Use of networks of low cost air quality sensors to quantify air quality in urban settings. (December 2018)

• Record Type: Journal Article
• Authors: Popoola, Olalekan A.M.; Carruthers, David; Lad, Chetan; Bright, Vivien B.; Mead, Mohammed I.; Stettler, Marc E.J.; Saffell, John R.; Jones, Roderic L.
• Is Part Of: Atmospheric environment. Volume 194(2018)
• Page Start: 58
• Page End: 70
• Publication Date: 2018-12
• Subjects: Air quality -- Low cost sensors -- Dense sensor network -- Scale separation -- Air quality model -- Long term measurements; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2018.09.030
• Languages: English
• ISSNs: 1352-2310

Abstract

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: Network of low cost sensors for high spatio-temporal pollution measurements at LHR. Unprecedented source apportionment information at LHR, including emission factors. Show that majority of the local pollution was not from airport activities. Network data used to constrain an air quality model. Using the refined model prediction to show future impacts of developments at LHR.