Research PCBs in Dungeness Crab Reflect Distinct Source Fingerprints among Harbor/Industrial Sites in British Columbia M I C H A E L G . I K O N O M O U , * ,† MARC P. FERNANDEZ,† WAYNE KNAPP,‡ AND PAULA SATHER† Contaminants Science, Institute of Ocean Sciences, Department of Fisheries and Oceans, 9860 W. Saanich Road, Sidney, British Columbia, Canada, V8L 4B2, and Habitat Enhancement Branch, DFO-RHQ, 360-555 West Hastings Street, Vancouver, British Columbia, Canada, V6B 5G3
Dungeness crab (Cancer magister) samples were collected from various pulp mill and principal harbor sites on the West Coast of Canada. Full congener PCB analysis was performed on several composite and single hepatopancreas samples from each site, and the spatial variability of PCB patterns was explored. A recently developed direct mixing model (DMM) which relies on iteration of representative Aroclor end-members was used to make source predictions based on congener-specific PCB data from biota. Additionally, factor analysis and principal component analysis (FA/PCA) were applied to examine the intersite variability for potential PCB-source patterns. This unsupervised exploratory analysis (i.e., FA/PCA) revealed three distinct clusters of variables containing either low (di-tetra), moderate (penta-hexa), and high (hepta-nona) levels of chlorination, which were related to common Aroclor mixtures (e.g., A1242, A1248, A1254, and A1260). Overall, the PCA scores for each site qualitatively agreed with the source predictions provided by the DMM, and distinct source compositions were predicted for various sites examined.
Introduction Principle harbors and receiving waters of pulp and paper mills in Canada have been the subject of numerous scientific reports on organochlorine contaminants, in particular polychlorinated dibenzo-p-dioxins and furans (PCDD/F) (e.g., refs 1-3). However, the levels of co-occurring polychlorinated biphenyls (PCBs) have generally been overlooked at these sites and, in several cases, are higher than those of all other organochlorines. Certain PCB congeners are known to be highly bioaccumulative and exhibit toxicities similar to the highly toxic 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), which include dermal toxicity, immunotoxicity, carcinogenicity, and adverse effects on reproduction, development, and endocrine functions (4, 5). Furthermore, recent reports of persistently high PCB concentrations in the tissues of higher trophic organisms such as Pacific killer whales (6) and bald * Corresponding author phone: (250) 363-6804; fax: (250) 3636807; e-mail:
[email protected]. † Institute of Ocean Sciences, Department of Fisheries and Oceans. ‡ Habitat Enhancement Branch, DFO-RHQ. 10.1021/es011209v CCC: $22.00 Published on Web 05/17/2002
Published 2002 by the Am. Chem. Soc.
eagles (7) off Canada’s West Coast warrant efforts to understand the potential sources for these contaminants in this region. PCBs have been used in industrial applications as dielectric fluids (capacitors, transformers), industrial fluids (in hydraulic systems, gas turbines, and vacuum pumps), fire retardants, heat transfer applications, and plasticizers (adhesives, textiles, surface coatings, sealants, printing, copy paper) (8). Furthermore, MacDonald et al. (2) have suggested that certain PCBs may be produced in situ during the bleaching of wood pulp with chlorine. Although many companies around the world have produced PCB mixtures under such trade names as Clophen (Bayer, Germany), Phenoclor or Pyralene (Prodelec, France), Kanechlor (Kanegafuchi, Japan), Fenclor (Caffaro, Italy), and Soval (Caffaro, Russia), the major source of PCB contamination in North America is derived from a group of technical mixtures known as Aroclors, produced and sold by Monsanto Industrial Chemicals Company in the United States (9). Between 1957 and 1975, Aroclor 1242 (A1242), Aroclor 1254 (A1254), and Aroclor 1260 (A1260) were sold in larger quantity (3.011 × 105 tonnes) than any other PCB-containing substances in the U.S. (9). Thus, not surprisingly, it is these three Aroclors that are most commonly found in the environment (9, 10). Benthic macroinvertebrates are commonly used to monitor bioaccumulative organic contaminants found in the environment (11). Dungeness crab (Cancer magister), the most commonly found commercial crustacean in the shallow waters of British Columbia (BC), is a territorial bottom scavenger which feeds primarily on clams but also on smaller crustaceans, fish, and opportunistically on fish carcasses (12). Because of their high trophic level, detritivorous benthic invertebrates such as Dungeness crab tend to accumulate a high degree of lipophilic contaminants including organochlorines such as dioxins and furans (13). Also, because they spend much of their lifecycle in intimate contact with sediment, large crustaceans will bioaccumulate sedimentderived contaminants (14, 15). Combined with its long life (ca. 3-6 years), fecundity, widespread distribution, and relatively stationary habit (16), Dungeness crab can serve as a useful biomonitor for site-specific time-integrated bioaccumulative-contaminants monitoring (e.g., refs 1, 13, 17, and 18). Six of the 16 coastal pulp mills in operation in 1998 on the West Coast of BC were included in the sampling, featuring mills utilizing various pulp/paper processes. In most cases, these mills were the only potential industrial sources present within each geographically isolated sampling domain. Furthermore, two principal harbor sites were included to represent mixed industrial and domestic sources. The spatial trends in PCB congener patterns detected in Dungeness crab were investigated, and two complementary numerical procedures, a direct mixing model (DMM) and factor and principal component analysis (FA/PCA), were applied to interpret the multivariate data with respect to potential PCB sources. FA/PCA have been extensively used to facilitate PCB congener pattern analysis in environmental samples (e.g., refs 2 and 19-22), whereas the DMM, which relies on experimentally determined compositions of A1242, A1254, and A1260, was recently developed and validated by Sather et al. (23).
Materials and Methods Sample Collection and Analysis. Mature male Dungeness crab samples were collected in the spring using commercial VOL. 36, NO. 12, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Sampling locations for Dungeness crab on the West Coast of British Columbia. Solid bars indicate average ∑PCBs (µg/g lipid) in crab hepatopancreas tissue at each site. crab traps as part of the organochlorine monitoring program organized by the Habitat Enhancement Branch of the Canadian Department of Fisheries and Oceans (see Appendix A of the Supporting Information for sample site descriptions, meristic information, and total PCB levels). Sixty-five samples (several of these samples were composites of 2-10 individual crabs) were collected in the spring from various coastal locations in BC (Figure 1) from 1996 to 1999 inclusively. Additionally, Pacific rock crab (Cancer antennarius) and red rock crab (Cancer productus) samples were collected from East Sooke Park (reference site) and Plumper Bay, Esquimalt (site 8g), respectively. Samples were weighed, sized, and dissected. Composite hepatopancreases were combined and homogenized using a Sorvall Omni-Mixer (Ivan Sorvall Inc., Newtown, CT). An aliquot from each sample was taken for percent lipid (gravimetry of lipid extraction) and moisture (oven drying) analysis. Approximately 10 g of tissue from each sample was extracted with dichloromethane/hexane (1:1) for PCB analysis, which was performed via a gas chromatography-highresolution mass spectrometry (GC-HRMS) isotope dilution method as described in Ikonomou et al. (24). Full congener (167 congener peaks including 34 coeluting bands: BZ# 4/10, 7/9, 8/5, 16/32, 27/24, 33/20, 42/59, 47/48/75, 52/73, 56/60, 64/71/41, 70/76, 74/61, 83/109, 84/92, 87/115, 97/86, 101/ 90, 102/93, 107/108, 117/125/116, 118/106, 131/142, 134/ 143, 135/144, 138/163/164/160, 139/140, 146/161, 153/132, 170/190, 172/192, 174/181, 187/182, 203/196) analysis was performed including non-ortho, mono-ortho, and di-ortho fractions. Similarly, Aroclor 1242, 1254, and 1260 standards (purchases from Accustandard, RFR Corp., and Ultrascientific) were analyzed in triplicate as described in Sather et al. (23). The resultant normalized profiles for each Aroclor mixture were consistent with those reported by Frame et al. (25). 2546
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Data Processing and Numerical Analyses. Initially, undetected congeners were assigned the value of the detection limit. Congeners with levels below the detection limit for greater than 30% of the samples were removed from the data set as recommended by Meglen (26) to avoid significant detection limit artifacts in FA/PCA. Twenty-nine variables (i.e., congeners or coeluting congener groups) were eliminated based on the previous criteria. Further preprocessing of the resulting 138 variables involved adjusting concentrations to tissue lipid content followed by estimating missing values. Missing values (comprising 0.5 for n ) 65) to the variable with missing values (i ) m) was used to calculate a ratio (eq 1) for each sample.
xm/(xa)
or
(
∑x over a, b, c, etc.) i
(1)
Next, site means, or the overall mean (i.e., if too few samples with nonmissing values remained for the site in question) of this ratio were calculated and multiplied by xa or ∑xi, correspondingly, for the sample in question to estimate the missing value. Once a complete data set was obtained, the values were normalized by one of two normalization techniques: (1) values were normalized to a sum of 100% or (2) a selective normalization procedure was used as described in Johansson et al. (27), where the normalization factor was based on values with midrange standard deviations and means rather than the entire set of variables. Method 1 was used for data subject to the DMM, whereas Method 2 was used for data subject to FA/PCA to avoid any spurious negative correlations between large variables and positive correlations between small variables as a result of closure of the data.
FIGURE 2. Distribution of chlorohomologues in A1242, A1254, and A1260 and the average found in Dungeness crab hepatopancreases from eight locations on the West Coast of British Columbia. (Note: error bars are standard deviations for site averaged data.) Initially, the Aroclor composition of each sample was estimated using a DMM developed by Sather et al. (23). The DMM uses an iterative technique involving a matrix of linear equations constructed from the congener profiles of Aroclor standards A1242, A1254, and A1260 determined by GCHRMS as per Ikonomou et al. (24). (See Supporting Information Appendix B for details and a worked example of the DMM.) FA/PCA were performed on the selectively normalized and standardized data using Pirouette version 2.03 (Infometrix, Woodinville, WA) which uses the nonlinear iterative partial least-squares (NIPALS) algorithm (28) to determine the first k factors with out computing all factors. Initially, 11 variables were excluded from the PCA model (BZ# 13, 19, 72, 96, 102/93, 103, 111, 123, 139/140, 154, 168) based on a low relative modeling power (MP) determined for these variables by a preliminary PCA. These variables were poorly modeled by PCA either due to a low total variance in the original data set or a high residual variance between the original and reconstructed data sets; thus, they were excluded with minimal effect on the sample scores determined for each sample. Although 138/163/164/160 also showed a low MP, upon reexamination it was found that this was predominantly due to a high residual variance caused mainly by an anomalously high value for this variable in one of the samples (sample 1e; see Appendix A) from site 1; thus, this variable was retained. The optimal number of factors to include in the unvalidated model was then determined by examining the percentage variance accounted for by each of the first seven factors as well as inspecting the sample residuals plots for extreme outliers and structure for each factor level from 7 to 1.
Results and Discussion Chlorohomologue Series. The distribution (by weight percent) of each chlorohomologue series total (determined as the sum of each congener detected by GC/HRMS in each chlorohomologue series) in Aroclor 1242, 1254, and 1260 compared to average composition over the eight industrial sites examined in BC is shown in Figure 2. The predominant chlorohomologues seen in the West Coast crab samples were hexa > penta > hepta, comprising 89% of the total PCBs. Porte and Albaige´s (29) also found these three chlorohomologues to be the most predominant PCB homologues in several biota samples including crab taken from the Western Mediterranean. The chlorohomologue pattern for the West Coast samples shown in Figure 2 is similar in composition to a mixture of A1254/A1260. However, the bioaccumulation of PCB congeners by biota is known to be dependent on the
FIGURE 3. DMM results averaged by site. (Note: error bars are standard deviations for each site; n ) number of subsite samples which were used to calculate average) (See Appendix B for subsite samples which were determined to be outliers based on a RSS > 100.) octanol-water partitioning coefficient (Kow) and membrane permeability (among other factors) which, in turn, are highly dependent on chlorination level and thus chlorohomologue series (30). Because of the enrichment and exclusion of certain PCBs in biota based on chlorination level, PCB source determination based on homologue series totals alone will be misleading. Direct Mixing Model (DMM). Because of the availability of full congener data in this work, a more reliable method of source prediction may be achieved by comparing the congener-specific profile observed in biota to that of common Aroclor mixtures. The results of applying the DMM utilizing experimentally determined concentrations of A1242, A1254, and A1260 to the normalized sample compositions are illustrate in Figure 3. These results show that there are significant differences in the projected Aroclor mixture between sampling locations. In general, the composition predicted for the harbor sites (numbers 8 and 9) is approximately a 45:55 mixture of A1254/A1260, whereas that predicted for most mill sites excluding Ocean Falls (number 6) consists of even higher proportions of A1260. Furthermore, Ocean falls is the only site where a heavy A1254 contamination is predicted. Sites 1, 4, and 7 contain larger intrasite variation in predicted mixtures compared with other sites because certain subsites samples from these locations contained PCB patterns which either reflected actual heterogeneity in source composition at these sites or were inadequately modeled by the DMM (see Appendix B for details). Inevitably, physical or biological transformation along with differential bioaccumulation rates will lead to a certain degree of systematic error in the comparison between environmental PCB patterns to those predicted by a direct mixing model using pure Aroclor end-members. It is apparent from Figure 4 that the largest discrepancy between the PCB composition predicted by the DMM and that actually found in the biota samples was elevated proportions of PCB 153/ 132 and PCB 138/163/164/160 in the latter. Similarly, but to a lesser extent, PCB 101/90, PCB 52/73, and PCB 99 were higher in the biota samples than predicted by the projected Aroclor composition. Conversely, PCB 180, PCB 174/181, PCB 70/76, PCB 105, and PCB 170/190 were found in lower proportions in the biota samples than in the predicted Aroclor mixture. PCB 138/163/164/160 can be approximated as PCB 138 and PCB 153/132 as PCB 153, both of which are predominant congeners in Aroclors due to their 2,4,5substitution pattern that is a preferential substitution pattern during the production of Aroclors (31). PCBs 153 and 138 were also the most predominant PCBs found in a range of VOL. 36, NO. 12, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 4. Summary of large residuals from DMM. The boxes represent the 25th to 75th percentile, and the upper and lower fences are the maximum and minimum. (Note: outliers are represented by open circles and extremes by asterisks; samples with large RSS and outlying residual patterns identified in Appendix B are not included in this plot.) biota samples from various environments including the Spanish Coast, Dutch Coast, Italian Coast, and Lake Erie (4, 15, 29, 32). Fisk et al. (33) determined that the dietary biomagnification factor was highest for PCB 153 followed by PCB 138 in juvenile rainbow trout as compared to a subset of 16 PCBs predicted to have the slowest elimination and greatest bioaccumulation potential. Additionally, the relatively high log Kow of hexachlorobiphenyls (6.4-7.6) such as PCB 153 and 138 predicts a high sediment deposition rate and favors bioaccumulation from sediments for these congeners (15, 34). Finally, the 2,4,5-substitution pattern, shown in both phenyl groups of PCB 153 and one phenyl group of PCB 138, is exceptionally recalcitrant to biotransformation and elimination in biota (29). Similar arguments can be made for PCB 101/90, PCB 52/73, and PCB 99, which can be approximated as PCB 101, PCB 52, and PCB 99, respectively, due to the more favorable ring substitution pattern in these latter congeners over their coeluting counterparts. Conversely, PCBs that were found to occur at lower levels than expected in the hepatopancreas may be easily metabolised by crab or have a low uptake potential in this species or its prey. PCBs 174/181, 170/190, and 180 found in lower than expected concentrations are heptachlorobiphenyls which may be too large to efficiently pass through cell membranes in the GI tract and gills in various biota including crab (30, 35). Also, PCBs 174 (favored congener in 174/181), 170/190, 105, and 70 (favored congener in 70/76) all contain ortho-,meta-, or meta-,para-vicinal H atoms, which are known to facilitate the biotransformation and subsequent elimination of PCBs (21). It becomes quite complex if one attempts to take into account these various effects to try and correct a model such that the predictions are more accurate for a given data set; also, one will end up with a model that only applies to the conditions used in that particular experiment. Because the DMM revealed the presence of distinct Aroclor source compositions in our data set, we have chosen to reexamine the data set using an unsupervised exploratory technique which is based on the variance between samples. A better understanding of the interrelationship between congeners, and the spatial variation across the samples without any a priori assumption of Aroclor sources, was sought. Factor and Principal Component Analyses (FA/PCA). FA/PCA was used to examine the data for all sources of 2548
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FIGURE 5. Variable loadings for (a) PC1 and 2 and (b) PC1 and 3 from FA/PCA using 127 PCB variables averaged by subsite. (Note: see list of identified variables in Table 1.) variation between subsites (not restricted to only A1242, A1254, and A1260) and to determine the relative contributions of each identified factor for each subsite. It was determined that the data set could be sufficiently described with a 3 PC model which accounted for 66% of the total intersample variance. Although some sample residual outliers still occurred, the major sources of variation were modeled by the first three PCs. Upon inspection of the subsequent PC (PC 4-6.5% of the variance), it was determined that the small variance captured by this component was mainly due to two slightly anomalous samples (4d and 4b) in the data set. A 3 PC model is optimal because it explains the major sources of variation in the smallest number of dimensions possible. Variable loadings (Figure 5a) show that there are three predominant clusters of variables on the 2D surface of PC1 and PC2 accounting for 57.5% of the total variance between samples (see Table 1 for variable identification). Two sets of variables consisting of predominant congeners found in A1254 and A1260, respectively, formed clusters on opposite ends of the most important factor, PC1 (accounting for 35.4% of the total variance), indicating there may be a partial negative correlation between these two factors. A less cohesive set of variables showing di- through tetrachlorinated congeners clustered on the positive end of PC2, which accounts for 22.1% of the total variance. This cluster of variables may correspond to either A1242 which has an average CL per molecule of 3.10 or another common Aroclor, A1248, which has an average CL per molecule of 3.90 and a very similar congener pattern to that of A1242 (8). However, congeners 4/10, 8/5, and 15, all of which were found in this variable cluster, occur in A1242 13-18 times greater than in A1248 where they collectively make up less than 1 wt % (25). Furthermore, between 1957 and 1975, A1242 outsold A1248 in the U.S. by more than 7 to 1 (9). Thus, we have assigned variable cluster C (Table 1) to A1242-type variation.
TABLE 1. Summary List of Variables Identified in PCA Loadings Plot (Figure 5, Parts a and b) location of cluster on PC A (ellipse) B (ellipse) C (ellipse) D (above line) E (below line)
PCB variables f 137a, 166a, 156a*, 128a*, 143/134b, 167a, 114a, 124a, 97/86a*, 101/90a*, 118/106a*, 157a, 110a*, 105a*, 84/92a*, 87/115a*, 83/109a, 107/108a, 122a91a, 82a*, 85a*, 95a*, 99a*, 89d, 162a, 158a, 119a, 130a 193b, 187/182b*, 207b, 178b*, 175b, 183b*, 202b, 191b, 170/190b*, 179b*, 177b*, 198b, 197b, 180b*, 203/196b*, 172/192b, 201b*, 194b*, 195b, 200b, 185b, 171b*, 174/181b*, 176b, 199b, 189b, 188 88d, 58, 169, 78, 16/32c*,100c, 4/10c*, 39c, 47/75/48d*, 17c*, 27/24c, 79c, 35c, 57c, 42/59d*, 40d, 67c, 18c*, 49d*, 15c*, 28c*, 31c*, 81d, 33/20c*, 64/71/41d*, 63d, 68c, 80a, 37c*, 55c, 22c*, 56/60d*, 77d, 45c,d*, 51d, 53d, 74/61d*, 25c, 8/5c*, 36, 11c, 66c* 169e, 79e, 126e, 80e, 39e, 11e, 184, 78e, 162, 36e, 81e, 153/132, 35e, 68, 57, 99, 146/161, 189, 178, 63, 85, 193, 6, 74/61, 77e 25, 46, 26, 40, 22, 16/32, 64/71/41, 51, 45, 27/24, 42/59, 28, 53, 136, 152, 141, 149
a
PCB congener(s) found in A1254 in highest concentration (A-C only). b PCB congener(s) found in A1260 in highest concentration (A-C only). PCB congener(s) found in A1242 in highest concentration (A-C only). d PCB congeners(s) found in A1248 in highest concentration (A-C only) (composition of this Aroclor from Frame et al. (25)). e Coplanar congener (D and E only). (*) Greater than 1% in corresponding Aroclor. c
PC1 and PC2 account for a significant and explainable amount of the total variation, whereas PC3 (Figure 5b), which captured only 8.2% of the variance, showed a less explainable distribution of variables. Variable loadings over PC1 and PC2 suggest that A1242, A1254, and A1260 are the primary sources of PCB contamination in the sites examined in this work, which justifies the use these Aroclors as end-members in the DMM previously. Additionally, from Table 1, it is apparent that the positive PC3 variables consisted of 11 of the 20 coplanar PCB congeners in contrast to the negative PC3 variables which were similar in chlorohomologue distribution but contained no coplanar PCBs. The interpretation of this result is less clear; however, various reports have suggested that coplanar CBs may have distinct bioaccumulative properties from those of nonplanar congeners of comparable chlorination number (reviewed in ref 36). Thus, the differential bioaccumulation of coplanar versus noncoplanar congeners of similar chlorination enhanced by differences in PCB concentrations between subsites (see PCB levels for each subsite sample in Appendix A and subsite distribution over PC3 in Figure 6b) may have led to the variance captured by this component. To view the distribution of the sites in terms of these identified factors, the PCA scores are plotted in Figure 6. In most cases, the DMM results (Figure 3) agree with the position of sites on the 2D score plot (PC1 and 2 in Figure 6a) based on the identified factors in Figure 5a. From the PCA scores, Crofton (1c, e, and f), Nanaimo (5a and b), and Prince Rupert (7a only) samples show predominant characteristics of an A1260 source. Similarly, A1254/A1260 was estimated by DMM to be approximately 1:3 for each of these sites (see Figure 3). The PCA scores for Ocean Falls (6 a-d) indicate a high contribution of A1254 which is consistent with the DMM results showing a 75-80% contribution of A1254 to the total composition at this site. The PCA scores for both harbor sites (sites 8 and 9) show no strong relationship with either PC1 or 2 and, thus, do not contradict the 45:55 mix of A1254/ A1260 predicted by the DMM. Subsite deviations from the general trends discussed here either reflect actual heterogeneity in the PCB composition within each sampling domain or may be due to slightly different life histories of the organisms which made up each sample taken within each site. Additionally, some of the subsite variation was due to temporal variation in PCB composition from 1996 to the 1999 sampling period (e.g., sample 1b was sampled in 1996 versus the rest of site 1 subsites which were sampled in 1999). Howe Sound (site 4) and, to a lesser extent, Elk Falls (site 2) show characteristics of an A1242 source. However, although the DMM predicted a 35% contribution of A1242 at Queen Charlotte Channel (4d) which caused the slight A1242
FIGURE 6. PCA score plot for (a) PC1 and 2 (b) and PC1 and 3 for 127 PCB variables averaged for each subsite. (Note: 7a* was the site 7 average excluding the two DMM and preliminary PCA outliers (see Appendix A); 8g** was the red rock crab sample taken at this location.) estimate seen for the average Howe Sound composition (site 4, Figure 3), no %A1242 was predicted for other subsites samples from Howe Sound or Elk Falls. Because FA/PCA is based on equally weighted (due to autoscaling) congener specific variance, it may not have been as susceptible to bias or error from altered PCB congener patterns in environmental samples. This is evidenced by an average residual sum of squares (42 ( 35; based on the nonautoscaled selectively normalized data) of less than half that found for the DMM. A1242 would be most susceptible to environmental alterations as it consists of a significant amount of dichloro- and trichlorobiphenyls which have very unfavorable octanolVOL. 36, NO. 12, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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water partition coefficients, along with favorable biotransformation/elimination characteristics (i.e., lack 2,4,5-substitution and contain ortho-,meta- and meta-,para-vicinal hydrogen). This is coupled with the fact that tetrachloroand pentachlorobiphenyl congeners contained in A1242 have a much greater biomagnification potential which would result in a “weathered” congener pattern that poorly reflects the A1242 end-member used in DMM; thus, the composition of this mixture may be underestimated by this technique. Furthermore, estimates of A1260 by DMM may also be effected by bias due to the enrichment of recalcitrant and highly bioaccumulative PCBs which are found in great amount, namely, PCBs 153 and 138 (previously shown to have large negative residuals in the DMM because they were underpredicted), both of which occur in greater amounts in A1260 than in A1254. In PCA, because all congeners are equally weighted by autoscaling, the numerous other minor congeners which may not have had such largely skewed environmental concentrations will have outweighed this bias to some extent in assigning sources. Summary of PCB Contamination at Mill and Harbor Sites. The total PCB concentrations (see bars in Figure 1) as well as the predicted PCB Aroclor source composition vary significantly with site. Elk Falls and Howe Sound both show the lowest levels of PCB contamination; also, both of their PCB patterns exhibit the largest A1242 characteristics. Thus, the full extent of PCB contamination at these sites may be underestimated due to the unfavorable biomagnification potential and ease of biotransformation/elimination of the lower chlorinated congeners, which make up a large proportion of A1242. Nanaimo, Crofton, Prince Rupert, and Esquimalt Harbor show slightly higher levels of PCBs, and compositionally, these sites seem to have a predominant A1260 pattern, with lesser amounts of A1254. Finally, Victoria Harbor and Ocean Falls have the highest levels of total PCBs out of all the eight sites investigated. The composition of Victoria Harbor was estimated to be approximately 45:55 A1254/A1260, whereas Ocean Falls was predicted to have a much higher A1254 contamination. A large variation in total PCBs occurred at Ocean falls from 61 µg/g lipid at the old mill site to 2 µg/g lipid at the farthest sample from the mill. This large range in PCB levels, together with the similarity in predicted Aroclor composition for all Ocean Falls sampling locations, suggests a point source of PCB contamination from the former pulp mill site. Although Environment Canada inspections did not reveal the presence of any PCB containing oils at this location, improper disposal of PCB containing transformer or capacitors upon the closure of the pulp and paper mill in 1975 may have led to these high levels. The highly restricted circulation at this Fjord is also a contributing factor to these high levels. Nanaimo and Crofton, which despite large differences in pulp production modes and capacities (see Appendix A, Supporting Information), along with the absence of any paper production at Nanaimo, were predicted to have almost identical A1254/A1260 patterns. However, total PCBs at Crofton are higher than those at Nanaimo, which is consistent with the higher production capabilities at the former mill.
Acknowledgments The authors thank Margaret Wright for collecting the Dungeness crab, all Regional Dioxin Laboratory chemists and laboratory assistants involved in the processing and analysis of these samples, Dr. R. F. Addison for revising the manuscript, M. Fischer for assisting with the editing, and B.A. Bailey from Inspection/Enforcement Division, Environmental Protection Branch, Environment Canada, for requested information. In addition, we extend our thanks to the anonymous reviewers of this manuscript whose suggestions and comments have helped us improve the clarity 2550
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of this publication. Financial support was provided by the Fisheries and Oceans Canada.
Supporting Information Available Appendix A is a table of sampling site information, meristic data, and PCB levels for male Dungeness crab samples taken from the coastal waters of British Columbia. Appendix B is and expanded description with a graphical illustration of the direct mixing model (DMM). This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review August 20, 2001. Revised manuscript received April 1, 2002. Accepted April 2, 2002. ES011209V
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