Prediction of Air Quality in Urban Area, Chennai

Abstract

The quality of air in Alandur, Chennai is polluted by Particulate Matter (PM2.5) over the years. Reports prove that particulates affect the health of humans and environment. Development of accurate forecasting models to find PM2.5 concentration in air helps to take control measures, early warning and mitigative measures. In this study, the performance of non-linear model (Feed Forward Back Propagation using LEARNGD function) with meteorological data and gaseous pollutants as input parameters from the year 2015-2019 at Alandur with different surrounding activities of urban area. In this paper, the prediction of PM2.5 in the study area is mainly focused to find the effects of harmful emissions. To predict PM2.5, an artificial neural network (ANN) prediction model is developed. The data obtained from the monitoring station on the Alandur Bus depot of Alandur area in Chennai is given as input variable. The prediction model is validated and evaluated by statistical calculations, and then it was found that it performed well in the prediction of PM2.5. The performance of the developed model was evaluated by Mean Square Error (MSE) and value of R 2. The best prediction performance was observed in the model for Purelin transfer function with R 2 value of 0.96 and MSE of 0.094 and for Tansig transfer function with R 2 value of 0.97 and MSE of 0.103 from the framed networks.