Wavelet and multiple linear regression analysis for identifying factors affecting particulate matter PM2.5 in Mumbai City, India

Abstract

Purpose The purpose of this paper is to identify significant factors of environmental variables and pollutants that have an effect on PM2.5 through wavelet and regression analysis. Design/methodology/approach In order to provide stable data set for regression analysis, multiresolution analysis using wavelets is conducted. For the sampled data, multicollinearity among the independent variables is removed by using principal component analysis and multiple linear regression analysis is conducted using PM2.5 as a dependent variable. Findings It is found that few pollutants such as NO2, NOx, SO2, benzene and environmental factors such as ambient temperature, solar radiation and wind direction affect PM2.5. The regression model developed has high R2 value of 91.9 percent, and the residues are stationary and not correlated indicating a sound model. Research limitations/implications The research provides a framework for extracting stationary data and other important features such as change points in mean and variance, using the sample data for regression analysis. The work needs to be extended across all areas in India and for various other stationary data sets there can be different factors affecting PM2.5. Practical implications Control measures such as control charts can be implemented for significant factors. Social implications Rules and regulations can be made more stringent on the factors. Originality/value The originality of this paper lies in the integration of wavelets with regression analysis for air pollution data.