Ambient PM2.5 Human Health Effects—Findings in China and Research Directions
Bibliographic record
Abstract
Exposure to fine particulate matter (PM) results in adverse health outcomes. Although this is a global concern, residents of China may be particularly vulnerable due to frequent severe air pollution episodes associated with economic growth, industrialization, and urbanization. Until 2012, PM2.5 was not regulated and monitored in China and annual average concentrations far exceeded the World Health Organizations guidelines of 10 μg/m3. Since the establishment of PM2.5 Ambient Air Quality Criteria in 2012, concentrations have decreased, but still pose significant health risks. A review of ambient PM2.5 health effect studies is warranted to evaluate the current state of knowledge and to prioritize future research efforts. Our review found that recent literature has confirmed associations between PM2.5 exposure and total mortality, cardiovascular mortality, respiratory mortality, hypertension, lung cancer, influenza and other adverse health outcomes. Future studies should take a long-term approach to verify associations between exposure to PM2.5 and health effects. In order to obtain adequate exposure assessment at finer spatial resolutions, high density sampling, satellite remote sensing, or models should be employed. Personal monitoring should also be conducted to validate the use of outdoor concentrations as proxies for exposure. More research efforts should be devoted to seasonal patterns, sub-population susceptibility, and the mechanism by which exposure causes health effects. Submicron and ultrafine PM should also be monitored and regulated.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".