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Industrial Wastewater Treatment by Coagulation–Flocculation and Advanced Oxidation Processes: A Review
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Microvascular Responses in the Dermis and Muscles After Balneotherapy: Results from a Prospective Pilot Histological Study
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Simultaneous Heterotrophic Nitrification and Aerobic Denitrification of High C/N Wastewater in a Sequencing Batch Reactor
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Urban Geochemical Contamination of Highland Peat Wetlands of Very High Ecological and First Nations Cultural Value
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Numerical Study of Turbulent Open-Channel Flow Through Submerged Rigid Vegetation
Journal Description
Water
Water
is a peer-reviewed, open access journal on water science and technology, including the ecology and management of water resources, and is published semimonthly online by MDPI. Water collaborates with the Stockholm International Water Institute (SIWI). In addition, the American Institute of Hydrology (AIH), The Polish Limnological Society (PLS) and Japanese Society of Physical Hydrology (JSPH) are affiliated with Water and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, GEOBASE, GeoRef, PubAg, AGRIS, CAPlus / SciFinder, Inspec, and other databases.
- Journal Rank: JCR - Q2 (Water Resources) / CiteScore - Q1 (Aquatic Science)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 19.1 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the first half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Water include: GeoHazards.
- Journal Clusters of Water Resources: Water, Journal of Marine Science and Engineering, Hydrology, Resources, Oceans, Limnological Review, Coasts.
Impact Factor:
3.0 (2024);
5-Year Impact Factor:
3.3 (2024)
Latest Articles
Development of Hydrological Criteria for the Hydraulic Design of Stormwater Pumping Stations
Water 2025, 17(20), 3007; https://doi.org/10.3390/w17203007 (registering DOI) - 19 Oct 2025
Abstract
For the design of stormwater pumping stations, there is often uncertainty regarding the selection of an appropriate rainfall event to determine the required pumping capacity and temporary storage volume for managing extreme events of a given magnitude. To account for the risk of
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For the design of stormwater pumping stations, there is often uncertainty regarding the selection of an appropriate rainfall event to determine the required pumping capacity and temporary storage volume for managing extreme events of a given magnitude. To account for the risk of system failure, the return period is considered, as recommended based on the size of the catchment’s drainage area or other considerations, depending on the local regulations of a country. This study focused on analysing the direct runoff volume from the catchment, the storage volume required for the operation of the pumping system, and the order of magnitude of the design flow rate. The results indicate that a rainfall event with a duration of at least twice the time of concentration should be used. The design flow rate should range between 50% and 70% of the peak discharge, and designing for flow rates near the peak is not advisable, as it can lead to intermittent pump operation and result in an oversized installed capacity. The methodology developed in this research was applied to the Coastal Protection Project located in the city of Cartagena, Colombia, which includes a 2045.6-m-long box culvert with a cross-sectional area of 2 × 2 m, and three pumping stations, each equipped with three pumps rated at 0.75 m3/s, for a total installed capacity of 6.75 m3/s.
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(This article belongs to the Special Issue Sustainable Water Resources Management in a Changing Environment)
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Open AccessSystematic Review
Management of Conventional and Non-Conventional Water Sources: A Systematic Literature Review
by
Oleg Dashkevych and Mashor Housh
Water 2025, 17(20), 3006; https://doi.org/10.3390/w17203006 (registering DOI) - 19 Oct 2025
Abstract
A global transition in water management is currently underway, marked by the declining reliability of conventional sources and the accelerated adoption of non-conventional alternatives. This shift is driven by escalating pressures from climate change, population growth, and freshwater overexploitation. While the literature on
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A global transition in water management is currently underway, marked by the declining reliability of conventional sources and the accelerated adoption of non-conventional alternatives. This shift is driven by escalating pressures from climate change, population growth, and freshwater overexploitation. While the literature on management of water sources (WSs) is extensive, empirical clarity on Hybrid Water Systems Management (HWSM)—the integration of conventional and non-conventional WSs within a single system—remains limited. The present study addresses this gap through a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, which ensures methodological transparency and applicability. From over 9000+ peer-refereed articles retrieved from three major scientific databases (ScienceDirect, Scopus, and Web of Science Core Collections), published between 1999 and 2024, 44 studies were identified as the most relevant and consequently analyzed. The literature review refines the classification of WSs, distinguishing conventional sources, such as groundwater and surface water, from non-conventional alternatives, such as desalinated water, treated wastewater, gray water, and rainwater harvesting. The analysis also indicates that non-conventional WSs are now more prominent in the literature than conventional ones. Overall, the present study demonstrated that modern water management strategies increasingly emphasize optimization and circular reuse. In contrast, earlier approaches tend to focus more on water conservation and economic efficiency. The literature also indicates a gradual shift from traditional supply-dominant models toward integrated, cost-effective, and sustainability-oriented approaches that combine multiple sources and advanced allocation techniques.
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(This article belongs to the Section Water Resources Management, Policy and Governance)
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Open AccessArticle
Environmental Drivers of Macrozoobenthos Structure Along a Discontinuous Tributary of the Oder River (North-Western Poland)
by
Nadhira Benhadji, Jarosław Dąbrowski, Adam Brysiewicz, Przemysław Czerniejewski and Łukasz Hałasa
Water 2025, 17(20), 3005; https://doi.org/10.3390/w17203005 (registering DOI) - 19 Oct 2025
Abstract
The Myśla River, a right-bank tributary of the Oder catchment, was the focus of our study on the impact of environmental parameters on macrozoobenthos diversity and composition. We surveyed 18 sites along the Myśla catchment, from upstream to the outlet, recording environmental features
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The Myśla River, a right-bank tributary of the Oder catchment, was the focus of our study on the impact of environmental parameters on macrozoobenthos diversity and composition. We surveyed 18 sites along the Myśla catchment, from upstream to the outlet, recording environmental features and sampling macrozoobenthos. The taxa composition (31 taxa) was dominated by insect larvae, particularly Diptera Chironomidae, with moderate contributions from mollusc families such as Sphaeriidae, Bithyniidae, and Planorbidae, which are primarily filter-feeders or grazers. Based on environmental affinities, the river was divided into three sections. Sites within lake areas and those with diverse sediment types exhibited the highest biodiversity. Conductivity, flow rate, nitrogen compound levels, dissolved oxygen, suspended particles, and current velocity most strongly influenced biodiversity, while substrate type shaped taxa composition. Lakes heavily disrupt the ecological continuity of the Myśla River, significantly altering natural ecological processes and causing deviations from the River Continuum Concept (RCC), whereas artificial structures exert only minor additional influence. We examined the applicability of the RCC by analyzing macrozoobenthos structure along the upstream-to-downstream gradient. This preliminary study contributes to ongoing regional research, highlighting the role of lakes in shaping the Myśla River ecosystem and assessing the relevance of RCC in unique river systems.
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(This article belongs to the Special Issue Advances in Monitoring of Hydrological and Ecological Processes Under Climate Change)
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Open AccessArticle
Research on Internal Flow and Runner Force Characteristics of Francis Turbine
by
Jianwen Xu, Peirong Chen, Yanhao Li, Xuelin Yang and An Yu
Water 2025, 17(20), 3004; https://doi.org/10.3390/w17203004 (registering DOI) - 19 Oct 2025
Abstract
Francis turbines are widely used due to their large capacity and broad head adaptability, placing higher demands on the internal flow characteristics and runner performance of the units. In this paper, numerical simulations of a Francis turbine model were conducted using ANSYS CFX
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Francis turbines are widely used due to their large capacity and broad head adaptability, placing higher demands on the internal flow characteristics and runner performance of the units. In this paper, numerical simulations of a Francis turbine model were conducted using ANSYS CFX 2022 R1. The SST turbulence model, ZGB cavitation model, and VOF multiphase flow model were selected for the calculations. The internal flow characteristics and pressure pulsations in the runner and draft tube under different operating conditions were analyzed, and the variations in normal and tangential forces acting on the runner blades during operation were investigated. The results indicate significant differences in the internal flow within the runner and draft tube under various guide vane opening conditions. The pressure pulsation in the unit is influenced by both the internal flow in the draft tube and the rotation of the runner. The mechanical load on the runner blades is affected by multiple factors, including the wake from upstream fixed guide vanes, rotor–stator interaction, and downstream vortex ropes. Under low-flow conditions, the variation in forces acting on the runner blades is relatively small, whereas under high-flow conditions, the runner blades are prone to abrupt force fluctuations at 0.6–0.8 times the rotational frequency. This is manifested as periodic abrupt force changes in both the X and Y directions of the runner blades under high-flow conditions. The normal force in the Z-direction of the runner blades increases instantaneously and then decreases immediately, while the tangential force decreases instantaneously and then increases promptly.
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(This article belongs to the Section Hydraulics and Hydrodynamics)
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Open AccessArticle
Hybrid RSM–ANN Modeling for Optimization of Electrocoagulation Using Aluminum Electrodes (Al–Al) for Hospital Wastewater Treatment
by
Khanit Matra, Yanika Lerkmahalikit, Sirilak Prasertkulsak, Amnuaychai Kongdee, Raweeporn Pomthong, Suchira Thongson and Suthida Theepharaksapan
Water 2025, 17(20), 3003; https://doi.org/10.3390/w17203003 (registering DOI) - 18 Oct 2025
Abstract
Electrocoagulation (EC) employing aluminum–aluminum (Al–Al) electrodes was investigated for hospital wastewater treatment, targeting the removal of turbidity, soluble chemical oxygen demand (sCOD), and total dissolved solids (TDS). A hybrid modeling framework integrating response surface methodology (RSM) and artificial neural networks (ANN) was developed
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Electrocoagulation (EC) employing aluminum–aluminum (Al–Al) electrodes was investigated for hospital wastewater treatment, targeting the removal of turbidity, soluble chemical oxygen demand (sCOD), and total dissolved solids (TDS). A hybrid modeling framework integrating response surface methodology (RSM) and artificial neural networks (ANN) was developed to enhance predictive reliability and identify energy-efficient operating conditions. A Box–Behnken design with 15 experimental runs evaluated the effects of pH, current density, and electrolysis time. Multi-response optimization determined the overall optimal conditions at pH 7.0, current density 20 mA/cm2, and electrolysis time 75 min, achieving 94.5% turbidity, 69.8% sCOD, and 19.1% TDS removal with a low energy consumption of 0.34 kWh/m3. The hybrid RSM–ANN model exhibited high predictive accuracy (R2 > 97%), outperforming standalone RSM models, with ANN more effectively capturing nonlinear relationships, particularly for TDS. The results confirm that EC with Al–Al electrodes represent a technically promising and energy-efficient approach for decentralized hospital wastewater treatment, and that the hybrid modeling framework provides a reliable optimization and prediction tool to support process scale-up and sustainable water reuse.
Full article
(This article belongs to the Special Issue Advanced Treatment Technologies for Emerging Contaminants in Wastewater)
Open AccessArticle
An Adaptive Management-Oriented Approach to Spatial Planning for Estuary National Parks: A Case Study of the Yangtze River Estuary, China
by
Wanting Peng, Ziyu Zhu, Jia Liu, Yunshan Lin, Qin Zhao, Wenhui Yang, Chengzhao Wu and Wenbo Cai
Water 2025, 17(20), 3002; https://doi.org/10.3390/w17203002 (registering DOI) - 18 Oct 2025
Abstract
Estuaries represent quintessential coupled human–natural systems (CHNS) where the dynamic interplay between ecological processes and anthropogenic pressures (e.g., shipping, water use exploitation) challenges conventional static spatial planning approaches. Focusing on the Yangtze River Estuary—a globally significant yet intensely utilized ecosystem—this study develops an
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Estuaries represent quintessential coupled human–natural systems (CHNS) where the dynamic interplay between ecological processes and anthropogenic pressures (e.g., shipping, water use exploitation) challenges conventional static spatial planning approaches. Focusing on the Yangtze River Estuary—a globally significant yet intensely utilized ecosystem—this study develops an adaptive management (AM)-oriented spatial planning framework for estuarine protected areas. Our methodology integrates systematic identification of optimal zones using multi-criteria assessments of biodiversity indicators (e.g., flagship species habitats), ecological metrics (e.g., ecosystem services), and management considerations; delineation of a three-tier adaptive zoning system (Control–Functional–Seasonal) to address spatiotemporal pressures; and dynamic management strategies to mitigate human-environment conflicts. The proposed phased conservation boundary (Phase I: 664.38 km2; Phase II: 1721.94 km2) effectively balances ecological integrity with socio-economic constraints. Spatial–temporal analysis of shipping activities over five years demonstrates minimal operational interference, confirming the framework’s efficacy in reconciling conservation and development priorities. By incorporating ecological feedback mechanisms into spatial planning, this work advances a transferable model for governing contested seascapes, contributing to CHNS theory through practical tools for adaptive, conflict-sensitive conservation. The framework’s implementation in the Yangtze context provides empirical evidence that science-driven, flexible spatial planning can reduce sectoral conflicts while maintaining ecosystem functionality, offering a replicable pathway for sustainable water management of similarly complex human–natural systems worldwide.
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(This article belongs to the Section Oceans and Coastal Zones)
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Open AccessArticle
Quantifying Flood Impacts on Ecosystem Carbon Dynamics Using Remote Sensing and Machine Learning in the Climate-Stressed Landscape of Emilia-Romagna
by
Jibran Qadri and Francesca Ceccato
Water 2025, 17(20), 3001; https://doi.org/10.3390/w17203001 (registering DOI) - 18 Oct 2025
Abstract
Flood events, intensified by climate change, pose significant threats to both human settlements and ecological systems. This study presents an integrated approach to evaluate flood impacts on ecosystem carbon dynamics using remote sensing and machine learning techniques. The case of the Emilia-Romagna region
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Flood events, intensified by climate change, pose significant threats to both human settlements and ecological systems. This study presents an integrated approach to evaluate flood impacts on ecosystem carbon dynamics using remote sensing and machine learning techniques. The case of the Emilia-Romagna region in Italy is presented, which experienced intense flooding in 2023. To understand flood-induced changes in the short term, we quantified the differences in net primary productivity (NPP) and above-ground biomass (AGB) before and after flood events. Short-term analysis of NPP and AGB revealed substantial localized losses within flood-affected areas. NPP showed a net deficit of 7.0 × 103 g C yr−1, and AGB a net deficit of 0.5 × 103 Mg C. While the wider region gained NPP (6.7 × 105 g C yr−1), it suffered a major AGB loss (3.3 × 105 Mg C), indicating widespread biomass decline beyond the flood zone. Long-term ecological assessment using the Remote Sensing Ecological Index (RSEI) showed accelerating degradation, with the “Fair” ecological class shrinking from 90% in 2014 to just over 50% in 2024, and the “Poor” class expanding. “Good” and “Very Good” classes nearly disappeared after 2019. High-hazard flood zones were found to contain 9.0 × 106 Mg C in AGB and 1.1 × 107 Mg C in soil organic carbon, highlighting the vulnerability of carbon stocks. This study underscores the importance of integrating flood modeling with ecosystem monitoring to inform climate-adaptive land management and carbon conservation strategies. It represents a clear, quantifiable carbon loss that should be factored into regional carbon budgets and post-flood ecosystem assessments.
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(This article belongs to the Special Issue Water-Induced Geo-Disaster Reduction in the Context of Climate Change: Hydrology, Management Strategies, and Ecological Geological Engineering)
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Open AccessReview
Advances in Remote Sensing and Sensor Technologies for Water-Quality Monitoring: A Review
by
Huilun Chen, Xilan Gao and Rongfang Yuan
Water 2025, 17(20), 3000; https://doi.org/10.3390/w17203000 (registering DOI) - 18 Oct 2025
Abstract
Water-quality monitoring plays a vital role in protecting and managing water resources, maintaining ecological balance and safeguarding human health. At present, the traditional monitoring technology is associated with risks of low sampling efficiency, long response time, high economic cost and secondary pollution of
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Water-quality monitoring plays a vital role in protecting and managing water resources, maintaining ecological balance and safeguarding human health. At present, the traditional monitoring technology is associated with risks of low sampling efficiency, long response time, high economic cost and secondary pollution of water samples, and cannot guarantee the accuracy and real-time determination of monitoring data. Remote sensing (RS) technology and sensors are used to automatically realize the real-time monitoring of water quality. In this paper, the principles and composition of remote monitoring systems are systematically summarized. For the RS technology, indicators including chlorophyll-a, turbidity and total suspended matter/solids, colored dissolved organic matter, electrical conductivity (EC), dissolved oxygen (DO), temperature and pH value were considered, and for sensors monitoring, the parameters of pH value, temperature, oxidation reduction potential, DO, turbidity, EC and salinity, and total dissolved solids were analyzed. The practical applications of remote monitoring in surface water, marine water and wastewater are introduced in this context. In addition, the advantages and disadvantages of remote monitoring systems are evaluated, which provides some basis for the selection of remote monitoring systems in the future.
Full article
(This article belongs to the Section New Sensors, New Technologies and Machine Learning in Water Sciences)
Open AccessArticle
Environmental Surveillance of ESKAPE Bacteria in Wastewater and Rivers in the Vhembe District, South Africa: Public Health Risks from a One Health Perspective
by
Natasha Potgieter, Mpumelelo Casper Rikhotso, Leonard Owino Kachienga, Rohudzwa Badzhi and Afsatou Ndama Traoré
Water 2025, 17(20), 2999; https://doi.org/10.3390/w17202999 (registering DOI) - 18 Oct 2025
Abstract
The One Health approach is used to assess health-associated risks resulting from human exposure to antibiotic-resistant bacteria (ARB) that pose a significant public health risk. In this approach, wastewater treatment plants (WWTPs) play an important role in reducing bacteria and antibiotic-resistant genes (ARGs)
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The One Health approach is used to assess health-associated risks resulting from human exposure to antibiotic-resistant bacteria (ARB) that pose a significant public health risk. In this approach, wastewater treatment plants (WWTPs) play an important role in reducing bacteria and antibiotic-resistant genes (ARGs) in the environment. The ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) are of significant concern due to their ability to evade the effects of multiple antibiotics, including last-resort treatments such as carbapenems and glycopeptides. This study aimed to investigate the environmental surveillance of ESKAPE bacteria in wastewater and their adjacent receiving water bodies in Limpopo Province, South Africa. Methodology: Over a period of 6 months, all isolates were identified phenotypically, and genomic DNA was extracted using the QIAamp 96 DNA QIAcube® HT Kit. Species-specific PCR was performed, followed by Sanger sequencing. The relevant sequences were compared to NCBI GenBank references using BLAST for confirmation and to assess the potential human health-associated risks. Results: ESKAPE organisms identified phenotypically were confirmed using PCR in both WWTP samples. Bacteria such as Acinetobacter baumannii and Enterobacter spp. were not detected in upstream or downstream river samples, particularly during August and September. In December and January, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa were not detected in effluent samples at both sites. Phylogenetic analysis revealed a diverse range of clinically significant genera, including Pseudomonas, Klebsiella, Enterobacter, and Staphylococcus, with strains closely related to global clinical isolates. Many of the isolates were associated with resistance to carbapenems, fluoroquinolones, and aminoglycosides. In addition, some strains clustered with both methicillin-sensitive and methicillin-resistant lineages. Conclusions: The findings emphasise the urgent need for increased genomic surveillance in environmental settings affected by wastewater discharge and highlight the importance of integrated antimicrobial resistance monitoring that connects clinical and environmental health sectors.
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(This article belongs to the Section Water and One Health)
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Open AccessArticle
Citric Acid-Modified Sepiolite as an Efficient and Sustainable Adsorbent for the Removal of Methylene Blue from Aqueous Solutions
by
Zhuangzhuang Tian, Ziyi Chen, Qing Wang, Xin Gao and Wei Wei
Water 2025, 17(20), 2998; https://doi.org/10.3390/w17202998 - 17 Oct 2025
Abstract
Eco-friendly clay-based adsorbents with low cost and high adsorption capacity for toxic dyes have attracted significant attention. In this study, a novel citric acid-modified sepiolite (CA-SEP) composite was developed for the efficient removal of methylene blue (MB) from aqueous solutions. The morphological, crystalline,
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Eco-friendly clay-based adsorbents with low cost and high adsorption capacity for toxic dyes have attracted significant attention. In this study, a novel citric acid-modified sepiolite (CA-SEP) composite was developed for the efficient removal of methylene blue (MB) from aqueous solutions. The morphological, crystalline, and structural properties of the composite were characterized using XRD, FTIR, SEM, and BET analyses. Compared to pristine SEP, CA-SEP exhibited a 2.6-fold increase in adsorption capacity for MB and demonstrated excellent reusability. The effects of key parameters—including solution pH (2.0–10.0), contact time (0–300 min), adsorbent dosage (0.2–2.0 g/L), and initial MB concentration (10–150 mg/L)—on adsorption performance were systematically investigated. Modeling results indicated that the Sips isotherm provided the optimal fit for the equilibrium data. In kinetic studies, the adsorption process was best described by the pseudo-second-order model. The maximum adsorption capacity of CA-SEP for MB was estimated to be 40.61 mg/g. Moreover, the adsorbent retained high removal efficiency after five adsorption-desorption cycles, demonstrating good regenerability. These results indicate that CA-SEP is a highly efficient, sustainable, and economically viable adsorbent for the elimination of MB from contaminated water.
Full article
(This article belongs to the Special Issue Development of New Wastewater Treatments for the Efficient Removal of Micropollutants)
Open AccessArticle
Spatiotemporal Water Quality Assessment in Spatially Heterogeneous Horseshoe Lake, Madison County, Illinois Using Satellite Remote Sensing and Statistical Analysis (2020–2024)
by
Anuj Tiwari, Ellen Hsuan and Sujata Goswami
Water 2025, 17(20), 2997; https://doi.org/10.3390/w17202997 - 17 Oct 2025
Abstract
Inland lakes across the United States are increasingly impacted by nutrient pollution, sedimentation, and algal blooms, with significant ecological and economic consequences. While satellite-based monitoring has advanced our ability to assess water quality at scale, many lakes remain analytically underserved due to their
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Inland lakes across the United States are increasingly impacted by nutrient pollution, sedimentation, and algal blooms, with significant ecological and economic consequences. While satellite-based monitoring has advanced our ability to assess water quality at scale, many lakes remain analytically underserved due to their spatial heterogeneity and the multivariate nature of pollution dynamics. This study presents an integrated framework for detecting spatiotemporal pollution patterns using satellite remote sensing, trend segmentation, hierarchical clustering and dimensionality reduction. Taking Horseshoe Lake (Illinois), a shallow eutrophic–turbid system, as a case study, we analyzed Sentinel-2 imagery from 2020–2024 to derive chlorophyll-a (NDCI), turbidity (NDTI), and total phosphorus (TP) across five hydrologically distinct zones. Breakpoint detection and modified Mann–Kendall tests revealed both abrupt and seasonal trend shifts, while correlation and hierarchical clustering uncovered inter-zone relationships. To identify lake-wide pollution windows, we applied Kernel PCA to generate a composite pollution index, aligned with the count of increasing trend segments. Two peak pollution periods, late 2022 and late 2023, were identified, with Regions 1 and 5 consistently showing high values across all indicators. Spatial maps linked these hotspots to urban runoff and legacy impacts. The framework captures both acute and chronic stress zones and enables targeted seasonal diagnostics. The approach demonstrates a scalable and transferable method for pollution monitoring in morphologically complex lakes and supports more targeted, region-specific water management strategies.
Full article
(This article belongs to the Special Issue Use of Remote Sensing Technologies for Water Resources Management)
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Open AccessArticle
AI-Assisted Monitoring and Prediction of Structural Displacements in Large-Scale Hydropower Facilities
by
Jianghua Liu, Chongshi Gu, Jun Wang, Yongli Dong and Shimao Huang
Water 2025, 17(20), 2996; https://doi.org/10.3390/w17202996 - 17 Oct 2025
Abstract
Accurate prediction of structural displacements in hydropower stations is essential for the safety and long-term stability of large-scale water-related infrastructure. To address this challenge, this study proposes an AI-assisted monitoring framework that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction with Gated
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Accurate prediction of structural displacements in hydropower stations is essential for the safety and long-term stability of large-scale water-related infrastructure. To address this challenge, this study proposes an AI-assisted monitoring framework that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction with Gated Recurrent Units (GRUs) for temporal sequence modeling. The framework leverages long-sequence prototype monitoring data, including reservoir level, temperature, and displacement, to capture complex spatiotemporal interactions between environmental conditions and dam behavior. A parameter optimization strategy is further incorporated to refine the model’s architecture and hyperparameters. Experimental evaluations on real-world hydropower station datasets demonstrate that the proposed CNN–GRU model outperforms conventional statistical and machine learning methods, achieving an average determination coefficient of R2 = 0.9582 with substantially reduced prediction errors (RMSE = 4.1121, MAE = 3.1786, MAPE = 3.1061). Both qualitative and quantitative analyses confirm that CNN–GRU not only provides stable predictions across multiple monitoring points but also effectively captures sudden deformation fluctuations. These results underscore the potential of the proposed AI-assisted framework as a robust and reliable tool for intelligent monitoring, safety assessment, and early warning in large-scale hydropower facilities.
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(This article belongs to the Special Issue Intelligent Safety Diagnosis and Reinforcement of Water-Related Buildings)
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Open AccessArticle
Could ChatGPT Automate Water Network Clustering? A Performance Assessment Across Algorithms
by
Ludovica Palma, Enrico Creaco, Michele Iervolino, Davide Marocco, Giovanni Francesco Santonastaso and Armando Di Nardo
Water 2025, 17(20), 2995; https://doi.org/10.3390/w17202995 - 17 Oct 2025
Abstract
Water distribution networks (WDNs) are characterized by complex challenges in management and optimization, especially in ensuring efficiency, reducing losses, and maintaining infrastructure performances. The recent advancements in Artificial Intelligence (AI) techniques based on Large Language Models, particularly ChatGPT 4.0 (a chatbot based on
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Water distribution networks (WDNs) are characterized by complex challenges in management and optimization, especially in ensuring efficiency, reducing losses, and maintaining infrastructure performances. The recent advancements in Artificial Intelligence (AI) techniques based on Large Language Models, particularly ChatGPT 4.0 (a chatbot based on a generative pre-trained model), offer potential solutions to streamline these processes. This study investigates the ability of ChatGPT to perform the clustering phase of WDN partitioning, a critical step for dividing large networks into manageable clusters. Using a real Italian network as a case study, ChatGPT was prompted to apply several clustering algorithms, including k-means, spectral, and hierarchical clustering. The results show that ChatGPT uniquely adds value by automating the entire workflow of WDN clustering—from reading input files and running algorithms to calculating performance indices and generating reports. This makes advanced water network partitioning accessible to users without programming or hydraulic modeling expertise. The study highlights ChatGPT’s role as a complementary tool: it accelerates repetitive tasks, supports decision-making with interpretable outputs, and lowers the entry barrier for utilities and practitioners. These findings demonstrate the practical potential of integrating large language models into water management, where they can democratize specialized methodologies and facilitate wider adoption of WDN managing strategies.
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(This article belongs to the Section Hydraulics and Hydrodynamics)
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Open AccessReview
A Bibliometric-Systematic Literature Review (B-SLR) of Machine Learning-Based Water Quality Prediction: Trends, Gaps, and Future Directions
by
Jeimmy Adriana Muñoz-Alegría, Jorge Núñez, Ricardo Oyarzún, Cristian Alfredo Chávez, José Luis Arumí and Lien Rodríguez-López
Water 2025, 17(20), 2994; https://doi.org/10.3390/w17202994 - 17 Oct 2025
Abstract
Predicting the quality of freshwater, both surface and groundwater, is essential for the sustainable management of water resources. This study collected 1822 articles from the Scopus database (2000–2024) and filtered them using Topic Modeling to create the study corpus. The B-SLR analysis identified
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Predicting the quality of freshwater, both surface and groundwater, is essential for the sustainable management of water resources. This study collected 1822 articles from the Scopus database (2000–2024) and filtered them using Topic Modeling to create the study corpus. The B-SLR analysis identified exponential growth in scientific publications since 2020, indicating that this field has reached a stage of maturity. The results showed that the predominant techniques for predicting water quality, both for surface and groundwater, fall into three main categories: (i) ensemble models, with Bagging and Boosting representing 43.07% and 25.91%, respectively, particularly random forest (RF), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGB), along with their optimized variants; (ii) deep neural networks such as long short-term memory (LSTM) and convolutional neural network (CNN), which excel at modeling complex temporal dynamics; and (iii) traditional algorithms like artificial neural network (ANN), support vector machines (SVMs), and decision tree (DT), which remain widely used. Current trends point towards the use of hybrid and explainable architectures, with increased application of interpretability techniques. Emerging approaches such as Generative Adversarial Network (GAN) and Group Method of Data Handling (GMDH) for data-scarce contexts, Transfer Learning for knowledge reuse, and Transformer architectures that outperform LSTM in time series prediction tasks were also identified. Furthermore, the most studied water bodies (e.g., rivers, aquifers) and the most commonly used water quality indicators (e.g., WQI, EWQI, dissolved oxygen, nitrates) were identified. The B-SLR and Topic Modeling methodology provided a more robust, reproducible, and comprehensive overview of AI/ML/DL models for freshwater quality prediction, facilitating the identification of thematic patterns and research opportunities.
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(This article belongs to the Special Issue Machine Learning Applications in the Water Domain)
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Open AccessArticle
Aquaculture Water Quality Classification Using XGBoost ClassifierModel Optimized by the Honey Badger Algorithm with SHAP and DiCE-Based Explanations
by
S M Naim, Prosenjit Das, Jun-Jiat Tiang and Abdullah-Al Nahid
Water 2025, 17(20), 2993; https://doi.org/10.3390/w17202993 - 16 Oct 2025
Abstract
Water quality is an essential part of maintaining a healthy environment for fish farming. The quality of the water is related to a few of the chemical and biological characteristics of water. The conventional evaluation methods of the water quality are often time-consuming
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Water quality is an essential part of maintaining a healthy environment for fish farming. The quality of the water is related to a few of the chemical and biological characteristics of water. The conventional evaluation methods of the water quality are often time-consuming and may overlook complex interdependencies among multiple indicators. This study has proposed a robust machine learning framework for aquaculture water quality classification by integrating the Honey Badger Algorithm (HBA) with the XGBoost classifier. The framework enhances classification accuracy and incorporates explainability through SHAP and DiCE, thereby providing both predictive performance and transparency for practical water quality management. For reliability, the dataset has been randomly shuffled, and a custom 5-fold cross-validation strategy has been applied. Later, through the metaheuristic-based HBA, feature selections and hyperparameter tuning have been performed to improve and increase the prediction accuracy. The highest accuracy of 98.45% has been achieved by a particular fold, whereas the average accuracy is 98.05% across all folds, indicating the model’s stability. SHAP analysis reveals Ammonia, Nitrite, DO, Turbidity, BOD, Temperature, pH, and CO2 as the topmost water quality indicators. Finally, the DiCE analysis has analyzed that Temperature, Turbidity, DO, BOD, CO2, pH, Ammonia, and Nitrite are more influential parameters of water quality.
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(This article belongs to the Section New Sensors, New Technologies and Machine Learning in Water Sciences)
Open AccessArticle
The Risk Assessment for Water Conveyance Channels in the Yangtze-to-Huaihe Water Diversion Project (Henan Reach)
by
Huan Jing, Yanjun Wang, Yongqiang Wang, Jijun Xu and Mingzhi Yang
Water 2025, 17(20), 2992; https://doi.org/10.3390/w17202992 - 16 Oct 2025
Abstract
Water conveyance channels, as critical components of water diversion projects, feature numerous structures, complex configurations, and intensive operational management requirements, making them vulnerable to multiple risks, such as extreme flooding, channel blockage, structural failures, and management deficiencies. To ensure an accurate assessment of
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Water conveyance channels, as critical components of water diversion projects, feature numerous structures, complex configurations, and intensive operational management requirements, making them vulnerable to multiple risks, such as extreme flooding, channel blockage, structural failures, and management deficiencies. To ensure an accurate assessment of the operational safety risk, this study proposes a comprehensive risk assessment framework that integrates risk probability and risk loss. The former is quantified using the Consequence Reverse Diffusion Method (CRDM), which systematically identifies and categorizes key factors of primary dike failure modes into four domains: hydrological characteristics, channel morphology, engineering structures, and operational management. The latter is assessed by integrating socioeconomic impacts, including population exposure, infrastructure investment, and industrial and agricultural production. A structured assessment framework is established through systematic indicator selection, justified weight assignment, and standardized scoring criteria. Application of the framework to Yangtze-to-Huaihe Water Diversion Project (Henan Reach) reveals that the risk probability across four segments falls within the (1, 3) range, indicating a generally low to moderate risk profile, while channel morphology shows greater spatial variability than hydrological, structural, and management indicators, driven by local differences in crossing structure density, sinuosity, and regime coefficients. Meanwhile, the segments along the Qingshui River face higher risk losses owing to their upstream location and large-scale water supply capacity, resulting in a relatively higher comprehensive risk level.
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(This article belongs to the Section Water Resources Management, Policy and Governance)
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Open AccessArticle
Freeze–Thaw-Driven Dynamics of Soil Water–Salt and Nitrogen: Effects and Implications for Irrigation Management in the Hetao Irrigation District
by
Weili Ge, Jiaqi Jiang, Chunli Su, Xianjun Xie, Qing Zhang, Chunming Zhang, Yanlong Li, Xin Li, Jiajia Song and Yinchun Su
Water 2025, 17(20), 2991; https://doi.org/10.3390/w17202991 - 16 Oct 2025
Abstract
This study investigated the mechanisms of soil water–salt and nitrogen transport and optimal strategies under freeze–thaw (F-T) cycles in the salinized farmlands of the Hetao Irrigation District. A combined approach of field monitoring and laboratory simulation, utilizing both undisturbed and repacked soil columns
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This study investigated the mechanisms of soil water–salt and nitrogen transport and optimal strategies under freeze–thaw (F-T) cycles in the salinized farmlands of the Hetao Irrigation District. A combined approach of field monitoring and laboratory simulation, utilizing both undisturbed and repacked soil columns subjected to 0–15 F-T cycles and five irrigation treatments, was employed to analyze the spatiotemporal dynamics in Gleyic Solonchaks. The results demonstrated that freeze–thaw processes play an important role in salt migration in surface soil layers, driving salt redistribution through phase changes of soil moisture. Increased freeze–thaw cycles reduced surface soil moisture content while promoting upward salt accumulation, salt dynamics exhibited pronounced spatial heterogeneity and irrigation source dependency, and the surface layer exhibited lower salinity levels after irrigation compared to pre-irrigation levels. These cycles also enhanced short-term soil nitrogen transformation and facilitated inorganic nitrogen accumulation. Different irrigation regimes exhibited a significant impact on the dynamics of water–salt and nitrogen in soil, with low-salinity treatment (S2) and moderate-nitrogen irrigation (N2) effectively reducing surface salt accumulation while improving nitrogen utilization efficiency (moderate-nitrogen irrigation exhibited higher mineralization rates, which facilitated the release of inorganic nitrogen from soil). This study reveals the synergistic transport mechanisms of water–salt and nitrogen under freeze–thaw driving forces and provides a scientific basis and practical pathway for sustainable agricultural management in cold arid irrigation districts.
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(This article belongs to the Section Soil and Water)
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Open AccessArticle
Bottle Test Free Chlorine Bulk Decay Coefficient Statistical Fitting for Water Supply Systems via State Estimation Techniques
by
Elena Cejas, Sarai Díaz and Javier González
Water 2025, 17(20), 2990; https://doi.org/10.3390/w17202990 (registering DOI) - 16 Oct 2025
Abstract
Free chlorine residual is the most widely adopted disinfectant residual in water supply systems. Chlorine is usually applied at treatment works, but it decays as water flows and spends time within the network. Chlorine decay is the result of a bulk and a
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Free chlorine residual is the most widely adopted disinfectant residual in water supply systems. Chlorine is usually applied at treatment works, but it decays as water flows and spends time within the network. Chlorine decay is the result of a bulk and a wall decay component. Bulk decay may be considered invariable through the pipe network (it only depends on water composition) and is often characterized at the entrance to the system through bottle tests, which measure chlorine evolution over time in a laboratory environment to then adjust a model (dependent on one or more coefficients) that represents its behavior. Previous studies have acknowledged that the bulk decay coefficient varies widely and that free chlorine measurements are subject to measurement errors, but they have not quantified the impact of these errors on the bulk decay coefficient. The aim of this paper is to provide a methodology that statistically fits chlorine’s bulk decay coefficient based on bottle test results, with appropriate management of uncertainty effects. The proposal is to use state estimation techniques, which combine free chlorine measurements and system knowledge (in this case, a first-order bulk decay model) to provide the most likely chlorine behavior and its associated uncertainty. This approach goes one step beyond previous studies, which report only a single value of the bulk decay coefficient without accounting for randomness, and thus fail to assess true variability, leading to unrepresentative comparisons. Results for water samples from different sources demonstrate the importance of controlling the fitting process through state estimation to understand and compare the bulk decay coefficient.
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(This article belongs to the Section Urban Water Management)
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Open AccessArticle
Hybrid Reconstruction of Sea Level at Dokdo in the East Sea Using Machine Learning and Geospatial Interpolation (1993–2023)
by
MyeongHee Han and Hak Soo Lim
Water 2025, 17(20), 2989; https://doi.org/10.3390/w17202989 - 16 Oct 2025
Abstract
Sea level variability in the East Sea (Sea of Japan) and the Northwest Pacific poses challenges for coastal risk management due to the scarcity of long-term observations at remote locations such as Dokdo (Dok Island). This study reconstructs a continuous monthly sea level
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Sea level variability in the East Sea (Sea of Japan) and the Northwest Pacific poses challenges for coastal risk management due to the scarcity of long-term observations at remote locations such as Dokdo (Dok Island). This study reconstructs a continuous monthly sea level record at Dokdo from 1993 to 2023 by imputing gaps in 13 nearby Permanent Service for Mean Sea Level tide gauge stations using eight machine learning models and geospatial interpolation methods. The ensemble mean of Machine Learning-based imputations produced physically realistic and temporally coherent timeseries, preserving both seasonal and interannual variability. Sea level at Dokdo, estimated via inverse distance weighting, aligned well with satellite altimetry from Copernicus Marine Service and exhibited strong regional coherence with nearby stations. These results demonstrate that a hybrid framework combining statistical imputation, Machine Learning, and inverse barometric correction can effectively reconstruct sea level in data-sparse marine regions. The methodology provides a scalable tool for monitoring long-term trends and validating satellite and model products in marginal seas like the East Sea.
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(This article belongs to the Special Issue Advanced Remote Sensing for Coastal System Monitoring and Management)
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Open AccessArticle
Copper (II) Complex Decorated PVDF Membranes for Enhanced Removal of Organic Pollutants from Textile and Oily Wastewater
by
Felipe P. da Silva, Aline C. F. Pereira, Juliana C. Pinheiro, Annelise Casellato, Cristiano P. Borges and Fabiana V. da Fonseca
Water 2025, 17(20), 2988; https://doi.org/10.3390/w17202988 - 16 Oct 2025
Abstract
This study reports the development of polyvinylidene fluoride (PVDF) membranes decorated with a copper(II) complex (CuL) for the removal of organic pollutants from wastewater. Using Drimaren Red X-6BN (DRX-6BN) as a probe, the PVDF membrane with the lowest CuL loading (PVDF/PDA/CuL-4) reached an
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This study reports the development of polyvinylidene fluoride (PVDF) membranes decorated with a copper(II) complex (CuL) for the removal of organic pollutants from wastewater. Using Drimaren Red X-6BN (DRX-6BN) as a probe, the PVDF membrane with the lowest CuL loading (PVDF/PDA/CuL-4) reached an adsorption capacity of 19.78 mg/g at 300 min, with removal of up to 50% DRX-6BN. Kinetic analysis favored Elovich (R2 > 0.9928; RMSE < 0.4489) and the pseudo-second-order model (R2 > 0.9540; RMSE < 1.1388), consistent with chemisorption. Intraparticle diffusion occurred in two steps. In the presence of 20 mg/L of hydrogen peroxide (H2O2), the removal was >80% within 180 min at higher CuL loadings (PVDF/PDA/CuL-40). In oily wastewater, PVDF/PDA/CuL-4 achieved ~100% COD removal in 120 min with H2O2, whereas pristine PVDF achieved 38.5%. Storage stability tests demonstrated the preservation of catalytic and separation performance for at least three months. All tests were conducted at pH ≈ 6.0 and a temperature of 25 °C. In contrast to many catalytic membranes, these membranes operate at near-neutral pH and ambient temperature in the absence of radiation. The results highlight PVDF membranes decorated with CuL as a robust and sustainable approach for the treatment of oily effluents, particularly by combining Fenton-like processes under mild conditions.
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(This article belongs to the Section Wastewater Treatment and Reuse)
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