Application of Multiple Linear Regression and Geographically Weighted Regression Model for Prediction of PM 2.5
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RESEARCH ARTICLE
Application of Multiple Linear Regression and Geographically Weighted Regression Model for Prediction of PM2.5 Tripta Narayan1 • Tanushree Bhattacharya1 Swapan Konar1
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Soubhik Chakraborty1
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Received: 16 May 2019 / Revised: 1 October 2020 / Accepted: 6 October 2020 The National Academy of Sciences, India 2020
Abstract The present study deals with the assessment of the spatial distribution of PM2.5 over a decade in Jharkhand state of eastern India, which is a prominent site for mining and industries. Since there are very few monitoring stations on the ground to monitor the air quality of the entire state, satellite data have been utilised. The study period is from the year 2005 to 2016. The selection of the study period is based on the availability of satellite as well as ground station data. Multiple linear regression and geographically weighted regression (GWR) model was employed to predict the concentration of PM2.5 spatially, and the results were compared with the help of Akaike information criterion to identify the better representative model. Results showed that the GWR model performed better in predicting the spatial distribution of PM2.5. PM2.5 concentration of this state exceeds the permissible limit set by the world health organisation. The north-eastern districts of the state (29.36% of the total area) had exceeded even the Indian national ambient air quality standard. The identification of the possible reasons for high concentration was made through visual examination of satellite imageries over the study period. Also, possible health effects were discussed. Keywords Geographically weighted regression model Kriging interpolation Resampling PM2.5 Multiple linear regression
& Tanushree Bhattacharya [email protected] 1
Birla Institute of Technology, Ranchi, India
1 Introduction Air pollution is a process through which the natural balance of its constituents is disturbed to such extent that is harmful not only to humans but to all the living creatures and the surrounding environment [1]. After observing the environmental conditions, it has been found that even at low concentration of air pollutants, the exposure risk is high. Air pollution harms the lungs and causes respiratory diseases that lead to more than two million deaths worldwide every year [2, 3]. It has been reported that out of these deaths, approximately 2.1 million were caused by PM2.5 alone [4]. Several studies have been published that particulate matters with size less than or equal to 2.5 lm (PM2.5) may cause respiratory and cardiovascular diseases [5–7]. In India, suspended particulate matter has a significant contribution to air pollution [8, 9]. The climate of the Indian subcontinent has tropical and subtropical characteristics. Such types of climatic conditions put considerable variation in the concentration of particulate matter over India on both spatial and temporal scales [8]. Many Indian cities have the level of particulate matters above the permissible limit in their ambient air, which
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