Water Quality Modeling and Monitoring

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Quality and Contamination".

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 53907

Special Issue Editors


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Guest Editor
Department of Civil and Environmental Engineering, Auburn University, Auburn, AL 36849, USA
Interests: water quality modeling in aquatic systems; lakes; water quality monitoring; climate change impacts; ecological modeling; fish habitat modeling; eutrophication; surface hydrology; hydrological modeling and analysis; stormwater management
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Guest Editor

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Guest Editor
Department of Civil and Environmental Engineering, Youngstown State University, Youngstown, OH 44555, USA
Interests: watershed modeling; water quality modeling; hydrologic investigation; climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water quality in watersheds and waterbodies is a critical issue due to its direct influence on public health, the biological integrity of natural resources, and the economy. Anthropogenic influences on climate change and variability, land use, and land cover change at the watershed scale can have various impacts on the hydrological, biological, and chemical processes within freshwater watersheds, bringing significant changes in the water quality of rivers, lakes, and reservoirs. Water quality issues in watersheds and inland waterbodies eventually affect water quality in estuaries and oceans. The Earth has a tremendous variety of waterbodies, from small ponds to Lake Superior, and from humanmade reservoirs to natural lakes. Even though waterbodies are only a small part of our planet, they play a critical and important role in the Earth’s biosphere. Understanding the impacts of changes from upstream or surrounding watersheds on water quality is important to people who live or visit the waterbody and is also fundamental in providing better ecological and environmental strategies and mitigation methods to protect our ecosystems.

Dissolved oxygen and other water quality constituents have implications for the growth, reproduction, and survival of freshwater organisms such as phytoplankton, zooplankton, benthic organisms, and fish. Climate variations (seasonal or inter-annual) and global climate warming directly affect the heat budget of an aquatic system through the surface heat exchange between the water and the atmosphere and then influence water quality characteristics. Climate warming will alter water temperature, ice/snow cover, and water quality characteristics in aquatic systems. Nutrients and other chemicals to aquatic systems affect freshwater organism populations and biodiversity. Monitoring and modeling approaches have been used by citizen volunteers, biologists, water resources managers, engineers, and scientists to understand and study water quality issues in watersheds and waterbodies. Different monitoring techniques and modern monitoring devices/sensors allow us to get more in-depth information that we could not obtain before. Advanced models or modeling methods also allow us to better understand water quality dynamics and spatial distributions in watersheds and waterbodies that discrete data collections or monitoring cannot reveal. Over the last few decades, significant improvements in watershed models have been achieved to explore water quality modeling in waterbodies due to various anthropogenic interventions. Monitoring data are necessary for model calibration and validation before the model can be used for scenario study, sensitivity analysis, and future projection under certain changes in watersheds.

In this Special Issues, we would like to invite journal articles related to watershed- and waterbody-scale studies on water quality bringing innovative approaches to address the emerging environmental problems. The Special Issue covers recent advanced modeling and monitoring studies related to water quality and includes but is not limited to the following:

  • Modeling and monitoring of point sources from urban areas and non-point source pollution from the agricultural land at the watershed scale;
  • Development of new tools or the improvement of existing models to investigate and evaluate the consequences of point and non-point source pollution on the downstream water quality;
  • Application of watershed models for small to large-scale watershed studies to simulate the water quality both at the surface and at the subsurface level to incorporate the environmental impact of land use development, and land management strategies;
  • The studies of the nutrient losses from the agricultural land, instream nutrient transport processes, and in-stream water quality processes from the river;
  • Innovative research to potentially improve the watershed models to address the complexity of watersheds in terms of better representation of land use change, forest management, agricultural best management practices, and various scenarios of real-world systems in watershed modeling to improve water quality;
  • Modeling and monitoring the impact of land development and agricultural practices in the water quality in field-, watershed- and basin-scale studies;
  • To identify the knowledge gap in watershed and waterbody modeling processes;
  • The improvement of the watershed and waterbody models to integrate the advanced processes and new sciences for appropriate representation of land development and agricultural growth processes at the spatial and temporal scales within a watershed;
  • Pollutant build-ups at the watershed scale and their impact on downstream water bodies;
  • Development of models to incorporate the nutrient transport and various transformation processes of several forms of nutrients (nitrogen, phosphorus, etc.), plant growth, pesticides, bacteria, micro/nano plastics, and sediments at the watershed scale or inside waterbodies;
  • Modeling and monitoring of emerging contaminants in the water cycle including, but not limited to, per- and polyfluoroalkyl substances (PFAS), perfluorooctanoic acid (PFOA), and perfluorooctanesulfonic acid (PFOS), which act as so-called endocrine disruptors (EDCs);
  • Watershed- and basin-scale water quality assessment/projection under both existing and project land use and land management conditions;
  • Development of management scenarios for water quality improvement in waterbodies using various best management practices;
  • Research incorporating complex water quality processes at the watershed scale and developing scientific support tools for decision-making systems;
  • Uncertainty in model application, sensitivity analysis of watershed and waterbody model parameters.

Both reviews as well as new research papers are welcome.

Prof. Dr. Xing Fang
Prof. Dr. Jiangyong Hu
Dr. Suresh Sharma
Guest Editors

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Keywords

  • watershed modeling of water quality
  • water quality modeling in waterbodies
  • water quality monitoring in watersheds and waterbodies
  • anthropogenic influence
  • climate change impact on water quality
  • land use change impact

Published Papers (20 papers)

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Editorial

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4 pages, 187 KiB  
Editorial
Water Quality Modeling and Monitoring
by Xing Fang, Jiangyong Hu and Suresh Sharma
Water 2023, 15(18), 3216; https://doi.org/10.3390/w15183216 - 9 Sep 2023
Viewed by 1322
Abstract
This Special Issue, “Water Quality Modeling and Monitoring”, comprises 19 papers [...] Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)

Research

Jump to: Editorial

17 pages, 2514 KiB  
Article
Uncertainty and Sensitivity Analysis of the Effective Implementation of Water Quality Improvement Programs for Citarum River, West Java, Indonesia
by Iwan Juwana, Nur A. Rahardyan, Didin A. Permadi and Arief D. Sutadian
Water 2022, 14(24), 4077; https://doi.org/10.3390/w14244077 - 14 Dec 2022
Cited by 8 | Viewed by 2708
Abstract
Pollution of rivers is a challenge for many countries. In the Citarum watershed, Indonesia, where pollution has been an emerging issue nationwide, many programs and policies have been set up. However, implementation of all the planned programs and the significance of their contributions [...] Read more.
Pollution of rivers is a challenge for many countries. In the Citarum watershed, Indonesia, where pollution has been an emerging issue nationwide, many programs and policies have been set up. However, implementation of all the planned programs and the significance of their contributions toward water quality improvement of the Citarum River have not been analyzed. In this paper, we present original research on evaluating water quality programs planned to achieve outputs by using uncertainty and sensitivity analysis for a river. The essential inputs included: (1) key parameters, (2) priority planned programs, and (3) interrelationships between programs, parameters, and the level of successfulness of water quality control programs. The first and second inputs were prepared simultaneously using Principal Component Analysis (PCA) and Analytical Hierarchy Process (AHP). The latter was obtained using the Delphi method to obtain the related stakeholders’ opinions. Finally, we explore Monte Carlo simulation to analyze parameter uncertainty and sensitivity contributing to the program’s effectiveness. By implementing all the water quality control programs, the results showed that cadmium, BOD, and fecal coliform were the most affected parameters. In addition, the most effective programs to improve the pollution index were domestic waste, farming, solid waste, and water resource programs. If those programs were implemented collectively, the probability of reducing the pollution index was within a range 2.01–36.22% from the base case. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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19 pages, 3617 KiB  
Article
Field-Monitoring Sediment Basin Performance during Highway Construction
by Jaime C. Schussler, Michael A. Perez, Jarrell Blake Whitman and Bora Cetin
Water 2022, 14(23), 3858; https://doi.org/10.3390/w14233858 - 27 Nov 2022
Cited by 2 | Viewed by 2479
Abstract
Stormwater regulations require erosion and sediment control practices to be implemented during construction to prevent discharging polluted water offsite and mitigate downstream effects. Sediment basins are a common practice used to detain suspended sediment from stormwater runoff by providing residence time and storage [...] Read more.
Stormwater regulations require erosion and sediment control practices to be implemented during construction to prevent discharging polluted water offsite and mitigate downstream effects. Sediment basins are a common practice used to detain suspended sediment from stormwater runoff by providing residence time and storage to promote gravitational settling. Sediment basin design, and thus pollutant removal efficiency, vary regionally due to local design standards and preferences. This manuscript presents the results of a case study from Highway U.S. 30 construction in Tama County, Iowa, USA where two sediment basin systems were created within a conveyance channel by constructing an earthen berm across the channel to detain sediment-laden stormwater. A dewatering riser pipe was routed through the earthen berm to provide primary dewatering. The in-channel sediment basin was constructed with a 3% slope and a 10 ft. bottom width. The first system consisted of one basin created by a single earthen berm damming sediment-laden runoff, whereas the second system included two earthen berms, creating two in-channel sediment basins in series. Field monitoring was conducted on in-situ basins by deploying a rain gauge and automated water samplers positioned at the inflow and discharge points of a (a) single basin and (b) two basins in series within a roadside channel. During the monitoring period, no maintenance or dredging was recorded. Water samples were taken from the monitored basins at regular time intervals and analyzed for turbidity. Inflow turbidities often reached magnitudes up to the 103 NTU and discharge samples indicated negligible turbidity reduction after residence. On several occasions, the in-channel sediment basins acted as a sediment source, with discharge turbidities measuring higher than inflow. Despite their initial performance, there was interest in improving the in-channel basin design due to the potential to maximize length-to-width flow ratios, and use of existing infrastructure, which reduced the amount of right of way needed for basin construction, installation time and cost. As a result, several potential design improvements and techniques were recommended to enhance in-channel sediment basin performance. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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16 pages, 4665 KiB  
Article
Remote Sensing Retrieval of Total Nitrogen in the Pearl River Delta Based on Landsat8
by Yu Guo, Ruru Deng, Jiayi Li, Zhenqun Hua, Jing Wang, Ruihao Zhang, Yeheng Liang and Yuming Tang
Water 2022, 14(22), 3710; https://doi.org/10.3390/w14223710 - 16 Nov 2022
Cited by 4 | Viewed by 1619
Abstract
The Pearl River Delta in South China is subject to severe eutrophication, which is significantly exacerbated by the total nitrogen (TN). Remote sensing technology with large-scale synchronous observations in the Pearl River Delta can effectively monitor organic pollution. Statistical methods based on remote [...] Read more.
The Pearl River Delta in South China is subject to severe eutrophication, which is significantly exacerbated by the total nitrogen (TN). Remote sensing technology with large-scale synchronous observations in the Pearl River Delta can effectively monitor organic pollution. Statistical methods based on remote sensing images have been widely used in water quality parameter retrieval for inland rivers, reservoirs, and lakes, but have seldom been applied in the Pearl River Delta. TN is also a non-optically active substance, so it is difficult to retrieve TN through analysis methods. This study retrieves the concentration of total nitrogen (TN) based on Landsat8 images of the Pearl River Delta using a statistical method. The stepwise regression function is built by analyzing the TN concentration and the single-band, two-band, and three-band spectral information groups measured by an ASD FieldSpec3 spectrometer. The retrieval results show that the proposed method performs well with a small mean absolute error (MAE) (0.36 mg/L for TN) and high agreement (R2 = 0.61 for TN) between the in situ data and the retrieval concentration. The results demonstrate that the concentration of TN in the east of the Pearl River Delta was higher than in the west. Dachan Bay and Shenzhen Bay had the highest TN concentrations, which were around 3.02 mg/L and 3.67 mg/L. The 750–850 nm band could be an important reference for further exploring the spectral characteristics and retrieval of TN. The retrieval method in this study is easy to implement and convenient for local TN distribution capture, which can provide a timely reference for daily water quality supervision and management in the Pearl River Delta. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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16 pages, 3688 KiB  
Article
Trace Element Compositions and Water Quality Assessment in the Angara River Source (Baikal Region, Russia)
by Vera I. Poletaeva, Mikhail V. Pastukhov and Pavel G. Dolgikh
Water 2022, 14(21), 3564; https://doi.org/10.3390/w14213564 - 6 Nov 2022
Cited by 3 | Viewed by 1925
Abstract
The relevance of studying the spatial-temporal dynamics in the trace element composition of the water at the Angara River source is associated not only with determining the degree of anthropogenic load on the local area of the water body but also with the [...] Read more.
The relevance of studying the spatial-temporal dynamics in the trace element composition of the water at the Angara River source is associated not only with determining the degree of anthropogenic load on the local area of the water body but also with the use of the water chemical composition of the Angara River source as an integral indicator of the hydrochemical state of the entire Lake Baikal. The current work is based on monthly monitoring studies conducted from March 2021 to February 2022 along the left shore, middle part, and right shore of the Angara River source. In water samples, the concentrations of Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Cd, Sn, Cs, Tl, Pb, Th, and U were measured by inductively coupled plasma mass spectrometry. The results indicated that the concentrations of the trace elements lie within mean + 1SD: Cd in 97% of samples; U in 94% of samples; Tl in 92%; Al, Cr, Co, Ni, Cu, Zn, Sn, Pb, and Cs in over 80%; Fe in 78% of samples; and Mn and Th in over 60% of samples. Such results show a high degree of consistency in the water trace element composition at the source of the Angara River. The major factors responsible for the water hydrochemistry at the Angara River source include the runoff of Lake Baikal, the anthropogenic effect of Listvyanka and Port Baikal settlements, and water transport activity. The concentrations of all trace elements in the water of the Angara River source are substantially below the standards for drinking water. At the same time, the single-factor pollution index revealed water samples with considerable contamination by Fe, Zn, Sn, Al, Cs, Mn, Cu, Tl, Cd, Pb, and Th and very high contamination by Cr, Fe, Co, Cs, Tl, Pb, and Th. The pollution load index has classified most of the water samples as having baseline levels of pollutants. Three samples taken from the left shore, four from the middle part, and seven from the right shore were classified as polluted. This means that the ongoing anthropogenic impact may worsen the water quality and have a negative impact on living organisms. The obtained results make a clear case for strengthening environmental protection measures to minimize the anthropogenic effect on the ecosystems of Lake Baikal and Angara River. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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14 pages, 2309 KiB  
Article
Long-Term Monitoring of an Urban Stormwater Infiltration Trench in South Korea with Assessment Using the Analytic Hierarchy Process
by Minsu Jeon, Heidi B. Guerra, Hyeseon Choi and Lee-Hyung Kim
Water 2022, 14(21), 3529; https://doi.org/10.3390/w14213529 - 3 Nov 2022
Cited by 4 | Viewed by 1848
Abstract
Evaluating the functionality of small and decentralized low-impact development (LID) technologies often requires extensive labor, time, and costs for water quality analysis. In order to reduce these in an infiltration trench in South Korea, monitoring data gathered over a period of 8 years [...] Read more.
Evaluating the functionality of small and decentralized low-impact development (LID) technologies often requires extensive labor, time, and costs for water quality analysis. In order to reduce these in an infiltration trench in South Korea, monitoring data gathered over a period of 8 years were used to determine its long-term performance, establish a stormwater quality estimation model, and develop a comprehensive evaluation tool. Our findings show that the infiltration trench can treat up to 90% of the stormwater runoff from a paved road but would require annual maintenance to minimize the reduction in infiltration capacity. The facility was able to remove an average of 83% of total suspended solids (TSS), 75% of biochemical oxygen demand (BOD), 80% of chemical oxygen demand (COD), 76% of total nitrogen (TN), and 79% of total phosphorus (TP), with the highest removal efficiencies observed after maintenance was conducted. Rainfall depth and air quality parameters (i.e., PM2.5 and PM10) were found to be positively correlated with TSS, COD, TN, and TP. These parameters were then used to develop a model for the estimation of influent stormwater quality, which can help in estimating the effluent water quality based on the average removal efficiencies. Furthermore, a comprehensive evaluation tool considering indicators such as treatment efficiency, cultural benefits, and facility and operating conditions was established through the analytic hierarchy process (AHP). Aside from determining the facility’s overall efficiency, this can also serve as a diagnostic tool to identify whether maintenance is needed or not. While atmospheric and hydrological characteristics differ in different regions, and the results may vary if applied in other facilities, this study can serve as a guide to the effective and efficient evaluation of similar stormwater management facilities in South Korea. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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12 pages, 4904 KiB  
Article
Physicochemical Parameters in the Generation of Turbidity Episodes in a Water Supply Distribution System
by Ricardo Juncosa, José Luis Cereijo and Ricardo Vázquez
Water 2022, 14(21), 3383; https://doi.org/10.3390/w14213383 - 25 Oct 2022
Cited by 2 | Viewed by 3819
Abstract
Water is necessary for the development and support of human life. The ability of water to supply the different populations has different origins: water taken from river diversions, water from underground catchments, water from lakes and reservoirs, water from the recirculation of treated [...] Read more.
Water is necessary for the development and support of human life. The ability of water to supply the different populations has different origins: water taken from river diversions, water from underground catchments, water from lakes and reservoirs, water from the recirculation of treated water, etc. Episodes of turbidity and color changes in the water supply in pipe distribution systems are non-isolated problems that occur in many cities and towns. In particular, sedimentation in water supply pipelines and the subsequent resuspension of these particles in the system have created the need to investigate the processes and variables that promote turbidity episodes, including why, when, and where these episodes occur. In this study, different physicochemical parameters were investigated and analyzed in the water supply distribution network of the city of La Coruña (northwest Spain) through a pipe monitoring panel under real operating conditions. The supply waters come from the Mero river basin, a basin made up of siliceous materials, a unique condition with respect to the majority of studies that have been carried out using waters coming from basins made of basic materials. In this case, the relationships between different variables were studied, including the number of particles, particle size, turbidity, color, concentration of particulate materials, and mineralogy. In this article, only those parameters that are better correlated have been noted. The results revealed a predominant relationship between color and the concentration and mineralogy of particulate materials, as well as between turbidity and the number and size of particles. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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20 pages, 11411 KiB  
Article
Water Quality Index Classification Based on Machine Learning: A Case from the Langat River Basin Model
by Illa Iza Suhana Shamsuddin, Zalinda Othman and Nor Samsiah Sani
Water 2022, 14(19), 2939; https://doi.org/10.3390/w14192939 - 20 Sep 2022
Cited by 18 | Viewed by 3448
Abstract
Traditionally, water quality is evaluated using expensive laboratory and statistical procedures, making real-time monitoring ineffective. Poor water quality requires a more practical and cost-effective solution. Water pollution has been a severe issue, hurting water quality in recent years. Therefore, it is crucial to [...] Read more.
Traditionally, water quality is evaluated using expensive laboratory and statistical procedures, making real-time monitoring ineffective. Poor water quality requires a more practical and cost-effective solution. Water pollution has been a severe issue, hurting water quality in recent years. Therefore, it is crucial to create a model that forecasts water quality to control water pollution and inform consumers in the event of the detection of poor water quality. For effective water quality management, it is essential to accurately estimate the water quality class. Motivated by these considerations, we utilize the benefits of machine learning methods to construct a model capable of predicting the water quality index and water quality class. This study aims to investigate the performance of machine learning models for multiclass classification in the Langat River Basin water quality assessment. Three machine learning models were developed using Artificial Neural Networks (ANN), Decision Trees (DT), and Support Vector Machines (SVM) to classify river water quality. Comparative performance analysis between the three models indicates that the SVM is the best model for predicting river water quality in this study. In addition, there is a statistically significant difference in performance between the SVM, DT, and ANN models at the 0.05 level of confidence. The use of the kernel function, the grid search method, and the multiclass classification technique used in this study significantly impacts the effectiveness of the SVM model. The findings bolster the idea that machine learning models, particularly SVM, can be used to forecast WQI with a high degree of accuracy, hence enhancing water quality management. Consequently, the model based on machine learning lowered the cost and complexity of calculating sub-indices of six water quality parameters and classifying water quality compared to the standard IKA-JAS formula. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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13 pages, 4825 KiB  
Article
The University of West Florida Campus Ecosystem Study: Spatial and Temporal Variation in Water Quality at Thompson Bayou
by Frank S. Gilliam, Jacob W. Hardin, Jacob A. Williams and Rachel L. Lackaye
Water 2022, 14(18), 2916; https://doi.org/10.3390/w14182916 - 17 Sep 2022
Cited by 2 | Viewed by 2191
Abstract
Much of our understanding of factors influencing stream chemistry comes from studies of montane forests, whereas far less work has focused on streams of coastal areas that integrate a homogeneous, flat topography and interactions with the bodies of water into which they drain, [...] Read more.
Much of our understanding of factors influencing stream chemistry comes from studies of montane forests, whereas far less work has focused on streams of coastal areas that integrate a homogeneous, flat topography and interactions with the bodies of water into which they drain, especially involving tidal fluxes. Fewer still do so in the context of an urban interface, especially that of a college campus. This study assessed the water quality of Thompson Bayou, a freshwater stream entering the University of West Florida campus in a wetland after flowing through the urban property with impacted water quality. We measured temperature, pH, dissolved O2 (DO), and specific conductivity (SC) for one year at eight sites along Thompson Bayou from campus to the Escambia River. All variables, except temperature, varied spatially, with consistent increases in DO and SC toward the river of 10% and 75%, respectively. Variables exhibited temporal patterns of significant seasonal variation, especially temperature, increasing from a January minimum of 14 °C to a summer maximum of 28 °C. These results suggest that, in general, the biogeochemistry of coastal streams such as Thompson Bayou can be influenced by numerous factors, including (1) wetland processes, (2) interactions of the stream channel with forested uplands, and (3) tidal fluxes. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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19 pages, 5789 KiB  
Article
An Evaluation of À Trous-Based Record Extension Techniques for Water Quality Record Extension
by Samah Anwar, Bahaa Khalil, Mohamed Seddik, Abdelhamid Eltahan and Aiman El Saadi
Water 2022, 14(14), 2264; https://doi.org/10.3390/w14142264 - 20 Jul 2022
Cited by 2 | Viewed by 1533
Abstract
Hydrological data in general and water quality (WQ) data in particular frequently suffer from missing records and/or short-gauged monitoring/sampling sites. Many statistical regression techniques are employed to substitute missing values or to extend records at short-gauged sites, such as the Kendall-Theil robust line [...] Read more.
Hydrological data in general and water quality (WQ) data in particular frequently suffer from missing records and/or short-gauged monitoring/sampling sites. Many statistical regression techniques are employed to substitute missing values or to extend records at short-gauged sites, such as the Kendall-Theil robust line (KTRL), its modified version (KTRL2), ordinary least squares regression (OLS), four MOVE techniques, and the robust line of organic correlation (RLOC). In this study, in aspiring to achieve better accuracy and precision, the À Trous-Haar wavelet transform (WT) was adopted as a data denoising preprocessing step prior to applying record extension techniques. An empirical study was performed using real WQ data, from the National WQ monitoring network in the Nile Delta in Egypt, to evaluate the performance of these eight record-extension techniques with and without the WT data preprocessing step. Evaluations included the accuracy and precision of the techniques when used for the restoration of WQ missing values and for the extension of the WQ short-gauged variable. The results indicated that for the restoration of missing values, the KTRL and WT-KTRL outperformed other techniques. However, for the extension of short-gauged variables, WT-KTRL2, WT-MOVE3, and WT-MOVE4 techniques showed more accurate and precise results compared with both other techniques and their counterparts without the WT. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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17 pages, 2138 KiB  
Article
Annual Evaluation of 17 Oestrogenic Endocrine Disruptors and Hazard Indexes in the Douro River Estuary—The Atlantic Discharge of the Highest-Flow River of Southwestern Europe
by Maria João Rocha, Frederico Silva and Eduardo Rocha
Water 2022, 14(13), 2046; https://doi.org/10.3390/w14132046 - 26 Jun 2022
Cited by 1 | Viewed by 1847
Abstract
The concentrations of seventeen endocrine disruptor compounds (EDCs) that included oestrogens, phytoestrogens, sitosterol, and banned industrial pollutants were investigated at ten sites of the Douro River estuary. Surface waters were collected during 2019. After evaluating the physicochemical data (ammonia, nitrates, nitrites and phosphates), [...] Read more.
The concentrations of seventeen endocrine disruptor compounds (EDCs) that included oestrogens, phytoestrogens, sitosterol, and banned industrial pollutants were investigated at ten sites of the Douro River estuary. Surface waters were collected during 2019. After evaluating the physicochemical data (ammonia, nitrates, nitrites and phosphates), the waters were filtrated and submitted to solid-phase extraction (SPE) to extract and pre-concentrate (4000-fold) the EDCs. The extracts were derivatized with BSTFA + 1% TMS and analysed by gas chromatography-mass spectrometry (GC-MS). All EDCs showed a high detection rate (97%, on average), exhibiting ubiquity in this estuary. The finding of biologically relevant amounts of oestrogens (up to 8.5 ng/L for oestradiol, E2), phytoestrogens (up to 827 ng/L for biochanin A, BIO-A) and industrial pollutants (up to 2.7 µg/L for nonylphenol di-ethoxylated, NP2EO) strongly support ongoing risks of endocrine disruption for the local aquatic wildlife. Globally, there was an E2-equivalents (E2-EQs) concentration of 25 ng/L and a hazard index (HI) of 26, which further indicates considerable potential for adverse effects on local biota. Moreover, the physicochemical data suggest direct sewage discharges. Beyond possible toxicological effects on fauna, the detected contaminants may pose risks to humans via direct contact (bathing at local fluvial beaches) or by ingestion (local fish). Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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36 pages, 15452 KiB  
Article
Multi-Location Emulation of a Process-Based Salinity Model Using Machine Learning
by Siyu Qi, Minxue He, Zhaojun Bai, Zhi Ding, Prabhjot Sandhu, Yu Zhou, Peyman Namadi, Bradley Tom, Raymond Hoang and Jamie Anderson
Water 2022, 14(13), 2030; https://doi.org/10.3390/w14132030 - 24 Jun 2022
Cited by 4 | Viewed by 2097
Abstract
Advances in machine-learning techniques can serve practical water management needs such as salinity level estimation. This study explores machine learning, particularly deep-learning techniques in developing computer emulators for a commonly used process model, the Delta Simulation Model II (DSM2), used for salinity estimation [...] Read more.
Advances in machine-learning techniques can serve practical water management needs such as salinity level estimation. This study explores machine learning, particularly deep-learning techniques in developing computer emulators for a commonly used process model, the Delta Simulation Model II (DSM2), used for salinity estimation in California’s Sacramento-San Joaquin Delta (Delta). We apply historical daily input data to DSM2 and corresponding salinity simulations at 28 study locations from 1990 to 2019 to train two machine-learning models: a multi-layer perceptron (MLP) and Long-Short-Term Memory (LSTM) networks in a multi-task learning framework. We assess sensitivity of both networks to the amount of antecedent input information (memory) and training data to determine appropriate memory size and training data length. We evaluate network performance according to several statistical metrics as well as visual inspection. The study further investigates two additional networks, the Gated Recurrent Unit (GRU) and Residual Network (ResNet) in salinity modeling, and compares their efficacy against MLP and LSTM. Our results demonstrate strong performance of the four neural network models over the study period, achieving absolute bias below 4%, plus near-perfect correlation coefficients and Nash–Sutcliffe efficiency coefficients. The high complexity LSTM shows slight performance edge. We further show that deeper and wider versions of MLP and LSTM yield only marginal benefit over their baseline counterparts. We also examined issues related to potential overfitting by the proposed models, training data selection strategies, and analytical and practical implications. Overall, this new study indicates that machine-learning-based emulators can efficiently emulate DSM2 in salinity simulation. They exhibit strong potential to supplement DSM2 in salinity modeling and help guide water resource planning and management practices for the Delta region. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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18 pages, 3217 KiB  
Article
Machine Learning Algorithms for Biophysical Classification of Lithuanian Lakes Based on Remote Sensing Data
by Dalia Grendaitė and Edvinas Stonevičius
Water 2022, 14(11), 1732; https://doi.org/10.3390/w14111732 - 28 May 2022
Cited by 5 | Viewed by 2066
Abstract
Inland waters are dynamic systems that are under pressure from anthropogenic activities, thus constant observation of these waters is essential. Remote sensing provides a great opportunity to have frequent observations of inland waters. The aim of this study was to create a data-driven [...] Read more.
Inland waters are dynamic systems that are under pressure from anthropogenic activities, thus constant observation of these waters is essential. Remote sensing provides a great opportunity to have frequent observations of inland waters. The aim of this study was to create a data-driven model that uses a machine learning algorithm and Sentinel-2 data to classify lake observations into four biophysical classes: Clear, Moderate, Chla-dominated, and Turbid. We used biophysical variables such as water transparency, chlorophyll concentration, and suspended matter to define these classes. We tested six machine learning algorithms that use spectral features of lakes as input and chose random forest classifiers, which yielded the most accurate results. We applied our two-step model on 19,292 lake spectra for the years 2015–2020, from 226 lakes. The prevalent class in 67% of lakes was Clear, while 19% of lakes were likely affected by strong algal blooms (Chla-dominated class). The models created in this study can be applied to lakes in other regions where similar lake classes are found. Biophysical lake classification using Sentinel-2 MSI data can help to observe long-term and short-term changes in lakes, thus it can be a useful tool for water management experts and for the public. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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25 pages, 5516 KiB  
Article
Estimating Point and Nonpoint Source Pollutant Flux by Integrating Various Models, a Case Study of the Lake Hawassa Watershed in Ethiopia’s Rift Valley Basin
by Semaria Moga Lencha, Mihret Dananto Ulsido and Jens Tränckner
Water 2022, 14(10), 1569; https://doi.org/10.3390/w14101569 - 13 May 2022
Cited by 4 | Viewed by 2848
Abstract
Increasing pollutant emissions in the Lake Hawassa watershed (LHW) has led to a severe water quality deterioration. Allocation and quantification of responsible pollutant fluxes are suffering from scarce data. In this study, a combination of various models with monitoring data has been applied [...] Read more.
Increasing pollutant emissions in the Lake Hawassa watershed (LHW) has led to a severe water quality deterioration. Allocation and quantification of responsible pollutant fluxes are suffering from scarce data. In this study, a combination of various models with monitoring data has been applied to determine the fluxes for Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD5), Total Dissolved Solid (TDS), Total Nitrogen (TN), Nitrate and Nitrite-nitrogen (NOx-N), Total Phosphorous (TP) and phosphate (PO4-P). Water, wastewater and stormwater samples were collected and analyzed at eight monitoring stations from rivers and point sources and six monitoring stations of stormwater samples. The flow simulated with soil and water assessment tool (SWAT) could be very well calibrated and validated with gauge data. This flow from SWAT model, measured flow during monitoring and pollutant concentrations were used in FLUX32 to estimate pollutant fluxes of main rivers and point sources in LHW. The formulas provided by Ethiopian Roads Authority and Gumbel’s theory of rainfall frequency analysis was employed to determine the 2-years return period rainfall depth for the City of Hawassa. The integration of HEC-GeoHMS and SCS-CN with the catchment area enabled to determine stormwater pollution load of Hawassa City. The estimated pollutant flux at each monitoring stations showed that the pollutant contribution from the point and nonpoint sources prevailing in the study area, where the maximum fluxes were observed at Tikur-Wuha sub-catchments. This station was located downstream of the two point sources and received flow from the upper streams where agricultural use is predominant. Furthermore, Hawassa city has been identified as a key pollutant load driver, owing to increased impacts from clearly identified point sources and stormwater pollutant flux from major outfalls. Agricultural activities, on the other hand, covers a large portion of the catchment and contributes significant amount to the overall load that reaches the lake. Thus, mitigation measures that are focused on pollutant flux reduction to the lake Hawassa have to target on the urban and agricultural activities. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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23 pages, 7625 KiB  
Article
Potential Risk of Agrochemical Leaching in Areas of Edaphoclimatic Suitability for Coffee Cultivation
by Gleissy Mary Amaral Dino Alves dos Santos, Antônio Augusto Neves, Maria Eliana Lopes Ribeiro de Queiroz, Vagner Tebaldi de Queiroz, Carlos Antonio Alvares Soares Ribeiro, Efraim Lázaro Reis, Ana Carolina Pereira Paiva, José Romário de Carvalho, Samuel Ferreira da Silva, Ronie Silva Juvanhol, Taís Rizzo Moreira, Luciano José Quintão Teixeira, Sérgio Henriques Saraiva, Adilson Vidal Costa, Camila Aparecida da Silva Martins, Fábio Ribeiro Pires, Thuelem Azevedo Curty, Plinio Antonio Guerra Filho, Marcelo Henrique de Souza, Waldir Cintra de Jesus Junior and Alexandre Rosa dos Santosadd Show full author list remove Hide full author list
Water 2022, 14(9), 1515; https://doi.org/10.3390/w14091515 - 9 May 2022
Cited by 3 | Viewed by 2508
Abstract
Studies show that agricultural activities around the world still present a strong dependence on agrochemicals that can leach into the soil profile, causing its contamination, as well as that of water resources. In this context, the present study evaluates the potential risk of [...] Read more.
Studies show that agricultural activities around the world still present a strong dependence on agrochemicals that can leach into the soil profile, causing its contamination, as well as that of water resources. In this context, the present study evaluates the potential risk of pesticide leaching in areas of edaphoclimatic suitability for coffee cultivation in Espírito Santo state, Brazil. As a methodology, the areas of edaphoclimatic suitability for conilon and arabica coffee were defined, and subsequently, the risk of leaching of active agrochemical ingredients in these areas was evaluated using the Groundwater Ubiquity Score (GUS), Leaching Index (LIX) and Attenuation Factor/Retardation Factor (AF/RF) methods. Of the ten active ingredients evaluated, sulfentrazone and thiamethoxam present a potential risk of leaching into the groundwater level. The study allowed us to evaluate the potential risk of agrochemical leaching in tropical soils cultivated with coffee using geographic information system (GIS) techniques. The methodological proposal can be adapted for other agricultural areas and crops. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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18 pages, 2651 KiB  
Article
Oscillation Flow Dam Operation Method for Algal Bloom Mitigation
by Jungwook Kim, Jaewon Kwak, Jung Min Ahn, Hongtae Kim, Jihye Jeon and Kyunghyun Kim
Water 2022, 14(8), 1315; https://doi.org/10.3390/w14081315 - 18 Apr 2022
Cited by 5 | Viewed by 2008
Abstract
Green algae play an important role in ecosystems as primary producers, but they can cause algal blooms, which are socio-environmental burdens as responding to them requires water resources from dam reservoirs. This study proposes an alternative for reducing algal blooms through dam operation [...] Read more.
Green algae play an important role in ecosystems as primary producers, but they can cause algal blooms, which are socio-environmental burdens as responding to them requires water resources from dam reservoirs. This study proposes an alternative for reducing algal blooms through dam operation without using additional water resources. A novel oscillation flow concept was suggested: oscillating discharge of dam for irregular flow. To examine its effect, the Environmental Fluid Dynamics Code—National Institute of Environment Research (EFDC-NIER) model was constructed and calibrated for the Namhan River, South Korea, from downstream of the Chungju Dam to downstream of Gangcheon Weir. The water quality in the study area were simulated and analyzed for August 2019, which is when the largest number of harmful cyanobacteria had been reported in recent years. Our results showed that the oscillation flow produced significant variance of flow velocity, and algal bloom density in the Namhan River was reduced by 20–30% through the operation of the Chungju Dam. However, further study and investigation are required before practical application. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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25 pages, 5931 KiB  
Article
On the Retrieval of the Water Quality Parameters from Sentinel-3/2 and Landsat-8 OLI in the Nile Delta’s Coastal and Inland Waters
by Alaa A. Masoud
Water 2022, 14(4), 593; https://doi.org/10.3390/w14040593 - 15 Feb 2022
Cited by 13 | Viewed by 3478
Abstract
Reduced water quality due to the eutrophication process causes large economic losses worldwide. Multi-source remotely-sensed water quality monitoring can help provide effective water resource management. The research evaluates the retrieval of the water quality parameters: chlorophyll-a (Chl-a), total suspended matter [...] Read more.
Reduced water quality due to the eutrophication process causes large economic losses worldwide. Multi-source remotely-sensed water quality monitoring can help provide effective water resource management. The research evaluates the retrieval of the water quality parameters: chlorophyll-a (Chl-a), total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM), over optically different water types. Cross-sensor performance analysis of three satellite data sources: Sentinel-3 Ocean Land Color Imager (OLCI), Sentinel-2A Multi-Spectral Instrument (MSI), and Landsat-8 Operational Land Imager (OLI), acquired during a 45 min overpass on the Nile Delta coast on 22 March 2020 was performed. Atmospheric correction using the case 2 Regional Coast Color (C2RCC) was applied using local water temperature and salinity averages. Owing to the lack of ground-truth measurements in the coastal water, results were inter-compared with standard simultaneous color products of the Copernicus Marine Environment Monitoring Service (CMEMS), OLCI water full resolution (WFR), and the MODIS Aqua, in order to highlight the sensor data relative performance in the Nile Delta’s coastal and inland waters. Validation of estimates was carried out for the only cloud-free MSI data available in the 18–20 September 2020 period for the Burullus Lake nearly contemporaneous with in situ measurements in the 22–25 September 2020. Inter-comparison of the retrieved parameters showed good congruence and correlation among all data in the coastal water, while this comparison returned low positive or negative correlation in the inland lake waters. In the coastal water, all investigated sensors and reference data showed Chl-a content average of 3.14 mg m−3 with a range level of 0.39–4.81 mg m−3. TSM averaged 7.66 g m−3 in the range of 6.32–10.18 g m−3. CDOM clarified mean of 0.18 m−1 in the range level of 0.13–0.30 m−1. Analysis of the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) clarified that the MSI sensor was ranked first achieving the smallest MAE and RMSE for the Chl-a contents, while the EFR proved superior for TSM and CDOM estimates. Validation of results in Burullus Lake indicated a clear underestimation on average of 35.35% for the Chl-a induced by the land adjacency effect, shallow bottom depths, and the optical dominance of the TSM and the CDOM absorption intermixed in turbid water loaded with abundant green algae species and counts. The underestimation error increased at larger estimates of the algal composition/abundance (total counts, Chlorophyacea, Euglenophycaea, and Bacillariophycaea) and the biological contents (carbohydrates, lipids, and proteins), arranged in decreasing order. The largest normalized RMSE estimates marked the downstream areas where the inflow of polluted water persistently brings nutrient loads of nitrogen and phosphorous compounds as well as substantial amounts of detrital particles and sediments discharged from the agricultural and industrial drains and the land use changes related to agricultural practices, resulting in the increase of water turbidity giving rise to inaccurate Chl-a estimates. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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23 pages, 9259 KiB  
Article
Groundwater Suitability for Drinking and Irrigation Using Water Quality Indices and Multivariate Modeling in Makkah Al-Mukarramah Province, Saudi Arabia
by Maged El Osta, Milad Masoud, Abdulaziz Alqarawy, Salah Elsayed and Mohamed Gad
Water 2022, 14(3), 483; https://doi.org/10.3390/w14030483 - 6 Feb 2022
Cited by 76 | Viewed by 5569
Abstract
Water shortage and quality are major issues in many places, particularly arid and semi-arid regions such as Makkah Al-Mukarramah province, Saudi Arabia. The current work was conducted to examine the geochemical mechanisms influencing the chemistry of groundwater and assess groundwater resources through several [...] Read more.
Water shortage and quality are major issues in many places, particularly arid and semi-arid regions such as Makkah Al-Mukarramah province, Saudi Arabia. The current work was conducted to examine the geochemical mechanisms influencing the chemistry of groundwater and assess groundwater resources through several water quality indices (WQIs), GIS methods, and the partial least squares regression model (PLSR). For that, 59 groundwater wells were tested for different physical and chemical parameters using conventional analytical procedures. The results showed that the average content of ions was as follows: Na+ > Ca2+ > Mg 2+ > K+ and Cl > SO42− > HCO32− > NO3 > CO3. Under the stress of evaporation and saltwater intrusion associated with the reverse ion exchange process, the predominant hydrochemical facies were Ca-HCO3, Na-Cl, mixed Ca-Mg-Cl-SO4, and Na-Ca-HCO3. The drinking water quality index (DWQI) has indicated that only 5% of the wells were categorized under good to excellent for drinking while the majority (95%) were poor to unsuitable for drinking, and required appropriate treatment. Furthermore, the irrigation water quality index (IWQI) has indicated that 45.5% of the wells were classified under high to severe restriction for agriculture, and can be utilized only for high salt tolerant plants. The majority (54.5%) were deemed moderate to no restriction for irrigation, with no toxicity concern for most plants. Agriculture indicators such as total dissolved solids (TDS), potential salinity (PS), sodium absorption ratio (SAR), and residual sodium carbonate (RSC) had mean values of 2572.30, 33.32, 4.84, and −21.14, respectively. However, the quality of the groundwater in the study area improves with increased rainfall and thus recharging the Quaternary aquifer. The PLSR models, which are based on physicochemical characteristics, have been shown to be the most efficient as alternative techniques for determining the six WQIs. For instance, the PLSR models of all IWQs had determination coefficients values of R2 ranging between 0.848 and 0.999 in the Cal., and between 0.848 and 0.999 in the Val. datasets, and had model accuracy varying from 0.824 to 0.999 in the Cal., and from 0.817 to 0.989 in the Val. datasets. In conclusion, the combination of physicochemical parameters, WQIs, and multivariate modeling with statistical analysis and GIS tools is a successful and adaptable methodology that provides a comprehensive picture of groundwater quality and governing mechanisms. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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18 pages, 2639 KiB  
Article
Impact of Sediment Layer on Longitudinal Dispersion in Sewer Systems
by Marek Sokáč and Yvetta Velísková
Water 2021, 13(22), 3168; https://doi.org/10.3390/w13223168 - 10 Nov 2021
Cited by 4 | Viewed by 2185
Abstract
Experiments focused on pollution transport and dispersion phenomena in conditions of low flow (low water depth and velocities) in sewers with bed sediment and deposits are presented. Such conditions occur very often in sewer pipes during dry weather flows. Experiments were performed in [...] Read more.
Experiments focused on pollution transport and dispersion phenomena in conditions of low flow (low water depth and velocities) in sewers with bed sediment and deposits are presented. Such conditions occur very often in sewer pipes during dry weather flows. Experiments were performed in laboratory conditions. To simulate real hydraulic conditions in sewer pipes, sand of fraction 0.6–1.2 mm was placed on the bottom of the pipe. In total, we performed 23 experiments with 4 different thicknesses of sand sediment layers. The first scenario is without sediment, the second is with sediment filling 3.4% of the pipe diameter (sediment layer thickness = 8.5 mm), the third scenario represents sediment filling 10% of the pipe diameter (sediment layer thickness = 25 mm) and sediment fills 14% of the pipe diameter (sediment layer thickness = 35 mm) in the last scenario. For each thickness of the sediment layer, a set of tracer experiments with different flow rates was performed. The discharge ranges were from (0.14–2.5)·10−3 m3·s−1, corresponding to the range of Reynolds number 500–18,000. Results show that in the hydraulic conditions of a circular sewer pipe with the occurrence of sediment and deposits, the value of the longitudinal dispersion coefficient Dx decreases almost linearly with decrease of the flow rate (also with Reynolds number) to a certain limit (inflexion point), which is individual for each particular sediment thickness. Below this limit the value of the dispersion coefficient starts to rise again, together with increasing asymmetricity of the concentration distribution in time, caused by transient (dead) storage zones. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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40 pages, 5250 KiB  
Article
Data-Driven System Dynamics Model for Simulating Water Quantity and Quality in Peri-Urban Streams
by Gregory G. Lemaire, Shane A. Carnohan, Stanislav Grand, Victor Mazel, Poul L. Bjerg and Ursula S. McKnight
Water 2021, 13(21), 3002; https://doi.org/10.3390/w13213002 - 26 Oct 2021
Cited by 6 | Viewed by 3214
Abstract
Holistic water quality models to support decision-making in lowland catchments with competing stakeholder perspectives are still limited. To address this gap, an integrated system dynamics model for water quantity and quality (including stream temperature, dissolved oxygen, and macronutrients) was developed. Adaptable plug-n-play modules [...] Read more.
Holistic water quality models to support decision-making in lowland catchments with competing stakeholder perspectives are still limited. To address this gap, an integrated system dynamics model for water quantity and quality (including stream temperature, dissolved oxygen, and macronutrients) was developed. Adaptable plug-n-play modules handle the complexity (sources, pathways) related to both urban and agricultural/natural land-use features. The model was applied in a data-rich catchment to uncover key insights into the dynamics governing water quality in a peri-urban stream. Performance indicators demonstrate the model successfully captured key water quantity/quality variations and interactions (with, e.g., Nash-Sutcliff Efficiency ranging from very good to satisfactory). Model simulation and sensitivity results could then highlight the influence of stream temperature variations and enhanced heterotrophic respiration in summer, causing low dissolved oxygen levels and potentially affecting ecological quality. Probabilistic uncertainty results combined with a rich dataset show high potential for ammonium uptake in the macrophyte-dominated reach. The results further suggest phosphorus remobilization from streambed sediment could become an important diffuse nutrient source should other sources (e.g., urban effluents) be mitigated. These findings are especially important for the design of green transition solutions, where single-objective management strategies may negatively impact aquatic ecosystems. Full article
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
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