Passive Air Sampling Networks Combined with Multivariate Statistics Reveal Widespread Non-Aroclor Polychlorinated Biphenyl Sources to the Canadian Atmosphere
Bibliographic record
Abstract
Polychlorinated biphenyls (PCBs) in the North American atmosphere were originally thought to arise through volatilization of commercial Aroclor mixtures, but there is growing evidence of atmospheric emissions of non-Aroclor, i.e., unintentionally produced, PCBs. Here, we report on measurements of all 209 PCB congeners in 169 passive air samples collected between 2019 and 2022 using networks established around the Salish Sea, British Columbia (BC), and along the St. Lawrence River and Estuary, Quebec (QC), in Canada. Hierarchical cluster analysis and positive matrix factorization were employed to identify, distinguish, and quantify different PCB sources to the atmosphere. PCBs were detected at every single site, with elevated levels found in the urban centers of the region (Vancouver, BC; Montreal, Quebec City, QC), including in the vicinity of a municipal waste incinerator. We found evidence that suggests legacy Aroclor emissions, e.g., associated with electrical equipment storage in Pointe-Claire, QC, and building emissions in Burnaby, BC. We also identified several locations (e.g., in Sept-Îles and Alma, QC) where non-Aroclor sources are estimated to contribute over 40% of PCBs. In particular, PCB congeners 47, 51, and 68, known byproducts of 2,4-dichlorobenzoyl peroxide (2,4-DCBP) decomposition during silicone rubber and polyester production, were strongly associated with PCB-7 and -25. Although Aroclors were estimated to remain the main contributors of PCBs to the Canadian atmosphere, unintentional production is making a non-negligible contribution (estimated to be at least 10%). Of the known non-Aroclor sources, 2,4-DCBP is likely still used in North America with little to no regulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".