Probabilistic estimates of variability in exposure to traffic-related air pollution in the Greater Vancouver Regional District - a spatial perspective
Bibliographic record
Abstract
A probabilistic spatial exposure simulation model (SESM) was designed to investigate the effect of time spent at work and commuting on estimates of chronic exposure to traffic-related air pollution in large populations. The model produces distributions of exposure estimates in six microenvironments (home indoor, work indoor, other indoor, outdoor, transit to work and transit other) for workers and non-workers, using randomly sampled time-activity patterns from the Canadian Human Activity Pattern Survey and work flow data from Statistics Canada. The SESM incorporates geographic detail through the use of property assessment data, shortest route analysis, and the use of a geographic information system (GIS) to develop pollution concentration distributions. The SESM was implemented and tested using data for 382 census tracts in the Greater Vancouver Regional District of British Columbia. Simulation results were found to be relatively insensitive to the choice of distance used to represent the typical range of non-work related trips; the use of a simple annual average pollution estimate versus a time-stratified annual average; and the use of different indoor/outdoor ratios representing the infiltration of ambient pollution into indoor locations. Substantial sensitivity was observed based on the use of different methods for producing spatial estimates of ambient air pollution. The SESM was used to explore variability in annual total exposure of workers to traffic-related nitrogen dioxide (NO2). Total exposure ranged from 8 μg/m3 to 35 μg/m3 of iv annual average hourly NO2 and was highest where ambient pollution levels are highest, reflecting the regional gradient of pollution in the study area and the relatively high percentage of time spent at home locations. Within census tract variation was observed in the partial exposure estimates associated with time spent at work locations, particularly in suburban areas where longer commuting distances are more prevalent. In these areas, some workers may have exposures 1.3 times higher than other workers residing in the same census tract. Exposures to NO2 associated with the activity of commuting to work were negligible. No statistically significant difference in total exposure estimates was found between female and male commuters, although there were small but observable differences at the upper end of the exposure distributions associated specifically with the work indoor microenvironment. These differences were highest in suburban areas (up to 3 μg/m3 of annual hourly average NO2 higher for female commuters, in relation to 99th percentile total exposures levels of approximately 37 μg/m3), illustrating the impact of systematically different work locations for female compared to male commuters in these same census tracts. Simulated exposures for workers, non-workers, and a base scenario where all time is spent at the residence only were compared. Statistically significant differences were found in the exposure distributions for workers versus non-workers, workers versus residence only, and non-workers versus residence only. Differences in exposure within census tracts were highest at the 10th and 90th percentiles, on the order of -5.4 to +6.5 μg/m3 of annual average hourly NO2 respectively for workers compared to non-workers, in relation to exposure estimates between 10 and 40 μg/m3 of annual average hourly NO2 on average.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".