Exposure to noise and air pollution by mode of transportation during rush hours in Montreal
Bibliographic record
Abstract
According to the World Health Organization, air pollution and road traffic noise are two important environmental nuisances that could be harmful to the health and well-being of urban populations. Earlier studies suggest that motorists are more exposed to air pollutants than are active transportation users. However, because of their level of physical activity, cyclists also inhale more air pollutants. The main objective of this paper is to measure individuals' levels of exposure to air pollution (nitrogen dioxide – NO 2 ) and road traffic noise according to their use of different modes of transportation. Three teams of three people each were formed: one person would travel by bicycle, one by public transit, and the third by car. Nearly one hundred trips were made, from various outlying Montreal neighbourhoods to the downtown area at 8 am, and in the opposite direction at 5 pm. The use of mixed models demonstrated that public transit commuters' and cyclists' levels of exposure to noise are significantly greater than motorists' exposure. Again, using mixed models, we found that although the levels of exposure to the NO 2 pollutant do not significantly differ among the three modes, the inhaled doses of NO 2 pollutant are more than three times higher for cyclists than for motorists due to their stronger ventilation rate. It is hardly surprising that the benefits of physical activity are of course greater for cyclists: they burn 3.63 times more calories than motorists. This ratio is also higher for public transport users (1.73) who combine several modes (walking, bus and/or subway and walking).
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".