Assessment and prediction of PM10 concentration using ARIMA

Abstract

Urban air pollution is rapidly becoming an environmental issue of global concern, affecting public health, regional weather, and climate. The air quality crisis in cities is mainly caused by vehicle emissions. A city's air quality is assessed using the Air Quality Index (AQI), which analyses concentrations of air pollutants such as PM10, sulphur dioxide SO2, and nitrogen dioxide NO2. The current study on air pollution was carried out in the SG Halli area of Bangaluru and required air pollutant data was collected from the Karnataka State Pollution Control Board (CPCB). With the exception of PM10, the pollutant concentration was within the permissible limits set by the Central Pollution Control Authority (CPCB). Pollutant concentrations were analyzed using meteorological variables such as relative humidity (RH), temperature (T), wind direction (WD), wind speed (WS), and solar radiation (SR). PM10 concentrations are predicted using an auto-regressive integrated moving average (ARIMA) model. PM10 concentrations were well predicted by ARIMA model. On the AQI scale, it can be seen that the atmospheric environment of SG Halli is moderately polluted.