Impact of Oxidant Gases on the Relationship between Outdoor Fine Particulate Air Pollution and Nonaccidental, Cardiovascular, and Respiratory Mortality
Bibliographic record
Abstract
Abstract Outdoor fine particulate air pollution (PM2.5) is known to increase mortality risk and is recognized as an important contributor to global disease burden. However, less is known about how oxidant gases may modify the chronic health effects of PM2.5. In this study, we examined how the oxidant capacity of O3 and NO2 (using a redox-weighted average, Ox) may modify the relationship between PM2.5 and mortality in the 2001 Canadian Census Health and Environment Cohort. In total, 2,448,500 people were followed over a 10.6-year period. Each 3.86 µg/m3 increase in PM2.5 was associated with nonaccidental (Hazard Ratio (HR) = 1.095, 95% CI: 1.077, 1.112), cardiovascular (HR = 1.088, 95% CI: 1.059, 1.118), and respiratory mortality (HR = 1.110, 95% CI: 1.051, 1.171) in the highest tertile of Ox whereas weaker/null associations were observed in the middle and lower tertiles. Analysis of joint non-linear concentration-response relationships for PM2.5 and Ox suggested threshold concentrations between approximately 23 and 25 ppb with Ox concentrations above these values strengthening PM2.5-mortality associations. Overall, our findings suggest that oxidant gases enhance the chronic health risks of PM2.5. In some areas, reductions in Ox concentrations may have the added benefit of reducing the public health impacts of PM2.5 even if mass concentrations remain unchanged.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".