Search Results (174)

The growing environmental crisis, particularly water pollution from detergents, necessitates a shift from reactive compliance to proactive eco-innovation, as current methods often fail to systematically resolve trade-offs between performance, safety, and ecology. This study develops and illustrates the application of the Evolutionary-Driven Design Framework (EDDF), an integrated methodology that combines PESTEL analysis, historical evolutionary pattern analysis, Quality Function Deployment (QFD) with a novel contradiction index, Theory of Inventive Problem Solving (TRIZ), and environmental assessment. The framework was applied to redesign a conventional laundry detergent with the objectives of zero phosphates, superior biodegradability (>85%), maintained efficacy, and controlled cost. The quantitative contradiction index matrix prioritized critical unsustainable parameters (e.g., EDTA, Cocamide DEA) for substitution over mere optimization. Through an iterative feedback loop, the process evolved from a biobased concentrate to an “enzymatic power tablet” (Concept B). This waterless, solid formulation uses sodium citrate as a biodegradable builder and an encapsulated multi-enzyme system, achieving an estimated >90% biodegradability and zero phosphates while meeting technical and economic targets. The EDDF provides a structured, anticipatory roadmap that transforms regulatory and market pressures into drivers of innovation, offering companies a promising method for designing sustainable products by proactively resolving contradictions and avoiding historical mistakes. Full article

To support refined water quality evaluation and short-term early warning in multi-section river systems, this study developed three percentile-based composite indices: the Water Quality Index (WQI), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Entropy Weight Method (EWM). Monthly multi-parameter monitoring data from 2019 to 2025 were used, covering ten river sections (P1–P5, M1–M5). The three indices were compared in terms of statistical distribution, methodological consistency, and anomaly response. An integrated assessment–prediction framework was further established. Within this framework, a one-step prediction scheme was applied to evaluate four models: Long Short-Term Memory networks (LSTM), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). The results show that WQI scores were generally high and fluctuated within a narrow range. A clear “ceiling effect” was observed in the moderate-to-high grade intervals. WQI also showed weak consistency with TOPSIS and EWM (r ≈ 0.29–0.32). In contrast, TOPSIS and EWM were more sensitive to water quality fluctuations and extreme risks, and were moderately correlated with each other (r ≈ 0.53). Using TOPSIS < 50 as the threshold, 49 severe anomalous events were identified. These events were mainly clustered in February–April 2020, April–July 2023, and June–September 2025, with sections P4, M1, and M2 acting as high-incidence sites. In several typical events, WQI values remained high, indicating that reliance on WQI alone may delay early warning. Prediction results further reveal that the choice of index strongly affects sequence predictability. Taking XGBoost as the reference, the median validation R² followed a stable gradient: WQI (0.807) > TOPSIS (0.723) > EWM (0.594). XGBoost yielded positive R² values across all indices and sections. It also achieved the most robust overall performance and the strongest cross-site, cross-index generalization capability. Full article

Shortwave infrared (SWIR) imagery plays an important role in land–water boundary delineation, coastal monitoring, and complex aquatic environment observation. However, the spatial resolution of SWIR bands is usually lower than that of visible bands, which limits their capability to represent fine-scale targets and boundary structures. To address this problem, this study proposes MLE-ResUNet, a SWIR image super-resolution method that integrates along-track oversampling with visible-light-guided deep learning. The proposed method first exploits dual-view SWIR observations with sub-pixel displacement generated by increasing the sampling line rate in the push-broom imaging process. A maximum likelihood estimation (MLE)-based physical prior module is then introduced to transform multi-view degraded observations into a physically consistent latent high-resolution prior. Finally, high-resolution visible images are used to provide edge, texture, and structural guidance, and a ResUNet-based network is employed for multi-source feature fusion and residual reconstruction. Based on multi-region measured data acquired by the LHRSI (Lightweight High-Resolution Spectral Imager) payload onboard the BlueCarbon-1A satellite, a SWIR super-resolution dataset covering typical urban, farmland, and coastal scenarios was constructed. Comparative experiments were conducted against PCA, BDSD, PanNet, GPPNN, and two additional lightweight-guided deep learning baselines, namely LGPConv and a CANConv-style visible-guided baseline. The results show that MLE-ResUNet achieves the best performance across different scenarios and consistently outperforms the comparison methods in terms of SSIM, SAM, ERGAS, and Q-index. The proposed method effectively enhances spatial detail recovery while maintaining favorable spectral consistency. Ablation experiments further demonstrate that both along-track oversampling information and the MLE-based physical prior contribute to improved reconstruction quality and more stable training convergence. These findings indicate that the proposed method can enhance fine-scale SWIR observation capability without substantially increasing hardware complexity, providing an effective technical solution for shoreline identification, land–water boundary extraction, and complex surface target monitoring. Full article

Groundwater quality assessment in urban and peri-urban environments is often constrained by incomplete monitoring records, irregular sampling frequencies, and heterogeneous environmental datasets. The primary objective of this study is to predict the Water Quality Index (WQI) in the Attica River Basin, Greece, using advanced machine learning (ML) techniques. A groundwater quality dataset comprising 958 observations from 80 monitoring stations was analyzed using six physicochemical parameters, namely electrical conductivity, ammonium, nitrate, nitrite, chloride, and sulphate. Three modeling approaches, namely TabNet (with Winsorization), SVM, and Gradient Boosting Machines (GBM), were implemented to estimate groundwater quality conditions. To address the challenge of missing data, Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching (PMM) was implemented and systematically compared against conventional imputation approaches, including smoothed averages, interpolation, and forward-fill methods. The novelty of this study lies in the integration of open-access groundwater chemistry data, advanced multivariate imputation (MICE-PMM), and attention-based deep learning (TabNet) for groundwater quality prediction in a Mediterranean peri-urban area under data-scarce conditions. Using a multi-year groundwater monitoring dataset, the results indicate that the integrated MICE-PMM and TabNet framework achieved the highest predictive performance, with R² = 0.91, NSE = 0.91, RMSE = 52.21, and MAE = 25.68. Feature importance and sensitivity analyses identified nitrate as the dominant driver of WQI variability, highlighting the strong influence of anthropogenic nutrient loading on groundwater quality. Overall, the proposed framework provides a transferable, data-driven approach for groundwater quality prediction, environmental monitoring, and groundwater resource management in urban and peri-urban aquifer systems characterized by incomplete environmental datasets. Full article

Cascade hydropower alters river hydrological regimes and threatens aquatic ecosystems, calling for robust ecological risk assessment (ERA). Conventional assessments often rigidly apply the full five-layer Driving Force–Pressure–State–Impact–Response framework, leading to indicator redundancy and unbalanced weighting. Single weighting methods also fail to reconcile expert judgment with data variability. To address these issues, we developed a three-layer (target–element–indicator) evaluation system embedding DPSIR logic without its full structure, focusing on hydrological regime, water environmental quality, and aquatic ecology with ten indicators. We used an improved group AHP-CRITIC coupling method for weighting: AHP aggregates expert judgments via geometric mean, and CRITIC integrates data variability and inter-indicator conflict. Multi-attribute utility theory normalized indicators into a unified security index, applied to four cascade stations in the middle Jinsha River using 66-year (1953–2018) hydrological and seven-year (2013–2019) in situ monitoring data. The evaluation obtained a comprehensive index of 0.71 to 0.74, which is generally safe. River connectivity loss was the primary limiting factor. Hydrological alteration was mild overall with a value of 0.139, while extreme flow decline rate variation reached a high level of 0.83. Weekly regulated stations achieved over 97% ecological flow guarantee, which is much higher than daily regulated stations. This streamlined framework improves interpretability for cascade basins and supports sustainable watershed management. Full article

Estuarine and coastal ecosystems are facing significant threats from compounded pressures, such as land-based pollution and mariculture activities. These ecosystems confront severe challenges, including increasing environmental burdens and declining ecological health. Traditional evaluation methods that rely on statistical data struggle to meet the requirements for refined management of estuarine and coastal water environments. Taking Qinzhou Bay as a case study, this research incorporated multi-source data (including water quality indicators retrieved from remote sensing imagery, mariculture distribution, and land use information) into an integrated ecological health assessment system that combined remotely sensed data with the Pressure–State–Response (PSR) model. This approach enables a spatially continuous and quantitative evaluation of ecological health conditions for August 2015 (flood season), December 2015 (non-flood season), May 2022 (flood season), and December 2022 (non-flood season). The results indicated significant seasonal differences in the ecological health of Qinzhou Bay, with conditions generally better during the non-flood season than the flood season. Based on a comparison between the indicative estimation for 2015 and the inversion results for 2022, the overall ecological health index in 2022 showed an increasing trend, although some nearshore and estuarine areas experienced a declining trend. This study incorporated multi-source data, including remote sensing, into the PSR model framework, thereby advancing ecological health assessment from conventional discrete station-based evaluation to spatially continuous assessment. The effectiveness of this methodological approach in identifying spatiotemporal variations in the ecological health of estuarine and coastal zones was validated, providing scientific support for the refined management of estuarine and coastal water environments and ecological restoration. Full article

Groundwater serves as a vital source of domestic and agricultural water in rural areas of the Songnen Plain. Its chemical composition and water quality directly impact public health and regional sustainable development, making them subjects of significant concern. This study employed a comprehensive analytical framework, integrating Piper trilinear diagrams, ionic ratio analysis, the Water Quality Index (WQI), and the Human Health Risk Assessment (HHRA) model, to preliminarily evaluate groundwater conditions in a rural township of the Songnen Plain. The multi-method approach was designed to provide scientific insights for groundwater pollution prevention and remediation strategies in the region. Results indicate that the predominant groundwater chemical type in the study area is HCO₃-Ca. The hydrochemical process is primarily controlled by weathering and dissolution of silicate and carbonate minerals, accompanied by cation exchange. The WQI ranged from 84.78 to 192.82, with an average of 132.68, indicating overall moderate water quality. Fe and Mn are significant factors affecting water quality. The potential non-carcinogenic risks posed by groundwater to children, females, and males (0.988, 0.701, 0.534) and carcinogenic risks (1.77 × 10⁻⁵, 6.27 × 10⁻⁵, 4.81 × 10⁻⁵) are both below the USEPA recommended threshold (1.0, 1 × 10⁻⁴), indicating that the health risks were generally acceptable, though the HI for children approached the threshold. The results underscore the need for targeted mitigation of elevated Fe/Mn concentration (e.g., via aeration biofilters) while highlighting the region’s low health risks under current conditions. This work provides a template for integrating geochemical and health risk paradigms in groundwater management. Full article

Background and Objectives: Knee osteoarthritis (KOA) is a degenerative joint disease that affects quality of life through pain, impaired functional performance, and altered gait patterns. Hydrotherapy is a well-tolerated form of physical rehabilitation, especially suitable for patients with severe pain, as water’s properties support movement while reducing joint load. Its effects have been widely studied, primarily focusing on patient-reported outcomes, with limited synthesis of functional performance and gait-related outcomes. Materials and Methods: A systematic search was conducted in PubMed, Web of Science, Cochrane, PEDro, SpringerLink, ScienceDirect, and Google Scholar, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search strategy included a combination of Medical Subject Headings (MeSH) terms and keywords. For example, the PubMed search strategy was as follows: (“knee osteoarthritis” OR “knee OA”) AND (“hydrotherapy” OR “aquatic therapy” OR “water-based exercise”) AND (“gait” OR “walking” OR “functional performance”). Randomized controlled trials (RCTs) from the last 10 years involving patients with KOA undergoing aquatic therapy were included. Primary outcomes included functional performance assessed by measures such as the 6 min walking test (6MWT), the Timed Up and Go (TUG) test, the five sit-to-stand (5 STS) and stair climb (SC) tests, and by using gait-related parameters (e.g., speed, cadence, and step length) assessed clinically or using technology. Patient-reported outcomes, including the Visual Analog Scale (VAS), Western Ontario and McMaster University’s Osteoarthritis Index (WOMAC), and Knee Injury and Osteoarthritis Outcome Score (KOOS), were analyzed as a secondary objective. Results: A total of 479 studies were identified, of which 13 met the eligibility criteria. The results revealed improvements in functional performance, with increases in 6MWT in five studies, the TUG test in four trials, and better performance in the 5-STS and SC tests in five studies. Benefits in gait parameters were noted in four studies. Additionally, one of the articles reported improvements in static and dynamic balance, another showed enhanced proprioception, and a third described more efficient muscle activation during gait following hydrotherapy. Consistent benefits in pain reduction, joint stiffness, and activities of daily living, as reflected by VAS, WOMAC, and KOOS, were also noted immediately and maintained at follow-up. The variability in outcome measures and intervention characteristics limited the possibility of data integration and the calculation of effect sizes. Conclusions: Hydrotherapy as a rehabilitation intervention may be associated with improvements in functional capacity, mobility, and self-reported physical ability in patients with KOA, with some evidence supporting a beneficial effect on gait; however, the certainty of evidence remains low to moderate due to heterogeneity among studies and limited sample sizes. These findings should be interpreted in light of the methodological limitations identified across the included trials. Full article

Water quality monitoring remains a critical challenge for achieving Sustainable Development Goal 6, particularly in rural and resource-constrained environments where conventional laboratory-based methods are costly and slow. This study presents the development and field validation of an Artificial Intelligence of Things (AIoT) ecosystem for intelligent, low-cost, and real-time water quality assessment using edge computing and generative artificial intelligence. The system integrates a laboratory-developed multiparameter probe measuring temperature, pH, dissolved oxygen, and electrical conductivity with a mobile application and a cloud-based backend. Field validation was conducted in riverine environments in the municipality of Pueblo Bello (Cesar, Colombia), where the system was deployed for in situ data acquisition and real-time inference. A supervised Artificial Neural Network (ANN) was trained to classify water quality based on a Water Quality Index (WQI) ground truth derived from a public dataset, employing KNN-based missing data imputation, interquartile range outlier filtering, stratified balancing, and grid search hyperparameter optimization. The best-performing model achieved 85.1% accuracy and an AUC of 0.87 using only four physical parameters and was successfully deployed in TensorFlow Lite format on both the embedded probe and the mobile application with sub-millisecond inference time. Integration with a generative AI backend provides contextual natural-language interpretations of measurements. These results demonstrate that reduced-parameter edge AI systems can provide reliable environmental diagnostics while enhancing accessibility and citizen engagement for participatory water monitoring. Full article

Water pollution poses significant risks to human health and environmental sustainability, highlighting the need for accurate water quality assessment and prediction. This review examines the application of machine learning (ML) in Water Quality Index (WQI) assessments, focusing on WQI formulation, predictive modelling approaches, and explainable artificial intelligence (XAI) techniques. A structured literature review is conducted using major scientific databases, including ScienceDirect, Springer, and other relevant sources, following a systematic study selection process. The review analyzes commonly used water quality parameters and highlights how the deterministic structure of WQI influences machine learning modelling, often leading to high predictive performance that reflects predefined formulations rather than independent pattern learning. A comprehensive comparison of single, hybrid, and ensemble ML models is presented, showing that hybrid approaches generally provide improved robustness and accuracy in complex water quality scenarios. In addition, the role of XAI methods in enhancing model interpretability and supporting transparent decision-making is discussed. Key challenges, including limited generalization, model complexity, and interpretability constraints, are identified, and future research directions are proposed to develop more reliable and practical AI-based water quality monitoring systems. Overall, this review provides insights into the integration of machine learning and WQI, emphasizing the importance of balancing predictive accuracy with interpretability for sustainable water resource management. Full article

Energy consumption and different methods for determining energy efficiency were evaluated for drying of extruded rainbow trout feed pellets using a pilot-scale, heated air, integrated conveyor dryer and cooler. Impact of drying parameters on product quality, especially final moisture and pellet durability index (PDI), was also studied. From an initial moisture of 21.2 to 22.1% wet basis (wb), the drying–cooling process reduced the pellet moisture to 3.5 to 5.0% wb. Dryer throughput (82–121 kg/h) did not have statistically significant impact on final moisture (p = 0.0965) although the highest throughput corresponded to highest moisture; but increase in drying temperature from 93 to 115 °C led to a significant decrease in final moisture (p = 0.0285). Increase in dryer throughput led to a significant increase in PDI from 82.8 to 88.0% (p = 0.0003), while increase in drying temperature resulted in a slight decrease in PDI from 84.3 to 83.6%, although not statistically significant (p = 0.0811). Sorghum-based aquatic feed had a slightly lower PDI than wheat-based feed (82.8 versus 83.7%, respectively), but the difference was not statistically significant (p = 0.3009). Differences in pellet durability were attributed primarily to structural weakness induced by product shrinkage during drying, which in turn was impacted by drying rates. Specific energy consumption (SEC) during drying decreased from 136.3 to 101.1 MJ/kg-water with increase in throughput and increased from 122.5 to 150.1 MJ/kg-water with increase in drying temperature. An inverse trend was observed for various measures of dryer energy efficiency, with increase in efficiency for higher throughput and decrease for higher temperature. Sorghum-based aquatic feed had a higher drying SEC as compared to wheat-based feed and a lower energy efficiency. Overall, the results highlighted trade-offs between throughput, drying efficiency and pellet quality during drying of aquatic feeds. Full article

Due to the destruction of natural aquatic ecosystems, developing comprehensive biological index evaluation methods is critical for river restoration and regeneration. However, research on spatial multiple-scale biological assessments remains lacking. This study used the biological integrity index methodology to examine the effectiveness of fish and macrobenthos in ecological assessments, as well as to analyze environmental factors impacting aquatic ecosystem health assessments. The Daning River basin in Chongqing was selected as the study area, and aquatic ecosystem health assessments were conducted at both regional and local scales. The results indicated that benthos were more abundant than fish, but there were no significant differences in species richness between sub-basins (p > 0.05). The assessment results for F-IBI and B-IBI showed partial discrepancies at the local fine-scale level but were nearly consistent at the regional broad-scale sub-basin level, with only small differences between the F-IBI and B-IBI ratings in one sub-basin. This discrepancy may be due to the diverse water environment. Woodland areas have significant negative relationships with F-IBI, while water areas have significant positive relationships with it. Comprehensively, the assessment findings of F-IBI beat those of B-IBI, implying that F-IBI may be better suited for regional assessments. However, the impact of local water quality issues remains unclear. To enhance ecological restoration, it is vital to conduct additional research into the degree of interference caused by water quality variables. Full article

Physicochemical and microbiological parameters are important indicators of drinking water quality. This study assessed the quality of groundwater used for drinking in four regions of Kosovo at 16 locations using an integrated assessment framework that combined physicochemical, microbiological, and Water Quality Index (WQI) approaches. The results reveal substantial spatial variability in water quality. While most physicochemical parameters remained within recommended limits, elevated values of total dissolved solids (up to 2792.5 mg/L), electrical conductivity (up to 2768.5 µS/cm), nitrate (up to 60.75 mg/L), and phosphate (up to 0.875 mg/L) were observed at several locations, indicating localized hydrogeochemical and anthropogenic influences. Dissolved oxygen levels were generally low (0.68–5.49 mg/L), reflecting limited aeration conditions in groundwater systems. Microbiological analysis revealed critical contamination, with Escherichia coli concentrations up to 299.9 CFU/100 mL, and all sampling sites exceeded permissible limits, indicating widespread fecal pollution and rendering the groundwater unsafe for direct consumption. WQI assessment further confirmed this condition, where 93.75% of locations were classified as medium quality using the NSF-WQI method, whereas the WA-WQI method categorized 68.75% of samples as poor and 6.25% as very poor. The novelty of this study lies in the integrated evaluation of hydrogeochemical processes and microbiological contamination using dual WQI methods and multivariate statistical analysis, providing a comprehensive understanding of groundwater degradation pathways. The findings are significant for policymakers, environmental managers, and public health authorities, highlighting the urgent need for groundwater treatment, improved sanitation infrastructure, and sustainable water resource management strategies in vulnerable regions. Full article

Landscape visual impact assessment is a key component of environmental impact studies, as it enables the identification and management of negative effects on the territory. Traditional methods are often subjective, rely on expert judgement, and consider limited criteria. To address these limitations, this study proposes a quantitative index based on the integration of grey clustering and Shannon entropy complemented with Geographic Information System (GIS). This approach allows classification under uncertainty and the objective weighting of indicators related to physiographic, biotic, and anthropic factors of visual quality, fragility, and accessibility. The methodology was applied to an open-pit mine in Peru. Results show that terrain modifications, presence of artificial elements, and the alteration of water bodies significantly affect visual quality, while the absence of restoration measures, observer exposure, and vegetation type increase fragility and reduce landscape resilience. The proposed method provides a robust, transparent, and reproducible framework that overcomes subjectivity in traditional approaches, supporting more reliable environmental planning and management. Full article

The rapid expansion of seaweed aquaculture has profound impacts on coastal ecosystems, yet the lack of long-term, high-precision spatiotemporal monitoring methods has constrained systematic understanding of aquaculture dynamics and their environmental effects. This study integrated Landsat (1984–2025) and Sentinel-2 (2015–2025) imagery with an attention-enhanced U-Net deep learning model to achieve 41 years of continuous monitoring of seaweed aquaculture in the Dongtou Archipelago, Zhejiang Province, China. The model achieved high extraction accuracy for both Landsat and Sentinel-2 aquaculture areas (F1 scores of 0.972 and 0.979, respectively). On this basis, the cultivation zones were further classified into Porphyra sp. and Sargassum fusiforme cultivation areas by incorporating local aquaculture planning and field survey data. Results showed that the aquaculture area underwent three developmental stages: slow initiation (1984–2000, <3 km²), rapid expansion (2001–2015, 3–8 km²), and high-level fluctuation (post-2015, typically 8–20 km²), reaching a peak of ~30 km² during 2018–2019. Long-term retrieval of water quality parameters revealed that the decline in total suspended matter (from ~80 to 60 mg/L) and chlorophyll (from ~3 to 2 μg/L) within aquaculture zones was significantly greater than that in non-aquaculture areas, providing direct observational evidence for local water quality improvement by appropriately scaled aquaculture. Meanwhile, sea surface temperature showed a sustained increasing trend, with extremely high-temperature days (≥25 °C) exhibiting strong interannual variability, posing potential thermal stress risks to cold-preferring seaweed species. The NDVI (Normalized Difference Vegetation Index) and FAI (Floating Algae Index) indices effectively captured aquaculture phenology (seeding, growth, maturation, harvest), with their interannual peaks exhibiting an inverted U-shaped correlation with corresponding yields (R = 0.82 and 0.79, respectively, based on quadratic regression fitting), preliminarily demonstrating the potential of remote sensing in indicating density-dependent effects. This study systematically demonstrates the comprehensive capability of multi-source satellite remote sensing in long-term dynamic monitoring, environmental effect assessment, and yield relationship analysis of seaweed aquaculture, providing key technical support and scientific basis for aquaculture carrying capacity management and ecological risk prevention in island waters. Full article