Polychlorinated biphenyls (PCBs) have entered the environment in North America as Aroclor technical mixtures. Most methods used for the determination of total PCB levels in environmental samples visually match patterns of sample peaks to those in Aroclor standards. Concern over the accuracy of Aroclor-based measurements on compositionally modified samples coupled with advancements in analytical techniques have led to congener-specific PCB analysis. In this study, the PCB data from 27 tissue samples determined by an Aroclor-based method and a full congener method were compared in terms of total PCB concentration to assess the reliability of this Aroclor technique for total PCB determination. Our data show a strong correlation between the sum of Aroclors and the total PCBs obtained from the full congener determinations. We also developed a model using the compositional data from three Aroclors (1242, 1254, and 1260) to determine the amount of compositional alteration from original Aroclor patterns in environmental samples. Full congener data, from a variety of tissue types and trophic levels, examined using this method showed that compositional modification from original Aroclor patterns increases with trophic level, with the greatest modification observed in seal and killer whale samples. This result agrees both with expectation and with what has been found in other studies. Such techniques, which connect congener-specific PCB data to Aroclor contamination, may prove useful to investigations into environmental and metabolic fate and transfer processes.

No abstract

Polychlorinated biphenyl (PCB) congener patterns based on full congener PCB analyses of three farmed and five wild species of salmon from coastal British Columbia, Canada are compared using principal components analysis (PCA) and the best fit linear decomposition of the observed PCB composition in terms of Aroclor 1242, 1254, and 1260 end-members. The two complementary analysis methods are used to investigate congener composition pattern differences between species, trophic levels, feeding preferences, and farmed or wild feeding regimes, with the intent of better understanding PCB processes in both salmon and salmon consumers. PCA supports classification of PCB congeners into nine groups based on a) structure activity groups (SAG) related to the bioaccumulation potential in fish-eating mammals, b) Cl number, and c) the numbers of vicinal meta- and para-H. All three factors are needed to interpret congener distributions since SAGs by themselves do not fully explain PCB distributions. Farmed salmon exhibit very similar congener patterns that overlap the PCA and Aroclor composition of their food, while wild salmon separate into two distinct groups, with chinook and "coastal" coho having higher proportions of the higher chlorinated, Aroclor 1260 type, nonmetabolizable congeners, and chum, pink, sockeye, and "remote" coho having higher proportions of the lower chlorinated, more volatile and metabolizable Aroclor 1242 type, congeners. Wild chinook have the highest PCB and toxic equivalent (TEQ) concentrations, and the highest proportions of A1254 A1260, and PCB congeners in the most refractory SAG. Because both "coastal" and "remote" coho groups are likely to be consuming prey of similar size and trophic level, the PCB delivery mechanism (e.g., atmosphere vs runoff) apparently has more influence on the salmon PCB profile than biotransformation, suggesting that the wild chinook PCB profile is determined by feeding preference. Overall, wild salmon distributions primarily relate to trophic level, feeding preferences, and longevity, while metabolism appears at most a minor factor. The new classification protocol takes better advantage of individual congener PCB analyses and provides a better framework for understanding the PCB distributions in salmon and, potentially, the movement of individual PCB congeners through marine food chains than previous classification schemes.

This paper compares two previously published methods, an Aroclor estimation method and a mixing model method, that relate Aroclor contamination to congener specific data in environmental samples. The Aroclor estimation method, which is consistent with U.S. EPA Method 8082, uses a limited set of congener specific data to estimate Aroclor contributions to the sample, while the mixing model method uses the full congener data to model sample compositions as linear combinations of Aroclors. The performance of these methods are compared, using 181 samples at a variety of trophic levels, in terms of (a) total PCB concentrations, (b) compositional modification levels from original Aroclors, and (c) determination of the Aroclor mixture or mixtures best describing the sample (Aroclor speciation). We find that the two methods agree in all three terms for samples of low trophic level, but disagree for samples of higher tropic levels. Most significantly, the comparison reveals systematic overestimation of total PCB content by the Aroclor estimation method for samples at high trophic levels. The implication is that Aroclor determinations using persistent congeners cannot reliably be used as surrogates for total PCB concentration. The strengths and weaknesses of each method are detailed.

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.