Gender Differences in Chronic Exposure to Traffic-Related Air Pollution—A Simulation Study of Working Females and Males
Bibliographic record
Abstract
The objective of this study was to identify spatial variability in exposure to outdoor traffic-related air pollution with specific emphasis on comparing exposure estimates for working females and males across a metropolitan area. A spatial exposure simulation model was used to estimate annual average exposure to traffic-related nitrogen dioxide for males and females reporting regular work in census tracts other than that of their residence, in Vancouver, British Columbia. The model produced estimates of annual average exposure in six microenvironments (home indoor, work indoor, other indoor, outdoor, in-vehicle to work, and in-vehicle other) using time-activity patterns and work flow data, for males and females in each of 382 census tracts. This allowed for the identification of spatial variations in exposure estimates for each gender, due to mobility within the study region. Indoor sources of nitrogen dioxide were not included in the simulation. No significant differences in estimated total exposure were found between working females and males in general. Small but observable spatial differences, however, were found between working females and males at the 90th percentile of the exposure distributions associated specifically with the work indoor microenvironment. These were highest in suburban areas (+3 μ g/m3 for females, relative to total exposures in the range of 26 to 37 μ g/m3 annual average hourly nitrogen dioxide). These results identify specific geographic locations in the study area where personal monitoring studies might be warranted and suggest that the inclusion of workplace locations in multivariate modeling could be useful to further understand differences in estimated exposures.
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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 | 0.001 |
| 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) | 0.001 | 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".