Simulating Risk of Exposure to Traffic-related Air Pollution in Working and Non-working Populations
Bibliographic record
Abstract
P-662 Introduction: Individual-level exposure assessments used for large population epidemiological analyses or risk assessments of air pollution health effects most often use pollution levels at residential address to indicate exposure. For individuals working away from home or attending school, using pollution levels at residential location only may cause exposure misclassification and undermine subsequent analyses. A spatial exposure simulation model (SESM) was used to investigate the effects of commuting on exposure in working and non-working populations in Vancouver, British Columbia as part of the Border Air Quality Study. Methods: The SESM simulates working and non-working populations in census tracts by randomly sampling from time-activity patterns and distributions of pollution levels in six microenvironments: indoor residential, indoor other, outdoor, indoor work, commute, and other travel. Unlike other simulation models using this approach, the SESM uses a geographic information system to create the distributions for sampling, and provides output for each census tract in the study area rather than one result for the entire region. We use census data to identify commute destinations for each census tract. Exposure to nitrogen dioxide (NO2) was simulated for one 24-hour period, as it indicates traffic-related air pollution and there is a consistent gradient from high (urban core) to low (suburban fringe) in our study area. Results: The SESM results show that for the day simulated, workers have a different risk of exposure to ambient NO2 than non-workers living in the same census tract, and that risk may be higher or lower depending on geographic location. Exposure risk in the urban core is higher overall, but, for example, 75 percent of the workers in a census tract located in the urban core have a lower risk of exposure than non-workers in the same census tract, while 60 percent of workers in a suburban census tract have higher risk compared to non-workers (Figure 1). Male and female non-workers do not show significant differences in risk of exposure in any census tract. Figure 2 shows the difference in exposure risk at the 90th percentile between workers and non-workers for selected census tracts.FigureFigureDiscussion: Using pollution levels at residential location introduces significant exposure misclassification for working populations. Nonworking populations may also be misclassified to a lesser degree. The SESM provides map-able results for every census tract in the study area, thus highlighting where misclassification errors occur, the spatial distribution of exposure risk, and where targeted policy may have the greatest impact. Figure 1. Cumulative frequency distribution for a census tract 20 km away from the urban and a census tract in the urban core Figure 2. Map showing the difference in exposure risk at the 90th percentile between workers and nonworkers for selected census tracts
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".