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
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
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基于WPI指数的干旱区浅水湖泊水质时空变化趋势——以中国乌兰素海为例
DOI：
10.3390/w11071410
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发表时间：
2019-07
期刊：
影响因子：
3.4
通讯作者：
Lixin Wang
中科院分区：
文献类型：
--
作者：
Qi Zhang;Ruihong Yu;Ye Jin;Zhuangzhuang Zhang;Xinyu Liu;Hao Xue;Yanling Hao;Lixin Wang
乌兰苏海是我国黄河流域最大的浅水湖泊，是河套地区灌溉系统的重要组成部分。其水质问题已成为人们关注的焦点，影响到当地的水安全和经济的可持续发展。采用水污染指数（WPI）这一有效的水质评价方法，比较各污染指标的污染程度，确定主要污染指标。状态转移指数（RSI）的方法来识别水质趋势。采用聚类分析和丹尼尔趋势检验方法，分析了典型水质指标（如，总氮（TN）、总磷（TP）、溶解氧（DO）和化学需氧量（COD）。结果表明，1998 - 2017年，乌兰素海水质总体呈改善趋势; WPITN、WPITP和WPIDO的空间变化规律为：入口>中心和出口; WPICOD的空间变化规律为：出口>入口>中心。TN是全年的关键污染指标。2017年，利用聚类分析确定了旱季和雨季。旱季WPICOD高于WPITN、WPITP和WPIDO，而雨季WPITN、WPITP和WPIDO高于WPICOD。WPI可分为3类：重度污染区、中度污染区和轻度污染区，但这3类污染区在旱季和雨季的划分上存在差异。WPICOD是2017年所有污染指标中最高的。年内或年际污染、农业面源污染、点源污染和内源污染是导致水质恶化的主要污染源。本研究为当局有效管理水质和控制水污染提供了有用的信息。
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.
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DOI：
--
发表时间：
2013
期刊：
Environmental Monitoring in China
影响因子：
--
作者：
Wang Mei
通讯作者：
Wang Mei
影响因子：
11.4
作者：
M. SanClements;G. Oelsner;D. McKnight;J. Stoddard;S. Nelson
通讯作者：
M. SanClements;G. Oelsner;D. McKnight;J. Stoddard;S. Nelson
基于双齿形结构的电信波长集成液晶光开关
影响因子：
1.3
作者：
Zhu, Jia Hu;Huang, Xu Guang;Tao, Jin;Jin, Xiao Ping;Mei, Xian;Zhu, Yun Jin
通讯作者：
Zhu, Yun Jin
影响因子：
3.2
作者：
Ren Chun-tao
通讯作者：
Ren Chun-tao
影响因子：
3.3
作者：
He-Long Jiang
通讯作者：
He-Long Jiang