Examining the social distributions in neighbourhood black carbon and ultrafine particles in Montreal and Toronto, Canada
Bibliographic record
Abstract
• Outdoor BC and UFP concentrations exhibited high within-cities spatial variations. • Inequities in BC and UFP distributions were low to moderate and context dependent. • High priority areas were adjacent to local sources of BC and UFP. Socioeconomic inequities in outdoor ultrafine particles (UFP) and black carbon (BC) are understudied in Canada, where metropoles like Montreal and Toronto feature distinct sociodemographic diversity and urban characteristics compared to U.S. cities. We collected vulnerability indicators, including social, economic, household composition, and immigration status, at the dissemination area level for Montreal and Toronto using data from the 2006 and 2021 Canadian Census of Population. Areas were classified as disadvantaged, intermediate, or advantaged following K-means clustering analysis. We aggregated and calculated population-weighted average concentrations of BC and UFP, and UFP size at the dissemination area and cluster levels using high-resolution exposure surfaces, derived from year-long mobile monitoring campaigns conducted in each city during 2020–2021. Final exposure surfaces were generated by integrating predictions from land-use regression models and deep convolutional neural network models. We observed high within-city variations in aggregated air pollutant levels, with higher outdoor BC and UFP concentrations and smaller UFP sizes in areas near local sources such as major roads, railways, airports, and densely populated regions. Advantaged areas experienced the lowest median UFP concentrations in both Montreal (10,707 pt/cm 3 ) and Toronto (10,988 pt/cm 3 ), as well as the lowest BC concentrations (650 ng/m 3 ) in Montreal. The highest median UFP concentrations were observed in intermediate areas in Montreal (15,709 pt/cm 3 ) and disadvantaged areas in Toronto (12,228 pt/cm 3 ). Conversely, the highest BC concentrations were observed in disadvantaged and intermediate areas in Montreal (805–811 ng/m 3 ), and disadvantaged and advantaged areas in Toronto (1,228–1,252 ng/m 3 ). Notably, high priority areas for the double burden of vulnerability and high BC and UFP concentrations were located near air pollutants local emission sources. Our findings highlight the importance of prioritizing exposure mitigation for populations residing near local sources and to understand contextual factors influencing inequities across cities and pollutants.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".