Air Pollution in Indian Cities and Comparison of MLR, ANN and CART Models for Predicting PM10 Concentrations in Guwahati, India

Indian cities are increasingly becoming susceptible to PM10 induced health hazards, thereby creating concern for the country's policymakers. Air pollution is engulfing the comparatively smaller cities as the rapid pace of urbanization, and economic development seem not to lose steam. A review of air pollution of 28 cities of India, which includes tier-I, II, and III cities of India, found to have grossly violated both WHO (World Health Organisation) and NAAQS (National Ambient Air Quality Standard of India) in respect of acceptable daily average PM10 (particulate matter less than 10 mu m in aerodynamic diameter) concentrations by a wide margin. Predicting the city level PM10 concentrations in advance and accordingly initiate prior actions is an acceptable solution to save the city dwellers from PM10 induced health hazards. Predictive ability of three models, linear Multiple Linear Regression (MLR), nonlinear Multi-Layer Perceptron class of Artificial Neural Network (MLP ANN), and nonlinear Classification and Regression Tree (CART), for one day ahead PM10 concentration forecasting of tier-II Guwahati city, were tested with 2016-2018 daily average observed climate data, PM10, and gaseous pollutants. The results show that the non-linear algorithm MLP with feedforward backpropagation network topologies of ANN class, gives the best prediction value compared with linear MLR and nonlinear CART model. Therefore, ANN (MLP) approach may be useful to effectively derive a predictive understanding of one day ahead PM10 concentration level and thus provide a tool to the policymakers for initiating in situ measures to curb air pollution and improve public health.