RealTimeAir

Abstract

Poor air quality has been responsible for millions of premature deaths. Acknowledging the critical role air quality plays in the future of their populations, governments across the world have been installing networks of fixed location air quality measurement instruments. But these monitoring stations are expensive and therefore spatially sparse, typically publishing summaries of hourly averages of pollutant measurements once per day. Data so sparse spatially and temporally offers little to inform the street user or policy maker as to what is happening at a more granular level, thus reducing the ability to avoid pollutants. This paper investigates the feasibility of using consumer grade mobile sensors as a means to contribute to a real time federated hyper-local crowd sensing air quality data service, RealTimeAir (RTA), underpinned by government reference sensors. We compare two mobile sensors and examine the correlation of the measurements between them. We investigate the correlation between these sensors and the more expensive fixed monitoring stations. We consider the variation of measurements over time and space to investigate the need for greater granularity of these measurements. Finally, we present a low pollutant exposure route finder as a use case for the proposed system.