Neighborhood Green Space and Pregnancy Outcomes: Disentangling Effects from Air Pollution and Noise Exposures
Bibliographic record
Abstract
Background: While growing evidence suggests urban green space may be associated with improved health, few studies have attempted to disentangle the effects of green space from other spatially clustered physical and social factors. Here we examine the correlation between neighborhood green space, and fine-scale exposure to air pollution, noise and area-level socioeconomic status (SES) within a large birth-cohort. Methods: Using linked administrative data, we identified 70,249 singleton births (from 1999–2002) in Vancouver, British Columbia, Canada. Seasonal residential green space was estimated using 30m Normalized Difference Vegetation Index (NDVI) data for 100m buffers around residential postal codes. Residential noise exposure was estimated using CadnaA software with a focus on transportation-related sources. Residential air pollution exposure was assessed using a number of land use regression models, of which nitrogen dioxide (NO2) is presented here. Area-level SES was measured using household median income data at the census dissemination area level. We assessed the correlation between exposure measures for postal codes of all study participants as well as exposure levels stratified by the lowest and highest quartile income areas. Results: The average NDVI value within 100m of the residential postal code of study participants was 0.25, with small season variation between winter (0.17) and summer (0.26) values. Moderate negative correlations were observed between NDVI values and NO2 air pollution (-0.52) and noise (-0.32) levels. For participants living in the lowest income areas, greenness was significantly lower (0.20 vs. 0.29 NDVI units) and NO2 (18.0 vs. 15.3 µg/m3) and noise (65.0 vs. 62.5 dB) significantly higher compared to those living in the highest income areas. Conclusions: Neighborhood green space is moderately correlated with air pollution and noise exposures and all three exposures vary by area-level SES.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".