Aged and Obscured Wildfire Smoke Associated with Downwind Health Risks
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Fine-mode particulate matter (PM 2.5 ) is a highly detrimental air pollutant, regulated without regard for chemical composition and a chief component of wildfire smoke. As wildfire activity increases with climate change, its growing continental influence necessitates multidisciplinary research to examine smoke’s evolving chemical composition far downwind and connect chemical composition-based source apportionment to potential health effects. Leveraging advanced real-time speciated PM 2.5 measurements, including an aerosol chemical speciation monitor in conjunction with source apportionment and health risk assessments, we quantified the stark pollution enhancements during peak Canadian wildfire smoke transport to New York City over June 6–9, 2023. Interestingly, we also observed lower-intensity, but frequent, multiday wildfire smoke episodes during May–June 2023, which risk exposure misclassification as generic aged organic PM 2.5 via aerosol mass spectrometry given its extensive chemical transformations during 1 to 6+ days of transport. Total smoke-related organic PM 2.5 showed significant associations with asthma exacerbations, and estimates of in-lung oxidative stress were enhanced with chemical aging, collectively demonstrating elevated health risks with increasingly frequent smoke episodes. These results show that avoiding underestimated aged biomass burning PM 2.5 contributions, especially outside of peak episodes, necessitates real-time chemically resolved PM 2.5 monitoring to enable next-generation health studies, models, and policy under far-reaching wildfire impacts in the 21st century.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".