Neighborhood walkability and air pollution exposure
Bibliographic record
Abstract
Background: Residing in high-walkability neighborhoods may be associated with health benefits (increased physical activity) and risks (exposure to traffic-related air pollution). More research is needed to characterize the overall health impacts of residence in high- and low-walkability neighborhoods. Aim: To explore how ambient air pollution in the US differs by level of neighborhood walkability. Methods: We obtained year-2010 ambient monitor data for PM2.5, NOx, and O3 for all available EPA monitors. A walkability score was assigned to each monitor from a publicly available database. We used regression analysis to estimate the relationship between ambient air pollution concentrations and walkability while controlling for city-specific background air pollution concentrations. We used cities with 5 or more monitors to estimate air pollution concentrations for midpoint walkscores in high- and low-walkability neighborhoods. We then use health risk calculations to estimate the health impacts of residence in high- and low-walkability neighborhoods (i.e., incorporating both physical [in]activity and exposure to air pollution). Results: Air pollution concentrations differed between high- and low-walkability neighborhoods, with higher concentrations in high- vs. low-walkability neighborhoods for PM2.5, (8.2 vs. 7.7 µg/m3) and NOx (24.5 vs. 13.7 ppb), and with the reverse trend for O3 (50.1 ppb in high- vs. 51.2 ppb in low-walkability neighborhoods). Conclusions: Health risks from air pollution may be important when planning for compact, walkable urban areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".