Water Quality Management Research Articles

Estimating the vertical profile of water quality variables in reservoirs: Application of remotely sensed data and machine learning techniques.

Water quality assessment and management of reservoirs depend on accurate, large-scale, and continuous monitoring of the vertical profile of Water Quality Variables (WQVs). Remote sensing data have been widely used to retrieve high spatiotemporal water quality data; however, their application has practically been limited to evaluating surface WQVs. In this paper, a novel and efficient approach is introduced for assessing the profile of WQVs in reservoirs that depend on stratification, by taking into account the shape of profile as prior knowledge. First, an appropriate function is fitted to the WQVs vertical profile, and second, the parameters of that function as representative of the WQVs vertical profile are estimated using machine learning techniques. The model's inputs are day, maximum depth of point, and remote sensing data. Finally, PAWN sensitivity analysis is applied to show the extent to which each input influences different parts of the vertical profile. This method is applied in the Wadi Dayqah Reservoir, the largest dam in Oman, to evaluate water temperature, dissolved oxygen, pH, and chlorophyll-a profile. The results show that the predicted profiles are properly representative of in situ measurements, with a mean absolute error of 0.28 °C, 0.25 mg/L, 0.052, and 0.33 μg/L on test data sets of water temperature, dissolved oxygen, pH, and chlorophyll-a, respectively. Finally, PAWN sensitivity analysis illustrates that satellite data not only influence the parameters representing surface WQVs but also contribute to the estimation of other curve parameters.

Heavy metal bioaccumulation based on seasonal monsoon impact in benthic macroinvertebrates of Korean streams.

This study investigates the influence of seasonal monsoon flooding on heavy metal contamination and bioaccumulation in benthic macroinvertebrate communities within a stream ecosystem. We analyzed sediment and benthic macroinvertebrate samples for eight heavy metals [zinc (Zn), chromium (Cr), nickel (Ni), lead (Pb), copper (Cu), arsenic (As), cadmium (Cd), and mercury (Hg)] ) before (BF) and after (AF) a major flooding event. Significant spatial and temporal variations in heavy metal concentrations were observed, with generally higher levels detected after the flood. Chironomidae consistently exhibited high bioaccumulation factors (BAFs) for several metals, highlighting their role as bioindicators. Notably, elevated Cu accumulation was observed in multiple species, including Radix auricularia (R. auricularia), Cipangopaludina chinensis malleata (C. c. malleata), and Palaemon spp. Non-metric multidimensional scaling (NMDS) analysis revealed shifting correlations between environmental variables and bioaccumulation patterns before and after flooding. Pre-flood, total nitrogen (TN) showed a strong positive correlation with Hg bioaccumulation, while post-flood, large sand content emerged as a more influential factor for Zn, Cr, Ni, and Pb bioaccumulation. Our findings emphasize the complex interplay between seasonal flooding, environmental factors, and heavy metal dynamics, with potential implications for ecological risk assessment and water quality management.

Brackish water parameters monitoring dashboard using Internet of things and industry 4.0

INTRODUCTION: Brackish water aquaculture plays a crucial role in meeting the growing global demand for seafood. It offers an opportunity to diversify aquaculture production and reduce pressure on overexploited marine resources. OBJECTIVES: By harnessing the unique properties of brackish ecosystems, this practice contributes to food security, economic growth, and sustainable resource management, while also promoting the conservation of valuable marine habitats. The development of a cutting-edge Indigenous Water Quality Monitoring Prototype named "Aqua BuoySis" for precision brackish water aquaculture utilizing machine intelligence. METHODS: The prototype integrates sensors for Dissolved Oxygen (DO), pH, Temperature, Turbidity, and Total Dissolved Solids (TDS). These sensors are calibrated using a dynamic temperature-based machine-learning approach to ensure accuracy in real-time environments. Sensor calibration constants are uploaded to a server for comprehensive data calibration. RESULTS: The system collects data at 20-second intervals, associating it with specific pond IDs. Data refinement is achieved through Long Short-Term Memory (LSTM) processing. An Android and Web application, available in native languages such as Tamil and Telugu, has been developed to provide live updates to aqua farmers, facilitating informed decision-making. CONCLUSION: This technology represents a significant step towards enhancing precision in brackish water aquaculture through the fusion of machine intelligence and water quality management.

Information and Analytical System Monitoring and Assessment of the Water Bodies State in the Mineral Resources Complex

Currently, one of the most pressing global issues is ensuring that human activities have access to water resources that meet essential quality standards. This challenge is addressed by implementing a series of organizational and technical measures aimed at preserving the ecology of water basins and reducing the level of harmful industrial emissions and other pollutants in the aquatic environment. To guarantee the necessary quality of water resources, monitoring is conducted based on selected parameters using various methods and means of technical quality control. From these results, suitable measures are formulated and applied to maintain water quality. Various scientific works extensively discuss different approaches to water quality management and compliance with specified requirements. Modern strategies for developing water monitoring systems leverage the capabilities of information systems that collect, process, store, and transmit information, enabling the resolution of issues in geographically distributed water bodies in real time. This paper proposes an approach that employs mathematical methods to identify the most significant factors determining water quality and to assess their interrelations using methods of a priori ranking, multivariate correlation regression analysis, and integral quantitative assessment. A hardware and software solution for the development of a unified integrated information and analytical system is proposed. This system enables continuous monitoring and assessment of water bodies based on a set of key parameters, addressing a range of critical tasks. This paper provides a detailed description of the software product, presents a demonstration using real-world data, and discusses the anticipated benefits of implementing such an information and analytical system.

Identifying potential introduced and natural sources of pollution in Delaware watersheds.

Managing water quality with microbial impairment caused by Enterococcus poses unique challenges regarding the determination of fecal host origin. Most water monitoring is performed based on Enterococcus counts that neither detect the location of the introduction of pollution nor identify the type of contaminating Enterococcus. The use of sequenced-based microbial source tracking could allow for identification of fecal origin and potential remediation of pollution. The state of Delaware has numerous waterways with high microbial impairment from unknown sources, so we used sequence-based microbial source tracking to investigate potential microbial pollution in three watersheds with significant variation in land use and population density. In this study, we use a 16S rRNA sequence reference library of microbial communities from relevant fecal sources (wild animal, domestic animal, sediment, and septic/wastewater) to determine the most likely sources of microbial impairment in three Delaware watersheds. This study assigned sources of microbial contamination to mostly human-related sources (septic and wastewater) or unknown sources indicating that waste infrastructure may have a larger influence on microbial community structure in Delaware watersheds than previously considered. Our results suggest that long-term source tracking is valuable for ruling out native or domesticated animals as contributors to water pollution.IMPORTANCETraditional microbial pollution monitoring utilizes specific fecal indicator bacteria that need to grow in the laboratory for detection. Here, we show the use of sequence information from whole microbial communities and an expanded reference library in microbial source tracking. Expanding the host detection range by including the whole microbial community may allow for a wider range of potential fecal origin identification even when specific fecal indicators are absent or in low concentration. We show that many Delaware waterways bear the signature of human influence compared to natural sources. In addition, the robust reference library built in this study can be used to conduct source tracking studies in the mid-Atlantic USA.

Review of Recent Advances in Remote Sensing and Machine Learning Methods for Lake Water Quality Management

This review examines the integration of remote sensing technologies and machine learning models for efficient monitoring and management of lake water quality. It critically evaluates the performance of various satellite platforms, including Landsat, Sentinel-2, MODIS, RapidEye, and Hyperion, in assessing key water quality parameters including chlorophyll-a (Chl-a), turbidity, and colored dissolved organic matter (CDOM). This review highlights the specific advantages of each satellite platform, considering factors like spatial and temporal resolution, spectral coverage, and the suitability of these platforms for different lake sizes and characteristics. In addition to remote sensing platforms, this paper explores the application of a wide range of machine learning models, from traditional linear and tree-based methods to more advanced deep learning techniques like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). These models are analyzed for their ability to handle the complexities inherent in remote sensing data, including high dimensionality, non-linear relationships, and the integration of multispectral and hyperspectral data. This review also discusses the effectiveness of these models in predicting various water quality parameters, offering insights into the most appropriate model–satellite combinations for different monitoring scenarios. Moreover, this paper identifies and discusses the key challenges associated with data quality, model interpretability, and integrating remote sensing imagery with machine learning models. It emphasizes the need for advancements in data fusion techniques, improved model generalizability, and the developing robust frameworks for integrating multi-source data. This review concludes by offering targeted recommendations for future research, highlighting the potential of interdisciplinary collaborations to enhance the application of these technologies in sustainable lake water quality management.

Comprehensive evaluation of water quality grades based on improved CRITIC and multidimensional connection cloud combination

AbstractThis study proposes a comprehensive water quality assessment method for nine major plateau lakes in Yunnan Province, based on an improved CRITIC method combined with a multidimensional connectivity cloud model. Key water quality monitoring indicators (NH3‐N, COD, TP, TN, permanganate index, DO, pH) were selected, and the weights were determined using the improved CRITIC method, highlighting the impact of NH3‐N, COD, and TP. These weights were then integrated with a multidimensional connectivity cloud model to classify lake water quality levels. The results indicated the following water quality grades for the lakes: Dianchi (III), Fuxian Lake (I), Erhai (II), Xingyun Lake (I), Qilu Lake (II), Yilong Lake (II), Lugu Lake (I), Yangzonghai (II), and Chenghai (II). Compared to five conventional methods, the proposed approach better addresses the issues of fuzziness, randomness, and discreteness in water quality indicators, avoiding the boundary selection problems inherent in traditional methods. By combining the improved CRITIC method with a multidimensional connectivity cloud model, the study achieves more precise and reliable evaluations through objective weighting and comprehensive consideration of multiple indicators. This method offers a more accurate reflection of lake water quality conditions and provides a scientific basis for water quality management and decision‐making, demonstrating significant potential for application in the field of environmental science.

Advancing aquaculture: fuzzy logic-based water quality monitoring and maintenance system for precision aquaculture

AbstractAquaculture 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.

A Comprehensive Review of Water Quality Analysis

The effluent discharge of domestic wastewater and industrial effluents into watercourses has necessitated the development of monitoring systems with the purpose of characterizing levels of pollution. Substantial levels of pollution in emerging nations have been exacerbated by rapidly growing populations and inefficient enforcement of sustainable management initiatives. The Water Quality Index (WQI) can help fill the gap between water quality reporting and monitoring by offering a simple and effective way to assimilate and communicate results from a large amount of data. WQIs are the kind of communication tools that can facilitate knowledge sharing between scholars and the general public. The degradation of natural water resources, such as lakes, streams, and estuaries, is the most significant problem facing humanity. Water that is not clean has far-reaching effects on all aspects of life. Water resource management is therefore essential if it is to maximize water quality. If data are analyzed and water quality can be predicted in advance, water pollution may be efficiently addressed. Although this subject has been the subject of numerous earlier studies, additional research is still required to fully understand the effectiveness, reliability, accuracy, and application of the present approaches to water quality management. In this research work, a comprehensive review has been conducted on water quality assessment using artificial techniques. It reviews 75 research works on water quality analysis. The data collected in each research work have been analyzed. Moreover, the type of water resource surface water, groundwater, drinking water as well has been analyzed. The parameters considered in each work are “Dissolved Oxygen (DO), Potential of Hydrogen (pH), Biochemical Oxygen Demand (BOD), Total Dissolved Solids (TDS), and Chemical Oxygen Demand (COD), chloride, hardness, alkalinity, nitrate–nitrite”. In addition, the Artificial Intelligence technique utilized for water quality analysis is also assessed. Finally, the research gaps identified in water quality detection are exhibited.

Discriminative characteristics of hydrochemical components and sedimentary organic matter in Korean coastal aquaculture systems during summer

Understanding the spatial distribution and sources of sedimentary organic matter (OM) in coastal environments is crucial for effective water quality management and the preservation of ecosystem health. Although extensive research has been conducted on OM dynamics, there remains a gap in understanding the ongoing biogeochemical processes in Korean coastal aquaculture zones, particularly during the summer season. To address this gap, we investigated the spatial variation of water chemical properties and isotopic composition of sedimentary OM to trace the composition, source, and reactivity of mixed OM in aquaculture systems along the Korean coast during the summer season. The isotopic approach was applied to surface sediments from five sections: western (W)-1, W-2, southern (S)-1, S-2, and eastern (E)-1. With respect to increased nutrients (mainly nitrate; 1.2 ± 0.6 mg/L) by dam-water discharge near W sections, our isotopic signatures revealed that a substantial fraction of sedimentary OM might dominantly originated from autochthonous OM source (algae; 36.5%) related to the increase of terrestrial nutrients. Simultaneously, the deposition of allochthonous OM (aquacultural feces; 44%) was predominant in the S-2 sections. The 34S-depleted patterns (approximately -7.2‰) in the S-2 section was indicative of active sulfate reduction occurring at the sedimentary boundary. Therefore, together with the precise determination of ongoing OM, our isotopic results provide valuable insights for effectively managing water-sedimentary qualities under the increase of anthropogenic contamination.

Coupling of anthropogenic activities and natural factors on dissolved organic matter: Insights from coastal urban rivers in southern China

Accelerated urbanization has reshaped the biogeochemistry of dissolved organic matter (DOM) in urban rivers, yet the interplay between anthropogenic and natural influences on riverine DOM remains unclear. This study utilized absorbance and fluorescence spectroscopy coupled with parallel factor analysis (PARAFAC), to explore the spatiotemporal distribution of DOM in twenty coastal rivers located in a highly urbanized area. Findings highlight increased DOM abundance, especially the protein-like component (C2: 1.48 RU in urban areas and 0.24 RU in suburban areas), in the rivers of urban areas, contrasted by seasonal shifts of chromophoric DOM (CDOM) and humic-like DOM in the rivers of suburban areas (a350: 3.18 m−1 in the wet season and 1.23 m−1 in the dry season). Tidal mixing’s role in DOM distribution is evidenced by diurnal salinity changes and its inverse relationship with DOM abundance. Notably, the Shenzhen River’s unique DOM composition (a higher abundance of humic-like DOM and a lower abundance of protein-like DOM) suggests agricultural impacts. Strong correlations (R2 values reaching up to 0.87) between spectral DOM indicators and chemical oxygen demand (COD) underscore the great potential of spectral methods for water quality monitoring. Overall, this study illuminates the coupled effects of urbanization and natural factors on riverine DOM, offering insights for the biogeochemical cycling of DOM and water quality management in urban rivers, with implications for similar urbanized regions globally.

Standardization of FTIR-Based Methodologies for Microplastics Detection in Drinking Water: A Meta-Analysis Indeed and Practical Approach

The detection of microplastics (MPs) in drinking water presents significant environmental and public health challenges. This study comprises two stages: a meta-analysis aimed at standardizing Fourier Transform Infrared Spectroscopy (FTIR) methods for MP detection, followed by the practical implementation of these findings in the laboratory. The review of studies conducted from 2019 to 2023 identifies 0.45 μm cellulose nitrate filters and Nile red staining as the most effective techniques for fluorescent detection. Experimental results demonstrate the superior retention capabilities of cellulose nitrate filters and the uniformity of Nile red staining. This dual approach not only optimizes water treatment processes but also enhances the accuracy of MP detection. The findings contribute to improved water quality management and public health protection by establishing robust protocols for MP analysis.

Introducing a prediction method for the photodegradation of p-cresol, a phenolic contaminant of emerging concern, in wastewater effluent

Despite extensive efforts to understand the photodegradation of phenolic contaminants of emerging concern (PhCECs) in aquatic systems, prediction methods, especially in waters containing effluent organic matter (EfOM), remain underdeveloped. This study introduces a prediction method for p-cresol, a representative PhCECs, based on correlations between EfOM optical parameters and p-cresol kinetic parameters. We examined p-cresol photodegradation in various EfOM samples, characterized by their optical properties, and used the reaction rate coefficient between EfOM and p-cresol, α3EfOM⁎, to quantify and predict p-cresol degradation in different wastewater effluent samples. Results showed significant correlations between p-cresol's photodegradation rate constant (0.144 to 0.441 h−1) and EfOM characteristics, with α3EfOM⁎ values ranging from 4 × 1011 to 10 × 1011 M−1 s−1. The method was validated with p-cresol at concentrations ranging from 25 to 100 μM and multiple EfOM samples. The method's applicability was further evaluated using propranolol, a pharmaceutical contaminant of emerging concern, demonstrating its versatility for predicting the degradation behavior of other contaminants in different wastewater samples. The method accurately predicted p-cresol and propranolol degradation across diverse wastewater samples, suggesting its potential for expansion to other classes of contaminants, aiding in water quality management, improving wastewater treatment processes, and enhancing environmental risk assessments.

Stormwater treatment in constrained urban spaces through a hybrid Sequential Sedimentation Biofiltration System

Urban areas face increasing pressures on water resources, necessitating innovative approaches to climate adaptation and water quality management. Nature-based Solutions (NbS) offer a sustainable pathway, yet their integration with existing infrastructure in urban settings remains occasional. This study presents a novel hybrid system—Sequential Sedimentation Biofiltration System (SSBS)—designed for stormwater treatment within confined urban spaces. The system was adjusted to the existing stormwater infrastructure by integrating a sedimentation tank (SED), three Permeable Reactive Barriers (PRBs), and a biofiltration zone (BIO). The SSBS was evaluated for its efficiency in removing nutrients and sediments, focusing on the performance of PRBs. Our findings showed limited sediment removal in SED and PRBs due to spatial constraints and a high Hydraulic Loading Rate (HLR = 1.31 m/d), achieving an average of 13.6% Total Suspended Solids (TSS) removal. However, PRBs demonstrated effective removal of ammonium (43.4%) and phosphate (59.3%), potentially due to sorption and biofilm activity, with dominant microbial communities including Proteobacteria, Bacteroidetes, and nutrient-transforming taxa such as Nitrospirae. Interestingly, PRBs increased nitrite levels (57.1%) but did not significantly impact nitrate, chloride, or TSS. The BIO zone further enhanced nutrient retention (56% PO4–P) and served as a sink for TSS (52%). This study underscores the potential for integrating traditional urban infrastructure with NbS in a sequential stormwater treatment system, demonstrating its effectiveness in space-constrained urban environments.

Scientometric Analysis of Water Ecology Research: Insights from Chinese Ocean Universities

In response to increasing concerns about water ecology sustainability, this paper employs scientometric analysis to examine research trends at seven leading Chinese ocean universities from 2010 to 2021. Using CiteSpace software to analyze data from the Web of Science, we explored a comprehensive dataset of dissertations and published papers across disciplines such as water ecology, marine biology, fisheries, and environmental sciences. Our analysis shows a significant rise in research output after 2016, highlighting the pivotal role these universities play in advancing water ecology research. Through keyword co-occurrence and clustering analyses, we identified shifting research focuses, ranging from biodiversity and ecosystem dynamics to emerging topics like water quality management and ecological restoration. This study not only consolidates existing research achievements but also provides valuable insights for shaping future research directions in water ecology.

Development of a deep learning–based feature stream network for forecasting riverine harmful algal blooms from a network perspective

Global increases in the occurrence of harmful algal blooms (HABs) are of major concern in water quality and resource management. A predictive model capable of quantifying the spatiotemporal associations between HABs and their influencing factors is required for effective preventive management. In this study, a feature stream network (FSN) model is proposed to provide daily forecasts of cyanobacteria abundance at multiple monitoring sites simultaneously in a river network. The spatial connectivity between monitoring sites was expressed as a directed acyclic graph comprising edges and nodes representing flows and monitoring sites, respectively. Furthermore, a segment-wise node connection structure was developed to extract the latent features of a river segment comprising individual nodes and sequentially transfer them to the downstream segment(s). In addition, a feature engineering–attention hybrid mechanism was employed to address temporal mismatches among different monitoring schemes while adding explainability to the model. Consequently, the FSN showed improved predictive performance, temporal resolution, and explainability for multi-site forecasts of HAB in a single model framework. The developed model was applied to a bloom-prone middle course of the Nakdong River, South Korea. Various hydrological, environmental, and biological factors were utilized for forecasting the cyanobacteria abundance. The FSN exhibited a high degree of accuracy across the sites for the test data with a coefficient of determination in the range of 0.64–0.71 and root mean square error in the range of 2.06–2.26 cells/mL on natural log scales. Although the relative importance of input features varied across the sites, the features extracted from nearby nodes consistently exhibited high importance in forecasting the cyanobacteria abundance. These explanations indicate that the proposed model can successfully characterize the spatial hierarchy of a river network. A scenario analysis suggested that reduced total nitrogen loads in the effluents from the wastewater treatment plant and the combined operations of upstream and downstream weirs were effective in managing HABs.

The impact of temperature on the growth of Pseudomonas aeruginosa in mineral waters originated from different wells: A predictive approach

This study aimed to evaluate the behavior of Pseudomonas aeruginosa (PSA) in natural mineral water sourced from three different extraction wells and stored at various temperatures (10, 12, 20, 23, and 30 °C) to calculate the kinetic growth parameters of this microorganism through predictive modeling. The physicochemical characterization of waters was also evaluated at the time of collection, and included the analysis of 40 different minerals, and quality parameters such as pH, conductivity, oxidation-reduction potential (ORP), total dissolved solids (TDS), salinity (PSU), and temperature (T). PSA survived in raw mineral water incubated at 12, 20, 23, and 30 °C; however, no growth was observed at 10 °C. Growth curves started with an initial population of ~ 2.5–3 log CFU/mL, and final PSA populations ranged from 3.5 to 4.9 log CFU/mL. The maximum specific growth rate (μmax) at 30 °C varied among the wells, with Well P-07 showing the highest growth rate (0.2 h−1), followed by Well P-08 (0.195 h−1) and well P-01 (0.133 h−1). At 12 °C, well P-01 exhibited the highest growth rate (μmax = 0.22 h−1), indicating a influence of mineral composition in the growth of PSA. The lag time (λ) also varied, with minimum values of 2.4 ± 0.1 h at 30 °C and maximum values of 41.6 ± 0.2 h at 12 °C. From these primary estimated parameters, it was possible to obtain five robust secondary models to describe the influence of temperature on the maximum growth rates and lag phase of PSA in the well. The estimated PSA growth parameters at 20 and 23 °C were subjected to a hierarchical cluster analysis and correlation plots to verify the influence of the physicochemical composition of the waters on the PSA behavior at each well's specific annual average temperature. This analysis confirmed a positive relationship (p < 0.05) between the presence of minerals (Ca, Fe, Sr, Mn, Na) and ions (SO4−3, Cl−) and the PSA lag phase time. These results underscore the need for tailored water quality management strategies that consider chemical composition and temperature to address specific microbial contamination risks.

Water quality and pollution source apportionment responses to rainfall in steppe lake estuaries: A case study of Hulun Lake in northern China

Hulun Lake, the largest inland steppe lake in China, is encountering severe water quality degradation. Estuaries play important roles in material and energetic exchange between rivers and lakes. The water quality at the estuaries of Hulun Lake directly reflects the impact of both human activities and natural factors on the lake’s overall water quality, especially during rainfall events. From July 28, 2021, to August 4, 2021, water samples from 62 sites were collected in the three estuaries of Hulun Lake before and after a moderate rainfall event. 13 water parameters, including dissolved oxygen (DO), Turbidity (Tur), Total Nitrogen (TN), Total Phosphorus (TP), Total Organic Nitrogen (TON), and Total Organic Phosphorus (TOP) were measured. The spatio-temporal distribution of water quality in the estuaries was assessed based on water quality index (WQI). Besides, an improved approach integrating stepwise linear regression (SLR) and principal component analysis (PCA) was utilized to construct a WQImin model for an effective assessment of water quality in these estuaries. Furthermore, the absolute principal component scores-multiple linear regression (APCS-MLR) model was employed to identify and quantify the environmental drivers underlying the water quality in the estuaries. The results of WQI indicated that the water quality of the sites in the estuaries of Hulun Lake was “medium” or “poor”, both before and after the rainfall, with a general deterioration in water quality in response to the rainfall. The simplified WQImin model consisted of 5 crucial parameters (i.e., TN, TP, ammonium (NH4+-N), Tur, and permanganate index (CODMn)), and it performed well without parameter weights. Spatial differences in some water parameters among the estuaries were detected, which were attributed to the natural factors and human activities upstream. The principal environmental factors affecting the water quality in the estuaries consisted of hydrodynamic processes, internal phosphorus release, external phosphorus input, external nitrogen input, nitrification in the estuaries, and external organic input and internal organic release. Therefore, we propose basin management strategies such as limiting grazing pressure, adopting enclosed pasture, wetland restoration, optimizing water renewal cycle in Hulun Lake, and transboundary water quality management to tackle water contamination in Hulun Lake.

Automation System of Water Circulation and Fish Feeding in Tilapia Fish Pond with Solar Panel Source

Tilapia cultivation requires good management of water quality and proper feeding to ensure optimal fish growth. One of the main challenges in this process is maintaining the water pH level within ideal limits and providing feed according to the needs of the fish based on their size. In addition, limited access to electricity in some locations makes fish pond management less efficient. This study aims to design an automated water circulation system that is regulated based on the water pH level and automatic feeding according to the needs of the fish, by utilizing solar panels as a power source. This system uses a pH sensor to monitor water conditions in real time and regulate water circulation to maintain ideal pH levels. Feeding is regulated based on the size and number of fish to ensure optimal feed efficiency and fish growth. This system is powered by solar panel energy, so it can operate independently without relying on conventional electricity networks. Test results show that the system is able to maintain water pH stability and automate feeding with high accuracy, while utilizing renewable energy effectively. The use of solar panels not only reduces dependence on electricity, but also reduces operational costs and supports environmental sustainability. This system is expected to increase the efficiency and productivity of tilapia cultivation, especially in areas with limited access to electricity.

The trends of per-and polyfluoroalkyl substances (pfas) in drinking water systems in Maryland United States

Per- and polyfluoroalkyl substances (PFAS) have become a major concern in water quality management because of their persistence in the environment and associated health risks. In Maryland, the diverse water resources and densely populated areas, faces unique challenges culminating from PFAS contamination. This research paper presents a comprehensive overview of PFAS contamination trends in Maryland's drinking water systems across four distinct phases, spanning from 2019 to 2022, it highlights the trends of PFAS contamination, environmental and public health risks, and strategies for effective management. Utilizing data from extensive monitoring efforts conducted in the state, the study reveals a persistent and evolving environmental health challenge characterized by the dominance of different PFAS compounds, particularly PFOS and PFOA, found at concerning concentrations. Mean concentrations of total PFOA/PFOS across the sampling periods were at 18.78 ng/L, 7.28 ng/L, 14.60 ng/L, 12.46 ng/L, significantly surpassing the EPA's 2024 maximum contaminant levels of 4 ng/L for PFOA and PFOS. Despite fluctuations observed across sampling phases, PFAS levels consistently surpass EPA health advisory levels, indicating widespread contamination. Potential sources, such as industrial sites and wastewater treatment plants, underscore the need for robust regulatory enforcement and innovative remediation strategies to safeguard public health. The findings emphasize the necessity for continuous monitoring and multi-faceted mitigation approaches to address PFAS contamination effectively, ensuring the safety of Maryland's water resources and the health of its residents.