The impacts of comprehensive urbanization on PM2.5 concentrations in the Yangtze River Delta, China
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 large...
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2021
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oai:doaj.org-article:ef4c6c0a61454431891f346c49cc0a442021-12-01T05:02:56ZThe impacts of comprehensive urbanization on PM2.5 concentrations in the Yangtze River Delta, China1470-160X10.1016/j.ecolind.2021.108337https://doaj.org/article/ef4c6c0a61454431891f346c49cc0a442021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21010025https://doaj.org/toc/1470-160XAir 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 R2 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 road 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.Qiannan SheShanshan CaoShiqing ZhangJianpeng ZhangHongkai ZhuJiehuan BaoXing MengMin LiuYang LiuElsevierarticlePM2.5Air pollutionUrbanizationSpatiotemporal statistical modelYangtze River DeltaEcologyQH540-549.5ENEcological Indicators, Vol 132, Iss , Pp 108337- (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
PM2.5 Air pollution Urbanization Spatiotemporal statistical model Yangtze River Delta Ecology QH540-549.5 |
| spellingShingle |
PM2.5 Air pollution Urbanization Spatiotemporal statistical model Yangtze River Delta Ecology QH540-549.5 Qiannan She Shanshan Cao Shiqing Zhang Jianpeng Zhang Hongkai Zhu Jiehuan Bao Xing Meng Min Liu Yang Liu The impacts of comprehensive urbanization on PM2.5 concentrations in the Yangtze River Delta, China |
| description |
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 R2 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 road 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. |
| format |
article |
| author |
Qiannan She Shanshan Cao Shiqing Zhang Jianpeng Zhang Hongkai Zhu Jiehuan Bao Xing Meng Min Liu Yang Liu |
| author_facet |
Qiannan She Shanshan Cao Shiqing Zhang Jianpeng Zhang Hongkai Zhu Jiehuan Bao Xing Meng Min Liu Yang Liu |
| author_sort |
Qiannan She |
| title |
The impacts of comprehensive urbanization on PM2.5 concentrations in the Yangtze River Delta, China |
| title_short |
The impacts of comprehensive urbanization on PM2.5 concentrations in the Yangtze River Delta, China |
| title_full |
The impacts of comprehensive urbanization on PM2.5 concentrations in the Yangtze River Delta, China |
| title_fullStr |
The impacts of comprehensive urbanization on PM2.5 concentrations in the Yangtze River Delta, China |
| title_full_unstemmed |
The impacts of comprehensive urbanization on PM2.5 concentrations in the Yangtze River Delta, China |
| title_sort |
impacts of comprehensive urbanization on pm2.5 concentrations in the yangtze river delta, china |
| publisher |
Elsevier |
| publishDate |
2021 |
| url |
https://doaj.org/article/ef4c6c0a61454431891f346c49cc0a44 |
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