Journal Description
Water
Water
is a peer-reviewed, open access journal on water science and technology, including the ecology and management of water resources, and is published semimonthly online by MDPI. Water collaborates with the International Conference on Flood Management (ICFM) and Stockholm International Water Institute (SIWI). In addition, the American Institute of Hydrology (AIH), The Polish Limnological Society (PLS) and Japanese Society of Physical Hydrology (JSPH) are affiliated with Water and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, GEOBASE, GeoRef, PubAg, AGRIS, CAPlus / SciFinder, Inspec, and other databases.
- Journal Rank: JCR - Q2 (Water Resources) / CiteScore - Q1 (Water Science and Technology)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.5 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Water include: GeoHazards and Hydrobiology.
Impact Factor:
3.4 (2022);
5-Year Impact Factor:
3.5 (2022)
Latest Articles
Forecasting the River Water Discharge by Artificial Intelligence Methods
Water 2024, 16(9), 1248; https://doi.org/10.3390/w16091248 (registering DOI) - 26 Apr 2024
Abstract
The management of water resources must be based on accurate models of the river discharge in the context of the water flow alteration due to anthropic influences and climate change. Therefore, this article addresses the challenge of detecting the best model among three
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The management of water resources must be based on accurate models of the river discharge in the context of the water flow alteration due to anthropic influences and climate change. Therefore, this article addresses the challenge of detecting the best model among three artificial intelligence techniques (AI)—backpropagation neural networks (BPNN), long short-term memory (LSTM), and extreme learning machine (ELM)—for the monthly data series discharge of the Buzău River, in Romania. The models were built for three periods: January 1955–September 2006 (S1 series), January 1955–December 1983 (S2 series), and January 1984–December 2010 (S series). In terms of mean absolute error (MAE), the best performances were those of ELM on both Training and Test sets on S2, with MAETraining = 5.02 and MAETest = 4.01. With respect to MSE, the best was LSTM on the Training set of S2 (MSE = 60.07) and ELM on the Test set of S2 (MSE = 32.21). Accounting for the R2 value, the best model was LSTM on S2 (R2Training = 99.92%, and R2Test = 99.97%). ELM was the fastest, with 0.6996 s, 0.7449 s, and 0.6467 s, on S, S1, and S2, respectively.
Full article
(This article belongs to the Special Issue Hydrological Simulation and Forecasting Based on Artificial Intelligence)
Open AccessArticle
Rainfall-Runoff Parameter Estimation from Ungauged Flat Afforested Catchments Using the NRCS-CN Method
by
Szymon Kobus
Water 2024, 16(9), 1247; https://doi.org/10.3390/w16091247 - 26 Apr 2024
Abstract
Of the numerous methods applied in rainfall-runoff models, the most common is the NRCS-CN method that is applied to calculate raised-water runoffs and compare them with the runoff values measured for 12 selected rainfall-runoff events. This study was conducted on three experimental forest
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Of the numerous methods applied in rainfall-runoff models, the most common is the NRCS-CN method that is applied to calculate raised-water runoffs and compare them with the runoff values measured for 12 selected rainfall-runoff events. This study was conducted on three experimental forest catchments with an area ranging from 67.6 to 747 ha. Total rainfall values ranging from 22.2 to 84.1 mm were analysed. Relatively low effective rainfall values were obtained for the lowest average for catchment 1 (Pe = 0.23 mm) and the runoff coefficient (α = 0.40%) and for the highest average for catchment 3 (Pe = 1.35 mm) and an average runoff coefficient (α = 3.12%). The maximum potential retention Si value, corresponding to each pair of P-Pe events, was the effect of the catchment’s moisture and absorptive capacity conditions. The lowest retention S value was calculated for catchment 3. The highest average retention value was calculated for catchment 1, in which the lightest soils were found. The best fit of the initial loss coefficient for the majority of rainfall-runoff events occurred for the λ coefficient values of 0.05 and 0.075. At higher λ, the effective rainfall Pe was not generated. LAG times calculated using 10 methods yielded diverse values. The fit of a specific formula was largely influenced by the size of the catchment, as well as the number and type of parameters considered during model calibration. The method based on catchment width demonstrated the best fit for all catchments, with R² ranging from 0.77 to 0.78 and RMSE from 0.52 for catchment 2 to 1.11 for catchment 1.
Full article
(This article belongs to the Special Issue Water Resources Science and Management in Forested and Mixed-Land-Use Watersheds)
Open AccessArticle
Precipitation Modeling Based on Spatio-Temporal Variation in Lake Urmia Basin Using Machine Learning Methods
by
Sajjad Arbabi, Mohammad Taghi Sattari, Nasrin Fathollahzadeh Attar, Adam Milewski and Mohamad Sakizadeh
Water 2024, 16(9), 1246; https://doi.org/10.3390/w16091246 - 26 Apr 2024
Abstract
The amount of rainfall in different regions is influenced by various factors, including time, place, climate, and geography. In the Lake Urmia basin, Mediterranean air masses significantly impact precipitation. This study aimed to model precipitation in the Lake Urmia basin using monthly rainfall
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The amount of rainfall in different regions is influenced by various factors, including time, place, climate, and geography. In the Lake Urmia basin, Mediterranean air masses significantly impact precipitation. This study aimed to model precipitation in the Lake Urmia basin using monthly rainfall data from 16 meteorological stations and five machine learning methods (RF, M5, SVR, GPR, and KNN). Eight input scenarios were considered, including the monthly index, longitude, latitude, altitude, distance from stations to Lake Urmia, and distance from the Mediterranean Sea. The results revealed that the random forest model consistently outperformed the other models, with a correlation rate of 0.968 and the lowest errors (RMSE = 5.66 mm and MAE = 4.03 mm). This indicates its high accuracy in modeling precipitation in this basin. This study’s significant contribution is its ability to accurately model monthly precipitation using spatial variables and monthly indexes without measuring precipitation. Based on the findings, the random forest model can model monthly rainfall and create rainfall maps by interpolating the GIS environment for areas without rainfall measurements.
Full article
(This article belongs to the Section Water and Climate Change)
Open AccessArticle
Occurrence and Risk Assessment of Perfluoroalkyl Substances in Surface Water of Hefei City, Southeast China
by
Yu Zhang, Chuanjun Jiang, Liangpu Zhang, Hua Cheng and Ning Wang
Water 2024, 16(9), 1245; https://doi.org/10.3390/w16091245 - 26 Apr 2024
Abstract
In this work, the spatial distribution, potential sources, and risk assessment of perfluoroalkyl substances (PFASs) were investigated at 22 surface water sampling sites in Hefei City. The study encompassed 11 distinct types of PFASs, which included 7 perfluoroalkyl carboxylic acids (PFCAs) and 4
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In this work, the spatial distribution, potential sources, and risk assessment of perfluoroalkyl substances (PFASs) were investigated at 22 surface water sampling sites in Hefei City. The study encompassed 11 distinct types of PFASs, which included 7 perfluoroalkyl carboxylic acids (PFCAs) and 4 perfluoroalkyl sulfonic acids (PFSAs). The findings indicated that the overall concentration of PFASs varied between 12.96 to 545.50 ng/L, with perfluorooctanoic acid (PFOA), perfluorobutanesulfonic acid (PFBS), perfluorobutyric acid (PFBA), and perfluorohexanoic acid (PFHxA) being the most prevalent, contributing to an average of 71% of the total PFASs concentration. Principal component analysis (PCA) elucidated the primary sources of PFASs, which included industrial emissions, fluoropolymer production and treatment, textile processing, and the impact of the electroplating industry. Employing the risk quotient (RQ) method facilitated the assessment of ecological risks associated with PFASs in surface water within the study area, suggesting that the current concentrations of PFASs in Hefei’s surface water pose a relatively low ecological risk. However, the long-term ecological effects of PFASs cannot be overlooked due to their potential for long-range transport and the cumulative nature of biological food chains.
Full article
Open AccessArticle
Simultaneous Synthesis of Single- and Multiple-Contaminant Water Networks Using LINGO and Excel Software
by
Abeer M. Shoaib, Amr A. Atawia, Mohamed H. Hassanean, Abdelrahman G. Gadallah and Ahmed A. Bhran
Water 2024, 16(9), 1244; https://doi.org/10.3390/w16091244 - 26 Apr 2024
Abstract
Controlling the distribution of water and wastewater between industrial processes is vital to rationalize water usage and preserve the environment. In this paper, a mathematical technique is proposed to optimize water–wastewater networks, and a nonlinear program is introduced to minimize the consumption of
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Controlling the distribution of water and wastewater between industrial processes is vital to rationalize water usage and preserve the environment. In this paper, a mathematical technique is proposed to optimize water–wastewater networks, and a nonlinear program is introduced to minimize the consumption of freshwater and, consequently, the flowrate of wastewater discharge. A general mathematical model, able to handle industrial plants containing up to eight sources and eight sinks, is developed using LINGO optimization software to facilitate dealing with complex case studies. The introduced model can handle single-contaminant networks as well as multiple-contaminant ones. The optimal water network is synthesized through two steps; the first step involves the introduction of the case study data into the developed mathematical model. The second step considers using the optimal solution produced after running the developed LINGO model as feed data for a pre-designed Excel sheet able to deal with these results and simultaneously draw the optimal water–wastewater network. The proposed mathematical model is applied to two case studies. The first case study includes actual data from four fertilizer plants located in Egypt; the water resources and requirements are simultaneously integrated to obtain a sensible cutting in both freshwater consumption (lowered by 52.2%) and wastewater discharge (zero wastewater discharge). The second case study regards a Brazilian petrochemical plant; the obtained results show noticeable reductions in freshwater consumption by 12.3%, while the reduction percentage of wastewater discharge is 4.5%.
Full article
(This article belongs to the Special Issue Contaminants in the Water Environment)
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Open AccessArticle
Determinants of Farmers’ Acceptance of the Volumetric Pricing Policy for Irrigation Water: An Empirical Study from China
by
Xuan Fang and Ying Zhu
Water 2024, 16(9), 1243; https://doi.org/10.3390/w16091243 - 26 Apr 2024
Abstract
Volumetric-based pricing for irrigation water was introduced as part of a comprehensive reform of agricultural water prices in China. However, operational deficiencies and farmers’ lack of willingness to adopt the volumetric pricing policy (VPP) hinder the coordinated implementation of the reform. To address
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Volumetric-based pricing for irrigation water was introduced as part of a comprehensive reform of agricultural water prices in China. However, operational deficiencies and farmers’ lack of willingness to adopt the volumetric pricing policy (VPP) hinder the coordinated implementation of the reform. To address these practical challenges, we employed a binary logistic regression model to analyse farmers’ acceptance of the VPP for agricultural irrigation water usage in Suqian City, Jiangsu Province. A variable set was formed by selecting potential variables from four types of influencing factors: the subject (water users), the object (water supply departments), natural condition factors, and social condition factors. Our results revealed seven factors that determine whether farmers accept the VPP: irrigation water measurement at the water inlet of a lateral canal, the irrigation water-saving rewards scale, enforcement efforts of charging by volume, the irrigation water source type, the use of agricultural water-saving for trade, financial investment in water-saving technology, and the level of irrigation water pricing. We determined the degree of influence of the seven determining factors, among which the irrigation water-saving rewards scale and enforcement efforts of charging by volume most influence farmers’ decisions on the VPP for irrigation water. The results of this study can be used as a reference for innovation of the agricultural water-saving system in Suqian City, optimisation of an accurate fiscal subsidy scale, quantification of irrigation water rights, optimisation of the measurement facility layout, and effective implementation of agricultural water rights trading. More broadly, this study provides a valuable reference for solving the difficulties faced in the comprehensive reform of agricultural water pricing in China, which includes irrigation water pricing mechanisms, management systems, subsidy mechanisms, and water-saving incentive measures.
Full article
(This article belongs to the Section Water Resources Management, Policy and Governance)
Open AccessReview
Harnessing the Potential of Extracellular Polymeric Substances in Enhancing ANAMMOX Processes: Mechanisms, Strategies, and Perspectives
by
Lijing Fan, Cancan Jiang, Xu Wang, Yang Yang, Yawen Xie, Jiaqi Su, Hong Sun, Shengjun Xu and Xuliang Zhuang
Water 2024, 16(9), 1242; https://doi.org/10.3390/w16091242 - 26 Apr 2024
Abstract
Anaerobic ammonium oxidation (ANAMMOX) has emerged as a promising sustainable nitrogen removal technology that offers significant advantages over conventional nitrification–denitrification processes, such as reduced energy consumption, a 60% reduction in oxygen demand, and a 90% reduction in sludge production. However, the practical application
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Anaerobic ammonium oxidation (ANAMMOX) has emerged as a promising sustainable nitrogen removal technology that offers significant advantages over conventional nitrification–denitrification processes, such as reduced energy consumption, a 60% reduction in oxygen demand, and a 90% reduction in sludge production. However, the practical application of ANAMMOX is hindered by several challenges, including the slow growth of ANAMMOX bacteria, long start-up periods, and high sensitivity to environmental disturbances. Recent studies have highlighted the crucial role of extracellular polymeric substances (EPSs) in the formation, activity, and stability of ANAMMOX biofilms and granules. An EPS is a complex mixture of high-molecular-weight polymers secreted by microorganisms, mainly composed of polysaccharides, proteins, nucleic acids, and lipids. The diverse physicochemical properties and functional groups of EPSs enable them to serve as a structural scaffold, protective barrier, sorption site, electron shuttle, and nutrient source for ANAMMOX bacteria. This review aims to provide an overview of the latest research progress on harnessing the potential of EPSs to enhance the ANAMMOX process. The characteristics, compositions, and extraction methods of ANAMMOX-derived EPSs are summarized. The mechanisms of how EPSs facilitate the enrichment, immobilization, aggregation, and adaptation of ANAMMOX bacteria are elucidated. The strategies and effects of EPS supplementation on improving the performance and robustness of ANAMMOX reactors under various stresses are critically reviewed. The challenges and future perspectives of the EPS-mediated optimization of the ANAMMOX process are also discussed. This review sheds new light on exploiting EPSs as a renewable bioresource to develop more efficient and stable ANAMMOX applications for sustainable wastewater treatment.
Full article
(This article belongs to the Section Wastewater Treatment and Reuse)
Open AccessArticle
Novel Oxidation Strategies for the In Situ Remediation of Chlorinated Solvents from Groundwater—A Bench-Scale Study
by
Alicia Cano-López, Lidia Fernandez-Rojo, Leónidas Pérez-Estrada, Sònia Jou-Claus, Marta Batriu, Carme Bosch, Xavier Martínez-Lladó, Joana Baeta Trias, Ricard Mora Vilamaña, Mònica Escolà Casas and Víctor Matamoros
Water 2024, 16(9), 1241; https://doi.org/10.3390/w16091241 - 26 Apr 2024
Abstract
Industrial chlorinated solvents continue to be among the most significant issues in groundwater (GW) pollution worldwide. This study assesses the effectiveness of eight novel oxidation treatments, including persulfate (PS), ferrous sulfate, sulfidated nano-zero valent iron (S-nZVI), and potassium ferrate, along with their combinations,
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Industrial chlorinated solvents continue to be among the most significant issues in groundwater (GW) pollution worldwide. This study assesses the effectiveness of eight novel oxidation treatments, including persulfate (PS), ferrous sulfate, sulfidated nano-zero valent iron (S-nZVI), and potassium ferrate, along with their combinations, for the potential in situ remediation of GW polluted with chlorinated solvents (1,2-dichloroethylene, trichloroethylene, and tetrachloroethylene). Our bench-scale results reveal that the combined addition of PS and S-nZVI can effectively eliminate trichloroethylene (10 µg/L), achieving removal rates of up to 80% and 92% within 1 h, respectively, when using synthetic GW. In the case of real GW, this combination achieved removal rates of 69, 99, and 92% for cis-1,2-dichloroethylene, trichloroethylene, and tetrachloroethylene, respectively, within 24 h. Therefore, this proposed remediation solution resulted in a significant reduction in the environmental risk quotient, shifting it from a high-risk (1.1) to a low-risk (0.2) scenario. Furthermore, the absence of transformation products, such as vinyl chloride, suggests the suitability of employing this solution for the in situ remediation of GW polluted with chlorinated solvents.
Full article
(This article belongs to the Special Issue New Technologies for Soil and Groundwater Remediation)
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Open AccessArticle
Groundwater Chemical Trends Analyses in the Piedmont Po Plain (NW Italy): Comparison with Groundwater Level Variations (2000–2020)
by
Daniele Cocca, Manuela Lasagna and Domenico Antonio De Luca
Water 2024, 16(9), 1240; https://doi.org/10.3390/w16091240 - 26 Apr 2024
Abstract
The concentrations of chemicals in the groundwater chemical values in the Piedmont Po Plain (NW Italy) show significant temporal variability and need to be characterised due to the lack of regional-scale assessments. The aim of this study was to analyse the trends (period
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The concentrations of chemicals in the groundwater chemical values in the Piedmont Po Plain (NW Italy) show significant temporal variability and need to be characterised due to the lack of regional-scale assessments. The aim of this study was to analyse the trends (period 2000–2020) in the main physicochemical parameters and main ions in 227 wells in the shallow aquifer and to identify the potential causes. The identification of change points (points of sudden change) and comparisons with groundwater level variations were also performed. Results highlight general increasing trends for Na, Cl and HCO3, decreasing trends for SO4 and NO3, stationary conditions for pH and heterogeneous behaviours for electrolytic conductivity, Ca and Mg. Change points occurred in at least 50% of the monitoring wells, mainly during the 2008–2011 period. The comparison between groundwater levels and chemistry highlights a direct proportionality. Superimposed processes that induce an absence of proportionality are shown. The comparison of results with those of previous studies conducted under similar conditions revealed similar variations.. In conclusion, the potential responsible factors (e.g., road-salt dissolution and agricultural practices) and the relevant role of groundwater level variation were identified.
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(This article belongs to the Section Hydrogeology)
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Open AccessArticle
Identification of Trends in Dam Monitoring Data Series Based on Machine Learning and Individual Conditional Expectation Curves
by
Miguel Á. Fernández-Centeno, Patricia Alocén and Miguel Á. Toledo
Water 2024, 16(9), 1239; https://doi.org/10.3390/w16091239 - 26 Apr 2024
Abstract
Dams are complex systems that involve both the structure itself and its foundation. Rheological phenomena, expansive reactions, or alterations in the geotechnical parameters of the foundation, among others, result in non-reversible and cumulative modifications in the dam response, leading to trends in the
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Dams are complex systems that involve both the structure itself and its foundation. Rheological phenomena, expansive reactions, or alterations in the geotechnical parameters of the foundation, among others, result in non-reversible and cumulative modifications in the dam response, leading to trends in the monitoring data series. The accurate identification and definition of these trends to study their evolution are key aspects of dam safety. This manuscript proposes a methodology to identify trends in dam behavioural data series by identifying the influence of the time variable on the predictions provided by the ML models. Initially, ICE curves and SHAP values are employed to extract temporal dependence, and the ICE curves are found to be more precise and efficient in terms of computational cost. The temporal dependencies found are adjusted using a GWO algorithm to different function characteristics of irreversible processes in dams. The function that provides the best fit is selected as the most plausible. The results obtained allow us to conclude that the proposed methodology is capable of obtaining estimates of the most common trends that affect movements in concrete dams with greater precision than the statistical models most commonly used to predict the behaviour of these types of variables. These results are promising for its general application to other types of dam monitoring data series, given the versatility demonstrated for the unsupervised identification of temporal dependencies.
Full article
(This article belongs to the Special Issue Managing Impacts on Baseflows in Streams and the Associated Impacts on Ecosystems and Water Quality)
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Open AccessArticle
A Novel Method for Anomaly Detection and Signal Calibration in Water Quality Monitoring of an Urban Water Supply System
by
Jincheng Liu, Di Wu, Hadi Mohammed and Razak Seidu
Water 2024, 16(9), 1238; https://doi.org/10.3390/w16091238 - 26 Apr 2024
Abstract
Water quality monitoring plays a crucial role in urban water supply systems for the production of safe drinking water. However, the traditional approach to water monitoring in Norway relies on a periodic (weekly/biweekly/monthly) sampling and analysis of biological indicators, which fails to provide
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Water quality monitoring plays a crucial role in urban water supply systems for the production of safe drinking water. However, the traditional approach to water monitoring in Norway relies on a periodic (weekly/biweekly/monthly) sampling and analysis of biological indicators, which fails to provide a timely response to changes in water quality. This research addresses this issue by proposing a data-driven solution that enhances the timeliness of water quality monitoring. Our research team applied a case study in Ålesund Kommune. A sensor platform has been deployed at Lake Brusdalsvatnet, the water source reservoir in Ålesund. This sensor module is capable of collecting data for 10 different physico-chemical indicators of water quality. Leveraging this sensor platform, we developed a CNN-AutoEncoder-SOM solution to automatically monitor, process, and evaluate water quality evolution in the lake. There are three components in this solution. The first one focuses on anomaly detection. We employed a recurrence map to encode the temporal dynamics and sensor correlations, which were then fed into a convolutional neural network (CNN) for classification. It is noted that this network achieved an impressive accuracy of up to . Once an anomaly is detected, the data are calibrated in the second component using an AutoEncoder-based network. Since true values for calibration are unavailable, the results are evaluated through data analysis. With high-quality calibrated data in hand, we proceeded to cluster the data into different categories to establish water quality standards in the third component, where a self-organizing map (SOM) is applied. The results revealed that this solution demonstrated significant performance, with a silhouette score of 0.73, which illustrates a small in-cluster distance and large intra-cluster distance when the water was clustered into three levels. This system not only achieved the objective of developing a comprehensive solution for continuous water quality monitoring but also offers the potential for integration with other cyber–physical systems (CPSs) in urban water management.
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(This article belongs to the Topic Hydrology and Water Resources Management)
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Open AccessArticle
Photochlorination of Anthracene in Saline Ice under Simulated Solar Light
by
Yujie Li, Xuefeng Hu, Hao Xie, Beichuan Cai and Yaxing Bai
Water 2024, 16(9), 1237; https://doi.org/10.3390/w16091237 - 26 Apr 2024
Abstract
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Polycyclic aromatic hydrocarbons (PAHs) can undergo photochemical reactions in chlorine-containing environments, generating chlorinated polycyclic aromatic hydrocarbons (ClPAHs). This phenomenon has been confirmed in aqueous and soil environments, while was previously overlooked in saline ice. Thus, this study aimed to investigate the photochemical chlorination
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Polycyclic aromatic hydrocarbons (PAHs) can undergo photochemical reactions in chlorine-containing environments, generating chlorinated polycyclic aromatic hydrocarbons (ClPAHs). This phenomenon has been confirmed in aqueous and soil environments, while was previously overlooked in saline ice. Thus, this study aimed to investigate the photochemical chlorination behavior of anthracene (ANT) in saline ice. Under photoexcitation, the ground state ANT generates the singlet state ANT (1ANT*), which is transformed into the triplet state ANT (3ANT*) via intersystem crossing. Simultaneously, the oxygen receives electrons and further reacts to form the hydroxyl radical (·OH). The ·OH reacts with chloride ions (Cl−) to produce chlorine radicals (·Cl). The ·Cl then reacts with 3ANT* to form monochloroanthracene (9-ClANT, 2-ClANT). The resulting monochloroanthracene further reacts with ·Cl to form dichloroanthracene (9,10-Cl2ANT). Lower temperature, higher salinity, and dissolved organic matter are facilitated to generate ClPAHs, which may show negative impacts on the ecological environment.
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Open AccessArticle
Water Value Ambivalence: A Qualitative Exploration of the Multitude of Water Values
by
Lien Dieleman, Robbe Geerts, Frédéric Vandermoere and Stijn Brouwer
Water 2024, 16(9), 1236; https://doi.org/10.3390/w16091236 - 26 Apr 2024
Abstract
Tap water and its pricing have predominantly interested economists, partly due to the perception of water services primarily as production processes. As a result, much of the existing literature focuses on the economic value of water, leaving the social and cultural importance of
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Tap water and its pricing have predominantly interested economists, partly due to the perception of water services primarily as production processes. As a result, much of the existing literature focuses on the economic value of water, leaving the social and cultural importance of water for citizens underexplored. This study adopts a sociological lens to explore the significance of water, delving into citizens’ experiences and perceptions regarding their water usage. Applying a social practice approach to value creation, we conducted 15 in-depth interviews. The results show that although the price of tap water is a concern for people, the actual value of water extends well beyond its price. Water has direct values for citizens in their everyday lives, as well as indirect value by contributing to broader societal systems. In their everyday lives, citizens use water not so much for the sake of water itself, but in various household water practices (e.g., showering) associated with certain values: hygiene, health, relaxation, warmth, and so on. Finally, our study directs attention towards the tensions people may experience between the various values they attach to tap water and the sense of responsibility to use it prudently. Future research needs to consider this water value ambivalence when encouraging water conservation.
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(This article belongs to the Section Water Use and Scarcity)
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Open AccessArticle
Greedy Weighted Stacking of Machine Learning Models for Optimizing Dam Deformation Prediction
by
Patricia Alocén, Miguel Á. Fernández-Centeno and Miguel Á. Toledo
Water 2024, 16(9), 1235; https://doi.org/10.3390/w16091235 - 25 Apr 2024
Abstract
Dam safety monitoring is critical due to its social, environmental, and economic implications. Although conventional statistical approaches have been used for surveillance, advancements in technology, particularly in Artificial Intelligence (AI) and Machine Learning (ML), offer promising avenues for enhancing predictive capabilities. We investigate
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Dam safety monitoring is critical due to its social, environmental, and economic implications. Although conventional statistical approaches have been used for surveillance, advancements in technology, particularly in Artificial Intelligence (AI) and Machine Learning (ML), offer promising avenues for enhancing predictive capabilities. We investigate the application of ML algorithms, including Boosted Regression Trees (BRT), Random Forest (RF), and Neural Networks (NN), focussing on their combination by Stacking to improve prediction accuracy on concrete dam deformation using radial displacement data from three dams. The methodology involves training first-level models (experts) using those algorithms, and a second-level meta-learner that combines their predictions using BRT, a Linear Model (LM) and the Greedy Weighted Algorithm (GWA). A comparative analysis demonstrates the superiority of Stacking over traditional methods. The GWA emerged as the most suitable meta-learner, enhancing the optimal expert in all cases, with improvement rates reaching up to 16.12% over the optimal expert. Our study addresses critical questions regarding the GWA’s expert weighting and its impact on prediction precision. The results indicate that the combination of accurate experts using the GWA improves model reliability by reducing error dispersion. However, variations in optimal weights over time necessitate robust error estimation using cross-validation by blocks. Furthermore, the assignment of weights to experts closely correlates with their precision: the more accurate a model is, the more weight that is assigned to it. The GWA improves on the optimal expert in most cases, including at extreme values of error, with improvement rates up to 41.74%. Our findings suggest that the proposed methodology significantly advances AI applications in infrastructure monitoring, with implications for dam safety.
Full article
(This article belongs to the Special Issue Safety Evaluation of Dam and Geotechnical Engineering, Volume II)
Open AccessArticle
Study on Properties of Micro-Nano Magnetic Composite Prepared by Mechanochemical Method of NdFeB Secondary Waste and Removal of As (V) from Mine Water
by
Xiujuan Feng and Yicheng Rao
Water 2024, 16(9), 1234; https://doi.org/10.3390/w16091234 - 25 Apr 2024
Abstract
The secondary waste produced by NdFeB waste after rare earth recycling, with an annual output of more than tens of thousands of tons, is the largest solid waste emission source in the rare earth industry, and long-term storage causes land resource occupation and
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The secondary waste produced by NdFeB waste after rare earth recycling, with an annual output of more than tens of thousands of tons, is the largest solid waste emission source in the rare earth industry, and long-term storage causes land resource occupation and environmental pollution. Arsenic-containing mine wastewater has serious harm, wide distribution, and long duration of pollution. In this study, the mechanical ball milling method was used to activate NdFeB secondary waste to prepare micro-nano magnetic composite materials, the main components of which are Fe2O3, Fe3O4, and C. Under mechanical mechanochemical action, the particles are more dispersed, the particle size decreases, the specific surface area increases significantly, the crystal structure changes to amorphous structure, the degree of amorphous shape increases, and the content of Fe-OH increases. Applied to the treatment of As (V) in simulated mine water, it was found that the removal of As (V) by this material was mainly based on chemisorption and monolayer adsorption, and the maximum adsorption amount reached 10.477 mg/g. Zeta, FT-IT, and XPS characterization confirmed that the removal of As (V) was a coordination exchange reaction between the material and As (V) to form an inner sphere complex. The removal rate of As (V) decreased from 94.33% to 73.56% when the initial concentration of solution was 10 mg/L, pH value was 3.0, and material dosage was 1 g/L after 5 times of regrowth. This study provides a new way for the application of NdFeB secondary waste, which has low cost, green environmental protection, and wide application prospects.
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(This article belongs to the Special Issue Water, Wastewater and Waste Management for Sustainable Development)
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Open AccessArticle
Determination of Critical Damage Size of Inclined Waterproof Coal Pillar under Asymmetric Load
by
Xingping Lai, Xiaoqian Yuchi, Helong Gu, Pengfei Shan and Wenhua Yang
Water 2024, 16(9), 1233; https://doi.org/10.3390/w16091233 - 25 Apr 2024
Abstract
Quantitative determination of the critical size of an inclined coal pillar in an old goaf water-affected area is of great significance for water damage prevention and safe mining. The critical size of the inclined waterproof coal pillar is derived by using mechanical analyses,
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Quantitative determination of the critical size of an inclined coal pillar in an old goaf water-affected area is of great significance for water damage prevention and safe mining. The critical size of the inclined waterproof coal pillar is derived by using mechanical analyses, numerical calculations, and field engineering practices to determine the stability of the waterproof coal pillar in the old goaf water-affected area of the 1303 working face of Dananhu No. 1 Mine in the Xinjiang region. Firstly, a force model of the inclined waterproof coal pillar was established to reveal the law that the critical size of the coal pillar increases with the increase in coal seam inclination under the action of asymmetric load. Then, numerical simulation was applied to reveal the dynamic evolution processes of plastic deformation–destabilization of the coal pillar under the influence of mining and single-side water pressure, and the critical size of the coal pillar in the study area was determined to be 19.09 m. Finally, measures such as pumping pressure relief and slurry reinforcement were adopted to reduce the deformation rate of the roadway on the side of the coal pillar, which ensured the stability of the waterproof coal pillar and the safe mining of the working face.
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(This article belongs to the Special Issue Theory and Technology of Mine Water Disaster Prevention and Resource Utilization)
Open AccessArticle
Parameter Optimization of Frazil Ice Evolution Model Based on NSGA-II Genetic Algorithm
by
Yunfei Chen, Jijian Lian, Xin Zhao and Deming Yang
Water 2024, 16(9), 1232; https://doi.org/10.3390/w16091232 - 25 Apr 2024
Abstract
This study is based on the research results of frazil ice evolution in recent years and proposes an improved frazil ice evolution mathematical model. Based on the NSGA-II genetic algorithm, seven key parameters were used as optimization design variables, the minimum average difference
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This study is based on the research results of frazil ice evolution in recent years and proposes an improved frazil ice evolution mathematical model. Based on the NSGA-II genetic algorithm, seven key parameters were used as optimization design variables, the minimum average difference between the number of frazil ice, the mean and the standard deviation of particle diameter of the simulation results, and the observed data were used as the optimization objective, the Pareto optimal solution set was optimized, and the importance of each objective function was analyzed and discussed. The results show that compared to previous models, the improved model has better agreement between simulation results and experimental results. The optimal parameters obtained by the optimization model reduces the difference rate of water temperature process by 5.75%, the difference rate of quantity process by 39.13%, the difference rate of mean particle size process by 47.64%, and the difference rate of standard deviation process by 56.84% during the period of intense evolution corresponding to the initial parameter group. The results prove the validity of the optimization model of frazil ice evolution parameters.
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Open AccessArticle
The Influence of Arctic Conditions on the Formation of Algae and Cyanobacteria Diversity and on the Water Quality of Freshwater Habitats on Kotelny Island, Lena Delta Wildlife Reserve, Yakutia
by
Sophia Barinova and Viktor Gabyshev
Water 2024, 16(9), 1231; https://doi.org/10.3390/w16091231 - 25 Apr 2024
Abstract
The significant interest in the islands in the Russian Arctic has been in terms of available oil reserves, which determine the direction of economic development and associated environmental risks for this sector of the Arctic in the near future. Kotelny Island is the
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The significant interest in the islands in the Russian Arctic has been in terms of available oil reserves, which determine the direction of economic development and associated environmental risks for this sector of the Arctic in the near future. Kotelny Island is the largest island of the New Siberian Islands Archipelago included in the protected zone of the Lena Delta Nature Reserve, which is located at 76° N, washed from the west by the Laptev Sea, washed from the east by the East Siberian Sea in a permafrost zone, and characterized by harsh climatic conditions defined by the northeast winds that prevail in vegetative season. January sees temperatures ranging from −32 to −35 °C, and July from +6 to +8 °C, which causes a short growing season. Samples were taken between August 3 and 8, 2018 in 12 freshwater bodies where 210 taxa were revealed. Aquatic communities were dominated by zygnematophycean and diatom algae, grouped in the basins of two rivers and associated with the position on the island’s landscape, which suggests the influence of cold north-east winds, leading to the avoidance of habitats in open and high places, which was revealed by statistical methods and also confirms the high individuality of taxa composition. Bioindication methods showed that water bodies are slightly alkaline, with low ion concentrations, with the presence of sulfides in low-lying habitats, and average saturation with organic matter. The mesotrophic status of the studied water bodies was evaluated through an assessment and the type of nutrition in the communities of algae and cyanobacteria indicates they formed there as true autotrophs, which corresponds to the status of a protected area and can serve as a reference level for monitoring anthropogenic impact.
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Open AccessArticle
Using the Heavy Metal and Biotic Indices to Assess Ecological Quality in the Central Area of the East Sea, South Korea
by
Jian Liang, Chae-Woo Ma and Dae-Sun Son
Water 2024, 16(9), 1230; https://doi.org/10.3390/w16091230 - 25 Apr 2024
Abstract
With the rapid development of the South Korean economy, human activities have extensively affected Korea’s coastal environment. A precise ecological quality assessment remains paramount despite the relatively lower impact of human activities on the East Sea compared to the West and South Seas
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With the rapid development of the South Korean economy, human activities have extensively affected Korea’s coastal environment. A precise ecological quality assessment remains paramount despite the relatively lower impact of human activities on the East Sea compared to the West and South Seas of South Korea. Accurate ecological quality assessments can provide valuable marine environmental protection and management references. In our study, we employed seven indices based on heavy metals and macrobenthos to comprehensively assess ecological quality. Our results indicated the final ecological quality in the central East Sea of South Korea was acceptable at most stations; however, the ecological quality in winter marginally falls short compared to that in spring. The concentration of heavy metals emerges as a significant determinant of the final ecological quality, underscoring the need for subsequent studies to investigate the origins of heavy metals in the central East Sea of South Korea and the influence of anthropogenic activities on heavy metal concentrations. Furthermore, employing a single biotic index proves challenging for accurately assessing ecological quality in the East Sea of South Korea.
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(This article belongs to the Special Issue Marine Ecological Monitoring, Assessment and Protection)
Open AccessArticle
Effects of Bio-Organic Fertilizers Substitution on Gaseous Nitrogen Losses in Rice Fields
by
Zhengdi Han, Huijing Hou, Xianzi Yao, Xiang Qian, Qin Tao and Mingyao Zhou
Water 2024, 16(9), 1229; https://doi.org/10.3390/w16091229 - 25 Apr 2024
Abstract
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Show Figures
Traditional practices for managing irrigation and fertilizer in Chinese rice fields have historically consumed large amounts of water resources and caused serious gaseous nitrogen losses (ammonia volatilization and N2O), resulting in low water and fertilizer use efficiency. While both water-saving irrigation
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Traditional practices for managing irrigation and fertilizer in Chinese rice fields have historically consumed large amounts of water resources and caused serious gaseous nitrogen losses (ammonia volatilization and N2O), resulting in low water and fertilizer use efficiency. While both water-saving irrigation and substituting organic fertilizer for chemical fertilizer can impact ammonia volatilization and N2O emissions, the impact of their combined application on gaseous nitrogen loss in rice fields remains unclear. To achieve this goal, we conducted a two-year experiment using two irrigation methods and three bio-organic fertilizer substitution modes. The experiment investigated the effect of different irrigation and fertilizer management techniques on gaseous nitrogen losses in rice fields. The result indicated that controlled irrigation could reduce the peak value of ammonia volatilization by 36.8~75.9% and ammonia volatilization accumulation by 45.8%. However, it also leads to a 71.4% increase in N2O accumulation emissions, resulting in a 43.0% reduction in gaseous nitrogen losses. Compared to full chemical fertilizers, bio-organic fertilizer substitution could effectively reduce the peak of N2O and ammonia volatilization. Cumulative ammonia volatilization and N2O emissions went down by 22.7~60.0% and 38.6~42.6%, respectively. This then led to a 23.4~52.9% drop in total gaseous nitrogen losses. In contrast, the utilization of controlled irrigation and bio-organic fertilizer substitution did not have a significant impact on rice yield. However, it did reduce the intensity of gaseous nitrogen loss from rice fields by 42.7% and 22.5% to 56.5%, respectively. When taken together, the substitution of bio-organic fertilizer in controlled irrigation can effectively reduce gaseous nitrogen losses while maintaining rice yields. This study has significant practical implications for reducing nitrogen loss from paddy fields, improving water and fertilizer utilization, and achieving sustainable agricultural development.
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