Increasing disparities in air pollution health burdens in the United States
Bibliographic record
Abstract
Ambient nitrogen dioxide (NO2) and fine particulate matter (PM2.5) pollution threaten public health in the United States (U.S.), and systemic racism has led to modern-day disparities in the distribution and associated health impacts of these pollutants. Many studies on environmental injustices related to ambient air pollution focus only on disparities in pollutant concentrations or provide only an assessment of pollution or health disparities at a snapshot in time. In this study we aim to document changing disparities in pollution-attributable health burdens over time and, for the first time, disparities in NO2-attributable health impacts across the entire U.S. We show that, despite overall decreases in the public health damages associated with NO2 and PM2.5, ethnoracial relative disparities in NO2-attributable pediatric asthma and PM2.5-attributable premature mortality in the U.S. have widened during the last decade. Racial disparities in PM2.5 attributable premature mortality and NO2-attributable pediatric asthma have increased by 19% and 16%, respectively, between 2010 and 2019. Similarly, ethnic disparities in PM2.5-attributable premature mortality have increased by 40% and NO2-attributable pediatric asthma by 10%. These widening trends in air pollution disparities are reversed when more stringent air quality standard levels are met for both pollutants. Our methods provide a semi-observational approach to tracking changes in disparities in air pollution and associated health burdens across the U.S.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".