Multi-year Analyses Reveal Different Trends, Sources, and Implications for Source-Related Human Health Risks of Atmospheric Polycyclic Aromatic Hydrocarbons in the Canadian Great Lakes Basin
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are of high concern to public health due to their carcinogenic and mutagenic properties. Here, we present the first comprehensive and quantitative analysis of sources, potential source regions according to source sectors and source-related human health risks of multi-year atmospheric measurements of PAHs in the Canadian Great Lakes Basin (GLB). The highest PAH concentrations were observed at a rural residential site (Egbert), followed by two regionally representative remote sites [Point Petre (PPT) and Burnt Island]. The levels of most PAHs in the GLB atmosphere significantly decreased between 1997 and 2017, broadly consistent with the decreasing trends of anthropogenic emissions. Coal, liquid fossil fuel, and biomass burning were the most common potential sources. The potential source regions for most source sectors were identified south or southwest of the sampling sites. Risk assessment suggests potential health risks associated with the inhalation of atmospheric PAHs. On a positive note, health risks from coal combustion, liquid fossil fuel combustion, and petrogenic sources at PPT significantly decreased, directly demonstrating the success of emission control in reducing health impacts. In contrast, the health risk from forest fire-related PAH emissions may play an increasing role in the future due to climate change.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".