Search Results (20,115)

Superhydrophobic magnetic nanomaterials (SHMNMs) are emerging as multifunctional platforms for efficient oil–water separation due to their combination of extreme water repellency, strong oil affinity, and external magnetic responsiveness. This review presents a comprehensive analysis of recent advances in the design, synthesis, and environmental application of SHMNMs. The theoretical foundations of superhydrophobicity and the physicochemical behavior of magnetic nanoparticles are first outlined, followed by discussion of their synergistic integration. Key fabrication techniques—such as sol–gel synthesis, electrospinning, dip-coating, laser-assisted processing, and the use of biomass-derived precursors—are critically assessed in terms of their ability to tailor surface morphology, chemical functionality, and long-term durability. The review further explores the mechanisms of oil adsorption, magnetic separation, and material reusability under realistic environmental conditions. Special attention is paid to the scalability, mechanical resilience, and environmental compatibility of SHMNMs in the context of water treatment technologies. Current limitations, including reduced efficiency in harsh media, potential environmental risks, and challenges in material regeneration, are discussed. This work provides a structured overview that could support the rational development of next-generation superhydrophobic materials tailored for sustainable and high-performance separation of oil and organic pollutants from water. Full article

Monitoring water quality parameters in coastal and estuarine environments is critical for assessing their ecological status and addressing environmental challenges. However, traditional in situ sampling programs are often constrained by limited spatial and temporal coverage, making it difficult to capture the complex variability in these dynamic systems. This study introduces a novel upscaling framework that leverages limited in situ measurements and airborne hyperspectral data to generate multiple conditional realizations of water quality parameter fields. These pseudo-measurements are statistically consistent with the original data and are used to calibrate inversion algorithms that relate satellite-derived reflectance data to water quality parameters. The approach was applied to Kaštela Bay, a semi-enclosed coastal area in the eastern Adriatic Sea, to map seasonal variations in water quality parameters such as Chlorophyll-a. The upscaling framework captured spatial patterns that were absent in sparse in situ observations and enabled regional mapping using Sentinel-2A satellite data at the appropriate spatial scale. By generating realistic pseudo-measurements, the method improved the stability and performance of satellite-based retrieval algorithms, particularly in periods of high productivity. Overall, this methodology addresses data scarcity challenges in coastal water monitoring and its application could benefit the implementation of European water quality directives through enhanced regional-scale mapping capabilities. Full article

Dam monitoring tracks environmental variables (water level, temperature) and structural responses (deformation, seepage, and stress) to assess safety and performance. Structural health monitoring (SHM) refers to the systematic observation and analysis of the structural condition over time, and it is essential in maintaining the safety, functionality, and long-term performance of dams. This review examines monitoring data applications, covering structural health assessment methods, historical motivations, and key challenges. It discusses monitoring components, data acquisition processes, and sensor roles, stressing the need to integrate environmental, operational, and structural data for decision making. Key objectives include risk management, operational efficiency, safety evaluation, environmental impact assessment, and maintenance planning. Methodologies such as numerical modeling, statistical analysis, and machine learning are critically analyzed, highlighting their strengths and limitations and the demand for advanced predictive techniques. This paper also explores future trends in dam monitoring, offering insights for engineers and researchers to enhance infrastructure resilience. By synthesizing current practices and emerging innovations, this review aims to guide improvements in dam safety protocols, ensuring reliable and sustainable dam operations. The findings provide a foundation for the advancement of monitoring technologies and optimization of dam management strategies worldwide. Full article

Thermal cameras are becoming popular in several applications of precision agriculture, including crop and soil monitoring, for efficient irrigation scheduling, crop maturity, and yield mapping. Nowadays, these sensors can be integrated as payloads on unmanned aerial vehicles, providing high spatial and temporal resolution, to deeply understand the variability of crop and soil conditions. However, few commercial software programs, such as PIX4D Mapper, can process thermal images, and their functionalities are very limited. This paper reports on the implementation of a custom MATLAB^® R2024a script to extract agronomic information from thermal orthomosaics obtained from images acquired by the DJI Mavic 3T drone. This approach enables us to evaluate the temperature at each point of an orthomosaic, create regions of interest, calculate basic statistics of spatial temperature distribution, and compute the Crop Water Stress Index. In the authors’ opinion, the reported approach can be easily replicated and can serve as a valuable tool for scientists who work with thermal images in the agricultural sector. Full article

In arid regions with limited water resources, improving cropland water-use efficiency (WUEc) is crucial for maintaining crop production. This study aims to investigate how changes in meteorological and vegetation factors affect WUEc in drylands and to identify its primary drivers, which are essential for understanding how cropland ecosystems respond to complex environmental changes. Using remote sensing data, we analyzed the spatiotemporal patterns of WUEc in Xinjiang from 2002 to 2022 by applying STL decomposition, Sen’s slope combined with the Mann–Kendall test, and an XGBoost–SHAP model, quantifying its key controlling factors. The results indicate that from 2002 to 2022, WUEc in Xinjiang showed an overall declining trend. Prior to 2007, WUEc increased at 0.05 gC·m⁻¹·m⁻²·a⁻¹, after which it fluctuated downward at −0.01 gC·m⁻¹·m⁻²·a⁻¹. Intra-annual peaks consistently occurred in May and during September–October. Spatially, WUEc exhibited significant heterogeneity, increasing from south to north, with 53.26% of the region showing declines. Temperature (T) and leaf area index (LAI) emerged as the primary meteorological and vegetation drivers, respectively, influencing WUEc change in 45.7% and 17.6% of the area. Both variables were negatively correlated with WUEc, with negative correlations covering 60% of the region for T and 83% for LAI. These findings provide scientific guidance for optimizing crop structure and water-resource management strategies in arid regions. Full article

Soil moisture is a key factor in the exchange of energy and matter between the soil and atmosphere, playing a vital role in the hydrological cycle and agricultural management. Traditional monitoring methods are limited in achieving large-scale, real-time observations, while deep learning offers new avenues to model the complex nonlinear relationships between spectral features and soil moisture content. This study focuses on the Wei-Ku Oasis in Xinjiang, using multi-source remote sensing data (Landsat series and Sentinel-1) and in situ multi-layer soil moisture measurements. The BOSS feature selection algorithm was applied to construct 46 feature parameters, including vegetation indices, soil indices, and microwave indices, and to identify optimal variable sets for each depth. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and their hybrid model (CNN-LSTM) were used to build soil moisture inversion models at various depths. Their performances were systematically compared on both training and testing sets, and the optimal model was used for spatiotemporal mapping. The results show that the CNN-LSTM-based multi-depth soil moisture inversion model achieved superior performance, with the 0–10 cm model showing the highest accuracy and a testing R² of 0.64, outperforming individual models. The testing R² values for the soil moisture inversion models at depths of 10–20 cm, 20–40 cm, and 40–60 cm were 0.59, 0.54, and 0.59, respectively. According to the mapping results, soil moisture in the 0–60 cm profile of the Wei-Ku Oasis exhibited a vertical gradient, increasing with depth. Spatially, soil moisture was higher in the central oasis and lower toward the periphery, forming a “center-high, edge-low” pattern. This study provides a high-accuracy method for multi-layer soil moisture remote sensing in arid regions, offering valuable data support for oasis water resource management and precision irrigation planning. Full article

Endotoxins, lipopolysaccharides released from the outer membrane of Gram-negative bacteria, pose a significant risk in healthcare environments, particularly in Central Sterile Supply Departments (CSSDs), where the delivery of sterile pyrogen-free medical devices is critical for patient safety. Traditional methods for controlling endotoxins in water systems, such as ultraviolet (UV) disinfection, have proven ineffective at reducing endotoxin concentrations to comply with regulatory standards (<0.25 EU/mL). This limitation presents a significant challenge, especially in the context of reverse osmosis (RO) permeate used in CSSDs, where water typically has very low conductivity. Despite the established importance of endotoxin removal, a gap in the literature exists regarding effective chemical-free methods that can meet the stringent endotoxin limits in such low-conductivity environments. This study addresses this gap by evaluating the effectiveness of the eBooster™ electrochemical technology—featuring proprietary electrode materials and a reactor design optimized for potable water—for endotoxin removal from water, specifically under the low-conductivity conditions typical of RO permeate. Laboratory experiments using the B250 reactor achieved >90% endotoxin reduction (from 1.2 EU/mL to <0.1 EU/mL) at flow rates ≤5 L/min and current densities of 0.45–2.7 mA/cm². Additional real-world testing at three hospitals showed that the eBooster™ unit, when installed in the RO tank recirculation loop, consistently reduced endotoxin levels from 0.76 EU/mL (with UV) to <0.05 EU/mL over 24 months of operation, while heterotrophic plate counts dropped from 190 to <1 CFU/100 mL. Statistical analysis confirmed the reproducibility and flow-rate dependence of the removal efficiency. Limitations observed included reduced efficacy at higher flow rates, the need for sufficient residence time, and a temporary performance decline after two years due to a power fault, which was promptly corrected. Compared to earlier approaches, eBooster™ demonstrated superior performance in low-conductivity environments without added chemicals or significant maintenance. These findings highlight the strength and novelty of eBooster™ as a reliable, chemical-free, and maintenance-friendly alternative to traditional UV disinfection systems, offering a promising solution for critical water treatment applications in healthcare environments. Full article

Waste activated sludge (WAS), a byproduct of livestock wastewater treatment, poses significant disposal challenges due to its low biodegradability and potential environmental impact. Anaerobic digestion (AD) offers a sustainable approach for methane recovery and sludge stabilization. This study evaluates the biomethane potential (BMP) of WAS and its co-digestion with swine slurry (SS), water lily (Nymphaea spp.), and lotus (Nelumbo nucifera) shoot biomass to enhance methane yield. Batch BMP assays were conducted at substrate-to-inoculum (S/I) ratios of 1.0 and 0.5, with methane production kinetics analyzed using the modified Gompertz model. Mono-digestion of WAS yielded 259.35–460.88 NmL CH₄/g VS_added, while co-digestion with SS, water lily, and lotus increased yields by 14.89%, 10.97%, and 16.89%, respectively, surpassing 500 NmL CH₄/g VS_added. All co-digestion combinations exhibited synergistic effects (α > 1), enhancing methane production beyond individual substrate contributions. Lower S/I ratios improved methane yields and biodegradability, highlighting the role of inoculum availability. Co-digestion reduced the lag phase limitations of WAS and plant biomass, improving process efficiency. These findings demonstrate that co-digesting WAS with nutrient-rich co-substrates optimizes biogas production, supporting sustainable sludge management and renewable energy recovery in livestock wastewater treatment systems. Full article

Small water bodies are widely spread and play crucial roles in supporting regional agricultural and aquaculture activities. PlanetScope imagery has a high resolution (3 m) with daily global coverage and has obviously enhanced small water body mapping. Recent studies have demonstrated the effectiveness of deep learning for mapping small water bodies using PlanetScope; however, a persistent challenge remains in the scarcity of high-quality, manually annotated water masks used for model training, which limits the generalization capability of data-driven deep learning models. In this study, we propose a transfer learning framework that leverages Sentinel-2 data to improve PlanetScope-based small water body mapping, capitalizing on the spectral interoperability between PlanetScope and Sentinel-2 bands and the abundance of open-source Sentinel-2 water masks. Eight state-of-the-art segmentation models have been explored. Additionally, this paper presents the first assessment of the VMamba model for small water body mapping, building on its demonstrated success in segmentation tasks. The models were pre-trained using Sentinel-2-derived water masks and subsequently fine-tuned with a limited set (1292 image patches, 256 × 256 pixels in each patch) of manually annotated PlanetScope labels. Experiments were conducted using 5648 image patches and two areas of 9636 km² and 2745 km², respectively. Among the evaluated methods, VMamba achieved higher accuracy compared with both CNN- and Transformer-based models. This study highlights the efficacy of combining global Sentinel-2 datasets for pre-training with localized fine-tuning, which not only enhances mapping accuracy but also reduces reliance on labor-intensive manual annotation in regional small water body mapping. Full article

Synthetic Aperture Radar (SAR) technology has the characteristics of all-day and all-weather functionality; accordingly, it is not affected by rainy weather, overcoming the limitations of optical remote sensing, and it provides irreplaceable technical support for efficient water body extraction. To address the issues of low accuracy and unstable results in water body extraction from Sentinel-1 SAR images using a single method, a water body extraction method fusing the Sentinel-1 dual-polarized water index and random forest is proposed. This novel method enhances water extraction accuracy by integrating the results of two different algorithms, reducing the biases associated with single-method water body extraction. Taking Dalu Lake, Yinfu Reservoir, and Huashan Reservoir as the study areas, water body information was extracted from SAR images using the dual-polarized water body index, the random forest method, and the fusion method. Taking the normalized difference water body index extraction results obtained via Sentinel-2 optical images as a reference, the accuracy of different water body extraction methods when used with SAR images was quantitatively evaluated. The experimental results show that, compared with the dual-polarized water body index and the random forest method, the fusion method, on average, increased overall water body extraction accuracy and Kappa coefficients by 3.9% and 8.2%, respectively, in the Dalu Lake experimental area; by 1.8% and 3.5%, respectively, in the Yinfu Reservoir experimental area; and by 4.1% and 8.1%, respectively, in the Huashan Reservoir experimental area. Therefore, the fusion method of the dual-polarized water index and random forest effectively improves the accuracy and reliability of water body extraction from SAR images. Full article

As the demand for sustainable materials continues to grow, calcium caseinate (Cas) biopolymer films have emerged as promising alternatives to fossil-based plastics. However, their mechanical fragility and high-water sensitivity limit their application in packaging. In this study, we reinforced Cas films with zinc hydroxide nitrate (ZHN) using two incorporation methods: wet (ZHN-w) and dry (ZHN-d). We evaluated how each method affected the dispersion of the filler and, consequently, the functional properties of the films. To our knowledge, this is the first report of ZHN being used in biopolymeric films. Structural and morphological analyses showed better dispersion of ZHN in the wet-incorporated films. These samples exhibited a substantial increase in tensile strength, from 0.75 ± 0.00 MPa to 9.62 ± 2.45 MPa, along with a marked improvement in Young’s modulus. The films also became less soluble in water, more resistant to swelling, and structurally more cohesive. In antimicrobial tests, the ZHN-w films showed stronger inhibition against E. coli and S. aureus. Overall, this approach offers a simple and effective way to enhance protein-based films using food-safe materials, making them suitable for active and bio-based packaging applications. Full article

This systematic review critically examines the benefits and challenges of high-compression-ratio (CR) implementation in hybrid-dedicated engines, recognizing CR increase as a pivotal strategy for enhancing the indicated thermal efficiency to achieve carbon peak and carbon neutrality goals. However, excessively high CRs face critical constraints, including intensified knock propensity, increased heat transfer (HTR) losses, reduced combustion stability, augmented dissociation losses, and cold-start misfire risks. The feasibility and necessity of CR enhancement in hybrid systems were comprehensively evaluated based on these factors, with fundamental mechanisms of the detrimental effects elucidated. To address these challenges, optimized countermeasures were synthesized: knock suppression via high-octane fuels, EGR technology, lean combustion, and in-cylinder water injection; heat transfer reduction through thermal barrier coatings and independent CR/expansion-ratio control; misfire risk monitoring using ion current or cylinder pressure sensors. These approaches provide viable pathways to overcome high-CR limitations and optimize engine performance. Nevertheless, current research remains confined to isolated solutions, warranting future focus on integrated optimization mechanisms investigating synergistic interactions of multiple strategies under high-CR conditions. Full article

As a globally renowned alpine gorge region and seismically active zone, the eastern margin of the Qinghai–Tibet Plateau (QTP) is highly prone to landslide dam formation. Considering unstable landslide dams often pose catastrophic risks to downstream areas, current research on landslide dams along QTP primarily focuses on the breach mechanisms of unstable dams, while studies on the formation mechanisms of stable landslide dams—which can provide multiple benefits to downstream regions—remain limited. This paper selected the Conaxue Co landslide dam on the eastern margin of the QTP as one case example. Field investigation, sampling, numerical simulation, and comprehensive analysis were carried out to disclose its formation mechanisms. Field investigation shows that the Conaxue Co landslide dam was formed by a high-speed long-runout landslide blocking the river, with its structure exhibiting a typical inverse grading pattern characterized by coarse-grained rock overlying fine-grained layers. The inverse grading structure plays a critical role in the stability of the Conaxue Co landslide dam. On one hand, the coarse, hard rock boulders in the upper dam mitigate fluvial erosion of the lower fine-grained sediments. On the other hand, the fine-grained layer in the lower dam acts as a relatively impermeable aquitard, preventing seepage of dammed lake water. Additionally, the step-pool system formed in the spillway of the Conaxue Co landslide dam contributes to the protection of the dam structure by dissipating 68% of the river’s energy (energy dissipation rate η = 0.68). Understanding the formation mechanisms of the Conaxue Co landslide dam can provide critical insights into managing future landslide dams that may form in the QTP, both in emergency response and long-term strategies. Full article

The effect of humic substances on growth performance, excretion, and the digestive organs was studied in pheasant chickens. Three hundred 1-day-old pheasant chicks were assigned to two equal groups. Chicks in the experimental group were fed diets supplemented with humic substances (5 g/kg). Chickens in the experimental group reached significantly higher average daily weight gains (p ˂ 0.05) in the second phase of rearing (29–49 d) at a significantly better feed conversion ratio (p ˂ 0.05). The supplementation of humic substances led to a significant increase in the average body weight of pheasants at the end of the experiment (p ˂ 0.05), and to a significant increase in average daily weight gains for the entire monitored period (p ˂ 0.05). In the experimental group, significantly higher relative weights of the gizzard (p ˂ 0.05), small intestine (p ˂ 0.05), and jejunum (p ˂ 0.05), along with a significantly larger relative length of the duodenum (p ˂ 0.05), were recorded. A significantly higher concentration of dry matter in the droppings of pheasants was found in the experimental group than in the control group (p ˂ 0.05). According to our results, feeding humic substances had the most beneficial effect on pheasant growth performance during the growing phase. Humic substances can also have a positive effect on the microclimate within the breeding facility. Reductions in the water content of droppings contribute to a reduction in the humidity of the litter, thus limiting the activity of nitrogenic bacteria. Full article

Precipitation concentration reflects the uneven temporal distribution of rainfall. It plays a critical role in water resource management and flood–drought risk under climate change. However, its long-term trends, associations with atmospheric teleconnections as potential drivers, and links to extreme heavy precipitation events remain poorly understood in complex basins like the Yangtze River Basin. This study analyzes these aspects using ground station data from 1960 to 2019 and conducts a comparison using the Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (GPM IMERG) satellite product. We calculated three indices—Daily Precipitation Concentration Index (PCID), Monthly Precipitation Concentration Index (PCIM), and Seasonal Precipitation Concentration Index (SPCI)—to quantify rainfall unevenness, selected for their ability to capture multi-scale variability and associations with extremes. Key methods include Mann–Kendall trend tests for detecting changes, Hurst exponents for persistence, Pettitt detection for abrupt shifts, random forest modeling to assess atmospheric teleconnections, and hot spot analysis for spatial clustering. Results show a significant basin-wide decrease in PCID, driven by increased frequency of small-to-moderate rainfall events, with strong spatial synchrony to extreme heavy precipitation indices. PCIM is most strongly associated with El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). GPM IMERG captures PCIM patterns well but underestimates PCID trends and magnitudes, highlighting limitations in daily-scale resolution. These findings provide a benchmark for satellite product improvement and support adaptive strategies for extreme precipitation risks in changing climates. Full article