PM2.5 estimation using multiple linear regression approach over industrial and non-industrial stations of India

Abstract

PM2.5 (particulate matter size less than 2.5 µm, also called Respirable suspended particulate matter (RSPM)) is causing devastating effects on various living entities and is deleterious more than any other pollutants. As ambient air pollution is a scourge to India, in the present research work, PM2.5 is considered and the current study aims to estimate surface level PM2.5 concentrations using satellite-derived aerosol optical depth (AOD) along with meteorological data obtained from reanalysis and in-situ measurements over two different cities of India, namely: Agra, a non-industrial site for a study period of 2011–2015 and Rourkela, a highly industrialized location for 2009–2013, respectively. From the average daily variation of PM2.5, the pollution levels are critical and exceeding the threshold values defined by the pollution control board for most of the days at both the sites. Satellite-observed AOD values were also found to be very high over Agra (average AOD 0.76–0.8) and Rourkela (average AOD 0.4–0.46) during the study period. The annual exceedance factor (AEF) values over Agra and Rourkela were found to be always > 1.5 which indicates the above critical state of pollution. Traditional simple linear regression method (Model I), multiple linear regression (Model II (a–e)), log-linear regression (Model III) and conditional based MLR (Model IV and Model V) methods are applied to estimate the PM2.5 concentrations over Taj for Agra region for a study period of 2011–2015 and Sonaparbat for Rourkela region for a study period of 2009–2013. The models obtained over Taj and Sonaparbat are applied to Rambagh (2011–2015) and Rourkela (2009–2013) sites for validation. The coefficient of determination (R) between observed and estimated values are found to be statistically significant for model II (e) during training and validation at both the sites and model performance is adequate. The Model II (e) can thus be used as a unified explanatory model for the estimation of PM2.5 over these two monitoring stations.