Intra-Continental Transport of Western Wildfire Smoke Heightens Health Risks Across North America
Bibliographic record
Abstract
Wildfires in North America, particularly in western states, have caused widespread environmental, economic, social, and health impacts. Smoke from these fires travels long distances, spreading pollutants and worsening the air quality across continents. Vulnerable groups, such as children, the elderly, and those with preexisting conditions, face heightened health risks, as do firefighters working in extreme conditions. Wildfire firefighters are of particular concern as they are fighting fires in extreme conditions with minimal protective equipment. This study examined wildfire smoke during July–August 2021, when intense fires in Canada and the western U.S. led to cross-continental smoke transport and caused significant impacts on the air quality across North America. Using the GEOS-Chem model, we simulated the transport and distribution of PM2.5 (particulate matter with a diameter of 2.5 μm or smaller), identifying significant carcinogenic risks for adults, children, and firefighters using dosimetry risk methodologies established by the U.S. EPA. Significant carcinogenic risks for adult, child, and firefighter populations due to exposure to PM2.5 were identified over the two-month period of evaluation. The findings emphasize the need for future studies to assess the toxic chemical mixtures in wildfire smoke and consider the risks to underrepresented communities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".