Environmental Injustice in Peel Region: A Cross-Sectional Analysis of Air Pollution and Social Vulnerability
Bibliographic record
Abstract
Background: Nitrogen dioxide (NO2), a traffic-related air pollutant, is co-emitted with greenhouse gasses that contribute to climate change and negatively affect health. This research examines the relationship between air pollution exposure and social vulnerability to determine whether communities at a social disadvantage experience environmental injustice. Methods: The 2016 census measures for Peel Region were assessed. Peel Region has 1,381,739 people across Mississauga, Brampton, and Caledon; the spatial unit of analysis was the dissemination area (DA) with an average size of 837 people in Peel. Average ambient NO2 concentrations for 2016 came from the Canadian Urban Environmental Health Research Consortium and resampled to DAs. Social disadvantage was defined by the Ontario Marginalization Index (ON-Marg). Measures within index components came from the Canadian census. Environmental injustice was identified through correlation analysis between ON-Marg and NO2 exposure and an analysis of index components with exposure. Results: The 2016 annual NO2 concentrations were positively correlated with the overall ON-Marg quintiles (Spearman: 0.34, p < .001, 95% CI 0.29 to 0.37). The components and their correlations with NO2 were residential instability (Spearman: 0.37, p < .001, 95% CI 0.33 to 0.42), material deprivation (Spearman: 0.15, p < .001, 95% CI 0.10 to 0.19), dependency (Spearman: 0.31, p < .001, 95% CI 0.26 to 0.35), and ethnic concentration (Spearman: −0.06, p = .02, 95% CI −0.11 to −0.01). Conclusions: In Peel Region, a correlation exists between social vulnerability and air pollution, indicating inequitable exposure to NO2. Marginalized communities may not be able to choose their living environment. Environmental justice research in the context of NO2 will support policy aiming to reduce inequitable exposure.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".