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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]

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]

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]

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]

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]

This research explores the strategic optimization of secondary chlorination in water distribution systems (WDSs), in order to enhance the efficiency of disinfection while mitigating odor and operational costs and promoting sustainability in water quality management. The methodology integrates EPANET simulations for water hydraulic and quality modeling with a deep belief network (DBN) within the deep learning framework for accurate chloric odor prediction. Utilizing the non-dominated sorting genetic algorithm-II (NSGA-II), this methodology systematically balances the objectives of chloride dosage and chloramine formation. It combines a chloric odor intensity assessment, a multi-component kinetic model, and dual-objective optimization to conduct a comparative analysis of case studies on secondary chlorination strategies. The optimal configuration with five secondary chlorination stations reduced chloric odor intensity to 1.20 at a cost of USD 40,020.77 per year in Network A while, with eight stations, chloric odor intensity was reduced to 0.88 at a cost of USD 71,405.38 per year in Network B. The results demonstrate a balanced trade-off between odor intensity and operational cost on one hand and sustainability on the other hand, highlighting the importance of precise chlorine management to improve both the sensory and safety qualities of drinking water while ensuring the sustainable use and management of water resources. [ABSTRACT FROM AUTHOR]

Our research project specifically focuses on evaluating groundwater quality in six West Texas counties. We aim to determine whether environmental changes have any impact on the levels of Total Dissolved Solids (TDS) in the water supplied to the public. To achieve this goal, we will be utilizing advanced machine learning algorithms to analyze TDS levels and create geospatial maps for each year between the 1990s and 2010s. To ensure the accuracy of our data, we have gathered information from two trusted sources: the Texas Water Development Board (TWDB) and the Groundwater Database (GWDB). We have analyzed the TDS and other elemental analyses from TWDB–GWDB lab reports and compared them with the quality cutoff set by the World Health Organization (WHO). Our approach involves a thorough examination of the data to identify any emerging patterns. The machine learning algorithm has been successfully trained and tested, producing highly accurate results that effectively predict water quality. Our results have been validated through extensive testing, highlighting the potential of machine learning approaches in the fields of environmental research. Overall, our findings will contribute to the development of more effective policies and regulations in predicting groundwater quality and improving water resource management in Texas. Therefore, this research provides important information for groundwater protection and the development of plans for water resource use in the future. [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]

Cyanobacteria are cosmopolitan organisms; nonetheless, climate change and eutrophication are increasing the occurrence of cyanobacteria blooms (cyanoblooms), thereby raising the risk of cyanotoxins in water sources used for drinking, agriculture, and livestock. This study aimed to determine the presence of cyanobacteria, including toxigenic cyanobacteria and the occurrence of cyanotoxins in the El Pañe reservoir located in the high-Andean region, Arequipa, Peru, to support water quality management. The study included morphological observation of cyanobacteria, molecular determination of cyanobacteria (16S rRNA analysis), and analysis of cyanotoxins encoding genes (mcyA for microcystins, cyrJ for cylindrospermopsins, sxtl for saxitoxins, and AnaC for anatoxins). In parallel, chemical analysis using Liquid Chromatography coupled with Mass Spectrometry (LC-MS/MS) was performed to detect the presence of cyanotoxins (microcystins, cylindrospermopsin, saxitoxin, and anatoxin, among others) and quantification of Microcystin-LR. Morphological data show the presence of Dolichospermum sp., which was confirmed by molecular analysis. Microcystis sp. was also detected through 16S rRNA analysis and the presence of mcyA gene related to microcystin production was found in both cyanobacteria. Furthermore, microcystin-LR and demethylated microcystin-LR were identified by chemical analysis. The highest concentrations of microcystin-LR were 40.60 and 25.18 µg/L, in May and November 2022, respectively. Microcystins were detected in cyanobacteria biomass. In contrast, toxins in water (dissolved) were not detected. Microcystin concentrations exceeded many times the values established in Peruvian regulation and the World Health Organization (WHO) in water intended for human consumption (1 µg/L). This first comprehensive report integrates morphological, molecular, and chemical data and confirms the presence of two toxigenic cyanobacteria and the presence of microcystins in El Pañe reservoir. This work points out the need to implement continuous monitoring of cyanobacteria and cyanotoxins in the reservoir and effective water management measures to protect the human population from exposure to these contaminants. [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]

In aquaponic farming, there is a potential risk that heavy metals will contaminate the water, which can lead to heavy metal accumulation in the plants. Our research investigated the accumulation of mercury (Hg) and lead (Pb) under aquaponic conditions and the effect of their increased presence on the uptake of other macro- and micronutrients using watercress (Nasturtium officinale) as a model plant. The potential modifying effect of humic acid on heavy metal accumulation was also investigated. Adding Hg and Pb increased the mercury and lead levels of the watercress plants to over 300 µg kg−1, while the addition of humic acid significantly reduced the concentration of both mercury and lead in the plants compared to plants treated with heavy metals alone, from 310.647 µg kg−1 to 196.320 µg kg−1 for Hg and from 313.962 µg kg−1 to 203.508 µg kg−1 for Pb. For Fe and Mn, higher values were obtained for the Hg + humic acid treatments (188.13 mg kg−1 and 6423.92 µg kg−1, respectively) and for the Pb + humic acid treatments (198.26 mg kg−1 and 6454.31 µg kg−1, respectively). Conversely, the Na, K, Cu levels were lower compared to those in plants treated with heavy metals alone. Our results demonstrated that watercress can accumulate mercury, leading to high levels, even above food safety standards, highlighting the importance of water quality control in aquaponic systems. Furthermore, these results suggest that watercress could be used as a natural filter in recirculation systems. The addition of humic acid significantly reduced the accumulation of heavy metals and altered the element content in the plant. [ABSTRACT FROM AUTHOR]

Eutrophication occurs frequently in urban water bodies, and rapid measurement of phosphorus (P) is needed for water quality control, since P has been one of the limiting factors. In this study, approximately 400 water samples were collected from typical urban water bodies in Nanjing city, and Fourier transform infrared attenuated total reflectance spectroscopy (FTIR-ATR) was applied for rapid P determination. Both silicon ATR (Si-ATR) and ZnSe-ATR were employed in the recording of FTIR-ATR spectra, and different algorithms, including partial least squares regression (PLSR), support vector machines for regression (SVRs), extreme learning machines (ELMs), and self-adaptive partial least squares model (SA–PLS), were applied in the analysis of spectra data. The results showed that the water quality varied significantly for different water bodies in different seasons, and both Si-ATR and ZnSe-ATR could achieve good P prediction. The PLSR and SVR models showed poor P prediction effects while the ELM model was excellent, and the SA-PLS model was the best one. For the SA-PLS model, the prediction accuracy of Si-ATR (Rv2 = 0.973, RMSEV = 0.015 mg L−1, RPDV = 6.05) was slightly better than that of ZnSe-ATR (Rv2 = 0.942, RMSEV = 0.011 mg L−1, RPDV = 4.13). Therefore, the FTIR-ATR technology coupled with the SA-PLS model achieved rapid P determination in urban water, providing an effective option for water quality monitoring. [ABSTRACT FROM AUTHOR]

Assessing groundwater quality is essential for ensuring the sustainability of agriculture and ecosystems. This study evaluates groundwater contamination by heavy metals (HMs) using GIS approaches, multivariate statistical analysis (MSA), pollution indices (heavy metal pollution index (HPI), metal index (MI), degree of contamination (Cd), ecological risk index (ERI), and pollution index (PI)), and human health risk assessment (HHRA). The results revealed significant variations in heavy metal concentrations across the study area, with the highest concentrations found in the southern and southeastern parts, characterized by intense agricultural activities and uncontrolled landfills. Statistical analyses indicated both natural and anthropogenic sources of contamination. Pollution indices showed medium to high water pollution levels, with HPI values ranging from 20.23 to 128.60, MI values from 3.34 to 12.17, and Cd values from 2.90 to 11.73, indicating varying degrees of contamination. ERI values suggested a low ecological risk across all samples. However, health risk assessments highlighted significant non-carcinogenic and carcinogenic risks, particularly for children, with TCR values for some heavy metals like Ni and Cr exceeding safe limits, indicating potential health hazards. The findings provide a valuable framework for policymakers to develop targeted strategies for mitigating groundwater contamination and ensuring sustainable water quality management. [ABSTRACT FROM AUTHOR]

The Water Quality Index (WQI) is a tool designed to provide a singular figure representing the overall water quality status of a water body. This study applies Malaysia WQI, National Sanitation Foundation WQI (NSFWQI) and statistical analysis to investigate the impact of diverse pollution sources on the Brunei River's water quality, a critical aquatic ecosystem affected by the rapid escalation of urbanization, industrial activities, and agricultural runoff. Principal component analysis (PCA), expert judgement, and correlation analysis were used to propose parameters for developing Brunei River's WQI. Eight monitoring stations were selected to analyze 16 water quality parameters (pH, water temperature (T), dissolved oxygen (DO), oxidation-reduction potential (ORP), chemical oxygen demand (COD), the five-day biochemical oxygen demand (BOD5), salinity, electrical conductivity (EC), total dissolved solids (TDS), turbidity, total suspended solids (TSS), ammoniacal-nitrogen (NH3-N), fecal coliform (FC), total coliform (TC), phosphate and nitrate (NO3−) in this study. The results showed that NSFWQI classified the Brunei River as moderately polluted, while Malaysia (WQI) status was classified as slightly polluted except for Station Q around the quarry area. Statistical analysis revealed that the primary pollution sources are anthropogenic activities such as quarrying, domestic waste, and agricultural and urban runoff. Other specific areas of concern with low WQI and significant pollution levels are situated at Kampong Ayer Stations (N and J) due to proximal anthropogenic activities. The proposed water quality parameters for developing Brunei River's WQI are pH, DO, EC, FC, NO3−, BOD5, T, TSS, turbidity and phosphate. This study addresses the current pollution status of the Brunei River and sets a precedent for future research emphasizing collaborative data-driven strategies for water quality management. [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]

Natural resource management (NRM) plays a pivotal role in ensuring the sustainability of ecosystems, which are essential for human health and well-being. This systematic review examines the impact of various NRM practices on water quality and their subsequent effects on public health. Specifically, it focuses on interventions such as watershed management, pollution control, land use management, water treatment, and ecosystem restoration. We conducted a comprehensive search across PubMed, Scopus, and Web of Science, supplemented by gray literature from Google Scholar, WHO reports, and government and NGO publications, covering studies published between 2014 and 2024. A total of 42 studies met the inclusion criteria, encompassing diverse geographical regions with significant representation from developing countries. The findings indicate that effective NRM practices, particularly those aimed at reducing pollutants, managing watersheds, and promoting sustainable land use, significantly improve water quality by lowering levels of chemical contaminants, microbial pathogens, and physical pollutants. Improved water quality directly correlates with reduced incidences of waterborne diseases, chronic health conditions from long-term chemical exposure, and acute health effects from immediate pollutant exposure. The review underscores the need for tailored NRM strategies that consider local environmental and socio-economic contexts. It also highlights the importance of community involvement, regulatory frameworks, and continuous monitoring to enhance the effectiveness of NRM interventions. Despite the positive impacts, barriers such as limited financial resources, technical expertise, and community engagement pose challenges to the implementation of these practices. In conclusion, the systematic review demonstrates that comprehensive and context-specific NRM practices are crucial for improving water quality and public health outcomes. Policymakers and practitioners are encouraged to adopt integrated water resource management approaches, prioritize sustainable practices, and engage local communities to achieve long-term health and environmental benefits. [ABSTRACT FROM AUTHOR]

Nitrates can cause severe ecological imbalances in aquatic ecosystems, with considerable consequences for human health. Therefore, monitoring this inorganic form of nitrogen is essential for any water quality management structure. This research was conducted to develop a novel Nitrate Portable Measurement System (NPMS) to monitor nitrate concentrations in water samples. NPMS is a reagent-free ultraviolet system developed using low-cost electronic components. Its operation principle is based on the Beer–Lambert law for measuring nitrate concentrations in water samples through light absorption in the spectral range of 295–315 nm. The system is equipped with a ready-to-use ultraviolet sensor, light emission diode (LED), op-amp, microcontroller, liquid crystal display, quartz cuvette, temperature sensor, and battery. All the components are assembled in a 3D-printed enclosure box, which allows a very compact self-contained equipment with high portability, enabling field and near-real-time measurements. The proposed methodology and the developed instrument were used to analyze multiple nitrate standard solutions. The performance was evaluated in comparison to the Nicolet Evolution 300, a classical UV–Vis spectrophotometer. The results demonstrate a strong correlation between the retrieved measurements by both instruments within the investigated spectral band and for concentrations above 5 mg NO3−/L. [ABSTRACT FROM AUTHOR]

East African lakes support the food and water security of millions of people. Yet, a lack of continuous long-term water quality data for these waterbodies impedes their sustainable management. While satellite-based water quality retrieval methods have been developed for lakes globally, African lakes are typically underrepresented in training data, limiting the applicability of existing methods to the region. Hence, this study aimed to (1) assess the accuracy of existing and newly developed water quality band algorithms for East African lakes and (2) make satellite-derived water quality information easily accessible through a Google Earth Engine application (app), named LAndsat water QUality retrieval tool for east African lakes (LAQUA). We collated a dataset of existing and newly collected in situ surface water quality samples from seven lakes to develop and test Landsat water quality retrieval models. Twenty-one published algorithms were evaluated and compared with newly developed linear and quadratic regression models, to determine the most suitable Landsat band algorithms for chlorophyll-a, total suspended solids (TSS), and Secchi disk depth (SDD) for East African lakes. The three-band algorithm, parameterised using data for East African lakes, proved the most suitable for chlorophyll-a retrieval (R2 = 0.717, p < 0.001, RMSE = 22.917 μg/L), a novel index developed in this study, the Modified Suspended Matter Index (MSMI), was the most accurate for TSS retrieval (R2 = 0.822, p < 0.001, RMSE = 9.006 mg/L), and an existing global model was the most accurate for SDD estimation (R2 = 0.933, p < 0.001, RMSE = 0.073 m). The LAQUA app we developed provides easy access to the best performing retrieval models, facilitating the use of water quality information for management and evidence-informed policy making for East African lakes. [ABSTRACT FROM AUTHOR]

Assessing bacterial communities across water resources is crucial for understanding ecological dynamics and improving water quality management. This study examines the functional profiles of bacterial communities in drinking water resources in Mega Manila, Philippines, including Laguna Lake tributaries, pre-treatment plant sites, groundwater sources, and post-treatment plant sites. Using eDNA sequencing, flux balance analysis, and taxonomy-to-phenotype mapping, we identified metabolic pathways involved in nutrient metabolism, pollutant degradation, antibio- tic synthesis, and nutrient cycling. Despite site variations, there are shared metabolic pathways, suggesting the influence of common ecological factors. Site-specific differences in pathways like ascorbate, aldarate, and phenylalanine metabolism indicate localized environmental adaptations. Antibiotic synthesis pathways, such as streptomycin and polyketide sugar unit biosynthesis, were detected across sites. Bacterial communities in raw and pre-treatment water showed potential for pollutant degradation such as for endocrine-disrupting chemicals. High levels of ammonia-oxidizing and sulfate-reducing bacteria in pre- and post-treatment water suggest active nitrogen removal and pH neutralization, indicating a need to reassess existing water treatment approaches. This study underscores the adaptability of bacterial communities to environmental factors, as well as the importance of considering their functional profiles in assessing drinking water quality resources in urban areas. [ABSTRACT FROM AUTHOR]

Conserving water resources from scarcity and pollution is the basis of water resource management and water quality monitoring programs. However, due to industrialization and population growth in Malaysia, which have resulted in poor water quality in many areas, this program needs to be improved. A smart water quality monitoring system based on the internet of things (IoT) paradigm was designed to analyze water conditions in real time and enable effective water management. Long-range (LoRa) application of the low-power, wide-area networking concept has become a phenomenon in IoT smart monitoring applications. This study proposes the implementation of a LoRa network in a water quality monitoring system-based IoT approach. The LoRa nodes were embedded with measuring sensors pH, turbidity, temperature, total dissolved solids, and dissolved oxygen, in the designated water stations. They operate at a transmission power of 14 dB and a bandwidth of 125 kHz. The network properties were tested with two different antenna gains of 2.1 dBi and 3 dBi, with three different spread factors of 7, 9, and 12. The water stations were located on the Sungai Pantai and Sungai Anak Air Batu rivers on the Universiti Malaya campus, Malaysia. Following a dashboard display and K-means analysis of the water quality data received by the LoRa gateway, it was determined that both rivers are Class II B rivers. The results from the evaluation of LoRa performance on the received strength signal indicator, signal noise ratio, loss packet, and path loss at best were −83 dBm, 7 dB, <0%, and 64.41 dB, respectively, with a minimum received sensitivity of −129.1 dBm. LoRa has demonstrated its efficiency in an urban environment for smart river monitoring purposes. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we investigated the seasonal effects of constructed wetlands on water quality characteristics in a gate dam lake. The research reveals pronounced seasonal changes in the lake's water environment index. During winter, nitrate nitrogen and BOD5 interact significantly, jointly becoming the primary factors restricting lake water quality. In summer, the constructed wetlands effectively improve TN levels. Furthermore, water depth plays a regulatory role in Chl-a content, ammonia–nitrogen, and nitrite–nitrogen by influencing light penetration and water temperature. Urban lakes commonly suffer from nutrient over-enrichment, resulting in water quality deterioration and eutrophication. Constructed wetlands are widely employed for ecological restoration in such lakes but their efficacy in water purification noticeably fluctuates with the seasons. This study takes the constructed wetland of Jinshan Lake as an example. By analyzing the water quality parameters at three depths during both summer and winter, this study explores the influence of the constructed wetland on the water quality of each layer during different seasons and elucidates the potential mechanisms underlying these seasonal effects. The results indicate that the constructed wetland significantly enhances total nitrogen (TN) concentration during summer and exhibits the capacity for nitrate–nitrogen removal in winter. However, its efficacy in removing total phosphorus (TP) is limited, and may even serve as a potential phosphorus (P) source for the lake during winter. Water quality test results of different samples indicated they belong to Class III or IV. Restrictive factors varied across seasons: nitrate–nitrogen and BOD5 jointly affected water quality in winter, whereas TP predominantly constrained water quality in summer. These results could provide a reference for water quality monitoring and management strategies of constructed wetlands in different seasons in Jiangsu Province. [ABSTRACT FROM AUTHOR]

Heavy metal pollution seriously threatens the drinking water safety and ecological environment in karst lead–zinc mines. Fifteen groundwater and surface water samples were collected in a karst lead–zinc mine in Daxin, Chongzuo. Ten heavy metal (Mn, Zn, As, Pb, Cr, Cd, Ni, Co, Cu, and Fe) concentrations were detected. Correlation and cluster analysis were utilized to explore the distribution characteristics and sources. The health risks were appraised using the health risk assessment model. The groundwater had more heavy metal types than the surface water, of which the concentrations and average concentrations exceeded the class III water quality standard. The mine drainage contributed most (65.10%) to the heavy metal concentrations. Pb, Zn, Cd, Mn, Co, Ni, Cu, and Fe primarily originated from the mining of the lead–zinc mine, Cr primarily came from the fuel combustion and wear of metals, and As was primarily connected with the regional geological background. The groundwater had a higher total health risk (5.12 × 10−4 a−1) than the surface water (2.17 × 10−4 a−1). In comparison with the non-carcinogenic risk, the carcinogenic risk increased by three to five orders of magnitude. The carcinogenic risk distribution of Cr and Cd represented the health risk pattern. The drinking pathway posed two to three orders of magnitude the amount of health risks that the dermal contact pathway posed. Children suffered greater health risks. Water security for children should be more strictly controlled. Zn, Cd, Pb, Mn, and Cr must be paid more attention in terms of water quality protection and management. [ABSTRACT FROM AUTHOR]

In southwest Florida, the Caloosahatchee River flows from Lake Okeechobee into a biologically productive tidal estuarine system. A combination of excess water during the wet season, insufficient fresh water in the dry season, and poor quality of the river water are damaging the estuarine ecosystem. To better control the quality and quantity of the water entering the estuary, reservoirs are being constructed to store excess, poor quality water during the wet season and return it to the river for discharge into the estuary at an appropriate time. This stored water is enriched in nutrients and organic carbon. Because of the subtropical nature of the climate in southwest Florida and potential increases in temperature in the future, the return flow of water from the reservoirs must be treated before it can be returned to the river. Hence, an experimental water treatment system was developed and operated to compare biological treatment processes consisting of solely wetland plants versus adding some engineered processes, including slow sand filtration and a combination of slow sand filtration and ultraviolet (UV) treatment. These three treatment trains were operated and monitored through a seasonal cycle in 2021–2022. All three treatment methods significantly reduced the concentrations of nutrients and total organic carbon. While the enhanced engineered wetlands' treatment trains did slightly outperform the wetland train, a comparison of the three process trains showed no statistically significant difference. It was concluded that upscaling of the slow sand filtration and UV process could improve the treatment efficiency, but this change would have to be evaluated within a framework of long-term economic benefits. It was also concluded that the Caloosahatchee River water quality is quite enriched in nutrients so that reservoir storage would increase the organic carbon concentrations, making it imperative that it be treated before being returned to the river. It was also discovered that the green alga Cladophora sp. grew rapidly in the biological treatment tubs and will present a significant challenge for the treatment of the reservoir discharge water using the currently proposed alum treatment. [ABSTRACT FROM AUTHOR]

Change in land use and land cover (LULC) is crucial to freshwater ecosystems as it affects surface runoff, groundwater storage, and sediment and nutrient transport within watershed areas. Ecosystem components such as wetlands, which can contribute to the reduction of water pollution and the enhancement of groundwater recharge, are altered by LULC modifications. This study evaluates how wetlands in the Big Sunflower River Watershed (BSRW) have changed in recent years and quantified their impacts on streamflow, water quality, and groundwater storage using the Soil and Water Assessment Tool (SWAT). The model was well calibrated and validated prior to its application. Our study showed that the maximum increase in wetland areas within the sub-watersheds of interest was 26% from 2008 to 2020. The maximum changes in reduction due to the increase in wetland areas were determined by 2% for streamflow, 37% for total suspended solids, 13% for total phosphorus (TP), 4% for total nitrogen (TN), and the maximum increase in shallow groundwater storage by 90 mm from 2008 to 2020 only in the selected sub-basins. However, the central part of the watershed experienced average declines of groundwater levels up to 176 mm per year due to water withdrawal for irrigation or other uses. This study also found that restoration of 460 to 550 ha of wetlands could increase the reduction of discharge by 20%, sediment by 25%, TN by 18%, and TP by 12%. This study highlights the importance of wetland conservation for water quality improvement and management of groundwater resources. [ABSTRACT FROM AUTHOR]

Due to the high uncertainty of model predictions, it is often challenging to draw definitive conclusions when evaluating river water quality in the context of management options. The major aim of this study is to present a statistical evaluation of the Hydrologic Simulation Program FORTRAN (HSPF), which is a water quality modeling system, and how this modeling system can be used as a valuable tool to enhance monitoring planning and reduce uncertainty in water quality predictions. The authors' findings regarding the sensitivity analysis of the HSPF model in relation to water quality predictions are presented. The application of the computer model was focused on the Ave River watershed in Portugal. Calibration of the hydrology was performed at two stations over five years, starting from January 1990 and ending in December 1994. Following the calibration, the hydrology model was then validated for another five-year period, from January 1995 to December 1999. A comprehensive evaluation framework is proposed, which includes a two-step statistical evaluation based on commonly used hydrology criteria for model calibration and validation. To thoroughly assess model uncertainty and parameter sensitivity, a Monte Carlo method uncertainty evaluation approach is integrated, along with multi-parametric sensitivity analyses. The Monte Carlo simulation considers the probability distributions of fourteen HSPF water quality parameters, which are used as input factors. The parameters that had the greatest impact on the simulated in-stream fecal coliform concentrations were those that represented the first-order decay rate and the surface runoff mechanism, which effectively removed 90 percent of the fecal coliform from the pervious land surface. These parameters had a more significant influence compared to the accumulation and maximum storage rates. When it comes to the oxygen governing process, the parameters that showed the highest sensitivity were benthal oxygen demand and nitrification/denitrification rate. The insights that can be derived from this study play a critical role in the development of robust water management strategies, and their significance lies in their potential to contribute to the advancement of predictive models in the field of water resources. [ABSTRACT FROM AUTHOR]

Background: This study provides a comprehensive analysis of Acinetobacter baumannii in aquatic environments and fish microbiota by integrating culture-dependent methods, 16S metagenomics, and antibiotic resistance profiling. Methods: A total of 83 A. baumannii isolates were recovered using culture-dependent methods from intra-hospital infections (IHI) and wastewater (WW) and surface water (SW) samples from two southern Romanian cities in August 2022. The antibiotic susceptibility was screened using disc diffusion, microdilution, PCR, and Whole Genome Sequencing assays. Results: The highest microbial load in the analyzed samples was found in Glina, Bucharest, for both WW and SW samples across all investigated phenotypes. For Bucharest isolates, the resistance levels corresponded to fluoroquinolones > aminoglycosides > β-lactam antibiotics. In contrast, A. baumannii from upstream SW samples in Târgoviște showed the highest resistance to aminoglycosides. The blaOXA-23 gene was frequently detected in IHI, WW, and SW isolates in Bucharest, but was absent in Târgoviște. Molecular phylogeny revealed the presence of ST10 in Târgoviște isolates and ST2 in Bucharest isolates, while other minor STs were not specifically correlated with a sampling point. Using 16S rRNA sequencing, significant differences in microbial populations between the two locations was identified. The low abundance of Alphaproteobacteria and Actinobacteria in both locations suggests environmental pressures or contamination events. Conclusions: These findings indicate significant fecal contamination and potential public health risks, emphasizing the need for improved water quality monitoring and management. [ABSTRACT FROM AUTHOR]

Due to rapid urbanization, population growth, industrialization, and agricultural activities, there is an increasing demand for freshwater resources, leading to heightened pressure on watershed ecosystems. This study focused on the Nishan Reservoir, Qufu Sihe River, and Qufu Yihe River, conducting field investigations on these water bodies during the spring of 2021 and 2022. Water samples were collected and analyzed for key water quality indicators, including chemical oxygen demand (COD), biochemical oxygen demand (BOD), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (AN), chlorophyll A, and algal cell density. This study unveiled notable correlations among various water quality parameters, including positive associations between BOD and COD, chlorophyll A and algal cell density, and inverse relationships with total phosphorus. Moreover, significant positive correlations were identified between total nitrogen and ammonia nitrogen, as well as between ammonia nitrogen and chlorophyll A. The study highlighted that the TP concentration surpassed the threshold of 0.20 mg/L in 2021, potentially exacerbating the proliferation of algae, leading to algal blooms and adversely affecting the aquatic ecosystem. This study emphasizes the significance of broadening the geographical scope and utilizing long-term datasets to discern trends, determinants, and management approaches pertinent to water quality. Furthermore, this study underscores the imperative of investigating the influence of nitrogen-to-phosphorus nutrient ratios on the composition and proliferation of algal populations, while also taking into account the potential impact of additional factors like light availability, temperature, and water flow on the dynamics of algal communities. [ABSTRACT FROM AUTHOR]

An accurate quantification of flow components and an understanding of water source dynamics are essential for effective water resource and quality management. However, the complexity of hydrological processes and the interference of intensive human activities pose significant challenges in precisely separating water discharge into distinct components such as surface runoff, interflow, and groundwater. The Xinanjiang (XAJ) model, a conceptual watershed hydrological model, has been developed and successfully implemented for rainfall–runoff simulations and hydrograph separations across various Chinese watersheds. While the model framework is robust, it fails to account for agricultural irrigation water withdrawals and the variations in in-stream water travel times across different hydrological regimes, introducing considerable uncertainty in simulating low-flow conditions. This study introduced modifications to the XAJ model by allowing parameter adjustments across different flow regimes and incorporating irrigation withdrawals into the runoff routing process. Utilizing a decade of hydrometeorological data (2013–2022) from the Yongan River watershed in eastern China, the modified model demonstrated improved efficiency metrics in low- and medium-flow regimes compared to the original model, with a Nash–Sutcliffe coefficient improvement from −4.43~−0.49 to 0.40~0.46, R2 from 0.21~0.36 to 0.53~0.63, and BIAS reduction from 7.60~89.08% to 2.06~12.71%. Furthermore, the modified XAJ model provided a more accurate estimation of the spatial and temporal distribution of streamflow components across sub-watersheds. The original model tended to overestimate groundwater contributions (13%) and underestimate interflow (14%), particularly in low-flow conditions. The enhanced XAJ model, thus, offers a more effective tool for identifying streamflow components, providing essential insights into hydrological processes for better management decisions. [ABSTRACT FROM AUTHOR]

This study provides an in-depth analysis of the hydrochemical characteristics and their controlling factors in the lower reaches of the Yellow River. Through water quality sampling and analysis over two hydrological periods within a year, combined with hydrochemical methods and machine learning techniques, the study reveals the joint impact of natural factors and human activities on the spatiotemporal variations in hydrochemical constituents. The findings indicate that the water in the lower reaches of the Yellow River exhibits weak alkalinity (the pH is between 7 and 8), with the primary hydrochemical type being HCO3·SO4—Ca·Na·Mg. The temporal variation in the hydrochemical constituents is mainly influenced by rainfall, where nitrate levels are higher during the flood season due to the flushing effect of rainfall, whereas other hydrochemical constituents show an opposite temporal pattern due to the dilution effect of rainfall. The spatial variation in the Yellow River's hydrochemistry is primarily controlled by a combination of human activities and rainfall. Using Gibbs diagram analysis, it is identified that rock weathering is the main source of ionic constituents, while agricultural fertilization, industrial emissions, and domestic wastewater discharge have significant impacts on the hydrochemical constituents. Compared to other rivers worldwide, the concentration of hydrochemical constituents in the lower reaches of the Yellow River is relatively high, especially nitrate and sulfate, which is closely related to the geological characteristics of the Yellow River basin and intense human activities in the middle and lower reaches. Principal component analysis reveals that the main controlling factors for hydrochemical constituents during the dry season in the lower reaches of the Yellow River are rock weathering dissolution and industrial activities, followed by domestic wastewater; during the flood season, the main controlling factors are rock weathering dissolution and industrial activities, followed by agricultural activities and domestic wastewater. The research findings provide theoretical support for water resource management and water quality protection in the lower reaches of the Yellow River. [ABSTRACT FROM AUTHOR]

This document discusses the importance of water, wastewater, and waste management in the agriculture and agri-food industry. It highlights the need for sustainable development and access to clean water for all. The document also covers various research papers on topics such as water management, wastewater management, fertilizers, and irrigation. The papers provide insights into the impact of land cover on water quality, the ecological status of rivers, rainwater usage, groundwater quality, wastewater treatment, and more. Overall, the document emphasizes the significance of water and waste management in the context of agriculture and invites further scientific research in this area. [Extracted from the article]

Water quality assessment stands as a fundamental step in water resource management. In addition to substances and organisms present in the water, land use and land cover are two factors also affecting water quality. This study analyzed the correlation and influence between water quality and land use and land cover in the Miranda River Basin (MRB) and the natural wetlands in the Pantanal Biome. Using a watershed-wide approach, results suggested that water quality management policies can be considered in terms of sub-basins (smaller planning units). The Water Quality Index (WQI) was considered GOOD or EXCELLENT throughout the MRB. It was observed that the natural wetlands have the capacity to contribute to improving the WQI, with reductions in thermotolerant coliforms and turbidity; however, high values were found for thermotolerant coliforms, a temporal increasing trend of biochemical oxygen demand (BOD), and a reduction in dissolved oxygen and total phosphorus. Conversely, in these flooded areas, trends of a decrease in dissolved oxygen and an increase in BOD levels were found. Natural wetlands play important hydrological and ecological roles in water quality, storing, removing, and cycling nutrients. They are highly relevant areas for defining watershed management and conservation strategies, environmental protection, and providing ecosystem services. [ABSTRACT FROM AUTHOR]

Nanling County, situated on the southern bank of the Yangtze River's middle and lower reaches in China, and has not yet carried out hydrogeochemical geological surveys. This study is pivotal in ensuring the reliability of the drinking water supply, particularly during emergencies. Utilizing an array of analytical methods—statistical analysis, Shularev classification, Piper trilinear diagram, Gibbs diagram, ion ratio method, and mineral saturation index—this research elucidates the hydrogeochemical characteristics and principal water–salt interactions in Nanling's shallow groundwater. Our findings, derived from the Shularev classification and Piper trilinear diagram, reveal that, in the southern mountainous and river valley plain regions, the primary hydrogeochemical type of groundwater is HCO3-Ca. Conversely, in the northern area of Sanli Town and the adjoining plain, groundwater predominantly falls under the HCO3-Na•Ca category, with some regions showing the characteristics of HCO3•Cl-Ca, HCO3•Cl-Na•Ca, and, occasionally, HCO3•SO4-Na•Ca. According to the Gibbs diagram analysis, the predominant source of groundwater in this region is attributed to water–rock dissolution processes occurring during groundwater runoff. The increase in Na+, Ca2+, Cl−, HCO3−, and SO42− concentrations in the water–rock interaction in the study area is mainly due to the dissolution of rock salt, gypsum, calcite, and dolomite, and the alternating cation adsorption occurs during the reaction. Finally, the mineral saturation index points to the ongoing dissolution of gypsum, calcite, and dolomite, until a state of precipitation–dissolution equilibrium is reached. This comprehensive study provides vital insights into the hydrogeochemical dynamics of Nanling County's groundwater, contributing significantly to our understanding of regional water quality and its management. [ABSTRACT FROM AUTHOR]

Heavy metals play a crucial role in the environment due to their toxicity, persistence, and bioaccumulation ability, which can lead to severe ecological and health risks. This study aimed to investigate the impact of urbanization and agricultural practices on the heavy metal content in the sediments of the Bug River catchment. To this end, 96 surface sediment samples were collected from various points in the Bug River catchment, including from urban, agricultural, and forested areas. The samples for laboratory analysis were collected in July 2018, 2019, and 2020 in the Polish part of the Bug River watershed. Heavy metal (Zn, Pb, Cr, Ni, Cu, Fe, Mn, and Cd) concentrations were determined using atomic absorption spectroscopy (AAS). The geoaccumulation index (Igeo), contamination factor (CF), and pollution load index (PLI) were used to assess the degree of sediment contamination. The results indicate higher concentrations of heavy metals in urban sediments, where cadmium concentrations reached up to 2.5 mg/kg, compared to agricultural and forested areas, where concentrations were significantly lower. The average Igeo value for cadmium was 0.24 in agricultural areas and 0.15 in urban areas, suggesting the predominance of anthropogenic influences over natural sources. The highest PLI values were found in urban areas, reaching a maximum of 0.33, indicating higher pollution levels. Statistical analysis revealed that urban emissions and agricultural activities significantly influenced the presence of these metals in the Bug River sediments. This study's conclusions emphasize that effective river water quality management requires continuous monitoring and an understanding of anthropogenic and natural pollution sources. The results contribute to a better understanding the interactions between human activities and water quality, crucial for planning protection and remediation strategies. Additionally, this study provides critical insights into optimizing pollution management strategies and developing remediation methods, serving local and regional policymakers in planning protective actions. [ABSTRACT FROM AUTHOR]

This paper analyses the effects of environmental flows on water quality within a highly regulated basin, focusing on the Turia River basin in the eastern Iberian Peninsula. Through water management and water quality models, a series of simulations were conducted, introducing variations in the outflows of the Loriguilla reservoir to evaluate the effects of different environmental flow scenarios on water quality, particularly at the location of the intake for the water supply to Valencia. Three environmental flow scenarios were analyzed, alongside an alternative management scenario, considering their implications on water quality and reliability of water demand. The findings of this paper, particularly the nitrate (NO3−) concentration evolution, highlight the influence of minimum e-flow and e-flow regimes on water quality within the basin. These results suggest that while modifying the current flow regime can lead to some improvements in nitrate concentrations at the Valencia supply intake point, the primary cause of high nitrate concentrations is attributed to irrigation return flow and the pre-existing contamination of the aquifer. This analysis offers valuable insights into the complexities of water quality management in regulated basins, emphasizing the need for a multi-faceted approach to address the diverse factors influencing water quality and demand supply reliability. [ABSTRACT FROM AUTHOR]

The climate crisis poses a grave threat to numerous small island developing states (SIDS), intensifying risks from extreme weather events and sea level rise (SLR). This vulnerability heightens the dangers of coastal erosion, chronic water quality degradation, and dwindling coastal resources, demanding global attention. The resultant loss of ecological persistence, functional services, and ecosystem resilience jeopardizes protection against wave action and SLR, endangering coastal habitats' economic value, food security, infrastructure, and livelihoods. Implementing integrated strategies is imperative. A thorough discussion of available strategies and best management practices for coastal ecosystem restoration is presented in the context of SIDS needs, threats, and major constraints. Solutions must encompass enhanced green infrastructure restoration (coral reefs, seagrass meadows, mangroves/wetlands, urban shorelines), sustainable development practices, circular economy principles, and the adoption of ecological restoration policies. This requires securing creative and sustainable funding, promoting green job creation, and fostering local stakeholder engagement. Tailored to each island's reality, solutions must overcome numerous socio-economic, logistical, and political obstacles. Despite challenges, timely opportunities exist for coastal habitat restoration and climate change adaptation policies. Integrated strategies spanning disciplines and stakeholders necessitate significant political will. [ABSTRACT FROM AUTHOR]

As the management areas for NPS pollution continue to increase, it is essential to conduct a situation analysis considering the regional characteristics and the scope of pollution reduction. In this study, the focus is on differentiating regional (urban, agricultural) characteristics to enhance water quality and reduce pollution loads in the increasing management areas for NPSs. Furthermore, priority management areas are identified based on urgency and vulnerability, and management strategies are proposed. The assessment involved evaluating both streamflow and water quality (T-P) using long-term monitoring data and watershed models (SWAT and HSPF) that take into account regional characteristics. The results indicated notable regional improvements, with T-P pollution reductions ranging from 20.7% to 26.8% and T-P concentration reductions ranging from 16.4% to 24.7% compared to baseline conditions in unmanaged areas. Based on these research findings, it is anticipated that the efficient and effective management of NPS pollution can be implemented on a regional basis. Moreover, the results of this study will not only contribute to the establishment of pollution standards, but also significantly impact the evaluation and proposal of management objectives, thereby making a substantial contribution to national water quality policies. [ABSTRACT FROM AUTHOR]

Airborne sensing images harness the combined advantages of hyperspectral and high spatial resolution, offering precise monitoring methods for local-scale water quality parameters in small water bodies. This study employs airborne hyperspectral remote sensing image data to explore remote sensing estimation methods for total nitrogen (TN) and total phosphorus (TP) concentrations in Lake Dianshan, Yuandang, as well as its main inflow and outflow rivers. Our findings reveal the following: (1) Spectral bands between 700 and 750 nm show the highest correlation with TN and TP concentrations during the summer and autumn seasons. Spectral reflectance bands exhibit greater sensitivity to TN and TP concentrations compared to the winter and spring seasons. (2) Seasonal models developed using the Catboost method demonstrate significantly higher accuracy than other machine learning (ML) models. On the test set, the root mean square errors (RMSEs) are 0.6 mg/L for TN and 0.05 mg/L for TP concentrations, with average absolute percentage errors (MAPEs) of 23.77% and 25.14%, respectively. (3) Spatial distribution maps of the retrieved TN and TP concentrations indicate their dependence on exogenous inputs and close association with algal blooms. Higher TN and TP concentrations are observed near the inlet (Jishui Port), with reductions near the outlet (Lanlu Port), particularly for the TP concentration. Areas with intense algal blooms near shorelines generally exhibit higher TN and TP concentrations. This study offers valuable insights for processing small water bodies using airborne hyperspectral remote sensing images and provides reliable remote sensing techniques for lake water quality monitoring and management. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (Chl-a) concentration monitoring is very important for managing water resources and ensuring the stability of marine ecosystems. Due to their high operating efficiency and high prediction accuracy, backpropagation (BP) neural networks are widely used in Chl-a concentration inversion. However, BP neural networks tend to become stuck in local optima, and their prediction accuracy fluctuates significantly, thus posing restrictions to their accuracy and stability in the inversion process. Studies have found that metaheuristic optimization algorithms can significantly improve these shortcomings by optimizing the initial parameters (weights and biases) of BP neural networks. In this paper, the adaptive nonlinear weight coefficient, the path search strategy "Levy flight" and the dynamic crossover mechanism are introduced to optimize the three main steps of the Artificial Ecosystem Optimization (AEO) algorithm to overcome the algorithm's limitation in solving complex problems, improve its global search capability, and thereby improve its performance in optimizing BP neural networks. Relying on Google Earth Engine and Google Colaboratory (Colab), a model for the inversion of Chl-a concentration in the coastal waters of Hong Kong was built to verify the performance of the improved AEO algorithm in optimizing BP neural networks, and the improved AEO algorithm proposed herein was compared with 17 different metaheuristic optimization algorithms. The results show that the Chl-a concentration inversion model based on a BP neural network optimized using the improved AEO algorithm is significantly superior to other models in terms of prediction accuracy and stability, and the results obtained via the model through inversion with respect to Chl-a concentration in the coastal waters of Hong Kong during heavy precipitation events and red tides are highly consistent with the measured values of Chl-a concentration in both time and space domains. These conclusions can provide a new method for Chl-a concentration monitoring and water quality management for coastal waters. [ABSTRACT FROM AUTHOR]

Effective management of water quality in watersheds is crucial because it is directly linked to the sustainability of aquatic ecosystems. In conventional watershed management, best management practices (BMPs) have been instrumental in addressing deteriorating water quality issues caused by non-point source pollution. Greenhouse gas (GHG) emissions have emerged as a global concern, necessitating immediate and diverse environmental actions to mitigate their impacts. This study aims to explore BMPs that maximize total phosphorus (TP) load removal efficiencies, while minimizing costs and GHG emissions within watersheds, using the Soil and Water Assessment Tool (SWAT) and non-dominated sorting genetic algorithm III (NSGA-III). The Yeongsan River Watershed between 2012 and 2021 was selected as the study area. Hydrological and BMP data were analyzed. Applying identical BMPs to the watershed showed that the BMPs with high TP removal efficiency may not be effective in terms of cost and GHG emissions. Therefore, the optimal combination of BMPs for the Yeongsan River Watershed was determined using NSGA-III considering TP removal efficiency, cost, and GHG emissions. This study is the first to consider GHG emissions at the watershed level when applying BMPs and is expected to contribute to the development of BMP implementation incorporating GHG emissions. [ABSTRACT FROM AUTHOR]

Marine water quality significantly impacts human livelihoods and production such as fisheries, aquaculture, and tourism. Satellite remote sensing facilitates the predictions of large-area marine water quality without the need for frequent field work and sampling. Prediction of diffuse attenuation coefficient (Kd), which describes the speed at which light decays as it travels through water, obtained from satellite-derived ocean color products can reflect the overall water quality trends. However, current models inadequately explore the complex nonlinear features of Kd, and there are difficulties in achieving accurate long-term predictions and optimal computational efficiency. This study innovatively proposes a model called Remote Sensing-Informer-based Kd Prediction (RSIKP). The proposed RSIKP is characterized by a distinctive Multi-head ProbSparse self-attention mechanism and generative decoding structure. It is designed to comprehensively and accurately capture the long-term variation characteristics of Kd in complex water environments while avoiding error accumulation, which has a significant advantage in multi-dataset experiments due to its high efficiency in long-term prediction. A multi-dataset experiment is conducted at different prediction steps, using 70 datasets corresponding to 70 study areas in Hangzhou Bay and Beibu Gulf. The results show that RSIKP outperforms the five prediction models based on Artificial Neural Networks (ANN, Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Long Short-Term Memory Networks (LSTM)). RSIKP captures the complex influences on Kd more effectively to achieve higher prediction accuracy compared to other models. It shows a mean improvement of 20.6%, 31.1%, and 22.9% on Mean Absolute Error (MAE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). Particularly notable is its outstanding performance in the long time-series predictions of 60 days. This study develops a cost-effective and accurate method of marine water quality prediction, providing an effective prediction tool for marine water quality management. [ABSTRACT FROM AUTHOR]

Water effluent pollution in the Ecuadorian Amazon occurs mainly due to the lack of sewage infrastructure, wastewater treatment plants in urban and rural areas, and agricultural and livestock activities. Consequently, understanding water quality is crucial because of its dynamic nature, influenced by various activities along its course. We evaluated and compared the water quality status of the El Salvador River with the current standards of the Ministry of the Environment, Water, and Ecological Transition in Ecuador and with Decree No. 115/2003 on water quality and water pollution management. The water quality index was determined through random sampling at seven locations along the river. The results show good water quality, with contamination indices ranging from 84 to 87. When comparing the results with the standards, all water quality parameters met the standards for recreational purposes. However, considering the river's uses for agricultural activities, we compared the water with additional standards from legislation outlined by the Environment Ministry and found that the nitrate content exceeded permissible limits due to runoff from the surrounding crops, causing a potential risk to human health. Therefore, incorporating helophyte plants is a promising option that would promote the health of this aquatic ecosystem and others. [ABSTRACT FROM AUTHOR]

This study investigates the influence of urban pollution and climate dynamics on water quality and the bacterial communities in an Argelian watershed. Twenty-one sampling campaigns were conducted over two years at six sites along the Oued Boussellam, a river impacted by the effluent of a sewage treatment plant, from a low-polluted site to a water reservoir within a 50 km distance. Fecal indicators and the human fecal marker crAssphage were monitored. Illumina 16S rRNA amplicon sequencing was used to assess water microbial populations' changes. Urban sewage discharge had an impact on the river quality and microbial ecosystem, which was attenuated along the river course. Significant reductions (>4 log10 for E. coli and somatic coliphages, >3 log10 for crAssphage) occurred, particularly during high-temperature periods. crAssphage correlated strongly with somatic coliphages downstream the river. Seasonal differences were observed in the diversity of the bacterial communities, with higher values during the high-temperature period. The genus-level community structure was similar at highly polluted river sites, also displaying seasonal differences. Despite high pollution levels, natural processes reduced fecal indicators to acceptable levels in the reservoir as well as shaped the bacterial communities along the river, highlighting the importance of understanding indicator persistence and microbial community resilience for effective water quality management within the context of the global warming scenario. [ABSTRACT FROM AUTHOR]

Featured Application: Industrial application of temperature control in coal-based exothermic reactions. The control of steam drums, used to remove heat from Fischer–Tropsch synthesis or diethyl oxalate hydrogenation, is confronted with a challenge on controlling quality. The traditional proportional–integral–differential (PID) controllers with fixed parameters are dissatisfying upon deployment. The backward-propagation neural network (BPNN) self-tuning PID control algorithm was thus developed and implemented via a Python and KINGVIEW software combination. Application experiments showed that, in both setpoint control and step change control of the steam drum pressure, static deviation and the maximum error were less with the BPNN self-tuning PID controller, in comparison to the conventional PID controller. Moreover, it seemed that certain adaptations occurred to the nonlinear change in the reaction system, revealing that it was superior to the traditional PID controller. It is shown that the backward-propagation neural network will improve the control quality in boiling water drum systems for exothermic reactions. It can be predicted that the backward-propagation neural network is qualified for process condition control in the chemical industry. [ABSTRACT FROM AUTHOR]

The first flush (FF) phenomenon is commonly associated with a relevant load of pollutants, raising concerns about water quality and environmental management in agro-urban areas. An FF event can potentially transport contaminated water into a receiving water body by activating combined sewer overflow (CSO) systems present in the drainage urban network. Therefore, accurately characterizing FF events is crucial for the effective management of sewer systems and for limiting environmental degradation. Given the ongoing controversy in the literature regarding the delineation of FF event occurrences, there is an unavoidable necessity for further investigations, especially experimental-based ones. This study presents the outcomes of an almost two-year field campaign focused on assessing the water quantity and quality of two combined sewer systems in Northern Italy. For this purpose, various hydro-meteorological variables, including precipitation, flow rate, temperature, and solar radiation, in addition to water quality analytics, were measured continuously to capture stormwater events. Throughout the monitoring period, sixteen stormwater events were identified and analyzed using five indices usually adopted in the literature to identify FF occurrences. The results indicate that there is a strong positive correlation between the mass first flush ratios calculated for nutrients and three factors, including maximum rainfall intensity, maximum flow rate, and antecedent dry weather period. Furthermore, rainfall duration was found to possess a strong negative correlation with the mass first flush ratios calculated for nutrients. However, for the same rainfall event, the occurrence of FF has never been unanimously confirmed by the indices examined in this study. Moreover, different macro-groups of pollutants can behave differently. Thus, it becomes apparent that relying solely on a priori analyses, without the support of data from experimental monitoring campaigns, poses a risk when designing actions for the mitigation of FF occurrences. [ABSTRACT FROM AUTHOR]