MULTIPOLLUTANT ASSESSMENT OF SHORT-TERM MORTALITY EFFECTS OF FINE PARTICULATE MATTER, ITS CHEMICAL CONSTITUENTS AND GASEOUS POLLUTANTS IN U.S. CITIES.
Bibliographic record
Abstract
Background and Aims: U.S., European, and Canadian multi-city studies have shown relatively consistent short-term mortality effects of particulate matter (PM) and ozone. However, there is less information available for the effects of other gaseous pollutants and the role of PM chemical components. The objective of this study was to identify specific characteristics (e.g., source type) of the air pollution mixture in major U.S. cities. Methods: We assembled and analyzed mortality and air pollution data for 64 major U.S. cities where fine particulate matter (PM2.5), key PM2.5 chemical constituents, ozone (O3), nitrogen dioxide (NO2), carbon monoxide, and sulfur dioxide were all available for the years 2001-2006. In addition to analyzing individual pollutants, we conducted factor analysis with PM2.5 chemical constituents and gaseous pollutants. All-cause mortality risk estimates for the pollutants at lag 0 through 3 days were estimated using Poisson regression models in individual cities, adjusting for temporal trends, immediate and delayed temperature, and day of week. Risk estimates from individual cities were combined in a second-stage random effects model. Results: Of the criteria pollutants, PM2.5, NO2, and O3 were each associated with all-cause daily deaths, with NO2 showing the strongest association (e.g., percent excess death of 0.33% [95%CI: 0.17, 0.49] per 10 ppb increase in 24-hr average at lag 1 day). Factor analysis yielded several components that could be interpreted as traffic (EC, OC, NO2), soil (Al, Si), metals (Pb, Zn), coal (As, Se), sea salt (Na, Cl), and residual oil (Ni, V). Soil and traffic factors showed significant or nearly significant associations with mortality, with magnitudes similar to those for the criteria pollutants per comparable distributional increment, despite the smaller sample size. Conclusion: Both regional and local pollutants contribute to short-term mortality effects. Acknowledgement: This research supported by the Health Effects Institute’s National Particle Component Toxicity Initiative.
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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 | 0.001 |
| 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".