Excess Morbidity and Mortality Associated with Air Pollution above American Thoracic Society Recommended Standards, 2017–2019
Bibliographic record
Abstract
Abstract Rationale Over the past year, the American Thoracic Society (ATS), led by its Environmental Health Policy Committee, has reviewed the most current air quality scientific evidence and has revised their recommendations to 8 μg/m3 and 25 μg/m3 for long- and short-term fine particulate matter (PM2.5) and reaffirmed the recommendation of 60 ppb for ozone to protect the American public from the known adverse health effects of air pollution. The current U.S. Environmental Protection Agency (EPA) standards, in contrast, expose the American public to pollution levels that are known to result in significant morbidity and mortality. Objectives To provide county-level estimates of annual air pollution-related health outcomes across the United States using the most recent federal air quality data, and to support the ATS’s recent update to the long-term PM2.5 recommended standard. This study is presented as part of the annual ATS/Marron Institute “Health of the Air” report. Methods Daily air pollution values were obtained from the EPA’s air quality system for monitored counties in the United States from 2017–2019. Concentration–response functions used in the EPA’s regulatory review process were applied to pollution increments corresponding to differences between the rolling 3-year design values and ATS-recommended levels for long-term PM2.5 (8 μg/m3), short-term PM2.5 (25 μg/m3), and ground-level ozone (O3; 60 ppb). Health impacts were estimated at the county level in locations with valid monitoring data. Results Meeting ATS recommendations throughout the country prevents an estimated 14,650 (95% confidence interval [CI], 8,660–22,610) deaths; 2,950 (95% CI, 1,530–4,330) lung cancer incidence events; 33,100 (95% CI, 7,300–71,000) morbidities, and 39.8 million (95% CI, 14.6–63.3 million) impacted days annually. This prevents 11,850 more deaths; 2,580 more lung cancer incidence events; 25,400 more morbidities; and 27.2 million more impacted days than meeting EPA standards alone. Conclusions Significant health benefits to be gained by U.S. communities that work to meet ATS-recommended air quality standards have now been identified under scenarios meeting the new ATS recommendation for long-term PM2.5 (8 μg/m3). The “Health of the Air” report presents an opportunity for air quality managers to quantify local health burdens and EPA officials to update their standards to reflect the latest science.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".