Water

15 pages, 1905 KiB

Open AccessEditor’s ChoiceArticle

Effect of Light Intensity on the Growth and Nutrient Uptake of the Microalga Chlorella sorokiniana Cultivated in Biogas Plant Digestate

by Thomas L. Palikrousis, Christos Manolis, Sotirios D. Kalamaras and Petros Samaras

Water 2024, 16(19), 2782; https://doi.org/10.3390/w16192782 - 30 Sep 2024

Cited by 8 | Viewed by 2698

Abstract

This study investigated the effect of light intensity on the growth and nutrient uptake of Chlorella sorokiniana cultivated in nitrogen-rich anaerobic digestion wastewater. Three light intensities (20, 68, and 162 µmol m⁻² s⁻¹) were applied over a 30-day period with [...] Read more.

This study investigated the effect of light intensity on the growth and nutrient uptake of Chlorella sorokiniana cultivated in nitrogen-rich anaerobic digestion wastewater. Three light intensities (20, 68, and 162 µmol m⁻² s⁻¹) were applied over a 30-day period with a 16:8 h light–dark photoperiod. The goal was to understand how light affects biomass productivity, nutrient assimilation, and biochemical composition under varying nitrogen concentrations originating from biogas plant digestate, up to 5 g L⁻¹. The results showed that higher light intensities significantly boosted biomass production, achieving a five-fold increase at 162 µmol m⁻² s⁻¹ compared to 20 µmol m⁻² s⁻¹. Nutrient uptake followed a similar pattern, with 94% of ammonium nitrogen removed in 7 days under high light, compared to 55% after 30 days under low light. Phosphorus content was also completely removed after 7 days under light intensities of 68 and 162 µmol m⁻² s⁻¹. Additionally, elevated light intensity led to increased lipid accumulation (from 29.7% to 34%) and reduced protein content (from 30.9% to 26.1%), with carbohydrate content not being affected by light intensity. These findings highlight light intensity as a critical factor for optimizing microalgae cultivation in nitrogen-rich biogas digestate, promoting both effective nutrient removal and biomass production for potential bioenergy applications. Full article

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29 pages, 7501 KiB

Open AccessEditor’s ChoiceArticle

Water Resources’ AI–ML Data Uncertainty Risk and Mitigation Using Data Assimilation

by Nick Martin and Jeremy White

Water 2024, 16(19), 2758; https://doi.org/10.3390/w16192758 - 27 Sep 2024

Cited by 3 | Viewed by 1135

Abstract

Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), learns by training and is restricted by the amount and quality of training data. Training involves a tradeoff between prediction bias and variance controlled by model complexity. Increased model complexity decreases prediction [...] Read more.

Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), learns by training and is restricted by the amount and quality of training data. Training involves a tradeoff between prediction bias and variance controlled by model complexity. Increased model complexity decreases prediction bias, increases variance, and increases overfitting possibilities. Overfitting is a significantly smaller training prediction error relative to the trained model prediction error for an independent validation set. Uncertain data generate risks for AI–ML because they increase overfitting and limit generalization ability. Specious confidence in predictions from overfit models with limited generalization ability, leading to misguided water resource management, is the uncertainty-related negative consequence. Improved data is the way to improve AI–ML models. With uncertain water resource data sets, like stream discharge, there is no quick way to generate improved data. Data assimilation (DA) provides mitigation for uncertainty risks, describes data- and model-related uncertainty, and propagates uncertainty to results using observation error models. A DA-derived mitigation example is provided using a common-sense baseline, derived from an observation error model, for the confirmation of generalization ability and a threshold identifying overfitting. AI–ML models can also be incorporated into DA to provide additional observations for assimilation or as a forward model for prediction and inverse-style calibration or training. The mitigation of uncertain data risks using DA involves a modified bias–variance tradeoff that focuses on increasing solution variability at the expense of increased model bias. Increased variability portrays data and model uncertainty. Uncertainty propagation produces an ensemble of models and a range of predictions. Full article

(This article belongs to the Section Water Resources Management, Policy and Governance)

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16 pages, 2262 KiB

Open AccessEditor’s ChoiceArticle

Decontamination Potential of Ultraviolet Type C Radiation in Water Treatment Systems: Targeting Microbial Inactivation

by Abayomi Olusegun Adeniyi and Modupe Olufunmilayo Jimoh

Water 2024, 16(19), 2725; https://doi.org/10.3390/w16192725 - 25 Sep 2024

Cited by 3 | Viewed by 3519

Abstract

Access to safe water and sanitation is a critical global challenge, posing significant health risks worldwide due to waterborne diseases. This study investigates the efficacy of ultraviolet type C radiation as a disinfection method for improving water quality. The research elucidates UV-C’s mechanism [...] Read more.

Access to safe water and sanitation is a critical global challenge, posing significant health risks worldwide due to waterborne diseases. This study investigates the efficacy of ultraviolet type C radiation as a disinfection method for improving water quality. The research elucidates UV-C’s mechanism of action, highlighting its ability to disrupt DNA and RNA replication, thereby inactivating pathogens. Furthermore, the study analyses the influence of key factors on UV-C disinfection effectiveness, including water turbidity and the presence of dissolved organic matter, which can attenuate UV-C penetration and reduce treatment efficiency. The experimental results demonstrate a substantial reduction in microbial content following UV-C treatment. River water samples exhibited a 57.143% reduction in microbial load, while well water samples showed a 50% reduction. Notably, Escherichia coli (E. coli) concentrations decreased significantly, with an 83.33% reduction in well water and a 62.5% reduction in borehole water. This study makes a novel contribution to the understanding of UV-C disinfection by identifying the presence of resistant organisms, including Adenoviruses, Bacterial spores, and the Protozoan Acanthamoeba, in water samples. This finding expands the scope of UV-C research beyond easily culturable bacteria. To address this challenge, future investigations should explore synergistic disinfection strategies, such as combining UV-C treatment with advanced oxidation processes. Optimising UV-C system designs and developing robust, real-time monitoring systems capable of detecting and quantifying known and emerging UV-resistant pathogens are crucial for ensuring comprehensive water decontamination. Full article

(This article belongs to the Section Wastewater Treatment and Reuse)

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25 pages, 398 KiB

Open AccessEditor’s ChoiceReview

Beyond Bioremediation: The Untapped Potential of Microalgae in Wastewater Treatment

by Davide Liberti, Filipa Pinheiro, Beatriz Simões, João Varela and Luísa Barreira

Water 2024, 16(19), 2710; https://doi.org/10.3390/w16192710 - 24 Sep 2024

Cited by 9 | Viewed by 4791

Abstract

Microalgae-based wastewater bioremediation has emerged as a promising and sustainable solution for water purification by harnessing the natural ability of microalgae to absorb and transform pollutants. In the literature, it is possible to find diverse microalgae applications in wastewater treatment, highlighting their efficiency [...] Read more.

Microalgae-based wastewater bioremediation has emerged as a promising and sustainable solution for water purification by harnessing the natural ability of microalgae to absorb and transform pollutants. In the literature, it is possible to find diverse microalgae applications in wastewater treatment, highlighting their efficiency in nutrient removal, heavy metal sequestration, and overall water quality enhancement. Although microalgae demonstrate remarkable potential for wastewater treatment, there is a critical gap in research concerning the utilization of biomass produced during the treatment process, including large-scale biomass harvesting methods, economic viability assessments, and the exploration of innovative downstream applications. By shedding light on these deficiencies, the aim of this review is to encourage further research and development to maximize the potential of microalgae in removing wastewater pollution and the application of biomass derived from the treatment. In conclusion, this review not only underscores the overall efficiency of microalgae in wastewater bioremediation but also emphasizes the necessity of a more comprehensive approach that considers the full lifecycle of microalgae, from wastewater treatment to innovative applications of biomass, addressing both environmental and economic concerns. Full article

(This article belongs to the Special Issue Persistent and Emerging Organic Contaminants in Natural Environments)

28 pages, 910 KiB

Open AccessEditor’s ChoiceReview

Microplastics’ Impact on the Environment and the Challenging Selection of Reliable Key Biomonitors

by Luigi Rosati, Federica Carraturo, Fiore Capozzi, Teresa Chianese, Alessandra La Pietra, Michela Salamone, Valeria Spagnuolo, Ida Ferrandino and Simonetta Giordano

Water 2024, 16(18), 2637; https://doi.org/10.3390/w16182637 - 17 Sep 2024

Cited by 3 | Viewed by 4297

Abstract

Microplastics (MPs) persist for long periods in the environment, causing adverse effects on aquatic and terrestrial ecosystems. The accumulation of MPs in various trophic levels mostly depends on weathering phenomena, their reduced dimensions and the improved bioavailability; this ultimately causes their ingestion by [...] Read more.

Microplastics (MPs) persist for long periods in the environment, causing adverse effects on aquatic and terrestrial ecosystems. The accumulation of MPs in various trophic levels mostly depends on weathering phenomena, their reduced dimensions and the improved bioavailability; this ultimately causes their ingestion by organisms living in different niches. The modern concern about MPs toxicity collides with the current unavailability of standardized and reliable methodologies to assess the risks associated with the exposure of organisms from different habitats. Hence, the identification and selection of appropriate biomonitors for MPs pollution risk assessment should focus on the identification of easy-to-implement assays, rapidly interpretable results (e.g., based on the MPs bioaccumulation capabilities in their tissues) and standardizable methodologies. The present review analyzed some emerging biomonitors exploited for MPs evaluation, selected and examined according to their potential use as specific biological indicators for diverse environments. The research was focused on plants, as biological models for airborne microfibers toxicity evaluation; mussels, as key organisms for the establishment of MPs accumulation in marine environments; land snails, representing emerging organisms selected for studies of MPs’ impact on soil. Furthermore, recent findings evidenced the influence of microplastics on the composition of environmental microbiota, enhancing pathogenic biofilms formation, leading to increased water, soil, food, crops and waste contamination. Disposing of harmonized and validated methods to study MPs’ impact on the environment, integrated with promising machine learning tools, might sensibly support the risk management strategies protecting human and animal health. Full article

(This article belongs to the Section Water and One Health)

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22 pages, 3249 KiB

Open AccessEditor’s ChoiceArticle

LSTM-Autoencoder Based Detection of Time-Series Noise Signals for Water Supply and Sewer Pipe Leakages

by Yungyeong Shin, Kwang Yoon Na, Si Eun Kim, Eun Ji Kyung, Hyun Gyu Choi and Jongpil Jeong

Water 2024, 16(18), 2631; https://doi.org/10.3390/w16182631 - 16 Sep 2024

Cited by 5 | Viewed by 2448

Abstract

The efficient management of urban water distribution networks is crucial for public health and urban development. One of the major challenges is the quick and accurate detection of leaks, which can lead to water loss, infrastructure damage, and environmental hazards. Many existing leak [...] Read more.

The efficient management of urban water distribution networks is crucial for public health and urban development. One of the major challenges is the quick and accurate detection of leaks, which can lead to water loss, infrastructure damage, and environmental hazards. Many existing leak detection methods are ineffective, especially in complex and aging pipeline networks. If these limitations are not overcome, it can result in a chain of infrastructure failures, exacerbating damage, increasing repair costs, and causing water shortages and public health risks. The leak issue is further complicated by increasing urban water demand, climate change, and population growth. Therefore, there is an urgent need for intelligent systems that can overcome the limitations of traditional methodologies and leverage sophisticated data analysis and machine learning technologies. In this study, we propose a reliable and advanced method for detecting leaks in water pipes using a framework based on Long Short-Term Memory (LSTM) networks combined with autoencoders. The framework is designed to manage the temporal dimension of time-series data and is enhanced with ensemble learning techniques, making it sensitive to subtle signals indicating leaks while robustly dealing with noise signals. Through the integration of signal processing and pattern recognition, the machine learning-based model addresses the leak detection problem, providing an intelligent system that enhances environmental protection and resource management. The proposed approach greatly enhances the accuracy and precision of leak detection, making essential contributions in the field and offering promising prospects for the future of sustainable water management strategies. Full article

(This article belongs to the Special Issue Prediction and Assessment of Hydrological Processes)

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20 pages, 2569 KiB

Open AccessEditor’s ChoiceArticle

Seasonal Variability and Hydrological Patterns Influence the Long-Term Trends of Nutrient Loads in the River Po

by Edoardo Cavallini, Pierluigi Viaroli, Mariachiara Naldi, Mattia Saccò, Alessandro Scibona, Elena Barbieri, Silvia Franceschini and Daniele Nizzoli

Water 2024, 16(18), 2628; https://doi.org/10.3390/w16182628 - 16 Sep 2024

Cited by 4 | Viewed by 1475

Abstract

This study investigates the long-term trends (1992–2022) of nitrogen and phosphorus loadings exported by the River Po to the Adriatic Sea, to better analyse how changes in hydrology are affecting the timing and magnitude of river nutrient loads. We used 30 years of [...] Read more.

This study investigates the long-term trends (1992–2022) of nitrogen and phosphorus loadings exported by the River Po to the Adriatic Sea, to better analyse how changes in hydrology are affecting the timing and magnitude of river nutrient loads. We used 30 years of monitoring data in order to (a) identify the main temporal patterns and their interactions at a decadal, annual and seasonal scale, (b) estimate precipitation effects on load formation and evaluate whether and to which extent the hydrological regime affects nutrient export across the years and (c) analyse the nutrient export regime at a monthly scale and the main transport dynamic of N and P chemical species (hydrological vs. biogeochemical control). The long-term analysis shows a general decrease of both P and N loadings, but the trends are different between the elements and their chemical species, as well as undergoing different seasonal variations. We found a statistically significant relationships between precipitation and loads, which demonstrates that precipitation patterns drive the exported load at the intra- and interannual time scales considered in this study. Precipitation-induced load trends trigger seasonal changes in nutrient deliveries to the sea, peaking in spring and autumn. The nitrogen decrease is mainly concentrated in the summer dry period, while total phosphorus diminishes mainly in spring and autumn. This mismatch of N and P results in variable molar N:P ratios within the year. The effects of extreme drought and flood events, along with the progressive decrease in the snowmelt contribution to water fluxes, are expected to exacerbate the variability in the N and P loadings, which in turn is expected to perturbate the biodiversity, food webs and trophic state of the Northern Adriatic Sea. Full article

(This article belongs to the Special Issue Anthropogenic and Climatic Disturbances in Aquatic Ecosystems: Multiscale Spatio-Temporal Evolution and Effects)

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24 pages, 853 KiB

Open AccessEditor’s ChoiceReview

The Convergence of Antibiotic Contamination, Resistance, and Climate Dynamics in Freshwater Ecosystems

by Marcelo Pedrosa Gomes

Water 2024, 16(18), 2606; https://doi.org/10.3390/w16182606 - 14 Sep 2024

Cited by 11 | Viewed by 7297

Abstract

The convergence of antibiotic contamination, antimicrobial resistance (AMR), and climate dynamics poses a critical environmental and public health challenge. Freshwater ecosystems are increasingly threatened by the persistent presence of antibiotics, which, coupled with rising global temperatures, accelerate the development and spread of AMR. [...] Read more.

The convergence of antibiotic contamination, antimicrobial resistance (AMR), and climate dynamics poses a critical environmental and public health challenge. Freshwater ecosystems are increasingly threatened by the persistent presence of antibiotics, which, coupled with rising global temperatures, accelerate the development and spread of AMR. This review examines the sources, pathways, and mechanisms through which antibiotics enter freshwater systems and how climate change exacerbates these processes. This review discusses this convergence’s ecological and human health impacts, highlighting the implications for biodiversity and public health. It also explored the current monitoring and mitigation strategies, including advanced oxidation processes, natural-based solutions, and policy interventions. Finally, this review identifies critical research gaps and proposes future directions for managing the intertwined threats of antibiotic contamination, resistance, and climate change. It emphasizes the need for integrated, multidisciplinary approaches to protect freshwater resources in an increasingly volatile global environment. Full article

(This article belongs to the Special Issue Toxicology in Freshwater Ecosystems)

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41 pages, 2467 KiB

Open AccessEditor’s ChoiceArticle

Comprehensive Resilience Assessment Framework for Water Distribution Networks

by Joana Carneiro, Dália Loureiro, Marta Cabral and Dídia Covas

Water 2024, 16(18), 2611; https://doi.org/10.3390/w16182611 - 14 Sep 2024

Cited by 4 | Viewed by 1621

Abstract

A novel comprehensive resilience assessment framework for drinking water systems is proposed integrating different resilience perspectives (i.e., robustness, autonomy, flexibility, reliability, preparedness and recovery), oriented by objectives, criteria and metrics, applicable at the tactical level. The resilience assessment framework is applied to a [...] Read more.

A novel comprehensive resilience assessment framework for drinking water systems is proposed integrating different resilience perspectives (i.e., robustness, autonomy, flexibility, reliability, preparedness and recovery), oriented by objectives, criteria and metrics, applicable at the tactical level. The resilience assessment framework is applied to a Portuguese real water distribution network, enabling the evaluation of the system’s resilience. The infrastructure dimension is the main contributor to the low resilience results, particularly in terms of infrastructural robustness, as the infrastructure has exceeded the average service life and has low rehabilitation rates. In terms of autonomy, the system highly depends on external water and energy sources. Regarding the service dimension, most of the drinking water available is used for non-potable uses (e.g., irrigation), without alternative sources. The detailed diagnosis identified network area R6 as the priority area. Assets rehabilitation, increasing storage capacity, finding alternative water and energy sources, and minimizing non-potable uses are relevant improvement measures that promote the reinforcement of the system’s resilience. The resilience assessment framework is a very useful tool for the daily and tactical management of drinking water systems. Full article

(This article belongs to the Section Urban Water Management)

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28 pages, 26581 KiB

Open AccessEditor’s ChoiceArticle

Empirical Bayesian Kriging, a Robust Method for Spatial Data Interpolation of a Large Groundwater Quality Dataset from the Western Netherlands

by Mojtaba Zaresefat, Reza Derakhshani and Jasper Griffioen

Water 2024, 16(18), 2581; https://doi.org/10.3390/w16182581 - 12 Sep 2024

Cited by 11 | Viewed by 2898

Abstract

No single spatial interpolation method reigns supreme for modelling the precise spatial distribution of groundwater quality data. This study addresses this challenge by evaluating and comparing several commonly used geostatistical methods: Local Polynomial Interpolation (LPI), Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging [...] Read more.

No single spatial interpolation method reigns supreme for modelling the precise spatial distribution of groundwater quality data. This study addresses this challenge by evaluating and comparing several commonly used geostatistical methods: Local Polynomial Interpolation (LPI), Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging (UK), and Empirical Bayesian Kriging (EBK). We applied these methods to a vast dataset of 3033 groundwater records encompassing a substantial area (11,100 km²) in the coastal lowlands of the western Netherlands. To our knowledge, no prior research has investigated these interpolation methods in this specific hydrogeological setting, exhibiting a range of groundwater qualities, from fresh to saline, often anoxic, with high natural concentrations of PO₄ and NH₄. The prediction performance of the interpolation methods was assessed through statistical indicators such as root means square error. The findings indicated that EBK outperforms the other geostatistical methods in forecasting groundwater quality for the five variables considered: Cl, SO₄, Fe, PO₄, and NH₄. In contrast, SK performed worst for the species except for SO₄. We recommend not using SK to interpolate groundwater quality species unless the data exhibit low spatial variation, high sample density, or evenly distributed sampling. Full article

(This article belongs to the Special Issue Water, Geohazards, and Artificial Intelligence, 2nd Edition)

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15 pages, 6817 KiB

Open AccessEditor’s ChoiceArticle

A Fully Connected Neural Network (FCNN) Model to Simulate Karst Spring Flowrates in the Umbria Region (Central Italy)

by Francesco Maria De Filippi, Matteo Ginesi and Giuseppe Sappa

Water 2024, 16(18), 2580; https://doi.org/10.3390/w16182580 - 12 Sep 2024

Cited by 4 | Viewed by 1351

Abstract

In the last decades, climate change has led to increasingly frequent drought events within the Mediterranean area, creating an urgent need of a more sustainable management of groundwater resources exploited for drinking and agricultural purposes. One of the most challenging issues is to [...] Read more.

In the last decades, climate change has led to increasingly frequent drought events within the Mediterranean area, creating an urgent need of a more sustainable management of groundwater resources exploited for drinking and agricultural purposes. One of the most challenging issues is to provide reliable simulations and forecasts of karst spring discharges, whose reduced information, as well as the hydrological processes involving their feeding aquifers, is often a big issue for water service managers and researchers. In order to plan a sustainable water resource exploitation that could face future shortages, the groundwater availability should be assessed by continuously monitoring spring discharge during the hydrological year, using collected data to better understand the past behaviour and, possibly, forecast the future one in case of severe droughts. The aim of this paper is to understand the factors that govern different spring discharge patterns according to rainfall inputs and to present a model, based on artificial neural network (ANN) data training and cross-correlation analyses, to evaluate the discharge of some karst spring in the Umbria region (Central Italy). The model used is a fully connected neural network (FCNN) and has been used both for filling gaps in the spring discharge time series and for simulating the response of six springs to rainfall seasonal patterns from a 20-year continuous daily record, collected and provided by the Regional Environmental Protection Agency (ARPA) of the Umbria region. Full article

(This article belongs to the Special Issue Recent Advances in Karstic Hydrogeology, 2nd Edition)

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31 pages, 15174 KiB

Open AccessEditor’s ChoiceArticle

Flood Susceptibility Assessment for Improving the Resilience Capacity of Railway Infrastructure Networks

by Giada Varra, Renata Della Morte, Mario Tartaglia, Andrea Fiduccia, Alessandra Zammuto, Ivan Agostino, Colin A. Booth, Nevil Quinn, Jessica E. Lamond and Luca Cozzolino

Water 2024, 16(18), 2592; https://doi.org/10.3390/w16182592 - 12 Sep 2024

Cited by 5 | Viewed by 3160

Abstract

Floods often cause significant damage to transportation infrastructure such as roads, railways, and bridges. This study identifies several topographic, environmental, and hydrological factors (slope, elevation, rainfall, land use and cover, distance from rivers, geology, topographic wetness index, and drainage density) influencing the safety [...] Read more.

Floods often cause significant damage to transportation infrastructure such as roads, railways, and bridges. This study identifies several topographic, environmental, and hydrological factors (slope, elevation, rainfall, land use and cover, distance from rivers, geology, topographic wetness index, and drainage density) influencing the safety of the railway infrastructure and uses multi-criteria analysis (MCA) alongside an analytical hierarchy process (AHP) to produce flood susceptibility maps within a geographic information system (GIS). The proposed methodology was applied to the catchment area of a railway track in southern Italy that was heavily affected by a destructive flood that occurred in the autumn of 2015. Two susceptibility maps were obtained, one based on static geophysical factors and another including triggering rainfall (dynamic). The results showed that large portions of the railway line are in a very highly susceptible zone. The flood susceptibility maps were found to be in good agreement with the post-disaster flood-induced infrastructural damage recorded along the railway, whilst the official inundation maps from competent authorities fail to supply information about flooding occurring along secondary tributaries and from direct rainfall. The reliable identification of sites susceptible to floods and damage may provide railway and environmental authorities with useful information for preparing disaster management action plans, risk analysis, and targeted infrastructure maintenance/monitoring programs, improving the resilience capacity of the railway network. The proposed approach may offer railway authorities a cost-effective strategy for rapidly screening flood susceptibility at regional/national levels and could also be applied to other types of linear transport infrastructures. Full article

(This article belongs to the Special Issue Advances in Flood Hazard and Risk Analysis: Theory, Methods, Numerical Models, Strategies, and Applications)

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29 pages, 5060 KiB

Open AccessEditor’s ChoiceArticle

Effectiveness of Collars and Hooked-Collars in Mitigating Scour around Different Abutment Shapes

by Zaka Ullah Khan, Afzal Ahmed, Manousos Valyrakis, Ghufran Ahmed Pasha, Rashid Farooq, Nadir Murtaza and Diyar Khan

Water 2024, 16(17), 2550; https://doi.org/10.3390/w16172550 - 9 Sep 2024

Cited by 5 | Viewed by 1116

Abstract

Abutment scour is a major cause of bridge failures worldwide, leading to disruptions, economic losses, and loss of life. The present experimental study examines countermeasures against abutment scour using hooked-collar protections on vertical-wall and wing-wall abutments (at 45° and 60°) under different flow [...] Read more.

Abutment scour is a major cause of bridge failures worldwide, leading to disruptions, economic losses, and loss of life. The present experimental study examines countermeasures against abutment scour using hooked-collar protections on vertical-wall and wing-wall abutments (at 45° and 60°) under different flow conditions. All 60 experiments were performed under sub-critical flow conditions by investigating scour around an abutment 20 cm long, 20 cm wide, and 25 cm tall. Two distinct values of the Froude number, 0.154 and 0.179, and a sediment particle diameter (d₅₀) of 0.88 mm were used throughout the experimental phase. The resulting equilibrium scour around the abutments was compared to those with collar and hooked-collar protections. It was determined that the maximum abutment scour depth reduction was 83.89% when hooked collars were placed on vertical wall abutments beneath the bed surface level, and for wing-wall abutments at 45° and 60°, it was 74.2% and 73.5%, respectively, at the bed surface level. Regression analysis was conducted to assess the non-dimensional scour depth (D_s/Y_f) and scour reduction (R_Ds/Y_f), with a high enough coefficient of determination (R² values of 0.96 and 0.93, respectively), indicating high confidence in the analysis. The sensitivity analysis findings demonstrate that the width of the collar (W_c) and L_a are the most influencing factors affecting D_s/Y_f and RD_s/Y_f. Full article

(This article belongs to the Special Issue Advances in Hydraulic and Water Resources Research (2nd Edition))

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64 pages, 5373 KiB

Open AccessEditor’s ChoiceReview

Harmful Algal Blooms in Eutrophic Marine Environments: Causes, Monitoring, and Treatment

by Jiaxin Lan, Pengfei Liu, Xi Hu and Shanshan Zhu

Water 2024, 16(17), 2525; https://doi.org/10.3390/w16172525 - 5 Sep 2024

Cited by 36 | Viewed by 15308

Abstract

Marine eutrophication, primarily driven by nutrient over input from agricultural runoff, wastewater discharge, and atmospheric deposition, leads to harmful algal blooms (HABs) that pose a severe threat to marine ecosystems. This review explores the causes, monitoring methods, and control strategies for eutrophication in [...] Read more.

Marine eutrophication, primarily driven by nutrient over input from agricultural runoff, wastewater discharge, and atmospheric deposition, leads to harmful algal blooms (HABs) that pose a severe threat to marine ecosystems. This review explores the causes, monitoring methods, and control strategies for eutrophication in marine environments. Monitoring techniques include remote sensing, automated in situ sensors, modeling, forecasting, and metagenomics. Remote sensing provides large-scale temporal and spatial data, while automated sensors offer real-time, high-resolution monitoring. Modeling and forecasting use historical data and environmental variables to predict blooms, and metagenomics provides insights into microbial community dynamics. Control treatments encompass physical, chemical, and biological treatments, as well as advanced technologies like nanotechnology, electrocoagulation, and ultrasonic treatment. Physical treatments, such as aeration and mixing, are effective but costly and energy-intensive. Chemical treatments, including phosphorus precipitation, quickly reduce nutrient levels but may have ecological side effects. Biological treatments, like biomanipulation and bioaugmentation, are sustainable but require careful management of ecological interactions. Advanced technologies offer innovative solutions with varying costs and sustainability profiles. Comparing these methods highlights the trade-offs between efficacy, cost, and environmental impact, emphasizing the need for integrated approaches tailored to specific conditions. This review underscores the importance of combining monitoring and control strategies to mitigate the adverse effects of eutrophication on marine ecosystems. Full article

(This article belongs to the Section Water Quality and Contamination)

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36 pages, 2483 KiB

Open AccessEditor’s ChoiceReview

A Review of the Efficiency of Phosphorus Removal and Recovery from Wastewater by Physicochemical and Biological Processes: Challenges and Opportunities

by Sima Abdoli, Behnam Asgari Lajayer, Zahra Dehghanian, Nazila Bagheri, Amir Hossein Vafaei, Masoud Chamani, Swati Rani, Zheya Lin, Weixi Shu and G. W. Price

Water 2024, 16(17), 2507; https://doi.org/10.3390/w16172507 - 4 Sep 2024

Cited by 9 | Viewed by 8459

Abstract

Phosphorus (P) discharge from anthropogenic sources, notably sewage effluent and agricultural runoff, significantly contributes to eutrophication in aquatic environments. Stringent regulations have heightened the need for effective P removal technologies in wastewater treatment processes. This paper provides a comprehensive review of current P [...] Read more.

Phosphorus (P) discharge from anthropogenic sources, notably sewage effluent and agricultural runoff, significantly contributes to eutrophication in aquatic environments. Stringent regulations have heightened the need for effective P removal technologies in wastewater treatment processes. This paper provides a comprehensive review of current P removal methods, focusing on both biological and chemical approaches. Biological treatments discussed include enhanced biological P removal in activated sludge systems, biological trickling filters, biofilm reactors, and constructed wetlands. The efficiency of microbial absorption and novel biotechnological integrations, such as the use of microalgae and fungi, are also examined. Chemical treatments reviewed encompass the application of metal salts, advanced oxidation processes such as chlorination, ozonation, and the Fenton reaction, as well as emerging techniques including the Electro-Fenton process and photocatalysis. Analytical methods for P, including spectrophotometric techniques and fractionation analyses, are evaluated to understand the dynamics of P in wastewater. This review critically assesses the strengths and limitations of each method, aiming to identify the most effective and sustainable solutions for P management in wastewater treatment. The integration of innovative strategies and advanced technologies is emphasized as crucial for optimizing P removal and ensuring compliance with environmental regulations. Full article

(This article belongs to the Section Wastewater Treatment and Reuse)

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20 pages, 9509 KiB

Open AccessEditor’s ChoiceArticle

High-Performance Crown Ether-Modified Membranes for Selective Lithium Recovery from High Na⁺ and Mg²⁺ Brines Using Electrodialysis

by Xiaochun Yin, Pei Xu and Huiyao Wang

Water 2024, 16(17), 2489; https://doi.org/10.3390/w16172489 - 2 Sep 2024

Cited by 4 | Viewed by 2246

Abstract

The challenge of efficiently extracting Li⁺ from brines with high Na⁺ or Mg²⁺ concentrations has led to extensive research on developing highly selective separation membranes for electrodialysis. Various studies have demonstrated that nanofiltration membranes or adsorbents modified with crown ethers [...] Read more.

The challenge of efficiently extracting Li⁺ from brines with high Na⁺ or Mg²⁺ concentrations has led to extensive research on developing highly selective separation membranes for electrodialysis. Various studies have demonstrated that nanofiltration membranes or adsorbents modified with crown ethers (CEs) such as 2-OH-12-crown-4-ether (12CE), 2-OH-18-crown-6-ether (18CE), and 2-OH-15-crown-5-ether (15CE) show selectivity for Li⁺ in brines. This study aims to develop high-performance cation exchange membranes (CEMs) using CEs to enhance Li⁺ selectivity and to compare the performance of various CE-modified membranes for selective electrodialysis. The novel CEM (CR671) was modified with 12CE, 18CE, and 15CE to identify the optimal CE for efficient Li⁺ recovery during brine electrodialysis. The modification process included polydopamine (PDA) treatment and the deposition of polyethyleneimine (PEI) complexes with the different CEs via hydrogen bonding. Interfacial polymerization with 1,3,5-benzenetricarbonyl trichloride-crosslinked PEI was used to create specific channels for Li⁺ transport within the modified membranes (12CE/CR671, 15CE/CR671, and 18CE/CR671). The successful application of CE coatings and Li⁺ selectivity of the modified membranes were verified through Fourier-transform infrared spectroscopy, zeta-potential measurements, and electrochemical impedance spectroscopy. Bench-scale electrodialysis tests showed significant improvements in permselectivity and Li⁺ flux for all three modified membranes. In brines with high Na⁺ and Mg²⁺ concentrations, the 15CE/CR671 membrane demonstrated more significant improvements in permselectivity compared to the 12CE/CR671 (3.3-fold and 1.7-fold) and the 18CE/CR671 (2.4-fold and 2.6-fold) membranes at current densities of 2.3 mA/cm² and 2.2 mA/cm², respectively. At higher current densities of 14.7 mA/cm² in Mg²⁺-rich brine and 15.9 mA/cm² in Na⁺-rich brine, the 15CE/CR671 membrane showed greater improvements in Li⁺ flux, approximately 2.1-fold and 2.3-fold, and 3.2-fold and 3.4-fold compared to the 12CE/CR671 and 18CE/CR671 membranes. This study underscores the superior performance of 15CE-modified membranes for efficient Li⁺ recovery with low energy demand and offers valuable insights for advancing electrodialysis processes in challenging brine environments. Full article

(This article belongs to the Section Wastewater Treatment and Reuse)

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22 pages, 2666 KiB

Open AccessEditor’s ChoiceReview

Future Agricultural Water Availability in Mediterranean Countries under Climate Change: A Systematic Review

by André M. Claro, André Fonseca, Helder Fraga and João A. Santos

Water 2024, 16(17), 2484; https://doi.org/10.3390/w16172484 - 1 Sep 2024

Cited by 6 | Viewed by 3905

Abstract

Warming and drying trends in the Mediterranean Basin exacerbate regional water scarcity and threaten agricultural production, putting global food security at risk. This study aimed to review the most significant research on future water availability for the Mediterranean agricultural sector under climate change [...] Read more.

Warming and drying trends in the Mediterranean Basin exacerbate regional water scarcity and threaten agricultural production, putting global food security at risk. This study aimed to review the most significant research on future water availability for the Mediterranean agricultural sector under climate change (CC) scenarios published during 2009–2024. Two searches were performed in the Scopus and Web of Science databases, to which previously identified significant studies from different periods were also added. By applying a methodology duly protocoled in the PRISMA2020-based guideline, a final number of 44 particularly relevant studies was selected for review. A bibliometric analysis has shown that most of the published research was focused on Southwestern European countries (i.e., Spain, Italy, Portugal) and grapevine and olive tree crops. Overall, the reviewed studies state that future Mediterranean water reserves may not meet agricultural water demands, due to reduced reservoir inflows and higher irrigation demands under future CC and socioeconomic scenarios. Regarding adaptation measures to improve water-use management in agriculture, the majority of the reviewed studies indicate that the use of integrated modelling platforms and decision–support systems can significantly contribute to the development and implementation of improved water/land-management practices. Full article

(This article belongs to the Section Water, Agriculture and Aquaculture)

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19 pages, 8448 KiB

Open AccessEditor’s ChoiceReview

State-of-the-Art Techniques for Real-Time Monitoring of Urban Flooding: A Review

by Jiayi Song, Zhiyu Shao, Ziyi Zhan and Lei Chen

Water 2024, 16(17), 2476; https://doi.org/10.3390/w16172476 - 30 Aug 2024

Cited by 7 | Viewed by 2873

Abstract

In the context of the increasing frequency of urban flooding disasters caused by extreme weather, the accurate and timely identification and monitoring of urban flood risks have become increasingly important. This article begins with a bibliometric analysis of the literature on urban flood [...] Read more.

In the context of the increasing frequency of urban flooding disasters caused by extreme weather, the accurate and timely identification and monitoring of urban flood risks have become increasingly important. This article begins with a bibliometric analysis of the literature on urban flood monitoring and identification, revealing that since 2017, this area has become a global research hotspot. Subsequently, it presents a systematic review of current mainstream urban flood monitoring technologies, drawing from both traditional and emerging data sources, which are categorized into sensor-based monitoring (including contact and non-contact sensors) and big data-based monitoring (including social media data and surveillance camera data). By analyzing the advantages and disadvantages of each technology and their different research focuses, this paper points out that current research largely emphasizes more “intelligent” monitoring technologies. However, these technologies still have certain limitations, and traditional sensor monitoring techniques retain significant advantages in practical applications. Therefore, future flood risk monitoring should focus on integrating multiple data sources, fully leveraging the strengths of different data sources to achieve real-time and accurate monitoring of urban flooding. Full article

(This article belongs to the Special Issue Urban Flooding Control and Sponge City Construction)

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16 pages, 3426 KiB

Open AccessEditor’s ChoiceArticle

Mapping Flood Impacts on Mortality at European Territories of the Mediterranean Region within the Sustainable Development Goals (SDGs) Framework

by Iraklis Stamos and Michalis Diakakis

Water 2024, 16(17), 2470; https://doi.org/10.3390/w16172470 - 30 Aug 2024

Cited by 4 | Viewed by 1485

Abstract

Despite significant advances in technology and flood risk management, as well as the countless risk prevention initiatives undertaken by governments and institutions in recent decades, flood hazards persist in threatening human life and health, especially under the effects of climate change. To assess [...] Read more.

Despite significant advances in technology and flood risk management, as well as the countless risk prevention initiatives undertaken by governments and institutions in recent decades, flood hazards persist in threatening human life and health, especially under the effects of climate change. To assess the effectiveness of the various programs or measures devised to protect human life and health from floods, it is crucial to measure and understand its impacts on society, establishing the capability to track indicators or metrics that reflect the spatial distribution and temporal progress of floods and their impacts. In this context, this study uses disaster loss data derived from international disaster databases adapted in regional context following the Nomenclature of Territorial Units for Statistics level 2 (or NUTS2), to examine the spatial distribution and temporal evolution of deaths, directly attributable to flood disasters. In addition, we explore the potential of currently available datasets in understanding and monitoring flood-related mortality, an important standardized progress indicator of flood disaster impacts. This study is framed within the United Nations’ Sustainable Development Goals (SDGs), recently adopted by the European Union, and is focused on the Union’s territories in the Mediterranean region, an area particularly sensitive to climate change. Results show interesting spatial patterns, and generally inconclusive temporal trends, although locally we see evidence of both an increase and a decline in flood mortality. In addition, this work discusses the currently available datasets potential, weaknesses and limitations, as well as the importance of tracking flood impacts on human life in a future increasingly influenced by extreme weather events and climate change. Full article

(This article belongs to the Special Issue Risks of Hydrometeorological Extremes)

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29 pages, 5562 KiB

Open AccessEditor’s ChoiceArticle

On the Necessity for Improving Water Efficiency in Commercial Buildings: A Green Design Approach in Hot Humid Climates

by A. Chandana Hemantha J. Thebuwena, S. M. Samindi M. K. Samarakoon and R. M. Chandima Ratnayake

Water 2024, 16(17), 2396; https://doi.org/10.3390/w16172396 - 26 Aug 2024

Cited by 4 | Viewed by 4022

Abstract

Water, a fundamental and indispensable resource necessary for the survival of living beings, has become a pressing issue in numerous regions worldwide due to scarcity. Urban areas, where the majority of the global population resides, witness a substantial consumption of blue water, particularly [...] Read more.

Water, a fundamental and indispensable resource necessary for the survival of living beings, has become a pressing issue in numerous regions worldwide due to scarcity. Urban areas, where the majority of the global population resides, witness a substantial consumption of blue water, particularly in commercial buildings. This study investigates the potential for enhancing water efficiency within an ongoing high-rise office building construction situated in a tropical climate. The investigation utilizes the green building guidelines of leadership in energy and environmental design (LEED) through a case-study-based research approach. Strategies included using efficient plumbing fixtures (such as high air–water ratio fixtures and dual-flush toilets), the selection of native plants, implementing a suitable irrigation system, introducing a rainwater harvesting system (RWHS) and improving the mechanical ventilation and air conditioning (MVAC) system. The results showed a 55% reduction in water use from efficient fixtures, a 93% reduction in landscaping water needs and a 73% overall water efficiency with a RWHS from the baseline design. Additionally, efficient cooling towers and the redirection of condensed water into the cooling tower make-up water tank improved the overall water efficiency to 38%, accounting for the water requirements of the MVAC system. The findings of this study can contribute to more sustainable and water-efficient urban development, particularly in regions facing water scarcity challenges. The significance of these findings lies in their potential to establish industry standards and inform policymakers in the building sector. They offer valuable insights for implementing effective strategies aimed at reducing blue water consumption across different building types. Full article

(This article belongs to the Special Issue Water-Sensitive and Sustainable Urban Development)

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21 pages, 4526 KiB

Open AccessEditor’s ChoiceReview

Sustainable Solutions for Mitigating Water Scarcity in Developing Countries: A Comprehensive Review of Innovative Rainwater Storage Systems

by Geoffrey Ssekyanzi, Mirza Junaid Ahmad and Kyung-Sook Choi

Water 2024, 16(17), 2394; https://doi.org/10.3390/w16172394 - 26 Aug 2024

Cited by 14 | Viewed by 12112

Abstract

As global water resources decline and demand increases due to population growth and climate change, innovative rainwater storage systems (IRSSs) have become crucial. This review examines the potential of IRSSs to sustainably address rainwater challenges by analyzing key factors that influence their success. [...] Read more.

As global water resources decline and demand increases due to population growth and climate change, innovative rainwater storage systems (IRSSs) have become crucial. This review examines the potential of IRSSs to sustainably address rainwater challenges by analyzing key factors that influence their success. Drawing on research from Scopus and Google Scholar, it evaluates IRSSs in both urban and rural settings across different countries and regions, focusing on their contribution to Sustainable Development Goal (SDG) 6. This review highlights how social, environmental, economic, and policy factors affect the success of IRSS compared to traditional systems common in developing nations. IRSSs can outperform traditional methods in sustainability, encouraging their adoption. However, there is a significant gap in policy integration that needs to be addressed for successful implementation. Further research is needed to better understand the contributing factors and their role in achieving sustainability. Integrating rainwater harvesting into national water policies could offer valuable guidance for policymakers and water resource managers in addressing issues like urban floods, water scarcity, and related social and environmental challenges in developing countries. Full article

(This article belongs to the Section Water Resources Management, Policy and Governance)

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15 pages, 6230 KiB

Open AccessEditor’s ChoiceArticle

Modern Treatment Using Powdered Chlorella vulgaris for Adsorption of Heavy Metals from Freshwater

by Eleonora Sočo, Dorota Papciak, Andżelika Domoń and Dariusz Pająk

Water 2024, 16(17), 2388; https://doi.org/10.3390/w16172388 - 25 Aug 2024

Cited by 3 | Viewed by 2331

Abstract

In the face of current challenges related to climate change, maintaining the appropriate quality of freshwater becomes crucial. This study examined the effectiveness of removing heavy metals (Cu(II) and Co(II)) using Chlorella vulgaris biosorbents (dietary supplements in the form of powder). This study [...] Read more.

In the face of current challenges related to climate change, maintaining the appropriate quality of freshwater becomes crucial. This study examined the effectiveness of removing heavy metals (Cu(II) and Co(II)) using Chlorella vulgaris biosorbents (dietary supplements in the form of powder). This study determined the parameters of the biosorbent (point of zero charge (PZC) analysis using scanning electron microscopy with back-scattered electron (SEM-BSE) and Fourier transform infrared spectroscopy (FT-IR) analysis). Batch tests were also performed to determine the kinetic constants and adsorption equilibrium of Cu(II) and Co(II) ions. Based on the conducted research, it was found that a pseudo-second-order equation describes the kinetics of the biosorption process. Among the studied adsorption isotherms, the Langmuir and Freundlich models fit best. The results indicate that single-layer adsorption took place and Chlorella vulgaris is a microporous adsorbent. The maximum sorption capacity in the single-component system for Cu(II) and Co(II) was 30.3 mg·g⁻¹ and 9.0 mg·g⁻¹, respectively. In contrast, in the binary system, it was 20.8 mg·g⁻¹ and 19.6 mg·g⁻¹ (extended Langmuir model) and 23.5 mg·g⁻¹ and 19.6 mg·g⁻¹ (Jain-Snoeyinka model). Chlorella vulgaris is an effective biosorbent for removing heavy metals from freshwater. This technology offers an ecological and economical solution for improving water quality, making it a promising alternative to traditional purification methods. Full article

(This article belongs to the Special Issue Freshwater Quality Challenges in Southern Europe under an Increasingly Warmer and Drier Climate Scenario)

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42 pages, 16420 KiB

Open AccessEditor’s ChoiceArticle

Multi-Objective and Multi-Variable Optimization Models of Hybrid Renewable Energy Solutions for Water–Energy Nexus

by João S. T. Coelho, Maaike van de Loo, Juan Antonio Rodríguez Díaz, Oscar E. Coronado-Hernández, Modesto Perez-Sanchez and Helena M. Ramos

Water 2024, 16(17), 2360; https://doi.org/10.3390/w16172360 - 23 Aug 2024

Cited by 8 | Viewed by 2107

Abstract

A new methodology, called HY4RES models, includes hybrid energy solutions (HESs) based on the availability of renewable sources, for 24 h of water allocation, using WaterGEMS 10.0 and PVGIS 5.2 as auxiliary calculations. The optimization design was achieved using Solver, with GRG nonlinear/evolutionary [...] Read more.

A new methodology, called HY4RES models, includes hybrid energy solutions (HESs) based on the availability of renewable sources, for 24 h of water allocation, using WaterGEMS 10.0 and PVGIS 5.2 as auxiliary calculations. The optimization design was achieved using Solver, with GRG nonlinear/evolutionary programming, and Python, with the non-dominated sorting genetic algorithm (NSGA-II). The study involves the implementation of complex multi-objective and multi-variable algorithms with different renewable sources, such as PV solar energy, pumped hydropower storage (PHS) energy, wind energy, grid connection energy, or battery energy, and also sensitivity analyses and comparisons of optimization models. Higher water allocations relied heavily on grid energy, especially at night when solar power was unavailable. For a case study of irrigation water needs of 800 and 1000 m³/ha, the grid is not needed, but for 3000 and 6000 m³/ha, grid energy rises significantly, reaching 5 and 14 GWh annually, respectively. When wind energy is also integrated, at night, it allows for reducing grid energy use by 60% for 3000 m³/ha of water allocation, yielding a positive lifetime cashflow (EUR 284,781). If the grid is replaced by batteries, it results in a lack of a robust backup and struggles to meet high water and energy needs. Economically, PV + wind + PHS + grid energy is the most attractive solution, reducing the dependence on auxiliary sources and benefiting from sales to the grid. Full article

(This article belongs to the Special Issue Water and Energy Synergies)

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16 pages, 3069 KiB

Open AccessEditor’s ChoiceArticle

Source-Oriented Health Risks and Distribution of BTEXS in Urban Shallow Lake Sediment: Application of the Positive Matrix Factorization Model

by Ivana Trajković, Milica Sentić, Jelena Vesković, Milica Lučić, Andrijana Miletić and Antonije Onjia

Water 2024, 16(16), 2302; https://doi.org/10.3390/w16162302 - 15 Aug 2024

Cited by 4 | Viewed by 1265

Abstract

The degradation of sediments in urban environments worldwide is driven by population growth, urbanization, and industrialization, highlighting the need for thorough quality assessment and management strategies. As a result of these anthropogenic activities, benzene, toluene, ethylbenzene, xylenes, and styrene (BTEXS) are persistently released [...] Read more.

The degradation of sediments in urban environments worldwide is driven by population growth, urbanization, and industrialization, highlighting the need for thorough quality assessment and management strategies. As a result of these anthropogenic activities, benzene, toluene, ethylbenzene, xylenes, and styrene (BTEXS) are persistently released into the environment, polluting sediment. This study employed self-organizing maps (SOMs), positive matrix factorization (PMF), and Monte Carlo simulation of source-oriented health risks to comprehensively investigate sediment in an urban shallow lake in a mid-sized city in central Serbia. The results indicated a mean ∑BTEXS concentration of 225 µg/kg, with toluene as the dominant congener, followed by m,p-xylene, benzene, ethylbenzene, o-xylene, and styrene. Three contamination sources were identified: waste solvents and plastic waste due to intensive recreational activities, and vehicle exhaust from heavy traffic surrounding the lake. Both non-carcinogenic and carcinogenic health risks were below the permissible limits. However, children were more susceptible to health risks. Benzene from vehicle exhaust is the most responsible for non-carcinogenic and carcinogenic health risks in both population groups. The results of this study can help researchers to find a suitable perspective on the dynamics and impacts of BTEXS in lake sediments. Full article

(This article belongs to the Special Issue Fate, Transport, Removal and Modeling of Pollutants in Water)

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21 pages, 13626 KiB

Open AccessEditor’s ChoiceArticle

Numerical Simulation Study of Factors Influencing Ultrasonic Cavitation Bubble Evolution on Rock Surfaces during Ultrasonic-Assisted Rock Breaking

by Jinyu Feng, Tie Yan and Zhaokai Hou

Water 2024, 16(16), 2234; https://doi.org/10.3390/w16162234 - 8 Aug 2024

Cited by 6 | Viewed by 1918

Abstract

With the increasing demand for deep oil and gas exploration and CCUS (Carbon Capture, Utilization, and Storage) engineering, improving rock-crushing efficiency stands as a pivotal technology. Ultrasonic vibration-assisted drilling has emerged as a novel rock-breaking technology. The high-frequency vibrations of ultrasonic waves impact [...] Read more.

With the increasing demand for deep oil and gas exploration and CCUS (Carbon Capture, Utilization, and Storage) engineering, improving rock-crushing efficiency stands as a pivotal technology. Ultrasonic vibration-assisted drilling has emerged as a novel rock-breaking technology. The high-frequency vibrations of ultrasonic waves impact rocks, inducing resonance and accelerating their fragmentation. At the same time, ultrasonic waves generate cavitation bubbles in the liquid near rock surfaces; the expansion and collapse of these bubbles further contribute to rock damage, thereby improving crushing efficiency. Therefore, investigating the dynamics and failure characteristics of cavitation bubbles near rock surfaces under ultrasonic influence is crucial for advancing ultrasonic-assisted rock-breaking technology. This study treats the liquid as compressible flow and investigates the movement and rupture of bubbles near rock surfaces under varying ultrasonic parameters, rock properties, characteristics of the circulating medium, and other relevant factors. The findings show that ultrasonic waves induce the oscillation, translation, collapse, and rebound of bubbles near rock surfaces. Higher ultrasonic frequencies correspond to larger collapse pressures and amplitudes near surrounding rocks, as well as longer expansion times and shorter collapse durations. In addition, bubble movement and collapse lead to rock material deformation, influenced by the rheological properties of the liquid medium. The study outcomes serve as a foundation for optimizing engineering parameters in ultrasonic-assisted rock breaking and provide theoretical support for the advancement of this technology. Full article

(This article belongs to the Special Issue Hydrodynamic Science Experiments and Simulations)

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38 pages, 1050 KiB

Open AccessEditor’s ChoiceReview

Sludge Composting—Is This a Viable Solution for Wastewater Sludge Management?

by Elena Elisabeta Manea and Costel Bumbac

Water 2024, 16(16), 2241; https://doi.org/10.3390/w16162241 - 8 Aug 2024

Cited by 3 | Viewed by 3227

Abstract

Wastewater treatment plants generate significant amounts of sludge, a residual product that is rich in nutrients, usually considered waste, and traditionally eliminated by storage or incineration, methods that are expensive, environmentally damaging, and often unsustainable. Composting is increasingly recognized as an ecological and [...] Read more.

Wastewater treatment plants generate significant amounts of sludge, a residual product that is rich in nutrients, usually considered waste, and traditionally eliminated by storage or incineration, methods that are expensive, environmentally damaging, and often unsustainable. Composting is increasingly recognized as an ecological and durable solution for managing biodegradable waste, including sludge resulting from wastewater treatment. The composting of residual sludge usually requires mixing with bulking agents, such as green waste or agricultural residues, to ensure a well-balanced carbon–nitrogen ratio. This mixture undergoes a controlled aerobic decomposition, sometimes followed by post-treatment, resulting in a stabilized final product that is nutrient-rich and pathogen-free and can be used as soil amendment or fertilizer in different agricultural or landscaping applications. By using composting, communities can reduce elimination costs, reduce greenhouse gas emissions, and minimize the environmental impact of sludge management. This paper reviews recent reported experiences in the laboratory regarding full-scale sludge composting, highlighting the particularities of the processes, the influence factors, the quality of the final product, and the environmental and regulatory constraints. Composting is a sustainable and ecological solution for managing wastewater sludge, contributing to nutrient circularity, and minimizing the environmental impact. Full article

(This article belongs to the Special Issue Resource Use of Sewage Sludge for Soil Application)

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18 pages, 3055 KiB

Open AccessEditor’s ChoiceArticle

Projected Climate Change Impacts on the Number of Dry and Very Heavy Precipitation Days by Century’s End: A Case Study of Iran’s Metropolises

by Rasoul Afsari, Mohammad Nazari-Sharabian, Ali Hosseini and Moses Karakouzian

Water 2024, 16(16), 2226; https://doi.org/10.3390/w16162226 - 6 Aug 2024

Cited by 4 | Viewed by 1588

Abstract

This study explores the impacts of climate change on the number of dry days and very heavy precipitation days within Iran’s metropolises. Focusing on Tehran, Mashhad, Isfahan, Karaj, Shiraz, and Tabriz, the research utilizes the sixth phase of the Coupled Model Intercomparison Project [...] Read more.

This study explores the impacts of climate change on the number of dry days and very heavy precipitation days within Iran’s metropolises. Focusing on Tehran, Mashhad, Isfahan, Karaj, Shiraz, and Tabriz, the research utilizes the sixth phase of the Coupled Model Intercomparison Project (CMIP6) Global Circulation Models (GCMs) to predict future precipitation conditions under various Shared Socioeconomic Pathways (SSPs) from 2025 to 2100. The study aims to provide a comprehensive understanding of how climate change will affect precipitation patterns in these major cities. Findings indicate that the SSP126 scenario typically results in the highest number of dry days, suggesting that under lower emission scenarios, precipitation events will become less frequent but more intense. Conversely, SSP585 generally leads to the lowest number of dry days. Higher emission scenarios (SSP370, SSP585) consistently show an increase in the number of very heavy precipitation days across all cities, indicating a trend towards more extreme weather events as emissions rise. These insights are crucial for urban planners, policymakers, and stakeholders in developing effective adaptation and mitigation strategies to address anticipated climatic changes. Full article

(This article belongs to the Special Issue Impacts of Climate Change on Water Resources: Assessment and Modeling, 2nd Edition)

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24 pages, 6681 KiB

Open AccessEditor’s ChoiceArticle

A Machine Learning Approach to Monitor the Physiological and Water Status of an Irrigated Peach Orchard under Semi-Arid Conditions by Using Multispectral Satellite Data

by Pasquale Campi, Anna Francesca Modugno, Gabriele De Carolis, Francisco Pedrero Salcedo, Beatriz Lorente and Simone Pietro Garofalo

Water 2024, 16(16), 2224; https://doi.org/10.3390/w16162224 - 6 Aug 2024

Cited by 10 | Viewed by 2607

Abstract

Climate change is making water management increasingly difficult due to rising temperatures and unpredictable rainfall patterns, impacting crop water availability and irrigation needs. This study investigated the ability of machine learning and satellite remote sensing to monitor water status and physiology. The research [...] Read more.

Climate change is making water management increasingly difficult due to rising temperatures and unpredictable rainfall patterns, impacting crop water availability and irrigation needs. This study investigated the ability of machine learning and satellite remote sensing to monitor water status and physiology. The research focused on predicting different eco-physiological parameters in an irrigated peach orchard under Mediterranean conditions, utilizing multispectral reflectance data and machine learning algorithms (extreme gradient boosting, random forest, support vector regressor); ground data were acquired from 2021 to 2023 in the south of Italy. The random forest model outperformed in predicting net assimilation (R² = 0.61), while the support vector machine performed best in predicting electron transport rate (R² = 0.57), Fv/Fm ratio (R² = 0.66) and stomatal conductance (R² = 0.56). Random forest also proved to be the most effective in predicting stem water potential (R² = 0.62). These findings highlighted the potential of integrating machine learning techniques with high-resolution satellite imagery to assist farmers in monitoring crop health and optimizing irrigation practices, thereby addressing the challenges determined by climate change. Full article

(This article belongs to the Special Issue Water Resources, Environment, and Ecosystems: Application of New Technology)

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14 pages, 3414 KiB

Open AccessEditor’s ChoiceArticle

Trimethoprim Removal from Aqueous Solutions via Volcanic Ash-Soil Adsorption: Process Modeling and Optimization

by Roberto Lavecchia, Antonio Zuorro, Oussama Baaloudj and Monica Brienza

Water 2024, 16(15), 2209; https://doi.org/10.3390/w16152209 - 5 Aug 2024

Cited by 4 | Viewed by 1730

Abstract

Antibiotic contamination of water sources is a significant environmental and public health concern. This contamination is classified among the most dangerous types of pollution currently because of their harmful effects. Therefore, it is essential to identify effective and environmentally friendly ways to deal [...] Read more.

Antibiotic contamination of water sources is a significant environmental and public health concern. This contamination is classified among the most dangerous types of pollution currently because of their harmful effects. Therefore, it is essential to identify effective and environmentally friendly ways to deal with those dangerous compounds. Within this context, this work looked into whether soils made from volcanic ash could be used as cost-effective adsorbents to remove the antibiotic trimethoprim (TRM) from aqueous solutions. To examine the impacts of the main operating parameters on TRM removal, which are the initial antibiotic concentration (C), contact time (t), stirring speed (S), and solid-to-liquid ratio (R), a Central Composite Design (CCD) based on the Response Surface Methodology (RSM) was employed. Full quadratic polynomial models were used to correlate the experimental data, allowing for the estimation of each factor’s influence. With a predicted removal efficiency of 77.59%, the removal process optimization yielded the following set of optimal conditions: C = 4.5 mg/L, t = 45.5 min, S = 747 rpm, and R = 0.04 g/mL. Experiments conducted under predicted ideal conditions supported both the result and the previously developed model’s capacity for prediction. Additionally, the adsorption mechanism was also proposed based on the characterization of the adsorbent before and after the treatment. The study’s findings provide the possibility of using soils formed from volcanic ash as a cost-effective adsorbent material for the removal of TRM and likely other similar pollutants from contaminated waters. Full article

(This article belongs to the Section Wastewater Treatment and Reuse)

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18 pages, 1133 KiB

Open AccessEditor’s ChoiceReview

A Review of Drip Irrigation’s Effect on Water, Carbon Fluxes, and Crop Growth in Farmland

by Hui Guo and Sien Li

Water 2024, 16(15), 2206; https://doi.org/10.3390/w16152206 - 4 Aug 2024

Cited by 6 | Viewed by 7805

Abstract

The substantial depletion of freshwater reserves in many pivotal agricultural regions, attributable to the dual pressures of global climate change and the excessive extraction of water resources, has sparked considerable apprehension regarding the sustainability of future food and water security. Drip irrigation, as [...] Read more.

The substantial depletion of freshwater reserves in many pivotal agricultural regions, attributable to the dual pressures of global climate change and the excessive extraction of water resources, has sparked considerable apprehension regarding the sustainability of future food and water security. Drip irrigation, as an efficient and precise irrigation method, reduces water loss caused by deep percolation, soil evaporation, and runoff by controlling the irrigation dosage and frequency, thus improving the efficiency of water resource utilization. Studies have shown that compared with traditional irrigation methods, drip irrigation can significantly decrease water consumption, optimize the water–energy relationship by reducing soil evaporation, increase the leaf area index, and promote crop growth, thereby enhancing plant transpiration. Although more wet and dry soil cycles from drip irrigation may increase soil CO₂ emissions, it also enhances crop photosynthesis and improves crop net ecosystem productivity (NEP) by creating more favorable soil moisture conditions, indicating greater carbon sequestration potential. The advantages of drip irrigation, such as a short irrigation cycle, moderate soil moisture, and obvious dry and wet interfaces, can improve a crop’s leaf area index and biomass accumulation, improve root dynamics, promote the distribution of photosynthetic products to the aboveground parts, and thus enhance crop yields. This study highlights the potential for the application of drip irrigation in arid regions where resource optimization is sought, providing strong technical support for the achievement of sustainable agricultural development. Future research needs to consider specific agricultural practices, soil types, and environmental conditions to further optimize the implementation and effectiveness of drip irrigation. Full article

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24 pages, 2913 KiB

Open AccessEditor’s ChoiceArticle

Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins

by Haoyuan Yu and Qichun Yang

Water 2024, 16(15), 2199; https://doi.org/10.3390/w16152199 - 2 Aug 2024

Cited by 8 | Viewed by 2196

Abstract

Machine learning models’ performance in simulating monthly rainfall–runoff in subtropical regions has not been sufficiently investigated. In this study, we evaluate the performance of six widely used machine learning models, including Long Short-Term Memory Networks (LSTMs), Support Vector Machines (SVMs), Gaussian Process Regression [...] Read more.

Machine learning models’ performance in simulating monthly rainfall–runoff in subtropical regions has not been sufficiently investigated. In this study, we evaluate the performance of six widely used machine learning models, including Long Short-Term Memory Networks (LSTMs), Support Vector Machines (SVMs), Gaussian Process Regression (GPR), LASSO Regression (LR), Extreme Gradient Boosting (XGB), and the Light Gradient Boosting Machine (LGBM), against a rainfall–runoff model (WAPABA model) in simulating monthly streamflow across three subtropical sub-basins of the Pearl River Basin (PRB). The results indicate that LSTM generally demonstrates superior capability in simulating monthly streamflow than the other five machine learning models. Using the streamflow of the previous month as an input variable improves the performance of all the machine learning models. When compared with the WAPABA model, LSTM demonstrates better performance in two of the three sub-basins. For simulations in wet seasons, LSTM shows slightly better performance than the WAPABA model. Overall, this study confirms the suitability of machine learning methods in rainfall–runoff modeling at the monthly scale in subtropical basins and proposes an effective strategy for improving their performance. Full article

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24 pages, 2321 KiB

Open AccessEditor’s ChoiceArticle

Legionnaires’ Disease Surveillance and Public Health Policies in Italy: A Mathematical Model for Assessing Prevention Strategies

by Vincenzo Romano Spica, Paola Borella, Agnese Bruno, Cristian Carboni, Martin Exner, Philippe Hartemann, Gianluca Gianfranceschi, Pasqualina Laganà, Antonella Mansi, Maria Teresa Montagna, Osvalda De Giglio, Serena Platania, Caterina Rizzo, Alberto Spotti, Francesca Ubaldi, Matteo Vitali, Paul van der Wielen and Federica Valeriani

Water 2024, 16(15), 2167; https://doi.org/10.3390/w16152167 - 31 Jul 2024

Cited by 3 | Viewed by 2699

Abstract

Legionella is the pathogen that causes Legionnaires’ disease, an increasingly prevalent and sometimes fatal disease worldwide. In 2021, 97% of cases in Europe were caused by Legionella pneumophila. We present a mathematical model that can be used by public health officials to [...] Read more.

Legionella is the pathogen that causes Legionnaires’ disease, an increasingly prevalent and sometimes fatal disease worldwide. In 2021, 97% of cases in Europe were caused by Legionella pneumophila. We present a mathematical model that can be used by public health officials to assess the effectiveness and efficiency of different Legionella monitoring and control strategies to inform government requirements to prevent community-acquired Legionnaires’ disease in non-hospital buildings. This simulation model was built using comprehensive data from multiple scientific and field-based sources. It is a tool for estimating the relative economic and human costs of monitoring and control efforts targeting either L. pneumophila or Legionella species and was designed to analyze the potential application of each approach to specific building classes across Italy. The model results consistently showed that targeting L. pneumophila is not only sufficient but preferable in optimizing total cost (direct and economic) for similar human health benefits, even when stress-tested with extreme inputs. This cost–benefit analytical tool allows the user to run different real-life scenarios with a broad range of epidemiological and prevalence assumptions across different geographies in Italy. With appropriate modifications, this tool can be localized and applied to other countries, states, or provinces. Full article

(This article belongs to the Special Issue Legionella: A Key Organism in Water Management)

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19 pages, 10941 KiB

Open AccessEditor’s ChoiceArticle

Assessment and Driving Factors of Wetland Ecosystem Service Function in Northeast China Based on InVEST-PLUS Model

by Xiaolin Zhu, Ruiqing Qie, Chong Luo and Wenqi Zhang

Water 2024, 16(15), 2153; https://doi.org/10.3390/w16152153 - 30 Jul 2024

Cited by 8 | Viewed by 1567

Abstract

Wetland ecosystem service function provides and maintains the Earth’s life system, which supports human and social development. However, in recent years, with the intensification of human social activities, the wetland area in northeast China has been reduced, and wetland ecosystem service function has [...] Read more.

Wetland ecosystem service function provides and maintains the Earth’s life system, which supports human and social development. However, in recent years, with the intensification of human social activities, the wetland area in northeast China has been reduced, and wetland ecosystem service function has been damaged. This paper evaluates the ecosystem service function of wetlands in northeast China based on the InVEST model, taking 40 prefecture-level cities as the evaluation unit, calculating the carbon stock, soil retention, and habitat quality of the wetlands in the study area and analyzing the drivers of changes in ecosystem service function using the PLUS model. The following results were obtained: temporally, the wetland carbon stock decreased from 754 Tg in 2000 to 688 Tg in 2020; the wetland soil retention increased from 24,424 Tg in 2000 to 33,160 Tg in 2010, and then decreased to 28,765 Tg in 2020; and the quality of wetland habitats was roughly unchanged. The wetland habitats in the study area were categorized into 5 types, classified as I, II, III, IV, or V, and the spatial changes in the 40 prefecture-level cities in northeast China were analyzed. The driving factors affecting the change in the wetland ecosystem service function were further analyzed, mainly focusing on changes in the wetland area itself. The influence of other land-use types and the influence of related policies were analyzed in three aspects, among which the GDP and spatial density of the population are social factors, and the elevation and slope are natural factors that provide larger contributions to the change in wetland area. The reduction in forest and grassland areas and the increase in cultivated land and construction land areas have a negative effect on the ecosystem service function of wetlands, and the implementation of relevant wetland protection policies promotes the ecosystem service function of wetlands. According to the problems faced by wetlands in different regions, the government formulates strategies that are in line with local development, with a view to implementing wetland ecological development in the northeast region in the new context, which will help to realize intensive land use and stimulate the vitality of the region. Full article

(This article belongs to the Section Ecohydrology)

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18 pages, 1518 KiB

Open AccessEditor’s ChoiceArticle

Solar-Powered Desalination as a Sustainable Long-Term Solution for the Water Scarcity Problem: Case Studies in Portugal

by Rita Apolinário and Rui Castro

Water 2024, 16(15), 2140; https://doi.org/10.3390/w16152140 - 29 Jul 2024

Cited by 5 | Viewed by 7207

Abstract

The challenge of global water scarcity, exacerbated by population growth, pollution, and uneven resource distribution, demands innovative solutions. Seawater desalination, particularly Reverse Osmosis (RO) desalination technology, offers a promising remedy due to its efficiency, economic attractiveness, and enduring durability. This study explores the [...] Read more.

The challenge of global water scarcity, exacerbated by population growth, pollution, and uneven resource distribution, demands innovative solutions. Seawater desalination, particularly Reverse Osmosis (RO) desalination technology, offers a promising remedy due to its efficiency, economic attractiveness, and enduring durability. This study explores the potential of solar-powered desalination to replace grid-imported electricity as a cost-effective solution to water scarcity, emphasizing economic and environmental aspects. We delve into the economic viability of desalination by developing a model that considers desalination capacity, input electricity prices, and specific energy consumption. Applying this model to case studies in Portugal (Porto Santo Island in the Madeira Archipelago and Algarve in the southern mainland) demonstrates that integrating photovoltaic (PV) solar energy systems to supply the electricity required in the desalination process can reduce the unit production costs of desalinated water by about 33%. The obtained unit production cost of desalinated water using solar PV input is lower than current water tariffs, underscoring the economic feasibility of this approach. The proposed solution is in line with the United Nations Sustainable Development Goals (SDGs), contributing to Goal 6 (Clean Water and Sanitation), Goal 7 (Affordable and Clean Energy), and Goal 8 (Decent Work and Economic Growth). Full article

(This article belongs to the Special Issue Membrane Separation Technology for Water Purification and Power Generation Using Water)

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29 pages, 9301 KiB

Open AccessEditor’s ChoiceArticle

Baffle-Enhanced Scour Mitigation in Rectangular and Trapezoidal Piano Key Weirs: An Experimental and Machine Learning Investigation

by Chonoor Abdi Chooplou, Ehsan Kahrizi, Amirhossein Fathi, Masoud Ghodsian and Milad Latifi

Water 2024, 16(15), 2133; https://doi.org/10.3390/w16152133 - 27 Jul 2024

Cited by 11 | Viewed by 1719

Abstract

The assessment of scour depth downstream of weirs holds paramount importance in ensuring the structural stability of these hydraulic structures. This study presents groundbreaking experimental investigations highlighting the innovative use of baffles to enhance energy dissipation and mitigate scour in the downstream beds [...] Read more.

The assessment of scour depth downstream of weirs holds paramount importance in ensuring the structural stability of these hydraulic structures. This study presents groundbreaking experimental investigations highlighting the innovative use of baffles to enhance energy dissipation and mitigate scour in the downstream beds of rectangular piano key weirs (RPKWs) and trapezoidal piano key weirs (TPKWs). By leveraging three state-of-the-art supervised machine learning algorithms—multi-layer perceptron (MLP), extreme gradient boosting (XGBoost), and support vector regression (SVR)—to estimate scour hole parameters, this research showcases significant advancements in predictive modeling for scour analysis. Experimental results reveal that the incorporation of baffles leads to a remarkable 18–22% increase in energy dissipation and an 11–14% reduction in scour depth for both RPKWs and TPKWs. Specifically, introducing baffles in RPKWs resulted in a noteworthy 26.7% reduction in scour hole area and a 30.3% decrease in scour volume compared to RPKWs without baffles. Moreover, novel empirical equations were developed to estimate scour parameters, achieving impressive performance metrics with an average R² = 0.951, RMSE = 0.145, and MRPE = 4.429%. The MLP models demonstrate superior performance in predicting maximum scour depth across all scenarios with an average R² = 0.988, RMSE = 0.035, and MRPE = 1.036%. However, the predictive capabilities varied when estimating weir toe scour depth under diverse circumstances, with the XGBoost model proving more accurate in scenarios involving baffled TPKWs with R² = 0.965, RMSE = 0.048, and MRPE = 2.798% than the MLP and SVR models. This research underscores the significant role of baffles in minimizing scouring effects in TPKWs compared to RPKWs, showcasing the potential for improved design and efficiency in water-management systems. Full article

(This article belongs to the Section Water Erosion and Sediment Transport)

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34 pages, 10129 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Meteorological Drought Analysis and Regional Frequency Analysis in the Kızılırmak Basin: Creating a Framework for Sustainable Water Resources Management

by Gaye Aktürk, Hatice Çıtakoğlu, Vahdettin Demir and Neslihan Beden

Water 2024, 16(15), 2124; https://doi.org/10.3390/w16152124 - 26 Jul 2024

Cited by 11 | Viewed by 2548

Abstract

Drought research is needed to understand the complex nature of drought phenomena and to develop effective management and mitigation strategies accordingly. This study presents a comprehensive regional frequency analysis (RFA) of 12-month meteorological droughts in the Kızılırmak Basin of Turkey using the L-moments [...] Read more.

Drought research is needed to understand the complex nature of drought phenomena and to develop effective management and mitigation strategies accordingly. This study presents a comprehensive regional frequency analysis (RFA) of 12-month meteorological droughts in the Kızılırmak Basin of Turkey using the L-moments approach. For this purpose, monthly precipitation data from 1960 to 2020 obtained from 22 meteorological stations in the basin are used. In the drought analysis, the Standard Precipitation Index (SPI), Z-Score Index (ZSI), China-Z Index (CZI) and Modified China-Z Index (MCZI), which are widely used precipitation-based indices in the literature, are employed. Here, the main objectives of this study are (i) to determine homogeneous regions based on drought, (ii) to identify the best-fit regional frequency distributions, (iii) to estimate the maximum drought intensities for return periods ranging from 5 to 1000 years, and (iv) to obtain drought maps for the selected return periods. The homogeneity test results show that the basin consists of a single homogeneous region according to the drought indices considered here. The best-fit regional frequency distributions for the selected drought indices are identified using L-moment ratio diagrams and ZDIST goodness-of-fit tests. According to the results, the best-fit regional distributions are the Pearson-Type 3 (PE3) for the SPI and ZSI, generalized extreme value (GEV) for the CZI, and generalized logistic distribution (GLO) for the MCZI. The drought maps obtained here can be utilized as a useful tool for estimating the probability of drought at any location across the basin, even without enough data for hydrological research. Full article

(This article belongs to the Section Hydrology)

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26 pages, 5275 KiB

Open AccessEditor’s ChoiceArticle

Adsorption of a Multicomponent Pharmaceutical Wastewater on Charcoal-Based Activated Carbon: Equilibrium and Kinetics

by Mina Asheghmoalla and Mehrab Mehrvar

Water 2024, 16(15), 2086; https://doi.org/10.3390/w16152086 - 24 Jul 2024

Cited by 6 | Viewed by 3297

Abstract

The treatment of pharmaceutical wastewater is a critical environmental challenge, necessitating efficient removal methods. This study investigates the adsorption of a synthetic multicomponent pharmaceutical wastewater (SPWW) containing methanol, benzene, methylene chloride, 4-aminophenol, aniline, and sulfanilic acid onto charcoal-based activated carbon (AC). Batch experiments [...] Read more.

The treatment of pharmaceutical wastewater is a critical environmental challenge, necessitating efficient removal methods. This study investigates the adsorption of a synthetic multicomponent pharmaceutical wastewater (SPWW) containing methanol, benzene, methylene chloride, 4-aminophenol, aniline, and sulfanilic acid onto charcoal-based activated carbon (AC). Batch experiments were conducted to study the effects of pH, contact time, and initial concentrations of the adsorbates. The results show that longer contact time and higher initial concentrations increase the adsorption capacity, whereas pH shows no significant effect on the adsorption capacity at a value of less than 10, eliminating the need for pH adjustment and reducing process costs. The pseudo-second order (PSO) kinetic model best describes the adsorption process, with intraparticle diffusion playing a key role, as confirmed by the Weber and Morris (W-M) model. Six models describing the adsorption at equilibrium are applied to experimental data, and their parameters are estimated with a nonlinear regression model. Among isotherm models, the Langmuir-Freundlich model provides the best fit, suggesting multilayer adsorption on a heterogeneous granular activated carbon (GAC) surface. The maximum adsorption capacity is estimated to be 522.3 mgC/gAC. Experimental results confirm that GAC could effectively treat highly concentrated pharmaceutical wastewater, achieving up to 52% removal efficiency. Full article

(This article belongs to the Topic Wastewater Treatment by Physical, Chemical, Photochemical, and Biological Processes, and Their Combinations)

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16 pages, 1663 KiB

Open AccessEditor’s ChoiceArticle

Crop Water Use and a Gravity Model Exploration of Virtual Water Trade in Ghana’s Cereal Agriculture

by Alexander Sessi Kosi Tette, Golden Odey, Mirza Junaid Ahmad, Bashir Adelodun and Kyung-Sook Choi

Water 2024, 16(15), 2077; https://doi.org/10.3390/w16152077 - 23 Jul 2024

Cited by 3 | Viewed by 1687

Abstract

Agricultural water productivity is crucial for sustainability amidst the escalating demand for food. Cereals are pivotal in providing nutritious food at affordable prices. This study was based on Ghanaian data spanning from 1992 to 2021 to evaluate water usage in the cultivation of [...] Read more.

Agricultural water productivity is crucial for sustainability amidst the escalating demand for food. Cereals are pivotal in providing nutritious food at affordable prices. This study was based on Ghanaian data spanning from 1992 to 2021 to evaluate water usage in the cultivation of major cereals. It also examined the virtual water losses or gains in cereal trade alongside influencing factors. The analysis utilized secondary data encompassing the virtual water content, production quantity, export and import quantities, distance, GDP per capita, population, and land per capita of Ghana and its 75 trade partners. In the last 5 years, crop water use (CWU) reached an average of 7.08 billion m³/yr for maize, 3.48 billion m³/yr for rice, 1.08 billion m³/yr for sorghum, and 0.63 billion m³/yr for millet production. Ghana’s major partners for exported virtual water (EVW) were Niger, Burkina Faso, South Africa, and Togo. Major partners for imported virtual water (IVW) were Argentina, South Africa, Ukraine, Togo, Russia, Burkina Faso, Canada, Senegal, Nigeria, Portugal, UK, Niger, and the USA. The Panel Least Squares Method of regression was used to apply the Gravity Model principle in assessing influencing factors. The findings indicate that Ghana is a net importer of virtual water in the cereal trade, with significant influences from geographical distance, GDP per capita, population, land per capita, and cereal water use. Full article

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19 pages, 4611 KiB

Open AccessEditor’s ChoiceArticle

Anaerobic Digestion of Dye Wastewater and Agricultural Waste with Bio-Energy and Biochar Recovery: A Techno-Economic and Sustainable Approach

by Albert Tumanyisibwe, Mahmoud Nasr, Manabu Fujii and Mona G. Ibrahim

Water 2024, 16(14), 2025; https://doi.org/10.3390/w16142025 - 17 Jul 2024

Cited by 5 | Viewed by 2843

Abstract

While several researchers have investigated the anaerobic digestion (AD) of textile wastewater for dye degradation, their studies suffer from lower biogas productivity due to substrate inhibition and the occurrence of secondary pollution from digestate disposal. Hence, this study focuses on using the extract [...] Read more.

While several researchers have investigated the anaerobic digestion (AD) of textile wastewater for dye degradation, their studies suffer from lower biogas productivity due to substrate inhibition and the occurrence of secondary pollution from digestate disposal. Hence, this study focuses on using the extract of wheat straw (WS) as a co-substrate to facilitate the dye AD process, followed by recycling the digestate sludge for biochar production. In the first study, the batch digesters were operated at different dye wastewater (DW)/WS ratios (0–50% v/v), substrate-to-inoculum ratio of 0.28–0.50 g/g, pH 7.0 ± 0.2, and 37 °C. The digester operated at a DW/WS fraction of 65/35% (v/v) showed the best chemical oxygen demand (COD) removal efficiency of 68.52 ± 3.40% with bio-CH₄ of 270.52 ± 19.14 mL/g COD_removed. About 52.96 ± 3.61% of the initial COD mass was converted to CH₄, avoiding inhibition caused by volatile fatty acid (VFA) accumulation. In the second experiment, the dry digestate was thermally treated at 550 °C for 2 h under an oxygen-deprived condition, yielding 0.613 ± 0.031 g biochar/g. This biochar exhibited multiple functional groups, mineral contents, and high stability (O/C = 0.193). The combined digestion/pyrolysis scenario treating 35 m³/d (106.75 kg COD/d) could maintain profits from pollution reduction, biogas, biochar, and carbon trading, obtaining a 6.5-year payback period. Full article

(This article belongs to the Topic Advanced Processes and Technologies for Wastewater: Collection, Treatment, and Resource)

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18 pages, 1949 KiB

Open AccessEditor’s ChoiceReview

Unraveling the Potential of Microbial Flocculants: Preparation, Performance, and Applications in Wastewater Treatment

by Yang Yang, Cancan Jiang, Xu Wang, Lijing Fan, Yawen Xie, Danhua Wang, Tiancheng Yang, Jiang Peng, Xinyuan Zhang and Xuliang Zhuang

Water 2024, 16(14), 1995; https://doi.org/10.3390/w16141995 - 14 Jul 2024

Cited by 8 | Viewed by 3319

Abstract

Microbial flocculants (MBFs), a class of eco-friendly and biodegradable biopolymers produced by various microorganisms, have gained increasing attention as promising alternatives to conventional chemical flocculants in wastewater treatment and pollutant removal. This review presents a comprehensive overview of the current state of MBF [...] Read more.

Microbial flocculants (MBFs), a class of eco-friendly and biodegradable biopolymers produced by various microorganisms, have gained increasing attention as promising alternatives to conventional chemical flocculants in wastewater treatment and pollutant removal. This review presents a comprehensive overview of the current state of MBF research, encompassing their diverse sources (bacteria, fungi, and algae), major categories (polysaccharides, proteins, and glycoproteins), production processes, and flocculation performance and mechanisms. The wide-ranging applications of MBFs in removing suspended solids, heavy metals, dyes, and other pollutants from industrial and municipal wastewater are critically examined, highlighting their superior efficiency, selectivity, and environmental compatibility compared to traditional flocculants. Nonetheless, bioflocculants face significant challenges including high substrate costs, low production yields, and intricate purification methodologies, factors that impede their industrial scalability. Moreover, the risk of microbial contamination and the attendant health implications associated with the use of microbial flocculants (MBFs) necessitate thorough evaluation. To address the challenges of high production costs and variable product quality, strategies such as waste valorization, strain improvement, process optimization, and biosafety evaluation are discussed. Moreover, the development of multifunctional MBF-based flocculants and their synergistic use with other treatment technologies are identified as emerging trends for enhanced wastewater treatment and resource recovery. Future research directions are outlined, emphasizing the need for in-depth mechanistic studies, advanced characterization techniques, pilot-scale demonstrations to accelerate the industrial adoption of MBF, and moreover, integration with novel wastewater treatment processes, such as partial nitrification and the anammox process. This review is intended to inspire and guide further research and development efforts aimed at unlocking the full potential of MBFs as sustainable, high-performance, and cost-effective bioflocculants for addressing the escalating challenges in wastewater management and environmental conservation. Full article

(This article belongs to the Special Issue Water Quality Engineering and Wastewater Treatment III)

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29 pages, 4994 KiB

Open AccessEditor’s ChoiceReview

A Systematic Literature Review for Addressing Microplastic Fibre Pollution: Urgency and Opportunities

by Carmen Ka-Man Chan, Chris Kwan-Yu Lo and Chi-Wai Kan

Water 2024, 16(14), 1988; https://doi.org/10.3390/w16141988 - 13 Jul 2024

Cited by 10 | Viewed by 3346

Abstract

Microplastic fibre (MPF) pollution is a pressing concern that demands urgent attention. These tiny synthetic textile fibres can be found in various ecosystems, including water and air, and pose significant environmental risks. Despite their size (less than 5 mm), they can harm aquatic [...] Read more.

Microplastic fibre (MPF) pollution is a pressing concern that demands urgent attention. These tiny synthetic textile fibres can be found in various ecosystems, including water and air, and pose significant environmental risks. Despite their size (less than 5 mm), they can harm aquatic and terrestrial organisms and human health. Studies have demonstrated that these imperceptible pollutants can contaminate marine environments, thereby putting marine life at risk through ingestion and entanglement. Additionally, microplastic fibres can absorb toxins from the surrounding water, heightening their danger when consumed by aquatic organisms. Traces of MPFs have been identified in human food chains and organs. To effectively combat MPF pollution, it is crucial to understand how these fibres enter ecosystems and their sources. Primary sources include domestic laundry, where synthetic textile fibres are released into wastewater during washing. Other significant sources include industrial effluents, breakdown of plastic materials, and atmospheric deposition. Additionally, MPFs can be directly released into the environment by improperly disposing of consumer products containing these fibres, such as non-woven hygienic products. A comprehensive approach is necessary to address this pressing issue, including understanding the sources, pathways, and potential risks of MPFs. Immediate action is required to manage contamination and mitigate MPF pollution. This review paper provides a systematic literature analysis to help stakeholders prioritise efforts towards reducing MPFs. The key knowledge gaps identified include a lack of information regarding non-standardised test methodology and reporting units, and a lack of information on manufacturing processes and products, to increase understanding of life cycle impacts and real hotspots. Stakeholders urgently need collaborative efforts to address the systematic changes required to tackle this issue and address the proposed opportunities, including targeted government interventions and viable strategies for the industry sector to lead action. Full article

(This article belongs to the Special Issue Water Quality Engineering and Wastewater Treatment III)

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13 pages, 1163 KiB

Open AccessEditor’s ChoiceArticle

Application of Low-Pressure Nanofiltration Membranes NF90 and NTR-729HF for Treating Diverse Wastewater Streams for Irrigation Use

by Charith Fonseka, Seongchul Ryu, Sukanyah Devaisy, Jaya Kandasamy, Lee McLod, Harsha Ratnaweera and Saravanamuthu Vigneswaran

Water 2024, 16(14), 1971; https://doi.org/10.3390/w16141971 - 11 Jul 2024

Cited by 4 | Viewed by 1585

Abstract

The application of low-pressure nanofiltration (NF) was investigated for three different applications: water reuse from acid mine drainage (AMD), surface water containing natural organic matter (NOM) and agricultural reuse of microfiltered biologically treated sewage effluent (MF-BTSE). AMD contains many valuable rare earth elements [...] Read more.

The application of low-pressure nanofiltration (NF) was investigated for three different applications: water reuse from acid mine drainage (AMD), surface water containing natural organic matter (NOM) and agricultural reuse of microfiltered biologically treated sewage effluent (MF-BTSE). AMD contains many valuable rare earth elements (REEs) and copper (Cu) that can be recovered with fresh water. The NF90 membrane was investigated for recovery of fresh water from synthetic AMD. A steady permeate flux of 15.5 ± 0.2 L/m²h was achieved for pretreated AMD with over 98% solute rejection. NF90 achieved a high dissolved organic carbon (DOC) rejection of 95% from surface water containing NOM where 80% of the organic fraction was hydrophilic, mainly humics. The NF process maintained a high permeate flux of 52 LMH at 4 bars. The MF-BTSE was treated by NTR-729HF for agricultural reuse. NTR-729HF membranes were capable of rejecting DOC and inorganics such as sulfates and divalent ions (SO₄²⁻, Ca²⁺ and Mg²⁺) from MF-BTSE, with less than 20% rejection of monovalent (Na⁺ and Cl⁻) ions. The sodium adsorption ratio (SAR) was significantly reduced from 39 to 14 after treatment through NTR-729HF at 4 bar. The resulting water was found to be suitable to irrigate salt-sensitive crops. Full article

(This article belongs to the Special Issue Application of Membrane Separation Technology in Water and Wastewater Treatment)

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19 pages, 984 KiB

Open AccessEditor’s ChoiceReview

Assessing International Transboundary Water Management Practices to Extract Contextual Lessons for the Nile River Basin

by Mekdelawit M. Deribe, Assefa M. Melesse, Belete B. Kidanewold, Shlomi Dinar and Elizabeth P. Anderson

Water 2024, 16(14), 1960; https://doi.org/10.3390/w16141960 - 11 Jul 2024

Cited by 4 | Viewed by 4028

Abstract

Transboundary waters account for a significant portion of global freshwater resources, yet their management is often challenging. The Nile River basin faces significant challenges owing to the complex history and unique context of the basin. Examining the experience of other transboundary basins can [...] Read more.

Transboundary waters account for a significant portion of global freshwater resources, yet their management is often challenging. The Nile River basin faces significant challenges owing to the complex history and unique context of the basin. Examining the experience of other transboundary basins can offer insights for the effective management of the Nile waters. This paper aims to extract contextual lessons for the Nile from global transboundary water management practices. To that end, we performed a scoping literature search to identify well-researched transboundary water management practices from across the world, selected key case studies, and analyzed their management practices. We discussed the context of the Nile and organized the unique challenges of the basin in five themes, and we discussed how global experiences could provide valuable insights for the Nile basin within each theme. Trust building, the need for equitable water use frameworks, a strong river basin organization, the nuanced role of external actors, and the impact of broader political context were major themes that emerged from the analysis of the Nile context. Within each theme, we presented experiences from multiple basins to inform transboundary water management in the Nile basin. Full article

(This article belongs to the Special Issue Conflict and Cooperation: The Duality of Cross-Border Water Governance)

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16 pages, 1280 KiB

Open AccessEditor’s ChoiceArticle

Are Harmful Algal Blooms Increasing in the Great Lakes?

by Karl R. Bosse, Gary L. Fahnenstiel, Cal D. Buelo, Matthew B. Pawlowski, Anne E. Scofield, Elizabeth K. Hinchey and Michael J. Sayers

Water 2024, 16(14), 1944; https://doi.org/10.3390/w16141944 - 10 Jul 2024

Cited by 3 | Viewed by 1886

Abstract

This study used satellite remote sensing to investigate trends in harmful algal blooms (HABs) over the last 21 years, focusing on four regions within the Laurentian Great Lakes: western Lake Erie, Green Bay, Saginaw Bay, and western Lake Superior. HABs in the water [...] Read more.

This study used satellite remote sensing to investigate trends in harmful algal blooms (HABs) over the last 21 years, focusing on four regions within the Laurentian Great Lakes: western Lake Erie, Green Bay, Saginaw Bay, and western Lake Superior. HABs in the water column were identified from remote sensing-derived chlorophyll concentrations, and surface HAB scums were classified based on the Normalized Difference Vegetation Index (NDVI) band ratio index. Using imagery from the Moderate Resolution Imaging Spectroradiometer sensor on the Aqua satellite (MODIS-Aqua) from 2002 to 2022, we generated daily estimates of the HAB and surface scum extents for each region, which were then averaged to generate mean annual extents. We observed a significant decline in the Saginaw Bay mean annual HAB extents over the 21-year study period. Otherwise, no significant changes were observed over this period in any region for either the HAB or surface scum mean annual extents, thus suggesting that HABs are not increasing in the Great Lakes. Despite the lack of increasing trends, the blooms are still recurring annually and causing a negative impact on the nearby communities; thus, we believe that it is crucial to continue studying Great Lakes HABs to monitor the impact of current and future abatement strategies. Full article

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21 pages, 4041 KiB

Open AccessEditor’s ChoiceArticle

Application of Dynamic Programming Models for Improvement of Technological Approaches to Combat Negative Water Leakage in the Underground Space

by Sérgio Lousada, Svitlana Delehan and Andrii Khorolskyi

Water 2024, 16(14), 1952; https://doi.org/10.3390/w16141952 - 10 Jul 2024

Cited by 3 | Viewed by 1228

Abstract

The article solves an urgent problem, which is to develop a new approach to finding solutions to improve technological methods to combat negative water leakage in underround spaces. We propose the use of dynamic programming methods to select the optimal technology to secure [...] Read more.

The article solves an urgent problem, which is to develop a new approach to finding solutions to improve technological methods to combat negative water leakage in underround spaces. We propose the use of dynamic programming methods to select the optimal technology to secure such spaces. In accordance with the algorithm proposed in this paper, the problem was broken into a number of stages. At each stage, an optimal solution was sought (organisation of transport, delivery of materials to the destination, selection of materials, etc.). Thus, we applied a decomposition approach that allowed us to take into account the variety of parameters that affect the efficiency of the process. All these stages and their corresponding technological solutions were formalised by building network models. In these network models, vertices corresponded to solutions, and the distances between vertices (edges) corresponded to the value of the optimisation parameter. Thus, the shortest route from the initial to the final vertex corresponded to the optimal technological solution to combat negative water leakage in underground spaces. Based on the systematisation of data on technologies to combat water inflow into underground spaces, basic and refined models were developed. These models allowed us to take into account the risks associated with water breakthroughs into underground spaces. To minimise the risks, additional measures to combat water inflows are envisaged. In the practical part of this study, the results of the selection of a method with which to control water inflows are presented. This method involves the use of anchoring to reduce water filtration. According to the results of field observations, no water breakthroughs into the underground space were recorded. Full article

(This article belongs to the Special Issue Water-Related Geoenvironmental Issues, 2nd Edition)

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21 pages, 3527 KiB

Open AccessEditor’s ChoiceArticle

Quantifying Predictive Uncertainty and Feature Selection in River Bed Load Estimation: A Multi-Model Machine Learning Approach with Particle Swarm Optimization

by Xuan-Hien Le, Trung Tin Huynh, Mingeun Song and Giha Lee

Water 2024, 16(14), 1945; https://doi.org/10.3390/w16141945 - 10 Jul 2024

Cited by 5 | Viewed by 1556

Abstract

This study presents a comprehensive multi-model machine learning (ML) approach to predict river bed load, addressing the challenge of quantifying predictive uncertainty in fluvial geomorphology. Six ML models—random forest (RF), categorical boosting (CAT), extra tree regression (ETR), gradient boosting machine (GBM), Bayesian regression [...] Read more.

This study presents a comprehensive multi-model machine learning (ML) approach to predict river bed load, addressing the challenge of quantifying predictive uncertainty in fluvial geomorphology. Six ML models—random forest (RF), categorical boosting (CAT), extra tree regression (ETR), gradient boosting machine (GBM), Bayesian regression model (BRM), and K-nearest neighbors (KNNs)—were thoroughly evaluated across several performance metrics like root mean square error (RMSE), and correlation coefficient (R). To enhance model training and optimize performance, particle swarm optimization (PSO) was employed for hyperparameter tuning across all the models, leveraging its capability to efficiently explore complex hyperparameter spaces. Our findings indicated that RF, GBM, CAT, and ETR demonstrate superior predictive performance (R score > 0.936), benefiting significantly from PSO. In contrast, BRM displayed lower performance (0.838), indicating challenges with Bayesian approaches. The feature importance analysis, including permutation feature and SHAP values, highlighted the non-linear interdependencies between the variables, with river discharge (Q), bed slope (S), and flow width (W) being the most influential. This study also examined the specific impact of individual variables on model performance by adding and excluding individual variables, which is particularly meaningful when choosing input variables for the model, especially in limited data conditions. Uncertainty quantification through Monte Carlo simulations highlighted the enhanced predictability and reliability of models with larger datasets. The correlation between increased training data and improved model precision was evident in the consistent rise in mean R scores and reduction in standard deviations as the sample size increased. This research underscored the potential of advanced ensemble methods and PSO to mitigate the limitations of single-predictor models and exploit collective model strengths, thereby improving the reliability of predictions in river bed load estimation. The insights from this study provide a valuable framework for future research directions focused on optimizing ensemble configurations for hydro-dynamic modeling. Full article

(This article belongs to the Special Issue Application of Machine Learning in Hydrologic Sciences)

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21 pages, 4849 KiB

Open AccessEditor’s ChoiceArticle

Leak and Burst Detection in Water Distribution Network Using Logic- and Machine Learning-Based Approaches

by Kiran Joseph, Jyoti Shetty, Ashok K. Sharma, Rudi van Staden, P. L. P. Wasantha, Sharna Small and Nathan Bennett

Water 2024, 16(14), 1935; https://doi.org/10.3390/w16141935 - 9 Jul 2024

Cited by 4 | Viewed by 3083

Abstract

Urban water systems worldwide are confronted with the dual challenges of dwindling water resources and deteriorating infrastructure, emphasising the critical need to minimise water losses from leakage. Conventional methods for leak and burst detection often prove inadequate, leading to prolonged leak durations and [...] Read more.

Urban water systems worldwide are confronted with the dual challenges of dwindling water resources and deteriorating infrastructure, emphasising the critical need to minimise water losses from leakage. Conventional methods for leak and burst detection often prove inadequate, leading to prolonged leak durations and heightened maintenance costs. This study investigates the efficacy of logic- and machine learning-based approaches in early leak detection and precise location identification within water distribution networks. By integrating hardware and software technologies, including sensor technology, data analysis, and study on the logic-based and machine learning algorithms, innovative solutions are proposed to optimise water distribution efficiency and minimise losses. In this research, we focus on a case study area in the Sunbury region of Victoria, Australia, evaluating a pumping main equipped with Supervisory Control and Data Acquisition (SCADA) sensor technology. We extract hydraulic characteristics from SCADA data and develop logic-based algorithms for leak and burst detection, alongside state-of-the-art machine learning techniques. These methodologies are applied to historical data initially and will be subsequently extended to live data, enabling the real-time detection of leaks and bursts. The findings underscore the complementary nature of logic-based and machine learning approaches. While logic-based algorithms excel in capturing straightforward anomalies based on predefined conditions, they may struggle with complex or evolving patterns. Machine learning algorithms enhance detection by learning from historical data, adapting to changing conditions, and capturing intricate patterns and outliers. The comparative analysis of machine learning models highlights the superiority of the local outlier factor (LOF) in anomaly detection, leading to its selection as the final model. Furthermore, a web-based platform has been developed for leak and burst detection using a selected machine learning model. The success of machine learning models over traditional logic-based approaches underscores the effectiveness of data-driven, probabilistic methods in handling complex data patterns and variations. Leveraging statistical and probabilistic techniques, machine learning models offer adaptability and superior performance in scenarios with intricate or dynamic relationships between variables. The findings demonstrate that the proposed methodology can significantly enhance the early detection of leaks and bursts, thereby minimising water loss and associated economic costs. The implications of this study are profound for the scientific community and stakeholders, as it provides a scalable and efficient solution for water pipeline monitoring. Implementing this approach can lead to more proactive maintenance strategies, ultimately contributing to the sustainability and resilience of urban water infrastructure systems. Full article

(This article belongs to the Special Issue Advances in Management of Urban Water Supply System)

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31 pages, 2593 KiB

Open AccessEditor’s ChoiceReview

Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets

by Fahad Hasan, Paul Medley, Jason Drake and Gang Chen

Water 2024, 16(13), 1904; https://doi.org/10.3390/w16131904 - 3 Jul 2024

Cited by 10 | Viewed by 6332

Abstract

Machine learning (ML) applications in hydrology are revolutionizing our understanding and prediction of hydrological processes, driven by advancements in artificial intelligence and the availability of large, high-quality datasets. This review explores the current state of ML applications in hydrology, emphasizing the utilization of [...] Read more.

Machine learning (ML) applications in hydrology are revolutionizing our understanding and prediction of hydrological processes, driven by advancements in artificial intelligence and the availability of large, high-quality datasets. This review explores the current state of ML applications in hydrology, emphasizing the utilization of extensive datasets such as CAMELS, Caravan, GRDC, CHIRPS, NLDAS, GLDAS, PERSIANN, and GRACE. These datasets provide critical data for modeling various hydrological parameters, including streamflow, precipitation, groundwater levels, and flood frequency, particularly in data-scarce regions. We discuss the type of ML methods used in hydrology and significant successes achieved through those ML models, highlighting their enhanced predictive accuracy and the integration of diverse data sources. The review also addresses the challenges inherent in hydrological ML applications, such as data heterogeneity, spatial and temporal inconsistencies, issues regarding downscaling the LSH, and the need for incorporating human activities. In addition to discussing the limitations, this article highlights the benefits of utilizing high-resolution datasets compared to traditional ones. Additionally, we examine the emerging trends and future directions, including the integration of real-time data and the quantification of uncertainties to improve model reliability. We also place a strong emphasis on incorporating citizen science and the IoT for data collection in hydrology. By synthesizing the latest research, this paper aims to guide future efforts in leveraging large datasets and ML techniques to advance hydrological science and enhance water resource management practices. Full article

(This article belongs to the Special Issue Water Resource Management in Artificial Intelligence and Big Data Analytics)

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41 pages, 10492 KiB

Open AccessEditor’s ChoiceReview

Water Dams: From Ancient to Present Times and into the Future

by Andreas N. Angelakis, Alper Baba, Mohammad Valipour, Jörg Dietrich, Elahe Fallah-Mehdipour, Jens Krasilnikoff, Esra Bilgic, Cees Passchier, Vasileios A. Tzanakakis, Rohitashw Kumar, Zhang Min, Nicholas Dercas and Abdelkader T. Ahmed

Water 2024, 16(13), 1889; https://doi.org/10.3390/w16131889 - 1 Jul 2024

Cited by 8 | Viewed by 6084

Abstract

Since ancient times, dams have been built to store water, control rivers, and irrigate agricultural land to meet human needs. By the end of the 19th century, hydroelectric power stations arose and extended the purposes of dams. Today, dams can be seen as [...] Read more.

Since ancient times, dams have been built to store water, control rivers, and irrigate agricultural land to meet human needs. By the end of the 19th century, hydroelectric power stations arose and extended the purposes of dams. Today, dams can be seen as part of the renewable energy supply infrastructure. The word dam comes from French and is defined in dictionaries using words like strange, dike, and obstacle. In other words, a dam is a structure that stores water and directs it to the desired location, with a dam being built in front of river valleys. Dams built on rivers serve various purposes such as the supply of drinking water, agricultural irrigation, flood control, the supply of industrial water, power generation, recreation, the movement control of solids, and fisheries. Dams can also be built in a catchment area to capture and store the rainwater in arid and semi-arid areas. Dams can be built from concrete or natural materials such as earth and rock. There are various types of dams: embankment dams (earth-fill dams, rock-fill dams, and rock-fill dams with concrete faces) and rigid dams (gravity dams, rolled compacted concrete dams, arch dams, and buttress dams). A gravity dam is a straight wall of stone masonry or earthen material that can withstand the full force of the water pressure. In other words, the pressure of the water transfers the vertical compressive forces and horizontal shear forces to the foundations beneath the dam. The strength of a gravity dam ultimately depends on its weight and the strength of its foundations. Most dams built in ancient times were constructed as gravity dams. An arch dam, on the other hand, has a convex curved surface that faces the water. The forces generated by the water pressure are transferred to the sides of the structure by horizontal lines. The horizontal, normal, and shear forces resist the weight at the edges. When viewed in a horizontal section, an arch dam has a curved shape. This type of dam can also resist water pressure due to its particular shape that allows the transfer of the forces generated by the stored water to the rock foundations. This article takes a detailed look at hydraulic engineering in dams over the millennia. Lessons should be learned from the successful and unsuccessful applications and operations of dams. Water resource managers, policymakers, and stakeholders can use these lessons to achieve sustainable development goals in times of climate change and water crisis. Full article

(This article belongs to the Section Soil and Water)

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14 pages, 3512 KiB

Open AccessEditor’s ChoiceArticle

Behavioral and Biochemical Effects of Glyphosate-Based Herbicide Roundup on Unionid Mussels: Are Mussels Good Indicators of Water Pollution with Glyphosate-Based Pesticides?

by Agnieszka Drewek, Jan Lubawy, Piotr Domek, Jan Polak, Małgorzata Słocińska, Aleksandra Dzięgelewska and Piotr Klimaszyk

Water 2024, 16(13), 1882; https://doi.org/10.3390/w16131882 - 1 Jul 2024

Cited by 3 | Viewed by 2001

Abstract

The behavioral (filtration activity) and biochemical (oxidative stress) effects of Roundup 360 Plus (active substance glyphosate) herbicide on two species of unionid mussels, Unio tumidus (Philipsson, 1788) and Anodonta anatina (L.), were evaluated at concentrations ranging from 15 to 1500 μg L⁻¹ [...] Read more.

The behavioral (filtration activity) and biochemical (oxidative stress) effects of Roundup 360 Plus (active substance glyphosate) herbicide on two species of unionid mussels, Unio tumidus (Philipsson, 1788) and Anodonta anatina (L.), were evaluated at concentrations ranging from 15 to 1500 μg L⁻¹ of glyphosate for five days. During all experiments, we did not record the mortality of the studied mussel species. Exposure to Roundup herbicide induced dose-dependent filtration disruptions in both U. tumidus and A. anatina. Exposure of the mussels to a low and environmentally relevant concentration 15 µg glyphosate L⁻¹ resulted in a slight (<20%) and temporary decrease in mean valve dilation. Exposure of the mussels to Roundup at relatively high concentrations caused drastic and prolonged shell closure and a reduction in the mussel shell opening rate. Exposure of both mussel species to herbicide resulted in oxidative stress; an increase in superoxide dismutase enzymatic activity was detected. The most significant increase in SOD activity was observed after the exposure to the highest Roundup concentration. However, no correlation between the Roundup concentration and enzymatic activity was found. The use of unionid mussels to detect environmentally relevant concentrations of Roundup, as a part of biological early warning system for pollution, is limited, but they can serve to detect the incidental pollution of aquatic ecosystems with high concentrations of this herbicide. Full article

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