Associations between short-term ambient PM2.5, oxidant gases and respiratory hospitalizations in children: effect modification by PM2.5 transition metals, sulfur and oxidative potential
Bibliographic record
Abstract
Background and Aim: Exposure to particulate and gaseous pollutants impair the respiratory health of children. Mass-based measures of PM2.5 do not capture spatiotemporal variability in particle composition/toxicity. The aim of this study is to examine whether associations between short-term ambient PM2.5 or oxidant gases and respiratory hospitalizations in children are modified by metals or sulfur content in PM2.5, or particle oxidative potential. Methods: This is a case-crossover study of 10,500 children (0-17 years) in Canada. Daily PM2.5 mass concentration and the combined weighted oxidant capacity of NO2 and O3 (Ox) were collected. Monthly estimates of transition metals (copper, iron, nickel, manganese, zinc) and sulfur in PM2.5, and three metrics of particle oxidative potential (OPAA, OPGSH, OPDTT) were measured at each monitoring site. Conditional logistic regression models were used to estimate associations between PM2.5 or Ox and respiratory hospitalizations, above and below median metals, sulfur, and particle oxidative potential. Results: Without stratifying above and below median metals, sulfur, or particle oxidative potential, there were no associations between PM2.5 mass and respiratory hospitalizations (OR and 95% CI per 10 μg/m3 increase in PM2.5: 1.004 [0.955, 1.056]). However, when the analyses were performed above/below median metals, sulfur, and oxidative potential, positive associations were observed when metals, sulfur and OPGSH were above the median. For example, when copper was above the median, the OR and 95% CI per 10 μg/m3 increase in PM2.5 was 1.084 [1.007, 1.167], while the OR and 95% CI was 0.970 [0.929, 1.014] when copper was below the median. Stronger associations between Ox and respiratory hospitalizations were also observed when metals, sulfur and oxidative potential were above the median. Conclusions: Stronger associations between short-term PM2.5, oxidant gases and respiratory hospitalizations in children were observed when metals, sulfur and particle oxidative potential were elevated. Keyword: PM2.5, respiratory health, children, oxidant gases
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".