Search Results (4,753)

The preparation for next-generation Earth Observation missions, such as the European Space Agency’s (ESA) Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and Land Surface Temperature Monitoring (LSTM), requires robust pre-launch proxy datasets. Because current simulation methodologies frequently rely on isolated, platform-specific approaches, this study proposes a comprehensive, unified multisensor framework capable of dynamically generating operationally realistic CHIME and LSTM datasets from diverse airborne and satellite sources. Three distinct processing pipelines were established. For hyperspectral data simulation, precursor satellite imagery (PRISMA and EnMAP) and high-resolution airborne measurements (HySpex) were harmonized to CHIME’s 30 m specifications utilizing Spectral Response Function (SRF) adjustments, Point Spread Function (PSF) spatial resampling, and 6S atmospheric radiative transfer modeling. For thermal data simulation, archive Landsat 8/9 and ASTER imagery were transformed into LSTM’s target 50 m, 5-band configuration using a synergistic two-step approach: a physics-based Spectral Super-Resolution (SSR) module followed by an AI-driven Spatial Super-Resolution (SpSR) transformer network. Evaluated across highly diverse inland, coastal, and riverine testbeds in Italy, the simulated products demonstrated high spectral, spatial, and radiometric fidelity. While inherently constrained by the native spectral ranges of the input sensors and by the current lack of absolute on-orbit mission data for validation, the downscaled images closely reproduced complex thermal patterns and water-quality gradients. Ultimately, this scalable framework provides the remote sensing community with early access to representative datasets and mission performance assessments, while accelerating pre-launch algorithm development and testing for environmental monitoring applications—particularly those focused on water discharges. Full article

Addressing the environmental challenges posed by the massive stockpiling of phosphogypsum (PG) has become a global concern, highlighting the urgency of developing large-scale, low-cost and resource-efficient utilization approaches for PG. This study was conducted in the rocky desertification areas of southwestern China, where land and water resources are scarce. Two land creation techniques—layered reconstruction (GA) and integrated construction (GB)—were adopted with modified PG to systematically investigate their impacts on soil properties and potato growth, yield and quality. The results showed that both techniques significantly improved soil conditions and enhanced potato yield and quality, with each presenting distinct characteristics in soil improvement. Specifically, the GA technique showed relatively better performance in soil nutrient enrichment, while the GB technique was more conducive to enhancing soil enzyme activity. Compared with the local red soil control, both techniques reduced heavy metal accumulation in potato tubers; however, Pb and Cd contents still exceeded national food safety limits, indicating potential food safety risks. In summary, land creation using modified PG can effectively increase arable land area, improve soil quality in rocky desertification regions, and simultaneously promote potato yield and quality. Nevertheless, as the current results are based on a single-season field trial, they cannot reflect the long-term patterns of heavy metal migration and accumulation. Therefore, for large-scale application, it is necessary to strengthen the monitoring of heavy metal levels in imported soil and long-term regional environmental impacts so as to ensure the quality and safety of agricultural products from reclaimed land. Full article

Water pollution control for wetland lakes has undergone a fluctuating development process. Effective pollution management requires not only scientific water quality monitoring data but also clear identification of pollution sources within the study area. Accordingly, this study investigated Mudong Lake, the core area of the Huixian Wetland, and conducted water quality monitoring in January 2023 (dry season) and June 2023 (wet season). Based on the Water Quality Index (WQI) assessment results, water quality was better in the wet season than in the dry season. To identify pollution sources, the Absolute Principal Component Score-Multiple Linear Regression (APCS-MLR) model was applied. The results showed that pollution in the dry season was mainly derived from aquaculture and agricultural non-point source pollution, anthropogenic point source pollution, and internal release from sediments, while pollution in the wet season exhibited mixed characteristics, driven by agricultural non-point sources, domestic sewage discharge, and natural factors. Source apportionment analysis indicated that composite pollution sources (domestic sewage and aquaculture wastewater), agricultural non-point source pollution, and other unidentified sources contributed 43.71%, 34.11%, and 22.18% of the total pollution load, respectively. The findings of this study can provide a scientific basis for pollution control, emission reduction, and the targeted management of Mudong Lake. Full article

The discharge of effluents from shrimp farms into coastal lagoons can alter food availability, water quality, and pollutant load, potentially affecting the biological performance and safety of farmed bivalves. This study evaluated the influence of shrimp farm effluents on the growth, total weight, and condition index of the Pacific oyster Crassostrea (Magallana) gigas. Two oyster cultivation zones were established in the coastal lagoon of Los Melagos (Sonora, Mexico): one near a shrimp effluent zone (EZ) and the other in a reference effluent-free zone (FZ). Shell height and length, total weight, and condition index were measured monthly, along with environmental variables, including chlorophyll “a” concentration and sea surface temperature obtained from satellite imagery. Oysters cultivated in EZ showed significantly higher total weight, condition index, and growth rates compared to those in FZ. Seasonal fluctuations in chlorophyll “a” were observed, reflected in growth patterns, suggesting greater food availability in waters influenced by effluents. However, these environments pose health risks that require continuous, integrated environmental and health monitoring. Full article

Freshwater ecosystems are increasingly threatened by eutrophication and other anthropogenic and climate-driven pressures that undermine ecological functioning and biodiversity. This study evaluates the transferability of a GIS-based multi-criteria decision analysis (GIS–MCDA) framework with Fuzzy Analytic Hierarchy Process (F-AHP), originally developed for a shallow coastal lake, to a morphologically distinct deep upland lake (Lough Tay, Ireland). Monthly in situ measurements at a single monitoring point in 2024 were analysed together with meteorological variables using Spearman rank correlations. Because spatial interpolation of in-lake water quality parameters was not feasible, eutrophication susceptibility was mapped using four external spatial drivers: distance from water resources (River Cloghoge inflows), land-based nitrogen export potential, distance from environmental pollutants represented by the transportation network, and a wind exposure index derived from a DEM and wind-rose analysis. Criteria were standardized with fuzzy membership functions, weighted using F-AHP (consistency index 0.056), and aggregated using weighted linear combination at 25 m resolution. The resulting Eutrophication Susceptibility Index (ESI) ranged from 0.18 to 0.81, indicating generally moderate to good conditions, with higher ESI values concentrated in the northern lake sector near inflow zones. The results demonstrate that GIS–MCDA can be adapted to lakes with limited monitoring by relying on external drivers, providing a spatial proxy for susceptibility rather than measured trophic status. Full article

The objective of this study was to assess and model the condition and resilience of the Cunas River using integrated indices and multivariate statistics in order to determine the impact of anthropogenic pressure and enhance water security in the Peruvian Andes. Stations in the upper, middle, and lower reaches of the river were monitored during the rainy and dry seasons, applying quality indices (NSF-WQI, WA-WQI, CCME-WQI, and I-WQI), principal component analysis (PCA), hierarchical cluster analysis (HCA), and Spearman’s rank correlation (ρ) to assess the intensity and direction of associations between physical–chemical parameters. The results reveal severe degradation in the lower section of the river, with critical hypoxia and extreme coliform levels during the dry season, drastically exceeding the levels in the upper reach. The I-WQI demonstrated superior performance (322.24; Unfit) by being more sensitive than the NSF-WQI (53.15–59.87). PCA confirmed that low flow explains the greatest variance in pollution (PC1 71.55%), while HCA identified maximum synergy (rescaling distance < 1) between biochemical oxygen demand (BOD₅) and total phosphorus, indicating the collapse of self-purification capacity. The HCA identified a maximum synergy between BOD₅ and total phosphorus during the low-flow season, while the PCA confirmed that low discharge intensifies pollutant concentrations. These findings support the need for resilience-based governance that prioritizes the protection of natural infrastructure. Full article

Studying zooplankton in freshwater ecosystems is crucial for ecological research, providing insight into ecosystem health, biodiversity, and water quality. This study focuses on developing a hybrid approach for studying and analyzing zooplankton communities using machine learning and human expert analysis. The goal of the study was to automate the labor-intensive process of zooplankton analysis as part of a long-term Lake Baikal monitoring program (since 1945), while maintaining continuity with traditional methods. A software and algorithmic system were developed to automate the analysis: images were processed using a two-stage pipeline (object detection using YOLO V11, classification using metric learning and visual transformers), and complex cases and new species were sent to specialists for verification. Over 240,000 images from 811 samples were processed, and models are updated using verified data to adapt to seasonal changes. An updatable database of labeled zooplankton images suitable for statistical analysis and research has been created. A comparison of manual and machine analysis revealed no significant differences in species composition, with accurate detection in 87% of images. This approach allows for scalable monitoring and the accumulation of labeled data arrays for the development of computer vision methods and the assessment of the state of Lake Baikal’s ecosystem. Full article

In response to the increasingly severe issue of heavy metal pollution in water, this paper proposes a method for the robust quantitative analysis of copper ions in purified water and real water samples based on the feature-level fusion of ultraviolet-visible absorption (UV-Vis) spectra and fluorescence spectra, combined with the Extreme Gradient Boosting (XGBoost) algorithm. Specifically, this study introduces a feature-level fusion strategy to overcome the limitations of single-spectrum detection, while the optimized XGBoost algorithm is employed to model the complex non-linear relationships that are difficult to capture using traditional linear regression methods. An optimization algorithm is introduced to fine-tune the model’s hyperparameters, thereby enhancing its predictive performance. Using the coefficient of determination (R²) and root mean square error (RMSE) as evaluation metrics, rapid and accurate detection of copper ions in water is achieved. Experimental results show that, for standard solutions, the optimized XGBoost model achieves a coefficient of determination of 0.9915 and a root mean square error of 2.6663 mg/L; for actual water samples, the optimized XGBoost model achieved a coefficient of determination of 0.9892 and RMSE of 1.2738 mg/L. This demonstrates the model’s strong generalization ability in overcoming the physical limitations of optical probes. This method effectively identifies and quantifies copper ions in water samples, demonstrating good accuracy and stability. Full article

The availability of high-resolution oceanographic data is critical for evidence-based coastal environmental management and climate resilience planning, yet it remains constrained by observational gaps and the prohibitive computational cost of fine-scale hydrodynamic modeling. While downscaling techniques provide a viable pathway, current data-driven approaches often lack statistical physical associations, overlook multivariate environmental interactions, and struggle to represent complex coastal topography. To address these limitations, we present MEOFGAN—an environmentally informed downscaling framework that integrates multivariate empirical orthogonal function (MEOF) decomposition with a generative adversarial network (GAN). The model extracts physically interpretable spatial modes of coupled ocean variables, learns their cross-scale transitions through adversarial training, and systematically incorporates high-resolution bathymetry as a static environmental constraint to enhance spatial fidelity. When applied to the Bohai Sea, MEOFGAN successfully downscales sea surface temperature (SST) and sea surface height (SSH) from 1/4° to 1/12°, achieving error reductions of 30–68% compared to benchmark methods while preserving ecologically relevant structural patterns (SSIM > 0.92). The framework demonstrates strong generalization by reconstructing 500 m resolution distributions of chlorophyll-a (Chl-a), dissolved oxygen (DO), and salinity in Bohai Bay, capturing fine-scale environmental gradients during a documented algal bloom event. This work establishes a methodological framework that can be transferred as a paradigm for generating high-resolution coastal datasets. Rather than serving as a universally transferable pre-trained model, the framework requires region-specific training and application. Data generated in this manner can directly support water quality monitoring, eutrophication assessment, habitat mapping, and regionally tailored climate adaptation strategies. Full article

Surface and bottom water samples collected at 20 sampling points west of Jeju Island, Korea, in the northern East China Sea in July 2025 were analyzed using Temperature–Salinity diagrams and multivariate analyses to characterize water-quality features. A thermocline and halocline were observed within the upper 40 m throughout the study area, together with a west-to-east salinity gradient. In surface waters, Changjiang Diluted Water (CDW) was identified in the western and central parts of the study area, whereas the Taiwan Warm Current was identified in the eastern part. In contrast, bottom-water masses were not distinctly separated, and Yellow Sea Cold Water and Kuroshio-origin Water overlapped in the central part of the study area. Multivariate analyses showed that the southwestern part of the study area was characterized by potential CDW influence in surface water. The results highlight layer-specific clustering patterns, with surface water clustering mainly in relation to water-mass characteristics, whereas bottom waters clustered primarily along a water-depth gradient. CDW may also transport nutrient-imbalanced water characterized by excess nitrogen and relative phosphorus deficiency. This study delineates the summer extent of CDW influence west of Jeju Island and provides a basis for distinguishing potentially CDW-influenced zones in the study area under summer hydrographic conditions. These findings provide an observation-based basis for sustainable marine monitoring in the northern East China Sea, with implications for assessing nutrient imbalance and contaminant transport under summer hydrographic conditions. Full article

The estuarine and coastal regions of India and Taiwan are under increasing threat from pollutants such as microplastics (MPs) and heavy metals including cadmium (Cd). These contaminants are known to have adversely affect biodiversity and water quality. In this study, the combined toxic effects of polyethylene microplastics (PE-MPs) and Cd were evaluated using Poecilia sphenops, a euryhaline fish species, selected for its adaptability to varying salinity conditions. P. sphenops were exposed to Cd (20, 40, and 60 μg/L), MPs (8, 16, 24 mg/L), and co-exposure combinations ranging from Cd 5 μg/L + MPs 4 mg/L to Cd 20 μg/L + MPs 16 mg/L Results showed significant (p< 0.05) negative effects on growth parameters including body weight gain, specific growth rate (SGR), and survival rate. Hematological analysis revealed significant (p< 0.05) decreases in hemoglobin (Hb), red blood cells (RBCs), and white blood cells (WBCs), indicating impaired oxygen transport and compromised immune function. Elevated blood glucose levels indicated physiological stress, while reduced total protein levels suggested a compromised nutritional status. Antioxidant enzyme activities, including catalase (CAT), superoxide dismutase (SOD), and glutathione peroxidase (GPx), were significantly (p < 0.05) decreased in the toxicant-treated groups compared with the control. Digestive enzyme activities (proteases, amylases, and lipases) were also reduced, suggesting impaired digestion and nutrient assimilation. The study also included a comparative assessment of water quality between the exposed and control tanks. Water quality parameters such as turbidity, salinity, hardness, alkalinity, chloride, fluoride, and total suspended solids (TSSs) were elevated in the toxicant-treated media, accompanied by a notable decline in dissolved oxygen (DO) levels. These findings highlight the urgent need for integrated pollution control and water quality monitoring, particularly in coastal regions vulnerable to desalination discharges and plastic contamination. Sustainable management strategies must address these complex interactions between multiple pollutants to protect aquatic ecosystems. 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

Urban squares remain underrepresented in conservation-oriented literature compared with parks, street trees and green infrastructure. This scoping review uses CS-derived categories as an analytical lens to examine how the literature on urban squares frames conservation targets, direct threats, contributing factors and conservation actions. Following PRISMA-ScR, we searched Scopus and Web of Science for English-language peer-reviewed articles (2014–2024). After screening, 69 studies were included. Full texts were coded into CS-derived components and synthesised through frequency distributions, a cross-case conceptual synthesis, and an exploratory clustering of recurrent CF-DT-CT configurations. The reviewed literature is strongly centred on human-centred outcomes, particularly health, air quality and water quality, while biodiversity-related targets remain comparatively underrepresented. The most frequently investigated direct threats are pollution-related and linked to natural system management and modification, whereas other pressures are addressed less consistently. Contributing factors are dominated by meteorological conditions and vegetation coverage and composition. Reported conservation actions emphasise monitoring technologies, regulatory policy and green infrastructure, while others receive limited attention. Together, these analytical steps help make recurrent pathways and underrepresented dimensions more explicit, providing a more transparent evidence base for context-sensitive urban planning and nature-based solutions. Full article

Introduction: Healthcare-associated infections are a significant public health challenge, particularly in resource-limited settings. While hand hygiene is critical for infection prevention, contaminated water from hand hygiene stations (HHSs) in healthcare facilities (HCFs) may undermine infection control efforts. Chlorination can reduce microbial contamination in HHSs, ensuring that water intended for hygiene does not become an infection source. Methods: Water quality was monitored before and after the installation of on-site chlorine dispensers (CDs) in water tanks and HHSs of HCFs in Quetzaltenango, Guatemala, to evaluate their effectiveness in improving water quality. Focus groups were conducted to develop action plan proposals to ensure the intervention’s sustainability. Results: Before the intervention, 75% of HHS water samples tested positive for total coliforms, with 50% testing positive for presumptive extended-spectrum beta-lactamase (ESBL)-producing total coliforms, while 20% were E. coli-positive, with 50% presumptive ESBL-producing E. coli. After installing CD, 1% of samples were coliform-positive over a six-month period. Focus groups identified resource limitations and political barriers and proposed solutions such as developing operational manuals, strengthening inter-institutional relationships, and forming alliances with external organizations. Conclusion: Localized chlorination was successfully implemented using a community participatory approach to improve water quality in resource-limited HCFs. These findings have important implications for infection prevention and control. Full article