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Objective: The objective of this study was to test the compatibility and performance of a developed photonic sensor system, which can serve as a dependable and practical device for continuous monitoring of turbidity changes in aquaculture tanks. Methods: The fabricated photonic sensor system consisted of an integrated data logger and sensor probe. The sensor probe exhibited a precise emission of infrared light at a wavelength of 850 nm. Moreover, the sensor evaluates the ambient light across the red‐green‐blue spectrum. To ensure accuracy and reliability, the entire system underwent a thorough calibration process, referencing nephelometric turbidity unit values acquired through a specialized handheld turbidimeter. Rigorous trials were systematically conducted in 600‐L seawater tanks featuring tubular sea cucumber Holothuria tubulosa and Gilt‐head Sea Bream Sparus auratus to ensure the sensitivity and robustness of the photonic sensor system to the aquaculture environment. Result: A calibration curve revealed a significant correlation between the infrared channel values of the sensor (photon counts) and the turbidity values measured by the turbidimeter. The photonic sensor effectively captured turbidity changes in the aquaculture tanks, with significant differences observed between the tanks. The sensor performance was evaluated in trials with Gilt‐head Sea Bream, which showed sensitivity to high turbidity changes. The photonic sensor system accurately reflects turbidity changes continuously using its own active light source, independent of ambient light intensity, which is essential for turbid water conditions or for taking measurements in total darkness. Conclusion: The photonic sensor is a reliable tool for the continuous and accurate monitoring of turbidity changes in aquaculture systems. However, there are specific usage limitations under low‐turbidity conditions that can be improved in further studies. Impact statementThe photonic sensor system, as designed for this study, is capable of accurately measuring turbidity changes using its own active light source independent of ambient light intensity, allowing 24‐h monitoring of turbid water conditions or taking measurements in total darkness. Moreover, this system may be useful in detecting turbidity changes associated with algal blooms in sea cages or from poor water treatment performance of filtration systems in recirculating aquaculture. [ABSTRACT FROM AUTHOR]

The unutilized and unproductive lands in alkaline and waterlogged areas can be used for income and employment generation through fisheries activities. Apart from the income generation, the reclamation of the land is also possible if the scientific practices are followed. Karnataka is bestowed with vast variety of water resources for fisheries activities, saline, alkaline and waterlogged areas are also present is large extent in the state where the land is unused in majority of the affected areas. In this study, Krishna command area was selected for demonstration of composite fish culture using alkaline and waterlogged lands. Four beneficiaries were identified and empowered on using of these affected areas for fish culture. They were provided with quality carp seed and 2 tons of supplementary feed to each and scientific fish culture practices were demonstrated. Meanwhile, the water quality, fish survival, health and growth were monitored throughout the demonstration period. The pH of the water gradually decreased from 8.9 to 7.5, majorly due to addition of organic load to the pond. Other water quality parameters were scientifically monitored and they exhibited a decreasing trend with optimal limits favoring to fish culture. Catla, sliver carp, grass carp, common carp and amur carp showed a better growth rate with others being in moderate condition. The total mean fish production was found to be 3.8 t/acre from the demonstration sites and with the mean gross revenue of 4.25 lakhs. [ABSTRACT FROM AUTHOR]

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

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

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

Abstract 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 Chlorophyll a (Chl-a) is a key indicator of marine ecosystems, and certain hydro-meteorological parameters (HMPs) are highly correlated with its fluctuations. Here, relevant and accessible HMPs were used as inputs, combined with machine learning (ML) algorithms for estimating 3D Chl-a in the South China Sea (SCS). With the inputs of temperature, salinity, depth, wind speed, wind direction, sea surface pressure, and relative humidity, the LightGBM-based model performed well, achieving high R2 values of 0.985 and 0.789 in validation and testing sets, respectively. Based on a large number of in situ measurements, this model enables the estimation of the 3D distribution of summer Chl-a in the SCS over the past fifteen years using a 3D hydrographic dataset combined with surface meteorological parameters. The results show that the 3D distribution of the model estimated Chl-a is characterized similarly to the previous studies and can capture the effect of hydro-meteorological conditions on Chl-a distribution. The environmental variables affecting Chl-a were considered more comprehensively in this study, and the methodological framework has the potential to be applied to the low-cost monitoring of the remaining water quality parameters.

[Display omitted] • Dual-functional paper-based materials are fabricated through a surface engineering strategy. • Rapid and sensitivity detection towards Cr 2 O 7 2− is achieved through multiple detection methods. • N and B co-doped MXene quantum dots possess longer maximum emission wavelengths and strong oxidation stability. • Outstanding enrichment capacity towards Cr 2 O 7 2− (162.4 mg g−1) is exhibited. Dichromate ion (Cr 2 O 7 2−) is a highly toxic chromium-containing compound that poses significant hazards to the digestive, respiratory systems, skin, and mucous membranes. Currently, the detection and adsorption of Cr 2 O 7 2− face significant challenges, including the time-consuming and low sensitivity nature of traditional analytical methods. The limited efficiency and capacity of existing adsorbents hinder their practical application in real-time water quality monitoring and environmental remediation. Herein, using polyethyleneimine-functionalized (PEI) pulp fiber paper as the substrate, we developed smart paper-based materials (designated as NB-MQDs@PP) incorporated with nitrogen and boron co-doped MXene quantum dots (NB-MQDs) for rapid adsorption and sensitive detection of Cr 2 O 7 2−. Compared to undoped MQDs, NB-MQDs exhibited longer excitation wavelength and enhanced oxidation stability. As anticipated, NB-MQDs achieved rapid (response time of 10 s) and sensitive (detection limit of 1.2 μM) recognition of Cr 2 O 7 2− within a wide pH range with a quenching efficiency of 99.9%. Simultaneously, two on-site detection methods, immersion and cyclic filtration, were constructed based on NB-MQDs@PP. The detection limit of the immersion method was 17.0 nM, while the cyclic filtration method had a detection limit as low as 3.8 nM, surpassing the majority of those reported literatures. Remarkably, NB-MQDs@PP exhibited outstanding enrichment capacity towards Cr 2 O 7 2−, with an adsorption capacity of up to 162.4 mg/g. This work provides a novel strategy for creating unique paper-based materials with excellent capture and monitoring dual-function, which can be customized according to the requirements of various application scenarios. [ABSTRACT FROM AUTHOR]

Aquaculture plays a vital role in global food production, and maintaining optimal water quality is essential for the health and growth of aquatic species. This research addresses the need for an efficient, adaptive solution by introducing an innovative water quality monitoring and maintenance system for aquaculture ponds. Unlike conventional systems, our approach uniquely integrates fuzzy logic with IoT technologies to optimise the precision and adaptability of pond management. The system stands out with its continuous monitoring of critical parameters such as temperature, pH, dissolved oxygen (DO), weather conditions and salinity and its ability to autonomously adjust operational controls, such as aerators and water pumps, based on dynamic environmental changes. This ensures ideal water conditions without manual intervention, providing a reliable and effective solution for aquaculture pond management. This work’s novelty lies in applying fuzzy Logic to handle the complexity and variability of aquaculture environments, allowing for nuanced control decisions that improve water quality management. The system’s efficiency was demonstrated through a 72-h operational test, where it maintained optimal DO and salinity levels, showcasing its reliability and effectiveness in real-world conditions. The fuzzy logic model has demonstrated a commendable accuracy rate of 98%. These results validate the system’s performance and underscore its practical benefits, meeting the demands of aquaculture production and significantly enhancing operational efficiency by enabling remote monitoring and rapid issue identification. This research contributes a robust technological solution for aquafarmers, offering a promising advancement in aquaculture management by improving productivity and ensuring the health and growth of aquatic species. [ABSTRACT FROM AUTHOR]

Water quality assessment presents challenges, primarily the paucity of available data and ongoing system maintenance. This research develops an automated monitoring and control of water quality parameters in aquaponic systems with internet of thing (IoT) technology. Proper fish feeding management is important, which is why the fish were fed at 12:00, 16:00 and 07:00. The most significant relative error recorded during the validation of the DS18B20, PH-4502C, SEN0244, SEN0237-A, SEN0189 and DFR0300 sensors is 5.0%. The maximum standard deviation between the mentioned sensors was 1.96, and the highest coefficient of variation reached 7.24%. Before the installation of the aquaponic system, the specific growth rate (SGR) of fish was 4.89±0.17% and after implementing the automated aquaponics system, the SGR of fish increased to 6.21±0.24%. The feed conversion ratio values of the fish, both before and after the installation of the control system, were 1.98±0.14% and 1.53±0.09%, respectively. In addition, an improvement in plant growth was observed, evidenced by the difference in the values of height, number of leaves, leaf length, and weight of the plants before and after the installation of the control system, which was 7.74 cm, 5 leaves, 5.6 cm, and 41.6 g respectively. [ABSTRACT FROM AUTHOR]

Fish farmers struggle with the problem of fish mortality due to climate change, poisoning, and bad water quality. Estimates on the mortality of fishes ranges from 60% to 80% depending on the type of fish. Traditional approaches in monitoring the quality of water such as observation or laboratory testing prove to be costly and time-consuming. This study aims to develop a portable water quality monitoring kit for fish farms. The device allows users to monitor the water's dissolved oxygen, nitrogen, total dissolved solids, turbidity, temperature, and pH level. It is powered by a rechargeable battery, uses an Arduino microcontroller, and has a real-time alert system. The device's limitations include the battery life and the sensors will need to be recalibrated at least monthly to ensure the accuracy of the readings. However, the results show that the implementation of the device garnered high acceptability in all parameters in the evaluation with a total mean rating of 4.66. This implies that the device can be used in monitoring the water quality in aquaculture sectors as corroborated by the respondent's evaluation. It is recommended to enhance the device by adding more water quality sensors and integrating Internet of Things (IoT) capabilities to further improve its functionality and reliability.

The protection of the water's quality is an essential component of the planning, control, prevention, and preventative actions that must be taken to reduce the pollution of the water and the deterioration of aquatic habitat. Monitoring the water's quality on a consistent basis is an essential activity that contributes to keeping track of the hydrological particulars of any body of water. Many surviving systems and methods have been shown to be ineffective in achieving the same purpose, and only a small handful of other conventional methods have shown a remarkable outcome that has helped to reinstate and keep up water resources from piles of nutrients and organic composites that cause quality degradation. However, the figures collected from all corners of the world suggest that there is an immediate demand for an upgrade in the monitoring system for water quality. Based on the findings of the survey, the concept presented here entails the suggestion of a unique system that is able to maintain tabs on the quality of water, hence assisting the ongoing pollution control trends.. In this paper, a methodical approach is suggested for collecting the parametric quality indicators, most of which are chemical, which then leads to the production of a characteristic drawing for each of the variables relevant to the specific water resource that is the subject of the observation. The information that was gathered is, to put it another way, coupled with clouds. The suggested approach eliminates the most significant drawbacks of the present techniques, which results in an increase in the effectiveness of monitoring water quality and a reduction in water pollution.

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.

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

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

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

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

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

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

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

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

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

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

In recent years, heavy rainfall disasters linked to climate change have become more frequent, raising concerns about the release of chemicals stored in factories. Assessing chemical contamination during such emergencies therefore necessitates the development of a quick and easy method for evaluating hazardous contaminants in combination with toxicity testing. This study proposes a "toxicity screening" method that combines biological response testing and chemical analysis to systematically evaluate hazardous contaminants in emergency situations. The toxicity screening method evaluates the water quality in three steps, including water quality measurements and a delayed fluorescence (DF) assay, metal content measurements and a DF assay, and targeted screening analysis and a DF assay. The efficacy of this method was tested using industrial wastewater from 14 locations. Seven of the samples were non-toxic, while the other seven samples were toxic, displaying no observed effect concentration (NOEC) values ranging from 0.625 to 20%. Two toxic samples in the first phase possessed high total chlorine concentrations (0.4 mg L−1) and conductivities (2200 mS m−1), indicating that the main sources of toxicity were residual chlorine and a high salt concentration. In the second phase, metal content analysis identified metals as the toxicity cause in four samples. In the third phase, the organic contaminants were analyzed, and tri-n-octyl phosphate (TNOP) was detected at a concentration of 0.00027 mg L−1. The results of solid-phase extraction experiments and exposure tests with TNOP alone indicated that the contribution of TNOP to the toxicity was negligible and that chemicals not adsorbed on the solid-phase extraction cartridges were the cause of toxicity. The proposed method can therefore be considered effective for disaster-related water quality assessment, delivering results within 12 days. [ABSTRACT FROM AUTHOR]

One of the primary ways utilities prevent and address water quality concerns in distribution systems is via regular hydrant flushing, yet rigorous research to evaluate the impact of full‐scale flushing programs is limited. This study employed time‐series and correlation analyses to evaluate the efficacy of a utility's five‐month repeated conventional flushing program to reduce nitrification and improve disinfectant residual concentrations in the distribution system. Short‐term water quality improvements during flushes were common across the 16 locations flushed, but lasting improvements were inconsistent as demonstrated by continued nitrification and heterogeneous water quality changes. Flushing frequencies and flow rates may need to be tailored to individual sites, even when similar water quality challenges exist. Water quality monitoring data and related analyses helped to prioritize flush sites, maximize efficiency during and between flushes, and inform decisions to implement additional interventions. [ABSTRACT FROM AUTHOR]

Together with increasing people's need for water, water needs to be monitored due to the pressure created by factors such as drought and pollution. In this study, water quality was monitored by determining 10 points on the Karasu River in Bilecik Province and it was aimed to examine the seasonal change in water quality. pH, EC, Na, K, Ca, Mg, CO3, HCO3, Cl, B, SO4, Sodium Adsorption Rate (SAR), Residual Sodium Carbonate (RSC) and %Na parameters were determined in water. And then using these parameters, quality classes were determined with the help of water quality classification systems developed by scientists such as Schofield, Wilcox, Thorne, Doneen and Soifer. The study were temporally diveded into 4 periods. They are period 1, period 2 (Spring), period 3 (Summer) and period 4 (Fall). After all, for the period 3, heavy metal pollution, especially Al, attracts attention. Except for the period 2 of point 7 and period 4 of point 9, the class of all periods and points in terms of sulphate was "Very good (class 1)". According to Schofield (1933 and 1935) systems, point 7 is in particularly bad situation in terms of EC. According to Wilcox (1948), point 7 is not suitable for irrigation in the period 2. At point 7, water pollution in period 2 has been identified as common to most classification systems. According to Christiansen et al. (1977), there is intense pollution in terms of EC and Na2CO3 parameters. According to Soifer (1987), point 2 is the cleanest point. As a result, Karasu river is not suitable for irrigation in terms of Al and B. It was understood that heavy metal pollution did not cause much of a problem in non-industrialized cities such as Bilecik. Besides, it was understood that the most common element among heavy metals was Pb in Karasu River. The SAR and RSC values of all periods, seasons and points were classified as "Very Good-Safe (C1S1)". [ABSTRACT FROM AUTHOR]

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

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

Highlights A 10-year dataset of time-series MODIS imagery and in situ Chl-a concentration were curated for Lake Okeechobee. LSTM significantly outperformed KNN, SVR, and RF for Chl-a prediction and subsequent HAB detection. The optimal window length was found to be 13 days with a 4-day temporal resolution for the LSTM model. KNN, SVR, and RF models were not effective at utilizing the temporal dynamics of the input features. Harmful algal blooms (HABs) in inland waterbodies are a global concern due to their negative impact on human, animal, and ecosystem health. Chlorophyll-a (Chl-a) concentration is an important water quality parameter for monitoring HABs. While statistical and machine learning (ML) models have been widely studied to predict Chl-a concentration and HABs based on single-time-point satellite data, this work assessed whether long short-term memory (LSTM) can improve both tasks by leveraging temporal features in time-series MODIS satellite images compared to three classical ML models, including k-nearest neighbor (KNN), support vector regression (SVR), and random forest (RF). A dataset of daily MODIS images and monthly in situ Chl-a concentration measurements from 2011 to 2020 was curated for Lake Okeechobee, Florida. A window size of 13 days with a temporal resolution of four days was found to produce the optimal performance for LSTM, which significantly outperformed KNN, SVR, and RF for Chl-a prediction with a root mean square error of 11.95 µg/L, a mean absolute error of 8.55 µg/L, and a R2 value of 0.43. The superior performance of LSTM for Chl-a prediction was likely due to its ability to leverage the temporal dynamics in the features associated with HAB development. The Chl-a predictions were further used to determine HAB events, showing better accuracy and a significantly higher F1 score for LSTM over the other models. The study suggested that combining LSTM with high-temporal-resolution time-series data should be preferred over applying common ML models on time-series or single-time-point remote sensing data for Chl-a and HAB monitoring. [ABSTRACT FROM AUTHOR]

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

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

Antibiotic resistant bacteria are prevalent environmental contaminants in freshwaters, and antibiotic resistance genes circulate throughout the urban water cycle. The increase of antibiotic resistant pathogens threatens public health through direct and indirect exposure, and natural resource managers need information on the spatial patterns of antibiotic resistant bacteria and environmental factors associated with their distribution to improve water quality monitoring and to better assess human, animal, and environmental health risks. We collected water and epilithic biofilm samples and measured physicochemical environmental variables at 29 sites distributed longitudinally in the Green-Duwamish River basin, Washington, USA. We characterized catchment-wide patterns of gram-negative fecal indicator bacteria and hypothesized that the presence of antibiotic resistance would be associated with environmental heterogeneity, bacterial primary ecology, stream compartment, and stream type. Antibiotic resistance was determined by microbial growth on selective media supplemented with 3 different antibiotics (ampicillin, chloramphenicol, or tetracycline). Phenotypic antibiotic resistance was positively associated with disturbance, but resistance to at least 1 antibiotic was also detected in undeveloped river segments, with an 83% overall detection rate (i.e., 24 out of 29 sites, 17 in the mainstem and 7 in tributaries). The most probable number of Escherichia coli was associated with higher levels of antibiotic resistance of non- E. coli coliforms across the basin (ρ = 0.38, p < 0.01) but was not associated with antibiotic resistance of E. coli. Phenotypic resistance was highest among non- E. coli coliforms in the water column of tributaries draining moderately to extensively developed subcatchments. Generalized linear mixed-effects model results showed that 18% of the variance in presence of antibiotic resistance was explained by the fixed effects (summed CV across environmental variables, stream type, primary ecology, and stream compartment), and when a spatial random effect was included, the model explained 27% of the variance. Our study provides new evidence that environmental factors and bacterial primary ecology are important underlying factors associated with spatial patterns of antibiotic resistant enteric coliforms. We used macroecological concepts and a riverscape approach to characterize the distribution of antibiotic resistance with methods applicable to municipalities. [ABSTRACT FROM AUTHOR]

Dissolved oxygen concentration is one of the important indicators of water quality. In order to achieve low-cost and stable measurement of dissolved oxygen in water, this paper reported a single-optical-path dissolved oxygen sensor based upon quenching of single optical path fluorescence. A sol-gel method was used to prepare an oxygen sensitive membrane with tetraethyl orthosilicate (TEOS) and triethoxy(octyl)silane (octyl-triEOS) as the precursor embedded with tris(4,7-diphenyl-1,10-phenanthroline)ruthenium(II) dichloride (Ru(dpp)3Cl2) to optimize the nitrogen-oxygen quenching ratio and improve the performance of the sensor. Optical stability experiments were also conducted. A single-optical-path structure was adopted to reduce complexity, and the fluorescence intensity was calculated using the fast Fourier transform (FFT). The number of calculations were effectively reduced by measuring and storing the fluorescence intensity at 0 mg/L in advance. Calibration experiments were performed on the sensor, and the oxygen sensitive membrane with the highest sensitivity was prepared by the control variable method. The results showed that the fluorescence dyes in the prepared oxygen sensitive membrane were uniformly dispersed, and a fitted Stern–Volmer equation was obtained with a linearity of 0.994 and a sensitivity of 0.115; moreover, the light stability was good. Performance testing and anti-interference experiments were conducted, and the results showed that the standard deviation was 0.076 mg/L and the relative standard deviation was 0.805%. The errors due to changes in salinity and pH were both less than 0.25 mg/L. The sensor offers high accuracy and anti-interference ability, and is suitable for water quality monitoring and marine science. [ABSTRACT FROM AUTHOR]

Water quality monitoring is imperative in providing the objective evidence required to make sound decisions about water quality management. This study aimed to examine the water quality status of the Migori River by determining spatio-seasonal variations in water quality parameters, along with associated influencing factors and potential health risks. Therefore, eighteen physico-chemical and bacteriological variables were sampled and analyzed monthly for six months covering the wet and dry seasons from the upstream, midstream, and downstream stations, and a health risk survey was conducted with 90 watershed households. ANOVA and T-test were used to test for the significant spatial and seasonal variations (p < 0.05), respectively; whereas Pearson's correlation was used to identify relationships between parameters. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) were used to find various spatial patterns in the river water quality datasets, while the Canadian Council of Ministries of the Environment Water Quality Index (CCME-WQI) showed the suitability status of the river water quality. The assessed variables showed significant seasonal variability but no significant spatial differences in the river. HCA generated 3 clusters suggesting that water quality deteriorated downstream from the upstream of the watershed. The PCA extracted four PCs explaining 80.5% of the total variance, which suggested that the variations in water quality are attributed to point and nonpoint sources of pollution. While most of the physico-chemical variables were within maximum permissible limits, the bacteriological levels exceeded the prescribed standards. The index ranked the river's water condition between 'poor' to 'marginal'; upstream has better water condition that gradually decreases toward the downstream, and water quality is better in the wet season than the dry season. The study revealed that the water of the Migori River is polluted and potentially hazardous for human usage, and thus suitable pollution control measures are urgently needed to safeguard public health. [ABSTRACT FROM AUTHOR]

This work is focused on finding G‐optimal designs theoretically for kriging models with two‐dimensional inputs and separable exponential covariance structures. For design comparison, the notion of evenness of two‐dimensional grid designs is developed. The mathematical relationship between the design and the supremum of the mean squared prediction error (SMSPE) function is studied and then optimal designs are explored for both prospective and retrospective design scenarios. In the case of prospective designs, the new design is developed before the experiment is conducted and the regularly spaced grid is shown to be the G‐optimal design. Retrospective designs are constructed by adding or deleting points from an already existing design. Deterministic algorithms are developed to find the best possible retrospective designs (which minimizes the SMSPE). It is found that a more evenly spread design under the G‐optimality criterion leads to the best possible retrospective design. For all the cases of finding the optimal prospective designs and the best possible retrospective designs, both frequentist and Bayesian frameworks have been considered. The proposed methodology for finding retrospective designs is illustrated with a spatiotemporal river water quality monitoring experiment. [ABSTRACT FROM AUTHOR]

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

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

This study analyzed data from the quarterly results of water quality monitoring in the State of Santa Catarina (Southern Brazil) from the Qualiágua Program. The results present water quality collections carried out in 2019-2021 of 23 analytical parameters in 40 municipalities of the State. The Principal Component Analysis (PCA) technique was applied to the analyzed parameters and to verify sources of contamination. The data on land use of the hydrographic basins of each monitoring point were also analyzed. The results showed that electrical conductivity, total and dissolved solids and chloride concentration are responsible for most of the data variability, having more influence at monitoring points close to the ocean. On the other hand, nitrate and turbidity are influenced by soil management due to land use. Total phosphorus, ammoniacal nitrogen and thermotolerant coliforms had a greater relationship with urban areas. The results provide helpful information for decision-making processes related to public policies for water uses in the State. [ABSTRACT FROM AUTHOR]

Understanding the effects of precipitation on freshwater properties in different environmental contexts can make an important contribution to maintaining the quality of hydrological resources. The present work aimed to assess the influence of rainfall events on the modulation of stream Water Quality Index (WQI) and its parameters in a basin predominantly covered by a tropical montane forest. The focus was on the post-stormflow legacy. The main parameters that varied correlated to precipitation were turbidity, total dissolved solids, and dissolved oxygen. However, these parameters varied within a small range of values and had little effect on WQI. Total phosphorus did not correlate with the precipitation. Nitrates had a very weak correlation and inverse. There was no seasonal trend for WQI due to weak modulation of parameters influenced by precipitation. In this way, the natural forest cover of the basin protects the stream from the arrival of pollutants in large quantities by hindering the runoff formation. It is the main factor contributing to a high water quality index and provides some protection against quality modulation caused by precipitation. Even at relatively low values, thermotolerant coliforms were the parameter most affecting WQI, without correlating with precipitation; they were controlled by tourism dynamics, warning about the impacts from land use/cover changes since anthropogenic cover represented less than 1% of the basin area. However, it must be considered that the Coliforms protagonism was reinforced by the low modulation of the other parameters. [ABSTRACT FROM AUTHOR]

Ensuring the protection of the aquatic environment and addressing the water scarcity and degradation of water quality in the Mediterranean region pose significant challenges. This study specifically aims to assess the impact of urban and industrial pollution on the ZAT River water quality. The study exploits a combination of field measurements and mathematical simulations using the PEGASE model. The objective is to evaluate how water quality changes throughout the different seasons and to determine whether olive oil factories discharge industrial wastewater into the river. The study reveals that the river water quality remains relatively stable along its course, up to km 64 in winter and km 71.77 in summer, where poor water quality is recorded. This degradation can be attributed to multiple factors. One of these factors is the discharge of industrial wastewater, which accounts for 47% of the COD pollution load. This industrial wastewater is released into the river without treatment during the production period (January–February) and inactivity period (March–May). The combined impact of urban and industrial wastewater is also associated with the decrease in water flow resulting from water withdrawals due to irrigation canals and groundwater recharge, which both contribute to the observed changes in river water quality. Importantly, field measurements combined with results obtained from the calibrated model provide compelling evidence of unauthorized wastewater discharges from the olive oil factories into the river. These results emphasize the need for stricter regulation, such as developing water quality monitoring strategies based on the use of modeling methodologies. They also emphasize the importance of improving wastewater management practices, such as setting up treatment plants for different sources of pollution or developing a co-treatment plant to mitigate the adverse impact of industrial pollution on river water quality. [ABSTRACT FROM AUTHOR]