Association of fine particulate matter air pollution and its constituents with lung function: The China Pulmonary Health study
Bibliographic record
Abstract
The associations of long-term exposure to various constituents of fine particulate matter (≤2.5 μm in aerodynamic diameter, PM2.5) air pollution with lung function were not clearly elucidated in developing countries. The aim was to evaluate the associations of long-term exposure to main constituents of PM2.5 with lung function in China. This is a nationwide, cross-sectional analysis among 50,991 study participants from the China Pulmonary Health study. Multivariable linear regression models were used to obtain differences of forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), FEV1/FVC, peak expiratory flow (PEF), and forced expiratory flow at 25–75% of exhaled FVC (FEF25-75%) associated with an interquartile range (IQR) change of PM2.5 or its constituents. Residential annual PM2.5 levels varied from 26 μg/m3 to 92 μg/m3 (average: 53 μg/m3). An IQR increase of PM2.5 concentrations was associated with lower FEV1 (19.82 mL, 95% CI: 11.30–28.33), FVC (17.45 mL, 95% CI: 7.16–27.74), PEF (86.64 mL/s, 95% CI: 59.77–113.52), and FEF25-75% (31.93 mL/s, 95% CI: 16.64–47.22). Black carbon, organic matter, ammonium, sulfate, and nitrate were negatively associated with most lung function indicators, with organic matter and nitrate showing consistently larger magnitude of associations than PM2.5 mass. This large-scale study provides first-hand epidemiological evidence that long-term exposure to ambient PM2.5 and some constituents, especially organic matter and nitrate, were associated with lower large- and small- airway function.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".