Multi-Pollutant Analysis of Reproductive Outcomes and Air Pollution Using the CMAQ Model
Bibliographic record
Abstract
ISEE-0645 Background and Objective: A growing body of research has reported associations between exposure to ambient air pollution and adverse reproductive outcomes. We previously reported associations in a population-based cohort between small for gestational age (SGA) and pre-term birth with regulatory monitor-based and land use regression model exposure estimates. Here we present findings using estimates from the Community Multi-scale Air Quality (CMAQ) modeling system for additional air pollution metrics in the region of Vancouver, Canada. Methods: We identified 70,249 singleton births born between 1999 and 2002 with complete covariate and residential history data. We estimated risk of mean pregnancy exposures on SGA and preterm birth in logistic regression models. We obtained daily average CMAQ model estimates from May 30, 2004 to May 29, 2005, at a grid resolution of 4 x 4 km2; these were linked by month and day to the residential (6-digit postal code) histories of mothers during pregnancy with the actual birth year being retained as a covariate. CMAQ estimates for 20 particle components and 7 gaseous species were included in analyses. Results: For SGA, elevated ORs were observed for NO/NO2, CO, NH3 and particulate NH4 (accumulation mode). Other particle species associated with elevated ORs were soil and coarse mass, elemental carbon, accumulation mode nitrate and sulfate and primary (but not secondary) organic mass. Although the number of cases was small (N = 241) we observed consistent associations with pre-term birth <30 weeks for unspecified anthropogenic accumulation mode mass, coarse mass, and ammonia. Conclusion: Exposure estimates derived from the CMAQ model showed associations with birth outcomes that generally were consistent with previous observations based upon monitoring network data and land use regression models. Associations with preterm births <30 weeks were also consistent with prior findings of an association with PM2.5 mass, but also suggest a non-traffic source for this relationship.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".