scholarly journals Assessment of Lake Water Quality and Eutrophication Risk in an Agricultural Irrigation Area: A Case Study of the Chagan Lake in Northeast China

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2380 ◽  
Author(s):  
Xuemei Liu ◽  
Guangxin Zhang ◽  
Guangzhi Sun ◽  
Yao Wu ◽  
Yueqing Chen
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Lake Water ◽  
Northeast China ◽  
Water Quality Management ◽  
Chemical Properties ◽  
Principal Component ◽  
Trophic State Index ◽  
Lake Water Quality ◽  
Chagan Lake

Water quality safety is the key factor to maintain the ecosystem service functions of lakes. Field investigations and statistical analyses were carried out to study the water quality of a large, agriculture-stressed lakes (e.g., Chagan Lake) in Northeast China. The hydro-chemical properties of the Chagan Lake are HCO3·CO3-Na. Nutrient (N and P) and non-nutrient (pH and F−) were found to be the major factors that threaten water quality safety of the lake. The concentration of total nitrogen (TN) and total phosphorus (TP) was found to vary seasonally and at different locations. The overall lake water had mean TN and TP values of 2.19 mg/L and 0.49 mg/L, respectively, in summer. TN was the major factor for water quality deterioration in the western region of the lake, while TP was the principal factor in the other regions, as determined by a principal component analysis (PCA). Fluoride (F−) concentration in the lake water were related to the values of total dissolved solid (TDS), pH, and electrical conductivity (EC). In addition, eutrophication is a fundamental index that has been affecting the ecological evaluation of water quality. The results showed that trophic level index (TLI), trophic state index (TSI), and eutrophication index (EI) were evaluated to quantify the risk of eutrophication. However, TLI and TSI can better describe the purification effect of the wetland. These indices showed that the lake water was hyper-eutrophic in summer, with TLI, TSI, and EI values of 60.1, 63.0, and 66.6, respectively. Disparities in water quality were observed among whole areas of the lake. Overall, this study revealed that controlling agriculture drainage is crucial for lake water quality management. The study generated critical data for making water quality management plans to control the risk.

Development of spatial similarity-based modelling to improve integrated lake water quality management in Malaysia

2018 ◽  
Vol 23 (1) ◽  
pp. 24-33
Author(s):  
Bashirah Fazli ◽  
Aziz Shafie ◽  
Azuhan Mohamed ◽  
Mohd F. Mohamad ◽  
Nasehir K. E. M. Yahaya ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Lake Water ◽  
Water Quality Management ◽  
Lake Water Quality ◽  
Spatial Similarity

A WIND–PHYTOPLANKTON MODEL FOR LAKE WATER QUALITY MANAGEMENT

1986 ◽  
Vol 2 (1) ◽  
pp. 220-224 ◽  
Author(s):  
Sally L. Marquis ◽  
Brian W. Mar ◽  
Eugene B. Welch
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Lake Water ◽  
Water Quality Management ◽  
Lake Water Quality

scholarly journals Economic Instruments for Lake Water Quality Management in Japan.

1997 ◽  
Vol 25 ◽  
pp. 623-628 ◽  
Author(s):  
Thomas Ballatore ◽  
Masahisa Nakamura ◽  
Tomonori Matsuo
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Lake Water ◽  
Water Quality Management ◽  
Economic Instruments ◽  
Lake Water Quality

scholarly journals Modelling Water Quality Improvements in a South Korean Inter-Basin Water Transfer System

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3173
Author(s):  
Hye Won Lee ◽  
Bo-Min Yeom ◽  
Jung Hyun Choi
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Constructed Wetlands ◽  
Management Practices ◽  
Water Quality Management ◽  
Quality Data ◽  
Watershed Model ◽  
Selective Withdrawal ◽  
Lake Water Quality ◽  
Water Quality Data

In this study, we investigated the feasibility of using constructed wetlands for non-point source pollution reduction. The effect of constructed wetlands in reducing suspended solids (SS) was analyzed using an integrated modeling system of watershed model (HSPF), reservoir model (CE-QUAL-W2), and stream model (EFDC) to investigate the behavior and accumulation of the pollution sources based on 2017 water quality data. The constructed wetlands significantly reduced the SS concentration by approximately 30%, and the other in-lake management practices (e.g., artificial floating islands and sedimentation basins) contributed an additional decrease of approximately 7%. Selective withdrawal decreased in the average SS concentration in the influents by ~10%; however, the effluents passing through the constructed wetlands showed only a slight difference of 1.9% in the average SS concentration. In order to meet the water quality standards, it was necessary to combine the constructed wetlands, in-lake water quality management, and selective withdrawal practices. Hence, it was determined that the model proposed herein is useful for estimating the quantitative effects of water quality management practices such as constructed wetlands, which provided practical guidelines for the application of further water quality management policies.

scholarly journals Identification of High-Priority Tributaries for Water Quality Management in Nakdong River Using Neural Networks and Grade Classification

2020 ◽  
Vol 12 (21) ◽  
pp. 9149
Author(s):  
Kang-Young Jung ◽  
Sohyun Cho ◽  
Seong-Yun Hwang ◽  
Yeongjae Lee ◽  
Kyunghyun Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Quality Management ◽  
Water Quality Management ◽  
Oxygen Demand ◽  
Principal Component ◽  
Pollution Load ◽  
Nakdong River ◽  
Neural Network Models ◽  
Grade Classification

To determine the high-priority tributaries that require water quality improvement in the Nakdong River, which is an important drinking water resource for southeastern Korea, data collected at 28 tributaries between 2013 and 2017 were analyzed. To analyze the water quality characteristics of the tributary streams, principal component analysis and factor analysis were performed. COD (chemical oxygen demand), TOC (total organic carbon), TP (total phosphorus), SS (suspended solids), and BOD (biochemical oxygen demand) were classified as the primary factors. In the self-organizing maps analysis using the unsupervised learning neural network model, the first factor showed a highly relevant pattern. To perform the grade classification, 11 parameters were selected. Six parameters are concentrations of the main parameters for the water quality standard assessment in South Korea. We added the pollution load densities for the selected five primary factors. Joochungang showed the highest pollution load density despite its small watershed area. According to the results of the grade classification method, Joochungang, Topyeongcheon, Hwapocheon, Chacheon, Gwangyeocheon, and Geumhogang were selected as tributaries requiring high-priority water quality management measures. From this study, it was concluded that neural network models and grade classification methods could be utilized to identify the high-priority tributaries for more directed and effective water quality management.

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