Data science and IoT based mobile monitoring framework for hyper-local PM2.5 assessment in urban setting

Abstract

Ambient air quality shows a high degree of spatiotemporal variation. It is known to impact human life and health. Obtaining meaningful estimates of human exposure to air pollutants is key to mitigating its ill effects. In order to ascertain individual exposure, pollutant information is required at high resolution. Current air quality monitoring approaches utilize static monitoring stations that are sparsely distributed across a city which in turn results in poor spatial resolution. Such stations allow only for gross assessment of air quality across a city. This work proposes a comprehensive framework for hyperlocal assessment of ambient air quality through a mobile monitoring approach. PM2.5 concentration is considered in this work as a specific air pollutant to demonstrate the framework. In order to test the efficacy of the approach, two case studies were undertaken in the city of Chennai, India as a part of this work. The first study aims at locating diurnal, spatio-temporal PM2.5 hotspots in an urban environment. These hotspots are then associated with daily anthropogenic activity. The second study enables an event centric understanding of spatio-temporal short-lived extremities in PM2.5 concentration. The results are then retrospectively associated with land use profile and anthropological activities. The mobile sensing paradigm provides a unique perspective in the assessment of such localized events. This micro-level assessment offers significant insights on the spatio-temporal variations within a city in a regular case and an extreme, event centric case. Real world applications of hyperlocal air quality assessment, such as least exposure route estimation, are also explored at the end of this work.