[Analysis of the Temporal and Spatial Variation of PM_2.5 in China Based on the LUR Model]

The land use regression (LUR) model is one of the most important systematic methods to simulate the temporal and spatial differentiation of the atmospheric pollutant concentration. To explore the adaptability of the LUR model to the simulation of air pollutants at the national scale in China and the temporal and spatial variation characteristics of fine air particulate matter (PM_2.5) in China in 2015 and its correlation with different geographical elements, we built a LUR model. The LUR model is based on a geographically weighted algorithm using PM_2.5 data acquired from the national control monitoring site in 2015 as the dependent variable and applying factors such as the type of land use, altitude, population, road traffic, and meteorological elements as independent variables. Based on model regression mapping, we obtained the distributions of monthly and annual PM_2.5 concentrations nationwide in 2015 and analyzed the temporal and spatial variation characteristics of PM_2.5 concentrations using the Hu line as a reference line. The results indicate that introducing the geographically weighted algorithm can significantly reduce the residual Moran's Ⅰ of the LUR model, weaken the spatial autocorrelation of residuals, and improve the coefficient of determination R², which is better to reveal the complex relationship between the spatial distribution and impact factors of PM_2.5. Cropland, forest, grass and urban industrial and residential land, and meteorological elements and major roads noticeably impact the PM_2.5 concentration. Different spatial distributions of different geographical elements have distinct effects on PM_2.5. The PM_2.5 shows distinct temporal and spatial differences on both sides of the Hu line. The PM_2.5 concentration is relatively high in developed cities with a large population and high industrialization levels. The concentration of PM_2.5 is higher in winter and gradually decreases in autumn, spring, and summer.