Machine learning based estimation of Ozone using spatio-temporal data from air quality monitoring stations

Abstract

In this paper, models are created to predict the levels of ground level Ozone at particular locations based on the cross-correlation and spatial-correlation of different air pollutants whose readings are obtained from several different air quality monitoring stations in Gauteng province, South Africa, including the City of Johannesburg which is on the cusp of being one of the world's megacities and is currently the most polluted city in the country. Datasets spanning several years collected from the monitoring stations and transmitted through the Internet-of-Things are used. Big data analytics and cognitive computing is used to get insights on the data and create models that can estimate levels of Ozone without requiring massive computational power or intense numerical analysis.