Fire Smoke Elevated the Carbonaceous PM<sub>2.5</sub> Concentration and Mortality Burden in the Contiguous U.S. and Southern Canada
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Despite emerging evidence on the health impacts of fine particulate matter (PM 2.5 ) from wildland fire smoke, the specific effects of PM 2.5 composition on health outcomes remain uncertain. We developed a three-level, chemical transport model-based framework to estimate daily full-coverage concentrations of smoke-derived carbonaceous PM 2.5, specifically organic carbon (OC) and elemental carbon (EC), at a 1 × 1 km 2 spatial resolution from 2002 to 2019 across the contiguous U.S. (CONUS) and Southern Canada (SC). A 10-fold random cross-validation confirmed robust performance, with daily R 2 = 0.77 (OC) and 0.80 (EC) in the smoke-off scenario and 0.67 (OC) and 0.71 (EC) in the smoke-on scenario, and exceeded 0.90 at the monthly scale after residual adjustment. Modeling results indicated that increases in wildland fire smoke have offset approximately one-third of the improvements in background air quality. In recent years, wildland fire smoke has become more frequent and carbonaceous PM 2.5 concentrations have intensified, especially in the Western CONUS and Southwestern Canada. Wildfire season is also starting earlier and lengthens throughout the year, leading to more population being exposed. We estimated that long-term exposure to fire smoke carbonaceous PM 2.5 is responsible for approximately 7455 and 259 non-accidental deaths annually in the CONUS and SC, respectively, with associated annual monetized damage of 68.3 billion USD for the CONUS and 1.9 billion CAD for SC. The Southeastern CONUS, where prescribed fires are prevalent, contributed most to these health impacts and monetized damages. Our findings offer critical insights to inform policy development and assess future health burdens associated with fire smoke exposure.
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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) | 0.000 | 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 | 0.000 |
| 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".