Estimation of Air Pollutants using Time Series Model at Coalfield Site of India - Arti Choudhary; Pradeep Kumar (Proceedings of The International Conference on Data Science and Official Statistics, 2021)
Assessment of air pollutants and quality is an intricate task because of dynamic nature, unpredictability and high inconsistency in space and time. In this study, a time series moving average (MA) model is employed to estimate air pollutants (PM2.5, PM10, NO2, NOX, O3, SO2 and CO) over the coalfield site of India. The estimated O3 with Adj. R2 = 0.958 was identified as the most accurate estimation followed by other estimated pollutants. Though, results for the estimated PM2.5 (Adj. R2 = 0.950) and NO2 (Adj. R2 = 0.949) were found almost similar to the results of O3 (Adj. R2 = 0.958). The estimated CO with Adj. R2 = 0.887 was identified lower among all the estimated pollutants was also found very well. The existing results of the study demonstrate that MA model permits us to precisely estimate daily basis pollutant concentrations, for the different sites of India.