Impacts of Subway System Modifications on Air Quality in Subway Platforms and Trains
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Subway PM 2.5 can be substantially sourced from the operation of the system itself. Improvements in subway air quality may be possible by examining the potential to reduce these emissions. To this end, PM 2.5 was measured on the trains and station platforms of the Toronto subway system. A comparison with previously published data for this system reveals significant changes in below ground platform PM 2.5 . A reduction of nearly one-third (ratio (95% CI): 0.69 (0.63, 0.75)) in PM 2.5 from 2011 to 2018 appears to have resulted from a complete modernization of the rolling stock on one subway line. In contrast, below ground platform PM 2.5 for another line increased by a factor of 1.48 (95% CI; 1.42, 1.56). This increase may be related to an increase in emergency brake applications, the resolution of which coincided with a large decrease in PM 2.5 concentrations on that line. Finally, platform PM 2.5 in two newly opened stations attained, within one year of operation, typical concentrations of the neighboring platforms installed in 1963. Combined, these findings suggest that the production of platform PM 2.5 is localized and hence largely freshly emitted. Further, PM 2.5 changed across this subway system due to changes in its operation and rolling stock. Thus, similar interventions applied intentionally may prove to be equally effective in reducing PM 2.5 . Moreover, establishing a network of platform PM 2.5 monitors is recommended to monitor ongoing improvements and identify impacts of future system changes on subway air quality. This would result in a better understanding of the relationship between the operations and air quality of subways.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".