Spatiotemporal Trends and Environmental Inequity of Polycyclic Aromatic Hydrocarbon (PAH) Air Pollutants
Bibliographic record
Abstract
Hamilton, Ontario, an industrial city in Canada, experiences high pollutant concentrations and poor air quality, leading to adverse health outcomes. This study examined the spatial and temporal dynamics of polycyclic aromatic hydrocarbons (PAHs) in Hamilton and their relation to environmental justice and gentrification using field sampled data from 2009 and 2022-2023, the Ontario Marginalization Index, and the Canadian Census. Evidence suggests that in Hamilton, PAHs are increasing and are more concentrated where marginalized communities live. Land use regression (LUR) prediction surfaces showed that annually, 2022 - 2023 PAH values and cancer risks were higher than those in 2009. Seasonally, winter PAH concentrations exceeded those of summer. While the spatial patterns of LUR predictions were generally consistent, the spatial pattern of summer 2009 concentrations was distinct from other seasons. Correlation analysis indicated worsening environmental justice, with stronger links between PAH concentrations and marginalization in 2022-2023 than in 2009, driven by socioeconomic status more than demographics. PAH levels were significantly higher in gentrified communities compared to non-gentrified communities. These findings enhance understanding of PAH-related environmental justice in Hamilton from the past to the present, highlighting the interplay between pollution, socioeconomic factors, and urban form.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".