Cyclists' exposure to air pollution and road traffic noise in central city neighbourhoods of Montreal
Bibliographic record
Abstract
It is well known that bicycling in urban areas has beneficial effects on people's health and well-being. On the other hand, urban cycling, especially during the morning and evening commute, may be associated with health and safety risks due to potentially high levels of exposure to air pollution, road noise, and high traffic density. Few studies have, however, measured cyclists' exposure to noise and air pollution simultaneously. The objective of this research is to evaluate cyclists' exposure to air pollution and noise in central city neighbourhoods of Montreal and to identify the impact on exposure of associated local factors such as weather conditions, the day and time, the type of road, bicycle path or lane used and the characteristics of the immediate environment around the cyclist's route. A total of 85 bicycle trips were analyzed, representing 422 km of travel and nearly 25 h of data collection. The mean exposure levels were 70.5 dB(A) for noise and 76 μg/m 3 for nitrogen dioxide (NO 2 ). A very weak negative correlation was found between the two measures of exposure (R 2 = − 0.07, p = 0.005). The results of the spatial regression models show that the morning commute and trips on collector roads and on-street bike lanes and shared bike lanes have significant and positive impacts on exposure to air pollution and noise. On the other hand, some factors are only significant for one or the other of the two types of 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".