Citation

Shoari N, Beevers S, Brauer M, Blangiardo M. Environ. Int. 2022; 165: e107286.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.envint.2022.107286

PMID

35660953

Abstract

Creating healthy environments around schools is important to promote healthy childhood development and is a critical component of public health. In this paper we present a tool to characterize exposure to multiple urban environment features within 400 m (5-10 min walking distance) of schools in Greater London. We modelled joint exposure to air pollution (NO(2) and PM(2.5)), access to public greenspace, food environment, and road safety for 2,929 schools, employing a Bayesian non-parametric approach based on the Dirichlet Process Mixture modelling. We identified 12 latent clusters of schools with similar exposure profiles and observed some spatial clustering patterns. Socioeconomic and ethnicity disparities were manifested with respect to exposure profiles. Specifically, three clusters (containing 645 schools) showed the highest joint exposure to air pollution, poor food environment, and unsafe roads and were characterized with high deprivation. The neighbourhood of the most deprived cluster of schools had a median of 2.5 ha greenspace, 29.0 µg/m(3) of NO(2), 19.3 µg/m(3) of PM(2.5), 20 fast food retailers, and five child pedestrian crashes over a three-year period. The neighbourhood of the least deprived cluster of schools had a median of 21.8 ha greenspace, 15.6 µg/m(3) of NO(2), 15.1 µg/m(3) of PM(2.5), 2 fast food retailers, and one child pedestrian crash over a three-year period. To have a school-level understanding of exposure levels, we then benchmarked schools based on the probability of exceeding the median exposure to various features of interest. Our study accounts for multiple exposures, enabling us to highlight spatial distribution of exposure profile clusters, and to identify predominant exposure to urban environment features for each cluster of schools. Our findings can help relevant stakeholders, such as schools and public health authorities, to compare schools based on their exposure levels, prioritize interventions, and design local policies that target the schools most in need.

Language: en

Keywords

Air quality; Greenspace; Bayesian nonparametrics; Food environment; Pedestrian child crash; School exposure