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

The Yeongsan River is one of the four major rivers in South Korea. Since the construction of two weirs as part of the Four Major Rivers Project to secure water resources in 2011, issues with algal blooms have frequently arisen, prompting the Ministry of Environment of Korea to conduct continuous monitoring of water quality and algal outbreaks. This study, conducted between 2019 and 2023, examined the relationship between the phytoplankton community structure and physicochemical factors at the Seungchon and Juksan weirs. Phytoplankton were categorized into four groups (Bacillariophyceae, Chlorophyceae, Cyanophyceae, and other phytoplankton), and 20 dominant genera were selected for analysis. As microalgal species vary depending on environmental conditions, understanding the specific relationships among the microalgae observed in the study area can help explain their occurrence mechanisms and contribute to the development of effective management strategies. Therefore, we used principal component analysis (PCA) to analyze the seasonal variation patterns of the four microalgal groups and visualize key data features through dimensionality reduction. Additionally, PCA was employed to identify and visualize environmental factors related to seasonal variations in phytoplankton communities. PCA helped elucidate how different environmental factors influence phytoplankton fluctuations across seasons. We used canonical correspondence analysis (CCA) to investigate the relationships among the 20 dominant genera in each group and environmental factors. Additionally, CCA was used to analyze the relationship between the distribution of the top five dominant phytoplankton taxa in each group and various environmental factors. CCA allowed for a detailed examination of how these dominant taxa interact with environmental conditions. PCA revealed significant correlations between other phytoplankton and Chl-a in spring and Cyanophyceae and water temperature in summer. Bacillariophyceae was positively correlated with nitrogen-based nutrients but negatively with phosphate phosphorus (PO4-P). CCA revealed significant correlations between dominant genera and environmental factors. Stephanodiscus sp. was associated with nitrogen-based nutrients, whereas Microcystis sp. and Dolichospermum sp. were associated with water temperature and PO4-P. Stephanodiscus sp. affected water treatment through filtration and sedimentation issues, whereas Microcystis sp. and Dolichospermum sp. produced the toxin microcystin. These findings offer valuable insights for water quality management. [ABSTRACT FROM AUTHOR]

The temporal dynamics of solute export from catchments are challenging to quantify and model due to confounding hydrological and biogeochemical processes and sparse measurements. Conventionally, the concentrationdischarge relationship (C-Q) and statistical approaches to describe it, such as the Weighted Regressions on Time, Discharge and Seasons (WRTDS), have been widely used. Recently, deep learning (DL) approaches, especially Long-Short-Term-Memory (LSTM) models, have shown predictive capability for discharge, temperature, and dissolved oxygen. However, it is not clear if such advances can be expanded to water quality variables driven by complex subsurface biogeochemical processes. This work evaluates the performance of LSTM and WRTDS for 20 water quality variables across ~500 catchments in the continental US. We find that LSTM does not markedly outperform WRTDS in our dataset, potentially limited by the current measurement capabilities of water quality across CONUS. Both models present similar performance patterns across water quality variables, with the LSTM displaying better performance for nutrients compared to weathering-derived solutes. Additionally, the LSTM does not benefit from flexibility in the inputs. For example, incorporation of climate data that constrains streamflow generation, does not significantly improve the LSTM performance. We also find that data availability is not a straightforward predictor of LSTM model performance, although higher availability tends to stabilize performance. To fully assess the potential of the LSTM model, it may be necessary to use a higher frequency dataset across the CONUS, which does not exist today. To evaluate the dynamics of C-Q patterns relative to model performance, we introduce a "simplicity index" considering both the seasonality in the concentration pattern and the linearity in the C-Q relationship, or the C-Q-t pattern. The simplicity index is strongly correlated with model performance and differentiates the underlying controls on water quality dynamics. Further DL experiments and model-intercomparison highlight the strengths and deficiencies of existing frameworks, pointing to the need for further hydrogeochemical theories that are amenable to complex basins and solutes. [ABSTRACT FROM AUTHOR]

Chlorophyll‐a (Chl‐a) concentrations, a key indicator of algal blooms, were estimated using the XGBoost machine learning model with 23 variables, including water quality and meteorological factors. The model performance was evaluated using three indices: root mean square error (RMSE), RMSE‐observation standard deviation ratio (RSR), and Nash–Sutcliffe efficiency. Nine datasets were created by averaging 1 hour data to cover time frequencies ranging from 1 hour to 1 month. The dataset with relatively high observation frequencies (1–24 h) maintained stability, with an RSR ranging between 0.61 and 0.65. However, the model's performance declined significantly for datasets with weekly and monthly intervals. The Shapley value (SHAP) analysis, an explainable artificial intelligence method, was further applied to provide a quantitative understanding of how environmental factors in the watershed impact the model's performance and is also utilized to enhance the practical applicability of the model in the field. The number of input variables for model construction increased sequentially from 1 to 23, starting from the variable with the highest SHAP value to that with the lowest. The model's performance plateaued after considering five or more variables, demonstrating that stable performance could be achieved using only a small number of variables, including relatively easily measured data collected by real‐time sensors, such as pH, dissolved oxygen, and turbidity. This result highlights the practicality of employing machine learning models and real‐time sensor‐based measurements for effective on‐site water quality management. Practitioner Points: XAI quantifies the effects of environmental factors on algal bloom prediction modelsThe effects of input variable frequency and seasonality were analyzed using XAIXAI analysis on key variables ensures cost‐effective model development [ABSTRACT FROM AUTHOR]

This paper showcases the successful development and implementation of two Digital Twin prototypes within the Lab Digital Twins project, designed to enhance the efficiency and quality control of Aigües de Barcelona's drinking water network. The first prototype focuses on asset management, using (near) real‐time data and statistical models, and achieving a 70% success rate in predicting pump station failures 137 days in advance. The second prototype addresses water quality monitoring, leveraging machine learning to accurately forecast trihalomethane levels at key points in the distribution system, and enabling proactive water quality management strategies, ensuring compliance with stringent safety standards and safeguarding public health. The paper details the methodology of both prototypes, highlighting their potential to revolutionize water network management. Practitioner Points: Digital representation of assets and processes in the drinking water treatment networkEarly fault detection in assets, and predictions of trihalomethane formation in the drinking water distribution networkReduction on monitoring time and incident response for target assets by means of Digital TwinsImprovement in visualization, prediction, and proactive measures for asset management and water quality controlContribution to the growing knowledge on Digital Twins and their potential to revolutionize water network operations [ABSTRACT FROM AUTHOR]

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]

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]

Eutrophication occurs frequently in urban water bodies, and rapid measurement of phosphorus (P) is needed for water quality control, since P has been one of the limiting factors. In this study, approximately 400 water samples were collected from typical urban water bodies in Nanjing city, and Fourier transform infrared attenuated total reflectance spectroscopy (FTIR-ATR) was applied for rapid P determination. Both silicon ATR (Si-ATR) and ZnSe-ATR were employed in the recording of FTIR-ATR spectra, and different algorithms, including partial least squares regression (PLSR), support vector machines for regression (SVRs), extreme learning machines (ELMs), and self-adaptive partial least squares model (SA–PLS), were applied in the analysis of spectra data. The results showed that the water quality varied significantly for different water bodies in different seasons, and both Si-ATR and ZnSe-ATR could achieve good P prediction. The PLSR and SVR models showed poor P prediction effects while the ELM model was excellent, and the SA-PLS model was the best one. For the SA-PLS model, the prediction accuracy of Si-ATR (Rv2 = 0.973, RMSEV = 0.015 mg L−1, RPDV = 6.05) was slightly better than that of ZnSe-ATR (Rv2 = 0.942, RMSEV = 0.011 mg L−1, RPDV = 4.13). Therefore, the FTIR-ATR technology coupled with the SA-PLS model achieved rapid P determination in urban water, providing an effective option for water quality monitoring. [ABSTRACT FROM AUTHOR]

The Water Quality Index (WQI) is a tool designed to provide a singular figure representing the overall water quality status of a water body. This study applies Malaysia WQI, National Sanitation Foundation WQI (NSFWQI) and statistical analysis to investigate the impact of diverse pollution sources on the Brunei River's water quality, a critical aquatic ecosystem affected by the rapid escalation of urbanization, industrial activities, and agricultural runoff. Principal component analysis (PCA), expert judgement, and correlation analysis were used to propose parameters for developing Brunei River's WQI. Eight monitoring stations were selected to analyze 16 water quality parameters (pH, water temperature (T), dissolved oxygen (DO), oxidation-reduction potential (ORP), chemical oxygen demand (COD), the five-day biochemical oxygen demand (BOD5), salinity, electrical conductivity (EC), total dissolved solids (TDS), turbidity, total suspended solids (TSS), ammoniacal-nitrogen (NH3-N), fecal coliform (FC), total coliform (TC), phosphate and nitrate (NO3−) in this study. The results showed that NSFWQI classified the Brunei River as moderately polluted, while Malaysia (WQI) status was classified as slightly polluted except for Station Q around the quarry area. Statistical analysis revealed that the primary pollution sources are anthropogenic activities such as quarrying, domestic waste, and agricultural and urban runoff. Other specific areas of concern with low WQI and significant pollution levels are situated at Kampong Ayer Stations (N and J) due to proximal anthropogenic activities. The proposed water quality parameters for developing Brunei River's WQI are pH, DO, EC, FC, NO3−, BOD5, T, TSS, turbidity and phosphate. This study addresses the current pollution status of the Brunei River and sets a precedent for future research emphasizing collaborative data-driven strategies for water quality management. [ABSTRACT FROM AUTHOR]

Rapid urbanization and industrialization have caused water quality issues in urban rivers. Appropriate measures based on water quality monitoring systems and prediction methods are needed for water quality management. While South Korea has operated a water quality monitoring system that measures various environmental factors and has accumulated water quality data, a water quality prediction system is not in place. This study suggests a water quality prediction method based on a long short-term model using water quality and meteorological monitoring data. Additionally, we present a derived input set of the prediction model that can improve the prediction model performance. The prediction model's performance was evaluated by the coefficient of determination under various conditions, such as the hyperparameters, temporal resolution of input data, and application of upstream and downstream data. As a result, using the temporal resolution of the input data as hourly data improved predictions by an average of 25.6% over three days of the prediction period compared to daily data. Meanwhile, it was analyzed that the hyperparameters and using upstream and downstream data have a minor effect on the model performance. The results of this study underscore the crucial role of the number, duration, and temporal resolution of available monitoring data in water quality management. [ABSTRACT FROM AUTHOR]

Natural resource management (NRM) plays a pivotal role in ensuring the sustainability of ecosystems, which are essential for human health and well-being. This systematic review examines the impact of various NRM practices on water quality and their subsequent effects on public health. Specifically, it focuses on interventions such as watershed management, pollution control, land use management, water treatment, and ecosystem restoration. We conducted a comprehensive search across PubMed, Scopus, and Web of Science, supplemented by gray literature from Google Scholar, WHO reports, and government and NGO publications, covering studies published between 2014 and 2024. A total of 42 studies met the inclusion criteria, encompassing diverse geographical regions with significant representation from developing countries. The findings indicate that effective NRM practices, particularly those aimed at reducing pollutants, managing watersheds, and promoting sustainable land use, significantly improve water quality by lowering levels of chemical contaminants, microbial pathogens, and physical pollutants. Improved water quality directly correlates with reduced incidences of waterborne diseases, chronic health conditions from long-term chemical exposure, and acute health effects from immediate pollutant exposure. The review underscores the need for tailored NRM strategies that consider local environmental and socio-economic contexts. It also highlights the importance of community involvement, regulatory frameworks, and continuous monitoring to enhance the effectiveness of NRM interventions. Despite the positive impacts, barriers such as limited financial resources, technical expertise, and community engagement pose challenges to the implementation of these practices. In conclusion, the systematic review demonstrates that comprehensive and context-specific NRM practices are crucial for improving water quality and public health outcomes. Policymakers and practitioners are encouraged to adopt integrated water resource management approaches, prioritize sustainable practices, and engage local communities to achieve long-term health and environmental benefits. [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]

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]

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]

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]

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]

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]

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.

Due to rapid urbanization, population growth, industrialization, and agricultural activities, there is an increasing demand for freshwater resources, leading to heightened pressure on watershed ecosystems. This study focused on the Nishan Reservoir, Qufu Sihe River, and Qufu Yihe River, conducting field investigations on these water bodies during the spring of 2021 and 2022. Water samples were collected and analyzed for key water quality indicators, including chemical oxygen demand (COD), biochemical oxygen demand (BOD), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (AN), chlorophyll A, and algal cell density. This study unveiled notable correlations among various water quality parameters, including positive associations between BOD and COD, chlorophyll A and algal cell density, and inverse relationships with total phosphorus. Moreover, significant positive correlations were identified between total nitrogen and ammonia nitrogen, as well as between ammonia nitrogen and chlorophyll A. The study highlighted that the TP concentration surpassed the threshold of 0.20 mg/L in 2021, potentially exacerbating the proliferation of algae, leading to algal blooms and adversely affecting the aquatic ecosystem. This study emphasizes the significance of broadening the geographical scope and utilizing long-term datasets to discern trends, determinants, and management approaches pertinent to water quality. Furthermore, this study underscores the imperative of investigating the influence of nitrogen-to-phosphorus nutrient ratios on the composition and proliferation of algal populations, while also taking into account the potential impact of additional factors like light availability, temperature, and water flow on the dynamics of algal communities. [ABSTRACT FROM AUTHOR]

Seasonal quality assessment of surface water provides not only a better understanding about pollution dynamics in water bodies but also information for the implementation of sustainable water-use management strategies. Such vital information is lacking for surface waters in most cities in sub-Sahara Africa region, especially Nigeria. The study aimed, therefore, to examine the seasonal physio-chemical characteristics of surface waters in Enugu urban, southeast Nigeria, during the dry and wet seasons. Twelve Surface water samples were collected from the six major rivers in Enugu urban during two seasons (wet and dry seasons). Their physico-chemical characteristics were determined by specific analyses between 2020 and 2022. Some of the parameters were measured in situ whereas bacterial analysis were analysed in the laboratory. Seasonal fluctuations in surface water quality characteristics were determined and the pollution status was compared to WHO standards. The results revealed that the parameters like Turbidity, Ammonia, Nitrate, Nitrogen, Fecal coliform and total coliform were above the WHO permissible limit at all sites and for all seasons. Various anthropogenic activities, untreated sewage and effluent were the major sources of pollution to the river ecology and the surface water. Overall monitoring network results must be used to control pollution in catchments where industrial and urban activities are increasing in other to ensure effective water quality management, sustainability and safety. [ABSTRACT FROM AUTHOR]

Numerous individuals in Riau Province are engaged in the fish cultivation due to the province's ample water supply and the substantial market demand, which generates relatively high selling prices. The significance of water quality issues in fish aquaculture stems from the fact that fish inhabit water. Water quality is determined by a number of parameters, including acidity, temperature, turbidity, and the quantity of dissolved substances. Therefore, this study aimed to develop a device capable of monitoring water quality. The water parameters observed through the utilization of sensors and the Internet of Things were temperature, acidity, dissolved solids concentration, and turbidity. Utilizing the Rapid Application Development (RAD) Method, the research was completed through the following phases: Analysis, design, development, evaluation, implementation, and simulation. A device comprised of electronic components, including a microcontroller and sensors, was the result of this research. Monitoring parameter data was collected in real time via sensors and subsequently stored on a cloud server. The tools developed as a consequence of this research enable fish producers to acquire parameter data in real time. These tools serve the purpose of generating responses to normalize water quality and to facilitate action in response to changes in water quality. [ABSTRACT FROM AUTHOR]

Considering the growing importance of sustainable development and value engineering in various fields, especially in water quality management, superior attention has been paid to reducing costs in water quality monitoring systems. One of the methods, which reduces environmental and economic costs in water quality monitoring networks, is to decrease the number of Water Quality Monitoring Stations (WQMS). In the present study, the optimization of the WQMS in the Mond River basin located in the south of the Fars province in Iran was studied. Three algorithms, including Tabu Search (TS), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), were applied to the existing WQMS to determine the optimal number of stations with the maximum number of station combinations for different applications. Then, we compared the performance of these three algorithms. The results presented that the number of existing WQMS can be reduced from 16 to 12 for irrigation and 16 to 11 for drinking purposes. As a result, the number of stations suggested for the Mond River basin was selected 12 out of 16 monitoring stations for both water usages. The results are consistent with previous studies that investigated the application of the Genetic Algorithm (GA) and Dynamic Programming Approach (DPA) for the optimization of WQMS in the Mond River. [ABSTRACT FROM AUTHOR]

The water source reservoirs are the important urban water source in northern China. Although external pollution has been greatly improved, the internal pollutants in reservoirs continue to accumulate with the complex deposition and release processes, resulting in potential risks to water supply safety. To address the aforementioned issue, this paper proposed a simulation model of water quality named ECOlab EU1-WSR to simulate the spatio-temporal changes of water quality under the influence of internal pollution for the water source reservoirs. Based on the analysis of the water quality characteristics and the distribution of benthic vegetation in the reservoir, a three-dimensional hydrodynamic model was established based on MIKE3, the corresponding parameters and the related state variables were set, the ECOlab EU1-WSR model was established by secondly developing the original ECOlab EU1 template, and the real-time dynamic outputs of pollutant content in sediment were added to link the water quality index with sediment nutrition index for better revealing the impact of the internal pollution on the water quality. The performance of the model was evaluated by the case application on the water quality simulation of Daye reservoir and the optimization of the connection project between Daye reservoir and Xueye reservoir in Shandong Province China. The results showed that the model can accurately and simultaneously simulate the pollution in water and sediment by the comparative verification of hydrodynamics, water temperature, and water quality. Moreover, the model can effectively reflect the influence of the accumulation, deposition, and release of internal pollution on water quality by analyzing the correlation between the content of various pollution in water body and those in sediment, such as the total nitrogen and total phosphorus in the water body at the bottom of the water intake, were negatively correlated with the total nitrogen and total phosphorus in the sediments with correlation coefficients of 0.538 and 0.917, respectively. In addition, the optimal water inlet position and water flow rate of the connection project can be optimized and determined by using the model to effectively control water quality. The established model will be a useful tool for the design and management of a reservoir, the interconnection projects, and other water bodies by adaptively recoded. [ABSTRACT FROM AUTHOR]

Airborne sensing images harness the combined advantages of hyperspectral and high spatial resolution, offering precise monitoring methods for local-scale water quality parameters in small water bodies. This study employs airborne hyperspectral remote sensing image data to explore remote sensing estimation methods for total nitrogen (TN) and total phosphorus (TP) concentrations in Lake Dianshan, Yuandang, as well as its main inflow and outflow rivers. Our findings reveal the following: (1) Spectral bands between 700 and 750 nm show the highest correlation with TN and TP concentrations during the summer and autumn seasons. Spectral reflectance bands exhibit greater sensitivity to TN and TP concentrations compared to the winter and spring seasons. (2) Seasonal models developed using the Catboost method demonstrate significantly higher accuracy than other machine learning (ML) models. On the test set, the root mean square errors (RMSEs) are 0.6 mg/L for TN and 0.05 mg/L for TP concentrations, with average absolute percentage errors (MAPEs) of 23.77% and 25.14%, respectively. (3) Spatial distribution maps of the retrieved TN and TP concentrations indicate their dependence on exogenous inputs and close association with algal blooms. Higher TN and TP concentrations are observed near the inlet (Jishui Port), with reductions near the outlet (Lanlu Port), particularly for the TP concentration. Areas with intense algal blooms near shorelines generally exhibit higher TN and TP concentrations. This study offers valuable insights for processing small water bodies using airborne hyperspectral remote sensing images and provides reliable remote sensing techniques for lake water quality monitoring and management. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (Chl-a) concentration monitoring is very important for managing water resources and ensuring the stability of marine ecosystems. Due to their high operating efficiency and high prediction accuracy, backpropagation (BP) neural networks are widely used in Chl-a concentration inversion. However, BP neural networks tend to become stuck in local optima, and their prediction accuracy fluctuates significantly, thus posing restrictions to their accuracy and stability in the inversion process. Studies have found that metaheuristic optimization algorithms can significantly improve these shortcomings by optimizing the initial parameters (weights and biases) of BP neural networks. In this paper, the adaptive nonlinear weight coefficient, the path search strategy "Levy flight" and the dynamic crossover mechanism are introduced to optimize the three main steps of the Artificial Ecosystem Optimization (AEO) algorithm to overcome the algorithm's limitation in solving complex problems, improve its global search capability, and thereby improve its performance in optimizing BP neural networks. Relying on Google Earth Engine and Google Colaboratory (Colab), a model for the inversion of Chl-a concentration in the coastal waters of Hong Kong was built to verify the performance of the improved AEO algorithm in optimizing BP neural networks, and the improved AEO algorithm proposed herein was compared with 17 different metaheuristic optimization algorithms. The results show that the Chl-a concentration inversion model based on a BP neural network optimized using the improved AEO algorithm is significantly superior to other models in terms of prediction accuracy and stability, and the results obtained via the model through inversion with respect to Chl-a concentration in the coastal waters of Hong Kong during heavy precipitation events and red tides are highly consistent with the measured values of Chl-a concentration in both time and space domains. These conclusions can provide a new method for Chl-a concentration monitoring and water quality management for coastal waters. [ABSTRACT FROM AUTHOR]

River water quality management and monitoring are essential responsibilities for communities near rivers. Government decision-makers should monitor important quality factors like temperature, dissolved oxygen (DO), pH, and biochemical oxygen demand (BOD). Among water quality parameters, the BOD throughout 5 days is an important index that must be detected by devoting a significant amount of time and effort, which is a source of significant concern in both academic and commercial settings. The traditional experimental and statistical methods cannot give enough accuracy or solve the problem for a long time to detect something. This study used a unique hybrid model called MVMD-LWLR, which introduced an innovative method for forecasting BOD in the Klang River, Malaysia. The hybrid model combines a locally weighted linear regression (LWLR) model with a wavelet-based kernel function, along with multivariate variational mode decomposition (MVMD) for the decomposition of input variables. In addition, categorical boosting (Catboost) feature selection was used to discover and extract significant input variables. This combination of MVMD-LWLR and Catboost is the first use of such a complete model for predicting BOD levels in the given river environment. In addition, an optimization process was used to improve the performance of the model. This process utilized the gradient-based optimization (GBO) approach to fine-tune the parameters and better the overall accuracy of predicting BOD levels. To assess the robustness of the proposed method, we compared it to other popular models such as kernel ridge (KRidge) regression, LASSO, elastic net, and gaussian process regression (GPR). Several metrics, comprising root-mean-square error (RMSE), R (correlation coefficient), U95% (uncertainty coefficient at 95% level), and NSE (Nash–Sutcliffe efficiency), as well as visual interpretation, were used to evaluate the predictive efficacy of hybrid models. Extensive testing revealed that, in forecasting the BOD parameter, the MVMD-LWLR model outperformed its competitors. Consequently, for BOD forecasting, the suggested MVMD-LWLR optimized with the GBO algorithm yields encouraging and reliable results, with increased forecasting accuracy and minimal error. [ABSTRACT FROM AUTHOR]

Water quality and the management of nitrogenous compounds are of key importance within aquaculture. In this paper, automated and portable analysers for in situ analysis of nitrite and nitrate in water were deployed in freshwater and saline recirculating aquaculture systems (RAS). The analysers were based upon ion chromatography (IC) and employed a NaCl eluent with an anion exchange guard column for low backpressure anion separation, in combination with selective 235 nm ultra-violet light-emitting diode (UV-LED) based absorbance detection. The analysers were monitored and delivered real-time concentration data using a cellular internet of things (IoT) module and cloud-based dashboard. Overall performance and chromatographic repeatability were tested across various temperature profiles and 500 sequential runs within the laboratory. Deployments in freshwater and saline RAS, with concentrations ranging between 0.1–3.6 mg L−1 nitrite and 0.6–392 mg L−1 nitrate, were successful and the analytical performance was comparable to that of accredited lab-based instrumentation. [ABSTRACT FROM AUTHOR]

The ability to infer ocean chlorophyll- a concentrations (Chl a) from spaceborne instruments is key to assessments of global ocean productivity and monitoring of water quality. Here, we present a novel parametric algorithm, OCG, trained on a set of global in situ high-performance liquid chromatography (HPLC) data that leverages Level-3 remote sensing reflectance (R rs ) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite. The OCG algorithm leverages more bands than existing algorithms and also provides pixel-wise uncertainty assessments that enable the calculation of the probability of exceeding specific Chl a thresholds. This feature has significant implications for water quality management, particularly in monitoring harmful algal blooms. The OCG surpasses existing algorithms in bias and accuracy without overfitting, especially in coastal areas, where it outperforms the current standard product (CI OC3) by 20 % in median symmetric accuracy. Moreover, the OCG reduces the signed symmetric percentage bias (SSPB) in coastal regions from 41 % (CI OC3) to below 5 %. Globally, the OCG algorithm yields lower Chl a in coastal regions, the Southern Ocean and the Mediterranean Sea, and higher values in the open ocean, particularly in ocean gyres and polar regions. For the Chesapeake Bay and the Baltic Sea, for example, daily OCG estimates for 2002 to 2021 are, on average, 2.9 g/L and 3.7 g/L lower than CI OC3 estimates, respectively. The presented approach also shows great potential for other existing and upcoming sensors, enabling widespread application in remote sensing. [ABSTRACT FROM AUTHOR]

This study classified surface water quality in Can Tho city using the Eutrophication index, Harmony Degree Equation (HDE), and Technique of Order Preference by Similarity to Ideal Solution (TOPSIS). Water quality data were collected in two seasons at 38 locations with 18 parameters, including temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), nitrite (N-NO2 - ), nitrate (N-NO3 - ), ammonium (N-NH4 + ), orthophosphate (P-PO4 3- ), Fe, F -, Pb, As, Hg, coliform, chlorine-, and phosphorus-based pesticides. Water quality parameters are compared with national technical regulations on surface water quality (QCVN 08- MT:2015/BTNMT). The HDE method based on entropy weight has been applied to evaluate the comprehensive harmony degree of water quality for various purposes. In addition, the TOPSIS was also used to rank water quality at each location and determine the priority level that required mitigation and treatment solutions. Surface water quality in the study area had low dissolved oxygen content and was contaminated with TSS and coliform in both seasons. Water quality in the rainy season tends to decrease compared to the dry season. Based on HDE results, water quality in the study area in the dry season was assessed as suitable for domestic activities (needs treatment), irrigation, and navigation (HDII = 0.922), while the rainy season was suitable for irrigation and navigation (HDIII = 1.00). Moreover, surface water in the study area was in a state of potential eutrophication (EI > 0), in which eutrophication was higher during the dry season. The SW25 and SW28 were the most seriously eutrophic in the dry and rainy seasons, respectively. TOPSIS analysis indicated that SW22 and SW28 need treatment measures in both seasons; furthermore, SW2-SW4 (dry season) and SW23 (rainy season) also need appropriate management and impact mitigation solutions. SW4 was affected by the most significant seasonal impacts, which have high priority in the dry season and are lowest in the rainy season. Therefore, future studies are needed to identify specific sources of variation at these locations to reduce impacts. The study results provide helpful information for the decision-making process and water quality management. [ABSTRACT FROM AUTHOR]

The water quality index (WQI) is a globally accepted guideline to indicate the water quality standard of any groundwater resource. Water levels in existing groundwater sources are declining in several coastal zones. Therefore, for monitoring water quality and improving water management, the prediction and identification of groundwater status by an effective technique with higher accuracy is urgently needed. Therefore, this research aims to find an effective model for WQI prediction by comparing entropy and critic weight-based WQI (ENW-WQI and CRITIC-WQI) with multi-layer perceptron artificial neural network (MLP-ANN) technique and also to identify contaminated zones using GIS. Initially, 1000 water sampling datasets with concentrations of several water quality parameters of different coastal blocks of eastern India during 2018 to 2022 are considered for the estimation of ENW-WQI and CRITIC-WQI. It shows 65% and 67% of the samples are excellent to good for drinking. ENW-WQI and CRITIC-WQI-based MLP-ANN models have been established considering different data portioning and hidden neuron numbers. Input variables and appropriate dataset partitioning with hidden neurons for models obtained from correlation and trial–error analysis. Spatial distribution maps are also produced for calculated WQIs using inverse distance weighted interpolation approaches. Three fitting models are obtained: ENW-WQI-MLP-ANN, CRITIC-WQI-MLP-ANN-I and CRITIC-WQI-MLP-ANN-II. CRITIC-WQI-MLP-ANN-II model (data ratio 85:15, network structure 6–12-1, R2 = 0.986, NSE = 0.98, and error rate 0.49%) provides the best accuracy in WQI prediction. The GIS-based WQI maps record several areas related to drinking water quality. The results of this research can help in planning the provision of safe drinking water in the future. [ABSTRACT FROM AUTHOR]

• Composition and structures of actinobacterial community showed seasonal changes. • Actinobacterial community diversity and richness became highest in winter. • Sporichthya spp., Rhodococcus spp., and Mycobacterium spp. were major actinobacteria genus. • Water temperature and DO mainly controlled community seasonal variation. Actinobacterial community is a conspicuous part of aquatic ecosystems and displays an important role in the case of biogeochemical cycle, but little is known about the seasonal variation of actinobacterial community in reservoir ecological environment. In this study, the high-throughput techniques were used to investigate the structure of the aquatic actinobacterial community and its inducing water quality parameters in different seasons. The results showed that the highest diversity and abundance of actinobacterial community occurred in winter, with Sporichthya (45.42%) being the most abundant genus and Rhodococcus sp. (29.32%) being the most abundant species. Network analysis and correlation analysis suggested that in autumn the dynamics of actinobacterial community were influenced by more factors and Nocardioides sp. SX2R5S2 was the potential keystone species which was negatively correlated with temperature (R = -0.72, P < 0.05). Changes in environmental factors could significantly affect the changes in actinobacterial community, and the dynamics of temperature, dissolved oxygen (DO), and turbidity are potential conspicuous factors influencing seasonal actinobacterial community trends. The partial least squares path modeling further elucidated that the combined effects of DO and temperature not only in the diversity of actinobacterial community but also in other water qualities, while the physiochemical parameters (path coefficient = 1.571, P < 0.05) was strong environmental factors in natural mixture period. These results strengthen our understanding of the dynamics and structures of actinobacterial community in the drinking water reservoirs and provide scientific guidance for further water quality management and protection in water sources. [Display omitted] [ABSTRACT FROM AUTHOR]

Efficient monitoring of water quality parameters (WQPs) is crucial for environmental health. Drone hyperspectral images have offered the potential for the flexible and accurate retrieval of WQPs. However, a machine learning (ML)-based multi-process strategy for WQP inversion has yet to be established. Taking a typical urban river in Guangzhou city, China, as the study area, this paper proposes a machine learning-based strategy combining spectral preprocessing and ML regression models with ground truth WQP data. Fractional order derivation (FOD) and discrete wavelet transform (DWT) methods were used to explore potential spectral information. Then, multiple methods were applied to select sensitive features. Three modeling strategies were constructed for retrieving four WQPs, including the Secchi depth (SD), turbidity (TUB), total phosphorus (TP), and permanganate index (CODMn). The highest R2s were 0.68, 0.90, 0.70, and 0.96, respectively, with corresponding RMSEs of 13.73 cm, 6.50 NTU, 0.06 mg/L, and 0.20 mg/L. Decision tree regression (DTR) was found to have the potential with the best performance for the first three WQPs, and eXtreme Gradient Boosting Regression (XGBR) for the CODMn. Moreover, tailored feature selection methods emphasize the importance of fitting processing strategies for specific parameters. This study provides an effective framework for WQP inversion that combines spectra mining and extraction based on drone hyperspectral images, supporting water quality monitoring and management in urban rivers. [ABSTRACT FROM AUTHOR]

As the demand for high-quality food rises, especially amidst the COVID-19 pandemic and the continuous development of society meaning that people demand to eat well, ensuring food security has become increasingly urgent. Agricultural technology is evolving, with aquaponic systems emerging as a promising solution to urban food needs. However, these systems present challenges, such as maintaining optimal water quality and minimizing environmental control errors. In this study, we propose a comprehensive approach combining a literature review and controlled experiments. Through the literature review, the recent findings on water management and sustainability in food production were analyzed, providing crucial insights for enhancing aquaponic system performance. Building on this, a series of experiments were conducted to develop and test a water quality management system using PID control. The integration of PID control showed good performance and reduced errors in SIMULINK, and we applied three controls to manage the stability and responsiveness of the aquaponic system. The optimal values obtained from the controller of the vegetable tank system were 4,706,691,503 and −174.418; for the fish tank, they were 36,167, 0.00126, and −174.418; and for the heater system, they were 4.761, 0.0488, and −31.88. This solution is expected to be responsive and provide stable control over various variables. [ABSTRACT FROM AUTHOR]

Seawater pollution is highly detrimental to human life and marine biota. Quality of seawater in a particular location also affects fish caught by fishermen. This is the reason why early detection of water conditions is crucial for the preservation of the marine ecosystem with a focus on the measurements of temperature, pH, and salinity. Separate researches have been conducted on the application of sensors and IoT to measure seawater quality as well as fish caller device to increase the possibility of catching fish. Therefore, this research has been used to integrate both devices in order to ensure fishermen contribute to the provision of information on seawater quality while catching fish by using fish caller device. This has been achieved through the implementation of NodeMCU ESP8266 to control frequency of fish caller device as well as to assess the parameters of seawater quality. Moreover, data obtained from the sensors have been first stored in a microSD card in order to overcome the lack of internet connectivity at the location and automatically sent to the server once the NodeMCU established an internet connection. An Android smartphone application has been used to select frequency of fish caller device according to the desired fish species as well as to view the seawater quality data stored on the server. This effort is directed at ensuring that an integrated system is developed to increase frequency of catching fish using fish caller device while contributing to the process of monitoring seawater quality, which is crucial for the preservation of the marine ecosystem. [ABSTRACT FROM AUTHOR]

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There is a need for an effective monitoring solution for water quality control in tailings dumps and adjacent water bodies in order to prevent environmental pollution. This article highlights the importance of water quality monitoring and surveillance to prevent pollution. It is proposed to develop a mobile robotic complex equipped with sensors for monitoring water bodies and tailings, which is also capable of measuring underwater topographic data. The objects of study were a tailings pond and water bodies. The authors analyzed existing technical monitoring solutions, designed and developed a robotic complex, echolocation device, tested them on specific sites (the tailings dump of the Zhayrem Mining and Processing Plant and the Ishim River), conducted laboratory analysis of water samples, classified the results. Additionally, they obtained 2D and 3D maps of the bottom, and entered all collected data into a developed database and software. The developed complex demonstrated high accuracy of movement (an error of about 0.2 m on the x axis and 0.1 m on the y axis) and the ability to register environmental parameters such as temperature, humidity, PH. Data analysis for 2021–2023 showed a significant excess of recycled water discharged into the evaporator pond, which emphasizes the importance of monitoring and management of water resources. The research applies ARIMA models, neural networks to predict water body parameters. The results obtained indicate the high efficiency of the developed robotic complex and methods for analyzing data on water resources. These methods can be used in industry, scientific research and environmental projects to regularly monitor water quality and take measures to protect it. [ABSTRACT FROM AUTHOR]

Stormwater ponds are primarily designed for flood control and water quality protection, but they often serve as habitats of differing quality for a variety of organisms. Floral and faunal distributions within stormwater ponds are dependent on the environmental and biological characteristics of each pond and surrounding ecosystem which are inherently linked to management. Increasing stormwater pond size and habitat complexity can promote more diverse floral and faunal communities. However, aquatic pollution and the presence of invasive species can reduce biodiversity. Robust submerged and emergent macrophyte assemblages are critical in maintaining rich and abundant populations of other taxa. Phytoplankton are key primary producers in stormwater ponds; however, some groups are toxic. Zooplankton distribution in stormwater ponds is primarily determined by aquatic connectivity, trophic status, salinity, and predator presence. Fish may be naturally or unnaturally introduced to stormwater ponds, and impaired water quality can threaten their health. Various bird species use stormwater ponds as temporary or permanent habitat, and emergent macrophytes can provide roosting habitat. In some cases, birds may become nuisances and impair stormwater pond water quality. Semi-aquatic mammals may inhabit stormwater ponds with the potential to physically alter these habitats through shelter construction. For some taxa, stormwater ponds represent ecological traps which reduce organismal fitness. This review critically examines the many factors that determine floral and faunal communities within stormwater ponds, with insight into best management practices to promote healthy pond ecosystems, advance stakeholder engagement and awareness, and avoid the creation of ecological traps in an increasingly urbanizing world. [ABSTRACT FROM AUTHOR]

With the rapid development of port construction and the shipping industry, port water quality issues are of great concern. This is always a challenging task due to the frequent human activities and dynamic processes involved. A parallel intelligent water quality monitoring system is therefore proposed to ensure the effective monitoring and intelligent control of water pollutants. The real monitoring system and the artificial monitoring system of port water quality are established by applying artificial systems, computational experiments and parallel execution (ACP method). Both systems interact with each other and execute in parallel. The artificial monitoring system simulates complex scenarios, while the real monitoring system feeds the artificial monitoring system with actual monitoring data. By means of data-driven and model-driven approaches, the two systems can compute, observe and evaluate to control, manage and train models. Through the continuous optimization between the two systems, the efficiency and accuracy of the water quality monitoring system could be improved. Technical support can be further provided for the planning of water quality monitoring sites, implementation of monitoring tasks, allocation of emergency resources, etc. As in-situ monitoring data are obtained, computational experiments and parallel executions could be conducted to achieve the ultimate goal of port water quality management. [ABSTRACT FROM AUTHOR]

Traditional aquaculture systems appear challenged by the high levels of total ammoniacal nitrogen (TAN) produced, which can harm aquatic life. As demand for global fish production continues to increase, farmers should adopt recirculating aquaculture systems (RAS) equipped with biofilters to improve the water quality of the culture. The biofilter plays a crucial role in ammonia removal. Therefore, a biofilter such as a moving bed biofilm reactor (MBBR) biofilter is usually used in the RAS to reduce ammonia. However, the disadvantage of biofilter operation is that it requires an automatic system with a water quality monitoring and control system to ensure optimal performance. Therefore, this study focuses on developing an Internet of Things (IoT) system to monitor and control water quality to achieve optimal biofilm performance in laboratory-scale MBBR. From 35 days into the experiment, water quality was maintained by an aerator's on/off control to provide oxygen levels suitable for the aquatic environment while monitoring the pH, temperature, and total dissolved solids (TDS). When the amount of dissolved oxygen (DO) in the MBBR was optimal, the highest TAN removal efficiency was 50%, with the biofilm thickness reaching 119.88 μm. The forthcoming applications of the IoT water quality monitoring and control system in MBBR enable farmers to set up a system in RAS that can perform real-time measurements, alerts, and adjustments of critical water quality parameters such as TAN levels. [ABSTRACT FROM AUTHOR]

The water quality is the most important parameter for aquatic species health and growth. The condition is very critical and is essential to monitor continuously. Poor water quality will affect health, growth and ability of the animal to survive. These also affected their harvesting yields based on the amount and size of the animal. The main water parameters such dissolved oxygen (DO), pH, temperature, salinity and turbidity are monitored and control for good water quality. The data were acquired by the developed instrument and send wirelessly through GPRS/GSM module to cloud-based database. The data were retrieved and the water quality is predicted using fuzzy logic and multi-layer perceptron. MATLAB software was used for the model which is developed based on Mamdani fuzzy interface system. The membership functions of fuzzy were generated, as well as the simulation and analysis of the water quality system. Results show that the performance of fuzzy method can improve system performance in monitoring the water quality. This system also provides alert signals to farmers based on specific limit value for the water quality parameters. This will help the breeders to make certain adjustment to ensure suitable water quality for the aquaculture system. [ABSTRACT FROM AUTHOR]

Chromophoric dissolved organic matter (CDOM) is a significant contributor to the biogeochemical cycle and energy dynamics within aquatic ecosystems. Hence, the implementation of a systematic and comprehensive monitoring and governance framework for the CDOM in inland waters holds significant importance. This study conducted the retrieval of CDOM in Lake Khanka. Specifically, we use the GBDT (R2 = 0.84) algorithm which performed best in retrieving CDOM levels and an empirical relationship based on the situ data between CDOM and dissolved organic carbon (DOC) to indicate the distribution of DOC indirectly. The performance of the CDOM-DOC retrieval scheme was reasonably good, achieving an R2 value of 0.69. The empirical algorithms were utilized for the analysis of Sentinel-3 datasets from the period 2016 to 2020 in Lake Khanka. The potential factors that contributed to the sources of DOM were also analyzed with the humification index (HIX). The significant relationship between CDOM and DOC (HIX and chemical oxygen demand (COD)) indicated the potential remote sensing application of water quality monitoring for water management. An analysis of our findings suggests that the water quality of the Great Khanka is superior to that of the Small Khanka. Moreover, the distribution of diverse organic matter exhibits a pattern where concentrations are generally higher along the shoreline compared to the center of the lake. Efficient measures should be promptly implemented to safeguard the water resources in international boundary lakes such as Lake Khanka and comprehensive monitoring systems including DOM distribution, DOM sources, and water quality management would be essential for water resource protection and government management. [ABSTRACT FROM AUTHOR]

A wholesale/retail model delivers drinking water to over 5 million residents in metropolitan Melbourne (Australia). Water Safety Plans were implemented in 1999 ahead of being regulatory mandated in 2003. With over 20 years of WSP application, this collaborative paper by the wholesaler and retailer utilities presents practical examples of drinking water quality risk management in challenging operational environments, highlighting lessons learnt, improvements made and outcomes achieved. Melbourne's supply comprises multiple sources, necessitating different tailored treatment configurations. Source waters range from open catchment with multiple treatment barriers, to protected catchment source waters requiring solely disinfection treatment (unfiltered) with gravity driven supply. Potable supply is a combination of unfiltered, filtered, desalinated and blended supplies. This makes for diversity in case studies brought to this paper, and a range of lessons likely to be of interest to the global WSP community. The Melbourne utility experience highlights the importance of developing and continually improving control measures for ongoing (adaptive) risk reduction. A robust emergency management plan is also fundamental to ensure preparedness for complex and unpredictable events. Furthermore, leveraging learnings from audits and incidents has been valuable for process improvement. WSP implementation has also facilitated timely communication with consumers and other stakeholders. [ABSTRACT FROM AUTHOR]

The composition of emulsion is complex. In the process of coal mining, the emulsion required by the hydraulic equipment can enter the mine water through a variety of channels, thereby affecting the water quality of the mine. In this study, the mine water of a super large coal mine, Daliuta Coal Mine, was collected, and static and dynamic simulation tests were designed to analyze the changes in various water quality indicators of mine water containing different concentrations of emulsions over time. Furthermore, the health risk assessment model was applied to evaluate the health risks of hexavalent chromium (Cr6+) and chloride in mine water containing emulsions on different populations. The results indicate that the alkaline substances such as sodium castor oil in the emulsion can increase the pH value of mine water, but it is more obvious when the emulsion concentration is high. The mine water itself contains chloride and sulfate exceeding 300 and 400 mg/L, respectively, and the addition of emulsion under static conditions has little effect on them, while the amount of emulsion added under dynamic conditions reaches 20 mL/L, which has a significant impact. The emulsion contains a certain amount of Cr6+ and can affect the total dissolved solid content, total hardness, and other indicators of mine water through mechanisms such as adsorption, solubilization, and chemical precipitation. The calculation results of the health risk assessment model indicate that the excessive chloride in the mine water in the area poses a potential non-carcinogenic risk to all populations exposed to their environment. The health risk index of Cr6+ increases with the increase in emulsion content in mine water, and the potential non-carcinogenic risk is higher for children. Overall, emulsions can significantly deteriorate the quality of mine water. This research can provide a scientific theoretical basis for subsequent study of mine water pollutant treatment, water quality monitoring and management, and health risk assessment, thereby contributing to reducing the health risks it brings and protecting the safety of local groundwater quality. [ABSTRACT FROM AUTHOR]

Lake water clarity, phytoplankton biomass, and hypolimnetic oxygen concentration are metrics of water quality that are highly degraded in eutrophic systems. Eutrophication is linked to legacy nutrients stored in catchment soils and in lake sediments. Long lags in water quality improvement under scenarios of nutrient load reduction to lakes indicate an apparent ecosystem memory tied to the interactions between water biogeochemistry and lake sediment nutrients. To investigate how nutrient legacies and ecosystem memory control lake water quality dynamics, we coupled nutrient cycling and lake metabolism in a model to recreate long‐term water quality of a eutrophic lake (Lake Mendota, Wisconsin, USA). We modeled long‐term recovery of water quality under scenarios of nutrient load reduction and found that the rates and patterns of water quality improvement depended on changes in phosphorus (P) and organic carbon storage in the water column and sediments. Through scenarios of water quality improvement, we showed that water quality variables have distinct phases of change determined by the turnover rates of storage pools—an initial and rapid water quality improvement due to water column flushing, followed by a much longer and slower improvement as sediment P pools were slowly reduced. Water clarity, phytoplankton biomass, and hypolimnetic dissolved oxygen differed in their time responses. Water clarity and algal biomass improved within years of nutrient reductions, but hypolimnetic oxygen took decades to improve. Even with reduced catchment loading, recovery of Lake Mendota to a mesotrophic state may require decades due to nutrient legacies and long ecosystem memory. Plain Language Summary: Lake water quality, as measured by algae concentration near the lake surface, the clarity of the water, and the availability of dissolved oxygen, is greatly reduced in lakes with nutrient pollution from phosphorus. In Lake Mendota, Wisconsin, phosphorus applied to the surrounding landscape for more than a century has accumulated in catchment soils and in the lake water column and sediments (i.e., "legacy phosphorus"), leading to poor water quality. To investigate how water quality in Lake Mendota might respond to nutrient reduction, we used computer models to simulate the elimination of phosphorus inputs from the catchment and track water quality change. Phosphorus in the lake water column initially decreased quickly, due to water column flushing, but then decreased very slowly due to release of legacy phosphorus from lake sediments. Water quality recovery lagged that of phosphorus, indicating an inherent "ecosystem memory" for past phosphorus levels. Ecosystem memory was due to biological activity that remained elevated, even when phosphorus was declining in the water column. When nutrient inputs to the lake were eliminated in the model, recovery of algae concentrations and water clarity to pristine conditions required decades, and a return to a fully oxygenated condition required a century. Key Points: Legacy phosphorus in lake sediments controls long term lake water quality response to nutrient remediationCoupled cycles of nutrients, physics, and metabolism explain ecosystem memory of lake phosphorus, water clarity, and oxygen habitatImprovement in lake water quality to pristine levels will require decades of commitment to nutrient load reductions [ABSTRACT FROM AUTHOR]

Dissolved oxygen (DO) is a key indicator of water quality and the health of an aquatic ecosystem. Aspiring to reach a more accurate forecasting approach for DO levels of natural streams, the present work proposes new graph-based and transformer-based deep learning models. The models were trained and validated using a network of real-time hydrometric and water quality monitoring stations for the Credit River Watershed, Ontario, Canada, and the results were compared with both benchmarking and state-of-the-art approaches. The proposed new Graph Neural Network Sample and Aggregate (GNN-SAGE) model was the best-performing approach, reaching coefficient of determination (R2) and root mean squared error (RMSE) values of 97% and 0.34 mg/L, respectively, when compared with benchmarking models. The findings from the Shapley additive explanations (SHAP) indicated that the GNN-SAGE benefited from spatiotemporal information from the surrounding stations, improving the model's results. Furthermore, temperature has been found to be a major input attribute for determining future DO levels. The results established that the proposed GNN-SAGE model outperforms the accuracy of existing models for DO forecasting, with great potential for real-time water quality management in urban watersheds. [ABSTRACT FROM AUTHOR]

Safe drinking water sources are crucial for human health. Consequently, water quality management, including continuous monitoring of water quality and algae at sources, is critical to ensure the availability of safe water for local residents. This study aimed to construct statistical prediction models considering probability distributions relevant to cyanophyte cell counts and compare their prediction performance. In this study, water quality parameters at Juam Lake and Tamjin Lake, representative water sources in the Yeongsan and Seomjin rivers, South Korea, were investigated. We used a water quality monitoring network, algae alert system, and hydraulic and hydrological data measured every 7 days from January 2017 to December 2022 from the Water Environment Information System of the National Institute of Environmental Research. Using data for 2017–2021 as a training set and data for 2022 as a test set, the performances of seven models were compared for predicting cyanophyte cell counts. Environmental factors associated with algae in water sources were observed based on the monitoring data, and a prediction model appropriate for the cyanophyte distribution was generated, which also included the risk of toxicity. The extreme gradient boosting with the random forest model had the best predictive performance for cyanophyte cell counts. The study results are expected to facilitate water quality management in various water systems, including water sources. [ABSTRACT FROM AUTHOR]

Effective water quality prediction techniques are essential for the sustainable development of water resources and implementation of emergency response mechanisms. However, the water environment conditions are complex, and the presence of a large amount of noise in the water quality data makes it difficult to reveal the long-term trends or cycles of the data, affecting the acquisition of serial correlation in the data. In addition, the loss function based on the vertical Euclidean distance will produce a prediction lag problem, and it is difficult to make an accurate multi-step prediction of water quality series. This paper presents a multi-step water quality prediction model for watersheds that combines Savitzky-Golay (SG) filter with Transformer optimized networks. Among them, the SG filter highlights data trend change and improves sequence correlation by smoothing the potential noise of original data. The transformer network adopts a sequence-to-sequence framework, which contains a position encoding module and a self-attentive mechanism to perform multi-step prediction while effectively obtaining the sequence correlation. Moreover, the DIstortion Loss including shApe and TimE (DILATE) loss function is introduced into the model to solve the problem of prediction lag from two aspects of shape error and time error to improve the model's generalization ability. An example validates the model with the benchmark model at four monitoring stations in the Lanzhou section of the Yellow River basin in China. The results show that the predictions of the proposed model have the correct shape, temporal positioning, and the best accuracy in a multi-step prediction task for four sites. It can provide a decision-making basis for comprehensive water quality control and pollutant control in the basin. [ABSTRACT FROM AUTHOR]