From Traffic to Residential Heating: Modeling the Environmental Justice Implications of Sector Specific Air Pollution Policies
Bibliographic record
Abstract
Outdoor air pollution is a significant public health concern and is responsible for approximately 4 million premature deaths worldwide annually. The burden of exposure to air pollution is disproportionately borne by socially disadvantaged and marginalized communities, making air pollution a major focus of environmental justice research. Air pollution related environmental justice research aims to capture disparities in exposure to air pollution, understand their drivers, and determine equitable mitigation measures. In this dissertation, we present three novel approaches that aim to better quantify the impact of two major sources of air pollution in Canada, transportation and residential wood burning, and understand their environmental justice implications. In the first study, we spatially distribute emissions from light-duty vehicles and trucks in Toronto between 2006 and 2020. We then use these emissions to investigate whether these two vehicle fleets disproportionately impact marginalized communities and how patterns have changed over time given stricter emission standards. In the second study we develop a novel approach to downscale emission scenario outputs from Chemical Transport Models to a high-spatial resolution, for the purpose of capturing the burden of buses, cars, and trucks on air pollution exposure and environmental justice. This study also investigates whether the spatial resolution of exposure surfaces and the type of model used (Chemical Transport Models vs Land Use Regression) have an impact on environmental justice analyses. In the third study, we present a data-driven method to estimate and spatially distribute emissions from residential wood burning in Quebec and examine the influence of socioeconomic factors on wood burning usage. This dissertation makes significant contributions to the fields of air pollution and environmental justice by improving our understanding of how emissions from major sources are distributed and investigating how different factors such as spatial resolution, type of model used and accuracy of emission inventories might influence environmental justice analyses. This dissertation includes three unique studies investigating the impacts of specific sectors on environmental justice in Canada. By addressing gaps in the literature, this research aims to support the development of equitable strategies to mitigate air pollution exposure and advance environmental justice research and policy in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".