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作者:

Liu, Yu (Liu, Yu.) | Lan, Shuang-Shuang (Lan, Shuang-Shuang.) | Zhang, Yong-Xiang (Zhang, Yong-Xiang.) (学者:张永祥) | Li, Fang-Chun (Li, Fang-Chun.) | Hou, Shu-Kai (Hou, Shu-Kai.)

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摘要:

The distribution patterns of human activities affecting groundwater vulnerability vary with time. Studying the temporal and spatial changes in groundwater vulnerability, exploring the distribution characteristics of each period, and predicting the trends of development are important to formulate an effective development plan and reduce the risk of groundwater pollution at the same time. Based on the hydrogeological data as well as humanities and social data for 2004, 2010, and 2016 for the Chaoyang District of Beijing, a comprehensive evaluation model considering the human factors such as the land use types was established using the DRASTIC model. The spatiotemporal pattern of groundwater vulnerability was quantitatively characterized by calculating the Global Moran's I and Getis-Ord Gi* index, and the distribution characteristics and variations in groundwater vulnerability were analyzed by the centroid of the G index and the standard deviation ellipse of the study area. The results indicate that in 2004, 2010, and 2016, the areas of high vulnerability have gradually reduced. The groundwater vulnerability in the study area shows a strong spatial aggregation; high concentration areas are mainly distributed in the northeast and southwest regions. The vulnerability of the northeast region has been decreasing each year, while the vulnerability of the northwest region has not changed much. The main reasons are the land use changes and the reductions in fertilizer use. © 2017, Science Press. All right reserved.

关键词:

Autocorrelation Groundwater Groundwater pollution Land use Petroleum reservoir evaluation

作者机构:

  • [ 1 ] [Liu, Yu]College of Architecture and Civil Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Liu, Yu]Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Lan, Shuang-Shuang]College of Architecture and Civil Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Lan, Shuang-Shuang]Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Zhang, Yong-Xiang]College of Architecture and Civil Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 6 ] [Zhang, Yong-Xiang]Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 7 ] [Li, Fang-Chun]College of Architecture and Civil Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 8 ] [Li, Fang-Chun]Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 9 ] [Hou, Shu-Kai]College of Architecture and Civil Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 10 ] [Hou, Shu-Kai]Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Beijing; 100124, China

通讯作者信息:

  • [lan, shuang-shuang]key laboratory of beijing for water quality science and water environment recovery engineering, beijing university of technology, beijing; 100124, china;;[lan, shuang-shuang]college of architecture and civil engineering, beijing university of technology, beijing; 100124, china

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来源 :

Environmental Science

ISSN: 0250-3301

年份: 2017

期: 10

卷: 38

页码: 4236-4244

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 7

ESI高被引论文在榜: 0 展开所有

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