Air pollution mixture complexity and its effect on PM2.5-related mortality: A multicountry time-series study in 264 cities
Bibliographic record
Abstract
Background: Fine particulate matter (PM 2.5 ) occurs within a mixture of other pollutant gases that interact and impact its composition and toxicity. To characterize the local toxicity of PM 2.5 , it is useful to have an index that accounts for the whole pollutant mix, including gaseous pollutants. We consider a recently proposed pollutant mixture complexity index (PMCI) to evaluate to which extent it relates to PM 2.5 toxicity. Methods: The PMCI is constructed as an index spanning seven different pollutants, relative to the PM 2.5 levels. We consider a standard two-stage analysis using data from 264 cities in the Northern Hemisphere. The first stage estimates the city-specific relative risks between daily PM 2.5 and all-cause mortality, which are then pooled into a second-stage meta-regression model with which we estimate the effect modification from the PMCI. Results: We estimate a relative excess risk of 1.0042 (95% confidence interval: 1.0023, 1.0061) for an interquartile range increase (from 1.09 to 1.95) of the PMCI. The PMCI predicts a substantial part of within-country relative risk heterogeneity with much less between-country heterogeneity explained. The Akaike information criterion and Bayesian information criterion of the main model are lower than those of alternative meta-regression models considering the oxidative capacity of PM 2.5 or its composition. Conclusions: The PMCI represents an efficient and simple predictor of local PM 2.5 -related mortality, providing evidence that PM 2.5 toxicity depends on the surrounding gaseous pollutant mix. With the advent of remote sensing for pollutants, the PMCI can provide a useful index to track air quality.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".