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

被引：25

作者：

Zhang, Qi ^{[1
]}

Yu, Ruihong ^{[1
,2
]}

Jin, Ye ^{[1
]}

Zhang, Zhuangzhuang ^{[1
]}

Liu, Xinyu ^{[1
]}

Xue, Hao ^{[1
]}

Hao, Yanling ^{[1
,2
]}

Wang, Lixin ^{[1
,2
]}

机构：

[1] Inner Mongolia Univ, Sch Ecol & Environm, Key Lab River & Lake Inner Mongolia Autonomous Re, Hohhot 010021, Inner Mongolia, Peoples R China

[2] Minist Educ, Key Lab Ecol & Resources Use Mongolian Plateau, Hohhot 010021, Inner Mongolia, Peoples R China

来源：

WATER | 2019年 / 11卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Lake Ulansuhai; spatio-temporal variation; water pollution index (WPI); daniel trend test; cluster analysis; regime shift; POLLUTION INDEX; RIVER; MANAGEMENT; PHOSPHORUS; DANUBE;

D O I：

10.3390/w11071410

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页数：20

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