Personal Exposures to Traffic-Related Air Pollution and Acute Respiratory Health among Bronx Schoolchildren with Asthma
Bibliographic record
Abstract
BACKGROUND: Previous studies have reported relationships between adverse respiratory health outcomes and residential proximity to traffic pollution, but have not shown this at a personal exposure level. OBJECTIVE: We compared, among inner-city children with asthma, the associations of adverse asthma outcome incidences with increased personal exposure to particulate matter mass ≤ 2.5 μm in aerodynamic diameter (PM(2.5)) air pollution versus the diesel-related carbonaceous fraction of PM2.5. METHODS: Daily 24-hr personal samples of PM(2.5), including the elemental carbon (EC) fraction, were collected for 40 fifth-grade children with asthma at four South Bronx schools (10 children per school) during approximately 1 month each. Spirometry and symptom scores were recorded several times daily during weekdays. RESULTS: We found elevated same-day relative risks of wheeze [1.45; 95% confidence interval (CI), 1.03-2.04)], shortness of breath (1.41; 95% CI, 1.01-1.99), and total symptoms (1.30; 95% CI, 1.04-1.62) with an increase in personal EC, but not with personal PM(2.5) mass. We found increased risk of cough, wheeze, and total symptoms with increased 1-day lag and 2-day average personal and school-site EC. We found no significant associations with school-site PM(2.5) mass or sulfur. The EC effect estimate was robust to addition of gaseous pollutants. CONCLUSION: Adverse health associations were strongest with personal measures of EC exposure, suggesting that the diesel "soot" fraction of PM(2.5) is most responsible for pollution-related asthma exacerbations among children living near roadways. Studies that rely on exposure to PM mass may underestimate PM health impacts.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".