Abstract
Fine particulate matter (i.e. particles with diameters smaller than 2.5 microns, PM2.5) has become a critical environmental issue in China. Land use and land cover (LULC) is recognized as one of the most important influence factors, however very fewer investigations have focused on the impact of LULC on PM2.5. The influences of different LULC types and different land use and land cover change (LULCC) types on PM2.5 are discussed. A geographically weighted regression model is used for the general analysis, and a spatial analysis method based on the geographic information system is used for a detailed analysis. The results show that LULCC has a stable influence on PM2.5 concentration. For different LULC types, construction lands have the highest PM2.5 concentration and woodlands have the lowest. The order of PM2.5 concentration for the different LULC types is: construction lands > unused lands > water > farmlands >grasslands > woodlands. For different LULCC types, when high-grade land types are converted to low-grade types, the PM2.5 concentration decreases; otherwise, the PM2.5 concentration increases. The result of this study can provide a decision basis for regional environmental protection and regional ecological security agencies.
Highlights
Fine particulate matter has become a critical environmental issue in China
The P M2.5 concentration of the construction lands was still the highest. In both 2 years, the order of PM2.5 concentration for the different Land use and land cover (LULC) types was the same: construction lands > unused lands > water > farmlands > grasslands > woodlands, meaning that the LULC type had an important influence on the P M2.5 concentrations
A Geographical weighted regression (GWR) method was used for the general analysis, and a spatial analysis method based on the geographic information system was used for the detailed analysis
Summary
Fine particulate matter (i.e. particles with diameters smaller than 2.5 microns, PM2.5) has become a critical environmental issue in China. Previous research has revealed that PM2.5 is severely affected by meteorological factors at the macro-scale[18] in terms of temperature19, precipitation[20], wind c onditions[21,22], etc., while at the micro-scale, PM2.5 is strongly associated with land use and land cover (LULC) type[23]. Due to the discontinuous spatial distribution of sites monitoring P M2.5 data, several methods have been employed to solve this problem, including spatial interpolation[30], chemical transport models[31], land-use regression models[32] and aerosol optical depth (AOD) based statistical models[33]. The PM2.5 data used in these studies was station monitoring data which is spatially discontinuous and cannot reveal the spatial relationship between P M2.5 and LULCC types. The results of this study can provide a decision basis for regional environmental protection and regional ecological security agencies
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