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
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