INFLUENCE OF THE BUILT ENVIRONMENT ON PERSONAL EXPOSURE TO FINE PARTICULATE MATTER IN CYCLISTS DURING THE MORNING COMMUTE IN A CANADIAN CITY
Bibliographic record
Abstract
Background and Aims: The health impacts of exposure to fine particulate matter air pollution (PM2.5) conflict with the health benefits of commuting by active transport. This is of special concern to commuting cyclists as they are often near air pollution generating traffic and respiring at an elevated rate. Long-term and short-term exposure to PM2.5 has been implicated epidemiologically in numerous cardiovascular and respiratory ailments. Personal exposure studies are critical as single-site ambient monitors do not adequately capture environmental variations and consistently report lower pollution measurements. Methods: Participants were equipped with a GPS and a portable device which measures PM2.5 both recording at one second intervals. Five routes totalling approximately 50km were cycled during the morning commute for 5 days across the city bicycle network in a midsized Canadian city (London, ON). GIS was employed to spatially analyze over one hundred thousand pollution data points. Results: This study found that mean individual exposure of 22.8µg/m3 was significantly greater than mean hourly ambient measures of 12.88µg/m3 (p = 0.03). Although atmospheric conditions such as wind direction (r = 0.72) were found to correlate with daily personal exposure to PM2.5, intraurban variation was still present in the data normalized to control for temporal meteorological effects. Conclusions: This study aims to be the first to incorporate elements of the built environment, such as the presence of street trees, traffic volume, traffic speed, urban morphology (e.g. block length, distance to nearest intersection), and land uses into a regression model in order to account for the variation and hotspots of PM2.5.
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".