Inequities in exposure to ambient fine particulate matter in Canada
Bibliographic record
Abstract
BACKGROUND AND AIM: Exposure to fine particulate matter (PM2.5) is associated with various adverse health outcomes. Previous cross-sectional analyses of environmental injustice in Canada found inequitable exposure to PM2.5 in low-income populations, visible minorities and immigrants. We expand on this literature by investigating if communities with different demographic characteristics benefit equitably from changes in ambient concentrations of PM2.5 from 2001 to 2016 in Canada. METHODS: We use census tract level estimates of average annual PM2.5 derived from satellite-based observations to investigate how the spatial distribution of PM2.5 has evolved over time. We use decennial census data to determine if demographic characteristics are associated with changes in exposure to PM2.5, accounting for geographic boundary changes between census periods. RESULTS:Ambient PM2.5 concentrations have decreased from 2001 (median of 9.1 μg/m³ across tracts) to 2016 (median of 6.4 μg/m³ across tracts), with varying provincial patterns. Across census tracts, ranked estimates of PM2.5 in 2001 and in 2016 are correlated (Spearman correlation coefficient = 0.75). Tracts with higher concentrations of PM2.5 in 2001 tend to remain among the most polluted tracts in 2016. Accounting for provincial differences and baseline PM2.5, census tracts with greater proportions of individuals with lower education, unemployed individuals or individuals who have lower income experience smaller absolute decreases in PM2.5 from 2001 to 2016. CONCLUSIONS:Identifying demographic groups that benefit least from changes in ambient concentrations of PM2.5 provides direction for research on reducing environmental injustice due to differential exposure. KEYWORDS: Environmental justice, Particulate matter, Socio-economic factors
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".