Search

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]

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]

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]

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]

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]

[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]

Rapidly changing climate is disrupting the High Arctic's water systems. As tracers of hydrological processes, stable water isotopes can be used for high quality monitoring of Arctic waters to better reconstruct past changes and assess future environmental threats. However, logistical challenges typically limit the length and scope of isotopic monitoring in High Arctic landscapes. Here, we present a comprehensive isotopic survey of 535 water samples taken in 2018 and 2019 of the lakes and other surface waters of the periglacial Pituffik Peninsula in far northwest Greenland. The δ18O, δ2H, and deuterium‐excess values of these samples, representing 196 unique sites, grant unprecedented insight into the environmental drivers of the regional hydrology and water isotopic variability. We find that the spatial variability of lake water isotopes can best be explained through evaporation and the hydrological ability of a lake to replace evaporative water losses with precipitation and snowmelt. Temporally, summer‐long evaporation can drive lake water isotopes beyond the isotopic range observed in precipitation, and wide interannual changes in lake water isotopes reflect annual weather differences that influenced evaporation. Following this, water isotope samples taken at individual times or sites in similar periglacial landscapes may have limited regional representativeness, and increasing the spatiotemporal extent of isotopic sampling is critical to producing accurate and informative High Arctic paleoclimate reconstructions. Overall, our survey highlights the diversity of isotopic compositions in Pituffik surface waters, and our complete isotopic and geospatial database provides a strong foundation for future researchers to study hydrological changes at Pituffik and across the Arctic. Plain Language Summary: Water isotopes can help us track how rapidly changing climate is disrupting High Arctic water systems, but the challenging Arctic environment has limited the monitoring required to understand these isotopes. To address this, we collected 535 water isotope samples from lakes and other waters on the Pituffik Peninsula in northwest Greenland in 2018 and 2019. We found that differences in lake water isotopes are mainly due to water evaporation and how connected a lake is to sources of precipitation and snowmelt that can replace evaporated water in the summer. The information we collected about isotopes is a good starting point for other scientists who want to study how water is changing, not just in Pituffik, but also in the whole Arctic. Our findings tell us that if we only collect water samples once or twice, or only in one place, we might not get the full picture of what is happening with the isotopes across the whole region. To get a better understanding of how the climate is changing in the High Arctic, water isotopic samples should be collected from a wide range of locations over long periods of time. Key Points: Five hundred and thirty five water isotope samples taken over 2 years in Pituffik, Greenland, provide insight into High Arctic isotope hydrologySpatially, lake water isotopic composition reflects the degree that evaporation losses are offset by precipitation and snowmelt rechargeEvaporation drives summer‐long lake water isotopic evolution and best explains interannual isotopic differences [ABSTRACT FROM AUTHOR]

Satellite data are extensively used for water quality monitoring purposes, offering a significantly reduced cost compared to in situ data sampling. Using past measurements to predict future conditions remains a challenging task, because of the complexity of the natural phenomena that are involved, with great potential in terms of water resources management. This paper proposes a model that can be used to forecast Chlorophyll‐α $\alpha $ (Chl‐α $\alpha $) values in water bodies, which are a common water quality indicator. The operation of the model lays on the fact that typically Chl‐α $\alpha $ increases and decreases periodically. First, we apply C2RCC, which is a common atmospheric correction algorithm, to Sentinel‐2 images to get Chl‐α $\alpha $ maps for 15 lakes for 12 consecutive months around Europe. Then, we use this data set (∼ ${\sim} $1,000 Sentinel‐2 images) to train a Generative Adversarial Network (GAN) to recognize spatiotemporal patterns. To accomplish this task, pix2pix algorithm is employed, matching consecutive past and current Chl‐α $\alpha $ maps to future Chl‐α $\alpha $ maps. This model has been applied to 3 water bodies around Europe that are not included in the 15‐lakes training data set and has been found to perform accurately, achieving high Pearson and Spearman correlations and low RMSE values. Overall, the model can be used to make Chl‐α $\alpha $ maps' predictions with low computational cost and without using any in situ data and without the requirement of training for every water body. Key Points: Use of Sentinel 2 data for creating inland water bodies' Chlorophyll‐α $\alpha $ time‐seriesGenerative Adversarial Networks are trained to recognize Chlorophyll‐α $\alpha $ spatio‐temporal patternsA continental‐scale model is created for Chlorophyll‐α $\alpha $ short term predictions [ABSTRACT FROM AUTHOR]

Microfluidics are crucial for managing small-volume analytical solutions for various applications, such as disease diagnostics, drug efficacy testing, chemical analysis, and water quality monitoring. The precise control of flow control devices can generate diverse flow patterns using pneumatic control with solenoid valves and a microcontroller. This system enables the active modulation of the pneumatic pressure through Arduino programming of the solenoid valves connected to the pressure source. Additionally, the incorporation of solenoid valve sets allows for multichannel control, enabling simultaneous creation and manipulation of various microflows at a low cost. The proposed microfluidic flow controller facilitates accurate flow regulation, especially through periodic flow modulation beneficial for droplet generation and continuous production of microdroplets of different sizes. Overall, we expect the proposed microfluidic flow controller to drive innovative advancements in technology and medicine owing to its engineering precision and versatility. [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]

Agricultural phosphorus (P) loss constitutes a significant factor in agricultural non-point source pollution (ANSP). Due to the widespread occurrence and complexity of ANSP, emphasis on risk prevention and control is preferable to retroactive treatment, to reduce costs. Effective risk identification is an issue that needs to be addressed urgently. Henan Province, a typical intensive agricultural region in China, was used as a case study to develop a straightforward and precise model for assessing the risk of P loss. Total phosphorus (TP) emission intensity at the county level in Henan Province was estimated based on planting, livestock and poultry breeding, and rural domestic activities. Subsequently, influential factors were selected to determine the extent of P loss in rivers. Finally, the model was validated using water quality data. The results indicate that (1) TP emission and rainfall are the primary contributors to the risk of P loss, whereas vegetation coverage has negligible effects. (2) The primary sources of TP emission, in descending order of magnitude, are livestock and poultry breeding, rural domestic activities, and planting. Livestock and poultry breeding represents the largest proportion at approximately 50%. (3) High-risk areas for P loss are concentrated in the plains of the central, eastern, and northern Henan Province, while low-risk areas are mainly located in the western mountainous and hilly regions. (4) The model exhibits high accuracy with a determination coefficient (R2) of 0.81 when compared to surface water quality monitoring data. This study provides a new framework for assessing the risk of P loss in intensive agricultural settings. [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]

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]

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]

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]

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]

Water quality is deteriorating in the world's freshwater bodies, and Lake Tana in Ethiopia is becoming unpleasant to biodiversity. The objective of this study is to retrieve non-optical water quality data, specifically total nitrogen (TN) and total phosphorus (TP) concentrations, in Lake Tana using Machine Learning (ML) techniques applied to Landsat 8OLI imagery. TheMLmethods employed include Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest Regression (RF), XGBoost Regression (XGB), AdaBoost Regression (AB), and Gradient Boosting Regression (GB). The XGB algorithm provided the best result for TN retrieval, with determination coefficient (R²), mean absolute error (MARE), relative mean square error (RMSE) and Nash Sutcliff (NS) values of 0.80, 0.043, 0.52, and 0.81 mg/L, respectively. The RF algorithm was most effective for TP retrieval, with R² of 0.73, MARE of 0.076, RMSE of 0.17 mg/L, and NS index of 0.74. These methods accurately predicted TN and TP spatial concentrations, identifying hotspots along river inlets and northeasters. The temporal patterns of TN, TP, and their ratios were also accurately represented by combining in-situ, RS and ML-based models. Our findings suggest that this approach can significantly improve the accuracy of water quality retrieval in large inland lakes and lead to the development of potential water quality digital services. [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]

Fruit- and vegetable-processing facilities may contaminate wastewater via contaminants found in the produce and disinfecting chemicals used. These contaminants may include agrochemicals, pesticides, and disinfectants such as chlorine and quaternary ammonium compounds (QACs). Some compounds may exhibit harmful endocrine-disrupting activity. This study investigated the impact of a minimally processed vegetable facility on wastewater quality via in vitro bioassays and chemical screening. Estrogen activity was assessed via a yeast estrogen screen (YES), and (anti-)androgenic and glucocorticoid activities were evaluated via an MDA-kb2 reporter gene assay. The samples were screened via gas and liquid chromatography-tandem mass spectrometry (GC-MS/MS and LC-MS/MS) to identify target compounds, and GC coupled with time-of-flight mass spectrometry (GC-TOFMS) was used for non-targeted screening. Sample complexity and chemical profiles were assessed using GC-TOFMS. Estrogenic activity was detected in 16 samples (n = 24) with an upper limit of 595 ± 37 ng/L estradiol equivalents (EEqs). The final wastewater before discharge had an EEq of 0.23 ng/L, which is within the ecological effect-based trigger value range for the estrogenic activity of wastewater (0.2-0.4 ng/L EEq). Androgenic activity was detected in one sample with a dihydrotestosterone equivalent (DHTEq) value of 10 ± 2.7 ng/L. No antiandrogenic activity was detected. The GC-MS/MS and LC- MS/MS results indicated the presence of multiple pesticides, nonylphenols, triclocarban, and triclosan. Many of these compounds exhibit estrogenic activity, which may explain the positive YES assay findings. These findings showed that wastewater from the facility contained detergents, disinfectants, and pesticides and displayed hormonal activity. Food-processing facilities release large volumes of wastewater, which may affect the quality of the water eventually being discharged into the environment. We recommend expanding conventional water quality monitoring efforts to include additional factors like endocrine activity and disinfectant byproducts. [ABSTRACT FROM AUTHOR]

Background: Algae play important roles in urban river ecosystems and are the cornerstones of most water quality monitoring programs. Thus, a better understanding of algal community dynamics is needed to support sustainable management of water resources in urban rivers. Results: In this study, we quantified the seasonal variations in planktonic and periphytic algal community structure in the highly urban Fenhe River and identified environmental factors affecting algal community structure and diversity. We monitored planktonic (drifting) and periphytic (attached) algal communities in the Taiyuan section of the Fenhe River over one year. The results indicated that Cyanophyta was the dominant phylum in both communities, followed by Bacillariophyta and Chlorophyta. Significant differences were observed in the composition of the planktonic and periphytic algal communities. In particular, the periphytic algal community was more diverse than the planktonic community. Water temperature and pH were the main environmental factors affecting planktonic and periphytic algal community structure, respectively, while nutrients were the most significant factor affecting planktonic and periphytic algal diversity. Ecological modeling indicated that the variations in the algal communities of the Fenhe River are mainly driven by stochastic processes. A co-occurrence network developed for the communities displayed positive interactions between the planktonic and periphytic algae. Conclusions: These findings deepen our understanding of the seasonal interaction between planktonic and periphytic algae and the driving factors affecting community structure in the Fenhe River. They also provide a theoretical basis for the managing and protecting water resources in urban river ecosystems. [ABSTRACT FROM AUTHOR]

Objective To analyze the monitoring data of rural drinking water quality and water supply project information in Gansu Province, and to provide a basis for the guarantee of rural drinking water health security. Methods Based on the water quality monitoring data of rural centralized water supply project in Gansu Province from 2018 to 2023, the water quality index evaluation method was used to compare the differences in water quality, and the chi-square test was used to compare the status of water supply projects in each year. Results The results showed that the automatic monitoring system, the proportion of purification and disinfection facilities, and the comprehensive index of water quality in rural centralized water supply project in Gansu Province were improved, and the differences between each year were statistically significant (P < 0.05). In the single monitoring index, the total standard rate of total coliforms in microbial index increased from 68.66% to 90.95% from 2018 to 2023, and the turbidity qualified rate in sensory index increased from 92.26% to 98.65%. Conclusion The rural drinking water supply situation in Gansu from 2018 to 2023 was improved, and the water quality was on the rise. It is necessary to continue to promote the scale and standardization construction of water supply project to ensure the health safety of drinking water. [ABSTRACT FROM AUTHOR]

In remote watersheds or large water bodies, monitoring water quality parameters is often impractical due to high costs and time-consuming processes. To address this issue, a cost-effective methodology based on remote sensing was developed to predict water quality parameters over a large and operationally challenging area, especially focusing on East Lake. Sentinel-2 satellite image data were used as a proxy, and a multiple linear regression model was developed to quantify water quality parameters, namely chlorophyll-a, total nitrogen, total phosphorus, ammonia nitrogen and chemical oxygen demand. This model was then applied to East Lake to obtain the temporal and spatial distribution of these water quality parameters. By identifying the locations with the highest concentrations along the boundaries of East Lake, potential pollution sources could be inferred. The results demonstrate that the developed multiple linear regression model provided a satisfactory relationship between the measured and simulated water quality parameters. The coefficients of determination R2 of the multiple linear regression models for chlorophyll-a, total nitrogen, total phosphorus, ammonia nitrogen and chemical oxygen demand were 0.943, 0.781, 0.470, 0.624 and 0.777, respectively. The potential pollution source locations closely matched the officially published information on East Lake pollutant discharges. Therefore, using remote sensing imagery to establish a multiple linear regression model is a feasible approach for understanding the exceedance and distribution of various water quality parameters in East Lake. [ABSTRACT FROM AUTHOR]

This study examines the dynamics of limnological parameters of a South American lake located in southern Chile with the objective of predicting chlorophyll-a levels, which are a key indicator of algal biomass and water quality, by integrating combined remote sensing and machine learning techniques. Employing four advanced machine learning models (recurrent neural network (RNNs), long short-term memory (LSTM), recurrent gate unit (GRU), and temporal convolutional network (TCNs)), the research focuses on the estimation of chlorophyll-a concentrations at three sampling stations within Lake Ranco. The data span from 1987 to 2020 and are used in three different cases: using only in situ data (Case 1), using in situ and meteorological data (Case 2), using in situ, and meteorological and satellite data from Landsat and Sentinel missions (Case 3). In all cases, each machine learning model shows robust performance, with promising results in predicting chlorophyll-a concentrations. Among these models, LSTM stands out as the most effective, with the best metrics in the estimation, the best performance was Case 1, with R2 = 0.89, an RSME of 0.32 µg/L, an MAE 1.25 µg/L and an MSE 0.25 (µg/L)2, consistently outperforming the others according to the static metrics used for validation. This finding underscores the effectiveness of LSTM in capturing the complex temporal relationships inherent in the dataset. However, increasing the dataset in Case 3 shows a better performance of TCNs (R2 = 0.96; MSE = 0.33 (µg/L)2; RMSE = 0.13 µg/L; and MAE = 0.06 µg/L). The successful application of machine learning algorithms emphasizes their potential to elucidate the dynamics of algal biomass in Lake Ranco, located in the southern region of Chile. These results not only contribute to a deeper understanding of the lake ecosystem but also highlight the utility of advanced computational techniques in environmental research and management. [ABSTRACT FROM AUTHOR]

Many natural and artificial liquid environments, such as rivers, oceans, lakes, water storage tanks, aquariums, and urban water distribution systems, are difficult to access. As a result, technology is needed to enable autonomous liquid sampling to monitor water quality and ecosystems. Existing in situ sample acquisition and handling systems for liquid environments are currently limited to a single use and are semi-autonomous, relying on an operator. Liquid sampling systems should be robust and light and withstand long-term operation in remote locations. The system components involved in liquid sampling should be sterilisable to ensure reusability. Here, we introduce a prototype of a liquid sampler that can be used in various liquid environments and may be valuable for the scientific characterisation of different natural, remote, and planetary settings. The Autonomous Planetary Liquid Sampler (APLS) is equipped with pre-programmed, fully autonomous extraction, cleaning, and sterilisation functionalities. It can operate in temperatures between −10 °C and 60 °C and pressure of up to 0.24 MPa (~24 m depth below mean sea level on Earth). As part of the control experiment, we demonstrate its safe and robust autonomous operation in a laboratory environment using a liquid media with Bacillus subtilis. A typical sampling procedure required 28 s to extract 250 mL of liquid, 5 s to fill the MilliQ water, 25 s for circulation within the system for cleaning and disposal, and 200 s to raise the system temperature from ~30 °C ambient laboratory temperature to 150 °C. The temperature is then maintained for another 3.2 h to sterilise the critical parts, allowing a setup reset for a new experiment. In the future, the liquid sampler will be combined with various existing analytical instruments to characterise the liquid solution and enable the autonomous, systematic monitoring of liquid environments on Earth. [ABSTRACT FROM AUTHOR]

Evaluating the efficiency of wastewater treatment plants (WWTPs) and their impact on receiving surface water bodies is a complex and highly significant task due to its regulatory implications for both environmental and public health. The monitoring of many water quality parameters related to the compliance of treated wastewater with environmental standards has led to the development of a unitless metric, the Wastewater Quality Index (WWQI), which serves as a practical tool for regulatory authorities. The aim of this research is to propose an appropriate WWQI methodology, incorporating a set of water quality indicators and a weighting approach, to evaluate wastewater effluents under operational monitoring. In this study, WWQI was successfully applied to access the operation of 21 WWTPs' effluents within a single monitoring campaign, outside the mandatory monitoring schemes. The WWQI was computed for physical-chemical parameters including chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), total suspended solids (TSS), electrical conductivity (EC) and pH, priority substances (Cd, Ni and Pb) and a specific contaminant (Cr) using the weighted approach in the WWQI calculation, based on equal weighting, expert judgement and PCA weighing using factor loadings. The three approaches give similar results for the calculated WWQI. The expert judgment approach is more suitable for evaluating WWTP performance during a single monitoring campaign due to its simplicity compared to the PCA-based approach and its ability to prioritize specific water quality parameters over an equal weightage method. [ABSTRACT FROM AUTHOR]

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

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

The Xiao Bei mainstream, located in the middle reaches of the Yellow River, plays a vital role in regulating the quality of river water. Our study leveraged 73 years of hydrological data (1951–2023) to investigate long-term runoff trends and seasonal variations in the Xiao Bei mainstream and its two key tributaries, the Wei and Fen Rivers. The results indicated a significant decline in runoff over time, with notable interannual fluctuations and an uneven distribution of runoff within the year. The Wei and Fen Rivers contributed 19.75% and 3.59% of the total runoff to the mainstream, respectively. Field monitoring was conducted at 11 locations along the investigated reach of Xiao Bei, assessing eight water quality parameters (temperature, pH, dissolved oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen (NH3-N), total phosphorus (TP), permanganate index (CODMn), and 5-day biochemical oxygen demand (BOD5)). Our long-term results showed that the water quality of the Xiao Bei mainstream during the monitoring period was generally classified as Class III. Water quality parameters at the confluence points of the Wei and Fen Rivers with the Yellow River were higher compared with the mainstream. After these tributaries merged into the mainstream, local sections show increased concentrations, with the water quality parameters exhibiting spatial fluctuations. Considering the mass flux process of transmission of the quantity and quality of water, the annual NH3-N inputs from the Fen and Wei Rivers to the Yellow River accounted for 11.5% and 67.1%, respectively, and TP inputs accounted for 6.8% and 66.18%. These findings underscore the critical pollutant load from tributaries, highlighting the urgent need for effective pollution management strategies targeting these tributaries to improve the overall water quality of the Yellow River. This study sheds light on the spatiotemporal changes in runoff, water quality, and pollutant flux in the Xiao Bei mainstream and its tributaries, providing valuable insights to enhance the protection and management of the Yellow River's water environment. [ABSTRACT FROM AUTHOR]

The Heilongjiang River is one of the largest rivers in the cool temperate zone and has an abundant fish source. To date, the microbiota community in water samples and fish guts from the Heilongjiang River is still unclear. In the present study, water samples and fish guts were collected from four locations of the Heilongjiang River during both the dry season and the wet season to analyze the spatio-temporal dynamics of microbiota communities in the water environment and fish guts through 16s ribosome RNA sequencing. The water qualities showed seasonal changes in which the pH value, dissolved oxygen, and total dissolved solids were generally higher during the dry season, and the water temperature was higher during the wet season. RDA indicated that higher pH values, dissolved oxygen, and total dissolved solids promoted the formation of microbiota communities in the water samples of the dry season, while higher water temperature positively regulated the formation of microbiota communities in the water samples of the wet season. LEFSe identified five biomarkers with the most abundant difference at the genus level, of which TM7a was upregulated in the water samples of the dry season, and SM1A02, Rheinheimera, Gemmatimonas, and Vogesella were upregulated in the water samples of the wet season. Pearson analysis revealed that higher pH values and dissolved oxygen positively regulated the formation of TM7a and negatively regulated the formation of SM1A02, Rheinheimera, Gemmatimonas, and Vogesella (p < 0.05), while higher water temperature had the opposite regulatory roles in the formation of these biomarkers. The relative abundance of microbiota diversity in fish guts varies greatly between different fish species, even if the fishes were collected from the same water source, indicating that dietary habits and fish species may be key factors, affecting the formation and construction of microbiome community in fish gut. P. glenii, P. lagowskii, G. cynocephalus, and L. waleckii were the main fish resources, which were collected and identified from at least six sample points. RDA indicated that the microbiota in the water environment regulated the formation of microbiota community in the guts of G. cynocephalus and L. waleckii and had limited regulated effects on P. glenii and P. lagowskii. The present study identified the regulatory effects of water qualities on the formation of microbiota communities in the water samples and fish guts, providing valuable evidence for the protection of fish resources in the Heilongjiang River. [ABSTRACT FROM AUTHOR]

The present study was conducted in one of the most significant water sources in Iraq, the Tigris River in Misan Province. The aim was to examine the zooplankton community, specifically Cladocera, Copepoda, and Rotifera. Samples were collected 30cm below the water surface using a plankton haul net with a mesh size of 64μm from September 2023 to February 2024. Sampling took place at three stations: the first in Kumait, located in the north of Misan Province; the second in Amarah City, the capital of Misan Province, and the third about 20km south of Amarah City. The results revealed seventeen species of zooplankton, belonging to two phyla, three classes, four orders, and eight families. A comparison between the historical and current status in the study area indicated a noticeable shift in zooplankton composition toward species more tolerant of pollution. For instance, the appearance of the family Brachionidae and its common species Brachionus sp. in the study area, alongside the absence of Daphnia species, suggests ecological degradation in the inland waters of Iraq, particularly in Misan Province. This study recommends incorporating zooplankton into water quality monitoring programs (WQMPS) in Iraq. [ABSTRACT FROM AUTHOR]

BACKGROUND: Access to safe drinking water remains a critical challenge in Sub-Saharan Africa, driven by a complex mix of environmental, political, social, economic, and infrastructural factors. This scoping review aims to map the literature on water access in Sub-Saharan Africa over the past decade. METHODS: We conducted a comprehensive search of academic databases and grey literature from January 2013 to the present. We included peer-reviewed quantitative, qualitative, and mixedmethods research, as well as reviews and reports focusing on factors influencing water access and related interventions in SubSaharan Africa. Data were extracted on study characteristics, key determinants, proposed solutions, and outcomes. RESULTS: A total of 137 studies were included. Commonly reported determinants included droughts, climate change, conflict, governance, gender, wealth, education, poverty, and inadequate infrastructure. Identified potential interventions included infrastructure development, water quality monitoring, climate adaptation, governance reforms, decentralized management, targeted subsidies, and integrated water resources management. However, most studies described barriers rather than evaluating solutions. CONCLUSIONS: Persistent inequities in water access are driven by interconnected factors such as poverty, governance, gender, and infrastructure. Implementing integrated solutions is crucial, with a shift from problem identification to evaluating contextualized interventions across sectors. Dedicated implementation research is needed to translate knowledge into action, advancing water security and achieving Sustainable Development Goal 6 in the region. [ABSTRACT FROM AUTHOR]

Water quality indicator algorithms often separate marine and freshwater systems, introducing artificial boundaries and artifacts in the freshwater to ocean continuum. Building upon the Ocean Colour- (OC) and Lakes Climate Change Initiative (CCI) projects, we propose an improved tool to assess the interactions across river–sea transition zones. Fuzzy clustering methods are used to generate optical water types (OWT) representing spectrally distinct water reflectance classes, occurring within a given region and period (here 2016–2021), which are then utilized to assign membership values to every OWT class for each pixel and seamlessly blend optimal in-water algorithms across the region. This allows a more flexible representation of water provinces across transition zones than classic hard clustering techniques. Improvements deal with expanded sensor spectral band-sets, such as Sentinel-3 OLCI, and increased spatial resolution with Sentinel-2 MSI high-resolution data. Regional clustering was found to be necessary to capture site-specific characteristics, and a method was developed to compare and merge regional cluster sets into a pan-regional representative OWT set. Fuzzy clustering OWT timeseries data allow unique insights into optical regime changes within a lagoon, estuary, or delta system, and can be used as a basis to improve WQ algorithm performance. [ABSTRACT FROM AUTHOR]

Simple Summary: The Internet of Things (IoT) can improve small-scale aquaponics, a sustainable farming method that combines fish farming with plant growing in water without soil, by making the process more efficient and profitable by optimizing resource use, closely monitoring water quality, and ensuring the best conditions for both fish and plants to thrive. Aquaponics is beneficial for the environment and can help ensure a steady food supply but presents challenges for small-scale farmers due to a lack of expertise in water chemistry and system upkeep, as well as high operational costs. Identified challenges in aquaponics operation include high water and energy costs, maintaining the right balance of fish and plants, and the risk of mosquitoes breeding in the water. This systematic review offers a comprehensive guide to setting up and maintaining an aquaponics system, including choosing the right fish and plants, designing the system, monitoring water quality, and feeding the fish. The importance of knowledge sharing among farmers is also highlighted to improve aquaponics practices. The integration of IoT into these systems can reduce the need for manual work and improve the availability of information related to system control, which could facilitate further adoption and optimization of aquaponics farming practices. Aquaponics combines aquaculture and hydroponics to offer a sustainable approach to agriculture, addressing food security issues with minimal environmental harm. However, small-scale practitioners face challenges due to a lack of professional knowledge in water chemistry and system maintenance. Economic hurdles, such as operational costs and energy-intensive components, hinder the viability of small-scale aquaponics. Selecting suitable fish and plant species, along with appropriate stocking densities, is crucial. Media Bed (MB), Deep Water Culture (DWC), and the Nutrient Film Technique (NFT) are commonly used hydroponic techniques. This study outlines optimal conditions, including water quality, temperature, pH, and nutrient concentrations, essential for symbiotic fish and plant cultivation. Integrating IoT technology enhances efficiency and profitability by optimizing resource utilization, monitoring water quality, and ensuring optimal growth conditions. Knowledge sharing among practitioners fosters innovation and sustainability through collaborative learning and best practices exchange. Establishing a community for knowledge sharing is vital for continuous improvement, advancing small-scale aquaponics towards a more efficient and sustainable future. [ABSTRACT FROM AUTHOR]

Monitoring of stream water quality is a key element of water resource management worldwide, but methods that are commonly used in temperate habitats may not be appropriate in humid tropical systems. We assessed the influence of four land uses on microbial water quality in 21 streams in the Panama Canal Watershed over a one-year period, using a common culture-based fecal indicator test and 16S rDNA metabarcoding. Each stream was located within one of four land uses: mature forest, secondary forest, silvopasture, and traditional cattle pasture. Culturing detected total coliforms and Escherichia coli across all sites but found no significant differences in concentrations between land uses. However, 16S rDNA metabarcoding revealed variability in the abundance of coliforms across land uses and several genera that can cause false positives in culture-based tests. Our results indicate that culture-based fecal indicator bacteria tests targeting coliforms may be poor indicators of fecal contamination in Neotropical oligotrophic streams and suggest that tests targeting members of the Bacteroidales would provide a more reliable indication of fecal contamination. [ABSTRACT FROM AUTHOR]

The volume of water loss because of leakage in the conveyance pipe has been alarming. Old and poorly constructed pipelines, inadequate corrosion protection, poorly maintained valves, and mechanical damage contribute to leakage. Water-carrying pipes were buried underground, so tracing leak points manually could be tasking, if not impossible. This work was to report on the effectiveness of a developed Automated Leak and Water Quality Detection (ALWQD) system. This device can detect leaks in the piped water system automatically and can also report any deterioration in the quality of water that flows through affected pipes. The ALWQD consisted of several drainpipe connections, pipe accessories, electronic components, and sensors to monitor water quality impairment. The control signal was the solenoid valves that interfaced with the ESP-32 microcontroller boards placed on the pipe manifold at intervals, along with water quality monitoring sensors of turbidity, Total Dissolved Solids (TDS), and pH. The fabrication and testing of the device followed standard procedures. Testing of ALWQD was done at 0, 5, and 10 minutes under load and no-load conditions, with average variation in reading recorded after three trials. The findings indicated that the efficiency of ALWQD was between 70% and 80%, which could be improved upon. The trend in the results of the monitored parameters was not different from that of similar previous work. Leaks caused pressure drops and disallowed the full flow of water found at pipe joints, which could be a pathway for the intrusion of contaminants into the water conveyance system. [ABSTRACT FROM AUTHOR]

One of the relatively affordable and safe waste treatment is biological treatment by utilising certain plants as biofilters. The water lettuce (Pistia stratiotes L.) is an aquatic plant that is usually considered a weed by the community. These plants can be used to absorb toxic elements in wastewater. The purpose of this study was to analyse the relationship between Pistia stratiotes L. and the decrease in the organic matter content of fishery processing wastewater. The research method involved varying the effectiveness of the relationship between the bacteria and the water lettuce, the cover rate of the water slide plants and measuring water quality. Water quality measurements include: analysis of BOD5, COD, TSS, pH and temperature. Data analysis with regression and correlation tests using SPSS 28 software. In this study, the density of Pistia stratiotes L. used was 0 %, 25 %, 50 %, 75 % and 100 %. Based on the results, it can be concluded that the effectiveness of the Pistia stratiotes L. in reducing levels of BOD5, COD and TSS is highest at a density of 100 % with the effectiveness values of each 23.7 mg/L, 58.7 mg/L and 2.67 mg/L. [ABSTRACT FROM AUTHOR]

Surface glint significantly reduces the measurement accuracy of remote sensing reflectance of water, Rrs, making it difficult to effectively use field measurements for studying water optical properties, accurately retrieving water quality parameters, and validating satellite remote sensing products. To accurately assess the effectiveness of various glint removal methods and enhance the accuracy of water reflectance measurements, a portable multiprobe high-resolution System (PMHRS) is designed. The system is composed of a spectrometer, fiber bundles, an irradiance probe, and three radiance probes. The reliability and measurement accuracy of the PMHRS are ensured through rigorous laboratory radiometric calibration and temperature correction. The comprehensive uncertainty of laboratory calibration ranges from 1.29% to 1.43% for irradiance calibration and from 1.47% to 1.59% for radiance calibration. Field measurement results show a strong correlation with both synchronous ASD data, and Sen2Cor-atmospherically corrected Sentinel-2B data (R2 = 0.949, RMSE = 0.013; R2 = 0.926, RMSE = 0.0105). The water-leaving radiance measurements obtained under different solar elevation angles using three methods (M99 method, polarization method, and SBA) demonstrate that the improved narrow field-of-view polarization probe effectively removes surface glint across various solar elevation angles (with overall better performance than the traditional M99 method). At a solar elevation angle of 69.7°, the MAPD and MAD between the measurements of this method and those of the SBA are 5.8% and 1.4 × 10−4, respectively. The results demonstrate that the PMHRS system outperforms traditional methods in sun glint removal, significantly enhancing the accuracy of water remote sensing reflectance measurements and improving the validation quality of satellite data. This work provides a crucial technical foundation for the development of high-resolution continuous observation platforms in complex aquatic environments. It holds significant implications for improving the accuracy of field-based water remote sensing reflectance measurements and for enhancing the quality of water ecological monitoring data and satellite validation data. [ABSTRACT FROM AUTHOR]

The Mundeswari River, an ecologically distressed river in eastern India, has been subjected to water quality deterioration largely due to anthropogenic activities in its vicinity. This study aimed to comprehensively evaluate the current state of pollution in the river and assess the appropriateness of river water for irrigation, given its extensive use for agricultural purposes. Monthly water quality monitoring was undertaken at four distinct sampling sites (SP1–SP4) over a two-year period (2020–2022), considering seventeen water quality parameters. This research employed principal component analysis/factor analysis (PCA/FA) and absolute principal component score-multiple linear regression (APCS-MLR) receptor modelling. These methodologies were used to discern and quantify potential sources of pollution influencing the water quality of the Mundeswari River. The study revealed that the water quality of the Mundeswari River was most degraded during the pre-monsoon season. Among the four sampling sites, SP3 exhibited the highest level of pollution with mean biochemical oxygen demand (BOD) and chemical oxygen demand (COD) values of 5.36 mg/L and 44.72 mg/L, respectively. According to the one-way analysis of variance (ANOVA), there was considerable spatial and seasonal disparities (P < 0.05) in most water quality parameters. The PCA/FA extracted four latent pollution sources, accounting for 81.5% of the total variance. The primary factors influencing the quality of river water are natural weathering processes, discharge of domestic effluent and waste, and agricultural runoff. The APCS-MLR receptor model further revealed that agricultural drainage factors and the discharge of domestic effluent and waste had a greater impact on the Mundeswari River. The investigation concluded that the mean values of all indicators for irrigation suitability were below the defined threshold limits, indicating that the water of the studied river appears suitable for irrigation. The outcomes of this study may significantly contribute to the formulation of sustainable strategies for the ecological rejuvenation of the Mundeswari River. [ABSTRACT FROM AUTHOR]