Physicochemical Characterization of the Particulate Matter in New Jersey/New York City Area, Resulting from the Canadian Quebec Wildfires in June 2023
Bibliographic record
Abstract
The global increase in wildfires, primarily driven by climate change, significantly affects air quality and health. Wildfire-emitted particulate matter (WFPM) is linked to adverse health effects, yet the toxicological mechanisms are not fully understood given its physicochemical complexity and the lack of spatiotemporal exposure data. This study focuses on the physicochemical characterization of WFPM from a Canadian wildfire in June 2023, which affected over 100 million people in the US Northeast, particularly around New Jersey/New York. Aerosol systems were deployed to characterize WFPM during the 3 day event, revealing unprecedented mass concentrations mainly in the WFPM 0.1 and WFPM 0.1–2.5 size fractions. Peak WFPM 2.5 concentrations reached 317 μg/m 3, nearly 10 times the National Ambient Air Quality Standard (NAAQS) 24 h average limit. Chemical analysis showed a high organic-to-total carbon ratio (96%), consistent with brown carbon wildfires nanoparticles. Large concentrations of high-molecular-weight PAHs were found predominantly bound to WFPM 0.1, with retene, a molecular marker of biomass burning and a known teratogen, being the most abundant (>70%). Computational modeling estimated a total lung deposition of 9.15 mg over 72 h, highlighting the health risks of WFPM, particularly due to its long-distance travel capability and impact on densely populated areas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".