Inequities in ambient fine particulate matter: A spatiotemporal analysis in Canadian communities
Bibliographic record
Abstract
Exposure to fine particulate matter (PM2.5) is associated with adverse health outcomes but communities are not randomly exposed to PM2.5. Previous cross-sectional environmental injustice analyses in Canada found disproportionately higher exposure to PM2.5 in low-income populations, visible minorities and immigrants. Beyond static surveillance, it is also important to evaluate how changes in PM2.5 exposure over time may differentially impact disadvantaged communities. We examine whether communities with different sociodemographic characteristics benefited equitably from the overall decreases in ambient concentrations of PM2.5 from 2001 to 2016 in Canada. We derived census tract level estimates of average annual PM2.5 using validated satellite-based estimations of annual average PM2.5 concentration surfaces. We investigated how the spatial distribution of PM2.5 has evolved over 15 years (2001–2016) by comparing absolute values and rank percentiles of census tract level annual average PM2.5 concentrations in 2001 and 2016. Using decennial census data and multivariable linear regression, we determined if sociodemographic characteristics are associated with changes in exposure to PM2.5, accounting for geographic boundary changes between census periods. Overall, ambient PM2.5 concentrations decreased from 2001 (median of 9.1 μg/m3) to 2016 (median of 6.4 μg/m3), with varying provincial patterns. Across communities, ranked census tract specific PM2.5 in 2001 and in 2016 are highly correlated (Spearman's rho = 0.75). We found that, on average and accounting for provincial differences and baseline PM2.5, communities with greater density of aboriginal population, lower education, higher shelter-cost-to-income ratio, unemployment or lower income experienced smaller absolute decreases in PM2.5 from 2001 to 2016. Identifying sociodemographic groups that benefit least from decreasing exposure to PM2.5 highlights the need to consider environmental injustice when designing or revising air pollution policies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".