Impacts of the 2023 Canadian Wildfires on the Oxidative Potential of Particulate Matter
Bibliographic record
Abstract
Particulate matter (PM) from wildfire (WF) smoke is increasingly impacting air quality. However, knowledge about its adverse health effects is limited, particularly regarding its oxidative properties relative to other PM types. In 2023, Canada experienced its worst recorded WF season, with smoke strongly impacting air quality across North America. Here we collected ambient PM samples from two urban (Toronto and Champaign) and one rural (East Trout Lake) sites in North America in 2023 and assessed their oxidative potential (OP; mass-normalized) and burden (OB; volume-normalized) using the dithiothreitol (DTT) assay. Our results indicate that OP of urban WF PM was not greater than non-WF PM. At the rural site, WF PM showed slightly higher OP than background PM, which may be related to a lesser extent of atmospheric aging of WF PM, or low OP of background PM in rural areas. During WF-influenced periods in Toronto, mass fractions of OP-active metals were significantly lower, but there was no significant difference in OP, indicating the importance of nonmetallic components to OP. Across all sites, OB was greater during WF periods, driven by elevated PM concentrations. Our findings suggest that while the OP of WF PM is similar to urban PM, higher PM concentrations during WF episodes can increase exposure to potentially harmful DTT-active species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".