Coarse Particulate Air Pollution and Daily Mortality: A Global Study in 205 Cities
Bibliographic record
Abstract
Abstract Rationale The associations between ambient coarse particulate matter (PM2.5–10) and daily mortality are not fully understood on a global scale. Objectives To evaluate the short-term associations between PM2.5–10 and total, cardiovascular, and respiratory mortality across multiple countries/regions worldwide. Methods We collected daily mortality (total, cardiovascular, and respiratory) and air pollution data from 205 cities in 20 countries/regions. Concentrations of PM2.5–10 were computed as the difference between inhalable and fine PM. A two-stage time-series analytic approach was applied, with overdispersed generalized linear models and multilevel meta-analysis. We fitted two-pollutant models to test the independent effect of PM2.5–10 from copollutants (fine PM, nitrogen dioxide, sulfur dioxide, ozone, and carbon monoxide). Exposure–response relationship curves were pooled, and regional analyses were conducted. Measurements and Main Results A 10 μg/m3 increase in PM2.5–10 concentration on lag 0–1 day was associated with increments of 0.51% (95% confidence interval [CI], 0.18%–0.84%), 0.43% (95% CI, 0.15%–0.71%), and 0.41% (95% CI, 0.06%–0.77%) in total, cardiovascular, and respiratory mortality, respectively. The associations varied by country and region. These associations were robust to adjustment by all copollutants in two-pollutant models, especially for PM2.5. The exposure–response curves for total, cardiovascular, and respiratory mortality were positive, with steeper slopes at lower exposure ranges and without discernible thresholds. Conclusions This study provides novel global evidence on the robust and independent associations between short-term exposure to ambient PM2.5–10 and total, cardiovascular, and respiratory mortality, suggesting the need to establish a unique guideline or regulatory limit for daily concentrations of PM2.5–10.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".