American Thoracic Society and Marron Institute Report. Estimated Excess Morbidity and Mortality Caused by Air Pollution above American Thoracic Society–Recommended Standards, 2011–2013
Bibliographic record
Abstract
Estimates of the health impacts of air pollution are needed to make informed air quality management decisions at both the national and local levels. Using design values of ambient pollution concentrations from 2011-2013 as a baseline, the American Thoracic Society (ATS) and the Marron Institute of Urban Management estimated excess morbidity and mortality in the United States attributable to exposure to ambient ozone (O3) and fine particulate matter (PM2.5) at levels above the American Thoracic Society-recommended standards. Within the subset of counties with valid design values for each pollutant, 14% had PM2.5 concentrations greater than the ATS recommendation, whereas 91% had O3 concentrations greater than the ATS recommendation. Approximately 9,320 excess deaths (69% from O3; 31% from PM2.5), 21,400 excess morbidities (74% from O3; 26% from PM2.5), and 19,300,000 adversely impacted days (88% from O3; 12% from PM2.5) in the United States each year are attributable to pollution exceeding the ATS-recommended standards. California alone is responsible for 37% of the total estimated health impacts, and the next three states (Pennsylvania, Texas, and Ohio) together contributed to 20% of the total estimates. City-specific health estimates are provided in this report and through an accompanying online tool to help inform air quality management decisions made at the local level. Riverside and Los Angeles, California have the most to gain by attaining the ATS recommendations for O3 and PM2.5. This report will be revised and updated regularly to help cities track their progress.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.019 |
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".