This study has employed Autoregressive Integrated Moving Average (ARIMA) model to analyze the time-series data of air pollutants at Chennai city in Tamil Nadu. Data on pollutants containing SO2, NO2 and PM10 for 12 months for four locations, Adayar, Anna Nagar, T.Nagar and Kilpauk are collected for the period 2010 to 2015. The stationary of the data is verified with the Augmented Dickey-Fuller test and null hypothesis. The best-suited order selection for prediction is done using Akaike Information Criterion (AIC). For the prediction of individual pollutants, dataset is broken into training and testing at a ratio of 66% and 33% respectively. The proposed model predicts the SO2 and NO2 with the least mean square error. The root mean squared error of PM10 prediction is high due to non-stationary.
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