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Publication Type : Journal Article
Publisher : Advances in Intelligent Systems and Computing, Springer Verlag,
Source : Advances in Intelligent Systems and Computing, Springer Verlag, Volume 696, p.413-426 (2018)
ISBN : 9789811073854
Keywords : Air pollution, Air pollution control, Air quality, Air quality prediction, ARIMA, Auto-regressive integrated moving average, Bengaluru, Continuous monitoring, Environmental quality, Forecasting, Housing, Nitrogen oxides, pollution monitoring, Stationary conditions, Time series analysis
Campus : Bengaluru
School : School of Engineering
Department : Chemistry
Year : 2018
Abstract : Air pollution control measures in India are still in its infancy, while the country is developing at a faster rate. Development is known to affect the air quality of a place adversely. The key to manage the air quality of a place is proper planning, and for that, robust forecasting system based on continuous monitoring is required. Bengaluru is a city which has grown in size and population in the past decades. This rapid growth has affected its environmental quality. The present work deals with development of air quality prediction model based on Autoregressive Integrated Moving Average (ARIMA). For this, pollution data of NO2, PM10 and SO2 from January 2013 to March 2016, 14 pollution monitoring stations has been used. The results show that data which satisfies the stationary condition can be used as an accurate prediction model. NO2 residential and RSPM residential satisfy this condition. © Springer Nature Singapore Pte Ltd. 2018.
Cite this Research Publication : A. M.S.K., Dr. Amrita Thakur, Dr. Deepa Gupta, and B. Sreevidya, “Time series analysis of air pollution in bengaluru using ARIMA model”, Advances in Intelligent Systems and Computing, vol. 696, pp. 413-426, 2018.