Reducing Exposures to Traffic-Related Air Pollution in Urban Areas: Regional Planning, Neighborhood Design, and Individual Behavior
Bibliographic record
Abstract
The assessment of exposure to traffic-related air pollution has seen advances along various dimensions. Air pollution dispersion models with fine spatial resolution and ability to reflect near-road air quality in street canyons, have made possible the development of exposure surfaces associated with strategic long-range scenarios affecting land-use and transportation in urban areas. Meanwhile, personal monitoring and GPS-enabled applications have motivated the development of a wide range of tools intended to inform users of their own exposure and ways to reduce it. While scientific evidence points to the success of these tools in identifying measures, at the policy or personal level, able to reduce air pollution exposures, there much left to learn about the impact of these tools on human behavior. How do policy makers use scenario-based information on air quality to formulate policy decisions? And how do individuals respond to air quality information provided to them? Are there individuals more inclined to respond to exposure reduction advice in a positive manner? This presentation will detail how high resolution air pollution data has informed a range of interventions to reduce traffic-related air pollution exposures in Canadian cities, from clean routes applications to large investments in transportation infrastructures and urban design. New evidence will also be presented from a stated-preference experiment examining how air pollution information impacts individual behavior.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".