Clearing the air: Which pollution source matters most for health?
Bibliographic record
Abstract
Abstract Objectives Air pollution remains a significant public health challenge, contributing to substantial morbidity and mortality. The aim of this study is to identify effective intervention strategies for mitigating air pollution and its health effects in southern Quebec. Methods We employ the Polair3D chemical transport model to estimate population-weighted concentrations of fine particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) at the census division level under four scenarios: replacing residential wood stoves with U.S. Environmental Protection Agency (EPA)–certified models (EPA), eliminating industrial emissions (IND), full vehicle electrification (EV), and removing emissions from refineries and smelters (RS). Health impacts were quantified with the Air Quality Benefits Assessment Tool for chronic PM 2.5 exposure and chronic NO 2 exposure, in addition to the commonly assessed acute NO 2 exposure and acute O 3 exposure. Results All scenarios reduced air pollutant concentrations and associated mortality to varying degrees. The EPA and EV interventions are the most effective in reducing mortality, lowering deaths attributable to pollutants by 15.26% (789 deaths from 5169 to 4380) and 16.13% (834 deaths from 5169 to 4335), respectively. The EPA scenario yields the greatest reduction in PM 2.5 -related mortality, while the EV scenario provides the most benefit for NO 2 -related mortality. Conclusion Targeted interventions can significantly reduce air pollution-related mortality. Replacing residential wood stoves and fully electrifying vehicles are particularly effective, with distinct benefits for PM 2.5 - and chronic NO 2 -related health outcomes. A multi-sectoral approach is essential to maximize public health gains.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".