An IoT based Sensing System for Modeling and Forecasting Urban Air Quality

Abstract

Rapid urbanization, vehicular emissions, rise in industrial activities, burning of crop residues and garbage in nearby areas, thermal power plants, emissions from diesel generators, dust from construction sites and household fuel use have been the cause of severe deterioration of urban air quality, resulting in a large number of deaths every year. In this work, an Internet of Things based system has been developed to monitor, analyze and forecast outdoor air quality. Air quality data is collected using our sensing system which is integrated with a vehicle, and collects data while the vehicle moves on the road. The sensed data is transferred and stored in cloud using an Android application. Stored data is used to forecast air quality with the help of statistical and stochastic forecasting models-quantile regression and ARMA/ARIMA. The forecast performance of these prediction models is measured using mean absolute deviation, mean percentage error, mean absolute percentage error, mean square error and root mean square error to find their efficacy.