SOURCES OF DIOXINS TO BALTIC AIR - DiVA portalumu.diva-portal.org/smash/get/diva2:535832/FULLTEXT01.pdf · sources of dioxins to baltic air ... low water solubility, ... 3.2 factors

SOURCES OF DIOXINS TO BALTIC AIR Volatilization and Resuspension As Potential Secondary Sources of Dioxins to Air

VAN ANH LE

Student Degree Thesis in Swedish School of Environmental Chemistry 45 ECTS

Master’s Level

Supervisors: Ian Cousins

Volatilization and Resuspension

as Potential Secondary Sources

of Dioxins to Air

VAN ANH LE

Supervisor: Ian Cousins

Master’s Thesis in Swedish School of Environmental Chemistry

Department of Applied Environmental Science

[email protected]

Master’s Thesis 2011

I

ABSTRACT

Persistent organic pollutants (POPs) are ubiquitous contaminants characterized by semi-

volatility, low water solubility, high lipophilicity and inherent toxicity. A combination of these

properties results in long-rang transport, bioaccumulation and biomagnification through food

webs. Elimination of the production, use and emissions of these POPs has been ongoing since

the 1970s. However, the levels of some POPs are still unacceptably high in some parts of the

environment and due to their high persistence levels only decline very slowly over a long

period of time. This is especially true for POPs in the Baltic Sea due to long water residence

time of approximately 40 years. Numerous studies have been carried out to explore the

behavior and fate of the POPs in Baltic regions using analytical methods or modeling

approaches.

Air-soil exchange plays an important role in controlling the environmental fate of POPs in

surface media. Air is a transport medium, which spreads chemicals far away from sources.

Soils have received an input of POPs from the atmosphere over a long time period. These

chemicals have accumulated in soil solids and, as primary emissions are released, can

potentially be rereleased to other environmental media. Therefore, soil could become a

significant “secondary” source of some POPs to the air. In this study, the aim was to

determine if volatilization and/or resuspension are potential sources of polychlorinated

dibenzo-p-dioxins and dibenzofurans (PCDDs/Fs) (“dioxins”) to Baltic air. Sources of these

compounds to Baltic air are particularly interesting because levels of dioxins in fatty fish in

the Baltic exceed the levels that are considered fit for human consumption in the European

Union guideline.

The fugacity quotient approach has been previously shown to be a useful method for

exploring the equilibrium status of two connected environmental compartments. Fugacity

quotients between the atmosphere and soil are calculated for seventeenth toxic 2, 3, 7, 8,-

substituted dioxin congeners . A multimedia mass balance model designed for the Baltic Sea

region (POPCYCLING-Baltic) is also employed to study the long-term exchange between air

and soil. Estimated fugacity ratios from model simulations are compared with calculated

fugacity quotients. Moreover, sensitive analysis is undertaken in order to evaluate the relative

effect of background concentration, resuspension and bioturbation transport to the transfer

flux from soil to air.


II

Fugacities of dioxins in soil are additionally measured directly using equilibrium passive

sampling devices. Among available passive samplers, polyoxymethylene 17 µm (POM-17) are

chosen to absorb freely dissolved PCDD/Fs molecules in soil. Total soil concentrations are

measured to provide input data for the POPCYCLING-Baltic multimedia fate and transport

model. Estimated fugacities of dioxins will be compared with directly “measured” fugacities in

soil. The predictive ability of the model is assesses by comparing estimated and “measured”

fugacity.

Calculated fugacity quotients showed that lower chlorinated dibenzofuran are close to

equilibrium between soil and air while other congeners show disequilibrium. Estimated

soil/air fugacity ratios are higher than one but soil still accumulates dioxins because transport

process is very slow and non-equilibrium can be maintained for a long period of time. Due to

the seasonal variation in concentration, volatilization is higher in summer than in winter.

Therefore, net gaseous flux between soil and air can be observed in summer.

Sensitivity analysis revealed that volatilization flux is proportional to background soil

concentration. High background soil concentration results in high volatilization fluxes and

vice versa. The simulation showed that the contribution of resuspension flux to air pollution

levels is relatively small in comparison to the influence of variation in background soil

concentration. If relatively high and unrealistic resuspension velocities are used as inputs in

the model, resuspension is a significant source to the atmosphere. In contrast to background

soil concentration and resuspension, bioturbation has no effect on volatilization flux even

though high bioturbation rates are used as model inputs. In conclusion, except for light

congeners, soil is still a sink of PCDD/Fs present in Baltic air. However, the increase in soil/air

fugacity ratios suggest an increasing important of soil-to-air transport in the near future.

Equilibrium passive samplers using POM strips are considered as a very simple, reproducible,

and inexpensive partitioning method. However, the largest disadvantage of using passive

samplers for dioxins is the long time to reach equilibrium. It takes 6 months for PCDD/Fs to

obtain equilibrium between soil and POM strips, which exceeded the time for doing a 45

credit thesis. The analytical phase of the experiment is still on-going, and thus it was not

possible to include the experimental results in this study.


III

Key word: PCDD/Fs, air-soil exchange, volatilization, resuspension, bioturbation,

POPCYLING-Baltic model, POM-17


IV

ACKNOWLEDGEMENT

Having finished my thesis, it is a great pleasure to take an opportunity to all those who accompanied

and supported me along the way.

First of all, I would like to express my appreciation and gratitude to my extraordinarily supervisor,

Assoc. Prof. Ian Cousins for all his support and invaluable advice, in the achievement of my academic

goals and my way into scientific world. I am deeply in debt of your endless patience and sympathy that

enable me to complete my thesis. It is such luck for me to have you as my supervisor.

I would like to address the most special word of thanks to Dr. James Armitage who instructed me from

the very early stage of my thesis as well as helped me a lot to stay calm even in the most “thrilling

moments”. You are the brilliant mind-guide who always know how and when to trigger the ideas that

pull me out from the state of chaos.

I am also grateful for discussions, comments and suggestions from Assoc. Prof. Gerard Cornelissen who

provided me with valuable advice on the experimental analysis part.

From deep inside, I would like to express my heartfelt thanks to Assoc. Prof. Karin Wiberg for her

kindness and helpful during my studies and agreement to be my examiner in this thesis project.

To my dear teachers of the Department of Chemistry - Umeå University and Department of

Environmental Material - Stockholm University, I would like to express my gratitude to you for all the

knowledge and skills I have been taught during this Master’s program.

As well, I also would like to thank my office-mate Li Zhe for her patience, tolerance and inspiration all

the time.

From bottom of my heart, it is hard to find a word to express my gratitude to my grandparents, my

parents for their care and shares. Family is the most precious treasure that I will give my greatest effort

to keep and devote to. The most special thanks to my father who taught me how to pursue my dreams

till I achieve them and how to believe in myself. Thank you, Mom, for your big and generous heart that

gives me eternal love and caring both in my life and studying.

Lastly, I wish to thank Chinh Nguyen, my boyfriend, for his eternal love, encouragement and

unyielding support through the process. I also offer my regards and blessings to all my beloved friends

who supported me in any respect during the completion of my thesis.

Stockholm, May 2011

ANH LE


V

TABLES OF CONTENTS

ABSTRACT ...................................................................................................................................................... I

ACKNOWLEDGEMENT ............................................................................................................................. IV

TABLES OF CONTENTS .............................................................................................................................. V

LIST OF FIGURES .................................................................................................................................... VIII

LIST OF TABLES ......................................................................................................................................... XI

ABBREVIATIONS.................................................................................................................................... XII

1. INTRODUCTION ...................................................................................................................................... 1

BACKGROUND ................................................................................................................................................ 1

THE AIMS OF PROJECT ............................................................................................................................... 3

2. PERSISTENT ORGANIC POLLUTANTS (POPS) ............................................................................... 3

2.1 DEFINITION, CLASSIFICATION ........................................................................................................ 3

2.2 ENVIRONMENTAL FATE .................................................................................................................... 4

2.3 CHEMICAL ANALYSIS .......................................................................................................................... 6

2.4 MODELING ............................................................................................................................................... 7

2.6 DIOXINS ..................................................................................................................................................... 8

2.6.1 Dioxins And Their Physical Chemical Properties .................................................... 8

2.6.2 Sources And Environmental Fate .................................................................................. 9

2.6.3 Degradation ........................................................................................................................ 10

2.6.4 Long - Range Transport ................................................................................................. 10

2.6.5 Bio-Accumulation, Bio-Magnification And Toxicity ............................................ 11

3. AIR-SOIL EXCHANGE ........................................................................................................................... 12

3.1. PROCESSES INVOLVED IN AIR – SOIL EXCHANGE............................................................... 12

Dry Deposition .............................................................................................................................. 13

Wet Deposition ............................................................................................................................. 13

Volatilization ................................................................................................................................. 14


VI

Bioturbation .................................................................................................................................. 15

Resuspension ................................................................................................................................ 15

3.2 FACTORS AFFECTING THE AIR-SOIL EXCHANGE PROCESS ............................................. 16

3. METHODOLOGY TO STUDY AIR-SOIL EXCHANGE .................................................................... 18

3.1 FUGACITY QUOTIENT CONCEPT.................................................................................................. 18

3.2 MULTIMEDIA FATE AND TRANSPORT MODEL OF DIOXINS ............................................ 20

3.3 METHODS TO MEASURE FUGACITY IN SOIL .......................................................................... 20

3.3.1 Fugacity Meter ................................................................................................................... 20

3.3.2 Equilibrium Passive Samplers ..................................................................................... 21

4. METHODS ............................................................................................................................................... 24

4.1 FUGACITY QUOTIENT ....................................................................................................................... 24

4.2 POPCYCLING-BALTIC MODEL (VERSION 1.05)...................................................................... 24

Environmental Input Parameters ......................................................................................... 26

Physical-Chemical Input Parameters ................................................................................... 26

Initial Concentrations ................................................................................................................ 29

Alterations To Popcycling/Baltic Model ............................................................................. 29

4.3 ANALYSIS FUGACITY IN SOIL USING PASSIVE SAMPLER .................................................. 30

Sampling ......................................................................................................................................... 30

Dry Weight Determination ...................................................................................................... 30

Development Of Pom-17 Samplers ...................................................................................... 30

5. RESULT AND DISCUSSION ................................................................................................................. 32

5.1 FUGACITY QUOTIENT CONCEPT.................................................................................................. 32

5.2 MODEL .................................................................................................................................................... 33

5.2.1 Default Values .................................................................................................................... 33

5.2.2 Sensitivity Analysis .......................................................................................................... 40

5.3 EXPERIMENT WITH PASSIVE SAMPLER .................................................................................. 43


VII

6. CONCLUSION ......................................................................................................................................... 43

7. RECOMMENDATION ........................................................................................................................... 44

APPENDIX A_ALTERATION TO MODEL ............................................................................................. 55

APPENDIX B_INPUT PARAMETERS .................................................................................................... 57

APPENDIX C_SIMULATION FOR OTHERS CONGENERS ................................................................ 58

APPENDIX D-SENSITIVE ANALYSIS OF 17 CONGENERS .............................................................. 75


VIII

LIST OF FIGURES

Figure 1. Important fluxes and partition coefficients (Wiberg et al., 2009) ........................................ 5

Figure 2. General Structure of PCDDs and PCDFs and numbering of carbon atoms ........................ 8

Figure 3. A schematic of illustration of the sources and environmental fate of PCDD/Fs ............. 9

Figure 4. A schematic picture of vertical soil aerosol suspension under action of wind (Qureshi

et al., 2009) ................................................................................................................................................................. 16

Figure 5. The POPCYCLING-Baltic Model aims to quantify the pathways of POPs from the

terrestrial environment to the marine environment via the atmosphere and rivers (Wania et

al., 2000). ..................................................................................................................................................................... 25

Figure 6. Compartments in POPCYCLING-Baltic Model (Armitage et al., 2009) ............................. 26

Figure 7. Illustration of shaking soil with POM-17 ..................................................................................... 31

Figure 8. Seasonal air fugacity of 2, 3, 7, 8-TCDD ........................................................................................ 35

Figure 9. Seasonal soil fugacity of 2, 3, 7, 8-TCDD in Swedish Baltic Proper .................................... 35

Figure 10. Time trend of air fugacity of 2, 3, 7, 8-TCDD in four Baltic Sea regions ....................... 35

Figure 11. Time trend in soil fugacity of 2, 3, 7, 8-TCDD in ten terrestrial regions. ...................... 36

Figure 12. Fugacity ratios between agricultural soil and air in ten terrestrial regions................ 36

Figure 13. Seasonal net gaseous fluxes of 2, 3, 7, 8-TCDD in Swedish Baltic Proper ..................... 36

Figure 14. Net flux of dioxins in ten terrestrial regions ............................................................................ 37

Figure 15. Air Fugacity of 17 Dioxins in Swedish Baltic Proper (A4 west) ....................................... 38

Figure 16. Soil fugacity of 17 Dioxins in Swedish Baltic Proper ............................................................ 38

Figure 17. Net gaseous fluxes of seventeen congeners in Swedish Baltic Proper .......................... 39

Figure 18. Net total flux (µg TEQ h-1) of 17 Dioxins in Swedish Baltic Proper ................................. 39

Figure 19. Changing in air fugacity of 2, 3, 7, 8-TCDD in Swedish Baltic Proper............................. 42

Figure 20. Changing in soil fugacity of 2, 3, 7, 8-TCDD in Swedish Baltic Proper ........................... 42

Figure 21. Net flux of 2, 3, 7, 8-TCDD between air and soil in Swedish Baltic Proper .................. 42

Figure 22. Seasonal net gaseous flux from agricultural soil to the atmosphere in Swedish Baltic

Proper 58

Figure 23. Air, soil fugacity and net flux of PECDD in Swedish Baltic Proper .................................. 59

Figure 24. Air, soil fugacity and net flux of 1,2,3,4,7,8-HXCDD in Swedish Baltic Proper............ 60



Figure 27. Air, soil fugacity and net flux of HPCDD in Swedish Baltic Proper .................................. 63

Figure 28. Air, soil fugacity and net flux of OCDD in Swedish Baltic Proper ..................................... 64

Figure 29. Air, soil fugacity and net flux of TCDF in Swedish Baltic Proper...................................... 65


IX

Figure 30. Air, soil fugacity and net flux of 1,2,3,7,8-PeCDF in Swedish Baltic Proper ................. 66

Figure 31. Air, soil fugacity and net flux of 2,3,4,7,8-PeCDF in Swedish Baltic Proper ................. 67

Figure 32. Air, soil fugacity and net flux of 1,2,3,4,7,8-HxCDF in Swedish Baltic Proper............. 68

Figure 33. Air, soil fugacity and net flux of 1,2,3,6,7,8-HXCDF in Swedish Baltic Proper ............ 69

Figure 34. Air, soil fugacity and net flux of 1,2,3,7,8,9-HXCDF in Swedish Baltic Proper ............ 70

Figure 35. Air, soil fugacity and net flux of 2,3,4,6,7,8-HxCDF in Swedish Baltic Proper............. 71

Figure 36. Air, soil fugacity and net flux of 1,2,3,4,6,7,8-HpCDF in Swedish Baltic Proper ......... 72

Figure 37. Air, soil fugacity and net flux of 1,2,3,4,7,8,9-HpCDF in Swedish Baltic Proper ......... 73

Figure 38. Air, soil fugacity and net flux of OCDF in Swedish Baltic Proper ..................................... 74

Figure 39. Compare of soil fugacities, net fluxes of PeCDD (A, B) in different cases .................... 75

Figure 40. Compare of soil fugacities, net fluxes of 1,2,3,4,7,8-HxCDD (A, B) in different cases ...

..................................................................................................................................................................... 76


..................................................................................................................................................................... 76


..................................................................................................................................................................... 77

Figure 43. Compare of soil fugacities, net fluxes of HpCDD (A, B) in different cases .................... 78

Figure 44. Compare of soil fugacities, net fluxes of OCDD (A, B) in different cases ....................... 78

Figure 45. Compare of soil fugacities, net fluxes of 1,2,3,4,7,8-HxCDF (A, B) in different cases ....

..................................................................................................................................................................... 79

Figure 46. Compare of soil fugacities, net fluxes of 1,2,3,7,8-PeCDF (A, B) in different cases ... 80

Figure 47. Compare of soil fugacities, net fluxes of 2,3,4,7,8-PeCDF (A, B) in different cases ... 80


..................................................................................................................................................................... 81


..................................................................................................................................................................... 82


..................................................................................................................................................................... 82


..................................................................................................................................................................... 83

Figure 52. Compare of soil fugacities, net fluxes of 1,2,3,4,6,7,8-HxCDF (A, B) in different cases

..................................................................................................................................................................... 84


..................................................................................................................................................................... 84


X

Figure 54. Compare of soil fugacities, net fluxes of OCDF (A, B) in different cases ........................ 85


XI

LIST OF TABLES

Table 1. List of POPs under Stockholm Convention (2009) ....................................................................... 4

Table 2. Summary of fugacity calculations of different levels of complexity used to describe

multimedia contaminant fate (Mackay, 2001) ................................................................................................ 7

Table 3. TEF schemes for some PCDD/F congeners ................................................................................... 12

Table 4. The formulae to calculate fugacity capacity for different compartments (Cousins and

Jones, 1998; Mackay, 2001) ................................................................................................................................. 18

Table 5. Summary of Aspvreten air (Sellström et al., 2009) and soil concentrations (Gawlik et

al., 2000) for selected PCDD/Fs. ........................................................................................................................ 24

Table 6. Terrestrial and atmospheric compartments in POPCYCLING-Baltic Model .................... 26

Table 7. Half-life of PCDD/Fs in different media (Sinkkonen and Paasivirta, 2000) ..................... 27

Table 8. Physical chemical properties of PCDD/Fs congeners at 250C (Aberg et al., 2008;

Govers and Krop; Trapp and Matthies, 1997) .............................................................................................. 28

Table 9. Sample preparation ................................................................................................................................ 31

Table 10. Henry’s law constant at 3 0C, organic carbon-water partition coefficient and fugacity

capacity in air and soil of 17 congeners. ......................................................................................................... 32

Table 11. Calculated fugacity in air, soil and fugacity quotient of 17 congeners ............................ 32

Table 12. Sensitivity analysis .............................................................................................................................. 40

Table 13. Formulae to calculate various transport processes within and between air and soil ....

..................................................................................................................................................................... 56

Table 14. Total atmospheric concentration ................................................................................................... 57


XII

ABBREVIATIONS AOC Amorphous organic carbon

BC Black carbon

CPW,free Freely dissolved pore water concentration

Cfree Freely dissolved water concentration

DF Dibenzofuran

DD Dibenzo-p-dioxin

DOM Dissolved organic matter

d.w. Dry weight

EC European Commission

EMEP European Monitoring and Evaluation Program

H Henry’s law constant

HCB Hexachlorobenzene

HELCOM Helsinki convention

HRGC High Resolution Gas Chromatography

HRMS High Resolution Mass Spectrometry

HxCDD Hexachlorinated dibenzo-p-dioxin

HxCDF Hexachlorinated dibenzofuran

HpCDD Heptachlorinated dibenzo-p-dioxin

HpCDF Heptachlorinated dibenzofuran

I-TEF Toxic equivalency factors according to NATO/CCMS 1988

I-TEQ Toxic equivalents according to I-TEFs

KAW Air – water partition coefficient

KOA Octanol – air partition coefficient

KOW Octanol – water partition coefficient

MeOH Metanol

OC Organic carbon

OCDD Octachlorinated dibenzo-p-dioxin

OCDF Octachlorinated dibenzofuran

OM Organic matter

PAHs Polycyclic aromatic hydrocarbons

PCB(s) Polychlorinated biphenyl(s)


XIII

PCDD/F(s) Polychlorinated dibenzo-p-dioxin(s) and polychlorinated

dibenzofuran(s); commonly known as dioxins

PCP Pentachlorophenol

PDMS Polydimethylsiloxane (passive sampler)

PeCDD Pentachlorinated dibenzo-p-dioxin

PeCDF Pentachlorinated dibenzofuran

POC Particulate organic carbon

POM Polyoxymethylene (material used for passive sampling)

POP(s) Persistent organic pollutant(s)

PUF Polyurethane foam

SPM Settling (or suspended) particulate matter

TCDD Tetrachlorinated dibenzo-p-dioxin

TCDF Tetrachlorinated dibenzofuran

TEF Toxic equivalency factor

TEQ Toxic equivalent

TOC Total organic carbon

WHO World Health Organization

WHO-TEF Toxic equivalency factor according to WHO; two sets issued, in

1998 and 2006

WHO-TEQ Toxic equivalents according to one of the WHO-TEF sets

w.w. Wet weight

μg Micrograms (1 μg = 0.001 mg)


1

1. INTRODUCTION

Background

Industrialization and modernization in recent decades has made a big step in improving our

daily life. However, the environment is being threatened with the numerous contaminants

released from modern industrial activities. According to the European inventory of

existing commercial chemical substances, there are more than 56,072 chemicals used in

industry in appreciable quantities. Many of them have been used without thoroughly

understanding their physico-chemical properties, fate, and toxicology. An example is the use

of persistent organic pollutants (POPs) in the early 20th century; their detrimental

ecotoxicological effects were not realized until the 1960s and bans were not introduced until

the 1970s.

POPs are organic chemicals, which are toxic, persistent, bio-accumulative, and susceptible for

long-range atmospheric transport (PBT-LRT) (Knut Breiveik, 2006). The ability to undergo

long-range transport to pristine environments (e.g. Arctic) far away from their emission

sources make POPs one of the most problematic environmental issues facing society today.

One of regions with high levels of POPs in its ecosystems is the Baltic Sea region, which makes

this area one of the most studied sea areas in the world.

The Baltic Sea is the largest body of brackish water in the world. The Baltic covers an area of

roughly 415 000 square kilometers. About 16 million people live along the coastline, and a

total of 80 million people in the entire catchment area (Helcom, 1993). A large amount of

domestic, industrial, and agricultural runoff is discharged into the sea through rivers, outfalls,

pipelines, and others effluent points. Harmful and toxic substances, e.g. chlorinated

hydrocarbon pesticides (DDT, dieldrin, and endrin), polychlorinated biphenyls (PCBs),

polychlorinated dibenzo-p-dioxins (PCDDs), and dibenzofurans (PCDFs) have found their

ways into the Baltic Sea. All these substances are toxic to the organisms in the marine

environment and probably also to humans due to resistance to degradation and

bioaccumulation in marine and terrestrial food chains and webs.

The concentrations of PCDD/Fs in fatty fish from the Baltic Sea have exceeded permitted

values allowed for human consumption in the European Union (Bignert et al., 2007).

Therefore, it is important to determine sources of chemicals impacting the sea. A few studies


2

have shown that the bulk of PCDD/Fs accumulated in the Baltic Sea mainly come from

atmospheric deposition (Sellström et al., 2009; Wiberg et al., 2009). Therefore, an

understanding of concentration and sources of PCDD/Fs to the atmosphere is necessary in

order to build a strategy for risk reduction of dioxins.

PCDD/Fs are formed and released in the environment mainly through combustion processes

or through the production, use, and disposal of chlorinated aromatic compounds. Accidental

fires, volatilization from treated wood factories, recycling plants, contamination of

commercial products, etc.…are other potential sources to the air. A report for European

Monitoring and Evaluation Programme (EMEP) about behavior of PCDD/Fs in air showed that

only 1% of the annual PCCD/Fs emissions remain in the atmosphere, about 5% degrade, and

38% are transported outside this region (EMEP, 03/2004). The remaining part deposits to

other media: about 47%-to soil and vegetation and about 9%-to the sea. Soil has received

continuously an amount equivalent to 47% of total annual emission over a period of several

decades. Besides, the half-life of PCDD/Fs in soil has been reported to vary from 10 to 150

years, which means that their degradation is very slow under natural conditions. As a result,

soil accumulates a significant amount of PCDD/Fs (Cousins and Jones, 1998; Duarte-Davidson

et al., 1996; EMEP, 03/2004; Harner et al., 1995). It is hypothesized that soil is an important

potential “secondary” source of dioxins to the air in the case of their primary emission

reduction (Duarte-Davidson et al., 1996).

A study focusing on PCBs has shown that their volatilization from soil is about 50% of the

total emission to the atmosphere (Shatalov et al., 2001). Lighter PCB congeners have a

stronger tendency to move from soil to air than heavier congeners (Backe et al., 2004). A

study in the UK also claims soil to be a source of PCB and lighter PAHs to the air (Cousins and

Jones, 1998). It is therefore hypothesized here that PCDD/Fs present in soils in the Baltic

region could potentially be secondary sources to the atmosphere through gaseous transport

(i.e. volatilization) or through resuspension of soil solids. To date, we are not aware of any

studies conducted in the Baltic region that have examined the potential role of soils as a

secondary source of dioxins to the atmosphere. The present study was therefore initiated to

explore the central hypothesis using several techniques.


3

The Aims of Project

Seventeen (2,3,7,8-substituted) of the 210 congeners of dioxins (210 = 75 PCDDs plus 135

PCDFs) were chosen due to their known high toxicity to mammals and thus potential toxic

effects on humans (Kutz et al., 1990; Van den Berg et al., 2006). Firstly, fugacities are

calculated from the physical-chemical properties of dioxins, properties of environmental

media and their concentration in each medium. The equilibrium state between soil and air is

assessed based on the calculated fugacity quotient. Secondly, a multimedia fate and transport

model, used to estimate the fate of POPs in the Baltic Sea region (POPCYCLING-Baltic

(Armitage et al., 2009)), is applied to obtain estimated fugacities in soil and air as well as long-

term fluxes between the two media. Moreover, some sensitive analyses were undertaken in

order to investigate the effect of initial soil concentration, bioturbation and resuspension to

the rate of transfer from soil to air. Thirdly, fugacities in soil were directly measured using

passive sampling devices (polyoxymethylene 17 µm). The aim of this last experiment was to

compare measured fugacities with those estimated by the model to assess the model’s

predictive capability.

2. PERSISTENT ORGANIC POLLUTANTS (POPs)

2.1 Definition, classification

Persistent organic pollutants (POPs) are defined as organic substances that are toxic and

persistent, could bio-accumulate in food webs, as well as undergo long-range trans-boundary

atmospheric transport (Breivik et al., 2004; El-Shahawi et al., 2010; Lohmann et al., 2007). In

recent years, attempts have been made to identify the behavior of these substances once

released to the environment. Many studies have shown that these chemical do not only bio-

accumulate but also bio-magnify in food chains and webs, resulting in adverse health effects

to wildlife and humans. The Convention on Long-range Trans-boundary Air Pollutant in 1998

in Aarhus ( Denmark) has provided the basic steps for global and regional control of POPs. In

2009 there were 21 compounds which had been listed as POPs by the Stockholm Convention.

POPs can be grouped according to their formation and primary origins. POPs can be formed

by unwanted by-products of combustion or intentionally produced (Breivik et al., 2004; El-

Shahawi et al., 2010; Lohmann et al., 2007). Table 1 summarized the list of present (in 2009)

POPs as well as their origins and classifications.


4

Table 1. List of POPs under Stockholm Convention (2009)

Groups Primary Origin POPs

Intentionally

produced

Pesticides/biocides

Aldrin, chlordane, chlordecone, dieldrin, endrin,

mirex, toxaphene,

dichlorodiphenyltrichloroethane(DDT),

heptachlor, hexachlorocyclohexane (HCH)

including lindane and hexachlorobenzene (HCB)

Industrial chemicals

Polychlorinated biphenyls (PCBs)

Hexabromobiphenyl (HBBP), perfluorooctane

sulfonic acid (PFOS), perflourooctane sulfonyl

fluoride(PFOS-F), pentachlorobenzene (PeCB)

Tetra to heptabromodiphenyl ethers (PBDEs)

Unintentionally

formed as by-

products

-Specific high

temperature

environment with

presence of chlorines

-Combustion derived

-Chemical-industrial

processes

Polychlorinated dibenzo-p-dioxins and

dibenzofurans (PCDD/Fs)

Polychlorinated biphenyls (PCBs)

Poly-aromatic hydrocarbon (PAHs)

Hexachlorobenzene (HCB)

Pentachlorobenzene (PeCB)

2.2 Environmental fate

The behavior and fate of POPs depends upon their physical chemical properties and the

nature of environment they reside in (Wiberg et al., 2009). The distribution of POPs in

environmental compartments is mainly governed by three equilibrium partitioning

coefficients, i.e. the air-water, water-octanol and octanol-air partition coefficients, in which

octanol is used as a surrogate for lipid and organic matter (Mackay, 2001). POPs are

transported between environmental compartments by various transport processes, which are

often broadly classified in multimedia models as diffusive and advective transport processes

(Figure 1). Diffusive transport between soil and air is a reversible two-way process, comprising

dry gaseous deposition, volatilization, sorption and dissolution. Advective transport is the

transport of chemical when it present in a moving media, including, in the case of transport

between soil an air or vice versa, wet and dry deposition, sedimentation, resuspension, and

erosion. Degradation is a pathway to irreversibly remove POPs from an environmental


5

compartment. The most important environmental property controlling soil/air exchange is

the organic carbon content of the soil. Due to their high lipophilicity and low water solubility,

POPs prefer to accumulate in media with high organic carbon or lipid content.

Figure 1. Important fluxes and partition coefficients (Wiberg et al., 2009)

When present in the atmosphere, POPs can sorb to particles or be present in the gaseous

phase due to their semi-volatile nature. POPs are removed from the atmosphere both by

physical and chemical processes. Physical removal from the air can occur by wet and dry

deposition of vapor and particle-sorbed species. For most organic chemicals, reaction with

hydroxyl radical is the dominant degradation process. However, for some compounds,

dominant degradation processes could be the reaction with ozone, or nitrate radical or

photolysis by sun light. Chemicals associated with particulate matter are suspended not to

undergo degradation (Watterson, 1999).

Beyond physical chemical removal processes stated above, POPs can undergo biotic

degradation in surface water, soil and sediments. Biotic degradation consists mainly of

microbial degradation. Abiotic degradation includes hydrolysis, direct and indirect photolysis,

and oxidation/reduction reactions. Most POPs accumulate in soil and sediment after

deposition from the atmosphere. These accumulated POPs can potentially volatilize back to

the atmosphere when levels in the air reduced. On the other hands, POPs in soil can be

leached to ground water or be degraded. In water, POPs partition between the particle and


6

dissolved phases. They can also be deposited to bottom sediments or be taken up by aquatic

biota. POPs in sediment can be transported back to the water column via diffusion or

resuspension in processes analogous to those in soil/air exchange.

Another important property of POPs is the potential to undergo long-range transboundary

atmospheric transport. POPs can travel a long distance in the atmosphere before depositing

on the Earth’s surface. Various evidence shows that POPs have been found in remote regions

(e.g. Arctic) where they have never been produced or used. Long range atmospheric and

ocean water transport are the two main pathways for global transport of POPs, resulting in

their ubiquitous presence (Lohmann et al., 2007).

2.3 Chemical Analysis

The method described here is the method used at Umeå University to analyze chlorinated

aromatic compounds in environmental samples. POPs are usually present in very low

concentration in background environmental samples. In order to compensate for loss of

analyte during extraction and cleanup procedures, isotope labeled recovery standards have

been used. Isotope-labeled standards ( 13C- and 37Cl-labeled) are added to the samples prior to

extraction. Since the analytes and the internal standard in any sample receive the same

treatment, the ratio of their signals will be unaffected by any lack of the reproducibility in the

procedure.

Most of extraction methods for organic pollutants are based on their preference to dissolve in

organic solvents. There are various types of extraction techniques and solvents, and the

design of the extraction procedure depends on the sample matrix and physical-chemcial

properties (e.g. polarity) of the analytes. With gaseous or aqueous samples, solid phase

extraction or lipid/lipid extraction is used. For solid samples, Soxhlet extraction or Soxhlet-

Dean-Stark extraction is preferred to be used, depending on the water content of samples.

After extraction, fat from biological samples and other interfering substances still remain in

the samples. Cleanup and fractionation procedures are applied, using dialysis or acid/base

columns, or multi-layer columns to separate the analytes from the matrix. The cleaned-up

sample extracts then undergo separation and quantification using gas chromatography (GC)

combined with mass spectrometry (MS). All the GCs have pressure control of the column and


7

temperature programming of the oven. The MS, which is connected with the GC through an

interface, can be low or high resolution.

2.4 Modeling

Due to the complexity of understanding chemical fate processes in the environment, as well as

the great expense of measuring levels of and conducted experiments on organic pollutants,

the interest in developing and applying models for estimating environmental fate is

increasing. Many different models have been developed that attempt to describe or predict

the fate of chemicals in the environment (Armitage et al., 2009; Mackay et al., 1996a; Mackay

et al., 1996b; Mackay et al., 1992; Mackay and Wania, 1995; McKone, 1996; Paterson and

Mackay, 1989; Sweetman et al., 2002; Wania and Mackay, 1995; Wania and Mackay, 1999).

The models proposed here for calculating partitioning and behavior of POPs in the

environment are based on the standard unit-world fugacity modeling concept as developed

by Mackay and co-worker. This multimedia mass balance approach was first developed in the

late 1970s and it is now widely accepted as a useful, essential tool for understanding of the

behavior of POPs in the environment.

Table 2. Summary of fugacity calculations of different levels of complexity used to describe multimedia contaminant fate (Mackay, 2001)

Type of fugacity calculation

Key as assumptions Information garnered

Level I -Equilibrium partitioning -Steady state -Closed system

-General partitioning tendencies for persistent chemicals

Level II -Equilibrium partitioning -Steady state -Opened system

-Estimate of overall persistence -Important compartments for removal processes -Relative importance of advection and degradation as removal pathways

Level II

-Non-equilibrium partitioning -Steady state -Open system

-Influence of mode of entry on fate and transport -Rates of inter-media transport -Refined assessment of overall persistence and loss pathways

Level IV

-Non-equilibrium partitioning -Dynamic -Open system

-Influence of mode of emission on fate and transport -Time course of respond of contaminant inventory by compartment to any time- varying condition


8

Fugacity models can be used to predict environmental fate of chemicals in a unit world. A unit

world is a model world with well-mixed compartments such as air, water, soil, sediment, ect…

A unit world is supposed to reflect the real world or a part of a real world. Fugacity models

increase in complexity from Level I to IV. Level I assumes equilibrium partitioning and is the

simplest and possible least realistic type of mass balance while level IV allows time-

dependent concentrations to be predicted (i.e. it is dynamic model) and may often provide the

most realistic type of mass balance. One of the advantages of fugacity models is the ability to

increase complexity depending on available information and the requirement of accuracy as

well as the purpose of users (Mackay, 2001; Mackay and Paterson, 1991; Mackay et al., 1992).

A detailed explanation of these different levels is included in table 2.

2.6 Dioxins

2.6.1 Dioxins and their physical chemical properties

Dioxins are a group of chlorinated organic chemicals with similar chemical structures.

Chlorine atoms can attach to eight different places on two benzene rings, carbon atom 1 to 4

and 6 to 9. The common term “dioxins” includes 210 congeners, in which 75 congeners are

polychlorinated dibenzo-p-dioxins (PCDDs) and 135 are polychlorinated dibenzo-furans

(PCDFs). A general chemical structure of PCDDs and PCDFs is presented as Figure 2.

Figure 2. General Structure of PCDDs and PCDFs and numbering of carbon atoms

Because of the unique environmental properties of dioxins and furans, such as low vapor

pressure, extremely low water solubility in water, high lipophilic, resistance to photolytic,

biological and chemical degradation, and tendency to bioaccumulation, they are categorized

as one of the most harmful organic pollutants. Physical chemical properties of dioxins vary

among congeners. In contrast to lipophilicity, vapor pressure and water solubility decrease

with increasing the number of chlorine atoms in the corresponding congeners.


9

2.6.2 Sources and environmental fate

Dioxins are mainly derived from human activities, but can also be generated naturally by

forest fires or volcanic activity. They are not produced for any industrial purpose but

unintentionally by-products of numerous industrial and combustion processes. Industrial

processes, waste incineration, fuels combustion (wood, coal or oil), chlorine bleaching from

pulp and paper mill, and chlorinated pesticides manufacturing were believed to be the main

sources of dioxins (Duarte-Davidson et al., 1996). Since the introduction of regulation on

dioxins, emission from chlorinated pesticides manufacturing which was historically the

biggest source has now become a minor contributor . Therefore, combustion processes have

become the most important global contributor to the dioxin source inventory (Deriziotis,

2004). In addition, cigarette smoke, home-heating systems, and exhaust from cars also

contain small amounts of dioxins.

Figure 3. A schematic of illustration of the sources and environmental fate of PCDD/Fs

Dioxins enter the environment as mixtures containing a variety of individual components and

impurities. Once released to environment, they distribute between environmental

compartments as seen in Figure 3. Dioxins can be found in both vapor and particles phases

due their semi-volatile nature. Their gas-particle partitioning depends on temperature,

amount and nature of particulate matter in the air, and the chlorination of dioxin congeners.

The large fraction of the less chlorinated dioxin congeners are present in the gaseous phase in

the summer since the temperature is high (Bobet et al., 1990; Eitzer and Hites, 1989;

Watterson, 1999).


10

Two main pathways by which dioxins are physically removed from the air are wet and dry

deposition. When deposited to terrestrial environments, dioxins tend to be associated with

soil solids or any surface with a high organic content, such as plant leaves. Large amounts of

dioxins accumulate in soil and can be gradually released to other media.

Most of the PCDD/Fs deposited from the atmosphere bind strongly to dissolve or particulate

organic matter in water. These particles deposit into sediments but can also be transported

back to water via resuspension. However, the reverse process is quite slow, resulting in the

accumulation of large amounts of dioxins in sediment. This is why sediments are regarded as

an important reservoir of dioxins in aquatic environment. Fish and other aquatic biota can

uptake PCDD/Fs through diffusion across gills or ingestion of contaminated prey.

2.6.3 Degradation

Photo-degradation can occur to dioxins in the gaseous phase, but mostly not in the particle

phase (Brubaker, 1997; Knut Breiveik, 2006; Watterson, 1999). Dioxins attached to

particulate matter are thought to be resistant to degradation. Less chlorinated compounds are

more easily degraded than others (Orth et al., 1989; Pennise and Kamens, 1996). The half-life

of PCDD/Fs in the atmosphere was found to be in a wide range from 0.4 up to 62 hours,

depending on light intensity and the chlorination of dioxins. Chemicals in surface waters,

which receive much sunlight, have higher rates of removal than bottom water or sediments.

The degradation half-live of dioxins in sediments has been estimated to up to 550 days (EPA,

Technical Factsheet on Dioxin; Ward et al, 1979), although this may be an overestimate of

their degradability.

Biodegradation has a minor impact on dioxin destruction because of their high resistance to

microbial activity. Volatilization also is not an important removal of dioxins from the water

column in comparison to the incorporation in sediments. The most important loss processes

for dioxin deposited to terrestrial soils are thought to be photolysis and volatilization. The

persistence half-life of TCDD on top soil surfaces may vary from less than one to three years

but half-lives in soil interiors may be as long as 12 to 150 years (EMEP, 03/2004).

2.6.4 Long - range transport

Physical and chemical properties of high persistence and semi-volatility, coupled with other

unique characteristics of PCDD/Fs, have resulted in their being widely distributed through the


11

global environment, even in remote regions where they have never been used, i.e. Arctic and

Antarctic regions. Dioxins can move long distances in the atmosphere before deposition.

Dioxins were found in soil and sediments in the Arctic (Brzuzy, 1996; Cleverly D.A. and

Carthy, 1996; Oehme, 1993; Wagrowski, 2000).

2.6.5 Bio-accumulation, Bio-magnification and Toxicity

Dioxins are global contaminants due to their toxicity, resistance to degradation, tendency to

bio-accumulate and bio-magnify up in the food chain. Dioxins have been detected in mussels,

crabs, herring, salmon, guillemot and seal (Kiviranta et al., 2003; Rappe et al., 1987; Sakurai et

al., 2000). Fatty fish caught in the Bothnian Sea (within the Baltic Sea) have exceeded the

maximum levels for human consumption in European Union guideline (Bignert et al., 2007;

Kiviranta et al., 2003). Bio-accumulation in such organisms occurs by the ingestion of

sediment or by direct uptake of dioxins from water through gill membranes. Since these

substances are harmful to aquatic organisms, they threaten the survival of predatory animals

and human health.

Dioxins can have varying harmful health effects depending on the number and position of the

chlorine atoms (Duarte-Davidson et al., 1996; Kutz et al., 1990; Van den Berg et al., 2006) . 2,

3, 7, 8-TCDD or simply TCDD, a molecule with 4 chlorine atoms, is the most toxic dioxin

congener. Dioxins are slowly bio-transformed in the body and are not easily eliminated. They

tend to accumulate in fat and in the liver. By interacting with a cellular receptor, dioxins can

trigger biological effects such as hormonal disturbances and alterations in cell functions.

Dioxins and dioxin-like compounds that have the ability to interact with Ah-receptors and

cause toxic effects are specified by a toxic factor called “Toxic Equivalency Factor” (TEF)

(Van den Berg et al., 2006) as shown in Table 3. This factor indicates the degree of toxicity of

each congener compared to 2, 3, 7, 8-TCDD, which is given a reference value of 1. All other

congeners are assigned lower TEFs comparable to their relative toxicity. TEF values vary for

different species and congeners. The TEF values of individual congeners in combination with

their concentration give us the total TCDD Toxic Equivalent (TEQ). To calculate TEQ of a

dioxin mixture, the amounts of each toxic compound are multiplied with their TEF values and

then summed together. The older International Toxic Equivalent (I-TEQ) and the World

Health Organization Toxic Equivalent (WHO-TEQ) are the two available schemes.


12

Table 3. TEF schemes for some PCDD/F congeners (Kutz et al., 1990; Van den Berg et al., 2006)

Congeners WHO-TEF (2006) I-TEF (1998)

2,3,7,8-TCDD 1 1 1,2,3,7,8-PeCDD 1 0.5 1,2,3,4,7,8-HxCDD 0.1 0.1 1,2,3,6,7,8-HxCDD 0.1 0.1 1,2,3,7,8,9-HxCDD 0.1 0.1 1,2,3,4,6,7,8-HpCDD 0.01 0.01 OCDD 0.0003 0.001 2,3,7,8-TCDF 0.1 0.1 1,2,3,7,8-PeCDF 0.03 0.05 2,3,4,7,8-PeCDF 0.3 0.5 1,2,3,4,7,8-HxCDF 0.1 0.1 1,2,3,6,7,8-HxCDF 0.1 0.1 1,2,3,7,8,9-HxCDF 0.1 0.1 2,3,4,6,7,8-HxCDF 0.1 0.1 1,2,3,4,6,7,8-HpCDF 0.01 0.01 1,2,3,4,7,8,9-HpCDF 0.01 0.01 OCDF 0.0003 0.001

3. AIR-SOIL EXCHANGE

3.1. Processes involved in air – soil exchange

As a result of regulations, the production and use of dioxins as pesticides and herbicides have

been prohibited and combustion processes have now become the dominant sources of dioxins

in the environment (Cousins and Jones, 1998). Once released into the air, dioxins move away

from primary sources before being deposited to terrestrial or water surfaces. Soil can receive

inputs of dioxins directly from air deposition or indirectly from plant growing on it. Due to

their resistance to biodegradation, the application of herbicide and pesticide containing

dioxins the 1960s and early 1970s still remain in soil today. Soil retains dioxins and thus is

considered as a large reservoir of PCDD/Fs, which can potentially be gradually released to the

atmosphere or surface waters (Cousins and Jones, 1998).

Transport processes between the air and soil play an important role in the accumulation and

fate of PCDD/Fs for many reasons. Firstly, one of the main pathways that humans are exposed

to dioxins occurs via the agricultural food chain; air-plant-cow-human (Cousins et al., 1999a;

Duarte-Davidson et al., 1996). For this reason, the levels of dioxins in air are key in controlling

the levels of dioxins in human. Secondly, the atmosphere is the major transport medium for

dioxins, controlling the regional and global transport of dioxins. Understanding the exchange


13

processes between air and soil is an important part of studies of the behavior and spreading

of dioxins in these environments.

Dry deposition

The two main processes contributing to air-soil exchange of semi-volatile organic compounds

(SVOCs) are : atmospheric deposition and volatilization from the soil (Cousins et al., 1999a).

Atmospheric deposition to soil includes dry and wet deposition. If soil is covered with

vegetation, it will receive another input from plant decay. Due to its large surface area,

vegetation is considered as an effective scavenger of dioxins in the atmosphere present in

both particle and gaseous phases (Simonich and Hites, 1995). Dry deposition refers to any

physical removal process in the atmosphere that does not involve precipitation (Hemond and

Fechner-Levy, 2000). There are three dry deposition mechanisms: gravitational settling,

impaction and absorption (Hemond and Fechner-Levy, 2000). Gravitational settling is a

significant removal process for particulate matter with diameter is larger than 1 µm (Kaupp et

al., 1994; Mackay, 2001). Impaction occurs when air containing particles moves past

stationary objects e.g. vegetation or buildings. Some of the airborne particles collide with the

objects and stick. Dry deposition of particles depends on the size and density of the aerosol

particle, terrestrial surface properties such as roughness and atmospheric conditions such as

humidity and wind speed. Atmospheric gases are absorbed by liquid or solid surfaces (soil,

vegetation, etc.) (Hemond and Fechner-Levy, 2000). The process depends on the physical-

chemical properties of the substance, the characteristics of the soil surface (i.e. concentration

in soil, roughness and especially the type of vegetation) and the environmental conditions

(e.g. wind speed) (Cousins et al., 1999a).

Wet deposition

Wet deposition refers to processes in which atmospheric chemicals are accumulated in rain,

snow, or fog droplets and are subsequently deposited onto Earth’s surface. Rain and snow are

very efficient scavengers of particles. Compounds are removed from the atmosphere both as

vapors (which dissolve in the raindrops) and bound to atmospheric particles (which are

incorporate in the rain within or below clouds) (Cousins et al., 1999a). When incorporation of

chemicals into water droplets occurs within a cloud (nucleation scavenging), the process is

called rainout. When incorporation occurs beneath a cloud (scavenging of particles and gases

by droplets), the process is called washout. Gases and vapors in the atmosphere are removed


14

from the air effectively by dissolving into raindrops. Particulate chemicals may also be

removed from the atmosphere through wet deposition processes. Particles play a role as

nucleation sites from condensation at the onset of water droplet or ice crystal formation.

Particles can also be incorporated into already-formed water droplets within a cloud by

collision. Removal of particles by rainout is far more effective than dry deposition of particles.

The total wet scavenging ratios in the air can be calculated with equation:

WT = WP Ф + WG (1-Ф)

Where: WT is the total wet scavenging ratios.

WP and WG are the sum of the effective scavenging ratios for the substance in the

particle and gas phases.

Ф is the fraction of chemical in air that sorbed to the aerosol.

In conclusion, dry and wet deposition control the deposition of PCDD/Fs to soils. In the case of

dry gaseous deposition, PCDD/Fs present in the vapor phase subsequently diffuse into the

soil. Association with particles that deposit to soils by gravitational settling or impaction is

another pathway. The size of particles is the key parameter determining the dry deposition

pathway of PCDD/Fs. However, particle size of PCDD/Fs are not dependent on the degree of

chlorination, therefore deposition pathways should be similar for all PCDD/Fs. It is

hypothesized that impaction is an important pathway of deposition for PCDD/Fs because

enrichment of PCDD/F particles are associated with diameter smaller than 0.45 µm (Kaupp et

al., 1994). In the case of wet deposition, PCDD/Fs are dissolved in precipitation. Alternatively,

they are associated with atmospheric aerosols scavenged by precipitation. Deposition is in

general dominated by the higher chlorinated congeners, notably octa-chlorinated dibenzo-p-

dioxins (OCDD), which typically accounts for 20-40% of the total PCDD/F flux (Lohmann and

Jones, 1998).

Volatilization

Volatilization from soil refers to the sum of processes that contribute to the evaporation of a

compound from the soil surface and subsequent transport to the atmosphere (Cousins et al.,

1999a). In soil dioxins can be sorbed to organic matter (reversibly or irreversibly), leached to

ground water, removed by erosion or degraded (biotic or abiotic), or volatilized to the air.

With soil covered by vegetation, losses by erosion are less than 1% per year (Mackay, 2001).

Most of PCDD/Fs remain in the soil at least 9 years because of their high immobility and half-

life value (10-150 years) (EMEP, 03/2004; Hagenmaier et al., 1992). Predicted soil-water


15

distribution coefficients for dioxins ranging from 104 to 106 reveal that PCDD/Fs sorb strongly

to soils (Brzuzy and Hites, 1995). Net volatilization losses can occur only when the fugacity of

the substance in the soil exceeds the fugacity of it in the overlying air. The substance needs to

be desorbed from the soil, migrate to the soil surface and then be transferred across the

soil/air interface to the air. There are typically three mechanisms to transfer a compound to

the soil surfaces. For most SVOCs, the main transport route is through mass transfer with

evaporating water. More volatile compounds under very dry conditions, are transferred by

upward gas and/or liquid phase diffusion. The main route for compounds that are immobile

and highly persistent is soil disturbance (tilling or bioturbation).

Bioturbation

Many animals spends most of or all their lives below ground seeking food, shelter and mates.

Earthworns and other invertebrates usually push their way vertically and horizontally

through the soil, displacing particles for short distances away from their bodies. These

activity, in turn, yield indirect effects on the volatilization flux of chemicals by transferring

chemicals to the surface. A study from McLachlan and co-worker showed that the influence of

vertical sorbed phase transport to gaseous exchange between surface soil and the atmosphere

is very important for lipophilic compounds (McLachlan et al., 2002). Dioxins posses ability to

sorb in soil, results in transport via the gas and liquid phases is very slow. Therefore,

bioturbation is believed to be another, often neglected, important transport mechanism in the

soil. Earthworms bring chemicals to the surfaces by turning over soil layers. When chemicals

reach the surface, they need to move across a layer called the stagnant air boundary layer.

Substances are transported through this layer by molecular diffusion. The rate of transfer is

dependent on the diffusion coefficient and vapor density of substances at the interface.

Resuspension

Surface soil particles can enter the atmosphere by three different mechanisms, depending on

their sizes, as shown in Figure 4. Large particles with diameter > 1500 µm can only roll along

the surface. That movement of soil particles is called creep. Particles have diameters in the

range of 70 to 1500 µm have the ability to lift up from the surface. However, these particles

are still too heavy to be present in the atmosphere for a long time. Saltation is the

phenomenon when particles are suspended from the surface but rapidly fall back. Only

particles with diameter smaller than 70 µm can suspend freely under suspension mode. The


16

smaller the particles are, the longer they can remain in the atmosphere. However, very small

particles with diameter < 20 µm act like a gas and can only suspend in the air for a few days

(Shao, 2001). Other factors that affect soil suspension are wind velocity, the roughness of the

soil surface and the effectiveness of saltation.

Figure 4. A schematic picture of vertical soil aerosol suspension under action of wind (Qureshi et al., 2009)

3.2 Factors affecting the air-soil exchange process

The soil/air partition coefficient (KSA) is used to describe partitioning of compounds between

the air and soil. It can be calculated from these equations

(1)

Where:

foc is the soil organic carbon fraction

Kow is the octanol/water partition coefficient

H is Henry’s law constant

Equation (1) shows that the behavior of a compound to partition from soil to air becomes

increasingly effective with a higher KOW/H ratio, which indicates the dependence of KSA on the

properties of chemical e.g. water solubility, vapor pressure, molecular weight, etc.

In addition to physical-chemical properties of a compound, environmental factors also play an

important role in controlling the partitioning between air and soil (Cousins et al., 1999a;


17

Duarte-Davidson et al., 1996). These environmental factors are temperature, wind speed,

humidity, soil properties, and vegetation cover. SVOCs have a tendency to partition into the

particle phase at low temperature. In addition, compounds that exit in the vapor phase are

also easily adsorb on solid surfaces at low temperatures thus increasing total deposition of a

substance. When the temperature increases 100C, vapor pressure also increases three to four

times. Therefore, higher temperature are usually associated with higher volatilization rates.

Increasing wind speed not only proportionally increases the dry gaseous deposition flux but

also intensifies volatilization and resuspension (Duarte-Davidson et al., 1996). Relative

humidity also has an effect on the volatilization rate. Reducing relative humidity can lead to

an increase in the volatilization rate due to loss of water in the soil surface. Soil properties

such as organic matter content, moisture, texture, porosity have strong effect on soil-air

partition coefficients KSA. According to equation (1), KSA increase proportionally with organic

carbon content in soil. Soil moisture content has an effect on volatilization due to increasing

the migration rate of a substance to the surface. Low soil-moisture content (0.3-0.8%) has a

strong effect on the soil-air partition coefficient, but KSA is not affected if soil moisture content

is from 1.9 to 12% (Hippelein and McLachlan, 2000). Soil texture is less important than

porosity and moisture content in influencing volatilization losses.

The effects of vegetation cover on air-soil exchange are expressed by the Leaf Area Index

(LAI) value. LAI is the ratio between the total surface area of the leaves and the ground-

surface area a plant or tree occupies. Chemicals can be deposited onto the plant by different

processes. They exist on the plant until they are washed off by rain or volatilized. If not

removed, they may enter the soil when the plants die and decay into the soil. The deposition

flux becomes increasingly effective with higher LAI values. There is a paucity of information in

the literature on the effects of vegetation cover on volatilization losses from soil. The

vegetation covers and shelters the soil and prevents chemicals from exposure to high

temperatures. As a result, the re-volatilization process can be decreased compared to bare

soil. However, vegetation can make water evaporate. Chemicals in deeper layers may be

transported to the soil surface by convection in the soil water, which may make volatilization

losses increase.


18

3. METHODOLOGY TO STUDY AIR-SOIL EXCHANGE

3.1 Fugacity quotient concept

The fugacity concept is used to evaluate the contamination status of environmental media as

well as to investigate and predict diffusive transport fluxes (Backe et al., 2004; Cousins and

Jones, 1998; Duarte-Davidson et al., 1996; Horstmann and McLachlan, 1992). Fugacity can be

thought of the fleeing or escaping tendency, and is equivalent to the partial pressure in air

(Mackay, 2001). A chemical present in two compartments is in equilibrium when their

fugacity values in both compartments are equal. The fugacity of a compound in a

compartment is calculated from its concentration

f = C / (Z*M)

Where:

C is the concentration of compound in the compartment (g m-3)

M is the molecular mass (g mol-1)

Z is the fugacity capacity (mol m-3 Pa-1)

Table 4. The formulae to calculate fugacity capacity for different compartments (Cousins and Jones, 1998;

Mackay, 2001)

Fugacity capacity (mol m-3 Pa-1)

Air Za = 1/RT R is the gas constant (8.314 Pa m3 mol-1 K-1) T is the absolute temperature (K)

Soil Zs = focρsKoczw Koc = 0.41Kow(*)

foc is the fraction of organic carbon ρs is the soil density (assumed to be 1.5 g cm-3 for all calculation (Lohmann and Jones, 1998)) Koc is the organic carbon/water partition coefficient Kow is the octanol/water partition coefficient

Water ZW = 1/H H is Henry’s law constant ( Pa m3 mol-1) at 250C

(*) according to Karickoff (Mackay, 2001)

The fugacity capacity, or Z-value, is a measurement of the compartment’s capacity to hold or

store a given chemical. Its value depends on the properties of media and properties of the

chemical. The fugacity capacities of different compartments can be calculated from either

thermodynamic theory and/or equilibrium partition coefficients. Table 4 shows the formulae

to calculate fugacity capacity of air, soil and water.


19

Temperature correction

Since most available physicochemical data are reported at 250C, it is necessary to adjust

parameters which are likely temperature dependent such as KOC and H. KOC is calculated from

KOW and these partition coefficients are not usually very temperature-sensitive. However,

Henry’s law constant is a temperature-sensitive parameter. It is necessary to adjust Henry’s

law constant value for the sampling site temperature. Temperature correction can be done

straightforwardly by using the integrated van’t Hoff equation (Backe et al., 2004; Beyer et al.,

2002; Cousins and Jones, 1998).

(

)

Where:

H1, H2 are Henry’s law constants at two temperatures

T1, T2 are temperature (K)

ΔHaw is the enthalpy of air-water exchange (J mol-1)

The relative fugacity of two environmental compartments is expressed by fugacity quotients.

The fugacity quotients of soil and air are calculated as fs/fa where fs is the fugacity of soil and fa

is the fugacity of air. The fugacity quotient concept is a useful method because the fluxes are

usually low and difficult to measure experimentally. This concept has been used previously in

the literature (Backe et al., 2004; Cousins and Jones, 1998; Duarte-Davidson et al., 1996).

Fugacity quotient values near one show equilibrium between the two phases. Values which

differ from one indicate a tendency for the compound to move from one compartment to the

others in attempt to establish equilibrium conditions. When the soil/air fugacity quotient is

larger than one, the compound tends to volatilize (i.e. the net gaseous flux is from soil to air)

from soil to the air. As the result, the soil may become a “secondary” source to the air.

Beside its convenience, the use of fugacity quotient approach has some limitations. The

approach only provides a snapshot for a set of environmental conditions. The partition

between air and soil can be affected by many factors such as the distribution of compounds

within surface soils, rates of transport and resistance to transport. However, these factors are

not accounted for in the calculation of fugacity quotients from field data. Multimedia fate and

transport model can help to solve these problems by estimating fugacity quotients as a

function of time/temperature etc..


20

3.2 Multimedia fate and transport model of dioxins

Several mass balance models have been developed to simulate the exchange of POPs between

air and soil (Cousins et al., 1999b; Duarte-Davidson et al., 1996; Harner et al., 1995). For

example, a study, which used a two-compartment model, predicted that soil is a significant

source of PCDD/Fs to the air (Duarte-Davidson et al., 1996). In UK, each year, the soils

released smaller than 0.15 kg ΣTEQ to the atmosphere. However, it was discussed that the

model overestimated soil-air fluxes at that time. They also made a conclusion that the soil

would be an important source to the air if the primary sources were reduced in the future. A

more complex model applied in Germany showed that background soil and air were in

equilibrium. However, for highly polluted soils, desorption from soil was a significant

secondary source for atmospheric pollution (Trapp and Matthies, 1997). It should be noted

though that it took a long time for dioxin to volatize from soil to air, for instance 2, 3, 7, 8-

TCDD was transported 0.1 m in sandy soil in 12 years (Freeman and Schroy, 1986). Herein,

the non-steady state, multi-compartmental, fugacity-based model is employed to simulate the

environmental fate of PCDD/Fs in the Baltic Sea. Detailed description of the model is

presented in Section 4.2.

3.3 Methods to measure fugacity in soil

3.3.1 Fugacity meter

The exchange of gaseous chemical between the atmosphere and soil is a diffusive process

(Hippelein and McLachlan, 1998). The direction and magnitude of the diffusion gradient is

determined by the concentrations in the air and soil and by the soil/air equilibrium partition

coefficient KSA. KSA can be measured using a solid-phase fugacity meter (Hippelein and

McLachlan, 1998; Hippelein and McLachlan, 2000). In the fugacity meter, a soil sample is

placed inside a glass column through which air is passed. Equilibrium between the air and the

surface of soil is established by adjusting the air flow rate passing through the column and

comparing measured concentrations in the exhaust air. The output air is collected with a

sorbent trap, which is extracted with solvent and analysed on a GC-MS to determine the levels

in the exhaust air. The concentration in the soil is also determined. KSA is calculated from the

ratio of concentrations in soil and air at equilibrium. The fugacity meter is believed to be a

valuable tool for investigating the fate of semi - volatile organochlorine compounds in a solid

phases (Horstmann and McLachlan, 1992). Apart from bulky apparatus, this method has some

other advantages. KSA is sensitive with temperature, therefore it is necessary to keep


21

temperature stable during the operation of the system. In addition, the rate of the air flow

passing through the column need to be adjusted in order to ensure equilibrium between the

air and the surface soil.

3.3.2 Equilibrium passive samplers

Freely dissolved concentrations (Cfree) refer to those molecules in an aqueous solution that are

not bound to particles or associated with dissolved organic carbon. Cfree can be understood as

an effective “available” concentration for bio-uptake or partitioning. Because it is an effective

measure of bioavailability, it is important for assessing the risk associated with a chemical in

a compartment. The Cfree of organic contaminants have been successfully measured in the

studies of chemical fate and transport (Cornelissen et al., 2010; Cornelissen et al., 2008a;

Jonker and Koelmans, 2001). One methodological approach for measuring Cfree that has found

widespread use in recent years is the use of equilibrium passive sampling devices. In order to

assess the availability of PCDD/Fs in soil, the soil pore-water concentration and total soil

concentration of dioxins have been measured.

Freely dissolved concentration

In this project, polyoxymethylene 17 µm (POM-17) is used to absorb freely dissolved

PCDD/Fs molecules in soil. Firstly, soil samples are shaken horizontally with POM-17 for a

long time enough to achieve equilibrium. The extraction time for PCDD/Fs achieved

equilibrium with soils is still unknown. It is assumed that 6 months is long enough for

equilibration of the system. The expected equilibrium time of 6 months used here is based on

observations of equilibration time of other SVOCs, e.g. PCBs (10-40 days using POM-17)

(Cornelissen et al., 2008b), PAHs, PCBs, and PCDD/Fs using POM-55 ( 10-14 days)

(Cornelissen et al., 2010). In order to check equilibrium status of the system, the POM strips

are taken out after 3 and 6 months and stored for analysis. After being sampled, POM strips

are cleaned and extracted using liquid chromatographic columns. The samples are injected

and quantified on a GC-MS system.

The freely dissolved (and “available”) soil pore-water concentrations (Cpw, free) are deduced

from the chemical contents in the POM samplers (CPOM) with measured passive sampler-water

partition coefficients (KPOM) values (Cornelissen et al., 2010; Cornelissen et al., 2008a; Jonker

and Koelmans, 2001).


22

Methods used to determined passive sampler-water partition coeffcients outlined by

Cornelissen et al. (2008a). KPOM are measured by shaking POM with PCDD/F stock solution,

without soil. KPOM of 2, 3, 7, 8-substituted congeners are deduced from the KPOM-KOW linear

regression of measured KPOM vs. KOW for the non-2, 3, 7, 8-substituted congeners.

KPOM for each non-2, 3, 7, 8-substituted congener is measure by shaking POM with stock

solution, without soil. A range of methanol-water co-solvent systems is used as substitute for

pure water because of the difficulty in measuring PCDD/F concentration at pg to ng per liter

in pure water (Cornelissen et al., 2008a). In addition, KPOM (non-2, 3, 7, 8-substituted

congeners) for pure water is deduced by extrapolating to 0% methanol. The analytical

procedure is similar to the procedure described above.

The POM strips are shaken with soil and water until the equilibrium condition is established.

Therefore, the fugacity of soil is equal to fugacity of water and fugacity of POM. It can be

calculated as follow:

(H: Henry’s law contants)

The calculated fugacity will be compared with the estimated fugacity to assess the predictive

ability of the model.

POM can accumulate larger amounts of contaminants due to its large surface area. By

extracting the plastic phase and concentrating the extract to a small volume, the POM method

is able to detect very low aqueous concentration. The method is 400 times more sensitive

than a standard 7 µm PDMS-SPME (Jonker and Koelmans, 2001). Equilibrium passive

samplers using POM strips are considered to be a simple, reproducible, and inexpensive

partitioning method. In contrast to active sampling, freely dissolved concentration can be

directly measured using POM without dissolve organic carbon (DOC) correction (Cornelissen

et al., 2010; Cornelissen et al., 2009). However, the biggest disadvantage of passive samplers

for dioxins is the long time to achieve equilibrium.


23

Total soil concentration

Total soil concentrations are also measured so that soil/water partition coefficients (KSW) can

be derived. Non-dried soils are extracted with toluene, PUF absorbents and internal standard

for 17 hours (Cornelissen et al., 2008a; Danielsson et al., 2005). The extracts is cleaned using

liquid chromatographic columns and analyzed with HRGC/HRMS.

Total organic carbon and black carbon contents

The freely dissolve aqueous concentration in soil can be affected by the strong binding to

organic carbon. The sorption of hydrophobic organic chemicals has been proposed to consist

of linear absorption of amorphous organic carbon and nonlinear adsorption of black carbon

(Cornelissen and Gustafsson, 2004). The concentration in the soil is calculated with

CS = fAOCKAOCCW + fBCKBCCW (Cornelissen et al., 2008a)

KSW = fAOC*KAOC + fBC*KBC (1)

Where

KSW is the soil/water partition coefficient.

fAOC and fBC are the soil mass fractions of AOC and BC, respectively.

KAOC is the AOC-water distribution ratio.

KBC is the BC-water distribution ratio.

KAOC and KBC is compared to assess the relative important of AOC and BC to sorption. AOC-

water distribution ratio is widely calculated using equation:

Log KAOC = logKOW -(0.48 ± 0.42) (Seth et al., 1999)

The soil mass fractions of AOC and BC are analyzed, and thus the BC-water distribution ratio

can be deduced from equation (1). The mass fraction of BC can be measure directly as

described below while the mass fraction of AOC is the difference between TOC and BC. The

methods used for determining TOC and BC are exactly the same procedure presented in

Cornelissen et al. (2008) and Gustafsson et al. (1997). Total organic carbon is determined

with catalytic combustion elemental analysis at 1030 0C after micro-acidification to remove

inorganic carbonates. Black carbon contents were determined by forming a small amount of

soil samples into balls and burning samples at 375 0C for 18 h in the presence of excess

oxygen.


24

4. METHODS

4.1 Fugacity quotient

Air concentrations of PCDD/Fs were taken from a study that was previously undertaken at

Aspvreten (south of Stockholm) during the winter of 2006-2007. The average temperature at

Aspvreten during the winter of 2006-2007 was 30C. The Henry’s law constant values were

recalculated at this temperature. Soil concentrations were taken from data reported for

European reference soils (Gawlik et al., 2000). The organic carbon fraction in soil was chosen

to be a typical value of 0.04, in the absence of measured values (analysis was ongoing at the

time of writing). The particle and gaseous air concentrations as well as soil concentrations of

17 congeners of PCDD/Fs are shown in Table 5.

Table 5. Summary of Aspvreten air (Sellström et al., 2009) and soil concentrations (Gawlik et al., 2000) for

selected PCDD/Fs.

Compound

Mean air concentration (fg m-3) Mean soil

concentration (ng kg-1)

Aspvreten (A2, A3, A4)

Pallas Average of

(Aspvreten+Pallas) (A1)

2,3,7,8-TCDD 0.19 0.03 0.11 0.24 1,2,3,7,8-PeCDD 0.68 0.07 0.38 0.86

1,2,3,4,7,8-HxCDD 0.69 0.21 0.45 1.11 1,2,3,6,7,8-HxCDD 2.11 0.45 1.28 2.01 1,2,3,7,8,9-HxCDD 1.43 0.28 0.86 2.62

1,2,3,4,6,7,8-HpCDD 18.75 1.25 10.00 20.3 OCDD 41.88 3.25 22.56 74.79

2,3,7,8-TCDF 2.06 0.29 1.18 0.72 1,2,3,7,8-PeCDF 1.87 0.22 1.04 1.68 2,3,4,7,8-PeCDF 2.81 0.34 1.57 1.28

1,2,3,4,7,8-HxCDF 3.06 0.49 1.78 2.54 1,2,3,6,7,8-HxCDF 3.15 0.55 1.85 2.39 1,2,3,7,8,9-HxCDF 0.40 0.06 0.23 0.12 2,3,4,6,7,8-HxCDF 3.41 0.51 1.96 2.88

1,2,3,4,6,7,8-HpCDF 12.50 1.88 7.19 22.54 1,2,3,4,7,8,9-HpCDF 1.69 0.15 0.92 1.48

OCDF 11.25 1.88 6.56 21.09

4.2 POPCYCLING-Baltic Model (Version 1.05)

The non-steady state, multi-compartmental, fugacity-based model employed to simulate the

environmental fate of PCDD/Fs in the Baltic Sea used in this study is an adapted version of

the POPCYCLING-Baltic model (version 1.05) (Armitage et al., 2009). The original


25

POPCYCLING-Baltic model (Wania et al., 2000) can be downloaded by following this link

http://www.utsc.utoronto.ca/~wania/downloads2.html. The model takes the form of a mass

balance statement with expressions for all relevant process rates. The air-soil fugacity ratios

provided by the model are compared with the calculated fugacity quotients above. The

predictive ability of the model is assessed by comparing model predictions with empirical

observations.

Figure 5. The POPCYCLING-Baltic Model aims to quantify the pathways of POPs from the terrestrial

environment to the marine environment via the atmosphere and rivers (Wania et al., 2000).

The modification has been previous undertaken by Armitage et al. (2009) and enabled the

user to define the initial concentration in all compartments. The scenarios for atmospheric

concentration can be defined as a function of the initial concentration. Air concentrations are

set as the driving function in the model, thus it is not necessary to define any emissions to the

model. Enhanced sorption to organic carbon was introduced into the model to account for

sorption to black carbon. However, it is currently assumed that there is no enhanced sorption

to black carbon. Seasonal variability in atmospheric concentration was taken into account as a

sinusoidal function of median value. The model was run in the environment of Visual Basic

6.0. Due to essential differences in the properties of toxic PCDD/Fs congeners affecting their

environmental behavior, simulations were performed separately for seven 2, 3, 7, 8-

substituted dibenzo-p-dioxins and ten 2, 3, 7, 8-substituted dibenzofurans.

http://www.utsc.utoronto.ca/~wania/downloads2.html


26

Environmental Input Parameters

Figure 6. Compartments in POPCYCLING-Baltic Model (Armitage et al., 2009)

The POPCYCLING-Baltic model consists of 85 compartments. Each compartment was

considered to be well-mixed (i.e. homogenous) both with respect to environmental and

chemical properties. The compartmentalization of the terrestrial (a), marine (b), and

atmospheric (c) environment of the Baltic Sea drainage basin in the POPCYCLING-Baltic

model are presented in Figure 6. Each terrestrial environment is correlative with its overlying

atmospheric compartment, as shown in Table 6. Environmental parameters used are the

default parameterizations of the model.

Table 6. Terrestrial and atmospheric compartments in POPCYCLING-Baltic Model

Physical-chemical Input Parameters

Physical chemical properties including phase partition coefficients, the corresponding heats of phase transfer, and first-order rate constants for chemical degradation in different compartment are shown in Table 8. The Henry’s law constants, vapor pressures, and water solubilities for the 17 selected

congeners were taken from (Govers and Krop). Enthalpy of phase change was taken from

Terrestrial Region Atmospheric Region

Terrestrial Region Atmospheric Region

T1 Bothnian Bay A1 North T6 Southern Baltic Coast A3 South

T2 Bothnian Sea A1 North T7 Swedish Baltic Coast A4 West

T3 Gulf of Finland A2 East T8 Danish Straits A4 West

T4 Neva A2 East T9 Kattegat A4 West

T5 Gulf of Riga A2 East T10


27

(Aberg et al., 2008). Three partition coefficients, i.e. octanol-water, air-water, and octanol-air

were used to describe environmental phase partitioning. Only two have to provide as input,

because the third can be calculated from the other two.

Table 7. Half-life of PCDD/Fs in different media (Sinkkonen and Paasivirta, 2000)

Congeners Half-life times (h) Air Water Soil Sediment

2,3,7,8-TCDD 200 4000 900000 900000 1,2,3,7,8-PeCDD 360 7200 1000000 1000000 1,2,3,4,7,8-HxCDD 740 14800 2400000 2400000 1,2,3,6,7,8-HxCDD 740 14800 550000 550000 1,2,3,7,8,9-HxCDD 740 14800 700000 700000 1,2,3,4,6,7,8-HpCDD 1500 30000 900000 900000 OCDD 3950 79000 1300000 1300000 2,3,7,8-TCDF 320 6400 550000 550000 1,2,3,7,8-PeCDF 660 13200 450000 450000 2,3,4,7,8-PeCDF 660 13200 450000 450000 1,2,3,4,7,8-HxCDF 1400 28000 600000 600000 1,2,3,6,7,8-HxCDF 1400 28000 700000 700000 1,2,3,7,8,9-HxCDF 1400 28000 500000 500000 2,3,4,6,7,8-HxCDF 1400 28000 450000 450000 1,2,3,4,6,7,8-HpCDF 3200 64000 350000 350000 1,2,3,4,7,8,9-HpCDF 3200 64000 300000 300000 OCDF 9600 192000 250000 250000


28

Table 8. Physical chemical properties of PCDD/Fs congeners at 250C (Aberg et al., 2008; Govers and Krop; Trapp and Matthies, 1997)

Congeners M

(g.mol-1) -logH

(kPa m3mol-1) -log S

(mol l-1) -logP (Pa)

logKow ΔUAW

(Jmol-1) 2,3,7,8-TCDD 322 2.79 7.47 4.24 6.96 78097 1,2,3,7,8-PeCDD 356 2.83 8.11 4.92 7.50 93851 1,2,3,4,7,8-HxCDD 391 2.84 8.59 5.41 7.94 108706 1,2,3,6,7,8-HxCDD 391 2.84 8.65 5.48 7.98 108706 1,2,3,7,8,9-HxCDD 391 2.84 4.63 4.31 7.47 108706 1,2,3,4,6,7,8-HpCDD 425 3.08 9.17 6.23 8.4 122258 OCDD 460 3.29 9.60 6.87 8.75 109657 2,3,7,8-TCDF 306 2.57 6.87 3.43 6.46 83535 1,2,3,7,8-PeCDF 340 2.72 7.50 4.21 6.99 89592 2,3,4,7,8-PeCDF 340 2.59 7.68 4.26 7.11 91903 1,2,3,4,7,8-HxCDF 375 2.72 8.15 4.86 7.53 99345 1,2,3,6,7,8-HxCDF 375 2.72 8.22 4.92 7.57 97961 1,2,3,7,8,9-HxCDF 375 3.02 8.64 5.65 7.76 97961 2,3,4,6,7,8-HxCDF 375 2.75 8.38 5.12 7.56 97961 1,2,3,4,6,7,8-HpCDF 409 2.85 8.76 5.60 8.01 106161 1,2,3,4,7,8,9-HpCDF 409 3.00 9.20 6.18 8.23 106142 OCDF 444 3.11 9.64 6.74 8.60 106965 Where M, H, S, P, KOW and ΔUAW indicate molecular weight, Henry’s law constant, solubility in water, vapor pressure, octanol-water partition coefficients and enthalpy of

phase change, respectively.


29

Initial concentrations

Background soil, sediment concentration were kept as default values used by Armitage et al.

(2009). The initial sediment concentration is derived from the current sediment

concentration (Sundqvist et al., 2009) and assuming that it has declined at the same rate as

atmospheric concentrations. The EU reference background concentration of soil (Gawlik et al.,

2000) was employed as the initial soil concentration. There was no distinction between

agricultural and forest soil. Initial air concentrations were derived from measurements

carried out in Aspvreten (South of Sweden) and Pallas (North of Finland) during the winter of

2006/2007. The average atmospheric concentrations during winter were obtained from the

field measurements. The average atmospheric concentrations in summer were assumed to be

lower than the corresponding ones in winter by a factor of 4. Input atmospheric concentration

for compartment A2, A3, and A4 were based on measurements undertaken at Aspvreten. The

average concentration of Aspvreten and Pallas was applied to compartment A1. The input

data for atmospheric concentration are presented in Table 3. The simulation was conducted

for a period of 20 years, from 1986 to 2006. In that time, atmospheric concentrations were

assumed to decrease linearly by a factor of 4 according to measurements taken in pine

needles (Armitage et al., 2009; Rappolder et al., 2007).

Alterations to POPCYCLING/Baltic model

The previous version of the POPCYCLING-Baltic model adapted by Amitage et al. (2009), only

focused on the fate of POPs between the atmosphere and marine environment, thus some

additional programming was necessary to obtain information about air-soil exchange.

Furthermore, some environmental input parameters were changed to investigate the

sensitive of volatilization rate, as discussed below.

Firstly, initial soil concentrations were changed to evaluate the effect of background soil

concentrations. The model was run with the same configuration except for the alteration of

initial soil concentration. Background soil concentrations were set at one magnitude lower

and one magnitude higher than the default values.

Secondly, two advective transport processes that have previously been shown to affect the

transfer of chemicals from soil to air were added to the model. Bioturbation helps to

physically transport the chemical through the soil layer, which could transport chemicals to

the surface where they can volatilize. Resuspension can directly transport chemicals to the


30

atmosphere. Resuspension is the transfer process of chemicals associated with soil particulate

matter to the air under the influence of wind. The rates of bioturbation and resuspension are

the products of the relevant mass transfer coefficients (MTC), concentrations in the soil, and

soil area. MTCs were chosen from literature with default values as 2.3 x 10-8 m h-1 (McLachlan

et al., 2002) and 6 x 10-10 m h-1 (Qureshi et al., 2009) for bioturbation and resuspension,

respectively.

4.3 Analysis Fugacity in Soil Using Passive Sampler

Sampling

Surface soils (0-2 cm) were sampled at Aspvreten (south of Stockholm). Agricultural soil was

taken from an open field while forest soils were taken in a pine forest nearby. All the samples

were kept in dark brown flasks and brought back to laboratory. At the laboratory, soil

samples were sieved and homogenized using a sieve with 2 mm diameter. The homogenous

soil samples were weighed in cleaned flasks and kept in the freezer to prevent degradation.

Dry weight determination

Approximately 3.0 gram aliquots of soil were weighed in small cups for each sample. The

small cups were covered with aluminum foil and put in an oven (60 0C). After a few days, they

were taken out and put in desiccator until they reaching room temperature. The water

content of the soil samples was calculated from the difference in weight of the cups before and

after drying.

Development of POM-17 samplers

The passive sampling material (POM) from was pre-cleaned by submerging it in MeOH and

then putting it in ultrasonic machine. After one day, POM was taken out of the MeOH and

placed in an oven (at 60 0C) until they were dry. Sodium chlorine was dissolved with MiliQ-

water to obtain a solution with 1% (g/g). As shown in Table 9, soil (non-dried, 6 g dry weight)

was shaken horizontally in the laboratory with sodium chlorine 1 % (250 ml), POM-17 (0.4 g)

and NaN3 (0.2 g) until they reached equilibrium. POM strips were collected after three months

and six months. After equilibration, POM strips were sampled, cleaned with water, and keep

in freezer.


31

Table 9. Sample preparation

Sample Non-dried soil (g) POM (g) NaN3 (g) VA-GL-1 14.92 0.39699 0.20041 VA-GL-2 14.88 0.40675 0.20483 VA-GL-3 14.97 0.40212 0.20422 VA-GL-4 14.98 0.40337 0.20440 VA-FS-1 16.02 0.40136 0.20024 VA-FS-2 15.98 0.40262 0.20538 VA-FS-3 15.94 0.40586 0.20404 VA-FS-4 16.09 0.40441 0.20745

Blk-1 - 0.40428 0.20825 Blk-2 - 0.40670 0.20326

The method used at Umeå University to analyze PCDD/Fs in sediment is described by

Sundqvist et al. (2009). Sediment, which was spiked with 13C-labelled internal standards of all

2,3,7,8-substituted PCDD/Fs, was weighed into clean thimbles and extracted with toluene

using a Soxhlet-Dean-Stark extractor. The extraction was stopped after 15 hours. The sample

was purified with activated copper and fractioned by four open liquid chromatographic

columns. The first column contained multiple layers i.e., glass wool, 3g KOH-silica, 3g neutral

silica, 6g of 40% (w/w) H2SO4 silica and 3g Na2SO4. N-hexane (60 ml), used to rinse and elute

analytes from the column. After evaporation, interfering sulphur present in the extract was

removed by adding activated copper. The second column had similar ingredients to the first

one, but each absorbent and eluent were only half of the amount. The third column was a

glass pipette that was packed with glass wool on either side and a mixture of AX21 carbon

(7.9%) and Celite in the middle. The column was first eluted with a mixture of n-hexane-

dichloromethane (1:4) (40 ml). It was subsequently turned upside down and eluted with 40

ml toluene to collect PCDD/Fs. The elution was transferred to a multilayer silica column

containing KOH-silica, silica, 40% H2SO4 silica, and Na2SO4. This last column was eluted with

n-hexane. 13C recovery standards (1,2,3,4-TCDD, 1,2,3,4,6-PeCDF, 1,2,3,4,6,9-HxCDF, and

1,2,3,4,6,8,9-HpCDF) were added to samples before injecting and analyzing on the GC/MS

system.

Figure 7. Illustration of shaking soil with POM-17


32

5. RESULT AND DISCUSSION

5.1 Fugacity quotient concept

Calculation of fugacity quotient was undertaken for the four atmospheric Baltic regions, namely A1 to A4. A1 regions were separated from the others due to differences in concentrations. Table 10 expresses the results of temperature correction for Henry’s law constant and

calculation of organic carbon-water partition coefficients, fugacity capacity of air and soil.

Fugacity capacity of air is the same for all substances at 25 0C. Fugacity capacity of soil is

much higher than those of air.

Table 10. Henry’s law constant at 3 0C, organic carbon-water partition coefficient and fugacity capacity in air

and soil of 17 congeners.

Congeners H KOC ZA ZS

2,3,7,8-TCDD 1.62E-03 3.74E+06

4.36E-04

1.73E+08

1,2,3,7,8-PeCDD 1.48E-03 1.30E+07 6.57E+08

1,2,3,4,7,8-HxCDD 1.45E-03 3.57E+07 1.85E+09

1,2,3,6,7,8-HxCDD 1.45E-03 3.92E+07 2.03E+09

1,2,3,7,8,9-HxCDD 1.45E-03 1.21E+07 6.28E+08

1,2,3,4,6,7,8-HpCDD 3.08E+00 1.03E+08 2.51E+06

OCDD 3.29E+00 2.31E+08 5.26E+06

2,3,7,8-TCDF 2.57E+00 1.18E+06 3.45E+04

1,2,3,7,8-PeCDF 2.72E+00 4.01E+06 1.10E+05

2,3,4,7,8-PeCDF 2.59E+00 5.28E+06 1.53E+05

1,2,3,4,7,8-HxCDF 2.72E+00 1.39E+07 3.83E+05

1,2,3,6,7,8-HxCDF 2.72E+00 1.52E+07 4.20E+05

1,2,3,7,8,9-HxCDF 3.02E+00 2.36E+07 5.86E+05

2,3,4,6,7,8-HxCDF 2.75E+00 1.49E+07 4.06E+05

1,2,3,4,6,7,8-HpCDF 2.85E+00 4.20E+07 1.10E+06

1,2,3,4,7,8,9-HpCDF 3.00E+00 6.96E+07 1.74E+06

OCDF 3.11E+00 1.63E+08 3.94E+06

Most of the calculated soil/air fugacity quotients (See table 11) are smaller than one,

indicating that the air phase is not in equilibrium with soil phase. This result suggests most of

PCDD/Fs have a tendency to remain in the soil. Previous studies also showed a similar result

(Cousins and Jones, 1998; Duarte-Davidson et al., 1996). Lighter PCDD/Fs have a stronger


33

tendency to move from soil to air than the heavier congeners. The chemicals with high

molecular weight have properties of low ability to volatize and high tendency to partition in

organic phase. As expected, 2, 3, 7, 8-TCDF, 1,2,3,7,8-PeCDF, and 2,3,4,7,8-PeCDF has fugacity

quotient larger than one, which point out the tendency to volatize from soils. Fugacity

quotients of heavier congeners showed that soil is nearly in equilibrium with the atmosphere.

Therefore, higher chlorinated congeners still remain in the soil.

Table 11. Calculated fugacity in air, soil and fugacity quotient of 17 congeners

Congeners fA(2-4) fA1 fS fS/fA(2-4) fS/fA1

2,3,7,8-TCDD 1.4E-15 7.8E-16 4.3E-18 3.2E-03 5.5E-03

1,2,3,7,8-PeCDD 4.4E-15 2.4E-15 3.7E-18 8.4E-04 1.5E-03

1,2,3,4,7,8-HxCDD 4.0E-15 2.6E-15 1.5E-18 3.8E-04 5.8E-04

1,2,3,6,7,8-HxCDD 1.2E-14 7.5E-15 2.5E-18 2.0E-04 3.4E-04

1,2,3,7,8,9-HxCDD 8.4E-15 5.0E-15 1.1E-17 1.3E-03 2.1E-03

1,2,3,4,6,7,8-HpCDD 1.0E-13 5.4E-14 1.9E-14 1.9E-01 3.5E-01

OCDD 2.1E-13 1.1E-13 3.1E-14 1.5E-01 2.8E-01

2,3,7,8-TCDF 1.5E-14 8.8E-15 6.8E-14 4.4E+00 7.7E+00

1,2,3,7,8-PeCDF 1.2E-14 7.0E-15 4.5E-14 3.5E+00 6.3E+00

2,3,4,7,8-PeCDF 1.9E-14 1.1E-14 2.5E-14 1.3E+00 2.3E+00

1,2,3,4,7,8-HxCDF 1.9E-14 1.1E-14 1.8E-14 9.4E-01 1.6E+00

1,2,3,6,7,8-HxCDF 1.9E-14 1.1E-14 1.5E-14 7.9E-01 1.3E+00

1,2,3,7,8,9-HxCDF 2.4E-15 1.4E-15 5.5E-16 2.2E-01 3.9E-01

2,3,4,6,7,8-HxCDF 2.1E-14 1.2E-14 1.9E-14 9.1E-01 1.6E+00

1,2,3,4,6,7,8-HpCDF 7.0E-14 4.0E-14 5.0E-14 7.1E-01 1.2E+00

1,2,3,4,7,8,9-HpCDF 9.5E-15 5.2E-15 2.1E-15 2.2E-01 4.0E-01

OCDF 5.8E-14 3.4E-14 1.2E-14 2.1E-01 3.7E-01

5.2 Model

To illustrate the use of fugacities and fugacities quotients in the interpretation of the fate of

dioxins, we discuss the results for 2, 3, 7, 8-TCDD in the Baltic Proper. The results for all other

congeners are listed in Appendix C.

5.2.1 Default values

As mentioned above, seasonality in atmospheric concentration was taken into account in this

version of POPCYCLING-Baltic model. Seasonality in air fugacity also follows a sinusoidal

function with the highest values in summer and lowest in winter. According to the assumption

used in the simulation, atmospheric concentrations decreased linearly by a factor of 4 from

1986 to 2006 (Bergknut et al., 2010). Figure 8 showed the same trend of air fugacity and

changes in concentration. In contrast to the trend in air fugacity, soil fugacity (Figure 9)

changed very slowly during the simulation time. As previously discussed, PCDD/Fs are


34

strongly sorbed to soil solids, where the rate of degradation is very slow. In combination with

the continuous input from the atmosphere over a very long time period, the level of PCDD/Fs

in soil are likely to be stable during the simulation time of 20 years (1986-2006).

The spatial distribution of PCDD/Fs is shown clearly in Figure 10 and Figure 11. The fugacity

in air of 2, 3, 7, 8-TCDD showed high values in the East, South, and West regions, while it was

lower in the Northern Baltic Sea. Figure 10 showed the spartial distribution of 2, 3, 7, 8-TCDD

over ten terrestrial regions of Baltic Sea. The abbreviation of T1 to T10 can be found in Table

4. The spatial distribution in air concentration over the Baltic Sea results in the different

levels of PCDD/Fs in ten terrestrial regions. 2, 3, 7, 8-TCDD have highest level in T5-Gulf of

Riga, lowest in T8-Danish Straits.

The model estimated soil/air fugacity quotients in different terrestrial regions are presented

in Figure 12. All the fugacity quotients are higher than one, meaning that soil fugacities are

higher than air fugacities. Due to disequilibrium between soil and air, dioxins in soil have a

tendency to move to the atmosphere until they reach equilibrium. Seasonal net gaseous soil-

to-air flux shown in Figure 13 revealed that net transfer from agricultural soil to the

atmosphere occurs in summer. However, the net flux between soil and air in Figure 14 shows

that there was almost no net transfer to the atmosphere. This result can be explained because

the exchange between soil and air is not only governed by diffusive transport processes but

also the advective transport processes. In this case, the rate of advective transport processes

is much larger than the rate of diffusive transport. Therefore, even though soil fugacities are

higher than air fugacities, soil is still estimated to be a storage reservoir of dioxins. However,

it can also be observed that the fluxes from soil to air increase during the simulation time

(1986-2006), and therefore volatilization may become an important processes in the near

future.


35

Figure 8. Seasonal air fugacity of 2, 3, 7, 8-TCDD

Figure 9. Seasonal soil fugacity of 2, 3, 7, 8-TCDD in Swedish Baltic Proper

Figure 10. Time trend of air fugacity of 2, 3, 7, 8-TCDD in four Baltic Sea regions

0

5

10

15

20

25

30

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005

Air

fu

gaci

ty (

10

16

Pa)

Year

0

20

40

60

80

100

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005

Soil

Fuga

city

( 1

01

6 P

a)

Year

0.00

5.00

10.00

15.00

20.00

25.00

1985 1990 1995 2000 2005

Air

fu

gaci

ty 1

016

Pa

Year

North

East

South

West


36

Figure 11. Time trend in soil fugacity of 2, 3, 7, 8-TCDD in ten terrestrial regions.

Figure 12. Fugacity ratios between agricultural soil and air in ten terrestrial regions

Figure 13. Seasonal net gaseous fluxes of 2, 3, 7, 8-TCDD in Swedish Baltic Proper

0.00

20.00

40.00

60.00

1985 1990 1995 2000 2005

Soil

Fuga

city

(1

01

6 P

a)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

0

2

4

6

8

10

1985 1990 1995 2000 2005

f S/f A

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

-20

0

20

40

60

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005

f S/f A

Year


37

Figure 14. Net flux of dioxins in ten terrestrial regions

The fugacities in air and soil as well as the net gaseous and total fluxes of 17 congeners in the

Baltic Proper are shown in Figure 14 to 18. High air and soil fugacities for PCDFs were

observed. The occurrence of net gaseous transfer from soil to the atmosphere was observed

for the lower chlorinated congeners (e.g. 2,3,7,8-TCDD; PeCDD; TCDF; PeCDF). However, the

net total flux from soil to air only occurred to TCDF. Other congeners tend to be close to

equilibrium between the air and the soil thus there is negligible net diffusive flux on an annual

basic. However, seasonal net gaseous fluxes of these congeners show in Figure 22 reveal a

higher volatilization tendency in summer than in winter. A low volatilization flux may occur

during the summer period. High lipophilicity means dioxins strongly sorb to organic matter,

resulting in their immobility and low degradation. These properties help dioxins accumulate

in the soil for a long time period. Besides, the nature of the soil as well as the contamination

patterns in that soil also determine the differences in the volatilization flux.

-1000

-800

-600

-400

-200

0

200

1985 1990 1995 2000 2005F

lux

(µg

TE

Q h

-1)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10


38

Figure 15. Air Fugacity of 17 Dioxins in Swedish Baltic Proper (A4 west)

Figure 16. Soil fugacity of 17 Dioxins in Swedish Baltic Proper

0

100

200

300

400

500

1985 1990 1995 2000 2005

Air

FU

gaci

ty (

10

16

Pa)

Year

TCDD

PECDD

123478-HXCDD

123678-HXCDD

123789-HXCDD

HPCDD

OCDD

TCDF

12378-PECDF

23478-PECDF

123478-HXCDF

123678-HXCDF

123789-HXCDF

234678-HXCDF

1234678-HPCDF

1234789-HPCDF

OCDF

0

100

200

300

400

500

1985 1990 1995 2000 2005

So

il F

uga

city

(1

0 1

6 P

a)

Year

TCDD

PECDD

123478-HXCDD

123678-HXCDD

123789-HXCDD

HPCDD

OCDD

TCDF

12378-PECDF

23478-PECDF

123478-HXCDF

123678-HXCDF

123789-HXCDF

234678-HXCDF

1234678-HPCDF

1234789-HPCDF

OCDF


39

Figure 17. Net gaseous fluxes of seventeen congeners in Swedish Baltic Proper

Figure 18. Net total flux (µg TEQ h-1) of 17 Dioxins in Swedish Baltic Proper

-15

-10

-5

0

5

10

15

20

25

1985 1990 1995 2000 2005

Flu

x (µ

g T

EQ

h-1

)

Year

TCDD

PECDD

123478-HXCDD

123678-HXCDD

123789-HXCDD

HPCDD

OCDD

TCDF

12378-PECDF

23478-PECDF

123478-HXCDF

123678-HXCDF

123789-HXCDF

234678-HXCDF

1234678-HPCDF

1234789-HPCDF

OCDF

-400

-300

-200

-100

0

100

1985 1990 1995 2000 2005

TCDD PECDD 123478-HXCDD 123678-HXCDD

123789-HXCDD HPCDD OCDD TCDF

12378-PECDF 23478-PECDF 123478-HXCDF 123678-HXCDF

123789-HXCDF 234678-HXCDF 1234678-HPCDF 1234789-HPCDF

OCDF


40

5.2.2 Sensitivity Analysis

Sensitivity analysis is quantification of changes in model results as a result of changes

individual model parameter (McKone and MacLeod, 2003). Some sensitivity tests were

undertaken to investigate the effect of background soil concentration and advective transport

on fugacities in air and soil, rate of transfer from soil to air (Table 12).

Table 12. Sensitivity analysis

Parameters Case Changing Initial soil concentration A Decrease initial soil concentration ten times

B Increase initial soil concentration ten times Bioturbation C Add into model Resuspension D Add into model

As a result of defined scenario for the atmospheric compartment, fugacity in air is not affected

by changes in the background concentration of soil or by adding bioturbation and

resuspension in the model as shown in Figure 19. Figure 20 displays the fugacity of 2, 3, 7, 8-

TCDD in soil for all 4 model scenarios listed in Table 12. Firstly, the trend and magnitude of

fugacity in soil is not affected by resuspension or bioturbation. Not surprisingly, however, the

observed changes fugacity in soil are directly proportional to changes the initial soil

concentration. Therefore, if background soil concentration is decreased ten times (Case A),

fugacity in soil is also reduced 10 times. The same trend is observed when the initial soil

concentration is increased ten times (Case B). Other congeners show a similarly tendency as

2, 3, 7, 8-TCDD, but with various levels due to the differences in background soil

concentrations and environmental conditions, e.g. surface covers, temperature.

The net flux between soil and air is strongly affected by the assumed background

concentration in soil, especially when its concentration is high (Figure 21). With the default

value, there is no 2, 3, 7, 8-TCDD transfer to the air, but when the background soil

concentration is increased ten times, there is a net flux from soil to air. Soil would become a

“secondary” source of dioxins in this case. Decreasing in soil concentration resulted in a

corresponding reduction of volatilization flux. It is observed that the volatilization of lower

chlorinated congeners are more sensitive to initial soil concentration than higher chlorinated

congeners, which is also reasonable due to their preferences to sorb strongly to soil solids.

When resuspension is added to the model, there is a change in the volatilization flux.

However, the magnitude of change is smaller than increasing the background concentration


41

and not high enough for soil aerosol resuspension to have a significant influence on

volatilization flux. The same result was observed in a study of Qureshi and co-workers (2009).

When very high mass transfer coefficients is added, the effect of resuspension to soil-to-air

transport is significant. High mass transfer coefficient could only be obtained in those regions

where are hot, dry, and windy,e.g. desert, regions locates in the lower latitudes. The obtained

result is reasonable because the mass transfer coefficient is not considered to be very high in

cold climate conditions. The Baltic Sea is located in a cold temperature region just below the

Arctic Circle, which has relatively long winters when snow covering the soils prevents soil

resuspension occurring. It was shown that the effect of resuspension flux on volatilization

varied among congeners. The observed trend revealed that the extent of the effect could

depend on the concentration of congeners in the soil, if environmental conditions are the

same. Congeners with high soil concentrations showed higher sensitivity to resuspension

than others. Similarly, a study of EMEP reported that soil concentrations have influenced on

the resuspension flux of PAHs (Gusev et al., 03/2008).

PCDD/Fs are mostly associated with soil organic matter within the soil compartment.

Transport processes associated with soil solids are considered to be more important than

diffusive soil-air and soil-water processes. Bioturbation is a often modeled as a diffusive

transport of soil solids within soil. In order to volatilize to the air, chemicals must be

transferred to the soil/air interface. Chemicals must diffuse through a thin stagnant boundary

layer of air above the soil surface. The rate of transfer within the soil combined with the rate

of diffusion through this layer are the two key transport processes determined the rate of

volatilization. The volatilization is air-side controlled when the rate of diffusion through the

stagnant layer is dominant. By contrast, the process is termed to be soil-side controlled if soil

transport is dominant. In this study, the sensitivity of bioturbation on soil-to-air transport is

examined by changing the mass transfer coefficients for this process. Modeling results shown

in Figure 18 to 20 suggest that bioturbation has no effect on the exchange of dioxins between

the atmosphere and soil of PCDD/Fs, even though high mass transfer coefficients were added.

This suggests that soil-to-air transport is air-side controlled. In others words, diffusion

through the atmospheric boundary layer is the main process controlling the air-soil exchange

of PCDD/Fs. It has been previously observed that sorbed phase transport has the strongest

effect on soil fugacities of chemicals with a log KOA between 7 and 8 and a log KAW > -3

(McLachlan et al., 2002). It is clear that the partition coefficients of PCDD/Fs are outside these

regions and thus bioturbation has no effect on predicted soil fugacities in the model.


42

Figure 19. Changing in air fugacity of 2, 3, 7, 8-TCDD in Swedish Baltic Proper

Figure 20. Changing in soil fugacity of 2, 3, 7, 8-TCDD in Swedish Baltic Proper

Figure 21. Net flux of 2, 3, 7, 8-TCDD between air and soil in Swedish Baltic Proper

0

4

8

12

16

20

1985 1990 1995 2000 2005

Air

fuga

city

( 1

01

6 P

a)

Year

Default values

Case A

Case B

Case C

Case D

0

100

200

300

400

1985 1990 1995 2000 2005

So

il fu

gaci

ty (

10

16 P

a)

Year

Default values

Case A

Case B

Case C

Case D

-200

-100

0

100

200

1985 1990 1995 2000 2005

Flu

x (

µg

TE

Q h

-1)

Year

Default values

Case A

Case B

Case C

Case D


43

5.3 Experiment With Passive Sampler

Because the time required to achieve equilibrium between soil and POM strips exceeded the

time available for doing a Master thesis it was not possible to include results from the

experiment in the thesis. The analytical phase of the experiment is still underway (See figure

7). For training purposes, instead of analyzing PCDD/Fs in the soil samples collected, available

sediment samples were analyzed. This provided the training in analyses of PCDD/Fs that was

initially an important part of this Master’s project.

6. CONCLUSION

An analysis of the time trend of the fugacities in the atmosphere and agricultural soils

combined with fugacity quotients for the 17 selected 2, 3, 7, 8-substituted dioxin congeners

resulted in the following conclusions:

(1) Analysis of the fugacity quotients between air and agricultural soil:

Lower chlorinated substituted congeners, i.e. 2, 3, 7, 8-TCDD; 2, 3, 7, 8-TCDF;

PeCDF in agricultural soil tend to be close to equilibrium with the atmosphere.

When emissions are reduced, reversal of air-soil exchange occurs and soils change

from being recipients to being sources of dioxins.

Higher chlorinated substituted congeners are relatively far from equilibrium,

indicating that soil is still an important storage reservoir for these compounds. The

net gaseous soil-to-air transport fluxes increase during summer and decrease

during winter, i.e. there is a low soil volatilization flux of PCDD/Fs during the

summer.

Fugacity quotients are a convenient way to express the relative fugacities of two

compartments. However, using fugacity quotients has some limitations. They only

provide a snapshot of behavior and do not account for non-diffusive processes

which are often dominant for dioxins.

(2) The fugacities in all studied media decrease as a result of the reductions in emission.

The rate of decrease is different within various media (e.g. soil responds more slowly

than air), which are a function of storage capacity (e.g. soils have a higher Z-value for

dioxins than air) and rate of loss processes (loss processes in soil are slower than in


44

air). The impact of temperature on the partitioning between gaseous and particle–

sorbed phase, results in seasonality in air fugacities. Fugacities in soil drop very slowly

though emissions have been reduced, due to the reasons given above. The same trend

was observed for all 17 congeners in any region of Baltic Sea. There are also

differences in environmental behavior of dioxins between regions that are due to

environmental characteristics, such as temperature, surface cover or the regional

hydrological conditions.

(3) The model predictions proved to be very sensitive to the background soil

concentration. Volatilization flux is directly proportional to initial soil concentration.

Therefore, it is important to select accurate input data for the background soil

concentration. Current data were taken from a European soil database as accurate data

specifically for the Baltic region were not available.

(4) Resuspension was shown to affect the air-soil exchange of PCDD/Fs. However, the

magnitude of change is quite low.

(5) Bioturbation is a much more important transport process than others processes within

soil. Nevertheless, results of this study have shown that volatilization of PCDD/Fs is

air-side controlled and thus bioturbation has no effect on the flux of dioxins from soil

to air.

In general, the contribution of soil-to-air transport processes, i.e. volatilization and

resuspension to the levels of dioxins in the atmosphere are small. As other inputs into

environment continue to be reduced, their contribution may become increasingly important

in the future. Despite the high fugacity quotient between soil and air, soil still acts as a storage

reservoir of PCDD/Fs and not as a significant secondary source.

7. RECOMMENDATION

Although soil/air fugacity quotients have provided useful information about the current

equilibrium state of the soil and air with regard to dioxins, there are always uncertainties

worthy of further investigation. Fugacity capacities of dioxins in soil are proportional to the

fraction of organic carbon. In this calculation, a typical value of the fraction of organic carbon


45

was chosen. In the work that continues after this Master’s thesis, it will be important to

measure soil organic carbon in the soils used in the passive sampling experiment. It would

further be interesting to determine the black carbon content of the soils, because black carbon

has been shown to strongly adsorb planar compounds, including dioxins (Cornelissen et al.,

2008a). Strong sorption to black carbon would limit soil-air transport of dioxins even further.

Since there were a lack of reliable data for PCDD/Fs concentrations in the Baltic Sea,

background soil concentrations were based on an EU reference. There is a need to carried out

monitoring studies to properly determine the concentration of dioxins in various types of

background soils in the Baltic region. In this model, there was no difference in background

concentrations of agricultural and forest soils, resulting in the similar output between for

these two kinds of soil. Another monitoring survey of dioxins in soils should take into account

different kinds of vegetation cover (e.g. forests versus grasslands).


46

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54

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Environmental Protection Agency.


55

APPENDIX A_ALTERATION TO MODEL

The POPCYLING-Baltic model version 1.05 has been developed to simulate the fate of POPs

in the Baltic region and in particular in the Baltic Sea. The previous application of the modified

model (Armitage et al., 2009) focused only on the marine environment so in this work it was

necessary to further modify the model code to investigate the exchange between the

atmosphere and terrestrial compartments. Firstly, some new variables were added to

calculate the fluxes between air and soil. Flux from one compartment to others is the product

of D-values, its fugacity, molecular weight of chemicals, and TEF-values. It is assumed that

toxic effects of a mixture of PCDD/Fs can be assessed addition once the concentration are all

normalized using TEF-values (Van den Berg et al., 2006). TEF-values is added when calculated

the fluxes in order to obtain output automatically normalized by TEF-values. The equations

used to calculated the flux from air to soil and the reverse process are listed below:

Total flux from air to agricultural soil: NAEK(i) = DAE(i) * FA * WM/1000 * TEF

Gaseous fluxes from air to agricultural soil: NAEKgas(i) = DAEgas(i) * FA * WM/1000 * TEF

Total deposition fluxes from air to soil: NAEKwetdry(i) = DAEwetdry(i)* FA* WM/1000* TEF

Volatilization fluxes from agricultural soil to air: NEAK(i) = DEA(i) * FE(i) * WM/1000 * TEF

Where: DAE(i), DAEgas(i), DAEwetdry(i), DEA(i) are the inter-media transport D-values.

FA, FE are air and agricultural soil fugacities, respectively.

WM is the molecular weight of the substance

TEF is the Toxic Equivalency Factor

There are different ways to calculate inter-media transport D-values, depending on the type of

transport processes. D-values for diffusive transport are the product of fugacity capacity, area

and mass transfer coefficient. D-values for advective transport are calculated from the

velocity of moving medium multiplied by the fugacity capacity of the same medium. Formulae

to calculate different inter-media transport D-values between air and soil are shown below.

Diffusive transport of soil solids (or bioturbation) and resuspension of soil solids have been

added as new transport processes into model.


56

Table 13. Formulae to calculate various transport processes within and between air and soil

Transport processes Equations Notes

Soil air diffusion DSA = BAE * Area * ZW * BAE and BWE is effective diffusivities calculted by Millington-Quirk expression * ZW, ZA, and ZPOC is fugacity capacity of water, air and particulate organic carbon * kSS is mass transfer coefficient associated with bioturbation * kEA ia air side mass transfer coefficient over soil kSR is mass transfer coefficient associated with resuspension * Area is area of soil occurred transfer.

Soil water diffusion DSW = BWE * Area * ZA Soil solids diffusion (or bioturbation)

DSS = kSS * Area * ZPOC

Diffusive across soil-air boundary layer

DSB = kEA * Area * ZA

Soil solids resuspension DSR = kSR * Area * ZPOC


57

APPENDIX B_INPUT PARAMETERS

Table 14. Total atmospheric concentration

Congeners Winter Summer

Average of winter + summer

Input

A1 A2-A4 A1 A2-A4 A1 A2-A4 A1 A2-A4 2,3,7,8-TCDD 0.18 0.31 0.04 0.08 0.11 0.19 0.44 0.78 1,2,3,7,8-PeCDD 0.60 1.09 0.15 0.27 0.38 0.68 1.51 2.73 1,2,3,4,7,8-HxCDD 0.72 1.11 0.18 0.28 0.45 0.69 1.80 2.78 1,2,3,6,7,8-HxCDD 2.04 3.37 0.51 0.84 1.27 2.11 5.10 8.43 1,2,3,7,8,9-HxCDD 1.37 2.29 0.34 0.57 0.86 1.43 3.42 5.73 1,2,3,4,6,7,8-HpCDD 16.0 30.0 4. 00 7.50 10.0 18.8 40.0 75.0 OCDD 36.1 67.0 9.02 16.8 22.6 41.8 90.2 167.5 2,3,7,8-TCDF 1.88 3.30 0.47 0.82 1.17 2.06 4.70 8.25 1,2,3,7,8-PeCDF 1.67 2.99 0.42 0.75 1.04 1.87 4.18 7.48 2,3,4,7,8-PeCDF 2.52 4.50 0.63 1.12 1.57 2.81 6.30 11.2 1,2,3,4,7,8-HxCDF 2.84 4.90 0.71 1.225 1.78 3.06 7.11 12.2 1,2,3,6,7,8-HxCDF 2.96 5.04 0.74 1.26 1.85 3.15 7.40 12.6 1,2,3,7,8,9-HxCDF 0.37 0.64 0.09 0.16 0.23 0.40 0.92 1.59 2,3,4,6,7,8-HxCDF 3.14 5.46 0.79 1.36 1.96 3.41 7.85 13.7 1,2,3,4,6,7,8-HpCDF 11.5 20.0 2.88 5.00 7.19 12.5 28.8 50.0 1,2,3,4,7,8,9-HpCDF 1.47 2.70 0.37 0.68 0.92 1.69 3.68 6.75 OCDF 10.5 18.0 2.62 4.50 6.56 11.2 26.2 45.0

Environmental Input Parameters

Terrestrial environment (10 zones): fresh water and associated sediments, vegetation (forest

canopy) and soil (forest and agricultural).

Parameters Agricultural soil Forest soil

Soil depth (m) 0.2 0.1 Volume fraction of air in soil 0.250 0.250 Volume fraction of water in soil 0.250 0.250 Mass fraction of OC in soil solids 0.018 0.018 Soil air boundary layer MTC(m.h-1) 2.080 0.416 Minimum MTC within soil(m.a-1) 0.010 5*10-3 Dry particle deposition to soil(m.h-1) 1.030 0.206 Volume fraction of solids in run-off 5*10-5 1*10-5

Parameters North East South West Height (m) 6000 6000 6000 6000 Volume (km3) 3.66*106 3.59*106 3.6*106 2.8*106 Volume fraction aerosols 2*10-12 2*10-12 2*10-12 2*10-12 Temperature (0C) -4.4 -3.1 -1.65 -6.05 Air residence time (h) 15.3 16.4 15.4 10.3 OH concentration (molecule.cm-3)

1*105 1.2*105 1.4*105 1.25*105


58

APPENDIX C_SIMULATION FOR OTHERS CONGENERS

Figure 22. Seasonal net gaseous flux from agricultural soil to the atmosphere in Swedish Baltic Proper

-15

0

15

30

45

60

1985 1990 1995 2000 2005 2010

Flu

x (µ

g T

EQ

h-1

)

Year

TCDD

PECDD

123478-HXCDD

123678-HXCDD

123789-HXCDD

HPCDD

OCDD

-60

-40

-20

0

20

40

60

80

1985 1990 1995 2000 2005 2010

Flu

x (µ

g T

EQ

h-1

)

Year

TCDF

12378-PECDF

23478-PECDF

123478-HXCDF

123678-HXCDF

123789-HXCDF

234678-HXCDF

1234678-HPCDF

1234789-HPCDF

OCDF


59

Figure 23. Air, soil fugacity and net flux of PECDD in Swedish Baltic Proper

0

5

10

15

20

25

1985 1990 1995 2000 2005

Air

fu

gaci

ty 1

01

6 P

a

Year

North

East

South

West

0

10

20

30

40

1985 1990 1995 2000 2005

Soil

Fuga

city

(1

01

6 P

a)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

-6000

-5000

-4000

-3000

-2000

-1000

0

1985 1990 1995 2000 2005

Flu

x (µ

g h

-1)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10


60

Figure 24. Air, soil fugacity and net flux of 1,2,3,4,7,8-HXCDD in Swedish Baltic Proper

0

2

4

6

8

10

12

1985 1990 1995 2000 2005

Air

fu

gaci

ty 1

01

6 P

a

Year

North

East

South

West

0

5

10

15

20

1985 1990 1995 2000 2005

Soil

Fuga

city

(1

01

6 P

a)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

-600

-500

-400

-300

-200

-100

0

1985 1990 1995 2000 2005

Flu

x (µ

g h

-1)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10


61


0

10

20

30

40

1985 1990 1995 2000 2005

Air

fu

gaci

ty 1

01

6 P

a

Year

North

East

South

West

0

10

20

30

1985 1990 1995 2000 2005

Soil

Fuga

city

(1

01

6 P

a)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

-2000

-1600

-1200

-800

-400

0

1985 1990 1995 2000 2005

Flu

x (µ

g h

-1)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10


62


0

2

4

6

8

10

12

1985 1990 1995 2000 2005

Air

fu

gaci

ty 1

01

6 P

a

Year

North

East

South

West

0

5

10

15

20

1985 1990 1995 2000 2005

Soil

Fuga

city

(1

01

6 P

a)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

-1200

-1000

-800

-600

-400

-200

0

1985 1990 1995 2000 2005

Flu

x (µ

g h

-1)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10


63

Figure 27. Air, soil fugacity and net flux of HPCDD in Swedish Baltic Proper

0

20

40

60

80

100

1985 1990 1995 2000 2005

Air

fu

gaci

ty 1

01

6 P

a

Year

North

East

South

West

0

20

40

60

1985 1990 1995 2000 2005

Soil

Fuga

city

(1

01

6 P

a)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

-1600

-1200

-800

-400

0

1985 1990 1995 2000 2005

Flu

x (µ

g h

-1)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10


64

Figure 28. Air, soil fugacity and net flux of OCDD in Swedish Baltic Proper

0

10

20

30

40

50

1985 1990 1995 2000 2005

Air

fu

gaci

ty 1

01

6 P

a

Year

North

East

South

West

0

20

40

60

1985 1990 1995 2000 2005

Soil

Fuga

city

(1

01

6 P

a)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

-120

-100

-80

-60

-40

-20

0

1985 1990 1995 2000 2005

Flu

x (µ

g h

-1)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10


65

Figure 29. Air, soil fugacity and net flux of TCDF in Swedish Baltic Proper

0

100

200

300

400

500

1985 1990 1995 2000 2005

Air

fu

gaci

ty 1

01

6 P

a

Year

North

East

South

West

0

200

400

600

800

1000

1985 1990 1995 2000 2005

Soil

Fuga

city

(1

01

6 P

a)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

-600

-400

-200

0

200

400

1985 1990 1995 2000 2005

Flu

x (µ

g h

-1)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10


66

Figure 30. Air, soil fugacity and net flux of 1,2,3,7,8-PeCDF in Swedish Baltic Proper

0

40

80

120

160

200

1985 1990 1995 2000 2005

Air

fu

gaci

ty 1

01

6 P

a

Year

North

East

South

West

0

100

200

300

400

1985 1990 1995 2000 2005

Soil

Fuga

city

(1

01

6 P

a)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

-400

-300

-200

-100

0

1985 1990 1995 2000 2005

Flu

x (µ

g h

-1)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10


67

Figure 31. Air, soil fugacity and net flux of 2,3,4,7,8-PeCDF in Swedish Baltic Proper

0

50

100

150

200

250

1985 1990 1995 2000 2005

Air

fu

gaci

ty 1

01

6 P

a

Year

North

East

South

West

0

40

80

120

160

200

240

1985 1990 1995 2000 2005

Soil

Fuga

city

(1

01

6 P

a)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

-6000

-5000

-4000

-3000

-2000

-1000

0

1985 1990 1995 2000 2005

Flu

x (µ

g h

-1)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10


68

Figure 32. Air, soil fugacity and net flux of 1,2,3,4,7,8-HxCDF in Swedish Baltic Proper

0

20

40

60

80

100

120

1985 1990 1995 2000 2005

Air

fu

gaci

ty 1

01

6 P

a

Year

North

East

South

West

0

20

40

60

80

100

1985 1990 1995 2000 2005

Soil

Fuga

city

(1

01

6 P

a)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

-2500

-2000

-1500

-1000

-500

0

1985 1990 1995 2000 2005

Flu

x (µ

g h

-1)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10


69

Figure 33. Air, soil fugacity and net flux of 1,2,3,6,7,8-HXCDF in Swedish Baltic Proper

0

20

40

60

80

100

120

140

1985 1990 1995 2000 2005

Air

fu

gaci

ty 1

01

6 P

a

Year

North

East

South

West

0

20

40

60

80

100

1985 1990 1995 2000 2005

Soil

Fuga

city

(1

01

6 P

a)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

-2500

-2000

-1500

-1000

-500

0

1985 1990 1995 2000 2005

Flu

x (µ

g h

-1)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10


70

Figure 34. Air, soil fugacity and net flux of 1,2,3,7,8,9-HXCDF in Swedish Baltic Proper

0

2

4

6

8

1985 1990 1995 2000 2005

Air

fu

gaci

ty 1

01

6 P

a

Year

North

East

South

West

0

1

2

3

4

5

1985 1990 1995 2000 2005

Soil

Fuga

city

(1

01

6 P

a)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

-400

-300

-200

-100

0

1985 1990 1995 2000 2005

Flu

x (µ

g h

-1)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10


71

Figure 35. Air, soil fugacity and net flux of 2,3,4,6,7,8-HxCDF in Swedish Baltic Proper

0

20

40

60

80

100

120

1985 1990 1995 2000 2005

Air

fu

gaci

ty 1

01

6 P

a

Year

North

East

South

West

0

20

40

60

80

100

120

1985 1990 1995 2000 2005

Soil

Fuga

city

(1

01

6 P

a)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

-3000

-2500

-2000

-1500

-1000

-500

0

1985 1990 1995 2000 2005

Flu

x (µ

g h

-1)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10


72

Figure 36. Air, soil fugacity and net flux of 1,2,3,4,6,7,8-HpCDF in Swedish Baltic Proper

0

50

100

150

200

250

1985 1990 1995 2000 2005

Air

fu

gaci

ty 1

01

6 P

a

Year

North

East

South

West

0

100

200

300

400

1985 1990 1995 2000 2005

Soil

Fuga

city

(1

01

6 P

a)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

-1200

-1000

-800

-600

-400

-200

0

1985 1990 1995 2000 2005

Flu

x (µ

g h

-1)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10


73

Figure 37. Air, soil fugacity and net flux of 1,2,3,4,7,8,9-HpCDF in Swedish Baltic Proper

0

5

10

15

1985 1990 1995 2000 2005

Air

fu

gaci

ty 1

01

6 P

a

Year

North

East

South

West

0

2

4

6

8

10

1985 1990 1995 2000 2005

Soil

Fuga

city

(1

01

6 P

a)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

-140

-120

-100

-80

-60

-40

-20

0

1985 1990 1995 2000 2005

Flu

x (µ

g h

-1)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10


74

Figure 38. Air, soil fugacity and net flux of OCDF in Swedish Baltic Proper

0

5

10

15

20

25

30

1985 1990 1995 2000 2005

Air

fu

gaci

ty 1

01

6 P

a

Year

North

East

South

West

0

10

20

30

40

50

1985 1990 1995 2000 2005

Soil

Fuga

city

(1

01

6 P

a)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

-30

-25

-20

-15

-10

-5

0

1985 1990 1995 2000 2005

Flu

x (µ

g h

-1)

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10


75

APPENDIX D-SENSITIVE ANALYSIS OF 17 CONGENERS

Figure 39. Compare of soil fugacities, net fluxes of PeCDD (A, B) in different cases

0

100

200

300

400

1985 1990 1995 2000 2005

So

il fu

gaci

ty (

10

16 P

a)

Year

Default values

Case A

Case B

Case C

Case D

-1000

-800

-600

-400

-200

0

200

1985 1990 1995 2000 2005

Flu

x (

µg

h-1

)

Year

Default values

Case A

Case B

Case C

Case D

0

100

200

300

400

1985 1990 1995 2000 2005

So

il fu

gaci

ty (

10

16 P

a)

Year

Default values

Case A

Case B

Case C

Case D


76

Figure 40. Compare of soil fugacities, net fluxes of 1,2,3,4,7,8-HxCDD (A, B) in different cases


-120

-80

-40

0

40

1985 1990 1995 2000 2005F

lux

( µ

g h

-1)

Year

Default values

Case A

Case B

Case C

Case D

0

100

200

300

400

1985 1990 1995 2000 2005

So

il fu

gaci

ty (

10

16 P

a)

Year

Default values

Case A

Case B

Case C

Case D

-400

-300

-200

-100

0

1985 1990 1995 2000 2005

Flu

x (

µg

h-1

)

Year

Default values

Case A

Case B

Case C

Case D


77


0

100

200

300

400

1985 1990 1995 2000 2005

So

il fu

gaci

ty (

10

16 P

a)

Year

Default values

Case A

Case B

Case C

Case D

-200

-100

0

100

1985 1990 1995 2000 2005

Flu

x (

µg

h-1

)

Year

Default values

Case A

Case B

Case C

Case D

0

100

200

300

400

1985 1990 1995 2000 2005

So

il fu

gaci

ty (

10

16 P

a)

Year

Default values

Case A

Case B

Case C

Case D


78

Figure 43. Compare of soil fugacities, net fluxes of HpCDD (A, B) in different cases

Figure 44. Compare of soil fugacities, net fluxes of OCDD (A, B) in different cases

-400

-300

-200

-100

0

1985 1990 1995 2000 2005F

lux

( µ

g h

-1)

Year

Default values

Case A

Case B

Case C

Case D

0

100

200

300

400

1985 1990 1995 2000 2005

So

il fu

gaci

ty (

10

16 P

a)

Year

Default values

Case A

Case B

Case C

Case D

-20

-10

0

10

1985 1990 1995 2000 2005

Flu

x (

µg

h-1

)

Year

Default values

Case A

Case B

Case C

Case D


79

Figure 45. Compare of soil fugacities, net fluxes of 1,2,3,4,7,8-HxCDF (A, B) in different cases

0

1000

2000

3000

4000

5000

6000

1985 1990 1995 2000 2005

So

il fu

gaci

ty (

10

16 P

a)

Year

Default values

Case A

Case B

Case C

Case D

-200

-100

0

100

200

300

1985 1990 1995 2000 2005

Flu

x (

µg

h-1

)

Year

Default values

Case A

Case B

Case C

Case D

0

400

800

1200

1600

2000

1985 1990 1995 2000 2005

So

il fu

gaci

ty (

10

16 P

a)

Year

Default values

Case A

Case B

Case C

Case D


80

Figure 46. Compare of soil fugacities, net fluxes of 1,2,3,7,8-PeCDF (A, B) in different cases

Figure 47. Compare of soil fugacities, net fluxes of 2,3,4,7,8-PeCDF (A, B) in different cases

-80

-40

0

40

1985 1990 1995 2000 2005

Flu

x (

µg

h-1

)

Year

Default values

Case A

Case B

Case C

Case D

0

400

800

1200

1600

1985 1990 1995 2000 2005

So

il fu

gaci

ty (

10

16 P

a)

Year

Default values

Case A

Case B

Case C

Case D

-1600

-1200

-800

-400

0

400

1985 1990 1995 2000 2005

Flu

x (

µg

h-1

)

Year

Default values

Case A

Case B

Case C

Case D


81


0

100

200

300

400

500

1985 1990 1995 2000 2005

So

il fu

gaci

ty (

10

16 P

a)

Year

Default values

Case A

Case B

Case C

Case D

-600

-500

-400

-300

-200

-100

0

1985 1990 1995 2000 2005

Flu

x (

µg

h-1

)

Year

Default values

Case A

Case B

Case C

Case D

0

100

200

300

400

500

600

1985 1990 1995 2000 2005

So

il fu

gaci

ty (

10

16 P

a)

Year

Default values

Case A

Case B

Case C

Case D


82



-600

-500

-400

-300

-200

-100

0

1985 1990 1995 2000 2005F

lux

( µ

g h

-1)

Year

Default values

Case A

Case B

Case C

Case D

0

100

200

300

400

1985 1990 1995 2000 2005

So

il fu

gaci

ty (

10

16 P

a)

Year

Default values

Case A

Case B

Case C

Case D

-100

-80

-60

-40

-20

0

1985 1990 1995 2000 2005

Flu

x (

µg

h-1

)

Year

Default values

Case A

Case B

Case C

Case D


83


0

100

200

300

400

1985 1990 1995 2000 2005

So

il fu

gaci

ty (

10

16 P

a)

Year

Default values

Case A

Case B

Case C

Case D

-800

-600

-400

-200

0

1985 1990 1995 2000 2005

Flu

x (

µg

h-1

)

Year

Default values

Case A

Case B

Case C

Case D

0

400

800

1200

1600

2000

1985 1990 1995 2000 2005

So

il fu

gaci

ty (

10

16 P

a)

Year

Default values

Case A

Case B

Case C

Case D


84



-200

-100

0

100

1985 1990 1995 2000 2005

Flu

x (

µg

h-1

)

Year

Default values

Case A

Case B

Case C

Case D

0

10

20

30

40

50

1985 1990 1995 2000 2005

So

il fu

gaci

ty (

10

16 P

a)

Year

Default values

Case A

Case B

Case C

Case D

-40

-30

-20

-10

0

1985 1990 1995 2000 2005

Flu

x (

µg

h-1

)

Year

Default values

Case A

Case B

Case C

Case D


85

Figure 54. Compare of soil fugacities, net fluxes of OCDF (A, B) in different cases

0

100

200

300

1985 1990 1995 2000 2005

So

il fu

gaci

ty (

10

16 P

a)

Year

Default values

Case A

Case B

Case C

Case D

-6

-4

-2

0

2

1985 1990 1995 2000 2005

Flu

x (

µg

h-1

)

Year

Default values

Case A

Case B

Case C

Case D

Documents

SOURCES OF DIOXINS TO BALTIC AIR - DiVA portalumu.diva-portal.org/smash/get/diva2:535832/FULLTEXT01.pdf · sources of dioxins to baltic air ... low water solubility, ... 3.2 factors