Elemental Composition of Particulate Matter and the Association with Lung Function
Bibliographic record
Abstract
BACKGROUND: Negative effects of long-term exposure to particulate matter (PM) on lung function have been shown repeatedly. Spatial differences in the composition and toxicity of PM may explain differences in observed effect sizes between studies. METHODS: We conducted a multicenter study in 5 European birth cohorts-BAMSE (Sweden), GINIplus and LISAplus (Germany), MAAS (United Kingdom), and PIAMA (The Netherlands)-for which lung function measurements were available for study subjects at the age of 6 or 8 years. Individual annual average residential exposure to copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc within PM smaller than 2.5 μm (PM2.5) and smaller than 10 μm (PM10) was estimated using land-use regression models. Associations between air pollution and lung function were analyzed by linear regression within cohorts, adjusting for potential confounders, and then combined by random effects meta-analysis. RESULTS: We observed small reductions in forced expiratory volume in the first second, forced vital capacity, and peak expiratory flow related to exposure to most elemental pollutants, with the most substantial negative associations found for nickel and sulfur. PM10 nickel and PM10 sulfur were associated with decreases in forced expiratory volume in the first second of 1.6% (95% confidence interval = 0.4% to 2.7%) and 2.3% (-0.1% to 4.6%) per increase in exposure of 2 and 200 ng/m, respectively. Associations remained after adjusting for PM mass. However, associations with these elements were not evident in all cohorts, and heterogeneity of associations with exposure to various components was larger than for exposure to PM mass. CONCLUSIONS: Although we detected small adverse effects on lung function associated with annual average levels of some of the evaluated elements (particularly nickel and sulfur), lower lung function was more consistently associated with increased PM mass.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".