Identifying inequitable exposure to toxic air pollution in racialized and low-income neighbourhoods to support pollution prevention
Bibliographic record
Abstract
Numerous environmental justice studies have confirmed a relationship between population characteristics such as low-income or minority status and the location of environmental health hazards. However, studies of the health risks from exposure to harmful substances often do not consider their toxicological characteristics. We used two different methods, the unit-hazard and the distance-based approach, to evaluate demographic and socio-economic characteristics of the population residing near industrial facilities in the City of Toronto, Canada. In addition to the mass of air emissions obtained from the national pollutant release inventory (NPRI), we also considered their toxicity using toxic equivalency potential (TEP) scores. Results from the unit-hazard approach indicate no significant difference in the proportion of low-income individuals living in host versus non-host census tracts (t(107) = 0.3, P = 0.735). However, using the distance-based approach, the proportion of low-income individuals was significantly higher (+5.1%, t(522) = 6.0, P <0.001) in host tracts, while the indicator for "racialized" communities ("visible minority") was 16.1% greater (t(521) = 7.2, P <0.001) within 2 km of a NPRI facility. When the most toxic facilities by non-carcinogenic TEP score were selected, the rate of visible minorities living near the most toxic NPRI facilities was significantly higher (+12.9%, t(352) = 3.5, P = 0.001) than near all other NPRI facilities. TEP scores were also used to identify areas in Toronto that face a double burden of poverty and air toxics exposure in order to prioritise pollution prevention.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".