Spatiotemporal characteristics of PM2.5 concentrations and responses to land-use change in Urumqi, China

The acceleration of urbanization has increasingly exacerbated air pollution in Northwest China. However, existing studies have relatively few analyses of PM2.5 concentrations in response to land-use changes. This study quantitatively evaluated the impact of land-use changes on PM2.5 concentrations in Urumqi (2014 to 2023) using remote sensing techniques and machine learning methods. The MCD19-A2 aerosol optical depth (AOD) product, with gaps filled using a singular spectrum analysis algorithm (99.63% AOD coverage), was used to predict PM2.5 concentrations based on the light gradient boosting machine method (10-CV R-2=0.93, root mean square error=17.98 mu g/m(3)). The spatial correlation between land-use changes and PM2.5 concentrations showed that PM2.5 concentrations were highest in central urban areas but decreased by an average of 27.41 mu g/m(3) over the decade. Land-use type transitions (barren-grassland, grassland-barren, and grassland-cropland) were significantly negatively correlated with PM2.5, indicating these changes reduced aerosol concentrations during the research period in Urumqi. The reaction of dynamic PM2.5 to land-use and land-cover changes showed a local overlap but was not entirely consistent, as reflected by the geographically weighted regression model. Geodetector quantified the contribution of land-use change to PM2.5 reduction, particularly barren-grassland conversion, which notably reduced PM2.5 (contribution coefficient = 0.161), highlighting the importance of protecting vegetated areas for PM2.5 control in Urumqi. These findings clarify the impact of land-use change on PM2.5, supporting improvements in land management and atmospheric control strategies for sustainable development in Urumqi.