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Pollutant PM2.5 Multi Step Prediction Under Seasonal Influences across 13 Indian Cities

The effect of pollutants in our lives is growing significantly with every passing hour. Air Quality Index or AQI plays a pivotal role in the sustainability of human life today. One of the most significant contributors to AQI is PM2.5. Predicting future values of PM2.5 provides an edge in terms of atmospheric and health concerns by judging the quality of air around us. Various algorithms have gained popularity in such domains. However, with a data of a few years only, basic models like Auto Regressive Integrated Moving Average (ARIMA) and their derivatives can be optimized to provide multistep prediction. In the present work, we have used data obtained from Central Pollution Control Board (CPCB), under the Ministry of Environment, Forest and Climate Change, India for 13 highly polluted, spatially distributed cities across India. k-NN algorithm is used to fill the missing values in the time series. Using the training dataset of approximately 2 years' hourly data, a 24-hour multistep forecast is obtained for 31stDec 2020 and the same is repeated for all the days in the month of January 2021. ARIMA and Seasonal ARIMA (SARIMA) models are used in the proposed work to predict the values of PM2.5. Comparative results of the methods studied is shared in pictorial graphs. Error indices-MAE, RMSE, NRMSE for the proposed algorithm obtained is as low as 0.3 micro grams/cubic meter (Peenya, Bangalore), 0.36 micro grams/cubic meter (Peenya, Bangalore) and 0.002 micro grams/cubic meter (Kanpur, Uttar Pradesh) respectively for the 24 multistep prediction of 31stDecember, 2020. Individual hour wise accuracy for the ARIMA and SARIMA models are also calculated and represented.