Abstract
Exposure to polychlorinated biphenyls (PCBs) remains a potential human health risk due to their persistence in the environment, despite a global ban on their production. Understanding the composition of PCB mixtures is essential for the application of a mixtures-based approach to assessing health risks of PCB exposure. This work represents the most extensive effort to date to compile and make publicly available the PCB congener profiles for mixtures with toxicological data, providing a foundation for understanding toxicological potency of PCB mixtures in the environment. We searched for published congener profiles across 29 commercial and simulated environmental PCB mixtures, including various Aroclors, Phenoclors, Clophens, and Kanechlors, among others. A total of 117 references containing 401 distinct complete or partial tabularized profiles were found. Aroclor 1254 had the most published profiles, with 79 unique datasets characterizing multiple mixture lots. In contrast, no congener-specific composition data were identified for Fenclors, Clophen C, or Pyralenes. Eighty-seven of the most complete and clearly reported profiles underwent a detailed extraction of the congener data, PCB mixture source, and analytical methods. Challenges encountered during data extraction included congener coelutions, incomplete methods reporting, and inconsistencies in PCB nomenclature. These factors complicate data visualization, comparisons across datasets, and use of the data in subsequent analyses. Where possible, we have converted profiles to the same units and congener numbering convention to allow for easier comparison. The extracted data are publicly available online as interactive visuals and as a downloadable Microsoft Excel® workbook. This dataset provides researchers with an overview of the current PCB mixture profile landscape that can serve as a tool to support efforts to minimize the health impacts of environmental PCB exposure, including the exploration of links between mixture composition and toxicity and the identification of the most efficient and effective remediation strategies at contaminated sites.