Fugacity-Based Model of PCB Bioaccumulation in Complex Aquatic

Environ. Sci. Technol. 1997, 31, 577-583

Fugacity-Based Model of PCB Bioaccumulation in Complex Aquatic Food Webs JAN CAMPFENS Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada M5S 1A4 DONALD MACKAY* Environmental and Resource Studies, Trent University, Peterborough, Ontario, Canada K9J 7B8

Ontario. The primary focus is on presenting the methodology, recognizing that comprehensive validation requires accurate values for a wide variety of food webs, physiological parameters, and chemicals. It is hoped that the availability of the model will encourage such validation and model modification. To clarify the development, we first describe uptake by a single organism, then in a linear food chain, and finally the general case of a food web.

Single Organism Uptake In the fugacity approach suggested by Clark et al. (7) and Mackay (6), the steady-state concentration or fugacity of chemical in a fish exposed to contaminated food and water can be expressed by

fWDW + fADA ) fF(DW + DE + DM + DG)

A fugacity-based model is developed to simulate the phenomena of bioconcentration and biomagnification of organic contaminants in complex food webs in aquatic systems comprising contaminated water and sediment. The food web consists of N classes of organisms, which may feed on all organisms including their own class, and in which each organism may experience chemical uptake from benthic or pelagic food organisms and water with clearance by respiration, egestion, and metabolism. The expressions reduce to a single equation involving an N × N matrix of food preference parameters that is readily solved to give concentrations and fluxes throughout the food web. The model is applied illustratively to bioaccumulation of PCB congeners in Lake Ontario yielding results generally within a factor of 3 of measured values. This approach quantifies the roles of exchange with water, food uptake, and food web structure as determinants of bioaccumulation in aquatic systems and has the potential to be extended to treat broader food webs including terrestrial and avian organisms.

Introduction It is widely accepted that the bioaccumulation of hydrophobic organic chemicals in aquatic food chains is an important phenomenon both from the point of view of adverse effects on aquatic organisms and effects on their predatorssespecially humans and fish-eating wildlife. There have been several reviews of this issue, and several approaches have been suggested to quantify the bioaccumulation phenomenon, notably the conventional concentration-based approaches proposed by Thomann (1), Thomann et al. (2), Gobas (3), and Gobas et al. (4), by Barber et al. (5) in their FGETS models, and by Mackay and co-workers using the fugacity approach (6, 7). The objective of this paper is to develop and verify a model that describes bioaccumulation in a food web consisting of N organisms in which each organism may feed on all organisms including its own class. Furthermore, the organisms may respire or absorb chemicals by diffusion from the water column or from sediment pore water. It is shown that if the expressions are formulated using the fugacity concept, the total system can be described by a single equation involving an N × N dietary preference matrix and a respiration vector that is readily solved regardless of the size or complexity of the food web. The approach is illustrated by treating bioaccumulation of PCB congeners and total PCBs in Lake * Corresponding author telephone: (705) 748-1489; fax: (705) 7481569; e-mail: [email protected].

S0013-936X(96)00478-6 CCC: $14.00

 1997 American Chemical Society

(1)

where the various fugacities (f) are subscripted W for water, A for food, and F for fish. Z values, which relate concentration to fugacity, and D values are subscripted W for exchange with water, A for food uptake, E for egestion, M for metabolism, and G for growth dilution as defined in Tables 1 and 2. The steady-state fugacity in the fish fF is given by

fF ) (fWDW + fADA)/(DW + DE + DM + DG)

(2)

If the egestion parameter DE is expressed as a fraction of the food intake parameter DA, i.e., as DA/Q, eq 2 becomes

fF ) (fWDW + fADA)/(DW + DA/Q + DM + DG)

(3)

where Q is a maximum or limiting biomagnification factor. This equation includes the assumption that growth rate is linear, that all processes are first-order in chemical concentration, and that the organism has maintained constant D values for a period of time, which is long relative to the clearance time of the contaminant. It does not treat the effect of historical differences in contaminant levels or exposure. A more complex model of lifetime exposure is required to treat such situations. Metabolism is expressed as a firstorder rate constant kM applicable to the total chemical in the organism and is expressed as a half-life, i.e., 0.693/kM. In the case of PCBs, kM is assigned an illustrative but negligible value. It is noteworthy that DW (gill ventilation) depends on ZW while DA (uptake from food) depends on ZA, which in turn depends on KOW. The ratio of DA to DW is thus proportional to KOW. It is small for non-hydrophobic chemicals and is large for hydrophobic chemicals. Uptake from water dominates, when fWDW . fADA, which occurs when DW . (DA/Q + DM + DG) and most loss is by respiration. The fugacity in the organism then approaches the fugacity in water, and simple equilibrium bioconcentration applies. Uptake from food dominates when fWDW , fADA, which occurs when DA/Q . (DW + DM + DG), and losses by respiration, metabolism, and growth are negligible as compared to loss by egestion. Equation 3 then reduces to

fF ) QfA

(4)

Q being the maximum or limiting biomagnification factor as discussed by Mackay (6) and Gobas et al. (4). Separating the terms for uptake from water and food gives

fF ) fWDW/(DW + DA/Q + DM + DG) + fADA/(DW + DA/Q + DM + DG) ) fWW + fAA (5) We refer to the dimensionless quantities W and A as,

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TABLE 1. Z Values (mol/m3‚Pa), i.e., Ratio of Concentration (mol/m3) to Fugacity (Pa) Z value

compartment air

ZA ) 1/RT

water

ZW ) 1/H or Cs/Ps

solid sorbent (e.g., sediment)

ZS ) KPFS/H

lipid and octanol fish/organism

ZO ) KOW/H ZF ) KBFB/H ) LBZO

food

ZA ) LAZO

TABLE 2. Definition of D Values for Uptake and Clearance Processes equation

DA ) EAGAZA DE ) DA/Q DM ) VFZFkM DG ) ZF (dVF/dt) DW ) k1VFZW

parameter definition

DA) net chemical uptake from food GA) gross food ingestion rate (m3/h) EA ) gut absorption efficiency DE ) chemical loss by egestion Q ) limiting biomagnification factor DM ) chemical loss by metabolism VF ) volume of organism (m3) kM ) metabolic rate constant (h-1) DG ) growth dilution term, t is time (h) k1 ) gill ventilation rate constant (h-1)

respectively, fugacity factors for respiration (or in some cases diffusive uptake) from water and for food. W can range from zero to 1.0, while A can range from zero to a maximum value of Q. A fugacity factor is similar in concept to a partition coefficient, but is a ratio of fugacities rather than concentrations, and it does not reflect equilibrium conditions. fWW is the fugacity in the fish attributable to waterborne contaminant, thus W is the ratio of that fugacity to the fugacity in water. Note that fW depends on the dissolved chemical concentration in water, not the total concentration. Similarly, fAA is the fugacity in the fish attributable to food, A being the ratio of that fugacity to the fugacity in food. Because of the linear nature of the equations, the fugacities fWW and fAA add to give the total fugacity fF. W controls the extent of bioconcentration, and A controls the extent of biomagnification. Bioaccumulation is thus clearly expressed as the sum of bioconcentration and biomagnification. This approach of separating the contributions of respiration and food is not novel and is merely a reformulation of similar concentration, rate constant equations in fugacity format. For example, Gobas (3) used a conventional rate constant approach to express the fish concentration as a function of the concentrations in water and in food in terms of the rate constants for uptake by respiration from food and the rate constants for loss by respiration egestion, metabolism, and growth yielding his eq 8, which can be shown to be algebraically equivalent to eq 5. The equation can also be shown to be similar to the formulations of food chain transfer developed by Thomann (8) and Barber et al. (5). An advantage of the fugacity formulation is that the parameters A and W have similar values for chemicals of similar KOW, whereas the rate constants have values that may range more widely and are very sensitive to KOW. The fugacity matrix discussed later is thus better conditioned and contains no extreme values that complicate solution. Finally, W and A have readily understandable meanings in terms of equilibrium status within the organism relative to water and food.

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R ) 8.314 Pa‚m3/mol‚K T ) temp (K) Cs ) aqueous solubility (mol/m3) Ps ) vapor pressure (Pa) H ) Henry’s law constant (Pa‚m3/mol) KP ) partition coeff (L/kg) FS ) solid density (kg/L) KOW ) octanol water partition coefficient KB ) equilibrium bioconcentration factor (L/kg) ) lipid content LBKOW FB ) organism density (kg/L) LA ) lipid content of food (g/g)

Food Web Model Linear Food Chain. If there is a series of organisms each having an exclusive diet of the organism one trophic level lower, we can write one equation for each organism. It is likely that the first organism in the food chain is an algal species that can be assumed to be in, or to approach, equilibrium with water, thus the food uptake term DA and the egestion term DA/Q can be ignored. Growth and metabolism are however included. The equations are then

f1 ) DW1fW/(DW1 + DM1 + DG1)

(6)

f2 ) (DW2fW + DA2fA2)/(DW2 + DA2/Q2 + DM2 + DG2) f3 ) (DW3fW + DA3fA3)/(DW3 + DA3/Q3 + DM3 + DG3) etc. where the subscripts 1, 2, and 3 refer to trophic levels within the food chain. But fA2 is f1, fA3 is f2, etc. Separating the terms in the numerator as in eq 5 and designating the denominators as DTi i.e., the total loss D value, gives

f1 ) W1fW

(7)

f2 ) W2fW + A2f1 f3 ) W3fW + A3f2

etc.

where

W1 ) DW1/(DW1 + DM1 + DG1) ) DW1/DT1

(8)

W2 ) DW2/(DW2 + DA2/Q2 + DM2 + DG2) ) DW2/DT2 In general

Wi ) DWi/(DWi + DAi/Qi + DMi + DGi) ) DWi/DTi and

A2 ) DA2/(DW2 + DA2/Q2 + DM2 + DG2) ) DA2/DT2

(9)

In general

Ai ) DAi/(DWi + DAi/Qi + DMi + DGi) ) DAi/DTi Substitution yields

f1/fW ) W1 f2/fW ) W2 + A2W1

(10)

f3/fW ) W3 + A3(W2 + A2W1) f4/fW ) W4 + A4(W3 + A3(W2 + A2W1)

etc.

Again, these equations and the following discussions are essentially re-statements of descriptions of the food chain bioaccumulation phenomena by Thoman (8). Two limiting conditions are worthy of note. If Ai is small as compared to Wi, which implies that most exposure is through the water (as is likely for less hydrophobic chemicals), then only the first term Wi is significant and fi will approximate fWWi at all trophic levels. For non-metabolising chemicals and slow growth, the fugacity of the chemical in all organisms will be similar to that of the chemical in water. Simple bioconcentration will then apply throughout the food chain and no biomagnification will occur. If Ai is large as compared to Wi, which implies that most exposure is by food (as is likely for more hydrophobic substances), then approximately

f2 ) A2fW

(11)

f3 ) A2A3fW

FIGURE 1. Simple, linear pelagic food chain showing how organism fugacities are attributable to respiration (thin arrows) and food uptake (thick arrows).

f4 ) A2A3A4fW If again metabolism and growth are unimportant and (for illustrative purposes only) the coefficients Qi are equal, then for a food chain containing n species, A will approach Q and

fn ) Q(n - 1)fW

(12)

The fugacities then rise by a biomagnification factor of Q at each trophic level. Biomagnification is likely only when Ai exceeds 1.00, or equivalently when DA > DW/(1 - 1/Q). Because Q is generally about 3 (6), this implies that DA must exceed 1.5 DW. In general, however, A and W are both significant and must be considered, if only to establish their relative importance. If metabolism is significant, the terms DTi become large; Wi and Ai become small; and the organisms will have lower fugacities than the water. It should also be noted that the equations are linear in fugacity, thus doubling fW will double all fugacities in fish. The contributions of water and food can be readily identified. For example, for trophic level 3, the relative contributions are W3 direct from water and A3 (W2 + A2W1) from food. But this food consists of A3W2, which represents contaminant absorbed by organism 2 from water, and A3A2W1 representing uptake by organism 2 from food organism 1 (see Figure 1). Organism 1 obtains all its chemical from water, thus ultimately all contaminant was derived from water, but by passage through differing numbers of organisms. Sediment- and Water-Based Food Chains. If the first trophic level is benthos, which is in contact with sediment of fugacity fS rather than water, similar equations apply but f1 is W1fS or possibly (W1 + A1)fS. Some organisms such as mysids may migrate daily from sediments into the water column and may thus respire for a fraction of time xW in water and a fraction xS in sediment. The respiration term then becomes

W(xWfW + xSfS)

(13)

Pelagic organisms have an xW of 1.0, while xW is zero for benthic organisms such as oligochaetes. If a food chain is based on both sediment and water as discussed by Thomann et al. (2) and as illustrated in Figure 2, then the equations can be structured with multiple A values for each organism designated Aji where j is the prey and i is the predator as dictated by food preference. More complex food webs can be constructed by following this approach. The combinations are again obvious. In Figure 2, the inputs

FIGURE 2. Food chain including pelagic and benthic organisms with thin arrows indicating respiration and thick arrows indicating food uptake. to pelagic organism 3 are W3fW from water directly, A13f1 or A13W1fW from pelagic food organism 1, and A23f2 or A23W2fS from benthic food organism 2. Contaminant thus reaches organism 3 from both water and sediment in proportions dictated by diet and respiration. These equations can be readily solved by successive substitution starting at the bottom of the food chain. Some algebra is necessary if an organism consumes its own species. It is possible to compile the equations for a complex food web by writing the expression for each route as illustrated in Figure 2, using as a basis a simple sketch of the food chain. General Food Web in Matrix Form. These equations can be shown to be special cases of a general matrix equation for N species or the same species at different life stages, all of which can potentially consume each other, including themselves. The general equation for organism i, which may respire in water and/or sediment, is

DWi(xWfW + xSfS) +

∑D

Ajifj

)

fi(DWi + DMi + DGi +

∑D

Aji/Qi)

) fiDTi (14)

where the summations are over all organisms including i. This can be rewritten as

fi )

∑A f + W (x ji j

i

Wf W

+ xSfS)

(15)

where Wi ) DWi/DTi and Aji ) DAji/DTi. The set of i equations

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FIGURE 3. Logarithmic plot of calculated and observed concentration of PCB congeners and total PCBs in organisms in Lake Ontario.

TABLE 3. Physical Chemical Properties of PCBs (11, 12) and Concentrations in Total Water and Sediment Solids in Lake Ontario (9) concentration

compound congener 18 congener 52 congener 101 congener 138 congener 153 congener 194 total PCB

MW H sediment (g/mol) (Pa‚m3/mol) log KOW (ng/g)

water (pg/L)

257.5 292.0 326.4 360.9 360.9 429.8 326

72 63 130 28 50 7.8 1100

58.1 47.6 32.7 48.6 42.9 47.5 12.2

5.6 6.1 6.4 6.73 6.9 7.4 6.6

4.0 25 27 15 25 3.7 570

TABLE 4. Environmental Conditions for Lake Ontario (3, 9, 12) Lake Ontario suspended particulate matter concentration (g/m3) 1.25 volume fraction sediment solids 0.1 organic carbon content of suspended matter 0.2 organic carbon content of sediment particles 0.02 density of particles in water column (kg/m3) 1500 density of sediment particles (kg/m3) 1500

TABLE 5. Dietary Preference Matrix for Aquatic Organismsa food (prey)

can be written in matrix form

Af ) E

(16)

where A is the food consumption or diet matrix, f is the vector of organism fugacities, and E is a respiration vector. For example, if there are four organisms, eq 17 becomes

[

(1 - A11) -A12 -A13 -A14

-A21 (1 - A22) -A23 -A24

-A31 -A32 (1 - A33) -A34

-A41 -A42 -A43 (1 - A44)

[

][ ]

f1 f2 ) f3 f4

W1(x1WfW + x1SfS) W2(x2WfW + x2SfS) W3(x3WfW + x3SfS) W4(x4WfW + x4SfS)

]

9

1

2

3

4

5

6

7

8

1* 2 3* 4* 5 6 7 8

0.00 0.80 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.18 0.60 0.54 0.00

0.00 0.20 0.00 0.00 0.82 0.40 0.21 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10

0.00 0.00 0.00 0.00 0.00 0.00 0.25 0.50

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.40

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

a 1, plankton; 2, mysid; 3, pontoporeia; 4, oligochaete; 5, sculpin; 6, alewife; 7, smelt; 8, salmonids (3, 10). An asterisk (*) indicates that these organisms either photosynthesize or consume organic carbon and detritus.

(17)

The coefficients in the A matrix represent the consumption of each organism by another. For the linear food chain described earlier all Aji values are zero except A12, A23, and A34. The vector of fugacity values (f1 - fN) is solved using a Gaussian elimination subroutine. From these fugacities all concentrations, amounts, and chemical fluxes can be calculated resulting in a complete steady-state mass balance throughout the entire food web. The behavior of larger and more complex food webs can be assessed with no increase in mathematical

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organism predator

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 31, NO. 2, 1997

complexity, and the sensitivity of concentrations to food web structure and dietary preferences can be readily explored. Rewriting eq 17 to express f as a function of E and the inverse of A is an attractive further step because it shows directly how fugacities respond to changes in exposure concentrations.

Model Application The model was applied to PCB data from Lake Ontario reported by Oliver and Niimi (9), with feeding relationships from Flint (10). PCB concentrations in sediment and water

FIGURE 4. Schematic diagram of concentrations, fugacities, and fluxes (µg/day) in Lake Ontario organisms of PCB congener 18.

TABLE 6. Organism Properties Used for Model Simulations (3, 9, 10)

plankton mysid pontoporeia oligochaete sculpin alewife smelt salmonid

mass (g)

lipid fraction

feeding rate (g g-1 d-1)

growth rate (g g-1 d-1)

metabolism half-life (days)

fraction water respiration

0 0.1 0.02 0.1 5.4 32 16 2410

0.015 0.04 0.03 0.01 0.08 0.07 0.04 0.16

0 0.2 0.224 0.17 0.04 0.035 0.04 0.02

0.025 0.02 0.02 0.015 0.005 0.004 0.005 0.002

500000 50000 50000 50000 5000 5000 5000 5000

1 1 0 0 1 1 1 1

PCB for six congeners, and total PCBs were used as model inputs. The six congeners were selected as having the highest and, thus, more reliable concentrations and covering a range of hydrophobicity. Model Parameters. Chemical parameters input were log KOW, molecular mass, and Henry’s law constant. Congenerspecific values were taken from Mackay et al. (11), and values for the PCB mixture were from Mackay (12) as listed in Table 3. Environmental conditions are documented in Table 4 with data from Gobas, (3), Oliver and Niimi (9), and Mackay (12). Feeding relationships given in Table 5 for Lake Ontario are from Gobas (3) and Flint (10). Other organism properties were taken from Oliver and Niimi (9), Gobas (3), and Flint (10) and are shown in Table 6. The water uptake rate constant k1, used to estimate DW, was based on the correlation of Gobas and Mackay (13) and Gobas (3):

1/k1 ) (VF/QW) + (VF/QL)/KOW ) 1/(LKOWk2)

(18)

where VF is the fish volume (L), L is the fish lipid content, k1 has units of days-1, QW and QL are transport parameters (L/ day) that express water and lipid phase conductivities correlated as

QW ) 88.3VF0.6

(19)

QL ) 0.001QW

(20)

Only the term VF/QW is significant for PCBs. The gut absorption efficiency E was correlated as (14)

1/E ) 5 × 10-8KOW + 2.3

(21)

All limiting biomagnification factors Q were set at 3.0. The metabolism half-lives were set at arbitrarily large times, i.e., 14 years or longer for all congeners and total PCBs such that

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FIGURE 5. Schematic diagram of concentrations, fugacities, and fluxes (µg/day) in Lake Ontario organisms of PCB congener 101. metabolism is negligible. Plankton are essentially at equilibrium with water, with only a small contribution to losses from growth and metabolism. Bioavailability in the water column, expressed as fugacity, was calculated using the organic carbon contents of the suspended matter (Table 4) and assuming that KOC is 0.41KOW (6). Fugacities in the sediment were calculated similarly from sediment concentrations and organic carbon contents from Table 4, again assuming KOC to be 0.41KOW.

ences in lipid content and to biomagnification. Beyond log KOW of 6.7, bioavailability in the water column is reduced, and gut absorption efficiency starts to fall. Since the fugacity in the sediment generally exceeds that of the water, benthic organisms and those which feed on them are more contaminated.

Figure 3 is a logarithmic plot of calculated versus measured wet weight concentrations for the six PCB congeners and total PCBs in Lake Ontario. Predicted plankton concentrations are close to those measured, although model predictions exceed measured concentrations for higher KOW congeners (153 and 194). Predicted values for mysids are generally within a factor of 2 of measured values. Values for pontoporeia exceed measured values by a factor of 2 or 3. Predictions for oligochaetes are lower than measured for PCBs 18-101, but higher for PCBs 138-194. Predictions for fish are generally within a factor of 3 of measured values. Estimation of benthos concentrations are less accurate but generally within a factor of 4. Overall the standard deviation on a log scale is 0.34 units corresponding to a factor of 2.2 difference between estimation and observation.

Figures 4-6 show the detailed contaminant concentrations, fugacities, and fluxes for three congeners. Fugacities generally increase with trophic level, but this is complicated by the different water and sediment fugacities. As KOW increases, so too does the relative intake from food. For congener 18, intake by salmonids by respiration is 3%; for congener 101, this drops to 0.3%, and for congener 194, it is only 0.2%. The major loss processes are egestion and growth dilution. In all cases, because the fugacity in fish exceeds that of the water, there is net loss through the gills, i.e., the quantity lost to gill water is 5 to 10 times the quantity absorbed. The concentration achieved by each organism is thus a function of its diet, its respiration rate, the bioavailability of the contaminant in the water and/or sediment, and the rates of loss by egestion, growth dilution, respiration, and metabolism. For species that have a diet of several other species, the concept of a simple bioaccumulation factor no longer applies, and even the concept of a food chain multiplier is excessively simplistic. Any change in diet, especially from pelagic to benthic organisms, can profoundly affect bioaccumultion. We view these diagrams as containing a wealth of information about the bioaccumulation process in a complex food web.

Inspection of the results shows that plankton are approximately at equifugacity with the water. At higher trophic levels there are higher concentrations attributable to differ-

On a species basis, model predictions were consistently high for benthos. There are several possible explanations. The fractional respiration value for interstitial sediment versus

All parameter values were set a priori, no reported bioaccumulation data being used to select or adjust values.

Results

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FIGURE 6. Schematic diagram of concentrations, fugacities, and fluxes (µg/day) in Lake Ontario organisms of PCB congener 194. overlying water may be in error, and there may be greater exposure to the overlying water column. The PCBs in Lake Ontario sediments are old and therefore may be very tightly bound to organic carbon. These PCBs may therefore not be as available to the benthic biota, i.e., the fugacity is less than that calculated from the concentration. This could be modeled by increasing Z for the PCBs in the sediment, thereby decreasing fugacity. At the time of sampling, PCB concentrations were falling, and there may be a lag in the response of the salmonids that is not treated by the model. We believe that this approach can be readily extended to include air-breathing organisms such as birds and marine mammals, which rely primarily on a diet drawn from the aquatic or marine environments. The respiration vector in eq 17 would then also include the chemical’s fugacity in air. Obviously the D values for respiration must be re-defined. It may even be possible to include vegetation as a component of the food web. Examination of the fugacity factors A and W shows that, for fish, the respiration value W is significant, even for very hydrophobic compounds. For air-breathing organisms, W is generally much smaller than A, thus contributions from and losses to air are often negligible except for very volatile compounds. This is caused by the lower concentration of oxygen in water (∼10 g/m3) as compared to air (∼250 g/m3), which forces the organism to respire a greater volume of water. Furthermore, air-water partition coefficients are usually small, thus the capacity of air to remove chemical from the organism is reduced. Organisms such as certain birds, whales, and terrestrial mammals such as mink and otter that feed from the aquatic environment but respire air are thus penalized with potentially high contaminant intake rates coupled to potentially low rates of loss. The model, written in BASIC, is available from the Trent University web site.

Acknowledgments The authors are grateful to NSERC, the Trent UniversityChemical Industry Consortium, and the Great Lakes University Research Fund for financial support.

Literature Cited (1) Thomann, R. V. Environ. Sci. Technol. 1989, 23 (6), 699-707. (2) Thomann, R. V.; Connolly, J. P.; Parkerton, T. F. Environ. Toxicol. Chem. 1992, 11, 615-629. (3) Gobas, F. A. P. C. A. Ecol. Modell. 1993, 69, 1-17. (4) Gobas, F. A. P. C.; Zhang, X.; Wells, R. Environ. Sci. Technol. 1993, 27, 2855-2863. (5) Barber, M. C.; Suarez, L. A.; Lassiter, R. R. Can. J. Fish. Aquat. Sci. 1991, 48, 318-337. (6) Mackay, D. Multimedia Environmental Models: the Fugacity Approach; Lewis Publishers Inc.: Chelsea, MI, 1991; 257 pp. (7) Clark, K. E.; Gobas, F. A.; Mackay, D. Environ. Sci. Technol. 1990, 24 (8), 1203-1213. (8) Thomann, R. V. Can. J. Fish. Aquat. Sci. 1981, 38, 280-296. (9) Oliver, B. G.; Niimi, A. J. Environ. Sci. Technol. 1988, 22, 388397. (10) Flint, R. W. J. Great Lakes Res. 1986, 12 (4), 344-354. (11) Mackay, D.; Shiu, W. Y.; Ma, K. C. Illustrated handbook of physicalchemical properties and environmental fate for organic chemicals. Volume 1: monoaromatic hydrocarbons, chlorobenzenes and PCBs; Lewis Publishers: Chelsea, MI, 1992; 697 pp. (12) Mackay, D. J. Great Lakes Res. 1989, 15, 283-297. (13) Gobas, F. A. P. C.; Mackay, D. Environ. Toxicol. Chem. 1987, 6, 495-504. (14) Gobas, F. A. P. C.; Muir, D. C.; Mackay, D. Chemosphere 1988, 17, 943-962.

Received for review June 4, 1996. Revised manuscript received September 25, 1996. Accepted September 27, 1996.X ES960478W X

Abstract published in Advance ACS Abstracts, December 15, 1996.

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