4.7 Article

School neighbourhood and compliance with WHO-recommended annual NO2 guideline: A case study of Greater London

Journal

SCIENCE OF THE TOTAL ENVIRONMENT
Volume 803, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.scitotenv.2021.150038

Keywords

Air pollution; Bayesian spatial models; Nitrogen dioxide; School's exposure; Neighbourhood attributes

Funding

  1. MRC Early Career Research Fellowship via the MRC Centre for Environment and Health [MR/T502613/1]

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This study investigated factors affecting the odds of schools in Greater London exceeding the WHO-recommended concentration of NO2. Transport-related factors were found to increase the likelihood of non-compliance, while distance from roads, green space, and vehicle speed were associated with reduced likelihood of exceeding the recommended NO2 concentration. The study suggests the importance of adopting clean fuel technologies, installing green barriers, and reducing motorised traffic to improve air quality for school-aged children in urban settings.
Despite several national and local policies towards cleaner air in England, many schools in London breach the WHO-recommended concentrations of air pollutants such as NO2 and PM2.5. This is while, previous studies highlight significant adverse health effects of air pollutants on children's health. In this paper we adopted a Bayesian spatial hierarchical model to investigate factors that affect the odds of schools exceeding the WHO-recommended concentration of NO2 (i.e., 40 mu g/m(3) annual mean) in Greater London (UK). We considered a host of variables including schools' characteristics as well as their neighbourhoods' attributes from household, so-cioeconomic, transport-related, land use, built and natural environment characteristics perspectives. The results indicated that transport-related factors including the number of traffic lights and bus stops in the immediate vi-cinity of schools, and borough-level bus fuel consumption are determinant factors that increase the likelihood of non-compliance with the WHO guideline. In contrast, distance from roads, river transport, and underground sta-tions, vehicle speed (an indicator of traffic congestion), the proportion of borough-level green space, and the area of green space at schools reduce the likelihood of exceeding the WHO recommended concentration of NO2. We repeated our analysis under a hypothetical scenario in which the recommended concentration of NO2 is 35 mu g/m(3) - instead of 40 mu g/m(3). Our results underscore the importance of adopting clean fuel technologies on buses, installing green barriers, and reducing motorised traffic around schools in reducing exposure to NO2 concentrations in proximity to schools. Also, our findings highlight the presence of environmental inequalities in the Greater London area. This study would be useful for local authority decision making with the aim of im-proving air quality for school-aged children in urban settings. (C) 2021 Elsevier B.V. All rights reserved.

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