Reducing Exposures to Traffic-Related Air Pollution in Urban Areas: Regional Planning, Neighborhood Design, and Individual Behavior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it