Environ. Sci. Technol. 2005, 39, 5716-5721
Comparison of Biota-Sediment Accumulation Factors across Ecosystems LAWRENCE P. BURKHARD,* PHILIP M. COOK, AND MARTA T. LUKASEWYCZ Mid-Continent Ecology Division, National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, 6201 Congdon Boulevard, Duluth, Minnesota 55804
Sets of biota-sediment accumulation factors (BSAFs) for fish were compared across ecosystems for nonionic organic chemicals. The sets of BSAFs, when plotted against each other, in log-log space, formed linear relationships and demonstrated that the relative scaling or ranking of the individual BSAFs within a set are consistent, if not the same, across ecosystems. This behavior holds for chemicals that either are, or are not, metabolized by fish. These results demonstrate that sets of BSAF values can differ but with parallel shifts in magnitude between ecosystems (for example, all of the BSAFs in the set are uniformly larger in one ecosystem, while in another they all are uniformly smaller) in response to underlying differences in ecosystem conditions and parameters such as trophic level, diet of the organisms, and distribution of the chemical between the sediment and water column.
Introduction Biota-sediment accumulation factors (BSAFs) are expressions of net bioaccumulation of chemicals by an organism as a result of uptake from all environmental sources and processes. BSAFs for fish and other organisms not in intimate contact with the sediments can only be determined by use of field data. The BSAF (1) (kilograms of organic carbon/ kilogram of lipid) is defined as the ratio of the lipid-normalized concentration of a chemical in an organism (Cl , kilograms of chemical/kilogram of lipid) to the organic carbonnormalized concentration of the chemical in surficial sediment (Csoc, kilograms of chemical/kilogram of organic carbon). Meaningful BSAFs, that is, values that enable accurate prediction of chemical residues in fish, require that the sediment samples be reflective of the organism’s recent exposure history. The use of lipid- and organic carbonnormalized concentrations makes the BSAF an approximate fugacity ratio (2). BSAFs are used for predicting chemical residues in aquatic organisms for sediments contaminated with PBTs (persistent bioaccumulative toxicants), especially for Superfund sites (3). When making predictions, a site-specific BSAF is clearly most desirable, that is, a BSAF measured at the site of interest, because this BSAF incorporates all processes and conditions influencing bioaccumulation at the site. However, for many sites, site-specific BSAFs are unavailable, especially during * Corresponding author phone: (218)529-5164; fax: (218)529-5003; e-mail:
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the initial phases of the risk assessment. Thus, BSAFs measured in different ecosystems are used in the assessment of the ecosystem of interest. When BSAFs from one ecosystem are used in another ecosystem, this application of the BSAF assumes that the underlying conditions and parameters affecting bioaccumulation are the same between the ecosystem of interest and the ecosystem where the BSAFs were measured. This implicit assumption is often not appreciated by users of BSAF data. As discussed by Burkhard et al. (2), the major conditions and parameters incorporated into a measured BSAF are (1) the distribution of the chemical between the sediment and water column, (2) the relationship of the food web to water and sediment, (3) the length of the food web (or trophic level of the organism), (4) bioavailability of the chemical due to amounts and types of organic carbon in the ecosystem, and (5) metabolic transformation rates of the chemical within the food web. The first four factors can vary widely different among ecosystems. In contrast, the fifth factor will in all likelihood vary much less among ecosystems. Additionally, when a BSAF is measured, the connection, that is, the relationship existing between the organism and sediment samples collected and analyzed, is incorporated into the measurement. By connection, we mean how reflective are sediment samples of the actual organism’s recent exposure? Often, this connection is loosely made by assuming that sediment samples collected from the same location as the organism samples are in total agreement. The effects of differing conditions, parameters, and connections upon the value of the BSAF have been captured in a field study by Wong et al. (4), where measured BSAFs for white suckers ranged from 1.7 to 27 (with a median value of 8.8) for p,p′-DDE across 36 different ecosystems. The difficulties and uncertainties in establishing the connection between fish and sediment samples at sampling locations makes it difficult to compare individual BSAFs across ecosystems. In part, one cannot distinguish differences in BSAFs caused by differences in parameters and conditions from those caused by differences in sample connections. In this report, rather than compare BSAFs for individual chemicals like p,p′-DDE, sets of BSAFs will be compared across ecosystems to determine if the relative scaling/ranking of individual chemicals within the sets remains the same. This comparison is not dependent upon the absolute value of the BSAFs but rather the consistency of the set of chemicals; that is, the sediment samples analyzed provide a good measure of the relative distribution of the chemicals across the organism’s immediate home range. If the relative scaling can be shown to be the same across ecosystems, then a technique for extrapolating BSAFs from one ecosystem to another appears possible. In this approach, a high-quality set of BSAFs (based upon well-defined connections between the organisms and sediment samples) could be extrapolated to another ecosystem by developing appropriate adjustment factors from the conditions and parameters for the two ecosystems. The development of appropriate and defensible BSAF adjustment factors is beyond the scope of this report and will not be addressed.
Experimental Procedures Sets of BSAFs were assembled from reported BSAFs (4-9). Additional BSAFs were derived from reported chemical concentrations in fish and sediment (10-15). Some care should be taken in interpreting the BSAFs. First, organic carbon contents in the sediment were estimated for a few reports; that is, we assumed 3% organic carbon for sediments 10.1021/es050308w Not subject to U.S. copyright. Publ. 2005 Am. Chem.Soc. Published on Web 07/01/2005
FIGURE 1. White sucker BSAFs (kilograms of organic carbon/kilogram of lipid) from six ecosystems plotted against BSAFs (kilograms of organic carbon/kilogram of lipid) for white sucker from sampling station 01208869 for p,p′-DDD (turquoise diamond), p,p′-DDT (brown diamond), p,p′-DDE (green diamond), cis-chlordane (blue circle), trans-chlordane (orange circle), trans-nonachlor (purple square), dieldrin (blue triangle), and cis-nonachlor (blue square) (4). The sampling locations are those reported by Wong et al. (4). The correlation coefficient (r), slope (standard deviation, number of data points) for geometric mean regression line (solid), Spearman’s coefficient of rank correlation (G) and significance level (r), and 1:1 line (dotted) are provided. Note: the y-axes have different scales in some of the subgraphs. from the Detroit River study (5), and we estimated organic carbon contents from the mass loss by ignition, assuming 40% of the loss was organic carbon, for the Lake Kasumigaura study (10). Second, most reports did not assess and/or report how reflective the sediment samples were of the actual exposure environment for the fishes collected. Third, sediment samples in some studies did not consist of the surficial sediment layer (or the biologically active sediment layer) but rather sediment samples extending from the sediment surface to some unreported depth. In determining BSAFs, the best sediment samples are the uppermost surficial sediment layer, because these sediments are the actual sediments exposed to the fish and to the benthic food web for the fish. Sediment samples extending from the sediment surface to much deeper levels in the sediments, for example, 0-20 cm, could represent time periods extending to decades or more depending upon sedimentation rates. Additionally, if loading histories for some but not all of the chemicals have changed, sediment samples extending to deeper levels might provide skewed representations of the distribution of chemicals; more or less exposure for the fish for some chemicals. In general, we believe that the biases will be relatively consistent across all chemicals for an individual study because the individual studies measured chemicals of the same class, for example, polychlorinated biphenyls (PCBs) or dichlorodiphenyltrichloroethanes (DDTs). Therefore, the BSAFs assembled from the literature data might be biased, high or low, depending upon
the individual study. The biases in the absolute values of the BSAFs do not necessarily affect the relative scaling or ranking of the BSAFs, and the latter issue is being evaluated in this investigation. In analyzing the BSAF data, geometric mean regressions (16) were performed because both x and y variables contained substantial error. The geometric mean regressions were performed on the BSAF data after log transformation with the equations of Halfon (16). Spearman’s coefficient of rank correlation (F) (17) and their levels of significance (R) were determined by use of SAS System for Windows, version 9.0 software.
Discussion From the field study of Wong et al. (4), BSAFs for white suckers were obtained for chlorinated pesticides for variety of riverine ecosystems. BSAFs for six or more chlorinated pesticides (principally p,p′-DDD, p,p′-DDT, p,p′-DDE, cis-chlordane, trans-chlordane, and trans-nonachlor) are compared in Figure 1 for different sites. In general, the BSAFs when plotted against each other fall on a line. There is one notable exception, Figure 1F, where p,p′-DDD, p,p′-DDT, and p,p′-DDE are all about an order of magnitude higher than the other chlorinated pesticides. This unusual behavior was observed only once for the 24 ecosystems evaluated. Consistent with the linear relationship, the order of the individual VOL. 39, NO. 15, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 2. BSAFs (kilograms of organic carbon/kilogram of lipid) for PCDD/Fs with nonzero mammalian toxicity equivalence factors (TEFs) from four ecosystems (10-15) plotted against BSAFs (kilograms of organic carbon/kilogram of lipid) for 6-year-old lake trout from Lake Michigan (8). The symbol-color combination represents the same chemical in all four subgraphs: 2,3,7,8-TeCDD (green diamond); 1,2,3,7,8PeCDD (orange diamond); 1,2,3,4,7,8-HxCDD (yellow diamond); 1,2,3,6,7,8-HxCDD (pink up triangle); 1,2,3,7,8,9-HxCDD (purple up triangle); 1,2,3,4,6,7,8-HpCDD (blue up triangle), OCDD (turquoise up triangle); 2,3,7,8-TeCDF (green up triangle); 1,2,3,7,8/1,2,3,4,8-PeCDF (orange up triangle); 2,3,4,7,8-PeCDF (yellow up triangle); 1,2,3,4,7,8/1,2,3,4,7,9-HxCDF (pink down triangle); 1,2,3,6,7,8-HxCDF (purple down triangle); 2,3,4,6,7,8-HxCDF (blue down triangle); 1,2,3,4,6,7,8-HpCDF (green down triangle); 1,2,3,4,7,8,9-HpCDF (orange down triangle); OCDF (yellow down triangle). The correlation coefficient (r), slope (standard deviation, number of data points) for geometric mean regression line (solid), Spearman’s coefficient of rank correlation (G) and significance level (r), and 1:1 line (dotted) are provided. Confidence limits (95%) on the Lake Michigan BSAFs are provided. BSAFs from low to high is also very consistent across the seven sampling locations. For example, trans-nonachlor and cis-chlordane have practically identical BSAFs in each sample. Additionally, if Figure 1F is ignored, p,p′-DDT, transchlordane, and p,p′-DDE have the smallest (all samples), second smallest (all samples), and largest (six of the seven samples) BSAFs, respectively. The relative scaling of the individual chemicals between ecosystems are statistically significant; that is, the Spearman’s coefficients of rank correlation (F) are close to 1.0 (that is, perfect agreement) and the probability of the rank correlation equaling 0 due to chance are quite small (Figure 1). This ranking behavior was generally observed at the other 17 white sucker sampling locations of the field study where fewer BSAFs were measured (data not reported). Although variances associated with the field-measured BSAFs of Wong et al. (4) are not available, the BSAF data for the chlorinated pesticides strongly suggests that sets of BSAFs differ uniformly among ecosystems and, thus, maintain the relative scaling of the BSAFs, within the set, across ecosystems. The above analysis was expanded by examining BSAFs for polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/ Fs) with mammalian toxicity equivalence factors (TEFs), that is, chemicals with 2,3,7,8- substitution patterns (Figure 2). The behavior observed with chlorinated pesticides is replicated with the PCDD/Fs, where the BSAFs when plotted against each other fall on a line, and the relative scaling of the BSAFs is similar across the five ecosystems. The quality of agreement for the relative scaling is less than that observed with the chlorinated pesticides; however, there are more chemicals in this comparison; the chemicals were present at 5718
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much lower concentrations in the environment, causing, we believe, higher analytical uncertainties; and BSAFs for four of the ecosystems were derived from less than optimal data. Given these considerations, the agreement is rather remarkable, and further, the relative scaling of the BSAFs among ecosystems are statistically significant (Figure 2). The above analysis was further expanded by examining BSAFs for PCBs from four field studies that used PCBs with measured BSAFs in most of the four studies (Figure 3). The behavior observed with the chlorinated pesticides and PCDD/Fs is replicated with the PCBs; that is, the BSAFs form a linear relationship when plotted against each other and the scaling/ranking of the individual BSAFs relative to each other remains relatively constant across sites. The observed behavior also extends to sets of BSAFs encompassing more than one chemical class as illustrated by the comparison of BSAFs for PCBs and PCDD/Fs from three ecosystems (Figure 4). The comparisons shown in this report are for fish species only. The relative scaling/ranking of individual BSAFs should not be as consistent across a wider range of organisms, for example, fish to birds, due to physiological and biochemical differences among the species. The slopes of BSAF comparisons in this report ranged from 0.57 to 1.58, with a central tendency toward a slope of 1.0 (Figures 1-4). At this time, the theoretical slope tendency for such BSAF comparisons has not been established. There are a few BSAFs with unusual behavior, that is, Figure 1F as described previously, octachlorodibenzofuran (OCDF) in Lake Shinji and possibly, Lake Kasumigaura (Figure 2), and PCB146 (2,2′,3,4′,5,5′-hexachlorobiphenyl) (Figure 3).
FIGURE 3. BSAFs (kilograms of organic carbon/kilogram of lipid) for PCBs from Green Bay (6), Hudson River (6), and Detroit River (5) plotted against BSAFs (kilograms of organic carbon/kilogram of lipid) for 6-year-old lake trout from Lake Michigan (8). The symbol-color combination represents the same chemical in all six subgraphs: PCB-18 (blue diamond), PCB-22 (turquoise diamond), PCB-26 (yellow square), PCB-28/31 (purple diamond), PCB-49 (purple square), PCB-52 (green square), PCB-56/60 (pink diamond), PCB-66 (orange square), PCB-85 (yellow circle), PCB-87 (orange circle), PCB-91 (pink square), PCB-97 (turquoise square), PCB-99 (blue circle), PCB-118 (blue square), PCB-141 (turquoise circle), PCB-146 (pink circle), PCB-149 (green circle), and PCB-180 (purple circle). The correlation coefficient (r), slope (standard deviation, number of data points) for geometric mean regression line (solid), Spearman’s coefficient of rank correlation (G) and significance level (r), and 1:1 line (dotted) are provided. Confidence limits (95%) are provided for each BSAF when available. The unusual behavior, we believe, is caused primarily by analytical biases. However, other causes could include differences in rates of chemical metabolism between fish species or greater relative differences in the sediment-water column chemical disequilibria for the chemicals (caused by differences in each chemical’s past and current loadings to the ecosystem). The data presented here demonstrate that relative scaling/ ranking of individual BSAFs for fish are consistent across different ecosystems, given the uncertainties associated with the measured BSAFs. Unfortunately, many of the data used in this analysis do not have reported uncertainties, but given the uncertainties that are available, the data presented here are consistent with the hypothesis that scaling/ranking of individual BSAFs across different ecosystems is the same. This behavior holds for chemicals metabolized by fish, that is, PCDD/Fs (18, 19), as well as for chemicals with substantially lower rates of metabolism in fish, that is, PCBs. This evaluation was performed by using sets of BSAF data for fish from rivers and streams (4-6), lakes and reservoirs (7, 8, 10, 11, 13-15), and marine/estuarine (9, 12) ecosystems. Admittedly, some of the BSAF data are from ecosystems where measurement of the BSAFs are very challenging due to temporal and spatial variabilities associated with the field
site (fish movement, flow changes, establishing the connection between sediment and fish exposure history, etc.). Additionally, some of the data used in the evaluation are from studies not necessarily designed for measuring BSAFs. Given these difficulties, the demonstration of consistent scaling/ranking of individual BSAFs across ecosystems with the available data is quite remarkable. The relative values of BSAFs for different chemicals are similar, if not the same, across ecosystems for fish species. This highly significant relative ranking phenomenon appears to occur in all ecosystems despite their differences and the errors or biases that may be associated with the measurements used to determine different sets of BSAFs. The consistent pattern of the BSAFs creates opportunities for improving and understanding bioaccumulation processes in aquatic ecosystems. First, the comparison of sets of BSAFs across ecosystems allows for evaluation of newly measured BSAFs to determine their reasonableness and validity. Clearly, site-specific BSAFs that diverge from the linear relationships found for other sites would be considered suspect and in need of further evaluation. Second, measurement of one or two BSAFs in one ecosystem would allow prediction of BSAFs for other chemicals in the ecosystem by use of sets of BSAF data from other ecosystems. Third, as discussed previously, VOL. 39, NO. 15, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 4. BSAFs (kilograms of organic carbon/kilograms of lipid) for PCBs and PCDD/Fs from Lake Ontario (7) and Tokyo Bay (9) plotted against BSAFs (kilograms of organic carbon/kilogram of lipid) for 6-year-old lake trout from Lake Michigan (8). The symbol-color combination represents the same chemical in both subgraphs, and their descriptions are listed in Figures 2 and 3. The correlation coefficient (r), slope (standard deviation, number of data points) for geometric mean regression line (solid), 95% confidence (- ‚ -) and prediction (- ‚‚ -) limits for the regression, Spearman’s coefficient of rank correlation (G) and significance level (r), and 1:1 line (dotted) are provided. Confidence limits (95%) are provided for each BSAF when available. The Tokyo Bay data set had only one PCB in common with the PCBs used in Figure 3. Consequently, nine of the other 11 PCBs reported in this study (i.e., PCBs with nonzero TEFs) were plotted against BSAFs for Lake Michigan 6-year-old lake trout as dotted circles: PCB-77 (brown), PCB-81 (yellow), PCB-105 (orange), PCB-114 (blue), PCB-123 (pink), PCB-126 (purple), PCB-156 (turquoise), PCB-167 (red), and PCB-169 (green). extrapolation of high-quality BSAFs from one ecosystem to another appears possible by developing adjustment factors based on basic conditions and parameters for the two ecosystems. Such adjustment factors may be developed by using predictions from food web models (20), and these adjustment factors can also be made for extrapolations between species and across different time periods within an ecosystem. Fourth, the consistency of the scaling/ranking of sets of BSAFs may presently be significantly underappreciated by scientists, regulators, and users of bioaccumulation data. The across-ecosystem consistency of BSAFs should translate to sets of bioaccumulation factors (BAFfd l ; that is, the ratio of the lipid-normalized concentration of the chemical in fish to that freely dissolved in the water column). However, detection of the consistency of the scaling in sets of measured BAFfd l values will probably be more difficult than with BSAFs due to the tendency of concentrations in water to fluctuate and to errors arising from the measurement of extremely low levels of the chemicals in the water. As previously reported (21), a BSAF can be related conveniently to an average BAFfd l by multiplying the BSAF by a predicted, estimated, or measured sediment to water concentration quotient (Πsocw).
Acknowledgments We thank Charles Wong, Kenneth Rygwelski, David Mount, and two anonymous reviewers for their reviews on earlier drafts of this report. The information in this document has been funded wholly by the U.S. Environmental Protection Agency. This paper has been subjected to review by the National Health and Environmental Effects Research Laboratory and approved for publication. Approval does not signify that the contents reflect the views of the Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.
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(20) Burkhard, L. P.; Cook, P. M.; Lukasewycz, M. T. A hybrid empirical-mechanistic modeling approach for extrapolating BSAFs and BAFs across species, time, and/or ecosystems. Environ. Toxicol. Chem. 2005 (submitted for publication). (21) U.S. Environmental Protection Agency. Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health 2000. Technical Support Document Volume 2: Development of National Bioaccumulation Factors; EPA-822-B-03-030; Office of Water: Washington, DC, 2003.
Received for review February 15, 2005. Revised manuscript received May 13, 2005. Accepted May 20, 2005. ES050308W
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