Search Results (986)

Background: Effective prevention of jellyfish stings is crucial for human safety during marine activities. Traditional protective methods are often limited in terms of coverage area and duration of protection; Methods: This study designed and tested a novel jellyfish-repellent textile by coating waterproof polyester fabric with copper ion-loaded multicompartmental nanoparticles, which repel jellyfish by disrupting their cellular membranes and physiological functions. The nanoparticles were synthesized to enable spatial separation of components, enhance stability, and allow controlled copper ion release. They were applied to the fabric in one step via high-voltage electrostatic spray technology, followed by characterization using SEM and FT-IR. The copper sulfate release profile and nanoparticle adhesion were analyzed. Jellyfish-repellent efficacy was evaluated, along with biocompatibility tests including skin sensitization (Magnusson and Kligman method), skin irritation (Draize test), and cytotoxicity (MTT assay on L929 cells and human dermal fibroblasts). Results: SEM confirmed the formation of uniform multicompartmental nanoparticles with sizes ranging from 2.28 to 3.15 μm. FT-IR verified successful anchoring of Cu²⁺ ions to fabric fibers through coordination with hydroxyl groups. Drug release tests demonstrated water-triggered controlled release of copper ions lasting over 168 h, with nanoparticle retention rates exceeding 70% on all fabrics. The textile showed significant effectiveness in repelling jellyfish. Moreover, no apparent sensitization, irritation, or cytotoxicity was observed. Conclusions: A novel jellyfish-repellent textile was successfully developed using copper ion-loaded multicompartmental nanoparticles. This textile provides a promising solution for preventing jellyfish stings and contributes to the advancement of protective gear for marine activities. Full article

Accurate precipitation data is vital for hydrological modeling, climate research, and water resource management, especially in arid regions like the United Arab Emirates (UAE), where rainfall is sparse and highly variable. This study assesses the performance of the Multi-Source Weighted-Ensemble Precipitation v2.8 (MSWEP) dataset against ground-based gauge data and three satellite precipitation products—CMORPH, IMERG, and GSMaP—across the UAE from 2004 to 2020. Evaluation metrics include statistical, categorical, and extreme precipitation indices. MSWEP shows a moderate correlation with gauge data (mean CC = 0.62), performing better than CMORPH (0.54) but below IMERG (0.68). It also yields lower RMSE and MAE than CMORPH and GSMaP, indicating improved error metrics. However, MSWEP overestimates light rainfall and underestimates extreme events, reflected in a lower KGE (0.42) and weak performance in the 95th percentile rainfall, especially in coastal and mountainous areas. Seasonal analysis reveals overestimation in winter and underestimation during summer convective storms. While MSWEP offers strong global coverage and temporal consistency, its application in arid environments like the UAE requires bias correction. These findings highlight the need for integrating multiple datasets and regional adjustments to enhance rainfall estimation accuracy for hydrological and climate-related applications. Full article

The Guizhou Plateau, with the most extensive and representative karst landforms worldwide, is characterized by severe soil erosion and a highly fragile ecological environment. However, large-scale assessments of soil conservation services in this region remain limited. A key challenge lies in identifying appropriate datasets and methodologies for regional-scale soil conservation service evaluations, particularly under conditions of data scarcity or limited data accuracy. In this study, Unmanned Aerial Vehicle imagery, runoff plot observations, ground survey data, and multi-source satellite remote sensing data were integrated to refine LS and C in the Revised Universal Soil Loss Equation (RUSLE), thereby establishing a parameterized and localized soil erosion model. This improvement provided a methodological foundation for soil conservation service research in the region. Subsequently, the spatiotemporal variations in soil conservation services in the Guizhou Plateau over the past two decades were assessed. Furthermore, the relationships between soil conservation services and other key ecosystem services, including water conservation and carbon sequestration, were quantitatively examined, and the driving factors were analyzed. Soil conservation on the Guizhou Plateau exhibited an improving trend from 2000 to 2020. In karst areas, the relationship between soil conservation and water conservation was primarily influenced by temperature, altitude, and vegetation coverage, whereas in non-karst areas, it was regulated by rainfall and slope. Ecological restoration projects have enhanced the synergy between soil conservation and carbon sequestration by promoting vegetation cover. These findings could contribute to the next stage of ecological engineering initiatives and ecological policy implementation in Guizhou. Full article

Surface soil moisture (SSM) is essential for crop growth, irrigation management, and drought monitoring. However, conventional field-based measurements offer limited spatial and temporal coverage, making it difficult to capture environmental variability at scale. This study introduces a multimodal soil moisture estimation framework that combines synthetic aperture radar (SAR), optical imagery, vegetation indices, digital elevation models (DEM), meteorological data, and spatio-temporal metadata. To strengthen model performance and adaptability, an intermediate fine-tuning strategy is applied to two datasets comprising 10,571 images and 3772 samples. This approach improves generalization and transferability across regions. The framework is evaluated across diverse agro-ecological zones, including farmlands, alpine grasslands, and environmentally fragile areas, and benchmarked against single-modality methods. Results with RMSE 4.5834% and $R^2$ 0.8956 show consistently high accuracy and stability, enabling the production of reliable field-scale soil moisture maps. By addressing the spatial and temporal challenges of soil monitoring, this framework provides essential information for precision irrigation. It supports site-specific water management, promotes efficient water use, and enhances drought resilience at both farm and regional scales. Full article

Amid intensifying global climate change and accelerating urbanization, maintaining a balance between carbon emission reduction has become essential for achieving sustainable development. This research investigates the spatiotemporal evolution and driving mechanisms of carbon sequestration services in the ecological development zone of northern Guangdong, China. By integrating Big Geodata technology with the InVEST model, the study quantitatively evaluates both the supply and demand dimensions of carbon sequestration services using land-use, nighttime light, and socioeconomic data. Carbon storage capacities were estimated for different land-use types (including cropland, forest, grassland, water body, built-up land, and undeveloped land), while carbon emissions were spatially distributed based on nighttime light intensity, providing a holistic perspective on the regional carbon budget. The findings indicate significant spatial heterogeneity: the western region exhibits an average carbon sequestration capacity approximately 20% higher than the eastern region, due to extensive forest and grassland coverage, whereas urban areas exhibit higher carbon demand coupled with insufficient supply. Through an analysis of land-use transfer matrices and contribution assessment, land-use transformations, particularly the conversion of ecological land to urban built-up areas, were quantitatively identified as the primary factor disrupting the regional carbon balance. This study proposes actionable territorial spatial planning strategies, such as prioritizing ecological conservation in high-carbon-supply areas and promoting low-carbon urban renewal in high-demand zones, directly derived from the spatial mismatch patterns revealed by the InVEST model outputs. These insights contribute significantly to regional sustainable development practices and global climate governance. Full article

Transitional attack represents a pivotal tactic in modern firefighting, whose efficacy is profoundly contingent upon the impact characteristics of water streams and their subsequent distribution patterns. This study integrates computational fluid dynamics (CFD) simulations with experimental validation to develop a momentum decomposition model for jet impingement on a ceiling. The model analyzes the dominant mechanisms of tangential spread and normal rebound on water distribution and optimizes water application strategies. Theoretical analysis reveals that upon ceiling impact, the normal velocity component of the stream undergoes rapid attenuation, causing the flow to be predominantly governed by tangential diffusion. This phenomenon results in an asymmetrically elliptical ground distribution, characterized by a significant concentration of water volume at the terminus of the diffusion path, while wall boundaries induce further water accumulation. A comparative analysis of the stream impact process and water distribution demonstrates a high degree of concordance between experimental and simulation results, thereby substantiating the reliability of the proposed model. Numerical simulations demonstrate that an increased jet angle markedly improves both coverage area and flux density. Higher water pressure enhances jet kinetic energy, leading to improved distribution uniformity. Appropriately extending the horizontal projection distance of the water jet further contributes to broadening the effective coverage. The parametric combination of a 49° jet angle, water pressure of 0.2–0.25 MPa, and a relative horizontal distance of 1.5–2.0 m is identified as optimal for overall performance. This research provides a scientific foundation and practical operational guidelines for enhancing the efficiency and safety of the transitional attack methodology. Full article

Remediated landfills require long-term monitoring due to ongoing processes such as settlement, water infiltration, leachate migration, and biogas emissions, which may lead to cover degradation and environmental risks. Traditional ground-based inspections are often time-consuming, costly, and limited in terms of spatial coverage. This study presents the application of Unmanned Aerial Vehicle (UAV)-based remote sensing methods for the structural assessment of a remediated landfill. A multi-sensor approach was employed, combining geometric data (Light Detection and Ranging (LiDAR) and photogrammetry), hydrological modeling (surface water accumulation and runoff), multispectral imaging, and thermal data. The results showed that subsidence-induced depressions modified surface drainage, leading to water accumulation, concentrated runoff, and vegetation stress. Multispectral imaging successfully identified zones of persistent instability, while UAV thermal imaging detected a distinct leachate-related anomaly that was not visible in red–green–blue (RGB) or multispectral data. By integrating geometric, hydrological, spectral, and thermal information, this paper demonstrates practical applications of remote sensing data in detecting cover degradation on remediated landfills. Compared to traditional methods, UAV-based monitoring is a low-cost and repeatable approach that can cover large areas with high spatial and temporal resolution. The proposed approach provides an effective tool for post-closure landfill management and can be applied to other engineered earth structures. Full article

Tropical forests are critical regulators of global water and energy cycles, with evapotranspiration ($ET$) being a key ecohydrological process. However, monitoring $ET$ over tropical forests is a challenge due to their complex structure, and the logistical difficulties in obtaining observations that are both spatially representative and have wide coverage. Remote sensing data offer an alternative to these limitations, although the effectiveness of $ET$ remote sensing-based models over these areas is not well-known. Thus, this study evaluates the performance of four remote sensing-based $ET$ models (SSEBop, geeSEBAL, PT-JPL and T-SEB) in tropical forests. We compared models’ estimations against flux tower observations and assessed the uncertainty in models’ outputs driven by different meteorological input forcings. Additionally, we conducted a spatial–temporal analysis of models’ response to the impact of deforestation on $ET$ patterns. Our results showed a good agreement between modeled and observed $ET$ using the most accurate meteorological input dataset (RMSEs ranging from 1.1 to 1.3 mm.day⁻¹ for ERA5-Land). The deforestation analysis for sites in Africa, America and Asia revealed an agreement of the models in demonstrating the impact of deforestation on $ET$, though performance varied due to different deforestation patterns. For the long-term results, models showed different responses to forest removal, highlighting the uncertainties of the individual models and underscoring the necessity of multi-model approaches in providing more accurate information. These findings demonstrate that current high-resolution remote sensing models can effectively monitor $ET$ in tropical forests on a global scale, especially for assessing the impacts of deforestation in data-scarce regions. Full article

The central Bohai Sea (CBS) is the distribution center and wintering grounds for economically important species of fish, shrimp, and crabs migrating from the Yellow Sea and the BS. However, the frequency of hypoxia in the CBS has gradually increased, posing a threat to its ecology. Therefore, we analyzed data from an on-site investigation of the cold-water mass coverage area in the southern part of the BS in the spring, summer, and autumn of 2022. We investigated the characteristics of seasonal variation in water quality parameter, the main characteristics and leading factors affecting the distribution of bottom hypoxia using stratification data and the Nutritional Status Quality Index. The “boot-shaped” distribution of hypoxia in summer was primarily the result of the intrusion of cold and highly saline water from the northern part in the study area, as well as the intrusion of high-temperature and low-salinity water from the Yellow River estuary (YRE) and the high-salinity water in the northeast corner of the study area, which had altered the stratification effect of the region. This is also the main reason that affects the accuracy of the prediction for occurrence of hypoxia stations in summer. The results show that the cold-water mass in the northern part of the Bohai Sea invades the cold-water mass in the southern part in summer 2022. Thus, this study provides novel insights into the formation and distribution of hypoxia in the CBS. Full article

Climate change and water scarcity are major threats to the sustainability of wheat production in Mediterranean regions. Thus, timely and reliable water demand assessments are crucial to drive decisions on crop management strategies that are useful for agricultural adaptation to climate change challenges. Although the AquaCrop model is widely used to infer crop yields, it requires continuous field-based observations (mainly soil water content and crop coverage). Often, these areas suffer from a scarcity of in situ data, suggesting the need for remote sensing and model-based decision support. In this framework, this research intends to compare the performance of the AquaCrop model using four different input combinations, with one employing ERA5-Land and crop cover retrieved by satellite images exclusively. A field experiment was conducted on durum wheat (highly sensitive to water stress and playing a strategic role in national food security) in northwest Tunisia during the growing season of 2024–2025, where meteorological variables, green Canopy Cover (gCC), Soil Water Content (SWC), and final yields (biological and grain) were monitored. The AquaCrop model was applied. Four model input combinations were evaluated. In situ meteorological data or ERA5-Land (E5L) reanalysis were combined with either measured-gCC (measured-gCC) or Sentinel-2 NDVI-derived gCC (NDVI-gCC). The results showed that E5L reproduced temperature with RMSE < 2.4 °C (NSE > 0.72) and ETo with RMSE equal to 0.57 mm d⁻¹ (NSE = 0.79), while precipitation presented larger discrepancies (RMSE = 4.14 mm d⁻¹, NSE = 0.58). Sentinel-2 effectively captured gCC dynamics (RMSE = 15.65%, NSE = 0.73) and improved AquaCrop perfomance (RMSE = 5.29%, NSE = 0.93). Across all combinations, AquaCrop reproduced yields within acceptable deviations. The simulated biological yield ranged from 9.7 to 11.0 t ha⁻¹ compared to the observed 10.3 t ha⁻¹, while grain yield ranged from 3.0 to 3.5 t ha⁻¹ against the observed 3.3 t ha⁻¹. As expected, the best agreement with measured yield data was obtained using in situ meteorological data and measured-gCC, even if the use of in situ meteorological data coupled with NDVI-gCC, or E5L-based meteorological data coupled with NDVI-gCC, produced realistic estimates. These results highlight that the application of AquaCrop employing E5L and Sentinel-2 inputs is a feasible alternative for crop monitoring in data-scarce environments. Full article

The use of satellite-based remote sensing imagery for water quality monitoring of inland and coastal waters has become widespread over the last few decades, with the expansion of, and investment in, operational Earth-observing missions. Satellite-based sensors are uniquely suited to provide synoptic, system-wide water quality parameter estimates that supplement traditional field-based sampling methods. The remote sensing of water quality parameter estimates is particularly valuable in systems with high temporal and spatial variability, as well as in areas that are difficult to access, or where agencies lack funding for routine monitoring. However, optically complex inland and coastal waters pose additional challenges for developing robust remote sensing retrieval models for optical properties and water quality parameters. One of the biggest challenges is collecting high quality field measurements that are used to calibrate and validate the retrieval algorithms. Here, we present the current status of satellite missions, field methods that include instruments used and commonly measured parameters, and repositories of historical field data that are relevant to inland and coastal water studies. We then present data requirements for model validation and highlight gaps in validation coverage. Finally, we provide considerations for future field campaigns to improve coordination with remote sensing data collection and ensure that field data is well suited for use in model or algorithm development. Full article

Sabina vulgaris is a keystone shrub species endemic to arid northwestern China, renowned for its exceptional drought tolerance, sand fixation capabilities, and critical role in desert ecosystem stability. This study investigates the impact of coal mining activities on the spatiotemporal dynamics of S. vulgaris shrublands in the ecologically fragile Mu Us Sandy Land, focusing on the Longde Coal Mine adjacent to the Shenmu S. vulgaris Nature Reserve. Utilizing seven periods (2013–2025) of 2 m resolution Gaofen-1 (GF-1) satellite imagery spanning 12 years of mining operations, we implemented a deep learning approach combining UAV-derived hyperspectral ground truth data and the SegU-Net semantic segmentation model to map shrub distribution via GF-1 data with high precision. Classification accuracy was rigorously validated through confusion matrix analysis (incorporating the Kappa coefficient and overall accuracy metrics). Results reveal contrasting trends: while the S. vulgaris Protection Area exhibited substantial expansion (e.g., Southern Section coverage grew from 2.6 km² in 2013 to 7.88 km² in 2025), mining panels experienced significant degradation. Within Panel 202, coverage declined by 15.4% (58.4 km² to 49.5 km²), and Panel 203 showed a 18.5% decrease (3.16 km² to 2.57 km²) over the study period. These losses correlate spatially and temporally with mining-induced groundwater depletion and land subsidence, disrupting the shrub’s shallow-root water access strategy. The study demonstrates that coal mining drives fragmentation and coverage reduction in S. vulgaris communities through mechanisms including (1) direct vegetation destruction, (2) aquifer disruption impairing drought adaptation, and (3) habitat fragmentation. These findings underscore the necessity for targeted ecological restoration strategies integrating groundwater management and progressive reclamation in mining-affected arid regions. Full article

The sudden turn of tropical cyclones (TCs) can rapidly alter the affected disaster-prone regions and associated rainfall distributions, posing severe threats to coastal areas and creating major challenges for operational forecasting. However, most of these events occur over the open ocean, where the scarcity of in situ observations limits our understanding of how precipitation and cloud microphysical processes evolve during the sudden turning. In this study, we analyzed the precipitation evolution and associated microphysical characteristics during the sudden turn of Super Typhoon Vongfong (2014) using the latest GPM satellite observations. The main findings are as follows: (1) During the sudden-turning period, the precipitation coverage expanded significantly. Strong convective precipitation was distributed from the inner eyewall to the outer eyewall and spiral rainbands and weakened in intensity, whereas stratiform precipitation broadened in coverage and intensified. (2) The increase in stratiform precipitation was attributed primarily to increased cloud water content, which strengthened collision–coalescence processes, promoted the formation of larger and more numerous raindrops, and consequently increased precipitation efficiency and intensity. (3) The weakening of convective precipitation was related to the reduction in eyewall updrafts, which suppressed ice-phase processes and limited the development of deep convection. Full article

Metal–Organic Frameworks (MOFs) have diverse applications due to their tunable porosity, large surface area, and diverse chemical functionalities. Among them, one of the most researched MOFs is MIL-101(Cr), which, in addition, is very stable in water. We have used a commercially available substance with approximately 300 nm large crystals for the preparation of a sensing nano-thin layer for the emerging water contaminant PFOS, due to its high selectivity towards this compound. Here, we have synthesized 20 nm sized crystals of MIL-101(Cr), which are among the smallest reported, and compared them to the same material with 300 nm sized crystals. The material was characterized by TEM and XPS. It was possible to prepare insoluble monolayers at the air–water interface (Langmuir films), which were characterized with film compression isotherms, Brewster angle microscopy, and surface potential measurements. The Langmuir–Blodgett (LB) method was used to deposit monolayers on Si wafers and 434 MHz Surface Acoustic Wave resonator simultaneously. The LB layers were very stable over time. The smaller-sized MIL-101 (Cr) crystals exhibit denser, more homogeneous water coverage and packing upon compression, with no observable 10–100 µm aggregates. LB monolayers from the 20 nm particles have approximately six times lower surface roughness. The LB monolayer is far from being smooth, but this will allow excellent access to the MOF pores by the tested analyte in a chemical sensing application. The lack of research on depositing presynthesized MOFs using probably the best method for nanoarchitectonics—the LB method—is addressed. The 20 nm sized MOF crystals are the smallest deposited by this method so far. Full article

Southern China, despite its humid climate, has increasingly faced severe hydrological droughts (HDs) in recent decades, highlighting the complexity of drought propagation. Most existing studies primarily examined the relationship between drought propagation and climatic factors, whereas quantitative analyses of interactive effects of underlying surface characteristics on drought propagation remain insufficient. This study introduces an integrated framework combining GRACE satellite-derived terrestrial water storage anomalies with topography, land use, geology, and climate data to examine HD formation and its drivers. The results show a clear divergence between meteorological drought (MD) and HD patterns, revealing that underlying surface characteristics, rather than precipitation deficits alone, drive HD spatial patterns. Among drought propagation indicators, intensity has the strongest link to environmental factors, positively correlating with elevation and slope, and negatively with mean annual precipitation and temperature. Forest coverage helps mitigate drought intensification, while karst geology and land use influence propagation timing. HD intensity follows an elevational gradient, with severe droughts in high-altitude areas and mild, frequent droughts in low-lying basins. These insights provide a mechanistic basis for developing early-warning systems and spatially adaptive water management strategies, thereby supporting sustainable drought resilience and promoting long-term water resource sustainability in Southern China. Full article