Publication Type:

Journal Article


Advances in Intelligent Systems and Computing, Springer Verlag, Volume 696, p.413-426 (2018)





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


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.


cited By 0; Conference of International Conference on Recent Advancement in Computer, Communication and Computational Sciences, RACCCS 2016 ; Conference Date: 2 September 2017 Through 3 September 2017; Conference Code:212189

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.