Influence of the Natural and Built Environment on Personal Exposure to Fine Particulate Matter (PM2.5) in Cyclists Using City Designated Bicycle Routes
Bibliographic record
Abstract
Urban cyclists are exposed to many traffic-related air pollutants including particulate matter (PM) that may increase vulnerability to health effects. This study investigates second-by-second personal exposure to PM2.5 (fine particulate matter that is 2.5 microns in diameter and less) along bicycle commuting paths, and assesses elements of the natural and built environment for the relative importance of these factors in understanding the variability in PM2.5 personal exposure. Urban cyclists were carrying high resolution PM2.5 monitors (placed in a backpack) in combination with portable GPS trackers to provide a spatial identity to each one-second pollutant measurement. The results of this study indicate that daily averages of PM2.5 concentrations from all bicycle routes were weakly correlated with meteorological variables, however, a strong influence of regional levels of PM2.5 was observed. Geospatial analysis of PM2.5 personal exposure concentrations showed a considerable variation within routes, correlated with land use (with lower concentrations in parks and higher in industrial areas) and clustered at four areas: busiest bridge, heavily trafficked road segments, the downtown urban core, and two construction sites. This study has found many incidences of personal exposure to PM2.5 exceeding the provincial guidelines for healthy activity (e.g., very poor (PM2.5 > 91 μg/m3) pollution concentrations are clustered in three regions: approaching the bridge in the west part of the city; the downtown urban core; and two under construction spots), which suggests behavioural and infrastructure modifications in balancing the health benefits of cycling with the environmental exposure to air pollutants.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".