Search

Seasonal variations in surface water quality are essential for assessing temporal variations in wetlands pollution due to natural or anthropogenic inputs from both point and non-point sources. The goal of this study was to use established methodologies to estimate the water quality of the Tianfu National Wetland Park based on physicochemical features; sampling was done from eight monitoring locations throughout the wetland region in the summer of 2019 and winter of 2020. The water quality index (WQI) is calculated using the following parameters: Turbidity (NTU), Nitrate, Chlorophyll, TOC, DOC, COD, BOD, Chroma, Ammonium nitrogen, pH, Electric Conductivity, and Total Phosphorus. One-way ANOVA and Tukey's pairwise comparisons with a 5% significance level were used to compare water quality parameters among the monitoring point's data. T-test analysis was used to compare the parameters between summer 2019 and winter 2020 and the difference between the water inlet and the water outlet. Cluster analysis was done on the WQI results using Ward's linkage approach. The analyses of variance (ANOVA) of data revealed statistically significant differences between points of sampling (p < 0.05). The paired t-test revealed significant differences in parameters between summer 2019 and winter 2020; however, all parameters in summer show higher values in the water inlet than water outlet. In winter, TOC, DOC, COD, BOD, Chroma, pH, and Electric Conductivity showed higher values than Turbidity (NTU), Nitrate, Chlorophyll, Ammonium Nitrogen, and Total Pospurus. In general, summer showed higher pollution than winter, and water inlets were more polluted than water outlets indicating that other factors may affect the water quality, such as vegetation cover, temperature, water level, and activities in the wetland during the seasons. [ABSTRACT FROM AUTHOR]

Aquaculture is an important economic and food source in many countries. Due to environmental restrictions and the effects of aquatic diseases, aquaculture requires a lot of labor and expensive materials, and it relies on the expertise of aquaculture experts. The quality of water is essential for aquaculture development. Therefore, in this paper, a novel aquaculture water quality monitoring using autonomous sensors (AquaSense) has been proposed which uses autonomous sensors for efficient monitoring of water in the aquaculture environment. In the AquaSense framework, temperature, pH, dissolved oxygen, and salinity measurements are recorded and collected using a variety of autonomous sensors. Based on the information gathered, users can assess the condition of their farm through the Internet. The MATLAB R2012b platform is employed to verify the effectiveness of the suggested water quality (WQ) monitoring technique and analyze the data. Several criteria including accuracy, precision, recall, specificity, and f1-score have been used to assess the effectiveness of the suggested strategy. AquaSense achieves the high accuracy ranges of 96.98%, and existing techniques ISAS, AquaStat, and IoT-WQI FIS achieve 91.24%, 93.39%, and 88.92%, respectively. [ABSTRACT FROM AUTHOR]

This research focuses on developing a multipurpose riparian zone designed to effectively reduce erosion and control nutrient flow into Lake Tondano. The goal is to enhance both conservation and pollution control strategies for sustainable ecosystem management. Conducted at Lake Tondano in Minahasa, Indonesia, the research followed three main stages: data collection, analysis, and design. The data collection included a vegetation survey of riparian species, land use mapping, and measuring nitrogen and phosphorus levels in lake sediments. A total of 91 plant species from 60 genera and 42 families were documented. Based on their Importance Value Index (IVI), non-invasive status, and nutrient absorption capabilities, 15 species were selected for restoration in areas with high nutrient concentrations, with sediment levels recorded at 0.09% nitrogen and 0.06% phosphorus in impacted zones. These plant species were carefully identified and rigorously tested, originating from intact riparian zones. They will be strategically employed in areas facing significant challenges from nutrient overloading. Using QGIS analysis, a riparian zone measuring 100 x 30 m was designed at coordinates 1.1745604 latitude and 124.8972748 longitude, targeting areas most impacted by nutrient pollution, which poses a risk of eutrophication and negatively affects aquatic ecosystems. The multipurpose riparian zone incorporates distinct wet, transition, and dry zones, employing a zigzag planting pattern to optimize pollutant filtration and nutrient uptake. This design effectively addresses critical issues of erosion and nutrient excessive enrichment, promoting ecological health and biodiversity in the region. The novel contributions of this study include identifying specific plant species capable of thriving in nutrient-rich sediments and quantifying their nutrient absorption capacities, thereby providing a scientifically grounded model for similar conservation efforts in vulnerable ecosystems and enhancing overall resilience. However, long-term impacts on water quality require further research to assess nutrient mitigation efficacy. [ABSTRACT FROM AUTHOR]

This study assesses the hydrochemical dynamics of surface waters in Ukraine's border regions with Poland and Slovakia over a 15-year period. Key water quality parameters, including sulfates (SO42-), biochemical oxygen demand (BOD5), dissolved oxygen (DO), and total suspended solids (TSS), were analysed to determine the ecological state of these transboundary water bodies. The results indicate that sulfate concentrations remain below the maximum permissible concentrations (MPC) for both household and fishery water use. BOD5 and DO levels generally comply with environmental standards, though localized areas show signs of organic pollution. TSS concentrations remain within acceptable limits, likely influenced by natural erosion and occasional anthropogenic activities. Pearson correlation analysis revealed significant relationships between nitrogen and nitrates (r = 0.814), underscoring the role of agricultural runoff in nutrient dynamics. Negative correlations between DO and several pollutants suggest that organic and chemical contamination affects oxygen availability. These findings emphasize the need for continued monitoring and transboundary collaboration to safeguard water quality in the region. [ABSTRACT FROM AUTHOR]

Accurate forecasting of dissolved oxygen (DO) levels is vital for river ecosystem health. A novel methodology, MVMD-TSA-GPR, combines Multivariate Variational Mode Decomposition (MVMD), the Tunicate Swarm Algorithm (TSA), and Gaussian Process Regression (GPR) to improve DO level predictions. This study also incorporated Generalized Additive Model and Regression Bagged Ensemble (RBE) for 1- and 3-month forecasts using monthly data (1974–2023) from 16 water quality parameters across five Mississippi River basin sites. Key predictors identified through cross-correlation include lagged values and parameters like water temperature, discharge, pH, total phosphorus, potassium, and sulfate, which significantly influence DO levels. The MVMD-TSA-GPR model outperformed others, especially at site 5, showing substantial improvements in accuracy with decreased RMSE values across various scenarios. Model ranking via the Taylor Diagram indicated MVMD-TSA-GPR had the highest performance, followed by MVMD-TSA-RBE and others. Notably, the GPR model's RMSE at site 3 decreased from 2.11 to 1.01 (109% reduction) for the 1-month forecast, while at site 4 for the 3-month forecast, it dropped from 1.85 to 1.04 (106% reduction). The results revealed that the MVMD-TSA-GPR model demonstrated the highest performance for DO (t + 1), achieving R = 0.90, PBIAS = 0.73%, and WI = 0.804, as well as for DO (t + 3), with R = 0.88, PBIAS = 0.54%, and WI = 0.779, at the Lower Mississippi site during the test phase. Additionally, the MVMD-TSA-RBE model excelled for DO (t + 1) in the Missouri River at the Hermann site, achieving R = 0.91, PBIAS = 0.86%, and WI = 0.805 during the test phase. These results underscore the effectiveness of the MVMD-TSA hybrid approach. Interpretative analysis using SHapley Additive exPlanations (SHAP) revealed water temperature, pH, and potassium as key factors affecting DO levels. The speed and accuracy of MVMD-TSA-GPR make it a promising tool for monitoring river water quality. [ABSTRACT FROM AUTHOR]

Aim: 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. Method: 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. Results: 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. Conclusion: 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]

Turbidity (Turb) and Chlorophyll-a (Chl-a) are crucial indicators of water quality because they can reveal the presence of suspended particles and algae, respectively. Understanding the health of rivers and spotting long-term water quality changes can both benefit from monitoring these measures. Traditional methods of monitoring these parameters, like in-situ measurements, is time-consuming, expensive, and inconvenient in some places. Sentinel-2, a multispectral satellite, might offer a more workable and economical option for monitoring water quality, though. This study used 100 in-situ data collected from the Ganga River near Varanasi in the pre-monsoon season (pre-MS) and post-monsoon season (post-MS) in order to create a model for the prediction of optically active water quality parameters by combining Multispectral Instrument (MSI) data and machine learning method (Random Forest). To create spatial distribution maps for Chl-a and Turb, 14 spectral indices and band ratios were employed as independent variables. The results showed that the prediction accuracy for Turb (R2 = 0.91, MAE = 1.13 and MAPE=7.76 % during pre-MS and R2 = 0.93, MAE = 0.88 and MAPE=2.29 % during post-MS) and for Chl-a (R2 = 0.97, MAE = 0.59, and MAPE=2.07 % during pre-MS and R2 = 0.95, MAE = 0.61, and MAPE = 2.71 % during post-MS). The Ganga near Varanasi abruptly turned green due to an increase in algal bloom in May and June 2021. This study not only revealed the reasons behind the green appearance but also identified potential areas of concern or hotspots. In order to identify hotspot locations, drainage networks, point source discharge locations and LU-LC were used. [ABSTRACT FROM AUTHOR]

Lake Taupō (Taupō moana) in the central North Island is the largest freshwater lake in Aotearoa/New Zealand. Taupō is also a frequently active and potentially hazardous caldera volcano. Water quality monitoring in Lake Taupō shows possible chemical linkages between the lake and hydrothermal systems under the lake around the Horomatangi Reef. We found that hydrothermal venting, discovered in Lake Taupō in 1998, is not a steady emission of bubbles and geothermal fluid but occasional larger fluid discharges also occur as pulses. These larger discharges contain sulphate and manifest in vertical water column profiles as conductivity anomalies. These appear to be linked to magmatic activity, which can cause earthquakes and other volcanic unrest under the lake. Rising plumes of warm water from these larger hydrothermal events can entrain sediment and other nutrients such as dissolved organic nitrogen up to the lake surface and they can continue for several months. Periods of volcanic unrest are well documented at Taupō volcano and have recently occurred in 2008–9, 2019 and 2022–23. Lake water quality data suggest that the conductivity anomalies may reflect a magmatic 'pulse' of this active volcano and provide evidence of large scale hydrothermal venting in Lake Taupō. [ABSTRACT FROM AUTHOR]

Lakes are important in supporting ecosystem services and livelihoods. However, their food webs and ecological functioning are continuously threatened by anthropogenic influences. Food web models have been widely used in studying trophodynamics, fisheries impacts, and ecological functioning of temperate lakes, but less often in Afrotropical lake systems. We used Ecopath mass‐balanced trophic models annually in 1999, 2010, and 2020 to assess trends in ecosystem function, and the impact of fisheries on the Lake Baringo Ecosystem, a shallow freshwater lake in Kenya. Pre‐balance (PREBAL) and Pedigree analyses supplemented Ecopath models. Model input data were from field sampling, published and gray literature. Food web trophic models indicated a bottom‐up grazer and detrital food chains in all 3 years. Odum's ecosystem development indicators (total productivity to total biomass and total respiration ratios; TPP/TB and TPP/TR) showed that the lake was in a low to intermediate developmental stage, with room for bio‐manipulation, and a highly reduced mean transfer efficiency (TE) (6.4%–0.49%) indicated low trophic transfer of internal production. System omnivory (SOI) and connectance (CI) indices that varied among years indicated temporal variation in food web complexity. Indices of system resilience (overhead and ascendency) indicated an increasing potential for the lake to recover from perturbations. The mean trophic level of the catch (MTLc) increased from 1999 to 2010 and decreased in 2020, by fishing down the food chain as fishing pressure increased. Oreochromis niloticus, an endemic cichlid, was the keystone species (KSi >0) controlling community structure, while the lungfish Protopterus aethiopicus, the top predator in the lake, was not a keystone species (KSi <0). We recommend an integrated approach to lake management that incorporates watershed regulations, regulates fishing effort on the keystone species (O. niloticus), and monitors water quality for sustainable management of the Lake Baringo ecosystem. [ABSTRACT FROM AUTHOR]

Global food security, economic growth, and biodiversity preservation are impacted significantly by aquaculture. Water quality monitoring (WQM) and water quality prediction (WQP) are essential for profitable as well as sustainable aquaculture. Empirical techniques lead to erroneous WQP, which has a negative impact on aquaculture by generating disease outbreaks, oxygen depletion, nutrient imbalances, chemical pollution, and unfavorable environmental effects. In this work, we propose attention-driven long short-term memory (A-LSTM) and gated recurrent unit (A-GRU) deep learning recurrent neural network (DL-RNN) models for aquaculture WQP. This study utilizes two datasets. The first dataset consists of 3 years of data with 1096 samples collected from aquaculture farms under the Agency for Development of Aquaculture Kerala (ADAK) in India. The second dataset is publicly available, where data is collected from the marine aquaculture base in Xincun Town, LingShui County, Hainan Province, China, which consists of 23200 samples collected over 80 days. Additionally, this work presents a thorough analysis of the effects of hyperparameters ( h p ) on the performance of the proposed models using two different water quality datasets. The prediction performance of proposed A-LSTM as well as A-GRU are compared with conventional LSTM and GRU DL-RNN models in terms of prediction accuracy and computational efficiency. Prediction accuracy in the range of 98.30 to 99.70% is observed for various water quality parameters. The findings demonstrate that the proposed A-LSTM and A-GRU models significantly improve prediction accuracy with lesser computation time. [ABSTRACT FROM AUTHOR]

Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of the most significant indicators of the quality of river water. The current study aimed to estimate the minimum, maximum, and mean DO concentrations (DO min, DO max, DO mean) at a gauging station located on Tualatin River, United States. To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. The modeling results demonstrated that the SVR and MLP surpassed the RF and GB models. Despite this, the SVR was concluded to be the best-performing method when used to estimate the DO min, DO max, and DO mean. The best error statistics in the testing phase were related to the SVR model with full (four) inputs to estimate DO mean concentration (RMSE = 0.663 mg/l, MAE = 0.508 mg/l, R = 0.945, NSE = 0.875). Finally, the explainability of the superior models (i.e. SVR models) was conducted using SHapley Additive exPlanations (SHAP) for the first time to estimate DO concentration. In fact, evaluating the explainability of machine learning models can provide useful information about the impact of each of the input estimators used in the procedure of models development. It was concluded that the specific conductance (SC) and followed by water temperature (WT) could provide the most contributions for estimating the DO min, DO max, and DO mean concentrations. [ABSTRACT FROM AUTHOR]

This study marks the first report on the genetic characterization of Penicillium expansum strain capable of mycotoxin production isolated from river water. Situated in Ganagalawanipeta village, Srikakulam, Andhra Pradesh, India, where river water serves as a vital resource, our investigation probed the presence of pathogenic opportunistic fungi adept at mycotoxin synthesis. Over six months, 30 samples were collected to assess their occurrence. This article revolves around the use of morphological traits for Penicillium genus identification. Precise species determination involved PCR analysis using universal primers ITS1 and ITS4, followed by sequence analysis through NCBI-BLASTn and the ITS2 database. The analysis indicated a striking 99.49% genetic similarity to Penicillium expansum isolate MW559596 from CSIR-National Institute of Oceanography, Goa, an Indian isolate, with a resultant 600-base pair fragment. This sequence was officially cataloged as OR536221 in the NCBI GenBank database. Sequence and phylogenetic assessments were conducted to pinpoint the strain and geographical origin. Notably, the ribosomal nuclear ITS region displayed significant inter- and intra-specific divergence, manifested in DNA barcodes and secondary structures established via minimum free energy calculations. These findings provide crucial insights into the genetic diversity and potential mycotoxin production of P. expansum isolates, shedding light on the environmental repercussions and health risks associated with river water contamination from agricultural and aquaculture effluents. This pioneering research advances our understanding of mycotoxin-producing fungi in aquatic environments and underscores the imperative need for water quality monitoring in regions reliant on such water sources for their sustenance and livelihoods. [ABSTRACT FROM AUTHOR]

Copyright of World Nuclear Geoscience is the property of World Nuclear Geoscience Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Deep learning (DL) has recently been applied to surface water quality prediction, whereas its online monitoring data consists of multiple indicators and time series, which are challenging for prediction models due to complex temporal dependencies and inter-indicator mechanisms. Convolutional neural network (CNN) and long short term memory (LSTM) can be used for indicator and temporal domains respectively, but still lack the ability to represent complex patterns in surface water quality. Since attention mechanisms are designed to effectively focus on the most crucial information, spatial attention mechanism (SAM) and temporal attention mechanism (TAM) are suitable for dealing with the above multi-indicator and time series issues. This work incorporates SAM and TAM into the CNN-LSTM model to form 4 DL models for water quality prediction including CNN-LSTM, SAM-enhanced CNN-LSTM, TAM-enhanced CNN-LSTM, and the CNN-LSTM enhanced by both attention mechanisms. Four surface water online monitoring sites are used as case studies to examine the models in predicting three water quality indicators including dissolved oxygen (DO), ammonia nitrogen (NH3-N), and total organic carbon (TOC). According to the case results of the 4 models after training with similar training epochs, the prediction accuracies of attention-enhanced models are better than the CNN-LSTM model, and the model with both attention mechanisms generally achieves the best performance among the 4 models. The prediction NSE of DO by the four models are 0.817, 0.948, 0.952, and 0.967 respectively in a representative case Jiujiang. The results demonstrate that spatial and temporal attention can analyze correlations from multiple indicators and time series of water quality data respectively, to improve the accuracy of surface water quality prediction. [ABSTRACT FROM AUTHOR]

Indonesia, an archipelagic nation with about 70% ocean territory, relies on oceanographic data for efficient marine environment monitoring and natural resource sustainability. Current data collection is limited by tools measuring only single parameters and lengthy data collection times. This study proposes a marine coastal water quality monitoring tool based on the internet of things (IoT), capable of simultaneously measuring temperature, electrical conductivity, pH, and dissolved oxygen. Utilizing an Atmega328 and a battery lasting up to 119 hours, this system offers a cost-effective solution for real-time oceanographic data collection. Employing the ADDIE methodology, the results demonstrate high measurement accuracy compared to traditional methods, with accuracy of 90.5% for temperature, 93.50% for electrical conductivity, 93.67% for pH, and 96.82% for dissolved oxygen. The development of this tool aims to reduce costs and labor in capturing oceanographic data integrated with IoT, facilitate access and monitoring of water data, and make a significant contribution to achieving SDGs targets. The main focus on the goals of addressing climate change and life underwater, especially in the aspects of water resources management and protection of marine ecosystems in Indonesian. [ABSTRACT FROM AUTHOR]

In present decade, because of fast growth of industries and rapid urbanization, the quality of natural water resources is deteriorated in higher rates, results in harmful and life-threatening diseases. Hence, there is a significant requirement in models for Water Quality Test and Analysis, which is time and cost effective. With that note, this research develops a novel model called Water Quality Evaluation and Monitoring Model (WQEM) that utilizes machine learning and Internet of Things (IOT) technique for performing water quality test. Moreover, Water Quality Index is estimated for providing the basic water qualities and based on that Quality Classification is also performed with the Multilayer Perceptron (MLP) based classification model. This work mainly focuses on the water fluoride content other factors such as Dissolved Oxygen, Total Dissolved Solids and pH. The experimentation is carried out using Raspberry Pi3 test kit and WEKA tool using the real-time data. The evaluation results evidence that the proposed model produces higher rate of classification results with minima Mean Absolute Error (MAE) than other compared water quality test models. [ABSTRACT FROM AUTHOR]

This work attempts to design an elemental method for detecting heavy metals in water. The presence of heavy metals in water is a critical issue that needs a check at every level of water consumption. To facilitate the checking, a simple method needs to be identified and developed. Electrochemical sensing is essentially a surface phenomenon and requires a higher surface area for greater accuracy and reliability. We have attempted to use a readily available Cu wire for detecting Pb to 50 μM concentration with 90% reliability. It is important to note that the sensing electrode (Cu wire) utilized for this work has been employed in a facile manner that enhances the ease of use for heavy metal electrochemical sensor. Moreover, post‐usage, the replacement of sensor material for subsequent usage is easy. The low cost and simplicity of the method make it ideal for resource‐constrained environments and portability, resulting in increasing the accessibility of water quality monitoring. The study examines the reliability of a low‐cost electrode for Pb concentration detection in water samples to the concentration of 50 μM using a simple low‐cost electrochemical sensor arrangement. [ABSTRACT FROM AUTHOR]

The increasing popularity of fish keeping in aquariums and the need for electronic equipment to maintain an optimal environment. This article focuses on monitoring water purity to ensure fish health and longevity, addressing the issue of water pollution caused by chemicals and waste produced by fish. Solutions such as mechanical and biological filters are explored, highlighting the use of the mechanical filter composed of zeolite, ceramic rings, and activated carbon, which work to remove solid particles, toxic compounds, and pollutants from the aquarium water. The article presents the implementation of a mechanical filter controlled by a PIC18F4550 microcontroller using a pH sensor. The results indicate the stability of the pH of the water in the established range of 6.5 to 7.5, with a maximum error of 3% at the upper limit of the range and no error below the established lower limit. It is concluded that the system effectively maintains the desired levels and ensures the fish's health. A technological solution for monitoring and controlling water quality is presented, recognizing the possibility of improvements in aquaculture. [ABSTRACT FROM AUTHOR]

To meet the Sustainable Development Goal (SDG) target 6.1, China has undertaken significant initiatives to address the uneven distribution of water resources and to enhance water quality. Since 2000, China has invested heavily in the water infrastructure of numerous reservoirs, with a total storage capacity increase of 4.704 × 1011 m3 (an increase of 90.8%). These reservoirs have significantly enhanced the available freshwater resources for drinking water. Concurrently, efforts to improve water quality in lakes and reservoirs, facilitated by nationwide water quality monitoring, have been successful. As a result, an increasing lakes and reservoirs are designated as centralized drinking water sources (CDWSs) in China. Among the 3441 CDWSs across all provinces, 40.8% are sourced from lakes and reservoirs, 32.6% from rivers, and 26.6% from groundwater in 2023. Notably, from 2016 to 2023, the percentage of lakes and reservoirs categorized as CDWSs has increased consistently across all 29 provinces. This progress has enabled 561.4 million urban residents to access improved drinking water sources in 2022, compared to 303.4 million in 2004. Our findings underscore the pivotal role of water infrastructure construction and water quality improvement jointly promoting lakes and reservoirs as vital drinking water sources. Nevertheless, the nationwide occurrence of algal blooms has surged by 113.7% from the 2000s to the 2010s , which is a considerable challenge to drinking water safety. Fortunately, algal blooms have been markedly alleviated in past four years. However, it is still crucial to acknowledge that lakes and reservoirs face the challenges of algal blooms, and associated toxic microcystin and odor compounds. [ABSTRACT FROM AUTHOR]

Groundwater salinization, exacerbated by natural and anthropogenic factors, poses a significant threat to agricultural production and ecosystems, particularly in coastal areas in China. The accumulation of trace contaminants in saline-alkaline water, such as antibiotics and heavy metals, further compounds the issue, impacting human health and the environment. The article highlights Electrodialysis (ED) technology and nano-activated carbon (NAC) as promising solutions for desalinating saline-alkaline water and removing trace pollutants, offering a sustainable and efficient approach to water treatment. The ED-NAC coupling process shows potential in addressing the challenges of saline-alkaline water treatment, enhancing efficiency, and reducing environmental impacts, with a focus on sustainable resource utilization and technological innovation. [Extracted from the article]

ABSTRACT This research introduces an innovative power management approach for wireless sensor networks in aquatic environmental monitoring. The study presents a dynamic algorithm that optimizes energy consumption in long range (LoRa) communication nodes by adaptively adjusting transmission power based on sensor–gateway distances. Leveraging GPS data and a log‐normal shadowing model, the method enables efficient power allocation. Field testing in an aquaculture setting demonstrates the energy‐aware power control (EAPC) algorithm's efficacy. Nodes at 500 m from gateways achieve up to 39% reduction in power consumption compared to fixed‐power systems. At 1250 m, savings decrease to 22%, and at 1500 m, to 11%, while maintaining reliable communication. This research advances sustainable aquatic resource management, offering applications in aquaculture, environmental conservation, and water resource management. By optimizing power usage, the approach contributes to more effective long‐term monitoring of aquatic environments. [ABSTRACT FROM AUTHOR]

This study provides a comprehensive bibliometric and in-depth analysis of artificial intelligence (AI) and machine learning (ML) applications in environmental monitoring, based on 4762 publications from 1991 to 2024. The research highlights a notable increase in publications and citations since 2010, with China, the United States, and India emerging as leading contributors. Key areas of research include air and water quality monitoring, climate change modeling, biodiversity assessment, and disaster management. The integration of AI with emerging technologies, such as the Internet of Things (IoT) and remote sensing, has significantly expanded real-time environmental monitoring capabilities and data-driven decision-making. In-depth analysis reveals advancements in AI/ML methodologies, including novel algorithms for soil mapping, land-cover classification, flood susceptibility modeling, and remote sensing image analysis. Notable applications include enhanced air quality predictions, water quality assessments, climate impact forecasting, and automated wildlife monitoring using AI-driven image recognition. Challenges such as the "black-box" nature of AI models, the need for high-quality data in resource-constrained regions, and the complexity of real-time disaster management are also addressed. The study highlights ongoing efforts to develop explainable AI (XAI) models, which aim to improve model transparency and trust in critical environmental applications. Future research directions emphasize improving data quality and availability, fostering interdisciplinary collaborations across environmental and computer sciences, and addressing ethical considerations in AI-driven environmental management. These findings underscore the transformative potential of AI and ML technologies for sustainable environmental management, offering valuable insights for researchers and policymakers in addressing global environmental challenges. [ABSTRACT FROM AUTHOR]

Efficient water quality monitoring is a central aspect of water resources management, especially in developing countries, where water quality is under high anthropogenic pressure and resources for monitoring are usually limited. Here, we evaluated an alternative to optimize water quality parameters (WQPs) in the water quality monitoring network (WQMN) of the most populous state in Brazil (São Paulo State). We focused on the monitoring goal of identifying water quality temporal trends, selecting WQPs with high statistical explanatory power and those that were particularly sensitive to natural and anthropogenic perturbations. We considered 12 initial WQPs (dissolved copper, total zinc, total lead, total chromium, total mercury, total nickel, total cadmium, total iron, total manganese, total aluminum, total copper, and surfactant) with data from 2004 to 2018 for 56 monitoring sites distributed across four major watersheds with contrasting land uses in the state. We performed principal component analysis, followed by objective criteria to refine WQPs recommendation for the WQMN. Our results indicated the opportunity of reducing at least one parameter from the initial set of WQPs in all watersheds. Total iron, total manganese, and total aluminum were the most relevant initial WQPs, since their maintenance in monitoring were indicated in all the analyzed cases. Natural watershed conditions (e.g., geomorphology and water geochemistry) potentially governed their concentrations in surface water. On the other hand, total mercury, total chromium, and dissolved copper had the maintenance indicated in only one watershed, especially due to concentrations consistently below the respective limits of quantification (LoQs). Future investigations can complement our recommendations for these parameters, since changes in LoQs could throw another light on water quality spatial and temporal variations and the need for reference areas for assessing baseline conditions can also be relevant. Moreover, we argue that depending on the monitoring goals of the WQMN, additional sampling of biota and sediments could be useful as many of the studied WQPs' bioconcentrate. Our results illustrated an alternative approach towards adaptive monitoring in São Paulo state in accordance with the intended monitoring goal (i.e., water quality temporal trends), converging with the more flexible monitoring adopted in well-structured networks worldwide. While we did not cover other monitoring goals in our study (as the control of illegal discharge of effluents or industrial spills, for example), we expect our methodology can contribute to establishing technical guidelines for reviewing the existing WQMNs in Brazil and other developing countries with similar challenges. [ABSTRACT FROM AUTHOR]

The colorimetric method, due to its rapid and low-cost characteristics, demonstrates a wide range of application prospects in on-site water quality testing. Current research on colorimetric detection using deep learning algorithms predominantly focuses on single-target classification. To address this limitation, we propose a multi-task water quality colorimetric detection method based on YOLOv8n, leveraging deep learning techniques to achieve a fully automated process of "image input and result output". Initially, we constructed a dataset that encompasses colorimetric sensor data under varying lighting conditions to enhance model generalization. Subsequently, to effectively improve detection accuracy while reducing model parameters and computational load, we implemented several improvements to the deep learning algorithm, including the MGFF (Multi-Scale Grouped Feature Fusion) module, the LSKA-SPPF (Large Separable Kernel Attention-Spatial Pyramid Pooling-Fast) module, and the GNDCDH (Group Norm Detail Convolution Detection Head). Experimental results demonstrate that the optimized deep learning algorithm excels in precision (96.4%), recall (96.2%), and mAP50 (98.3), significantly outperforming other mainstream models. Furthermore, compared to YOLOv8n, the parameter count and computational load were reduced by 25.8% and 25.6%, respectively. Additionally, precision improved by 2.8%, recall increased by 3.5%, mAP50 enhanced by 2%, and mAP95 rose by 1.9%. These results affirm the substantial potential of our proposed method for rapid on-site water quality detection, offering new technological insights for future water quality monitoring. [ABSTRACT FROM AUTHOR]

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]

Assessing diverse parameters like water quality, quantity, and occurrence of hydrological extremes and their management is crucial to perform efficient water resource management (WRM). A successful WRM strategy requires a three-pronged approach: monitoring historical data, predicting future trends, and taking controlling measures to manage risks and ensure sustainability. Artificial intelligence (AI) techniques leverage these diverse knowledge fields to a single theme. This review article focuses on the potential of AI in two specific management areas: water supply-side and demand-side measures. It includes the investigation of diverse AI applications in leak detection and infrastructure maintenance, demand forecasting and water supply optimization, water treatment and water desalination, water quality monitoring and pollution control, parameter calibration and optimization applications, flood and drought predictions, and decision support systems. Finally, an overview of the selection of the appropriate AI techniques is suggested. The nature of AI adoption in WRM investigated using the Gartner hype cycle curve indicated that the learning application has advanced to different stages of maturity, and big data future application has to reach the plateau of productivity. This review also delineates future potential pathways to expedite the integration of AI-driven solutions and harness their transformative capabilities for the protection of global water resources. [ABSTRACT FROM AUTHOR]

Rural wetlands are complex landscapes where rivers, croplands, and villages coexist, making water quality monitoring crucial for the well-being of nearby residents. UAV-based imagery has proven effective in capturing detailed features of water bodies, making it a popular tool for water quality assessments. However, few studies have specifically focused on drone-based water quality monitoring in rural wetlands and their seasonal variations. In this study, Xiangfudang Rural Wetland Park, Jiaxin City, Zhejiang Province, China, was taken as the study area to evaluate water quality parameters, including total nitrogen (TN), total phosphors (TP), chemical oxygen demand (COD), and turbidity degree (TUB). We assessed these parameters across summer and winter seasons using UAV multispectral imagery and field sample data. Four machine learning algorithms were evaluated and compared for the inversion of the water quality parameters, based on the situ sample survey data and UAV multispectral images. The results show that ANN algorithm yielded the best results for estimating TN, COD, and TUB, with validation R2 of 0.78, 0.76, and 0.57, respectively; CatBoost performed best in TP estimation, with validation R2 and RMSE values of 0.72 and 0.05 mg/L. Based on spatial estimation results, the average COD concentration in the water body was 16.05 ± 9.87 mg/L in summer, higher than it was in winter (13.02 ± 8.22 mg/L). Additionally, mean TUB values were 18.39 Nephelometric Turbidity Units (NTU) in summer and 20.03 NTU in winter. This study demonstrates the novelty and effectiveness of using UAV multispectral imagery for water quality monitoring in rural wetlands, providing critical insights into seasonal water quality variations in these areas. [ABSTRACT FROM AUTHOR]

Globally, over 80% of wastewater is discharged into water bodies without adequate treatment (UNESCO 2017:10–15), making accurate water quality prediction essential for safeguarding aquatic ecosystems and public health. This study presents a novel QPSO-CNN-LSTM model that significantly advances water quality prediction by combining Quantum Particle Swarm Optimization (QPSO) with a CNN-LSTM architecture. Unlike traditional models, the QPSO-CNN-LSTM leverages CNN to capture complex spatial features from water quality data and LSTM to model long-term temporal dependencies. The QPSO algorithm optimizes key hyperparameters, mitigating the need for manual tuning and improving the model's adaptability to dynamic environmental data. The model outperforms traditional methods with a 15–50% improvement in RMSE, MSE, MAE, and MAPE for dissolved oxygen and pH predictions. These enhancements demonstrate the model's superior accuracy and robustness, making it an invaluable tool for real-time water quality monitoring, pollution prevention, and cost-effective water management strategies. The practical implications of this model offer a step forward in preserving aquatic ecosystems through data-driven environmental stewardship. Highlights: A novel model combining deep learning and optimization offers enhanced accuracy in water quality predictions. The model effectively forecasts dissolved oxygen and pH levels, supporting real-time environmental monitoring. This approach can help improve water management and protect ecosystems by predicting changes before they occur. [ABSTRACT FROM AUTHOR]

The editorial discusses the significant transformations in coastal and estuarine ecological environments due to hydrodynamic variability. It emphasizes the importance of understanding hydrodynamic processes through advanced methodologies like remote sensing and numerical modeling to unravel ecological changes. The editorial presents six papers that highlight research on ecological environment changes in response to hydrodynamic variability, covering topics such as dissolved oxygen patterns, boundary current transitions, hypoxia/anoxia evolution, geochemical behavior in sediments, remote sensing estimation of nitrogen concentration, and coastal water darkening. These studies underscore the need for advanced technologies and long-term data to manage and protect vulnerable coastal ecosystems in the face of climate change. [Extracted from the article]

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]

Cyanobacteria and their toxins can have multiple effects on agricultural productivity and water bodies. Cyanotoxins can be transported to nearby crops and fields during irrigation and may pose a risk to animal health through water sources. Spatial and temporal variations in cyanotoxin concentrations have been reported for large freshwater sources such as lakes and reservoirs, but there are fewer studies on smaller agricultural surface water bodies. To determine whether spatiotemporal patterns of the cyanotoxin microcystin occurred in agricultural waters used for crop irrigation and livestock watering, three agricultural ponds on working farms in Georgia, USA, were sampled monthly within a fixed spatial grid over a 17-month period. Microcystin concentrations, which ranged between 0.04 and 743.75 ppb, were determined using microcystin–ADDA ELISA kits. Temporal stability was assessed using mean relative differences between microcystin concentrations at each location and averaged concentrations across ponds on each sampling date. There were locations or zones in all three ponds that were consistently higher or lower than the average daily microcystin concentrations throughout the year, with the highest microcystin concentrations occurring in winter. Additionally, microcystin patterns were strongly correlated with the patterns of chlorophyll, phycocyanin, and turbidity. The results of this work showed that consistent spatiotemporal patterns in cyanotoxins can occur in produce irrigation and livestock watering ponds, and this should be accounted for when developing agricultural water monitoring programs. [ABSTRACT FROM AUTHOR]

Chilika Lake, a RAMSAR site, is an environmentally and ecologically pivotal coastal lagoon in India facing significant emerging environmental challenges due to anthropogenic activities and natural processes. Traditional in situ water quality monitoring methods are often labor intensive and time consuming. This study presents a novel approach for ex situ water quality monitoring in Chilika Lake, located on the east coast of India, utilizing Google Earth Engine (GEE) and spectral indices, such as the Normalized Difference Turbidity Index (NDTI), Normalized Difference Chlorophyll Index (NDCI), and total suspended solids (TSS). The methodology involves the integration of multi-temporal satellite imagery and advanced spectral indices to assess key water quality parameters, such as turbidity, chlorophyll-a concentration, and suspended sediments. The NDTI value in Chilika Lake increased from 2019 to 2021, and the Automatic Water Extraction Index (AWEI) method estimated the TSS concentration. The results demonstrate the effectiveness of this approach in providing accurate and comprehensive water quality assessments, which are crucial for the sustainable management of Chilika Lake. Maps and visualization are presented using GIS software. This study can effectively detect floating algal blooms, identify pollution sources, and determine environmental changes over time. Developing intuitive dashboards and visualization tools can help stakeholders engage with data-driven insights, increase community participation in conservation, and identify pollution sources. [ABSTRACT FROM AUTHOR]

Land inspection robots, endowed with autonomous navigation, obstacle avoidance, and mobility capabilities, are increasingly showcasing their unique advantages and practical utility across various sectors, particularly in fisheries and aquaculture. This paper delves into the critical technologies underpinning land inspection robots and explores their potential application in modernizing and adding intelligence to the aquaculture industry. The paper comprehensively analyzes the trifecta of land inspection robots: sensor technology, mechanical design, and control systems. Sensors play a pivotal role in capturing an array of environmental parameters, including temperature, humidity, light, pressure, sound, and imagery, providing a comprehensive dataset for decision-making. The mechanical structure serves as the backbone, enabling the robots to traverse challenging terrains, negotiate obstacles, and perform a diverse range of inspection tasks. The control technology, meanwhile, governs autonomous movement, task execution, and decision-making, encompassing motion control, path planning, and task scheduling. In the context of aquaculture, land inspection robots hold immense promise. They facilitate real-time monitoring of critical water quality parameters such as dissolved oxygen, pH, and ammonia, ensuring optimal conditions for fish health and enhancing production efficiency and product quality. The robots' ability to detect fish abnormalities early on enables proactive management, reducing risks and improving disease control. Furthermore, their remote observation and control capabilities streamline intelligent management of fish breeding facilities, lowering labor costs and enhancing operational efficiency. However, the application of land inspection robots in aquaculture faces unique challenges stemming from the complex and variable nature of the environment. The robots must exhibit a high degree of adaptability, interference resistance, and selflearning capabilities to accommodate diverse fishery scenarios and species. Additionally, the stringent requirements for information accuracy and real-time data necessitate robust stability and reliability in data gathering and transmission processes. Technical bottlenecks, such as limitations in environmental perception, image quality degradation due to water reflections and turbidity, and energy management in wet and corrosive environments, hinder widespread adoption. To overcome these challenges and unlock the full potential of land inspection robots in aquaculture, several development suggestions are proposed. Firstly, enhancing environmental adaptability designs, including waterproofing and corrosion resistance, is crucial for stable operation in harsh conditions. Secondly, advancing image processing technologies can improve image quality and fish recognition accuracy. Thirdly, establishing a comprehensive data processing platform, integrating cloud computing and big data analytics, will streamline data collection, storage, and analysis, enabling smarter decision-making. Lastly, optimizing energy management systems, including high-capacity battery technologies and autonomous charging capabilities, will prolong operational durations and reduce downtime. In conclusion, while the application of land inspection robots in aquaculture faces technical hurdles, their potential to revolutionize the industry remains substantial. With continued technological advancements and strategic development efforts, these robots are poised to play a pivotal role in driving the modernization and intelligent transformation of fisheries and aquaculture. [ABSTRACT FROM AUTHOR]

Dissolved oxygen can play a key role in the production and living of aquatic ecosystems. New materials and artificial intelligence (AI) technologies can be expected to promote the process of smart fisheries in recent years. It is very necessary to equip it with light, small, soft, and biocompatible sensors. Flexible electronics and sensing technology can be combined to detect dissolved oxygen. In this study, a flexible dissolved oxygen sensor was prepared to measure the temperature function by magnetron sputtering. The inkjet was also dispensed on both sides of the flexible substrate material. A multi-layer structure of the planar electrode was then adopted for the vertical distribution of oxygen-permeable film to encapsulate the electrolyte and the planar electrode. The polarization voltage was determined for the dissolved oxygen sensor by the linear sweep voltammetry (LSV) scanning in the electrochemical workstation. The polarization time of the sensor was measured by the response experiment. In addition, a comparison was also made on the difference between the prepared and commercial electrodes. The surface morphology of dissolved oxygen and temperature sensors was characterized using optical microscopy. A series of tests were carried out on the linearity, sensitivity, response time, drift, stability, and mechanical bending properties of the sensor. Finally, the sensing circuit and intelligent processing were designed to verify the feasibility of the sensor in the detection of dissolved oxygen in aquaculture water. The experimental results showed that the optimal polarization voltage of the prepared dissolved oxygen sensor was −0.6 V and the polarization time was 42 s. There was less difference between the flexible sensor and commercial electrodes in CV scanning, indicating the better performance of ion diffusion. Both the dissolved oxygen sensor and the temperature sensor exhibited homogeneous and better surface morphology at high magnification. There was an excellent linear relationship between the collection current and the dissolved oxygen content (R²=0.994 5) at room temperature. The sensitivity of the sensor was −0.03 μA·L/mg, the response time was 16.8 s (the maximum difference of multiple measurements was 3.3 s), and the maximum difference within 7 days was 0.0195 μA. The resistance of the flexible temperature sensor shared a better linear relationship with temperature in the range of 0-150 ℃ and 0- 30 ℃ (R² were 0.994 9 and 0.997 6, respectively). The sensitivity of the sensor was −2.47 kΩ/℃, the response time was 3 s, and the hysteresis error was 2.17%. The flexible sensor maintained better performance for the measurement of dissolved oxygen and temperature in the range of 0-60°. The maximum error of the prepared sensor was less than 5% when detecting different content of dissolved oxygen in aquaculture water at various temperatures, compared with the commercial sensor. A better temperature compensation can be obtained to rapidly and accurately detect the content of dissolved oxygen and the temperature of the water body in fishery applications. [ABSTRACT FROM AUTHOR]

Land inspection robots, endowed with autonomous navigation, obstacle avoidance, and mobility capabilities, are increasingly showcasing their unique advantages and practical utility across various sectors, particularly in fisheries and aquaculture. This paper delves into the critical technologies underpinning land inspection robots and explores their potential application in modernizing and adding intelligence to the aquaculture industry. The paper comprehensively analyzes the trifecta of land inspection robots: sensor technology, mechanical design, and control systems. Sensors play a pivotal role in capturing an array of environmental parameters, including temperature, humidity, light, pressure, sound, and imagery, providing a comprehensive dataset for decision-making. The mechanical structure serves as the backbone, enabling the robots to traverse challenging terrains, negotiate obstacles, and perform a diverse range of inspection tasks. The control technology, meanwhile, governs autonomous movement, task execution, and decision-making, encompassing motion control, path planning, and task scheduling. In the context of aquaculture, land inspection robots hold immense promise. They facilitate real-time monitoring of critical water quality parameters such as dissolved oxygen, pH, and ammonia, ensuring optimal conditions for fish health and enhancing production efficiency and product quality. The robots' ability to detect fish abnormalities early on enables proactive management, reducing risks and improving disease control. Furthermore, their remote observation and control capabilities streamline intelligent management of fish breeding facilities, lowering labor costs and enhancing operational efficiency. However, the application of land inspection robots in aquaculture faces unique challenges stemming from the complex and variable nature of the environment. The robots must exhibit a high degree of adaptability, interference resistance, and self- learning capabilities to accommodate diverse fishery scenarios and species. Additionally, the stringent requirements for information accuracy and real-time data necessitate robust stability and reliability in data gathering and transmission processes. Technical bottlenecks, such as limitations in environmental perception, image quality degradation due to water reflections and turbidity, and energy management in wet and corrosive environments, hinder widespread adoption. To overcome these challenges and unlock the full potential of land inspection robots in aquaculture, several development suggestions are proposed. Firstly, enhancing environmental adaptability designs, including waterproofing and corrosion resistance, is crucial for stable operation in harsh conditions. Secondly, advancing image processing technologies can improve image quality and fish recognition accuracy. Thirdly, establishing a comprehensive data processing platform, integrating cloud computing and big data analytics, will streamline data collection, storage, and analysis, enabling smarter decision-making. Lastly, optimizing energy management systems, including high-capacity battery technologies and autonomous charging capabilities, will prolong operational durations and reduce downtime. In conclusion, while the application of land inspection robots in aquaculture faces technical hurdles, their potential to revolutionize the industry remains substantial. With continued technological advancements and strategic development efforts, these robots are poised to play a pivotal role in driving the modernization and intelligent transformation of fisheries and aquaculture. [ABSTRACT FROM AUTHOR]

The use of biological monitoring (biomonitoring) to assess water quality is recognised alongside the use of chemical and physicochemical parameters due to its ability and efficiency in providing information about both current and long-term changes. Indeed, biomonitoring is applied in several developed and developing countries. Nevertheless, some developing countries, such as Rwanda, are yet to adopt such a monitoring system. This paper presents results of an assessment of the knowledge and perceptions by water resource managers about the use of biomonitoring and bioindicators, and challenges to their integration into existing routine water quality monitoring systems in Rwanda. Qualitative research, using semistructured interviews, was conducted with nine water resource practitioners from six water governance institutions between August and November 2021. The results show that participants are aware that macroinvertebrates, algae and fish are bioindicators that have potential applications in Rwanda to complement the chemical and physicochemical parameters already being collected in the country's water monitoring system. The main challenges for integration of biological indicators, as indicated by participants, include the need for equipment, funding, technical skills and taxonomic knowledge. Training in the use of bioindicators and taxonomy are needed to raise the awareness and skills of staff from the institutions involved in freshwater ecosystem management in Rwanda, and to encourage integration of biomonitoring results into national water monitoring frameworks. [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]

Groundwater, widely used for supplying drinking water to populations, is a vital resource that must be managed sustainably, which requires a thorough understanding of its diverse physico-chemical and bacteriological characteristics. This study, based on a 27-year extraction from the Sise-Eaux database (1993–2020), focused on the island of Corsica (72,000 km2), which is diverse in terms of altitude and slopes and features a strong lithological contrast between crystalline Corsica and metamorphic and sedimentary Corsica. Following logarithmic conditioning of the data (662 water catchments, 2830 samples, and 15 parameters) and distinguishing between spatial and spatiotemporal variances, a principal component analysis was conducted to achieve dimensionality reduction and to identify the processes driving water diversity. In addition, the spatial structure of the parameters was studied. The analysis notably distinguishes a seasonal determinism for bacterial contamination (rain, runoff, bacterial transport, and contamination of catchments) and a more strictly spatial determinism (geographic, lithological, and land use factors). The behavior of each parameter allowed for their classification into seven distinct groups based on their average coordinates on the factorial axes, accounting for 95% of the dataset's total variance. Several strategies can be considered for the inventory and mapping of groundwater, namely, (1) establishing quality parameter distribution maps, (2) dimensionality reduction through principal component analysis followed by two sub-options: (2a) mapping factorial axes or (2b) establishing a typology of parameters based on their behavior and mapping a representative for each group. The advantages and disadvantages of each of these strategies are discussed. [ABSTRACT FROM AUTHOR]

The impact of drought is intensifying due to climate change, leading to significant environmental consequences, particularly concerning river water quality. While drought is typically classified as meteorological or hydrological, studies assessing its environmental impacts remain limited. Drought-induced hydrological alterations in rivers often degrade water quality, necessitating the development of an environmental drought index. This study introduces a novel methodology for calculating an index to evaluate the effects of drought on river water quality, specifically applied to tributaries of the Nakdong River in South Korea. The index was constructed by reviewing existing water quality and drought indices, selecting relevant parameters, and weighting each factor following the National Sanitation Foundation Water Quality Index (NSFWQI) methodology. Factors integrated into the index encompass both meteorological and hydrological indicators, with priority given to variables measurable in real time. Real-time parameters—such as flow rate, cumulative precipitation, days without rainfall, and sensor-based metrics (pH, electrical conductivity [EC], dissolved oxygen [DO], and total organic carbon [TOC])—were incorporated. Additionally, for rivers with upstream dams, dam discharge data were included to reflect its influence on flow conditions. The applicability of the calculated index was assessed by comparing index values to observed water quality data. A class interval structure was implemented to enhance the index's usability across diverse riverine conditions. Furthermore, the utility of the index was validated by comparing it to the basin's target water quality, thereby assessing its sensitivity to drought-induced water quality deterioration. The environmental drought index proposed in this study enables the proactive and real-time monitoring of water quality under drought conditions. When applied to 10 tributaries of the Nakdong River, the index demonstrated a clear correlation between drought conditions and water quality deterioration. This index provides a practical tool for river management, facilitating early response strategies to mitigate water quality impacts associated with environmental drought. [ABSTRACT FROM AUTHOR]

Globally, the health of coastal water bodies continues to be threatened by climate change and mounting anthropogenic pressures related to population increase and associated development. Land use changes have increased the direct runoff of freshwater, nutrients, and other contaminants from watersheds into coastal systems. Exacerbated by increased temperatures, these changes have contributed to a worldwide decline in seagrass coverage and losses of critical habitat and ecosystem functions. For restricted estuaries and lagoons, the influx of nutrients is particularly damaging due to high water residence times and impaired flushing. The result is eutrophication and associated declines in water quality and ecosystem function. To mitigate degraded water quality, engineered ocean–estuary exchanges have been carried out and studied with examples in Australia, New Zealand, India, Denmark, the Netherlands, Portugal, and the United States of America. Based on successes including decreased nutrient concentrations, turbidity, and chlorophyll and increased faunal abundance in some past studies, this option is considered as a management tool for combatting worsening water quality in other estuaries including the Indian River Lagoon, a subtropical, lagoon-type estuary on the central east coast of Florida, USA. Decreased residence times, lower nutrients, higher dissolved oxygen (DO), higher salinity, lower temperature, and lower turbidity all combine for improved ecosystem health. In this review, the successes and failures of past projects intended to increase ocean–estuary exchanges, including biological and geochemical processes that contributed to observed outcomes, are evaluated. The primary indicators of water quality considered in this review include nutrient contents (e.g., nitrogen and phosphorus) and dissolved oxygen levels. Secondary indicators include changes in temperature and salinity pre- and post- engineering as well as turbidity, which can also impact seagrass growth and overall ecosystem health. Each of the sites investigated recorded improvements in water quality, though some were more pronounced and occurred over shorter time scales. Overall, enhanced ocean exchange in restricted, impaired water bodies resulted in system-specific response trajectories, with many experiencing a net positive outcome with respect to water quality and ecosystem health. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)