doi:10.12202/j.0476-0301.2020241裴川乐^1,,
连炎清^2,,
1.北京师范大学地球科学前沿交叉研究中心，100875，北京
2.中国科学院地球环境研究所，710061，陕西西安
基金项目:中国科学院先导性科技专项（B类）资助项目（XDB40020100）

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通讯作者:连炎清（1962—），男，博士，研究员. 研究方向：水文与水环境模拟. E-mail：lianyq@ieecas.cn

中图分类号:X87

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收稿日期:2020-06-17
网络出版日期:2021-07-05
刊出日期:2021-10-01

Spatio-temporal correlation between energy consumption and PM_2.5 concentration based on nighttime light images

Chuanle PEI^1,,
Yanqing LIAN^2,,
1. Interdisciplinary Research Center of Earth Science Frontier, Beijing Normal University, 100875, Beijing, China
2. Institute of Earth Environment, Chinese Academy of Sciences, 710061, Xi’an, Shaanxi, China



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摘要
摘要: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28.5 μg·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．
关键词:PM_2.5/
能源消费/
时空关系/
空间相关性/
随机森林
Abstract: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．
Key words:PM_2.5/
energy consumption/
spatio-temporal relationship/
spatial correlation/
random forest