4.7 Article

A comprehensive insight into water pollution and driving forces in Western China-case study of Qinghai

期刊

JOURNAL OF CLEANER PRODUCTION
卷 274, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2020.123950

关键词

Grey water footprint; Water pollution; STIRPAT (Stochastic impacts by regression on population Affluence and technology) model; Driving force; Water environment load

资金

  1. Second Tibetan Plateau Scientific Expedition and Research Program (STEP) [2019QZKK1005]
  2. Strategic Priority Research Program of Chinese Academy of Sciences, Pan-Third Pole Environment Study for a Green Silk Road(Pan-TPE) [XDA20040400]
  3. National Natural Science Foundation of China [41590840, 41590842]
  4. Start-up Research Program of IGSNRR, CAS (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences) [Y9V90115YZ]

向作者/读者索取更多资源

Rapid industrialization and urbanization have caused many serious resources and environmental problems, including water scarcity and water pollution. Grey water footprint (GWF), estimating water pollution according to the volume of water required to assimilate pollutants, has caught large attention because of the requirement for meeting water scarcity and water pollution challenges. Qinghai, an arid or semi-arid region in Western China, is an important ecological barrier providing multiple ecological services with water as its key element. Recently it has experienced rapid urbanization which may cause serious water problems. Therefore, this study aims to investigate water pollution in Qinghai between 2000 and 2017 and to diagnose the related driving forces using the GWF model and the extended STIRPAT model. Results show that the total GWF increased between 2000 and 2017, from 1.28 E+10 to 1.97 E+10 m3. Agricultural GWF took the largest share with a mean proportion of 60%, followed by domestic GWF(8%) and industrial GWF(32%). Water environment load increased by 19.67%, while the GWF efficiency increased by 5.49 times. The GWF intensity decreased by 84.58%, reflecting water pollution level resulted in per unit of GDP decreased largely. Population and GDP per capita are the major drivers with contribution degrees of 70.20% and 39.65%, respectively. Industrial level had the least contribution, 5.34%, whereas energy intensity had a considerable negative impact, -36.66%, on GWF's increase. In addition, policy suggestions are provided to mitigate GWF challenges of Qinghai. This study can provide scientific basis for decision-marking for sustainability. (C) 2020 Elsevier Ltd. All rights reserved.

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