Use of networks of low cost air quality sensors to quantify air quality in urban settings

Olalekan A.M Popoola,David Carruthers,Chetan Lad,Vivien B Bright,Mohammed I Mead,Marc E.J Stettler,John R Saffell,Roderic L Jones

doi:10.1016/j.atmosenv.2018.09.030

Abstract

Low cost sensors are becoming increasingly available for studying urban air quality. Here we show how such sensors, deployed as a network, provide unprecedented insights into the patterns of pollutant emissions, in this case at London Heathrow Airport (LHR). Measurements from the sensor network were used to unequivocally distinguish airport emissions from long range transport, and then to infer emission indices from the various airport activities. These were used to constrain an air quality model (ADMS-Airport), creating a powerful predictive tool for modelling pollutant concentrations. For nitrogen dioxide (NO2), the results show that the non-airport component is the dominant fraction (∼75%) of annual NO2 around the airport and that despite a predicted increase in airport related NO2 with an additional runway, improvements in road traffic fleet emissions are likely to more than offset this increase. This work focusses on London Heathrow Airport, but the sensor network approach we demonstrate has general applicability for a wide range of environmental monitoring studies and air pollution interventions.

Highlights

Poor air quality is known to affect human health (Bernstein et al, 2004; Brunekreef and Holgate, 2002; McConnell et al, 2002; Parnia et al, 2002; Pope and Dockery, 2006; Ren et al, 2017; Samoli et al, 2008; WHO, 2009)
Over the last few years, low cost sensors have been assessed for their viability in monitoring ambient air quality including measurements of gaseous and particulate matter (PM) pollutants
While this study focuses on the UK's Heathrow Airport, the techniques we describe, that exploit the emerging low-cost air quality sensor technologies and novel analysis approaches, have far wider applicability for environmental monitoring and air pollution interventions

Summary

Introduction

Poor air quality is known to affect human health (Bernstein et al, 2004; Brunekreef and Holgate, 2002; McConnell et al, 2002; Parnia et al, 2002; Pope and Dockery, 2006; Ren et al, 2017; Samoli et al, 2008; WHO, 2009). Traditionally the remit of expensive and complex reference instruments (Kumar et al, 2015; National Audit Office, 2009) can be achieved using readily deployable low cost instruments, revolutionising approaches to the study of urban pollution. Over the last few years, low cost sensors have been assessed for their viability in monitoring ambient air quality including measurements of gaseous and particulate matter (PM) pollutants. There have been attempts at deploying portable sensors as networks to better understand the spatial variability of air pollution (Mead et al, 2013; Miskell et al, 2017; Mueller et al, 2017; Penza et al, 2014; Sun et al, 2016). There have been attempts at deploying portable sensors as networks to better understand the spatial variability of air pollution (Mead et al, 2013; Miskell et al, 2017; Mueller et al, 2017; Penza et al, 2014; Sun et al, 2016). Schneider et al, 2017 used a data fusion technique to combine sensor network data with an air quality model which was used to simulate the spatial pattern of pollutants

Methods

Results

Conclusion