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The effluent discharge of domestic wastewater and industrial effluents into watercourses has necessitated the development of monitoring systems with the purpose of characterizing levels of pollution. Substantial levels of pollution in emerging nations have been exacerbated by rapidly growing populations and inefficient enforcement of sustainable management initiatives. The Water Quality Index (WQI) can help fill the gap between water quality reporting and monitoring by offering a simple and effective way to assimilate and communicate results from a large amount of data. WQIs are the kind of communication tools that can facilitate knowledge sharing between scholars and the general public. The degradation of natural water resources, such as lakes, streams, and estuaries, is the most significant problem facing humanity. Water that is not clean has far-reaching effects on all aspects of life. Water resource management is therefore essential if it is to maximize water quality. If data are analyzed and water quality can be predicted in advance, water pollution may be efficiently addressed. Although this subject has been the subject of numerous earlier studies, additional research is still required to fully understand the effectiveness, reliability, accuracy, and application of the present approaches to water quality management. In this research work, a comprehensive review has been conducted on water quality assessment using artificial techniques. It reviews 75 research works on water quality analysis. The data collected in each research work have been analyzed. Moreover, the type of water resource surface water, groundwater, drinking water as well has been analyzed. The parameters considered in each work are “Dissolved Oxygen (DO), Potential of Hydrogen (pH), Biochemical Oxygen Demand (BOD), Total Dissolved Solids (TDS), and Chemical Oxygen Demand (COD), chloride, hardness, alkalinity, nitrate–nitrite”. In addition, the Artificial Intelligence technique utilized for water quality analysis is also assessed. Finally, the research gaps identified in water quality detection are exhibited. [ABSTRACT FROM AUTHOR]

This study proposes a comprehensive water quality assessment method for nine major plateau lakes in Yunnan Province, based on an improved CRITIC method combined with a multidimensional connectivity cloud model. Key water quality monitoring indicators (NH3‐N, COD, TP, TN, permanganate index, DO, pH) were selected, and the weights were determined using the improved CRITIC method, highlighting the impact of NH3‐N, COD, and TP. These weights were then integrated with a multidimensional connectivity cloud model to classify lake water quality levels. The results indicated the following water quality grades for the lakes: Dianchi (III), Fuxian Lake (I), Erhai (II), Xingyun Lake (I), Qilu Lake (II), Yilong Lake (II), Lugu Lake (I), Yangzonghai (II), and Chenghai (II). Compared to five conventional methods, the proposed approach better addresses the issues of fuzziness, randomness, and discreteness in water quality indicators, avoiding the boundary selection problems inherent in traditional methods. By combining the improved CRITIC method with a multidimensional connectivity cloud model, the study achieves more precise and reliable evaluations through objective weighting and comprehensive consideration of multiple indicators. This method offers a more accurate reflection of lake water quality conditions and provides a scientific basis for water quality management and decision‐making, demonstrating significant potential for application in the field of environmental science. [ABSTRACT FROM AUTHOR]

This study evaluates the quality of drinking groundwater in the city of Moussoro using geochemical, bacterial, and multivariate statistical methods. Access to safe drinking water is a major concern for the population in these regions, as the distribution of this resource is unequal across continents and is often affected by human activities, making it vulnerable. The analysis revealed two categories of water: potable and non-potable. The presence of pollutant indicators, such as Escherichia coli (E. coli) and total coliforms, was observed in most samples. This presence indicates water contamination and requiring monitoring and treatment before it is consumed by the population. Overall, most of the physicochemical parameters comply with the World Health Organisation guidelines for drinking water. However, the concentration of ammonium (NH4+) was high. Pollution elements such as ammonium (NH4+) and E. coli, indicate that human activities also contribute to water mineralisation. The study could ensure sustainable access to safe drinking water, thereby enhancing the overall quality of life in Moussoro. [ABSTRACT FROM AUTHOR]

The freshwater ecological characteristics in terms of the daily inventory of thermal stratification, spatial variation of O2 distribution, and the mobility of potentially toxic elements (PTEs) at the water sediment interface (WSI) are prudent freshwater assessment indices for water quality management protocol. The study conducted daily observations within a monsoon-influenced region, utilizing high-resolution techniques such as HR Peeper, Yellow Spring Instrument (YSI), and ZrO-Chelex diffusive gradient technology (DGT) to analyze PTEs, specifically phosphorus (P) and iron (Fe),within the water–sediment interface (WSI) under different temperatures and oxygen conditions. The 66-day field study showed that high thermal structure contributed significantly to production Fe ions and P from sediment under reductive dissolution of FeOOH. The study also revealed that P and Fe exhibited comparable spatial distribution patterns at the WSI, indicating a linked relationship between these PTEs. This correlation was reinforced by high Pearson correlation coefficients ranging from 0.7 to 0.9 (bilateral, p < 0.05) indicating that the concentrations of labile P were predominantly influenced by the release of phosphorus bound to iron. The fluxes of the PTEs were positive with a range of Fe, 3.3–81.5 mg/m2 day and P, 0.03–0.5 mg/m2 day showing the sediments liberated the PTEs into the benthic water. Again, high positive fluxes (Fe≈60 mg/m2 day, P≈0.5 mg/m2 day) for PTEs were obtained when stratification was high (anoxic conditions) and low (Fe≈5 mg/m2 day, P≈0.08 mg/m2 day) when stratification did not exist. This depicts that Fe/P dynamics were hinged mainly on hypoxic conditions in the benthic water under the reductive dissolution of FeOOH. The findings showed that organic materials (both solid and dissolved) correlated (> 0.7) significantly with (positive high values) Fe. This indicates that their interaction contributed to the reservoir water deterioration. However, Ca2⁺ and Mg2⁺ had little impact on the liberation of Fe-DOC-P from sediments due to their inability to compete with Fe for binding to DOC and P, as shown by their low correlation values. The research provides in-depth insights into the dynamics of PTEs on a daily timescale and offers valuable information for water management practices in inland reservoirs, particularly concerning the cycling of phosphorus (P) and its effects on ecosystem health. [ABSTRACT FROM AUTHOR]

Adenosine triphosphate (ATP) assays have a faster turnaround time and higher sensitivity than traditional cultivation methods for microbial monitoring. Challenges implementing ATP testing include incompatibility with chlorine quenching agents and hold time sensitivity, which are not well-studied. Chlorinated distribution system samples were collected from two Canadian utilities, Metro Vancouver (n = 40 samples) and Halifax Water (n = 283). No significant correlations were observed between heterotrophic cell count (HPC) and cellular ATP, suggesting these do not correlate well in waters with low biological activity (median HPC < 2 CFU/mL). However, interpretation of HPC and cATP results (based on the HPC guideline of 100 CFU/mL and cATP of 10 pg/mL) yielded the same conclusion for 95% of samples, suggesting a potential decision-making framework to replace HPC with cATP. Moreover, cATP correlates better with free chlorine (p < 0.04) compared with HPC for one of the studied systems. Importantly, adding chlorine quench (10% sodium thiosulfate) did not produce significantly different cATP results, nor did analyzing at various hold times of 4-, 6-, and 24-h. This study supports the integration of ATP testing into existing sampling procedures for water utilities, as a sensitive, fast, and reliable monitoring method. [ABSTRACT FROM AUTHOR]

Changes driven by both unanticipated human activities and management actions are creating wicked management landscapes in freshwater and marine ecosystems that require new approaches to support decision-making. By linking a predictive model of nutrient-and temperature-driven bottom hypoxia with observed commercial fishery harvest data from Lake Erie (United States-Canada) over the past century (1928-2022) and climate projections (2030-2099), we show how simple, yet robust models and routine monitoring data can be used to identify tradeoffs associated with nutrient management and guide decision-making in even the largest of aquatic ecosystems now and in the future. Our approach enabled us to assess planned nutrient load reduction targets designed to mitigate nutrient-driven hypoxia and show why they appear overly restrictive based on current fishery needs, indicating tradeoffs between water quality and fisheries management goals. At the same time, our temperature results show that projected climate change impacts on hypoxic extent will require more stringent nutrient regulations in the future. Beyond providing a rare example of bottom hypoxia driving changes in fishery harvests at an ecosystem scale, our study illustrates the need for adaptive ecosystem-based management, which can be informed by simple predictive models that can be readily applied over long time periods, account for tradeoffs across multiple management sectors (e.g., water quality, fisheries), and address ecosystem nonstationarity (e.g., climate change impacts on management targets). Such approaches will be critical for maintaining valued ecosystem services in the many aquatic systems worldwide that are vulnerable to multiple drivers of environmental change. [ABSTRACT FROM AUTHOR]

Background The Mettler-Toledo 7000RMS analyzer is a bio-fluorescent particle counter (BFPC) used to monitor real-time bioburden results from purified water (PW). Objective Validation of the analyzer using 13 microorganisms and a low-intensity, fluorescent, polystyrene bead. Methods During the execution of the validation, a laboratory water system that met PW quality standards was connected to the 7000RMS, and a syringe pump was used to introduce various concentrations of microorganisms and fluorescent polystyrene beads to the analyzer. Samples were collected and tested via the traditional membrane filtration (MF) method and the colony-forming unit (CFU) plate count results were compared to the auto-fluorescent unit (AFU) of the 7000RMS analyzer. The validation study was designed to follow the guidance in United States Pharmacopeia (USP) Chapter <1223>, European Pharmacopeia (EP) Chapter 5.1.6, and parenteral drug association (PDA) Technical Report 33. Concepts and strategies were adapted from EP Chapter 2.6.12 Microbiological Examination of Non-Sterile Products: Microbial Enumeration Tests, EP Chapter 10.2, EP Chapter 2.6.1 Sterility, USP Chapter <61> Microbiological Examination of Non-Sterile Products: Microbial Enumeration Tests, USP Chapter <71> Sterility Tests, and Japanese Pharmacopoeia (JP) General Information Chapter G8 Water: Quality Control of Water for Pharmaceutical Use. Results All pre-determined validation acceptance criteria for accuracy, specificity, precision, LOD, LOQ, linearity, and range were met. Conclusions The 7000RMS demonstrated performance equivalence to the MF method per USP <1223> but characteristically lacked correlation to the CFU. Highlights This validation approach highlights the superior capabilities of the 7000RMS when compared against the traditional compendial MF testing method for PW. [ABSTRACT FROM AUTHOR]

Nature‐based solutions (NbS, and related concepts like natural infrastructure, Ecosystem‐based Adaptation, and green infrastructure) are increasingly recognized as multi‐benefit strategies for addressing the critical sustainability challenges of the Anthropocene, including the climate emergency and biodiversity crisis. Mainstreaming NbS in professional practice requires strategic, landscape‐level planning integrating multiple sources of benefits and their synergies and trade‐offs. Levee setbacks (LS) are among the best‐studied riverine NbS with recognized benefits for flood risk management, drought resilience, water quality management, recreational opportunities, and ecological restoration for biodiversity. Although awareness of the multifarious benefits of LS as forms of Natural Capital is growing, implementation remains ad‐hoc and opportunistic. To address this critical implementation gap for one major example of NbS, we review and synthesize literature across diverse disciplines to provide an overview of the primary social, economic, and ecological mechanisms that affect the co‐benefit delivery of LS projects. Next, to make this information relevant to NbS practitioners, we link these mechanisms to spatial metrics that can be used to approximate the relative magnitude of project benefits and costs across these mechanisms. Finally, we highlight examples of key synergies and trade‐offs among benefits that should be considered for LS planning. This synthetic approach is intended to familiarize readers with the diverse potential benefits of LS, and provide an understanding of how to select and prioritize potential sites for further study and implementation. Synergies and trade‐offs among important benefit drivers abound, and social equity concerns will be paramount in ensuring the successful implementation of LS and other NbS in the future. This article is categorized under:Engineering Water > Sustainable Engineering of WaterEngineering Water > Planning WaterWater and Life > Nature of Freshwater Ecosystems [ABSTRACT FROM AUTHOR]

The overall goal of urban water supply is to ensure the water quality from source to tap. As the "last mile", the building water supply system (BWSS) is the crucial part in ensuring tap water safety, and its deteriorating water quality has attracted increasing attention. In this work, we provide a comprehensive overview of the pollution, configuration, purification and regulation of BWSSs, with a focus on ensuring water quality safety. Periodic water usage in buildings is a unique feature that leads to intermittent water stagnation and reduced residual chlorine. Biological pollution has become a key focus of existing studies due to its acute effects on human health, compared to the chronic effects of chemical pollution. For new systems, water quality risks can be reduced at the source by optimizing pipe materials and reasonable layout. It is recommended to introduce secondary disinfection technologies, as they are important for ensuring biosecurity. Moreover, supervision and maintenance are the basis for long-term efficient operation of BWSSs. This review constructs a framework for controlling water quality deterioration based on the wholse process, which is instructive for the design, operation, maintenance and management of BWSSs, and provides relatively clear research directions for improving water quality. [ABSTRACT FROM AUTHOR]

Aquaculture stakeholders have mental models, which are the internal cognitive representations of how they understand and prioritize the different features of their aquaculture systems. Individuals and stakeholder groups are likely to have different mental models, with implications for making cooperative governance work and guiding the rapidly emerging sector's sustainable development. We apply a participatory approach called fuzzy cognitive mapping to capture and compare the mental models of community-based coastal pond aquaculture stakeholders in Indonesia, including farmers, government managers, and researchers who need to work together to govern a rapidly expanding aquaculture sector which faces critical sustainability challenges. To conceptually structure our comparison, we use Elinor Ostrom's social–ecological systems framework. Our results highlight important differences between stakeholder group mental models which represent potential conflicts of interest and barriers for collaborative governance. Fish farmer models emphasize resource system challenges relating to production instability and risk, while government managers emphasize increasing production intensity to meet sectoral growth targets. Researchers, in contrast, tend to focus on pond waste treatment and water quality management. Governance attributes were consistently perceived as less frequent and less influential compared to other social–ecological dimensions, reflecting perceptions of weak governance in the sector. We identify a critical need for programs aimed at strengthening community-level institutional arrangements for governing shared aquaculture resources, increasing technical knowledge capacity, and managing financial risk. By merging all stakeholder models into a single "community" model, we identify key consensus action situations across the three groups as potential focal points for aquaculture development which may serve as a starting point for actors to work together to identify context-appropriate institutional solutions to these sustainability challenges. [ABSTRACT FROM AUTHOR]

Deepwater bodies such as dam reservoirs are among the most prominent water resources that supply societies' water demand. Hence, water quality in these resources requires continuous and meticulous monitoring. This study evaluated the water quality of Changuleh Dam, a dam under construction in western Iran, using the CE‐QUAL‐W2 model to predict fluctuations in sulphate (SO42−) and hydrogen sulphide (H2S) concentrations. A sensitivity analysis was conducted to assess the dependence of model outputs on input variables. Additionally, an uncertainty analysis was performed using MATLAB to address the inherent uncertainties of the input variables through an ARIMA (2, 2, 1) time series model and Monte Carlo simulations. The results indicated a high sulphate concentration averaging 955.6 mg/l, considerably exceeding the drinking water standard of 400 mg/l, identifying the reservoir as a sulphate‐rich water resource. Seasonal fluctuations of H2S concentrations were also observed in the reservoir's anaerobic bed sediments, peaking at 87 mg/l. The sensitivity analysis showed that nitrate and total dissolved solids (TDS) varied more noticeably than dissolved oxygen (DO) at different depths, with the greatest variation at 50 m. This study highlights the critical need for continuous and meticulous monitoring of water quality in dam reservoirs to meet water supply standards. Highlights: Sulfate concentrations in the reservoir exceeded drinking water standards, averaging 955.6 mg/l.Hydrogen sulfide concentrations peaked at 87 mg/l during seasonal stratification.Sensitivity analysis revealed that nitrate and TDS were more impacted by input variations than dissolved oxygen.Uncertainty analysis indicated the greatest fluctuations in sulfate and hydrogen sulfide concentrations at a depth of 50 m.Aeration of the reservoir is recommended to reduce hydrogen sulfide levels and maintain water quality. [ABSTRACT FROM AUTHOR]

Today's increasing demand for aquaculture production is accompanied by various challenges such as diseases, broodstock improvement, domestication, development of suitable pellets and feeding methods, hatchery technology, and water quality management. Thus, probiotic usage has been reported as the ideal alternative to antibiotics, other chemotherapeutics, and additional supplements to other alternative ingredients. The main beneficial roles of probiotics include the enhancement of disease and stress resistance, immunity, promotion of growth and reproduction, improvement of digestion, provide several nutrients, and enhancement of water microbial composition. To guarantee safety, the probiotics provided must be noninvasive and non-pathogenic. The use of probiotics in aquaculture, either directly or in combination with alternative materials such as plant protein diets, vitamins, microalgae, fermented products, and so on, has been shown to improve the health and growth of aquatic animals and offer significant benefits to the sustainability of the industry. There is advocacy for a systematic approach to conducting innovative research to unearth new putative strains, which is substantial in ensuring sustainable probiotic usage and, thus, can help in the continuous development of the aquaculture industry especially in China. Some examples of the probiotics found in China are mainly photosynthetic bacteria (PSB) which are autotroph bacteria capable of photosynthesis, antagonistic bacteria (Pseudoalteromonas sp., Flavobacterium sp., Alteromonas sp., Phaeobacter sp., Bacillus sp., etc.), bacteria that contribute nutrients and enzymes during digestion (lactic acid bacteria, yeasts, etc.), bacteria that improve water quality (nitrifying bacteria, denitrifying bacteria, etc.), Bdellovibrio, and other probiotics. This review also focuses on the potential use of probiotics in aquaculture, especially in China, and probiotics' prospective future role. [ABSTRACT FROM AUTHOR]

Desalination brine can negatively impact the marine environment in several ways, although there are ongoing discussions regarding the severity and magnitude of environmental effects. A fascinating strategy to lessen any adverse effects is to undertake resource recovery from the brine, which also has the potential for additional revenue generation. More recently, the increasing demand for secure and less geographically restricted sources of precious or rare earth minerals, integrated with growing awareness of waste management and environmental sustainability, is driving the development of economically viable technologies to recover valuable materials from waste streams. This article provides an overview of different methods and technologies, including reverse osmosis (RO), electrodialysis (ED), and distillation, that can be used to recover precious materials, including Li, Mg, Na, and Rb and valuable blends from various waste sources and thus create a more sustainable and circular economy. The mechanisms are discussed in detail, including electrochemical processes (electrolysis, ED, and capacitive deionization), thermal desalination (multistage flash distillation and membrane distillation), pressure‐driven desalination (RO and nanofiltration), and microbial desalination cells. Challenges associated with recovering precious materials from waste streams, such as fouling, scaling, and environmental impact, along with further research directions and potential applications of desalination technologies, are also addressed. [ABSTRACT FROM AUTHOR]

The editorial "Insights in aquatic microbiology: 2023" published in Frontiers in Microbiology explores the diverse interactions and dynamics of microorganisms in aquatic ecosystems. The research covers various environments, from oceans to lakes, rivers, and other water bodies, focusing on nutrient cycling, energy flow, and ecosystem health. The publication highlights recent findings in freshwater and saltwater ecosystems, emphasizing the importance of understanding microbial communities for environmental conservation and management. The editorial calls for the adoption of new technologies, interdisciplinary collaboration, and science-based policies to advance research in aquatic microbiology and promote sustainable ecosystem development. [Extracted from the article]

In a global context, the hydrochemical characteristics of shallow groundwater in coalfields exhibit high degrees of diversity and complexity that are rooted in the intricate interplay of geological variations, diverse climatic conditions, and extensive human activities. The specific types and concentrations of ions, such as Ca2+, Cl−, and NO3−, show stark differences across geographical regions. Given the crucial roles of coalfields as energy suppliers, the potential environmental contamination risks posed by mining activities to groundwater cannot be overlooked as such pollution directly impacts human health and ecological safety. This study focuses on the Huainan coal mining area in northern Anhui Province (China), where shallow groundwater samples were systematically collected and analyzed to determine the hydrochemical characteristics and ascertain the water quality status. By integrating hydrogeochemical analysis techniques with inverse modeling methods, it was revealed that the groundwater in this region is predominantly HCO3-Ca type, exhibiting weak alkaline characteristics. The formation mechanisms are primarily governed by silicate rock weathering and mineral dissolution-precipitation processes, albeit with discernible influences from human activities. PHREEQC simulations were used to further confirm the precipitation tendencies of minerals like calcite, dolomite, and fluorite as well as the significant dissolution characteristics of halite. The inverse modeling pathway analysis reveals specific hydrochemical processes along different paths: paths I and IV are notably dominated by Ca2+ dissolution-precipitation and cation exchange-adsorption processes, whereas paths II and III are closely associated with the precipitation of calcium montmorillonite as well as dissolution of kaolinite, calcite, quartz, and mineral incongruents. Moreover, evaluations based on the entropy-weighted water quality index (EWQI) indicated overall positive trends of the groundwater quality indicators within the Huainan mining area, reflecting the effectiveness of regional water quality management efforts and providing a scientific basis for future water quality protection and improvement strategies. In summary, this study not only deepens our understanding of the groundwater chemistry in the Huainan coal mining area but also underscores the importance of scientifically assessing and managing groundwater resources to address the environmental challenges potentially arising from coal mining activities. [ABSTRACT FROM AUTHOR]

The Yeongsan River is one of the four major rivers in South Korea. Since the construction of two weirs as part of the Four Major Rivers Project to secure water resources in 2011, issues with algal blooms have frequently arisen, prompting the Ministry of Environment of Korea to conduct continuous monitoring of water quality and algal outbreaks. This study, conducted between 2019 and 2023, examined the relationship between the phytoplankton community structure and physicochemical factors at the Seungchon and Juksan weirs. Phytoplankton were categorized into four groups (Bacillariophyceae, Chlorophyceae, Cyanophyceae, and other phytoplankton), and 20 dominant genera were selected for analysis. As microalgal species vary depending on environmental conditions, understanding the specific relationships among the microalgae observed in the study area can help explain their occurrence mechanisms and contribute to the development of effective management strategies. Therefore, we used principal component analysis (PCA) to analyze the seasonal variation patterns of the four microalgal groups and visualize key data features through dimensionality reduction. Additionally, PCA was employed to identify and visualize environmental factors related to seasonal variations in phytoplankton communities. PCA helped elucidate how different environmental factors influence phytoplankton fluctuations across seasons. We used canonical correspondence analysis (CCA) to investigate the relationships among the 20 dominant genera in each group and environmental factors. Additionally, CCA was used to analyze the relationship between the distribution of the top five dominant phytoplankton taxa in each group and various environmental factors. CCA allowed for a detailed examination of how these dominant taxa interact with environmental conditions. PCA revealed significant correlations between other phytoplankton and Chl-a in spring and Cyanophyceae and water temperature in summer. Bacillariophyceae was positively correlated with nitrogen-based nutrients but negatively with phosphate phosphorus (PO4-P). CCA revealed significant correlations between dominant genera and environmental factors. Stephanodiscus sp. was associated with nitrogen-based nutrients, whereas Microcystis sp. and Dolichospermum sp. were associated with water temperature and PO4-P. Stephanodiscus sp. affected water treatment through filtration and sedimentation issues, whereas Microcystis sp. and Dolichospermum sp. produced the toxin microcystin. These findings offer valuable insights for water quality management. [ABSTRACT FROM AUTHOR]

To study the characteristics and sources of nitrogen in the Baiyangdian Lake, this research conducted water quality monitoring during three hydrological periods (normal period, flood period, and dry period), and 165 pieces of routine water quality monitoring data were collected from the three national control sections for Baiyangdian Lake and its inflow rivers. By integrating water chemical analysis with multivariate statistical techniques, the study comprehensively investigated the spatiotemporal variation patterns of nitrogen in Baiyangdian Lake and identified the sources of nitrogen pollution. The results showed that the concentration of total nitrogen (TN) was highest during the dry period, reaching an average of 0.924 mg/L, and 31.3% of the sites exceeded the national Grade III surface water quality standard, reflecting a potential risk of nitrogen pollution. Based on the ion ratio method and principal component analysis (PCA), the main sources of nitrogen pollution in Baiyangdian Lake were identified as manure and domestic sewage, with agricultural fertilizers also having a certain impact on water nitrogen pollution. In addition, the study also compared the nitrogen concentration in Baiyangdian Lake with several important lakes in China. The results showed that the concentrations of TN and ammonium nitrogen (NH4+-N) in Baiyangdian Lake are lower than those in lakes in areas with similar human activity intensity, indicating that the water quality of Baiyangdian is relatively good. This study can provide a scientific basis for water quality management and pollution prevention for Baiyangdian Lake. [ABSTRACT FROM AUTHOR]

Planning, managing and optimising surface water quality is a complex and multifaceted process, influenced by the effects of both climate uncertainties and anthropogenic activities. Developing an innovative and robust decision support framework (DSF) is essential for effective and efficient water quality management, so it can provide essential information on water quality and assist policy makers and water resource managers to identify potential causes of water quality deterioration. This framework is crucial for implementing actions such as infrastructure development, legislative compliance and environmental initiatives. Recent advancements in computational domains have created opportunities for employing artificial intelligence (AI), advanced statistics and mathematical methods for use in improved water quality management. This study proposed a comprehensive conceptual DSF to minimise the adverse effects of extreme weather events and climate change on water quality. The framework utilises machine learning (ML), deep learning (DL), geographical information system (GIS) and advanced statistical and mathematical techniques for water quality management. The foundation of this framework is the outcomes from our three studies, where we examined the application of ML and DL models for predicting water quality index (WQI) in reservoirs, utilising statistical and mathematical methods to find the seasonal trend of rainfall and water quality, exploring the potential connection between streamflow, rainfall and water quality, and employing GIS to show the spatial and temporal variability of hydrological parameters and WQI. Three potable water supply reservoirs in the Toowoomba region of Australia were taken as the study area for practical implementation of the proposed DSF. This framework can serve as a comprehensive mechanism to identify distinct seasonal characteristics and understand correlations between rainfall, streamflow and water quality. This will enable policy makers and water resource managers to enhance their decision making processes by selecting the management priorities to safeguard water quality in the face of future climate variability, including prolonged droughts and flooding. [ABSTRACT FROM AUTHOR]

The temporal dynamics of solute export from catchments are challenging to quantify and model due to confounding hydrological and biogeochemical processes and sparse measurements. Conventionally, the concentrationdischarge relationship (C-Q) and statistical approaches to describe it, such as the Weighted Regressions on Time, Discharge and Seasons (WRTDS), have been widely used. Recently, deep learning (DL) approaches, especially Long-Short-Term-Memory (LSTM) models, have shown predictive capability for discharge, temperature, and dissolved oxygen. However, it is not clear if such advances can be expanded to water quality variables driven by complex subsurface biogeochemical processes. This work evaluates the performance of LSTM and WRTDS for 20 water quality variables across ~500 catchments in the continental US. We find that LSTM does not markedly outperform WRTDS in our dataset, potentially limited by the current measurement capabilities of water quality across CONUS. Both models present similar performance patterns across water quality variables, with the LSTM displaying better performance for nutrients compared to weathering-derived solutes. Additionally, the LSTM does not benefit from flexibility in the inputs. For example, incorporation of climate data that constrains streamflow generation, does not significantly improve the LSTM performance. We also find that data availability is not a straightforward predictor of LSTM model performance, although higher availability tends to stabilize performance. To fully assess the potential of the LSTM model, it may be necessary to use a higher frequency dataset across the CONUS, which does not exist today. To evaluate the dynamics of C-Q patterns relative to model performance, we introduce a "simplicity index" considering both the seasonality in the concentration pattern and the linearity in the C-Q relationship, or the C-Q-t pattern. The simplicity index is strongly correlated with model performance and differentiates the underlying controls on water quality dynamics. Further DL experiments and model-intercomparison highlight the strengths and deficiencies of existing frameworks, pointing to the need for further hydrogeochemical theories that are amenable to complex basins and solutes. [ABSTRACT FROM AUTHOR]

River network connectivity (RC) describes the hydrologic exchange of water, nutrients, sediments, and pollutants between the river channel and other “sites” via heterogenous flowpaths along the river corridor. As water moves downstream it carries these constituents, creating a stream‐to‐ocean continuum of connectivity that regulates global water, carbon, and nutrient cycling. River network connectivity models have developed over many decades, culminating in recent years with network‐scale RC models that explicitly simulate the transport and exchange of water and elements from headwaters to coasts, sometimes requiring models to contain tens of millions of river reaches. These advances provide transformative insights into the aggregate effects of RC on water and material transport across scales from local to global. Yet, recent reviews have pointed to several challenges that need to be overcome to continue advancing network‐scale RC modeling. In service of these goals, I summarize recent network‐scale RC maps and models to identify similarities and differences across the large‐scale RC modeling landscape. Although our computational and upscaling abilities have significantly improved and have revealed new insights, current models are still limited by the quantity, quality, resolution, and lack of standardization of the available in situ databases and source data maps necessary for the modeling. This suggests that we can extend recent advances if we keep improving these source datasets, while continuously revisiting our physics and theory to explain those new data. In doing so, we will continue to expand the role of network‐scale RC models in informing water quality modeling and management into the future. [ABSTRACT FROM AUTHOR]

The urban rivers, including Changwang, Meishe, and Wuyuan in Haikou City, Hainan Island, are vital water sources for agricultural production and support industrial and domestic activities. Despite the rivers experiencing anthropogenic impacts, limited studies have assessed their water quality. Accordingly, this study assessed the phytoplankton community structure, utilized the river phytoplankton assemblage index (Qr index) to evaluate the ecological status, and compared its performance with the comprehensive trophic level index (TLI). Sample collection and microscopy analysis was conducted seasonally in 2019. Two hundred ninety-eight phytoplankton species belonging to 8 phyla were identified, predominated by Chlorophyta, Bacillariophyta, and Cyanophyta. The phytoplankton biomass ranged from 0.04 to 34.98 mg L−1, with averages of 3.06 ± 0.71, 5.16 ± 1.92, and 2.70 ± 0.76 mg L−1 in Changwang, Meishe, and Wuyuan, respectively. The phytoplankton biomass varied seasonally, recording the highest and lowest values in summer and autumn, respectively. The phytoplankton species were classified into 26 functional groups, which exhibited spatial and seasonal differences in their biomass and composition. The redundancy analysis (RDA) revealed that NH3-N, TP, CODMn, Chl-a, salinity, and temperature were the main environmental factors influencing phytoplankton functional groups. The average Qr index values in Changwang, Meishe, and Wuyuan were 3.39 ± 0.61, 3.44 ± 0.51, and 3.22 ± 0.67, and all the rivers were rated "good" in status. Seasonally, the Qr index and TLI revealed that the rivers' ecological condition was better in autumn and winter compared to spring and summer. Generally, the Qr index performed better, indicating that parameters such as NH3-N, CODMn, TP, and Chl-a decreased with improving ecological conditions from "poor" to "excellent" status. In addition, the Qr index exhibited a significant negative relationship with TLI, suggesting that low Qr index values may indicate increased eutrophication or deteriorated water quality. Thus, the ecological condition of the urban rivers could be adequately assessed using the Qr index to guide their water quality monitoring and management. [ABSTRACT FROM AUTHOR]

Water quality early warning is a key aspect in industrial recirculating aquaculture systems for high-density shrimp farming. The concentrations of ammonia nitrogen and nitrite in the water significantly impact the cultured animals and are challenging to measure in real-time, posing a substantial challenge to water quality early warning technology. This study aims to collect data samples using low-cost water quality sensors during the industrial recirculating aquaculture process and to construct predictive values for ammonia nitrogen and nitrite, which are difficult to obtain through sensors in the aquaculture environment, using data prediction techniques. This study employs various machine learning algorithms, including General Regression Neural Network (GRNN), Deep Belief Network (DBN), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM), to build predictive models for ammonia nitrogen and nitrite. The accuracy of the models is determined by comparing the predicted values with the actual values, and the performance of the models is evaluated using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) metrics. Ultimately, the optimized GRNN-based predictive model for ammonia nitrogen concentration (MAE = 0.5915, MAPE = 28.95%, RMSE = 0.7765) and the nitrite concentration predictive model (MAE = 0.1191, MAPE = 29.65%, RMSE = 0.1904) were selected. The models can be integrated into an Internet of Things system to analyze the changes in ammonia nitrogen and nitrite concentrations over time through aquaculture management and routine water quality conditions, thereby achieving the application of recirculating aquaculture system water environment early warning technology. [ABSTRACT FROM AUTHOR]

Chlorophyll‐a (Chl‐a) concentrations, a key indicator of algal blooms, were estimated using the XGBoost machine learning model with 23 variables, including water quality and meteorological factors. The model performance was evaluated using three indices: root mean square error (RMSE), RMSE‐observation standard deviation ratio (RSR), and Nash–Sutcliffe efficiency. Nine datasets were created by averaging 1 hour data to cover time frequencies ranging from 1 hour to 1 month. The dataset with relatively high observation frequencies (1–24 h) maintained stability, with an RSR ranging between 0.61 and 0.65. However, the model's performance declined significantly for datasets with weekly and monthly intervals. The Shapley value (SHAP) analysis, an explainable artificial intelligence method, was further applied to provide a quantitative understanding of how environmental factors in the watershed impact the model's performance and is also utilized to enhance the practical applicability of the model in the field. The number of input variables for model construction increased sequentially from 1 to 23, starting from the variable with the highest SHAP value to that with the lowest. The model's performance plateaued after considering five or more variables, demonstrating that stable performance could be achieved using only a small number of variables, including relatively easily measured data collected by real‐time sensors, such as pH, dissolved oxygen, and turbidity. This result highlights the practicality of employing machine learning models and real‐time sensor‐based measurements for effective on‐site water quality management. Practitioner Points: XAI quantifies the effects of environmental factors on algal bloom prediction modelsThe effects of input variable frequency and seasonality were analyzed using XAIXAI analysis on key variables ensures cost‐effective model development [ABSTRACT FROM AUTHOR]

This paper showcases the successful development and implementation of two Digital Twin prototypes within the Lab Digital Twins project, designed to enhance the efficiency and quality control of Aigües de Barcelona's drinking water network. The first prototype focuses on asset management, using (near) real‐time data and statistical models, and achieving a 70% success rate in predicting pump station failures 137 days in advance. The second prototype addresses water quality monitoring, leveraging machine learning to accurately forecast trihalomethane levels at key points in the distribution system, and enabling proactive water quality management strategies, ensuring compliance with stringent safety standards and safeguarding public health. The paper details the methodology of both prototypes, highlighting their potential to revolutionize water network management. Practitioner Points: Digital representation of assets and processes in the drinking water treatment networkEarly fault detection in assets, and predictions of trihalomethane formation in the drinking water distribution networkReduction on monitoring time and incident response for target assets by means of Digital TwinsImprovement in visualization, prediction, and proactive measures for asset management and water quality controlContribution to the growing knowledge on Digital Twins and their potential to revolutionize water network operations [ABSTRACT FROM AUTHOR]

Healthy soils contribute to a wide range of ecosystem services and virtually all of the UN Sustainable Development Goals, but most of the world's soil resources are in only fair, poor, or very poor condition, and conditions are getting worse in more cases than they are improving. A total of 33% of all soils are moderately to highly degraded as a result of erosion, loss of organic matter, poor nutrient balance, salinization and alkalinization, contamination, acidification, loss of biodiversity, sealing, compaction, and poor water status. Best management practices are available to limit or mitigate threats to soil health, and many of them mitigate multiple soil threats. In many regions of the world, policies or initiatives to protect or enhance the status of soils are in place, and they need to be strengthened and enforced. The Food and Agriculture Organisation will publish its second comprehensive assessment of the status of the world's soils in 2025, and this review provides an interim update on world soil status and offers an accessible overview of the topic. [ABSTRACT FROM AUTHOR]

There is a need for a comprehensive comparative analysis of spatiotemporal variations in surface water quality, particularly in regions facing multiple pollution sources. While previous research has explored the use of individual water quality indices (WQIs), there is limited understanding of how different WQIs perform in assessing water quality dynamics in complex environmental settings. The objective of this study is to evaluate the effectiveness of three WQIs (Canadian Council of Ministers of the Environment (CCME), National Sanitation Foundation (NSF) and System for Evaluation of the Quality of rivers (SEQ-Eau) and a national water quality regulation in assessing water quality dynamics. The pilot study area is the Acısu Creek in Antalya City of Turkey, where agricultural practices and discharge of treated wastewater effluents impair the water quality. A year-long intensive monitoring study was conducted includig on-site measurements, analysis of numerous physicochemical and bacteriological parameters. The CCME and SEQ-Eau indices classified water quality as excellent/good at the upstream, gradually deteriorating to very poor downstream, showing a strong correlation. However, the NSF index displayed less accuracy in evaluating water quality for certain monitoring stations/sessions due to eclipsing and rigidity problems. The regulatory approach, which categorized water quality as either moderate or good for different sampling sessions/stations, was also found less accurate. The novelty of this study lies in its holistic approach to identify methodological considerations that influence the performance of WQIs. Incorporating statistical analysis, artificial intelligence or multi-criteria decision-making methods into WQIs is recommended for enhanced surface water quality assessment and management strategies. [ABSTRACT FROM AUTHOR]

This groundbreaking study pioneers the application of state-of-the-art machine learning algorithms to predict pivotal water parameters, specifically pH, water depth, and salinity. Rigorously evaluating four leading algorithms (Random Forest Regressor, MLP Regressor, Support Vector Machine, and XGB Regressor) leveraging a substantial dataset and employing comprehensive metrics, including R², MSE, MAE, and cross-validation scores. Results unequivocally demonstrate the exceptional performance of MLP Regressor and XGB Regressor, consistently outclassing other models in predicting pH, with remarkable R² values and minimal errors. MLP Regressor excels as the preeminent model for water depth prediction, while XGB Regressor leads in accurately predicting salinity. The study underscores the paramount importance of cross-validation in meticulously assessing model robustness and generalization capabilities. A distinctive feature of this research lies in its innovative approach, incorporating geographic localization data (longitude, latitude, and altitude) as exclusive inputs for all models. This strategic integration showcases the algorithms' unprecedented ability to predict water parameters based solely on geographical coordinates, underscoring the transformative potential of machine learning in revolutionizing water resource management. The implications extend far beyond its immediate focus, encompassing critical areas such as geophysical exploration, environmental monitoring, water quality management, and ecological conservation. By providing invaluable insights into the application of machine learning algorithms for predicting key water parameters, this study positions itself at the forefront of scientific contributions, setting a new standard for excellence in sustainable water resource utilization. [ABSTRACT FROM AUTHOR]

Summary: Emulsions are combinations of two‐phases of immiscible liquids, where one liquid is dispersed throughout the other liquid with the help of emulsifiers such as surfactants. Surfactants, which are emulsifiers composed of small amphiphilic molecules, have several drawbacks. These include being non‐biodegradable and releasing various chemical compounds into the environment. Consequently, they are not ideal for prolonged use. Solid particles can be an alternative candidate to stabilise emulsions, forming what are known as Pickering emulsions (PEs). It has shown that PEs exhibited greater encapsulation efficiency (EE) of other substances up to 98%, which is more than twice compared with that of surfactant‐stabilised emulsions. Due to its irreversible adsorption, PEs has been recognised in various industries such as cosmetics, food, biomedical and aquaculture which have potential in targeted drug and nutrient delivery to the fish, water management, feed enhancement and stress reduction in aquaculture species. Thus, this review will be discussing on (i) emulsion stability and performance, (ii) solid particles in stabilising Pickering emulsions, (iii) use of PEs in various industries, (iv) nanotechnology in PEs to optimise aquaculture, (v) potential of PEs as drug delivery in fish culture, (vi) PEs as agents in water quality management, and (vii) PEs as encapsulation of stress‐relieving compounds, (viii) PEs as fish and fish feed enhancer, (ix) innovations of chitosan‐based PEs from aquaculture by‐product, and (x) innovations of PEs from fisheries by‐product. This review would provide insight into future research on enhancing the sustainability and functionality of Pickering emulsions by exploring novel, environmentally friendly solid particles and incorporating advanced nanotechnology techniques to further improve Pickering emulsions stability. [ABSTRACT FROM AUTHOR]

Good water quality in a water distribution system (WDS) can be reached by using flow control valves (FCV) to reduce water residence time (WRT), and chlorine booster stations to maintain acceptable free residual chlorine concentrations (FRCC). The research developed a cost-effective, adaptative approach for managing FCV and chlorine booster stations, while maintaining acceptable pressure and FRCC ranges. The methodology is applied to a real full-scale case study considering three optimization formulations: 1) FCV operation, 2) chlorine booster station operation, and 3) combining them in an adaptive management strategy. Results revealed that chlorine booster stations individually or combined with FCV enhanced water quality significantly at an acceptable cost, while FCV optimization alone or combined with chlorine booster stations reduced WRT by, at most, 3.8%. Chlorine booster stations were thus more effective than FCV in this case study. This formulation can be applied to other WDS to improve water quality cost-effectively. [ABSTRACT FROM AUTHOR]

Cyanobacterial harmful algal blooms can pose risks to ecosystems and human health worldwide due to their capacity to produce natural toxins. The potential dangers associated with numerous metabolites produced by cyanobacteria remain unknown. Only select classes of cyanopeptides have been extensively studied with the aim of yielding substantial evidence regarding their toxicity, resulting in their inclusion in risk management and water quality regulations. Information about exposure concentrations, co‐occurrence, and toxic impacts of several cyanopeptides remains largely unexplored. We used liquid chromatography–mass spectrometry (LC–MS)‐based metabolomic methods associated with chemometric tools (NP Analyst and Data Fusion‐based Discovery), as well as an acute toxicity essay, in an innovative approach to evaluate the association of spectral signatures and biological activity from natural cyanobacterial biomass collected in a eutrophic reservoir in southeastern Brazil. Four classes of cyanopeptides were revealed through metabolomics: microcystins, microginins, aeruginosins, and cyanopeptolins. The bioinformatics tools showed high bioactivity correlation scores for compounds of the cyanopeptolin class (0.54), in addition to microcystins (0.54–0.58). These results emphasize the pressing need for a comprehensive evaluation of the (eco)toxicological risks associated with different cyanopeptides, considering their potential for exposure. Our study also demonstrated that the combined use of LC–MS/MS‐based metabolomics and chemometric techniques for ecotoxicological research can offer a time‐efficient strategy for mapping compounds with potential toxicological risk. Environ Toxicol Chem 2024;43:2222–2231. © 2024 SETAC [ABSTRACT FROM AUTHOR]

This study aimed to assess the trophic status of Pegametan Bay, Bali, Indonesia, as influenced by floating net cage aquaculture activities during June 2019 (dry season) and March 2021 (wet season). The trophic status was assessed using 24 parameters developed from a literature review, and water samples were collected from four observation stations. Data were analyzed using field measurements and laboratory analyses. The results showed that dissolved oxygen ranged between 2.6–9.1 mg/l in June 2019 and 3.4–7.7 mg/l during the March 2021 measurement period, salinity (33.6–34.4 ppt and 31–34 ppt), turbidity (0.77–6.33 FNU and 1.2–3.42 FNU), water clarity (4.18–11.92 m and 4.44–8.44 m), Biological Oxygen Demand (10.5–26 mg/l and 1–17 mg/l), Chemical Oxygen Demand (3.68–11 mg/l and 15.5–46.5 mg/l), chlorophyll-a (0.11–0.24 mg/l and 0–0.56 mg/l), Total Dissolved Solids (20,400–22200 mg/l and 21,300–53400 mg/l), ammonia (0.038–0.178 mg/l and 0.01–0.3 mg/l), nitrate (0.02–0.706 mg/l and 0.01–0.03 mg/l), orthophosphate (0.014–0.174 mg/l and 0.04–1.59 mg/l, and culturable bacteria (0–1.74 ∙ 105 CFU/mL and 0–1.61 ∙ 108 CFU/mL). Pegametan Bay exhibited a significant decrease in water quality between June 2019 and March 2021, with mesotrophic and eutrophic trophic states. The parameters determining changes in the trophic state of Pegametan Bay were orthophosphate, TSS, organic content, E. coli, Salmonella, and Vibrio. This trophic state assessment can be used for early detection, monitoring, water quality control, and eutrophication control, thereby ensuring sustainable aquaculture activities and optimal marine resource production. [ABSTRACT FROM AUTHOR]

In shrimp farming, the development of alternative production systems, such as fermented products, can help reduce the high dependence on artificial diets and ensure more sustainable growth. The "synbiotic system" typically uses low stocking densities in traditional earth ponds. To increase profitability, the tendency is to increase stocking densities, especially in small production units. The objective of the following work was to use the "synbiotic system" in different stocking densities, analyzing the water quality, the microorganisms present, the proximal composition of the formed bioflocs and the zootechnical performance. For this, 12 tanks with an area of 35 m2 each were used. Stocking densities were 30, 60, 90 and 120 shrimp/m2. Fertilization was carried out for seven days before the shrimp stocking and then every two days until the end of the experiment. The experiment lasted 45 days, and no water was renewed. Significant differences were observed in several quality parameters, including alkalinity, nitrate, total suspended solids, and turbidity. However, no significant differences were found in the proximal composition of the formed bioflocs. The presence of microorganisms throughout the experimental period indicated the effectiveness of adding fermented rice bran for nutrient assimilation. Furthermore, significant differences were observed in the final weight, feed conversion rate and productivity. The ability of the synbiotic system to provide food supplementation, control water quality and present satisfactory zootechnical performance was demonstrated, proving to be efficient in the cultivation of Litopenaeus vannamei. [ABSTRACT FROM AUTHOR]

Nitrate pollution is a global environmental problem, and mean nitrate levels have risen by an estimated 36% in global waterways since 1990. Tracing nitrate sources is important for water quality management, and nitrate isotopes (δ15N-NO3− and δ18O-NO3−) are commonly used for this purpose because of the different isotopic compositions of different sources. However, the impact of nitrate sorption on matrix and desorption from matrix on N and O isotopic composition of nitrate in liquid phase has not been well clarified. To explore the mechanism for the changes in nitrate concentration and isotopes in liquid phase during sorption and desorption, this study took a shale sample (enriched in clay minerals and commonly exposed in the Earth), conducted a series of laboratory experiments for nitrate sorption and desorption, and studied the impact of sorption and desorption on nitrate N and O isotopic composition in liquid phase. The results showed that the shale sample exhibited a rapid sorption and desorption rate for nitrate in the surface water samples, with the nitrate concentration in the solution decreasing from 14.3 mg/L to 4.1 mg/L within 5 min. The sorption data fit the Langmuir model better than that of the Freundlich model. The maximum possible sorption (Qmax) for the shale sample was estimated to be 46 μg/g. Preliminary laboratory experiments showed that changes in δ15N-NO3− values were not obvious, and changes in δ18O-NO3− values in liquid phase were minor during sorption and desorption of the shale sample, suggesting that nitrogen isotopic fractionation can be neglected, and the sorption of nitrate by the shale sample has a very limited impact on the distribution of nitrate isotopes in liquid phase. However, the impact of nitrate desorption on the nitrate isotopes in liquid phase depends on the isotopic composition of exchangeable nitrate in the solid phase, which may be related to antecedent water–rock interactions. This study provides important information for elucidating the evolution mechanism of nitrate and its isotopic compositions following sorption-desorption, and is conducive to revealing the nitrogen cycle law in the environment. [ABSTRACT FROM AUTHOR]

Droughts negatively affect basins by reducing the river streamflow and increasing ion concentrations due to lower dilution. This study examines the impact of droughts in the Colorado River basin in Argentina. For this purpose, data were collected during the period from 2015 to 2021 at eight monitoring stations containing water from the river and drainage canals. The Standardized Precipitation Index (SPI) was used to analyze droughts from 1966 to 2020, and the Mann–Kendall test was used to evaluate the precipitation trends. In addition, water quality indices for human consumption (DWQI) and livestock (LWQI) were calculated by evaluating physicochemical parameters. The results show an intensification of drought since 2007, with an SPI of −1.5 in 2008, which affected the river streamflow regime and reduced the dilution capacity in the Casa de Piedra Dam. This reduction led to the deterioration of the water quality, with DWQI values indicating that 85% of the samples were not suitable for human consumption but were suitable for livestock consumption. In the drainage canals, most of the samples were of low quality for livestock consumption. The physicochemical analyses show that although some parameters (Na+, K+, CO32− + HCO3−, and Cl−) were at acceptable levels, others (electrical conductivity, SO42−, and Ca2+) exceeded the WHO's limits, representing risks to human and livestock health. This study provides insights into how droughts and streamflow regulation affect the water quality in semiarid basins and highlights the broader applicability to other regions that present similar challenges under climate change scenarios. [ABSTRACT FROM AUTHOR]

Umbria is an Italian region characterized by a highly fragmented water supply and distribution system, with many small systems fed by local sources. Chlorination in these small systems faces several challenges, including strong fluctuating demand, low economic significance, and limited access to infrastructure. Due to Italian regulations, the resulting frequent noncompliance with water quality standards negatively impacts performance indicators and tariffs. This research explores the possibility of implementing a low-cost chlorination system designed to adapt to varying environmental conditions (e.g., water and environmental temperature fluctuations, changes in pH, etc.) and demands. A prototype of the device was developed and tested in the Water Engineering Laboratory of the University of Perugia, Italy, to assess its ability to apply a programmable chlorination model. The effects of electronic environmental noise, along with the reliability of water meters, thermometer measurements, dosing pump control, local logging, and remote data transmission, were tested under different conditions. The results demonstrated the instrument's readiness for field applications in pilot studies under real-world conditions. [ABSTRACT FROM AUTHOR]

The research introduces a revolutionary Internet of Things (IoT)-based system for fish farming, designed to significantly enhance efficiency and cost-effectiveness. By integrating the NodeMcu12E ESP8266 microcontroller, this system automates the management of critical water quality parameters such as pH, temperature, and oxygen levels, essential for fostering optimal fish growth conditions and minimizing mortality rates. The core of this innovation lies in its intelligent monitoring and control mechanism, which not only supports accelerated fish development but also ensures the robustness of the farming process through automated adjustments whenever the monitored parameters deviate from desired thresholds. This smart fish farming solution features an Arduino IoT cloud-based framework, offering a user-friendly web interface that enables fish farmers to remotely monitor and manage their operations from any global location. This aspect of the system emphasizes the importance of efficient information management and the transformation of sensor data into actionable insights, thereby reducing the need for constant human oversight and significantly increasing operational reliability. The autonomous functionality of the system is a key highlight, designed to persist in adjusting the environmental conditions within the fish farm until the optimal parameters are restored. This capability greatly diminishes the risks associated with manual monitoring and adjustments, allowing even those with limited expertise in aquaculture to achieve high levels of production efficiency and sustainability. By leveraging data-driven technologies and IoT innovations, this study not only addresses the immediate needs of the fish farming industry but also contributes to solving the broader global challenge of protein production. It presents a scalable and accessible approach to modern aquaculture, empowering stakeholders to maximize output and minimize risks associated with fish farming, thereby paving the way for a more sustainable and efficient future in the global food supply. [ABSTRACT FROM AUTHOR]

River deltas are ecologically critical landscape components and are responsible as habitats for millions of people, wildlife, and plant communities. Deltas store vast quantities of eroded nutrient‐rich sediment and play a significant role in water quality management through nutrient cycling and storage. Many studies have been conducted in marine and tidal delta environments that characterize the sedimentology and geomorphic characteristics of these relatively young landscapes. However, little work has been completed in even younger inland reservoir deltas. This study characterized the sediments and geomorphology of the growing delta forming at the outlet of the Des Moines River into Red Rock Reservoir in south‐central Iowa and discussed the implications of the lower delta sediment and topographic characteristics in terms of sediment and nutrient storage, and the potential impact of the delta on other hydroecological factors. We found that the ca. 30‐year‐old lower delta is storing up to 60% of the contributing watershed's eroded sediment and total carbon, with total sediment depths of ca. 9 m across the 755‐ha area. Further, we identified two major geomorphic landscape positions within the lower delta that were categorized as distributary channels and delta plains. Sedimentological differences in these microscale landscape positions were identified. Sand content was greater at lowest distributary channel elevations and at the highest plain elevations whereas silt dominated the elevation range between these two extremes. The zone of silt accumulation corresponds to the range in elevation associated with normal lake level management. The silt‐dominated area is regularly inundated and covered with a variety of emergent vegetation and is the zone of critical importance for waterbird habitat and nutrient‐processing capacity. [ABSTRACT FROM AUTHOR]

This study assesses water quality and heavy metal concentrations in 17 main groundwater sources in Duhok City, Iraq's agriculturally vital Shekhan area. It is important to comprehend the possible health concerns associated with heavy metal pollution in this area because of its relevance to food production. With an emphasis placed on heavy metal concentrations in groundwater sources to support public health and sustainable practices, this study provides essential insights into controlling water quality for irrigation and safe consumption. The Water Quality Index (WQI) results ranged from 15.23 to 37.05, indicating good and excellent water quality, well-suited for drinking and agricultural purposes. The results of heavy metals concentration from Copper (Cu), Manganese (Mn), Lead (Pb), and Nickel (Ni) ranged from 0.0002 to 0.0111 ppm, 0.0023 to 0.0187 ppm, 0.0006 to 0.0024 ppm, and 0.007 to 0.032 ppm, respectively. The World Health Organization (WHO) criteria were satisfied by all heavy metal concentrations in the water samples, except Cadmium (Cd), which exceeded the recommended threshold in six analyzed sources and varied from 0.0015 to 0.0158 ppm. The water is appropriate for irrigation and consumption, according to the findings of the heavy metal content analysis and water quality evaluation, while continuous monitoring is needed to guarantee optimum water quality. [ABSTRACT FROM AUTHOR]

Understanding the spatial and temporal patterns of sediment loading in water bodies is crucial for effective water quality management. Remote sensing (RS) has emerged as a valuable and reliable tool for monitoring turbidity, which can provide insights into sediment dynamics in water bodies. In this study, we investigate the potential of turbidity data derived from RS to explain simulated spatial and temporal sediment loading patterns in the Lake Tana basin, Ethiopia. Utilizing existing RS lake turbidity data from Copernicus Global Land Service (CGLS) and simulated seasonal and multiyear trends of river sediment loadings into Lake Tana from the Soil and Water Assessment Tool (SWAT + model), we estimate correlations at different river inlets into Lake Tana. The results reveal a strong positive correlation (R2 > 0.66) between the multiyear monthly average sediment load from inflow rivers and RS lake turbidity at most river inlets. This indicates that the simulated river sediment loads and lake turbidity at river inlets exhibit similar seasonal patterns. Notably, higher turbidity levels are observed at the river inlet with the highest sediment load export. These findings highlight the potential of RS turbidity products in characterizing temporal and spatial patterns of sediment loadings, particularly in data-scarce regions, contributing to a better understanding of water quality dynamics in such areas. [ABSTRACT FROM AUTHOR]

The impacts of landscape patterns on river water quality are commonly acknowledged, but understanding the complex processes by which landscape patterns affect water quality is still limited, especially in densely populated urban areas. Exploring the mechanisms through which landscape characteristics influence water quality changes in urbanized rivers will benefit regional water resource protection and landscape-scale resource development and utilization. Utilizing daily water quality monitoring data from rivers in the urbanized area of the Pearl River Delta in 2020, our research employed canonical analysis and partial least squares structural equation modeling (PLS-SEM) to explore the processes and mechanisms of the influence of urbanized river landscape patterns on surface water quality. The results indicated that total nitrogen (TN) was the critical indicator limiting the water quality of rivers in the Pearl River Delta. The landscape composition and configuration indexes exhibited non-linear variations with scale, and the landscape fragmentation was higher closer to the river. Landscape patterns had the most significant influence on water quality under the characteristic scale of a 5.50 km circular buffer zone, and landscape composition dominated the change of water quality of urbanized rivers, among which 30.64% of the percentage patch area of construction (C_PLAND) contributed 46.40% to the explanation rate of water quality change, which was the key landscape index affecting water quality. Moreover, landscape patterns had a higher interpretive rate of 39.29% on water quality in the wet season compared to 36.62% in the dry season. Landscape composition had an indirect negative impact on water quality, with a value of 0.47, by affecting the processes of runoff and nutrient migration driven by human activities, while landscape configuration had an indirect negative impact on water quality, with a value of 0.11. Our research quantified the impacts of landscape patterns driven by human activities on surface water quality and proposed management measures to optimize the allocation of landscape resources in riparian zones of urbanized rivers. The results provide a scientific basis for water quality management and protection in urbanized rivers. [ABSTRACT FROM AUTHOR]

The Jialing River, a key watershed in the upper Yangtze River, faces challenges in maintaining stable water quality due to its extensive area and issues such as agricultural and cross-border pollution. This study examines the water quality variations in the Jialing River's main course and its two major tributaries, Fujiang and Qujiang, using 2021 monitoring data for five water quality indicators: dissolved oxygen, permanganate index, ammonia nitrogen, total phosphorus, and total nitrogen, across 48 river sections. Methods including Fuzzy Comprehensive Evaluation, Nemerow Index, and Nearest Neighbor Analysis were applied. The findings reveal that: 1 The Fuzzy Evaluation method effectively demonstrates the impact of each indicator on various water quality standards. 2 In the study area, 38 sections are rated below Grade III for total nitrogen, and one section is rated Grade IV for permanganate index, indicating significant pollution of total nitrogen and exceedances of permanganate standards. Agricultural activities are identified as a major contributor to the exceedances of these two indicators. The remaining three indicators meet the water quality control standards for the Jialing River. 3 Spatially, the sections with Class III water quality are the least in the main stream, indicating relatively optimal water quality, while the Fujiang exhibits the poorest water quality. Class V sections are mainly distributed in the middle and lower reaches, with better water quality in the upper reaches of the watershed. 4 Seasonally, the water quality of river sections categorized as Class III deteriorated during the dry season, with an increase of three sections compared to the wet season. The water quality during the dry season exhibited greater instability. Specifically, the Fujiang demonstrated relatively stable water quality throughout the year, whereas Qujiang exhibits a decreasing trend in the number of Class V sections from July to December, indicating an improvement in water quality during the year. Conversely, the main stream shows an increase in the number of Class V sections from July to December, suggesting a declining trend in water quality during this period. These results offer valuable insights for pollution control and aquatic ecosystem restoration in the Jialing River basin. [ABSTRACT FROM AUTHOR]

The prediction of sediment concentrations is of significant importance for watershed sediment control, water-sediment regulation, as well as water quality and environmental management. The upper reaches of the Yangtze River, vast area in size and abundant in tributaries with complex water and sediment sources, pose a great challenge to accurately predict the process of suspended sediment concentration (SSC) entering the Three Gorges Reservoir. In this study, we propose a deep learning model named RF-LSTM, which combines Random Forest (RF) algorithm and Long Short-Term Memory (LSTM) neural network for daily SSC prediction at Cuntan station. This model addresses the varying impacts of rainfall in the upper reaches of the Yangtze River, as well as the inflow of water and sediment from its main stream and tributaries, on the daily SSC observed at the station. Firstly, the RF algorithm is employed to identify water and sediment factors that exhibit a strong correlation with the SSC at Cuntan. These factors are then utilized as input variables for the LSTM neural network to discern the mapping relationship between the optimized set of factors and the SSC at Cuntan. Finally, the model is applied in the region spanning from Xiangjiaba to Cuntan of the upper Yangtze River to predict the daily SSC during flood season at Cuntan station under different forecast periods. Results show that, compared to the LSTM model, the RF-LSTM model can better account for the lagged effects of the predictor factors on the SSC, and effectively capture the features that are strongly correlated with the SSC at Cuntan station. Under all the four different forecast horizons considered, the RF-LSTM exhibits superior performance in terms of both prediction accuracy and overall capability. Specially, when considering no-forecast and 1-d forecast horizons, both models exhibit high prediction accuracy, with Nash-Sutcliffe efficiency coefficients exceeding 0.82 during the validation period. Notably, the RF-LSTM model achieves a Nash-Sutcliffe efficiency coefficient of 0.91 in the no-forecast horizon, outperforming the LSTM model by reducing mean absolute errors and root mean square errors by 8% and 13%, respectively. Furthermore, under both of these forecast horizons, the RF-LSTM model more precisely captures SSC peaks and their occurrence timings. However, as the forecast horizon increases to 2 days and 3 days, the accuracy of both models decrease significantly. Nevertheless, the RF-LSTM model continues to outperform the LSTM model in terms of computational accuracy, demonstrating its robustness and reliability across different forecast horizons. These findings highlight the potential of the RF-LSTM model as a valuable tool for SSC prediction in the upper reaches of the Yangtze River, offering a valuable reference for future studies in this domain. [ABSTRACT FROM AUTHOR]

The Changwang, Meishe, and Wuyuan are urban rivers in Haikou City, Hainan Island. They are vital water sources, serving various purposes, from farmland irrigation to domestic and industrial use. Unfortunately, anthropogenic activities have influenced these "rivers," negatively impacting water quality and ecological functions; however, there is limited research on their ecological status. Thus, this study assessed the zooplankton community structure and developed the zooplankton index of biotic integrity (Z-IBI) to evaluate the ecological status. Samples were collected and analyzed seasonally in 2019. Generally, 159 zooplankton species belonging to 85 genera, 55 families, and 3 phyla were identified, dominated by Rotifer and Protozoa, followed by Arthropoda (groups of Copepod and Cladocera). The zooplankton density varied from 0.00 to 126.04 ind L−1, with averages of 19.49 ind L−1, 31.40 ind L−1, and 20.38 ind L−1 in Changwang, Meishe, and Wuyuan, respectively. The zooplankton density varied seasonally as summer > spring > winter > autumn. Analysis of similarity (ANOSIM) indicated a significant seasonal difference (R = 0.070, p < 0.001) in zooplankton community but not spatially (R = 0.004, p = 0.413). Thus, SIMPER revealed that Brachionus forficula, Didinium nasutum, Keratella valga, Nauplius, and Polyarthra vulgaris contributed to the seasonal differences. The redundancy analysis (RDA) revealed environmental factors, including total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH3-N), chemical oxygen demand (CODMn), dissolved oxygen (DO), temperature, pH, Chl-a, and electrical conductivity (EC) influenced zooplankton species. The Z-IBI was established using Zooplankton abundance, Protozoa abundance, Copepod abundance, Cladoceran biomass %, and Shannon–Wiener diversity index. Based on Z-IBI evaluation, ecological status was "moderate" in Changwang and Wuyuan, while it was "low" in Meishe. Seasonally, the ecological status in autumn was better than in summer and spring. The Z-IBI showed that parameters, including pH, EC, CODMn, TN, TP, and Chl-a, decreased with improving water quality, while water transparency increased with improved water quality. Thus, the "rivers" ecological status could be adequately assessed using the Z-IBI to guide their water quality monitoring and management. [ABSTRACT FROM AUTHOR]

Fetzara Lake, considered one of the most important wetlands in northeastern Algeria, was designated a Ramsar site in 2002. The waters in its watershed are affected by salinity, which influences their suitability for irrigation. To identify the factors influencing the quality of these surface waters, geochemical and statistical analyses were carried out on the basis of the results of chemical analyses of 51 samples collected, during two monitoring campaigns, from all the tributaries in the watershed. The findings show the dominance of three hydrochemical facies over the two campaigns: Na-Cl facies (55.17% and 22.73%) characterizes the waters water from Fetzara Lake outlet (drainage channel and wadi Meboudja), in relation to the influx of saliferous elements due to water evaporation in the lake. Ca–Mg–Cl (27.59% and 40.91%) and Ca-Mg-HCO3 (13.79%. and 13.79%) facies characterize the waters of the remaining tributaries, reflecting the dissolution of carbonate formations and the alteration of the Edough metamorphic basement. Multivariate statistical analysis, using principal component analysis (PCA), shows three water types: highly mineralized (EC > 3000 µS/cm), moderately mineralized (1000 < EC < 3000 µS/cm), and weakly mineralized (EC < 1000 µS/cm). Evaporation and silicate weathering are the main mechanisms controlling water mineralization according to the different bivariate plots. Furthermore, cation exchange indices (CAI-I and CAI-II) reveal that these reactions involve the adsorption of Na+ and K+ onto clay minerals, as well as the simultaneous release of Ca2+ and Mg2+ ions. Finally, the various quality indices (SAR, %Na, RSC and KR) revealed that the water in 36% of tributaries is unsuitable for irrigation. These findings will provide important information on surface water quality in the study area, particularly for irrigation purposes, and will contribute to the thoughtful and sustainable management of this resource. [ABSTRACT FROM AUTHOR]

Efforts to reduce nitrogen and carbon loading from developed watersheds typically target specific flows or sources, but across gradients in development intensity there is no consensus on the contribution of different flows to total loading or sources of nitrogen export. This information is vital to optimize management strategies leveraging source reductions, stormwater controls, and restorations. We investigate how solute loading and sources vary across flows and land‐use using high frequency monitoring and stable nitrate isotope analysis from five catchments with different sanitary infrastructure, along a gradient in development intensity. High frequency monitoring allowed estimation of annual loading and attribution to storm versus baseflows. Nitrate loads were 16 kg/km2/yr. from the forested catchment and ranged from 68 to 119 kg/km2/yr., across developed catchments, highest for the septic served site. Across developed catchments, baseflow contributions ranged from 40% of N loading to 75% from the septic served catchment, and the contribution from high stormflows increased with development intensity. Stormflows mobilized and mixed many surface and subsurface nitrate sources while baseflow nitrate was dominated by fewer sources which varied by catchment (soil, wastewater, or fertilizer). To help inform future sampling designs, we demonstrate that grab sampling and targeted storm sampling would likely fail to accurately predict annual loadings within the study period. The dominant baseflow loads and subsurface stormflows are not treated by surface water management practices primarily targeted to surface stormflows. Using a balance of green and gray infrastructure and stream/riparian restoration may target specific flow paths and improve management. Key Points: Baseflows contributed nearly as much or more of nitrogen export as stormflows across low to moderate development intensity catchmentsStormflow NO3− sources were diverse and highly variable in time, while baseflows mobilized few sources with little temporal variabilityGrab sampling methods did not reliably estimate the loading observed from in situ monitoring for small catchments [ABSTRACT FROM AUTHOR]

The Yamuna River, vital for the sustenance of millions residing in Delhi, is heavily polluted with toxic heavy metals due to the effects of urbanization, industrialization, and untreated sewage discharge. Understanding the extent of heavy metal pollution and its associated health risks is crucial for effective environmental management. This study employs a comprehensive approach utilizing a modified Heavy Metal Pollution Index, Nemerrow Index, and health risk assessment to evaluate the pollution levels and potential health hazards in the Delhi stretch of the Yamuna River. Field observations, laboratory analyses, and statistical methods were employed during the research. Specifically, four sampling locations were selected, and 25 samples were collected from each site along a 22-km stretch in Delhi between March and June 2023. The findings reveal significant contamination of the Yamuna River in Delhi with heavy metals exceeding permissible limits set by regulatory bodies in Indian and International context. At the various sampling points, lead concentrations exhibited variability, with the highest concentration recorded at Wazirabad (2.332 mg/L) and the lowest at Okhla Barrage (1.092 mg/L). However, in terms of positive index (PI) and negative index (NI) values, Wazirabad registered the highest values of 2,639.97 and −0.02, respectively, while Okhla showed the lowest values of 31.21 for PI and −0.79 for NI. Conversely, at the Nizamuddin sampling site, the lowest PI and NI values were observed, standing at 31.21 and −0.79, respectively. Furthermore, the assessment of health risks related to heavy metal exposure involved metrics like the Hazard Quotient and carcinogenic risk models. The Total Hazard Index (THI) values ranged from 650 to 3,352 for infants, 341 to 1,843 for children, and 141 to 745 for adults. The THI values indicate that drinking water from the Yamuna River can result in various detrimental health conditions. This research enhances our understanding of the environmental health issues associated with heavy metal pollution in Delhi's Yamuna River. It underscores the importance of implementing holistic strategies for water quality management and pollution mitigation, stressing the integration of scientific assessments with policy actions and community engagement efforts. [ABSTRACT FROM AUTHOR]

The increasing pollution of river systems poses a significant challenge to water quality management and public health. This study was conducted to evaluate the water quality of the Gomati River in India, focusing on the impact of anthropogenic activities on pollution levels across different seasons. The primary objective was to develop a comprehensive assessment model using an effective weighted fuzzy soft expert system (WFSES) to derive a weighted water pollution score (WWP-score) for rating water pollution levels based on various water quality parameters. To achieve this, we employed a fuzzy soft set (FSS) methodology integrated with a weighted fuzzy soft set (WFSS) model to analyze water quality indices at six sampling stations along the Gomati River, the largest river in Tripura, India. Originating from the Raima and Sarma streams, the river flows westward through key regions, providing 249.39 million cubic meters of water annually, essential for drinking, agriculture, fishing, and transportation. The study utilized crisp data summaries of key water quality parameters, including pH, total dissolved solids, total suspended solids, electrical conductivity, biochemical oxygen demand, dissolved oxygen, total hardness, chloride, total alkalinity, and total coliform, across the pre-monsoon, monsoon, and post-monsoon seasons from May 2022 to April 2023, guided by the distribution of waste discharge points. Fuzzy membership functions were defined for these parameters, and the root mean square (RMSQR) operation was used to combine multiple FSSs. The results revealed that the WWP-scores, a measure of water quality, ranged from 0.744 to 0.872 across the sampling stations, with Srimantapur exhibiting the highest level of pollution and Udaipur showing comparatively better quality. These scores indicate that water quality at all sites falls into the poor category, with Srimantapur experiencing the most severe contamination due to high levels of coliform bacteria and organic pollutants. The analysis highlights the impact of domestic wastewater and agricultural runoff on the river's health. This research offers a novel application of FSS and WFSS theory in environmental management, providing a robust framework for evaluating and ranking water quality across multiple parameters. The findings are valuable to the scientific community as they offer detailed insights into the pollution dynamics of the Gomati River and propose a methodological approach for assessing water quality in other river systems facing similar challenges. The study's results underscore the need for targeted pollution control measures and continuous monitoring to safeguard water resources and public health. [ABSTRACT FROM AUTHOR]

Current potentiometric sensing methods are limited to detecting nitrate at parts-per-billion (sub-micromolar) concentrations, and there are no existing potentiometric chemical sensors with ultralow detection limits below the parts-per-trillion (picomolar) level. To address these challenges, we integrate interdigital graphene ion-sensitive field-effect transistors (ISFETs) with a nitrate ion-sensitive membrane (ISM). The work aims to maximize nitrate ion transport through the nitrate ISM, while achieving high device transconductance by evaluating graphene layer thickness, optimizing channel width-to-length ratio (RWL), and enlarging total sensing area. The captured nitrate ions by the nitrate ISM induce surface potential changes that are transduced into electrical signals by graphene, manifested as the Dirac point shifts. The device exhibits Nernst response behavior under ultralow concentrations, achieving a sensitivity of 28 mV/decade and establishing a record low limit of detection of 0.041 ppt (4.8 × 10−13 M). Additionally, the sensor showed a wide linear detection range from 0.1 ppt (1.2 × 10−12 M) to 100 ppm (1.2 × 10−3 M). Furthermore, successful detection of nitrate in tap and snow water was demonstrated with high accuracy, indicating promising applications to drinking water safety and environmental water quality control. [ABSTRACT FROM AUTHOR]

The components and sources of dissolved organic matter (DOM) in urban water bodies are extremely complicated due to multiple environmental and anthropogenic factors. The comprehensive analysis of the DOM molecular composition is crucial for deep understanding their reactivities, environmental behaviors, and fates in aquatic ecosystems, thereby improving our ability to monitor and protect aquatic systems. In this study, ultraviolet–visible spectroscopy (UV–Vis) and excitation-emission matrix fluorescence spectroscopy (EEM) were combined with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) to investigate the optical and molecular characteristics of DOM in the Yongding River (YDH) and Beiyun River (BYH), two typical urban rivers with distinct landscapes and anthropogenic influences in Beijing, China. Results showed that DOM in BYH was mainly composed of proteins from microbial and sewage sources with low aromaticity, molecular weight, and abundant saturated labile compounds, along with a significant presence of anthropogenic surfactants. Whereas the YDH was primarily composed of humic substances from plant- and soil-derived terrestrials, with higher aromaticity humification, unsaturation and refractory compounds. In addition, due to the accumulation of refractory compounds, the concentration of DOC and the degree of organic pollution increased from upstream to downstream of the rivers, manifested as an increase in humification and molecular weight at the molecular level. Our results confirmed that landscape urbanization broadly modified the structure of DOM to be more aliphatic and saturated. The findings of this study will provide important insights into the chemdiverse molecular composition of DOM undergoing different influences from human activities and provide significant guidance for water quality management in urban river ecosystems. Highlights: Distinct molecular composition was observed due to different landscapes and anthropogenic influences. The humification degree, molecular weight, and aromatics of DOM were higher in YDH than BYH. Terrestrial humic materials dominated in YDH, while sewage and microbial-derived proteins dominated in BYH. More anthropogenic surfactants were detected, especially in sewage influenced rivers. [ABSTRACT FROM AUTHOR]