Long-range PM2.5 pollution and health impacts from the 2023 Canadian wildfires
Bibliographic record
Abstract
Smoke from extreme wildfires in Canada adversely affected air quality in many regions in 20231,2. Here we use satellite observations, machine learning and a chemical transport model to quantify global and regional PM2.5 (particulate matter less than 2.5 μm in diameter) exposure and human health impacts related to the 2023 Canadian wildfires. We find that the fires increased annual PM2.5 exposure worldwide by 0.17 μg m–3 (95% confidence interval, 0.09–0.26 μg m–3). North America had the largest increase in annual mean exposure (1.08 μg m–3; 0.82–1.34 μg m–3), but there were also increases in Europe (0.41 μg m–3; 0.32–0.50 μg m–3) owing to long-range transport. Annual mean PM2.5 exposure in Canada increased by 3.82 μg m–3 (3.00–4.64 μg m–3). In the USA, the contribution of the Canadian fires to increased PM2.5 was 1.49 μg m–3 (1.22–1.77 μg m–3), four times as large as the contribution from the 2023 wildfires originating in the USA. We find that 354 million (277–421 million) people in North America and Europe were exposed to daily PM2.5 air pollution caused by Canadian wildfires in 2023. We estimate that 5,400 (3,400–7,400) acute deaths in North America and 64,300 (37,800–90,900) chronic deaths in North America and Europe were attributable to PM2.5 exposure to the 2023 Canadian wildfires. Our results highlight the far-reaching PM2.5 pollution and health burden that large wildfires can have in a single year. A modelling study based on satellite observations, machine learning and a chemical transport model quantifies the global and regional exposure to particulate-matter pollution and the human health impacts related to the 2023 Canadian wildfires.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".