Cumulative air pollution indicators highlight unique patterns of injustice in urban Canada
Bibliographic record
Abstract
Disparities in air pollution exposure are a form of distributional environmental injustice that has been documented in many jurisdictions around the world. In Canada, although there is a growing literature characterizing exposure inequalities, an important gap is research that captures the cumulative impact of the multiple air pollutants to which communities are exposed. Here, we present a screening-level analysis of inequalities in single pollutant and cumulative air pollution burdens in three major cities in Canada: Toronto, Montreal, and Vancouver. We construct three cumulative hazard indices (CHIs), using previously published national datasets for PM 2.5 , NO 2 , SO 2 , and O 3 concentrations for illustrative year 2012. We describe the ways in which patterns of inequality differ between pollutants, between ways of calculating cumulative burden, and between cities. Different methods of constructing CHIs can yield different understandings of the spatial distribution of pollution, and in turn, inequality. We find the largest spatial variations for a CHI based on whether each pollutant exceeds an external benchmark (here, air quality guidelines), which translates into the largest calculated disparities in cumulative air pollution burdens for marginalized groups. We observe distinct patterns of inequality between the cities, in terms of which marginalized groups consistently experience higher cumulative air pollution burdens (Vancouver: Indigenous residents, Montreal: immigrant residents, Toronto: low-income residents). Results also highlight the importance of using a suite of socio-demographic indicators as patterns can differ between individual racialized/ethnic groups, and between different measures of socio-economic status. This work illustrates how a range of cumulative hazard screening indicators could be used in a policy context in Canada and elsewhere, while highlighting some of the methodological complexities in how environmental and social risks are characterized and combined. Given these complexities, we suggest that community input should inform the design of environmental justice indicators, as an important component of procedural justice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".