Spatial distribution for assessing exposure of particulate matter (PM 10 ) in a densely populated coalfield using land use regression model

Abstract

Abstract It is prudent to explore the spatial distribution of air pollution especially in mining affected land use as the coal mining area are expanding with increasing energy demand. In this study, the LUR model is developed to predict the spatial distribution of respirable particulates (PM10) concentration in a cluster of coal mines situated in a thickly populated region. Taking the agglomeration of Kusunda-Bastacolla administrative areas in JCF of India as a case study, the modified LUR model was tested. Results revealed that the eastern zone of the study area had higher concentration levels due to high population density and opencast coal mines as the major cause of elevated PM10 concentration levels. The PM10 concentration levels also followed a seasonal trend with being more elevated in winter followed by post-monsoon and summer. This study's potential predictor variables were area proportions of land use, traffic road length, population density, elevation, and land surface temperature (LST). The model validation showed that the modified LUR model with LST as predictor variable performed moderately with obvious average cross-validation based R2 (0.47) and lower RMSE (30.6 µg/m3). It can be concluded that the modified LUR model can provide a feasible tool for mapping PM10 concentration and exposure on population while planning future coal mining expansion.