" Assessing The Impact of Land Use Changes on Pm2.5 Concentrations: A Geographically Weighted Regression Approach " 

ABSTRACT The increase in urbanization and migration has led to the unprecedented growth in anthropogenic pollutants especially the pollution caused by PM2.5. The effects of the land surface changes on these pollutants are significant. Research in the past indicates a close link between PM2.5 pollution and land use patterns at the micro-scale. The association of land and pollutants could be utilized as a proactive measure for reducing PM2.5 pollution and that’s what the current work proposes to do by taking Land use and land cover changes (LULCC) into consideration as one of the crucial and important factors influencing air quality. This study delves into the effects of different LULC categories and changes in land use on PM2.5 concentrations over Dehradun, Uttarakhand using a geographically weighted regression model.Between 2000 and 2020, the LULCC analysis shows that the Built-up area has increased by 249.25% while in the same time interval, the highest recorded PM2.5 value increased by 17%, surging from 41.6 µg/m³ to 50.1 µg/m³, the agriculture area is increased by 371.86% over the study area. The built-up area exhibits the highest PM2.5 concentrations, while the densely vegetated area shows the lowest levels. The GWR analysis represents the significant relationship between PM2.5 and LULCC. These findings provide valuable insights for making informed decisions concerning regional environmental conservation, health, and local ecological well-being.   Keywords: PM2.5, Land use, Land cover, Remote Sensing, Geographically Weighted Regression,