With the acceleration of urbanizationand industrialization, atmospheric pollution has become a major issue,restricting the sustainable development of the urban environment. Since 2013,Beijing has been among China's most seriously affected regions in terms of hazepollution. Atmospheric pollution is closely linked to land use, particularlythe spatial patterns of green and urban land. Therefore, the quantification ofthe relationship between fine particulate matter (PM2.5) concentration and itsdriving factors in Beijing is of considerable significance for environmentalmanagement and spatial epidemiological studies. A land use regression (LUR)model was constructed to simulate the spatio-temporal distribution of PM2.5concentration. In this study, the independent variables (driving factors)included land use, meteorological factors, population, roads, the digitalelevation model, and the normalized difference vegetation index. The fivemodels had adjusted R-2 of 0.887, 0.770, 0.742, 0.877, and 0.798, respectively.Land use and meteorological factors were the main factors affecting PM2.5concentration. The driving factors of land use on a large scale and roads on asmall scale had a significant impact on PM2.5 emissions. Beijing's PM2.5concentrations in 2015 showed clear spatio-temporal characteristics. Thehighest (lowest) average PM2.5 concentration was recorded in winter (summer).In terms of spatial distribution, PM2.5 concentrations showed a "low inthe northwest and high in the southeast" trend. The most polluted areaswere mainly distributed in the central city and the southeastern andsouthwestern regions. The PM2.5 concentration boundary was essentiallyconsistent with the boundary of land use type. Different land use typespromoted or inhibited PM2.5 concentrations, with a difference of more than 20mu g/m(3) PM2.5 between the two land use categories. Thus, PM2.5 concentrationsshould be controlled by optimizing the spatial and temporal patterns of landuse.
Kong,Lingqiang,TianGuangjin. Assessment of the spatio-temporal pattern of PM2.5 and its drivingfactors using a land use regression model in Beijing, China[J]EnvironmentalMonitoring and Assessment,2020,01.