The Characterization and Mitigation of Fine Particulate Matter Air Pollution in the Toronto Subway System
Bibliographic record
Abstract
Subways have become a vital and growing part of urban transportation globally. They provide affordable access to work, education, and healthcare while reducing private vehicle use and improving urban air quality. However, air quality in subways is often characterized as high concentrations of iron-rich fine particulate matter (PM2.5). These levels can be ten times higher than outdoor concentrations, significantly increasing daily exposure for subway commuters, which number in the millions worldwide. This thesis addresses gaps in subway pollution science by critically reviewing strategies to improve subway air quality and characterizing the age and sources of subway PM2.5. A systematic review of research identified effective methods to improve subway air quality including increasing mechanical ventilation in subways that rely on natural and piston effect-driven ventilation, exploring strategies to reduce emissions from brakes, wheels, and rails, and implementing real-time PM monitoring in subways. The thesis also presents a unique, long-term dataset on subway PM2.5 from four measurement campaigns over ten years. This revealed significant line-wide shifts in PM2.5 levels over this time. These shifts were coincident with changes in subway operations, highlighting the close relationship between subway operations and air quality. They also indicated that subway PM2.5 is predominantly freshly emitted rather than the result of the resuspension of accumulated dust, which is important to informing mitigation strategies. Source apportionment analyses revealed the majority of PM2.5 in Toronto’s subway system to be system-sourced, primarily from the friction of wheels and rails during train deceleration. This thesis affirms that the source profile of PM2.5 in unventilated subways is unique relative to systems that use regular mechanical ventilation, where outdoor sources contribute more to PM levels. The findings highlight the importance of understanding the impact of subway operations on air quality, particularly in systems without regular mechanical ventilation. The insights from this thesis are relevant to both ventilated and unventilated subways, offering guidance for improving air quality. Policy options for regulating subway PM2.5 are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".