Metro Commuter Exposures to Particulate Air Pollution and PM<sub>2.5</sub>-Associated Elements in Three Canadian Cities: The Urban Transportation Exposure Study
Bibliographic record
Abstract
Abstract System-representative commuter air pollution exposure data were collected for the metro systems of Toronto, Montreal, and Vancouver, Canada. Pollutants measured included PM2.5 (PM = particulate matter), PM10, ultrafine particles, black carbon, and the elemental composition of PM2.5. Sampling over three weeks was conducted in summer and winter for each city and covered each system on a daily basis. Mixed-effect linear regression models were used to identify system features related to particulate exposures. Ambient levels of PM2.5 and its elemental components were compared to those of the metro in each city. A microenvironmental exposure model was used to estimate the contribution of a 70 min metro commute to daily mean exposure to PM2.5 elemental and mass concentrations. Time spent in the metro was estimated to contribute the majority of daily exposure to several metallic elements of PM2.5 and 21.2%, 11.3% and 11.5% of daily PM2.5 exposure in Toronto, Montreal, and Vancouver, respectively. Findings suggest that particle air pollutant levels in Canadian metros are substantially impacted by the systems themselves, are highly enriched in steel-based elements, and can contribute a large portion of PM2.5 and its elemental components to a metro commuter’s daily exposure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".