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Due to the increasingly serious water environment pollution, the difficulty of Water Quality Monitoring (abbreviated as WQM for convenience) is also constantly increasing, which puts forward more requirements for the capabilities of various aspects of WQM systems. However, the current WQM method has drawbacks such as slow speed, long monitoring time, complex operation, poor stability, and the inability to obtain accurate information on water pollution in the first time, as well as the generation of toxic and harmful secondary pollutants after some measurement parameters are tested. To address these issues and ensure water quality safety, this paper investigated the algorithm for monitoring water quality parameters using artificial intelligence data mining optical systems. This article applied an artificial intelligence data mining system to detect water quality and designed various system through this method to improve system performance. To verify the actual effectiveness of artificial intelligence data mining systems, this article selected 10 water plants as experimental research subjects and compared the differences between traditional WQM methods and WQM methods based on artificial intelligence data mining systems in terms of WQM time, accuracy, sensitivity, and protective performance. The experimental results showed that the optical system based on artificial intelligence data mining took an average of 2.7 days in WQM and the average accuracy was 85.95%. The average sensitivity value was 84.19% and the average protective score was 8.46 points. This indicated that artificial intelligence data mining optical technology had vital significance and value for WQM. [ABSTRACT FROM AUTHOR]

The quality of water used in aquaculture ponds is one of the crucial factors influencing the smell and sensory properties of fish. The water samples were taken from the rainbow trout fish ponds from three different fish farms in three provinces in Türkiye in four different seasons. The samples were analyzed for the volatile components by employing HS-SPME/GC–MS. Seven different volatile groups including aldehydes, ketones, esters, alcohols, volatile phenols, terpenes and other aromatic substances were identified in the samples. Among these, aldehydes were found to be the most dominant. (E)-2-Heptenal, nonanal, acetophenone, and 2-ethyl-1-hexanol are thought to be responsible for the off-odors in the water that have the potential to cause off-odors in fish. It was also determined that the amounts of these compounds increases in winter due to lower water temperature. Fish producers should monitor water quality on a regular basis to prevent off-odor compounds that degrade fish quality. [ABSTRACT FROM AUTHOR]

Water quality monitoring reveals changing trends in the environmental condition of aquatic systems, elucidates the prevailing factors impacting a water body, and facilitates science-backed policymaking. A 2020 hiatus in water quality data tracking in the Lower Passaic River (LPR), New Jersey, has created a 5-year information gap. To gain insight into the LPR water quality status during this lag period and ahead, water quality indices computed with 16-year historical data available for 12 physical, chemical, nutrient, and microbiological parameters were used to predict water quality between 2020 and 2025 using seasonal autoregressive moving average (ARIMA) models. Average water quality ranged from good to very poor (34 ≤ µWQI ≤ 95), with noticeable spatial and seasonal variations detected in the historical and predicted data. Pollution source tracking with the positive matrix factorization (PMF) model yielded significant R2 values (0.9 < R2 ≤ 1) for the input parameters and revealed four major LPR pollution factors, i.e., combined sewer systems, surface runoff, tide-influenced sediment resuspension, and industrial wastewater with pollution contribution rates of 23–30.2% in the upstream and downstream study areas. Significant correlation of toxic metals, nutrients, and sewage indicators suggest similarities in their sources. [ABSTRACT FROM AUTHOR]

Lead ion (Pb2+) pollution is a critical global issue due to its ability to accumulate in the human body, resulting in severe health problems. Despite extensive research efforts devoted to the detection of Pb2+ contamination, practical, rapid, and field-deployable sensors for Pb2+ is yet to be developed to effectively safeguard the environment and public health. Herein, a label-free affinity-based sensing device is developed based on printed circuit board (PCB) for low-cost, easy-to-use, and real-time on-site detection of Pb2+ ions. The sensors are prepared by forming a self-assembled monolayer of glutathione (GSH) on the surface of gold-plated PCB electrodes, which serves as a molecular probe to recognize Pb2+. Rapid and sensitive detection is achieved by using capacitance sensing integrated with microfluidic enrichment. The sensor's interfacial capacitance is used to indicate specific binding, while the capacitance reading process simultaneously induces alternating current electrothermal (ACET) acceleration of analyte's travel towards the probes. Thus, the enrichment and detection are integrated into a single step, making pre-concentration unnecessary and shortening the assay time to 30 s. This Pb2+ sensor has demonstrated one of the lowest limits of detection reported so far (1.85 fM) with a linear range of 0.01–10 pM. To evaluate the sensor's specificity, non-target metal ions are tested, all showing negligible responses. Testing of tap water sample also yields reasonable results, validating the sensor's robustness. The above-mentioned features, together with a commercial portable readout, make this sensor well-suited for point-of-use Pb2+ detection at low cost. [ABSTRACT FROM AUTHOR]

Fernandez, P.N.J.; Fernandez, E.G.B.; Cadondon, J.G., and Subade, R.F., 2024. Enhancing coastal management through the design and development of an in situ water quality monitoring system. Journal of Coastal Research, 40(6), 1090–1102. Charlotte (North Carolina), ISSN 0749-0208. The Philippines, with its extensive coastline rich in water resources, faces challenges because of the heavy reliance of residents on coastal waters for recreation and livelihood. This leads to water quality deterioration. Balancing human development with environmental protection necessitates regular, close monitoring of water resources. Traditional methods of water quality analysis are time-consuming and labor-intensive, and regular monitoring is financially burdensome. This study introduces the design and development of a customized water quality monitoring device as an alternative to traditional laboratory analysis. The device is portable, user-friendly, and capable of rapidly gathering real-time data. It features a multiparameter sensor that simultaneously measures temperature, pH, dissolved oxygen (DO), and electrical conductivity (EC). After testing and calibration, the device showed a mean error of 0.91°C for temperature, –0.025 mg/L for DO, 0.09 for pH, and 0.033 mS/cm for EC. Forty seawater samples from nine Environmental Management Bureau coastline monitoring stations were analyzed using the device. Comparison with commercially available in situ devices showed a moderate coefficient of determination for DO and pH and a high coefficient of determination for EC and temperature, indicating that some environmental and user-related factors affect readings. Insights from empirical results and consultations with local stakeholders will inform future improvements of the device. Implementing this prototype can help to inform decisions on resource management, pollution control, and public health protection. Real-time data can aid in early detection of contaminants and pollution sources, which allows swift remedial action, and adaptive management practices. [ABSTRACT FROM AUTHOR]

The Anil River estuary (ARE), which is crucial for São Luís Island's socioeconomic well-being, faces substantial human pressure due to its urban location, hastening the eutrophication process. This study, which was conducted from 2022 to 2023, aimed to assess trophic levels across the ARE. Using a multiparametric probe, we analysed the pH, temperature, dissolved oxygen, oxygen saturation, salinity, and total dissolved solids quarterly during the rainy and dry seasons at six sampling points. The nutrient analysis included nitrite, nitrate, ammonium ions, phosphate, total nitrogen (TN), and total phosphorus (TP). Trophic index (TRIX) values categorised the ARE as eutrophic (sector 1) or hypereutrophic (sector 2). The dry season exhibited relatively high trophic levels, indicating that the ARE was hypereutrophic. Sector 2, influenced by concentrated nutrients and chlorophyll-a, exhibited increased trophic status from domestic and industrial effluent discharge. Rainy season data at the downstream from the point 3 recorded the maximum DIN:DIP ratio, indicating phosphorus limitation. Monitoring estuarine trophic states is vital for kerbing eutrophication and preserving local biodiversity in the anil river estuary. [ABSTRACT FROM AUTHOR]

Turbidity is commonly monitored as an important water quality index. Human activities, such as dredging and dumping operations, can disrupt turbidity levels and should be monitored and analysed for possible effects. In this paper, we model the variations of turbidity in Dublin Bay over space and time to investigate the effects of dumping and dredging while controlling for the effect of wind speed as a common atmospheric effect. We develop a Vector Auto-Regressive Integrated Conditional Heteroskedasticity (VARICH) approach to modelling the dynamical behaviour of turbidity over different locations and at different water depths. We use daily values of turbidity during the years 2017–2018 to fit the model. We show that the results of our fitted model are in line with the observed data and that the uncertainties, measured through Bayesian credible intervals, are well calibrated. Furthermore, we show that the daily effects of dredging and dumping on turbidity are negligible in comparison to that of wind speed. [ABSTRACT FROM AUTHOR]

Underground water were abstracted from HDWs to determine their suitability for drinking. Physical, chemical and bacteriological parameters were carried out following standard guidelines. Total coliform count was done using the membrane filtration method. Heavy metal was determined using an Atomic Absorption Spectrophotometer (AAS BULK SCIENTIFIC MODEL 210 VGP). Samples were subjected to statistical and multivariate analysis. Results of physicochemical parameters show that they were all within the WHO standard for drinking. Cation concentrations follow the order: Na+ > Ca2+ > Mg2+ > K+, while anions are: HCO3− > SO42-> Cl−> NO3− > PO42-. As, Cd and Pb were not detected in the sampled water, but other heavy metals Cr, Cu, Fe, Mn and Zn were detected. They were, however, within the WHO's recommended range. Based on E. coli analysed, all of the water samples were free from faecal contamination since none was discovered in the water samples. Based on the water quality index, only sample G hand-dug well is of poor quality (though it could be treated) for human consumption; all other samples are good for human consumption. Deductions from Pipers' and the Durov diagram, as well as principal component analysis, revealed that there was little geological and human activity within the hand-dug wells. Based on the physicochemical, microbiological, heavy metal and water quality indexes, this study indicates that all of the water samples examined are free of pollution, but that continual monitoring of the hand-dug wells should be prioritised. [ABSTRACT FROM AUTHOR]

Amid escalating global challenges such as population growth, pollution, and climate change, access to safe and clean water has emerged as a critical issue. It is estimated that there are 4 billion cases of water-related diseases worldwide that cause 3.4 million deaths every year. This underscores the urgent need for efficient water quality monitoring and assessment. Traditional assessment techniques include laboratory-based methods that are manual, costly, time-consuming, and risky. Although some studies leveraged Internet of Things (IoT)-based systems to examine water quality, they only relied on a limited number of water quality parameters (and thus do not offer a comprehensive and accurate water quality assessment), mainly due to the technical difficulties to integrate multiple sensors to a single device. In fact, due to the issues of multimodality, heterogeneity, and complexity of data, the interoperability among sensors with various measurements, sampling rates, and technical requirements makes it very challenging to seamlessly integrate multiple sensors into one device. This study overcame these technical challenges by leveraging multisensor data fusion capabilities to develop an intelligent cloud-based IoT multimodal edge sensing device to provide an automated, real-time, and comprehensive assessment process of water quality. First, a total of nine water quality parameters were identified and considered. Second, the sensing device was designed and developed using an ESP32 embedded system, which is a low-cost, low-power system on a chip (SoC) microcontroller integrated with Wi-Fi and dual-mode Bluetooth connectivity by fusing data from six different sensors that measure the nine identified water parameters on the edge. Third, the overall water quality was evaluated using the National Sanitation Foundation Water Quality Index (NSFWQI). Fourth, a cloud-based server was created to publish the data instantly, and a graphical user interface (GUI) was developed to provide easy-to-understand real-time visualization and information of the water quality. The real-world applicability and practicality of the developed IoT-enabled sensing device was tested and verified in a pilot project (i.e., a case study) of a building located in Newark, New Jersey, for a duration of 6 months. This paper adds to the body of knowledge by being the first research developing a single IoT-enabled device that is capable of reporting NSFWQI in real-time based on 9 water quality indicators encompassing both physical [temperature, total dissolved solids (TDS), turbidity, and pH] and chemical [potassium, phosphorus, nitrogen, dissolved oxygen (DO), and 5-day biochemical oxygen demand (BOD5)] parameters. Thus, this study serves as a multifaceted improvement across different dimensions, fostering healthier, more efficient, and technologically advanced environments. [ABSTRACT FROM AUTHOR]

The article discusses a project in Italy that addressed the issue of hexavalent chromium (Cr(VI)) in groundwater in the province of Piacenza. Due to new regulations, an iterative process was developed to assess the problem, identify treatment technologies, define project priorities, and implement solutions within budget and time constraints. The project included site identification, technology choices, intervention planning, and process improvements, resulting in improved water quality and reduced Cr(VI) levels ahead of regulatory deadlines. The project showcased efficient planning and technology selection for successful implementation. [Extracted from the article]

Key Takeaways: While sample collection to monitor drinking water quality is a routine practice, water utilities must be prepared to address emergencies stemming from contamination. Having a plan for collecting and analyzing water samples during emergency response or other unusual circumstances better ensures actionable results. Guidance is available to help laboratories prepare to support a utility's emergency response. [ABSTRACT FROM AUTHOR]

Measurements by volunteer scientists using participatory science methods in combination with high resolution remote sensing can improve our ability to monitor water quality changes in highly vulnerable and economically valuable nearshore and estuarine habitats. In the Chesapeake Bay (USA), tidal tributaries are a focus of watershed and shoreline management efforts to improve water quality. The Chesapeake Water Watch program seeks to enhance the monitoring of tributaries by developing and testing methods for volunteer scientists to easily measure chlorophyll, turbidity, and colored dissolved organic matter (CDOM) to inform Bay stakeholders and improve algorithms for analogous remote sensing (RS) products. In the program, trained volunteers have measured surface turbidity using a smartphone app, HydroColor, calibrated with a photographer's gray card. In vivo chlorophyll and CDOM fluorescence were assessed in surface samples with hand-held fluorometers (Aquafluor) located at sample processing "hubs" where volunteers drop off samples for same day processing. In validation samples, HydroColor turbidity and Aquafluor in vivo chlorophyll and CDOM fluorescence were linear estimators of standard analytical measures of turbidity, chlorophyll and CDOM, respectively, with R2 values ranging from 0.65 to 0.85. Updates implemented in a new version (v2) of HydroColor improved the precision of estimates. These methods are being used for both repeat sampling at fixed sites of interest and ad-hoc "blitzes" to synoptically sample tributaries all around the Bay in coordination with satellite overpasses. All data is accessible on a public database (serc.fieldscope.org) and can be a resource to monitor long-term trends in the tidal tributaries as well as detect and diagnose causes of events of concern such as algal blooms and storm-induced reductions in water clarity. [ABSTRACT FROM AUTHOR]

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

The editorial discusses the theory, methods, and applications of geospatial big data, which are large datasets with spatial components collected from various sources like satellite imagery, social media, and sensor networks. The editorial highlights the challenges in defining geospatial big data, collecting and analyzing the data, and addressing biases and ethical concerns. It also introduces the special issue that includes seven articles focusing on human activities, social interactions, disasters, health crises, and environmental characteristics using geospatial big data. The editorial concludes by discussing future challenges and opportunities in geospatial big data analytics, emphasizing the importance of privacy, multimodal GeoAI, and scale issues in data collection and analysis. [Extracted from the article]

Oceanic water quality monitoring is essential for environmental protection, resource management, and ecosystem vitality. Optical remote sensing from space plays a pivotal role in global surveillance of oceanic water quality. However, the spatial resolution of current ocean color data products falls short of scrutinizing intricate small-scale marine features. This study introduces a hybrid model that fuses MODIS (Moderate Resolution lmaging Spectroradiometer) ocean color products with Sentinel-2 's remote sensing reflectance data to generate high-resolution ocean color imagery, specifically investigating the diffuse attenuation coefficient at a wavelength of 490 nm (Kd490). To address the intricacies of coastal environments, we propose two complementary strategies to improve the accuracy of inversion. The first strategy leverages MODIS ocean color products alongside a geographic segmentation model to perform distinct inversions for separate marine zones, enhancing spatial resolution and specificity in coastal regions. The second strategy bolsters model interpretability during training by integrating predictions from conventional physical models into a Random Forestbased Regression Ensemble (RFRE) model. This study focuses on the coastal regions surrounding the Beibu Gulf, near Hainan Island in China. Our findings exhibit a strong concordance with MODIS products, achieving a monthly average coefficient of determination (R²) of 0.90, peaking at 0.97, and sustaining a monthly average rootmean-square error (RMSE) of less than 0.02. These results substantiate the model's efficacy. Moreover, the annual trend analysis and localized assessment of the reconstructed Kd490 offer nuanced insights that surpass MODIS data, establishing a robust foundation for high-resolution water quality monitoring in coastal zones. [ABSTRACT FROM AUTHOR]

Understanding water quality is crucial for environmental management and policy formulation. However, existing methods for assessing water quality are often unable to fully integrate with multi-source remote sensing data. This study introduces a method that employs a stacking algorithm within the Google Earth Engine (GEE) for classifying water quality grades in the Songhua River Basin (SHRB). By leveraging the strengths of multiple machine learning models, the Stacked Generalization (SG) model achieved an accuracy of 91.67%, significantly enhancing classification performance compared to traditional approaches. Additionally, the analysis revealed substantial correlations between the normalized difference vegetation index (NDVI) and precipitation with water quality grades. These findings underscore the efficacy of this method for effective water quality monitoring and its implications for understanding the influence of natural factors on water pollution. [ABSTRACT FROM AUTHOR]

Water quality monitoring of rivers is necessary in order to properly manage their basins so that steps can be taken to control the amount of pollutants and bring them to the allowable level. The ARIMA (autoregressive integrated moving average) model does not consider nonlinear patterns in modeling water quality components. Also, in modeling using the MLP (Multilayer Perceptrons) model, both linear and nonlinear pattern are not controlled equally. Therefore, in the present study, linear time series models (ARIMA), MLP model, and a hybrid model of MLP and ARIMA optimized by a Grasshopper optimization algorithm are used to predict water quality components in the statistical period of 2011–2019. In the proposed hybrid method, the ability of the ARIMA and the MLP model are exploited. Observational water quality data for forecasting time series in the hybrid method include dissolved oxygen, water temperature, and boron over 108 months. Since, the hybrid model is capable of realizing the nonlinear essence of complicated time series, it makes more reliable forecasts. In the hybrid model, the correlation coefficients between the observational data and the predicted values are 0.9 for dissolved oxygen, 0.91 for water temperature, and 0.91 for boron. To compare the three ARIMA, MLP, and hybrid models, the accuracy indices of each model are calculated. The results show that the hybrid model's higher accuracy compared with the other two models. [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]

The dissolved oxygen concentration (DOC) is an important indicator of water quality. Accurate DOC predictions can provide a scientific basis for water environment management and pollution prevention. This study proposes a hybrid DOC forecasting framework combined with Variational Mode Decomposition (VMD), a convolutional neural network (CNN), a Gated Recurrent Unit (GRU), and the Beluga Whale Optimization (BWO) algorithm. Specifically, the original DOC sequences were decomposed using VMD. Then, CNN-GRU combined with an attention mechanism was utilized to extract the key features and local dependency of the decomposed sequences. Introducing the BWO algorithm solved the correction coefficients of the proposed system, with the aim of improving prediction accuracy. This study used 4-h monitoring China urban water quality data from November 2020 to November 2023. Taking Lianyungang as an example, the empirical findings exhibited noteworthy enhancements in performance metrics such as MSE, RMSE, MAE, and MAPE within the VMD-BWO-CNN-GRU-AM, with reductions of 0.2859, 0.3301, 0.2539, and 0.0406 compared to a GRU. These results affirmed the superior precision and diminished prediction errors of the proposed hybrid model, facilitating more precise DOC predictions. This proposed DOC forecasting system is pivotal for sustainably monitoring and regulating water quality, particularly in terms of addressing pollution concerns. [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]

Aim Method Results Conclusion Optimal care for patients with kidney failure reduces the risks of adverse health outcomes, including cardiovascular events and death. We evaluated data from the third iteration of the International Society of Nephrology Global Kidney Health Atlas (ISN‐GKHA) to assess the capacity for quality service delivery for kidney failure care across countries and regions.We explored the quality of kidney failure care delivery and the monitoring of quality indicators from data provided by an international survey of stakeholders from countries affiliated with the ISN from July to September 2022.One hundred and sixty seven countries participated in the survey, representing about 97.4% of the world's population. In countries where haemodialysis (HD) was available, 81% (n = 134) provided standard HD sessions (three times weekly for 3–4 h per session) to patients. Among countries with peritoneal dialysis (PD) services, 61% (n = 101) were able to provide standard PD care (3–4 exchanges per day). In high‐income countries, 98% (n = 62) reported that >75% of centers regularly monitored dialysis water quality for bacteria compared to 28% (n = 5) of low‐income countries (LICs). Capacity to monitor the administration of immunosuppression drugs was generally available in 21% (n = 4) of LICs, compared to 90% (n = 57) of high‐income countries. There was significant variability between and within regions and country income groups in reporting the quality of services utilized for kidney replacement therapies.Quality assurance standards on diagnostic and treatment tools were variable and particularly infrequent in LICs. Standardization of delivered care is essential for improving outcomes for people with kidney failure. [ABSTRACT FROM AUTHOR]

The availability of safe water, sanitation, and hygiene (WASH) facilities in schools is essential for a healthy learning environment and achieving sustainable development goals (SDGs) 4 and 6. Despite its importance, comprehensive studies on drinking water quality, sanitation, and hygiene in schools are scarce. This study explicitly assessed the WASH services and gaps in 43 educational institutions, located in Tongi, Bangladesh, through field and laboratory investigations. Thirteen physicochemical and bacteriological parameters were analyzed, and water quality was classified using an Integrated Water Quality Index (IWQI). Hygiene and sanitation were evaluated through observations and data from school administrators on water sources, toilets, handwashing facilities, and solid waste management. Results showed that WASH services exceeded the national average, but all schools had dangerously high Escherichia coli levels (mean: 43.95 CFU/100 mL) in drinking water, posing health risks. Additionally, 89.72% of samples showed elevated manganese levels, 35% had high iron, and 41.86% had increased conductivity. About 35% of water was unsuitable for drinking based on IWQI. Schools lacked the capacity to monitor WASH quality, especially drinking water. A strategic framework for safe WASH facilities is recommended. The findings can lead the policymakers to prioritize the improvements in WASH facilities for attaining SDG 6. [ABSTRACT FROM AUTHOR]

Ostracods are tiny crustaceans found in aquatic habitats and the present paper deals with the role of the water quality index on their population diversity and seasonal fluctuations at the three different sampling stations of Cauvery River, Tiruchirappalli (Tamil Nadu, South India) on a regular biweekly basis from September 2019 to August 2020. The highest level of a hydrological parameter, atmospheric temperature 33.75±0.353°C on April-20 at S-II; water temperature 32.00±0.353°C on June-20 at S-III; pH 8.5±0.106 on December-20 at S-II; DO 8.78±0.155 mg/l on December 20 at S-III; salinity 0.996±0.000 on March at S-I; phosphate 2.38±0.304 mg/l on August 20 at S-III; turbidity 3.8±0.070 NTU on February 20 at S-III; total solids 681.0±7.778 mg/l on May 20 at S-III; total hardness 246.0±4.242 mg/l in September 2029 at S-III and diversity of Ostracods population [533.5±6.71 ind/l] in S-III; [521.5±6.01 ind/l] in S-II and [510.0±2.82 ind/l] in S-I were observed in May 2020. Thus, ostracod aggregations exhibit favorable hydrological conditions and the response offers the possibility of use as pollution indicators for the Cauvery River Tiruchirappalli. [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]

Microbial Fuel Cells (MFCs) represent an innovative approach for transforming biomass energy directly into electricity, which showed great promise in various applications beyond energy generation and wastewater treatment. The use of MFCs as biosensors for in‐situ and online monitoring has garnered increasing interest. These biosensors stand out for their compactness, ease of operation, affordability, and portability. They have proven effectively in the detection of various water quality indicators, including organic matter, nitrogen, heavy metals, pH levels, and dissolved oxygen. This comprehensive review aims to provide a critical analysis of the current research landscape and the latest advancements in MFC technology, with special emphasis on the challenges encountered in its application for wastewater and water quality monitoring. Moreover, strategies for performance improvement, such as the adoption of miniaturized structures, the exploration of innovative materials, and the application of mathematical modelling for analysis, are also discussed. The review also explores potential avenues for future research, especially in the realm of detecting mixed pollutants. Thus, it provides insightful perspectives on the evolving field of biosensor technology based on MFCs. [ABSTRACT FROM AUTHOR]

A miniature multi-channel surface-enhanced Raman scattering (SERS) sensor based on polydimethylsiloxane (PDMS) is constructed to achieve rapid delivery of polluted water and specific identification of multiple components. Hg2+, organic pollutants, and sodium nitrite are successfully identified by the multi-channel SERS sensor using Cy5, cyclodextrin, and urea in the corresponding detection area. This multi-channel sensor exhibits excellent sensitivity and specificity, with detection limits of 3.2 × 10−10 M for Hg2+, 1.0 × 10−8 M for aniline, 6.9 × 10−9 M for diphenylamine, 9.1 × 10−8 M for PCB-77, and 7.5 × 10−9 M for pyrene, and 5.0 × 10−7 M for sodium nitrite. Compared with traditional analysis techniques, this method exhibited excellent recovery for the water pollutants ranging from 82.1 to 115.8%. The PDMS-based microchannel allows for simultaneous and rapid identification of multiple environmental pollutants, offering a portable detection method for emergency testing of environmental pollutants and routine determination of water pollutants. [ABSTRACT FROM AUTHOR]

Socio-economic activities along the Citarum River basin in Indonesia can induce ecological disturbances in the structure of the benthic macroinvertebrate community. This research aims to (1) identify the environmental factors responsible (2) describe the ecological disturbances using biological measurements; (3) and to develop local biocriteria using a multi-metric conceptual approach. An inventory of benthic macroinvertebrates was carried out at eight stations over a sampling period of 3 months, along with water quality monitoring. The results confirm the change in the structure of benthic macroinvertebrate communities, explained by seven environmental parameters: dissolved oxygen (DO), ambient habitat quality, turbidity, nutrient enrichment, conductivity, temperature, and embeddedness percentage. The changes were then assessed using six biological measurements. Only four have been shown to be capable as alternatives to existing biocriteria. Their implementation enabled the development of local biocriteria compatible with the multimetric concept known as the cumulative biotic index (CBI). [ABSTRACT FROM AUTHOR]

This study investigates the water quality and soil characteristics associated with qanat systems in the Cebrail district of the Karabakh region, Azerbaijan. Qanat systems, traditional underground channels designed for water transport, play a crucial role in providing reliable water sources for drinking and irrigation. Water and soil samples were collected from seven qanat systems and analyzed for various physicochemical properties. Water quality parameters included pH, electrical conductivity, hardness, mineralization, and concentrations of calcium, magnesium, sodium, and other ions. Soil analyses focused on pH, electrical conductivity, organic matter content, salinization degree, and the presence of key ions like sulfate and nitrate. The results indicated that qanat water is generally of high quality, with pH levels suitable for both drinking and irrigation. However, some qanat systems exhibited high electrical conductivity and mineralization levels, suggesting potential salinity issues for sensitive crops. Soil samples showed favorable conditions for agriculture, with good pH levels, low salinity, and high organic matter content. The analysis revealed a significant interaction between water quality and soil characteristics, emphasizing the importance of integrated management practices. In the context of climate change, the sustainability of qanat systems is critical. Recommendations include regular monitoring of water and soil quality, soil amendments to mitigate salinity, efficient irrigation techniques, and the use of climate-resilient infrastructure. This study underscores the importance of qanat systems in arid and semi-arid regions and provides practical recommendations for sustainable land and water resource management, enhancing the socio-economic well-being of local communities. [ABSTRACT FROM AUTHOR]

Mthatha River is the main source of water for the fast-growing population of Mthatha communities and its surrounding. Human resident settlements, livestock grazing and watering, subsistence farming and commercial activities are the main water and land use activities in the catchment. Using the environmental forensic method, this study was carried out to assess water quality in Mthatha River and its selected tributaries for different uses (drinking and domestic use) and identify potential pollution sources. Seven sampling points were purposively selected for water quality testing along Mthatha River and the tributaries. Data was collected during wet and dry seasons of the year 2021. Microbiological and physico-chemical parameters were measured in this study. Microbiological samples (i.e., Total coliform and E. coli) were analysed in the laboratory within 24 h. Physico-chemical parameters (Dissolved Oxygen, water temperature, pH, Electrical Conductivity, Turbidity, Total Dissolved Solids and Nitrate) were measured in-situ. Multivariate statistical analysis, one-way analysis of variance (ANOVA), and Pearson's correlation analysis were employed to analyse the data using SPSS version 22, Primer version 7 and MS Excel 2013.The pH, turbidity and nitrate were found to be higher during wet season than dry season. Extremely high microbiological indicators (E. coli and total coliform counts) were recorded during both seasons and exceeding the WHO and DWAF permissible limits. The study also found that agricultural activities in the upper reaches of the river and a combination of refuse dumping and sewage pollution in the mid-stream section are the main causes of changes in the water quality of the Mthatha River and its tributaries. An assessment and monitoring of the river water quality is necessary for the Mthatha community and to communicate information on water quality for sustainable watershed management practices. [ABSTRACT FROM AUTHOR]

Salinity and temperature significantly affect the growth and production of giant freshwater prawn Macrobrachium rosenbergii. This research investigated the effect of these two vital parameters on behaviour, survival, haemocyte-based cellular immunity, and proximate composition of M. rosenbergii. Adolescent prawns were exposed at five different salinities (0, 5, 10, 15, 20 ppt) and temperatures (22, 26, 30, 34, 38 °C) at a stocking of 6-ind/25-L glass tank with three replication and nursed up to 168 h. Prawn showed normal behaviour and movement up to salinity of 10 ppt, while rising salinity to 20 ppt associated with abnormal behaviour caused 50% mortality within 72 h and showed complete mortality after 96 h. Prawns exposed at 22–30 °C exhibited normal behaviours, while uplifting temperature to 34 °C showed unusual behaviour and mortality. Further increase of temperature to 38 °C, survival rapidly decreased to 33.33% after 24 h, and complete mortality happened within 72 h. Total haemocyte count and differential haemocyte count did not exhibit significant variation at salinity ≤ 10 ppt and temperature ≤ 30 °C, while there was a significant declination at higher salinities (≥ 15 ppt) and temperatures (≥ 34 °C). Interestingly, after a significant fall in protein content at 30 °C, it started to increase at higher temperatures. Meanwhile, a significant decrease in lipid content was recorded at salinity ≥ 15 ppt. Water quality measurements were within acceptable norms for prawn aquaculture throughout the experiment. According to this study, prawns' growth, behaviour, and immune system were mostly unaffected by saline levels up to 10 ppt and temperatures below 30 °C; thus, ranges could be adopted for climatically altered coastal environments. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to explore the feasibility of using GF-1 satellite data to retrieve the concentration of dissolved nitrous oxide (N2O) in water so as to provide an effective way to realize low-cost and high-efficiency real-time monitoring of water quality. Methods The 1st and 5th drainage ditches in Qingtongxia Irrigation District of Ningxia were selected as the research objects, and the reflectance and water quality parameters of GF-1 satellite image band, which were highly correlated with the concentration of dissolved N2O in the drainage ditches, were selected as independent variables, and the optimal combination of independent variables was determined by optimal subset screening method. Multiple linear regression, BP neural network and support vector machine models were respectively constructed to predict and compare the concentration of dissolved N2O in water. [Results] The water temperature (T) and dissolved organic carbon (DOC) were the main factors affecting the concentration of dissolved N2O in water, and satellite bands such as near infrared (NIR) were significantly correlated with the variation trend of dissolved N2O concentration in water. When the independent variable including 7 factors such as T and NIR. the model had the best prediction effect. Among the three models, the R of BP neural network model was 0.64. which had the highest prediction accuracy. [Conclusion] There is a complex correlation between GF-1 satellite data and water quality parameters and dissolved N2O concentration in water bodies, and BP neural network can use GF-1 satellite data to retrieve dissolved N2O concentration in water bodies with high accuracy. [ABSTRACT FROM AUTHOR]

This work aims to study the spatio-temporal variability of turbidity in Lake Chascomús using 34 years (1987–2020) of Landsat (TM, ETM + , and OLI) and Sentinel-2-MSI optical data and to understand this variability in terms of environmental variables. A semi-analytical algorithm, using reflectance in the red and near-infrared bands, was calibrated for Landsat and Sentinel-2 bands and tested using in situ turbidity measurements. The best performance was found using only the near-infrared band with 12.84% median accuracy and -12.84% bias when comparing in situ radiometric measurements and field data. When satellite-derived turbidity was compared to in situ values, the median accuracy was 31.8% and the bias 13.22%. Monthly climatological turbidity maps revealed spatial heterogeneity in Lake Chascomús, with differences observed between the north-west and south-east regions, particularly in summer and winter. Turbidity showed marked seasonal dynamics, with a minimum in autumn and a maximum in spring. Annual climatological turbidity maps showed significant inter-annual variability. Generalized linear models showed turbidity was positively associated with wind speed and photosynthetic active radiation (26.2% of the variability explained). Remote sensing was found to be a fundamental complement to traditional field-based methods for monitoring water quality parameters and allowing a better description of their spatio-temporal variability. [ABSTRACT FROM AUTHOR]

Objective: In a changing climate, the effects of air temperature, precipitation, and groundwater on water temperature and thermal habitat suitability for Gulf Sturgeon Acipenser desotoi, listed as threatened under the U.S. Endangered Species Act, are not well understood. Hence, we incorporated these factors into thermal habitat models to forecast how Gulf Sturgeon may be affected by wide‐ranging climate change scenarios in 2024–2074. Methods: Using data from the Choctawhatchee River, Florida, we developed precipitation‐ and groundwater‐corrected air–water temperature models, compared their accuracy with that of conventional air–water temperature models used in fisheries management, and projected future Gulf Sturgeon thermal habitat suitability for normal physiological functioning and fieldwork (i.e., population sampling and telemetry surgeries) in summer (May–August) under 16 climate change scenarios. Result: Precipitation‐ and groundwater‐corrected models were more accurate than conventional air–water temperature models (mean improvement in adjusted R2 = +0.45; range = +0.09 to +0.75). Water temperature was projected to warm at widely variable rates across climate change scenarios encompassing different air temperature, precipitation, and groundwater regimes. Importantly, Gulf Sturgeon summer aggregation areas were cooler and influenced more by precipitation and groundwater and less by air temperature than were non‐aggregation areas. If precipitation and groundwater—as drivers of cooling—become warm in a changing climate, summer aggregation areas were projected to exhibit thermal habitat degradation equivalent to or greater than that of non‐aggregation areas. Conclusion: Our results add hydrological context to the premise that aggregation areas provide cool water and energetic savings for Gulf Sturgeon during summer, underscoring the importance of protecting these habitats through groundwater conservation, water quality monitoring, and riparian/watershed habitat management. Our findings indicate that identifying thermally appropriate times for fieldwork activities will be increasingly important and time‐restricted as climate change intensifies. However, our research provides managers with a portfolio of water temperature models and an accurate, cost‐effective, management‐relevant approach to forecasting thermal habitat conditions for Gulf Sturgeon and other species in a changing climate. Impact StatementWater temperature models with precipitation and groundwater are accurate and useful, allowing managers to locate key thermal habitats, forecast temperatures, and decide when and where to safely target, capture, tag, and monitor Gulf Sturgeon and other species amid climate change. [ABSTRACT FROM AUTHOR]

This document provides a summary of research articles on groundwater quality and its impact on human health. Access to clean drinking water is a global challenge, as contamination can come from natural sources or human activities. The studies cover regions such as China, Kazakhstan, Saudi Arabia, Pakistan, and South Africa, and examine factors like pH levels, dissolved minerals, chemical compositions, and the presence of contaminants. The research emphasizes the need for understanding hydro-geochemical processes, implementing water treatment methods, and promoting sustainable water resource management to ensure community safety and well-being. [Extracted from the article]

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The scarcity of groundwater monitoring in Kosovo, particularly in Lipjan, underscores the urgency to assess and safeguard this vital resource amidst escalating water demands and mounting pollution. This study addresses the critical gap in groundwater data by proposing the establishment of a comprehensive monitoring system. The primary goal was to develop a system capable of providing real-time data on groundwater quality and quantity within the capture area. Specific research objectives include the daily real-time monitoring of groundwater quality, identification, and quantification of contaminants in the aquifer, as a basis for further work on delineation of contaminant sources impacting the capture area, and monitoring and quantifying water extraction rates from individual wells, therefore establishing the necessary protection zones. Seven divers have been installed in 7 monitoring wells in Lipjan to measure the water level and pressure, as well as a multiparameter sensor for water quality monitoring for pH, temperature, specific conductivity, total dissolved solids, and dissolved oxygen. The digital monitoring system has been set up to input and log the incoming data. The aim was to gather this data, analyze it and use it to create a model and calibrate it to match the observed data. Concurrently, a sensitivity analysis was performed to prioritize data collection and establish which parameters have the most significant impact on the model outcomes. This ensures the establishment of a model which will, in the future, be used to predict and forecast groundwater levels and quality and determine protection zones. [ABSTRACT FROM AUTHOR]

The water quality is vital for the vannamei shrimp pond's productivity. Manual monitoring at Gunung Anyar's vannamei shrimp pond is timeconsuming, ineffective, and potentially harmful. In this research, we developed a real-time monitoring system for the water quality of the vannamei shrimp pond. This monitoring system is integrated with a solarbased aerator. To address this, water quality monitoring in a solar-based aerator system tracks the degree of acidity (pH), temperature, and total dissolved solids (TDS) remotely using a website and real-time mobile phone Android application with 98.57% accuracy and 1.43% error. Seven days of data revealed the degree of acidity between 6.92 and 7.34 is indicated poor conditions of the pond While the temperatures from 23.59 °C to 38.32 °C, and TDS from 628.65 to 652.34 ppm indicate the good condition of the shrimp pond. This real-time monitoring system can help vannamei shrimp farmers monitor the actual conditions of their ponds. [ABSTRACT FROM AUTHOR]

Integral estimate of the environmental conditions of water bodies in the Russian part of the Ural River basin is presented. The estimate is based on the data of long-term state water quality monitoring, statistical data, remote sensing data, the results of special researches, and data of the authors' field studies of water bodies and drainage areas, as well as laboratory studies of water samples. The collected materials were used to characterize the current environmental conditions of water objects and the spatial heterogeneity in the economic development of the drainage area. All available data were used to compile a catalog of water bodies ranked by the level of the load on them. For water bodies experiencing the largest load and characterized by significant violations of water quality standards, a system of measures is proposed for improving their environmental conditions. The choice of the measures is based on the analysis of the Russian and foreign experience in water protection activities, including nature-based restoration technologies of water bodies. The proposals have a comprehensive character and are based on a basin-scale approach. [ABSTRACT FROM AUTHOR]

The Coln is an ecologically sensitive river in a limestone dominated catchment with no major tributaries. Three in‐line turbidity sensors were installed to monitor changes in the dynamics of suspended sediment transport from headwaters to the confluence. The aims were to (i) provide estimates of yield (t km−2 year−1) and likely drivers of suspended sediment over ~3 years and (ii) assess turbidity dynamics during storm events in different parts of the catchment. In addition, the sensor installation allowed a novel wavelet analysis based on identifying groups of turbidity peaks to estimate transport times of suspended sediment through the catchment. Yearly suspended sediment yields calculated for the upper catchment were typically less than 4 t ha−1 year−1 being similar to other UK limestone or chalk‐based rivers. Time series autoregressive integrated moving average models including explanatory variable regression modelling indicated that river discharge, groundwater level and water temperature were all significant predictors of turbidity levels throughout the year. However, high model residuals demonstrate that the models failed to capture random turbidity events. Five parts of the time series data were used to examine sediment dynamics. Plots of scaled discharge verses turbidity demonstrated that in the upper catchment, after initial suspended sediment generation, sediment quickly became limited. In the lower catchment, hysteresis analysis suggested that sediment dilution occurred, due to increasing base flow. The novel wavelet analysis demonstrated that during winter 'sediment events' identified as groups of turbidity peaks, took ~18 h to pass from the first sensor in the upper catchment to the second sensor (10.3 km downstream of sensor 1) and 24 h to the third sensor (23.3 km from sensor 1). The work demonstrates the potential for using multiple turbidity sensors and time series statistical techniques in developing greater understanding of suspended sediment dynamics and associated poor water quality in ecologically sensitive rivers. [ABSTRACT FROM AUTHOR]

In the present day, the health of the populace is significantly jeopardized by the presence of contaminated water, and the majority of the population is unaware of the distinction between safe and unsafe water consumption. Agricultural, industrial, and other human-induced activities are causing a significant decline in the availability of drinking water. Consequently, the issue of ensuring the safety of ingesting water is becoming increasingly prevalent. People should be aware of the purity of the water and the locations where it can be used in order to resolve this situation. There are numerous IoT-based system architectures that are capable of monitoring water parameters; however, the majority of these architectures do not allow for real-time water quality prediction or visualization. In order to achieve this, we suggest a wireless framework that is based on the Internet of Things (IoT). The sensors are able to capture water parameters and transmit the data to the cloud, where a machine learning (ML) model operates to classify the water quality. After that, Grafana enables us to effortlessly visualize the real-time data and predictions from any location. We employed a multi-class dataset from China for the model's construction. GridSearchCV was implemented to identify the optimal parameters for model optimization. The proposed model is a combination of the Random Forest (RF), Extreme Gradient Boosting (XGB), and Histogram Gradient Boosting (HGB) models. The accuracy of the model for the China dataset was 99.80%. To assess the robustness of the proposed model, we acquired a new dataset from the Bangladesh Water Development Board (BWDB) and used it to test the proposed model. The model's accuracy for this dataset was 99.72%. In summary, the proposed wireless IoT framework enables individuals to effortlessly monitor the purity of water and view its parameters from any location. [ABSTRACT FROM AUTHOR]

Detecting the quantity and diversity of diatoms is of great significance in areas such as climate change, water quality assessment, and oil exploration. Here, an efficient and accurate object detection model, named SC-DiatomNet, is proposed for diatom detection in complex environments. This model is based on the YOLOv3 architecture and uses the K-means++ algorithm for anchor box clustering on the diatom dataset. A convolutional block attention module is incorporated in the feature extraction network to enhance the model's ability to recognize important regions. A spatial pyramid pooling module and adaptive anchor boxes are added to the encoder to improve detection accuracy for diatoms of different sizes. Experimental results show that SC-DiatomNet can successfully detect and classify diatoms accurately without reducing detection speed. The recall, precision, and F1 score were 94.96%, 94.21%, and 0.94, respectively. It further improved the mean average precision (mAP) of YOLOv3 by 9.52% on the diatom dataset. Meanwhile, the detection accuracy was improved compared with those of other advanced deep learning algorithms. SC-DiatomNet has potential applications in water quality analysis and monitoring of harmful algal blooms. [ABSTRACT FROM AUTHOR]

Real-time monitoring systems are crucial for the comprehensive management of operations and processes, as well as for assessing the impacts of coastal infrastructures on the marine environment. These systems not only support environmental protection and data-driven decision-making but also enable the early detection of adverse events and the issuance of timely warnings for prompt responses. Although water quality is a critical parameter in this monitoring framework, there are currently limited permanent systems in place dedicated to maintaining these objectives. Even fewer systems leverage their data for research purposes, leading to a gap in the literature regarding effective processing approaches for real-time water quality data. In this context, this study presents a real-time water quality monitoring system integrated into a broader in-field laboratory installed at a coastal area off the coast of Ayia Napa, Cyprus, as well as an initial measured data set of different qualitative quantities. It proposes a holistic approach for post-processing real-time seawater quality data, employing both time and frequency domain analyses, alongside filtering techniques. The study discusses the advantages of each method and emphasizes the importance of their combined use. Utilizing data collected from a three-month operational period, the study assesses the current state of marine seawater quality and examines both temporal and cyclic variations in various seawater quality parameters. The findings reveal that the examined seawater parameters are within reasonable values, indicating that the construction and operation of a nearby marina and the necessary infrastructures (e.g., breakwater) did not affect the seawater quality in the area. Additionally, the study identifies pronounced daily cyclic responses in different seawater quality parameters, including temperature, density, pH, dissolved oxygen, and turbidity. Finally, notable correlations are observed between temperature and dissolved oxygen, temperature and conductivity, oxidation–reduction potential (ORP) and salinity, ORP and dissolved oxygen, and ORP and TDS. [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]

In many developed and developing nations, lakes are the primary source of drinking water. In the current scenario, due to rapid mobilization in anthropogenic activities, lakes are becoming increasingly contaminated. Such practices not only destroy lake ecosystems but also jeopardize human health through water-borne diseases. This study employs advanced hierarchical clustering through multivariate analysis to establish a novel method for concurrently identifying significantly polluted lakes and critical pollutants. A systematic approach has been devised to generate rotating component matrices, dendrograms, monoplots, and biplots by combining R-mode and Q-mode analyses. This enables the identification of contaminant sources and their grouping. A case study analyzing five lakes in Bengaluru, India, has been conducted to demonstrate the effectiveness of the proposed methodology. Additionally, one pristine lake from Jammu & Kashmir, India, has been included to validate the findings from the aforementioned five lakes. The study explored correlations among various physical, chemical, and biological characteristics such as temperature, pH, dissolved oxygen, conductivity, nitrates, biological oxygen demand (BOD), fecal coliform (FC), and total coliform (TC). Critical contaminants forming clusters included conductivity, nitrates, BOD, TC, and FC. Factor analysis identified four primary components that collectively accounted for 85% of the overall variance. Following identification of pollution hotspots, the study recommends source-based pollution control and integrated watershed management, which could significantly reduce lake pollution levels. Continuous monitoring of lake water quality is essential for identifying actual contaminant sources. These findings provide practical recommendations for maximizing restoration efforts, enforcing regulations on pollutant sources, and improving water quality conditions to ensure sustainable development of lakes. [ABSTRACT FROM AUTHOR]

The design of a representative surface water quality monitoring network is vital for accurately capturing the dynamics of water bodies and variability in pollution across a catchment. The representativeness of a surface water monitoring network refers to how well it reflects the characteristics of all monitored surface water bodies. In this study, using a micro-watershed–based approach, a Geographic Information System (GIS) tool (Surface Water Quality Monitoring Point Locations ANalysis (SWQM_PLAN)) has been developed to optimize the design of surface water quality monitoring networks. In the first stage of the two-stage study, a digital elevation model and minimum watershed area size were taken as input parameters and micro-watersheds with defined upstream–downstream relations were created. In the second stage, input parameters including land use data, pollution sources, and micro-watershed data, along with specific criteria, were used to identify the basins and determine the optimal locations for surface water monitoring stations. The developed GIS tool was then applied to evaluate the existing surface water monitoring network in the Gediz River Basin, designed by the Republic of Türkiye, Ministry of Agriculture and Forestry. The tool assessed the effectiveness if the existing monitoring network in terms of assessing agricultural pollution and provided potential revision suggestions to enhance the effectiveness of implemented pollution reduction measures. [ABSTRACT FROM AUTHOR]

Hydrobiogeochemical processes governing water quantity and quality are highly variable in space and time. Focusing on thirty river locations in Québec, Canada, three water quality hotness indices were used to classify watersheds as contaminant transport hotspots. Concentration and load data for suspended solids (SS), total nitrogen (TN), and total phosphorous (TP) were used to identify transport hotspots, and results were compared across hotness indices with different data requirements. The role of hydroclimatic and physiographic characteristics on the occurrence and temporal persistence of transport hotspots was examined. Results show that the identification of transport hotspots was dependent on both the type of data and the hotness index used. Relationships between temporal and spatial predictors, however, were generally consistent. Annual transport hotspot occurrence was found to be related to temporal characteristics such as the number of dry days, potential evapotranspiration, and snow water equivalent, while hotspot temporal persistence was correlated to landcover characteristics. Stark differences in the identification of SS, TN, and TP transport hotspots were attributed to differences in mobilization processes and provided insights into dominant water and nutrient flowpaths in the studied watersheds. This study highlighted the importance of comparing contaminant dynamics across watersheds even when high-frequency water quality data or discharge data are not available. Characterizing hotspot occurrence and persistence, among hotness indices and water quality parameters, could be useful for watershed managers when identifying problematic watersheds, exploring legacy effects, and establishing a prioritization framework for areas that would benefit from enhanced routine monitoring or targeted mitigation strategies. [ABSTRACT FROM AUTHOR]

Water quality monitoring and assessment play crucial roles in efficient water resource management, particularly in the context of agricultural rrigation. Leveraging Internet of Things (IoT) devices equipped with various sensors simplifies this process. In this study, we propose a comprehensive framework integrating IoT technology and Machine Learning (ML) techniques for water quality monitoring and assessment in agri- cultural settings. Our framework consists of four main modules: sensing, coordination, data processing, and decision-making. To gather essential water quality data, we deploy an array of sensors along the Rohri Canal and Gajrawah Canal in Nawabshah City, measuring parameters such as temperature, pH, turbidity, and Total Dissolved Solids (TDS). We then utilize ML algorithms to assess the Water Quality Index (WQI) and Water Quality Class (WQC). Preprocessing steps including data cleansing, Z-score normalization, correlation analysis, and data segmentation are implemented within the ML-enhanced framework. Regression models are employed for WQI prediction, while classification models are used for WQC prediction. The accuracy and efficacy of these models are evaluated using various metrics such as boxplots, violin plots, con- fusion matrices, and precision-recall metrics. Our findings indicate that the water quality in the Rohri Canal is generally superior to that in the Gajrawah Canal, which exhibits higher pollution levels. However, both canals remain suitable for agricultural irrigation, farming, and fishing. [ABSTRACT FROM AUTHOR]

Urban lakes serve as vital ecological and recreational anchors within built environments, essential for enhancing urban resilience. Evaluating lake health predominantly focuses on water quality, assessing indicators such as nutrient levels, toxicity, pH balance, and water clarity to monitor changes. This study proposes a comprehensive evaluation framework that systematically describes specific spatiotemporal manifestations and periodic exogenous regulation characteristics across five dimensions: physical structure, water quality, shoreline dynamics, external regulation, and social service. Furthermore, it introduces an urban lake health assessment model based on synergistic development to evaluate the integrated development and interaction between water environments and social services. This model is applied across urban lakes in various developmental stages in China. Key findings include: 1) Urban development often impacts lake health disparately, with varying degrees of synergy observed between water environments and social services across different urban lakes. However, shifts in urban ideologies and improvements in governance, along with protective policies and project implementations, have contributed to improving water quality to some extent. 2) Engineering interventions do not consistently correspond with improvements in water quality, and governance measures sometimes yield mixed outcomes, underscoring the necessity for systematic solutions to lake health. Restoring hydrological processes emerges as crucial for enhancing sustainability. [ABSTRACT FROM AUTHOR]