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Temporal and Spatial Variation Trends in Water Quality Based on the WPI Index in the Shallow Lake of an Arid Area: A Case Study of Lake Ulansuhai, China

1
School of Ecology and Environment in Inner Mongolia University, Key Laboratory of River and Lake in Inner Mongolia Autonomous Region, Hohhot 010021, Inner Mongolia Autonomous, China
2
Key Laboratory of Ecology and Resources Use of the Mongolian Plateau of Ministry of Education, Hohhot 010021, Inner Mongolia Autonomous, China
*
Author to whom correspondence should be addressed.
Water 2019, 11(7), 1410; https://doi.org/10.3390/w11071410
Received: 7 May 2019 / Revised: 28 June 2019 / Accepted: 5 July 2019 / Published: 9 July 2019
(This article belongs to the Section Water Quality and Ecosystems)
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Abstract

Ulansuhai, the largest shallow lake of the Yellow River of China, is an important component of the Hetao region irrigation system. Many concerns have concentrated on its water quality, which affects the local water security and sustainable economic development. In this study, the water pollution index (WPI), an effective water quality evaluation method, was used to compare the pollution levels among pollution indicators and to determine the major pollution indicators. The regime shift index (RSI) approach was employed to identify the water quality trends. Cluster analysis and Daniel trend test methods were employed to analyse the inner-annual and inter-annual spatio-temporal trends of the typical water quality indicators (e.g., total nitrogen (TN), total phosphorus (TP), dissolved oxygen (DO), and chemical oxygen demand (COD)) in Lake Ulansuhai. The results show that the water quality of Ulansuhai improved from 1998 to 2017; spatial variations in the WPITN, WPITP, and WPIDO followed the order of inlet > centre and outlet, while spatial variations in the WPICOD showed the order of outlet > inlet > centre. TN was the critical pollution indicator throughout the year. In 2017, the dry season and wet season were determined using cluster analysis. The WPICOD was higher than the WPITN, WPITP, and WPIDO in the dry season, while the WPITN, WPITP, and WPIDO were higher than the WPICOD in the wet season. WPI was grouped into three clusters: highly polluted regions, moderately polluted regions, and less polluted regions, However, there is a discrepancy between the three polluted regions that were divided into the dry season and the wet season. The WPICOD was highest among all pollution indicators in 2017. Major sources of pollution that contribute to the deterioration of water quality include inner-annual or inter-annual pollution, agricultural non-point pollution, point source pollution, and internal pollution. This study provides useful information for authorities to effectively manage water quality and control water pollution. View Full-Text
Keywords: Lake Ulansuhai; spatio-temporal variation; water pollution index (WPI); daniel trend test; cluster analysis; regime shift Lake Ulansuhai; spatio-temporal variation; water pollution index (WPI); daniel trend test; cluster analysis; regime shift
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhang, Q.; Yu, R.; Jin, Y.; Zhang, Z.; Liu, X.; Xue, H.; Hao, Y.; Wang, L. Temporal and Spatial Variation Trends in Water Quality Based on the WPI Index in the Shallow Lake of an Arid Area: A Case Study of Lake Ulansuhai, China. Water 2019, 11, 1410.

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