A Kinetics Model for Predicting the Accumulation of PCBs in

Research A Kinetics Model for Predicting the Accumulation of PCBs in Phytoplankton ROBERT S. SKOGLUND,† KARI STANGE,‡ AND DEBORAH L. SWACKHAMER* Environmental and Occupational Health, School of Public Health, University of Minnesota, 420 Delaware Street SE, Box 807 UMHC, Minneapolis, Minnesota 55455

This study reports on the development and evaluation of a predictive model for the accumulation of polychlorinated biphenyls (PCBs) in phytoplankton which incorporates the kinetics of the process. The model includes a surface sorption term, uptake and loss rate coefficients, and a biomass dilution term. Laboratory data collected from the accumulation of 40 representative PCB congeners in four representative algal species were used to parameterize the model, and the performance of the model was evaluated on a set of independent laboratory data and a set of field data collected from Green Bay, Lake Michigan. Model predictions were compared to predictions from an equilibrium model. Under low growth conditions, the predictions of the kinetics model and the equilibrium model were similar. However, for data collected during periods of intense growth, equilibrium predictions deviated significantly from the observed values and from the kinetics model predictions by as much as 3 orders of magnitude. Concentrations calculated on a lipid basis by both models significantly underestimated the observed accumulation and questioned the hypothesis that PCBs accumulate in the lipid portion of phytoplankton. However, on a relative basis, the kinetics model reproduced the observed accumulation significantly better than the equilibrium approach.

Introduction The accumulation of recalcitrant hydrophobic organic compounds (HOCs) in phytoplankton plays a significant * To whom correspondence should be addressed: telephone: (612) 626-0435; fax: (612) 626-0650; e-mail address: [email protected]. umn.edu. † Present address: St. Paul-Ramsey Medical Center, 8100 34th Ave. S, P.O. Box 1309, Minneapolis, MN 55440. ‡ Present address: Institute of Marine Research, Box 1870 N-5024, Bergen, Norway.

S0013-936X(95)00206-9 CCC: $12.00

 1996 American Chemical Society

role in the transport and fate of these compounds in aquatic ecosystems. The sorption of these compounds to phytoplankton can either expedite their removal from the water column or facilitate their movement into the food web. HOC-laden phytoplankton that settle from the water column are an important transport mechanism for the removal and eventual burial of these compounds. However, those that are ingested by higher order organisms serve as the primary source of contaminants to the food web. As a result, an understanding of the forces that drive and control the accumulation of HOCs in phytoplankton and an ability to predict the magnitude of accumulation under various conditions are important. This paper describes the development and testing of a model for predicting the accumulation of a class of HOCs, the polychlorinated biphenyls (PCBs), in phytoplankton. PCBs were chosen to represent HOCs for several reasons. First, PCBs allow for the evaluation of compounds having a wide range of physical-chemical properties. As a group, they range from the moderately hydrophobic (log octanolwater coefficient, Kow, of 4-5) to the very hydrophobic (log Kow 7-8.5). Secondly, they are a relevant contaminant and ensure applicability of the model. Finally, PCBs are ubiquitous in the environment and are of particular concern in the Great Lakes ecosystem due to their potential to adversely affect aquatic life, wildlife, and human health (1-9). The consumption of contaminated foods, particularly fish, is second only to industrial exposures as a source of PCBs to humans (10). The significance of fish consumption is due primarily to the accumulation of PCBs in the aquatic food web. At each level of the food web, organisms bioconcentrate PCBs because of the hydrophobic nature of these compounds and their resistance to metabolism. In addition, as higher trophic levels consume contaminated foods, PCBs are retained more efficiently than is organic carbon and are thus biomagnified. A model for the bioaccumulation of PCBs in Lake Michigan lake trout estimated that greater than 99% of the top predator’s exposure to HOCs is the result of contaminated food rather than direct exposure to water (11). As the base of the food web, phytoplankton play a significant role in the bioaccumulation of PCBs. Phytoplankton account for the largest fractions of both the organic carbon and the surface area in the food web. As a result, PCBs that are both absorbed in cellular carbon or adsorbed to the cell surface area are efficiently transferred into the food web. Food-chain models that predict the accumulation of HOCs in fish from known water concentrations often have assumed that the primary trophic level is in equilibrium with water (e.g., refs 11-13) and have used Kow to approximate the lipid-normalized bioconcentration factor (14, 15). One of the leading sources of uncertainty and sensitivity in the model predictions of higher order trophic levels is the phytoplankton HOC concentration. Thus, accurate predictions of phytoplankton HOC accumulation are needed. However, it has been demonstrated that several

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factors can affect the accumulation of HOCs in phytoplankton and that the assumption of equilibrium with water leads to biased predictions (16-19). This paper describes the development and testing of a model for the accumulation of PCBs in phytoplankton which has incorporated parameters that have been shown to most influence this process. Data collected on the accumulation of PCBs in four species of phytoplankton were used to develop and fit the model. The performance of the model was evaluated using independent sets of laboratory and field data.

Experimental Methods Experimental Design. Data collection consisted of measuring the accumulation of PCBs in phytoplankton in laboratory cultures and in native assemblages and evaluating parameters thought to influence accumulation. Methods and experimental design are fully described elsewhere (18, 19). For the laboratory studies, species of phytoplankton were chosen that represent the major divisions of phytoplankton and are present in Great Lakes’ assemblages (20). However, the choice of phytoplankton species was limited by the commercial availability of freshwater unialgal cultures and the ease with which sufficient biomass could be generated. The four species used in these studies were two species of green algae, Scenedesmus quadricauda (division Chlorophyta, obtained from the University of Minnesota collection) and Selenastrum capricornutum (division Chlorophyta, Carolina Biological Supply Co.); a blue-green species, Anabena sp. (division Cyanophyta, Carolina Biological Supply Co.); and the diatom, Synedra sp. (division Chrysophyta, class Bacillariophyceae, Carolina Biological Supply Co.). Descriptions of the phytoplankton and culturing methods are described elsewhere (17, 19) and are summarized below. Stock solutions of phytoplankton were grown in large batch cultures in a synthetic media appropriate for each of the species. Cultures were maintained in the log growth phase by serial dilution. Media were prepared with filtered (0.2 µm) Lake Superior water. Phytoplankton were concentrated by centrifugation and added to a series of 1-L bottles at a concentration of approximately 10 mg/L (dry weight). After allowing the cultures to acclimate for several days, 40 PCB congeners were added to each bottle by subsurface injection in a 0.5-mL acetone carrier. Controls receiving only acetone indicated that the solvent had no effect on the cultures. Incubations were performed at 11 °C to minimize the confounding effects of growth. The average growth rates during these experiments ranged from 0.03 to 0.09 doubling day-1. At specific time points, duplicate bottles were removed for analysis. Phytoplankton and media were separated by centrifugation, and congenerspecific PCB measurements were made in both phases. At each time point, subsamples of the aqueous phase were collected for dissolved organic carbon analysis, and subsamples of the phytoplankton were collected for suspended solids, particulate organic carbon, and lipid analysis. The total concentration of PCBs in each sample was approximately 7 µg/L. Concentrations of individual PCB congeners ranged from 30 to 1575 ng/L and are listed in ref 17. These congeners were chosen to cover a broad range of physical-chemical properties and represented all homologs and all permutations of ortho-substitution in the PCB class of compounds (17).

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Field Collections. Phytoplankton samples were collected from Green Bay, Lake Michigan from a mid-bay site (44°42′37′′ N, 87°51′19′′ W) in April, June, July, and October 1989 and in February 1990 from either the EPA R/V Roger Simons or R/V Blue Water. Water was pumped from 5 m depth through netting to isolate the material between 10 and 102 µm, which was determined to be exclusively phytoplankton. The nature of this material was verified by light microscopy and pigment analysis (21). The aqueous phase was operationally defined as the filtrate from a glass fiber filter (Whatman, GF/F) with a nominal pore size of 0.7 µm; dissolved PCBs were collected on a 150-mL column of precleaned Amberlite nonionic polymeric absorbent (XAD-2 resin). As in the laboratory studies, subsamples of the aqueous phase were collected for dissolved organic carbon analysis, and subsamples of the phytoplankton were collected for suspended solids, particulate organic carbon, and lipid analysis. Analysis of the data consisted primarily of developing and testing an equation that was capable of reproducing the observed accumulation of PCBs in phytoplankton. Model development involved the identification of parameters, which correlated with accumulation, and the incorporation of these into a predictive equation. Data from laboratory studies were then used to fit the model and to verify that the calculated parameters were appropriate. Testing of the model involved using the model to predict accumulation in both laboratory and field settings and then to compare the predicted values with the observed accumulations. Furthermore, the new equation’s predictions were compared to predictions based on the strict equilibrium model. Analytical Methods and Quality Assurance. Samples collected for PCB analysis were stored at -20 °C (phytoplankton) or 4 °C (dissolved phase) until extracted. The laboratory dissolved phase and incubation flasks were batch extracted with hexane, and the field and laboratory phytoplankton and field XAD-2 resin were Soxhlet extracted (1:1 hexane:acetone). Prior to extraction, PCB congeners IUPAC Nos. 014 (3,5-dichlorobiphenyl), 065 (2,3,4,5-tetrachlorobiphenyl), and 166 (2,3,4,4′,5,6-hexachlorobiphenyl) were added to each sample as surrogate recovery standards. Interfering compounds were removed from the sample extracts by normal phase, liquid-solid chromatography. Laboratory samples were passed through 1 g of 6% deactivated silica and 5 g of 10% deactivated alumina and eluted with 3 bed volumes of hexane. Field samples were passed through 5 g of 100% activated alumina and eluted with 3 bed volumes of 2% dichloromethane in hexane. Final extracts were stored at -20 °C until analyzed. Immediately prior to analysis, PCB congeners IUPAC Nos. 030 (2,4,6-trichlorobiphenyl) and 204 (2,2′,3,4,4′,5,6,6′octachlorobiphenyl) were added to each sample as internal standards. Congener-specific PCB analysis was performed on a Hewlett-Packard 5890 gas chromatograph equipped with an autosampler, splitless injector, electron capture detector, 60-m DB-5 capillary column (J & W Scientific), and a computerized digital data acquisition system (Maxima, Millipore-Waters). Chromatographic conditions consisted of an injector temperature of 225 °C, detector temperature of 325 °C, and an initial and final oven temperature of 100 and 280 °C, respectively. Peak identification was done by retention time relative to that of the internal standards (( approximately 10 s). Quantitation was done by the internal standard method using peak areas and was specific for all

environmentally significant congeners (approximately 90 peaks) (22). Sample data were first examined for the acceptability of the internal and surrogate standards. Internal standard acceptability was based on the peak area ratio of the two internal standards, and surrogate standard acceptability was based on recovery. Laboratory samples with surrogate recoveries 105% and field samples with surrogate recoveries 115% were excluded from further analysis. Less than 2% of the laboratory samples and less than 6% of the field samples were excluded for these reasons. Each peak identified as a PCB congener was quantified by all accepted internal standards and normalized to the recovery of all accepted surrogate standards (i.e., recovery corrected). PCB data below the detection limit were not considered further. Values were reported without blank corrections. Lipid samples in the laboratory studies were collected by centrifugation and immediately extracted via a microextraction technique after the method described by Gardner et al. (23). Lipid samples in the field study were collected by subsampling the PCB extract. Lipid mass was determined gravimetrically, and all samples were analyzed in duplicate. Dissolved organic carbon (DOC) samples were collected in 100-mL acid-washed polycarbonate bottles and stored at -20 °C until analysis. Samples were analyzed on an Ionics 555 TC-TOC analyzer. Particulate organic carbon (POC) samples were collected in duplicate on pre-ashed 47-mm GF/F filters and stored at -20 °C until analysis. Samples were analyzed by a wet chemistry method described by Menzel and Vaccaro (24). Suspended particulate material (SPM) samples were collected in duplicate on preweighed polycarbonate filters, and SPM mass was determined gravimetrically.

centration in the phytoplankton, Cm is the concentration in the matrix, and Cs is the concentration on the surface:

Results and Discussion

This accomplished two things: conceptually, it tied the equation to the thermodynamics of the process by incorporating an equilibrium partitioning coefficient, and operationally, it reduced the number of fitted variables from two to one. The second enhancement was to incorporate the diluting effects of growth as an additional loss function (kd). Support for this addition and the calculations necessary to convert growth measurement into a rate constant are discussed in detail in Skoglund and Swackhamer (18). In brief, a comparison of accumulation data collected under different growth conditions clearly demonstrated that growth affected accumulation. Furthermore, calculated accumulation data plotted as a function of Kow and growth rate indicate that even low growth rates can significantly affect the observed accumulation of PCBs in phytoplankton, particularly congeners with high Kow values. The substitution of the modified eqs 2 and 3 into eq 1 yields eq 6, an equation for predicting the accumulation of PCBs in phytoplankton:

Historically, the sorption of PCBs to phytoplankton has been modeled by equilibrium partition coefficients (16). Fundamental to this approach is the assumption that equilibrium conditions are achieved rapidly and maintained. Data collected from both phytoplankton (17, 18) and higher organisms (25-28) have demonstrated that the thermodynamics-based model fails to predict the accumulation of many compounds, particularly the extremely hydrophobic ones. Non-equilibrium conditions were one of the proposed reasons for the model’s failure. We have proposed that equilibrium conditions in the sorption of PCBs to phytoplankton are not achieved instantaneously and, under certain conditions, are never achieved. Therefore, the strict equilibrium model is inadequate for predicting the accumulation of PCBs in phytoplankton. In its place, we propose a model based on the kinetics of the accumulation process. This model describes accumulation as a function of multiple uptake and loss mechanisms and attempts to incorporate all of the factors that have been shown to significantly affect the process. Equation Development. Empirical evaluation of the laboratory accumulation data reveal that they form an S-shaped or sigmoidal pattern (18). The sigmoidal accumulation pattern has to be quantitatively demonstrated for several of the congeners using a method reported by Brisbin et al. (29), indicating that accumulation is in multiple compartments. Thus, we divided accumulation into surface and matrix components (eq 1), in which Cp is the con-

Cp ) C m + C s

(1)

The matrix component of accumulation was best quantified by a basic single compartment accumulation model (eq 2), in which Flip is the phytoplankton lipid fraction, Cd is the dissolved concentration of PCBs (ng/L), ku is the uptake rate constant (day-1), kx is the depuration rate constant (day-1), and T is time (day):

ku Cm ) FlipCd (1 - e-kxT) kx

(2)

The surface component of accumulation was best quantified by a partitioning equation (eq 3), in which Ksa is the surface adsorption partitioning coefficient:

Cs ) FlipCdKsa

(3)

Two enhancements were made to eq 2. First, kx was expressed as a ratio of ku and an equilibrium partitioning coefficient. At equilibrium, the ratio of the uptake and depuration rate constants is equal to Kow:

Cp ku ) ) BAFlip ) Kow kx CdFlip

(4)

However, since the accumulation is divided in two components, ku and kx account for only the matrix portion of the accumulation, and therefore their ratio is equal to the difference between Kow and Ksa:

ku ) kx Kow - Ksa

[

Cp ) Flip

(5)

(

ku Cd 1 ku + kd Kow - Ksa ku exp + kd T Kow - Ksa

((

) ))

]

+ (CdKsa) (6)

The units used are as follows: Cp, ng/kg; ku, kx, and kd,


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TABLE 1

Summary of Calculated ku Valuesa log ku

Scenedesmus quadricauda Selenastrum capricornutum Anabena sp. Synedra sp. complete data set complete set less Selenastrum capricornutum

regression vs Kow

n mean 95% CI slope F value

species

34 35 29 29 127 90

5.67 4.76 5.63 4.78 5.21 5.38

0.43 1.85 0.25 0.61 1.35 0.91

-0.09 3.35* -1.11 109.68 0.04 0.05* -0.41 34.04 -0.45 30.68 -0.20 7.21*

a An asterisk indicates that the slope of a regression of the data set versus Kow is not statistically different from zero at R ) 0.025.

TABLE 2

Summary of Estimated Ksa Valuesa log Ksa

species

FIGURE 1. Calculated uptake rate constants (O) and surface adsorption partition coefficients (() as a function of Kow.

day-1; T, day; Kow, Ksa, and Flip, unitless. Even though complex, eq 6 is consistent with the fundamental hypotheses of lipid partitioning. If the system is allowed to reach equilibrium and the influence of other processes are negligible (T f ∞, kd ≈ 0), eq 6 reduces to

Cp ) FlipCdKow

(7)

Equation Parametrization. The input parameters for the kinetic equation to predict the accumulation of PCBs in phytoplankton are the congener-specific dissolved concentrations, congener Kow, lipid content and growth rate of the phytoplankton, and the exposure period. It was empirically determined that constants were adequate for the uptake rate constant and the surface adsorption partitioning coefficient. A discussion of this determination follows. Congener- and species-specific values for the uptake rate constants were calculated by iteration. Sample size for these calculations ranged from 7 to 9 (this is the number of time points for a specific congener and given species). A total of 127 independent rate constants for 35 different PCB congeners in four species of phytoplankton were calculated. Values for the calculated congener-specific uptake rate constants for each species are presented in Figure 1 as a function of Kow. Regression and summary data are presented in Table 1. Results for the monochlorobiphenyls and dichlorobiphenyls were not calculated because of their poor recovery in the mass balance calculations due to volatilization. Congeners for which iteration did not converge are also excluded. As a result, congener 104 was excluded from all data sets, and congener 054 was excluded from all data sets except Selenastrum sp. All congeners with log Kow < 6 failed to converge in the Anabena sp. and Synedra sp. experiments and were excluded. A total of approximately 12% of the entire data set was excluded. Regression data indicate that in Scenedesmus sp. and Anabena sp. the uptake rate constants were statistically independent of Kow. In Synedra sp. the uptake rate constant had a slight dependence on Kow but its range was only

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n

regression vs Kow

mean 95% CI slope

Scenedesmus quadricauda 34 4.41 Selenastrum capricornutum 35 4.66 Anabena sp. 29 4.15 Synedra sp. 29 4.40 complete data set 127 4.42 96 4.42 complete set less Synedra sp.

0.32 0.47 0.35 0.22 0.50 0.56

F value

0.03 0.63* -0.03 0.39* -0.26 59.33 -0.19 223.30 -0.11 11.13 -0.09 5.26*

a An asterisk indicates that the slope of a regression of the data set versus Kow is not statistically different from zero at R ) 0.025.

slightly more than 1 order of magnitude. Data from these three experiments indicated that a single value was adequate for the uptake rate constant. This observed independence is consistent with other reports in the literature (30, 31). In contrast, uptake rate constants calculated from the Selenastrum sp. data were inversely related to Kow and had a range of approximately 3 orders of magnitude. However, less weight was applied to these data because of experimental difficulties encountered when culturing these phytoplankton. Selenastrum tended to aggregate rather than remain dispersed, thus increasing the uncertainty associated with these experimental data. Most importantly, the utility of the model would be greatly diminished if it required species-specific input data. Surface adsorption coefficients were calculated from the PCB data collected at the earliest sampling points (0.02 day). Calculations were based on the hypothesis that accumulation at the early sampling points was primarily due to surface adsorption. Both the sigmoidal shape of the data set and the theoretical rates of surface and matrix sorption support this hypothesis. Even at the earliest sampling point there was significant accumulation, approximately 20-30% of the PCBs. Accumulation remained constant for approximately 24-48 h and then began to slowly increase. These data imply that there was an initial rapid sorption before a slower, but higher capacity, process began to dominate. Based on the assumption that the rapid portion of sorption was instantaneous relative to the other process, accumulation at the earliest sampling point was chosen as the best estimate of the portion of the phytoplankton’s PCB concentrations due to surface adsorption. Calculated congener-specific surface adsorption coefficient values for each species are also presented in Figure 1 as a function of Kow. Regression and summary data are presented in Table 2.

TABLE 3

Estimated Values Used To Predict Accumulation in Field Data Set

FIGURE 2. Comparison of the predictiveness of the two models. Log-transformed comparison of the measured accumulation in the independent set of laboratory data and the predictions of the equilibrium model ()) and kinetics model (b). The solid line represents a one-to-one match, and the dashed lines represent ( 1 log unit.

Regression data indicate that the estimated surface adsorption coefficients are independent of Kow in the Scenedesmus sp. and Selenastrum sp. data. Although there was a dependence on Kow in the other two data sets, the range of Ksa in both of these data (without Synedra) sets is less than 1 order of magnitude, and this Ksa was independent of Kow. Furthermore, the range of the combined data sets was only slightly greater than 1 order of magnitude. This independence of Ksa from both congener and species parameters may be an indication that surface area-tovolume ratio may be the primary factor controlling surface sorption. Estimates of gross surface area-to-volume ratios for the four species utilized in these studies were within 1 order of magnitude (19). Estimates were based on both literature values (32) and data collected from light and electron microscopy. Based on this hypothesis, as particle size decreases and surface area-to-volume ratios increase, the Kow may increase and thus increase the role of the surface sorption portion of the equation and reduce the influence of parameters such as growth rate, incubation time, and uptake rate constants. As a result, for very small particles the thermodynamic model’s assumption of instantaneous equilibrium may be relatively accurate. For both ku and Ksa, the geometric means of the four data sets were chosen for use in the kinetics model. Tables 1 and 2 list the log of the multi-species uptake rate constant (day-1) and surface adsorption coefficient (L/kg) as 5.38 and 4.42, respectively. For comparison, the equation for uptake rate constants in Connolly and Pederson (12) was extrapolated to phytoplankton and used to calculate a log uptake rate constant (day-1) of 4.00 (18). No estimates of surface adsorption coefficients for phytoplankton were located in the literature. The kx values for each PCB congener can be calculated from eq 3. The kx term is inversely related to Kow because of the gain in entropy associated with depuration.

sample date (month/yr)

growth rate (doubling day-1)

time (day) (35)

04/89 07/89 09/89 10/89 02/90

0.1 0.1 0.1 0.05 0.01

5 5 5 10 20

Model Performance. The performance capability of the new equation was tested using independent sets of laboratory and field data. In each case, eq 4 was used to predict congener-specific concentrations of PCBs in the phytoplankton, and these values were compared to measured values. The laboratory set consisted of additional data on the accumulation of the 40-PCB mixture in Scenedesmus sp. However, growth was not temperature-limited in this experiment. The average growth rate was approximately 0.15 doubling day-1 over a 30-day incubation period, and sampling point-specific growth rates ranged from 0.05 to 0.53 doubling day-1. The kinetics model was able to predict the measured concentrations in the independent laboratory data to within 1 order of magnitude over 95% of the time. Examination of the outliers revealed that they were exclusively from the latest sampling point (30 days). Laboratory records indicated that by day 30 the test cultures were no longer actively growing, but in fact had experienced a significant amount of cell death. Based on the questionable applicability of the model to this type of sample, data from day 30 was excluded from a plot of the measured versus predicted values (Figure 2). In contrast, the equilibrium model was only able to predict the measured concentrations in the same data to within 1 order of magnitude less than 65% of the time. In some cases, the predictions overestimate measured values by as much as three orders of magnitude. For the application of the kinetics model to the field data, estimates were made for growth rate and residence time in the water column. The estimated values used are listed in Table 3, and model performance is shown in Figure 3. Visual inspection of Figure 3 reveals that all of the predictions based on the kinetics model and a vast majority of the those based on the strict equilibrium model underestimate the observed accumulation. Regression data from Figure 3 confirm this (Table 4). The y-intercepts of the predictions of the kinetics and equilibrium models are -1.45 and -0.07, respectively. The slopes and R2 values of the predictions from the kinetics and equilibrium models were 0.94 and 0.73 and 0.77 and 0.47, respectively. Thus, both the slope and R2 values indicate that the kinetics model was the better predictor, but the absolute magnitude of the prediction (indicated by the intercept) was better provided by the equilibrium model. The kinetic model performance was better on field data than on lab data. This may be due to the higher DOC values and higher PCB concentrations in the lab samples. Because the y-intercept is largely a function of the lipid fraction normalization, each of the model’s predictions for accumulation were recalculated on the basis of dry weight and organic carbon rather than lipid (see Figures 4 and 5). Based on the regression data for Figure 4 (Table 4), application of the models on a dry-weight basis significantly


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FIGURE 3. Comparison of the predictiveness of the equilibrium ()) and kinetics (b) models on a lipid basis using field data. The solid line represents the least squared regression of the kinetic model’s predictions, and the dashed line represents the regression of the equilibrium model’s predictions.

FIGURE 4. Comparison of the predictiveness of the equilibrium ()) and kinetics (b) models on a dry-weight basis using field data. The solid line represents the least squared regression of the kinetic model’s predictions, and the dashed line represents the regression of the equilibrium model’s predictions.

TABLE 4

Regression Statistics for Figures 3-5a y-intercept

slope

figure

model

std value error

F and p values

std value error

R2

3

kinetics 0.94 equilibrium 0.73

0.03 F(1,658) ) 215 -1.45 0.11 0.77 0.04 p < 0.001 -0.07 0.17 0.47

4


0.03 F(1,658) ) 171 0.04 p < 0.001

1.45 0.11 0.65 2.83 0.16 0.28

5


0.03 F(1,658) ) 303 0.04 p < 0.001

0.18 0.10 0.73 1.56 0.17 0.36

a Within each figure the variances of the two data sets were heterogeneous (F(164, 164) ) 3.34, p < 0.001; F(329, 329) ) 2.25-2.62, p < 0.001).

reduces their predictive quality. On a dry weight basis, both models significantly overestimate the observed accumulation, and there is a significant decrease in both the slopes and the R2 values. However, application of the kinetics model on an organic carbon basis (Figure 5, Table 4) increases its predictive quality. The model does not categorically over- or underestimate the observed accumulation, and the slope and R2 value are acceptable. The equilibrium model normalized to organic carbon does not perform well however. The success of the carbon-based equation contradicts the hypothesis that PCBs partition only to the lipid portion of phytoplankton. There are at least three possible explanations for this observation. One is that PCBs have a greater affinity for phytoplankton lipids than they do for 1-octanol. Thus, predictions based on Kow would always underestimate accumulation. A second explanation is that phytoplankton components other than lipids, particularly those which are high in organic carbon, are capable of interacting with and stabilizing PCBs to an appreciable degree. A third explanation is that this observed anomaly was the result of a sampling or analytical bias. A source

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FIGURE 5. Comparison of the predictiveness of the equilibrium ()) and kinetics (b) models on an organic carbon basis using field data. The solid line represents the least squared regression of the kinetic model’s predictions, and the dashed line represents the regression of the equilibrium model’s predictions.

of analytical bias is the method used to measure the lipid content of the field samples. The hexane/acetone extraction of lipids that was necessitated by small field samples was not the most rigorous method available, as discussed in a report comparing lipid extraction methods (33). However, the analytical bias in the extraction of lipids would be less than a factor of 2 and cannot account for the magnitude of the differences between the observed accumulation and that predicted by each of the models. Secondly, our field lipid data may be underestimated by 25-30% due to degradation of the phospholipids during storage (unpub-

lished data). Again, this bias does not account for the difference seen in observed vs predicted values. Therefore, the likelihood of the other two possibilities needs to be investigated, particularly the role of nonlipid organic material. Regardless of whether the lipid or organic carbon fraction of the phytoplankton was used to define concentration, the kinetics model was a better predictor of the observed accumulation. In each of the three sets of data found in Figures 3-5, the kinetics model had a slope that was closer to 1 and a greater R2 value (Table 4). The predictive quality of the equilibrium model greatly decreases during periods of intense growth. Examination of outliers in the equilibrium predictions in the lipidnormalized data set revealed that they were exclusively data collected during the spring and early summer, periods during which intensive growth would be expected. This is consistent in both the laboratory and field data sets and was predicted on a theoretical basis (18). In contrast, the kinetics model performed equally well during periods of intense growth and low growth. The increased predictiveness of the kinetics model was primarily due to the incorporation of a phytoplankton growth parameter. A sensitivity analysis was performed to determine which parameters had the greatest influence on model predictions and to determine the largest source of uncertainty. This analysis indicated that the kinetic model’s predictions were virtually independent of changes in Ksa while changes in Cd and Flip had a direct linear effect. Although the effect of these two parameters on the model’s predictions were significant, the effects are identical in both the equilibrium and the kinetic models and therefore of little significance for a comparison of the two. In contrast, ku, kd, and T all had nonlinear effects on the model’s predictions and only appear in the kinetic model. The relationship between the changes in the parameter and the model’s predictions was a function of Kow. This analysis was done by performing multiple two-dimensional analyses of the model’s response to changes in key parameters. The application of the model is expected to be most sensitive to the growth rate of the phytoplankton. Within the anticipated range of growth rates (0-2 doubling day-1), congeners with log Kow g 6.5 experience an order of magnitude change in the predicted accumulation. In contrast, for congeners with log Kow ≈ 4.5, the predicted accumulation was independent of growth rate. Thus uncertainties in growth rate will lead to uncertainties in the model predictions. Exposure time (T) is expected to be the second most sensitive parameter in the application of the model. Again, the congeners with the higher Kow values (log Kow g 6.5) are most sensitive to changes in time. Changes in exposure time from 2 to 20 days can affect the predicted accumulation of these congeners by approximately an order of magnitude. For congeners with log Kow g 7.5, predicted accumulation continues to be influenced by time well beyond the reasonably anticipated residence time of phytoplankton in the water column. In contrast, for congeners with log Kow ≈ 4.5, the predicted accumulation was independent of exposure time. Furthermore, the effects of growth rate and exposure time are not independent. The incremental effect of growth on accumulation is greatest during periods of short exposure times (i.e., high turnover, high growth). In the field, phytoplankton assemblages experience a range

of exposure times. One must use an average time that is appropriate to the growth conditions being modeled. Our model uses a constant for ku, which was determined to be independent of Kow. However, model predictions for the more hydrophobic congeners (log Kow > 6.5) were more greatly affected by changes in ku than those for the less hydrophobic congeners. Our measured log ku values were generally between 5 and 6; this variation had almost no effect on the less hydrophobic congener predictions and affected the very hydrophobic congeners by up to 0.5 order of magnitude. Previous studies (17, 18, 25-27) have demonstrated that the equilibrium model was particularly ineffective in predicting the accumulation of extremely hydrophobic compounds. Evaluation of the differences between predicted and observed accumulations as a function of Kow demonstrated that in laboratory studies the accuracy of predictions of both models was inversely related to Kow, particularly in the range of log Kow 6-9. However, the magnitude of the deviation was more significant in the equilibrium model. In contrast, the deviations between observed and predicted values in the field data were independent of Kow in both models. A common occurrence during periods of intense growth, particularly in batch cultures, is a dramatic increase in the dissolved organic carbon (DOC). In light of the significance of intense growth periods on model performance, it is important to address the influence of DOC on the partitioning of PCBs between water and phytoplankton. The DOC values from the experiments used to develop model parameters were as follows: Scenedesmus sp., 3->20 mg/ L; Selenastrum sp., 45-93 mg/L; Anabena sp., 43-121 mg/ L; and Synedra sp., 117-122 mg/L. There was some question as to the accuracy of the data from the latter three experiments because of the exceptionally high DOC values. One possible explanation for these elevated values is that a less rigorous centrifuge was used to separate the dissolved and sorbed phases in these experiments. The Scenedesmus sp. samples were separated at 4500g while the other three were separated at 1500g. Other possible explanations include insufficient purging to remove inorganic carbon or that these levels were simply beyond the limits of the method used (19). DOC values from the independent laboratory data set ranged from 3 to 14 mg/L, and those from the field data set ranged from 2 to 6 mg/L. The presence of DOC and colloidal particles not removed from the dissolved phase by centrifuge alters the partitioning of PCBs between phytoplankton and the aqueous phase by forming a competitive third phase. This third phase has little effect on the measured PCB concentrations in the phytoplankton but can positively bias the measured dissolved concentrations (19). It would have been most accurate to correct all of the measured dissolved concentrations to their corresponding DOC concentrations. However, we decided against this course of action because the quality of some of the DOC measurements was in question. Furthermore, the effects of DOC on the predictions of the two equations were similar because both equations incorporated the dissolved concentration identically. Therefore, high DOC had little effect on the comparison of the predictiveness of the two models.

Summary In summary, the kinetics model describing the partitioning of PCBs between water and phytoplankton presented in


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this paper reproduced the observed accumulation of PCBs in phytoplankton better than did the traditional equilibrium model. Although the new model includes several new parameters, the incorporation of a phytoplankton growth parameter contributes most significantly to the increase in predictiveness. Increased predictiveness was best observed during periods of intense growth. During these periods, the equilibrium model grossly overestimated the observed accumulation. Furthermore, data presented in this paper challenge the hypothesis that PCBs accumulate only in the lipid portions of the phytoplankton. These data suggest that other, nonlipid organic materials may play an important role in the accumulation of PCBs and other hydrophobic organic compounds.

Acknowledgments This research was funded in part by the Minnesota Sea Grant College Program, supported by the National Oceanic and Atmospheric Administration Office of Sea Grant, Department of Commerce; by the U.S. Environmental Protection Agency Great Lakes National Program Office, Chicago, IL (R-005028); by the U.S. EPA Large Lakes Research Station, Grosse Ile, MI (CR-821960); and by the Biomedical Research Support Grant Program, Division of Research Resources, National Institutes of Health. The following people are gratefully acknowledged: Professor Orlando Rushmeyer for assistance with the culturing of phytoplankton; Dr. James Hurley of the Wisconsin Department of Natural Resources and the Trout Lake Research Station, University of Wisconsin, for assistance with the analysis of pigments; Captains Lincoln McGurk and Ronald Ingram and the crew of the R/V Bluewater and R/V Roger Simons for assistance with the collection of field samples; Dr. Robert V. Thomann for initial review of the manuscript; and Karen Brademeyer for preparation of the manuscript.

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Received for review March 24, 1995. Revised manuscript received March 6, 1996. Accepted March 11, 1996.X ES950206D X

Abstract published in Advance ACS Abstracts, May 1, 1996.

A Kinetics Model for Predicting the Accumulation of PCBs in

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