Integrating Atmospheric Physics and Spatiotemporal Machine Learning to Project Wildfire Smoke Exposure and Health Risks in Canada under Future Climate
Bibliographic record
Abstract
The catastrophic Canadian wildfire seasons of 2024-2025, including the devastating Jasper fire, underscore the escalating public health risk from wildfire smoke. Toronto and other Eastern Canadian cities repeatedly experienced the world's worst air quality during prolonged smoke episodes. Using machine learning, we project future wildfire activity and associated smoke exposure under SSP585 by deriving a joint spatiotemporal distribution of wildfire ignition, size, and smoke dispersion that explicitly accounts for regional correlation dependencies. Our framework integrates the Canadian National Fire Database, CMIP6-derived vapor pressure deficit (VPD), and quantile regression to distinguish the drivers of small, wind-driven events versus large, climate-driven fires. Preliminary results show a 35%-85% increase in population exposure to hazardous smoke (PM > 50 μg/m³) by mid-century, with Eastern Canada and U.S. cities most affected. These findings highlight urgent mitigation and adaptation needs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".