The impacts of comprehensive urbanization on PM2.5 concentrations in the Yangtze River Delta, China

被引:32
|
作者
She, Qiannan [1 ,2 ]
Cao, Shanshan [1 ]
Zhang, Shiqing [1 ]
Zhang, Jianpeng [3 ]
Zhu, Hongkai [1 ]
Bao, Jiehuan [1 ]
Meng, Xing [4 ,5 ]
Liu, Min [1 ,6 ]
Liu, Yang [2 ]
机构
[1] East China Normal Univ, Sch Ecol & Environm Sci, Shanghai Key Lab Urban Ecol Proc & Ecorestorat, Shanghai 200241, Peoples R China
[2] Emory Univ, Rollins Sch Publ Hlth, Dept Environm Hlth, Atlanta, GA 30322 USA
[3] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Reg Sustainable Dev Modeling, Beijing 100101, Peoples R China
[4] East China Normal Univ, Sch Geog Sci, Key Lab Geog Informat Sci, Minist Educ, Shanghai 200241, Peoples R China
[5] Henan Univ, Key Lab Geospatial Technol Middle & Lower Yellow, Minist Educ, Kaifeng 475004, Peoples R China
[6] Inst Ecochongming, Shanghai 200062, Peoples R China
基金
中国国家自然科学基金;
关键词
PM2.5; Air pollution; Urbanization; Spatiotemporal statistical model; Yangtze River Delta; AIR-QUALITY; ECO-ENVIRONMENT; TIME-SERIES; URBAN FORM; LAND-USE; POLLUTION; CITIES; LEVEL;
D O I
10.1016/j.ecolind.2021.108337
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
Air pollution is a serious global environmental problem, especially in developing countries. PM2.5 is a major air pollutant that poses critical risks in urban areas. In this study, we identified the influence of comprehensive urbanization on regional PM2.5 in the Yangtze River Delta (YRD), the largest metropolitan region in China, from 1998 to 2015. The impacts of four urbanization subsystems (Economic, Spatial, Demographic, and Social) on the spatiotemporal evolution of PM2.5 were investigated at the city level using a linear mixed effect model (LME). The annual average concentration of PM2.5 over the YRD increased during the study period, with rapid growth in the northern plains and slow growth in the mountainous south. The LME model showed good performance between the predicted and observed PM2.5, with an R-2 value for 10-fold cross-validation reaching 0.87 and a regression coefficient of 0.88. Urban population, GDP ratio of secondary industry, built-up area, total mad area, number of students in colleges and universities, and total retail sales of consumer goods all had positive associations with PM2.5 concentration, while the proportion of tertiary industry employment, GDP ratio of primary industry, forest area, and number of hospital beds all had negative associations. Economic had the strongest effect (44%) on PM2.5 early on (1998-2003), but over time the contribution of other subsystems gradually increased. The overall contributions of urbanization type to PM2.5 in the YRD were Spatial (33.10%) > Economic (26.76%) > Demographic (16.85%) > Social (12.72%). Our results help deepen the understanding of regional air pollution in China as well as its correlation with urbanization and its subsystems.
引用
收藏
页数:8
相关论文
共 50 条
  • [31] PM2.5 vertical variation during a fog episode in a rural area of the Yangtze River Delta, China
    Zhu, Jun
    Zhu, Bin
    Huang, Yong
    An, Junlin
    Xu, Jiaping
    SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 685 : 555 - 563
  • [32] Driving force heterogeneity of urban PM2.5 pollution: Evidence from the Yangtze River Delta, China
    Wang, Sufeng
    Xu, Ling
    Ge, Shijian
    Jiao, Jianling
    Pan, Banglong
    Shu, Ying
    ECOLOGICAL INDICATORS, 2020, 113 (113)
  • [33] Chemical Characterization and Source Apportionment of PM2.5 during Spring and Winter in the Yangtze River Delta, China
    Du, Wenjiao
    Zhang, Yanru
    Chen, Yanting
    Xu, Lingling
    Chen, Jinsheng
    Deng, Junjun
    Hong, Youwei
    Xiao, Hang
    AEROSOL AND AIR QUALITY RESEARCH, 2017, 17 (09) : 2165 - 2180
  • [34] Analysis of the driving factors of PM2.5 concentration in the air: A case study of the Yangtze River Delta, China
    Xu, Guoyu
    Ren, Xiaodong
    Xiong, Kangning
    Li, Luqi
    Bi, Xuecheng
    Wu, Qinglin
    ECOLOGICAL INDICATORS, 2020, 110
  • [35] Assessment of the health benefits of declining PM2.5 levels in the Yangtze River Delta
    Wei, Tong
    Li, Zheng-Lei
    Chen, Yu
    Zhao, Xiu-Ge
    Wang, Dan-Lu
    Hou, Ya-Xuan
    Liu, Fen
    Zhongguo Huanjing Kexue/China Environmental Science, 2023, 43 (06): : 3211 - 3219
  • [36] Personal exposure to PM2.5 in Chinese rural households in the Yangtze River Delta
    Hu, Ruolan
    Wang, Shuxiao
    Aunan, Kristin
    Zhao, Minjiang
    Chen, Lu
    Liu, Zhaohui
    Hansen, Mette H.
    INDOOR AIR, 2019, 29 (03) : 403 - 412
  • [37] The empirical relationship between PM2.5 and AOD in Nanjing of the Yangtze River Delta
    Shao, Ping
    Xin, Jinyuan
    An, Junlin
    Kong, Lingbin
    Wang, Bingyun
    Wang, Junxiu
    Wang, Yuesi
    Wu, Dan
    ATMOSPHERIC POLLUTION RESEARCH, 2017, 8 (02) : 233 - 243
  • [38] Estimating PM2.5 concentrations in Yangtze River Delta region of China using random forest model and the Top-of-Atmosphere reflectance
    Yang, Lijuan
    Xu, Hanqiu
    Yu, Shaode
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2020, 272
  • [39] Self-adaptive bandwidth eigenvector spatial filtering model for estimating PM2.5 concentrations in the Yangtze River Delta region of China
    Huangyuan Tan
    Yumin Chen
    John P. Wilson
    Annan Zhou
    Tianyou Chu
    Environmental Science and Pollution Research, 2021, 28 : 67800 - 67813
  • [40] Self-adaptive bandwidth eigenvector spatial filtering model for estimating PM2.5 concentrations in the Yangtze River Delta region of China
    Tan, Huangyuan
    Chen, Yumin
    Wilson, John P.
    Zhou, Annan
    Chu, Tianyou
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2021, 28 (47) : 67800 - 67813