Spatial Relationships Between Socio-Economic Status And Sources of Hazardous Air Pollution in the City of Toronto, Canada
Bibliographic record
Abstract
This research paper identifies and characterizes areas in the City of Toronto that may be impacted by facilities that emit air pollutants. The impacted areas were isolated using a combination of K-Means cluster analysis and kernel density estimation to determine whether disparity in socio-economic status can be correlated with the location of these facilities. Dissemination Area (DA) level data from the 2006 Canadian Census were evaluated against pollution data provided by Environment Canada’s 2006 National Pollutant Release Inventory (NPRI) database. A total of 67 socio-economic variables from the 2006 Statistics Canada census were analysed. The City of Toronto’s 3,577 DAs were assigned to one of two cluster groups: Underprivileged Areas, or Areas of Affluence. The DAs represented by the two cluster groups were then analyzed alongside the NPRI pollution data, which had been developed into generalized concentration ranges using a 5km search radius. Although the Areas of Affluence cluster contains 15% more facilities (141) than the Underprivileged Areas (104), the latter are generally impacted by higher concentrations of a more diverse range of pollution. For example, a larger percent of Toronto residents living in Underprivileged Areas are exposed to the highest concentrations of total emissions, heavy metals, miscellaneous compounds, and non-carcinogenic emissions when compared to the population of DAs designated as Areas of Affluence. Conversely, a larger percent of residents living in Areas of Affluence are generally exposed to the highest concentrations of volatile organic compounds and carcinogenic emissions. The findings suggest an environmental justice concern, with respect to industrial air pollution within the City of Toronto.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".