -DDE 1830 PCB-95 64o,p' -DDD 250 PCB-101 32p,p' -DDD 920 PCB-105 33o,p' -DDT 2480 PCB-118 102p,p' -DDT 8930 PCB-126 109

PCB-128 32PCB-136 12PCB-138 32

Spiked OCs ng g-1 PCB-149 7.613C6-α-HCH 19 PCB-153 3213C10-TC 8.4 PCB-170 3013C10-TN 7.5 PCB-180 3013C12-Dieldrin 9.8 PCB-187 3013C12-p,p' -DDT 710 PCB-195 30

Native OCs ng g-1 Spiked PCBs ng g-1

TC 20 PCB-8 34CC 19 PCB-18 33TN 15 PCB-28 36Dieldrin 600 PCB-32 6.0Endo I 23 PCB-44 55Endo II 380 PCB-52 33ESUL 720 PCB-66 36o,p' -DDE 56 PCB-77 110p,p' -DDE 1830 PCB-95 64o,p' -DDD 250 PCB-101 32p,p' -DDD 920 PCB-105 33o,p' -DDT 2480 PCB-118 102p,p' -DDT 8930 PCB-126 109

PCB-128 32PCB-136 12PCB-138 32

Spiked OCs ng g-1 PCB-149 7.613C6-α-HCH 19 PCB-153 3213C10-TC 8.4 PCB-170 3013C10-TN 7.5 PCB-180 3013C12-Dieldrin 9.8 PCB-187 3013C12-p,p' -DDT 710 PCB-195 30

77

ii) Effect of extraction time on HPCD extractability. Once the optimized HPCD extraction

strength was determined, the experiment was performed by a longer single extraction time of 40

h, and by four sequential 20-h extractions. The soil used in this experiment was not spiked and

only the native OCPs were determined.

iii) Effect of chemical concentration on HPCD extractability. After the optimized HPCD

strength and extraction time were determined, the procedure was used to extract soil that were

spiked with labeled OCPs (13C6-α-HCH, 13C10-TC, 13C10-TN, 13C12-dieldrin, 13C12-p,p’-DDT) at

a four-fold range of concentrations and details are listed in Figure Appendix (A) 4.1. These

were: 7.7 to 31 ng g-1 13C6-α-HCH; 3.9 to 15 ng g-1 13C10-TC, 3.3 to 13 ng g-1 13C10-TN, 2.7 to

11 ng g-1 13C12-dieldrin, and 275 to 1100 ng g-1 13C12-p,p’-DDT.

iv) HPCD extraction of chemicals from sand. Sand (1 g) was spiked with a suite of non-labeled

OCPs (50 ng) and non-labeled PCBs (30 ng), and extracted by the optimized HPCD procedure.

4.3.5 Exhaustive extraction

A Soxhlet apparatus was used to perform exhaustive extraction. Soil (1 g) was fortified with

known amount of 2H6-γ-HCH, 12C10-HEPX, 13C12-PCB-32, -77, -118 and -126 before mixed

thoroughly with 1 g of granular anhydrous sodium sulfate to remove water. It was then extracted

in an alundum thimble sequentially with dichloromethane (DCM), followed by hexane: acetone

(50:50) and finally by methanol, each for 22 h. Each extract was analyzed independently.

Blanks (n = 6) were run by extracting sodium sulfate and treating it as a sample. The extracts

were concentrated to 1 mL by using a rotary evaporator, blown down with a gentle stream of

nitrogen and solvent exchanged to isooctane. Extracts were cleaned up on a column of neutral

Al2O3 (3 g, 0.063–0.30 mm grain size, deactivated by adding 6% deionized water) overlain with

1 cm granular anhydrous Na2SO4. The column was pre-washed with 10 mL of 20% DCM in

hexane and eluted with 35 mL of 20% DCM in hexane. The sample was finally concentrated to 1

mL by nitrogen blow down and solvent exchanged to isooctane.

78

4.3.6 Soil aging experiment

The spiked soil was divided into 9 portions of 200 g, placed in individual glass jars and stored in

darkness. One jar was sampled after 2, 10, 45, 90, 135, 195, 255, 390 and 550 d of aging. The

sampled soil was extracted by using the optimized HPCD extraction procedure and exhaustively

using the Soxhlet apparatus with DCM, as described above.

4.3.7 Bacterial activity

Estimation of soil bacterial numbers in terms of colony forming units (CFUs) in soil was

performed. At each sampling event, 1 g of soil was weighed into a glass test tube. Sterile

Ringer’s solution (5 mL of 1/4 strength) was added, and the tube was sealed and shaken at the

long dimension at room temperature for 1 h. The test tube was then removed and left to stand for

2 h prior to sampling, after which a 0.5 mL portion of the supernatant was serially diluted and

plated out on agar. The plates were incubated at 25°C for 2 d prior to determination of CFUs.

4.3.8 Instrumental analysis

Sample extracts were adjusted to 1 mL, with 13C12-PCB105 added as an internal standard prior to

analysis. Samples were analyzed by capillary gas chromatography - mass spectrometry (GC-

MS) in the electron capture negative ion (ECNI) mode for OCPs and the electron impact (EI)

mode for PCBs. Instruments used were Agilent 6890 GC – 5973 MSD. In each case the analysis

was done on a 60-m DB-5 column (0.25 mm i.d., 0.25 μm film, J&W Scientific) with He carrier

gas at 30 cm s-1. Sample volumes of 2 μL were injected splitless (split opened after 1.0 min).

Inlet and transfer line temperatures were 265°C and 250°C. For OCPs, the GC oven temperature

program was: 90°C (1 min), ramped to 160°C at 15°C min-1, to 250°C at 2°C min-1, to 270°C at

15°C min-1 and held for 5 min. Methane was the reagent gas at 2.2 mL min-1. For PCBs, the GC

oven temperature program was: 90°C (1 min), ramped to 160°C at 15°C min-1, to 280°C at 3°C

min-1 and held for 15 min. Ion source and quadrupole temperatures for OCPs and PCBs analyses

were 150°C and 106°C. Compounds targeted for the study, along with their monitored ions were

as followed: 13C6-α-HCH (261, 263), 13C10-TC (420, 422), 13C10-TN (454), 13C12-dieldrin (390),

79

13C12-p,p’-DDT (260), 2H6-γ-HCH (261, 263), 12C10-HEPX (328), TC and CC (410, 412), TN

(444, 446), dieldrin (380, 382), ENDO I, II (404, 406) and ESUL (388, 386), o,p’-DDE, o,p’-

DDD, o,p’-DDT (246, 248), p,p’-DDE (316, 318), p,p’-DDD (248); p,p’-DDT (248, 250),

dichlorobiphenyls (di-CB) (222, 224), tri-CB (256, 258), tetra-CB (292, 290), penta-CB (326,

328), hexa-CB (360, 362); hepta-CB (394, 392), octa-CB (430, 428), 13C12-PCB-77 (304, 302), 13C12-PCB-32 (268, 270), 13C12-PCB-105, 118, 126 (338, 336).


The recoveries by DCM extraction of the surrogates fortified into the soil (n=24) were: 2H6-γ-

HCH 88±3%, 12C10-HEPX 111±7%, 13C12-PCB-32 103±17%, 13C12-PCB-77 107±4%, 13C12-

PCB-118 122±19% and 13C12-PCB-126 110±5%. For surrogates that were spiked into the HPCD

solution, recoveries were: 2H6-γ-HCH 92±8%, 12C10-HEPX 101±9%, 13C12-PCB-32 114±11%, 13C12-PCB-77 102±8%, 13C12-PCB-118 101±9% and 13C12-PCB-126 104±9%.

There were no target analytes found in the DCM soil extraction blanks and the HPCD blanks.

Limits of detection for the OCPs in soil were 0.16 to 1.6 ng g-1; and PCBs from 0.4 to 2.0 ng g-1

with 1 mL of sample extract and 1 g of soil. Throughout the entire aging experiment, the soil

was populated with active bacteria. The agar plate count showed 1.7 ×105 to 3.5 × 105 CFU of

bacteria in the soil from 2 to 550 d of aging.

Sequential Soxhlet extraction of the soil showed that most chemicals were recovered from the

DCM extraction (F1). The percent of chemicals extracted by hexane: acetone (50:50) (F2)

relative to the DCM extraction (F2/F1) was 0-16%, with an overall mean of 11%. The third

extraction using methanol (F3) yielded minimal amount of chemicals with F3/F1 of 0–7.7%

(mean 2%) (Table A4.1). F2/F1 and F3/F1 did not changed with the chemical aging time. There

was no difference between the spiked and native OCPs in these exhaustive extraction steps.

Hence, it is assumed that DCM Soxhlet extraction yielded the total extractable chemicals in soil.

80


4.4.1 Optimization of HPCD extraction method

The concentrations of the spiked and native chemicals, based on DCM Soxhlet extraction, are

shown in Table 4.1. “HPCD extractability” was defined as the percentage of chemical extracted

by HPCD relative to DCM Soxhlet extraction. In the following discussion, “spiked chemical”

refers to the isotopically labeled OCPs and non-labeled PCBs. “Native chemical” refers to the

non-labeled OCPs that resided in the soil for 25–40 y (DDTs, chlordanes, dieldrin) or an

indeterminate time for currently used endosulfans.

To optimize the concentration of HPCD, solutions of 10, 50, 100, 200, 400 and 500 mM were

tested using an extraction time of 20 h and after the spiked chemicals had been added to the soil

for 2 d. Results for selected native and spiked OCPs and spiked PCBs are shown in Figure 4.1,

and all results are presented in Table A4.2. HPCD extractability increased with higher HPCD

concentration, and reached plateaus between 200 to 500 mM for OCPs (Figures 4.1A to C).

Hence, 400 mM was chosen for subsequent extractions.

HPCD extractabilities of selected PCBs are shown in Figure 4.1D to F. Extractability of the low

molecular weight (LMW) PCBs (di-, tri- and tetra- congeners) showed trends similar to the

OCPs. The HPCD extractability of the high molecular weight (HMW) PCBs (penta-, hexa-,

hepta- and octa-congeners) increased steadily up to 500 mM HPCD and did not level off like the

LMW PCBs and OCPs. HPCD extractability of the HMW congeners are lower than the LMW

congeners. Possible reasons for this are discussed in Section 4.4.4.

Fresh portions of soil were then extracted with 400 mM HPCD for 40 h. Little or no significant

difference was found between the amount of native OCPs extracted for 20 vs. 40 h (Figure

A4.2), which suggested that 20 h was sufficient. No differences were found in HPCD

extractabilities of spiked chemicals over the range of concentrations (Figure A4.1).

81

Figure 4.1 Effect of HPCD concentration on the extractability of selected native and spiked

OCPs (A to C) and spiked PCBs (D to F)

0%

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p,p’-DDEp,p’-DDDo,p’-DDTp,p'-DDT

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13C12-Dieldrin

13C12-p,p'-DDT

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PCB 8PCB 28PCB 52PCB 77

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82

Sequential extractions of the soil were performed, each using 400 mM HPCD for 20 h. Results

for native TC and p,p’-DDT is shown in Figure A4.3. For TC, the first HPCD extraction

removed about 39% of the residue from the soil, while the second, third and fourth extractions

removed 15%, 10% and 7% respectively. For p,p’-DDT, the first HPCD extraction removed 36%

of the residue, while the second, third and fourth extractions removed 17%, 9% and 6%

respectively. The subsequent extractions probably liberated some of the recalcitrant residues

that are more strongly bound to the soil. Thus, HPCD is a mild extraction method that even with

four extractions did not liberate all chemical from the soil. Doick et al. (2006) performed

sequential HPCD extraction of phenanthrene that had been aged in soil for 0, 14, 26 and 58 days.

The authors reported that fractions of phenanthrene extracted by the first HPCD portion declined

over time, while second and third portions of HPCD did not change over time. Furthermore, the

first extraction provided a 1:1 correlation with the fraction of phenanthrene available for

microbial mineralization. They suggested that a single HPCD extraction is sufficient to liberate

the loosely bound fraction that is accesible for microbial degradation.

Hartnik et al. (2008) reported that the amount of chemical extracted by HPCD is controlled by

the soil-water-HPCD equilibrium system. HPCD would extract the loosely bound fraction if its

extraction capacity exceeds the sorption capacity of the soil. Otherwise, the system would

achieve equilibrium and the HPCD would not extract all the loosely bound fraction from the soil.

The extraction efficiency of the HPCD-soil-water system can be evaluated by calculating the

maximum extraction fraction, MEF = EC/(EC+SC). It is the maximum amount of compound in

soil that can be extracted with a certain amount of HPCD, where EC is the HPCD extraction

capacity (EC = QCDKCD) and SC is the soil sorption capacity (SC = QSOILfOCKOC). QCD and QSoil

are the masses of HPCD and soil, KCD and KOC are the partition coefficients between HPCD and

water, and soil organic carbon and water and fOC is the fraction of organic carbon in the soil.

MEF is calculated for this study using the optimized HPCD extraction conditions; i.e. 400 mM

concentration and 20 h. KCD was determined according to a log-linear regression between KOW

vs. KCD (log KCD = 0.62 log KOW + 0.34) reported by Wang and Brusseau (1993). KOC was

calculated according to Seth et al. (1999). Table A4.3 compares the calculated MEF and the

experimental results after 2 d of aging. The MEF is always greater than the experimental

extractability and indicates that the quantity of HPCD is sufficient to extract all the loosely

83

bound chemical from the soil. It is also noted that the HPCD-soil mass ratio used in this study is

5.8:1, which is higher than the ratios suggested by Hartnik et al. (ratio = 3.5:1) for extraction of

α-cypermethrin and chlorfenvinphos and Hickman et al. (2008) (ratio = 1.5:1) for extraction of

PAHs. Based on the above considerations, “HPCD extractability” using the optimized procedure

refers to the residue liberated (relative to DCM extraction) by 400 mM HPCD in a single 20-h

extraction.

4.4.2 Concentrations of chemicals in the soil over 550 d of Aging

Soil concentrations (CSOIL), as determined by DCM extraction, did not change over the 550 d of

aging for most native or spiked chemicals, with %RSD ranged from 5% to 16% for OCPs and 5-

11% for PCBs (Table A4.4). Exceptions were for spiked 13C6-α-HCH, native endosulfans

(ENDO I, II and ESUL), spiked PCBs 8 and 28, which showed losses over time. Ln CSOIL of

these chemicals are plotted against time in Figure 4.2A-B. Concentrations of 13C6-α-HCH

decreased from 19 ng g-1 to 4.1 ng g-1 over 550 d and resulted in a half live of 249 d assuming a

pseudo-first order degradation. Endosulfans were native to the soil, although the age of the

residues could not be established since it is a currently used pesticide. Over the time of

laboratory aging, ENDO I decreased from 23 to 4.7 ng g-1. From Figure 4.2B, the degradation

appears to be biphasic, with a slightly faster rate over 130 d and a slower rate thereafter. If a

pseudo-first order loss is assumed over the entire aging period, the half life is 240 d. ENDO II

and metabolite ESUL were in much higher abundance than ENDO I, as often reported (Weber et

al., 2009). ENDO II showed little change, from 375 ng g-1 initially to 287 ng g-1 at 550 d, while

ESUL declined from 718 ng g-1 to 494 ng g-1 at 550 d. Half lives of these were 1425 and 1020 d.

84

Figure 4.2 Degradation of 13C6-α-HCH, PCB 8, PCB 28, Endo I, II and ESUL in soils over 550

d of aging.

-1.0

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y = -0.0029x + 2.89r2 = 0.97

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13C6-α-HCH

PCB 8

PCB 28

y = -0.0007x + 6.62

r2 = 0.70

y = -0.0005x + 5.96r2 = 0.56

y = -0.0029x + 2.92r2 = 0.86

Endo I

Endo II

ESUL

A

B

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SO

ILln

CS

OIL

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13C6-α-HCH

PCB 8

PCB 28

y = -0.0007x + 6.62

r2 = 0.70

y = -0.0005x + 5.96r2 = 0.56

y = -0.0029x + 2.92r2 = 0.86

Endo I

Endo II

ESUL

A

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lnC

SO

ILln

CS

OIL

85

Losses of the two LMW PCBs were also biphasic. PCB 8 decreased rapidly within 10 d with

more than 90% of chemical lost. The rate decreased greatly afterwards, and 93% of PCB 8 was

lost between 10 to 500 d. Because of this great difference in rates, no half life was estimated.

Accelerated loss of PCB 28 took place in the first 10 d, but not to the extent as for PCB 8.

Treating the entire depletion as a pseudo-first order process led to a half life of 184 d. This is

much shorter than those suggested by Mackay et al. (2006) that ranged from 2 to 6 y for

trichlorobiphenyls.

4.4.3 Effect of aging on HPCD extractability

The HPCD extractability of spiked and native OCPs and spiked PCBs was measured after 2, 10,

45, 90, 135, 195, 255, 390, and 550 d of spiking, using the optimized HPCD procedure. The

HPCD extractabilities of selected OCPs and PCBs are presented in Figure 4.3 and details are

shown in Table A4.5. Overall, changes in extractability were small. The largest decreases were

between 2 and 10 d and occurred for both spiked and native compounds. This was not expected

since the native OCPs (other than endosulfans) have resided in the soil for decades. It may be

that the native OCPs were partly desorbed from the soil during the processes of sieving, addition

of spiking solution and further mixing. Extractability of the OCPs decreased more slowly

afterwards. Simple linear regression of percent extractability vs. time from 10-550 d showed

significant decreases for all spiked OCPs (p <0.05 for 13C6-α-HCH, 13C10-TC and 13C12-dieldrin;

p<0.06 for 13C10-TN and 13C12-p,p’-DDT) and some native OCPs (p<0.05 for dieldrin, ENDO I,

ESUL, DDDs). Extractabilities of the spiked OCPs were higher and approached those of the

native OCPs over time.

86

Figure 4.3 Effect of aging on the HPCD extractability of spiked and native OCPs, and spiked

PCBs.

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PCB 126 PCB 128PCB 138

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50%

60%

0 100 200 300 400 500 600

PCB 28 PCB 32PCB 52

20%

30%

40%

50%

60%

0 100 200 300 400 500 600

PCB 28 PCB 32PCB 52

10%

20%

30%

40%

0 100 200 300 400 500 600


10%

20%

30%

40%

0 100 200 300 400 500 600


0%

10%

20%

30%

40%

0 100 200 300 400 500 600

Time (d)


20%

30%

40%

50%

0 100 200 300 400 500 600

HP

CD

Ext

ract

abilit

y TC13C10-TC

0%

10%

20%

30%

40%

0 100 200 300 400 500 600

Time (d)


20%

30%

40%

50%

0 100 200 300 400 500 600

HP

CD

Ext

ract

abilit

y TC13C10-TC

10%

20%

30%

40%

0 100 200 300 400 500 600

HP

CD

Ext

ract

abilit

y TN13C10-TN

10%

20%

30%

40%

0 100 200 300 400 500 600

HP

CD

Ext

ract

abilit

y TN13C10-TN

20%

30%

40%

50%

60%

70%

0 100 200 300 400 500 600

HP

CD

Ext

ract

abilit

y

Dieldrin13C12-Dieldrin

20%

30%

40%

50%

60%

0 100 200 300 400 500 600

Time (d)

HP

CD

Ext

ract

abilit

y p,p'-DDT13C12-p,p'-DDT

87

Figure 4.4 presents the ratio of extractability of spiked/native OCPs against time. Decrease in

this ratio for TC, TN, dieldrin and p,p’-DDT was observed. This implies that the spiked OCPs

became more strongly bound to the soil with time. The spiked/native extractability ratio of TC,

TN and dieldrin approached 1.0 - 1.1 by the end of 550 d of aging. On the other hand, the

extractability of spiked p,p’-DDT remained ~30% higher than the native residue, even at 550 d.

Since the KOW of p,p’-DDT is higher than those of TC, TN and dieldrin, it is possible that

chemicals with higher hydrophobicity require more time to form bound residues or relocate to

micropores where they becomes inaccessible for microbial uptake. The behavior of spiked PCBs

was different from the OCPs. After an initial decline in extractability between 2 and 10 d, most

PCBs showed no significant change (p>0.05) up to 550 d, the only exception being PCB 32.

This agrees with Cousins et al. (1998), who reported lack of an aging effect for PCB partitioning

from soil to air.

Figure 4.4 Relative HPCD Extractability of spiked to native OCPs ratio over 550 days of aging.

0.9

1.0

1.1

1.2

1.3

1.4

1.5

0 200 400 600Time (d)

Spi

ked/

Nat

ive

HP

CD

E

xtra

ctab

ility TC

TNDieldrinp,p'-DDT

88

It was observed that the decrease in HPCD extractability was non-linear and there was always a

portion of the chemical remaining extractable. Previous studies have found that a pseudo first

order model could well describe the decreasing extractable fraction of chemicals aged in soil

(Menchai et al., 2008; Northcott and Jones, 2001). Zhang et al. (2007) found that a first-order

model with fast and slow transfer kinetics to two soil organic matter compartments described

changes in sorption of hexachlorobenzene and DDT to soil over time. Following Menchai et al.

(2008), eq. [4.1] is applied to model the data.

Yt = Y0 + A ekt Eq [4.1]

where Yt is the percent of chemical extracted by HPCD at a specific time (t), Y0 is the percent of

chemical that remains extractable at infinity, Y0+A is the extractable percent of chemical at t = 0,

k is the rate constant which describes the first-order movement of chemical from extractable to

non-extractable sites. Table A4.6 lists the modeled parameters, Y0, k, A and R2 for all the

chemicals and compares Y0 + A with the experimental data. The results have shown that Y0 + A

is reasonably similar to the experimental values at Day 2 for all spiked and most native OCPs.

Modeled results for Y0+A are greater than 100% for some chemicals, even though the R2 is high.

This is unrealistic as the initial amount cannot be greater than 100%. Within the OCPs class, this

occurred for native o,p’-DDE (p,p’-DDE was also unrealistically high, with Y0 + A = 80%), but

for over half of the PCB congeners. As noted earlier, the HPCD extractability of most PCBs

dropped sharply between 2 and 10 d and remained the same through 550 d. Thus, eq 1 is not a

good model for the spiked PCBs.

4.4.5 Effect of physical-chemical properties on HPCD extractability

As mentioned in Section 4.3.1, extractability of the HMW PCBs was lower than LMW PCBs.

Two factors may be responsible for this. They may be too large to be accommodated by the

HPCD cavity, and their sorption to the soil organic matter may be stronger. To investigate the

first possibility, the molecular volume (MV, nm3 molecule-1) of PCB was calculated by the ratio

of the LeBas molar volume (nm3 mol-1) (Mackay et al., 2006) to the Avogadro’s number

(molecule mol-1) and plotted against HPCD Extractability after Day 2 of spiking (Figure A4.4).

89

Negative correlation was observed (r2 = 0.70, p < 0.001). The cavity of β-HPCD was reported

from 0.262 – 0.346 nm 3 in volume (Shieh and Hedges, 1996; Wang and Brusseau, 1993). MV

of di- and tri-chlorinated PCBs ranged from 0.376 to 0.411 nm3 molecule-1, whereas the MV of

the tetra-, penta-, hexa-, hepta- and octa-PCBs ranged from 0.446 – 0.584 nm3 molecule-1.

Hence, the HMW PCBs may be too large for effective HPCD encapsulation which resulted in the

low extractability. Investigations using cyclodextrins with larger cavities would be worthwhile.

Reid et al. (2000) reported that pyrene and benzo[a]pyrene gave lower HPCD extractabilities

than phenanthrene due to their larger size and steric hindrance at the HPCD cavity that prevents

the complexation of the HMW PAHs, and a similar effect on the solubilization of PAHs by

cyclodextrins was noted by Shixiang et al. (1998).

The second possibility was investigated by extracting the target chemicals from spiked sand,

which has little or no organic matter and thus sorption of organic chemical is minimal.

HPCD extractability of chemicals from sand ranged from 65–97% for OCPs and 77–84% for

PCBs (Table A4.7). Overall, the extraction efficiency from sand was greater than soil. ENDO II

and ESUL had the lowest recovery from the sand (73% and 65%). Interestingly, there was no

difference between the extractability of the LMW and HMW PCBs from sand; all congeners

ranged from 76-84%. The sand experiment results suggest that the low extractability of the

HMW PCBs from soil is due to their higher hydrophobicity and stronger sorption to the organic

matter in soil, however the possibility of a molecular size effect is not negated. It may be that

inclusion of HMW PCBs in the HPCD, however ineffective, is still sufficient to compete with

sand in sorbing capability.

HPCD extractabilities of OCPs and PCBs were expressed as a partition coefficient between the

HPCD and the soil, KCD-Soil = mass of chemical per mass of HPCD (ng g-1)/mass of chemical per

mass of soil (ng g-1). Octanol-water partition coefficients (KOW) were obtained from Mackay et

al. (2006). Log KCD-Soil was plotted against log KOW for 2, 90, 255, 550 d of aging. Log KCD-Soil

was negatively correlated with log KOW of the spiked PCBs at Day 2 with r2 = 0.67 and p <0.05

(Figure 4.5A). Dieldrin, TC, TN and p,p’-DDT cluster close to the PCBs, but α-HCH with log

KOW = 3.7 deviates strongly from this correlation. This indicates that, within the PCB class, the

90

more hydrophobic chemicals are less readily partitioned to the aqueous solution of HPCD from

the soil organic matter, resulting in lower KCD-Soil. Significant negative correlation between

KCD-Soil and KOW was observed for PCBs aging after 90, 255 and 550 d (Figures 4.5B to D).

Wang and Brusseau (1993) reported that the log partition coefficient between water and HPCD

(log KCD) is linearly proportion to log KOW for trichloroethene, chlorobenzene, naphthalene and

DDT. However Shixiang et al. (1998) found that molecular size was a more important indicator

of cyclodextrin solubilization of PAHs than log KOW.

Figure 4.5 Log KCD-Soil of spiked OCPs and PCBs vs. Log KOW at Day 2, 90, 255 and 550 of

aging. Regression is performed on PCBs only.

Day 2

y = -0.37x + 1.30R2 = 0.67

-2.0

-1.5

-1.0

-0.5

3 4 5 6 7 8 9

Log KOW

Log

KC

D-S

oil

PCBs

OCs

Linear(PCBs)

Day 90y = -0.28x + 0.56

R2 = 0.60

-2.0

-1.5

-1.0

-0.5

3 4 5 6 7 8 9

Log KOW

Log

KC

D-S

oil

PCBs

OCs

Linear(PCBs)

Day 255y = -0.32x + 0.78

R2 = 0.60-2.0

-1.5

-1.0

-0.5

3 4 5 6 7 8 9

Log KOW

Log

KC

D-S

oil

PCBs

OCs

Linear(PCBs)

Day 550y = -0.31x + 0.74

R2 = 0.54

-2.0

-1.5

-1.0

-0.5

3 4 5 6 7 8 9

Log KOW

Log

KC

D-S

oil

PCBs

OCs

Linear(PCBs)

p<0.001p<0.001

p<0.001p<0.001

A B

C D

Day 2

y = -0.37x + 1.30R2 = 0.67

-2.0

-1.5

-1.0

-0.5

3 4 5 6 7 8 9

Log KOW

Log

KC

D-S

oil

PCBs

OCs

Linear(PCBs)

Day 90y = -0.28x + 0.56

R2 = 0.60

-2.0

-1.5

-1.0

-0.5

3 4 5 6 7 8 9

Log KOW

Log

KC

D-S

oil

PCBs

OCs

Linear(PCBs)

Day 255y = -0.32x + 0.78

R2 = 0.60-2.0

-1.5

-1.0

-0.5

3 4 5 6 7 8 9

Log KOW

Log

KC

D-S

oil

PCBs

OCs

Linear(PCBs)

Day 550y = -0.31x + 0.74

R2 = 0.54

-2.0

-1.5

-1.0

-0.5

3 4 5 6 7 8 9

Log KOW

Log

KC

D-S

oil

PCBs

OCs

Linear(PCBs)

p<0.001p<0.001

p<0.001p<0.001

Day 2

y = -0.37x + 1.30R2 = 0.67

-2.0

-1.5

-1.0

-0.5

3 4 5 6 7 8 9

Log KOW

Log

KC

D-S

oil

PCBs

OCs

Linear(PCBs)

Day 90y = -0.28x + 0.56

R2 = 0.60

-2.0

-1.5

-1.0

-0.5

3 4 5 6 7 8 9

Log KOW

Log

KC

D-S

oil

PCBs

OCs

Linear(PCBs)

Day 255y = -0.32x + 0.78

R2 = 0.60-2.0

-1.5

-1.0

-0.5

3 4 5 6 7 8 9

Log KOW

Log

KC

D-S

oil

PCBs

OCs

Linear(PCBs)

Day 550y = -0.31x + 0.74

R2 = 0.54

-2.0

-1.5

-1.0

-0.5

3 4 5 6 7 8 9

Log KOW

Log

KC

D-S

oil

PCBs

OCs

Linear(PCBs)

p<0.001p<0.001

p<0.001p<0.001

A B

C D

91

4.4.6 Comparison of HPCD extractability with other studies

In this study, the HPCD extractability of spiked p,p’-DDT was 41% while the native p,p’-DDT

was 32% at the end of the 550 d aging experiment. Menchai et al. (2008) reported that the

bioavailability of freshly spiked DDTs in sediments declined from 11 to 1.5% by end of one year

of incubation based on SPMD measurement. Tang et al. (1999) found that the earthworm

bioavailability of DDT from various soil ranged from 3-28% under laboratory conditions. In a

tropical soil, the methanol extractable fraction diminished from 98% to 29% after 356 d and the

bound residues based on acid digestion, increased from 0 to 9% of the total DDTs extracted

(Singh and Agarwal, 1999). DDT bioavailability to earthworms was about 30% of the total

extractable residue based on hexane: acetone Soxhlet extraction in soil aged for 40 y by

Morrison et al. (2000). Despite the different soil properties in these studies, it appears that the

bioavailability of DDTs is around 30% of the total extractable amount, and this is in line with the

HPCD extractability of p,p’-DDT from the muck soil found in this study.

4.5 CONCLUSION

A non-exhaustive chemical extraction method using HPCD to extract OCPs and PCBs from soil

was developed. Log KCD-soil was inversely correlated with log KOW for PCBs, which suggested

that the more hydrophobic congeners were more strongly bound to the soil, although the

possibility of size-exclusion by the HPCD cavity cannot be excluded. HPCD extractabilities of

the spiked chemicals were initially greater than for the natives. Their extractabilities declined and

approached to those of the natives over time, indicating greater binding to the soil with age.

Results support previous findings that HPCD could be an appropriate extractant for

distinguishing loosely and strongly bound residues and inferring the bioaccessibility of

chemicals in soil. Further studies of merit would be to expand the chemical classes, test

cyclodextrins with different cavity sizes and investigate correlations with bioavailability.

92


We acknowledge funding from the Chemicals Management Plan, and the Research Affiliate

Program through Environment Canada. We thank Frank Wania and Myrna Simpson of

University of Toronto for helpful discussions.

4.7 REFERENCES Allan, I. J., Semple, K. T., Hare, R., Reid, B. J., 2006. Prediction of mono- and polycyclic aromatic hydrocarbon degradation in spiked soil using cyclodextrin extraction. Environ. Pollut. 144, 562–571. Alexander, M., 2000. Aging, bioavailability, and overestimation of risk from environmental pollutants. Environ. Sci. Technol. 34, 4259–4265. Barriuso, E., Benoit, P., Dubus, I. G., 2008. Formation of pesticide nonextractable (Bound) residues in soil: Magnitude, controlling factors and reversibility. Environ. Sci. Technol. 42, 1845–1854. Barthe, M., Pelletier, E., 2007. Comparing bulk extraction methods for chemically available polycyclic aromatic hydrocarbons with bioaccumulation in worms. Environ. Chem. 4, 271–283. Cousins, I., McLachlan, M.S., Jones, K.C., 1998. Lack of an aging effect on the soil-air partitioning of polychlorinated biphenyls. Environ. Sci. Technol. 32, 2734–2740. Cuypers, C., Pancras, T., Grotenhuis, T., Rulkens, W., 2002. The estimation of PAH bioavailability in contaminated sediments using hydroxypropyl-beta-cyclodextrin and Triton X-100 extraction techniques. Chemosphere, 46, 1235–1245. Doick, K. J., Clasper, P. J., Urmann, K., Semple, K. T., 2006. Further validation of the HPCD-technique for the evaluation of PAH microbial availability in soil. Environ. Pollut. 144, 345–354. Hartnik, T., Jensen, J., Hermens, J. L. M., 2008. Nonexhaustive b-cyclodextrin extraction as a chemical tool to estimate bioavailability of hydrophobic pesticides for earthworms. Environ. Sci. Technol. 42, 8419–8425. Hickman, Z. A., Swindell, A.L., Allan, I. J., Rhodes, A. H., Hare, R., Semple, K. T., Reid, B. J., 2008. Assessing biodegradation potential of PAHs in complex multi-contaminant matrices. Environ. Pollut. 156, 1041–1045.

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Kurt-Karakus, P.B., Bidleman, T.F., Staebler, R.M., Jones, K.C., 2006. Measurement of DDT fluxes from a historically treated agricultural soil in Canada. Environ. Sci. Technol. 40, 4578–4585. Luthy, R. G., Aiken, G. R., Brusseau, M. L., Cunningham, S. D., Gschwend, P., Pignatello, J. J., Reinhard, M., Traina, S. J., Weber JR, W. J., Westall, J. C., 1997. Sequestration of hydrophobic organic contaminants by geosorbents. Environ. Sci. Technol. 31, 3341–3347. Mackay, D., Shiu, W.Y., Ma, K. C., Lee, S. C. Handbook of Physical-Chemical Properties and Environmental Fate for Organic Chemicals, 2nd ed.; CRC Press UK: London, 2006. Menchai, P., Van Zwieten,L., Kimber, S., Ahmad, N., Rao, P. S. C., Hose, G., 2008. Bioavailable DDT residues in sediments: Laboratory assessment of ageing effects using semi-permeable membrane devices. Environ. Pollut. 153, 110–118. Morrison, D. E., Robertson, B. K., Alexander, M., 2000. Bioavailability to earthworms of aged DDT, DDE, DDD and dieldrin in soil. Environ. Sci. Technol. 34, 709–713. Northcott, G. L., Jones, K. C., 2001. Partitioning, extractability, and formation of nonextractable PAH residues in soil. 1. Compound differences in aging and sequestration. Environ. Sci. Technol. 35, 1103–1110. Reid, B. J., Stokes, J., Jones, K. C., Semple, K. T., 2000. Nonexhaustive cyclodextrin-based extraction technique for the evaluation of PAH bioavailability. Environ. Sci. Technol. 34, 3174–3179. Semple, K. T., Doick, P. Burauel, A. Craven, H. Harms and Jones, K. C., 2004. Defining bioavailability and bioaccessibility of contaminated soil and sediment is complicated. Environ. Sci. Technol. 38, 228A–231A. Semple, K.T., Doick, K.J., Wick, L.Y., Harms, H., 2007. Microbial interactions with organic contaminants in soil: Definitions, processes and measurement. Environ. Pollut. 150, 166-176. Seth, R.; Mackay, D.; Muncke, J., 1999. Estimating the organic carbon partition coefficient and its variability for hydrophobic chemicals. Environ. Sci. Technol. 33, 2390–2394. Shieth, W. J., Hedges, A. R., 1996. Properties and applications of cyclodextrins. JMS Pure Appl. Chem. A33, 673–683. Shixiang, G., Liansheng, W., Qingguo, H., Sukui, H., 1998. Solubilization of polycyclic aromatic hydrocarbons by β-cyclodextrins and carboxymethyl-β-cyclodextrins. Chemosphere 37, 1299–1305. Singh, D., Agarwal, H. C., 1995. Persistent of DDT and nature of bound residues in soil at higher altitude. Environ. Sci. Technol. 29, 2301–2304.

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Stokes, J. D., Wilkinson, A., Reid, B. J., Jones, K. C., Semple, K. T., 2005. Prediction of polycyclic aromatic hydrocarbon biodegradation in contaminated soil using an aqueous hydroxypropyl-β-cyclodextrin extraction technique. Environ. Toxicol. Chem. 24, 1325–1330. Tang, J., Robertson, B. K., Alexander, M., 1999. Chemical-extraction methods to estimate bioavailability of DDT, DDE, and DDD in soil. Environ. Sci. Technol. 33, 4346–4351. Wang, X., Brusseau, M.L., 1993. Solublization of some low-polarity organic compounds by hydroxypropyl-b-cyclodextrin. Environ. Sci. Technol. 27, 2821–2825. Weber, J., Halsall, C. J., Muir, D., Teixeira, C., Small, J, Solomon, K., Hermansen, M., Hung, H., Bidleman, T. F., 2009. Endosulfan, a global pesticide: a review of its fate in the environment and occurrence in the Arctic. Sci. of Total Environ. Doi: 10.1016/j/scitotenv.2009.10.077 Zhang, J. J., Wen, B., Shan, X. Q., Zhang, S., Khan, S.U., 2007. Temporal change in the distribution patterns of hexachlorobenzene and dichlorodiphenyltrichloroethane among various soil organic matter fractions. Environ. Pollut. 150, 234–242.

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5 AGING OF ORGANOCHLORINE PESTICIDES AND POLYCHLORINATED BIPHENYLS IN MUCK SOIL: VOLATILIZATION, BIOACCESSIBILITY AND DEGRADATION

Fiona Wonga, b, Terry F. Bidlemana



b Department of Chemistry, University of Toronto Scarborough, 1265 Military Trail, Toronto,

Ontario, M1C 1A4, Canada

Contribution: Experiment designed by Terry F. Bidleman and F. Wong. F. Wong carried out

the experiments, analysis and prepared manuscript under supervision of Terry F. Bidleman.

Manuscript submitted to Environmental Science and Technology, August 18, 2010.

96

5.1 ABSTRACT

An organic rich muck soil which is highly contaminated with native organochlorine pesticide

(OCPs) was spiked with known amounts of 13C-labeled OCPs and non-labeled polychlorinated

biphenyls (PCBs). Spiked soils were aged under indoor, outdoor and sterilized conditions and

the change in volatility, surrogate bioaccessibility and degradation of chemicals was monitored

periodically over 730 d. Volatility was measured using a fugacity meter to characterize the soil-

air partition coefficient (KSA = CSOIL/CAIR). The fraction of bioaccessible residues was estimated

by comparing recoveries of chemical with a mild extractant, hydroxylpropyl-β-cyclodextrin

(HPCD) vs. a harsh extractant, dichloromethane. KSA of the spiked OCPs in the non-sterile

(Indoor, Outdoor) soils were initially lower and approached the KSA of native OCPs over time,

showing reduction of volatility upon aging. HPCD extractability of spiked OCPs and PCBs were

negatively correlated with KSA, which suggests that volatility can be used as a surrogate for

bioaccessibility. Degradation of endosulfans, PCB 8 and 28 was observed in the non-sterile

soils, and α-HCH showed selective degradation of the (+) enantiomer. Enantiomer fractions

(EF) in air and HPCD extracts were lower than in non-sterile soils, suggesting greater

sequestering of the (+) enantiomer in the soil during microbial degradation.

5.2 INTRODUCTION

Soil is an important medium in controlling the global cycling of organic chemicals. After a

chemical enters the soil, it can be leached, degraded, vaporized, or remain sorbed to soil particles

for years. Models have shown that soils are acting as a sink, initially absorbing chemicals from

the atmosphere during periods of increased emissions. As emissions decline, soils become a

source and chemicals are slowly being released back to the atmosphere. (1–4). This has been

demonstrated by field studies in which volatilization of legacy and current- use pesticides from

contaminated soils was measured (5–10).

97

The dimensionless soil-air partition coefficient (KSA = CSOILS/CAIR) has been conventionally

estimated by the modified Karickhoff model (11, 12):

SOILOAOCSA KK ρφ411.0= Eq [5.1]

where φOC is the fraction of soil organic carbon, ρSOIL is soil density (kg L-1), KOA is the octanol-

air partition coefficient and 0.411 is a constant with unit of L kg-1. Over the years, there have

been many studies carried out to directly measure KSA under various environmental conditions

(12–24). Hippelein and McLachlan (12, 17) reported that the above model systematically

underestimated the measured KSA by a factor of 1.5 for polychlorinated biphenyls (PCBs) and

chlorobenzenes. Other studies have shown differences up to an order-of-magnitude beteween

experimental and predicted KSA for organochlorine pesticides (OCPs), polyaromatic

hydrocarbons (PAHs) and PCBs (13, 15, 16, 18). Niederer et al. (21) found that the partition

coefficient of a chemical between natural organic matter/air was highly variable and showed up

to an order of magnitude difference depending on the source of the humic and fulvic acids (such

as terrestrial vs. aquatic).

KSA is a key parameter used in soil emission models, which assume that the availability of a

chemical for soil-air exchange does not vary over time. However, it has been well documented

that as the contact time of a chemical in soil increases, its bioaccessability and extractability may

decline as the chemical relocates within the soil matrix to sites where organisms cannot access,

or forms strong irreversible bonds with the soil (25–32). Similarly, aging may reduce the

volatilization of a chemical, a phenomenon which has largely been ignored. If only a fraction of

the chemical is available for emission to the atmosphere, this could lead to over-prediction in

soil-air emission models. Cousins et al. (14) reported that after spiking a low organic carbon soil

with PCBs, there was a short period of increased PCB volatilization for about 48 d and no further

changes in volatilization were observed for the subsequent 390 d.

This study investigated the effect of aging on the volatilization and bioaccessibility of OCPs and

PCBs in a high organic matter soil. A contaminated muck soil was spiked with known amount

of labeled chemicals and portions of the soil were aged in an indoor and outdoor environment.

98

The volatility and bioaccessibility of the native and spiked chemicals in soil were monitored

periodically up to 730 days of aging. The degradation kinetics of spiked OCPs and PCBs, and

the enantioselective degradation of 13C6-α-HCH, were also studied.

Volatilization under equilibrium conditions was determined using a fugacity meter as described

by Meijer et al. (20). Bioaccessibility was determined by a mild chemical extraction method

using an aqueous solution of hydroxypropyl-β-cyclodextrin (HPCD). HPCD extraction of PAHs

has been shown to correlate well with microbial mineralization rates (32–36) and earthworm

uptake of current-use pesticides from soils (37). Recently, an optimized HPCD extraction

method was developed to estimate the bioaccessibility of OCPs and PCBs from a high organic

matter soil (38). It was shown that the extractability of the aged residues was lower than that of

the freshly spiked chemicals, and the HPCD extractability of the spiked chemicals declined over

time.

5.3 EXPERIMENTAL METHOD

5.3.1 Soil preparation

Muck soil with an organic carbon content of 42% (39) and pH of 5.5 (deionized water) was

collected from a farm in Ontario, Canada. The soil was highly contaminated with OCPs with last

known application occurring about 25 to 40 years ago, except for endosulfan, which is currently

used. The soil contained 14400 ng g-1 total DDTs (p,p’- and o,p’- isomers of DDT, DDE and

DDD), 600 ng g-1 dieldrin, 54 ng g-1 chlordanes (trans- + cis- chlordane + trans-nonachlor = TC,

CC and TN), 1120 ng g-1 endosulfans (endosulfan I, II and endosulfan sulfate = ENDO I, ENDO

II, ESUL). PCBs (< 0.4 – 2.0 ng g-1, depending on the congener) and hexachlorocyclohexanes

(α- and γ- HCHs, <0.32 ng g-1) were below detection limits.

Muck soil was sieved with 2 mm mesh and stored at room temperature before use. An aliquot of

the soil was spiked with an acetone: isooctane (50:50) solution which contained isotopically 13C-

labeled OCPs and non-labeled PCBs: 16 ng g-1 of 13C6-α- HCH, 8 ng g-1 each of 13C10-TC, 13C10-

99

TN, 600 ng g-1 of 13C12-p,p’-DDT, and 10 to 160 ng g-1each of PCB-8, 18, 28, 32, 44, 52, 66, 77,

95, 101, 105, 118, 126, 128, 136, 138, 149, 153, 180, 187. Spiked soil was dried under the

fumehood to dissipate the residual solvent and mixed with the remaining soil by stirring for 30

min.

5.3.2 Aging

The spiked soil was separated into portions of 200 g and aged under different conditions as

followed: i) Indoor – soil portions were sealed in glass jars and aged in the laboratory, under

room temperature (20 to 26 °C) and darkness; ii) Outdoor – soil portions were placed in

aluminum cans with open ends covered with mesh. They were then returned to the farm and

buried at 5 cm underground and aged in the field; iii) Sterile – Prior to spiking, these soils were

sterilized by γ-irradiation with a dosage of 25 kGy at the Department of Chemical Engineering

and Applied Chemistry, University of Toronto. The Sterile soils were aged in the laboratory as

the Indoor soils. Replicate jars of Indoor and Sterile soils were sampled periodically over 550 d

and Outdoor soils over 730 d. During sampling the soils in the remaining jars were stirred briefly

to provide aeration. The volatility, bioaccessibility and concentration of the spiked and native

chemicals, and the bacterial activities were monitored. The soil moisture contents of the Indoor

and Sterile soils in the jars were stable throughout the incubation period at 24±4% and 31±2%

respectively. The soil moisture content of the Outdoor soil was variable depending on the time

of the sampling and ranged from 18 to 47% with the highest moisture content found during

spring. Hippelein and McLachlan (17) reported that soil water content has no influence on the

KSA above 0°C when the relative humidity of the air over the soil was maintained at 100%.

5.3.3 Volatilization measurements

The soil-air partition coefficient (KSA = CSOIL/CAIR) was employed as an indicator of

volatilization changes over time. KSA was made dimensionless by multiplying CSOIL/CAIR by the

dry soil solids density of 1530 kg m-3 for the muck soil (20). CAIR (ng m-3) was measured by

placing the soils into a fugacity meter as described by Meijer et al. (20). In the fugacity meter,

nitrogen was first passed through a water reservoir to achieve 100% relative humidity before it

100

entered the soil column, where soil-air exchange occurred. The effluent air was passed through

an adsorbent trap which contained C18-silica based sorbent (2.5 g, particle size 40-70 μm,

International Sorbent Technology Ltd, Mid Glamorgan, UK) to capture the volatilized chemical.

The C18 resin was rinsed with 10 mL of acetone three times and 10 mL of DCM before use.

After a sample was collected, a surrogate recovery solution containing 26 ng of 2H6-γ-HCH, 10

ng each of 12C10-heptachlor exo-epoxide (HEPX), 13C12-PCB-32, -77, and -126, and 1 ng of 13C12-PCB-118 was added to the C18 resin, then the C18 resin was eluted with 40 mL of 50:50

hexane: dichloromethane. The extract was blown down under a gentle stream of nitrogen and

solvent exchanged to 200 μl isooctane for quantitative analysis. A fugacity meter with no soil

was run as air blank (n = 19).

To ensure that measurements were performed under equilibrium condition, the flow rate was

varied from 0.14 to 0.76 L min-1 and stable air concentrations were maintained, see Figure

Appendix (A) 5.1. The flow rate used in this study was 0.60±0.10 L min-1 and 0.75 m3 of air

was sampled for each event.

CSOIL (ng g-1 dry weight) was obtained by an exhaustive extraction method, using 200 mL

dichloromethane (DCM), as fraction 1(F1), in a Soxhlet apparatus for 22 h followed by cleanup

with neutral alumina. Sequential Soxhlet extraction of the soil by 50:50 hexane/acetone (F2) and

methanol (F3) was performed ensure DCM yields the total extractable fraction. The average

100xF2/F1 and 100xF3/F1 were 11% and 2% respectively, so DCM removed most of the

extractable chemicals. Sodium sulfate was extracted as blanks (n=27). Details of the cleanup

procedure using alumina chromatography are given in Wong and Bidleman (38).

5.3.4 Bioaccessibility and bacterial activity

Bioaccessibility was measured by a mild chemical extraction method described in Wong and

Bidleman (38). Briefly, 1 g of soil was mixed with 10 mL of 400 mM hydroxypropyl-β-

cyclodextrane (HPCD) solution. The HPCD was shaken, centrifuged and extracted with

methanol. The HPCD-methanol solution was extracted with hexane to recover the chemicals

followed by blown down to 1 mL and solvent exchanged to isooctane for analysis. Estimation

101

of soil bacterial numbers in terms of colony forming units (CFUs) was performed by plate counts

following the method described in ref (38).

5.3.5 Quantitative analysis

13C12-PCB105 was added to the extracts as an internal standard. Quantitative analysis was

performed by capillary gas chromatography - mass spectrometry (Agilent 6890 GC – 5973

MSD) in electron capture negative ion mode (ECNI) for OCPs and electron impact mode (EI) for

PCBs. Analysis was done on a 60-m DB-5 column (0.25 mm i.d., 0.25 μm film, Agilent

Technologies), with helium as the carrier gas. GC oven temperature program, ions monitored

and settings on the MSD are detailed in ref (38).

Enantiomers of 13C6-α-HCH were separated using the β-DEXcst (BDXcst, proprietary

composition, 30 m × 0.25 mm i.d., 0.25 μm film thickness, Restek, Bellefonte, P.A, U.S.) as the

primary column and BGB-172 column (BGB, 20% tert-butyldimethylsilylated β-cyclodextrin in

OV-1701, 30 m x 0.25 mm i.d., 0.25 μm film thickness, BGB Analytik AG, Switzerland) as the

secondary column for confirmation purpose. Instrument operating conditions are reported in

Kurt-Karakus et al. (7). Results of enantiomer separations were expressed as enantiomer fraction

(EF), defined as the peak areas of the (+)/[(+) + (–)] enantiomers. EF = 0.500 indicates the

chemical is racemic, whereas EF ≠ 0.500 indicates nonracemic. Data presented are an average of

the primary and confirmation results.


There were no target analytes found in the soil blanks and HPCD blanks. Limits of detection for

the OCPs in soil were 0.16 to 1.6 ng g-1; and for PCBs ranged from 0.4 to 2.0 ng g-1 with 1 mL of

sample extract and 1 g of soil. No OCPs were found in the air blanks (n = 19) but trace levels

were found of PCBs 8, 18, 32, 28, 52, 44, 66, 95, 101, 118 and 149. Their blank values ranged

from 0.01 to 0.63 ng m-3 and are listed in Table A5.1.

102

The percentage recovery of surrogates in air, soil and HPCD solutions were: 2H6-γ-HCH

86±10%, 85±10%, 84±10%; 12C10-HEPX 104±15%, 93±20%, 123±20%; 13C12-PCB-32 90±8%,

84±2%, 86±8%; 13C12-PCB-77 99±9%, 106±11%, 105±10%; 13C12-PCB-118 91±9%, 102±21%,

94±10%; and 13C12-PCB-126 97±10%, 100±19%, 99±15%.

Heterogeneity of the soil was determined by extracting 5 sub-samples of the soil with DCM after

2 d of spiking. The relative standard deviations (%RSD) of chemical concentrations averaged

16%. The %RSD of KSA (25 sets of triplicate measurements) ranged from 7 to 23% (mean 15%)

for native and spiked OCPs and from 6 to 16% (mean 10 %) for PCBs.

5.4 RESULTS

Throughout the entire aging experiment, the Indoor and Outdoor soils were populated with active

bacteria. CFU measurements are presented in Table A5.2. The range of CFU for the Indoor

soils was 1.7 ×105 to 3.5 × 105 CFU, and for the Outdoor soils was 3.0 ×105 to 1.1 ×106. The

Sterile soils showed no CFU count at Day 0, but low bacterial activities were observed starting at

Day 45, with 710 CFU and up to 1300 CFU at Day 550 of aging. It is possible that radiation

did not completely kill all the bacteria in the soil and a stronger dose of radiation is needed to

keep the soil sterile for a prolonged period. Still, the CFU of the Sterile soils was 2 to 3 orders

of magnitude lower than those in the Indoor and Outdoor soils.

5.4.1 Effect of aging on volatility

Experimental KSA measurements are listed in Tables A5.3 to 5.5. Figure 5.1 shows the changes

in KSA for TC, 12C10-TC, TN, 13C10-TN, p,p’-DDT, 13C12-p,p’-DDT, and PCBs 18, 52, 95 and

149 with aging time for the Indoor, Outdoor and Sterile soils. KSA of most compounds in the

Indoor soils rose after spiking to plateau after 90-195 d. Slow increases up to the end of the

aging period were noted for some of the high molecular weight (HMW) PCBs and the

endosulfans (Table A5.3). The plateaus were reached more quickly (60 d) in Outdoor soils,

while Sterile soils showed no consistent changes in KSA over the aging period (Figure 5.1).

103

Cousins et al. (14) observed that a rapid increase of KSA one day after PCBs were spiked into a

low organic carbon soil, followed by a period of steady KSA for the next 392 d. Kobližková et

al. (18) reported similarly that the soil-to-air flux was highest the first day after spiking, and then

decreased and stabilized over the next 2 to 3 d, as seen with the Sterile soil in the current study.

The increase in KSA over time for some native compounds in the Indoor soils (Figure 5.1), was

unexpected, as these had resided in the soil for decades. The increase in the early aging period

for native TC, TN and p,p’-DDT in the Indoor soils was less than for the spiked compounds.

Slight increase in KSA was noted for the native compounds in the Outdoor soils between days 2 –

20. It may be that the native OCs were partly desorbed from the soil during the processes of

sieving, addition of spiking solution and further mixing. However, this was not seen with the

Sterile soil and we cannot offer an explanation for the KSA increase for native compounds in the

Indoor soils.

KSA of spiked OCPs were lower than KSA of the native compounds, with the greatest difference

seen for p,p'-DDT. Relative KSA were expressed as ratio of the spiked/native for the Indoor,

Outdoor and Sterile soils (Table A5.6). A ratio of 1 means the spiked and native chemicals have

the same soil volatility. No increase in relative KSA was observed upon aging for the Sterile soils

but a slow increase was seen for the Indoor and Outdoor soils for TC, TN and p,p’-DDT. This

suggests that the spiked chemicals become more tightly bound to the soil and less volatile over

the entire time of the experiment, slowly approaching the KSA of the natives.

104

Figure 5.1 Changes in log KSA over the aging time for selected OCPs and PCBs in Indoor (IN),

Outdoor (OUT) and Sterile (ST) soils.

7.2

7.4

7.6

7.8

8.0

0 200 400 600

Time (d)

log

KS

A

TC 13C10-TC

p,p'-DDT 13C12-p,p'-DDT

log

KS

Alo

g K

SAlo

g K

SA

PCB 18 PCB 52PCB 95 PCB 149

IN OUT ST

IN OUT ST

IN OUT ST

IN OUT ST

TC 13C10-TC TC 13C10-TC

p,p'-DDT 13C12-p,p'-DDT p,p'-DDT 13C12-p,p'-DDT



TN 13C10-TN TN 13C10-TN TN 13C10-TN

7.2

7.4

7.6

7.8

0 200 400 600

7.2

7.4

7.6

7.8

8.0

0 200 400 600

7.2

7.4

7.6

7.8

0 200 400 600

7.2

7.4

7.6

7.8

8.0

0 200 400 600 800

7.2

7.4

7.6

7.8

0 200 400 600 800

6.0

7.0

8.0

0 200 400 6006.0

7.0

8.0

0 200 400 6006.0

7.0

8.0

0 200 400 600 800

8.0

8.5

9.0

9.5

0 200 400 6008.0

8.5

9.0

9.5

0 200 400 6008.0

8.5

9.0

9.5

0 200 400 600 800

7.2

7.4

7.6

7.8

8.0

0 200 400 6007.2

7.4

7.6

7.8

8.0

0 200 400 6007.2

7.4

7.6

7.8

8.0

0 200 400 600

Time (d)

log

KS

A

TC 13C10-TCTC 13C10-TC

p,p'-DDT 13C12-p,p'-DDTp,p'-DDT 13C12-p,p'-DDT

log

KS

Alo

g K

SAlo

g K

SA

PCB 18 PCB 52PCB 95 PCB 149PCB 18 PCB 52PCB 95 PCB 149

IN OUT ST

IN OUT ST

IN OUT ST

IN OUT ST

TC 13C10-TCTC 13C10-TC TC 13C10-TCTC 13C10-TC

p,p'-DDT 13C12-p,p'-DDTp,p'-DDT 13C12-p,p'-DDT p,p'-DDT 13C12-p,p'-DDTp,p'-DDT 13C12-p,p'-DDT



TN 13C10-TNTN 13C10-TN TN 13C10-TNTN 13C10-TN TN 13C10-TNTN 13C10-TN

7.2

7.4

7.6

7.8

0 200 400 600

7.2

7.4

7.6

7.8

8.0

0 200 400 600

7.2

7.4

7.6

7.8

0 200 400 600

7.2

7.4

7.6

7.8

8.0

0 200 400 600 800

7.2

7.4

7.6

7.8

0 200 400 600 800

6.0

7.0

8.0

0 200 400 6006.0

7.0

8.0

0 200 400 6006.0

7.0

8.0

0 200 400 600 800

8.0

8.5

9.0

9.5

0 200 400 600

6.0

7.0

8.0

0 200 400 600 800

8.0

8.5

9.0

9.5

0 200 400 6008.0

8.5

9.0

9.5

0 200 400 6008.0

8.5

9.0

9.5

0 200 400 600 800

105

Plateau KSA were determined for the spiked chemicals by averaging the KSA measurements after

they have leveled off (i.e. after 195 d for Indoor, 390 d for Outdoor and 210 d for Sterile). The

plateau values are listed in Tables A5.3 to 5.5. Plateau KSA ranges for 13C6-a-HCH 6.82-7.06;

TC 7.64-7.68; 13C10-TC 7.55–7.62; p,p’-DDT 8.95-9.16; 13C12-p,p'-DDT 8.67-8.84. The plateau

KSA of selected native OCPs from the Indoor soils was compared to a previous study which

measured KSA on the same soil using the same fugacity meter as this study (20). Table A5.7

showed that our measurements agreed within a factor of two. Plateau KSA of the Sterile soils

were lower than the non-sterile soils (i.e. Indoor and Outdoor) for most chemicals except for the

OCPs (Figure A5.2). No difference in plateau KSA was observed bewteen the Indoor and

Outdoors soils for all chemicals. Possible reasons are discussed later.

5.4.2 Effect of aging on bioaccessibility and correlation with KSA

The bioaccessibile fraction was estimated by HPCD extraction and expressed as HPCD%, the

percentage of chemical accessed by HPCD relative to exhaustive extraction with

dichloromethane (38). Results are given in Tables A5.8 to 5.10. Figure A5.3 displays the HPCD

extractability of TC, 12C10-TC, TN, 12C10-TN, p,p’-DDT, 13C12-p,p’-DDT, PCB 18, 52, 95 and

149 for the Indoor, Outdoor and Sterile soils. At all time points, HPCD% for spiked OCPs were

higher than for the native compounds, which suggests that spiked chemicals were more loosely

bound to the soil and more bioaccessible. For the Indoor and Outdoor soils, a decrease in

HPCD% was observed early in the aging process and approached a plateau thereafter. There was

no change in the HPCD% for most compounds in the Sterile soils with the exception of

endosulfans, for which HPCD% decreased over time.

Figure 5.2 shows a plot of decreasing HPCD% vs. increasing KSA for the spiked OCPs and

selected PCBs for the Indoor soils over the course of aging. Significant negative correlations

were found for the OCPs and the lower molecular weight (LMW) PCBs - 18, 32, 52 (p-values

<0.001 to 0.02). No correlations between HPCD% and KSA were seen for the HMW PCBs (e.g.,

95, 149, p-values = 0.24 and 0.33 respectively). Wong and Bidleman (38) suggested that there is

a size dependence of HPCD extraction of PCBs. Reid et al. (40) reported lower extractability for

106

pyrene and benzo[a]pyrene than for phenanthrene which they attributed to their larger size and

steric hinderance at the HPCD cavity.

Figure 5.2 Changes in HPCD extractability % and KSA over the aging time for spiked OCPs and

PCBs in the Indoor soils.

30%

40%

50%

60%

0.5 1.0 1.5 2.0

25%

35%

45%

55%

0.2 0.4 0.6 0.8

22%

26%

30%

34%

1 2 3 4 522%

26%

30%

34%

0 5 10 15 20

30%

40%

50%

0.2 0.4 0.6 0.830%

40%

50%

1.5 2.0 2.5 3.0 3.5 4.0

35%

45%

55%

20 40 60 8030%

40%

50%

60%

0.1 0.3 0.5 0.7

20%

25%

30%

35%

1 2 3 4 5 6

KSA X 107

HP

CD

ext

ract

abilit

y %

r2 = 0.56p = 0.02

PCB 52

r2 = 0.76p = 0.002

PCB 32

r2 = 0.18, p = 0.24

PCB 95

r2 = 0.13p = 0.33

PCB 149

r2= 0.84p = 0.0005

13C6-α-HCH

r2 = 0.70p = 0.005

13C12-p,p'-DDT

r2 = 0.49p = 0.03

PCB 18

13C10-TC 13C10-TN

r2= 0.74p = 0.003

r2= 0.75p = 0.002

30%

40%

50%

60%

0.5 1.0 1.5 2.0

25%

35%

45%

55%

0.2 0.4 0.6 0.8

22%

26%

30%

34%

1 2 3 4 522%

26%

30%

34%

0 5 10 15 20

30%

40%

50%

0.2 0.4 0.6 0.830%

40%

50%

1.5 2.0 2.5 3.0 3.5 4.0

35%

45%

55%

20 40 60 8030%

40%

50%

60%

0.1 0.3 0.5 0.7

20%

25%

30%

35%

1 2 3 4 5 6

KSA X 107

HP

CD

ext

ract

abilit

y %

r2 = 0.56p = 0.02

PCB 52

r2 = 0.76p = 0.002

PCB 32

r2 = 0.18, p = 0.24

PCB 95

r2 = 0.13p = 0.33

PCB 149

r2= 0.84p = 0.0005

13C6-α-HCH

r2 = 0.70p = 0.005

13C12-p,p'-DDT

r2 = 0.49p = 0.03

PCB 18

13C10-TC 13C10-TN

r2= 0.74p = 0.003

r2= 0.75p = 0.002

107

5.4.3 Role of microbial activity and sterilization of the soil

The KSA and HPCD% of the Sterile soils generally showed no trend with aging time, with

exceptions noted above. Studies on sorption of organic chemicals in sterile vs. non-sterile soils

have shown that microbes may accelerate the formation of bound residues (27, 41–43).

However, MacLeod and Semple (42) reported that there was less pyrene extracted by butanol

from soils that have been sterilized. Gevao et al. (27) demonstrated that microbial activity not

only promoted the formation of bound residues but was also responsible for the release of non-

extractable residues of a herbicide, dicamba. As discussed earlier, log KSA in the Sterile soils

were generally lower than the non-sterile soils (i.e. Indoor and Outdoor), a discrepancy that

might due to an artifact produced by the sterilization process. Kelsey et al. (44) reported that the

conformation of the soil organic matter changes after gamma irradiation, which might alter the

sorption behavior of chemicals to the soil. For instance, the authors found that uptake of p,p’-

DDE by the earthworm, E. fetida increased in sterilized soil. It was suggested that different

sterilization processes led to different observations about earthworm uptake, and not all

compounds followed the same trends.

5.4.4 Comparison with the Karickhoff model

KSA was estimated using the modified Karickhoff model (11, 12) and compared to the plateau

KSA of the Indoor, Outdoor and Sterile soils. Figure 5.3 plots the modeled and plateau KSA

against KOA. KOA values were obtained from ref (45–48). Modeled KSA from the modified

Karickhoff model was 6 times greater than the Indoor and Outdoor plateau KSA and 13.5 times

greater than the Sterile plateau KSA. This indicates the chemicals are more volatile than

anticipated from the model. There is better agreement for the LMW PCBs than the HMW PCBs.

OCPs showed more scattering than the PCBs since PCBs have similar structures, while structure

between the OCPs are more diverse. He et al. (14, 15) reported the modeled KSA was greater that

those measured experimentally for PAHs, PCBs and OCPs and the difference was less than 1

order of magnitude. On the other hand, a soil-air flux chamber study showed the fugacity

calculations based on the Karickhoff model underestimated the volatilization of PCBs and OCPs,

particularly for the HMW chemicals and in high organic carbon soils (18). Moreover, Hippelein

108

and McLachlan (12, 17) found that the Karickhoff model under predicts the KSA of PCBs and

chlorobenzenes by a factor of 2.

Figure 5.3 Plateau log KSA of native OCPs, spiked OCPs and PCBs vs. log KOA for Indoor,

Outdoor and Sterile soils. Plateau log KSA of Indoor soils equals the mean log KSA from Day 195

to 550; Outdoor - from Day 390 to 730; Sterile - from Day 210 to 550. Solid line = log KSA

predicted by the modified Karickhoff model.

The muck soil used in this study is a highly organic peat soil (42%). The modified Karickhoff

model was developed based on experiments carried out with a whole range of different soils and

sedimantsand may not be applicable to this study. Niederer et al. (21) reported that organic

carbon-water sorption coefficient (KOC) of organic chemicals to ten humic and fulvic acids varied

by more than 1 order of magnitude, depending on the origin of the sorbent. This implies that a

single partition coefficient (e.g. KOA) may not be suitable to describe the sorption behavior of a

chemical in all soil types. KSA may be better predicted using the polyparameter linear free

energy relationships (pp-LFERs) that accounts for van der Waals as well as H-donor/acceptor

interactions between the chemical and the sorbent (21–23).

log

KS

A

log KOA

Spiked PCBs Native OCPs Spiked 13C-OCPs Modified Karickhoff model

Indoor

6

7

8

9

10

7 8 9 10 11

Outdoor

6

7

8

9

10

7 8 9 10 11

Sterile

6

7

8

9

10

7 8 9 10 11

log

KS

A

log KOA

Spiked PCBs Native OCPs Spiked 13C-OCPs Modified Karickhoff model

Indoor

6

7

8

9

10

7 8 9 10 11

Outdoor

6

7

8

9

10

7 8 9 10 11

Sterile

6

7

8

9

10

7 8 9 10 11

Indoor

6

7

8

9

10

7 8 9 10 11

Outdoor

6

7

8

9

10

7 8 9 10 11

Sterile

6

7

8

9

10

7 8 9 10 11

109

5.4.5 Degradation of chemicals in soils

Losses over time in the Indoor non-sterile soil were observed for 13C6-α-HCH, ENDO I, ENDO

II, ESUL, PCBs 8 and 28; with 23% to 100% loss by the end of 550 d. No other compounds

showed significant losses (p >0.05). Microbial degradation was probably the cause for the loss of

the above compounds because the soils were sealed in glass jars and not exposed to light. The

Sterile soils showed no changes in chemical concentrations over time, which rules out hydrolysis

as a contributing loss mechanism. Concentrations (ln CSOIL) of these chemicals in the Indoor and

Outdoor non-sterile soils vs. time are plotted in Figure A5.4. Losses of ENDO I, PCB 8 and 28

in the Indoor soils appear to be biphasic with a sharp decrease during the first 10 to 45 d of aging

followed by a slow decline during 45 to 550 d. Half lives were calculated for each phase

assuming pseudo first-order kinetics (Table 5.1). The degradation rate of 13C6-α-HCH, ENDO

II, ESUL and PCB 32 appeared to be constant and single-phase half-lives were calculated for

these (Table 5.1).

For the Outdoor soils, substantial loss of 13C6-α-HCH, ENDO I, PCBs 8, 18, 28 and 32 were

seen with 21 to 100% of loss. Slight loss (10 to 15 %) was observed for other PCBs. Dissipation

of ENDO II and ESUL was not observed. Endosulfan may have been applied to the field site

during the time of the study, which complicates the interpretation. Leaching, vaporization and

microbial degradation are possible contributors to the loss. Environmental conditions were an

important factor in which changes in CSOIL were modest during the two winter periods (i.e. from

day 60 to 230, and from day 390 to 620) compared to faster dissipation in warmer months

(Figure A5.4). The losses were treated as a single pseudo-first order process and half lives are

reported in Table 5.1.

110

Table 5.1 Half-lives of 13C6-α-HCH, endosulfans and PCB 8, 18, 28, 32 for Indoor and Outdoor

soils. ns = No significant degradation or degradation does not follow first order kinetics.

5.4.6 Enantioselective degradation of 13C6-α-HCH

The enantiomers of 13C6-α-HCH were separated and expressed as enantiomer fraction, EF =

peak areas of (+)/[(+) + (−)]. The EFSOIL of 13C6-α-HCH for the Indoor, Outdoor and Sterile

soils was plotted against time in Figure 5.4A. 13C6-α-HCH in the Sterile soil was racemic with

EFSOIL ranging from 0.498 to 0.503 during the 550 d of aging time. This is expected as CSOIL of 13C6-α-HCH was constant in the Sterile soil and no degradation occurred. EFSOIL of 13C6-α-

HCH in the Indoor soils decreased slowly from 0.501 to 0.198 which indicates preferential

degradation of the (+) enantiomer, and degradation of both enantiomers was pseudo first-order

(Figure 5.4B, p <0.001, r2 =0.97-0.99). Half-lives calculated for the 550 d experiment were 205

d for (+) and 255 d for (–) (Table 5.1). Kurt-Karakus (7) reported that about 44% of the α-HCH

Indoor Half-lives (d) Outdoor Half-lives (d)13C6-α-HCH Overall, 0 to 550 d 250 Overall, 0 to 730 d 230

(+) 0 to 550 d 155 (+) 0 to 730 d 205(–) 0 to 550 d 360 (–) 0 to 730 d 255

Endo I 0 to 45 d 110 0 to 730 d 96045 to 550 d 260

Endo II 0 to 550 d 1420 0 to 730 d ns

ESUL 0 to 550 d 1020 0 to 730 d ns

PCB 8 0 to 10 d 11 0 to 730 d 27045 to 550 d 230

PCB 18 0 to 550 d ns 0 to 730 d 2190

PCB 28 0 to 45 d 39 0 to 730 d 61045 to 550 d 270

PCB 32 0 to 550 d 1810 0 to 730 d 1260

Indoor Half-lives (d) Outdoor Half-lives (d)13C6-α-HCH Overall, 0 to 550 d 250 Overall, 0 to 730 d 230

(+) 0 to 550 d 155 (+) 0 to 730 d 205(–) 0 to 550 d 360 (–) 0 to 730 d 255

Endo I 0 to 45 d 110 0 to 730 d 96045 to 550 d 260

Endo II 0 to 550 d 1420 0 to 730 d ns

ESUL 0 to 550 d 1020 0 to 730 d ns

PCB 8 0 to 10 d 11 0 to 730 d 27045 to 550 d 230

PCB 18 0 to 550 d ns 0 to 730 d 2190

PCB 28 0 to 45 d 39 0 to 730 d 61045 to 550 d 270

PCB 32 0 to 550 d 1810 0 to 730 d 1260

111

in worldwide background soils was racemic, 38% was preferentially depleted in the (−) and 18%

depleted in the (+) enantiomer. The degradation pattern seen here is a strong evidence of

microbial activity as enantioselective degradation is a biotic process.

EFSOIL of the Outdoor soil showed the same pattern of degradation as the Indoor soils with

EFSOIL decreasing from 0.500 to 0.345 during the first 620 d. Outdoor EFSOIL slightly increased

during the last two sampling intervals (from day 620 to 730), reaching an EFSOIL of 0.381. The

reversal of enantioselective degradation suggests a change in microbial community.

Kurt-Karakus et al. (49) reported that enantioselective degradation is subject to high variability

and may differ locally and over distances of centimeters to meters. Change in environmental

conditions like pH, temperature, humidity, soil organic matter may also contribute to the reversal

of enantioselective degradation (50, 51). There was modest change in EFSOIL during the two

winter periods (Figure 5.4) and this agrees with a similarly modest change in CSOIL.

112

Figure 5.4 Changes in the enantiomer fractions (EF) of 13C6-α-HCH in the Indoor, Outdoor and

Sterile soils over time (A), and ln CSOIL of the (+) and (−) enantiomer in the Indoor (B) and

Outdoor Soils (C). Day 60 to 230 and Day 390 to 620 are the two winter periods for the Outdoor

soils.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0 200 400 600 800

EF o

f 13 C

6-α-H

CH

IndoorOutdoorSterile

Indoor

r2 = 0.97

r2 = 0.99

0.0

0.5

1.0

1.5

2.0

2.5

0 100 200 300 400 500 600

lnC

SOIL

(+) (−)

Outdoor

r2= 0.94

r2 = 0.88

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

0 200 400 600 800

lnC

SOIL

(+) (−)

Time (d)

A

B

C

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0 200 400 600 800

EF o

f 13 C

6-α-H

CH


Indoor

r2 = 0.97

r2 = 0.99

0.0

0.5

1.0

1.5

2.0

2.5

0 100 200 300 400 500 600

lnC

SOIL

(+) (−)

Outdoor

r2= 0.94

r2 = 0.88

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

0 200 400 600 800

lnC

SOIL

(+) (−)

Time (d)

A

B

C

113

5.4.7 Enantioselective volatilization

EFs of 13C6-α-HCH in the corresponding HPCD extracts (EFHPCD) and air (EFAIR) are presented

in Figure 5.5. In general, EFHPCD and EFAIR agreed with each other in the Indoor, Outdoor and

Sterile soils. Surprisingly, EFAIR and EFHPCD were lower than EFSOIL in the Indoor soils after 90

d and Outdoor soils after 390 d. However, this was not seen in the Sterile soil experiments in

which EFAIR, EFHPCD and EFSOIL were all close to racemic over 550 d.

Physicochemical properties (e.g., vapor pressure, solubility, KOA) of the enantiomers are the

same and hence volatilization and HPCD extraction efficiencies from the soil are expected to be

the same only unless the two enantiomers are not located in the same microenvironment. It is

hypothesized that during microbial degradation the residual (+) enantiomer is more tightly

sequestered within the soil matrix, which decreases its volatility relative to the (–) enantiomer.

This sequestered fraction may be not extractable by mild agents such as air or HPCD, but

recovered by stronger solvents such as DCM, leading to the discrepancy between EFAIR or

EFHPCD and EFSOIL. The agreement between EFAIR and EFHPCD suggested that they are accessing

the same pool of chemical and supports the hypothesis that volatility can be used as a surrogate

for bioaccessibility or vice versa.

Figure 5.5 Enantiomer fractions (EF) of 13C6-α-HCH in air, HPCD and soils that have been

aged under Indoor, Outdoor and Sterile conditions.

Time (d)

EF

Indoor0.10

0.20

0.30

0.40

0.50

0 200 400 600

SoilAirHPCD

Sterile0.40

0.50

0.60

0 200 400 600

SoilAirHPCD

Outdoor0.20

0.30

0.40

0.50

0 200 400 600 800

SoilAirHPCD

Time (d)

EF

Indoor0.10

0.20

0.30

0.40

0.50

0 200 400 600

SoilAirHPCD

Sterile0.40

0.50

0.60

0 200 400 600

SoilAirHPCD

Indoor0.10

0.20

0.30

0.40

0.50

0 200 400 600

SoilAirHPCD

Sterile0.40

0.50

0.60

0 200 400 600

SoilAirHPCD

Outdoor0.20

0.30

0.40

0.50

0 200 400 600 800

SoilAirHPCD

114


The authors thank Shawn Janse from University of Guelph for access to Muck Crops Research

Station and obtaining the muck soil. We are grateful to Frank Wania for his helpful comments.

We acknowledge funding from the Chemical Management Plan of Environment Canada and

Health Canada, and the Research Affiliate Program through Environment Canada.

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6 VISUALISING THE EQUILIBRIUM DISTRIBUTION AND MOBILITY OF ORGANIC CONTAMINANTS IN SOIL USING THE CHEMICAL PARTITIONING SPACE

Fiona Wonga, b, Frank Waniab




of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario, M1C 1A4, Canada

Contribution: F. Wong carried out the calculations and prepared the manuscript under

supervision of Frank Wania.

Manuscript prepared for submission to Journal of Environmental Monitoring

120

6.1 ABSTRACT

The fate of neutral organic contaminants in soil is visualized using the chemical partitioning

space defined by the air-water partition coefficient (KAW) and the humic acid-water partition

coefficient (KHW). Two types of maps are drawn in the KAW-KHW space, displaying either the

equilibrium distribution of contaminants between the air-filled pores, the pore water and the solid

phases of the bulk soil or indicating the relative importance of the three transport processes

removing contaminants from soil (evaporation, leaching and particle erosion). The partitioning

properties of a large suite of neutral organic chemicals (i.e. herbicides, pharmaceuticals,

polychlorinated biphenyls and volatile chemicals) were estimated using poly-parameter linear

free energy relationships and superimposed onto these maps. This allows instantaneous

estimation of the equilibrium phase distribution and mobility of neutral organic chemicals in soil.

Although there is a link between the major phase and the dominant transport process, such that

chemicals found in air-filled pore space are subject to evaporation, those in water-filled pore

space undergo leaching and those in the sorbed phase are associated with particle erosion, the

partitioning coefficient thresholds for distribution and mobility can often deviate by many orders

of magnitude. In particular, even small fractions of chemical in pore water or pore air allows for

evaporation and leaching to dominate over solid phase transport. Multiple maps that represent

soils that differ in the amount and type of soil organic matter show how and to what extent

contaminant phase distribution and mobility can vary between regions. Similarly, the impact of

variable water saturation, soil temperature, and dissolved organic carbon on organic contaminant

fate in soils can be graphically assessed with the soil partitioning space.

6.2 INTRODUCTION

Large quantities of organic contaminants enter the soil daily through different pathways, such as

direct application, atmospheric deposition, precipitation and addition of biosolids (Breivik et al.,

2004; Li and MacDonald, 2005). Once an organic contaminant enters the soil, it could undergo

sorption, volatilization, leaching, or biodegradation (Bidleman, 1999; Harner et al., 2001; Lalah

et al., 1996). The fate of an organic contaminant is dependent on its physical-chemical properties

121

as well as the characteristics of the soil (Grathwohl, 1990; Niederer et al., 2007; Prueger et al.,

2005; Reichman et al., 2000). Soil is a complex medium which is composed of air, water,

organic solids, mineral matter, and biota. Its composition depends on the origin of the parent

material which varies geographically (Brady and Weil, 1999). For example, desert soils are low

in organic carbon and dry. Soils in salt marshes are wet and rich in organic matter. Moreover,

these components are dynamic and can change over the course of days, months and years in

response to weather and climate (i.e. temperature, precipitation), topography, land use or

management practices.

The toxicity, bioaccumulation and persistence of organic contaminants which are applied to soil

have always been a concern (Harnly et al., 2005; Herrera-Portugal et al., 2005; Lock et al., 2002;

Welp et al., 1999). Despite numerous experimental studies to elucidate chemical fate in soil,

there are still many uncertainties related to sorption kinetics and transport processes, and many

chemical classes have not been investigated experimentally at all (Bedos et al., 2002). This is

primarily due to the large classes of chemicals involved and the dynamic and heterogeneous

nature of soil. The assessment of environmental fate would clearly benefit from a simple way to

quickly obtain information of the likely distribution and mobility of a substance in soil.

An organic chemical’s equilibrium phase partitioning coefficients control to a large extent its

distribution and transport in the environment. In an environment composed of three major

phases, two such coefficients are often sufficient to characterize chemical fate. This allows a

graphical representation of fate parameters as a function of a two dimensional coordinate system

defined by these two partitioning coefficients (Breivik et al., 2003). Such chemical partitioning

space maps have been shown to be an effective tool for displaying the distribution and fate of

neutral organic chemicals in the atmosphere (Lei and Wania, 2004) and in a snow pack (Meyer

and Wania, 2008). Differences in fate between different chemicals, the influence of changes in

environmental phase composition and phase volumes, the influence of temperature, and the

sensitivity of fate to uncertainties in the partitioning coefficients can all be displayed and

assessed graphically using the partitioning space. Here, this approach is employed to visually

assess the equilibrium phase distribution and mobility of a large suite of neutral organic

chemicals in different soils. The partitioning of herbicides, pharmaceutical and personal care

122

products, polychlorinated biphenyls and some volatile chemicals between the air, water and solid

phase of the bulk soil is investigated. The dominant transport processes are identified and their

dependence on temperature, water content, organic carbon content, depth of the surface soil

horizon, and dissolved organic carbon are evaluated.

6.3 METHODS

6.3.1 Calculating organic chemical phase distribution in soil at equilibrium

The fate of an organic contaminant in soil depends on its phase distribution among the three

major components of soil: air-filled pore space, water-filled pore space, and solids. The

equilibrium phase distribution between air and water is described by the air-water partition

coefficient, KAW and that between solids and water by the solid-water partition coefficient, KSW.

If it is assumed that only the organic fraction is responsible for partitioning to soil solids and that

the partitioning properties of humic acid can represent the partitioning properties of the entire

soil organic matter phase, we can substitute the humic acid-water partitioning coefficient KHW for

KSW. The equations used to calculate the mass fractions of a chemical in air, water and organic

solids at equilibrium are given in Table 6.1. Using different parameter combinations for the

phase composition of a soil, those mass fractions are calculated and plotted as function of KAW

and KHW.

123

Table 6.1 Equations and parameters used to derive mass fractions of chemicals in soil.

Mass fraction in water EOCEHWDOCDOCWWEWAW

DOCDOCWW

VFVFKKVFVFVFVFVFKKVFVFVF

+++−−+

)1()(

Mass fraction in air EOCEHWDOCDOCWWEWAW

EWAW

VFVFKKVFVFVFVFVFKVFVFK

+++−−−−

)1()1(

Mass fraction in organic solids EOCEHWDOCDOCWWEWAW

EOCEHW

VFVFKKVFVFVFVFVFKVFVFK

+++−− )1(

ρOC Density of organic carbon 106 g m-3 ρmm Density of mineral matters 2.4 ×106 g m-3 φ Porosity in soil 0.5 VFW Volume fraction of water in bulk soil Variable: 0.05, 0.25, 0.49 VFA Volume fraction of air in bulk soil 1 – VFw – VFE VFE Volume fraction of solids in bulk soil 1 – φ VFEOC

Volume fraction of organic solids

mmOCOCOC

OCOC

fff

ρρρ

/)1(//

−+ fOC Mass fraction of organic solids in soil Variable: 0.01, 0.05, 0.50 VFDOC Volume fraction of dissolved organic carbon Variable: 0, 0.000025

6.3.2 Calculating the relative importance of chemical transport processes in soil

A chemical in soil can undergo primarily three transport processes: evaporation, leaching and

particle erosion. The rates of these processes can be quantified using D-values (mol Pa-1 h-1).

Equations for the calculation of these D-values and default parameters are adapted from the

CoZMo-POP2 model (Wania et al., 2006) and are given in Table 6.2. In brief, evaporation of a

chemical from soil is treated as a two film process, whereby a substance’s vapour has to diffuse

sequentially through the soil pore space and through a stagnant air boundary layer above the soil.

Diffusion within the soil occurs in parallel in both the air-filled and water-filled pore space and is

corrected for tortuosity using the Millington-Quirk formula. Leaching is dependent on the rate of

water loss from the soil to groundwater and particle erosion is governed by the rate of solid

runoff from the soil. Analogous to the phase distribution plots, the relative contribution of each

124

transport process to the overall loss of the chemical from the soil was calculated and plotted as

function of KAW vs. KHW. Different combinations of input parameters yield different versions of

those plots.

6.3.3 Placing chemicals onto the chemical space maps

A number of potential soil contaminants belonging to a variety of chemical classes were placed

on the partitioning maps based on their KAW and KHW, which can be estimated using

polyparameter-linear free energy relationships (pp-LFER) (Niederer et al., 2006, Goss et al.,

2006):

Log Kixy = lxyLi + vxyVi + bxyBi + axyAi + sxySi + cxy Eq [6.1]

where Kixy is the equilibrium partition coefficient of a chemical i between phases x and y. Each

term on the right side of eq. [6.1] describe a type of interaction between the solute and the two

phases that contribute to the overall partitioning. The uppercase letters are the solute descriptors.

L is the log hexadecane-air partition coefficient determined by eq. [SI19] in Brown and Wania

(2009). V refers to the McGowan volume of the solute. Both L and V describe non-specific

interactions. B and A are measures for the solute’s hydrogen-bond basicity and acidity,

respectively. S is the solute’s dipolarity or polarizability. The lowercase letters are the

complementary phase (sorbent) descriptors and cxy is a system constant.

125

Table 6.2 Equations and parameters used to derive relative importance of chemical transport

processes in soil.

Relative importance of evaporation DE / (DE + DEW + DER) Relative importance of leaching DEW / (DE + DEW + DER) Relative importance of particle erosion DER / (DE + DEW + DER) DE D-value for evaporation, , mol h-1 Pa-1

wEAEAE

E

ZUZUZU

A

657

11+

+

DEW D-value for leaching, mol h-1 Pa-1 )()1( DOCWEEWE ZZAUfrU +−

DER D-value for erosion, mol h-1 Pa-1 EOCERE ZUA ZA Fugacity capacity in air, mol m-3 Pa-1

1/(RT) ZW Fugacity capacity in water, mol m-3 Pa-1

1/(RTKAW) ZEOC Fugacity capacity in organic solids, mol m-3

Pa-1 ZW KHWVFEOC ρOC/(106 kg L-1)

ZDOC Fugacity capacity in dissolved organc carbon, mol m-3 Pa-1

ZW *0.23KHWVFDOC ρDOC / (106 kg L-1)

U5E MTC for diffusion in the air-filled pore space, m h-1

2

3/10

)(2ln WA

A

E

A

VFVFVF

hB

+

U6E MTC for diffusion in the water-filled pore space, m h-1 2

3/10

)(2ln WA

W

E

W

VFVFVF

hB

+

U7E MTC through air boundary layer over soil 2.08 m h-1 BA molecular diffusivity in air 0.018 m2 h-1 BW molecular diffusivity in water 01.8×10-6 m2 h-1 hE Soil depth Variable: 0.18 m, 0.1 m, 0.01 m AE Area of soil 1 m2 frUE Fraction of water evaporation from soil 0.43 ρDOC Density of DOC 106 g m-3 UEW Leaching rate of water from soil 3.9×10-5 m h-1 UER Solid runoff rate from soil 2.3×10-8 m h-1 T Temperature 298 K R Gas constant 8.314 m3 Pa K-1 mol -1

126

Phase descriptors for the partitioning between five types of humic acids (HA) or fulvic acids

(FA) and water at 15 °C were taken from Niederer et al. (2007) to estimate KHW. For each

chemical, KHW for Leonardite HA and KAW were additionally calculated at 5, 15, 25 and 40 °C

using linear correlations between the phase descriptor and temperature reported in Niederer et al.

(2006) and Goss et al. (2006).

Solute descriptors for 19 herbicides and five pharmaceutical and personal care products (PPCP)

were obtained from Tülp et al. (2008), those for three polychlorinated biphenyls (PCB-52, -101, -

138) and two volatile chemicals, i.e. p-xylene, 1,1,1-trichloroethane (TCE) were taken from

Abraham et al. (2005) and Abraham et al. (1994), respectively. These organic chemicals cover a

wide range of partitioning properties.

6.4 RESULTS 6.4.1 Equilibrium phase partitioning and mobility of organic chemicals in a typical temperate

soil

The equilibrium phase distribution and dominant transport process of neutral organic chemicals

in a typical temperate soil containing 5% organic carbon (OC) and field capacity water content

(WC) of 25% (approximately half the pore space is filled with water) is plotted as function of

KAW vs. KHW in Figure 6.1A and B, respectively. Because the log KAW range of the selected

chemicals is much wider than the range in log KHW, the KAW-axis is compressed relative to the

KHW-axis.

Contaminants which are predominately (>90%) sorbed to organic solids are located in the green

region at the lower right of Figure 6.1A, those that strongly prefer the gas phase in the air-filled

pores fall in the red region at the upper left and those primarily dissolved in water-filled pores

are in the blue region on the lower left of the partitioning space. The light green, yellow and light

127

blue colours indicate the transition region, where the chemical is present in more than one phase

by at least 50 %. The transition from predominant partitioning to organic solids to that in water

in the typical temperate soil occurs in the log KHW range between 1.5 and -0.5, that from water to

air at log

KAW between -1 and 1, and that between organic solids and air at a KHA between 2.5 and 4.5.

Minor presence of a chemical in the air and water phases is denoted with the red and blue

(>0.1%, >1%, >10%) boundary lines, respectively.

The estimated relative contribution of each of the three transport loss process in the typical

temperate soil is plotted as a function of KHW and KAW in Figure 6.1B. Contaminants that are

primarily (>90%) transported by leaching fall into the blue region (lower left), erosion

predominates in the green region (lower right) and evaporation is the key transport pathway in

the red region (upper left). Obviously, the dominant transport processes in Figure 6.1A

correspond to the phase distribution in 6.1B, as contaminants in the dissolved phase are subject

to leaching, those in gas phase undergo evaporation, and those that are sorbed will be transported

with particles.

However, the boundaries in the phase distribution map do not coincide with those in the mobility

map. The log KHW threshold for transition from solids to water in the phase distribution map

(Figure 6.1A) is at ~0.5, almost 4 orders of magnitude smaller than the log KHW threshold

between erosion and leaching in the mobility map (Figure 6.1B), which is 4.5. The reason is that

the mass transfer coefficient for leaching (10-5 m h-1) is three orders of magnitude higher than

that for erosion (10-8 m h-1) and hence leaching is a much faster transport pathway than erosion.

The remaining difference is due to the larger volume of water relative to soil organic matter.

Similarly, the log KAW threshold for transition from air to water (50% each) lies at 0, which is

higher than the log KAW threshold between evaporation and leaching (50 % each) in the mobility

map of -2.5. Again, volatilisation from the typical temperate soil is more than two orders of

magnitude faster than leaching. This implies that even small fractions of chemical found in the

air- and water-filled pore space are important, because those fractions are so much more mobile

than those that are sorbed to solids.

128

Figure 6.1 Phase distribution (A) and mobility (B) of selected organic chemicals in a typical

temperate soil (OC 5%, WC 25%). Each chemical corresponds to a short diagonal line, which

indicates the temperature dependence of its partitioning properties. Herbicides are shown in

orange, volatile chemicals are in white. PCBs are in black and PPCP as dotted yellow lines.

Log KHW

1. Amitrole2. Atrazine3. Bromacil4. Chlorothalonil5. Chlorpyrifos6. Cypermethrin7. Cyromazine8. Desethylatrazine9. Endosulfan10. Endrin11. Metolachlor12. Parathion13. Sulfentrazone14. Trifluraline15. Carbamazepine16. Diazepam17. Fluorouracil18. Triclosan19. Zearalenone20. TCE21. p-xylene22. PCB5223. PCB10124. PCB138

> 90 % in air-filled pore space/ evaporation> 50 % in air-filled pore space/ evaporation> 90 % in water-filled pore space/ leaching> 50 % in water-filled pore space/ leaching> 90 % sorbed to organic solids/ erosion> 50 % sorbed to organic solids/ erosion

Log

K AW

Log

KAW

A. Phase Distribution

B. Mobility

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

AIR

Water

Organic solid

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

0.1%1%10%

0.1%

1%10%

0.1%

1%10%

10%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

AIR

Water

Organic solid

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

Evaporation

Leaching

Erosion

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

Evaporation

Leaching

Erosion

Log KHW




Log

K AW

Log

KAW

A. Phase Distribution

B. Mobility

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

AIR

Water

Organic solid

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

0.1%1%10%

0.1%

1%10%

0.1%

1%10%

10%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

AIR

Water

Organic solid

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

Evaporation

Leaching

Erosion

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

Evaporation

Leaching

Erosion

129

6.4.2 Placing the chemicals onto the space maps

Selected herbicides, PCBs and volatile organic chemicals were placed onto the maps for the

typical temperate soil (Figure 6.1A and B), allowing instantaneous visualization of their

dominant equilibrium phase distribution and mobility. Because of the temperature dependence of

its partitioning properties, each chemical is represented by a diagonal line. Herbicides are shown

as orange lines, PPCP as dotted yellow lines, PCBs are in black, and volatile chemicals in white.

The top of each line indicates the partitioning properties at 40 °C, those at the bottom correspond

to 5 °C. The more temperature dependent the partitioning properties of a substance are, the

longer the line. The horizontal displacement is a measure of the change in KHW upon a change in

temperature, the vertical displacement indicates the change in KAW. Partitioning coefficients

involving the gas phase (e.g. KAW) tend to be more dependent on temperature than those between

two condensed phases (e.g. KHW). This is not readily apparent in Figure 6.1 because of the

different scale of the two axes.

Amitrole (#1), cyromazine (#7), desethylatrazine (#8) and fluorouracil (#17) are found in the

blue region of both plots, thus are expected to partition into the pore water and to be subject to

leaching. Most other chemicals are found in the green region of the phase distribution plot,

which indicate that they are mainly (>90%) sorbed to the organic solids. Some examples are

PCBs (#22−#24), atrazine (#2), endosulfan (#9), metalochlor (#11), parathion (#12), and

chlorothalonil (#14). Many of them fall within the 0.1% boundary for the water-filled pores, e.g.

metolachlor (#11); chlorothaloni (#14) or in the air-filled pores, e.g. p-xylene (#20) and TCE

(#21).

As mentioned earlier, even if a chemical has only a small fraction in the water or air phase (e.g.

0.1 to 1%), the mobility of that fraction is relatively high, which is illustrated by the mobility

map. Chlorothalonil (#4) is predominantly associated in solids (>90%) and only 0.1% dissolves

in the pore water. However, the mobility map shows that more than 90% of it will be transported

by leaching rather than by particle erosion. Other examples are atrazine (#2), bromacil (#3),

metalochlor (#11), sulfentrazone (#13) and carbamazepine (#15). These chemicals have been

often found in groundwater and urban streams, likely due to leaching from surrounding soils

130

(Alavi et al., 2008; Kurt-Karakus et al., 2008; Sacher et al., 2001; Struger et al., 2007). Similarly,

although TCE (#20) and p-xylene (#21) are primarily found in the solid phase and only 0.1 to 1%

are found in the air pore space, they are very susceptible to evaporation as indicated by the

mobility map.

Figure 6.1 also illustrates the effect of temperature on phase distribution and mobility. With

increasing temperature the position of most chemicals in the maps shifts from the lower right to

the upper left, which indicates decreasing sorption from the organic solids to both water and air..

This implies that these chemicals are more likely to volatilize from soil and leach to groundwater

at higher temperatures. For example, cypermethrin (#6) and endosulfan (#9) are almost

exclusively sorbed to organic solids at low temperature (5°C) but >0.1% of them would be found

in the dissolved phase at higher temperature (i.e. 40 °C). Triclosan (#18) is sorbed to organic

solids at low temperature, but at high temperatures a small percentage dissolves in the water

phase and can undergo leaching. On the other hand, temperature has little effect on the phase

distribution on some chemicals such as the PCBs (#22–#24) as they are far away from the

transition regions.

6.4.3 Comparing different soils: role of the depth of the surface soil horizon, the amount and

type of soil organic matter, dissolved organic carbon

Phase distribution and mobility maps for three soils containing different amounts of soil organic

matter (low: OC 1%, typical OC 5%, peat soil OC 50%) are plotted in Figure 6.2. Water content

is assumed to remain at field capacity (WC = 25%). As the OC% increases, the green regions

expands to the top and the left; more contaminants are sorbed to the organic solids and are

transported by particle erosion. The log KHW threshold between solids and water in the phase

distribution map moves from 1.2 in the low OC soil to -0.2 in the peat soil. The corresponding

shift in log KHW threshold in the mobility map is from 5 to 3.2. In low organic carbon soil, a

greater number of chemicals dissolve appreciably into the pore water and may be subject to

leaching. For example, triclosan mainly sorbs (>90%) onto the solid phase in a peat soil.

However, in a low OC soil, 0.1 – 1% are found in the pore water and leaching becomes the

dominant transport pathway.

131

Not only the amount of soil organic matter is different between different soils, but also the

partitioning properties of different types of organic matter can vary considerably according to

Niederer et al. (2007). To show the influence of such variability on the estimated phase

distribution and mobility of chemicals, their KHW was estimated for five different types of soil

organic matter (SOM) and placed on the phase distribution and mobility maps of Figure 6.2.

Each chemical is thus represented by 5 markers. The log KHW of the Leonardite HA, used as

representative of soil humic acids in the typical temperate soil of section 6.4.1, is shown as a

black solid marker. It has the highest KHW for all chemicals, indicating that it has the highest

sorption capacity among the five humic substances investigated.

Figure 6.2 shows that there is great variability in KHW with a range of up to two orders of

magnitude for a single substance. For example, the log KHW of carbamazepine, ranged from -0.64

to 1.62, which suggest the contaminant could either be predominantly sorbed to the organic

solids or dissolved in pore water depending on the type of the humic substance. This is also true

for some herbicides such as desethylatrazine, bromacil, and cyromazine, which may leach from

soils with humic substances with a low sorption potential, but not in humic substances with high

sorption potentials.

Some chemicals, in particular non-polar chemicals that mostly interact with soil organic matter

by non-specific interactions, are less affected by the type of organic matter. For example, KHW

for each of the VOCs and PCBs ranges over little more than an order of magnitude and

accordingly the five markers are grouped much closer together than those for many herbicides

and PPCPs. As a result, the five markers of the PCBs, endrin, and cypermethrin cluster within

the green region of the panels in the bottom of Figure 6.2 and thus will not be subject to

significant leaching or evaporation irrespective of the sorption characteristics of the soil organic

matter. For the same reason, the two volatile chemicals, e.g. p-xylene and TCE, will evaporate

regardless of the organic matter type.

132

Figure 6.2 Phase distribution (top) and transport process (bottom) of selected chemicals at 15 °C in soils that differ in the amount of

organic matter (different panels: low organic carbon left, typical middle, peat soil right) and in the type of humic substance (five

different markers for one chemical, black marker indicates the Leonardite humic acid).

BromacilChlorothalonilChlorpyrifosCyromazineCypermethrinDesethylatrazineEndosulfanEndrinMetolachlorTrifluralineCarbamazepineDiazepamFluoruracilTriclosanZearalenoneTCEp-xylenePCB52

Log KHW

Log

KAW

Log

KAW

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

OC = 1%; WC 25% OC = 5%; WC 25% OC = 50%; WC 25%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

Phase Distribution

Mobility

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%0.1%

10%

0.1%1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

1%0.1%

10%

10%

1%0.1%

10%

1%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

0.1%1%10%

0.1%

1%10%

0.1%

1%10%

10%

OC = 1%; WC 25% OC = 5%; WC 25% OC = 50%; WC 25%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

0.1%1%10%

0.1%

1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

0.1%1%10%

0.1%

1%10%

0.1%

1%10%

10%

Phase Distribution

Mobility

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%0.1%

10%

0.1%1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%0.1%

10%

0.1%1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

1%0.1%

10%

10%

1%0.1%

10%

1%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

1%0.1%

10%

10%

1%0.1%

10%

1%

0.1%

10%

1%0.1%

10%

1%

0.1%10

%

1%

0.1%



Log KHW

Log

KAW

Log

KAW

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

OC = 1%; WC 25% OC = 5%; WC 25% OC = 50%; WC 25%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

Phase Distribution

Mobility

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%0.1%

10%

0.1%1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

1%0.1%

10%

10%

1%0.1%

10%

1%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

0.1%1%10%

0.1%

1%10%

0.1%

1%10%

10%

OC = 1%; WC 25% OC = 5%; WC 25% OC = 50%; WC 25%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

0.1%1%10%

0.1%

1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

0.1%1%10%

0.1%

1%10%

0.1%

1%10%

10%

Phase Distribution

Mobility

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%0.1%

10%

0.1%1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%0.1%

10%

0.1%1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

1%0.1%

10%

10%

1%0.1%

10%

1%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

1%0.1%

10%

10%

1%0.1%

10%

1%

0.1%

10%

1%0.1%

10%

1%

0.1%10

%

1%

0.1%

133

The surface soil horizon depth of 0.18 m used here is typical for agricultural soils. The surface

horizons of forest and bare soils are often much shallower. The influence of surface soil horizon

depth on the mobility of chemicals is investigated by setting this parameter to 0.1 and 0.01 m

(Figure 6.3). As the surface soil horizon decreases in depth the red region in the mobility plot

expands downward and to the right. This shift is small when the surface soil horizon depth is

reduced to 0.1 m but it is more pronounced when it is decreased to 0.01 m. There is a 2.5 orders

of magnitude difference in the log KAW threshold separating dominance of leaching vs.

volatilization between a deep and a shallow soil. The mass transfer coefficients (MTC) for

diffusion across the air (U5E) and water-filled pore space (U6E) increase as the transport distance

(he) shorten. As a result, more chemicals are removed by evaporation in a thin soil than a deep

soil. Some example of chemicals that would volatilize more readily from a shallow soil are

chlorothalonil (#4), chlorpyrifos (#5), desethylatrazine (#8) and the PCBs.

Figure 6.3 The influence of surface soil horizon depth (he) on the mobility of chemicals in a

typical temperate soil. The numbering of chemicals is the same as in Figure 6.1

he = 0.18 m

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

Evaporation

Leaching

Erosion

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

Evaporation

Leaching

Erosion

he = 0.1 m he = 0.01 m

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1%

10%

1%0.1%

10% 1%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1%

10%

1%0.1%

10% 1%

0.1%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

Log KHW

Log

KAW

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1%

10%1%0.1%

10% 1%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1%

10%1%0.1%

10% 1%

0.1%

10%

1%0.1%

10%1%0.1%

10% 1%

0.1%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

he = 0.18 m

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

Evaporation

Leaching

Erosion

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1% 10%

1%

1%0.1%

10%

0.1%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

Evaporation

Leaching

Erosion

he = 0.1 m he = 0.01 m

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1%

10%

1%0.1%

10% 1%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1%

10%

1%0.1%

10% 1%

0.1%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1%

10%

1%0.1%

10% 1%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1%

10%

1%0.1%

10% 1%

0.1%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

Log KHW

Log

KAW

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1%

10%1%0.1%

10% 1%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1%

10%1%0.1%

10% 1%

0.1%

10%

1%0.1%

10%1%0.1%

10% 1%

0.1%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1%

10%1%0.1%

10% 1%

0.1%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1%

10%1%0.1%

10% 1%

0.1%

10%

1%0.1%

10%1%0.1%

10% 1%

0.1%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

134

Dissolved organic carbon (DOC) can be a carrier of organic chemicals in soil pore water and is

particularly important for chemicals that are highly hydrophobic and sorb strongly to organic

matter (McCarthy and Zachara, 1989). The influence of DOC to the phase distribution and

mobility is examined by assuming the fraction of DOC in the pore water is 0.0025%, which is

equivalent to a DOC concentration of 25 mg/L. Equations for calculating the phase distribution

and mobility are shown in Tables 6.1 and 6.2. It is assumed that the DOC partition coefficient,

KDOC = 0.23 KHW (Burkhard 2000; Seth et al., 1999). Consideration of DOC in pore water does

not visibly change the phase distribution of organic chemicals in soils, but influences notably in

the mobility of organic chemicals (Figure 6.4). The transition from leaching to erosion shifts to

higher log KHW values, i.e. the importance of leaching raltaive to erosion increases for the more

sorptive chemicals. This can be attributed to the relative slow transport rate of erosion compared

to leaching. The result suggests that even a very hydrophobic chemical has the potential to leach

from the soil in the presence of DOC.

Figure 6.4 Influence of 25 mg/L of dissolved organic carbon (DOC) on the mobility of organic

chemicals in a typical temperate soil.

Log

KAW

Log KHW

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1%

10%1%

0.1%Leaching

Evaporation

Erosion

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

10%

Log

KAW

Log KHW

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1%

10%1%

0.1%Leaching

Evaporation

Erosion

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

2

10%

1%0.1%

10%1%

0.1%Leaching

Evaporation

Erosion

10%

1%0.1%

10%1%

0.1%Leaching

Evaporation

Erosion

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

10%

135

6.4.4 Rapid changes in phase distribution and mobility: the role of soil moisture

Soil water content changes in response to events such as precipitation, infiltration, snow melt,

flooding, and drought. The equilibrium phase partitioning of a chemical is studied for the typical

soil (5 % OC, Leonardite HA) under three different water content (WC) scenarios: at the wilting

point (WC = 5%); at field capacity (WC = 25%); and waterlogged (WC = 49%). The same suite

of chemicals is placed onto the phase distribution map, using the partitioning properties within

the temperature range from 5 to 40°C.

Figure 6.5 shows that with increasing WC, the blue region shift upwards and expands to the

right, i.e. more chemicals become dissolves in pore water at the expense of the pore and air and

the solids. The impact on the relative distribution between air and water filled pore space is quite

significant, the corresponding log KAW threshold shifts by almost 3 orders of magnitude from -1

in the dry soil to +1.8 in the water-logged soil. The impact on solidwater distribution is rather

limited, with a less than 1 order of magnitude shift in the KHW threshold. Water saturation of a

soil can result in the dissolution of chemicals in pore water. For instance, whereas cyromazine

(#7) and desethyatrazine (#8) are in the sorbed phase in a dry soil, they occur in the pore water of

a waterlogged soils. For chemicals such as the PCBs (#22–#24) and parathion (#12), which are

far from the transition zones, WC has little effect on phase partitioning, as they will always be

associated with the solid phase.

136

Figure 6.5 Phase distribution of selected organic chemicals in a soil at wilting point (WC =

5%), field capacity (WC = 25%) and waterlogged conditions (WC = 49%) soils. Each chemical

corresponds to a short diagonal line, which indicates the temperature dependence of its

partitioning properties. Herbicides are shown in orange, volatile chemicals are in white, PCBs

are in black and PPCP as dotted yellow lines.

Soil at Wilting Point: OC = 5%; WC=5%

Waterlogged Soil: OC = 5%; WC=49%

Log KHW

Log

KAW

Log

KAW

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

AIR

Water

Organic solid

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

AIR

Water

Organic solid

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C



Soil at Wilting Point: OC = 5%; WC=5%

Waterlogged Soil: OC = 5%; WC=49%

Log KHW

Log

KAW

Log

KAW

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

AIR

Water

Organic solid

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

AIR

Water

Organic solid

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

AIR

Water

Organic solid

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

-2 -1 0 1 2 3 4 5 6 7-14

-12

-10

-8

-6

-4

-2

0

20.1%1%10%

0.1%

1%10%

10%

0.1%1%10%

0.1%

1%10%

0.1%

1%10%

10%

AIR

Water

Organic solid

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

12

3

4

5

68

79

10

11

12

13

14

15

1617

18

19

2021

22 23 24

5°C

40°C




137

6.4.5 Discussion

We have aimed to demonstrate the effectiveness of using chemical space maps to graphically and

instantaneously predict the fate of a wide range of organic chemicals in soil, without the need for

any actual model calculations. In addition to the their use in the rapid and simple assessment of

chemical fate in soil, we believe such maps to have didactic value, in that they can illustrate

graphically how various factors related to both soils and chemicals can influence the phase

distribution and mobility of organic chemicals in soil. One could envisage a computer-based

version of those maps that displays the colours corresponding to primary phase distribution and

dominant transport pathways in response to user defined soil conditions (porosity, water content,

soil organic matter). A database containing solute descriptors could then be accessed to display a

user-defined selection of chemicals on the maps, whereby the user could either choose a specific

temperature or soil organic matter type or select to display the variability caused by such

parameters. It may even be worthwhile to dynamically display the changes in the maps, i.e. both

the movement of the thresholds and the movement of the chemicals that occur during scenarios

of time-variant soil temperature and water content.

One of the benefits of a simple tool such as the partitioning maps presented here is their potential

use in guiding more sophisticated fate assessments. It is quite easy to use the maps to identify the

parameters that would need to be known precisely for a more thorough assessment of a

chemical’s fate in soil. If a chemical is close to or within a transition area of interest, those

parameters defining the thresholds of that transition area and those parameters controlling the

placement of the chemicals of interest in that transition area should be known well. On the other

hand, if the chemicals of interest are located far from the transition area of interest, approximate

knowledge of such parameters can often be tolerated. For example, if the primary objective of an

assessment is whether a chemical has the potential to contaminate groundwater reserves, the area

of interest is the transition area around the blue region in the mobility maps. If the assessed

chemicals fall into the transition region, a more detailed assessment may be warranted. If

however, the chemicals fall clearly inside or outside of the blue region, no further assessment

may be required.

138

Finally, it should be stressed that the approach underlying the partitioning maps displayed here is

very simplistic and makes many assumptions that may often not be valid. The most important

assumption is that of thermodynamic equilibrium, i.e. there is no account of possible kinetic

limitations to achieving sorption equilibrium. There is also no consideration of irreversible or

non-linear sorption. Endo et al. (2009) observed that literature pp-LFERs may underestimate

experimental KOC at low concentrations by up to an order of magnitude, because of the non-

linearity of sorption. It would be possible to account for non-linear sorption, by plotting a

chemical with a range of KHW values that are valid a different concentrations – similar to the KHW

values at different temperatures (Figure 6.1) or for different types of soil organic matter (Figure

6.2).The role of organic matter-mineral matter assemblies in influencing sorption is ignored. It

has also been suggested that sorption to mineral surfaces may play a key role in dry soils with a

low organic matter content (Goss et al., 2004). Furthermore, the erosion rate is assumed to be

similar among the different soils studied here, although it is dependent on environmental

conditions and soil composition. The mobility maps presented here only display loss processes

occurring by transport, whereas all but the most persistent organic chemicals in soils will be lost

predominantly by degradation reactions.


We are grateful to Terry F. Bidleman and Trevor N. Brown for advice and helpful discussions.

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Bidleman, T. F. Exchange of pesticides. Water Air Soil Pollut. 1999, 115, 115–166. Brady, N. C., Weil, R. R. The Nature and Properties of Soils,12th, ed.; Prentice Hall: New Jersey, 1999. Breivik, K.; Wania, F. Expanding the applicability of multimedia fate models to polar organic chemicals. Environ. Sci. Technol. 2003, 37, 4934–4943. Breivik, K.; Alcock, R.; Li, Y. F.; Bailey, R. E.; Fiedler, H.; Pacyna, J. M. Primary sources of selected POPs: Regional and global scale emission inventories. Environ. Pollut. 2004, 128, 3–16. Brown, T.; Wania, F. Development and exploration of an organic contaminant fate model using poly-parameter linear free energy relationships. Environ. Sci. Technol. 2009, 43, 6676–6683. Burkhard, L. P. Estimating dissolved organic carbon partition coefficients for nonionic organic chemicals. Environ. Sci. Technol. 2000, 34, 4663–4668. Endo, S.; Grathwohl, P.; Haderlein, S. B.; Schmidt, T. C. LFERs for soil organic carbon-water distribution coefficients (KOC) at environmentally relevant sorbate concentrations. Environ. Sci. Technol. 2009, 43, 3094–3100. Goss, K.U.; Buschmann, J.; Schwarzenbach, R. P. Adsorption of organic vapors to air-dry soils: model predictions and experimental validation. Environ. Sci. Technol. 2004, 38, 3667–3673. Goss, K.U. Prediction of the temperature dependency of Henry's law constant using poly-parameter linear free energy relationships. Chemosphere 2006, 64, 1369–1374. Harner, T.; Bidleman, T. F.; Jantunen, L. M. M.; Mackay, D. Soil-air exchange model of persistent pesticides in the United States cotton belt. Environl Toxicol. Chem. 2001, 20, 1612–1621. Harnly, M.; McLaughlin, R.; Bradman, A.; Anderson M.; Gunier, R. Correlating agricultural use of organophosphates with outdoor air concentrations: A particular concern for children. Environ. Health Perspectives 2005, 113, 1184–1189. Kurt-Karakus, P. B.; Bidleman, T. F.; Muir, D. C. G.; Cagampan, S. J.; Struger, J.; Sverko, E.; Small, J. M.; Jantunen, L. M. Chiral current-use herbicides in Ontario streams. Environ. Sci. Technol. 2008, 41, 8452–8458 Lalah, J. O.; Wandiga, S. O.; Dauterman, W. C. Mineralization, volatilization, and degradation of carbofuran in soil samples from Kenya. Bull. Environ. Contamin. Toxicol. 1996, 56, 37–41. Lei, Y. D.; Wania, F. Is rain or snow a more efficient scavenger of organic chemicals? Atmos. Environ., 2004, 38, 3357–357.

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Li, Y. F.; Macdonald, R. W. Sources and pathways of selected organochlorine pesticides to the Arctic and the effect of pathway divergence on HCH trends in biota: a review. Sci. Total Environ. 2005, 342, 87–106. Lock, K.; De Schamphelaere, K. A. C.; Janssen, C. R. The effect of lindane on terrestrial invertebrates. Archives of Environ. Contamin. Toxicol. 2002, 42, 217–221. McCarthy, J. F.; Zachara, J. M. Subsurface transport of contaminants. Environ. Sci. Technol. 1989, 23, 496–502. Meyer, T.; Wania, F. Organic contaminant amplification during snowmelt. Water Research 2008, 42, 1847–1865. Niederer, C.; Goss, K. U.; Schwarzenbach, R. P. Sorption equilibrium of a wide spectrum of organic vapors in Leonardite humic acid: Experimental setup and experimental data. Environ. Sci. Technol. 2006, 40, 5368–5373. Niederer, C.; Schwarzenbach, R. P.; Goss, K. U. Elucidating differences in the sorption properties of ten humic- and fulvic acids for polar and non-polar organic chemicals. Environ. Sci. Technol. 2007, 41, 6711–6717. Prueger, J. H., Gish, T. J., McConnell, L. L., McKee, L.G., Hatfield, J. L., Kustas, W. P. Solar radiation, relative humidity, and soil water effects on metolachlor volatilization. Environ. Sci. Technol. 2005, 39, 5219–5226. Reichman, R.; Mahrer, Y.; Wallach, R. A combined soil-atmosphere model for evaluating the fate of surface-applied pesticides. 2. The effect of varying environmental conditions. Environ. Sci. Technol. 2000, 34, 1321–1330. Sacher, F.; Lange, F. T.; Brauch, H. J.; Blankenhorn, I. Pharmaceuticals in groundwaters. Analytical methods and results of a monitoring program in Beden-Wurttemberg, Germany. J. Chromatography A 2001, 938, 199–210. Seth, R.; Mackay, D.; Muncke, J. Estimating the organic carbon partition coefficient and its variability for hydrophobic chemicals. Environ. Sci. Technol. 1999, 33, 2390–2394. Struger, J.; Fletcher, T. Occurrence of lawn care and agricultural pesticides in the Don River and Humber River watersheds (1998-2002). J. Great Lakes Res. 2007, 33, 887–905. Tülp, H.; Goss, K. U.; Schwarzenbach, R. P.; Fenner, K. Experimental determination of LSER parameters for a set of 76 diverse pesticides and pharmaceuticals. Environ. Sci. Technol. 2008, 42, 2034–2040. Wania, F.; Breivik, K.; Persson, N. J.; McLachlan, M. S. CoZMo-POP2 – A fugacity-based dynamic multi-compartmental mass balance model of the fate of persistent organic pollutants. Environ. Modelling and Software, 2006, 21, 868–884.

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142

7 AIR-WATER EXCHANGE OF ANTHROPOGENIC AND NATURAL ORGANOHALOGENS ON INTERNATIONAL POLAR YEAR (IPY) EXPEDITIONS IN THE CANADIAN ARCTIC

Fiona Wonga, b, Liisa M. Jantunena, Monika Pućkoc, d, Tim Papakyriakoud, Ralf M. Staeblere,

Gary A. Sternc, d, Terry F. Bidlemana

a Centre for Atmospheric Research Experiments, Environment Canada, 6248 Eighth Line,

Egbert, ON, L0L 1N0, Canada

b Department of Chemistry and Department of Physical and Environmental Scicences, University


c Freshwater Institute, Department of Fisheries and Oceans, University of Manitoba,

501 University Crescent, Winnipeg, MB, R3T 2N6, Canada

d Centre for Earth Observation Science, University of Manitoba, 474 Wallace Building, 125

Dysard Road, Winnipeg, R3T 2N2, Canada

eScience and Technology Branch, Environment Canada, 4905 Dufferin St, Toronto, ON, M3H

5T4, Canada

Contribution: Sample collection during Leg 1 was carried out by F. Wong and L. Jantunen and

Legs 8 and 9 by F. Wong. Laboratory analysis performed by F. Wong, L. Jantunen, with

assistance from S. Wrigglesworth and A. Gawor. Sample collection and laboratory analysis of

HCHs in air and water samples from Legs 5 to 8 was done by M. Pućko and DFO personnel.

Micrometeorological data was collected by T.Papakyriakou. Manuscript was prepared by F.

Wong under supervision of Terry F. Bidleman and contributions from co-authors.

Manuscript submitted to Environment Science and Technology, May 30, 2010.

143

7.1 ABSTRACT

Shipboard measurements of organohalogen compounds in air and surface seawater were

conducted in the Canadian Arctic in 2007-2008. Study areas included the Labrador Sea, Hudson

Bay and the southern Beaufort Sea. Water concentration ranges (pg L-1) were: α-

hexachlorocyclohexane (α-HCH) 465 – 1013, γ-HCH 150 – 254, hexachlorobenzene (HCB) 4.0

– 6.4, 2, 4-dibromoanisole (DBA) 8.5 – 38, 2, 4, 6- tribromoanisole (TBA) 4.7 – 163. Air

concentration ranges (pg m-3) were: α-HCH 7.5 – 48, γ-HCH 2.1 – 7.7, HCB 48 – 71, DBA 4.8 –

25 and TBA 6.4 – 39. Fugacity gradients predicted net deposition of HCB in all areas, while

exchange directions varied for the other chemicals by season and locations. α-HCH in air over

the Beaufort Sea was racemic in winter (mean enantiomer fraction, EF = 0.504±0.008) and

nonracemic in late spring – early summer (mean EF = 0.476±0.010). This decrease in EF was

accompanied by a rise in air concentrations due to volatilization of nonracemic α-HCH from

surface water (EF = 0.457±0.019). Concurrent air samples were collected at 1 and 15 m above

water in the Beaufort Sea and coupled with micrometeorological data to estimate the fluxes of α-

HCH (FM) which were compared to fluxes calculated from the commonly used Whitman two-

film model (FTF). FM agreed with FTF within a factor of 1.5 for seven out of eight events.

7.2 INTRODUCTION

Recent studies suggest that the summer sea-ice in the Arctic could disappear by 2040 (1) and

some suggest as early as 2015 (2). Decreasing ice cover will open larger areas of the Arctic

Ocean and its regional seas to air-water gas exchange. It is expected that loss of ice cover will

increase rates of deposition and volatilization of semivolatile chemicals as the system strives to

achieve air-water equilibrium.

Declining emissions of the pesticide technical hexachlorocyclohexane (HCH) since the 1980s

have led to a reduction in atmospheric concentrations of its main component, α-HCH (3−7) and

reversal of its air-water flux in the Arctic Ocean from net deposition in the 1980s to near-

144

equilibrium or net volatilization after the early 1990s (4, 7−14). Evasion of α-HCH from the

Canadian Archipelago during ice breakup was followed by an increase in air concentrations and

appearance in the air of nonracemic α-HCH from seawater (12). Dieldrin was also volatilizing

from the central Archipelago during the early 1990s (15). Organochlorine pesticides (OCPs),

which are close to air-water equilibrium or undergoing net deposition are γ-HCH (7−14, 16),

hexachlorobenzene (HCB) (13, 16), chlordanes (13), endosulfan (16) and toxaphene (15). A

year-long study in the central Archipelago showed seasonal variation in flux direction and

magnitude for the above OCPs depending on air concentrations, advected water masses and ice

cover (15).

Departure from equilibrium is commonly assessed by comparing fugacities of the chemical in air

and water, and fluxes are estimated using the Whitman two-film model of gas exchange.

Alternatively, the flux of gases (or heat) can be estimated from a gradient in concentration (or

temperature) using various micrometeorological techniques (17−20). Few studies have applied

such methods to estimate the air-water flux of semivolatile chemicals, although this has been

done over soils (21, 22) and forest canopies (23). Fluxes of HCHs and HCB over Lake Superior

were derived by coupling shipboard measurements of air concentration at two heights and sensible

heat flux by the direct eddy covariance technique (24).

The purpose of this study was to investigate the influence of ice cover on the air-water gas

exchange of organohalogen compounds in the Canadian Arctic as part of International Polar

Year (IPY) Circumpolar Flaw Lead (CFL) system study and ArcticNet and expeditions in the

Labrador Sea, Hudson Bay and southern Beaufort Sea. Net exchange direction was determined

for α- and γ-HCH, HCB, and two naturally occurring compounds 2, 4-dibromoanisole (DBA)

and 2, 4, 6-tribromoanisole (TBA). Fluxes of α-HCH were estimated using a

micrometeorological approach and the Whitman two-film model.

145

7.3 EXPERIMENTAL METHOD

7.3.1 Air and water sampling, extraction and analysis

This study was conducted jointly by research groups from the Centre for Atmospheric

Research Experiments (CARE), Environment Canada and Freshwater Institute (FWI),

Department of Fisheries and Oceans, as part of the 2007-08 Circumpolar Flaw Lead (CFL) and

2007 ArcticNet expeditions on board the Canadian Coast Guard Ship (CCGS) Amundsen.

During the initial leg of the cruise (July 29 to August 16, 2007), the ship traveled northward from

the St. Lawrence Seaway to the Labrador Sea, Hudson Strait, south to Sanikiluaq in Hudson Bay,

and west to Churchill, Manitoba, Canada (Figure 7.1). Open water was encountered for the entire

trip. Legs 8 (May 16 to June 4, 2008) and 9 (June 27 to July 16, 2008) took place at a flaw lead

in the southern Beaufort Sea, moving south-north along the west coast of Banks Island. Legs 8

and 9 provided an opportunity to study air-water gas exchange during mainly ice-covered versus

open water conditions.

Sampling of air and water was carried out by the CARE team on these legs. HCHs in air (this

study) and water (25) were also measured in the southern Beaufort region by the FWI team

during January 6, 2008 to May 13, 2008 (Legs 5−8, N = 16). Methods are given in Appendix,

A7.1. Briefly, low volume air samples (LV Air, 75 m3, N = 34) were collected at 1 m and 15 m

above the water surface when the ship was stationary to measure concentration gradients in air

over 6 to 16 hours. Details of the sampling time are presented in Table A7.1. High volume air

samples (HV Air, 300−2500 m3, N = 41) were continuously taken at the deck level and each

sample was taken over 8.5 to 51 hours. Low volume and high volume water samples (LV Water,

4 L, N = 28; HV water, 40−100 L, N = 31) were collected to measure concentrations in the upper

7 m of the ocean. Methods for sample extraction, quantitative analysis, chiral analysis and

quality control are given in A7.1.

The potential for air-water exchange was examined using the fugacity approach. Details are

given in A7.2.

146

Figure 7.1 Map of the cruise track during Legs 1a and 1b, and sampling area in the southern

Beaufort Sea during Legs 5 – 9 (see insert). LV Air15 and LV Air1 = low volume air sampling

taken at 15 m and 1 m above water; HV Air = high volume air sampling taken at deck level. Star

denotes LV Air and Water sampling events during Legs 1b, 8 and 9.

LV Air15

HV Air

LV Air1

Labrador SeaLeg 1a

Churchill

Hudson Bay

1, 2,3 Leg 1b

Beaufort Sea

0 1000 km

Legs 5 to 9

LV Air15

HV Air

LV Air1

Labrador SeaLeg 1a

Churchill

Hudson Bay

1, 2,3 Leg 1b

Beaufort Sea

0 1000 km

LV Air15

HV Air

LV Air1

LV Air15LV Air15

HV AirHV Air

LV Air1LV Air1

Labrador SeaLeg 1a

Churchill

Hudson Bay

1, 2,3 1, 2,3 Leg 1b

Beaufort SeaBeaufort SeaBeaufort Sea

0 1000 km

Legs 5 to 9

147

7.3.2 Micrometeorological measurements

Flux of α-HCH was estimated using a micrometeorological approach. Methods for measuring

the micrometeorological data are presented in A7.3. This technique employed the LV Air

samples taken simultaneously at 1 m (C1) and 15 m (C15) above surface.

Based on the vertical concentration gradient, the net flux (FM, ng m-2 h-1, defined as positive for

volatilization) of α-HCH was calculated from Lenschow, p. 146 (26),

)/ln()(*

115

115

zzCCkuF

cmM ΦΦ

−−≈ Eq [7.1]

where u* is the average friction velocity (m s-1) for the sample periods, C15 and C1 are air

concentrations (ng m-3), z15 and z1 are the corresponding heights of C15 and C1, k is the von

Karman’s constant (0.4), Φ is the stability correction for momentum (m) and scalar (c)

concentration. Hourly friction velocity was derived from drag coefficients using ship

meteorological measurements following Smith (27). Drag coefficients were first corrected for

atmospheric stability using the Monin-Obukhov stability parameter, which was computed

following Abdella and McFarlane (28). Stability was assessed using the bulk Richardson

number (RB), a commonly used approximation to the gradient Richardson number (29). Method

used to estimate RB and details for the stability corrections are presented in A7.4. In applying Eq

(7.1) we assume the following: 1) similarity of the eddy diffusivities for momentum and scalars

under neutral atmospheric stability; 2) constancy of fluxes with height, i.e. no vertical flux

divergence or convergence beneath the upper measurement height, and 3) steady state

conditions. Further discussion on these assumptions is given in A7.4.

For comparison, fluxes were also calculated based on the Whitman two-film model (FTF) using

air concentration measured at 1 m above the water surface (C1). Details are presented in A7.5.

148


7.4.1 Air and water concentrations

Mean air and water concentrations of chemicals are summarized in Table 7.1 and presented for

each sample in Tables A7.2 to 7.5. Only the operationally defined gaseous and dissolved

concentrations were quantified, since previous studies found that GFF-retained quantities in air

and water were below detection for HCHs (10, 13) and HCB (13), and particulate HCB in

seawater averaged 5% of the total (13). The following measures of concentrations in 1) water

and 2) air were used in evaluating differences among legs and calculating air-water fugacity

gradients. 1) Water concentrations. α- and γ-HCH: average of LV and HV water. The average

percent differences between LV water and HV water were 15% and 5% for α-HCH and γ-HCH,

respectively. HCB: HV Water. DBA and TBA: LV Water. 2) Air concentrations. α- and γ-

HCH: HV Air. HV Air and LV Air (average at 1 and 15 m) agreed within 11% for α-HCH; γ-

HCH was not detected in some LV Air samples due to its lower concentrations. HCB, DBA and

TBA: LV Air.

Hexachlorocyclohexanes Mean concentrations of α-HCH in water of the Labrador Sea (Leg

1a) and Hudson Bay (Leg 1b) were 465 ± 118 and 628 ± 108 pg L-1, while means for γ-HCH

were 150 ± 59 and 178 ± 35 pg L-1. Water concentrations of α-HCH and γ-HCH in the southern

Beaufort Sea (Legs 8 and 9) averaged 964 ± 204 and 200 ± 64 pg L-1 and did not differ

significantly between the two legs. Pućko et al. (25) measured 922 ± 70 pg L-1 for α-HCH and

129 ± 23 pg L-1 for γ-HCH in the same region from March 16 to May 25, 2008. Their samples

were taken directly under the ice while those from the current study were from the ship’s water

intake at 7 m. A previous study in 1999 also found higher concentrations of HCHs in the

southern Beaufort Sea compared to the eastern Archipelago (30). Mean concentration of α-HCH

in the southern Beaufort Sea during 1999 (4700 pg L-1) was nearly five times higher than in 2008

(922−964 pg L-1), but less difference was seen for γ-HCH between 1999 (310 pg L-1) and 2008

(129−200 pg L-1).

149

Concentrations of α-HCH in air averaged 26 ± 8.8 pg m-3 over the Labrador Sea and Hudson Bay

during late spring to summer (Legs 1a, 1b), 16 ± 2.6 over the southern Beaufort Sea from mid-

May to early June (Leg 8) and 48 ± 13 pg m-3 from late June to mid-July (Leg 9). Corresponding

levels of γ-HCH were 2.3 ± 0.4 pg m-3 (Legs 1a, 1b), 2.1 ± 0.2 (Leg 8) and 4.6 ± 0.7 (Leg 9).

Both HCHs were significantly higher (p < 0.001) on Leg 9 than Leg 8. Air concentrations of α-

HCH in the southern Beaufort Sea were substantially lower in January to mid-May (Legs 5-8),

averaging 7.5 ± 2.3 pg m-3 and γ-HCH was slightly higher, 7.7 ± 3.7 pg m-3. Possible reasons are

discussed below. For comparison, mean air concentrations reported at Canadian arctic air

monitoring stations between 2000−2003 at Alert (82o30’N, 62o20’W), Kinngait (64o13’N,

76o32’W), and Little Fox Lake (61o21’N, 135o38’W), and the U.S. station at Point Barrow,

Alaska (71o18’N, 156o36’W) were in the range α-HCH = 19–48 pg m-3 and γ-HCH = 2.7–5.6 pg

m-3 (7).

Hexachlorobenzene HCB in water averaged 5.4 ± 2.0 pg L-1, with a narrow range from 4.0–6.4

pg L-1 and no significant differences among legs (p<0.05). These values were comparable to 5–6

pg L-1 in the southern Beaufort Sea and White Sea in 1999–2000 (31) and 4.6 pg L-1 in the North

Atlantic and eastern Arctic Ocean in 2008 (13) but a factor of 2–3 lower than 14–18 pg L-1 in the

central Archipelago at Resolute Bay in 1993 (15).

HCB in air ranged from 48−71 pg m-3 and averaged 61 ± 13 pg m-3. HCB in Beaufort Sea air

was significantly higher (p<0.01) on Leg 9 (71 ± 11 pg m-3) than Leg 8 (48 ± 6.5 pg m-3), while

HCB over Hudson Bay (Leg 1a) averaged 58 ± 2.6 pg m-3. Similar to this study, annual means

of HCB in Canadian Arctic air in 2000–2005 ranged from 29–69 pg m-3 (6, 7), while levels over

the northern North Atlantic and eastern Arctic Ocean were 23–87 pg m-3 (13).

Bromoanisoles Ranges (and means) concentration of DBA and TBA in water were 8.5–38 (19

± 6.9) pg L-1 and 4.7–163 (54 ± 11) pg L-1. Highest levels were seen during Leg 1a followed by

Legs 1b, 9 and 8. TBA in the waters of the Great Barrier Reef was estimated to be 540 pg L-1

using passive water samplers (32). TBA was also found in deep sea fish species (33) and marine

biota from Norway (34).

150

Ranges (and means) for DBA and TBA in air were: 4.8–25 (16 ± 6.7) pg m-3 and 6.4–39 (23 ±

7.3) pg m-3. Spatial trends were similar to water: Leg 1b > 9 > 8. Overall, levels in air were

comparable to previous measurements in Norway: <0.34–46 pg m-3 for DBA, and 0.1–37 pg m-3

for TBA (35), the Indian Ocean: 15 pg m-3 (mean) for TBA (36), New Zealand and American

Samoa: 18-19 pg m-3 and the Gulf of Mexico: 55 pg m-3 for TBA (37) and the Atlantic Ocean:

23–42 pg m-3 for DBA and 33–58 pg m-3 for TBA (38).

Bromoanisoles are formed naturally from reactions between haloperoxidase with humic

substances in oceans (32) and found in marine algae (39, 40) and sponges from the Great Barrier

Reef (32) and Antarctica (41). Führer and Ballschmiter et al. (38) reported that high levels of

atmospheric bromoanisoles were associated with regions with high primary productivity off the

west coast of Africa. They are also formed by biomethylation of bromophenols, which are

produced anthropogenically as fumigants, wood preservatives, industrial intermediates and as

by-products in chlorination of water containing bromide ions (39, 42). Half-lives of DBA and

TBA due to OH radical reactions are 50 h and 100 h respectively (43), which exceed the 2 d half

life criterion for long-range transport according to the Stockholm Convention. Hence,

bromoanisoles measured in this study may have been produced locally or have been advected

from other locations.

Table 7.1 Summary of air (pg m-3) and water (pg L-1) concentrations for α-HCH, γ-HCH, HCB,

DBA and TBA.

Leg Date LocationAir ( pg m-3) Mean SD Mean SD Mean SD Mean SD Mean SD1a Jul 29 - Aug 5, 2007 Labrador Sea 21 5.8 2.3 0.41b Aug 5 - 16, 2008 Hudson Bay 30 8.9 2.4 0.5 58 2.6 25 8.0 39 7.85-8 Jan 6 - May 13, 2008 Beaufort Sea 7.5 2.3 7.7 3.78 May 16 - Jun 4, 2008 Beaufort Sea 16 2.6 2.1 0.2 48 6.5 4.8 5.5 6.4 5.39 Jun 27 - Jul 16, 2008 Beaufort Sea 48 13 4.6 0.7 71 11 19 6.6 24 8.7

Water (pg L-1)1a Jul 29 - Aug 5, 2007 Labrador Sea 465 118 150 59 5.1 1.6 38 14 163 321b Aug 5 - 16, 2008 Hudson Bay 628 108 178 35 6.4 2.2 19 3.3 34 0.78 May 16 - Jun 4, 2008 Beaufort Sea 1013 198 254 74 6.4 2.1 9.6 1.2 4.7 2.99 Jun 27 - Jul 16, 2008 Beaufort Sea 942 209 175 43 4.0 1.9 8.5 8.9 12 8.4

α-HCH HCB DBA TBAγ-HCHLeg Date LocationAir ( pg m-3) Mean SD Mean SD Mean SD Mean SD Mean SD1a Jul 29 - Aug 5, 2007 Labrador Sea 21 5.8 2.3 0.41b Aug 5 - 16, 2008 Hudson Bay 30 8.9 2.4 0.5 58 2.6 25 8.0 39 7.85-8 Jan 6 - May 13, 2008 Beaufort Sea 7.5 2.3 7.7 3.78 May 16 - Jun 4, 2008 Beaufort Sea 16 2.6 2.1 0.2 48 6.5 4.8 5.5 6.4 5.39 Jun 27 - Jul 16, 2008 Beaufort Sea 48 13 4.6 0.7 71 11 19 6.6 24 8.7

Water (pg L-1)1a Jul 29 - Aug 5, 2007 Labrador Sea 465 118 150 59 5.1 1.6 38 14 163 321b Aug 5 - 16, 2008 Hudson Bay 628 108 178 35 6.4 2.2 19 3.3 34 0.78 May 16 - Jun 4, 2008 Beaufort Sea 1013 198 254 74 6.4 2.1 9.6 1.2 4.7 2.99 Jun 27 - Jul 16, 2008 Beaufort Sea 942 209 175 43 4.0 1.9 8.5 8.9 12 8.4

α-HCH HCB DBA TBAγ-HCH

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7.4.2 Air-water gas exchange

Fugacity of α-HCH, γ-HCH, HCB, DBA and TBA in water (fW) and air (fA) were calculated and

expressed as fractions, where ff = fW /(fW +fA) (Figure 7.2). A ff = 0.50 indicates equilibrium and

no net exchange between air and water; ff >0.50 indicates net volatilization and ff < 0.50

indicates net deposition. Uncertainties in ff was estimated according to Bruhn et al., (44) and

detailed in A7.2. The equilibrium "window" at 95% confidence for α-, γ-HCH, DBA and TBA

was ff = 0.40–0.64, and for HCB was ff = 0.37–0.73. The ff of α-HCH in all locations ranged

from 0.60–0.71, indicating equilibrium or net volatilization. The ff of α-HCH on Leg 8 (0.71 ±

0.04) in the Beaufort Sea was greater than on Leg 9 (0.60 ± 0.04), indicating that the

volatilization potential of α-HCH was higher during ice cover conditions. Loss of ice on Leg 9

allowed α-HCH evasion into the boundary layer, raising the air concentration (see below), and

lowering ff. Near equilibrium was observed in Hudson Bay (ff = 0.64 ± 0.06), and in the

Labrador Sea with ff = 0.60 ± 0.07. γ-HCH in Labrador Sea (Leg 1a) was near equilibrium as

indicated by ff of 0.64 ± 0.06. Net volatilization in Hudson Bay, Leg 1b (ff = 0.72 ± 0.05) and

near equilibrium in the Beaufort Sea (ff = 0.58±0.03 during Leg 8; and 0.62±0.03 during Leg 9).

Other reports indicate variable net exchanges in arctic waters. Volatilization or equilibrium of α-

HCH was usually reported in the western Arctic (4, 7, 12, 14, 15) and deposition or equilibrium

in the eastern Arctic (8, 9, 13). Net exchanges of γ-HCH generally ranged from equilibrium to

deposition in the western and eastern Arctic, with occasional reports of volatilization in some

areas (8, 9, 12–16). γ-HCH was added to the Stockholm Convention in 2009 and its

concentrations in arctic air may still be influenced by recent usage. Monthly records at six

circumpolar stations between 2000-2002 showed generally increasing concentrations of α-HCH

from spring through fall. No consistent trend was seen for γ-HCH, and some stations showed

summertime minima (7). The ff of HCB was <0.3 at all sampling periods and locations,

implying net deposition. Net deposition or equilibrium of HCB has been reported in previous

studies in the Arctic (7, 13, 15). Strong seasonal differences, and sometimes reversals, in net

exchange of these chemicals was noted in the central Archipelago, in response to changing air

concentrations, ice cover and advection of water masses (15).

152

DBA and TBA were near equilibrium and volatilizing, respectively, in Hudson Bay (ff = 0.51

and 0.69). Net exchanges in the Beaufort Sea were volatilization of DBA on Leg 8 (ff = 0.68 ±

0.20) and deposition on Leg 9 (ff = 0.37 ± 0.06), and near equilibrium for TBA on both legs (Leg

8, ff = 0.56 ± 0.18; Leg 9, ff = 0.55 ± 0.06).

Figure 7.2 Air-water gas exchange of α-HCH, γ-HCH, HCB, DBA and TBA. The dash lines are

the equilibrium window for α-HCH, γ-HCH, DBA and TBA (0.40-0.64). For HCB, the

equilibrium window was 0.37-0.73.

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153

7.4.3 Enantiomers as tracers of α-HCH volatilization

Enantiomers of α-HCH were determined to trace the sources of HCHs in the atmosphere; i.e.

volatilization vs. long-range transport. Results were expressed as enantiomer fraction, EF = peak

area of the (+)/[(+) + (–)] enantiomers. EFs in water were derived from the average of LV and

HV Water samples where EFs in air were obtained from HV Air samples only. Results are listed

in Tables A7.3 to 7.5. Nonracemic α-HCH was observed in all water samples, with preferential

degradation of the (+) enantiomer. Mean EFs during Legs 1a = 0.425 ± 0.010, 1b = 0.447 ±

0.005, 8 = 0.462 ± 0.020 and 9 = 0.455 ±0.019. Pućko et al. (25) reported EFs in south Beaufort

Sea water just under the ice ranging from 0.416–0.451 and averaging 0.438 ± 0.011 during

March to May, 2008. In air, EFs were lowest on Leg 1 (0.456 ± 0.008) and higher in the

Beaufort Sea (0.476 ± 0.010, not significantly different between Legs 8 and 9).

The relationship between α-HCH concentrations and EFs in air of the southern Beaufort Sea

from January to July 2008 is shown in Figure 7.3. During January 6 to May 13, 2008, the sea

was mostly covered by ice, α-HCH concentrations in air were low, and EFs were not

significantly different from racemic (0.504±0.008). EFs in air showed a decline from May 16 –

July 15, with a mean of 0.476 ± 0.010. The drop in EF corresponded to increased air

concentrations as ice receded and the net volatilization flux of α-HCH from the water to air

increased.

A similar trend of atmospheric α-HCH in air close to racemic before ice breakup and nonracemic

when ice receded was found at Resolute Bay in the central Archipelago (12). As in this study, a

decline in EF was accompanied by an increase in α-HCH concentration. Racemic α-HCH was

found over the ice-covered central Arctic Ocean in 1994, while α-HCH was nonracemic over the

ice-free Chukchi Sea and Greenland Sea (11). Nonracemic α-HCH in air was also reported over

other open sea (4, 8, 13, 45, 46, 47) and coastal regions (48, 49). Genualdi et al. (50) reported

that transport of air above the Pacific atmospheric boundary layer (ABL) was associated with

racemic α-HCH, while nonracemic α-HCH originated from transport below the ABL.

154

Figure 7.3 Concentration and EF of α-HCH in air of the southern Beaufort Sea from Legs 5 to 9, January to July 2008.

Enantiom

er fraction (EF)

Racemic EFSeawater EF

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7.4.4 Fluxes from micrometeorological measurements vs. Whitman two-film model

Vertical gradient LV Air samples were taken simultaneously at 1 m (C1) and 15 m (C15) above

surface. Figure A7.1 shows the air concentrations of α-HCH, HCB, DBA and TBA as a ratio of

C1/C15. Legs 1b and 9 were performed over water while Leg 8 was over ice (except for Event 8,

see Table A7.1). For most events over water, the α-HCH concentration at C1 was 10 to 22%

higher than C15. Other chemicals did not show significant differences between C1 and C15.

These gradient LV Air samples were employed to estimate the net fluxes of α-HCH using a

micrometeorological method and results are compared to those estimated using the Whitman

two-film model (51), which is based on the C1 samples. Focus was put on the Leg 9 samples

where there were micrometeorological data available and open water was encountered during

most of the sampling periods.

Equilibrium air concentration (Ceq, A) was calculated for Leg 9 samples based on the surface

water concentration and H (i.e. Ceq, A = CWH/RT), and it was found to be a factor of 1.2 to 1.7

higher than C1. This is expected as C1 is probably influenced by the ambient air to some extent,

whereas Ceq, A is the theoretical concentration at the air-water interface.

In applying the micrometeorological approach, the atmospheric stability of each sampling event

was examined in which hourly RB was calculated. It is found that 63-100% of the RB were

indicative of neutral (i.e. weakly stable or weakly unstable) conditions during most of the

sampling events. The only exception was, Event #13 for which only 50% of the RB was close to

neutral, and no flux is estimated for this event. Therefore the stability corrections required were

generally small.

Figure 7.4 compares net flux of α-HCH determined by the micrometeorological approach (FM)

and Whitman two-film model (FTF). Flux values for each sampling event are given in Table

A7.6. The flux range for FM was – 0.15 to 0.75 ng m-2 h-1 and for FTF was 0.17 to 0.52 ng m-2

h-1. In the 1999, the net volatilization flux of α-HCH in southern Beaufort Sea was 1.4 ng m-2 h-

1, estimated by the two-film model (12), but at that time the surface water concentration was

about 3-4 times higher than in this study. Both FM and FTF predicted net volatilization except for

156

sampling event #14 in which net deposition was predicted by FM. For other events, mean FM

was 0.44±0.2 ng m-2 h-1 and mean FTF was 0.29 ± 0.15 ng m-2 h-1.

Uncertainty associated with FM was estimated based on the standard deviation (SD) of u* and the

concentration terms; this is shown in Figure 7.4. Those associated with FTF were based on the

SDs of the concentration terms, Henry’s law constant, and KOL. The RSD range over all events

for FM was 83 to 277% and for FTF was 127 to 288%. Details of the error analysis are presented

in A7.6.

Figure 7.4 Flux of α-HCH in the southern Beaufort Sea during Leg 9, sampling events # 10 to

17 (Table A7.6). FM = flux determined from meteorological approach, FTF = flux determined

from the two-film model. Vertical lines indicate propagated standard deviations. Positive and

negative fluxes indicate volatilization and deposition, respectively. FM was not estimated for

event 13 because of non-neutral atmospheric stability.

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157

Despite the large uncertainties associated with FM and FTF, the fluxes generally agreed well with

each other. The Whitman two-film model is based on the molecular diffusion through the water

and gas films at the interface. The mass transfer coefficient varies with wind speed through its

effect on film thickness, but no account is taken of atmospheric stability. On the other hand, FM

might be biased as distortion of air flow by the bow of the ship could have affected concentration

gradients. Estimation of u* is made from measurements high above the ship’s deck with non-

disturbed airflow is assumed and that u* is assumed to be similar throughout the surface

atmospheric layer.

This study has demonstrated the potential of using the micrometeorological technique to estimate

flux of organic chemicals over water, particularly in situations where no water samples are

available and under neutral atmospheric condition. However, FM and FTF predicted opposite flux

at one event and the reason is unclear. It is caution that the micrometeorological technique must

be applied with consideration of the atmospheric stability, sampling time, and surface

homogeneity.


This work was funded by ArcticNet and the International Polar Year – Circumpolar Flaw Lead

(CFL) System Study and NSERC Discovery Grant Program (TP). FW acknowledges travel

grants from the Northern Science Training Program of Indian and Northern Affairs Canada, and

the Centre for Global Change Science, University of Toronto. FW was supported under the

Research Affiliate Program with Environment Canada. We thank the crew members of the

CCGS Amundsen for logistic support. FW is grateful to Allison MacHutchson (FWI) and Garry

Codling (Lancaster University, UK) for help in sampling and Frank Wania (University of

Toronto) for helpful discussions, Sonya Wrigglesworth and Anya Gawor (CARE) for laboratory

support. MP thank Bruno Rosenberg for help with laboratory analysis at FWI. At the University

of Manitoba we are grateful for the assistance from Brent Else and Bruce Johnston for field

support and data processing.

158

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8 CONCLUSIONS AND RECOMMENDATIONS

8.1 CONCLUSIONS

The present thesis contributes to our knowledge of air-soil and air-water gas exchange of

persistent organic pollutants (POPs) with emphasis on organochlorine pesticides (OCPs), by

examining factors which influence the air-surface exchange processes, the current net exchange

status in the environment, and comparison of different approaches to estimate the net surface

exchange. Key findings are summarized below.

Air-soil exchange of organochlorine pesticides in Mexico

Organochlorine pesticides are widespread in the air and soils of Mexico. DDTs, endosulfans and

toxaphenes are the most frequently detected compounds and the highest in concentration.

Highest concentrations of DDTs both in air and soil were found in southern Mexico and the

lowest in northern and central Mexico. A fresher DDT residue was observed at sites with greater

DDT use and in the southern part of the country, as evident from the higher p,p’-DDT proportion

and nearly racemic o,p’-DDT. This agrees with the former heavy use of DDT in the endemic

malarious area of the country.

Hexachlorocyclohexanes (HCHs), chlordanes, and dieldrin in air and soils of Mexico were

relatively low and evenly distributed across the country, likely due to them being aged residues

and more diffuse in the environment.

Examination of soil-air exchange using fugacity concepts showed that some soils are net

recipients of DDTs from the atmosphere, while other soils are net sources. Profiles of toxaphene

components in soils and air showed similar patterns which suggests that soil is the source to the

atmosphere. Non-racemic o,p’-DDT in air and soil samples from the central-northern regions

164

suggest emission of aged residues from soil. Endosulfan, a currently used pesticide, was

undergoing net deposition at most sites.. Other OCPs showed wide variability in fugacity,

suggesting a mix of net deposition and volatilization.

Aging of organochlorine pesticides and polychlorinated biphenyls in muck soil: volatility, bioaccessibility and degradation

A method using hydroxypropyl-β-cyclodextrin (HPCD) to non-exhaustively extract OCPs and

polychlorinated biphenyls (PCBs) from a high organic muck soil was optimized for the HPCD

concentration, extraction time, and concentration of chemicals in soil.

The HPCD-soil partition coefficient (KCD-soil) was inversely correlated with the octanol-water

partition coefficient (KOW) for PCBs, which suggested that the more hydrophobic congeners were

more strongly bound to the soil and more difficult to extract, although the possibility of size-

exclusion by the HPCD cavity cannot be excluded.

HPCD extractabilities and volatilities of spiked 13C-labelled OCPs in non-sterile muck soil were

initially greater than for the native OCPs, then declined and approached to those of the natives

over time, indicating greater binding to the soil with age. Spiked PCBs also showed similar

declining trend. No decline of HPCD extractability and volatility over time was observed for

chemicals in the sterile soils.

Based on correlations between HPCD extractability and bioaccessibility found by others, HPCD

could be an appropriate extractant for distinguishing loosely and strongly bound OCPs and PCBs

in the muck soil, and inferring their bioaccessibility. HPCD extractability was negatively

correlated with the soil-air partition coefficient (KSA), which suggests that volatility could also be

used as a surrogate for bioaccessibility.

KSA was over-predicted by the classical Karickhoff model when compared to the measured

values, which supports previous findings in the literature.

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Racemic 13C6-α-HCH spiked into the soil showed selective degradation of the (+) enantiomer

over time. Good agreement was found between nonracemic enantiomer proportions in air and

the HPCD extract, which suggests that the air and HPCD are accessing the same sequestered

pool of chemical in the soil. However, enantiomer proportions in soil where both air and HPCD

extract favoured the (–) enantiomer to a greater extent than soil which had been exhaustively

extracted with an organic solvent. These results show preferential volatilization and HPCD

extraction of the (–) enantiomer, which might be due to greater sequestering in soil of the (+)

enantiomer which is preferentially degraded. This was only observed in the non-sterile soils

where degradation occurred. Such a phenomenon is first to be documented and challenges the

conventional thinking of soil-air exchange of chiral chemicals that enantiomers from the same

pool of chemical in the soil.

Equilibrium distribution and mobility of organic contaminants in soil using the chemical partitioning space

Chemical partitioning space is a simple way of quickly obtaining information of the likely

equilibrium partitioning of a chemical in soil among pore water, pore air and the solid phase.

Chemical space maps, constructed using humic acid-water and air-water partition coefficients

(KHW, KAW) provide graphical and instantaneous prediction of the phase distribution and mobility

of a wide range of chemicals in different soils. They have didactic value in illustrating how

various soil properties (e.g. soil moisture, type and amount of organic matter, temperature,

surface soil depth) and chemical class (e.g. non-polar, polar) can influence the phase distribution

and mobility of organic chemicals in soil.

Assessment of the phase distribution and mobility of chemicals using the space maps revealed

that there is a link between the partitioning and transport in soil. However, their partitioning

thresholds can deviate by order of magnitudes depending on soil properties and magnitude of

partition coefficients. This implies that even a small fraction of a chemical in air or water is

important, because it may be more mobile than chemical in the solid phase which has a much

slower transport rate.

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Chemical partitioning space maps can be used as screening tools for more sophisticated fate

assessments by identifying the parameters that need to be known precisely vs. those that require

only approximate knowledge.

Air-water gas exchange of POPs in the Canadian Arctic

α-HCH concentrations in air and seawater declined significantly from an earlier study in 1999,

possibly attributed to the continuing bans of its usage and dissipation from storage reservoirs.

HCB in seawater also declined since last measured in 1993, but air concentrations remained

unchanged.

Bromoanisoles were highly variable and depended on season and location. Lower levels in

seawater were found in the southern Beaufort Sea were during spring compared to summer,

while the highest concentrations in seawater were found in Labrador Sea fjords in summer.

Seasonal trends in air concentrations mirrored those in seawater.

Water/air fugacity gradients predicted net deposition of HCB in all areas, while the net exchange

directions varied for HCHs and bromoanisoles by season and location. The mean water-to-air

flux determined from the micrometeorological approach was 1.4 times greater than the mean flux

estimated from the classic Whitman two-film model.

α-HCH in air over the Beaufort Sea was racemic in winter and nonracemic in late spring – early

summer. This shift from racemic to nonracemic pattern of α-HCH was accompanied by a rise in

air concentrations due to volatilization of nonracemic α-HCH from surface water. This “tracer”

approach provided unequivocal evidence of α-HCH volatilization from the sea and demonstrated

the importance of sea ice in governing exchange.

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8.2 RECOMMENDATIONS

Air-soil exchange of OCPs in Mexico

The survey of OCPs in air and soil of Mexico was the first large-scale effort and provided

baseline data on regional differences in contamination. It is recommended that the sites surveyed

in this study and the results obtained be considered if Mexico establishes a monitoring program

for OCPs in air and soil. These data could be also incorporated into continental atmospheric

transport models to forecast the movement of OCPs over a larger scale (e.g. U.S., Canada).

Monitoring efforts should be expanded to a wider range of chemicals such as brominated flame

retardants and currently used pesticides.

Sampling locations in Mexico should be extended to areas of potentially high emissions. These

include Oaxaca, the state with the highest DDT consumption in the country during 1989-1999

(Gallardo Diaz et al., 2000), inland agricultural areas of Sinaloa, communities in Chiapas where

high levels of DDT have been reported in soil (Herrera-Portugal et al., 2005), and cotton-

growing regions in Sonora.

Measurement of air-soil fluxes is recommended in areas with highly contaminated soil. Such

measurements can be accomplished by coupling micrometeorological data with vertical air

gradient measurements to estimate the flux of legacy chemicals from tropical soil to the

atmosphere, as has been done in temperate regions (Kurt-Karakus et al., 2006; Majewski et al.,

1993).

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Aging of organochlorine pesticides and polychlorinated biphenyls in muck soil: volatility, bioaccessibility and degradation

Recommendations for further method development on HPCD, include work on a wider range of

soils (i.e. low organic carbon) and extension to other chemical classes.

There is a need for systematic studies which explore the effect of physical chemical properties

(e.g., molecular size, octanol-water partition coefficient) and cyclodextrin cavity size on the

extractability of chemicals from soil.

Strong correlation was found between reduced chemical volatilization (higher KSA) and reduced

bioaccessibility (as measured by the surrogate, HPCD extractability) in a high organic muck soil.

Examining whether this correlation holds in a low organic soil for a wider range of chemical

classes is suggested.

Studies are needed to establish a direct link between HPCD extractability and chemical uptake

by earthworms or mineralization rate of microorganism for the OCPs and PCBs investigated

here.

An alternative method, such as pp-LFERs, is suggested to predict KSA. Application of pp-LFER

to muck soil would require development of a set of sorbent descriptors that is applicable to such

soils. pp-LFERs may be a powerful predictive tool for polar herbicides and other polar

emerging chemicals as it accounts for both specific and non-specific interaction between the

sorbate and sorbent. This may not be possible with the classic octanol-based sp-LFER.

The role of microorganisms and effect of sterilization on the formation of bound residues needs

to be better understood. A detail study on aging of chemicals in soil is required which

characterizes the type and amount of microorganisms present and the mechanisms by which they

lead to formation of bound residues. The changes in the conformation of soil organic matter due

to sterilization should be investigated, as this may alter sorption kinetics (Kelsey et al., 2010).

169

Further studies are required to test the hypothesis of preferential volatilization or sequestration of

enantiomers from soil. The hypothesis of tighter binding of the one enantiomer by the soil

during microbial degradation should be investigated.

Equilibrium distribution and mobility of organic contaminants in soil using the chemical

partitioning space

Develop an user-friendly, computer-based version of chemical space maps that displays the

colours corresponding to primary phase distribution and dominant transport pathways in

response to user defined soil conditions (porosity, water content, soil organic matter). A database

containing solute descriptors could then be accessed to display a user-defined selection of

chemicals on the maps, whereby the user could either choose a specific temperature or soil

organic matter type or select to display the variability caused by changes of these parameters.

The role of mineral sorption should be included, as mineral surfaces may play a key role in dry

soils with a low organic matter content (Goss et al., 2004). Non-linear sorption under low

chemical concentration scenarios and degradation loss processes should also be accounted for.

Air-water gas exchange of POPs in the Canadian Arctic

Further investigation of air-water exchange of persistent chemicals in the Arctic Ocean, focusing

on the Canadian Archipelago, is suggested. Concentrations of HCHs in water have declined

between 1999 and 2007/08. Other studies have documented seasonal changes in seawater

advection of HCHs in the central Archipelago (Hargrave et al., 1997) and the Beaufort Shelf

(Macdonald et al., 1999a, b). Thus, long-term monitoring of HCHs and other POPs in seawater

of the Archipelago is needed.

Air-water exchange should be studied for other classes of chemicals which have recently been

discovered in the Arctic, such as brominated flame retardants, perfluorinated chemicals, and

current-use pesticides. In particular, the net flux of endosulfan should be monitored on a regular

basis as this has been recently banned in U.S. and the European Union. Currently, net deposition

is reported for endosulfan I with fugacity ratio <0.2 in the Canadian Archipelago (Weber et al.,

170

2010). It is possible that endosulfan behaves similarly as α-HCH in the Arctic and a reversal of

air-water exchange from net deposition to net volatilization may occur as the pesticide is phased

out.

Improvement of the HCH gradient air sampling technique for flux estimation is recommended.

The experiment as done in this thesis was limited by the long sampling time (over 6-16 hours)

during which micrometeorological conditions probably changed. A diffusion denuder sampling

technique developed by Perlinger et al. (2005) or other thermally desorbable device might be

adapted to measure air concentrations of HCHs and other persistent chemicals over shorter time

periods, which would allow more accurate estimation of fluxes. The vertical concentration

gradient in air may be better characterized with sampling at additional heights.

More flux measurements should be made under different micrometeorological conditions, with

comparisons to the two-film model and other models of sea-air exchange.

A recommendation is to improve understanding of the relationship between ice-cover and gas

flux of chemicals, especially under mixed open water-ice covered conditions. This may be

improved by examining the percentage ice-cover with satellite images.

8.3 REFERENCES Gallardo Diaz, E. G., Borja Aburto, V. H., Méndez Galván, J. F., Sánchez Tejeda, G., Olguin Bernal, H., Ramierez Hernández, J. A. Situacion actual de la malaria y el uso de DDT en México. Centro Nacional de Salud Ambiental. Centro de Vigilancia Epidemiologica. Report 01-0206-HEQ, 2000, Ministry of Health of Mexico. Goss, K. U.; Buschmann, J.; Schwarzenbach, R. P., 2004. Adsorption of organic vapors to air-dry soils: model predictions and experimental validation. Environ. Sci. Technol. 38, 3667–3673. Hargrave, B. T., Barrie, L. A., Bidleman, T. F., Welch, H. E., 1997. Seasonality in exchange of organochlorines between arctic air and seawater. Environ. Sci. Technol. 31, 3258–3266. Herrera-Portugal, C., Ochoa, H., Franco-Sanchez, G., Yanez, L., Diaz-Barriga, F., 2005. Environmental pathways of exposure to DDT for children living in a malarious area of Chiapas, Mexico. Environ. Research, 99, 158–163.

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Kelsey, J. W., Slizovskiy, I. B., Peters, R. D., Melnick, A. M., 2010. Sterilization affects soil organic matter chemistry and bioaccumulation of spiked p,p’-DDE and anthracene by earthworms. Environ. Pollut. 158, 2251–2257. Kurt-Karakus, P. B., Bidleman, T. F., Staebler, R. M., Jones, K. C., 2006. Measurement of DDT fluxes from a historically treated agricultural soil in Canada. Environ. Sci. Technol. 40, 4578–4585. Macdonald, R. W., McLaughlin, F. A., Carmack, E. C., Stern, G., 1999a. Long-range transport of contaminants to the Canadian Basin and selective withdrawal through the Canadian Archipelago. In: Kalhok, S., editor. Synopsis of research conducted under the 1998/99 Northern Contaminants Program. Environmental Studies, vol. 76. Ottawa 7. Indian and Northern Affairs Canada; p. 39–42. Macdonald, R. W., McLaughlin, F. A., Carmack, E. C., Stern, G., 1999b. The seasonal cycle of organochlorine concentrations in the Canada Basin. In: Kalhok, S., editor. Synopsis of research conducted under the 1998/99 Northern Contaminants Program. Environmental Studies, vol. 76. Ottawa 7. Indian and Northern Affairs Canada; p. 43–47. Majewski, M. S., Desjardins, R., Rochette, P., Pattey, E., Seiber, J., Glotfelty, D. E. 1993. Field comparison of an eddy accumulation and an aerodynamic-gradient system for measuring pesticide volatilization fluxes. Environ. Sci. Technol. 27, 121–128. Perlinger, J. A., Tobias, D. E., Morrow, P. S., Doskey, P. V., 2005. Evaluation of novel techniques for measurement of air–water exchange of persistent bioaccumulative toxicants in Lake Superior. Environ. Sci. Technol. 39, 8411–8419. Weber, J., Halsall, C. J., Muir, D., Teixeira, C., Small, J., Solomon, K., Hermanson, M., Hung, H., Bidleman, T., 2010. Endosulfan, a global pesticide: A review of its fate in the environment and occurrence in the Arctic. Sci. Total Environ., 408, 15, 2966–2984.

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APPENDIX

CHAPTER 1 INTRODUCTION A1.1 Alegria et al. 2008

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174

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177

178

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180

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A1.2 Wong et al. 2008

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183

184

185

186

Note: y-axis for Figure 2 should read “Normalized to Parlar 40+41”

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188

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APPENDIX

CHAPTER 2 PASSIVE AIR SAMPLING OF ORGANOCHLORINE PESTICIDES IN

MEXICO

A2.1 Determining the sampling rates for passive samplers.

Table A2.1 Description of sampling sites and schedule.

Table A2.2 Sampling rates for each site at each sampling period.

Table A2.3 Organochlorine pesticides in Mexico air – annual arithmetic mean (pg m-3).

Table A2.4 Enantiomer fraction (EF) and its deviation from racemic (DEVrac) of o,p-

DDT. TP, MT, VC and TB = Data obtained from the 2002-2004 sampling

campaign. Nd = not detected. N = number of samples. DEVrac =

Deviation from racemic: absolute value of (EF – 0.5)

Figures A2.1.1

to 2.1.10

Three-day back trajectory airshed maps and seasonality of OCP

concentrations at each site.

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A2.1 Determining the sampling rates for passive samplers

Method

The uptake profile for a passive sampler is described by the following equatio (1, 2),

CPUF = KPUF-A ·CAIR · (1–exp–[(APUF/VPUF)( t · kA/KPUF-A)]) Eq [A2.1]

where CPUF (ng m-3) is the concentration of analyte on the PUF disk, KPUF-A is the PUF-air

partition coefficient, CAIR (ng m-3) is the concentration of analyte in air, APUF (m2) and VPUF (m3)

are the area and volume of the PUF disk, t (d) is the duration of deployment, and kA (m d-1)is the

air-side mass transfer coefficient. Since KPUF-A, equals to CPUF/CAIR, which is analogous to

VPUF/VAIR, we can estimate VAIR by substituting V to C. Subsequently, VAIR can be determined

by rearranging the equation as follows

VAIR = KPUF-A · VPUF · 1–exp–[(t · kA)/ (KPUF-A·δ)] Eq [A2.2]

δ (m) is the thickness of the PUF disk, which equals to VPUF/APUF (VPUF = 0.037 m3; APUF =

0.00021 m2). KPUF-A is correlated to KOA, and it is temperature dependent. It was estimated for

each compound at on a daily basis, and the average value over the deployment period was used.

To estimate kA, a set of depuration compounds (DCs) was added to the PUF disks prior to

deployment and the percent recovery was monitored. Recovery percentages of the DCs are

presented in SI-2.2. It is assumed that the uptake rate of analyte can be inferred from the loss

rate of DCs. kA is air-side controlled and defined as:

kA = –ln (Ct/C0) ·δ · (KPUF-A/t) Eq [A2.3]

where Ct/C0 is ratio of the amount of DCs remaining in the PUF disk at the end of the

deployment to the initial DCs amount. Gouin et al. (2) suggested the desired recovery of the

DCs should be within the range of 20 to 80% to ensure a linear sampling rate. Once kA is

192

estimated, the sampling rate (R, m3d-1) is determined as kA·APUF. R and kA are presented in

Table A2.2.

Recovery percentages of the depuration compounds

Recovery percentages for the depuration compounds in the blanks (field and laboratory blanks, n

=39) were: [2H6]-γ-HCH: 86±14%, [13C12]-PCB9: 90±17%, [13C12]-PCB32: 89±31%, [13C12]-

PCB52: 84±16%, PCB107: 85±11% and PCB198: 89±17%. Recovery percentages after

deployment were normalized to the DC recovery from the corresponding field blank and were:

[2H6]-γ-HCH: 34±17%, [13C12]-PCB-9: 9.6±6%, [13C12]-PCB32: 46±17%, [13C12]-PCB52:

53±21%, PCB107: 90±12% and PCB198: 94±16%. Gouin et al. (2) suggested the desired

recovery of the DCs should be within the range of 20 to 80% to ensure a linear sampling rate.

Accordingly, DCs of [2H6]-γ-HCH, [13C12]-PCB32 and [13C12]-PCB52 were used to estimate the

sampling rates for all events. The annual mean sampling rate at all sites ranged from 3.0 to 7.7

m3 d-1, with an overall mean of 4.5±1.7 m3d-1. This is in line with Gouin et al. (2) who reported

3.1 m3d-1 for samplers deployed in eastern Canada, 4.8±2.3 m3d-1 by Pozo et al. (1) in Chile; and

3.9±2 m3d-1 for the global atmospheric passive sampling study (3). The highest sampling rate

was recorded at Mazatlan (MAZ), with mean of 7.7 m3 d-1. MAZ has low DC recovery;

especially in the first two periods, with 5% for [2H6]-γ -HCH, 12% for [13C12]-PCB52 and 67%

for PCB107. The samplers in MAZ were placed on the roof top of a house, located on the beach

(approximately 10 m from the ocean) on the west coast of Sinaloa. Probably the high wind

penetrating the sampler increased the loss of DCs and thus the sampling rate increased. Such a

phenomenon has also been observed in earlier studies (4, 5).

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Table A2.1 Description of sampling sites and schedule

Site Category Description North West Eleva. (m) Period Start Date End Date Temp ( °C ) Precip (cm) NBaja California (BAJ) A Mexicali valley 32°35.38' 115°01.21' 2 1 3-Sep-05 4-Dec-05 23.2 2.3 2

2 04-Dec-05 17-Mar-06 14.4 4.1 2Celestun (CEL) R 20°51.55' 90°23.56' 10 1 06-May-05 05-Aug-05 28.1 265 2

2 05-Aug-05 08-Nov-05 26.7 258 23 8-Nov-05 23-Feb-06 23.2 82 24 23-Feb-06 29-May-06 27.1 79 2

Chihuahua (CHI) U 28°38.42' 106°01.16' 1269 1 25-May-05 25-Aug-05 24.7 25 22 25-Aug-05 26-Nov-05 17.5 15 23 26-Nov-05 24-Feb-06 10.2 3.6 24 24-Feb-06 29-May-06 18.7 4.1 2

Colima (COL) S 19°12.81' 103°48.19' 362 1 17-May-05 17-Aug-05 25.3 136 22 17-Aug-05 17-Nov-05 24.8 64 23 17-Nov-05 15-Feb-06 24.2 7.8 24 15-Feb-06 09-Aug-06 25.5 223 2

Cordoba (COR) S 18°51.64' 96°54.21' 920 1 11-May-05 16-Aug-05 26.8 706 22 16-Aug-05 16-Nov-05 25.5 806 13 16-Nov-05 6-Mar-06 21.5 46 24 06-Mar-06 01-Jul-06 25.3 62 1

Cuernavaca (CUE) S 18° 52.79' 99° 09.49' 1578 1 10-May-05 12-Aug-05 25.5 22 12 12-Aug-05 14-Nov-05 24.4 38 23 14-Nov-05 14-Feb-06 21.8 4.1 24 14-Feb-06 14-May-06 25.8 67 2

Mazatlan (MAZ) A 23°54.27' 106°57.52' 7 1 21-May-05 21-Aug-05 26.4 146 22 21-Aug-05 21-Nov-05 25.8 175 23 28-Nov-05 28-Feb-06 18.9 3.9 24 28-Feb-06 21-May-06 20.6 1.0 2

Mexico City (MEX) U 19°21.67' 99°4.35' 2240 1 06-Jun-05 8-Sep-05 15.9 296 22 8-Sep-05 8-Dec-05 13.3 460 23 8-Dec-05 13-Mar-06 11.2 15 24 13-Mar-06 16-Jun-06 15.0 215 2

Roof top of a building



Meteorological station, University of Veracruz

Lamp post

Roof top, facing the Pacific coast


194

Table A2.1 continued……

Site Category Description North West (m) Period Start Date End Date Temp ( °C ) Precip (cm) NMonterrey (MON) R 25°45.78' 99°57.84' 288 1 23-May-05 24-Aug-05 30.4 72 2

2 24-Aug-05 23-Nov-05 25.4 38 23 23-Nov-05 28-Feb-06 17.4 5.2 24 28-Feb-06 20-May-06 25.4 27 2

San Luis Potosi (SLP) U 22°08.65' 101°00.94' 1906 1 16-May-05 18-Aug-05 21.5 324 22 18-Aug-05 11-Nov-05 18.4 25 23 11-Nov-05 20-Feb-06 13.7 2.7 24 20-Feb-06 22-May-06 18.9 208 2

Tuxpan (TUX) A Hang on a tree of a farm 20°52.75' 97°21.20' 29 1 14-May-05 15-Aug-05 28-Jan-00 26-Mar-00 2

Abbreviations:A = Agricultural; R= Rural SU = Suburban; U= UrbanN = number of samples collectedEleva. = ElevationTemp = Mean Temperature of the sampling period (celcius)Precip. = Total precipitation of the sampling period (cm)


On a post in a rural area

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Table A2.2 Sampling rates for each site at each sampling period

R kA R kA R kA R kA R kA

(m3/day) (m/day) (m3/day) (m/day) (m3/day) (m/day) (m3/day) (m/day) (m3/day) (m/day)Period 1-A 4.5 121 6.6 177 5.8 157 4.6 123 4.8 131Period 1-B 3.7 100 6.6 178 7.8 211 4.6 125 5.0 135Period 2-A 3.2 88 4.4 118 5.4 147 5.9 160 3.3 89Period 2-B 3.0 82 4.2 114 6.1 165 5.4 145 na naPeriod 3-A na na 2.8 77 2.5 68 2.8 75 2.8 76Period 3-B na na 3.1 85 3.2 87 3.0 81 2.8 76Period 4-A na na 3.6 96 4.3 117 2.6 70 na naPeriod 4-B na na 4.3 115 5.6 151 2.2 60 2.4 66Mean 3.6 4.4 5.1 3.9 3.5

R kA R kA R kA R kA R kA R kA (m3/day) (m/day) (m3/day) (m/day) (m3/day) (m/day) (m3/day) (m/day) (m3/day) (m/day) (m3/day) (m/day)

Period 1-A 4.7 127 10 272 4.7 128 4.6 125 5.1 137 4.2 114Period 1-B na na 9.3 251 4.0 108 7.0 189 5.1 137 4.2 114Period 2-A 3.1 83 7.5 203 6.1 166 4.2 113 5.0 135 na naPeriod 2-B 3.5 95 7.5 202 5.9 159 4.2 113 5.8 157 na naPeriod 3-A 2.1 203 7.9 215 5.6 151 2.2 61 4.0 109 na naPeriod 3-B 2.7 268 5.5 150 4.9 134 2.4 65 4.3 117 na naPeriod 4-A 2.2 59 6.0 163 3.3 88 3.0 81 2.5 67 na naPeriod 4-B 2.4 65 na na 4.2 114 3.0 81 5.4 145 na naMean 3.0 7.7 4.8 3.8 4.6 4.2

na = no data available.

COL

TUX

COR

CUE MAZ MEX MON SLP

BAJ CEL CHI

196

Table A2.3 Organochlorine pesticides in Mexico air – annual arithmetic mean (pg m-3)

LODb BAJ CEL CHI COL COR CUE MAZ MEX MON SLP TUX TPd MTd VCd TBd

α -HCH 0.41 10 3.9 5.9 13 8.5 19 8.9 8.9 6.0 9.4 1.9 11 13 20 11γ -HCH 0.34 104 8.2 11 17 18 35 16 49 8.8 16 21 42 12 52 39HEPT 1.0 ndc nd nd nd 5.0 nd nd 2.8 nd nd nd nd nd 1.0 ndHEPX 1.3 nd nd nd nd nd 2.6 nd 5.9 nd nd nd nd nd nd ndTC 0.09 4.2 1.8 2.6 4.8 4.3 6.7 2.8 6.4 3.7 5.5 0.20 5.1 2.1 4.5 2.3CC 0.08 4.8 2.4 2.7 1.2 3.1 4.8 0.9 5.2 4.2 5.1 0.53 3.9 2.2 4.0 2.7TN 0.08 4.4 2.0 2.0 4.5 2.8 6.4 2.3 4.4 3.7 4.5 0.31 3.2 1.5 3.3 2.2Aldrin 0.64 nd nd nd nd nd nd nd nd nd nd nd nd nd nd ndDieldrin 0.85 7.8 2.3 2.2 3.9 3.6 5.2 3.8 4.7 3.6 1.6 1.8 2.9 11 2.3 0.86ENDO I 22 1660 36 351 2930 147 980 19000 320 89 200 29 296 182 83 77ENDO II 0.91 260 7.2 95 752 43 280 7400 68 16 40 6.3 39 38 13 12ESUL 1.9 20 nd 6.9 48 11 20 400 6.8 2.4 3.6 nd 5.6 7.3 2.5 3.2o,p' -DDE 0.64 16 53 3.3 24 2.5 38 1.8 3.2 1.2 1.2 1.5 21 17 38 2.7p,p' -DDE 0.77 293 1136 25 286 41 244 47 21 10 13 29 199 334 534 39o,p' -DDD 1.9 2.6 18 nd 3.7 2.8 2.3 nd nd nd nd nd nd 6.3 12 ndp,p' -DDD 1.9 5.9 54 nd 29 10.4 9.5 3.8 9.5 nd nd 4.2 8.6 42 35 13o,p' -DDT 0.78 18 173 1.7 103 26 73 7.3 17 nd 1.4 12 60 231 140 22p,p' -DDT 3.8 nd 540 nd 306 46 133 15 nd nd nd nd 258 1730 441 163ΣTOX 5.1 689 30 96 50 61 54 150 64 63 42 27 229 41 19 6ΣCHL 13 6.2 7.4 11 10 18 6.0 16 12 15 1.0 12 5.8 12 7.2ΣENDO 1940 45 453 3730 200 1280 26800 395 107 244 36 341 227 98 92ΣDDT 338 1975 34 750 129 500 76 55 15 21 50 547 2360 1200 18FTC

e 0.70 0.41 0.58 0.87 0.58 0.77 0.83 0.55 0.47 0.52 0.28 0.55 0.47 0.53 0.45

FENDOf 0.88 0.83 0.81 0.77 0.80 0.79 0.66 0.83 0.83 0.84 0.82 0.86 0.84 0.87 0.86

FDDTeg 0.01 0.32 0.08 0.52 0.56 0.34 0.25 0.10 0.16 0.14 0.06 0.55 0.83 0.45 0.74

FDDToh 0.10 0.75 0.69 0.75 0.62 0.63 0.69 0.28 0.83 0.70 0.14 0.80 0.88 0.76 0.86

a. ΣCHL = TC+CC+TN. ΣENDO = ENDO I + ENDO II + ESUL. ΣDDT = p,p'-DDT + o,p'-DDT + p,p'-DDE + o,p'-DDE + p,p'-DDD + o,p'-DDD. ΣTOX = quantified as technical toxaphene. b. LOD = mean blank + 3 x SD for endosulfans. If chemical is not detected in blanks, instrumental detection limit was used.c. nd = not detected. Summations used 1/2 LOD for ND values.d. Data are taken from Alegria et al. (6)e. FTC = TC/(TC +CC)f. FENDO = ENDO I/(ENDO I + ENDO II)g. FDDTe = p,p' -DDT/ (p,p' -DDT + p,p' -DDE)h. FDDTo = p,p' -DDT/ (p,p'- DDT + o,p' -DDT)e-h. Mean fraction of individual sample.

197

Table A2.4 Enantiomer fraction (EF) and its deviation from racemic (DEVrac) of o,p-DDT.

TP, MT, VC and TB = Data obtained from the 2002-2004 sampling campaign. Nd = not

detected. N = number of samples. DEVrac = Deviation from racemic: absolute value of (EF –

0.5)

Site Mean Stdev N DEVracBAJ 0.480 0.002 2 0.020CEL 0.496 0.004 4 0.004CHI 0.494 0.002 3 0.006COL 0.495 0.002 4 0.005COR 0.497 0.006 4 0.003CUE 0.492 0.001 4 0.008MAZ 0.510 0.005 3 0.010MEX 0.497 0.002 4 0.003MON 0.490 1 0.010SLP 0.484 0.003 2 0.016TUX 0.501 1 0.001TP 0.502 0.003 14 0.002MT 0.500 0.006 19 0.000VC 0.504 0.002 20 0.004TB 0.502 0.002 12 0.002

o,p'-DDT

nd

nd

Site Mean Stdev N DEVracBAJ 0.480 0.002 2 0.020CEL 0.496 0.004 4 0.004CHI 0.494 0.002 3 0.006COL 0.495 0.002 4 0.005COR 0.497 0.006 4 0.003CUE 0.492 0.001 4 0.008MAZ 0.510 0.005 3 0.010MEX 0.497 0.002 4 0.003MON 0.490 1 0.010SLP 0.484 0.003 2 0.016TUX 0.501 1 0.001TP 0.502 0.003 14 0.002MT 0.500 0.006 19 0.000VC 0.504 0.002 20 0.004TB 0.502 0.002 12 0.002

o,p'-DDTSite Mean Stdev N DEVracBAJ 0.480 0.002 2 0.020CEL 0.496 0.004 4 0.004CHI 0.494 0.002 3 0.006COL 0.495 0.002 4 0.005COR 0.497 0.006 4 0.003CUE 0.492 0.001 4 0.008MAZ 0.510 0.005 3 0.010MEX 0.497 0.002 4 0.003MON 0.490 1 0.010SLP 0.484 0.003 2 0.016TUX 0.501 1 0.001TP 0.502 0.003 14 0.002MT 0.500 0.006 19 0.000VC 0.504 0.002 20 0.004TB 0.502 0.002 12 0.002

o,p'-DDT

nd

nd

198

Figures A2.1.1 to 2.1.10 Three-day back trajectory airshed maps and seasonality of OCP

concentrations at each site.

The back trajectories were calculated at 10, 100, 500 m height at 6-h interval for each day the

passive samplers were deployed. The back trajectory probability map of an air parcel passing

through a grid was calculated as the number of trajectories per grid divided by the total number

of trajectories. Airshed probability density maps were produced to show where the air most

frequently passed through before arriving at the sampling site within the last three days.

Figure A2.1.1

Baja California (BAJ)

b) Temporal variation of OC concentration (pg m-3)

a) Back trajectory airshed maps for each sampling period

Period 1 – Sep 3, 2005 to Dec 4, 2005 Period 2 – Dec 4, 2005 to Mar 17, 2006

Lindane

0

100

200

BAJ-1 BAJ-2

ΣCHL

0

10

20

30

BAJ-1 BAJ-2

Dieldrin

0

5

10

BAJ-1 BAJ-2

p,p'-DDE

0

200

400

BAJ-1 BAJ-2

ENDO I

0

1000

2000

3000

BAJ-1 BAJ-2

ΣTOX

0

400

800

BAJ-1 BAJ-2

Baja California (BAJ)



Period 1 – Sep 3, 2005 to Dec 4, 2005 Period 2 – Dec 4, 2005 to Mar 17, 2006Period 1 – Sep 3, 2005 to Dec 4, 2005 Period 2 – Dec 4, 2005 to Mar 17, 2006

Lindane

0

100

200

BAJ-1 BAJ-2

ΣCHL

0

10

20

30

BAJ-1 BAJ-2

Dieldrin

0

5

10

BAJ-1 BAJ-2

Lindane

0

100

200

BAJ-1 BAJ-2

ΣCHL

0

10

20

30

BAJ-1 BAJ-2

Dieldrin

0

5

10

BAJ-1 BAJ-2

p,p'-DDE

0

200

400

BAJ-1 BAJ-2

ENDO I

0

1000

2000

3000

BAJ-1 BAJ-2

ΣTOX

0

400

800

BAJ-1 BAJ-2

p,p'-DDE

0

200

400

BAJ-1 BAJ-2

ENDO I

0

1000

2000

3000

BAJ-1 BAJ-2

ΣTOX

0

400

800

BAJ-1 BAJ-2

199

Figure A2.1.2

Celestun (CEL)



Period 2 – Aug 5, 2005 to Nov 8, 2005

Period 3 – Nov 8, 2005 to Feb 23, 2006 Period 4 – Feb 23, 2006 to May 29, 2006

Period 1 – May 6, 2005 to Aug 5, 2005

Lindane

0

5

10

CEL-1 CEL-2 CEL-3 CEL-4

ΣCHL

0

5

10


Dieldrin

0

1

2

3


ENDO I

0

30

60


p,p'-DDE

0

500

1000

1500


ΣTOX

0

20

40


Celestun (CEL)



Period 2 – Aug 5, 2005 to Nov 8, 2005


Period 1 – May 6, 2005 to Aug 5, 2005

Lindane

0

5

10


ΣCHL

0

5

10


Dieldrin

0

1

2

3


ENDO I

0

30

60


p,p'-DDE

0

500

1000

1500


ΣTOX

0

20

40


Lindane

0

5

10


ΣCHL

0

5

10


Dieldrin

0

1

2

3


Lindane

0

5

10


ΣCHL

0

5

10


Dieldrin

0

1

2

3


ENDO I

0

30

60


p,p'-DDE

0

500

1000

1500


ΣTOX

0

20

40


ENDO I

0

30

60


p,p'-DDE

0

500

1000

1500


ΣTOX

0

20

40


200

Figure A2.1.3

Chihuahua (CHI)



Period 1 – May 25, 2005 to Aug 25, 2005 Period 2 – Aug 25, 2005 to Nov 26, 2005


Lindane

0

10

20

CHI-1 CHI-2 CHI-3 CHI-4

ΣCHL

0

5

10

15


Dieldrin

0

2

4


ENDO I

0

250

500


p,p'-DDE

0

20

40


ΣTOX

0

60

120


Chihuahua (CHI)



Period 1 – May 25, 2005 to Aug 25, 2005 Period 2 – Aug 25, 2005 to Nov 26, 2005Period 1 – May 25, 2005 to Aug 25, 2005 Period 2 – Aug 25, 2005 to Nov 26, 2005

Period 3 – Nov 26, 2005 to Feb 24, 2006 Period 4 – Feb 24, 2006 to May 29, 2006Period 3 – Nov 26, 2005 to Feb 24, 2006 Period 4 – Feb 24, 2006 to May 29, 2006

Lindane

0

10

20


ΣCHL

0

5

10

15


Dieldrin

0

2

4


ENDO I

0

250

500


p,p'-DDE

0

20

40


ΣTOX

0

60

120


Lindane

0

10

20


ΣCHL

0

5

10

15


Dieldrin

0

2

4


Lindane

0

10

20


ΣCHL

0

5

10

15


Dieldrin

0

2

4


ENDO I

0

250

500


p,p'-DDE

0

20

40


ΣTOX

0

60

120


ENDO I

0

250

500


p,p'-DDE

0

20

40


ΣTOX

0

60

120


201

Figure A2.1.4

Colima (COL)



Period 3 – Nov 17, 2005 to Feb 15, 2006 Period 4 – Feb 15, 2006 to Aug 9, 2006


Lindane

0

10

20

30

COL-1 COL-2 COL-3 COL-4

ΣCHL

0

10

20


Dieldrin

0

5

10


ENDO I

0

5000

10000


p,p'-DDE

0

250

500


ΣTOX

0

40

80


Colima (COL)



Period 3 – Nov 17, 2005 to Feb 15, 2006 Period 4 – Feb 15, 2006 to Aug 9, 2006Period 3 – Nov 17, 2005 to Feb 15, 2006 Period 4 – Feb 15, 2006 to Aug 9, 2006


Lindane

0

10

20

30


ΣCHL

0

10

20


Dieldrin

0

5

10


ENDO I

0

5000

10000


p,p'-DDE

0

250

500


ΣTOX

0

40

80


Lindane

0

10

20

30


ΣCHL

0

10

20


Dieldrin

0

5

10


Lindane

0

10

20

30


ΣCHL

0

10

20


Dieldrin

0

5

10


ENDO I

0

5000

10000


p,p'-DDE

0

250

500


ΣTOX

0

40

80


ENDO I

0

5000

10000


p,p'-DDE

0

250

500


ΣTOX

0

40

80


202

Figure A2.1.5

Cuernavaca (CUE)



Period 1 – May 10, 2005 to Aug 12, 2005 Period 2 –Aug 12, 2005 to Nov 14, 2005


Lindane

0

30

60

CUE-1 CUE-2 CUE-3 CUE-4

ΣCHL

0

20

40


Dieldrin

0

5

10


ENDO I

0

500

1000

1500


p,p'-DDE

0

200

400


ΣTOX

0

40

80


Cuernavaca (CUE)



Period 1 – May 10, 2005 to Aug 12, 2005 Period 2 –Aug 12, 2005 to Nov 14, 2005Period 1 – May 10, 2005 to Aug 12, 2005 Period 2 –Aug 12, 2005 to Nov 14, 2005


Lindane

0

30

60


ΣCHL

0

20

40


Dieldrin

0

5

10


ENDO I

0

500

1000

1500


p,p'-DDE

0

200

400


ΣTOX

0

40

80


Lindane

0

30

60


ΣCHL

0

20

40


Dieldrin

0

5

10


Lindane

0

30

60


ΣCHL

0

20

40


Dieldrin

0

5

10


ENDO I

0

500

1000

1500


p,p'-DDE

0

200

400


ΣTOX

0

40

80


ENDO I

0

500

1000

1500


p,p'-DDE

0

200

400


ΣTOX

0

40

80


203

Figure A2.1.6

Cordoba (COR)



Period 3 - Nov 16, 2005 to Mar 6, 2005 Period 4 – Mar 6, 2006 to Jul 1, 2006

Period 1 – May 11, 2005 to Aug 16, 2005 Period 2 - Aug 16, 2005 to Nov 16, 2005

Lindane

0

10

20

30

COR-1 COR-2 COR-3 COR-4

ΣCHL

0

10

20

30


Dieldrin

0

5

10


ENDO I

0

100

200


p,p'-DDE

0

30

60


ΣTOX

0

50

100


Cordoba (COR)



Period 3 - Nov 16, 2005 to Mar 6, 2005 Period 4 – Mar 6, 2006 to Jul 1, 2006

Period 1 – May 11, 2005 to Aug 16, 2005 Period 2 - Aug 16, 2005 to Nov 16, 2005Period 1 – May 11, 2005 to Aug 16, 2005 Period 2 - Aug 16, 2005 to Nov 16, 2005

Lindane

0

10

20

30


ΣCHL

0

10

20

30


Dieldrin

0

5

10


Lindane

0

10

20

30


ΣCHL

0

10

20

30


Dieldrin

0

5

10


ENDO I

0

100

200


p,p'-DDE

0

30

60


ΣTOX

0

50

100


ENDO I

0

100

200


p,p'-DDE

0

30

60


ΣTOX

0

50

100


204

Figure A2.1.7

Mazatlan (MAZ)





Lindane

0

10

20

30

MAZ-1 MAZ-2 MAZ-3 MAZ-4

Dieldrin

0

5

10


ΣCHL

0

5

10


ENDO I

0

25000

50000


p,p'-DDE

0

50

100


ΣTOX

0

50

100

150


Mazatlan (MAZ)





Lindane

0

10

20

30


Dieldrin

0

5

10


ΣCHL

0

5

10


ENDO I

0

25000

50000


p,p'-DDE

0

50

100


ΣTOX

0

50

100

150


Lindane

0

10

20

30


Dieldrin

0

5

10


ΣCHL

0

5

10


Lindane

0

10

20

30


Dieldrin

0

5

10


ΣCHL

0

5

10


ENDO I

0

25000

50000


p,p'-DDE

0

50

100


ΣTOX

0

50

100

150


ENDO I

0

25000

50000


p,p'-DDE

0

50

100


ΣTOX

0

50

100

150


205

Figure A2.1.8

Mexico City (MEX)



Period 1 – Jun 6, 2005 to Sep 8, 2005 Period 2 – Sep 8, 2005 to Dec 8, 2005

Period 3 – Dec 8, 2005 to Mar 13, 2006 Period 4 – Mar 13, 2006 to Jun 16, 2006

Lindane

0

40

80

MEX-1 MEX-2 MEX-3 MEX-4

ΣCHL

0

5

10


Dieldrin

0

5

10


p,p'-DDE

0

20

40


ENDO I

0

200

400


ΣTOX

0

50

100


Mexico City (MEX)



Period 1 – Jun 6, 2005 to Sep 8, 2005 Period 2 – Sep 8, 2005 to Dec 8, 2005Period 1 – Jun 6, 2005 to Sep 8, 2005 Period 2 – Sep 8, 2005 to Dec 8, 2005

Period 3 – Dec 8, 2005 to Mar 13, 2006 Period 4 – Mar 13, 2006 to Jun 16, 2006Period 3 – Dec 8, 2005 to Mar 13, 2006 Period 4 – Mar 13, 2006 to Jun 16, 2006

Lindane

0

40

80


ΣCHL

0

5

10


Dieldrin

0

5

10


p,p'-DDE

0

20

40


ENDO I

0

200

400


ΣTOX

0

50

100


Lindane

0

40

80


ΣCHL

0

5

10


Dieldrin

0

5

10


Lindane

0

40

80


ΣCHL

0

5

10


Dieldrin

0

5

10


p,p'-DDE

0

20

40


ENDO I

0

200

400


ΣTOX

0

50

100


p,p'-DDE

0

20

40


ENDO I

0

200

400


ΣTOX

0

50

100


206

Figure A2.1.9

Monterrey (MON)





Lindane

0

5

10

15

MON-1 MON-2 MON-3 MON-4

ΣCHL

0

10

20


Dieldrin

0.0

2.5

5.0


p,p'-DDE

0

5

10

15


ENDO I

0

50

100

150


ΣTOX

0

50

100

150


Monterrey (MON)





Lindane

0

5

10

15


ΣCHL

0

10

20


Dieldrin

0.0

2.5

5.0


p,p'-DDE

0

5

10

15


ENDO I

0

50

100

150


ΣTOX

0

50

100

150


Lindane

0

5

10

15


ΣCHL

0

10

20


Dieldrin

0.0

2.5

5.0


Lindane

0

5

10

15


ΣCHL

0

10

20


Dieldrin

0.0

2.5

5.0


p,p'-DDE

0

5

10

15


ENDO I

0

50

100

150


ΣTOX

0

50

100

150


p,p'-DDE

0

5

10

15


ENDO I

0

50

100

150


ΣTOX

0

50

100

150


207

Figure A2.1.10

San Luis Potosi (SLP)





Lindane

0

10

20

SLP-1 SLP-2 SLP-3 SLP-4

ΣCHL

0

10

20


Dieldrin

0

1

2

3


p,p'-DDE

0

10

20


ENDO I

0

250

500


ΣTOX

0

30

60


San Luis Potosi (SLP)





Lindane

0

10

20


ΣCHL

0

10

20


Dieldrin

0

1

2

3


p,p'-DDE

0

10

20


ENDO I

0

250

500


ΣTOX

0

30

60


Lindane

0

10

20


ΣCHL

0

10

20


Dieldrin

0

1

2

3


Lindane

0

10

20


ΣCHL

0

10

20


Dieldrin

0

1

2

3


p,p'-DDE

0

10

20


ENDO I

0

250

500


ΣTOX

0

30

60


p,p'-DDE

0

10

20


ENDO I

0

250

500


ΣTOX

0

30

60


208

References

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sampler derived air concentrations of persistent organic pollutants on a north-south

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(2) Gouin, T.; Harner, T.; Blanchard, P.; Mackay, D. Passive and active air samplers as

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Basin. Environ. Sci. Technol., 2005, 39: 9115-9122.

(3) Pozo, K.; Harner, T.; Wania, F.; Muir, D.C.G.; Jones, K.C.; Barrie, L.A. Toward a global

network for persistent organic pollutants in air: Results from the GAPs study. Environ.

Sci. Technol., 2006, 40: 4867-4873.

(4) Tuduri, L.; Harner, T.; Hung, H. Polyurethane foam (PUF) disk passive air samplers:

Wind effect on sampling rates. Environ. Pollut., 2006, 144: 377-383.

(5) Soderstrom, H.S.; Bergqvist, P.A. Passive air sampling using semipermeable membrane

devices at different wind speeds in situ calibrated by performance reference compounds.

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(7) Wong, F.; Alegria, H.A.; Jantunen, L.M.; Bidleman, T.F.; Salvador-Figueroa, M.; Gold-

Bouchot, G.; Ceja-Moreno, V.; Waliszewski, S.M.; Infanzon, R. Organochlorine

pesticides in soils and air of southern Mexico: chemical profiles and potential for soil

emissions. Atmos. Environ., 2008, 42: 7737-7745.

209

APPENDIX

CHAPTER 3 ORGANOCHLORINE PESTICIDES IN SOILS OF MEXICO AND THE

POTENTIAL FOR SOIL-AIR EXCHANGE

Figure A3.1 Map of the sampling area in Mexico. Green = urban sites; yellow = agricultural

sites; red = rural sites. Data for Sites 19–29 were published in Wong et al., 2008.

Figure A3.2 Plots of deviation from racemic (DEVrac) of o,p’-DDT vs. (A) DDT used and (B)

latitude.

Figure A3.3 Deviation from Racemic values (DEVRac) of A) o’p-DDT, B) trans-chlordane (TC)

and C) cis-chlordane (CC) in soils and air in Mexico.

Table A3.1 Description of soil sampling sites.

Table A3.2 OCP concentration in Mexico soils (ng g -1, dry weight).

Table A3.3 Enantiomer fraction of o,p’-DDT, trans-chlordane (TC) and cis-chlordane (CC) in

Mexico soils.

Table A3.4 Fugacity fractions (ff) of OCPs in Mexico. ff = fS/(fS+fA), where fS = fugacity of

soil; fA = fugacity of air.

210

Figure A3.1 Map of the sampling area in Mexico. Green = urban sites; yellow = agricultural sites;

red = rural sites. Data for Sites 19–29 were published in Wong et al., 2008.

2

1412

16

5 725

1

24

29

17

3

15

1094 116

13 18

8

28

21- 22

2320

2726

19

211

Figure A3.2 Plots of deviation from racemic (DEVrac) of o,p’-DDT vs. (A) DDT used and (B)

latitude.

0.00

0.04

0.08

0.12

0.16

0 50 100 150

DDT Used (tons)

DE

VR

ac -

o,p

'-DD

TA Site 22

0.00

0.04

0.08

0.12

0.16

14 16 18 20 22 24 26 28 30

Latitude

DE

VR

ac -

o,p

'-DD

T

BSite 22

212

Figure A3.3 Deviation from Racemic values (DEVRac) of A) o’p-DDT, B) trans-chlordane (TC)

and C) cis-chlordane (CC) in soils and air in Mexico

0.000

0.001

0.010

0.100

1.000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Site ID

DE

VR

acof

TC

nd nd

B

0.000

0.001

0.010

0.100

1.000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Site ID

DE

VR

acof

TC

nd nd0.000

0.001

0.010

0.100

1.000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Site ID

DE

VR

acof

TC

0.000

0.001

0.010

0.100

1.000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Site ID

DE

VR

acof

TC

nd nd

B

* Racemic, i.e. DEVRac = 0; nd = not determinedSoil Air * Racemic, i.e. DEVRac = 0; nd = not determinedSoil Air

0.0001

0.0010

0.0100

0.1000

1.0000

3 4 5 8 9 10 11 12 13 15 17 19 20 21 22 23 25 26 27

Site ID

DE

VR

acof

o,p

'-D

DT

A

* ** nd nd0.0001

0.0010

0.0100

0.1000

1.0000

3 4 5 8 9 10 11 12 13 15 17 19 20 21 22 23 25 26 27

Site ID

DE

VR

acof

o,p

'-D

DT

0.0001

0.0010

0.0100

0.1000

1.0000

3 4 5 8 9 10 11 12 13 15 17 19 20 21 22 23 25 26 27

Site ID

DE

VR

acof

o,p

'-D

DT

A

* ** nd nd

0.0001

0.0010

0.0100

0.1000

1.0000

1 3 4 5 9 10 11 12 13 15 16Site ID

DE

VR

acof

CC

nd

C

0.0001

0.0010

0.0100

0.1000

1.0000

1 3 4 5 9 10 11 12 13 15 16Site ID

DE

VR

acof

CC

0.0001

0.0010

0.0100

0.1000

1.0000

1 3 4 5 9 10 11 12 13 15 16Site ID

DE

VR

acof

CC

nd

C

213

Table A3.1 Description of soil sampling sites.

Location Abbrev.Year of

Collection Site ID Description of site CategoryElevation

(m)North

(degree)West

(degree)# of

samplesOrganic carbon

Content (%)

Celestun CEL 2005 1 Pronatura building R 10 20.86 -90.39 5 10-11

2005 2 Maize farm A 1302 28.69 -105.99 3 2.1-4.2

2005 3 Residential Area U 1339 28.75 -105.98 2 1.3-4.6

2005 4 University of Colima U 362 19.21 -103.80 2 2-2.1

2005 5 Grazing field A 389 19.23 -103.72 4 1.0-1.2

2005 6 Maize farm A 389 19.23 -103.72 4 0.89-1.5

2005 7 Sugar Cane Farm A 920 18.86 -96.90 2 2.7-3.2

2005 8 Coffee Farm A 920 18.86 -96.90 2 4.1-4.5

2005 9 Mexican Institute of Water Technology U 1578 18.88 -99.16 1 1.7

2005 10 Sugar Cane Farm 1 A 1578 18.88 -99.16 2 1.8-2.4

2005 11 Sugar Cane Farm 2 A 1578 18.88 -99.16 2 1.5-2.3

2005 12 School lawn U 1906 22.14 -101.02 2 2.4-3.5

2005 13 Farm A 1906 22.14 -101.02 1 3.4

2005 14 Maize farm A 20 23.85 -106.89 2 1.2-1.5

2005 15 Red bell pepper farm A 46 23.90 -106.95 6 0.95-1.4

Monterrey MON 2005 16 Grain farm A 288 25.76 -99.96 3 2.4-2.6

2005 17 Citrus farm A 6 20.86 -97.34 2 1.9-2.6

2005 18 Cedar farm A 29 20.88 -97.35 3 1.2-2.8

2002 19 Low altitude farm A 30 14.89 -92.29 5 0.8-1.8

2002 20 High altitude farm A 1200 15.11 -92.24 9 2.8-7.2

2002 21 Cemetery U 30 14.89 -92.29 1 1.3

2002 22 Park U 30 14.89 -92.29 1 2.3

2002 23 Nature Reserve 1 R 1030 15.11 -92.24 5 2.4-4.8

2004 24 Nature Reserve 2 R 15 14.63 -93.24 6 1.0-2.1

2005 25 Residential Area 1 U 16 19.18 -96.17 5 0.9-1.6

2005 26 Residential Area 2 U 25 19.20 -96.20 4 0.5-0.8

2005 27 Roadside U 40 19.11 -96.10 3 0.9-1.9

2005 28 Beach R 15 19.24 -96.18 4 0.5-1.8Tabasco TB 2006 29 Fish farm A 5 17.86 -91.79 3 3.9-6.4Category: A = agricultural, R = rural, U = urban

Veracruz VC

Chihuahua

Cordoba

Mazatlan

Tuxpan

Cuernavaca

Colima

Chiapas

San Louis Potosi

CHI

COR

SLP

MAZ

TUX

CHP

CUE

COL

214


Location LOD2 CEL CHI CHI COL COL COL COR COR CUE CUE CUE SLP SLP MAZ MAZSite ID (ng/g) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Category R A U U A A A A U A A U A A A α -HCH 0.011 nd3 0.019 nd 0.015 nd nd nd 0.032 0.050 0.039 0.087 nd nd nd 0.021 β -HCH 0.011 nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd γ -HCH 0.011 nd 0.072 nd nd nd nd 0.13 0.019 0.067 0.029 0.052 0.020 nd nd 0.015 δ -HCH 0.011 nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd

ΣHCH1 0.011 0.091 0.011 0.024 0.011 0.011 0.14 0.051 0.12 0.068 0.14 0.026 0.011 0.011 0.036HEPT 0.011 nd nd nd 0.028 nd nd nd nd nd nd nd nd nd nd ndHEPX 0.011 nd 0.084 nd 0.045 nd nd 0.045 nd nd nd nd nd nd nd 0.59TC 0.0011 0.009 0.73 0.019 0.06 0.0011 0.0028 0.062 0.012 0.12 0.018 0.0051 0.059 0.17 nd 1.7CC 0.0011 0.014 0.64 0.018 0.03 nd nd 0.01 0.0064 0.14 0.014 0.0068 0.069 0.11 nd 0.75TN 0.0011 0.074 0.81 0.022 0.36 0.002 0.0042 0.04 0.0048 0.31 0.034 0.021 0.12 0.12 0.01 0.32

ΣCHL1 0.096 2.2 0.060 0.45 0.004 0.0075 0.12 0.023 0.57 0.067 0.033 0.25 0.40 0.01 2.7FTC

5 0.40 0.53 0.51 0.67 0.68 0.84 0.85 0.65 0.45 0.57 0.43 0.46 0.61 0.69Aldrin 0.011 nd nd nd nd nd nd nd nd nd nd nd nd nd nd ndDieldrin 0.011 nd 0.30 nd nd nd nd nd nd 0.04 nd nd nd nd nd 3.1ENDO I 0.0063 0.010 0.12 0.75 0.035 0.022 0.034 0.014 0.025 0.13 0.07 0.012 0.034 nd 0.39 243ENDO II 0.0025 0.0044 0.22 1.94 0.039 0.082 0.062 0.018 0.025 0.22 0.10 0.014 0.054 nd 0.77 440ESUL 0.0011 0.0040 0.29 1.36 0.11 0.26 0.15 0.049 0.070 0.24 0.24 0.051 0.10 0.19 1.04 227

ΣENDO1 0.018 0.63 4.05 0.18 0.36 0.25 0.081 0.12 0.59 0.41 0.078 0.19 0.19 2.20 909FENDO

5 0.69 0.37 0.28 0.48 0.21 0.35 0.44 0.50 0.38 0.41 0.46 0.39 0.00 0.33 0.36o,p' -DDE 0.027 nd nd 0.035 nd nd nd nd 0.18 0.11 nd 0.056 0.09 nd nd 0.11p,p' -DDE 0.011 0.11 1.3 2.1 1.5 0.055 0.057 0.69 18 2.5 1.4 8.3 7.1 0.62 0.032 20o,p' -DDD 0.027 nd 0.057 nd nd nd nd nd 1.6 0.13 nd 0.060 0.32 nd nd 0.18p,p' -DDD 0.027 nd 0.31 0.036 0.13 nd nd 0.078 7.2 0.45 0.12 0.56 1.4 nd nd ndo,p' -DDT 0.027 nd 0.42 0.10 0.17 nd nd 0.10 9.5 0.45 0.16 0.85 1.1 nd nd 0.48p,p' -DDT 0.053 nd nd nd 0.52 nd nd nd 46 1.7 0.62 2.3 nd nd nd 1.6

ΣDDT1 0.19 2.2 2.3 2.3 0.14 0.14 0.92 82 5.3 2.3 12 10 0.70 0.11 23FDDTe

5 0.19 0.020 0.012 0.26 0.33 0.32 0.04 0.72 0.41 0.31 0.22 0.004 0.04 0.45 0.07FDDTo

5 0.059 0.22 0.76 0.21 0.83 0.79 0.80 0.73 0.02 0.77ΣTOX1 0.060 0.15 3.6 1.2 0.24 0.094 0.12 0.20 0.60 0.27 0.52 0.43 9.9 2.5 0.13 334 1. ΣHCH = α−HCH +γ−HCH; ΣCHL = TC+CC+TN; ΣENDO = ENDO I + ENDO II + ESUL; ΣDDT = p,p'-DDT + o,p'-DDT + p,p'-DDE + o,p'-DDE + p,p'-DDD + o,p'-DDD; ΣTOX = quantified as technical toxaphene 2. LOD = mean blank + 3 x standard deviations. If chemical is not detected in blanks, instrumental detection limit was used. 3. nd = not detected. Summations used 1/2 LOD for nd values. 4. Data are taken from Wong et al., (2008) 5. FTC = TC/(TC +CC); FENDO = ENDO I/(ENDO I + ENDO II); FDDTe = p,p'-DDT/ (p,p'-DDT + p,p'-DDE); FDDTo = p,p'-DDT/ (p,p'-DDT + o,p'-DDT). Calculations were not performed when both species were not detected.

215


Location LOD2 MON TUX TUX CHP4 CHP4 CHP4 CHP4 CHP4 CHP4 VC4 VC4 VC4 VC4 TB4

Site ID (ng/g) 16 17 18 19 20 21 22 23 24 25 26 27 28 29Category A A A A A U U R R U U U R A (+)/N6 % α -HCH 0.011 nd 0.023 nd 0.012 nd 0.020 0.024 nd nd 0.037 nd nd nd nd 12 41% β -HCH 0.011 nd nd nd nd nd nd nd nd nd nd nd nd nd nd 0 0% γ -HCH 0.011 0.018 nd nd nd 0.014 0.025 0.023 nd nd 0.036 nd 0.026 0.018 0.016 16 55% δ -HCH 0.011 nd nd nd nd nd nd nd nd nd nd nd nd nd nd 0 0%

ΣHCH1 0.023 0.028 0.011 0.021 0.021 0.045 0.046 0.014 0.011 0.073 0.011 0.032 0.024 0.026HEPT 0.011 nd nd nd nd nd nd nd nd nd nd nd nd nd nd 1 3%HEPX 0.011 nd nd nd nd nd nd nd nd nd nd nd nd nd nd 4 14%TC 0.0011 0.02 nd nd 0.0058 0.0041 0.0086 0.014 0.0021 nd 0.043 0.015 0.023 0.011 0.004 25 86%CC 0.0011 0.03 nd nd 0.0022 0.0053 0.012 0.019 0.0027 nd 0.056 nd 0.016 0.0078 0.006 22 76%TN 0.0011 0.08 nd 0.0011 0.012 0.0096 0.023 0.066 0.0029 nd 0.22 0.026 0.026 0.0067 0.0051 27 93%

ΣCHL1 0.13 0.002 0.0022 0.020 0.019 0.044 0.10 0.0076 0.0 0.32 0.041 0.064 0.025 0.015FTC

5 0.40 0.72 0.44 0.42 0.42 0.43 0.43 0.59 0.58 0.41Aldrin 0.011 nd nd nd nd nd nd nd nd nd nd nd nd nd nd 0 0%Dieldrin 0.011 nd nd nd nd 0.048 0.028 0.056 nd nd nd nd nd nd nd 6 21%ENDO I 0.0063 0.011 nd nd 0.032 0.047 0.072 0.061 0.11 0.0089 0.026 0.023 0.028 0.025 0.0068 26 90%ENDO II 0.0025 0.026 0.0065 0.015 nd nd nd 0.050 0.13 nd nd nd nd nd nd 19 66%ESUL 0.0011 0.023 0.017 0.038 nd 0.03 0.02 0.09 0.25 nd 0.029 0.009 0.014 0.004 0.013 27 93%

ΣENDO1 0.060 0.028 0.059 0.03 0.08 0.09 0.20 0.49 0.01 0.056 0.033 0.043 0.030 0.021FENDO

5 0.29 0.38 0.28 0.96 0.97 0.98 0.55 0.46 0.88 0.95 0.95 0.96 0.95 0.85o,p' -DDE 0.027 nd nd nd 0.089 0.03 1.6 0.06 nd nd 0.062 nd nd nd nd 11 38%p,p' -DDE 0.011 0.83 0.32 0.054 8.5 6.5 203 1.6 0.09 0.06 7.9 0.11 0.26 0.016 0.17 29 100%o,p' -DDD 0.027 nd nd nd 0.11 0.13 4.0 0.11 nd nd 0.15 nd nd nd nd 11 38%p,p' -DDD 0.027 0.04 0.04 nd 0.45 1.5 23 0.39 0.034 nd 1.7 nd 0.034 nd nd 18 62%o,p' -DDT 0.027 nd 0.06 nd 0.85 0.69 20 0.67 nd nd 1.8 nd 0.071 nd nd 17 59%p,p' -DDT 0.053 nd 0.22 nd 3.5 10 109 2.2 0.08 nd 8.7 nd 0.23 nd nd 14 48%

ΣDDT1 0.94 0.67 0.13 14 19 360 5.1 0.26 0.14 20 0.19 0.62 0.10 0.25FDDTe

5 0.03 0.41 0.33 0.29 0.62 0.35 0.58 0.46 0.32 0.52 0.20 0.47 0.62 0.13FDDTo

5 0.79 0.81 0.94 0.84 0.77 0.77 0.83 0.76ΣTOX1 0.060 0.20 0.13 0.22 17 1.3 15 69 0.51 0.13 0.30 0.087 0.088 0.16 nd 28 97% 1. ΣHCH = α−HCH +γ−HCH; ΣCHL = TC+CC+TN; ΣENDO = ENDO I + ENDO II + ESUL; ΣDDT = p,p'-DDT + o,p'-DDT + p,p'-DDE + o,p'-DDE + p,p'-DDD + o,p'-DDD; ΣTOX = quantified as technical toxaphene 2. LOD = mean blank + 3 x standard deviations. If chemical is not detected in blanks, instrumental detection limit was used. 3. nd = not detected. Summations used 1/2 LOD for nd values. 4. Data are taken from Wong et al., (2008) 5. FTC = TC/(TC +CC); FENDO = ENDO I/(ENDO I + ENDO II); FDDTe = p,p'-DDT/ (p,p'-DDT + p,p'-DDE);

FDDTo = p,p'-DDT/ (p,p'-DDT + o,p'-DDT). Calculations were not performed when both species were not detected. 6. (+)/N = number of samples above LOD. % = (+)/N * 100%

216

Table A3.3 Enantiomer fraction of o,p’-DDT, trans-chlordane (TC) and cis-chlordane (CC) in

Mexico soils. Nd = not determined.

Location Site ID Category o,p' -DDT TC CCCEL 1 R nd 0.499 0.506CHI 2 A nd 0.444 ndCHI 3 U 0.469 0.461 0.530COL 4 U 0.511 0.473 0.537COL 5 A 0.504 0.480 0.513COL 6 A nd 0.475 ndCOR 7 A nd 0.455 ndCOR 8 A 0.468 0.486 ndCUE 9 U 0.500 0.466 0.532CUE 10 A 0.511 0.475 0.514CUE 11 A 0.497 0.457 0.498SLP 12 U 0.500 0.427 0.542SLP 13 A 0.500 0.427 0.542MAZ 14 A nd 0.484 ndMAZ 15 A 0.566 0.444 0.523MON 16 A nd 0.380 0.588TUX 17 A 0.506 0.478 ndTUX 18 A nd 0.460 ndCHP 19 A 0.456 nd ndCHP 20 A 0.495 nd ndCHP 21 U 0.518 nd ndCHP 22 U 0.647 nd ndCHP 23 R 0.489 nd ndCHP 24 R nd nd ndVC 25 U 0.519 nd ndVC 26 U 0.478 nd ndVC 27 U 0.492 nd ndVC 28 R nd nd ndTB 29 A nd nd nd

217

Table A3.4 Fugacity fractions (ff) of OCPs in Mexico. ff = fS/(fS+fA), where fS = fugacity of

soil; fA = fugacity of air.

Location Site ID Category α-HCH γ-HCH TC ENDO I p,p' -DDE p,p' -DDD o,p' -DDT p,p' -DDT ΣTOX

CEL 1 R 0.37 0.27 0.04 0.05 0.002 0.002 0.001 0.0002 0.005CHI 2 A 0.65 0.68 0.86 0.02 0.15 0.32 0.66 0.04 0.31CHI 3 U 0.35 0.14 0.15 0.09 0.23 0.05 0.31 0.04 0.13COL 4 U 0.65 0.33 0.51 0.002 0.07 0.02 0.04 0.02 0.16COL 5 A 0.56 0.37 0.04 0.002 0.01 0.005 0.01 0.002 0.13COL 6 A 0.53 0.34 0.08 0.003 0.005 0.004 0.01 0.002 0.14COR 7 A 0.36 0.80 0.41 0.01 0.11 0.02 0.05 0.003 0.08COR 8 A 0.70 0.27 0.08 0.01 0.69 0.57 0.76 0.79 0.15CUE 9 U 0.80 0.62 0.59 0.02 0.11 0.20 0.12 0.10 0.16CUE 10 A 0.76 0.42 0.18 0.01 0.07 0.06 0.04 0.04 0.26CUE 11 A 0.90 0.63 0.08 0.002 0.36 0.28 0.25 0.18 0.29SLP 12 U 0.29 0.34 0.24 0.01 0.70 0.72 0.89 0.05 0.78SLP 13 A 0.25 0.10 0.43 0.001 0.15 0.02 0.08 0.04 0.43MAZ 14 A 0.53 0.26 0.02 0.003 0.01 0.02 0.04 0.02 0.04MAZ 15 A 0.87 0.60 0.99 0.76 0.90 0.03 0.71 0.64 0.99MON 16 A 0.52 0.59 0.28 0.01 0.47 0.15 0.38 0.10 0.09TUX 17 A 0.97 0.29 0.31 0.04 0.22 0.09 0.18 0.69 0.27TUX 18 A 0.83 0.20 0.22 0.03 0.03 0.02 0.03 0.14 0.28CHP 19 A 0.64 0.27 0.17 0.03 0.54 0.34 0.39 0.21 0.87CHP 20 A 0.38 0.31 0.06 0.02 0.10 0.07 0.03 0.02 0.01CHP 21 U 0.75 0.52 0.24 0.07 0.97 0.97 0.94 0.90 0.86CHP 22 U 0.68 0.36 0.23 0.04 0.12 0.21 0.23 0.09 0.94CHP 23 R 0.41 0.16 0.02 0.01 0.01 0.01 0.01 0.002 0.04CHP 24 R 0.72 0.38 0.28 0.10 0.001 0.003 0.004 0.001 0.38VC 25 U 0.94 0.50 0.55 0.08 0.23 0.27 0.31 0.23 0.48VC 26 U 0.62 0.10 0.24 0.06 0.003 0.002 0.002 0.001 0.17VC 27 U 0.77 0.54 0.51 0.14 0.02 0.01 0.03 0.01 0.30VC 28 R 0.31 0.11 0.09 0.01 0.0003 0.0003 0.0004 0.0001 0.04TB 29 A 0.43 0.16 0.07 0.01 0.03 0.03 0.01 0.001 0.13

218

APPENDIX

CHAPTER 4 HYDROXYPROPYL-β-CYCLODEXTRIN AS NON-EXHAUSTIVE

EXTRACTANT FOR ORGANOCHLORINE PESTICIDES AND POLYCHLORINATED

BIPHENYLS IN MUCK SOIL

Figure A4.1 HPCD extractability of OCPs over a range of soil concentration. Soil concentration (ng g-1) for Level 1, 2 and 3 are shown in the table below.

Figure A4.2 HPCD extractability (%) of native OCPs from soils for 20 vs. 40 hrs. Figure A4.3 Sequential HPCD extraction of trans-chlordane (TC) and p,p’-DDT and from soil. Figure A4.4 HPCD extractability vs. molecular volume of PCBs. Molecular volume = LeBas

molar volume (nm3 mol-1) divided by Avogadro’s number (molecules mol-1). Table A4.1 Sequential Soxhlet extraction of soils using DCM (F1), acetone/hexane (F2) and

methanol (F3). Data are normalized to F1. Soils have been aged for 2, 90, 135, 195, 255, 390 and 550 d.

Table A4.2 Optimization of OCPs and PCBs using increasing strength of HPCD solution. Table A4.3 Sorption capacities (SC), extraction capacities (EC) and maximum extraction

fraction (MEF), and HPCD extractability measured at day 2 of this study. SC = QsoilfOCKOC. EC = QCDKCD. MEF = EC/(EC+SC). Qsoil = mass of soil. QCD = mass of HPCD.

Table A4.4 Soil concentration of OCPs and PCBs over 550 days of aging (ng g-1). Table A4.5 HPCD extraction of OCPs and PCBs from sand. Table A4.6 Fitted parameters for eq [4.1] and experimental data at day 2. Y0 = the percent of

chemical remain extractable overtime, Y0 + A = the initial fraction that is available, k = rate constant.


219

Figure A4.1 HPCD extractability of OCPs over a range of soil concentration. Soil concentration

(ng g-1) for Level 1, 2 and 3 are shown in the table below.

110055027513C12-p,p'-DDT

115.42.713C12-Dield

136.63.313C10-TN

157.73.913C10-TC

31157.713C6-α-HCH

L3L2L1

110055027513C12-p,p'-DDT

115.42.713C12-Dield

136.63.313C10-TN

157.73.913C10-TC

31157.713C6-α-HCH

L3L2L1

0%10%20%30%40%50%60%70%

13C6-α-HCH 13C10-TC 13C10-TN 13C12-Dieldrin 13C12-p,p’-DDT

Level 1 Level 2 Level 3

HPC

D E

xtra

ctab

ility

0%10%20%30%40%50%60%70%



0%10%20%30%40%50%60%70%



HPC

D E

xtra

ctab

ility

220

Figure A4.2 HPCD extractability (%) of native OCPs from soils for 20 vs. 40 hrs.

HP

CD

Ext

ract

abilit

y

0%

20%

40%

60%

80%

TC CC TNAldr

in

Dieldri

nEnd

o I

Endo I

I

ESUL

o,p'-D

DE

p,p'-D

DE

o,p'-D

DD

p,p'-D

DD

o,p'-D

DT

p,p'-D

DT

20 hr 40 hr

0%

20%

40%

60%

80%

TC CC TNAldr

in

Dieldri

nEnd

o I

Endo I

I

ESUL

o,p'-D

DE

p,p'-D

DE

o,p'-D

DD

p,p'-D

DD

o,p'-D

DT

p,p'-D

DT

20 hr 40 hr

221

Figure A4.3 Sequential HPCD extraction of trans-chlordane (TC) and p,p’-DDT and from soil.

0%

10%

20%

30%

40%

50%

1 2 3 4

Extraction #

HPC

D E

xtra

ctab

ility

TC

0%

10%

20%

30%

40%

50%

1 2 3 4

Extraction #

HPC

D E

xtra

ctab

ility

TC

0%

10%

20%

30%

40%

50%

1 2 3 4

Extraction #

HPC

D E

xtra

ctab

ility

p,p'-DDT

0%

10%

20%

30%

40%

50%

1 2 3 4

Extraction #

HPC

D E

xtra

ctab

ility

p,p'-DDT

222

Figure A4.4 HPCD extractability vs. molecular volume of PCBs. Molecular volume = LeBas

molar volume (nm3 mol-1) divided by Avogadro’s number (molecules mol-1).

y = -0.0035x + 1.33r2 = 0.70p<0.001

0%

10%

20%

30%

40%

50%

60%

0.35 0.45 0.55 0.65

Molecular volume (nm3 molecule-1)

HPC

D e

xtra

ctab

ility

(Day

2) y = -0.0035x + 1.33

r2 = 0.70p<0.001

0%

10%

20%

30%

40%

50%

60%

0.35 0.45 0.55 0.65

Molecular volume (nm3 molecule-1)

HPC

D e

xtra

ctab

ility

(Day

2)

223

Table A4.1 Sequential Soxhlet extraction of soils using DCM (F1), acetone/hexane (F2) and

methanol (F3). Data are normalized to F1. Soils have been aged for 2, 90, 135, 195, 255, 390

and 550 d.

F2/F1 Mean Stdev

F3/F1 Mean Stdev

TC 12% 3.6% 2.3% 2.0%CC 12% 3.4% 2.2% 1.8%TN 14% 4.2% 2.8% 2.4%Dieldrin 9.3% 3.1% 2.0% 2.1%Endo I 16% 6.5% 0% 0%Endo II 7.7% 2.6% 0% 0%ESUL 7.7% 2.8% 0% 0%o,p' -DDE 11% 2.6% 7.7% 9.2%p,p' -DDE 8% 1.8% 5.6% 4.5%o,p' -DDD 10% 4.4% 2.1% 1.9%p,p' -DDD 10% 4.9% 0% 0%o,p' -DDT 11% 2.9% 0% 0%p,p' -DDT 13% 4.6% 0% 0%13C6-α-HCH 0% 0% 0% 0%13C10-TC 10% 3.1% 0% 0%13C10-TN 11% 3.7% 0% 0%13C12-Dieldrin 5.1% 4.0% 0% 0%13C12-p,p' -DDT 15% 5.5% 0% 0%

F2/F1 Mean Stdev

F3/F1 Mean Stdev

TC 12% 3.6% 2.3% 2.0%CC 12% 3.4% 2.2% 1.8%TN 14% 4.2% 2.8% 2.4%Dieldrin 9.3% 3.1% 2.0% 2.1%Endo I 16% 6.5% 0% 0%Endo II 7.7% 2.6% 0% 0%ESUL 7.7% 2.8% 0% 0%o,p' -DDE 11% 2.6% 7.7% 9.2%p,p' -DDE 8% 1.8% 5.6% 4.5%o,p' -DDD 10% 4.4% 2.1% 1.9%p,p' -DDD 10% 4.9% 0% 0%o,p' -DDT 11% 2.9% 0% 0%p,p' -DDT 13% 4.6% 0% 0%13C6-α-HCH 0% 0% 0% 0%13C10-TC 10% 3.1% 0% 0%13C10-TN 11% 3.7% 0% 0%13C12-Dieldrin 5.1% 4.0% 0% 0%13C12-p,p' -DDT 15% 5.5% 0% 0%

224

Table A4.2 Optimization of OCPs and PCBs using increasing strength of HPCD solution.

OCs 10 mM 50 mM 100 mM 200 mM 400 mM 500 mMNative

TC 4% 14% 33% 38% 39% 37%CC 6% 23% 46% 51% 48% 44%TN 3% 10% 26% 24% 28% 29%Dieldrin 14% 36% 45% 59% 50% 46%Endo I 15% 39% 45% 55% 44% 47%Endo II 3% 14% 10% 26% 19% 15%ESUL 9% 27% 42% 50% 32% 31%o,p' -DDE 3% 11% 28% 32% 34% 35%p,p' -DDE 3% 9% 23% 23% 27% 30%o,p' -DDD 3% 11% 31% 34% 36% 36%p,p' -DDD 3% 9% 27% 31% 35% 28%o,p' -DDT 3% 13% 31% 34% 42% 39%p,p' -DDT 2% 10% 24% 31% 36% 35%

Spiked13C6-α-HCH 10% 28% 39% 55% 46% 46%13C10-TC 5% 16% 37% 42% 45% 44%13C10-TN 4% 12% 28% 28% 34% 33%13C12-Dieldrin 23% 52% 68% 67% 59% 75%13C12-p,p' -DDT 4% 14% 33% 42% 51% 50%

PCBs 10 mM 50 mM 100 mM 200 mM 400 mM 500 mMDi- and trichlorobiphenyls

PCB 8 13% 37% 57% 62% 55% 56%PCB 18 13% 38% 59% 62% 54% 54%PCB 28 5% 18% 35% 43% 41% 44%PCB 32 8% 28% 48% 49% 50% 51%

TetrachlorobiphenylsPCB 44 7% 22% 38% 41% 35% 36%PCB 52 8% 24% 45% 55% 52% 52%PCB 66 4% 14% 29% 33% 36% 38%PCB 77 3% 8% 18% 18% 24% 28%

PentachlorobiphenylsPCB 95 4% 12% 25% 27% 30% 33%PCB 101 5% 14% 30% 35% 38% 40%PCB 105 7% 10% 21% 23% 28% 33%PCB 118 3% 10% 23% 26% 30% 34%PCB 126 5% 6% 15% 9% 12% 15%

HexachlorobiphenylsPCB 128 7% 9% 21% 20% 26% 31%PCB 136 9% 12% 26% 27% 31% 35%PCB 138 7% 11% 22% 23% 29% 34%PCB 149 9% 12% 26% 27% 31% 35%PCB 153 7% 10% 21% 20% 26% 30%

Hepta-, OctachlorobiphenylsPCB 170 7% 8% 18% 12% 13% 16%PCB 180 6% 8% 18% 11% 13% 17%PCB 187 7% 9% 20% 16% 20% 22%PCB 195 7% 12% 18% 12% 8% 9%





PCB 8 13% 37% 57% 62% 55% 56%PCB 18 13% 38% 59% 62% 54% 54%PCB 28 5% 18% 35% 43% 41% 44%PCB 32





PCB 8 13% 37% 57% 62% 55% 56%PCB 18 13% 38% 59% 62% 54% 54%PCB 28 5% 18% 35% 43% 41% 44%PCB 32 8% 28% 48% 49% 50% 51%

TetrachlorobiphenylsPCB 44 7% 22% 38% 41% 35% 36%PCB 52 8% 24% 45% 55% 52% 52%PCB 66 4% 14% 29% 33% 36% 38%PCB 77 3% 8% 18% 18% 24% 28%

PentachlorobiphenylsPCB 95 4% 12% 25% 27% 30% 33%PCB 101 5% 14% 30% 35% 38% 40%PCB 105 7% 10% 21% 23% 28% 33%PCB 118 3% 10% 23% 26% 30% 34%PCB 126 5% 6% 15% 9% 12% 15%

HexachlorobiphenylsPCB 128 7% 9% 21% 20% 26% 31%PCB 136 9% 12% 26% 27% 31% 35%PCB 138 7% 11% 22% 23% 29% 34%PCB 149 9% 12% 26% 27% 31% 35%PCB 153 7% 10% 21% 20% 26% 30%

Hepta-, OctachlorobiphenylsPCB 170 7% 8% 18% 12% 13% 16%PCB 180 6% 8% 18% 11% 13% 17%PCB 187 7% 9% 20% 16% 20% 22%PCB 195 7% 12% 18% 12% 8% 9%

225

Table A4.3 Sorption capacities (SC), extraction capacities (EC) and maximum extraction

fraction (MEF), and HPCD extractability measured at day 2 of this study. SC = QsoilfOCKOC. EC

= QCDKCD. MEF = EC/(EC+SC). Qsoil = mass of soil. QCD = mass of HPCD.

log Kow log Koc log KCD SC EC MEF

Exp. Data at Day 2

TC 6.27 5.33 4.23 9.0E+04 9.8E+04 52% 39%CC 6.20 5.27 4.18 7.8E+04 8.9E+04 53% 48%TN 6.10 5.19 4.12 6.4E+04 7.7E+04 55% 28%Dieldrin 5.48 4.66 3.74 1.9E+04 3.2E+04 63% 50%Endo I 4.94 4.20 3.40 6.6E+03 1.5E+04 69% 44%Endo II 4.78 4.06 3.30 4.9E+03 1.2E+04 71% 23%ESUL 4.94 4.20 3.40 6.6E+03 1.5E+04 69% 39%o,p'-DDE 6.93 5.89 4.64 3.3E+05 2.5E+05 44% 34%p,p'-DDE 6.93 5.89 4.64 3.3E+05 2.5E+05 44% 27%o,p'-DDD 6.33 5.38 4.26 1.0E+05 1.1E+05 52% 36%p,p'-DDD 6.33 5.38 4.26 1.0E+05 1.1E+05 52% 35%o,p'-DDT 6.39 5.43 4.30 1.1E+05 1.2E+05 51% 42%p,p'-DDT 6.39 5.43 4.30 1.1E+05 1.2E+05 51% 36%13C6-α-HCH 3.94 3.35 2.78 9.4E+02 3.5E+03 79% 46%13C10-TC 6.27 5.33 4.23 9.0E+04 9.8E+04 52% 45%13C10-TN 6.10 5.19 4.12 6.4E+04 7.7E+04 55% 34%13C12-Dieldrin 5.48 4.66 3.74 1.9E+04 3.2E+04 63% 59%13C12-p,p'-DDT 6.39 5.43 4.30 1.1E+05 1.2E+05 51% 51%PCB 8 5.12 4.35 3.51 9.4E+03 1.9E+04 67% 55%PCB 18 5.60 4.76 3.81 2.4E+04 3.8E+04 61% 54%PCB 28 5.66 4.81 3.85 2.7E+04 4.1E+04 60% 42%PCB 32 5.75 4.89 3.91 3.2E+04 4.7E+04 59% 50%PCB 44 6.00 5.10 4.06 5.3E+04 6.7E+04 56% 36%PCB 52 5.91 5.02 4.00 4.4E+04 5.9E+04 57% 54%PCB 66 6.31 5.36 4.25 9.7E+04 1.0E+05 52% 39%PCB 77 6.50 5.53 4.37 1.4E+05 1.4E+05 49% 26%PCB 95 5.92 5.03 4.01 4.5E+04 6.0E+04 57% 32%PCB 101 6.33 5.38 4.27 1.0E+05 1.1E+05 51% 40%PCB 105 6.82 5.79 4.57 2.6E+05 2.1E+05 45% 29%PCB 118 6.69 5.68 4.49 2.0E+05 1.8E+05 47% 32%PCB 126 7.00 5.95 4.68 3.7E+05 2.8E+05 43% 13%PCB 128 7.32 6.22 4.88 7.0E+05 4.4E+05 39% 27%PCB 136 7.12 6.05 4.75 4.7E+05 3.3E+05 41% 33%PCB 138 7.21 6.13 4.81 5.7E+05 3.8E+05 40% 30%PCB 149 6.86 5.83 4.59 2.8E+05 2.3E+05 45% 33%PCB 153 6.87 5.84 4.60 2.9E+05 2.3E+05 44% 27%PCB 170 6.96 5.92 4.66 3.5E+05 2.6E+05 43% 13%PCB 180 7.16 6.09 4.78 5.1E+05 3.5E+05 41% 13%PCB 187 6.84 5.81 4.58 2.7E+05 2.2E+05 45% 20%PCB 195 7.76 6.60 5.15 1.7E+06 8.3E+05 33% 9%

log Kow log Koc log KCD SC EC MEFExp. Data at Day 2

TC 6.27 5.33 4.23 9.0E+04 9.8E+04 52% 39%CC 6.20 5.27 4.18 7.8E+04 8.9E+04 53% 48%TN 6.10 5.19 4.12 6.4E+04 7.7E+04 55% 28%Dieldrin 5.48 4.66 3.74 1.9E+04 3.2E+04 63% 50%Endo I 4.94 4.20 3.40 6.6E+03 1.5E+04 69% 44%Endo II 4.78 4.06 3.30 4.9E+03 1.2E+04 71% 23%ESUL 4.94 4.20 3.40 6.6E+03 1.5E+04 69% 39%o,p'-DDE 6.93 5.89 4.64 3.3E+05 2.5E+05 44% 34%p,p'-DDE 6.93 5.89 4.64 3.3E+05 2.5E+05 44% 27%o,p'-DDD 6.33 5.38 4.26 1.0E+05 1.1E+05 52% 36%p,p'-DDD 6.33 5.38 4.26 1.0E+05 1.1E+05 52% 35%o,p'-DDT 6.39 5.43 4.30 1.1E+05 1.2E+05 51% 42%p,p'-DDT 6.39 5.43 4.30 1.1E+05 1.2E+05 51% 36%13C6-α-HCH 3.94 3.35 2.78 9.4E+02 3.5E+03 79% 46%13C10-TC 6.27 5.33 4.23 9.0E+04 9.8E+04 52% 45%13C10-TN 6.10 5.19 4.12 6.4E+04 7.7E+04 55% 34%13C12-Dieldrin 5.48 4.66 3.74 1.9E+04 3.2E+04 63% 59%13C12-p,p'-DDT 6.39 5.43 4.30 1.1E+05 1.2E+05 51% 51%PCB 8 5.12 4.35 3.51 9.4E+03 1.9E+04 67% 55%PCB 18

log Kow log Koc log KCD SC EC MEFExp. Data at Day 2

TC 6.27 5.33 4.23 9.0E+04 9.8E+04 52% 39%CC 6.20 5.27 4.18 7.8E+04 8.9E+04 53% 48%TN 6.10 5.19 4.12 6.4E+04 7.7E+04 55% 28%Dieldrin 5.48 4.66 3.74 1.9E+04 3.2E+04 63% 50%Endo I 4.94 4.20 3.40 6.6E+03 1.5E+04 69% 44%Endo II 4.78 4.06 3.30 4.9E+03 1.2E+04 71% 23%ESUL 4.94 4.20 3.40 6.6E+03 1.5E+04 69% 39%o,p'-DDE 6.93 5.89 4.64 3.3E+05 2.5E+05 44% 34%p,p'-DDE 6.93 5.89 4.64 3.3E+05 2.5E+05 44% 27%o,p'-DDD 6.33 5.38 4.26 1.0E+05 1.1E+05 52% 36%p,p'-DDD 6.33 5.38 4.26 1.0E+05 1.1E+05 52% 35%o,p'-DDT 6.39 5.43 4.30 1.1E+05 1.2E+05 51% 42%p,p'-DDT 6.39 5.43 4.30 1.1E+05 1.2E+05 51% 36%13C6-α-HCH 3.94 3.35 2.78 9.4E+02 3.5E+03 79% 46%13C10-TC 6.27 5.33 4.23 9.0E+04 9.8E+04 52% 45%13C10-TN 6.10 5.19 4.12 6.4E+04 7.7E+04 55% 34%13C12-Dieldrin 5.48 4.66 3.74 1.9E+04 3.2E+04 63% 59%13C12-p,p'-DDT 6.39 5.43 4.30 1.1E+05 1.2E+05 51% 51%PCB 8 5.12 4.35 3.51 9.4E+03 1.9E+04 67% 55%PCB 18 5.60 4.76 3.81 2.4E+04 3.8E+04 61% 54%PCB 28 5.66 4.81 3.85 2.7E+04 4.1E+04 60% 42%PCB 32 5.75 4.89 3.91 3.2E+04 4.7E+04 59% 50%PCB 44 6.00 5.10 4.06 5.3E+04 6.7E+04 56% 36%PCB 52 5.91 5.02 4.00 4.4E+04 5.9E+04 57% 54%PCB 66 6.31 5.36 4.25 9.7E+04 1.0E+05 52% 39%PCB 77 6.50 5.53 4.37 1.4E+05 1.4E+05 49% 26%PCB 95 5.92 5.03 4.01 4.5E+04 6.0E+04 57% 32%PCB 101 6.33 5.38 4.27 1.0E+05 1.1E+05 51% 40%PCB 105 6.82 5.79 4.57 2.6E+05 2.1E+05 45% 29%PCB 118 6.69 5.68 4.49 2.0E+05 1.8E+05 47% 32%PCB 126 7.00 5.95 4.68 3.7E+05 2.8E+05 43% 13%PCB 128 7.32 6.22 4.88 7.0E+05 4.4E+05 39% 27%PCB 136 7.12 6.05 4.75 4.7E+05 3.3E+05 41% 33%PCB 138 7.21 6.13 4.81 5.7E+05 3.8E+05 40% 30%PCB 149 6.86 5.83 4.59 2.8E+05 2.3E+05 45% 33%PCB 153 6.87 5.84 4.60 2.9E+05 2.3E+05 44% 27%PCB 170 6.96 5.92 4.66 3.5E+05 2.6E+05 43% 13%PCB 180 7.16 6.09 4.78 5.1E+05 3.5E+05 41% 13%PCB 187 6.84 5.81 4.58 2.7E+05 2.2E+05 45% 20%PCB 195 7.76 6.60 5.15 1.7E+06 8.3E+05 33% 9%

226

Table A4.4 Soil concentration of OCPs and PCBs over 550 days of aging (ng g-1).

Day 2 Day 10 Day 45 Day 90 Day 135 Day 195 Day 255 Day 390 Day 550 Mean Stdev %RSDDi- and trichlorobiphenylsPCB 8 34.0 33.2 2.52 1.78 1.24 1.13 0.93 0.58 0.54 8.43 14.3 169%PCB 18 33.3 33.3 33.7 32.1 29.9 34.3 31.5 28.1 32.0 32.0 1.99 6%PCB 28 35.7 36.2 16.7 14.1 11.1 9.9 7.1 5.0 4.5 15.6 12.20 78%PCB 32 5.9 6.0 5.56 5.38 4.82 5.52 4.99 4.33 4.82 5.26 0.55 11%

TetrachlorobiphenylsPCB 44 54.7 41.2 46.8 42.7 40.0 44.2 42.6 38.4 43.1 43.7 4.76 11%PCB 52 33.4 34.0 37.3 36.0 34.0 38.3 37.9 34.9 39.8 36.2 2.27 6%PCB 66 35.7 36.2 39.3 37.7 35.4 39.6 38.2 34.4 39.1 37.3 1.90 5%PCB 77 110 107 119 112 103 116 113 100 112 110 6.02 5%PentachlorobiphenylsPCB 95 63.5 65.7 72.2 69.9 67.6 75.7 74.7 69.1 78.5 70.8 4.93 7%PCB 101 32.2 33.2 36.8 35.4 34.5 38.7 38.0 35.0 39.6 35.9 2.53 7%PCB 105 33.4 35.1 39.3 37.5 35.6 40.2 39.3 36.4 41.2 37.5 2.62 7%PCB 118 102 104 117 111 106 119 117 107 121 112 7.17 6%PCB 126 109 109 122 114 109 123 120 109 122 115 6.52 6%HexachlorobiphenylsPCB 128 32.3 33.2 37.4 35.5 34.7 39.1 38.0 35.2 39.9 36.1 2.61 7%PCB 136 12.0 12.5 13.7 13.3 13.0 14.5 14.3 13.1 15.0 13.5 0.98 7%PCB 138 32.2 33.3 37.2 35.5 34.6 38.7 37.9 35.0 39.8 36.0 2.53 7%PCB 149 7.6 7.9 8.69 8.41 8.24 9.16 9.03 8.28 9.51 8.54 0.62 7%PCB 153 31.7 32.9 36.7 35.1 34.3 38.1 37.2 34.3 39.2 35.5 2.49 7%Hepta-, OctachlorobiphenylsPCB 170 29.2 30.1 33.7 31.8 31.9 35.6 34.2 31.7 36.4 32.8 2.42 7%PCB 180 30.2 31.5 34.7 33.0 33.2 36.7 35.6 32.7 37.5 33.9 2.42 7%PCB 187 29.6 30.8 34.2 32.6 32.6 36.0 35.2 32.4 37.0 33.4 2.43 7%PCB 195 29.7 30.5 34.0 32.5 32.6 36.9 35.1 31.8 36.9 33.3 2.59 8%

Day 2 Day 10 Day 45 Day 90 Day 135 Day 195 Day 255 Day 390 Day 550 Mean Stdev %RSDNative OCs

TC 19.6 19.9 22.3 21.2 19.6 22.3 21.7 20.1 22.4 21.0 1.2 5.8%CC 19.2 19.6 21.8 20.7 19.1 21.9 21.2 19.6 21.9 20.6 1.2 5.9%TN 15.3 15.7 17.7 16.9 15.4 17.7 17.1 15.8 17.6 16.6 1.0 6.1%Dieldrin 600 609 700 695 553 701 732 675 699 663 60 9%Endo I 23.3 23.6 17.8 12.9 8.82 8.64 7.79 5.78 4.74 12.6 7.3 58%Endo II 375 355 415 405 313 375 373 301 287 355 46 13%ESUL 718 670 796 770 598 694 686 543 494 663 101 15%o,p' -DDE 56.4 60.0 68.3 67.9 54.8 68.0 69.4 65.6 71.6 64.7 6.1 9.4%p,p' -DDE 1827 1822 2076 1961 1934 2162 2170 2014 2267 2026 156 7.7%o,p' -DDD 252 258 293 282 229 304 278 260 315 275 27 10%p,p' -DDD 921 933 1055 985 755 1156 907 840 1254 979 155 16%o,p' -DDT 2476 2537 2922 2822 2572 2934 3070 2777 2926 2782 208 7.5%p,p' -DDT 8934 9242 10441 9908 9976 10653 10251 9418 10158 9887 576 5.8%

Spiked OCs13C6-α-HCH 19.0 18.4 17.3 13.5 10.5 10.4 8.32 5.23 4.14 11.9 5.6 47%13C10-TC 8.44 8.61 9.59 9.12 8.35 9.61 9.33 8.54 9.59 9.02 0.53 5.9%13C10-TN 7.54 7.79 8.59 8.11 7.47 8.51 8.27 7.55 8.54 8.04 0.46 5.7%13C12-Dieldrin 9.81 9.48 10.9 10.7 8.18 9.07 9.78 8.79 8.8 9.5 0.89 9.4%13C12-p,p' -DDT 710 736 828 783 780 823 807 741 798 779 41 5.3%




TetrachlorobiphenylsPCB 44 54.7 41.2 46.8 42.7 40.0 44.2 42.6 38.4 43.1 43.7 4.76 11%PCB 52 33.4 34.0 37.3 36.0 34.0 38.3 37.9 34.9 39.8 36.2 2.27 6%PCB 66 35.7 36.2 39.3 37.7 35.4 39.6 38.2 34.4 39.1 37.3 1.90 5%PCB 77 110 107 119 112 103 116 113 100 112 110 6.02 5%PentachlorobiphenylsPCB 95 63.5 65.7 72.2 69.9 67.6 75.7 74.7 69.1 78.5 70.8 4.93 7%PCB 101 32.2 33.2 36.8 35.4 34.5 38.7 38.0 35.0 39.6 35.9 2.53 7%PCB 105 33.4 35.1 39.3 37.5 35.6 40.2 39.3 36.4 41.2 37.5 2.62 7%PCB 118 102 104 117 111 106 119 117 107 121 112 7.17 6%PCB 126 109 109 122 114 109 123 120 109 122 115 6.52 6%HexachlorobiphenylsPCB 128 32.3 33.2



Day 2 Day 10 Day 45 Day 90 Day 135 Day 195 Day 255 Day 390 Day 550 Mean Stdev %RSDNative OCs

TC 19.6 19.9 22.3 21.2 19.6 22.3 21.7 20.1 22.4 21.0 1.2 5.8%CC 19.2 19.6 21.8 20.7 19.1 21.9 21.2 19.6 21.9 20.6 1.2 5.9%TN 15.3 15.7 17.7 16.9 15.4 17.7 17.1 15.8 17.6 16.6 1.0 6.1%Dieldrin 600 609 700 695 553 701 732 675 699 663 60 9%Endo I 23.3 23.6 17.8 12.9 8.82 8.64 7.79 5.78 4.74 12.6 7.3 58%Endo II 375 355 415 405 313 375 373 301 287 355 46 13%ESUL 718 670 796 770 598 694 686 543 494 663 101 15%o,p' -DDE 56.4 60.0 68.3 67.9 54.8 68.0 69.4 65.6 71.6 64.7 6.1 9.4%p,p' -DDE 1827 1822 2076 1961 1934 2162 2170 2014 2267 2026 156 7.7%o,p' -DDD 252 258 293 282 229 304 278 260 315 275 27 10%p,p' -DDD 921 933 1055 985 755 1156 907 840 1254 979 155 16%o,p' -DDT 2476 2537 2922 2822 2572 2934 3070 2777 2926 2782 208 7.5%p,p' -DDT 8934 9242 10441 9908 9976 10653 10251 9418 10158 9887 576 5.8%

Spiked OCs13C6-α-HCH 19.0 18.4 17.3 13.5 10.5 10.4 8.32 5.23 4.14 11.9 5.6 47%13C10-TC 8.44 8.61 9.59 9.12 8.35 9.61 9.33 8.54 9.59 9.02 0.53 5.9%13C10-TN 7.54 7.79 8.59 8.11 7.47 8.51 8.27 7.55 8.54 8.04 0.46 5.7%13C12-Dieldrin 9.81 9.48 10.9 10.7 8.18 9.07 9.78 8.79 8.8 9.5 0.89 9.4%13C12-p,p' -DDT 710 736 828 783 780 823 807 741 798 779 41 5.3%

227

Table A4.5 HPCD extraction of OCPs and PCBs from sand.

OCs Recovery Stdev PCBs Recovery Stdevα-HCH 83% 4% PCB 8 77% 4%γ-HCH 84% 4% PCB 18 76% 4%HEPX 97% 4% PCB 28 82% 4%TC 85% 4% PCB 32 80% 5%CC 86% 4% PCB 44 80% 4%TN 91% 3% PCB 52 80% 4%Dieldrin 89% 1% PCB 66 81% 4%Endo I 88% 3% PCB 77 79% 3%Endo II 73% 5% PCB 95 76% 4%ESUL 65% 5% PCB 101 80% 4%o,p' -DDE 82% 2% PCB 105 83% 4%p,p' -DDE 91% 1% PCB 118 79% 4%o,p' -DDD 88% 2% PCB 126 84% 5%p,p' -DDD 93% 2% PCB 128 83% 4%o,p' -DDT 97% 1% PCB 136 81% 5%p,p' -DDT 75% 8% PCB 138 82% 4%

PCB 149 82% 4%PCB 153 81% 5%PCB 170 83% 6%PCB 180 82% 6%PCB 187 81% 6%PCB 195 81% 7%

228

Table A4.6 Fitted parameters for eq [4.1] and experimental data at day 2. Y0 = the percent of

chemical remain extractable overtime, Y0 + A = the initial fraction that is available, k = rate

constant.

Native OC Y0 A Y0+A k R2Exp. Data at

Day 2

TC 31% 7% 38% -1.7E-02 0.88 39%CC 38% 10% 48% -2.0E-02 0.89 48%TN 23% 4% 28% -2.9E-02 0.71 28%Dieldrin 35% 12% 47% -9.7E-03 0.81 50%Endo I 5% 37% 42% -4.6E-03 0.84 44%Endo II 5% 18% 22% -2.4E-03 0.29 23%ESUL 1% 36% 37% -1.9E-03 0.94 39%o,p' -DDE 31% 134% 165% -1.8E+00 0.34 34%p,p' -DDE 24% 56% 79% -1.4E+00 0.51 27%o,p' -DDD 24% 10% 34% -1.2E-02 0.85 36%p,p' -DDD 21% 11% 32% -1.1E-02 0.77 35%o,p' -DDT 33% 5% 38% -5.8E-03 0.26 42%p,p' -DDT 30% 4% 34% -1.0E-02 0.43 36%

Spiked OC13C6-α-HCH 33% 15% 48% -7.5E-03 0.95 46%13C10-TC 33% 11% 44% -1.4E-02 0.95 45%13C10-TN 25% 8% 33% -2.6E-02 0.81 34%13C12-Dieldrin 38% 17% 55% -9.1E-03 0.84 59%13C12-p,p' -DDT 39% 10% 49% -9.8E-03 0.72 51%

Y0 A Y0+A k R2Exp. Data at

Day 2PCB 8 38% 19% 57% -4.6E-02 0.93 55%PCB 18 41% 16% 56% -6.0E-02 0.88 54%PCB 28 27% 14% 41% -6.4E-03 0.82 42%PCB 32 35% 14% 49% -1.9E-02 0.93 50%PCB 44 31% 234% 265% -1.9E+00 0.74 36%PCB 52 40% 9% 49% -1.5E-02 0.51 54%PCB 66 30% 15% 45% -2.8E-01 0.85 39%PCB 77 20% 202% 222% -1.8E+00 0.82 26%PCB 95 27% 107% 134% -1.5E+00 0.68 32%PCB 101 33% 114% 147% -1.4E+00 0.83 40%PCB 105 24% 9% 32% -2.9E-01 0.64 29%PCB 118 26% 263% 289% -1.9E+00 0.85 32%PCB 126 10% 131% 141% -1.9E+00 0.73 13%PCB 128 22% 237% 259% -1.9E+00 0.80 27%PCB 136 27% 256% 284% -1.9E+00 0.74 33%PCB 138 25% 251% 275% -1.9E+00 0.74 30%PCB 149 27% 249% 276% -1.9E+00 0.83 33%PCB 153 22% 232% 254% -1.9E+00 0.83 27%PCB 170 11% 1% 12% -2.9E-03 0.06 13%PCB 180 11% 50% 61% -1.6E+00 0.45 13%PCB 187 17% 157% 174% -1.9E+00 0.76 20%PCB 195 6% 1% 7% -2.2E-03 0.06 8.6%


Day 2




Day 2PCB 8 38% 19% 57%


Day 2




Day 2PCB 8 38% 19% 57% -4.6E-02 0.93 55%PCB 18 41% 16% 56% -6.0E-02 0.88 54%PCB 28 27% 14% 41% -6.4E-03 0.82 42%PCB 32 35% 14% 49% -1.9E-02 0.93 50%PCB 44 31% 234% 265% -1.9E+00 0.74 36%PCB 52 40% 9% 49% -1.5E-02 0.51 54%PCB 66 30% 15% 45% -2.8E-01 0.85 39%PCB 77 20% 202% 222% -1.8E+00 0.82 26%PCB 95 27% 107% 134% -1.5E+00 0.68 32%PCB 101 33% 114% 147% -1.4E+00 0.83 40%PCB 105 24% 9% 32% -2.9E-01 0.64 29%PCB 118 26% 263% 289% -1.9E+00 0.85 32%PCB 126 10% 131% 141% -1.9E+00 0.73 13%PCB 128 22% 237% 259% -1.9E+00 0.80 27%PCB 136 27% 256% 284% -1.9E+00 0.74 33%PCB 138 25% 251% 275% -1.9E+00 0.74 30%PCB 149 27% 249% 276% -1.9E+00 0.83 33%PCB 153 22% 232% 254% -1.9E+00 0.83 27%PCB 170 11% 1% 12% -2.9E-03 0.06 13%PCB 180 11% 50% 61% -1.6E+00 0.45 13%PCB 187 17% 157% 174% -1.9E+00 0.76 20%PCB 195 6% 1% 7% -2.2E-03 0.06 8.6%

229


OCs Day 2 Day 10 Day 45 Day 90 Day 135 Day 195 Day 255 Day 390 Day 550Native

TC 39% 36% 36% 33% 31% 32% 31% 32% 32%CC 48% 44% 44% 39% 37% 39% 38% 39% 38%TN 28% 25% 25% 24% 22% 23% 23% 24% 24%Dieldrin 50% 42% 43% 39% 39% 39% 35% 35% 35%Endo I 44% 41% 28% 28% 27% 25% 18% 0% 13%Endo II 23% 28% 8% 24% 17% 10% 26% 6% 10%ESUL 39% 34% 35% 29% 31% 28% 22% 17% 15%o,p' -DDE 34% 30% 33% 31% 29% 31% 28% 31% 33%p,p' -DDE 27% 23% 24% 24% 21% 24% 22% 24% 25%o,p' -DDD 36% 30% 32% 28% 25% 27% 25% 24% 24%p,p' -DDD 35% 26% 29% 25% 22% 23% 23% 20% 21%o,p' -DDT 42% 32% 38% 37% 34% 36% 33% 33% 34%p,p' -DDT 36% 31% 33% 34% 30% 31% 30% 30% 31%

Spiked13C6-α-HCH 46% 48% 45% 39% 39% 36% 37% 35% 32%13C10-TC 45% 41% 41% 36% 34% 34% 34% 34% 33%13C10-TN 34% 29% 28% 27% 24% 26% 25% 24% 25%13C12-Dieldrin 59% 48% 51% 45% 42% 44% 38% 38% 37%13C12-p,p' -DDT 51% 44% 46% 45% 40% 41% 38% 39% 40%

PCBs Day 2 Day 10 Day 45 Day 90 Day 135 Day 195 Day 255 Day 390 Day 550PCB 8 55% 51% 41% 35% 37% 39% 41% 38% 0%PCB 18 54% 51% 40% 39% 42% 43% 43% 40% 38%PCB 28 42% 37% na 38% 35% 29% 26% 29% 29%PCB 32 50% 46% 40% 39% 37% 40% 37% 33% 33%PCB 44 36% 30% 31% 30% 30% 32% 32% 32% 30%PCB 52 54% 41% 47% 44% 46% 42% 41% 39% 41%PCB 66 39% 31% 31% 28% 30% 32% 30% 30% 30%PCB 77 26% 19% 20% 20% 20% 21% 19% 18% 18%PCB 95 32% 25% 28% 28% 25% 27% 26% 27% 27%PCB 101 40% 32% 34% 33% 31% 33% 33% 33% 33%PCB 105 29% 24% 22% 23% 23% 25% 25% 24% 25%PCB 118 32% 24% 25% 26% 25% 27% 25% 26% 26%PCB 126 13% 9% 10% 11% 10% 11% 10% 10% 10%PCB 128 27% 20% 23% 23% 21% 23% 22% 22% 23%PCB 136 33% 26% 28% 29% 25% 28% 27% 27% 28%PCB 138 30% 23% 25% 26% 24% 26% 24% 24% 25%PCB 149 33% 26% 28% 28% 25% 28% 27% 27% 28%PCB 153 27% 21% 22% 22% 21% 23% 22% 22% 23%PCB 170 13% 11% 12% 15% 10% 12% 11% 11% 12%PCB 180 13% 10% 11% 13% 10% 12% 11% 11% 11%PCB 187 20% 16% 16% 18% 15% 17% 17% 16% 17%PCB 195 8.6% 5.9% 6.5% 9.2% 5.5% 7.6% 6.8% 6.2% 6.7%



Spiked13C6-α-HCH 46% 48% 45% 39% 39% 36% 37% 35% 32%13C10-TC 45% 41% 41% 36% 34% 34% 34% 34% 33%13C10-TN 34% 29% 28% 27% 24% 26% 25% 24% 25%13C12-Dieldrin



Spiked13C6-α-HCH 46% 48% 45% 39% 39% 36% 37% 35% 32%13C10-TC 45% 41% 41% 36% 34% 34% 34% 34% 33%13C10-TN 34% 29% 28% 27% 24% 26% 25% 24% 25%13C12-Dieldrin 59% 48% 51% 45% 42% 44% 38% 38% 37%13C12-p,p' -DDT 51% 44% 46% 45% 40% 41% 38% 39% 40%

PCBs Day 2 Day 10 Day 45 Day 90 Day 135 Day 195 Day 255 Day 390 Day 550PCB 8 55% 51% 41% 35% 37% 39% 41% 38% 0%PCB 18 54% 51% 40% 39% 42% 43% 43% 40% 38%PCB 28 42% 37% na 38% 35% 29% 26% 29% 29%PCB 32 50% 46% 40% 39% 37% 40% 37% 33% 33%PCB 44 36% 30% 31% 30% 30% 32% 32% 32% 30%PCB 52 54% 41% 47% 44% 46% 42% 41% 39% 41%PCB 66 39% 31% 31% 28% 30% 32% 30% 30% 30%PCB 77 26% 19% 20% 20% 20% 21% 19% 18% 18%PCB 95 32% 25% 28% 28% 25% 27% 26% 27% 27%PCB 101 40% 32% 34% 33% 31% 33% 33% 33% 33%PCB 105 29% 24% 22% 23% 23% 25% 25% 24% 25%PCB 118 32% 24% 25% 26% 25% 27% 25% 26% 26%PCB 126 13% 9% 10% 11% 10% 11% 10% 10% 10%PCB 128 27% 20% 23% 23% 21% 23% 22% 22% 23%PCB 136 33% 26% 28% 29% 25% 28% 27% 27% 28%PCB 138 30% 23% 25% 26% 24% 26%

59% 48% 51% 45% 42% 44% 38% 38% 37%13C12-p,p' -DDT 51% 44% 46% 45% 40% 41% 38% 39% 40%

PCBs Day 2 Day 10 Day 45 Day 90 Day 135 Day 195 Day 255 Day 390 Day 550PCB 8 55% 51% 41% 35% 37% 39% 41% 38% 0%PCB 18 54% 51% 40% 39% 42% 43% 43% 40% 38%PCB 28 42% 37% na 38% 35% 29% 26% 29% 29%PCB 32 50% 46% 40% 39% 37% 40% 37% 33% 33%PCB 44 36% 30% 31% 30% 30% 32% 32% 32% 30%PCB 52 54% 41% 47% 44% 46% 42% 41% 39% 41%PCB 66 39% 31% 31% 28% 30% 32% 30% 30% 30%PCB 77 26% 19% 20% 20% 20% 21% 19% 18% 18%PCB 95 32% 25% 28% 28% 25% 27% 26% 27% 27%PCB 101 40% 32% 34% 33% 31% 33% 33% 33% 33%PCB 105 29% 24% 22% 23% 23% 25% 25% 24% 25%PCB 118 32% 24% 25% 26% 25% 27% 25% 26% 26%PCB 126 13% 9% 10% 11% 10% 11% 10% 10% 10%PCB 128 27% 20% 23% 23% 21% 23% 22% 22% 23%PCB 136 33% 26% 28% 29% 25% 28% 27% 27% 28%PCB 138 30% 23% 25% 26% 24% 26% 24% 24% 25%PCB 149 33% 26% 28% 28% 25% 28% 27% 27% 28%PCB 153 27% 21% 22% 22% 21% 23% 22% 22% 23%PCB 170 13% 11% 12% 15% 10% 12% 11% 11% 12%PCB 180 13% 10% 11% 13% 10% 12% 11% 11% 11%PCB 187 20% 16% 16% 18% 15% 17% 17% 16% 17%PCB 195 8.6% 5.9% 6.5% 9.2% 5.5% 7.6% 6.8% 6.2% 6.7%

230

APPENDIX

CHAPTER 5 AGING OF ORGANOCHLORINE PESTICIDES AND

POLYCHLORINATED BIPHENYLS IN MUCK SOIL: VOLATILIZATION,

BIOACCESSIBILITY AND DEGRADATION


Figure A5.2 Plateau log KSA of spiked OCPs and selected PCBs. Plateau Log KSA for

Indoor (IN) ranged from Day 195 to 550, Outdoor (OUT) Day 230 to 730

and Sterile (ST) Day 210 to 550.

Figure A5.3 HPCD extractability of selected OCPs and PCBs for Indoor (IN), Outdoor

(OUT) and Sterile (ST) soils.

Figure A5.4 Ln CSOIL of 13C6-α-HCH, ENDO I, PCB 8 and 28 over the aging time for

Indoor, Outdoor and Sterile soils. Day 60 to 230, 390 to 620 are the winter

periods for the Outdoor soils.

Table A5.1 Mean blanks and limits of detection (LOD) of PCBs in air.

Table A5.2 Soil bacteria colony forming units (CFU) for the Indoor, Outdoor and

Sterile soils.

Table A5.3 Log KSA for the Indoor soils. Plateau is mean log KSA from 195 to 550d.


Table A5.5 Log KSA for the Sterile soils. Plateau is mean log KSA from 210 to 550 d.

Table A5.6 Relative KSA of OCPs (Spiked/Native).

Table A5.7 Indoor plateau log KSA for native OCPs compared to literature values

obtained from Meijer et al., (2003). Soil from the same farm was used in

the Meijer study.

Table A5.8 HPCD extractability% for the Indoor soils.

Table A5.9 HPCD extractability% for the Outdoor soils.


231


0.0

1.0

2.0

3.0

4.0

0.000 0.200 0.400 0.600 0.800

Flow Rate (L min-1)

TC a

nd E

ndo

I

0

10

20

30

40

50

2 H6-

γ-H

CH

and

p,p

’-DD

T

TCEndo I2H6-γ-HCHp,p’-DDT

0.00

0.50

1.00

1.50

0.000 0.200 0.400 0.600 0.800

Flow Rate (L min-1)

CC

10

20

30

40

50

p,p’

-DD

E an

d o,

p’-D

DT

CCp,p’-DDEo,p’-DDT

0.0

1.0

2.0

3.0

4.0

0.000 0.200 0.400 0.600 0.800

Flow Rate (L min-1)

TC a

nd E

ndo

I

0

10

20

30

40

50

2 H6-

γ-H

CH

and

p,p

’-DD

T

TCEndo I2H6-γ-HCHp,p’-DDT

0.00

0.50

1.00

1.50

0.000 0.200 0.400 0.600 0.800

Flow Rate (L min-1)

CC

10

20

30

40

50

p,p’

-DD

E an

d o,

p’-D

DT

CCp,p’-DDEo,p’-DDT

232

Figure A5.2 Plateau log KSA of spiked OCPs and selected PCBs. Plateau Log KSA for Indoor

(IN) ranged from Day 195 to 550, Outdoor (OUT) Day 230 to 730 and Sterile (ST) Day 210 to

550.

6

7

8

9

13 C 6-α

-HCH

13 C 10-TC

13 C 10-TN

13 C 12-p,

p'-DDT

PCB 18

PCB 44

PCB 52

PCB 95

PCB 101

PCB 149

PCB 153

Plat

eau

Log

KSA

INDOOR OUTDOOR STERILE

6

7

8

6

7

8

9

13 C 6-α

-HCH

13 C 10-TC

13 C 10-TN

13 C 12-p,

p'-DDT

PCB 18

PCB 44

PCB 52

PCB 95

PCB 101

PCB 149

PCB 153

Plat

eau

Log

KSA

INDOOR OUTDOOR STERILE

233

Figure A5.3 HPCD extractability of selected OCPs and PCBs for Indoor (IN), Outdoor (OUT)

and Sterile (ST) soils.

HP

CD

ext

ract

abilit

y %

TC 13C10-TC

p,p'-DDT 13C12-p,p'-DDT

25%

35%

45%

55%

0 200 400 600

IN25%

35%

45%

55%

0 200 400 600

ST25%

35%

45%

55%

0 200 400 600 800

OUT

20%

30%

40%

50%

60%

70%

0 200 400 600

ST

20%

30%

40%

50%

60%

70%

0 200 400 600

IN

20%

30%

40%

50%

60%

0 200 400 600 800

OUT

20%

30%

40%

50%

60%

0 200 400 600

IN

20%

30%

40%

50%

60%

0 200 400 600

ST

20%

30%

40%

50%

60%

0 200 400 600 800

OUT

TC 13C10-TC TC 13C10-TC

p,p'-DDT 13C12-p,p'-DDT p,p'-DDT 13C12-p,p'-DDT




TC 13C10-TCTC 13C10-TC

p,p'-DDT 13C12-p,p'-DDTp,p'-DDT 13C12-p,p'-DDT

25%

35%

45%

55%

0 200 400 600

IN25%

35%

45%

55%

0 200 400 600

ST25%

35%

45%

55%

0 200 400 600 800

OUT

20%

30%

40%

50%

60%

70%

0 200 400 600

ST

20%

30%

40%

50%

60%

70%

0 200 400 600

IN

20%

30%

40%

50%

60%

0 200 400 600 800

OUT

20%

30%

40%

50%

60%

0 200 400 600

IN

20%

30%

40%

50%

60%

0 200 400 600

ST

20%

30%

40%

50%

60%

0 200 400 600 800

OUT

25%

35%

45%

55%

0 200 400 600

IN25%

35%

45%

55%

0 200 400 600

ST25%

35%

45%

55%

0 200 400 600 800

OUT

20%

30%

40%

50%

60%

70%

0 200 400 600

ST

20%

30%

40%

50%

60%

70%

0 200 400 600

IN

20%

30%

40%

50%

60%

0 200 400 600 800

OUT

20%

30%

40%

50%

60%

0 200 400 600

IN

20%

30%

40%

50%

60%

0 200 400 600

ST

20%

30%

40%

50%

60%

0 200 400 600 800

OUT

TC 13C10-TCTC 13C10-TC TC 13C10-TCTC 13C10-TC

p,p'-DDT 13C12-p,p'-DDTp,p'-DDT 13C12-p,p'-DDT p,p'-DDT 13C12-p,p'-DDTp,p'-DDT 13C12-p,p'-DDT




Time (d)

15%

25%

35%

45%

0 200 400 600

TN 13C10-TN

IN

15%

25%

35%

45%

0 200 400 600

ST

15%

25%

35%

45%

0 200 400 600 800

OUTTN 13C10-TN TN 13C10-TN

15%

25%

35%

45%

0 200 400 600

TN 13C10-TNTN 13C10-TN

IN

15%

25%

35%

45%

0 200 400 600

ST

15%

25%

35%

45%

0 200 400 600 800

OUTTN 13C10-TNTN 13C10-TN TN 13C10-TNTN 13C10-TN

234

Figure A5.4 Ln CSOIL of 13C6-α-HCH, ENDO I, PCB 8 and 28 over the aging time for Indoor,

Outdoor and Sterile soils. Day 60 to 230, 390 to 620 are the winter periods for the Outdoor soils.

13C6-α-HCH

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 200 400 600 800


Endo I

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 200 400 600 800


PCB 8

-3

-2

-1

0

1

2

3

4

0 200 400 600 800


PCB-28

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0 200 400 600 800


Time (d)

LnC

SO

IL

13C6-α-HCH

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 200 400 600 800


Endo I

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 200 400 600 800


PCB 8

-3

-2

-1

0

1

2

3

4

0 200 400 600 800


PCB-28

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0 200 400 600 800


Time (d)

LnC

SO

IL

235

Table A5.1 Mean blanks and limits of detection (LOD) of PCBs in air.

1 LOD = blank + 3*STDEV2 N = 19

Mean (ng/m3) STDEV

LOD1

(ng/m3) N2

PCB 8 0.17 0.28 1.00 6PCB 18 0.50 0.42 1.75 18PCB 28 0.63 0.46 1.99 18PCB 32 0.18 0.16 0.66 17PCB 44 0.31 0.26 1.08 17PCB 52 0.50 0.36 1.57 18PCB 66 0.18 0.14 0.61 16PCB 77 0.00 0.00 0.00 0PCB 95 0.44 0.41 1.66 15PCB 101 0.21 0.21 0.85 13PCB 105 0.00 0.00 0.00 0PCB 118 0.01 0.02 0.07 1PCB 126 0.00 0.00 0.00 0PCB 128 0.00 0.00 0.00 0PCB 136 0.00 0.00 0.00 0PCB 138 0.00 0.00 0.00 0PCB 149 0.00 0.01 0.04 1PCB 153 0.00 0.00 0.00 0PCB 180 0.00 0.00 0.00 0PCB 187 0.00 0.00 0.00 01 LOD = blank + 3*STDEV2 N = 19

Mean (ng/m3) STDEV

LOD1

(ng/m3) N2

PCB 8 0.17 0.28 1.00 6PCB 18 0.50 0.42 1.75 18PCB 28 0.63 0.46 1.99 18PCB 32 0.18 0.16 0.66 17PCB 44 0.31 0.26 1.08 17PCB 52 0.50 0.36 1.57 18PCB 66 0.18 0.14 0.61 16PCB 77 0.00 0.00 0.00 0PCB 95 0.44 0.41 1.66 15PCB 101 0.21 0.21 0.85 13PCB 105 0.00 0.00 0.00 0PCB 118 0.01 0.02 0.07 1PCB 126 0.00 0.00 0.00 0PCB 128 0.00 0.00 0.00 0PCB 136 0.00 0.00 0.00 0PCB 138 0.00 0.00 0.00 0PCB 149 0.00 0.01 0.04 1PCB 153 0.00 0.00 0.00 0PCB 180 0.00 0.00 0.00 0PCB 187 0.00 0.00 0.00 0

236

Table A5.2 Soil bacteria colony forming units (CFU) for the Indoor, Outdoor and Sterile soils.

Day Indoor Day Outdoor Day Sterile2 2.7E+05 2 3.5E+05 2 0

10 3.5E+05 20 6.8E+05 10 045 2.8E+05 60 3.0E+05 45 71090 3.1E+05 230 4.2E+05 90 0

135 1.7E+05 335 7.2E+05 135 1300195 2.3E+05 390 8.7E+05 210 170250 2.0E+05 620 8.4E+05 370 0390 2.9E+05 680 1.1E+06 550 890550 3.0E+05 730 8.2E+05

237

Table A5.3 Log KSA for the Indoor soils. Plateau is mean log KSA from 195 to 550d.

INDOOR 2 10 45 90 135 195 255 390 550 Plateau Plat STD

TC 7.49 7.55 7.58 7.60 7.60 7.64 7.70 7.67 7.68 7.67 0.02CC 7.57 7.63 7.68 7.69 7.68 7.71 7.79 7.76 7.76 7.75 0.03TN 7.47 7.59 7.57 7.55 7.58 7.60 7.67 7.65 7.66 7.65 0.03Endo I 7.27 7.22 7.32 7.82 8.04 8.19 8.24 8.58 8.66 8.42 0.24Endo II 8.72 8.59 8.67 8.81 8.95 8.92 9.02 9.02 9.18 9.04 0.11ESUL 9.49 9.43 9.31 9.73 9.73 9.82 9.91 9.82 9.99 9.89 0.08o,p' -DDE 7.62 7.79 7.67 7.74 7.80 7.79 7.83 7.81 7.81 7.81 0.02p,p' -DDE 8.00 8.09 8.09 8.05 8.07 8.09 8.18 8.14 8.10 8.13 0.04o,p' -DDD 8.39 8.48 8.53 8.50 8.54 8.53 8.58 8.54 8.60 8.56 0.03p,p' -DDD 8.62 8.71 8.70 8.77 8.86 8.93 8.86 8.90 8.90 0.03o,p' -DDT 8.24 8.23 8.28 8.36 8.39 8.40 8.47 8.42 8.46 8.44 0.03p,p' -DDT 8.93 8.83 9.04 9.10 9.10 9.11 9.19 9.16 9.18 9.16 0.0413C6-α-HCH 6.50 6.58 6.65 6.68 6.69 6.79 6.83 6.83 6.81 6.82 0.0213C10-TC 7.27 7.39 7.45 7.42 7.46 7.50 7.58 7.57 7.56 7.55 0.0313C10-TN 7.30 7.43 7.47 7.50 7.52 7.57 7.65 7.63 7.62 7.62 0.0313C12-p,p' -DDT 8.37 8.48 8.73 8.72 8.78 8.77 8.87 8.85 8.87 8.84 0.05PCB 8 6.10 6.31 6.56 nd nd nd nd nd ndPCB 18 6.30 6.49 6.61 6.54 6.58 6.69 6.74 6.66 6.67 6.69 0.04PCB 28 6.69 6.84 6.87 6.92 nd nd nd nd ndPCB 32 6.43 6.60 6.70 6.67 6.72 6.83 6.85 6.80 6.81 6.82 0.02PCB 44 7.17 7.32 7.29 7.32 7.36 7.46 7.45 7.42 7.44 7.44 0.02PCB 52 6.82 6.97 7.10 7.08 7.12 7.23 7.24 7.22 7.23 7.23 0.01PCB 66 7.34 7.47 7.59 7.64 7.69 7.80 7.77 7.81 7.84 7.80 0.03PCB 77 8.08 8.23 8.33 8.43 8.42 8.57 8.62 8.62 8.67 8.62 0.04PCB 95 7.16 7.30 7.44 7.44 7.48 7.59 7.58 7.59 7.60 7.59 0.01PCB 101 7.33 7.45 7.58 7.62 7.67 7.78 7.76 7.79 7.82 7.79 0.02PCB 105 8.13 8.37 8.49 8.51 8.52 8.63 8.61 8.69 8.70 8.66 0.04PCB 118 7.89 8.01 8.10 8.27 8.26 8.40 8.44 8.43 8.50 8.44 0.04PCB 128 8.34 8.60 9.05 8.77 8.79 8.88 8.91 8.93 8.98 8.92 0.04PCB 136 7.38 7.57 7.73 7.73 7.75 7.86 7.89 7.87 7.89 7.88 0.02PCB 138 8.09 8.29 8.39 8.44 8.46 8.59 8.57 8.66 8.74 8.64 0.08PCB 149 7.59 7.67 7.80 7.89 7.98 8.07 8.01 8.10 8.17 8.09 0.06PCB 153 7.86 8.05 8.16 8.25 8.23 8.39 8.40 8.44 8.48 8.43 0.04PCB 180 8.54 8.72 9.02 8.93 8.84 9.02 9.02 9.17 9.02 9.06 0.08PCB 187 8.04 8.21 8.44 8.50 8.51 8.61 8.70 8.69 8.74 8.69 0.06

ndnd

ndnd

nd

INDOOR 2 10 45 90 135 195 255 390 550 Plateau Plat STDTC 7.49 7.55 7.58 7.60 7.60 7.64 7.70 7.67 7.68 7.67 0.02CC 7.57 7.63 7.68 7.69 7.68 7.71 7.79 7.76 7.76 7.75 0.03TN 7.47 7.59 7.57 7.55 7.58 7.60 7.67 7.65 7.66 7.65 0.03Endo I 7.27 7.22 7.32 7.82 8.04 8.19 8.24 8.58 8.66 8.42 0.24Endo II 8.72 8.59 8.67 8.81 8.95 8.92 9.02 9.02 9.18 9.04 0.11ESUL 9.49 9.43 9.31 9.73 9.73 9.82 9.91 9.82 9.99 9.89 0.08o,p' -DDE 7.62 7.79 7.67 7.74 7.80 7.79 7.83 7.81 7.81 7.81 0.02p,p' -DDE 8.00 8.09 8.09 8.05 8.07 8.09 8.18 8.14 8.10 8.13 0.04o,p' -DDD 8.39 8.48 8.53 8.50 8.54 8.53 8.58 8.54 8.60 8.56 0.03p,p' -DDD 8.62 8.71 8.70 8.77 8.86 8.93 8.86 8.90 8.90 0.03o,p' -DDT 8.24 8.23 8.28 8.36 8.39 8.40 8.47 8.42 8.46 8.44 0.03p,p' -DDT 8.93 8.83 9.04 9.10 9.10 9.11 9.19 9.16 9.18 9.16 0.0413C6-α-HCH 6.50 6.58 6.65 6.68 6.69 6.79 6.83 6.83 6.81 6.82 0.0213C10-TC 7.27 7.39 7.45 7.42 7.46 7.50 7.58 7.57

INDOOR 2 10 45 90 135 195 255 390 550 Plateau Plat STDTC 7.49 7.55 7.58 7.60 7.60 7.64 7.70 7.67 7.68 7.67 0.02CC 7.57 7.63 7.68 7.69 7.68 7.71 7.79 7.76 7.76 7.75 0.03TN 7.47 7.59 7.57 7.55 7.58 7.60 7.67 7.65 7.66 7.65 0.03Endo I 7.27 7.22 7.32 7.82 8.04 8.19 8.24 8.58 8.66 8.42 0.24Endo II 8.72 8.59 8.67 8.81 8.95 8.92 9.02 9.02 9.18 9.04 0.11ESUL 9.49 9.43 9.31 9.73 9.73 9.82 9.91 9.82 9.99 9.89 0.08o,p' -DDE 7.62 7.79 7.67 7.74 7.80 7.79 7.83 7.81 7.81 7.81 0.02p,p' -DDE 8.00 8.09 8.09 8.05 8.07 8.09 8.18 8.14 8.10 8.13 0.04o,p' -DDD 8.39 8.48 8.53 8.50 8.54 8.53 8.58 8.54 8.60 8.56 0.03p,p' -DDD 8.62 8.71 8.70 8.77 8.86 8.93 8.86 8.90 8.90 0.03o,p' -DDT 8.24 8.23 8.28 8.36 8.39 8.40 8.47 8.42 8.46 8.44 0.03p,p' -DDT 8.93 8.83 9.04 9.10 9.10 9.11 9.19 9.16 9.18 9.16 0.0413C6-α-HCH 6.50 6.58 6.65 6.68 6.69 6.79 6.83 6.83 6.81 6.82 0.0213C10-TC 7.27 7.39 7.45 7.42 7.46 7.50 7.58 7.57 7.56 7.55 0.0313C10-TN 7.30 7.43 7.47 7.50 7.52 7.57 7.65 7.63 7.62 7.62 0.0313C12-p,p' -DDT 8.37 8.48 8.73 8.72 8.78 8.77 8.87 8.85 8.87 8.84 0.05PCB 8 6.10 6.31 6.56 nd nd nd nd nd ndPCB 18 6.30 6.49 6.61 6.54 6.58 6.69 6.74 6.66 6.67 6.69 0.04PCB 28 6.69 6.84 6.87 6.92 nd nd nd nd ndPCB 32 6.43 6.60 6.70 6.67 6.72 6.83 6.85 6.80 6.81 6.82 0.02PCB 44 7.17 7.32 7.29 7.32 7.36 7.46 7.45 7.42 7.44 7.44 0.02PCB 52 6.82 6.97 7.10 7.08 7.12 7.23 7.24 7.22 7.23 7.23 0.01PCB 66 7.34 7.47 7.59 7.64 7.69 7.80 7.77 7.81 7.84 7.80 0.03PCB 77 8.08 8.23 8.33 8.43 8.42 8.57 8.62 8.62 8.67 8.62 0.04PCB 95 7.16 7.30 7.44 7.44 7.48 7.59 7.58 7.59 7.60 7.59 0.01PCB 101 7.33 7.45 7.58 7.62 7.67 7.78 7.76 7.79 7.82 7.79 0.02PCB 105 8.13 8.37 8.49 8.51 8.52 8.63 8.61 8.69 8.70 8.66 0.04PCB 118 7.89 8.01 8.10 8.27 8.26 8.40 8.44 8.43 8.50 8.44 0.04PCB 128 8.34 8.60 9.05 8.77 8.79 8.88 8.91 8.93 8.98 8.92 0.04PCB 136 7.38 7.57 7.73 7.73 7.75 7.86 7.89 7.87 7.89 7.88 0.02PCB 138 8.09 8.29 8.39 8.44 8.46 8.59 8.57 8.66 8.74 8.64 0.08PCB 149 7.59 7.67 7.80 7.89 7.98 8.07 8.01 8.10 8.17 8.09 0.06PCB 153 7.86 8.05 8.16 8.25 8.23 8.39 8.40 8.44 8.48 8.43 0.04PCB 180 8.54 8.72 9.02 8.93 8.84 9.02 9.02 9.17 9.02 9.06 0.08PCB 187 8.04 8.21 8.44 8.50 8.51 8.61 8.70 8.69 8.74 8.69 0.06

ndnd

ndnd

nd

238


OUTDOOR 2 20 60 230 335 390 620 680 730 Plateau Plat STDTC 7.62 7.65 7.68 7.63 7.63 7.66 7.65 7.63 7.62 7.64 0.02CC 7.68 7.72 7.77 7.68 7.68 7.71 7.71 7.65 7.69 7.69 0.03TN 7.67 7.72 7.76 7.67 7.69 7.71 7.71 7.68 7.67 7.69 0.02Endo I 7.45 7.57 7.89 7.89 8.03 8.31 8.43 8.44 8.56 8.43 0.10Endo II 8.54 8.89 8.96 8.94 8.76 8.92 8.88 8.93 8.72 8.86 0.10ESUL 9.40 9.61 9.62 9.67 9.52 9.67 9.68 9.74 9.45 9.64 0.13o,p' -DDE 7.72 7.77 7.88 7.76 7.74 7.78 7.76 7.79 7.68 7.75 0.05p,p' -DDE 8.13 8.08 8.08 7.91 7.97 7.94 7.91 7.85 7.91 7.90 0.04o,p' -DDD 8.59 8.65 8.67 8.61 8.65 8.63 8.55 8.46 8.57 0.09p,p' -DDD 9.45 9.50 9.42 9.29 9.44 9.35 9.43 9.21 9.28 9.32 0.09o,p' -DDT 8.16 8.36 8.40 8.29 8.22 8.35 8.26 8.32 8.15 8.27 0.08p,p' -DDT 8.96 9.02 9.11 9.04 9.09 9.08 9.06 9.03 9.07 9.06 0.0213C6-α-HCH 6.73 6.84 7.05 6.93 6.92 7.06 7.08 7.08 7.03 7.06 0.0313C10-TC 7.43 7.54 7.61 7.59 7.60 7.60 7.58 7.60 7.58 7.59 0.0113C10-TN 7.54 7.57 7.71 7.63 7.65 7.68 7.68 7.63 7.65 7.66 0.0213C12-p,p' -DDT 8.15 8.60 8.77 8.66 8.72 8.72 8.70 8.66 8.68 8.69 0.03PCB 8 6.43 6.58 6.68 6.62 6.54 6.64 6.77 6.65 6.69 0.07PCB 18 6.62 6.72 6.81 6.74 6.70 6.74 6.80 6.68 6.78 6.75 0.05PCB 28 6.96 7.12 7.25 7.16 7.16 7.22 7.30 7.02 7.52 7.26 0.21PCB 32 6.72 6.84 6.95 6.87 6.85 6.89 6.96 6.79 6.99 6.90 0.09PCB 44 7.42 7.53 7.58 7.49 7.48 7.51 7.59 7.47 7.54 7.53 0.05PCB 52 7.12 7.23 7.31 7.22 7.20 7.24 7.30 7.17 7.24 7.23 0.05PCB 66 7.65 7.79 7.88 7.77 7.76 7.80 7.86 7.70 7.80 7.79 0.06PCB 77 8.34 8.51 8.58 8.46 8.46 8.50 8.54 8.41 8.48 8.48 0.06PCB 95 7.47 7.59 7.67 7.57 7.55 7.60 7.65 7.53 7.58 7.59 0.05PCB 101 7.70 7.83 7.90 7.81 7.79 7.84 7.89 7.76 7.82 7.83 0.05PCB 105 8.53 8.70 8.78 8.66 8.64 8.69 8.74 8.58 8.63 8.66 0.07PCB 118 8.21 8.39 8.46 8.34 8.33 8.37 8.42 8.27 8.33 8.35 0.06PCB 126 8.92 9.15 9.17 9.08 9.05 9.14 9.17 9.03 9.06 9.10 0.07PCB 128 8.73 8.99 9.01 8.89 8.88 8.97 8.96 8.88 8.92 8.93 0.04PCB 136 7.74 7.89 8.00 7.89 7.85 7.91 7.95 7.83 7.88 7.89 0.05PCB 138 8.49 8.70 8.77 8.65 8.64 8.70 8.76 8.60 8.65 8.68 0.07PCB 149 7.85 8.05 8.18 8.04 8.03 8.07 8.16 7.98 8.07 8.07 0.07PCB 153 8.25 8.44 8.52 8.41 8.40 8.45 8.49 8.35 8.41 8.43 0.06PCB 180 8.92 9.19 9.26 9.16 9.13 9.22 9.28 9.12 9.17 9.20 0.07PCB 187 8.36 8.59 8.58 8.57 8.63 8.68 8.53 8.59 8.61 0.06nd

nd

nd

OUTDOOR 2 20 60 230 335 390 620 680 730 Plateau Plat STDTC 7.62 7.65 7.68 7.63 7.63 7.66 7.65 7.63 7.62 7.64 0.02CC 7.68 7.72 7.77 7.68 7.68 7.71 7.71 7.65 7.69 7.69 0.03TN 7.67 7.72 7.76 7.67 7.69 7.71 7.71 7.68 7.67 7.69 0.02Endo I 7.45 7.57 7.89 7.89 8.03 8.31 8.43 8.44 8.56 8.43 0.10Endo II 8.54 8.89 8.96 8.94 8.76 8.92 8.88 8.93 8.72 8.86 0.10ESUL 9.40 9.61 9.62 9.67 9.52 9.67 9.68 9.74 9.45 9.64 0.13o,p' -DDE 7.72 7.77 7.88 7.76 7.74 7.78 7.76 7.79 7.68 7.75 0.05p,p' -DDE 8.13 8.08 8.08 7.91 7.97 7.94 7.91 7.85 7.91 7.90 0.04o,p' -DDD 8.59 8.65 8.67 8.61 8.65 8.63 8.55 8.46 8.57 0.09p,p' -DDD 9.45 9.50 9.42 9.29 9.44 9.35 9.43 9.21 9.28 9.32 0.09o,p' -DDT 8.16 8.36 8.40 8.29 8.22 8.35 8.26 8.32 8.15 8.27 0.08p,p' -DDT 8.96 9.02 9.11 9.04 9.09 9.08 9.06 9.03 9.07 9.06 0.0213C6-α-HCH 6.73 6.84 7.05 6.93 6.92 7.06 7.08 7.08 7.03 7.06 0.0313C10-TC 7.43 7.54 7.61 7.59 7.60 7.60 7.58 7.60 7.58 7.59 0.0113C10-TN 7.54 7.57 7.71 7.63 7.65 7.68 7.68 7.63 7.65 7.66 0.0213C12-p,p' -DDT 8.15 8.60 8.77 8.66 8.72 8.72 8.70 8.66 8.68 8.69 0.03PCB 8 6.43 6.58 6.68 6.62 6.54 6.64 6.77 6.65 6.69 0.07PCB 18 6.62 6.72 6.81 6.74 6.70 6.74 6.80 6.68 6.78 6.75 0.05PCB 28 6.96 7.12 7.25 7.16 7.16 7.22 7.30 7.02 7.52 7.26 0.21PCB 32 6.72 6.84 6.95 6.87 6.85 6.89 6.96 6.79 6.99 6.90 0.09PCB 44 7.42 7.53 7.58 7.49 7.48 7.51 7.59 7.47 7.54 7.53 0.05PCB 52 7.12 7.23 7.31 7.22 7.20 7.24 7.30 7.17 7.24 7.23 0.05PCB 66 7.65 7.79 7.88 7.77 7.76 7.80 7.86 7.70 7.80 7.79 0.06PCB 77 8.34 8.51 8.58 8.46 8.46 8.50 8.54 8.41 8.48 8.48 0.06PCB 95 7.47 7.59 7.67 7.57 7.55 7.60 7.65 7.53 7.58 7.59 0.05PCB 101 7.70 7.83 7.90 7.81 7.79 7.84 7.89 7.76 7.82 7.83 0.05PCB 105 8.53 8.70 8.78 8.66 8.64 8.69 8.74 8.58 8.63 8.66 0.07PCB 118 8.21 8.39 8.46 8.34 8.33 8.37 8.42 8.27 8.33 8.35 0.06PCB 126 8.92 9.15 9.17 9.08 9.05 9.14 9.17 9.03 9.06 9.10 0.07PCB 128 8.73 8.99 9.01 8.89 8.88 8.97 8.96 8.88 8.92 8.93 0.04PCB 136 7.74 7.89 8.00 7.89 7.85 7.91 7.95 7.83 7.88 7.89 0.05PCB 138 8.49 8.70 8.77 8.65 8.64 8.70 8.76 8.60 8.65 8.68 0.07PCB 149 7.85 8.05 8.18 8.04 8.03 8.07 8.16 7.98 8.07 8.07 0.07PCB 153 8.25 8.44 8.52 8.41 8.40 8.45 8.49 8.35 8.41 8.43 0.06PCB 180 8.92 9.19 9.26 9.16 9.13 9.22 9.28 9.12 9.17 9.20 0.07PCB 187 8.36 8.59 8.58 8.57 8.63 8.68 8.53 8.59 8.61 0.06

OUTDOOR 2 20 60 230 335 390 620 680 730 Plateau Plat STDTC 7.62 7.65 7.68 7.63 7.63 7.66 7.65 7.63 7.62 7.64 0.02CC 7.68 7.72 7.77 7.68 7.68 7.71 7.71 7.65 7.69 7.69 0.03TN 7.67 7.72 7.76 7.67 7.69 7.71 7.71 7.68 7.67 7.69 0.02Endo I 7.45 7.57 7.89 7.89 8.03 8.31 8.43 8.44 8.56 8.43 0.10Endo II 8.54 8.89 8.96 8.94 8.76 8.92 8.88 8.93 8.72 8.86 0.10ESUL 9.40 9.61 9.62 9.67 9.52 9.67 9.68 9.74 9.45 9.64 0.13o,p' -DDE 7.72 7.77 7.88 7.76 7.74 7.78 7.76 7.79 7.68 7.75 0.05p,p' -DDE 8.13 8.08 8.08 7.91 7.97 7.94 7.91 7.85 7.91 7.90 0.04o,p' -DDD 8.59 8.65 8.67 8.61 8.65 8.63 8.55 8.46 8.57 0.09p,p' -DDD 9.45 9.50 9.42 9.29 9.44 9.35 9.43 9.21 9.28 9.32 0.09o,p' -DDT 8.16 8.36 8.40 8.29 8.22 8.35 8.26 8.32 8.15 8.27 0.08p,p' -DDT 8.96 9.02 9.11 9.04 9.09 9.08 9.06 9.03 9.07 9.06 0.0213C6-α-HCH 6.73 6.84 7.05 6.93 6.92 7.06 7.08 7.08 7.03 7.06 0.0313C10-TC 7.43 7.54 7.61 7.59 7.60 7.60 7.58 7.60 7.58

OUTDOOR 2 20 60 230 335 390 620 680 730 Plateau Plat STDTC 7.62 7.65 7.68 7.63 7.63 7.66 7.65 7.63 7.62 7.64 0.02CC 7.68 7.72 7.77 7.68 7.68 7.71 7.71 7.65 7.69 7.69 0.03TN 7.67 7.72 7.76 7.67 7.69 7.71 7.71 7.68 7.67 7.69 0.02Endo I 7.45 7.57 7.89 7.89 8.03 8.31 8.43 8.44 8.56 8.43 0.10Endo II 8.54 8.89 8.96 8.94 8.76 8.92 8.88 8.93 8.72 8.86 0.10ESUL 9.40 9.61 9.62 9.67 9.52 9.67 9.68 9.74 9.45 9.64 0.13o,p' -DDE 7.72 7.77 7.88 7.76 7.74 7.78 7.76 7.79 7.68 7.75 0.05p,p' -DDE 8.13 8.08 8.08 7.91 7.97 7.94 7.91 7.85 7.91 7.90 0.04o,p' -DDD 8.59 8.65 8.67 8.61 8.65 8.63 8.55 8.46 8.57 0.09p,p' -DDD 9.45 9.50 9.42 9.29 9.44 9.35 9.43 9.21 9.28 9.32 0.09o,p' -DDT 8.16 8.36 8.40 8.29 8.22 8.35 8.26 8.32 8.15 8.27 0.08p,p' -DDT 8.96 9.02 9.11 9.04 9.09 9.08 9.06 9.03 9.07 9.06 0.0213C6-α-HCH 6.73 6.84 7.05 6.93 6.92 7.06 7.08 7.08 7.03 7.06 0.0313C10-TC 7.43 7.54 7.61 7.59 7.60 7.60 7.58 7.60 7.58 7.59 0.0113C10-TN 7.54 7.57 7.71 7.63 7.65 7.68 7.68 7.63 7.65 7.66 0.0213C12-p,p' -DDT 8.15 8.60 8.77 8.66 8.72 8.72 8.70 8.66 8.68 8.69 0.03PCB 8 6.43 6.58 6.68 6.62 6.54 6.64 6.77 6.65 6.69 0.07PCB 18 6.62 6.72 6.81 6.74 6.70 6.74 6.80 6.68 6.78 6.75 0.05PCB 28 6.96 7.12 7.25 7.16 7.16 7.22 7.30 7.02 7.52 7.26 0.21PCB 32 6.72 6.84 6.95 6.87 6.85 6.89 6.96 6.79 6.99 6.90 0.09PCB 44 7.42 7.53 7.58 7.49 7.48 7.51 7.59 7.47 7.54 7.53 0.05PCB 52 7.12 7.23 7.31 7.22 7.20 7.24 7.30 7.17 7.24 7.23 0.05PCB 66 7.65 7.79 7.88 7.77 7.76 7.80 7.86 7.70 7.80 7.79 0.06PCB 77 8.34 8.51 8.58 8.46 8.46 8.50 8.54 8.41 8.48 8.48 0.06PCB 95 7.47 7.59 7.67 7.57 7.55 7.60 7.65 7.53 7.58 7.59 0.05PCB 101 7.70 7.83 7.90 7.81 7.79 7.84 7.89 7.76 7.82 7.83 0.05PCB 105 8.53 8.70 8.78 8.66 8.64 8.69 8.74 8.58 8.63 8.66 0.07PCB 118 8.21 8.39 8.46 8.34 8.33 8.37 8.42 8.27 8.33 8.35 0.06PCB 126 8.92 9.15 9.17 9.08 9.05 9.14 9.17 9.03 9.06 9.10 0.07PCB 128 8.73 8.99 9.01 8.89 8.88 8.97 8.96 8.88 8.92 8.93

7.59 0.0113C10-TN 7.54 7.57 7.71 7.63 7.65 7.68 7.68 7.63 7.65 7.66 0.0213C12-p,p' -DDT 8.15 8.60 8.77 8.66 8.72 8.72 8.70 8.66 8.68 8.69 0.03PCB 8 6.43 6.58 6.68 6.62 6.54 6.64 6.77 6.65 6.69 0.07PCB 18 6.62 6.72 6.81 6.74 6.70 6.74 6.80 6.68 6.78 6.75 0.05PCB 28 6.96 7.12 7.25 7.16 7.16 7.22 7.30 7.02 7.52 7.26 0.21PCB 32 6.72 6.84 6.95 6.87 6.85 6.89 6.96 6.79 6.99 6.90 0.09PCB 44 7.42 7.53 7.58 7.49 7.48 7.51 7.59 7.47 7.54 7.53 0.05PCB 52 7.12 7.23 7.31 7.22 7.20 7.24 7.30 7.17 7.24 7.23 0.05PCB 66 7.65 7.79 7.88 7.77 7.76 7.80 7.86 7.70 7.80 7.79 0.06PCB 77 8.34 8.51 8.58 8.46 8.46 8.50 8.54 8.41 8.48 8.48 0.06PCB 95 7.47 7.59 7.67 7.57 7.55 7.60 7.65 7.53 7.58 7.59 0.05PCB 101 7.70 7.83 7.90 7.81 7.79 7.84 7.89 7.76 7.82 7.83 0.05PCB 105 8.53 8.70 8.78 8.66 8.64 8.69 8.74 8.58 8.63 8.66 0.07PCB 118 8.21 8.39 8.46 8.34 8.33 8.37 8.42 8.27 8.33 8.35 0.06PCB 126 8.92 9.15 9.17 9.08 9.05 9.14 9.17 9.03 9.06 9.10 0.07PCB 128 8.73 8.99 9.01 8.89 8.88 8.97 8.96 8.88 8.92 8.93 0.04PCB 136 7.74 7.89 8.00 7.89 7.85 7.91 7.95 7.83 7.88 7.89 0.05PCB 138 8.49 8.70 8.77 8.65 8.64 8.70 8.76 8.60 8.65 8.68 0.07PCB 149 7.85 8.05 8.18 8.04 8.03 8.07 8.16 7.98 8.07 8.07 0.07PCB 153 8.25 8.44 8.52 8.41 8.40 8.45 8.49 8.35 8.41 8.43 0.06PCB 180 8.92 9.19 9.26 9.16 9.13 9.22 9.28 9.12 9.17 9.20 0.07PCB 187 8.36 8.59 8.58 8.57 8.63 8.68 8.53 8.59 8.61 0.06nd

nd

nd

239

Table A5.5 Log KSA for the Sterile soils. Plateau is mean log KSA from 210 to 550 d.

STERILE 2 10 45 90 135 210 370 550 Plateau Plat STDTC 7.75 7.67 7.75 7.66 7.62 7.64 7.72 7.69 7.68 0.04CC 7.79 7.72 7.80 7.69 7.65 7.69 7.77 7.75 7.74 0.04TN 7.84 7.77 7.84 7.74 7.70 7.73 7.82 7.78 7.78 0.05Endo I 7.81 7.74 7.82 7.59 7.59 7.66 7.69 7.71 7.69 0.02Endo II 8.99 8.93 9.05 8.75 8.67 8.75 8.82 8.91 8.82 0.08ESUL 9.70 9.68 9.80 9.49 9.44 9.52 9.54 9.50 9.52 0.02o,p' -DDE 7.88 7.81 7.91 7.78 7.77 7.79 7.87 7.87 7.85 0.05p,p' -DDE 8.05 7.98 8.07 7.95 7.89 7.89 7.99 7.94 7.94 0.05o,p' -DDD 8.55 8.38 8.50 8.33 8.22 8.29 8.36 8.26 8.31 0.05p,p' -DDD 9.05 8.95 9.04 8.97 8.86 8.97 8.99 9.01 8.99 0.02o,p' -DDT 8.17 8.24 8.29 7.98 7.93 7.95 8.14 8.14 8.08 0.11p,p' -DDT 8.91 8.91 8.94 8.76 8.80 8.83 9.02 9.00 8.95 0.1113C6-α-HCH 6.94 6.90 6.94 6.89 6.81 6.92 6.88 6.88 6.89 0.0313C10-TC 7.70 7.64 7.71 7.63 7.54 7.62 7.65 7.60 7.62 0.0313C10-TN 7.76 7.72 7.80 7.70 7.62 7.71 7.73 7.70 7.71 0.0113C12-p,p' -DDT 8.67 8.57 8.64 8.55 8.59 8.69 8.62 8.71 8.67 0.05PCB 8 6.30 6.25 6.29 6.33 6.34 6.31 6.25 6.26 6.27 0.03PCB 18 6.52 6.46 6.46 6.50 6.48 6.46 6.43 6.42 6.44 0.02PCB 28 6.85 6.82 6.85 6.83 6.86 6.81 6.84 6.78 6.81 0.03PCB 32 6.65 6.59 6.60 6.62 6.61 6.58 6.58 6.55 6.57 0.02PCB 44 7.26 7.22 7.23 7.22 7.20 7.18 7.23 7.17 7.20 0.03PCB 52 7.02 6.97 6.98 6.97 6.95 6.93 6.98 6.92 6.94 0.03PCB 66 7.50 7.48 7.51 7.46 7.43 7.42 7.50 7.43 7.45 0.05PCB 77 8.04 8.03 8.09 8.02 7.92 7.95 8.06 7.99 8.00 0.06PCB 95 7.10 7.07 7.08 7.07 7.03 7.02 7.08 7.01 7.04 0.04PCB 101 7.18 7.16 7.18 7.15 7.12 7.11 7.19 7.11 7.14 0.04PCB 105 8.07 8.07 8.11 8.02 7.95 7.96 8.11 8.01 8.03 0.07PCB 118 7.76 7.76 7.80 7.74 7.65 7.68 7.79 7.71 7.73 0.06PCB 126 8.47 8.50 8.56 8.49 8.38 8.42 8.51 8.47 8.47 0.04PCB 128 8.61 8.65 8.68 8.64 8.55 8.60 8.73 8.63 8.65 0.07PCB 136 7.62 7.61 7.63 7.61 7.52 7.54 7.63 7.56 7.58 0.05PCB 138 7.97 8.00 8.05 7.99 7.90 7.94 8.05 7.98 7.99 0.05PCB 149 7.81 7.79 7.84 7.77 7.73 7.73 7.89 7.81 7.81 0.08PCB 153 7.65 7.68 7.72 7.67 7.58 7.62 7.73 7.65 7.67 0.06PCB 180 8.22 8.27 8.36 8.31 8.22 8.29 8.35 8.33 8.32 0.03PCB 187 8.30 8.36 8.38 8.29 8.32 8.43 8.37 8.37 0.05nd

STERILE 2 10 45 90 135 210 370 550 Plateau Plat STDTC 7.75 7.67 7.75 7.66 7.62 7.64 7.72 7.69 7.68 0.04CC 7.79 7.72 7.80 7.69 7.65 7.69 7.77 7.75 7.74 0.04TN 7.84 7.77 7.84 7.74 7.70 7.73 7.82 7.78 7.78 0.05Endo I 7.81 7.74 7.82 7.59 7.59 7.66 7.69 7.71 7.69 0.02Endo II 8.99 8.93 9.05 8.75 8.67 8.75 8.82 8.91 8.82 0.08ESUL 9.70 9.68 9.80 9.49 9.44 9.52 9.54 9.50 9.52 0.02o,p' -DDE 7.88 7.81 7.91 7.78 7.77 7.79 7.87 7.87 7.85 0.05p,p' -DDE 8.05 7.98 8.07 7.95 7.89 7.89 7.99 7.94 7.94 0.05o,p' -DDD 8.55 8.38 8.50 8.33 8.22 8.29 8.36 8.26 8.31 0.05p,p' -DDD 9.05 8.95 9.04 8.97 8.86 8.97 8.99 9.01 8.99 0.02o,p' -DDT 8.17 8.24 8.29 7.98 7.93 7.95 8.14 8.14 8.08 0.11p,p' -DDT 8.91 8.91 8.94 8.76 8.80 8.83 9.02 9.00 8.95 0.1113C6-α-HCH 6.94 6.90 6.94 6.89 6.81 6.92 6.88 6.88 6.89 0.0313C10-TC 7.70 7.64 7.71 7.63 7.54 7.62 7.65 7.60 7.62 0.0313C10-TN 7.76 7.72 7.80 7.70 7.62 7.71 7.73 7.70 7.71

STERILE 2 10 45 90 135 210 370 550 Plateau Plat STDTC 7.75 7.67 7.75 7.66 7.62 7.64 7.72 7.69 7.68 0.04CC 7.79 7.72 7.80 7.69 7.65 7.69 7.77 7.75 7.74 0.04TN 7.84 7.77 7.84 7.74 7.70 7.73 7.82 7.78 7.78 0.05Endo I 7.81 7.74 7.82 7.59 7.59 7.66 7.69 7.71 7.69 0.02Endo II 8.99 8.93 9.05 8.75 8.67 8.75 8.82 8.91 8.82 0.08ESUL 9.70 9.68 9.80 9.49 9.44 9.52 9.54 9.50 9.52 0.02o,p' -DDE 7.88 7.81 7.91 7.78 7.77 7.79 7.87 7.87 7.85 0.05p,p' -DDE 8.05 7.98 8.07 7.95 7.89 7.89 7.99 7.94 7.94 0.05o,p' -DDD 8.55 8.38 8.50 8.33 8.22 8.29 8.36 8.26 8.31 0.05p,p' -DDD 9.05 8.95 9.04 8.97 8.86 8.97 8.99 9.01 8.99 0.02o,p' -DDT 8.17 8.24 8.29 7.98 7.93 7.95 8.14 8.14 8.08 0.11p,p' -DDT 8.91 8.91 8.94 8.76 8.80 8.83 9.02 9.00 8.95 0.1113C6-α-HCH 6.94 6.90 6.94 6.89 6.81 6.92 6.88 6.88 6.89 0.0313C10-TC 7.70 7.64 7.71 7.63 7.54 7.62 7.65 7.60 7.62 0.0313C10-TN 7.76 7.72 7.80 7.70 7.62 7.71 7.73 7.70 7.71 0.0113C12-p,p' -DDT 8.67 8.57 8.64 8.55 8.59 8.69 8.62 8.71 8.67 0.05PCB 8 6.30 6.25 6.29 6.33 6.34 6.31 6.25 6.26 6.27 0.03PCB 18 6.52 6.46 6.46 6.50 6.48 6.46 6.43 6.42 6.44 0.02PCB 28 6.85 6.82 6.85 6.83 6.86 6.81 6.84 6.78 6.81 0.03PCB 32 6.65 6.59 6.60 6.62 6.61 6.58 6.58 6.55 6.57 0.02PCB 44 7.26 7.22 7.23 7.22 7.20 7.18 7.23 7.17 7.20 0.03PCB 52 7.02 6.97 6.98 6.97 6.95 6.93 6.98 6.92 6.94 0.03PCB 66 7.50 7.48 7.51 7.46 7.43 7.42 7.50 7.43 7.45 0.05PCB 77 8.04 8.03 8.09 8.02 7.92 7.95 8.06 7.99 8.00 0.06PCB 95 7.10 7.07 7.08 7.07 7.03 7.02 7.08 7.01 7.04 0.04PCB 101 7.18 7.16 7.18 7.15 7.12 7.11 7.19 7.11 7.14 0.04PCB 105 8.07 8.07 8.11 8.02 7.95 7.96 8.11 8.01 8.03 0.07PCB 118 7.76 7.76 7.80 7.74 7.65 7.68 7.79 7.71 7.73 0.06PCB 126 8.47 8.50 8.56 8.49 8.38 8.42 8.51 8.47 8.47 0.04PCB 128 8.61 8.65 8.68 8.64 8.55 8.60 8.73 8.63 8.65 0.07PCB 136 7.62 7.61 7.63 7.61 7.52 7.54 7.63 7.56 7.58 0.05PCB 138 7.97 8.00 8.05 7.99 7.90 7.94 8.05 7.98 7.99 0.05PCB 149 7.81 7.79 7.84 7.77 7.73 7.73

0.0113C12-p,p' -DDT 8.67 8.57 8.64 8.55 8.59 8.69 8.62 8.71 8.67 0.05PCB 8 6.30 6.25 6.29 6.33 6.34 6.31 6.25 6.26 6.27 0.03PCB 18 6.52 6.46 6.46 6.50 6.48 6.46 6.43 6.42 6.44 0.02PCB 28 6.85 6.82 6.85 6.83 6.86 6.81 6.84 6.78 6.81 0.03PCB 32 6.65 6.59 6.60 6.62 6.61 6.58 6.58 6.55 6.57 0.02PCB 44 7.26 7.22 7.23 7.22 7.20 7.18 7.23 7.17 7.20 0.03PCB 52 7.02 6.97 6.98 6.97 6.95 6.93 6.98 6.92 6.94 0.03PCB 66 7.50 7.48 7.51 7.46 7.43 7.42 7.50 7.43 7.45 0.05PCB 77 8.04 8.03 8.09 8.02 7.92 7.95 8.06 7.99 8.00 0.06PCB 95 7.10 7.07 7.08 7.07 7.03 7.02 7.08 7.01 7.04 0.04PCB 101 7.18 7.16 7.18 7.15 7.12 7.11 7.19 7.11 7.14 0.04PCB 105 8.07 8.07 8.11 8.02 7.95 7.96 8.11 8.01 8.03 0.07PCB 118 7.76 7.76 7.80 7.74 7.65 7.68 7.79 7.71 7.73 0.06PCB 126 8.47 8.50 8.56 8.49 8.38 8.42 8.51 8.47 8.47 0.04PCB 128 8.61 8.65 8.68 8.64 8.55 8.60 8.73 8.63 8.65 0.07PCB 136 7.62 7.61 7.63 7.61 7.52 7.54 7.63 7.56 7.58 0.05PCB 138 7.97 8.00 8.05 7.99 7.90 7.94 8.05 7.98 7.99 0.05PCB 149 7.81 7.79 7.84 7.77 7.73 7.73 7.89 7.81 7.81 0.08PCB 153 7.65 7.68 7.72 7.67 7.58 7.62 7.73 7.65 7.67 0.06PCB 180 8.22 8.27 8.36 8.31 8.22 8.29 8.35 8.33 8.32 0.03PCB 187 8.30 8.36 8.38 8.29 8.32 8.43 8.37 8.37 0.05nd

240

Table A5.6 Relative KSA of OCPs (Spiked/Native).

Indoor

0.0

0.4

0.8

1.2

0 200 400 600

Ksa

Spi

ke/N

ativ

e

TCTNp,p'-DDT

Outdoor

0.0

0.4

0.8

1.2

0 200 400 600 800

Ksa

Spi

ke/N

ativ

e

TCTNp,p'-DDT

Sterile

0.0

0.4

0.8

1.2

0 200 400 600

Ksa

Spi

ke/N

ativ

e

TCTNp,p'-DDT

Indoor 2 10 45 90 135 195 255 390 550TC 0.60 0.70 0.74 0.66 0.73 0.73 0.77 0.78 0.77TN 0.68 0.70 0.79 0.89 0.88 0.93 0.95 0.96 0.91p,p'-DDT 0.28 0.44 0.49 0.41 0.47 0.45 0.48 0.49 0.49

Outdoor 2 20 60 230 335 390 620 680 730TC 0.64 0.78 0.84 0.90 0.92 0.88 0.86 0.93 0.93TN 0.74 0.71 0.88 0.90 0.92 0.92 0.95 0.91 0.96p,p'-DDT 0.16 0.38 0.46 0.41 0.44 0.44 0.43 0.43 0.41

Sterile 2 10 45 90 135 210 370 550TC 0.89 0.93 0.90 0.94 0.84 0.95 0.85 0.81TN 0.85 0.88 0.91 0.92 0.82 0.96 0.81 0.83p,p'-DDT 0.57 0.45 0.50 0.62 0.61 0.73 0.40 0.51

Time (d)Time (d)

Time (d)

Indoor

0.0

0.4

0.8

1.2

0 200 400 600

Ksa

Spi

ke/N

ativ

e

TCTNp,p'-DDT

Outdoor

0.0

0.4

0.8

1.2

0 200 400 600 800

Ksa

Spi

ke/N

ativ

e

TCTNp,p'-DDT

Sterile

0.0

0.4

0.8

1.2

0 200 400 600

Ksa

Spi

ke/N

ativ

e

TCTNp,p'-DDT







Time (d)Time (d)

Time (d)

241

Table A5.7 Indoor plateau log KSA for native OCPs compared to literature values obtained from

Meijer et al., (2003). Soil from the same farm was used in the Meijer study.

log KSA This Study Meijer et al., 2003

TN 7.62 7.32o,p'-DDE 7.80 7.53p,p'-DDE 8.11 7.97o,p'-DDD 8.55 8.74p,p'-DDD 8.86 8.82o,p'-DDT 8.42 8.27

log KSA This Study Meijer et al., 2003

TN 7.62 7.32o,p'-DDE 7.80 7.53p,p'-DDE 8.11 7.97o,p'-DDD 8.55 8.74p,p'-DDD 8.86 8.82o,p'-DDT 8.42 8.27

242

Table A5.8 HPCD extractability% for the Indoor soils.

INDOOR 2 10 45 90 135 195 255 390 550TC 39 36 36 33 31 32 31 32 32CC 48 44 44 39 37 39 38 39 38TN 28 25 25 24 22 23 23 24 24Endo I 44 41 28 28 27 25 18 0 13Endo II 23 28 8 24 17 10 26 6 10ESUL 39 34 35 29 31 28 22 17 15o,p' -DDE 34 30 33 31 29 31 28 31 33p,p' -DDE 27 23 24 24 21 24 22 24 25o,p' -DDD 36 30 32 28 25 27 25 24 24p,p' -DDD 35 26 29 25 22 23 23 20 21o,p' -DDT 42 32 38 37 34 36 33 33 34p,p' -DDT 36 31 33 34 30 31 30 30 3113C6-α-HCH 46 48 45 39 39 36 37 35 3213C10-TC 45 41 41 36 34 34 34 34 3313C10-TN 34 29 28 27 24 26 25 24 2513C12-p,p' -DDT 51 44 46 45 40 41 38 39 40PCB 8 55 51 41 35 37 39 41 38 0PCB 18 54 51 40 39 42 43 43 40 38PCB 28 42 37 0 38 35 29 27 29 29PCB 32 50 46 40 39 36 37 37 33 33PCB 44 36 30 31 30 30 32 32 32 30PCB 52 54 41 47 44 41 39 41 39 41PCB 66 39 31 31 28 30 32 30 30 30PCB 77 26 19 20 20 20 21 19 18 18PCB 95 32 25 28 28 25 27 26 27 27PCB 101 40 32 34 33 31 33 33 33 33PCB 105 29 24 22 23 23 25 25 24 25PCB 118 32 24 25 26 25 27 25 26 26PCB 126 13 9 10 11 10 11 10 10 10PCB 128 27 20 23 23 21 23 22 22 23PCB 136 33 26 28 29 25 28 27 27 28PCB 138 30 23 25 26 24 26 24 24 25PCB 149 33 26 28 28 25 28 27 27 28PCB 153 27 21 22 22 21 23 22 22 23PCB 170 13 11 12 15 10 12 11 11 12PCB 180 13 10 11 13 10 12 11 11 11PCB 187 20 16 16 18 15 17 17 16 17

INDOOR 2 10 45 90 135 195 255 390 550TC 39 36 36 33 31 32 31 32 32CC 48 44 44 39 37 39 38 39 38TN 28 25 25 24 22 23 23 24 24Endo I 44 41 28 28 27 25 18 0 13Endo II 23 28 8 24 17 10 26 6 10ESUL 39 34 35 29 31 28 22 17 15o,p' -DDE 34 30 33 31 29 31 28 31 33p,p' -DDE 27 23 24 24 21 24 22 24 25o,p' -DDD 36 30 32 28 25 27 25 24 24p,p' -DDD 35 26 29 25 22 23 23 20 21o,p' -DDT 42 32 38 37 34 36 33 33 34p,p' -DDT 36 31 33 34 30 31 30 30 3113C6-α-HCH 46 48 45 39 39 36 37 35 3213C10-TC 45 41 41 36 34 34 34 34 3313C10-TN 34 29 28 27 24 26 25 24 2513C12-p,p' -DDT 51 44 46 45 40 41 38 39 40PCB 8

INDOOR 2 10 45 90 135 195 255 390 550TC 39 36 36 33 31 32 31 32 32CC 48 44 44 39 37 39 38 39 38TN 28 25 25 24 22 23 23 24 24Endo I 44 41 28 28 27 25 18 0 13Endo II 23 28 8 24 17 10 26 6 10ESUL 39 34 35 29 31 28 22 17 15o,p' -DDE 34 30 33 31 29 31 28 31 33p,p' -DDE 27 23 24 24 21 24 22 24 25o,p' -DDD 36 30 32 28 25 27 25 24 24p,p' -DDD 35 26 29 25 22 23 23 20 21o,p' -DDT 42 32 38 37 34 36 33 33 34p,p' -DDT 36 31 33 34 30 31 30 30 3113C6-α-HCH 46 48 45 39 39 36 37 35 3213C10-TC 45 41 41 36 34 34 34 34 3313C10-TN 34 29 28 27 24 26 25 24 2513C12-p,p' -DDT 51 44 46 45 40 41 38 39 40PCB 8 55 51 41 35 37 39 41 38 0PCB 18 54 51 40 39 42 43 43 40 38PCB 28 42 37 0 38 35 29 27 29 29PCB 32 50 46 40 39 36 37 37 33 33PCB 44 36 30 31 30 30 32 32 32 30PCB 52 54 41 47 44 41 39 41 39 41PCB 66 39 31 31 28 30 32 30 30 30PCB 77 26 19 20 20 20 21 19 18 18PCB 95 32 25 28 28 25 27 26 27 27PCB 101 40 32 34 33 31 33 33 33 33PCB 105 29 24 22 23 23 25 25 24 25PCB 118 32 24 25 26 25 27 25 26 26PCB 126 13 9 10 11 10 11 10 10 10PCB 128 27 20 23 23 21 23 22 22 23PCB 136 33 26 28 29 25 28 27 27 28PCB 138 30 23 25 26 24 26 24 24 25PCB 149 33 26 28 28 25 28 27 27 28PCB 153 27 21 22 22 21 23 22 22 23PCB 170 13 11 12 15 10 12 11 11 12PCB 180 13 10 11 13 10 12 11 11 11PCB 187

55 51 41 35 37 39 41 38 0PCB 18 54 51 40 39 42 43 43 40 38PCB 28 42 37 0 38 35 29 27 29 29PCB 32 50 46 40 39 36 37 37 33 33PCB 44 36 30 31 30 30 32 32 32 30PCB 52 54 41 47 44 41 39 41 39 41PCB 66 39 31 31 28 30 32 30 30 30PCB 77 26 19 20 20 20 21 19 18 18PCB 95 32 25 28 28 25 27 26 27 27PCB 101 40 32 34 33 31 33 33 33 33PCB 105 29 24 22 23 23 25 25 24 25PCB 118 32 24 25 26 25 27 25 26 26PCB 126 13 9 10 11 10 11 10 10 10PCB 128 27 20 23 23 21 23 22 22 23PCB 136 33 26 28 29 25 28 27 27 28PCB 138 30 23 25 26 24 26 24 24 25PCB 149 33 26 28 28 25 28 27 27 28PCB 153 27 21 22 22 21 23 22 22 23PCB 170 13 11 12 15 10 12 11 11 12PCB 180 13 10 11 13 10 12 11 11 11PCB 187 20 16 16 18 15 17 17 16 17

243

Table A5.9 HPCD extractability% for the Outdoor soils.

OUTDOOR 2 60 230 335 390 620 680 730TC 39 40 36 36 36 36 36 35CC 48 47 43 43 42 40 42 40TN 28 27 27 27 28 30 28 28Endo I 44 46 36 42 6 19 25 6Endo II 23 30 10 1 0 19 2 0ESUL 39 30 27 28 23 24 25 21o,p' -DDE 34 21 22 18 20 20 20 19p,p' -DDE 27 30 31 30 32 33 31 36o,p' -DDD 36 35 34 27 28 30 30 29p,p' -DDD 35 34 32 24 27 29 29 28o,p' -DDT 42 37 35 37 36 37 36 36p,p' -DDT 36 25 24 24 24 23 22 2113C6-α-HCH 46 38 37 25 24 0 0 013C10-TC 45 44 40 39 39 37 39 3813C10-TN 34 30 29 30 29 30 30 2913C12-p,p' -DDT 51 37 36 35 34 33 32 30PCB 8 55 45 42 37 37 26 29 0PCB 18 54 44 38 36 37 36 38 36PCB 28 42 39 34 34 25 28 25 18PCB 32 50 45 38 31 31 33 35 31PCB 44 36 32 29 27 28 27 29 28PCB 52 54 38 35 35 36 34 35 35PCB 66 39 31 29 28 30 28 29 28PCB 77 26 22 21 19 21 20 20 21PCB 95 32 26 25 25 26 26 26 26PCB 101 40 31 30 28 31 29 30 30PCB 105 29 22 22 21 23 22 22 23PCB 118 32 27 26 25 27 26 27 27PCB 126 13 12 11 10 11 12 11 13PCB 128 27 22 21 19 21 22 21 22PCB 136 33 25 24 22 23 24 23 24PCB 138 30 25 23 21 24 24 24 25PCB 149 33 29 28 29 28 30 29 30PCB 153 27 21 20 19 20 21 21 21PCB 170 13 9 10 9 9 11 9 11PCB 180 13 11 11 10 10 11 10 12PCB 187 20 17 17 16 17 18 17 18

OUTDOOR 2 60 230 335 390 620 680 730TC 39 40 36 36 36 36 36 35CC 48 47 43 43 42 40 42 40TN 28 27 27 27 28 30 28 28Endo I 44 46 36 42 6 19 25 6Endo II 23 30 10 1 0 19 2 0ESUL 39 30 27 28 23 24 25 21o,p' -DDE 34 21 22 18 20 20 20 19p,p' -DDE 27 30 31 30 32 33 31 36o,p' -DDD 36 35 34 27 28 30 30 29p,p' -DDD 35 34 32 24 27 29 29 28o,p' -DDT 42 37 35 37 36 37 36 36p,p' -DDT 36 25 24 24 24 23 22 2113C6-α-HCH 46 38 37 25 24 0 0 013C10-TC 45 44 40 39 39 37 39 3813C10-TN 34 30 29 30 29 30 30 2913C12-p,p' -DDT 51 37 36 35 34 33 32 30PCB 8 55 45 42 37 37 26 29 0PCB 18 54 44 38 36 37 36 38 36PCB 28

OUTDOOR 2 60 230 335 390 620 680 730TC 39 40 36 36 36 36 36 35CC 48 47 43 43 42 40 42 40TN 28 27 27 27 28 30 28 28Endo I 44 46 36 42 6 19 25 6Endo II 23 30 10 1 0 19 2 0ESUL 39 30 27 28 23 24 25 21o,p' -DDE 34 21 22 18 20 20 20 19p,p' -DDE 27 30 31 30 32 33 31 36o,p' -DDD 36 35 34 27 28 30 30 29p,p' -DDD 35 34 32 24 27 29 29 28o,p' -DDT 42 37 35 37 36 37 36 36p,p' -DDT 36 25 24 24 24 23 22 2113C6-α-HCH 46 38 37 25 24 0 0 013C10-TC 45 44 40 39 39 37 39 3813C10-TN 34 30 29 30 29 30 30 2913C12-p,p' -DDT 51 37 36 35 34 33 32 30PCB 8 55 45 42 37 37 26 29 0PCB 18 54 44 38 36 37 36 38 36PCB 28 42 39 34 34 25 28 25 18PCB 32 50 45 38 31 31 33 35 31PCB 44 36 32 29 27 28 27 29 28PCB 52 54 38 35 35 36 34 35 35PCB 66 39 31 29 28 30 28 29 28PCB 77 26 22 21 19 21 20 20 21PCB 95 32 26 25 25 26 26 26 26PCB 101 40 31 30 28 31 29 30 30PCB 105 29 22 22 21 23 22 22 23PCB 118 32 27 26 25 27 26 27 27PCB 126 13 12 11 10 11 12 11 13PCB 128 27 22 21 19 21 22 21 22PCB 136 33 25 24 22 23 24 23 24PCB 138 30 25 23 21 24 24 24 25PCB 149 33 29 28 29 28 30 29 30PCB 153 27 21 20 19 20 21 21 21PCB 170 13 9 10 9 9 11 9 11PCB 180 13 11 11 10 10 11 10 12PCB 187 20 17 17 16 17 18 17 18

244


STERILE 2 10 45 90 135 210 370 550TC 37 41 44 39 44 40 41 40CC 37 35 38 36 35 34 35 33TN 25 26 25 22 26 26 25 24Endo I 41 36 42 37 42 40 41 37Endo II 19 22 19 26 10 11 25 6ESUL 41 36 36 34 35 30 30 29o,p' -DDE 27 25 27 23 34 26 27 25p,p' -DDE 36 36 35 28 37 31 32 30o,p' -DDD 38 34 38 33 38 35 37 35p,p' -DDD 48 47 51 43 48 47 45 44o,p' -DDT 38 36 34 33 37 34 34 32p,p' -DDT 33 38 38 35 36 38 36 3513C6-α-HCH 51 46 54 50 54 43 53 4913C10-TC 48 45 48 44 50 43 47 4513C10-TN 33 30 29 28 36 32 32 3013C12-p,p' -DDT 49 47 49 47 49 47 52 48PCB 8 46 49 50 48 49 51 52 49PCB 18 45 48 49 47 49 49 53 51PCB 28 37 39 38 35 40 40 42 40PCB 32 40 44 43 41 51 47 48 46PCB 44 43 46 46 43 53 44 49 48PCB 52 39 42 42 38 42 42 45 44PCB 66 31 34 32 29 40 31 36 34PCB 77 20 24 20 18 26 20 24 22PCB 95 25 29 27 24 30 26 30 29PCB 101 31 35 33 30 36 32 37 35PCB 105 24 27 25 21 16 25 29 27PCB 118 25 29 27 23 30 26 30 28PCB 126 10 16 11 9 13 10 12 12PCB 128 22 27 23 20 28 22 26 25PCB 136 28 33 29 26 33 28 33 31PCB 138 25 30 26 23 30 25 29 28PCB 149 45 53 49 42 55 48 54 52PCB 153 22 27 23 20 27 23 26 25PCB 170 11 13 12 10 15 11 13 12PCB 180 11 13 12 10 15 11 13 12PCB 187 16 23 18 16 20 17 20 19

STERILE 2 10 45 90 135 210 370 550TC 37 41 44 39 44 40 41 40CC 37 35 38 36 35 34 35 33TN 25 26 25 22 26 26 25 24Endo I 41 36 42 37 42 40 41 37Endo II 19 22 19 26 10 11 25 6ESUL 41 36 36 34 35 30 30 29o,p' -DDE 27 25 27 23 34 26 27 25p,p' -DDE 36 36 35 28 37 31 32 30o,p' -DDD 38 34 38 33 38 35 37 35p,p' -DDD 48 47 51 43 48 47 45 44o,p' -DDT 38 36 34 33 37 34 34 32p,p' -DDT 33 38 38 35 36 38 36 3513C6-α-HCH 51 46 54 50 54 43 53 4913C10-TC 48 45 48 44 50 43 47 4513C10-TN 33 30 29 28 36 32 32 3013C12-p,p' -DDT 49 47 49 47 49 47 52 48PCB 8 46 49 50 48 49 51 52 49PCB 18 45 48 49 47 49 49 53 51PCB 28

STERILE 2 10 45 90 135 210 370 550TC 37 41 44 39 44 40 41 40CC 37 35 38 36 35 34 35 33TN 25 26 25 22 26 26 25 24Endo I 41 36 42 37 42 40 41 37Endo II 19 22 19 26 10 11 25 6ESUL 41 36 36 34 35 30 30 29o,p' -DDE 27 25 27 23 34 26 27 25p,p' -DDE 36 36 35 28 37 31 32 30o,p' -DDD 38 34 38 33 38 35 37 35p,p' -DDD 48 47 51 43 48 47 45 44o,p' -DDT 38 36 34 33 37 34 34 32p,p' -DDT 33 38 38 35 36 38 36 3513C6-α-HCH 51 46 54 50 54 43 53 4913C10-TC 48 45 48 44 50 43 47 4513C10-TN 33 30 29 28 36 32 32 3013C12-p,p' -DDT 49 47 49 47 49 47 52 48PCB 8 46 49 50 48 49 51 52 49PCB 18 45 48 49 47 49 49 53 51PCB 28 37 39 38 35 40 40 42 40PCB 32 40 44 43 41 51 47 48 46PCB 44 43 46 46 43 53 44 49 48PCB 52 39 42 42 38 42 42 45 44PCB 66 31 34 32 29 40 31 36 34PCB 77 20 24 20 18 26 20 24 22PCB 95 25 29 27 24 30 26 30 29PCB 101 31 35 33 30 36 32 37 35PCB 105 24 27 25 21 16 25 29 27PCB 118 25 29 27 23 30 26 30 28PCB 126 10 16 11 9 13 10 12 12PCB 128 22 27 23 20 28 22 26 25PCB 136 28 33 29 26 33 28 33 31PCB 138 25 30 26 23 30 25 29 28PCB 149 45 53 49 42 55 48 54 52PCB 153 22 27 23 20 27 23 26 25PCB 170 11 13 12 10 15 11 13 12PCB 180 11 13 12 10 15 11 13 12PCB 187 16 23 18 16 20 17 20 19

245

CHAPTER 7 AIR-WATER EXCHANGE OF ANTHROPOGENIC AND NATURAL

ORGANOHALOGENS ON INTERNATIONAL POLAR YEAR (IPY) EXPEDITIONS IN

THE CANADIAN ARCTIC

A7.1 Sample collection, extraction, analysis and quality control

A7.2 Air-water gas exchange

A7.3 Micrometeorological measurements

A7.4 Estimation of fluxes using micrometeorology

A7.5 Whitman two-film model


Table A7.1 Details of low volume air (LV Air) sampling at ~1 m and 15 m above surface

during Legs 1b, 8 and 9.

Table A7.2 Concentrations (pg m-3) of α-HCH, γ-HCH, HCB, DBA and TBA in low

volume air samples (LV Air) collected on Legs 1b, 8 and 9

Table A7.3 Concentrations of α-HCH, γ-HCH, (pg m-3) in high volume air samples (HV

Air) collected on Legs 1a, 1b, 8 and 9.

Table A7.4 Concentrations of α-HCH, γ-HCH, DBA and TBA (pg L-1) in low volume

water samples (LV Water) collected on Legs 1a, 1b, 8 and 9

Table A7.5 Concentrations of α-HCH, γ-HCH and HCB (pg L-1) in high volume water

samples (HV Water) collected on Legs 1a, 1b, 8 and 9

Table A7.6 Flux of α-HCH at the southern Beaufort Sea determined using

micrometeorological method (FM) and the two-film model (FTF) for Leg 9 and

one event on Leg 8. u* = friction velocity. U10 = wind speed at 10 m height. Ri

= Richardson number. φ = stability parameter. Positive and negative flux

indicate volatilization and deposition respectively.

Figure A7.1 Ratio of LV Air collected at 1 m to 15 m (C1/C15) above the surface for events

1-17 (Table A7.2). Legs 1b and 9 samples were collected over water. Leg 8

samples were collected over ice, except sampling event #8.

246

A7.1 Sample Collection, Extraction and Analysis, Quality Control

Sample collection.

Low volume air samples (LV Air, Legs 1b, 8, 9) (Table A7.1) were collected when the ship was

stationary by drawing air through stainless steel tubes located 1 m and 15 m above the water

surface to characterize the vertical gradients of chemicals (Figure 7.1). Air volumes of 75 m3

with a sampling rate of 80 L min-1 were passed through cartridges at deck level containing a

glass fiber filter (GFF, 7.6 cm diameter, Whatman Ltd., Maidstone, U.K.) followed by 1-2

polyurethane foam (PUF) plugs (7.5 cm × 6 cm diameter, Pacwill Environmental, Ontario,

Canada). A total of 17 sets (n = 17 × 2 = 34 samples) were collected, of which 4 sets were taken

using two PUFs to evaluate breakthrough

High volume air samples (HV Air, Legs 1a, 1b, 8, 9, n = 41) (Table A7.3) were taken at the deck

level near the bow of the ship (Figure 7.1). Air volumes of 300-2500 m3 were sampled at ~ 600

L min-1 by passing through a GFF (EPM 2000 20 × 25 cm) followed by 2 PUF plugs (8 cm

diameter × 7.5 cm). Single or duplicate low volume water samples (LV Water, Legs 1a, 1b, 8, 9,

n=28) (Table A7.4) of 4 L were collected on Legs 1a, 1b, 8 and 9 by pumping through a

stainless steel line which ran from 7 m below the water surface to the interior of the ship, or by

surface grab. Water was passed through a 140 mm diameter GFF/F followed by solid phase

extraction (SPE) cartridges containing ENV+ solid adsorbent (1 g, hydroxylated polystyrene-

divinylbenzene copolymer, IST Isolute, Biotage, Charlottesville, WV, U.S.A.).

High volume water samples (HV Water, Legs 1a,1b, 8, 9, n = 31) (Table A7.5) of 40 −100 L

were collected the same manner as the LV water except that the dissolved phase was passed

through ~75 mL of XAD-2 resin (polystyrene-divinyl benzene copolymer, 20-6 mesh, Supelco,

Bellefonte, PA. U.S.A.

PUFs were pre-cleaned by rinsing with water, followed by washing with acetone in a beaker, and

Soxhlet extracting with acetone, and then by petroleum ether for 22 h each. GFFs were cleaned

by baking at 400 °C overnight. XAD resin was cleaned by Soxhlet extracting in acetone, hexane

and dichloromethane (DCM), each for 2 d. SPE cartridges were cleaned with 20 mL acetone

prior to use. PUF plugs and SPE cartridges were stored in a freezer at -22 °C and XAD-2 resin

247

was stored in a refrigerator at 4 °C until extraction. All solvents were supplied by EMD Science

(Gibbstown, NJ, US).

Extraction and analysis.

In the CARE laboratory, HV and LV Air PUFs were fortified with d6-α-HCH, and then Soxhlet

extracted with 400 mL petroleum ether, followed by volume reduction and solvent exchange to

isooctane with rotary evaporation and a gentle stream of nitrogen. The final volumes were 0.5

mL for LV Air and 1 mL for HV Air extracts. SPE cartridges for LV Water were eluted with 15

mL acetone and solvent exchanged to 0.5 mL isooctane. XAD resin for HV Water samples was

extracted by eluting with 350 mL DCM, which was dried over granular anhydrous sodium

sulfate, solvent exchanged and concentrated to 1 mL in isooctane. Mirex was added to the

samples as internal standard. The air and water GFFs were not analyzed, thus only the gas-phase

air concentrations and dissolved phase water concentrations are reported here.

Analysis of samples at CARE was done using gas chromatography-electron capture negative ion

mass spectrometry (Agilent 6890 GC-5973 MSD GC-ECNI-MS). The column for quantitative

analysis was a DB5 (30m × 0.25 mm i.d., 0.25 μm film thickness, Agilent Technologies). Ions

monitored (target/qualifying) were: HCH (255/257); HCB (258/260); DBA (79/81); TBA

(344/346) and mirex (404/406). Chromatographic conditions for HCHs and HCB were reported

in (1). For DBA and TBA, the GC oven temperature program was: initial temperature 90°C,

ramped to 170°C at 20°C min-1, and then to 240°C at 3°C min-1. Injection was splitless with

250°C inlet temperature and 2 μl injection volume. Quadrupole and ion source temperatures

were 150°C. Transfer line temperature was 250°C. Quantitative analysis of air samples for

HCHs at FWI was done by GC with electron capture detection (GC-ECD), following methods

reported for water samples (2).

Enantiomers of α-HCH were determined for HV Air, LV Water and HV Water. Separation of

the enantiomers was achieved using β-DEXcst (BDXcst, proprietary composition, 30 m × 0.25

mm i.d., 0.25 μm film thickness, Restek, Bellefonte, PA, U.S.A.) as the primary column. For

confirmation, some samples were analyzed using a BGB-172 column (BGB, 20% tert-

butyldimethylsilylated β-cyclodextrin in OV-1701, 30 m x 0.25 mm i.d., 0.25 μm film thickness,

BGB Analytik AG, Switzerland). Instrument operating conditions are given in (3). Results of

248

enantiomer separations are expressed as enantiomer fraction (EF), defined as the peak areas of

the (+)/[(+) + (–)] enantiomers. EF = 0.500 indicates that the chemical is racemic. Data

presented are averages of the primary and confirmation results. Chiral analysis at FWI was

carried out by GC-ECNI-MS on a Betadex-120 column (30 m x 0.25 mm i.d., 0.25 μm film,

Supelco, Bellefonte, PA, U.S.A.) (2).

Quality control.

Air and water blanks underwent the same extraction and analytical procedures as the samples.

Limit of detection (LOD) was defined as mean blank + 3 times the standard deviation. If a

chemical was not found in the blanks, LOD was defined as instrumental detection limit (IDL),

which was estimated by injecting standards of the target analytes until a ~3:1 signal: noise ratio

was obtained. LODs are given as footnotes in Tables A7.2 to 5. Traces of HCB were found in the

air and water blanks, but not HCHs, DBA or TBA. The LODs for HCB were well below sample

concentrations in HV Air, LV Air and HV Water, but not LV Water, and so only HV Water

samples were used for HCB. Half of the LOD was assumed in statistical calculations when the

target chemical was below this limit.

Four sets of LV Air samples were taken with double PUFs, and none of the chemicals was

detected in the second PUF. For HV Air, breakthrough (100 x Back/Front PUF) was observed

for α-HCH (2 – 46%), γ-HCH (0 – 9%), HCB (6 – 99%), DBA (9 – 234%) and TBA (5–151%).

The higher breakthrough percentages corresponded to warmer temperatures during Legs 1a, 1b

and 9, while lower breakthrough was observed during Leg 8 in May. HV Air data were reported

as the sum of the front and back PUFs.

Recovery percentages for the spiked surrogate d6-α-HCH in the CARE laboratory were: LV Air

(n = 32) 83 ± 8%; HV Air (n = 40) 90± 16%; LV Water (n=24) 100 ± 7%; HV Water (n=31) 86

± 21%. Recoveries of d6-α-HCH from spiked PUF plugs at FWI averaged 73 ± 15%. Results

are blank and recovery corrected. Duplicate LV Water samples (11 pairs) agreed within 4.2 ±

3%.

In chiral analysis, ion ratios for each enantiomer peak were required to fall within the 95%

confidence interval of standards for a satisfactory analysis; otherwise, the result was rejected.

249

Decisions as to whether a particular sample contained racemic or nonracemic residues were

made by determining whether its EF was significantly different from the mean EF of standards at

p <0.05. Standard EFs ± SD for the β-DEXcst was 0.500 ± 0.002 and the BGB-172 column was

0.502 ± 0.0002. The average difference in EF determined by the two chiral column was 0.8% (n

= 11).

Literature Cited. (1) Jantunen, L.M.; Helm, P. A.; Ridal, J.J.; Bidleman, T.F. Air water gas exchange of

organochlorine pesticides in the Great Lakes. Atmos. Environ. 2008, 42, 8533–8542. (2) Kurt-Karakus, P.; Bidleman, T.F.; Jones, K.C. Chiral organochlorine pesticide signatures

in global background soils. Environ Sci Technol. 2005, 39, 8671–8677. (3) Pućko, M.; Stern, G.A.; Barber, D.G.; Macdonald, R.W.; Rosenberg, B. The International

Polar Year (IPY) Circumpolar Flaw Lead (CFL) System Study: the important of brine processes for α- and γ-hexachlorocyclohexane (HCH) accumulation/rejection in the sea ice. Atmosphere-Ocean, 2010, accepted.

250

A7.2 Air-water gas exchange

The potential to volatilize from water to air was examined using the fugacity approach.

HCf WW = Eq. [A7.1]

AAA RTCf = Eq. [A7.2]

where fW and fA are fugacites of the chemical in water and air (Pa), and CW and CA are air and

water concentrations (mol m-3). H is the Henry’s law constant (Pa m3 mol-1), R is the gas

constant (8.314 Pa m3 mol-1 K-1), TA is the air temperature (K). Henry's law constants were

adjusted for water temperature for HCHs (1), HCB (2), TBA and DBA (3), and a 20% higher

value was assumed in seawater due to the salting out effect (4, 5). Fugacities were expressed as

fractions, where ff = fW/[ fW + fA]. A ff = 0.50 indicates equilibrium and no net exchange between

air and water; ff >0.50 indicates net volatilization and ff < 0.50 indicates net deposition.

Precision of the concentration measurements for HCHs, DBA and TBA were estimated from the

pooled relative standard deviations (%RSD) of d6-α-HCH recoveries; i.e. 10% for CA and 6.6%

for CW. Percent RSD for recovery of d6-α-HCH for HV water was 24% and was taken as the

uncertainty in HCB concentration in water. Uncertainties for H were assumed to be 20% for all

chemicals (5). The uncertainty in ff was estimated according to Bruhn et al. (6) by determining

whether (fW – fA) is significantly different from zero at 95% confidence. Using their eq [6], the

critical fugacity quotients (Q = fW/fA) for significant net volatilization and deposition are 1.8 and

0.66. Converting to ff gives a equilibrium window of 0.40–0.64, which means that ff > 0.64

indicates net volatilization and ff < 0.40 indicates net deposition for α-HCH, γ-HCH, DBA and

TBA. The equilibrium window for HCB was slightly larger, 0.37–0.73.

251

Literature Cited.

(1) Xiao, H.; Li, N.; Wania, F. Compilation, evaluation, and selection of physical-chemicalproperty data for α-, β-, and γ-hexachlorocyclohexane. J. Chem. Eng. Data 2004, 49, 173–185.

(2) Shen, L.; Wania, F. Compilation, evaluation, and selection of physical-chemical property data for organochlorine pesticides. J. Chem. Eng. Data 2005, 50, 742–768.

(3) Pfeifer, O.; Lohmann, U.; Ballschmiter, K. Halogenated methyl-phenyl ethers (anisoles) in the environment: Determination of vapor pressures, aqueous solubilities, Henry’s law constants, and gas/water (Kgw), n-octanol/water- (Kow) and gas/n-octanol (Kgo) partition coefficients. Fresenius J. Anal. Chem. 2001, 371, 598–606.

(4) Sahsuvar, L.; Helm, P.A.; Jantunen, L.M.; Bidleman, T.F. Henry’s law constants for α-, β- and γ-hexachlorocyclohexanes (HCHs) as a function of temperature and gas exchange in arctic regions. Atmos. Environ. 2003, 37, 983–992.

(5) Jantunen, L.M.; Helm, P.A.; Kylin, H.; Bidleman, T.F. Hexachlorocyclohexanes (HCHs) in the Canadian Archipelago. 2. Air-water gas exchange of α- and γ-HCHs. Environ. Sci. Technol. 2008, 42, 465–470.

(6) Bruhn, R.; Lakaschus, S.; McLachlan, M.S. Air/sea gas exchange of PCBs in the southern Baltic sea. Atmos. Environ. 2003, 37, 3445–3454.

252

A7.3 Micrometeorological measurements

A 10 m tower was erected on the foredeck of the ship to monitor basic meteorological variables.

Horizontal wind speed was measured at 14.5 m above the sea surface using a wind monitor

(R.M. Young Co., model 15106MA). Temperature and relative humidity were measured using a

relative-humidity temperature probe (Vaisala, model HMP45C212) housed in a vented sunshield

at 13.5 m above the sea surface. Atmospheric pressure was measured approximately 6.5 m

above the sea surface using a Vaisala barometric pressure sensor (model PTB101B). Surface

temperature was monitored using an infrared transducer (Everest Interscience, model 4000.4ZL),

mounted 5.5 m above the sea surface. Measurements were made every 3 s and recorded as 1 min

averages on a data logger (Campbell Scientific Inc. model CR3000). Relative wind speed and

direction was converted to true winds during post processing. Periods where the wind direction

was ± 100° aft of the ship’s bow were screened from the data set to avoid airflow distortion on

measured wind associated with the ship’s wheelhouse and associated superstructure. The

NOAA/COARE v3.0 algorithm (1) was used to correct wind speed to 10 m above the sea

surface.

Literature Cited. (1) Fairall, C. W.; Bradley, E. F.; Rogers, D. P.; Edson, J. B.; Young, G. S. Bulk

parameterization of air-sea fluxes for tropical ocean-global atmosphere coupled-ocean atmosphere response experiment. J. Geophys. Res. 1996, 101, 3747–3764.

253

A7.4 Estimation of fluxes using micrometeorology

Assumptions were made when applying Eq. (7.1) to estimate α-HCH fluxes: 1) similarity of the

eddy diffusivities for momentum and scalars under neutral atmospheric stability; 2) constancy of

fluxes with height, meaning no vertical flux divergence or convergence beneath the upper

measurement height, and 3) steady state conditions.

There is some evidence that that the eddy diffusivity for some pesticides differs from that for

momentum, heat or water vapor (1), however transport characteristics are not well known and

the similarity assumption is commonly evoked (1, 2, 3). Non-neutrality is addressed using

empirically derived stability functions in Eq (7.1).

The second assumption requires measurements to be made in a surface boundary layer fully

adjusted to the underlying surface, which itself is horizontally homogeneous with a spatially

uniform source or sink. In absence of strong atmospheric stability or instability, the surface

(constant flux) layer develops to a height of between 1/100 and 1/300 of the upwind fetch (4, p.

112). Measurements in this study were made within 15 m of the water surface, which should fall

within the fully adjusted layer for most periods, with the exception perhaps of some

measurements made over mixed ice-ocean environments and under a stably stratified

atmosphere. Open seas present an essentially infinite upwind fetch with presumably (locally)

uniform concentrations of α-HCH in surface water (946±204 pg L-1 on legs 8 and 9), unlike

terrestrial surfaces where the surface source/sink can be patchy.

Finally, the third assumption requires no marked shifts in the micrometeorological conditions,

i.e. radiation and wind fields during the measurement period. Concentration measurements of α-

HCH require long integration periods in order to exceed a signal-to-noise threshold, so meeting

the steady-state conditions (stationarity) may not be possible. However, by careful selection of

periods with steady ambient conditions, flux-gradient approaches have been successfully applied

using integration times of between 4 h and 24 h (1, 2, 3, 5). Classic log-linear profiles of DDT

concentration have been reported by Kurt-Karakus et al. (5) above an agricultural surface for

integration times of between 6 and 9 hours.

Majewski (6) observed that the effect of increasing integration time is to increase the calculated

flux of methyl bromide through neutralizing effects of averaged atmospheric stability terms in

254

flux calculations. In that study, measurements were taken over 2-4 h sampling periods.

Concentration and micrometeorological data were then combined to simulate fluxes over

intervals of 8, 12 and-24 h. Results showed that combining the data (i.e. simulating longer

integration times) increased the estimated cumulative flux over an 8-h period by 13% and over a

24-h period by 28%. Such changes are minimal when compared with the propagated uncertainty

in the flux calculations (see A7.6).

Data are not available in this study to explicitly examine the profile of α-HCH within the

atmosphere’s lower 15 m. Assuming the only source of α-HCH is the surface and that there are

no chemical reactions between the surface and the measurement level, its concentration should

follow a logarithmic decay away from the surface in the turbulent atmospheric surface layer,

consistent with other atmospheric gases (e.g. H2O and CO2). The issue of stationarity is revisited

below.

Hourly friction velocity was derived from drag coefficients using ship meteorological

measurements following Smith (7). Drag coefficients were first corrected for atmospheric

stability using the Monin-Obukhov stability parameter, which was computed following Abdella

and McFarlane (8). Average u* for the sample periods were used in Eq. (7.1). Stability was

assessed using the bulk Richardson number (RB), a commonly used approximation to the

gradient Richardson number (9).

201010

010

)]/([)/()(

zzUzzTT

TgR Tsfcv

vB −

−−×= Eq. [A7.3]

where g = the gravitational constant (9.8 m s-1), TV = virtual potential temperature, Tsfc = surface

temperature, U10 = the wind speed at z10=10 m, and z0T and z0 are respectively the roughness

lengths for temperature and wind. According to Stull (9, p. 183), under weakly unstable

condition, -1<RB<0; and under weakly stable condition, 0<RB<0.2. Stability correction (Φc) for

the scalar in Eq. [7.1] was approximated using (1-16 RB )-0.5 for weakly unstable conditions and

(1+5 RB ) for weakly stable conditions. Stability correction (Φm) for the momentum in Eq. (7.1)

255

was approximated using (1-16 RB )-0.25 for weakly unstable conditions and (1+5 RB) for weakly

stable conditions (4, p. 52).

RB is a reasonable approximation to the Monin Obukhov stability parameter over the range -

1<RB<0 and 0<RB<0.2 (9, p.183), so we are comfortable with our treatment of atmospheric

stability, in light of existing uncertainty in the exact form of a stability correction for pesticide

vapors (1). The fact that stability is calculated over a reasonably thick atmospheric layer (14 m)

introduces uncertainty in the calculations. To mitigate the effect of uncertainty on flux

measurements, events were selected in which 63-100% of hourly RB indicated weakly unstable

to weakly stable periods (Table A7.6). Event #13 for which only 50% of the RB was close to

neutral, and no flux is estimated for this event. Therefore the stability corrections required were

generally small.

Literature Cited (1) Majewski, M. S. Micrometeorologic method for measuring the post-application

volatilization of pesticides. Water, Soil, Air Pollut. 1999, 115, 83-113. (2) Choi, S. D.; Staebler, R. M.; Li, H.; Su, Y.; Gevao, B.; Harner, T.; Wania, F. Depletion of

gaseous polycyclic aromatic hydrocarbon by a forest canopy. Atmos. Chem. Phys. 2008, 8, 4105–4113.

(3) Preuger, J.H.; Gish, T.J.; McConnell, L.L.; McKee, L.G.; Hatfield, J.L.; Kustas, W.P. Solar

radiation, relative humidity, and soil water effects on metolachlor volatilization. Environ. Sci. Technol. 2005, 39, 5219-5226.

(4) Garratt, J. R. The Atmospheric Boundary Layer; Cambridge University Press: Cambridge,

U.K., 1992. (5) Kurt–Karakus, P. B.; Bidleman, T. F.; Staebler, R. M.; Jones, K. C. Measurement of DDT

fluxes from a historically treated agricultural soil in Canada. Environ. Sci. Technol. 2006, 40, 4578–4585.

(6) Majewski, M. S. Error evaluation of methyl bromide aerodynamic flux measurements. In

Fumigants, Environmental fate, exposure, and analysis; Seiber, J. N., Knuteson, J. A., Woodrow, J. E., Wolfe, N. L., Yates, M. V., Yates, S. R, Eds.; American Chemical Society: Washington 1997; pp 236.

(7) Smith, S. D. Coefficients for sea surface wind stress, heat flux, and wind profiles as a

function of wind speed and temperature. J. Geophys. Res. 1998, 93, 15467–15472.

256

(8) Abdella, K.; McFarlane, N. A. Parameterization of the surface-layer exchange coefficients

for atmospheric models. Boundary-layer Meteorology 1996, 80, 223–248. (9) Stull. R. B. An Introduction to Boundary Layer Meteorology; Kluwer Academic Publishers:

Boston, U.S.A., 1988.

257

A7.5 Whitman Two-film model

Net fluxes (FTF) of α-HCH were calculated using the Whitman two-film model (1). Equations

used are given below.

)( AWAWTF ffDF −= Eq. [A7.4]

HK

D OLAW = Eq. [A7.5]

ALOL HkRT

kK+=

11 Eq. [A7.6]

where DAW (mol m-2 s-1 Pa-1) is the overall mass transfer coefficient, fw and fa are fugacities in

water and air, KOL (m s-1) is the overall liquid phase mass transfer coefficient; kL and kA are the

water and air film mass transfer coefficients (m s-1). For H < 0.2 Pa m-3 mol-1, 95% of the

resistance to gas exchange lies in the air film (2). The average water temperature on Leg 9 was

278K, at which the salinity-adjusted H for α-HCH was 0.12, hence it is assumed that 1/KOL =

RT/HkA. The air-side mass transfer coefficient (kA, m s-1) is defined as:

670

105.0

1043 )63.01.6(1062.4101 .

AA ScUUk −−− ××+×+×= Eq. [A7.7]

where ScA is the Schmidt number for organic gases at 20–25 °C, and the value of 2.9 for α-HCH

was taken from Bidleman and McConnell (2). According to Eq [A7.4], volatilization fluxes are

positive and deposition fluxes are negative.

Literature Cited. (1) Mackay, D.; Yeun, A.T.K. Mass transfer coefficients for volatilization of organic solutes

from water. Environ. Sci. Technol. 1983, 17, 211–217. (2) Bidleman, T.F.; McConnell, L.L. A review of field experiments to determine air–water gas

exchange of persistent organic pollutants. Sci. Total Environ. 1995, 159, 101–117.

258


In order to estimate the uncertainty associated with FM, we propagated errors in Eq (7.1) based

on the standard deviation (SD) of u* and the concentration terms. RSD for u* ranged from 21-

83%; RSD for C1 and C15 was 10%. Uncertainties associated with FTF, Eq (A7.4-7.6), was also

estimated based on the SD of the concentration terms, Henry’s law constant (H), and KOL. The

RSDs for C1 was 10%, CW = 6.6%, H = 20%. The RSD of KOL was estimated based on RSDs of

H and ka. The RSD of ka was taken from Hornbuckle et al. (1) with value of 40%.

Literature Cited (1) Hornbuckle, K.C.; Jeremiason, J.D.; Sweet, C.D.; Eisenreich, S.J. Seasonal variations in the

air-water exchange of polychlorinated biphenyls in Lake Superior. Environ. Sci. Technol. 1994, 28, 1491-1501.

259

Table A7.1 Details of low volume air (LV Air) sampling at ~1 m and 15 m above surface

during Legs 1b, 8 and 9.

Event Sample ID Leg Start Date, Time End Date, Time Latitude Longtitude Surface TA TW

1 IPY-MH-4, 5 1b Aug 7, 2007/8:52 Aug 7, 2007/17:30 56.39 -79.11 water 12.00 6.102 IPY-MH-6, 7 1b Aug 7, 2007/17:45 Aug 8, 2007/4:26 56.39 -79.11 water 14.31 6.103 IPY-MH-8, 9 1b Aug 8, 2007/4:50 Aug 8, 2007/15:45 56.39 -79.11 water 10.80 6.104 IPY-MH-15,17 8 May 20, 2008/16:05 May 20, 2008/21:40 71.57 -119.61 ice -0.33 -0.885 IPY-MH-20, 22 8 May 24, 2008/11:10 May 24, 2008/18:00 72.62 -126.15 ice 2.28 -1.186 IPY-MH-25, 27 8 May 28, 2008/01:05 May 28, 2008/11:00 74.41 -124.90 ice -2.21 -1.167 IPY-MH-28, 30 8 May 30, 2008/10:30 May 30, 2008/19:00 71.59 -125.26 ice -0.04 -1.228 IPY-MH-31, 33 8 June 1, 2008/11:00 June 1, 2008/20:00 70.64 -123.55 water -1.48 -0.449 IPY-MH-34, 36 8 June 3, 2008/10:25 June 3, 2008/21:25 69.86 -123.75 ice 0.81 -0.0710 IPY-MH-37, 39 9 June 28, 2008/10:50 June 29, 2008/01:50 71.14 -125.78 water 6.00 4.0011 IPY-MH-40, 42 9 July 1, 2008/00:35 July 1, 2008/15:55 71.43 -133.89 water 6.44 4.7812 IPY-MH-43, 45 9 July 3, 2008/23:15 July 4, 2008/13:15 72.66 -128.53 water 9.79 6.4613 IPY-MH-46, 48 9 July 5, 2008/21:25 July 6, 2008/12:08 75.21 -120.42 water 3.18 0.2614 IPY-MH-49, 51 9 July 8, 2008/04:35 July 8, 2008/19:20 71.73 -126.63 water 11.59 7.3515 IPY-MH-52, 54 9 July 9, 2008/19:05 July 10, 2008/11:05 71.27 -127.84 water 10.51 7.1616 IPY-MH-55, 57 9 July 13, 2008/04:05 July 13, 2008/16:00 71.04 -121.67 water 9.67 7.4617 IPY-MH-58, 60 9 July 14, 2008/09:15 July 14, 2008/21:08 70.73 -117.83 water 6.65 4.85 Notes: No LV sampling was performed during Leg 1a.

TA = air temperature (°C) TW = water temperature (°C)

260

Table A7.2 Concentrations (pg m-3) of α-HCH, γ-HCH, HCB, DBA and TBA in low volume air

samples (LV Air) collected on Legs 1b, 8 and 9

Sample ID Leg EventHeight above surface (m) α-HCH γ-HCH HCB DBA TBA

d6-α-HCH Recovery

IPY-MH-04 1b 1 15 28.5 nd 62.7 17.0 39.2 88%IPY-MH-05 1b 1 1 31.6 nd 56.0 18.2 31.3 83%IPY-MH-06 1b 2 15 28.5 nd 56.8 26.9 42.1 108%IPY-MH-07 1b 2 1 35.6 nd 56.3 17.9 27.4 98%IPY-MH-08 1b 3 15 29.9 nd 58.5 34.0 44.8 94%IPY-MH-09 1b 3 1 33.5 nd 60.3 33.7 47.0 96%IPY-MH-015 8 4 15 8.64 nd 46.6 3.67 5.46 83%IPY-MH-017 8 4 1 9.55 nd 65.0 nd 4.60 80%IPY-MH-020 8 5 15 11.2 nd 45.0 1.05 7.28 naIPY-MH-022 8 5 1 11.9 nd 50.0 3.6 7.88 naIPY-MH-025 8 6 15 16.0 nd 48.8 1.80 1.60 90%IPY-MH-027 8 6 1 16.1 nd 53.1 1.71 1.26 81%IPY-MH-028 8 7 15 13.3 nd 42.7 nd 3.07 77%IPY-MH-030 8 7 1 13.1 nd 48.7 nd 4.24 80%IPY-MH-031 8 8 15 11.7 nd 47.8 1.95 4.42 75%IPY-MH-033 8 8 1 14.1 nd 45.5 1.25 2.88 85%IPY-MH-034 8 9 15 24.0 nd 43.2 16.2 18.3 79%IPY-MH-036 8 9 1 19.1 nd 39.5 12.2 15.4 79%IPY-MH-037 9 10 15 40.4 2.80 54.3 14.0 17.4 93%IPY-MH-039 9 10 1 47.6 3.23 62.2 16.8 20.4 67%IPY-MH-040 9 11 15 40.9 3.77 69.8 19.7 17.8 77%IPY-MH-042 9 11 1 46.0 2.63 74.0 19.7 18.7 77%IPY-MH-043 9 12 15 50.4 3.63 72.0 14.2 21.9 85%IPY-MH-045 9 12 1 58.0 4.31 90.4 21.4 24.5 73%IPY-MH-046 9 13 15 24.4 nd 67.0 9.02 13.9 75%IPY-MH-048 9 13 1 24.4 nd 57.1 8.84 11.7 79%IPY-MH-049 9 14 15 55.0 3.06 89.8 25.8 42.9 76%IPY-MH-051 9 14 1 52.1 3.04 76.7 19.6 31.9 80%IPY-MH-052 9 15 15 43.4 2.40 67.6 16.2 16.9 79%IPY-MH-054 9 15 1 52.0 3.01 77.0 18.5 18.6 86%IPY-MH-055 9 16 15 40.6 1.57 72.5 24.8 33.0 82%IPY-MH-057 9 16 1 49.5 2.58 80.3 34.6 35.3 83%IPY-MH-058 9 17 15 32.7 nd 57.0 20.5 24.9 76%IPY-MH-060 9 17 1 38.7 1.87 65.1 26.7 31.3 86% Notes: na = not available (not measured); nd = not detected, data is below detection limit. For statistical calculations, nd was replaced with 1/2 LOD. LOD for α-HCH = 1.3 pg m-3; γ-HCH = 0.67 pg m-3, HCB = 1.1 pg m-3, DBA = 0.67 pg m-3 and TBA = 0.82 pg m-3. Only HCB was found in LV Air blanks, and mean blank = 0.31 pg m-3. LOD was calculated based on 75 m3 of air and 500 ul of extract volume.

261

Table A7.3. Concentrations of α-HCH, γ-HCH (pg m-3) in high volume air samples (HV Air)

collected on Legs 1a, 1b, 8 and 9.

Leg α-HCH γ-HCH d6-α-HCH Recovery EF of α-HCHHV Air 04 1a 21.6 2.2 97% 0.447HV Air 05 1a 20.1 2.1 99% 0.438HV Air 06 1a 19.5 2.1 96% 0.459HV Air 07 1a 15.5 2.8 104% 0.446HV Air 08 1a 14.8 3.2 96% 0.473HV Air 09 1a 17.2 1.8 96% 0.440HV Air 10 1a 19.7 2.3 109% 0.451HV Air 11 1a 28.1 1.9 90% 0.468HV Air 12 1a 32.4 2.6 90% 0.455HV Air 13 1b 35.4 2.5 76% 0.442HV Air 14 1b 29.3 2.6 89% 0.456HV Air 15 1b 27.8 1.9 82% 0.454HV Air 16 1b 20.3 2.0 95% 0.465HV Air 17 1b 24.5 2.2 93% 0.472HV Air 18 1b 28.9 2.3 96% 0.455HV Air 19 1b 29.6 2.1 103% 0.450HV Air 21 1b 52.7 3.7 33% 0.459HV Air 22 1b 32.0 2.6 99% 0.456HV Air 23 1b 23.6 1.8 125% 0.451HV Air 24 8 12.7 2.2 85% 0.471HV Air 25 8 12.2 2.3 72% 0.465HV Air 26 8 15.0 2.4 75% 0.465HV Air 27 8 18.1 2.2 85% 0.481HV Air 28 8 18.2 2.0 96% 0.480HV Air 29 8 18.1 2.0 77% 0.483HV Air 30 8 13.2 2.0 92% 0.485HV Air 31 8 14.2 1.7 115% 0.488HV Air 32 8 17.1 2.2 103% 0.481HV Air 33 8 19.2 2.1 112% 0.464HV Air 34 9 31.8 4.1 81% 0.473HV Air 35 9 36.2 4.3 91% 0.455HV Air 38 9 50.8 4.3 61% 0.487HV Air 39 9 31.4 3.5 80% 0.484HV Air 40 9 61.0 5.4 84% 0.477HV Air 41 9 66.1 5.4 85% 0.471HV Air 42 9 56.2 5.0 75% 0.469HV Air 43 9 49.5 4.3 82% 0.487 Notes: Details about the samples are presented in Jantunen et al., (1). LOD for α-HCH = 0.13 pg m-3; γ-HCH = 0.06 pg m-3. LOD was calculated based on 1600 m3 of air and 1000 ul of extract volume. Literature Cited (1) Jantunen, L. M.; Wong, F.; Kylin, H.; Helm, P.; Stern, G. A.; Strachan, B.; Burniston, D.; Bidleman, T. F. Air-water gas legacy and currently used pesticides in the Arctic regions. Environ. Sci. Technol., (in prep).

262

Table A7.4 Concentrations of α-HCH, γ-HCH , DBA and TBA (pg L-1) in low volume water

samples (LV Water) collected on Legs 1a, 1b, 8 and 9.

Sample ID Leg Date Latitude Longitude α-HCH γ-HCH DBA TBA

d6-α-HCH Recovery

EF of α-HCH Temp (C) n

LV1 1a Aug 1, 2007 58.18 -62.35 542 131 48.1 186 113% 0.437 3.76 2LV2 1a Aug 1, 2007 58.34 -63.29 549 146 27.8 140 113% 0.425 3.76 2LV3 1b Aug 8, 2007 56.60 -79.18 791 165 21.0 34.0 102% 0.443 5.02 4LV4 1b Aug 10, 2007 54.07 -79.95 583 182 16.4 35.0 104% 0.439 10.30 2LV5 8 May 26, 2008 74.07 -129.03 900 261 9.54 nd na 0.477 -0.14 2LV6 8 May 30, 2008 71.57 -125.30 1168 344 8.44 5.34 95% 0.463 -1.19 2LV7 8 June 1, 2008 70.64 -123.18 1104 172 10.8 7.19 92% 0.459 -0.79 2LV9 9 June 27, 2008 71.55 -124.34 916 162 nd 10 97% 0.462 4.11 1LV10 9 June 28, 2008 71.44 -124.46 1077 223 nd 19 99% 0.468 4.00 1LV11 9 June 30, 2008 71.37 -133.88 966 216 8.44 10 100% 0.452 4.89 1LV12 9 July 1, 2008 71.47 -133.90 846 154 6.95 7.13 106% 0.472 4.78 2LV13 9 July 3, 2008 72.66 -128.35 1002 162 nd 6.05 99% 0.483 6.49 1LV14 9 July 5, 2008 75.13 -120.40 1255 97.1 nd 3.32 99% 0.473 0.26 2LV15 9 July 8, 2008 71.70 -126.49 954 146 8.39 5.78 106% 0.465 7.35 3LV16 9 July 9, 2008 71.30 -127.76 850 138 8.53 5.82 108% 0.469 7.16 2LV17 9 July 13, 2008 71.07 -121.81 1131 193 21.3 23.9 97% 0.433 7.46 2LV18 9 July 14, 2008 70.78 -118.53 1136 255 26.7 27.4 91% 0.429 4.85 2 Notes: na = not available (not measured); nd = not detected, data is below detection limit. For statistical calculations, nd was replaced with 1/2 LOD. n = number of duplicates, data are mean of duplicate samples. LOD for α-HCH = 25 pg L-1; γ-HCH = 13 pg L-1, DBA = 2.5 pg L-1 and TBA = 3.2 pg L-1. LOD was calculated based on 4L of water and 500 ul of extract volume.

263

Table A7.5 Concentrations of α-HCH, γ-HCH and HCB (pg L-1) in high volume water samples

(HV Water) collected on Legs 1a, 1b, 8 and 9.

Sample ID Leg α-HCH γ-HCH HCBd6-α-HCH Recovery EF of α-HCH

HV Water 02 1a 249 55 2.7 103% 0.412HV Water 03 1a na na na 117% 0.414HV Water 04 1a 489 171 6.03 111% 0.423HV Water 05 1a 414 159 5.56 128% 0.423HV Water 06 1a 547 235 6.02 107% 0.438HV Water 07 1b 551 196 5.72 119% 0.451HV Water 08 1b 765 147 11.8 55% 0.459HV Water 09 1b na na na 81% 0.446HV Water 10 1b 572 204 4.48 118% 0.448HV Water 11 1b 659 190 4.62 88% 0.448HV Water 12 1b 489 151 4.81 100% 0.445HV Water 13 1b 480 134 6.51 79% 0.448HV Water 14 1b 691 155 6.53 63% 0.441HV Water 15 1b 619 196 7.95 82% 0.443HV Water 16 1b 601 134 5.23 79% 0.443HV Water 17 1b 556 209 8.14 95% 0.453HV Water 18 1b 804 257 4.68 82% 0.452HV Water 19 8 906 185 6.60 78% 0.421HV Water 20 8 1066 207 4.56 79% 0.461HV Water 21 8 646 196 8.46 31% 0.469HV Water 22 8 1297 336 8.36 63% 0.490HV Water 23 8 1018 333 3.81 75% 0.461HV Water 24 9 855 159 4.09 78% 0.462HV Water 25 9 759 170 3.74 85% 0.464HV Water 26 9 332 99 0.95 71% naHV Water 27 9 831 169 3.57 92% 0.430HV Water 28 9 1259 244 8.03 65% 0.463HV Water 29 9 970 195 4.16 83% 0.458HV Water 30 9 891 188 4.12 79% 0.431HV Water 31 9 926 185 3.59 88% 0.421 Notes: na = not available (not measured). Details about the samples are presented in Jantunen et al., (in prep). LOD for α-HCH = 2 pg L-1; γ-HCH = 1 pg L-1, HCB = 0.26 pg L-1. LOD was calculated based on 100 L of water and 1000 ul of extract volume.

264

Table A7.6 Flux of α-HCH at the southern Beaufort Sea determined using micrometeorological

method (FM) and the two-film model (FTF) for Leg 9. u* = friction velocity. U10 = wind speed at

10 m height. RB = bulk Richardson number. Positive and negative flux indicates volatilization

and deposition respectively. % Neutral RB is the percentage of the hourly RB during one sample

event that was under neutral atmospheric stability. It is defined as -0.2<RB<1.

Event u* U10 RB % Neutral RB FM FTF

m s-1 m s-1 ng m-2 h-1 ng m-2 h-1

10 2.51 0.09 0.09 63% 0.24 0.1111 4.15 0.16 0.05 73% 0.36 0.2412 3.31 0.11 0.11 77% 0.30 0.1813 2.70 0.10 0.16 50% 0.00 0.1714 3.65 0.15 0.11 80% -0.15 0.3315 3.29 0.13 0.12 73% 0.38 0.2916 3.94 0.16 0.05 85% 0.60 0.4017 6.52 0.25 0.02 100% 0.75 0.52

265

Figure A7.1 Ratio of LV Air collected at 1 m to 15 m (C1/C15) above the surface for events 1-17

(Table A7.2). Legs 1b and 9 samples were collected over water. Leg 8 samples were collected

over ice, except sampling event #8.

0.60.70.80.91.01.11.21.31.41.5

0 1 2 3 4 5 6 7 8 9 10 1112 13 14 15 16 17

α-HCH0.60.70.80.91.01.11.21.31.41.5

0 1 2 3 4 5 6 7 8 9 1011 12 1314 1516 17

HCB

0.00.51.01.52.02.53.03.54.0

0 1 2 3 4 5 6 7 8 9 10 11 1213 14 15 16 17

DBA0.4

0.6

0.8

1.0

1.2

1.4

1.6

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

TBA

Leg 1b Leg 8 Leg 9 Leg 1b Leg 8 Leg 9

Leg 1b Leg 8 Leg 9Leg 1b Leg 8 Leg 9

0.60.70.80.91.01.11.21.31.41.5

0 1 2 3 4 5 6 7 8 9 10 1112 13 14 15 16 17

α-HCH0.60.70.80.91.01.11.21.31.41.5

0 1 2 3 4 5 6 7 8 9 1011 12 1314 1516 17

HCB

0.00.51.01.52.02.53.03.54.0

0 1 2 3 4 5 6 7 8 9 10 11 1213 14 15 16 17

DBA0.4

0.6

0.8

1.0

1.2

1.4

1.6

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

TBA

Leg 1b Leg 8 Leg 9 Leg 1b Leg 8 Leg 9

Leg 1b Leg 8 Leg 9Leg 1b Leg 8 Leg 9

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