Abstract: Air pollution is a global concern that has severe effects on the environment and public health. This research seeks to discuss various indoor and outdoor pollutants which are all predicted using the Auto Regressive Integrated Moving Average (ARIMA) model. An ARIMA model can accurately predict pollutant levels thus helping in making interferences aimed at improving air quality by employing historical information from specific sites within Agra City. Various error metrics determine the effectiveness of the ARIMA model in increasing awareness levels and the need for immediate action toward the reduction of harmful substances. The results indicate promising outcomes with Root Mean Square Error (RMSE) values around 1.358 for NO2, 2.2615 for SO2, and 1.2501 for PM10, respectively, hence suggesting that the predictions are highly accurate regarding the amount of pollutants present in the air. These findings have a significant effect on employing data-driven approaches to prevent air pollution as well as promoting environmental sustainability.

Keywords: Air pollution, ARIMA, World Health Organization (WHO), Pollutants, health risks

Downloads: | DOI: 10.17148/IARJSET.2025.12806