Search Results (597)

Water pollution by organic dyes poses serious environmental and health challenges, demanding efficient and selective remediation methods. In this study, we engineered tailored organo-clay nanocomposites by modifying montmorillonite with hexadecyltrimethylammonium bromide (HTAB) and intercalating polyethylene glycol (PEG) chains of two distinct molecular weights (PEG200 and PEG4000). Comprehensive characterization techniques (XRD, FTIR, SEM, zeta potential, and TGA) confirmed the successful modification of the composites. Notably, PEG4000 promoted significant interlayer expansion, as evidenced by the shift of the (00l) reflection corresponding to the basal spacing d, indicating an increase in basal spacing. This expansion contributed to the formation of a well-ordered porous framework with uniformly distributed pores. In contrast, PEG200 produced smaller pores with a more uniform distribution but induced less pronounced interlayer expansion. Adsorption tests demonstrated rapid kinetics, achieving equilibrium in under 15 min, and impressive capacities: 420 mg/g of methylene blue (MB) adsorbed on PEG200/MMT@HTAB, and 385 mg/g of Congo red (CR) on PEG4000/MMT@HTAB. The crucial role of PEG chain length in adsorption selectivity was assessed, showing that shorter PEG chains favored methylene blue adsorption by producing narrower pores and faster kinetics, while longer PEG chains enhanced CR uptake via a stable, interconnected pore network that facilitates diffusion of larger dye molecules. Thermodynamic and Dubinin–Radushkevich analyses confirmed that the adsorption was spontaneous, exothermic, and predominantly driven by physical adsorption mechanisms involving weak van der Waals and dipole interactions. These findings highlight the potential of PEG-modified montmorillonite nanocomposites as cost-effective, efficient, and tunable adsorbents for rapid and selective removal of organic dyes in wastewater treatment. Full article

Water quality monitoring is an important step in maintaining environmental sustainability and public health. Water turbidity is one of the main parameters in assessing water quality, because a high level of turbidity can indicate pollution that is harmful to aquatic ecosystems and humans. In the digital era, Internet of Things (IoT) technology has been applied to improve the effectiveness of real-time monitoring of water turbidity. This study aims to examine IoT-based water turbidity monitoring strategies and technologies using the Systematic Literature Review (SLR) method with the PRISMA protocol. In the process of searching for literature, this study identified 222 articles from the Scopus database, which, after going through the screening stage based on relevance, document type, and accessibility, resulted in seven main articles for further analysis. The results of the review show that the utilization of IoT sensors and wireless communication enables real-time monitoring of water turbidity, improves early detection of pollution, and improves effectiveness in water monitoring. However, challenges such as data security, sensor reliability, and communication network stability still need to be overcome to ensure the system works optimally. This study confirms that IoT can be a more efficient and sustainable solution in monitoring water turbidity. Full article

With the rapid development of socioeconomics and the continuous advancement of urbanization, water environment issues in plain river networks have become increasingly prominent. Accurate and reliable water quality (WQ) predictions are a prerequisite for water pollution warning and management. Data-driven modeling offers a promising approach for WQ prediction in plain river networks. However, existing data-driven models suffer from inadequate capture of spatiotemporal (ST) dependencies and misalignment between direct prediction strategy assumptions with actual data characteristics, limiting prediction accuracy. To address these limitations, this study proposes a spatiotemporal graph neural network (ST-GNN) that integrates four core modules. Experiments were performed within the Chengdu Plain river network, with performance comparisons against five baseline models. Results suggest that ST-GNN achieves rapid and accurate WQ prediction for both short-term and long-term, reducing prediction errors (MAE, RMSE, MAPE) by up to 46.62%, 37.68%, and 45.67%, respectively. Findings from the ablation experiments and autocorrelation analysis further confirm the positive contribution of the core modules in capturing ST dependencies and eliminating data autocorrelation. This study establishes a novel data-driven model for WQ prediction in plain river networks, supporting early warning and pollution control while providing insights for water environment research. Full article

In recent years, arresters in 10 kV distribution transformer areas of the Guangdong power grid have exhibited a rising trend of premature failures, posing a serious threat to distribution network reliability. This paper studied the failure causes of arresters through performance tests on failed arresters and through deterioration tests on new arresters and their prorated sections under typical operating stresses. The failed arresters and their internal varistors displayed varying degrees of physical damage and pronounced degradation in electrical performance, characterized by a strong polarity effect on the DC reference voltage (U_1mA), elevated DC leakage current (I_L) and resistive current (i_R), and excessive residual voltage (U_5kV). In the lightning impulse test, varistors primarily showed pinhole-type damage and significant polarity effects on ΔU_1mA. In the AC aging test, ΔU_5kV increased markedly. In the water immersion test, varistors exhibited salt deposits and aluminum electrode blackening, with ΔU_1mA decreasing, while I_L and Δi_R increased significantly. Overall, internal moisture superimposed on other operating stresses was identified as a major internal cause of arrester failure, while pollution flashover of the housing was considered the primary external factor. Corresponding improvement measures in material optimization, testing and inspection, and operation and maintenance are proposed to enhance arrester reliability. Full article

Global climate change, combined with the construction of impermeable urban elements, tends to increase runoff, which might cause flooding and reduce groundwater recharge. Moreover, the first flash of these areas might accumulate pollutants that might deteriorate groundwater quality. A digital elevation model (DEM) describes urban landscapes by representing the watershed relief at any given location. While, in concept, finer DEMs and land use classification (LUC) are yielding better hydrological models, it is suggested that over-accuracy overestimates minor tributaries that might be redundant. Optimal DEM resolution with integrated spectral and feature-based LUC was found to reflect the hydrological network’s significant tributaries. To cope with the karstic urban watershed complexity, ModClark Transform and SCS Curve Number methods were integrated over a GIS-HEC-HMS platform to a nominal urban watershed sub-basin analysis procedure, allowing for detailed urban runoff modeling. This precise urban karstic terrain modeling procedure can predict runoff volume and discharge in urban, mountainous karstic watersheds, and may be used for water-sensitive design or in such cities to control runoff and prevent its negative impacts. Full article

Accurate prediction of water quality involves early identification of future pollutant concentrations and water quality indicators, which is an important prerequisite for optimizing water environment management. Although deep learning algorithms have demonstrated considerable potential in predicting water quality parameters, their broader adoption remains hindered by limited interpretability. This study proposes an interpretable deep learning framework integrating an artificial neural network (ANN) model with Shapley additive explanations (SHAP) analysis to predict spatiotemporal variations in water quality and identify key influencing factors. A case study was conducted in the Poyang Lake Basin, utilizing multi-dimensional datasets encompassing topographic, meteorological, socioeconomic, and land use variables. Results indicated that the ANN model exhibited strong predictive performance for dissolved oxygen (DO), total nitrogen (TN), total phosphorus (TP), permanganate index (CODMn), ammonia nitrogen (NH₃N), and turbidity (Turb), achieving R² values ranging from 0.47 to 0.77. Incorporating land use and socioeconomic factors enhanced prediction accuracy by 37.8–246.7% compared to models using only meteorological data. SHAP analysis revealed differences in the dominant factors influencing various water quality parameters. Specifically, cropland area, forest cover, air temperature, and slope in each sub-basin were identified as the most important variables affecting water quality parameters in the case area. These findings provide scientific support for the intelligent management of the regional water environment. Full article

Background: Greek islands face mounting pressures on their marine water resources due to tourism growth, agricultural runoff, climate change, and emerging pollutants. Safeguarding seawater quality is critical for ecosystem integrity, public health, and the sustainability of tourism-based economies. Objectives: This scoping review synthesizes and evaluates the existing research on seawater quality in the Greek islands, with emphasis on pollution sources, monitoring methodologies, and socio-environmental impacts, while highlighting the gaps in addressing emerging contaminants and aligning with sustainable development goals. Methods: A systematic literature search was conducted in Scopus, Google Scholar, ResearchGate, Web of Science, and PubMed for English- and Greek-language studies published over the last two to three decades. The search terms covered physical, chemical, and biological aspects of seawater quality, as well as emerging pollutants. The PRISMA-ScR guidelines were followed, resulting in the inclusion of 178 studies. The data were categorized by pollutant type, location, water quality indicators, monitoring methods, and environmental, health, and tourism implications. Results: This review identifies agricultural runoff, untreated wastewater, maritime traffic emissions, and microplastics as key pollution sources. Emerging contaminants such as pharmaceuticals, PFASs, and nanomaterials have been insufficiently studied. While monitoring technologies such as remote sensing, fuzzy logic, and Artificial Neural Networks (ANNs) are increasingly applied, these efforts remain fragmented and geographically uneven. Notable gaps exist in the quantification of socio-economic impact, source apportionment, and epidemiological assessments. Conclusions: The current monitoring and management strategies in the Greek islands have produced high bathing water quality in many areas, as reflected in the Blue Flag program, yet they do not fully address the spatial, temporal, and technological challenges posed by climate change and emerging pollutants. Achieving long-term sustainability requires integrated, region-specific water governance linked to the UN SDGs, with stronger emphasis on preventive measures, advanced monitoring, and cross-sector collaboration. Full article

The inherent complexity of modern supply chains obscures significant hidden CO₂ and Water Pollution Equivalent (WPE) emissions, presenting mounting challenges for integrated environmental governance. While prior research has largely treated carbon and water pollution metabolic systems in isolation, this study addresses the critical gap in understanding their bidirectional interactions under socioeconomic dynamics. We develop a novel Three-Dimensional Evaluation Method for the Metabolic Interaction System of Industrial CO₂ and Water Pollution (TDE-ISCW). This framework integrates Environmental Input–Output Analysis and Ecological Network Analysis to: (1) identify key industrial sectors and utility relationships within individual CO₂ and WPE systems; (2) quantify the mutual disturbance responses between the CO₂ and WPE metabolic systems through changes in sectoral emissions/output, inter-sectoral relationships, and sector–system linkages; and (3) propose optimized industrial restructuring strategies for synergistic pollution and carbon reduction. Applied to the highly industrialized Yangtze River Economic Belt, key findings reveal: (i) substantial upstream dependency, exemplified by Advanced Equipment Manufacturing’s 95.7% indirect CO₂ emissions; (ii) distinct key sectors for CO₂ (e.g., MOO, FTO, MNM) and WPE (e.g., MPM, OTH, FTO) reduction based on competitive relationships; and (iii) complex trade-offs, where emission reductions in one system (e.g., CO₂ via FTO restructuring) can trigger heterogeneous responses in the other (e.g., altered WPE influence or downstream CO₂/economic shifts). The TDE-ISCW framework provides actionable insights for designing coordinated, adaptive emission reduction policies that account for cascading cross-system effects, ultimately supporting regional industrial upgrading and resource efficiency goals. Future research should incorporate temporal dynamics and full industrial–metabolic cycles. Full article

This study delivers a pioneering, high-resolution hydrogeochemical assessment of surface waters in the Upper Velhas and Upper Paraopeba river basins within Brazil’s Iron Quadrangle—an area of critical socioeconomic importance marked by intensive mining and urbanization. Through a dense sampling network of 315 surface water points (one every 23 km²), the research generates an unprecedented spatial dataset, enabling the identification of contamination hotspots and the differentiation between lithogenic and anthropogenic sources of potentially toxic elements (PTEs). Statistical methods, including exploratory data analysis and cluster analysis, were applied to determine background and anomalous concentrations of potentially toxic elements (PTEs). Geospatial distribution maps were generated using GIS. The results revealed widespread contamination by As, Cd, Cr, Ni, Pb, and Zn, with many samples exceeding Brazilian, European, and global drinking water standards. Arsenic and cadmium anomalies in rural and peri-urban communities raise concerns due to the direct consumption of contaminated water. The innovative application of dense spatial sampling and integrated geostatistical methods offers new insights into the pathways and sources of PTE pollution, identifying specific lithological units (e.g., gold schists, mafic intrusions) and land uses (e.g., urban effluents, mining sites) associated with elevated contaminant levels. By establishing robust regional geochemical baselines and source attributions, this study sets a new standard for environmental monitoring in mining-impacted watersheds and provides a replicable framework for water governance, environmental licensing, and risk management in similar regions worldwide. Full article

Understanding watershed-scale interactions among landscape patterns, river morphology, and water quality is essential for effective water management. However, quantitative assessment of their coupled effects remains challenging. Utilizing water quality observation data, this study analyzed the independent and interactive influences of landscape pattern and river structure on the water quality of inlet rivers in the Chaohu Lake Basin (CLB) using correlation analysis and partial least squares structural equation modelling (PLS-SEM). The main conclusions are as follows: (1) The river water quality showed significant spatial distribution characteristics, and the northwestern part of the CLB formed a pollution aggregation area. (2) Ammonia nitrogen correlated positively with impervious surfaces but negatively with forest cover and patch cohesion, permanganate index linked positively to water surface but negatively to forest cover, and water temperature exhibited a significant negative correlation with network connectivity. (3) PLS-SEM results showed that both river structure (path coefficient = 0.877, p < 0.001) and landscape pattern (path coefficient = 0.177, p < 0.05) significantly influenced CLB water quality, with river structure having a stronger effect. This study supports source-based water quality control for Chaohu Lake Basin. Full article

Water quality deterioration poses a critical threat to ecological security and sustainable development, particularly in rapidly urbanizing regions. To enable proactive environmental management, this study develops a novel hybrid deep learning model, the NGO-CNN-GRU, for high-precision time-series water quality prediction in the Xijiang River Basin, China. The model integrates a Convolutional Neural Network (CNN) for spatial feature extraction and a Gated Recurrent Unit (GRU) for temporal dependency modeling, with hyperparameters optimized via the Northern Goshawk Optimization (NGO) algorithm. Using historical water quality (pH, DO, CODMn, NH3-N, TP, TN) and meteorological data (precipitation, temperature, humidity) from 11 monitoring stations, the model achieved exceptional performance: test set R² > 0.986, MAE < 0.015, and RMSE < 0.018 for total nitrogen prediction (Xiaodong Station case study). Across all stations and indicators, it consistently outperformed baseline models (GRU, CNN-GRU), with average R² improvements of 12.3% and RMSE reductions up to 90% for NH3-N predictions. Spatiotemporal analysis further revealed significant pollution gradients correlated with anthropogenic activities in the Pearl River Delta. This work provides a robust tool for real-time water quality early warning systems and supports evidence-based river basin management. Full article

Water pollution poses serious threats to public health and the environment, therefore requiring efficient and scalable monitoring solutions. This paper presents a cyber–physical system (CPS) that integrates paper-based biosensors with an edge IoT architecture and long-range wide area network (LoRaWAN) for real-time assessment of water quality. The biosensors detect pollutants such as arsenic, lead, and nitrates with a detection limit of 0.5 ppb. The system proposed was compared with existing LSTM systems based on two performance metrics: detection accuracy and latency. Paper-based biosensors were fabricated using silver nanoparticle-functionalized substrates to show high sensitivity and low-cost pollutant detection. TCN algorithm deployment at the edge allows for real-time processing for time-series data analysis due to its high accuracy and low latency properties compared with LSTM models, which were mainly chosen due to their usage in most applications dealing with time-series-based analysis. Experimentation was carried out by deploying the developed CPS in controlled environments, simulating pollutants at different levels, and executing the models to test their accuracy in detecting pollutants and the latency of data processing. The TCN framework achieved a detection accuracy of 98.7%, which surpassed LSTM by 92.4%. In addition, TCN reduced latency in processing by 38% to enable fast data analysis and decision making. LoRaWAN allowed for perfect packet transmission of up to 15 km, while the loss rate stayed as low as 2.1%. These results establish the proposed CPS as reliable, efficient, and scalable for real-time water contamination monitoring. Thus, this research introduces the integration of paper-based biosensors with advanced computational frameworks. Full article

In the present study, the changes in the groundwater quality in a Hungarian settlement, Báránd, were examined, nine years after the construction of a sewerage network. The sewerage network in the study area was completed in 2014, with a household connection rate exceeding 97% in 2023. In the summer of 2023, water samples were taken from 37 dug groundwater wells. Changes in the water quality were assessed using three water quality indicators (the Water Quality Index (WQI), Contamination degree (Cd), and Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI)) and geographic information (GIS), data visualization systems, and artificial intelligence (AI). During the evaluation of the quality of the groundwater, eight water chemical parameters were used (pH, EC, NH₄⁺, NO₂⁻, NO₃⁻, PO₄³⁻, COD, Na⁺). Based on interpolated maps and water quality indices, it was established that while an increasing portion of the area exhibits adequate or good water quality compared to the pre-sewerage period, a deterioration has occurred relative to recent years. Even nine years after the sewerage network construction, elevated concentrations of inorganic nitrogen forms and organic matter persist, indicating the continued presence of accumulated pollutants, as confirmed by all three water quality indicators to varying degrees and spatial patterns. The interactive data visualization and cloud-based sharing of the data of the water quality geodatabase were made freely available with the help of Tableau Public. A Feed-Forward Neural Network (FFNN) was developed to predict the groundwater quality, estimating the water quality statuses of three water quality indicators based on water chemistry parameters. The results showed that the applied training algorithms and activation functions proved to be the most effective in the case of different network structures. The most accurate prediction of the WQI and CCME WQI indicators was provided by the Bayesian control algorithm (trainbr), which achieved the lowest mean-squared error (RMSE_WQI = 0.1205, RMSE_{CCME WQI} = 0.1305) and the highest determination coefficient (R²_WQI = 0.9916, R²_{CCME WQI} = 0.9838). For the Cd index, the accuracy of the model was lower (RMSE = 0.1621, R² = 0.9714), suggesting that this indicator is more difficult to predict. With regard to our study, it should be emphasized that data visualization is a particularly practical tool for the post-processing of spatial monitoring data, as it is suitable for displaying information in an intuitive, visual form, for discovering spatial patterns and relationships, and for performing real-time analyses. AI is expected to further increase visualization efficiency in the future, enabling the rapid processing of large amounts of data and spatial databases, as well as the identification of complex patterns. Full article

Insulator pollution flashover is a serious fault in power transmission systems, with surface wetting being a key prerequisite for its occurrence. The unique electrostatic properties of HVDC transmission networks promote pollution accumulation and alter wetting behavior. In southwest China’s warm, humid mountainous regions, algae adhesion on DC insulators significantly affects surface wetting, increasing the risk of flashover under extreme weather conditions. This study proposes a surface-conductivity–based method to measure the water absorption of pollution layers on insulators. It quantitatively assesses the impact of algae on wetting characteristics, including saturated water absorption and salt loss rate. Experimental results show that in fog, saturated water absorption decreases as the tilt angle increases, while soluble salt content decreases over wetting time. NSDD has a minimal effect on saturated absorption. Moreover, the presence of algae significantly alters wetting behavior, increasing saturated water absorption by 27–47% and reducing salt loss. These findings provide an insight into the role of biological contamination in pollution flashover processes in high-humidity regions. Full article

This comprehensive study investigates the spatial distribution of metals in surface water, their accumulation in fish tissues, and their impact on the gut microbiome dynamics of fish in the Qi River, Huanggang City, Hubei Province. Three distinct sampling regions were established: the mining area (A), the transition area (B), and the distant area (C). Our results revealed that metal concentrations were highest in the mining area and decreased with increasing distance from it. The bioaccumulation of metals in fish tissues followed the order of gut > brain > muscle, with some concentrations exceeding food safety standards. Analysis of the gut microbiota showed that Firmicutes and Proteobacteria dominated in the mining area, while Fusobacteriota were more prevalent in the distant area. Heavy metal pollution significantly altered the composition and network structure of the gut microbiota, reducing microbial associations and increasing negative correlations. These findings highlight the profound impact of heavy metal pollution on both fish health and the stability of their gut microbiota, underscoring the urgent need for effective pollution control measures to mitigate ecological risks and protect aquatic biodiversity. Future research should focus on long-term monitoring and exploring potential remediation strategies to restore the health of affected ecosystems. Full article