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

Abstract

PM 2.5 (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, PM 2.5 is considered and the current study aims to estimate surface level PM 2.5 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 PM 2.5 , 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 PM 2.5 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 (2011Rambagh ( -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 PM 2.5 over these two monitoring stations.

Estimating Airborne Particulate Matter in the National Capital Region, Philippines Using Multiple Linear Regression and Gradient Boosting Algorithm on Modis Maiac Aerosol Optical Depth

“…One of the fundamental requirements in conducting the said models is the availability of satellite data. Gogikar, et al (2020) conducted a study for the estimation of PM2.5 values from satellite-derived AOD using different kinds of regression models, namely, simple linear regression, MLR, log-linear regression, and conditional-based MLR. The study area is in Agra and Rourkela region, India with inclusive years ranging from 2009 to 2015.…”

PM2.5 Concentrations Variability in North China Explored with a Multi-Scale Spatial Random Effect Model

“…The corresponding effects were treated as the determinate part (or global trends) in classic spatial statistical modelling [ 18 ]. Depending on the geographic scale where covariates are measured, the recent literature has tended to decompose the deterministic trend into a global component and a local component [ 23 , 24 , 25 ]. In addition, localised variabilities in the associations between covariates and PM 2.5 concentrations, which are another important aspect of local variability, have also been modelled through a set of local spatial statistical approaches, such as geographically weighted regression models [ 26 , 27 , 28 , 29 ].…”

Reviewing the Crop Residual Burning and Aerosol Variations during the COVID-19 Pandemic Hit Year 2020 over North India

“…The concentration of air pollutants changes even if emissions are the same depending on changing weather conditions [24]. Weather parameters play critical roles in modulating air pollutants and are considered in all prediction modes for pollutants [25,26]. Therefore, to compare two particular years' data for any pollutant, one needs to account for different weather conditions.…”