A Statistical Model Explaining Air Pollution Exposures of Cyclists in Urban Environments
Bibliographic record
Abstract
Understanding the factors that influence a cyclist’s exposure to air pollution is an important component of designing a healthful, high quality urban cycling network. This study analyzes the results from a large mobile data collection exercise, conducted in Montreal, Quebec, Canada during the summer of 2012. Research assistants covered approximately 475 km of unique roadway on bicycles equipped with instruments to measure ultra-fine particle concentrations (UFP), black carbon (BC), and GPS coordinates at one-second intervals. The spatial extent of the data collection included a diverse array of cycling facilities and land use patterns. Linear regression models were estimated for UFP (R²=0.3963) and BC (R²=0.4528), along with descriptive statistical analysis on many of the variables processed. Levels of UFP (2,653-75,374 #/cm³) and BC (68-70,322 ng/m³) varied greatly across the study area. Presence in a downtown location was the strongest parameter in both models. Following this, the UFP model was strongly 12 influenced by wind speed (β= -0.247), temperature (β= -0.192), and distance from a restaurant 13 (β= -0.115), while the BC model was most influenced by the distance to the nearest highway (β= 14 -0.285), relative humidity (β=0.250), and congestion (β=0.133). No aggregate difference was 15 observed between in-street cycling and cycle tracks, however park trails had noticeably lower 16 pollution levels. This may be due in part to higher traffic density on streets with cycle tracks 17 offsetting reductions gained by their greater distance from tailpipe emissions.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".