Analysis of Ningxia Hui Autonomous District’s Gray Water Footprint from the Perspective of Water Sustainability

Chen Yue, Yong Qian (), Feng Liu, Xiangxiang Cui and Suhua Meng
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Chen Yue: Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences (CAGS), Shijiazhuang 050061, China
Yong Qian: Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences (CAGS), Shijiazhuang 050061, China
Feng Liu: Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences (CAGS), Shijiazhuang 050061, China
Xiangxiang Cui: Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences (CAGS), Shijiazhuang 050061, China
Suhua Meng: Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences (CAGS), Shijiazhuang 050061, China

Sustainability, 2023, vol. 15, issue 16, 1-18

Abstract: Gray water footprint (GWF) is an effective method to evaluate the degree of water pollution and water quality. It is the amount of freshwater needed to dilute water pollutants to meet ambient water quality standards. Accounting and analyzing the GWF will be significant for promoting an improved water environment and sustainable water ecology in Ningxia Autonomous District. We accounted for the GWF of all cities in Ningxia from 2012 to 2020 and evaluated its spatial-temporal variations by the GWF accounting method proposed by Hoekstra. Then, the Logarithmic Mean Divisia Index (LMDI) method was applied to investigate the contributions of four driving factors: the population scale effect, economic development effect, technological effect, and industrial structure effect. And then, the changes in the GWF in the Ningxia region were analyzed. The results showed that the GWF in the Ningxia region changed from 79.21 × 10 8 to 29.09 × 10 8 m 3 /yr during 2012–2020, making a significant decreasing trend. Among all cities, Wuzhong City contributes the most in terms of the GWF. More specifically, economic development and technology structure are the positive and negative drivers of the GWF, respectively. The water pollution levels in Ningxia (0.49–1.3) indicated that the waste assimilation capacity has fallen short of taking up the pollutant load, which had an unfavorable impact on the groundwater according to actual water quality data. NO 3 -N and NH 3 -N are detected in the groundwater throughout the Ningxia region, with the highest NH 3 -N content in the groundwater in Yinchuan, which almost exceeded the groundwater quality standard of category III. Above all, this study reflected the current water pollution situation better by combining the GWF with actual water quality data in Ningxia. The finding of this study is valuable for addressing water quality threats and developing sustainable development.

Keywords: gray water footprint; groundwater; actual water quality data; Logarithmic Mean Divisia Index (LMDI) method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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