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

TL;DR

This study uses a Bayesian spatial model to identify neighborhood and transport factors influencing NO2 pollution levels near schools in London, highlighting key determinants and potential policy interventions to improve air quality for children.

Contribution

It introduces a Bayesian spatial hierarchical approach to analyze multiple neighborhood and transport variables affecting NO2 levels near schools in London.

Findings

Traffic lights and bus stops increase NO2 exceedance risk.

Green spaces and distance from roads reduce NO2 levels.

Policy recommendations include clean fuel use and green barriers.

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 ug/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. As a sensitivity analysis, we repeated our analysis under a hypothetical scenario in which the recommended concentration of NO2 is 35 ug/m3, instead of 40 ug/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. This study would be useful for local authority decision making with the aim of improving air quality for school-aged children in urban settings.