School neighbourhood and compliance with WHO-recommended annual NO 2 guideline: A case study of Greater London

Sci Total Environ. 2022 Jan 10;803:150038. doi: 10.1016/j.scitotenv.2021.150038. Epub 2021 Sep 1.

Abstract

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 μg/m3 annual mean) in Greater London (UK). We considered a host of variables including schools' characteristics as well as their neighbourhoods' attributes from household, socioeconomic, 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 vicinity 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 stations, 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 μg/m3 - instead of 40 μg/m3. 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 improving air quality for school-aged children in urban settings.

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

MeSH terms

  • Air Pollutants* / analysis
  • Air Pollution* / analysis
  • Bayes Theorem
  • Child
  • Environmental Exposure / analysis
  • Environmental Monitoring
  • Humans
  • London
  • Nitrogen Dioxide / analysis
  • Particulate Matter / analysis
  • Schools
  • World Health Organization

Substances

  • Air Pollutants
  • Particulate Matter
  • Nitrogen Dioxide