The synergistic effects of PM2.5 and high temperature on community mortality in British Columbia
Bibliographic record
Abstract
Abstract Exposure to fine particulate matter (PM2.5), a primary component of wildfire smoke, and exposure to high temperatures both independently increase the risk of mortality, with evidence of synergistic effects. These environmental stressors often co-occur during wildfire season, and their synergistic effects are expected to worsen with climate change. However, the combined health risks of wildfire-related PM2.5 and temperature remain poorly understood, limiting the effectiveness of public health interventions. This study investigated the joint effects of PM2.5 and temperature on community all-cause mortality across 13 wildfire seasons (2010–2022) in southwest British Columbia, Canada. Daily estimates of ambient PM2.5 and temperature exposure were assigned from a machine learning-based prediction model and satellite data, respectively. Using a case-crossover design and conditional logistic regression, we examined non-linear associations between co-exposures and mortality across deciles and absolute exposure ranges. We found significant, non-linear interactions, with the highest mortality risk observed on days with PM2.5 levels of 12–14 µg/m³ and temperatures ≥ 26 °C (OR = 7.31, 95% CI: [5.34, 10.0]). Co-exposures exceeding the 90th percentile showed synergistic effects, contributing an excess mortality risk of 7.9% (95% CI: 6.1, 9.1). Moderate co-exposure levels posed substantial risks, underscoring the need for greater attention to their public health impacts. Our findings highlight the urgent need to understand and address the compounding effects of PM2.5 and temperature to mitigate risks in a changing climate.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".