Modelling of Air-Water Exchange of PCBs in the Great Lakes

8/14/2019 Modelling of Air-Water Exchange of PCBs in the Great Lakes

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Authors Accepted Manuscript

Modelling of air-water exchange of PCBs in the great

lakes

Fan Meng, Deyong Wen, James Sloan

PII: S1352-2310(08)00186-6

DOI: doi:10.1016/j.atmosenv.2008.02.050

Reference: AEA 8181

To appear in: Atmospheric Environment

Received date: 15 October 2007

Revised date: 17 January 2008

Accepted date: 20 February 2008

Cite this article as: Fan Meng, Deyong Wen and James Sloan, Modelling of

air-water exchange of PCBs in the great lakes, Atmospheric Environment (2008),

doi:10.1016/j.atmosenv.2008.02.050

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Modelling of Air-water Exchange of PCBs in the Great Lakes1

Fan Meng, Deyong Wen, James Sloan*2

Waterloo Center for Atmospheric Sciences, University of Waterloo, Canada3

[email protected]

Corresponding author: James J. Sloan, Waterloo Center for Atmospheric Sciences,5

University of Waterloo, Waterloo, ON N2L 3G1 Canada e-mail:6

[email protected]

Abstract8

Volatilization from water may be an important emission or reemission process for9

PCBs. In previous work, we have expanded the Community Multi-scale Air Quality10

(CMAQ) model to simulate the transport, chemical transformation, gas/aerosol11

partitioning, deposition and air-water surface exchange of PCBs. The air-water12

surface exchange algorithm is based on a two-film model of the air-water interface.13

Using this expanded version of CMAQ, we simulated the air-water exchange flux of14

gas phase PCBs in the Great Lakes and examined the concentrations and deposition15

patterns of PCBs in North America for 2002. For gas phase PCBs, the volatilization16

from water surfaces is often greater than the absorption (dry deposition) to the water17

surfaces. For example, the net flux of PCBs from the Great Lakes to the atmosphere18

is much larger than the dry and wet deposition of particle phase PCBs to the Great19

Lakes. Thus we conclude that the Great Lakes are currently a source rather than a20

sink for PCBs. For remote areas such as Lake Superior, this air-water exchange21


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appears to be the most important source of PCBs emission. Anthropogenic emission,1

however, is still the dominant source when averaged across the whole North2

American model domain. Transfer resistance calculations show that the transfer3

resistance at the water side of the interface is the biggest resistance, while the4

aerodynamic and air side resistances are approximately the same. The total air-water5

transfer resistance is more sensitive to wind speed than to temperature.6

1. Introduction7

Because they are semi-volatile, gas phase PCBs in the atmosphere can be absorbed to8

and volatilize from surface water, depending on the concentration differences between9

the air and water sides. Estimations based on measured PCBs loading to the Great10

Lakes based on Integrated Atmospheric Deposition Network (IADN) data [Hoff et al.,11

1996; IADN, 1998; IADN, 2000] showed that this surface exchange process can be12

dominant in some regions. When modelling the fate of PCBs in the atmosphere,13

therefore, the volatilization of gas phase PCBs from water should be considered in14

addition to the other particle and gas phase dry and wet deposition processes.15

Generally, the transfer of gas phase pollutants between the atmosphere and a surface16

can be represented by three processes: (1) aerodynamic transport by turbulence17

through the atmospheric surface layer to a very thin layer of stagnant air just adjacent18

to the surface (quasi-laminar sub-layer); (2) molecular transport across this19

quasi-laminar sub-layer; (3) transfer processes in the surface side. In the CMAQ20

system, it is customary to use resistance models to describe the dry deposition of gas21

phase model species [Byun and Ching, 1999].22


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Various models based on the concept of a diffusive sub-layer or two films have1

been used to describe molecular transfer at the air-water interface [Liss and Slater,2

1974a; Upstill-Goddard, 2006]. In the present work, we integrate a two-film3

air-water exchange model into the CMAQ system. We report the application of this4

model to the prediction of the temporally and spatially resolved air-water surface5

exchange flux of gas phase PCBs for the Great Lakes region. We also use it to6

calculate the air concentration and deposition processes of gas and particle phase7

PCBs for the entire year of 2002 in a larger domain covering most of North America.8

2. Model Description9

In a recent publication, [Meng et al., 2007] we reported the expansion of the CMAQ10

model to include 22 PCB congenersPCB5, 8, 18, 28, 31, 52, 70, 90, 101, 105, 110,11

118, 123, 132, 138, 149, 153, 158, 160, 180 and 194. We used existing CMAQ12

algorithms to model the atmospheric transport and diffusion of particle and gas phase13

PCBs, dry deposition of particle phase PCBs, and wet deposition of both gas and14

particle phase PCBs. We also added several new components to the gas/particle15

partitioning model, as well as chemical reactions with OH radicals produced by the16

CMAQ gas phase chemical mechanism. We summarize here a few aspects of this17

new model that are important for the present publication.18

Due to the semi-volatile nature of gas phase PCBs, their surface exchange flux with19

water can be either from the atmosphere to the water (negative, deposition or20

absorption) or from the water to the atmosphere (positive, emission). By analogy21

with the CMAQ one-way resistance model for dry deposition[Byun and Ching, 1999;22


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emission at the water side sub-layer.1

The mass transfer coefficients depend on wind speed, atmospheric stability and2

surface conditions (breaking waves, bubble injection). Therefore, many laboratory3

and field studies have tried to relate mass transfer coefficients to wind speed or4

friction velocity [Liss and Merlivat, 1986; Mackay and Yeun, 1983; Wanninkhof,5

Ledwell, and Crusius, 1991]. In this study, we use the equation for Kw proposed by6

[Wanninkhof, Ledwell, and Crusius, 1991] and the equation for Ka suggested by7

[Mackay and Yeun, 1983]8

The mass transfer coefficient for PCBs is correlated with the mass transfer9

coefficient for carbon dioxide [Bidleman and McConnell, 1995; Hornbuckle et al.,10

1994; Wanninkhof, Ledwell, and Crusius, 1991] according to the following relation:11

),(5.0

),(5.0

)()(// 2

2PCBwCOwPCBwCOw

ScSckk = (3)12

where Sc(CO2)

and Sc(PCB)

are the Schmidt numbers of CO2and PCBs.13

The mass transfer coefficient for carbon dioxide through the water film can be14

related to the 10 m height wind speed (u10), which is based on air-water transfer15

experiments using SF6

[Wanninkhof, Ledwell, and Crusius, 1991]:16

64.1

102, 45.0 uk cow = (4)17

The Schmidt number for PCBs can be calculated from:[Reid, Prausnitz, and Polling,18

1987]19

2,

2,

coww

wcow

DSc

= (5)20

PCBww

w

PCBwD

Sc,

,

= (6)21


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where wis the kinetic viscosity of water as a function of temperature. D

w,pcbandD

w,co21

are the solution phase diffusivities of the PCB molecules and carbon dioxide2

molecules calculated by the method of Hayduk and Laudie [Hayduk and Laudie,3

1974].4

For ka

of PCBs, we use Mackays empirical formulation in which wave breaking5

has been considered. The wave breaking increases the air side friction velocity6

non-linearly and therefore increases ka[Mackay and Yeun, 1983].7

67.0

,10

5.0

10

43][)63.01.6(1062.410 ++= pcbaa Scuuk (7)8

The Schmidt number, Sca,pcb

, can be calculated from:9

PCBaa

a

PCBaD

Sc,

,

= (8)10

where a is the kinetic viscosity of water as a function of temperature; D

a,pcbis the11

diffusivity of the PCB molecules in air, which can be calculated using the method of12

Fuller et al.[Polling, Prausnitz, and O'Connell, 2000]13

The two-films approach that we used in our volatilization model is similar to that of14

IADN [Hillery et al., 1998; Hoff, 1994; Hoff et al., 1996; IADN, 2000]. In our15

model, however, the fluxes from air to water (deposition) and from water to air16

(volatilization) are both calculated using equation (1). Either volatilization or17

deposition can dominate, depending on the direction of the gradient between the air18

and water concentrations19

The model described in the previous section was integrated into CMAQ and its20

meteorology preprocessor, Meteorology-Chemistry Interface Program (MCIP). In21


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the application of the model, all 22 PCB congeners are simulated separately for all1

model processes such as transport, transformation, gas/particle partitioning,2

deposition/volatilization etc. and the correct parameters for each congener are used in3

these calculations. The results for total PCBs are the sum of the results obtained for4

the 22 PCB congeners.5

3. Modelling configurations and input Data6

3.1 Modelling configurations and meteorology7

The modelling domain covers North America with 132 x 90 36 km grid squares on8

a Lambert projection centered at (40N, 90W) (Figure 1). We used 15 vertical9

terrain-following sigma layers of varying thickness, with the top at 100 hpa and the10

first level at about 75 m. The model simulation used the CB-IV gas-phase chemistry11

mechanism [Gery et al., 1989], which includes 36 species and 93 reactions, including12

9 primary organic species and 11 photolysis reactions.13

Meteorological fields for the year 2002 were derived from MM5, the14

Fifth-Generation PSU/NCAR mesoscale model [Grell and Dudhia, 1994] and15

processed using MCIP, the CMAQ meteorology preprocessor.16

3.2 Emission data17

The accuracy of model simulations depends strongly on the quality of emission data.18

Unfortunately, there are still many uncertainties in both the emission factors and the19

activity data for PCBs in the U.S. and Canada. Therefore we used the global20

emission estimates of Breivik [Breivik, 2002; Breivik et al., 2002] which were21

developed using a mass balance approach. In this work, we use the maximum22


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estimates of 67800 kg/yr and 9200 kg/yr for the U. S. and Canada for the year 20001

respectively. These total emissions were allocated to the model grids by population.2

The total emission was also speciated into the 22 congeners.[Meng et al., 2007].3

Emissions of criteria pollutants such as SO2, NOx, CO, VOCs and PM from point,4

area, biogenic, on-road and non-road mobile sources were obtained from the 1999 U.5

S. NEI emission inventory and 1995 Canadian dataset. All emissions were processed6

using the Sparse Matrix Operator Kernel Emissions Modelling system (SMOKE) of7

U.S. EPA, which is the emission preprocessor for the CMAQ model.8

3.3 PCB Water concentrations in the Great Lakes9

The benefit of the modeling approach for calculation of the exchange flux is to10

allow the use of temporally and spatially resolved air concentrations, water11

concentrations and meteorology data. The scarcity of measurement data for water12

bodies, however, is still a serious problem when modelling the exchange process13

between water and the atmosphere. Since there are no water concentration data with14

resolution comparable to that of atmospheric models, we interpolated water15

concentration data when appropriate information is available.16

In the 1993 survey by the Great Lakes National Program Office (GLNPO)17

[Anderson et al., 1999], there are data available for 28 PCBs at 24 locations in the18

open water of the Great Lakes. Generally, Lake Huron and Lake Superior are cleaner19

with lower dissolved-phase PCB concentrations ranging from 60-92 pg / L and20

63-160 pg / L respectively. The dissolved-phase PCB concentrations in Lake Erie,21

Lake Michigan and Lake Ontario range from 52-330 pg / L, 110-140 pg / L and22


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110-190 pg / L respectively. The highest concentrations occur near the west end of1

Lake Erie (close to Winsor, Ontario and Detroit, Michigan). The concentrations are2

highly variable within the individual lakes; in Lake Erie, for example, the3

concentration varies by a factor of 6.4

Other data available for comparisons include the lake-wide averaged data of IADN5

[IADN, 2000] and the Canadian Great Lakes database of Environment Canada6

[Waltho, 2006]. These data are summarized in Table 1, which shows that Lake7

Ontario and Lake Erie have higher lake-wide average concentrations (by a factor of 3)8

than Lake Huron, Georgian Bay and Lake Superior and the trends of these9

concentrations with time differ among the lakes. The limited number of samples is10

probably the major reason for this. The interpolated data of 1993 from the Great11

Lakes National Program Office (GLNPO) (see Figure 2) and the 1997-2002 lake-wide12

averaged data from IADN have been used in our work.13

5. Modelling Results and Discussion14

5.1 Temporal variation of air/gas exchange flux15

The aerodynamic resistance (Ra) is determined by the strength of the atmospheric16

turbulence and therefore is the same for all PCB congeners. However, the heavier17

PCB congeners have bigger air-side quasi-laminar layer resistance (Rg) and water-side18

quasi-laminar layer resistance(Rl), although their variations with wind speed are19

similar (Equations 3-8). Figure 3 shows an example ofRa,Rg

andRlof PCB18 for20

1 May 2002 0:00 GMT to 6 May 2002 0:00 GMT at two sites in Lake Ontario and21

Lake Superior (see Figure 2 for detailed locations ). This shows that the most22


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important barrier to exchange from the lowest level of the atmosphere to the water is1

the resistance of the water side. The aerodynamic resistance due to atmospheric2

turbulence and the resistance of the laminar layer on the air side are similar. Equations3

(4) shows that the wind speed is the controlling factor for the water side resistance.4

Figure 4 shows the hourly air-water surface exchange fluxes of total PCBs and5

Figure 5 shows the hourly wind speed at 10m height and five-day average water6

surface temperature at 5 Great Lakes locations (the red dots in Figure2) predicted by7

MM5 during 2002. Comparison of figures 4 and 5 shows that the exchange fluxes8

have an obvious seasonal variation and a dependence on wind speed. The highest9

fluxes, which can be as high as 360 g/hectare/hour, occur in spring and the lowest10

occur in summer, when they are usually below 100 g/hectare/hour. The air11

concentration of PCBs is much smaller than the equilibrium value corresponding to12

the water concentration, so the latter controls the direction of the flux. From the13

Figures 4 and 5 it can also be seen that the exchange fluxes do not increase with14

temperature, indicating that the wind speed is more important than temperature in15

controlling the air-water exchange transfer flux.16

Figure 6 (a) shows the air-water exchange flux of total PCBs and wind speed at 1017

m height at two sites in Lake Ontario and Lake Superior for a five day period from 118

May to 6 May, 2002. The strong correlation between the air-water exchange flux19

and wind speed can be seen clearly from this. The high values of the air-water20

exchange flux correspond to the high wind speeds and are therefore dominated by the21

water side resistance, Rl. Figure 6(b) shows that the temporal variations of the22


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air-water exchange flux do not have an obvious relationship with the air1

concentrations for Lake Ontario and Lake Superior.2

5.2 Spatial distribution of air/water gas exchange flux3

Absorption and volatilization are the two opposing transfer processes in the4

air-water exchange. In the model, the direction of the air-water exchange flux is5

driven by the relative sizes of the air and water concentrations and the relevant6

Henrys law constant. Figure 7 shows the net air-water exchange fluxes of PCB18,7

PCB52, PCB101 and total PCBs for the Great Lakes in 2002. Clearly, the net fluxes8

of PCB18, PCB52, PCB101 and total PCBs are positive except very a few locations9

near the lakeshores (in blue). The PCB concentrations in water are one or two orders10

of magnitude higher than the air concentrationsi.e. cg


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and Lake Michigan the highest temperatures (Figure 8).1

Figures 9, 10 and 11 show, respectively, the wet deposition of total gas phase PCBs2

and the dry and wet depositions of total particle phase PCBs to the Great Lakes. The3

distribution of depositions generally corresponds to the distribution of the air4

concentrations of PCBs (Figure 1). Both air concentrations and depositions are5

mainly controlled by anthropogenic emissions. The contribution of air-water surface6

exchange processes to the air concentration was not significant, especially for the sites7

with high anthropogenic emission, which we have noted based on a sensitivity8

analysis of air-water exchange processes (Figure 9 in [Meng et al., 2007]).9

5.3 Total air-water exchange flux and deposition loading of PCBs10

Figure12 shows the model predicted net air-water surface exchange of total PCBs11

for the Great Lakes using the 1993 PCB water concentration data of the GLNPO12

[Anderson et al., 1999] and the 1997-2000 lake-wide averaged water concentrations13

[IADN, 2000]. The air-water surface exchange flux has also been compared with the14

model predicted depositions as well as the IADN estimation [IADN, 2000] of the net15

exchange flux, volatilization and absorption of the total (suite) PCBs. The model16

predicted air-water surface exchange fluxes for Lakes Superior, Michigan, Huron,17

Erie, and Ontario for 2002, based on the IADN water concentration data of 1997-200018

are 1197.5kg/year, 797.4 kg/year, 993.83 kg/year, 864.3 kg/year and 389.9 kg/year19

respectively. All net exchange fluxes are positive, i.e. volatilization dominates. The20

largest net flux is from Lake Superior and the smallest is from Lake Ontario. The total21

net exchange flux from the Great Lakes for 2002 based on the 1997-2000 IADN water22


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concentration data is 4242.9kg/year, which is close to the estimation by1

IADN(3030.0 kg/year). When using the higher water concentrations of the 19932

GLNPO data, the net flux from Great Lakes is 7334.8kg/year, or about a factor of two3

larger than the estimation by IADN. The total emissions of PCBs of 2000 in the4

U.S. and Canada are 67800 kg/yr and 9200 kg/yr respectively, so the emission or5

reemission due to the air-water surface exchange processes from the Great Lakes is6

not a dominant fraction for whole domain, but it is a significant percentage and is the7

most important contribution for some remote areas.8

In previous work, [Meng et al., 2007], we showed that the contribution of9

volatilization to the air concentrations above the remote lakes (Lake Superior and10

Lake Huron) is more significant than that of the other Great Lakes. However, Lake11

Erie, Ontario and Michigan have the potential to be important PCB emission sources12

in the future, after the anthropogenic emissions decrease, because of their high13

dissolved PCB concentrations.14

PCB deposition to the Great Lakes predicted by the model includes wet deposition15

of gas phase and wet and dry deposition of particle phase. Among these three, the16

particle phase wet deposition is the largest. The total wet and dry deposition of17

particle phase and wet deposition of gas phase PCBs to the Great Lakes for 2002 are18

13.38kg/year, 0.74kg/year and 0.062kg/year respectively. By comparison with the net19

air-water exchange flux, however, these depositions are small. The Great Lakes20

currently act as emission sources, not sinks, of PCBs due to air-water exchange21

processes. For the whole domain, however, the wet and dry deposition of particle22


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phase and wet deposition of gas phase PCBs are 1676.79 kg/year, 102.71 kg/year and1

13.64 kg/year respectively. We ignore dry deposition from the gas phase [Meng et2

al., 2007].3

There are no direct air-water exchange measurement data for comparison with our4

modelling predictions. In the previous work by IADN [IADN, 2000], the net5

exchange fluxes for Lakes Superior, Michigan, Huron, Erie and Ontario are estimated6

to be 720kg/year, 570kg/year, 610kg/year, 810kg/year and 320kg/year respectively.7

These are lower than our predictions, which use the same PCB water concentrations.8

For Lakes Erie and Ontario, where the IADN estimated absorptions are lower, the9

model-predicted net exchange fluxes are very similar to those estimated by IADN.10

For the other three Great Lakes, where the IADN estimated absorptions are higher, the11

model predicted net exchange fluxes are larger than the IADN results. On the other12

hand, the volatilization estimated by IADN is comparable to the model-predicted net13

exchange fluxes (Figure 12), which are mostly volatilization. This is reasonable,14

because our approach to modelling the volatilization is similar to that used in the15

IADN estimation of this quantity and the water concentrations are the same. Thus ,16

the differences noted above must result from differences in the calculation of the17

absorption (dry deposition) of gas phase PCBs.18

5.4 Discussion and conclusion19

Using an expanded version of CMAQ that we have described earlier [Meng et al.,20

2007], we simulated the air-water exchange flux of gas phase PCBs in the Great21

Lakes. The calculation of this exchange flux is based on the simulated air22


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concentrations of PCBs and meteorology parameters. The depositions of PCBs in1

the North American domain for 2002 have also been calculated. For the whole year,2

the volatilization of PCBs dominates for most of the Great Lakes area. This upward3

net exchange flux from the water to the atmosphere is also much larger than the dry4

and wet deposition of particle phase PCBs and wet deposition of gas phase PCBs into5

the Great Lakes. Thus we conclude that the Great Lakes are currently a source6

rather than a sink for PCBs. For remote areas such as Lake Superior, this air-water7

exchange process is the dominant emission process. The emission of PCBs and their8

air concentrations are decreasing and the Great Lakes have a large reservoir of9

dissolved PCBs. Thus, for the Great Lakes region, it is likely that volatilization from10

the lakes will become dominant in the near future and remain so for a long time.11

The PCB water concentrations are one of the key factors affecting the strength of12

the air-water exchange flux. The water concentrations may result from more13

complicated processes such as input from rivers or runoff, or partitioning between14

sediment and suspended particulate matter, in addition to the water-atmosphere15

exchange. In this work, we used both the 1997-2000 IADN and 1993 GLNPO water16

concentration data to calculate the PCB loading of 2002. We assume that the water17

concentration of 2002 is similar to that of the periods 1993 and 1997-2002, based on18

the fact that there is no obvious decreasing or increasing trend of PCB water19

concentration in the Great Lakes. The possible explanations for the relatively stable20

water concentrations of PCBs may be either the huge capacity of the Great Lakes or a21

possible source of PCBs in the Great Lakes sediments. Increasing the spatial and22


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temporal resolution of these concentrations and developing a water PCB transport1

model that can predict spatially and temporally resolved water concentrations and2

incorporate sediment concentrations will improve our ability to model air-water3

exchange.4

In our modeling work, the PCB emission does not have any temporal variation.5

Therefore the air concentrations in the winter are at the same level as in other seasons.6

The small seasonal variation of air concentration only reflects the effects of7

meteorology and chemical reaction. Since the water concentration is 1~2 orders of8

magnitude higher than the air concentration (including the Henrys law constants), the9

use of fixed emission rates did not change the direction of the exchange flux. The10

resistance for the transfer process, which varies with wind therefore is the major11

controlling factor for the exchange flux in our model.12

The Henrys law constants of PCBs are also important factors and a source of some13

uncertainty in calculating the air-water surface exchange flux (Equation 1). In this14

study, we used mean values of these parameters from various authors, but there is still15

significant uncertainty. For example, for PCB52, the Henrys law constant can vary16

from 1.9 to 40 M/atm according to different literature sources ([Sander, 1999]).17

The transfer resistance calculation showed that the transfer resistance on the water18

side is the biggest resistance, while the aerodynamic resistance and resistance of the19

air side sub-layer are comparable. The total transfer resistance is more related to wind20

speed than temperature. In spite of impressive advances in recent years, our current21

understanding of air-water exchange processes is still rather limited. These are22


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particularly important, however, in costal zones or estuaries because of the interaction1

between waves and the atmosphere, wave breaking and bubble formation2

[Upstill-Goddard, 2006].3

Averaged across the whole North American modelling domain, however,4

anthropogenic emission is still the dominant PCB source. Because PCBs are5

semi-volatile, however, it has been hypothesized that their air-soil exchange process6

may be also important [Cousins, Beck, and Jones, 1999]. Recently, it has been found7

[Backe, Cousins, and Larsson, 2004; Cousins and Jones, 2007] that the dry deposition8

might not be the only important air-soil exchange process, the emission or reemission9

from soil might be important as well. Currently there are no PCB soil concentration10

data available for our modelling domain, so we have not considered air-soil exchange11

in our work. Since anthropogenic emissions are decreasing and air-soil exchange12

might become important in the future, the addition of these processes to the model13

will likely be necessary in the future to understand the fate of PCBs in the14

atmosphere.15

Acknowledgements16

The authors are grateful for the financial assistance of the Ministry of the17

Environment of the Province of Ontario, Ontario Power Generation Inc. and Canadian18

Ortech Inc. Also, we would like to thank Jasmine Waltho of Environment Canada19

for providing PCB water concentration data for the Great Lakes.20

21

22


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Hoff, R.M., 1994. An error budget for the detereminatioin fo the atmospheric mass1

loading of toxic chemical in the Great Lakes. J.Great Lakes Res. 20, 229-239.2

Hoff, R.M. et al., 1996. Atmospheric deposition of toxic chemicals to the Great Lakes:3

a review of data through 1994. Atmospheric Environment 30, 3505-3527.4

Hornbuckle, K.C. et al., 1994. Seasonal Variations in Air-Water Exchange of5

Polychlorinated Biphenyls in Lake Superior. Environ.Sci.TEchnol 28, 1491-1501.6

IADN, 1998. Atmospheric deposition of toxic substances to the Great Lakes: IADN7

results through 1998.8

http://www.msc-smc.ec.gc.ca/iadn/resources/loadings9798/final_9798_loadings_r9

eport_e.html10

IADN, 2000. Atmospheric deposition of toxic substances to the Great Lakes: IADN11

results through 2000.12

http://www.msc-smc.ec.gc.ca/iadn/resources/loadings_2000/loadings_2000_e.htm13

l#appc US EPA Report Number: 905-R-04-900,14

Liss, P.S. and Merlivat, L., 1986. Air-sea gas exchange rate: introduciton and15

synthesis. In. P. Buat-Menard (Ed), The Role of Air-Sea Gas Exchange in16

Geochemical Cycling. NATO-ASI Series 185,17

Liss, P.S. and Slater, P.G., 1974. Flux of Gases Across Air-Sea Interface18

2. Nature 247, 181-184.19

Mackay, D. and Yeun, A.T.K., 1983. Mass-Transfer Coefficient Correlations for20

Volatilization of Organic Solutes from Water21

18. Environmental Science & Technology 17, 211-217.22

Meng, F. et al., 2007. Models for Gas/Particle Partitioning, Transformation and23

Air/Water Surface Exchange of PCBs and PCDD/Fs in CMAQ. Atmos.Environ. 1,24

1.25

Polling, B.E., Prausnitz, J.M., and O'Connell, J.P., 2000. The Properties of Gases and26

Liquids; 5th Ed.. McGraw-Hill, Inc.;New York. ; pp. 11.10-11.1227

Reid, R.C., Prausnitz, J.M., and Polling, B.E., 1987. The Properties of Gases and28

Liquids. McGraw-Hill, Inc.;New York. 4th ed., pp. 586-60529

Sander, R., 1999. Compilation of Henry's Law Constants for Inorganic and Organic30

Species of Potential Importance in Environmental Chemistry.31

http://www.mpch-mainz.mpg.de/~sander/res/henry.htm32

Upstill-Goddard, R.C., 2006. Air-sea gas exchange in the coastal zone. Esturarine33

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Waltho, Jasmine, (2006) Ontario Science and Technology Branch, Water Quality1

Monitoring & Surveillance, Environment Canada, 867 Lakeshore Road, P.O.2

Box 5050, Burlington, Ontario L7R 4A6 (905)319-6996 FAX3

(905)336-46094

Wanninkhof, R., Ledwell, J., and Crusius, J., 1991. Gas Transfer Velocities on Lakes5

Measured with Sulfur Hexafluoride. In: Wilhelms, S.C. and Gulliver, J.S. (Eds.),6

Air Water Mass Transfer.7

Wesely, M.L., 1989. Parameterization of surface resistances to gaseous dry deposition8

in regional-scale numerical models. Atmospheric Environment (1967-1989) 23,9

1293-1304.10

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12

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Figure 1 Modeling domain and annual averaged gas phase PCB concentrations in2

20023

4


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1

2

Figure 2 Dissolved PCB concentrations in the Great Lakes from Great Lakes3

National Program Office [Anderson et al., 1999]. Red dots are the location where the4

modelling results are extracted (Figure 3, 4,5,6). .5

6

7


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1

10

100

1000

10000

100000

1000000

5/1/02

0:00

5/1/02

12

:00

5/2

/02

0:00

5/2

/02

12

:00

5/3/02

0:00

5/3/02

12

:00

5/4/02

0:00

5/4/02

12

:00

5/5

/02

0:00

5/5

/02

12

:00

5/6/02

0:00

s/m

Rl_On

Rg_OnRa_On

Rl_SuRg_Su

Ra_Su

2

Figure 3 Model calculated aerodynamic resistance (Ra), air side quasi-laminar3

boundary layer resistance (Rg) and water side quasi-laminar boundary layer4

resistance (Rl) of PCB18 from 1 May 2002 0:00 GMT to 6 May 2002 0:00 GMT at5

two sites in Lake Ontario (On, blue) and Lake Superior (Su, red).6

7


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1

0

50

100

150

200

250

300

350

400

12-3

1-01

0:00

1-2

0-02

0:00

2-9-02

0:00

3-1-02

0:0

0

3-2

1-02

0:0

0

4-10-02

0:0

0

4-3

0-02

0:0

0

5-2

0-02

0:0

0

6-9-02

0:0

0

6-2

9-02

0:0

0

7-19-02

0:0

0

8-8

-02

0:00

8-28

-02

0:0

0

9-17

-02

0:0

0

10-7-02

0:00

10-27

-02

0:0

0

11-16-02

0:0

0

12

-6-02

0:0

0

12

-2

6-02

0:0

0

ug/hectare/h

Lake Superio

Lake Huron

Lake MichiganLake Erie

Lake Ontario

2

Figure 4 Air/water surface exchange flux of total PCBs at the 5 locations in the Great3

Lakes. We use the convention that a positive exchange flux is upward from water to4

atmosphere.5

6


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1

220

230

240

250

260

270

280

290

300

310

1-1-02

1:00

1-26

-02

1:00

2-20

-02

1:00

3-17

-02

1:00

4-11

-02

1:00

5-6-02

1:00

5-31

-02

1:00

6-25

-02

1:00

7-20

-02

1:00

8-14

-02

1:00

9-8-02

1:00

10

-3-02

1:00

10-28

-02

1:00

11-22

-02

1:00

12-17

-02

1:00

Temperature(K)

0

5

10

15

20

25

30

35

40

WindSpeed(m/s)

Temp_Su(K)

Temp_Hu(K)

Temp_Mi(K)

Temp_Er(K)

Temp_On(K)

Wsped10_Su(m/s)

Wsped10_Er(m/s)

Wsped10_On(m/s)

2

Figure 5 MM5 predicted hourly wind-speed at 10m height and five-day average3

surface temperature at the 5 Great Lakes locations indicated by the red dots in Figure4

25

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1

0

5

10

15

20

25

30

5-1-02

0:00

5-1-02

12:00

5-2-02

0:00

5-2-02

12:00

5-3-02

0:00

5-3-02

12:00

5-4-02

0:00

5-4-02

12:00

5-5-02

0:00

5-5-02

12:00

5-6-02

0:00

Windspeed(m/s)

0

20

40

60

80

100

120

140

160

180

g/hectare

Wspd10_SuWspd10_OnFlux_Su

Flux_On

2

(a)3

0.00E+00

5.00E-09

1.00E-08

1.50E-08

2.00E-08

2.50E-08

5/1/02

1:00

5/1/02

13

:00

5/2

/02

1:00

5/2

/02

13

:00

5/3

/02

1:00

5/3

/02

13

:00

5/4/02

1:00

5/4/02

13

:00

5/5

/02

1:00

5/5

/02

13

:00

ppm

Superior

Ontario

4

(b)5

Figure 6 (a)Air-water exchange flux of PCBs (solid line) and wind speed at 10m6

height (dashed line) at two sites in Lake Ontario and Lake Superior; (b) averaged air7

concentrations of Lake Ontario and Lake Superior (first layer of the model); for 58

day period from May 1 0:00GMT to May 6 0:00GMT, 2002.9

10


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(a)1

2

(b)3

4

(c)5

6


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1

(d)2

3

(e)4

Figure 7 Total exchange flux of PCB18 (a), PCB52 (b), PCB101 (c) and PCBs (d) for5

Great Lakes during 2002, using IADN 2000 PCB water concentration data. (e)6

Exchange flux of PCBs of Great Lakes for 2002 using PCBs water concentration of7

1993 GLNPO survey data. Positive value is the volatilization from Lakes.8

9


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1

(a)2

3

(b)4

Figure 8 Annual averaged wind speed at 10 meter height (a) and averaged surface5

temperature (b) of Great Lake of 20026

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Figure 9 Wet deposition to Great Lakes of total PCBs in gaseous phase3

4


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1

2

Figure 10 Wet deposition to Great Lakes of total PCBs in particle phase3

4


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1

2

Figure 11 Dry deposition to Great Lakes of total PCBs in particle phase3

4


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1

-600

-400

-200

0

200

400

600

800

1000

1200

1400

1600

18002000

2200

2400

Exchan

gefluxa

Exchan

gefluxb

Dryd

ep.of

partic

leph

ase

Wet

dep.o

fparticle

phas

e

Wet

dep.o

fgas

phase

IADN

Net

exch

ange

flux

IADN

Volatili

zatio

ns

IADN

Abs

orpti

on

kg/year

Lake Superior

Lake Michigan

Lake Huron

Lake Erie

Lake Ontario

2

Figure 12 Exchange and deposition for Great Lakes. Model predicted exchange flux3

of PCBs of gas phase using water concentrations of a. the survey data of 1993 by4

GLNPO [Anderson et al., 1999]; b. 1997-2000 average data by IADN[IADN, 2000];5

model predicted dry deposition of PCBs in particle phase; wet deposition of PCBs in6

particle phase; wet deposition of PCBs in gas phase; net exchange flux, volatilization7

and absorption of PCBs estimated by IADN [IADN, 2000]. (Note: positive net fluxes8

are volatilization flux and negative flux is deposition).9

10

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Year Lake

Ontario

Lake

Erie

Lake

Huron

Lake

Michigan

Lake

Superior

2004a 381.71 217.63 102.36d / 58.86

1997-2000b 78.0b 132.0 53 47.0 47.0 b

1993c 15000 17367 6833 1300 930

Average 203.2 174.43 71.56 88.5 66.3

Table 1 Lake-wide average PCBs concentration (pg/L) a. 2004 data from2

Environment Canada[Waltho, 2006] b.1997-2000 average data[IADN, 2000]; the3

survey data of 1993 by GLNPO [Anderson et al., 1999]; d. average of Lake Huron4

(102.4) and Geogian Bay(84.3).5

6

7

Documents

Modelling of Air-Water Exchange of PCBs in the Great Lakes