A Forecasting Approach to Predict the Air Quality Index and Its Related Pollutants

Abstract

Nowadays, pollution has grown to be a major worry. People ought to be aware of theair they breathe. An approach for determining the current state of air quality is the AirQuality Index (AQI). AQI gives the concept about quality of air or at what degree theair in the particular location is polluted. The present study explores multiplemethodologies for estimation and forecasting of the Air Quality Index (AQI) byconsidering the synergistic effects of key pollutants, specifically PM10, PM2.5, SO 2, andNO2. The study has been conducted for the regions of New Delhi, India to compare theambient air quality. In this study various methodologies have been employed such asCenter-Line Moving Average (CMA) technique, in addition to conventional AQIestimate techniques together with machine learning techniques to forecast the AQIvalues for the subsequent month with certain degree of tolerance. The study region's airquality state was classified into good, moderate, satisfactory, and unacceptable classesfor different AQI calculations, according to seasonal and daily AQI calculations