Double Burden of Deprivation and High Concentrations of Ambient Air Pollution at the Neighbourhood Scale in Montreal, Canada
Bibliographic record
Abstract
ISEE-0050 Background and Objective: Some neighbourhoods in urban areas are characterised by concentrations of socially and materially deprived populations. Additionally, levels of ambient air pollution can be variable at the local scale, creating disparities in air quality between neighbourhoods. Socioeconomic and physical characteristics of neighbourhood environments can affect the health and well-being of local residents. We identify whether neighbourhoods in Montreal characterised by social and material deprivation have higher levels of ambient air pollution than do others. Methods: We collected two-week integrated samples of nitrogen dioxide (NO2) at 133 sites across Montreal during three seasons between 2005 and 2006. We used these data in a geographic information system, along with data describing characteristics of land use, roads, and traffic, to create a spatial model of predicted mean annual concentrations of NO2 across Montreal. Next, we collected neighbourhood socioeconomic information for 501 census tracts and overlaid their boundaries on the pollution surface. We calculated Pearson correlation coefficients and 95% confidence intervals (CI) between neighbourhood-level indicators of deprivation and levels of pollution. Results: We found associations between NO2 and neighbourhood-level indicators of material deprivation, including median household income (r = −0.38, 95% CI: −0.45- −0.30), and with indicators of social deprivation, including proportion of people living alone (r = 0.46, 95% CI: 0.39-0.53). We identified specific neighbourhoods characterised by a double burden of high levels of deprivation and high concentrations of ambient NO2. Conclusion: Because of the unique social geography in Montreal, we found that not all deprived neighbourhoods had high levels of pollution and that some affluent neighbourhoods in the downtown core had high levels. Our results underscore the importance of considering social contexts in interpreting general associations between social and environmental risks to population health.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".