Search Results (3,220)

The development of miniaturized sensors has become relevant for the detection of chemical/biological substances, since they use and detect low concentrations, such as flocculants based on amines for the mining industry. In this study, buckypaper (BP) films based on carboxylic acid functionalized multi-walled carbon nanotubes (f-MWCNTs) were produced through vacuum filtration on cellulose filter paper to carry out sensory function in samples containing ether-amine (volumes: 1%, 5%, 10% and 100%). The morphological characterization of the BPs by scanning electron microscopy showed f-MWCNT aggregates randomly distributed on the cellulose fibers. Vibrational analysis by Raman spectroscopy indicated bands and sub-bands referring to f-MWCNTs and vibrational modes corresponding to chemical bonds present in the ether-amine (EA). The electrical responses of the BP to the variation in analyte concentration showed that the sensor differentiates deionized water from ether-amine, as well as the various concentrations present in the different analytes, exhibiting response time of 3.62 ± 0.99 min for the analyte containing 5 vol.% EA and recovery time of 21.16 ± 2.35 min for the analyte containing 10 vol.% EA, revealing its potential as a real-time response chemiresistive sensor. Full article

Aquatic vegetation plays a crucial role in freshwater ecosystems by providing habitats, regulating water quality, and supporting biodiversity. This study aims to monitor and analyze the dynamics of aquatic vegetation in Chimaditida Shallow Lake, Greece, using Sentinel-2 satellite imagery, with validation from field measurements. Data processing was performed using Google Earth Engine and QGIS. The study focuses on discriminating and mapping two classes of aquatic surface conditions: areas covered with Floating and Emergent Aquatic Vegetation and open water, covering all seasons from 1 March 2024, to 28 February 2025. Spectral bands such as B04 (red), B08 (near infrared), B03 (green), and B11 (shortwave infrared) were used, along with indices like the Modified Normalized Difference Water Index and Normalized Difference Vegetation Index. The classification was enhanced using Otsu’s thresholding technique to distinguish accurately between Floating and Emergent Aquatic Vegetation and open water. Seasonal fluctuations were observed, with significant peaks in vegetation growth during the summer and autumn months, including a peak coverage of 2.08 km² on 9 September 2024 and a low of 0.00068 km² on 28 December 2024. These variations correspond to the seasonal growth patterns of Floating and Emergent Aquatic Vegetation, driven by temperature and nutrient availability. The study achieved a high overall classification accuracy of 89.31%, with producer accuracy for Floating and Emergent Aquatic Vegetation at 97.42% and user accuracy at 95.38%. Validation with Unmanned Aerial Vehicle-based aerial surveys showed a strong correlation (R² = 0.88) between satellite-derived and field data, underscoring the reliability of Sentinel-2 for aquatic vegetation monitoring. Findings highlight the potential of satellite-based remote sensing to monitor vegetation health and dynamics, offering valuable insights for the management and conservation of freshwater ecosystems. The results are particularly useful for governmental authorities and natural park administrations, enabling near-real-time monitoring to mitigate the impacts of overgrowth on water quality, biodiversity, and ecosystem services. This methodology provides a cost-effective alternative for long-term environmental monitoring, especially in regions where traditional methods are impractical or costly. Full article

The discharge of oily wastewater threatens the ecosystem and human health, and the efficient treatment of oily wastewater is confronted with problems of high mass transfer resistance at the oil-water-solid multiphase interface, significant light shielding effect, and easy deactivation of photocatalysts. Although traditional physical separation methods avoid secondary pollution by chemicals and can effectively separate floating oil and dispersed oil, they are ineffective in removing emulsified oil with small particle sizes. To address these complex challenges, photocatalytic technology and photocatalysis-based improved technologies have emerged, offering significant application prospects in degrading organic pollutants in oily wastewater as an environmentally friendly oxidation technology. In this paper, the degradation mechanism, kinetic mechanism, and limitations of conventional photocatalysis technology are briefly discussed. Subsequently, the surface interface modulation functions of metal doping and heterojunction energy band engineering, along with their applications in enhancing the light absorption range and carrier separation efficiency, are reviewed. Focus on typical studies on the separation and degradation of aqueous and oily phases using photocatalytic membrane technology, and illustrate the advantages and mechanisms of photocatalysts loaded on the membranes. Finally, other new approaches and converging technologies in the field are outlined, and the challenges and prospects for the future treatment of oily wastewater are presented. Full article

Water science has always been a central part of modern scientific research. In this study, the viscosity and hydrogen bond structures of methanol aqueous solutions with different molar ratios were investigated via confocal microscopic Raman spectroscopy. The Raman spectra of methanol in the CH and CO stretching regions were measured in order to investigate the structure of water/methanol molecules. The points of transition were identified by observing changes in viscosity following changes in concentration, and the bands were assigned to the C-H bond vibration shifts where the molar ratios of methanol and water were 1:3 and 3:1. Furthermore, the large band shift of 19 cm⁻¹ between the methanol solutions with the lowest and highest concentrations contained three hydrogen bond network modes, affecting the viscosity of the solution. This study provides an explanation for the relationship between the microstructures and macroscopic properties of aqueous solutions. Full article

One of the persistent challenges in multispectral image analysis is the interference caused by dense cloud cover and its resulting shadows, which can significantly obscure surface features. This becomes especially problematic when attempting to monitor surface changes over time using satellite imagery, such as from Landsat-8. In this study, rather than simply masking visual obstructions, we aimed to investigate the role and influence of clouds within the spectral data itself. To achieve this, we employed Independent Component Analysis (ICA), a statistical method capable of decomposing mixed signals into independent source components. By applying ICA to selected Landsat-8 bands and analyzing each component individually, we assessed the extent to which cloud signatures are entangled with surface data. This process revealed that clouds contribute to multiple ICA components simultaneously, indicating their broad spectral influence. With this influence on multiple wavebands, we managed to configure a set of components that could perfectly delineate the extent and location of clouds. Moreover, because Landsat-8 lacks cloud-penetrating wavebands, such as those in the microwave range (e.g., SAR), the surface information beneath dense cloud cover is not captured at all, making it physically impossible for ICA to recover what is not sensed in the first place. Despite these limitations, ICA proved effective in isolating and delineating cloud structures, allowing us to selectively suppress them in reconstructed images. Additionally, the technique successfully highlighted features such as water bodies, vegetation, and color-based land cover differences. These findings suggest that while ICA is a powerful tool for signal separation and cloud-related artifact suppression, its performance is ultimately constrained by the spectral and spatial properties of the input data. Future improvements could be realized by integrating data from complementary sensors—especially those operating in cloud-penetrating wavelengths—or by using higher spectral resolution imagery with narrower bands. Full article

(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate classification methods to support conservation and policy efforts. In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. All models were trained using eight-band SuperDove satellite imagery from PlanetScope, with a spatial resolution of ~3 m, and ancillary geospatial datasets representing topography and soil drainage characteristics, each of which is available globally. (3) Results: All four machine learning models performed well in detecting wetlands from SuperDove imagery and environmental covariates, with varying strengths. The highest accuracy was achieved using all eight image bands alongside features created from supporting geospatial data. For binary wetland classification, the highest F1 scores were recorded by XGB (0.73) and RF/HGB (both 0.72) when including all covariates. MLPC also showed competitive performance (wetland F1 score of 0.71), despite its relatively lower spatial consistency. However, each model over-predicts total wetland area at a national level, an issue which was able to be reduced by increasing the classification probability threshold and spatial filtering. (4) Conclusions: The comparative analysis highlights the strengths and trade-offs of RF, XGB, HGB and MLPC models for wetland classification. While all four methods are viable, RF offers some key advantages, including ease of deployment and transferability, positioning it as a promising candidate for scalable, high-resolution wetland monitoring across diverse ecological settings. Further work is required for verification of small-scale wetlands (<~0.5 ha) and the addition of fine-spatial-scale covariates. Full article

Despite significant progress in photoelectrochemical (PEC) water splitting, high fabrication costs and limited efficiency of photoanodes hinder practical applications. Bismuth vanadate (BiVO₄), with its low cost, non-toxicity, and suitable band structure, is a promising photoanode material but suffers from poor charge transport, sluggish surface kinetics, and photocorrosion. In this study, porous monoclinic BiVO₄ films are fabricated via a simplified successive ionic layer adsorption and reaction (SILAR) method, followed by borate treatment and PEC deposition of NiFeO_x. The resulting B/BiVO₄/NiFeO_x photoanode exhibits a significantly enhanced photocurrent density of 2.45 mA cm⁻² at 1.23 V vs. RHE—5.3 times higher than pristine BiVO₄. It also achieves an ABPE of 0.77% and a charge transfer efficiency of 79.5%. These results demonstrate that dual surface modification via borate and NiFeO_x is a cost-effective strategy to improve BiVO₄-based PEC water splitting performance. This work provides a promising pathway for the scalable development of efficient and economically viable photoanodes for solar hydrogen production. Full article

The transition to clean and renewable energy sources is critically dependent on efficient hydrogen production technologies. This review surveys recent advances in photocatalytic water splitting, focusing on MNb₂O₆ nanomaterials, which have emerged as promising photocatalysts due to their tunable band structures, chemical robustness, and tailored morphologies. The objectives of this work are to (i) encompass the current synthesis strategies for MNb₂O₆ compounds; (ii) assess their structural, electronic, and optical properties in relation to photocatalytic performance; and (iii) elucidate the mechanisms underpinning enhanced hydrogen evolution. Main data collection methods include a literature review of experimental studies reporting bandgap measurements, structural analyses, and hydrogen production metrics for various MNb₂O₆ compositions—especially those incorporating transition metals such as Mn, Cu, Ni, and Co. Novelty stems from systematically detailing the relationships between synthesis routes (hydrothermal, solvothermal, electrospinning, etc.), crystallographic features, conductivity type, and bandgap tuning in these materials, as well as by benchmarking their performance against more conventional photocatalyst systems. Key findings indicate that MnNb₂O₆, CuNb₂O₆, and certain engineered heterostructures (e.g., with g-C₃N₄ or TiO₂) display significant visible-light-driven hydrogen evolution, achieving hydrogen production rates up to 146 mmol h⁻¹ g⁻¹ in composite systems. The review spotlights trends in heterojunction design, defect engineering, co-catalyst integration, and the extension of light absorption into the visible range, all contributing to improved charge separation and catalytic longevity. However, significant challenges remain in realizing the full potential of the broader MNb₂O₆ family, particularly regarding efficiency, scalability, and long-term stability. The insights synthesized here serve as a guide for future experimental investigations and materials design, advancing the deployment of MNb₂O₆-based photocatalysts for large-scale, sustainable hydrogen production. Full article

Titanium dioxide (TiO₂), a widely used semiconductor in photocatalysis owing to its adequate potential for water hydrolysis, chemical stability, low toxicity, and low cost. However, its efficiency is limited by fast charge-carrier recombination and poor visible light absorption. Coupling TiO₂ with a p-type semiconductor, such as nickel oxide (NiO), forming a p-n heterojunction, decreases the recombination of charge carriers and increases photocatalytic activity. In this work, the surface of TiO₂ modified with NiO nanoparticles (NPs) induced by radiolysis for photocatalytic hydrogen production was studied. The photocatalytic activity of NiO/TiO₂ was evaluated using methanol as a hole scavenger under UV–visible light. All modified samples presented superior photocatalytic activity compared to bare TiO₂. The dynamics of the charge carriers, a key electronic phenomenon in photocatalysis, was investigated by time-resolved microwave conductivity (TRMC). The results highlight the crucial role of Ni-based NPs modification in enhancing the separation of the charge carrier and activity under UV–visible irradiation. Furthermore, the results revealed that under visible irradiation, NiO-NPs inject electrons into the conduction band of titanium dioxide. Full article

Stem radius growth (GRO), tree water deficit (TWD), and maximum daily shrinkage (MDS) were monitored throughout 2023 in a semi-arid Mediterranean forest stand in Burdur, Türkiye, where Pinus nigra subsp. pallasiana (Lamb.) Holmboe and Pinus brutia Ten. naturally co-occur. These indicators, derived from electronic band dendrometers, were analyzed in relation to key climatic variables. Results indicated that P. brutia had a longer growth period, while P. nigra exhibited a higher average daily increment under the environmental conditions of 2023 at the study site. Annual stem growth was nearly equal for both species. Based on dendrometer observations, P. brutia exhibited lower normalized TWD and higher normalized MDS values under varying vapor pressure deficit (VPD) and soil water potential (SWP) conditions. A linear mixed-effects model further confirmed that P. brutia consistently maintained lower TWD than P. nigra across a wide climatic range, suggesting a comparatively lower degree of drought-induced water stress. GRO was most influenced by air temperature and VPD, and negatively by SWP. TWD was strongly affected by both VPD and SWP, while MDS was primarily linked to minimum air temperature and VPD. Moreover, MDS in P. brutia appeared more sensitive to climate variability compared to P. nigra. Although drought limited stem growth in both species during the study year, the lower TWD and higher MDS observed in P. brutia may indicate distinct physiological strategies for coping with drought. These findings offer preliminary insights into interspecific differences in water regulation under the particular climatic conditions observed during the study year in this semi-arid Mediterranean ecosystem. Full article

Erosion gullies can reduce arable land area and decrease agricultural machinery efficiency; therefore, automatic gully extraction on a regional scale should be one of the preconditions of gully control and land management. The purpose of this study is to compare the effects of the grey level co-occurrence matrix (GLCM) and topographic–hydrologic features on automatic gully extraction and guide future practices in adjacent regions. To accomplish this, GaoFen-2 (GF-2) satellite imagery and high-resolution digital elevation model (DEM) data were first collected. The GLCM and topographic–hydrologic features were generated, and then, a gully label dataset was built via visual interpretation. Second, the study area was divided into training, testing, and validation areas, and four practices using different feature combinations were conducted. The DeepLabV3+ and ResNet50 architectures were applied to train five models in each practice. Thirdly, the trainset gully intersection over union (IOU), test set gully IOU, receiver operating characteristic curve (ROC), area under the curve (AUC), user’s accuracy, producer’s accuracy, Kappa coefficient, and gully IOU in the validation area were used to assess the performance of the models in each practice. The results show that the validated gully IOU was 0.4299 (±0.0082) when only the red (R), green (G), blue (B), and near-infrared (NIR) bands were applied, and solely combining the topographic–hydrologic features with the RGB and NIR bands significantly improved the performance of the models, which boosted the validated gully IOU to 0.4796 (±0.0146). Nevertheless, solely combining GLCM features with RGB and NIR bands decreased the accuracy, which resulted in the lowest validated gully IOU of 0.3755 (±0.0229). Finally, by employing the full set of RGB and NIR bands, the GLCM and topographic–hydrologic features obtained a validated gully IOU of 0.4762 (±0.0163) and tended to show an equivalent improvement with the combination of topographic–hydrologic features and RGB and NIR bands. A preliminary explanation is that the GLCM captures the local textures of gullies and their backgrounds, and thus introduces ambiguity and noise into the convolutional neural network (CNN). Therefore, the GLCM tends to provide no benefit to automatic gully extraction with CNN-type algorithms, while topographic–hydrologic features, which are also original drivers of gullies, help determine the possible presence of water-origin gullies when optical bands fail to tell the difference between a gully and its confusing background. Full article

Lake Chinchaycocha, Peru’s second-largest high-altitude lake and a Ramsar-designated wetland of international importance, is increasingly threatened by anthropogenic pollution and hydroclimatic shifts. This study integrates Sentinel-2 multispectral imagery with in situ water quality data from Peru’s National Water Observatory to assess spatiotemporal dynamics in 31 physicochemical parameters between 2018 and 2024. We evaluated 40 empirical algorithms developed globally for Sentinel-2 and tested their transferability to this ultraoligotrophic Andean system. The results revealed limited predictive accuracy, underscoring the need for localized calibration. Subsequently, we developed and validated site-specific models for ammoniacal nitrogen, electrical conductivity, major ions, and trace metals, achieving high predictive performance during the rainy season (R² up to 0.95). Notably, the study identifies consistent seasonal correlations—such as between total copper and ammoniacal nitrogen—and strong spectral responses in Band 1, linked to runoff dynamics. These findings highlight the potential of combining public monitoring data with remote sensing to enable scalable, cost-effective assessment of water quality in optically complex, high-Andean lakes. The study provides a replicable framework for integrating national datasets into operational monitoring and environmental policy. Full article

By integrating EarthCARE W-band doppler cloud radar observations with GPM Ku/Ka-band dual-frequency precipitation radar data, this study constructs a novel global “pseudo tripe-frequency” radar coincidence dataset comprising 2886 coincidence events (about one-third of the events detected precipitation), aiming to systematically investigating band-dependent responses to cloud and precipitation structure. Results demonstrate that the W-band is highly sensitive to high-altitude cloud particles and snowfall (reflectivity < 0 dBZ), yet it experiences substantial signal attenuation under heavy precipitation conditions, and with low-altitude reflectivity reductions exceeding 50 dBZ, its probability density distribution is more widespread, with low-altitude peaks increasing first, and then decreasing as precipitation increases. In contrast, the Ku and Ka-band radars maintain relatively stable detection capabilities, with attenuation differences generally within 15 dBZ, but its probability density distribution exhibits multiple peaks. As the precipitation rate increases, the peak value of the dual-frequency ratio (Ka/W) gradually rises from approximately 10 dBZ to 20 dBZ, and can even reach up to 60 dBZ under heavy rainfall conditions. Several cases analyses reveal clear contrasts: In stratiform precipitation regions, W-band radar reflectivity is higher above the melting layer than below, whereas the opposite pattern is observed in the Ku and Ka bands. Doppler velocities exceeding 5 m s⁻¹ and precipitation rates surpassing 30 mm h⁻¹ exhibit strong positive correlations in convection-dominated regimes. Furthermore, the dataset confirms the impact of ice–water cloud phase interactions and terrain-induced precipitation variability, underscoring the complementary strengths of multi-frequency radar observations for capturing diverse precipitation processes. Full article

This study evaluated how the summer circulation pattern in the Southern Gulf of California influences copepod communities. The evaluation was based on hydrographic data and zooplankton samples collected during a multidisciplinary research cruise conducted in June and July of 2019. The results revealed the presence of a cyclonic circulation with a diameter of approximately 100 km, located near the entrance of the Gulf, affecting the upper 200 m layer. A total of 30 copepod species were identified, including 20 from the order Calanoida and 10 from Cyclopoida. The most abundant Calanoida species were Canthocalanus pauper, Clausocalanus furcatus, and Subeucalanus subcrassus, with respective densities of 2316.80, 1593.60, and 1584.64 ind m⁻³. The most abundant Cyclopoida species were Oithona setigera, Dioithona rigida, and Oncaea venusta, which had densities of 963.44, 290.56, and 235.52 ind m⁻³, respectively. The horizontal distribution of these species showed variations influenced by the cyclonic circulation. Specifically, low abundance values were observed at the center of cyclonic circulation, while higher values were found at its periphery. This pattern was consistent among the dominant species, indicating that they do not benefit from the cold subsurface waters induced by circulation. In fact, the distribution of some species was higher in a band of warm water located in the eastern portion of the study area. Overall, our findings shed light on how the summer cyclonic circulation in the Southern Gulf of California affects the copepod community, an aspect that has not been previously explored. This research enhances our understanding of the processes influencing this group of organisms in a highly dynamic environment. Full article

The chlorophyll-a (Chl-a) concentration in lakes is a crucial parameter for monitoring water quality and assessing phytoplankton abundance. However, accurately retrieving Chl-a concentrations remains a significant challenge in remote sensing. To address the limitations of existing methods in terms of modeling efficiency and adaptability, this study focuses on Lake Nanyi in Anhui Province. By integrating Sentinel-2 satellite imagery with in situ water quality measurements and employing the AutoML framework AutoGluon, a Chl-a inversion model based on narrow-band spectral features is developed. Feature selection and model ensembling identify bands B6 (740 nm) and B7 (783 nm) as the optimal combination, which are then applied to multi-temporal imagery from October 2022 to generate spatial mean distributions of Chl-a in Lake Nanyi. The results demonstrate that the AutoGluon framework significantly outperforms traditional methods in both model accuracy (R²: 0.94, RMSE: 1.67 $\mathsf{\mu }$g/L) and development efficiency. The retrieval results reveal spatial heterogeneity in Chl-a concentration, with higher concentrations observed in the southern part of the western lake and the western side of the eastern lake, while the central lake area exhibits relatively lower concentrations, ranging from 3.66 to 21.39 $\mathsf{\mu }$g/L. This study presents an efficient and reliable approach for lake ecological monitoring and underscores the potential of AutoML in water color remote sensing applications. Full article