Occupational Exposure to Ambient Air Pollution: At-Risk Worker Groups, Regulatory and Research Needs An Official American Thoracic Society Workshop Report
Bibliographic record
Abstract
Although health effects of ambient air pollution are well established in the general population, the impact of exposure in working populations remains poorly understood. Outdoor workers are disproportionately exposed to ambient air pollution, particularly with increasing wildfire smoke events and global climate change. An international interdisciplinary group of experts including worker representation assembled to review the current state of knowledge regarding the impact of occupational air pollution exposure on worker health and develop recommendations for research and actions to evaluate, mitigate, and regulate occupational air pollution exposure. The group identified health risks likely resulting from air pollution based on studies of the general population, noting that additional risks may be encountered from coexposures, as well as exertion increasing the work of breathing. High-risk groups were identified, including agricultural workers, construction workers, and wildland firefighters; others working in warehouses and indoor spaces are likely at risk via ambient air pollutant infiltration. It was estimated that at least 20 million outdoor U.S. workers are exposed to air pollution at work, which limits productivity and increases absenteeism. Participants recommended using air quality to guide work modifications and adoption of the hierarchy of exposure controls as a model to reduce exposures, as used by some states and proposed by the National Institute for Occupational Safety and Health for agricultural and other outdoor workers. Existing research supports the urgent need for policies to protect workers from exposure. Research gaps remain, including medical surveillance strategies, improved technology to protect workers, and studies specifically evaluating the impacts of occupational air pollution exposure.
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 | 0.029 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".