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

Vivekanand Venkataraman (M.S. Ramaiah Institute of Technology, Bengaluru, India)
Syed Usmanulla (M.S. Ramaiah Institute of Technology, Bengaluru, India)
Appaiah Sonnappa (M.S. Ramaiah Institute of Technology, Bengaluru, India)
Pratiksha Sadashiv (M.S. Ramaiah Institute of Technology, Bengaluru, India)
Suhaib Soofi Mohammed (M.S. Ramaiah Institute of Technology, Bengaluru, India)
Sundaresh S. Narayanan (M.S. Ramaiah Institute of Technology, Bengaluru, India)

International Journal of Quality & Reliability Management

ISSN: 0265-671X

Article publication date: 29 August 2019

Issue publication date: 9 October 2019

639

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.

Keywords

Acknowledgements

The authors would like to thank the anonymous reviewers for providing valuable suggestion thereby enhancing the quality of the paper.

Citation

Venkataraman, V., Usmanulla, S., Sonnappa, A., Sadashiv, P., Mohammed, S.S. and Narayanan, S.S. (2019), "Wavelet and multiple linear regression analysis for identifying factors affecting particulate matter PM2.5 in Mumbai City, India", International Journal of Quality & Reliability Management, Vol. 36 No. 10, pp. 1750-1783. https://doi.org/10.1108/IJQRM-06-2018-0150

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

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