luk - wit press...respectively, yd' is the value of the food or prey, 'f' is the value of the fish,...

Modeling the accumulation of PCBs in Lake

Ontario salmonids

G. K. Luk

Department of Civil Engineering, Ryerson Polytechnic University,

350 Victoria Street, Toronto, Ontario, Canada

EMail: [email protected]

Abstract

This paper demonstrates the application of a newly developed mathematicalmodel on the bioaccumulation of polychlorinated biphenyls (PCBs) in LakeOntario salmonids. The novel approach, based on the bioenergetics concept,combines physiological information of the fish, such as diet, metabolism,respiration, habitat, age, and species, with the biochemical nature andenvironmental conditions of the pollutants in the water. Three species ofsalmonids were studied, namely: Lake Trout (Salvelinus namaycush), BrownTrout (Salmo trutta), and Chinook Salmon (Oncorhynchus tshawytscha), fortheir ontological pattern of total body burden of PCBs. Results demonstrate anotable variation of PCBs accumulation among species sharing the same habitat,a fact which was explained in the model by the genetic difference in metabolismof each species.

1 Introduction

The effect of toxic pollution on the aquatic food web is a major concern to thepopulation living close to the Great Lakes, who depend to different degrees onthe lake for food, water, and recreation. To improve our understanding on theextent of the bioaccumulation problem, mathematical models are widely used tosupplement the sparse and often expensive information provided by samplingand monitoring. The objective of the proposed paper is to demonstrate theapplication of a newly developed mathematical model on Lake Ontariosalmonids, for the ontological bioaccumulation of total PCBs . The model isbased on the concept of bioenergetics, which relates the energy expenditure of afish directly to its food and water consumption, the two main pathways fromwhich the pollutants enter the body of the fish.

Transactions on Ecology and the Environment vol 13, © 1997 WIT Press, www.witpress.com, ISSN 1743-3541

64 Measurements and Modelling in Environmental Pollution

The bioenergetics-based model, first proposed by Norstrom et al. [11]and subsequently refined in Luk [6] and Luk and Brockway [7], was used inthis study. This model takes into account pollutants entering the fish throughfood consumption and water ingestion across the gills, processes which areassociated through metabolism to fish size, activity levels, and growth rate.Although several species of salmonids may share the same habitat, it has beenobserved that large variations in PCBs accumulation occur in different speciesexposed to similar levels of contaminant concentrations. In addition, there aresubstantial fluctuations in PCBs concentrations between species that consumethe same amount and variety of preys. Therefore, this research investigates theaccumulation of PCBs in fishes of different age-groups among various speciesoccupying the same habitat, and attempt to explore the physiological factorswhich may affect the potential of contaminant bioaccumulation.

A cross-species application of the bioenergetics model was performedwithin this study. Care was given to use species-specific parameters as much aspossible, in an effort to deter species borrowing which can lead to inaccuratemodel predictions [Ney 9]. In order to achieve this, it was imperative that eachspecies be studied independently, and that the methods used in determiningparameters remain consistent among the species. Factors related to age, weight,and metabolism are significant in discovering the specific nature of each speciesin relation to their bioaccumulation properties. Three species of Lake Ontariofishes were chosen for the study, and they are namely, Lake Trout (Salvelinusnamaycush), Brown Trout (Salmo truttd), and Chinook Salmon (Oncorhynchustshawytschd).

2 Modeling method and database

The bioenergetics approach is based on the first law of thermodynamics,whereby all food consumed by a fish is either used as energy, stored, convertedto growth, or eliminated as wastes. Each of these components wasparametrized and incorporated into an energy balancing procedure. The finalfirst-order differential equation [Luk 6, Luk and Brockway 7], which states thatthe change of body burden of a pollutant is the net result of the uptake fromfood and water, less the losses associated with waste egestion and growthdilution, may be given as

where P (pig/g) is the total body burden of PCBs per unit wet weight offish; E(dimensionless) is the efficiency of assimilation; C (pg/g) is the concentration;


Measurements and Modelling in Environmental Pollution 65

q (kcal/g) is the energy equivalence; air (kcal/(wk g*)) is the low routinemetabolism; W (g) is the wet weight of the fish; r (dimensionless) is the bodyweight exponent for metabolism; J3 (dimensionless) is the proportion of growthrate that represents the energy for food conversion; p (g/mL) is water density,and K' (wk~*) is the clearance rate combining waste egestion and growthdilution. The subscripts '/?/' and "pw ' represent PCBs from food and waterrespectively, yd' is the value of the food or prey, 'F' is the value of the fish,and 'ox ' represents values of oxygen.

The fish concentration database for this study is provided by the OntarioMinistry of Environment and Energy, from a lakewide surveillance program forthe years 1989 - 1994. Fish data for each of the three species, comprised oflength (cm), weight (g), and PCBs level (jig/g), were collected for samplesfrom 22 sites in Lake Ontario. In total 899 samples were collected: 257 LakeTrout, 377 Brown Trout, and 305 Chinook Salmon. Water concentrations ofPCBs (ng/L) were measured by the Inland Waters Directorate, Ontario Regionof Environment Canada, for the years encompassing 1986-1990. The sampleswere taken in surface waters (at 1 m depth) during spring isothermal conditions.The mean concentration of total PCB's during the time period studied is 1.08ng/L. The location of the fish sampling and water quality monitoring sites areillustrated in the map in Figure 1 .

3 Model Parameters

Simulation studies indicate that differences in PCBs concentrations amongspecies are governed by many factors, among them are the environmental,physiological, chemical, and biological. Accordingly, the model parameters areclassified into five categories: growth, metabolism, energy equivalence,exposure concentrations, and absorption efficiencies. The calibration forparameters in each of these categories is described in the following, and thevalues adopted in the modeling are summarized in Table 1 .

Fish growth was modeled using the Von Bertalanffy growth function(VBGF), which has been proven to give the most accurate results as comparedto other existing functions [Chen et al. 3]. It is defined by a weight-agerelation, given as

where W(t) (g) represents the wet weight of a t-week-old fish, W*, (g) is theasymptotic weight , k (wk~*) is the growth coefficient, and b (dimensionless) isthe power term in the weight-length function,

PT(f) = 67 1(0 » (3)

where Lft) (cm) is the length of the fish of the same age. The values of theseparameters were defined for each of the three species by linear regression, and asample calibration for Lake Trout is given in Figure 2.



Table 1 Model parameters for each the three salmonid species

Symbol

Fish Grov,

k̂

b

K'

Metabolic

air

T

Energy EC


Lake Ontario, New York

15

Legend11 • fish sampling site3 fish sampling region

& B water sampling site

Toronto*

'ego

Rochester*

4

25 50 mi Not for navigationcopyright 1996 Underwater Technologies, Inc., all rights reserved

Map Number NY310400 http:yAvww.utmaps.com

Figure 1 : Map of Lake Ontario demonstrating the locations of fish andwater quality sampling sites

100

40

20

1000 2000 3000 4000Weight (grams)

5000 6000 7000

Figure 2 : Growth function for Lake Trout



The metabolic function, which was found to have the greatest influence onthe model performance, was calibrated a-prior with published metabolic data foreach species. The metabolic costs per unit weight offish, Qs (kcal/wk), may begiven as a power function,

2, = ̂%" (4)

where a\r (kcal/(wk g*)) is the low-routine metabolic cost and r (dimensionless)is the power term for metabolism. These parameters were derived usingregression analysis to existing metabolic models. For each species, the foodconsumption estimates were compared to two existing bioenergetics models,covering the full-range of a fish's activity level from 'low-routine' to 'activeforaging'. The first model, developed by Stewart et al. [12], describes weight-specific metabolism using functions for consumption, temperature and typicalin-situ swimming speeds. The second model was from Thomann & Connolly[14], who defined the most active, or foraging state of metabolic costs offish.Using the body weight information for the three fish species, the above modelswere converted to metabolic costs and plotted over the growth of each species.When the power function of metabolism in equation (4) is fitted to the twomodels, the maximum and minimum values of T , from which the average couldbe obtained, as well as a were defined.

Estimates of the relative energy densities of each of the prey items, #%, aswell as that of oxygen, q̂ , were chosen from the literature. For the fish masses,energy densities, qp, are highly dependent on the size, and must be obtained foreach species. For Lake Trout and Brown Trout, the energy equivalent wasgiven by the Stewart et al. [12] equation,

qp (W 1.472kg) = 2.172 + 0.186PF

and for Chinook Salmon, Stewart & Ibarra [13] further developed the followingrelationship,

qp (W< 4.0kg) = 1.377 + 0.236PF (6)

qp (W> 4.0kg) = 1.816 + 0.126PF

The level of PCBs in Lake Ontario salmonids is directly affected by the dietcomposition and contamination. The diet of Ontario salmonids is mainlycomposed of invertebrates, slimy sculpins, smelts, and alewife, with thepreference shifting to alewife as the fishes grow. Borgmann & Whittle [1]estimated the average proportion for each species in the diet of various age-class Lake Trout. For the purpose of this research, their suggestion was appliedto Brown Trout and Chinook Salmon. This assumption is based on thesimilarities between habitat and diet composition suggested by both Brandt [2]and Jones et al. [5]. From the measured concentrations by Borgmann & Whittle[1] for Lake Ontario species for the years 1977-1988, the average PCBsconcentrations for smelt and sculpin were 0.88 and 1.12 pg/g respectively. Thetotal PCBs concentrations for alewife was considered to be identical to that ofsmelt, based on the suggestion by Niimi & Oliver [10]. With these information,



the overall diet contamination for different age-classes was thus derived bydirect proportioning.

The transfer efficiency factors were taken from literature review. Bothof the efficiencies of oxygen transfer, Eox, and pollutant transfer across the gills,Ep* remain constant for most salmonids [Norstrom et al. 11 and Jensen et al.4]. The efficiency of assimilation of PCBs from food, £#, was chosen as 0.80for Lake Trout from Norstrom et al. [11], but this value was observed to be alot smaller for the other two species. According to Madenjian et al. [8], a valueofEpf= 0.50 is more appropriate for Brown Trout and Chinook Salmon. Theefficiency of food assimilation, £& for Lake Trout was chosen as 0.78 [Stewartet al. 12], and for Chinook Salmon and Brown Trout it was chosen as 0.82 fromJensen et al. [4].

4 Results and Discussion

The bioenergetics-based bioaccumulation model was applied to each of thethree chosen species of salmonids, and the results are given in Figure 3. Thefollowing observations are made based on the findings:1. The model results were in general agreement with the observed data on the

trend in all cases. However, the predictions have been consistently higherthan the data. The main reason for the discrepancy may lie in the methodfor PCBs measurement in the fishes. The data provided by the Ministry ofEnvironment and Energy was collected by homogenization of a boneless,skinless fillet of dorsal muscle flesh. This method will invariably under-estimate the actual concentrations since most of the PCBs are deposited inthe lipid fractions of the fish, such as skin, intestines, and head.

2. There is a continuous increase of total PCBs concentration in each of thethree species with increasing age. Normally, as the fish ages and grows insize, the body weight increases quite substantially, and the concentration ofaccumulated pollutants should come down because of this "growth-dilution"effect. However, it is apparent that in spite of this marked dilution, theconcentration has still increased with age for all species, which indicates thatthe rate of accumulation must have increased quite tremendously, so muchso that growth-dilution was completely masked. This supports the conceptof biomagnification, and the results generated from the model wereconsistent with observations.

3. After the fishes mature, at the age-group of between 3 to 4, their dietcomposition remains quite constant. However, the total body burden wasobserved to increase with time. This phenomenon was explained in themodel by the slowing down of fish metabolism with age. As the fish ages,the metabolism reaches a asymptotic value, and food consumed along withthe pollutants will more likely be stored. Also around the same time, thegrowth-dilution slows down because the fish does not grow as much.Therefore, the net effect of general increase in total body burden is resulted.



(a)

1000 2000 3000 4000

Weight (grams)

5000 6000

(b)

1000 2000 3000 4000Weight (grams)

5000 6000

(o

1000 2000 3000 4000 5000

Weight (grams)

6000

Figure 3 : Modeling Results for (a) Lake Trout; (b) Brown Trout;and (c) Chinook Salmon



4. Although the three species were exposed to basically the same environment,fed on a similar diet, and lived in the same habitat, the difference of the totalPCBs body burden was quite remarkable. In fact, from the collected data, itcan be seen that the magnitude of maximum concentration in Lake Troutwas twice that of Brown Trout and four times that of Chinook Salmon. Thistrend was also indicated in the model results. This can be explained by thedifference on metabolism and growth patterns among the species. Therefore,it may be inferred that the overall bioaccumulation of toxins for individualspecies is very much governed by their biological predisposition.

5 Conclusions

The proposed method of modeling PCBs bioaccumulation with thebioenergetics-based concept seems to provide a promising framework tobioaccumulation studies. When applied simultaneously to three different fishspecies, the trend of accumulation and order of magnitude was successfullyreproduced. With improved parametrization and more extended verification, themodel can become a useful predictive tool in the study of bioaccumulation ofdifferent toxins in top-predator fishes. This is especially important in light of thefact that the current method of transfer-kinetics based models have severerestrictions with persistent chemicals, and have been reported to produceerroneous results in many cases. Another significant finding the model hasdiscovered, is the fact that the frequently observed variation of pollutantbioaccumulation among different species may be explained by the geneticdisposition, characterized by the growth, activity level and metabolic patterns.

6 Acknowledgment

The author would like to thank Ms. Julia Paleopanos for her assistance in thepreparation of this paper. Mr. Serge L'ltalien of Environment Canada and MrCharles Cox of the Ontario Ministry of Environment and Energy have kindlyprovided the data for the study, and their contributions are greatly appreciated.

References

1. Borgmann, U. & Whittle, M.W DDE, PCB, and Mercury concentrationtrends in Lake Ontario Rainbow Smelt (Osmerus Mordax) and SlimySculpin (Cottus Cognatus). 1977 to 1988, J. Great Lakes Res., 1992, 18(2), 298-308.



2. Brandt, S. Food of Trout and Salmon in Lake Ontario, J. Great Lake Res.,1986, 12 (3), 200-205.

3. Chen, Y., Jackson, DA. & Harvey, H.H. A comparison of von Bertalanffyand polynomial functions in modelling fish growth data, Can. J. Fish.Aquat. Set., 1991, 49, 1228-1235.

4. Jensen, A.L., Spigarelli, S.A. & Thommes, M.M. PCB uptake by fivespecies of fish in Lake Michigan, Green Bay of Lake Michigan, and CayugaLake, New York, Can. J. Fish. Aquat. &%., 1982, 39, 700-709.

5. Jones, ML., Koonce, J.F. & CTGorman, R. Sustainability of hatchery-dependent Salmonine fisheries in Lake Ontario: the conflict betweenpredator demand and prey supply, Trans. Am. Fish. Soc., 1993, 122, 1002-1018.

6. Luk, O.K. Bioaccumulation model for two PCB congeners in Lake OntarioLake Trout: A bioenergetics approach, Proc. CSCE 1995 Annual Conf.,Ottawa, Ontario, 1995.

7. Luk, G.K. & Brockway, F Application of a PCBs bioaccumulation modelto Lake Ontario Lake Trout, Accepted for Ecological Modelling, 1996.

8. Madenjian, C.P., Carpenter, S.R. & Rand, P.S. Why are the PCBconcentrations of salmonine individuals from the same lake so highlyvariable?, Can. J. Fish. Aquat. &%., 1994, 51, 800-807.

9. Ney, J.J. Bioenergetics modeling today: growing pains on the cutting edge,Trans. Am. Fish. Soc., 1993, 122, 736-748.

10. Niimi, A.J. & Oliver, B.G. Distribution of polychlorinated biphenylcongeners and other halocarbons in whole fish and muscle among LakeOntario Salmonids, Environ. Sci. Technol., 1989, 23, 83-88.

11. Norstrom, R.J., McKinnon, A.E. , & DeFreitas, S.W. A bioenergetics-basedmodel for pollutant accumulation by fish, simulation of PCB methyl-mercuryresidue levels in Ottawa River Yellow Perch (Perca flavescens), J. Fish.Res. Board Can., 1976, 33, 248-267.

12. Stewart, D.J., Weininger, D, Rottiers, D.V. & Edsall, T.A. An energeticsmodel for Lake Trout, Salvelinus namaycush: application to the LakeMichigan population, Can. J. Fish. Aquat. Sci., 1983, 40, 681-698.

13. Stewart, D.J. & Ibarra, M Predation and production by salmonine fishes inLake Michigan, 1978-88, Can. J. Fish. Aquat. Sci., 1990, 48,909-922.

14. Thomann, R.V. & Connolly, J.P. Model of PCB in the Lake Michigan LakeTrout food chain, Environ. Sci. TechnoL, 1984, 18 (2), 65-71.


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