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
Vulnerability Assessment of Groundwater Influenced Ecosystems in the Northeastern United States
Water 2024, 16(10), 1366; https://doi.org/10.3390/w16101366 (registering DOI) - 11 May 2024
Abstract
Groundwater-influenced ecosystems (GIEs) are increasingly vulnerable due to groundwater extraction, land-use practices, and climate change. These ecosystems receive groundwater inflow as a portion of their baseflow or water budget, which can maintain water levels, water temperature, and chemistry necessary to sustain the biodiversity
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Groundwater-influenced ecosystems (GIEs) are increasingly vulnerable due to groundwater extraction, land-use practices, and climate change. These ecosystems receive groundwater inflow as a portion of their baseflow or water budget, which can maintain water levels, water temperature, and chemistry necessary to sustain the biodiversity that they support. In some systems (e.g., springs, seeps, fens), this connection with groundwater is central to the system’s integrity and persistence. Groundwater management decisions for human use often do not consider the ecological effects of those actions on GIEs. This disparity can be attributed, in part, to a lack of information regarding the physical relationships these systems have with the surrounding landscape and climate, which may influence the environmental conditions and associated biodiversity. We estimate the vulnerability of areas predicted to be highly suitable for the presence of GIEs based on watershed (U.S. Geological Survey Hydrologic Unit Code 12 watersheds: 24–100 km2) and pixel (30 m × 30 m pixels) resolution in the Atlantic Highlands and Mixed Wood Plains EPA Level II Ecoregions in the northeastern United States. We represent vulnerability with variables describing adaptive capacity (topographic wetness index, hydric soil, physiographic diversity), exposure (climatic niche), and sensitivity (aquatic barriers, proportion urbanized or agriculture). Vulnerability scores indicate that ~26% of GIEs were within 30 m of areas with moderate vulnerability. Within these GIEs, climate exposure is an important contributor to vulnerability of 40% of the areas, followed by land use (19%, agriculture or urbanized). There are few areas predicted to be suitable for GIEs that are also predicted to be highly vulnerable, and of those, climate exposure is the most important contributor to their vulnerability. Persistence of GIEs in the northeastern United States may be challenged as changes in the amount and timing of precipitation and increasing air temperatures attributed to climate change affect the groundwater that sustains these systems.
Full article
(This article belongs to the Special Issue Aquatic Ecosystems Health Assessment Using Biological and Geospatial Analyses)
Open AccessArticle
Remote Measurement of Tide and Surge Using a Deep Learning System with Surveillance Camera Images
by
Gaetano Sabato, Giovanni Scardino, Alok Kushabaha, Giulia Casagrande, Marco Chirivì, Giorgio Fontolan, Saverio Fracaros, Antonio Luparelli, Sebastian Spadotto and Giovanni Scicchitano
Water 2024, 16(10), 1365; https://doi.org/10.3390/w16101365 (registering DOI) - 11 May 2024
Abstract
The latest progress in deep learning approaches has garnered significant attention across a variety of research fields. These techniques have revolutionized the way marine parameters are measured, enabling automated and remote data collection. This work centers on employing a deep learning model for
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The latest progress in deep learning approaches has garnered significant attention across a variety of research fields. These techniques have revolutionized the way marine parameters are measured, enabling automated and remote data collection. This work centers on employing a deep learning model for the automated evaluation of tide and surge, aiming to deliver accurate results through the analysis of surveillance camera images. A mode of deep learning based on the Inception v3 structure was applied to predict tide and storm surges from surveillance cameras located in two different coastal areas of Italy. This approach is particularly advantageous in situations where traditional tide sensors are inaccessible or distant from the measurement point, especially during extreme events that require accurate surge measurements. The conducted experiments illustrate that the algorithm efficiently measures tide and surge remotely, achieving an accuracy surpassing 90% and maintaining a loss value below 1, evaluated through Categorical Cross-Entropy Loss functions. The findings highlight its potential to bridge the gap in data collection in challenging coastal environments, providing valuable insights for coastal management and hazard assessments. This research contributes to the emerging field of remote sensing and machine learning applications in environmental monitoring, paving the way for enhanced understanding and decision-making in coastal regions.
Full article
(This article belongs to the Section Oceans and Coastal Zones)
Open AccessArticle
Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020
by
Chaoxu Pu, Shuaibo Zhou, Peijun Sun, Yunchuan Luo, Siyi Li and Zhangli Sun
Water 2024, 16(10), 1364; https://doi.org/10.3390/w16101364 (registering DOI) - 11 May 2024
Abstract
The variations in snow cover on the Tibetan Plateau play a pivotal role in comprehending climate change patterns and governing hydrological processes within the region. This study leverages daily snow cover data and the NASA Digital Elevation Model (DEM) from 2003 to 2020
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The variations in snow cover on the Tibetan Plateau play a pivotal role in comprehending climate change patterns and governing hydrological processes within the region. This study leverages daily snow cover data and the NASA Digital Elevation Model (DEM) from 2003 to 2020 to analyze spatiotemporal snow cover days and assess their responsiveness to climatic shifts by integrating meteorological data. The results reveal significant spatial heterogeneity in snow cover across the Plateau, with a slight decreasing trend in annual average snow cover duration. Snow cover is predominantly observed during the spring and winter seasons, constituting approximately 32% of the total snow cover days annually. The onset and cessation of snow cover occur within a range of 120–220 days. Additionally, an increasing trend in snow cover duration below 5000 m altitude was observed, in addition to a decreasing trend above 5000 m altitude. Sub-basin analysis delineates the Tarim River Basin as exhibiting the lengthiest average annual snow cover duration of 83 days, while the Yellow River Basin records the shortest duration of 31 days. The decreasing trend in snow cover duration closely aligns with climate warming trends, characterized by a warming rate of 0.17 ± 0.54 °C per decade, coupled with a concurrent increase in precipitation at a rate of 3.09 ± 3.81 mm per year. Temperature exerts a more pronounced influence on annual snow cover duration variation compared to precipitation, as evidenced by a strong negative correlation (CC = −0.67). This study significantly augments the comprehension of hydrological cycle dynamics on the Tibetan Plateau, furnishing essential insights for informed decision-making in water resource management and ecological conservation efforts.
Full article
Open AccessArticle
Sorption-Based Removal Techniques for Microplastic Contamination of Tap Water
by
Natalya S. Salikova, Almagul R. Kerimkulova, Javier Rodrigo-Ilarri, Kulyash K. Alimova, María-Elena Rodrigo-Clavero and Gulzhanat A. Kapbassova
Water 2024, 16(10), 1363; https://doi.org/10.3390/w16101363 (registering DOI) - 11 May 2024
Abstract
This study investigates the presence of microplastics in tap drinking water and evaluates the efficacy of various sorbents for their removal in the context of Kazakhstan’s water treatment system. Water samples taken in the cities of Kokshetau and Krasny Yar (Akmola region) were
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This study investigates the presence of microplastics in tap drinking water and evaluates the efficacy of various sorbents for their removal in the context of Kazakhstan’s water treatment system. Water samples taken in the cities of Kokshetau and Krasny Yar (Akmola region) were analyzed. Microplastics were detected in all samples, with concentrations ranging from 2.0 × 10−2 to 6.0 × 10−2 particles/dm3, predominantly in fiber form (74.1%). Outdated technologies and non-compliance with treatment regimens contribute to poor water quality, including high turbidity (87% of samples), color deviations (40% of samples), and acidity issues (20% of samples). To address these challenges, the study examined the sorption efficiency of different sorbents, with results indicating high retention rates (82.7–97.8%) for microplastic particles. Notably, aliphatic structures like PE and PP exhibited higher retention than PET. Among the sorbents tested, the synthesized carbon sorption material (CSM) demonstrated the highest efficiency in both microplastic retention and improvement in water quality parameters, making it a promising option for water treatment facilities and household filters.
Full article
(This article belongs to the Topic Microplastics Pollution)
Open AccessArticle
An Efficient Water Quality Prediction and Assessment Method Based on the Improved Deep Belief Network—Long Short-Term Memory Model
by
Zhiyao Zhao, Bing Fan and Yuqin Zhou
Water 2024, 16(10), 1362; https://doi.org/10.3390/w16101362 (registering DOI) - 11 May 2024
Abstract
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The accuracy of water quality prediction and assessment has always been the focus of environmental departments. However, due to the high complexity of water systems, existing methods struggle to capture the future internal dynamic changes in water quality based on current data. In
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The accuracy of water quality prediction and assessment has always been the focus of environmental departments. However, due to the high complexity of water systems, existing methods struggle to capture the future internal dynamic changes in water quality based on current data. In view of this, this paper proposes a data-driven approach to combine an improved deep belief network (DBN) and long short-term memory (LSTM) network model for water quality prediction and assessment, avoiding the complexity of constructing a model of the internal mechanism of water quality. Firstly, using Gaussian Restricted Boltzmann Machines (GRBMs) to construct a DBN, the model has a better ability to extract continuous data features compared to classical DBN. Secondly, the extracted time-series data features are input into the LSTM network to improve predicting accuracy. Finally, due to prediction errors, noise that randomly follows the Gaussian distribution is added to the assessment results based on the predicted values, and the probability of being at the current water quality level in the future is calculated through multiple evolutionary computations to complete the water quality assessment. Numerical experiments have shown that our proposed algorithm has a greater accuracy compared to classical algorithms in challenging scenarios.
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Open AccessReview
The Role of Ferrate (VI) in the Pretreatment of Algal Cells and Algal Organic Matters: A Review
by
Saige Wang, Shuyi Yang, Huan Chen and Qiufeng Lin
Water 2024, 16(10), 1361; https://doi.org/10.3390/w16101361 (registering DOI) - 11 May 2024
Abstract
Algal blooms are caused by excessive levels of nitrogen, phosphorus, and other plant nutrients in water. Algae and algal organic matter (AOM) pose a great threat to the quality of drinking water. This manuscript offers a systematic review of algal removal by ferrate
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Algal blooms are caused by excessive levels of nitrogen, phosphorus, and other plant nutrients in water. Algae and algal organic matter (AOM) pose a great threat to the quality of drinking water. This manuscript offers a systematic review of algal removal by ferrate (Fe(VI)) oxidation, including the conditions for the removal of different algae by Fe(VI) and the factors affecting the removal efficiency. On this basis, the oxidation and coagulation mechanisms of algae removal by Fe(VI) are discussed. Then, the review introduces the process combining Fe(VI) pre-oxidation with aluminum sulfate action. The addition of aluminum sulfate can further enhance the coagulation effect and reduce the formation of disinfection byproducts (DBPs) in the subsequent chlorination process by effectively removing AOM, which is recognized as a precursor of DBPs. In addition, recent studies on the combined application of Fe(VI) and Fe(II) are also reviewed. In a reasonable dose range, the synergistic effect of Fe(VI) and Fe(II) can significantly improve the removal of algae and algal toxins. Finally, this review provides a comprehensive evaluation of the applicability of Fe(VI) in removing algal material, offers guidance for the harmless treatment of algae with Fe(VI), and identifies future research questions.
Full article
(This article belongs to the Special Issue Removal of Trace Organic Pollutants in Water Using Advanced Oxidation Technology)
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Open AccessArticle
Polystyrene Plastic Particles Result in Adverse Outcomes for Hyalella azteca When Exposed at Elevated Temperatures
by
Felix Biefel, Susanne M. Brander, Richard E. Connon and Juergen Geist
Water 2024, 16(10), 1360; https://doi.org/10.3390/w16101360 (registering DOI) - 10 May 2024
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Micro- and nano-plastics are pervasive pollutants in global ecosystems, yet their interactions with aquatic wildlife and abiotic factors are poorly understood. These particles are recognized to cause subtle detrimental effects, underscoring the necessity for sensitive endpoints in ecotoxicological exposure studies. We investigated the
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Micro- and nano-plastics are pervasive pollutants in global ecosystems, yet their interactions with aquatic wildlife and abiotic factors are poorly understood. These particles are recognized to cause subtle detrimental effects, underscoring the necessity for sensitive endpoints in ecotoxicological exposure studies. We investigated the effects of particle uptake, size, and temperature on Hyalella azteca. Organisms were exposed to blue fluorescent polystyrene beads (500 nm and 1000 nm in diameter) at 0.43 mg/L for 96 h at temperatures mirroring climate predictions (21 °C, 24 °C, 27 °C). Besides survival and growth, particle uptake, visualized via confocal microscopy, and swimming behavior were analyzed. Mortality rates increased at 27 °C, and particle presence and temperature affected organism growth. Particle treatments influenced various behaviors (thigmotaxis, cruising, movement, acceleration, meander, zone alternation, and turn angle), with hypoactivity observed with 1000 nm particles and hypo- as well as hyper-activity responses with 500 nm particles. Particle uptake quantities were variable and increased with temperature in 500 nm treatments, but no migration beyond the gut was observed. Particle size correlated with uptake, and relationships with behavior were evident. Elevated temperatures exacerbated particle effects, highlighting the urgency of addressing plastic pollution in light of climate change for aquatic organism welfare and ecosystem health.
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Open AccessArticle
Application of Oversampling Techniques for Enhanced Transverse Dispersion Coefficient Estimation Performance Using Machine Learning Regression
by
Sunmi Lee and Inhwan Park
Water 2024, 16(10), 1359; https://doi.org/10.3390/w16101359 (registering DOI) - 10 May 2024
Abstract
The advection–dispersion equation has been widely used to analyze the intermediate field mixing of pollutants in natural streams. The dispersion coefficient, manipulating the dispersion term of the advection–dispersion equation, is a crucial parameter in predicting the transport distance and contaminated area in the
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The advection–dispersion equation has been widely used to analyze the intermediate field mixing of pollutants in natural streams. The dispersion coefficient, manipulating the dispersion term of the advection–dispersion equation, is a crucial parameter in predicting the transport distance and contaminated area in the water body. In this study, the transverse dispersion coefficient was estimated using machine learning regression methods applied to oversampled datasets. Previous research datasets used for this estimation were biased toward width-to-depth ratio (W/H) values ≤ 50, potentially leading to inaccuracies in estimating the transverse dispersion coefficient for datasets with W/H > 50. To address this issue, four oversampling techniques were employed to augment the dataset with W/H > 50, thereby mitigating the dataset’s imbalance. The estimation results obtained from data resampling with nonlinear regression method demonstrated improved prediction accuracy compared to the pre-oversampling results. Notably, the combination of adaptive synthetic sampling (ADASYN) and eXtreme Gradient Boosting regression (XGBoost) exhibited improved accuracy compared to other combinations of oversampling techniques and nonlinear regression methods. Through the combined ADASYN–XGBoost approach, it is possible to enhance the transverse dispersion coefficient estimation performance using only two variables, W/H and bed friction effects (U/U*), without adding channel sinuosity; this represents the effects of secondary currents.
Full article
(This article belongs to the Special Issue Contaminant Transport Modeling in Aquatic Environments)
Open AccessArticle
Connecting Water Quality and Ecosystem Services for Valuation and Assessment of a Groundwater Reserve Area in South-East Mexico
by
Myrna L. López-Monzalvo, Eduardo Batllori-Sampedro, Jairo A. Ayala-Godoy, Eugenio Guerrero-Ruiz and Laura M. Hernández-Terrones
Water 2024, 16(10), 1358; https://doi.org/10.3390/w16101358 (registering DOI) - 10 May 2024
Abstract
Even though the role of ecosystem services is known, the identification and assessment of water-related services is usually absent or often less represented as an ecosystem service. Progress in water quality indicator definition and compliance with regulations has been made; however, the relationship
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Even though the role of ecosystem services is known, the identification and assessment of water-related services is usually absent or often less represented as an ecosystem service. Progress in water quality indicator definition and compliance with regulations has been made; however, the relationship between water quality degradation and benefits to individuals and ecosystems remains little recognized. Here, we present an assessment of water quality and identification of ecosystem services in south-east Mexico. This study was performed within the geohydrological reserve zone of the Ring of Sinkholes, Yucatán Peninsula. Thirteen ecosystem services provided by the aquifer were identified. Water quality was evaluated in sinkholes based on national and international norms, considering different sinkhole uses. Results show a dynamic system, without saltwater intrusion and good to excellent water quality. The research demonstrates the relationship between ecosystem services and water quality, showing pressure in services related to uses for aquatic life protection and to a lesser extent those related to consumption. Current productive activities showed no pressure at this time. Principal Component Analysis (PCA) and Analysis of Variance (ANOVA) exhibited a significant difference in parameters and campaigns, but not between sinkholes. A long-lasting monitoring program for water quality is necessary to accurately evaluate the status of ecosystem services provided by the aquifer. Moreover, it is necessary to assess aquifers as ecosystems with economic, ecologic and socio-cultural importance. Effective water governance requires a balance of interests between all parties, within a legal and institutional framework.
Full article
(This article belongs to the Section Water Quality and Contamination)
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Open AccessArticle
Testing 16S Primers for Proper Identification of Cyanobacterial Communities in Small Water Bodies
by
Łukasz Łach, Nataliia Khomutovska, Jan Kwiatowski and Iwona Jasser
Water 2024, 16(10), 1357; https://doi.org/10.3390/w16101357 (registering DOI) - 10 May 2024
Abstract
The majority of investigations on microbial communities from various environments are presently built on culture-independent methods. Many studies point to the pivotal, selective role of primers targeting hypervariable regions of 16S rRNA in the metabarcoding of bacteria, including cyanobacterial communities. The selectivity of
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The majority of investigations on microbial communities from various environments are presently built on culture-independent methods. Many studies point to the pivotal, selective role of primers targeting hypervariable regions of 16S rRNA in the metabarcoding of bacteria, including cyanobacterial communities. The selectivity of primers designed to amplify targeted regions of the 16S rRNA gene, which has been highlighted by many authors, limited effective amplification. Moreover, the type and specificity of the studied material can also negatively influence the results of 16S metabarcoding. Most of the studies of cyanobacterial communities have been performed for planktonic microbial communities that are often represented by common, well-studied species. In this study, we present the results of 16S metabarcoding analysis using three primer pairs—two already well-known and a third designed in this study—that amplify divergent regions of the 16S rRNA gene (V3–V4, V4–V6, V6) for benthic, microbial mat-forming cyanobacteria communities. Such communities can be a source of toxigenic cyanobacterial taxa and should be monitored with adequate primers. The comparison of three primer pairs suggested that those designed within the present study describe the structure and composition of highly heterogeneous cyanobacterial mats’ communities better than the others.
Full article
(This article belongs to the Special Issue Biodiversity of Freshwater Ecosystems: Monitoring and Conservation)
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Open AccessArticle
Classification of Pollution Sources and Their Contributions to Surface Water Quality Using APCS-MLR and PMF Model in a Drinking Water Source Area in Southeastern China
by
Ai Wang, Jiangyu Wang, Benjie Luan, Siru Wang, Dawen Yang and Zipeng Wei
Water 2024, 16(10), 1356; https://doi.org/10.3390/w16101356 - 10 May 2024
Abstract
Identifying the potential pollution sources of surface water pollutants is essential for the management and protection of regional water environments in drinking water source areas. In this study, absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models were applied
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Identifying the potential pollution sources of surface water pollutants is essential for the management and protection of regional water environments in drinking water source areas. In this study, absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models were applied to assess water quality and identify the potential pollution sources affecting the surface water quality of Xin’an River Basin. For this purpose, a 10-year (2011–2020) dataset of eight water quality indicators (including pH, EC, DO, COD, NH3-N, TN, TP, and FC) covering eight monitoring stations and 7248 monthly observations was used. The results indicated that Pukou section had the worst water quality among the eight monitoring stations, and TN was the most serious water quality index. Both the APCS-MLR and PMF models identified agricultural nonpoint source pollution, urban nonpoint source pollution and rural domestic pollution, and meteorological factors. The sum of these three sources was very close, accounting for 60% and 58%, respectively. The APCS-MLR results demonstrated that for EC, COD, and NH3-N, the major pollution sources were urban nonpoint sources and rural domestic pollution. The major contamination source of TN was agricultural nonpoint source pollution (30.4%). Meanwhile, the major pollution sources of pH, DO, TP, and FC were unidentified factors. The PMF model identified five potential sources, and pH and DO were affected by meteorological factors. NH3-N and TP were influenced mainly by agricultural nonpoint source pollution. Atmospheric deposition was the major pollution source (87.9%) of TN. FC was mostly derived from livestock and poultry breeding (88.3%). EC and COD were mostly affected by urban nonpoint sources and rural domestic pollution. Therefore, receptor models can help managers identify the major sources of pollution in watersheds, but the major factors affecting different pollutants need to be supplemented by other methods.
Full article
(This article belongs to the Section Urban Water Management)
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Open AccessArticle
The Elimination of Levofloxacin from High-Salinity Wastewater via the Electrochlorination Process
by
Mingfei Wei, Jingyu Li, Bingqing Jing, Xuankun Li and Guanghui Li
Water 2024, 16(10), 1355; https://doi.org/10.3390/w16101355 - 10 May 2024
Abstract
The electrochlorination (E-Cl) process has attracted much attention as it is a highly efficient method for treating organic compounds in hypersaline wastewater. In this study, the E-Cl process was utilized for the removal of antibiotics. The optimal experimental conditions were determined to be
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The electrochlorination (E-Cl) process has attracted much attention as it is a highly efficient method for treating organic compounds in hypersaline wastewater. In this study, the E-Cl process was utilized for the removal of antibiotics. The optimal experimental conditions were determined to be a NaCl concentration of 100 mM, a current density of 1.5 mA/cm2, a pH of 7.0, and a plate spacing of 1 cm, with a levofloxacin (LEV) degradation efficiency reaching as high as 99% using this setup. The effects of the presence of other ions and humic acid on the E-Cl process were investigated, and it was found that the degradation of LEV was not significantly affected by the presence of coexisting substances. In addition, free chlorine was identified as the primary active species for the degradation of LEV by means of a quenching experiment. It was demonstrated by 3D EEM and TOC that LEV was not completely mineralized and that intermediate products may be present. In order to reveal the degradation pathways of LEV, its degradation products were also analyzed via LC-MS, and some possible pathways of LEV degradation in this system were proposed. The successful degradation of LEV demonstrated that the E-Cl process is an efficient and promising technique for the treatment of organic pollutants in high-salinity wastewater.
Full article
(This article belongs to the Topic Advanced Processes and Technologies for Wastewater: Collection, Treatment, and Resource)
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Open AccessReview
Excess of Nutrients in Prefabricated or Compact Wastewater Treatment Plants: Review, Solution Alternative, and Modeling for Verification
by
Marco Antonio Díaz, David Blanco, Rosa Chandia-Jaure, Danny Lobos Calquin, Alejandra Decinti, Pedro Naranjo and María Belén Almendro-Candel
Water 2024, 16(10), 1354; https://doi.org/10.3390/w16101354 - 10 May 2024
Abstract
Chile has numerous areas that lack sewage collection, including in the capital city. Sanitation in these cases is managed through individual solutions like septic tanks or small wastewater treatment plants (WWTPs) that use biological treatment, usually activated sludge with extended aeration. In general,
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Chile has numerous areas that lack sewage collection, including in the capital city. Sanitation in these cases is managed through individual solutions like septic tanks or small wastewater treatment plants (WWTPs) that use biological treatment, usually activated sludge with extended aeration. In general, the design of these systems adheres to the quality standards mandated by regulations for discharge, infiltration, or irrigation. In this scenario, traditional methods like increasing dissolved oxygen (DO) or hydraulic retention time (HRT) were unable to effectively reduce excessive nutrients. Therefore, literature related to nutrient excess and denitrification systems is consulted and reviewed to compile different solutions suitable for the presented issue. Potential solutions were modeled and verified using the free simulation software WRc STOAT. The software accurately predicted the unsatisfactory results of the current setup and provided parameters for the proposed modifications. Experience, precise user definition, influential characteristics, and modeling are essential in the design of WWTPs.
Full article
(This article belongs to the Section Wastewater Treatment and Reuse)
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Open AccessArticle
Projected Changes in Extreme Precipitation Patterns across Algerian Sub-Regions
by
Yasmine Hamitouche, Ayoub Zeroual, Mohamed Meddi, Ali A. Assani, Ramdane Alkama, Zekâi Şen and Xinhua Zhang
Water 2024, 16(10), 1353; https://doi.org/10.3390/w16101353 - 10 May 2024
Abstract
Extreme precipitation events play a crucial role in shaping the vulnerability of regions like Algeria to the impacts of climate change. To delve deeper into this critical aspect, this study investigates the changing patterns of extreme precipitation across five sub-regions of Algeria using
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Extreme precipitation events play a crucial role in shaping the vulnerability of regions like Algeria to the impacts of climate change. To delve deeper into this critical aspect, this study investigates the changing patterns of extreme precipitation across five sub-regions of Algeria using data from 33 model simulations provided by the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP-CMIP6). Our analysis reveals a projected decline in annual precipitation for four of these regions, contrasting with an expected increase in desert areas where annual precipitation levels remain low, typically not exceeding 120 mm. Furthermore, key precipitation indices such as maximum 1-day precipitation (Rx1day) and extremely wet-day precipitation (R99p) consistently show upward trends across all zones, under both SSP245 and SSP585 scenarios. However, the number of heavy precipitation days (R20mm) demonstrates varied trends among zones, exhibiting stable fluctuations. These findings provide valuable foresight into future precipitation patterns, offering essential insights for policymakers and stakeholders. By anticipating these changes, adaptive strategies can be devised to mitigate potential climate change impacts on crucial sectors such as agriculture, flooding, water resources, and drought.
Full article
(This article belongs to the Special Issue Hydroclimatic Modeling and Monitoring under Climate Change)
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Open AccessArticle
Unveiling the Dynamics of Cryptosporidium in Urban Surface Water: A Quantitative Microbial Risk Assessment and Insights into Climatic and Seasonal Influences
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Hazrat Bilal, Xiaowen Li, Muhammad Shahid Iqbal, Roberto Xavier Supe Tulcan and Madan Thapa Chhetri
Water 2024, 16(10), 1352; https://doi.org/10.3390/w16101352 - 10 May 2024
Abstract
In response to global urbanization and economic development, urban surface water pollution has become a universal challenge and particularly affects densely populated megacities, and Dhaka is no exception. The discharge of 98% of untreated domestic sewage and massive volumes of industrial wastewater from
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In response to global urbanization and economic development, urban surface water pollution has become a universal challenge and particularly affects densely populated megacities, and Dhaka is no exception. The discharge of 98% of untreated domestic sewage and massive volumes of industrial wastewater from over 7000 industries escalate surface water crises. This study investigates microbial and fecal contamination with particular emphasis on Cryptosporidium in surface water, known for causing waterborne diseases, such as cryptosporidiosis. Findings reveal high Cryptosporidium oocyst concentrations and fecal contamination in various water bodies in Dhaka City. Among the investigated water bodies, the Buriganga River exhibits the highest Cryptosporidium oocyst concentration (46%), while the Balu River, Turag River, Shitalakkhya River, Dhanmondi Lake, Gulshan Lake, Banani Lake, Ramna Lake, and Crescent Lake also present high levels of oocyst concentrations ranging from 21–40%. This study also calculated infection risks and found that the infection risk of swimming is highest during the wet season and is (3.9 ± 2.2 (95% CI: 3.0–5.0)) × 10−2 per swimming event, whereas it is approximately (2.4 ± 1.9 (95% CI: 1.6–3.3)) × 10−2 during the dry season. Annual diving risks are approximately (1.2 ± 0.6 (95% CI: 0.9–1.4)) × 10−2, indicating considerably high risks. Most of the sampling sites generally show significantly higher risks than other study areas like the Mymensingh and Kushtia Districts. In light of these results, we strongly recommend immediate measures to address water quality issues and mitigate the risks associated with Cryptosporidium contamination in Dhaka’s surface water.
Full article
(This article belongs to the Special Issue Advance in Freshwater Conservation and Restoration in a Large River Basin)
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Open AccessArticle
Evaluating the Influence of Reverse Osmosis on Lakes Using Water Quality Indices: A Case Study in Saudi Arabia
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Mohammed T. Aljassim, Abdulaziz A. AlMulla, Mahmoud M. Berekaa and Abdulmalik S. Alsaif
Water 2024, 16(10), 1351; https://doi.org/10.3390/w16101351 - 10 May 2024
Abstract
A drastic level of resource degradation was revealed through a preliminary evaluation (completed in 2016) of water quality in a recreational lake in the second industrial city in Dammam, Saudi Arabia. The primary signs were a foul smell, algal bloom, high turbidity, and
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A drastic level of resource degradation was revealed through a preliminary evaluation (completed in 2016) of water quality in a recreational lake in the second industrial city in Dammam, Saudi Arabia. The primary signs were a foul smell, algal bloom, high turbidity, and lack of aquatic life. This study aims to evaluate the influence of reverse osmosis (RO) on lake water quality. The recreational lake consists of two connected lakes (Lakes 1 and 2), which receive treated effluent from an industrial wastewater treatment plant. Composite samples were collected from the lakes to analyze their physiochemical parameters. Descriptive analyses were performed, and two water quality indices were developed to observe the variations in water quality conditions between the two periods (2016 and 2021). The results indicated that the water parameters of total dissolved solids (TDS), sulphate (SO42−), biological oxygen demand (BOD), and dissolved oxygen (DO) in 2016 (3356, 4100, 516, and 1.32 mg/L, respectively) were significantly improved in 2021 (2502, 1.28, 9.39, and 7.79 mg/L, respectively). The results of the water quality index (WQI) and comprehensive pollution index (CPI) indicated that the water quality in Lake 1 was significantly enhanced in 2021 (WQI = 85, CPI = 1) in comparison with assessment data from 2016 (WQI = 962, CPI = 8). However, the data from Lake 2 revealed higher pollution levels in 2021 (WQI = 1722, CPI = 18) than those recorded in 2016 (WQI = 1508, CPI = 13). As indicated by the absence of bad smells, algal blooms, and restoration of aquatic life, the RO intervention successfully improved the water quality in Lake 1. The WQI and CPI were helpful tools for evaluating lake water quality.
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(This article belongs to the Special Issue Sustainable Water Treatment and Contaminants Control: Technologies and Strategies)
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Open AccessArticle
Comparative Study on the Determination of Chlorophyll-a in Lake Phytoplankton by a YSI Multi-Parameter Water Quality Meter and Laboratory Spectrophotometric Method
by
Jie Wang, Lizeng Duan, Donglin Li, Yuwei Zhang, Zheng Yuan, Huayu Li and Hucai Zhang
Water 2024, 16(10), 1350; https://doi.org/10.3390/w16101350 - 9 May 2024
Abstract
Algal blooms caused by eutrophication are a major global problem, and the monitoring and prediction of algal densities in lakes are important indicators of eutrophication management. However, the reliability of the commonly used chlorophyll-a (Chl-a) to characterize phytoplankton density in
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Algal blooms caused by eutrophication are a major global problem, and the monitoring and prediction of algal densities in lakes are important indicators of eutrophication management. However, the reliability of the commonly used chlorophyll-a (Chl-a) to characterize phytoplankton density in lake environments needs to be further investigated. In this paper, we sampled and analyzed 365 samples from nine plateau lakes in Yunnan Province during the dry and rainy seasons. The Chl-a data measured by the laboratory spectrophotometric method and the portable YSI multi-parameter water quality meter (YSI) directly used in the field were compared, and regression analysis and correlation analysis with phytoplankton density were performed. Most of the Chl-a values measured by the laboratory instrument were greater than those measured by the YSI, and the correlation between the two methods was weak (0.492, p < 0.001). The correlation between Chl-a and phytoplankton density measured by the YSI reached 0.67 (p < 0.001) in the dry season, while the laboratory methods used to measure Chl-a to characterize phytoplankton density may have led to an overestimation of phytoplankton density due to nonspecific sources of Chl-a. However, both methods are relatively inaccurate for characterizing phytoplankton density. For different trophic states of lakes, nutrient concentration changes affect the Chl-a concentration of phytoplankton. During different seasons, changes in the fluorescence intensity of phytoplankton in response to environmental conditions prevent the YSI results from reflecting the authentic phytoplankton density. Furthermore, high species diversity can lead to inconsistent changes in Chl-a and phytoplankton because the content of Chl-a in individual cells of different phytoplankton is different. The relationship between Chl-a and phytoplankton density was species specific. Therefore, when applying Chl-a to characterize phytoplankton density in lakes, it is necessary to consider environmental conditions, phytoplankton community structure and other practical conditions. In addition, laboratory analytical methods and instrumental techniques and instruments need to be improved.
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Open AccessArticle
Hazard Assessment of Debris Flow: A Case Study of the Huiyazi Debris Flow
by
Yuntao Guo, Zhen Feng, Lichao Wang, Yifan Tian and Liang Chen
Water 2024, 16(10), 1349; https://doi.org/10.3390/w16101349 - 9 May 2024
Abstract
The Bailong River Basin is situated at the northeastern edge of the Qinghai–Tibet Plateau and the western transition zone of the Loess Plateau, characterized by steep terrain and heavy rainfall. This area experiences frequent occurrences of debris flows, posing serious threats to towns
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The Bailong River Basin is situated at the northeastern edge of the Qinghai–Tibet Plateau and the western transition zone of the Loess Plateau, characterized by steep terrain and heavy rainfall. This area experiences frequent occurrences of debris flows, posing serious threats to towns and construction projects. Focusing on the Huaiyazigou debris flow in the Bailong River Basin, numerical simulations of debris flow processes were conducted using Digital Surface Model (DSM) data with a resolution of 5 m × 5 m for various recurrence periods. The simulation results indicate that the debris flow develops rapidly along the gully after formation, decelerating and beginning to deposit upon reaching the cement plant area near the mouth of the gully, eventually merging into the Bailong River. The primary destructive modes of debris flow disasters encompass impact and burial. When encountering buildings, their flow characteristics manifest as deposition and diversion. A debris flow hazard classification model, based on intensity and recurrence periods, was established according to Swiss and Austrian standards, dividing the hazard into low, medium, and high levels. This method generated a debris flow hazard zone map, offering guidance for risk prevention and monitoring. This research demonstrates that using high-precision Digital Surface Models (DSM) can accurately represent the digital information of debris flow gully terrains and buildings. During the simulation process, it realistically reflects the characteristics of the debris flow movement, allowing for the more precise delineation of hazard zones.
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Open AccessArticle
Vison Transformer-Based Automatic Crack Detection on Dam Surface
by
Jian Zhou, Guochuan Zhao and Yonglong Li
Water 2024, 16(10), 1348; https://doi.org/10.3390/w16101348 - 9 May 2024
Abstract
Dam is an essential structure in hydraulic engineering, and its surface cracks pose significant threats to its integrity, impermeability, and durability. Automated crack detection methods based on computer vision offer substantial advantages over manual approaches with regard to efficiency, objectivity and precision. However,
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Dam is an essential structure in hydraulic engineering, and its surface cracks pose significant threats to its integrity, impermeability, and durability. Automated crack detection methods based on computer vision offer substantial advantages over manual approaches with regard to efficiency, objectivity and precision. However, current methods face challenges such as misidentification, discontinuity, and loss of details when analyzing real-world dam crack images. These images often exhibit characteristics such as low contrast, complex backgrounds, and diverse crack morphologies. To address the above challenges, this paper presents a pure Vision Transformer (ViT)-based dam crack segmentation network (DCST-net). The DCST-net utilizes an improved Swin Transformer (SwinT) block as the fundamental block for enhancing the long-range dependencies within a SegNet-like encoder–decoder structure. Additionally, we employ a weighted attention block to facilitate side fusion between the symmetric pair of encoder and decoder in each stage to sharpen the edge of crack. To demonstrate the superior performance of our proposed method, six semantic segmentation models have been trained and tested on both a self-built dam crack dataset and two publicly available datasets. Comparison results indicate that our proposed model outperforms the mainstream methods in terms of visualization and most evaluation metrics, highlighting its potential for practical application in dam safety inspection and maintenance.
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(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)
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Open AccessArticle
Treatment of Dairy Farm Runoff in Vegetated Bioretention Systems Amended with Biochar
by
Md Yeasir A. Rahman, Nicholas Richardson, Mahmood H. Nachabe and Sarina J. Ergas
Water 2024, 16(10), 1347; https://doi.org/10.3390/w16101347 - 9 May 2024
Abstract
Nitrogen and fecal indicator bacteria (FIB) in runoff from concentrated animal feeding operations (CAFOs) can impair surface and groundwater quality. Bioretention systems are low impact nature-based technologies that can effectively treat CAFO runoff if modified with an internal water storage zone (IWSZ) or
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Nitrogen and fecal indicator bacteria (FIB) in runoff from concentrated animal feeding operations (CAFOs) can impair surface and groundwater quality. Bioretention systems are low impact nature-based technologies that can effectively treat CAFO runoff if modified with an internal water storage zone (IWSZ) or amended with biochar. In this study, the performances of four pilot-scale modified bioretention systems were compared to assess the impacts of (1) amending bioretention media with biochar and (2) planting the systems with Muhlenbergia. The system with both plants and biochar amendment had the best performance, with an average of 5.58 log reduction in E. coli and 98% removal of total nitrogen (TN). All systems treated the first pore volume well as new runoff flushed the treated water from the IWSZ. Biochar improved TN and FIB removal due to its high capacity to adsorb or retain ammonium (NH4+), dissolved organic nitrogen, dissolved organic carbon, and E. coli. Planting improved performance, possibly by increasing rhizosphere microbial activity.
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(This article belongs to the Section Wastewater Treatment and Reuse)
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