Multiple Regression Analysis of the Air Quality Index Using Time Series

Abstract

One of the biggest problems in Meerut, Uttar Pradesh, has been air pollution. The components that make up air pollution include PM10, NO2, and SO2. Forecasts of these pollutants can be used to design a plan to reduce air pollution. Using data gathered by the U.P. Pollution Control Board in prior years, this article examines the air quality index of Ghaziabad's Khora Colony in Uttar Pradesh, considering air pollution. The analysis makes use of multiple linear regression (MLR). This approach uses time series analysis to provide us with approximate findings. Four data points are used at each stage, and the first one is ignored in favor of the following four in the series at each subsequent step. The AQI's future values can be somewhat predicted by examining its historical values since we obtain 47.9% of the variability in the independent factors. We discovered that this approach outperformed previous prediction techniques.