From the City to the Lake: Loadings of PCBs, PBDEs, PAHs and PCMs from Toronto to Lake Ontario
Bibliographic record
Abstract
Loadings from Toronto, Canada to Lake Ontario were quantified and major sources and pathways were identified, with the goal of informing opportunities for loading reductions. The contaminants were polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), polycyclic aromatic hydrocarbons (PAHs) and polycyclic musks (PCMs). Loadings were calculated from measured concentrations for three major pathways: atmospheric processes, tributary runoff, and wastewater treatment plant (WWTP) effluents. Although atmospheric deposition to the Great Lakes has received the greatest attention, this was the dominant loading pathway for PCBs only (17 ± 5.3 kg/y or 66% of total loadings). PCB loadings reflected elevated urban PCB air concentrations due to, predominantly, primary emissions. These loadings contribute to consumption advisories for nearshore fish. PBDE loadings to the lake, again from mainly primary emissions, were 48% (9.1 ± 1.3 kg/y) and 42% (8.0 ± 5.7 kg/y) via tributaries and WWTPs, respectively, consistent with emissions deposited and subsequently washed-off of urban surfaces and emissions to the sewage system. PAHs loadings of 1600 ± 280 kg/y (71%) from tributaries were strongly associated with vehicle transportation and impervious surfaces. PCM loadings were 83% (±140 kg/y) from WWTP final effluent, reflecting their use in personal care products. Opportunities for source reduction lie in reducing the current inventories of in-use PCBs and PBDE-containing products, reducing vehicle emissions of PAHs and use of PAHs in the transportation network (e.g., pavement sealants), and improving wastewater treatment technology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".