Towards cleaner air: PM2.5 exposure and disparities around childcare providers in England
Bibliographic record
Abstract
ABSTRACT Air pollution poses a significant health risk for young children, particularly in urban and deprived areas. Exposure to fine particulate matter (PM 2.5 ) during early life may contribute to long-term adverse health outcomes. This study examined changes in PM 2.5 concentrations around Early Years Providers (EYPs; childcare providers) in England from 2018 to 2022. We assessed associations between small-area socio-demographic characteristics and exposure levels exceeding the World Health Organisation (WHO) 2021 annual air quality guideline (>5 μg/m 3 ). We integrated data on EYPs locations from Ordnance Survey with annual PM 2.5 estimates from DEFRA using Geographic Information Systems and socio-demographic indicators — deprivation, urbanicity, and ethnic composition. A Bayesian spatial regression model with random effects was used to estimate adjusted associations between PM 2.5 levels and local population characteristics. The number of EYPs ranged from 15,780 in 2018 to 18,427 in 2019. Mean PM 2.5 levels around EYPs changed by 17.8% over the study period (from 9.4 μg/m 3 [SD=1.8] in 2018 to 7.8 μg/m 3 [SD=1.5] in 2022). However, PM 2.5 levels at over 96% of EYPs remained above the WHO 2021 annual guideline throughout. Higher PM 2.5 concentrations were observed in EYPs located in more deprived, urban, and predominantly non-white communities. Despite recent improvements, PM 2.5 levels around most EYPs in England remain above recommended thresholds. Targeted interventions in deprived urban areas are needed to reduce young children’s exposure and address environmental health inequalities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".