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This study used an integrated approach to mainly assess the water quality of paddy field during cultivation and quantify its equivalent ecological damages. Accordingly, an isolated pilot area with 0.6 ha and subsurface drainage pipes was prepared for flow measurement and multiple pollutant examination (DO, EC, pH, COD, TKN, TN, TP, NO 3 , butachlor) under controlled condition during 94 days of rice cultivation. Based on life cycle impact assessment (LCIA) database, the indices of ReCiPe (2016) were used to convert the examined nutrient and herbicide pollution. Results showed that TKN and TP were significant pollutants and reached the maximum concentrations of 7.2 and 4.9 mg/L in pilot outflow, respectively. Here, their average discharged loads were 56.2 gN/day and 45.3 gP/day. These loads equal leaching 8.5% and 9.4% of applied urea and phosphate fertilizers, respectively. The nutrient export coefficients were 8.4 kgN/ha and 6.8 kgP/ha. Nevertheless, the majority of this pollution was transferred by inflow. The net export coefficients were 0.3 kgN/ha and 2.6 kgP/ha while net leaching rates were 0.3%TN and 3.3%TP. The trend of combined ecological damages also showed that the 11-17th day of cultivation imposed the highest ecological risks. The state-of-the-art index of ecological footprint per food production estimates the equivalent ratio of lost lives by impaired ecosystem against lives saved from starvation. This index showed that 7% of the potential of produced paddy rice in this area for saving lives would be spoiled by releasing pollution to the terrestrial ecosystem in the long term. Yet, it can be enhanced as a matter of direct discharge to the freshwater. Therefore, using suitable agricultural operations or improving farm management practices for pollution abatement or assimilation potential is highly recommended., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Surface water pollution has always posed a serious challenge to water quality management. Improving water quality management requires figuring out how to comprehend water quality conditions scientifically and effectively as well as quantitatively identify regional pollution sources. In this study, Xianghai Lake, a typical lake-type wetland on the Northeast China Plain, was taken as the research area. Based on a geographic information system (GIS) method and 11 water quality parameters, the single-factor evaluation and comprehensive water quality index (WQI) methods were used to comprehensively evaluate the water quality of the lake-type wetland in the level period. Four key water quality parameters were determined by the principal component analysis (PCA) method, and more convenient comprehensive water quality evaluation models, the minimum WQI considering weights (WQI min-w ) and the minimum WQI without considering weights (WQI min-nw ) were established. The multiple statistical method and the absolute principal component score-multiple liner regression (APCS-MLR) model were combined to analyse the lake pollution sources based on the spatial changes in pollutants. The findings demonstrated that the WQI min-nw model's water quality evaluation outcome was more accurate when weights were not taken into account. The WQI min-nw model can be used as a simple and convenient way to comprehend the variations in water quality in wetlands of lakes and reservoirs. It was concluded that the comprehensive water quality in the study area was at a "medium" level, and COD Mn was the main limiting factor. Nonpoint source pollution (such as agricultural planting and livestock breeding) was the most important factor affecting the water quality of Xianghai Lake (with a comprehensive contribution rate of 31.65%). The comprehensive contribution rates of sediment endogenous and geological sources, phytoplankton and other plants, and water diversion and other hydrodynamic impacts accounted for 25.12%, 19.65%, and 23.58% of the total impact, respectively. This study can provide a scientific method for water quality assessment and management of lake wetlands, and an effective support for migration of migratory birds, habitat protection and grain production security., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

A historical perspective of river BOD/DO modeling using the Streeter-Phelps equation in the past century is presented with cases in the United States, Taiwan, and India. The main emphasis is on the regulatory aspects of modeling during the five decades following the passage of the 1972 Clean Water Act (CWA) in the United States. Success of the CWA in river clean-up is demonstrated via BOD/DO modeling for management use. Continuing interests outside the United States in anaerobic rivers and eutrophication-related low dissolved oxygen problems are serving as new proving ground for applications of river BOD/DO modeling. In addition, road blocks on BOD/DO modeling under future conditions for water quality management are outlined. PRACTITIONER POINTS: Water quality-based control to set end-of-pipe limits prior to 1970s Switched to technology-based control following the 1972 Clean Water Act Back to water quality-based control with field data support since 1980 Modeling to support technology-based and water quality-based control for renewed river BOD/DO problems in the 21 st century., (© 2023 The Author. Water Environment Research published by Wiley Periodicals LLC on behalf of Water Environment Federation.)

Water is an important resource, and it is a worldwide struggle to provide water of good quality to the whole population. Despite good governing laws and guidelines set in place to help protect the water resources and ensure it is of good quality for various consumers, the water quality in South Africa is worsening due to lack of management. The deteriorating infrastructure is becoming progressively worse, due to corruption and insufficient funds. The ever-increasing number of toxicants, as well as the identification of emerging chemicals of concern, are also challenges South Africa is facing. Chemical analysis cannot determine the total biological effect of a mixture of chemical compounds, but this shortcoming can be addressed by adding effect-based methods (EBMs) to water quality monitoring programmes. In this paper, the current status of water quality monitoring in South Africa is discussed, as well as the capacity of the country to add EBMs to its water quality monitoring programmes to protect and improve human and animal life. Created in Biorender.com., (© 2022. The Author(s).)

Multi-factor risk assessment is an important prerequisite for water quality protection and the safe operation of mega hydro-projects. As the largest long-distance inter-basin water diversion project in the world, the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC) has been in operation for 8 years and has benefited 79 million people along the canal. However, concerns have been raised in recent years about the potential negative effects of abnormal algal proliferation in the MRSNWDPC. It is very important for the safety of water supply to carry out relevant risk analysis and formulate regulatory management. In order to quantitatively evaluate the risk of algal proliferation in the MRSNWDPC under the influence of multiple factors, a multivariate risk assessment method based on Vine Copula theory and Monte Carlo simulation was proposed. Five key factors (water temperature, flow velocity, flow rate, algal cell density, and dissolved oxygen) were used and multiple dependency models in each section of the MRSNWDPC from January 2016 to January 2019 were established to study the risk of algal proliferation under multiple scenarios. The results demonstrate that water temperature can be used as an appropriate early-warning indicator of algal proliferation. The early-warning interval (unit: °C) of water temperature in the upper, middle, and lower reaches are 26-29°C, 23-26°C, and 21-23°C, respectively. Unlike bivariate analysis, the multiple dependency model describes the relationship between variables more accurately and enriches the scenarios of multiple conditional probabilities. When the water temperature fluctuates in the early-warning interval, regulating the upstream, midstream, and downstream flow velocity to be higher than 0.6 m/s, 0.5 m/s, and 0.6 m/s, respectively, can effectively reduce the risk of algal proliferation. This research not only provides a reference for the ecological control of algae in the MRSNWDPC and similar mega hydro-projects but also enriches the application of the Vine Copula theory coupled with the random sampling method for multi-variable risk analysis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Although integrated simulation-optimization modeling can provide a comprehensive and reliable analysis for water quality management (WQM), it is usually not easy to implement in practice. This study proposed a new efficient simulation-optimization modeling approach by leveraging the power of data-driven modeling, to support WQM under various uncertainties. A water quality simulation model is integrated with the optimization model, and then substituted by a series of numerical surrogate models based on inexact linear regression. The transformation can significantly reduce the computational burden and make it possible to implement uncertainty quantification through hybrid inexact programming. The proposed model incorporates interval quadratic programming and credibility constrained programming to deal with nonlinearity and various uncertainties associated with the management system. The proposed approach is applied to a real case study of the Grand River watershed in Canada for controlling phosphorus concentration in river water. The Grand River Simulation Model (GRSM) is employed as the physical simulation model to estimate the total phosphorus concentration in the river. Interval solutions under different confidence levels of violating the effluent standards were obtained, which can be used to generate optimal phosphorus control strategies. The results indicate the proposed data-driven interval credibility constrained quadratic programming (DICCQP) model is able to provide reliable and robust solutions for WQM by considering nonlinearity and various uncertainties while maintaining a high computational efficiency. The proposed new framework can be extended and applied to the other watersheds. The high efficiency of the proposed model makes it possible to solve large-scale complex water quality management and planning problems., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Rosetta Branch (RB) is the Nile western branch, which is considered the main freshwater resource for the western governorates of the Nile Delta, in addition to El-Beheira governorate, Egypt. This work aims to investigate the effectiveness of applying six feasible water quality management scenarios for the RB at Kafr Elzayat (KZ) City, where two major pollution sources (industrial companies and the Tala Drain) are discharging their effluents to the branch. A hydro-ecological model for the branch was developed based on MIKE 11 modeling system (3 modules). The developed model was calibrated using field hydrodynamic and water quality records for the branch during the period from November 2014 to August 2015. The calibrated model results were considered a base case for the simulated management scenarios. According to the study results, diverting the Tala Drain effluents to the nearest main drain will significantly enhance the branch water quality, while diverting of the industrial companies' effluents to the city sewer system will significantly enhance the water quality conditions downstream of the study. The expected scenario of decreasing the RB discharge, due to future climatic changes and/or construction of the new Ethiopian dams, will significantly affect the RB water quality status in a negative way. Urgent management plans, based on the presented scenarios results (or a combination of them), should be applied to enhance the branch water quality. The Tala Drain should be, at least, provided by a water quality treatment plant, and the branch freshwater discharge should be increased., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Nutrient export from agricultural areas is among the main contributors to water pollution in various watersheds. Agricultural Beneficial Management Practices (BMPs) are commonly used to reduce excessive nutrient runoff and improve water quality. The successful uptake of BMPs not only depends on their effectiveness but also on their costs of implementation. This study conducts a set of cost-effectiveness analyses to help stakeholders identify their preferred combinations of BMPs in the Qu'Appelle River Basin, a typical watershed in the Canadian Prairies. The considered BMPs are related to cattle and cropping farms and are initially selected by agricultural producers in this region. The analyses use a water quality model to estimate the impact of implementing BMPs on nutrient export, and the cost estimation model to approximate the cost of implementing BMPs at tributary and watershed scales. Our results show that BMPs' effectiveness, total costs of implementation and costs per kilogram of nutrient abatement vary between tributaries. However, wetland conservation is among the optimal practices to improve water quality across the watershed. It is also found that the rates of BMP adoption by stakeholders can influence the effectiveness of practices in a large watershed scale, which highlights the importance of stakeholder engagement in water quality management. This type of analyses can help stakeholders choose single or a combination of BMPs according to their available budget and acceptable levels of reduction in nutrients., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Effective river water quality management and planning is a complex issue challenged by various complexities and uncertainties. A simulation-based interval chance-constrained quadratic programming (ICCQP) model is developed for the seasonal planning of water quality management (WQM) under various uncertainties. The proposed model incorporates interval quadratic programming, chance-constrained programming, and a seasonal water quality simulation model within a general framework for WQM. Uncertainties associated with the objective and the coefficients in the left-hand sides of the constraints are tackled as intervals. Meanwhile, parameter uncertainties on the right-hand sides are characterized using probability distributions. Nonlinearities in the cost function are reflected by quadratic programming. A multi-segment water quality model is used to simulate the dynamic interactions between wastewater discharges and river water quality. The proposed ICCQP-WQM model is applied in a real case study for the control of total phosphorus (TP) in the central Grand River in Ontario, Canada. The results demonstrate that the proposed model is able to incorporate uncertainties expressed as intervals and probability information into an optimization framework and provide interval solutions. Thus, different cost-effective schemes for seasonal WQM could be generated. The results show the Kitchener wastewater treatment plant (WWTP) affects the value of the objective function more than the other WWTPs in the study area. It is also found that the Kitchener WWTP's cost accounts for the highest proportion (approximately 35.1-37.9%) of the total annual cost, which implies the control of TP at the Kitchener plant is the most important to the system. Moreover, river water TP standards in spring and autumn are usually difficult to meet, indicating different TP control strategies are needed in these two seasons. The generated results are valuable for local decision makers to generate TP control strategies, and also to identify optimized solutions under various uncertainties. The proposed ICCQP-WQM model can be extended to other watersheds to support effective water quality management and planning., (Copyright © 2020 Elsevier Inc. All rights reserved.)

There has been a growing concern in the dry zone of Sri Lanka that the contaminated surface and ground waters and ecohydrological changes are possibly linked to a number of health issues and overall well-being of village communities, including the widespread renal failure termed as chronic kidney disease of unknown aetiology (CKDu). Two types of data were collected for this study, namely qualitative data from key informant interviews and abstracts of 33 peer-reviewed research articles during the past decade. A total of 35 key informants were recruited, mainly from communities in the northern dry zone region where a higher number of CKDu patients have been reported. The interview transcripts were examined using a range of techniques including content, thematic and semantic network analyses. The findings of the research articles were explored through a word cloud analysis. The study indicates that CKDu seems to be influenced by multifactorial ecohydrological changes linked to anthropogenic stressors such as inefficient use of fertilisers and weedicides in agriculture. This situation is made further complicated by factors such as high fluoride level and hardness of local groundwater. Inappropriate agricultural and water policies and poor governance are other factors, particularly little or no restriction on the use of agrochemicals and pumping of groundwater and limited access to clean municipal water supplies. The key informants indicated grave concerns on the flow on effects of poor surface and groundwater quality in the region on health, livelihood and well-being of communities. There are also inevitable economic implications such as loss of human capital and families losing assets to pay for ongoing health issues. The study highlighted the need for more in-depth research to better understand how the surface and groundwater quality influence CKDu and other health conditions in the region. Furthermore, there is a need for implementing an active water quality management strategy with emphasis on developing locally relevant guidelines, educational programmes and integrated heath monitoring programmes in the region.

A simulation-based interval stochastic bi-level multi-objective programming (SISBLMOP) model was proposed in this research, through integrating the global nutrient export from watersheds model, interval parameter programming and stochastic chance-constrained programming into a general bi-level multi-objective programming framework. The SISBLMOP model can handle multiple uncertainties expressed as discrete intervals and probability density functions in both the simulation and optimization processes. System complexities, including the hierarchy structure of upper- and lower-level decision makers, can also be addressed in the model. The proposed model is applied to a real-world case study of the Xinfengjiang Reservoir Watershed in South China to identify the satisfactory implementation levels of multiple best management practices (BMPs). The model results show that multiple BMP schemes for water quality management can be obtained under different upper- and lower-level decision-making and risk-violation scenarios, reflecting the cooperation and gaming results of the two-level decision makers. Consequently, the corresponding BMP implementation costs are acceptable to both the upper- and lower-level decision makers. The model is widely applicable and can be effectively used for water quality management under multiple uncertainties and complexities., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

In this study, a Bayesian risk-induced interval stochastic modeling framework (BRISF) is proposed for planning effluent trading program among point and nonpoint sources as well as identifying interactions of important trading factors under system risk. BRISF incorporates nutrient fate modeling with soil and water assessment tool (SWAT), Bayesian inference with random walk Metropolis algorithm (RWM), and constraint-violation risk-based two-stage stochastic programming (CRTSP) within a general framework. Bayesian inference is employed for uncertainty analysis of SWAT model parameters and uncertain prediction of nutrient loadings; this process provides the random inputs for optimization process. CRTSP is capable of dealing with multiple uncertainties in modeling effluent trading program as well as system risk of environmental allowance violation. BRISF is applied to a real case of Xiangxihe watershed in China for water quality management. Solutions for optimal trading scheme corresponding to different risk levels are generated. Thousands of scenarios are examined to analyze the individual and interactive effects of trading ratios and treatment rates on trading system. Comparison between cross-industry and intra-industry effluent trading scheme is also conducted. It is proved that cross-industry trading would bring about higher benefit with reduced pollution loading; cross-industry effluent trading scheme would be recommended to achieve optimal water quality management and system benefit., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Macroinvertebrate community assessment is used in most US states to evaluate stream health under the Clean Water Act. While water quality assessment and impairment determinations are reported to the US Environmental Protection Agency, there is no national summary of biological assessment findings. The objective of this work was to determine the national extent of invertebrate-based impairments and to identify pollutants primarily responsible for those impairments. Evaluation of state data in the US Environmental Protection Agency's Assessment and Total Maximum Daily Load Tracking and Implementation System database revealed considerable differences in reporting approaches and terminologies including differences in if and how states report specific biological assessment findings. Only 15% of waters impaired for aquatic life could be identified as having impairments determined by biological assessments (e.g., invertebrates, fish, periphyton); approximately one-third of these were associated with macroinvertebrate bioassessment. Nearly 650 invertebrate-impaired waters were identified nationwide, and sediment was the most common pollutant in bedded (63%) and suspended (9%) forms. This finding is not unexpected, given previous work on the negative impacts of sediment on aquatic life, and highlights the need to more specifically identify the mechanisms driving sediment impairments in order to design effective remediation plans. It also reinforces the importance of efforts to derive sediment-specific biological indices and numerical sediment quality guidelines. Standardization of state reporting approaches and terminology would significantly increase the potential application of water quality assessment data, reveal national trends, and encourage sharing of best practices to facilitate the attainment of water quality goals.

In this study, a multi-level-factorial risk-inference-based possibilistic-probabilistic programming (MRPP) method is proposed for supporting water quality management under multiple uncertainties. The MRPP method can handle uncertainties expressed as fuzzy-random-boundary intervals, probability distributions, and interval numbers, and analyze the effects of uncertainties as well as their interactions on modeling outputs. It is applied to plan water quality management in the Xiangxihe watershed. Results reveal that a lower probability of satisfying the objective function (θ) as well as a higher probability of violating environmental constraints (q i ) would correspond to a higher system benefit with an increased risk of violating system feasibility. Chemical plants are the major contributors to biological oxygen demand (BOD) and total phosphorus (TP) discharges; total nitrogen (TN) would be mainly discharged by crop farming. It is also discovered that optimistic decision makers should pay more attention to the interactions between chemical plant and water supply, while decision makers who possess a risk-averse attitude would focus on the interactive effect of q i and benefit of water supply. The findings can help enhance the model's applicability and identify a suitable water quality management policy for environmental sustainability according to the practical situations.

Water authorities and drinking water companies are challenged with the question if, where and how to abate contaminants of emerging concern in the urban water cycle. The most effective strategy under given conditions is often unclear to these stakeholders as it requires insight into several aspects of the contaminants such as sources, properties, and mitigation options. Furthermore the various parties in the urban water cycle are not always aware of each other's requirements and priorities. Processes to set priorities and come to agreements are lacking, hampering the articulation and implementation of possible solutions. To support decision makers with this task, a decision support system was developed to serve as a point of departure for getting the relevant stakeholders together and finding common ground. The decision support system was iteratively developed in stages. Stakeholders were interviewed and a decision support system prototype developed. Subsequently, this prototype was evaluated by the stakeholders and adjusted accordingly. The iterative process lead to a final system focused on the management of contaminants of emerging concern within the urban water cycle, from wastewater, surface water and groundwater to drinking water, that suggests mitigation methods beyond technical solutions. Possible wastewater and drinking water treatment techniques in combination with decentralised and non-technical methods were taken into account in an integrated way. The system contains background information on contaminants of emerging concern such as physical/chemical characteristics, toxicity and legislative frameworks, water cycle entrance pathways and a database with associated possible mitigation methods. Monitoring data can be uploaded to assess environmental and human health risks in a specific water system. The developed system was received with great interest by potential users, and implemented in an international water cycle network., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

This study was conducted to identify the key factors related to the spatiotemporal variations in phytoplankton abundance in a subtropical reservoir from 2006 to 2010 and to assist in developing strategies for water quality management. Dynamic factor analysis (DFA), a dimension-reduction technique, was used to identify interactions between explanatory variables (i.e., environmental variables) and abundance (biovolume) of predominant phytoplankton classes. The optimal DFA model significantly described the dynamic changes in abundances of predominant phytoplankton groups (including dinoflagellates, diatoms, and green algae) at five monitoring sites. Water temperature, electrical conductivity, water level, nutrients (total phosphorus, NO 3 -N, and NH 3 -N), macro-zooplankton, and zooplankton were the key factors affecting the dynamics of aforementioned phytoplankton. Therefore, transformations of nutrients and reactions between water quality variables and aforementioned processes altered by hydrological conditions may also control the abundance dynamics of phytoplankton, which may represent common trends in the DFA model. The meandering shape of Shihmen Reservoir and its surrounding rivers caused a complex interplay between hydrological conditions and abiotic and biotic variables, resulting in phytoplankton abundance that could not be estimated using certain variables. Additional water quality and hydrological variables at surrounding rivers and monitoring plans should be executed a few days before and after reservoir operations and heavy storm, which would assist in developing site-specific preventive strategies to control phytoplankton abundance.

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

This study provides a comprehensive analysis of the water quality in five major rivers in Türkiye: Sakarya, Yeşilırmak, Kızılırmak, Seyhan Rivers, and Niğde Creek. Utilizing hydrochemical diagrams and the River Pollution Index (RPI), we assess the temporal and spatial variations in water quality over the past three decades. The hydrochemical characteristics reveal a dominant calcium-bicarbonate (Ca-HCO3) type water, indicating strong geological control primarily influenced by carbonate weathering. Seasonal variations and anthropogenic influences, particularly agricultural runoff and industrial discharge, contribute to significant changes in ion concentrations, especially in the Kızılırmak and Yeşilırmak Rivers. The RPI results classify these rivers as 'Severely polluted' to 'Moderately polluted', driven by high levels of suspended solids and biochemical oxygen demand. This study underscores the urgent need for tailored pollution control measures and sustainable water management practices in order to mitigate the impacts of anthropogenic activities and protect the ecological health of these vital water resources. The findings provide a robust framework for future research and policymaking to enhance water quality monitoring and management strategies in the region. [ABSTRACT FROM AUTHOR]

The real-time monitoring and evaluation of water quality provides a scientific basis for water resource management and promotes regional sustainable development. This study established a database using Landsat-8 satellite data and water quality data from the Ningxia Yellow River basin in China, spanning 2021 to 2023, and this paper proposes a custom residual convolutional neural network model with a hybrid attention mechanism, referred to as PCWA-ResCNN. The accuracy of the model in predicting turbidity, permanganate, ammonia nitrogen, and dissolved oxygen concentration was more than 95%. Compared to convolutional neural networks and long short-term memory models, this model performed better in predicting water quality parameters with significantly improved prediction performance. In terms of spatial distribution, the pollution degree in the middle reaches of the basin is relatively serious. However, the overall water quality is good, being mainly Class I and Class II water quality. The hybrid model established in this paper can better capture the complex nonlinear relationship between the observed values and the surface water reflectance, showing strong robustness. This model can be used for the water quality monitoring of complex inland rivers and lakes, and it can also provide effective support for relevant government departments to formulate scientific and reasonable water quality management policies. [ABSTRACT FROM AUTHOR]

This study conducted a comprehensive analysis of flood control and water quality simulation for the urban water system in Runan County, with the aim of evaluating the conditions of urban waterlogging and changes in water quality before and after the management of the urban water system. This provides significant scientific basis and technical support for the water system management and water environment protection in Runan County and similar urban areas. The study employed a rapid modeling technique assisted by ArcGIS and the SWMM (Storm Water Management Model) for the first time to conduct an in-depth simulation study on the regulation of floods and the improvement of water quality in the central urban area of Runan County. By simulating design rainfall scenarios of 1-h and 6-h durations for return periods of 3, 5, 10, 20, and 50 years, a detailed analysis was performed on the extent of flood inundation in the urban area of Runan County under different rainfall conditions and the flood detention capacity of the river channels. Furthermore, for rainfall scenarios of 3, 10, and 50-year return periods, a meticulous simulation analysis was conducted on the water quality response and the attenuation process of pollutant concentrations in the main river channels. The results of the study revealed the significant effects of river channel regulation on enhancing the connectivity of the water system and the capacity for flood control: the regulation of river channels significantly increased the cross-sectional area and the volume of water in the channels, leading to a reduction in pollutant concentrations along the direction of the river flow. Comparing the data before and after the management, the extent of the flood-prone area and the volume of water entering the river in the non-managed water system were 1.4 times and 59% of those after management, respectively, indicating that the comprehensive management of the water system significantly reduced the risk of waterlogging. In addition, the self-purification capacity of the river channels and the efficiency of pollutant degradation improved significantly after the management, effectively enhancing the water quality environment. [ABSTRACT FROM AUTHOR]

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model's performance. Additionally, a hybrid model was employed to calculate the ideal parameter values for the LSTM model, which helped overcome the nonstationarity, unpredictability, and nonlinearity of the data about the water quality parameters. This model recruited the COOT method. The original weekly water quality values at the Vaigai River, Madurai, Tamil Nadu, India, were tested using the suggested hybrid model. An independent LSTM, the hybrid optimisation method takes its cues from the cuckoo bird's reproductive strategy and a novel meta-heuristic optimisation technique dubbed COOT, which is based on the behaviour of a flock of coot birds. If implemented, the suggested hybrid model might serve as an alternate framework for water quality prediction, laying the groundwork for basin-wide efforts to manage water quality and control pollutants. [ABSTRACT FROM AUTHOR]

We detail the effect of a small stream of dense inflow that significantly altered the stratification and water quality in a constructed water body in northern British Columbia, Canada. As the dense inflow passed through the epilimnion of the steep‐sided lake, it entrained relatively large quantities of water. The resulting mixture of dense inflow and entrained epilimnetic water sank to the bottom of the lake. The removal of water from the epilimnion due to entrainment reduced the epilimnetic thickness. This opposes the normal process of epilimnetic deepening due to wind and convective cooling. The flux of fluid entrained into the dense inflow was calculated to be between 4 and 14 times the inflow, depending primarily on the thickness of the epilimnion. The entrainment had four major effects: (1) it reduced the residence time of the epilimnion from half a year to less than a month; (2) it removed the freshwater cap that resulted from spring ice melt; (3) it enabled fall turnover, which further enhanced deep oxygen content and helped to prevent meromixis from developing in the lake; and (4) it produced a rapid decline in contaminant (i.e., zinc) concentrations in the epilimnion, which received dissolved metals inputs from oxidized sulfide minerals exposed in the subaerial walls of the lake. Given the wide variety of inflows to inland water bodies, some of which are at least seasonally dense, an understanding of the mechanisms detailed here can inform lake management in general, and more specifically, management of water quality in mine‐impacted water bodies. [ABSTRACT FROM AUTHOR]

Biofloc technology (BFT) has emerged as a promising solution to address challenges in the expanding field of aquaculture. This review synthesizes recent literature to provide a comprehensive overview of biofloc development techniques, their applications, and avenues for future improvement. BFT offers significant advantages, including enhanced pond health, improved water quality, and reduced operational costs. By cultivating and managing heterotrophic bacteria within the system, BFT minimizes water exchange and facilitates the formation of bioflocs that serve as nutrient-rich food sources for cultured organisms. This review explores the mechanisms underlying biofloc formation, their impact on water quality management, and their role in promoting the growth and health of aquaculture species. However, the implementation of bioflocs in newly established ponds presents challenges, such as limited control over microbial dynamics and the potential presence of bacterial pathogens, which can result in economic losses and jeopardize animal welfare. This review underscores the importance of ongoing innovation and research to optimize biofloc technologies, addressing issues viz. ammonia accumulation and the search for cost-effective feed alternatives. By examining emerging trends and technological advancements, this review underscores the potential of biofloc development techniques to enhance the sustainability and efficiency of aquaculture practices. [ABSTRACT FROM AUTHOR]

Water quality characterization of Himalayan streams that provide variety of ecosystem services including drinking water to zooming population is indispensable. In this background, water quality characterization of the Rambiarrah watershed was carried out from Autumn 2016 to Summer 2018, which revealed that all the drinking water quality parameters were within the prescribed limits of the World Health Organization (WHO) and Beauru of Indian Standards (BIS) except total coliform (TC) and fecal coliform (FC). Hierarchical Cluster Analysis (HCA) displayed 3 clusters, cluster I, II, III with least, moderate and high anthropogenic disturbance respectively. Principal component analysis (PCA) recognized 4 principal components (PCs) and explained a cumulative variance of 70.3%. Redundancy analysis (RDA) highlighted that agriculture, built-up, and plantation land-use classes explained 55% of the variance, while as 57% of the variance was highlighted by observable sensory features (waste disposal, sewage inflow and mineral extraction). The water quality index (WQI) varied from excellent to very poor water quality category. Physico-chemical parameters like pH, EC, TDS, TH, Ca2+, and Mg2+ decipher significant effective weights at upstream sites while as microbial load (TC and FC) highlighted significant effective weights at mid and downstream sites. The spatial–temporal pattern of water quality and aquascape landscape interrelationship identified in this study will contribute to the water quality management endeavor. [ABSTRACT FROM AUTHOR]

Water quality monitoring is imperative in providing the objective evidence required to make sound decisions about water quality management. This study aimed to examine the water quality status of the Migori River by determining spatio-seasonal variations in water quality parameters, along with associated influencing factors and potential health risks. Therefore, eighteen physico-chemical and bacteriological variables were sampled and analyzed monthly for six months covering the wet and dry seasons from the upstream, midstream, and downstream stations, and a health risk survey was conducted with 90 watershed households. ANOVA and T-test were used to test for the significant spatial and seasonal variations (p < 0.05), respectively; whereas Pearson's correlation was used to identify relationships between parameters. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) were used to find various spatial patterns in the river water quality datasets, while the Canadian Council of Ministries of the Environment Water Quality Index (CCME-WQI) showed the suitability status of the river water quality. The assessed variables showed significant seasonal variability but no significant spatial differences in the river. HCA generated 3 clusters suggesting that water quality deteriorated downstream from the upstream of the watershed. The PCA extracted four PCs explaining 80.5% of the total variance, which suggested that the variations in water quality are attributed to point and nonpoint sources of pollution. While most of the physico-chemical variables were within maximum permissible limits, the bacteriological levels exceeded the prescribed standards. The index ranked the river's water condition between 'poor' to 'marginal'; upstream has better water condition that gradually decreases toward the downstream, and water quality is better in the wet season than the dry season. The study revealed that the water of the Migori River is polluted and potentially hazardous for human usage, and thus suitable pollution control measures are urgently needed to safeguard public health. [ABSTRACT FROM AUTHOR]

Intensifying anthropogenic disturbances have caused water pollution in China in recent decades. China has a vast territory with diverse climate conditions, land use types, and human activities, leading to significant water quality variability. However, few studies have investigated nationwide spatiotemporal patterns of key water quality parameters. In this study, we analyze monthly water quality observations from 3647 gauge stations to understand how water quality changes over time and space in China. We group the stations by water resource regions and adopt Python and SPSS to analyze the spatiotemporal variability and intercorrelations of eight water quality parameters. Results indicate that the concentrations of biochemical oxygen demand of 5 days (BOD5), chemical oxygen demand (COD), dissolved oxygen (DO), ammonia nitrogen (NH3-N), total nitrogen (TN), and total phosphorus (TP) show similar spatial patterns, with higher concentrations in the northern parts than the southern regions of China. The concentrations of COD and TP are higher in the rainy season than in the dry season, while DO, NH3-N, and TN show the opposite seasonal patterns. Strong positive correlations were found between BOD and COD, NH3-N and TP. The annual cumulative distribution figures demonstrate that all parameters showed slightly lower concentrations in 2022 and 2023 than in 2021, except for DO and TN. The TN/TP ratios across different water resource regions in China are significantly higher than 16, indicating that phosphorus is the limiting factor of eutrophication. This investigation provides a comprehensive understanding of the spatiotemporal variability of water quality parameters across China. The results of this study are highly valuable for investigating mechanisms regulating water quality across large spatial scales, thus providing valuable implications for improving water quality and mitigating water pollution. [ABSTRACT FROM AUTHOR]

The Yellow River basin, an area of extreme water scarcity, has faced significant challenges in water quality management due to rapid economic and social development since the 1980s. This study analyzes the water quality evolution over nearly 40 years, focusing on primary pollutants like chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and permanganate index (CODMn). In the 1990s, sections of the river were severely polluted, with some areas failing to meet the lowest national standards. In 2000, 32% of the river water was classified as inferior Class V. However, enhanced water resource management and stricter pollutant regulations introduced after 2000 have significantly improved water quality. By 2010, water quality reached its nadir, with 16% of water classified as inferior Class V and 25% as Class IV–V. By 2020, water quality showed marked improvement, with a significant reduction in segments classified as inferior Class V and Class IV–V. Recent years have seen water quality stabilize, with COD meeting Class I standards and NH3-N and CODMn meeting Class II standards based on national criteria. The study also highlights discrepancies in water quality between the mainstream and tributaries of the Yellow River. While the mainstream generally maintains good water quality, many tributaries remain severely polluted. In 2022, 85% of the water in tributaries was classified as Class I to III, 12.3% as Class IV to V, and only 2.7% as Class V. However, all water in the mainstream reached Class I–III, with 86% achieving Class II and 14% achieving Class I. A detailed analysis of the Huayuankou section over the past three decades shows a general decline in pollution indicators. Seasonal water quality fluctuations, correlated with flow rates and temperatures, were observed, often exhibiting normal distribution patterns. These findings underscore the effectiveness of sustained pollution control and the need for continuous, adaptive management strategies to improve and maintain water quality in the Yellow River basin. [ABSTRACT FROM AUTHOR]

Rapid urbanization and industrialization have caused water quality issues in urban rivers. Appropriate measures based on water quality monitoring systems and prediction methods are needed for water quality management. While South Korea has operated a water quality monitoring system that measures various environmental factors and has accumulated water quality data, a water quality prediction system is not in place. This study suggests a water quality prediction method based on a long short-term model using water quality and meteorological monitoring data. Additionally, we present a derived input set of the prediction model that can improve the prediction model performance. The prediction model's performance was evaluated by the coefficient of determination under various conditions, such as the hyperparameters, temporal resolution of input data, and application of upstream and downstream data. As a result, using the temporal resolution of the input data as hourly data improved predictions by an average of 25.6% over three days of the prediction period compared to daily data. Meanwhile, it was analyzed that the hyperparameters and using upstream and downstream data have a minor effect on the model performance. The results of this study underscore the crucial role of the number, duration, and temporal resolution of available monitoring data in water quality management. [ABSTRACT FROM AUTHOR]