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

07/27/2021
by   Niloofar Shoari, et al.
0

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.

READ FULL TEXT
research
09/21/2019

Analysis of air pollution time series using complexity-invariant distance and information measures

Air pollution is known to be a major threat for human and ecosystem heal...
research
05/20/2020

A Route to School Informational Intervention for Air Pollution Exposure Reduction

Walking and cycling are promoted to encourage sustainable travel behavio...
research
08/24/2018

A hierarchical modelling approach to assess multi pollutant effects in time-series studies

When assessing the short term effect of air pollution on health outcomes...
research
04/07/2022

Bayesian vector autoregressive analysis of macroeconomic and transport influences on urban traffic accidents

The macro influencing factors analysis of urban traffic safety is import...
research
05/29/2021

Estimating air quality co-benefits of energy transition using machine learning

Estimating health benefits of reducing fossil fuel use from improved air...
research
01/04/2023

Exploring Machine Learning Techniques to Identify Important Factors Leading to Injury in Curve Related Crashes

Different factors have effects on traffic crashes and crash-related inju...

Please sign up or login with your details

Forgot password? Click here to reset