Associations Between Socioeconomic Status and Air Pollution Exposure in Canadian Cities: Implications for Environmental Justice and Epidemiological Research
Bibliographic record
Abstract
PP-31-033 Background/Aims: Research examining the association between socioeconomic status (SES) and exposure to air pollution across multiple cities will improve our understanding of the variability in, and potential determinants of, environmental inequities. Here we report a national study that explores the association between SES and proximity to air pollution sources in 144 Canadian metropolitan areas, as well as between SES and concentrations of nitrogen dioxide (NO2) levels derived from land use regression models developed previously for a subset of 7 cities. Methods: SES variables were compiled from census data for 41,485 dissemination areas (DAs), representing 400–700 individuals per DA. Air pollution exposures were calculated for block points within each DA, representing approximately 123 individuals per block. SES surrogates included 8 census variables broadly covering social and material deprivation. Air pollution indicators included proximity to major roads, industrial land use, and point source emission sources. Concentrations of NO2 were extracted from land use regression models available for 7 cities (Victoria, Vancouver, Edmonton, Winnipeg, Toronto, Sarnia, and Montreal). Analyses included Spearman/Pearson correlations, loess plots, and multiple logistic regression with a smoothing function to account for spatial autocorrelation. Results: Significant associations were found between SES and air pollution indicators across all 144 Canadian metropolitan areas, but large between-city variations exist. Similar results were found using NO2 estimates for 7 large cities. For example, the likelihood of a DA being in the bottom 10th percentile of median household income in Vancouver and Montreal increased by 1.81 (95% CI: 1.72–1.90) and 2.78 (95% CI: 2.44–3.19) times, respectively, with each 5 ppb increase in NO2. Current analyses are examining potential city and neighborhood level determinants of these environmental inequities. Conclusion: SES is associated with higher exposures to air pollution in several Canadian cities; however, the magnitude of these associations vary. Documenting and explaining this variation has important implications from both an environmental injustice and epidemiological perspective.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".