Search Results (6,488)

This study explores the environmental justice issues associated with landfill compensation policies in Bantargebang, Indonesia. Although compensation programs have been implemented for many years, communities living near landfills continue to experience ongoing environmental damage and significant health concerns. Using a qualitative descriptive method, this research explores systemic barriers through in-depth interviews, observations, and water quality analysis. The findings indicate that labeling the program as “Social Assistance” within the Local Government Information System (SIPD) redefines ecological compensation as a fixed form of charity, rather than as a mechanism for genuine environmental restitution. Laboratory data show severe bacteriological contamination, with Total Coliform levels reaching 95%, forcing residents to bear substantial “hidden costs” for clean water, perpetuating a cycle of financial dependence. The growing normalization of health hazards is evident in over 5000 annual cases of acute respiratory infections, and the deadly landslide in March 2026, in which claimed seven lives and injured six others. These incidents underscore the failure of existing remediation approaches to safeguard human dignity and well-being. To address these shortcomings, this study proposes the adoption of an Integrated Compensation Model based on Green Social Work. This model emphasizes structural investment, spatial risk-based indices using quantitative data, and budget coding adjustments within the SIPD. This approach highlights the urgent need to move beyond temporary charitable assistance and instead pursue meaningful environmental justice, while positioning social workers as “Social-Ecological Brokers” who help restore dignity and well-being in communities often treated as “sacrifice zones.” Full article

Against the backdrop of multi-source water transfers and increasingly frequent extreme rainfall, short-term deterioration of reservoir inflow water quality has become a key risk to intake safety, treatment operations, and urban water-supply security. Traditional assessments based on static thresholds and annual or seasonal averages often fail to identify high-risk periods at the event scale. Using continuous online monitoring data from 2021 to 2024 for the inflow of Yuqiao Reservoir, Tianjin, China, this study developed a month-specific dynamic-threshold framework and green/yellow/red risk windows and integrated a reach-wise river–reservoir routing scheme; a two-box decay model; and a three-class risk trigger into a unified analytical framework for long-term background characterization, event propagation analysis, source-contribution interpretation, and early-warning evaluation. Results show that the permanganate index (COD_Mn) exhibits an overall stable-to-declining background with pronounced wet-season pulses, whereas total nitrogen (TN) and total phosphorus (TP) remain at moderate-to-high levels, with yellow/red risk windows clustering markedly in the wet season. In typical red and yellow events, nitrogen contributions from upstream control sections progressively accumulate toward the reservoir inlet along the river–reservoir cascade system, whereas in some events the residual contribution from unmonitored near-inlet inflows becomes dominant. The COD_Mn-based three-class trigger achieves an overall accuracy of approximately 71.5% and shows comparatively strong identification of yellow-level risk, while remaining conservative for red-level alarms. These findings indicate that coupling month-specific dynamic thresholds with event-scale routing-decay analysis and trigger-based classification can support inflow monitoring, intake-risk early warning, and coordinated operation of key upstream reaches and near-reservoir control zones in water-transfer–reservoir integrated systems. Full article

South African irrigation schemes face critical challenges of water scarcity, infrastructure deterioration, and limited monitoring capacity, threatening agricultural productivity and food security. This scoping review systematically analyses 59 peer-reviewed publications (2000–2025) on sensor-based and acoustic irrigation monitoring technologies in South Africa, using transformer-based natural language processing (Sentence-BERT embeddings), unsupervised Machine Learning (UMAP dimensionality reduction, HDBSCAN clustering), and geospatial mapping applied to literature retrieved from Web of Science and Scopus. Results show that water quality monitoring (42.4% of studies) and remote sensing (25.4%) dominate the national research landscape, while soil moisture sensing and modelling remain comparatively limited. Notably, no peer-reviewed studies applying acoustic monitoring technologies to irrigation were identified, representing a critical gap despite proven international applications for leak detection (95–98% accuracy), widespread infrastructure aging (over 50% of schemes exceeding 30 years), and reported water losses of 30–60% in poorly managed systems. Reported experimental water savings range from 15% to 30%, yet applications remain largely confined to pilot-scale implementations concentrated within a limited number of Water Management Areas. Persistent adoption barriers include infrastructure unreliability, financial inaccessibility, limited digital literacy, and weak institutional coordination. The review recommends: (i) expanding research coverage across underrepresented regions and Water Management Areas; (ii) strengthening extension support and technical training to enable broader adoption; and (iii) integrating low-cost sensor networks with predictive, data-driven irrigation advisory systems. These priorities aim to support scalable, context-sensitive irrigation modernisation under increasing water scarcity pressures. Full article

Responding to rapid urbanization, this study examines the trade-offs and synergies of ecosystem services (ESs) at the county scale in the Zhengzhou metropolitan area and constructs an ecological security pattern. Using the InVEST model, we quantified carbon storage (CS), soil conservation (SC), habitat quality (HQ), water yield (WY), and food production (FP). We then analyzed their trade-offs and synergies using the geographically weighted regression model, identified driving factors with an optimal parameter-based geographical detector model, detected ecosystem service bundles via a Self-organizing map model, and constructed an ecological security pattern based on circuit theory. The results showed that: (1) From 2003 to 2023, ES spatial distribution remained stable overall, with weak trade-offs and synergies. Locally, WY and HQ declined, while SC and FP increased. (2) Slope and DEM enhanced SC, whereas urban expansion consistently weakened CS, HQ, and FP. Moreover, slope played an increasingly prominent role in regulating WY. (3) Key synergistic bundles with stable spatiotemporal distribution were identified as ecological sources, leading to the construction of ecological security pattern characterized by “four districts, one corridor, and one belt.” This provides a framework for integrating ecological space protection and restoration into urban development. Full article

Streamflow and sediment yield are key components of river systems and are strongly influenced by anthropogenic land use changes. Soil erosion remains a critical environmental concern, degrading crop productivity, water quality, aquatic ecosystems, and river morphology. Sediment transported from croplands to rivers and reservoirs introduces contaminants and exacerbates water pollution. This study evaluates the effectiveness of Best Management Practices (BMPs) in the Nagavali and Vamsadhara watersheds using a calibrated and validated Soil and Water Assessment Tool (SWAT) model, targeting high sediment-yielding areas. BMP scenarios—including filter strips, sedimentation ponds, contour farming, and contour stone bunding—were assessed at watershed and sub-watershed scales. At the watershed scale, 10 m filter strips reduced sediment yield by 29% and 53% in the Nagavali and Vamsadhara watersheds, respectively. Combined BMP implementation further reduced sediment yield by 37% and 72%, and streamflow by 16.5% and 54%, respectively. These reductions persisted under future climate scenarios. The results highlight the potential of targeted BMP implementation to enhance watershed sustainability and support informed land and water management decisions. Full article

Surface water systems are increasingly exposed to multiple pressures generated by climate variability, intensified water resource exploitation, and evolving geopolitical dynamics. This study provides a novel contribution by identifying critical threshold effects and non-linear interactions that influence nitrate concentrations through an integrated information systems framework. It develops an integrated information-system-based analytical framework that combines hydrological, climatic, geopolitical, and strategic indicators to shape the broader contextual framework within which hydrological and climatic pressures operate, rather than serving as direct predictors. Considering the nitrate concentration in rivers as a key parameter of water quality, the paper goes beyond univariate analysis of nitrite concentration, examining its relationship with four explanatory variables: the Water Exploitation Index Plus (WEI+), the number of heat stress days (Heat_Stress), the Geopolitical Risk Index (GPR), and a proxy variable representing the presence of strategic infrastructure (Nuclear_State) using a Reduced Error Pruning Tree (REPTree) decision tree algorithm with 10-fold cross-validation. The results indicate that climatic stress emerges as the primary predictor, with a critical threshold of approximately 7.83 heat stress days, beyond which nitrate concentrations increase significantly. Under conditions of high climatic stress and intensive water exploitation (WEI+ ≥ 67.39), predicted nitrate levels exceed 20 mg/L and can reach extreme values of up to 58.82 mg/L. In contrast, low hydrological pressure (WEI+ < 0.39) combined with moderate climatic stress is associated with very low nitrate concentrations, around 2.75 mg/L. The model demonstrates strong predictive performance, with a correlation coefficient of 0.976, a Mean Absolute Error (MAE) of 0.593, a Root Mean Squared Error (RMSE) of 2.046, and a Receiver Operating Characteristic (ROC) area exceeding 0.94 for classification tasks. While geopolitical and strategic variables do not act as direct predictors, they contribute to shaping the contextual framework influencing water resource management and environmental vulnerability. Overall, the study highlights the non-linear and systemic nature of water quality dynamics and demonstrates the effectiveness of decision tree-based models within integrated information systems for supporting environmental monitoring and decision-making under conditions of climate stress and geopolitical uncertainty. Full article

Many cities adopt greening strategies to reduce contamination from combined sewer overflows (CSOs). Nonetheless, quantifying the impact of urban greening on CSO-affected water quality at the city scale remains challenging. To address this challenge, this work leveraged supervised learning to link water swimmability with the greening of a CSO shed (the drainage area of a CSO outfall), using New York City (NYC) as a case study. Random forest classification models were built to predict water swimmability after rainfall at 46 sites in NYC water bodies impacted by CSOs. A 14-feature model (AUROC =0.81, accuracy = 0.78) revealed that greening improved local water quality. However, water flow speed, antecedent rain depth, and CSO shed area were also influential. A simplified four-feature model (AUROC = 0.8, accuracy = 0.75) explored links between levels of greening and the probability of non-swimmable waters ($P_{ns}$) following different 18 h rainfall depths. Increased greening was found to be most impactful in reducing $P_{ns}$ for CSO sheds discharging to water bodies with flow speeds < 6 cm/s. For CSO sheds discharging to water bodies with flow speeds $\ge$ 14.7 cm/s, urban greening had no impact on $P_{ns}$. The work illustrates the utility of supervised learning in supporting citywide decisions regarding urban greening investments. Full article

Accurate prediction of effluent water quality is essential for the intelligent and sustainable operation of wastewater treatment plants (WWTPs). However, this task remains challenging due to the strong nonlinearity, long-term temporal dependencies, and severe fluctuations inherent in influent characteristics. In this study, a novel data-driven framework termed the Multi-Scale Temporal Representation-Enhanced Informer (MTRE-Informer), is proposed to predict key effluent quality indicators, including total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD). To ensure data quality and computational efficiency, a generative recurrent learning framework is first employed for anomaly detection and correction, followed by variance inflation factor (VIF)-based feature selection to mitigate multicollinearity. Furthermore, feature contribution analysis is conducted to improve model interpretability. Subsequently, the core MTRE-Informer architecture utilizes hierarchical multi-scale temporal representation learning to simultaneously capture local patterns and long-term dependencies within the complex dynamics of the wastewater treatment process. Experimental results demonstrate that the MTRE-Informer achieves robust and stable predictive performance across diverse operational datasets. For TN prediction, the proposed framework attains a coefficient of determination () of 0.9637 and a mean absolute percentage error (MAPE) of 3.39%. Compared with baseline approaches, the improvement ranges from 3.8% to 14.2%, validating its superior capability. To further enhance model robustness, an anomaly detection and correction strategy based on a generative recurrent learning framework is employed. In addition, feature contribution analysis and VIF-based feature selection are conducted to improve interpretability, mitigate multicollinearity, and enhance computational efficiency. Overall, this framework provides a reliable and practical solution for real-time effluent quality prediction, facilitating the intelligent management of WWTPs. Full article

Aquaculture sustainability in rapidly urbanizing regions is increasingly threatened by heavy metal contamination originating from complex anthropogenic land-use patterns. This study used an integrated model to evaluate the molecular-to-human health continuum in hybrid catfish (Clarias gariepinus × Clarias macrocephalus) sourced from Pathum Thani, Thailand’s primary aquaculture hub. We integrated geospatial land-use data with heavy-metal quantification, oxidative-stress biomarkers, and transcriptional profiling to assess how canal-specific water quality modulates fish health and consumer risk. The results revealed significant spatial heterogeneity in metal concentrations, corresponding to the province’s 27% urban–industrial land-use footprint. While water quality generally met regulatory limits, a pronounced aqueous–biotic discrepancy, “bioaccumulation paradox” was identified at certain sites, where muscle and hepatic tissues exhibited lead (Pb), chromium (Cr), and nickel (Ni) levels that substantially exceeded international safety standards. Biochemical and molecular analyses provided functional evidence of physiological distress, specifically significantly elevated malondialdehyde (MDA) levels, and the transcriptional modulation of cat, cyp1a, gpx, met, tnf, and star genes indicated that chronic metal exposure overwhelmed antioxidant defenses and induced potential endocrine disruption. Moreover, human health risk assessments revealed that the hazard index (HI) and target cancer risk (TR) exceeded unacceptable thresholds at multiple hotspots, indicating that Cr is a primary carcinogenic driver. These findings highlight a “GAP Paradox,” where farm-level certifications are insufficient to mitigate risks posed by the surrounding canal network. This study presents vital evidence-based risk profiles that necessitate a transition to a spatially based regulatory framework, incorporating geospatial land-use monitoring into national food safety policies to protect both aquaculture viability and public health. Full article

This study proposes and analyzes an integrated framework for monitoring potable water quality and laboratory effluent management in universities, with emphasis on its practical application in a Brazilian public institution. Adopting a qualitative and documentary approach, the research was based on high-impact scientific publications, institutional reports, and environmental databases. The results demonstrate that effective water and effluent governance depends on the interaction of three core dimensions: regulatory compliance, technological innovation, and institutional governance. These elements operate synergistically to ensure transparency, risk prevention, and environmental accountability. The proposed University Laboratory Water Monitoring Framework (UL-WMF) illustrates how universities can transform water control into a managerial and educational tool aligned with sustainability goals. The illustrative institutional application revealed potential for integrating Internet of Things (IoT) and Laboratory Information Management System (LIMS) technologies into environmental management routines, reinforcing universities’ strategic role in achieving global sustainability objectives. Despite relying on secondary data, this study provides a scalable foundation for decision support systems and future empirical validation. The novelty of the University Laboratory Water Management Framework (UL-WMF) lies in its integration of potable water monitoring and laboratory effluent governance into a single operational framework, addressing a gap in the existing literature and offering a model specifically tailored to the context of universities in developing countries. The applied component of the study consists of an illustrative institutional case constructed exclusively from publicly available environmental and governance reports. This illustration serves to demonstrate the operational relevance of the proposed framework, without implying field measurements or primary data collection. Full article

This study proposes and evaluates a specialized Recreational Water Quality Index (IR-WQI) designed to prioritize the bathers’ safety and comfort. Focusing on the Năvodari–Mamaia sector (2022–2024), the research investigates how different weighting configurations—prioritizing either microbiological safety or physicochemical stability—affect the accuracy of bathing water assessments. The IR-WQI was tested across four scenarios, comparing the sensitivity of a specialized pH-based “bather-comfort” penalty function against models that include salinity as a weighted constant. Results demonstrate high categorical stability, with 93.3% of monitoring sites maintaining their qualitative classification regardless of the weighting scheme. However, the inclusion of salinity was found to inflate quality scores, potentially masking fecal contamination at vulnerable sites. Scenario 1, which prioritizes microbiological indicators (60% weight) and incorporates a pH filter, provides a transparent and conservative diagnostic tool for coastal managers, thereby supporting sustainable tourism and informed decision-making for beach safety. Full article

The extensive deployment of automatic water quality monitoring stations has generated substantial volumes of time-series data. Effectively utilizing these data is crucial for enhancing prediction accuracy. To address the limitations of existing models in capturing complex inter-indicator relationships and multi-scale temporal features, this paper proposes a hybrid prediction model integrating time series decomposition with deep learning techniques. Adopting a “decomposition–prediction–reconstruction” paradigm, the model first decomposes the raw time series into trend, seasonal, and residual components using STL (Seasonal–Trend decomposition using LOESS). For the trend component, an improved Graph Convolutional Network (GCN) is designed to explicitly model the spatial dependencies among different water quality indicators. For the seasonal component, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is employed for multi-scale signal analysis, followed by a coupled Long Short-Term Memory–Convolutional Neural Network (LSTM-CNN) unit to capture both long-term dependencies and local features. To validate the efficacy of the proposed model, experiments were conducted on three real-world water quality datasets from different watersheds. Experimental results demonstrate that the proposed model outperforms mainstream baseline models, including StemGCN, LSTM-CNN, CEEMDAN-LSTM-CNN, and Attention-CLX. Across the three datasets, the model consistently outperforms the best-performing baseline, achieving reductions in MAE ranging from 13.8% to 24.5% and up to a 45.3% reduction in RMSE on a single dataset, while the highest correlation coefficient between predicted and observed values reaches 0.855. These findings demonstrate that the proposed decomposition–integration framework effectively enhances the accuracy and stability of multivariate water quality prediction, offering a promising tool for supporting sustainable water resource management. Full article

The transition from starch-dominated to protein-enriched gluten-free systems represents a critical step in improving the functional and nutritional quality of composite flours. This study investigated the effects of progressive substitution of Amaranthus caudatus (amaranth) with Lupinus mutabilis (Andean lupin) on the physicochemical, rheological, and antioxidant properties of gluten-free flour blends. A multimodal dataset comprising 33 variables across six measurement domains (proximal composition, hydration properties, thermomechanical behavior, pasting profiles, particle size, and antioxidant activity) was analyzed using an integrated framework combining univariate inference (FDR-adjusted p-values), PCA, Multiple Factor Analysis (MFA), and sparse Partial Least Squares Discriminant Analysis (sPLS-DA). Results revealed that increasing lupin content (10–40%) significantly increased protein and fiber levels while decreasing starch content, leading to higher water absorption capacity and reduced peak viscosity and setback. Multivariate models showed that the protein/fiber–starch trade-off was the principal axis of compositional differentiation (PC1, ~41% variance), while PC2 captured rheological and antioxidant variability, with formulations containing higher proportions of amaranth exhibiting greater antioxidant activity. The sPLS-DA model achieved 72% separation accuracy with moisture, protein, water absorption, and torque parameters as top discriminants. These findings provide an evidence-based framework for gluten-free flour optimization using Andean crops and highlight how statistical modeling can inform targeted formulation decisions. The approach is transferable to other compositional transitions in food systems, underscoring the utility of multivariate analytics in applied food research. The multivariate framework further suggests that intermediate substitution levels may offer an optimal balance between nutritional enrichment and rheological functionality. Full article

Groundwater plays an indispensable role in daily life. However, with the continuous advancement of industrialization, more attention should be paid to the quality of groundwater and the associated potential health risks in areas surrounding industrial parks. In this study, groundwater samples collected in the city of the Tibetan Plateau during the wet season (WS) and dry season (DS) were analyzed using Piper diagrams, Gibbs diagrams, and correlation analysis. The results elucidated the hydrochemical characteristics, formation mechanisms, and controlling factors of groundwater in the area. Groundwater potability was assessed using the Entropy-weighted Water Quality Index (EWQI) method. In addition, the health risk assessment model was applied to evaluate potential risks for four population groups, with NO₃⁻ and F⁻ selected as representative groundwater pollutants. The findings revealed that groundwater in the study zone was typically moderately alkaline and characterized primarily as soft–fresh and hard–fresh. The groundwater in both seasons mainly exhibited HCO₃–Ca chemical facies. Water–rock interactions involving silicate and carbonate minerals were identified as key processes controlling the hydrochemical composition in both seasons. EWQI results showed that groundwater quality for drinking purposes was excellent in the seasons. Sensitivity analysis further showed that Cl− exerted the greatest influence on the drinking water quality evaluation in both seasons. Health risk assessments revealed that the risks posed by NO₃⁻ and F⁻ to infants, children, adult females, and adult males remained within acceptable limits (with max values of 0.63, 0.39, 0.28, and 0.33 in the WS, and 0.59, 0.36, 0.26, and 0.31 in the DS, respectively). However, infants exhibited greater susceptibility than the other groups across seasons, with a risk index approximately twice that of adults. Overall, the findings contribute valuable insights for the sustainable management and planning of groundwater resources in the study zone. Future research could refine the risk assessment model with localized data and explore mitigation strategies for elevated risks in specific seasons or regions. Full article

Lake Erhai is an important plateau freshwater lake in China. It serves not only as a crucial drinking water source for the local region but also as the core area of the Cangshan Erhai National Nature Reserve. Consequently, Lake Erhai plays an extremely significant role in the local economy, society, and ecology. Since 2000, the water quality of Lake Erhai has continuously deteriorated, showing a eutrophic trend. To identify the primary driving forces behind these water quality changes, this study employed stepwise regression analysis. Climate conditions, socio-economic development within the basin, and implementation of environmental protection measures (IEPMs) were considered influencing factors for a comprehensive and systematic analysis of Lake Erhai’s water quality. The results indicate that rising air temperature may increase total phosphorus (TP) concentration, while rainfall may elevate both TP and total nitrogen (TN) levels. In contrast, higher wind speed may reduce chemical oxygen demand (COD_Mn), TP, and TN concentrations. Socio-economic development, meanwhile, may contribute to increased COD_Mn concentration. Based on these findings, this paper proposes recommendations focusing on formulating more effective non-point source pollution control measures and strengthening water quality monitoring in Lake Erhai during summer. By identifying the key natural and anthropogenic drivers of water quality changes in Lake Erhai, this study provides a scientific basis for the development of targeted pollution control strategies and directly contributes to the protection of clean water sources. Moreover, its revelation of the coupled impacts of climate change and socio-economic activities enhances understanding of plateau lake ecosystem resilience. This insight is critical for ensuring regional ecological security and serves as a model for advancing sustainable development goals in similar lake systems worldwide. Full article