Effect of inter-basin water transfer on water quality in an urban lake: A combined water quality index algorithm and biophysical modelling approach

Feng, Tao; Chao, Wang; Hou, Jun; Wang, Peifang; Liu, Yao; Dai, Qing-song; Yang, Yangyang; You, Guoxiang

doi:10.1016/j.ecolind.2017.06.044

Cited by 37 publications

(6 citation statements)

References 26 publications

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“…Given the complexity of these models and the frequent data limitations (e.g., lack of continuous in situ data), it is rare to find studies performing both processes simultaneously, as recognized by Whitehead et al [65] and Tomic et al [66]. By these reasons, a large number of modeling studies perform only one of these processes (e.g., [67][68][69][70]), or alternatively perform both processes for a very limited period or even solely based on seasonal data ( [71][72][73][74][75][76][77][78]). For the present study, and despite the common data limitations, both processes were performed for the hydrodynamic and water quality module in the Delft3D model implementation, following the procedures described below, and data sets presented in Section 2.2.…”

Section: Model Calibration and Validationmentioning

confidence: 99%

Modeling the Impact of Extreme River Discharge on the Nutrient Dynamics and Dissolved Oxygen in Two Adjacent Estuaries (Portugal)

Oliveira

Sousa

Morgado

et al. 2019

JMSE

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The Minho and Lima are adjacent estuaries located in the north of Portugal, with high ecological and economic importance. To address gaps in knowledge about changes in nutrient patterns in adjacent estuaries subject to different freshwater inflows, a numerical model, Delft3D, was implemented and developed, using a single domain, which allowed physical communication between estuaries. Calibration and validation of the model was successfully performed. Three numerical simulations were carried out, in which only river flows were varied (1st corresponds to a baseline numerical run, the 2nd a flood scenario, and the 3rd a drought scenario). Under flooding conditions, similar patterns were verified in both estuaries, with high fluvial discharges showing to have a reduced impact on both estuarine dynamics. In this case the nutrients were not a limiting factor for the biota, both for summer and winter seasons, since there was no significant decrease in dissolved oxygen concentration. For the drought scenario, it was observed that the estuary with the lower inflow of freshwater (Lima) was the most affected, with a significant decrease in the concentration of nutrients and oxygen dissolved in the winter season (decrease of 2 mg O2/L). In conclusion, this work reveals that it is essential to continuously monitor dam-controlled estuarine systems, as a significant decrease in river discharge will cause significant changes in the variables analysed (O2, PO4, and NO3) and may cause loss of biodiversity.

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Section: Model Calibration and Validationmentioning

confidence: 99%

Modeling the Impact of Extreme River Discharge on the Nutrient Dynamics and Dissolved Oxygen in Two Adjacent Estuaries (Portugal)

Oliveira

Sousa

Morgado

et al. 2019

JMSE

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show abstract

“…The second stage is water quality prediction using the pre-processed data. Traditional water quality prediction methods mainly include: the Grey Markov chain model method [17,18], the fuzzy-set theory-based Markov model [19], the regression prediction method [20,21,22,23], the time series method [24,25], and the water quality model prediction method [26,27]. The water quality model prediction method has poor self-adaptability, and the other traditional prediction methods also have many shortcomings, such as low prediction accuracy, poor stability, and single factor prediction without considering dynamics characteristics.…”

Section: Introductionmentioning

confidence: 99%

A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture

Zhang

Zhao

et al. 2019

Sensors

123

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An accurate prediction of cage-cultured water quality is a hot topic in smart mariculture. Since the mariculturing environment is always open to its surroundings, the changes in water quality parameters are normally nonlinear, dynamic, changeable, and complex. However, traditional forecasting methods have lots of problems, such as low accuracy, poor generalization, and high time complexity. In order to solve these shortcomings, a novel water quality prediction method based on the deep LSTM (long short-term memory) learning network is proposed to predict pH and water temperature. Firstly, linear interpolation, smoothing, and moving average filtering techniques are used to repair, correct, and de-noise water quality data, respectively. Secondly, Pearson’s correlation coefficient is used to obtain the correlation priors between pH, water temperature, and other water quality parameters. Finally, a water quality prediction model based on LSTM is constructed using the preprocessed data and its correlation information. Experimental results show that, in the short-term prediction, the prediction accuracy of pH and water temperature can reach 98.56% and 98.97%, and the time cost of the predictions is 0.273 s and 0.257 s, respectively. In the long-term prediction, the prediction accuracy of pH and water temperature can reach 95.76% and 96.88%, respectively.

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“…Thực tế diễn biến chất lượng nước phụ thuộc rất nhiều vào các yếu tố khí hậu, điều kiện tự nhiên và điều kiện sử dụng đất, lượng phân bón nông nghiệp, thủy sản, nước thải từ các khu công nghiệp, nước thải sinh hoạt,…Do đó, việc ứng dụng mô hình toán để mô phỏng, tính toán, dự báo lan truyền chất ô nhiễm là phương pháp hữu hiệu và hiệu quả [6][7][8]. Trên thế giới đã có rất nhiều các nghiên cứu về vấn đề dự báo dòng chảy, chất lượng nước ở các lưu vực sông, hồ theo hướng sử dụng công cụ mô hình hóa kết hợp với việc phân tích ảnh viễn thám GIS [9][10][11][12].…”

Section: Giới Thiệuunclassified

Nghiên cứu ứng dụng mô hình MIKE 11 đánh giá chất lượng nguồn tiếp nhận nước thải từ khu công nghiệp ra sông Cẩm Giàng, Hải Dương

Thế¹,

Trí²,

Tuyết³

et al. 2022

VNJHM

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Nước thải công nghiệp cũng song song với sự phát triển của ngành, kéo theo nhiều hệ lụy không đáng có cho môi trường và ảnh hưởng trực tiếp đến môi trường và sức khỏe người dân. Nghiên cứu ứng dụng mô hình MIKE 11 để đánh giá sự thay đổi chất lượng nước sông Cẩm Giàng khi tiếp nhận thêm nguồn thải từ khu công nghiệp (KCN) Tân Trường. Kết quả cho thấy hầu hết nồng độ các thông số ô nhiễm (TSS, COD, BOD5, PO4) mô phỏng trên sông Cẩm Giàng vẫn đạt giới hạn cho phép theo cột B1 của QCVN 08-MT:2015/ BTNMT. Kết quả mô phỏng các kịch bản cho thấy hầu hết nồng độ các chất ô nhiễm tại nguồn tiếp nhận vần trong pham vi giới hạn cho phép, chỉ có nồng độ COD vượt ngưỡng giới hạn cho phép do nồng độ COD hiện trạng trên sông tiếp nhận nước thải từ KCN đã khá cao. Kết quả nghiên cứu cho thấy mô hình mô phỏng CLN MIKE 11 có thể sử dụng để mô phỏng đánh giá diễn biến chất lượng nước từ KCN vào các nguồn tiếp nhận.

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Effect of inter-basin water transfer on water quality in an urban lake: A combined water quality index algorithm and biophysical modelling approach

Cited by 37 publications

References 26 publications

Modeling the Impact of Extreme River Discharge on the Nutrient Dynamics and Dissolved Oxygen in Two Adjacent Estuaries (Portugal)

Modeling the Impact of Extreme River Discharge on the Nutrient Dynamics and Dissolved Oxygen in Two Adjacent Estuaries (Portugal)

A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture

Nghiên cứu ứng dụng mô hình MIKE 11 đánh giá chất lượng nguồn tiếp nhận nước thải từ khu công nghiệp ra sông Cẩm Giàng, Hải Dương

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