Commuting Exposures to Particulate Air Pollution in Three Canadian Bus Transit Systems: The Urban Transportation Exposure Study
Bibliographic record
Abstract
Background: Health Canada’s Urban Transportation Exposure study was designed to assess levels of air pollution in Canadian transport environments. As Canadians spend approximately 52 minutes of their day commuting, this environment may contribute a larger proportion of their daily exposure to traffic-related air pollution (TRAP)”. Identifying factors related to bus commuting TRAP exposure is key to its mitigation. Methods: From 2010-2013, sampling was conducted using personal monitors in bus transit environments of Toronto, Ottawa, and Vancouver. In each city, data were collected during 3-week sampling campaigns in the summer and winter for fine particulate matter (PM2.5), ultrafine particulates (UFPs), and black carbon (BC). Buses were classed as natural gas, electric, hybrid electric/diesel, clean diesel, post 2000 diesel, or pre 2000 diesel. Stations/stops were classed as bus stop, above grade bus station, or below grade / indoor bus station. Particulate air pollution levels were compared between cities, seasons and waiting vs riding. The impact of bus propulsion type and stop/station design on air pollution exposures were evaluated using linear mixed-effect models. Model development included screening for potential confounders such as meteorological factors and land use. Results: Levels of PM2.5, UFPs, and BC were similar between cities and with higher UFPs concentrations observed during winter. In Toronto, bus stations had higher (65%, 95%CI: 48-84%) PM2.5 levels relative to bus stops. Similar differences were seen for BC. Compared to pre-20000 diesels, hybrid electric/diesel buses reduced exposures for PM2.5, UFPs, and BC in all three cities. Conclusions: The personal exposure data collected in this study provides a benchmark of Canadian commuter exposure for bus transit and provides evidence of the potential air quality benefits of ventilating indoor/below grade bus stations and replacing older bus models.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".