Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area

Abstract

Meteorological conditions and other gaseous pollutants generally impacted the development of ozone (O3) in the atmosphere.The purpose of this study was to create the best O3model for forecasting O3concentrations in the industrial area and to determine the variables that affect O3concentrations.Five-year data of meteorological and gaseous pollutants were used to analyze and develop the prediction model. Based on three distinct techniques, three separate multiple linear regression (MLR) prediction models of O3concentration were developed.MLR3had the highest correlation coefficient of 0.792 during development as compared to models MLR1and MLR2. MLR2was deemed the best O3 prediction model, however, since it had the lowest error values of root mean square error (3.976) and mean absolute error (3.548) when compared to other models.The establishment of an O3prediction model can offer local governments with early information that could help them reduce and manage air pollution emissions.