基于夜间灯光影像的能源消费与PM_(2.5)时空关系研究

Spatio-temporal correlation between energy consumption and PM_(2.5) concentration based on nighttime light images

利用能源消费统计数据与夜间灯光影像对陕西省能源消费进行空间化处理,结合PM_(2.5)遥感数据,基于双变量空间相关性分析等方法,研究了陕西省能源消费与PM_(2.5)的时空关系,并利用随机森林回归模型探讨了影响ρ(PM_(2.5))变化的能源消费因素.结果表明:1)2001-2013年陕西省ρ(PM_(2.5))先增大后减小,最高值达到28.5μg·m^(-3),省内PM_(2.5)分布的空间异质性较强,其中关中地区的ρ(PM_(2.5))最高;2)陕西省能源消费量逐年上升,在空间上的分布与ρ(PM_(2.5))类似,关中地区的能源消费量最多;3)陕西省能源消费量与ρ(PM_(2.5))的Moran’s I达到了0.289,表明二者之间有着明显的空间正相关性,即高能源消费的区域有着高质量浓度的PM_(2.5)分布;4)人口密度、路网密度与能源消费总量是陕西省ρ(PM_(2.5))变化的重要驱动因素.

PM_(2.5)is the primary pollutant in urban air in China, causing serious harm to human physical and mental health,arousing widespread concern. Study on the spatial and temporal relationship between PM_(2.5)and energy consumption will provide some theoretical basis to formulate effective atmospheric environmental protection policies and to promote urbanization. Energy consumption statistical data and nighttime light images were used to define spatial patterns in energy consumption in Shaanxi Province. Time-space relationship between energy consumption and PM_(2.5)concentration with PM_(2.5)remote sensing data were studied by spatial correlation analysis. Random forest regression was used to dissect energy consumption factors affecting changes in PM_(2.5)concentration. It was found that from 2001 to 2013,PM_(2.5)concentrations in Shaanxi Province initially increased and then declined,with the highest value at 28.5 μg·m^(-3). The spatial heterogeneity in PM_(2.5)distribution in the province was marked, with the Guanzhong region showing the highest PM_(2.5)concentration. Energy consumption in Shaanxi Province was found to increase year by year,with a spatial distribution similar to that of PM_(2.5)concentration. Energy consumption in the Guanzhong region was the largest. The Moran’s index of energy consumption and PM_(2.5)concentration in Shaanxi Province reached 0.289,indicating an obvious positive spatial correlation-areas with high energy consumption had high concentrations of PM_(2.5). Population density,road network density and total energy consumption were found to be important driving factors for changes in PM_(2.5)concentration in Shaanxi Province.