Investigating disparities in air pollution exposure in Canada : from the local to the national scale
Bibliographic record
Abstract
Research to quantify and mitigate inequities in exposure to environmental risk is increasing around the world. However, in Canada, gaps remain in understanding around how to reduce air pollution exposure inequities effectively and efficiently. This thesis aims to help address these gaps through two national-scale and one local-scale study, that provide insight into exposure patterns, potential drivers, and how different sources of data, modelling, and measurement tools can be used to inform action. Chapter 3 investigates disproportionality in industrial emissions of PM₂.₅ and PM₂.₅ precursors in Canada due to the sector’s significant contribution to air pollution and its regulatory potential. The National Pollutant Release Inventory is used to determine whether industrial facilities in general, as well as the highest emitting facilities, are disproportionately sited near sociodemographic groups that are potentially vulnerable to pollution exposure. The chapter identifies several subsectors with high disproportionality in emissions, with disproportionate siting patterns generally varying across the urban/rural divide, as well as between individual facilities and facility clusters. Chapter 4 extends this work to focus on disparities in industrial PM₂.₅ exposure using a reduced complexity model. Findings show that urban Indigenous populations are exposed more than the total population, while in rural areas settlement patterns and model uncertainty make distinguishing disparities more complicated. Results from modeling scenarios suggest that urban and rural Indigenous populations are disproportionately exposed to PM₂.₅ from the highest emitting facilities. These chapters support the inclusion of equity in future policy planning and the need for more stringent permitting, more continuous emissions monitoring, and stricter permit enforcement in Canada. Chapter 5 uses community knowledge to add value to a community-scale air quality study in the Strathcona neighborhood in Vancouver, BC. Strathcona is home to both significant emissions sources and higher populations of Indigenous, low-income, and unhoused people than other Vancouver neighborhoods. A network of 11 low-cost sensors was deployed to measure PM₂.₅, NO, NO₂, and O₃ and a variety of community engagement and knowledge solicitations were conducted. This pilot study demonstrates methods to incorporate this often-qualitative data into a local Land Use Regression and peak analysis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".