Unveilingthe Impact of Wildfires on NanoparticleCharacteristics and Exposure Disparities through Mobile and Fixed-SiteMonitoring in Toronto, Canada
Bibliographic record
Abstract
This study investigates the impacts of wildfires on nanoparticle characteristics and exposure disparities in Toronto, integrating data from a large-scale mobile monitoring campaign and fixed-site measurements during the unprecedented 2023 wildfire season. Our results reveal changes in particle characteristics during wildfire days, with particle number concentrations decreasing by 60% and particle diameter increasing by 30% compared to nonwildfire days. Moreover, the median lung deposited surface area (LDSA) levels rose by 31% during wildfire events. We employed gradient boosting models to estimate near-road LDSA levels on both wildfire and nonwildfire days. The LDSA ratio (wildfire/nonwildfire) exceeded 2.0 in certain areas along highways and in downtown Toronto. Furthermore, our findings show that marginalized communities faced greater LDSA increases than less marginalized ones. Under wildfire conditions, the LDSA ratio difference between the most and least marginalized groups was 16% for recent immigrants and visible minorities and 7% for seniors and children, both statistically significant. This study delivers critical insights into the spatiotemporal variations of nanoparticle characteristics during wildfire and nonwildfire periods, demonstrating the substantial health risks posed by increased LDSA levels and the inequitable distribution of these risks among Toronto’s diverse population.
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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) | 0.000 | 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".