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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]

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]

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]

Deteriorating water quality poses a substantial risk to human health, with billions at risk of waterborne diseases due to contamination. Insufficient water quality data augment risks as conventional monitoring methods lack comprehensive coverage. Technologies like the Internet of Things and machine learning offer real-time water quality monitoring and classification. IoT nodes often provide point data insufficient for monitoring the quality of entire water bodies. Remote sensing, though useful, has limitations such as measuring only optically active parameters and being affected by climate and resolution issues. To address these challenges, an unmanned surface vehicle named `AquaDrone' has been developed. AquaDrone traverses water bodies, collecting data of four key parameters (pH, dissolved oxygen, electrical conductivity, and temperature) along with GPS coordinates. The data is transmitted to a web portal via LoRa communication and Wi-Fi, where visualizations like data tables, trendlines and color-coded heatmaps are generated. A multilayer perceptron classifies water quality into five categories, aiding in real-time classification. A comparative analysis of various oversampling techniques has been conducted in the context of water quality classification. The AquaDrone offers a feasible solution for monitoring quality of small to medium-sized water bodies, crucial for safeguarding public health. HIGHLIGHTS AquaDrone measures four physicochemical parameters of water along with GPS coordinates to monitor water quality in real-time.; A multilayer perceptron classifies water quality into five categories, with oversampling used to enhance model training.; AquaDrone wirelessly transmits data to the web portal, where trendlines, data table and a color-coded heatmap are generated to visualize water quality.; This ensures effective real-time monitoring in small to medium-sized water bodies, protecting both water sources and human health.;

Abstract By implementing a high-frequency intelligent network of sensors, this work explores continuous monitoring and alerting for dynamic changes in water quality. Life depends on water, yet pollution is a greater menace. For this reason, precautions and careful observation are necessary. Typically, the focus on conventional water quality system monitoring is too much on data collection and needs more on analysis and extraction, limiting its capacity to offer thorough solutions. Making informed decisions becomes more complicated when there are discrepancies like damaged data, loss from power outages, or transmission issues. The proposed High-Frequency Intelligent Sensing Network (HFISN) monitoring system uses cloud computing, IoT and Big data technologies for intelligent sensing. Researchers developed it to address various challenges. Researchers recommend Nephelometric Turbidity Unit (NTU) Sensor installation to enhance the system’s performance and facilitate better monitoring of sedimentation, particle issues, and water purity. This sensor makes it possible to make more informed decisions by expanding the platform’s dataset. The solution not only resolves data cleaning and analysis issues but also includes intelligent early-warning capabilities for timely alerts. Quantum Cloud (QC) technology is employed to enhance security and accessibility. Test findings confirm its robustness with extra features and a built-in turbidity sensor. Because the platform ensures data accuracy and dependability, it provides decision-makers with a solid foundation to protect water resources.

Accurate detection of algae in microscopic image plays a crucial role in water quality monitoring. However, the existing object detection methods still face challenges in accurately detecting different categories of algae in microscopic image. In order to improve the accuracy, an improved YOLOv5s model is proposed for microalgae detection, by combining a Receptive Field Enhancement (RFE) module, the Wise‐IoU v3 dynamic non‐monotonic focal loss function, and a Dynamic head (Dyhead), which is termed receptive field enhancement wise‐IoU dyhead (RWD)‐you only look once (YOLO). Firstly, to detect microalgae of various scales, the Bottleneck in the C3 module of YOLOv5s is replaced with a more reasonable RFE module. Secondly, Wise‐IoU v3 is applied to enhance detection accuracy by assigning varying weights between high‐quality and low‐quality images. Finally, Dyhead is introduced to enhance the representation capacity of the detection head by integrating three attention mechanisms: scale awareness, spatial awareness, and task awareness. The proposed RWD‐YOLO model significantly enhances the accuracy of algae detection in microscopic image. Specifically, the experimental results on the microalgae dataset show that the RWD‐YOLO achieves an mAP@0.5 of 93.2% and an mAP@0.5:0.95 of 65.1%. Compared to the original YOLOv5s, mAP@0.5 and mAP@0.5:0.95 are improved by 3.7% and 5.7%, respectively. [ABSTRACT FROM AUTHOR]

The Sentinel-2 Multi-Spectral Instrument (MSI) is characterized by short revisit times (5 days), red-edge spectral bands (665 nm and 705 nm), and a high spatial resolution (10 m), making it highly suitable for monitoring water quality in both inland and coastal waters. Unlike SeaWiFS, which can adjust its viewing angles to minimize sunglint, the Sentinel-2 MSI operates with fixed near-nadir angles, which makes it more susceptible to sunglint. Additionally, the complex optical properties of water pose challenges in accurately determining its water-leaving reflectance. Therefore, we compared the effectiveness of six atmospheric correction (AC) algorithms (POLYMER, MUMM, DSF, C2RCC, BP, and GRS) in correcting sunglint using two typical lakes in Xinjiang, China, as examples. The results indicated that POLYMER achieved the highest overall evaluation score (1.61), followed by MUMM (1.21), while BP exhibited the lowest performance (0.62). Specifically, POLYMER showed robust performance at the 665 nm band with RMSE = 0.0012 sr−1, R2 = 0.74, and MAPE = 30.68%, as well as at the 705 nm band with RMSE = 0.0014 sr−1, R2 = 0.42, and MAPE = 38.44%. At the 443, 490, and 560 nm bands, MUMM showed better performance (RMSE ≤ 0.0026 sr−1, R2 ≥ 0.86, MAPE ≤ 28.20%). In terms of band ratios, POLYMER exhibited the highest accuracy (RMSE ≤ 0.093 and MAPE ≤ 22.2%), particularly for the ratio Rrs(490)/Rrs(560) (R2 = 0.71). In general, POLYMER is the best choice for the sunglint correction of Xinjiang's clean lakes. This study assessed the capability of different AC algorithms for sunglint correction and enhanced the monitoring capability of MSI data in clean waters. [ABSTRACT FROM AUTHOR]

Restored wetlands should be closely monitored to fully evaluate the effectiveness of restoration efforts. However, regular post-restoration monitoring can be time-consuming and expensive, and is often absent or inadequate. Satellite and airborne remote sensing systems have proven to be cost-effective tools in many fields, but they have not been widely used to monitor ecological restoration. This study assessed the potential of airborne hyperspectral remote sensing to monitor water mass characteristics of experimental temporary ponds in the Mediterranean region. These ponds were created during marsh restoration in Doñana National Park (south-west Spain). We used hyperspectral images acquired by the CASI-1500 hyperspectral airborne sensor to estimate and map water depth, turbidity and chlorophyll a in a subset of the 96 new ponds. The high spatial and spectral resolution of the CASI sensor allowed us to detect differences between ponds in water depth, turbidity and chlorophyll a, providing accurate mapping of these three variables, and a useful method to assess restoration success. High levels of spatial variation were recorded between different ponds, which likely generates high diversity in the animal and plant species that they contain. These results highlight the great potential of hyperspectral sensors for the long-term monitoring of wetland complexes in the Mediterranean region and elsewhere. [ABSTRACT FROM AUTHOR]

ABSTRACT The first record of the kelp species Phyllariopsis brevipes in the Campania Region (Tyrrhenian Sea, South Italy) is here reported. The species has been observed in October 2021 in four sites along Sorrento Peninsula and Capri Island coast during scuba diving surveys. P. brevipes grew on living thalli of crustose coralline algae from 32 up to 55 m depth, occasionally forming dense aggregations with a maximum cover of about 0.2 m2. The DNA barcoding analysis on the basis of the cox1 gene supported the identification, with 98.36% of similarity between the sample collected in this study (NCBI accession number: PP622796) and a sequence obtained from a specimen from Provence, Mediterranean France (NCBI accession number: GQ368264). The study area can be considered a stepping stone for the species dispersion. The occurrence of P. brevipes can be related to upwelling and good water quality and the monitoring of its health status might be a sentinel of environmental changes. [ABSTRACT FROM AUTHOR]

This paper describes a prototype network of automated in situ measurements of hyperspectral water reflectance suitable for satellite validation and water quality monitoring. Radiometric validation of satellite-derived water reflectance is essential to ensure that only reliable data, e.g., for estimating water quality parameters such as chlorophyll a concentration, reach end-users. Analysis of the differences between satellite and in situ water reflectance measurements, particularly unmasked outliers, can provide recommendations on where satellite data processing algorithms need to be improved. In a massively multi-mission context, including Newspace constellations, hyperspectral missions and missions with broad spectral bands not designed for "water colour", the advantage of hyperspectral over multispectral in situ measurements is clear. Two hyperspectral measurement systems, PANTHYR (based on the mature TRIOS/ RAMSES radiometer) and HYPSTAR® (a newly designed radiometer), have been integrated here in theWATERHYPERNET network with SI-traceable calibration and characterisation. The systems have common data acquisition protocol, data processing and quality control. The choice of validation site and viewing geometry and installation considerations are described in detail. Three demonstration cases are described: 1. PANTHYR data from two sites are used to validate Sentinel-2/MSI (A- 2. HYPSTAR® data at six sites are used to validate Sentinel-3/OLCI (A- 3. PANTHYR and HYPSTAR® data in Belgian North Sea waters are used to monitor phytoplankton parameters, including Phaeocystis globosa, over two 5 month periods. Conclusion are drawn regarding the quality of Sentinel-2/MSI and Sentinel-3/OLCI data, including indications where improvements could be made. For example, a positive bias (mean difference) is found for ACOLITE_DSF processing of Sentinel-2 in clear waters (Acqua Alta) and clues are provided on how to improve this processing. The utility of these in situ measurements, even without accompanying hyperspectral satellite data, is demonstrated for phytoplankton monitoring. The future evolution of the WATERHYPERNET network is outlined, including geographical expansion, improvements to hardware reliability and to the measurement method (including uncertainty estimation) and plans for daily distribution of near real-time data. [ABSTRACT FROM AUTHOR]

Phytoplankton is a polyphyletic group of organisms that responds rapidly to environmental conditions and provides a reliable response to changes, making it a good ecological indicator for water quality monitoring. However, a gradient is almost essential for a reliable relationship between pressure and impact. In a low-gradient environment, ingenuity is required to outsmart the limitations of the commonly used linear relationship. Here, we examine changes in biomass and functional biodiversity by analysing larger data sets (2013–2022) in six ecologically diverse, natural, deep Croatian karst lakes with low nutrient gradients using nonlinear correlation coefficients and multivariate analyses in 209 samples. We found that phytoplankton biomass was most strongly influenced by nutrients, salinity and alkalinity, while light availability and total nitrogen strongly influenced phytoplankton functional biodiversity. An additional analysis of the TN:TP ratio revealed that the oligotrophic Lake Vransko is nitrogen-limited, and lakes Kozjak and Prošće are phosphorus-limited. This further clarified the relationship of phytoplankton to nutrients despite the low gradient. The complex analysis in this study provides a new perspective for predicting changes in the structure and succession of phytoplankton in deep karst lakes for successful management under apparent anthropogenic pressure and climate change. [ABSTRACT FROM AUTHOR]

In this work, the authors provide a case study of time series regression techniques for water quality forecasting. With the constant striving to achieve the Sustainable Development Goals (SDG), the need for sensitive and reliable water management tools has become critical. Continuous online surface water quality monitoring systems that record time series data about surface water parameters are essential for the supervision of water conditions and proper water management practices. The time series data obtained from these systems can be used to develop mathematical models for the prediction of the temporal evolution of water quality parameters. Using these mathematical models, predictions can be made about future trends in water quality to pinpoint irregular behaviours in measured data and identify the presence of anomalous events. We compared the performance of regression models with different structures for the forecasting of water parameters by utilizing a data set collected from the Kis-Balaton Water Protection System (KBWPS) wetland region of Hungary over an observation period of eleven months as a case study. In our study, autoregressive integrated moving average (ARIMA) regression models with different structures have been compared based on forecasting performance. Using the resulting models, trends of the oxygen saturation, pH level, electrical conductivity, and redox potential of the water could be accurately forecast (validation data residual standard deviation between 0.09 and 20.8) while in the case of turbidity, only averages of future values could be predicted (validation data residual standard deviation of 56.3). [ABSTRACT FROM AUTHOR]

Coastal ecosystems are facing critical water quality deterioration, while the most convenient passage to the South China Sea, Beibu Gulf, has been under considerable pressure to its ecological environment due to rapid development and urbanization. In this study, we characterized the spatiotemporal change in the water quality in Beibu Gulf and proposed a machine learning approach to predict the water pollution level in Beibu Gulf on the basis of 5-year (2018–2022) observation data of ten water quality parameters from ten selected sites. Random forest (rf) and linear algorithms were utilized. Results show that a high frequency of exceedance of water quality parameters was observed particularly in summer and autumn, e.g., the exceeding rate of Dissolved Inorganic Nitrogen (DIN) at GX01, GX03, GX06, and GX07 station were 28.2~78.1% (average is 52.0%), 6.0~21.7% (average is 52.0%), 23.0~44.7% (average is 31.9%), and 5.2~33.4% (average is 21.2%), respectively. With regard to the spatial distribution, the pH, Water Salinity (WS), and Dissolved Oxygen (DO) values of stations inside the bay were overall lower than those of corresponding stations at the mouth of the bay and stations outside the bay. The concentrations of Chlorophyll-a concentration (except QZB) and nutrient salts showed a clearly opposite trend compared with the above concerned three parameters. For instance, the average Chl-a value of station GX09 was 22.5% higher than that of GX08 and GX10 between 2018 and 2022. Correlation analysis among water quality factors shows a significant positive correlation (r > 0.85) between Dissolved Inorganic Nitrogen (DIN) and NO3-N, followed by NO2-N and NH4-N, indicating that the main component of DIN is NO3-N. The forecasting results with machine learning also demonstrate the possibility to estimate the water quality parameters, such as chl-a concentration, DIN, and NH4-N in a cost-effective manner with prediction accuracy of approximately 60%, and thereby could provide near-real-time information to monitor the water quality of the Beibu Gulf. Predicting models initiated in this study could be of great interest for local authorities and the tourism and fishing industries. [ABSTRACT FROM AUTHOR]

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

Rapid and precise detection of waterborne pathogens is critical for effective public health management and environmental safety. Traditional methods for water quality monitoring, considered the "Gold Standard," are time-consuming, costly, and rely on centralized laboratories and expertise. These methods are impractical for on-site, real-time monitoring, and requiring further improvements. This review explores various waterborne pathogens and cutting-edge molecular detection techniques. It highlights the growing importance of point-of-care and point-of-application methods to expedite results and improve health risk management. Loop-mediated isothermal amplification emerges as a reliable, rapid, and accessible tool in the realms of on-site diagnostics and surveillance. Moreover, the review emphasizes the crucial role of water sample preparation and in-field nucleic acid isolation in augmenting pathogen detection to enable precise assessments of water quality. The advancement of these techniques will guarantee access to safe water and improve the management of waterborne diseases. [ABSTRACT FROM AUTHOR]

In light of the crucial role of marine ecosystems and the escalating environmental conservation challenges, it is essential to conduct marine monitoring to help implement targeted environmental protection measures efficiently. Energy harvesting technologies, particularly triboelectric nanogenerators (TENGs), have great potential for prolonging the lifespan and enhancing the reliability of sensors in remote areas. However, the high internal resistance, low current, and friction-induced abrasion issues of TENGs limit their performance in practical applications. This work presents a rolling mode triboelectric nanogenerator that utilizes multi-tunnel grating electrodes and the opposite-charge-enhancement mechanism to harvest wave energy efficiently. The device achieves significant instantaneous and root mean square power density of 185.4 W/(m3·Hz) and 10.92 W/(m3·Hz), respectively. By utilizing stacked devices and an exclusively designed power management module, a self-powered ocean sensing system including computing and long-range wireless communication (0.8 km) capabilities was developed. Laboratory and in-situ ocean tests were conducted to assess and validate the system. This work offers a potential solution for the challenging deployment of marine self-powered sensing nodes. Limited current output hinders triboelectric nanogenerators for maritime applications. Authors design a rolling-mode TENG with multi-tunnel grating electrodes, achieving 185.4 W/(m3·Hz) power density. They demonstrate a self-powered ocean sensing system for water quality monitoring and wireless communication. [ABSTRACT FROM AUTHOR]

Regular groundwater quality monitoring in resource-constrained regions present formidable challenges in terms of funding, testing facilities and manpower; necessitating the development of easily implementable monitoring techniques. This study proposes a copula-based risk assessment model utilizing easily measurable indicators (e.g., turbidity, alkalinity, pH, total dissolved solids (TDS), conductivity), to monitor the contaminates in groundwater which are otherwise difficult to measure (i.e., iron, nitrate, sulfate, fluoride, etc.). Preliminary correlation between the indicators and the target contaminates were identified using Pearson coefficient. Best representative univariate distributions for these pairs were selected using the Akaike Information Criterion (AIC), which were used in the formulation of the copula model. Validation against observed data showcased the model's high accuracy, supported by consistent Kendall Tau correlation coefficients. Through this model, conditional probabilities of the contaminants not exceeding the permissible limits set by the Bureau of Indian Standards (BIS) were calculated using indicator concentration. Notably, an inverse correlation between iron concentration and conductivity was noted, with the likelihood of iron exceeding BIS limits decreasing from 90 to 50% as conductivity rose from 500 to 2000 micromhos/cm. TDS emerged as a pivotal indicator for nitrate and sulfate concentrations, with the probability of sulfate surpassing 10 mg/l decreasing from 75 to 25% as TDS increased from 250 to 750 mg/l. Likewise, the probability of nitrate exceeding 1 mg/l decreased from 90 to 60% with TDS levels reaching 1500 mg/l. Furthermore, a 63% probability of fluoride concentrations remaining below 1 mg/l was observed at turbidity levels of 0–10 NTU. These findings hold significant implications for policymakers and researchers since the model can provide crucial insights into the risks associated with the contaminates exceeding the permissible limit, facilitating the development of an efficient monitoring and management strategies to ensure safe drinking water access for vulnerable populations. [ABSTRACT FROM AUTHOR]

Interjurisdictional migrations lead to seasonally changing patterns of exploitation risk, emphasizing the importance of spatially explicit approaches to fishery management. Understanding how risk changes along a migration route supports time-area based fishery management, but quantifying risk can be complicated when multiple fishing methods are geographically segregated and when bycatch species are considered. Further, habitat selection in dynamic environments can influence migration behavior, interacting with other management objectives such as water quality and habitat restoration. As a case study, we examined a novel acoustic telemetry data set for Lake Whitefish in Lake Erie, where they migrate through multiple spatial management units that are variably affected by seasonal hypoxia and host a variety of fisheries. Combining telemetry results with fishery catch and water quality monitoring, we demonstrate three exploitation risk scenarios: (i) high risk due to high residency and high catch, (ii) high risk due to high residency in time-areas with moderate catch, and (iii) low risk due to residency in time-areas with low catch. Interestingly, occupation of low risk refugia was increased by the development of hypoxia in adjacent areas. Consequently, fishery management goals to sustainably manage other target species may be directly and indirectly linked to water quality management goals through Lake Whitefish. [ABSTRACT FROM AUTHOR]

This research assessed water quality, based on the purpose of water consumption, in households in the municipality of Barbaza, the Province of Antique, Philippines, according to the national water quality guidelines. The effects of the empirical/traditional water use actions taken by local people on the quality of the water they use were investigated through a descriptive study using water quality measurements. Most of the drinking water in the community did not meet the required standards of pH, total dissolved solids (TDS), or coliform. Tap water and well water samples generally met the pH and TDS standards. However, Escherichia coli (E. coli) and coliform were detected, and nitrogen pollution in well water was also confirmed. Local practices, such as using old clothes as filters for well pumps, increased the coliform concentration from 0-10 CFU/mL to too numerous to count (TNTC) levels of more than 100 CFU/mL. Storing well water in a bucket also affected both E. coli and coliform concentrations. Such empirical/traditional water use actions create a high risk of exposing local people to harmful microorganisms. This research integrated citizen science into the methodology for local water management, which could assist governors, practitioners, and citizens, particularly in Southeast Asia, where strong community relationships exist. [ABSTRACT FROM AUTHOR]

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

Excessive ammonia nitrogen can potentially compromise the safety of drinking water. Therefore, developing a rapid and simple detection method for ammonia nitrogen in drinking water is of great importance. Nickel–copper hydroxides exhibit strong catalytic capabilities and are widely applied in ammonia nitrogen oxidation. In this study, a self-supported electrode made of nickel–copper carbonate hydroxide was synthesized on a carbon cloth collector via a straightforward one-step hydrothermal method for rapid ammonia nitrogen detection in water. It exhibits sensitivities of 3.9 μA μM−1 cm−2 and 3.13 μA μM−1 cm−2 within linear ranges of 1 μM to 100 μM and 100 μM to 400 μM, respectively, using a simple and rapid i-t method. The detection limit is as low as 0.62 μM, highlighting its excellent anti-interference properties against various anions and cations. The methodology's simplicity and effectiveness suggest broad applicability in water quality monitoring and environmental protection, particularly due to its significant cost-effectiveness. [ABSTRACT FROM AUTHOR]

Radioactive effluents, originating from nuclear power plants, medical‐nuclear applications, and various extraction industries worldwide, present a significant and dangerous contamination challenge. The concentrations of radioactive substances in wastewater, surface water, and potable water vary widely depending on the source and location. For example, cesium‐137 levels in wastewater from nuclear facilities can range from 0.1 to 10 Bq/L, while tritium concentrations in surface water near nuclear plants can reach up to 100 Bq/L. Regulatory guidelines, like the maximum contaminant level of 0.185 Bq/L for combined radium‐226 and radium‐228 in drinking water, are critical for ensuring safety and environmental protection. Specifically, in Fukushima, Japan, cesium‐137 levels in surface water range from 0.1 to 10 Bq/L due to the nuclear accident. In contrast, regions with natural uranium deposits, like parts of the United States, have reported radium‐226 concentrations in potable water up to 1 Bq/L. These variations highlight the necessity for focused monitoring and evaluation to protect water quality and community health. Among various methods, Gamma spectrometry and inductively coupled plasma mass spectrometry are precise for radionuclide quantification, scintillation detectors, and ion exchange, and adsorption techniques efficiently remove radioactive substances from water. This critical review examines the sources, adverse effects, and analysis and remediation strategies for various radioactive elements in wastewater. By thoroughly evaluating the origins and potential dangers associated with radioactive effluents, this report emphasizes the urgent need for rigorous monitoring and effective treatment practices to maintain the integrity of water resources and ecosystems. Practitioner Points: Comprehensive analysis of the radioactive elements frequently found in wastewater and drinking water.Assess the negative effects of radioactive elements in water systems.Examine the treatment methods used to eliminate radioactive pollutants from water sources.Outline effective methods and tactics for addressing and controlling radioactive contamination occurrences.Analyze the latest advancements in technology, regulatory enhancements, and optimal methods to guarantee the safety of drinking water and the sustainable handling of radioactive substances in wastewater. [ABSTRACT FROM AUTHOR]

Low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks and provide valuable water quality information to the public. However, the accuracy and precision of the values measured by the sensors are critical for widespread adoption. In this study, 19 different low-cost sensors, commonly found in the literature, from four different manufacturers are tested for measuring five water quality parameters: pH, dissolved oxygen, oxidation-reduction potential, turbidity, and temperature. The low-cost sensors are evaluated for each parameter by calculating the error and precision compared to a typical multiparameter probe assumed as a reference. The comparison was performed in a controlled environment with simultaneous measurements of real water samples. The relative error ranged from - 0.33 to 33.77%, and most of them were ≤ 5%. The pH and temperature were the ones with the most accurate results. In conclusion, low-cost sensors are a complementary alternative to quickly detect changes in water quality parameters. Further studies are necessary to establish a guideline for the operation and maintenance of low-cost sensors. [ABSTRACT FROM AUTHOR]

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

The World Health Organization (WHO) recommends the introduction of a water safety plan (WSP) approach on drinking water, in all types of settings. This study represents the first WSP developed on the Neptune Fountain, in Bologna (Italy), based on an interdisciplinary approach, integrating hydraulic and microbiological features, in a Building Information Modeling (BIM). The aim was to develop a dynamic and digital platform to update and share the maintenance program, promoting collaboration among microbiologists, engineers, and municipal staff. Water samples were collected along fountain water distribution systems (WDS) from 2016 to 2021 to monitor water quality through the heterotrophic bacteria at 22 °C and 37 °C, as well as to conduct an Enterococci, Coliform bacteria, Escherichia coli, Pseudomonas aeruginosa, Clostridium perfringens, and Staphylococcus aureus assessment. Simultaneously, hydraulic measures were performed, and advanced geomatics techniques were used to detect the WDS structural components, with a focus on the water treatment system (WTS). The WTS consisted of 10 modules corresponding to specific treatments: descaling, carbon–sand filtration, reverse osmosis, and ultraviolet disinfection. Fecal indicators, heterotrophic bacteria, and P. aeruginosa exceeded the reference limits in most of the modules. Several disinfections and washing treatments, other than changing the maintenance procedure scheduling, were performed, improving the WTS and controlling the contamination. The developed microbiological results, hydraulic measurements, and maintenance procedures were integrated in the BIM model to optimize the data storage, updating procedures and the real-time data sharing. This approach improved the fountain management, operation, and material conservation, ultimately preserving the health of daily visitors. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board 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.)

The application of antimicrobials in aquaculture primarily aims to prevent and treat bacterial infections in fish, but their inappropriate use may result in the emergence of zoonotic antibiotic-resistant bacteria and the subsequent transmission of resistant strains to humans via food consumption. The aquatic environment serves as a potential reservoir for resistant bacteria, providing an ideal breeding ground for development of antimicrobial resistance (AMR). The mutual inter-connection of intensive fish-farming systems with terrestrial environments, the food processing industry and human population creates pathways for the transmission of resistant bacteria, exacerbating the problem further. The aim of this study was to provide an overview of the most effective and available risk mitigation strategies to tackle AMR in aquaculture, based on the One Health (OH) concept. The stringent antimicrobial use guidelines, promoting disease control methods like enhanced farm biosecurity measures and vaccinations, alternatives to antibiotics (ABs) (prebiotics, probiotics, immunostimulants, essential oils (EOs), peptides and phage therapy), feeding practices, genetics, monitoring water quality, and improving wastewater treatment, rather than applying excessive use of antimicrobials, can effectively prevent the development of AMR and release of resistant bacteria into the environment and food. The contribution of the environment to AMR development traditionally receives less attention, and, therefore, environmental aspects should be included more prominently in OH efforts to predict, detect and prevent the risks to health. This is of particular importance for low and middle-income countries with a lack of integration of the national AMR action plans (NAPs) with the aquaculture-producing environment. Integrated control of AMR in fisheries based on the OH approach can contribute to substantial decrease in resistance, and such is the case in Asia, where in aquaculture, the percentage of antimicrobial compounds with resistance exceeding 50% (P50) decreased from 52% to 22% within the period of the previous two decades. [ABSTRACT FROM AUTHOR]

A study was carried out in 345 water ecosystems (rivers, streams, ditches, lagoons, ponds, puddles, rockpools, and artificial containers) throughout the autonomous community of Galicia (NW Spain) during different seasons between 2020 and 2023. The results revealed the first detections of Anopheles (Anopheles) petragnani Del Vechio, 1939 (n  = 185) and Culex (Culex) mimeticus Noè, 1899 (n  = 12) in the territory, allowing to update and expand the overall knowledge about their distribution and ecology. The breeding preferences of the species were analyzed considering habitat characteristics (land use, water body type, and degree of insolation), geographical variables (latitude, longitude, and altitude), and physical–chemical water parameters (temperature, pH, dissolved oxygen, electrical conductivity, salinity, total dissolved solids, and turbidity). In addition, the relationship between the occurrence of these species and other mosquitoes present in the study area was discussed. Anopheles petragnani is widely distributed in the region and shows breeding preferences for water bodies of fluvial origin, forest environments, and shaded situations. Culex mimeticus was detected sporadically breeding in a pond and in a river in the south of the region, both in industrial and agricultural land uses, and always exposed to the sun. Although the presence of these species may not currently pose a health risk in the region, it is important not neglecting their study since the adequate characterization of their larval biotopes is relevant regarding vector surveillance and control measures. [ABSTRACT FROM AUTHOR]

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

Summary: The dense and toxic blooms formed by cyanobacteria in aquatic environments pose significant threats to public health and aquatic ecosystems. Timely monitoring and prevention of cyanobacterial blooms in freshwater bodies are thus imperative. Although object detection methods have been applied in the field of algae identification, existing research faces several challenges. A primary issue is the overly idealistic setting of training sets, which are disconnected from the actual water quality environments, impeding practical algae identification and water quality monitoring. In this paper, we collect 2024 microscopic images of algae from a reservoir in Southern China, forming a comprehensive and diverse dataset for object detection. Addressing the aforementioned challenges, we propose an attention‐based strategy for the detection of tiny algal objects, which effectively samples and extracts features from algal targets. Our model uniquely leverages the morphable characteristics of algae, enhancing the accuracy and efficiency of identification. The training and improvement results of this model presented in our study are expected to aid in establishing a future system for real‐time algae monitoring and water quality assessment. [ABSTRACT FROM AUTHOR]

Evaluating remote sensing techniques (RST)-based water quality monitoring systems in hydrological applications is a complex multi-attribute decision-making (MADM) problem due to several integrated issues. These issues include the need to consider 15 evaluation criteria, the importance of these criteria in relation to their application and network infrastructure, data variation, and trade-offs and conflicts between them. Therefore, this study proposes an MADM integrated modelling approach to weight these criteria and rank the selected RSTs using the "Fuzzy Weighted with Zero Inconsistency" (FWZIC) method coupled with the "Vlse-kriterijumska Optimizcija I Kaompromisno Resenje" (VIKOR) method. First, a decision matrix was developed for the evaluation criteria and their intersection with various RSTs in different network categories. Next, the assessment criteria were weighted using FWZIC, followed by ranking the RSTs for each category using VIKOR. The findings reveal that the criteria weighting varied across categories, as assigned by specialists-based FWZIC. For example, the "data acquisition system complexity" criterion received the maximum weight (w = 0.081) for mesh-based sensing and the lowest (w = 0.057) for cellular-based sensing. Using the obtained weights and based on the ascending order of the performance score value (Q), various RSTs were benchmarked using VIKOR. This includes (n = 22) RSTs in fixed star, (n = 8) RSTs in mesh sensing, (n = 6) RSTs in cellular sensing, (n = 3) RSTs in fixed cable sensing, and (n = 2) RSTs in movable sensing. Finally, the evaluation process for the benchmarked RSTs involved systematic ranking, sensitivity analysis, and comparison analysis, which confirmed the robustness of the proposed approach across all RST categories. [ABSTRACT FROM AUTHOR]

Introduction: Nitrate and nitrite are naturally occurring in both plant- and animal-sourced foods, are used as additives in the processing of meat, and are found in water. There is growing evidence that they exhibit a spectrum of health effects, depending on the dietary source. The aim of the study was to examine source-dependent associations between dietary intakes of nitrate/nitrite and both all-cause and cause-specific mortality. Methods: In 52,247 participants of the Danish Diet, Cancer and Health Study, associations between source-dependent nitrate and nitrite intakes––calculated using comprehensive food composition and national drinking water quality monitoring databases––and all-cause, cardiovascular disease (CVD)-related, and cancer-related mortality over 27 years were examined using restricted cubic splines within Cox proportional hazards models adjusting for demographic, lifestyle, and dietary confounders. Analyses were stratified by factors hypothesised to influence the formation of carcinogenic N-nitroso compounds (namely, smoking and dietary intakes of vitamin C, vitamin E, folate, and polyphenols). Results: Plant-sourced nitrate intake was inversely associated with all-cause mortality [HRQ5vsQ1: 0.83 (0.80, 0.87)] while higher risks of all-cause mortality were seen for higher intakes of naturally occurring animal-sourced nitrate [1.09 (1.04, 1.14)], additive permitted meat-sourced nitrate [1.19 (1.14, 1.25)], and tap water-sourced nitrate [1.19 (1.14, 1.25)]. Similar source-dependent associations were seen for nitrite and for CVD-related and cancer-related mortality except that naturally occurring animal-sourced nitrate and tap water-sourced nitrate were not associated with cancer-related mortality and additive permitted meat-sourced nitrate was not associated with CVD-related mortality. No clear patterns emerged in stratified analyses. Conclusion: Nitrate/nitrite from plant sources are inversely associated while those from naturally occurring animal-sources, additive-permitted meat sources, and tap water-sources are positively associated with mortality. [ABSTRACT FROM AUTHOR]

Phosphorus pollution is a major water quality issue impacting the environment and human health. Traditional methods limit the frequency and extent of total phosphorus (TP) measurements across many rivers. However, remote sensing can accurately estimate riverine TP; nevertheless, no large‐scale assessment of riverine TP using remote sensing exists. Large‐scale models using remote sensing can provide a fast and consistent method for TP measurement, important for data generalization and accessing extensive spatial‐temporal change in TP. Our study uses remote sensing and machine learning to estimate the TP in rivers in the contiguous United States (CONUS). Initially, we developed a national scale matchup data set for Landsat detectable rivers (river width >30 m) using in situ TP and surface reflectance. We used in situ data from the Water Quality Portal (WQP), alongside water surface reflectance data from Landsat 5, 7, and 8 spanning from 1984 to 2021. Then, we used this data set to develop a machine learning (ML) model using different preprocessing methods and algorithms. We found that using high‐level vegetation in the clustering approach and over‐sampling or under‐sampling our training data in the sampling approach improved our model estimation accuracy. We compared XGBLinear, XGBTree, Regularized Random Forest (RRF), and K‐Nearest neighbors ML algorithms and selected XGBLinear as the best model with an R2 of 0.604, RMSE of 0.103 mg/L, mean average error of 0.83, and NSE of 0.602. Finally, we identified human footprint, elevation, river area, and soil erosion as the main attributes influencing the accuracy of estimated TP from the ML model. Plain Language Summary: Phosphorus pollution in rivers is a big problem for the environment and humans. Traditional ways of measuring phosphorus levels in rivers are limited and slow. However, remote sensing can help estimate phosphorus levels on a large scale. But, there hasn't been a study using remote sensing to measure phosphorus levels in rivers on a large scale. We studied the potential of remote sensing and machine learning to estimate phosphorus in the United States rivers. First, we collected data from 1984 to 2021, both on‐site phosphorus measurements from the Water Quality Portal and satellite river data from the Landsat archive, and developed our model. We found that using vegetation data and changing our data volume improved our model's accuracy. Likewise, we discovered that river characteristics like human activity level, elevation, river size, and soil erosion influenced how accurately our model could estimate phosphorus levels using remote sensing. Key Points: We created a national‐level in situ total phosphorus and water reflectance matchup data set for rivers within CONUSWe developed a machine learning model to simulate riverine phosphorus using water reflectance data retrieved from LandsatWe identified river‐specific characteristics that influence the model performance on retrieving riverine phosphorus from satellite data [ABSTRACT FROM AUTHOR]

Given the use of machine learning-based tools for monitoring the Water Quality Indicators (WQIs) over lakes and coastal waters, understanding the properties of such models, including the uncertainties inherent in their predictions is essential. This has led to the development of two probabilistic NN-algorithms: Mixture Density Network (MDN) and Bayesian Neural Network via Monte Carlo Dropout (BNN-MCD). These NNs are complex, featuring thousands of trainable parameters and modifiable hyper-parameters, and have been independently trained and tested. The model uncertainty metric captures the uncertainty present in each prediction based on the properties of the model--namely, the model architecture and the training data distribution. We conduct an analysis of MDN and BNN-MCD under near-identical conditions of model architecture, training, and test sets, etc., to retrieve the concentration of chlorophyll-a pigments (Chl a), total suspended solids (TSS), and the absorption by colored dissolved organic matter at 440 nm (acdom (440)). The spectral resolutions considered correspond to the Hyperspectral Imager for the Coastal Ocean (HICO), PRecursore IperSpettrale della Missione Applicativa (PRISMA), Ocean Colour and Land Imager (OLCI), and MultiSpectral Instrument (MSI). The model performances are tested in terms of both predictive residuals and predictive uncertainty metric quality. We also compared the simultaneous WQI retrievals against a single-parameter retrieval framework (for Chla). Ultimately, the models' real-world applicability was investigated using a MSI satellite-matchup dataset (N - 3, 053) of Chla and TSS. Experiments show that both models exhibit comparable estimation performance. Specifically, the median symmetric accuracy (MdSA) on the test set for the different parameters in both algorithms range from 30% to 60%. The uncertainty estimates, on the other hand, differ strongly. MDN's uncertainty estimate is ~50%, encompassing estimation residuals for 75% of test samples, whereas BNN-MCD's average uncertainty estimate is ~25%, encompassing the residuals for 50% of samples. Our analysis also revealed that simultaneous estimation results in improvements in both predictive performance and uncertainty metric quality. Interestingly, the trends mentioned above hold across different sensor resolutions, as well as experimental regimes. This disparity calls for additional research to determine whether such trends in model uncertainty are inherent to specific models or can be more broadly generalized across different algorithms and sensor setups. [ABSTRACT FROM AUTHOR]

Taitema Lake, situated at the terminus of the Tarim River Basin in Northwest China, represents a crucial ecological resource impacted by climate variability and anthropogenic interventions. In this study, we investigate the dynamic changes in Taitema Lake's area and water quality resulting from the implementation of an ecological water transfer project in 2000. Leveraging Landsat remote sensing data and comprehensive water quality monitoring, we analyzed the relationship between lake area variations and shifts in water quality parameters. Notably, our findings reveal a significant increase in Taitema Lake's area from 9.4 km2 in 2000 to 320 km2 in 2013. Concurrently, water quality indicators exhibited marked fluctuations, with total salt content ranging from 45,323.6 mg/L in 2000 to 970.4 mg/L in 2010 before increasing to 14,586.3 mg/L by 2014. Furthermore, a linear regression analysis highlights the moderate positive correlation between lake area and mineralization (R2 = 0.506) and sodium levels (R2 = 0.4907). Additionally, chloride (R2 = 0.5681) and sulfate (R2 = 0.6213) concentrations demonstrated a strong negative correlation with the lake area, indicative of a dilution effect. Furthermore, a comparison of water quality indicators between the years of minimum (2008) and maximum (2013) lake area underscores improvements in pH, chemical oxygen demand, and anionic surfactant concentrations as the lake area increased. Our study provides valuable insights into the effectiveness of ecological water management strategies in restoring and maintaining the ecological health of Taitema Lake, thereby informing evidence-based decision-making for the sustainable management of freshwater resources in arid environments. [ABSTRACT FROM AUTHOR]

The article focuses on the difficulties in providing bulk water services in Dr. Ruth Segomotsi Mompati district municipality, North West Province, South Africa. Topics include operational inefficiencies, the need for strategic planning and dynamic master planning, and the exacerbation of challenges due to the COVID-19 pandemic.

The article focuses on the ecological factors affecting zooplankton diversity in the Ravi River and its implications for water quality. Topics include the dominant zooplankton species observed, the impact of environmental parameters like water temperature and turbidity on zooplankton distribution, and the influence of anthropogenic activities on river health and zooplankton diversity.

Small water supply systems (SWSSs) are often more vulnerable to waterborne disease outbreaks. In Japan, many SWSSs operate without regulation under the Waterworks Law, yet there is limited investigation into microbial contamination and the associated health risks. In this study, the microbiological water quality of four SWSSs that utilize mountain streams as water sources and do not install water treatment facilities were monitored for over 2 years. In investigated SWSSs, the mean heterotrophic plate counts were below 350 CFU/mL, and the total bacterial loads (16S rDNA concentration) ranged from 4.71 to 5.35 log10 copies/mL. The results also showed the consistent presence of fecal indicator bacteria (FIB), i.e., Escherichia coli and Clostridium perfringens, suggesting the potential of fecal pollution. E. coli was then utilized as an indicator to assess the health risk posed by E. coli O157:H7 and Campylobacter jejuni. The results indicated that the estimated mean annual risk of infection and disability-adjusted life years (DALYs) exceeded acceptable levels in all SWSSs for the two reference pathogens. To ensure microbial water safety, implementing appropriate water treatment facilities with an estimated mean required reduction of 5–6 log10 was necessary. This study highlighted the potential microbial contamination and health risk level in SWSSs that utilize mountain streams as water sources, even though the water sources were almost not affected by human activities. Furthermore, this study would also be helpful in supporting risk-based water management to ensure a safe water supply in SWSSs. [ABSTRACT FROM AUTHOR]

Spatial variation in the landscape factors climate, geomorphology, and lithology cause significant differences in water quality issues even when land use pressures are similar. The Physiographic Environment Classification (PEC) classifies landscapes based on their susceptibility to the loss of water quality contaminants. The classification is informed by a conceptual model of the landscape factors that control the hydrochemical maturity of water discharged to streams. In New Zealand, a case study using climatic, topographic, and geological data classified the country into six, 36, and 320 classes at Levels 1 (Climate), 1–2 (Climate + Geomorphology), and 1–3 (Climate + Geomorphology + Lithology), respectively. Variance partitioning analysis applied to New Zealand's national surface water monitoring network (n = 810 stations) assessed the contributions of PEC classes and land use on the spatial variation of water quality contaminants. Compared to land use, PEC explained 0.6× the variation in Nitrate Nitrite Nitrogen (NNN), 1.0× in Total Kjeldahl Nitrogen (TKN), 1.8× in Dissolved Reactive Phosphorus (DRP), 2.3× in Particulate Phosphorus (PP), 2.6× in E. coli, and 4.3× in Turbidity (TURB). Land use explained more variation in riverine NNN, while landscape factors explained more variation in DRP, PP, E. coli, and TURB. Overall, PEC accounted for 2.1× more variation in riverine contaminant concentrations than land use. The differences in contaminant concentrations between PEC classes (p < 0.05), after adjusting for land use, were consistent with the conceptual model of hydrochemical maturation. PEC elucidates underlying causes of contaminant loss susceptibility and can inform targeted land management across multiple scales. [ABSTRACT FROM AUTHOR]

The conservation of hydrological resources involves continuously monitoring their contamination. A multiagent system composed of autonomous surface vehicles is proposed herein to efficiently monitor the water quality. To achieve a safe control of the fleet, the fleet policy should be able to act based on measurements and fleet state. It is proposed to use local Gaussian processes and deep reinforcement learning to jointly obtain effective monitoring policies. Local Gaussian processes, unlike classical global Gaussian processes, can accurately model the information in a dissimilar spatial correlation which captures more accurately the water quality information. A deep convolutional policy is proposed, that bases the decisions on the observation on the mean and variance of this model, by means of an information gain reward. Using a double deep Q‐learning algorithm, agents are trained to minimize the estimation error in a safe manner thanks to a Consensus‐based heuristic. Simulation results indicate an improvement of up to 24% in terms of the mean absolute error with the proposed models. Also, training results with 1–3 agents indicate that our proposed approach returns 20% and 24% smaller average estimation errors for, respectively, monitoring water quality variables and monitoring algae blooms, as compared to state‐of‐the‐art approaches. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. 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.)