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Evaluation of the effectiveness of different indicator PCBs to estimating total PCB concentrations in environmental investigations

Author:
Megson, David, Benoit, Nadine B., Sandau, Courtney D., Chaudhuri, Sri R., Long, Tanya, Coulthard, Emma, Johnson, Glenn W.
Source:
Chemosphere 2019 v.237 pp. 124429
ISSN:
0045-6535
Subject:
World Health Organization, aroclors, data collection, fractionation, persistent organic pollutants, prediction, risk, weathering
Abstract:
Polychlorinated biphenyls (PCBs) are one of the most widely studied group of persistent organic pollutants (POPs). There are 209 different PCBs, however not all 209 can currently be individually quantified in one analytical run. This means that a subset of PCBs congeners are often determined and reported. Some of the most commonly reported subsets are the 7 indicator PCBs (28, 52, 101, 118, 138, 153 and 180) and the WHO 12 PCBs (77, 81, 105, 114, 118, 123, 126, 156, 157, 167, 169 and 189). The WHO 12 congeners are co-planar ‘dioxin like’ PCBs that are effective for establishing health risks. The 7 indicator PCBs were selected as some of the most common PCBs across the compositional range of the most common technical mixtures (such as Aroclors), and are used to give an indication of the total PCB concentrations. These groups of indicator PCBs were established several decades ago. However, in the environment commercial mixtures are subject to weathering and fractionation processes, and additional sources of non-Aroclor PCBs are also becoming more important. In this manuscript we use existing large scale comprehensive congener specific datasets to evaluate the effectiveness of indicator PCBs to predict total concentrations and establish if they are still fit for purpose. The results indicate that while these traditional indicators are a useful tool to estimate total concentrations in humans with background exposure there are many instances where they are not fit for purpose and can lead to significant under predictions in total PCB concentrations in environmental matrices.
Agid:
6543586