Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2015

被引:76
|
作者
Zhou Liang [1 ,2 ]
Zhou Chenghu [2 ]
Yang Fan [3 ]
Che Lei [4 ]
Wang Bo [5 ]
Sun Dongqi [2 ]
机构
[1] Lanzhou Jiaotong Univ, Fac Geomat, Lanzhou 730070, Gansu, Peoples R China
[2] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
[3] Nanjing Univ, Sch Geog & Oceanog Sci, Nanjing 210023, Jiangsu, Peoples R China
[4] Northwest Normal Univ, Coll Geog & Environm Sci, Lanzhou 730070, Gansu, Peoples R China
[5] Univ Hong Kong, Dept Geog, Hong Kong 999077, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
air pollution; PM2.5; haze; spatio-temporal evolution; environmental influence; China; PARTICULATE AIR-POLLUTION; AEROSOL OPTICAL DEPTH; LAND-USE REGRESSION; UNITED-STATES; CHEMICAL-COMPOSITION; MORTALITY; ASSOCIATION; QUALITY; URBAN; VARIABILITY;
D O I
10.1007/s11442-019-1595-0
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
High concentrations of PM2.5 are universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM2.5 concentrations for regional air quality control and management. In this study, PM2.5 data from 2000 to 2015 was determined from an inversion of NASA atmospheric remote sensing images. Using geo-statistics, geographic detectors, and geo-spatial analysis methods, the spatio-temporal evolution patterns and driving factors of PM2.5 concentration in China were evaluated. The main results are as follows. (1) In general, the average concentration of PM2.5 in China increased quickly and reached its peak value in 2006; subsequently, concentrations remained between 21.84 and 35.08 g/m(3). (2) PM2.5 is strikingly heterogeneous in China, with higher concentrations in the north and east than in the south and west. In particular, areas with relatively high PM2.5 concentrations are primarily in four regions, the Huang-Huai-Hai Plain, Lower Yangtze River Delta Plain, Sichuan Basin, and Taklimakan Desert. Among them, Beijing-Tianjin-Hebei Region has the highest concentration of PM2.5. (3) The center of gravity of PM2.5 has generally moved northeastward, which indicates an increasingly serious haze in eastern China. High-value PM2.5 concentrations have moved eastward, while low-value PM2.5 has moved westward. (4) Spatial autocorrelation analysis indicates a significantly positive spatial correlation. The High-High PM2.5 agglomeration areas are distributed in the Huang-Huai-Hai Plain, Fenhe-Weihe River Basin, Sichuan Basin, and Jianghan Plain regions. The Low-Low PM2.5 agglomeration areas include Inner Mongolia and Heilongjiang, north of the Great Wall, Qinghai-Tibet Plateau, and Taiwan, Hainan, and Fujian and other southeast coastal cities and islands. (5) Geographic detection analysis indicates that both natural and anthropogenic factors account for spatial variations in PM2.5 concentration. Geographical location, population density, automobile quantity, industrial discharge, and straw burning are the main driving forces of PM2.5 concentration in China.
引用
收藏
页码:253 / 270
页数:18
相关论文
共 50 条
  • [1] Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2015
    Liang Zhou
    Chenghu Zhou
    Fan Yang
    Lei Che
    Bo Wang
    Dongqi Sun
    [J]. Journal of Geographical Sciences, 2019, 29 : 253 - 270
  • [2] Spatio-temporal trends and influencing factors of PM2.5 concentrations in urban agglomerations in China between 2000 and 2016
    Caihong Huang
    Kai Liu
    Liang Zhou
    [J]. Environmental Science and Pollution Research, 2021, 28 : 10988 - 11000
  • [3] Spatio-temporal trends and influencing factors of PM2.5 concentrations in urban agglomerations in China between 2000 and 2016
    Huang, Caihong
    Liu, Kai
    Zhou, Liang
    [J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2021, 28 (09) : 10988 - 11000
  • [4] Spatio-temporal evolution patterns and influencing factors of PM2.5 in Chinese urban agglomerations
    Wang, Zhenbo
    Liang, Longwu
    Wang, Xujing
    [J]. Dili Xuebao/Acta Geographica Sinica, 2019, 74 (12): : 2614 - 2630
  • [5] Spatio-temporal Evolution of PM2.5 Concentration During 2000-2019 in China
    Xia, Xiao-Sheng
    Wang, Jun-Hong
    Song, Wei-Dong
    Cheng, Xian-Fu
    [J]. Huanjing Kexue/Environmental Science, 2020, 41 (11): : 4832 - 4843
  • [6] Spatio-Temporal Variation of PM2.5 Concentrations and Their Relationship with Geographic and Socioeconomic Factors in China
    Lin, Gang
    Fu, Jingying
    Jiang, Dong
    Hu, Wensheng
    Dong, Donglin
    Huang, Yaohuan
    Zhao, Mingdong
    [J]. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2014, 11 (01) : 173 - 186
  • [7] Spatio-temporal distribution and chemical composition of PM2.5 in Changsha, China
    Nan-Nan Zhang
    Yang Guan
    Lei Yu
    Fang Ma
    Yi-Fan Li
    [J]. Journal of Atmospheric Chemistry, 2020, 77 : 1 - 16
  • [8] Spatio-temporal Variations in PM2.5 and Its Influencing Factors in the Yangtze River Delta Urban Agglomeration
    Wu, Shu-Qi
    Yao, Jia-Qi
    Yang, Ran
    Zhang, Shan-Wen
    Zhao, Wen-Ji
    [J]. Huanjing Kexue/Environmental Science, 2023, 44 (10): : 5325 - 5334
  • [9] Spatio-temporal variation and influence factors of PM2.5 concentrations in China from 1998 to 2014
    Lu, Debin
    Xu, Jianhua
    Yang, Dongyang
    Zhao, Jianan
    [J]. ATMOSPHERIC POLLUTION RESEARCH, 2017, 8 (06) : 1151 - 1159
  • [10] Compositional Spatio-Temporal PM2.5 Modelling in Wildfires
    Sanchez-Balseca, Joseph
    Perez-Foguet, Agustii
    [J]. ATMOSPHERE, 2021, 12 (10)