Search Results (691)

Monitoring surface water quality is essential for assessing water resources and identifying their quality patterns. Traditional monitoring methods, based on conventional point-sampling stations, are reliable but costly and limited in frequency and spatial coverage. These constraints hinder the ability to evaluate water quality parameters at the temporal and spatial scales required to detect the effects of extreme events on aquatic systems. Satellite imagery offers a viable complementary alternative to enhance the temporal and spatial monitoring scales of traditional assessment methods. However, limitations related to spectral, spatial, temporal, and/or radiometric resolution still pose significant challenges to prediction accuracy. This study aimed to propose a methodology for predicting optically active and inactive water quality parameters in lotic and lentic environments using remote-sensing data and machine-learning techniques. Three remote-sensing datasets were organized and evaluated: (i) data extracted from Sentinel-2 imagery; (ii) data obtained from raw PlanetScope (PS) imagery; and (iii) data from PS imagery normalized using the methodology developed by Dias. Data on water quality parameters were collected from 24 monitoring stations located along the Paraopeba River channel and the Três Marias Reservoir, covering the period from 2016 to 2023. Four machine-learning algorithms were applied to predict water quality parameters: Random Forest, k-Nearest Neighbors, Support Vector Machines with Radial Basis Function Kernel, and Cubist. Model performance was evaluated using four statistical metrics: root-mean-square error, mean absolute error, Lin′s concordance correlation coefficient, and the coefficient of determination. Models based on normalized PS data achieved the best performance in parameter estimation. Additionally, decision-tree-based algorithms showed superior generalization capability, outperforming the other models tested. The proposed methodology proved suitable for this type of analysis, confirming not only the applicability of PS data but also providing relevant insights for its use in diverse environmental-monitoring applications. Full article

The strategic engineering of metal–organic frameworks (MOFs) through integration with clay minerals offers a promising route to tailor their functional properties and expand their application scope. In this study, a series of clay-MOF composites was constructed by introducing MOFs onto the surfaces of different clay minerals. By varying the type of clay mineral, the nature and strength of surface-active sites could be effectively modulated. Notably, the Kaolinite-based MOFs (Ka-MOF) composite exhibited superior sensitivity for the detection of aspartic acid (AA), outperforming other composite nanozymes using o-phenylenediamine (OPD) and hydrogen peroxide (H₂O₂) as substrates, with a linear detection range of 0–37.56 μM and a low detection limit of 55.7 nM. The enhanced peroxidase-like activity is attributed to the substitution of silicon in the kaolinite structure by MOF components, which increases the density of Lewis acid–base sites. These sites facilitate H₂O₂ adsorption and promote its decomposition to generate singlet oxygen (¹O₂), thereby enhancing the catalytic oxidation process. Furthermore, the probe yielded satisfactory recoveries of aspartic acid (94.2% to 98.5%) in different real water samples through spiking recovery experiments. This work not only elucidates the influence of crystal surface engineering on the optical and catalytic properties of nanozymes but also provides a robust platform for tracing amino acids and studying their environmental chemical behaviors. Full article

The present study evaluates the influence of industrialization on suspended particulate matter (SPM) dynamics along the northern coast of Rio de Janeiro, focusing specifically on the Açu Port Industrial Complex (APIC). A 20-year MODIS-Aqua (1 km) dataset (2002–2022) was processed using the OC-SMART atmospheric correction. For SPM estimation, a retrieval approach for coastal turbid waters that integrates two optimized bio-optical algorithms based on Optical Water Types (OWTs) was developed. The validity of this approach was substantiated through the utilization of the GLORIA in situ dataset and satellite matchups, which demonstrated its robust performance across a range of turbidity conditions. Its main innovation lies in the OWT-based fusion of two optimized SPM models, enabling robust retrievals across diverse coastal optical conditions. Statistical analyses based on Census X11 decomposition and the Seasonal Mann–Kendall test revealed strong spatial and temporal variability, with SPM concentrations increasing by up to 60% near the APIC during the study period, coinciding with dredging, port expansion, and sediment disposal. These findings indicate a pronounced anthropogenic signal, while spatial and temporal correlation analyses demonstrated that sediment dispersion is consistently directed northward, primarily controlled by currents and wind forcing. The results indicate that industrial activities augment the supply of sediments, while natural hydrodynamic processes govern their dispersion and transport, emphasizing the impact of human pressures and physical drivers on coastal sediments. Full article

Marine risers, critical structures connecting underwater production systems and surface floating platforms, stand freely in water and endure extremely complex marine environmental loads. To meet the multi-parameter observation demand for their overall state, a fiber-optic sensing-based marine riser buoy observation system was developed. Unlike traditional point-type and offline monitoring systems, it integrates marine buoys with sensing submarine cables to achieve long-term real-time online monitoring of risers’ overall state via fiber-optic sensing technology. Comprising two main modules (buoy monitoring module and fiber-optic sensing module), the buoy’s stability was verified through theoretical derivation, simulation, and stability curve plotting. Frequency domain analysis of buoy loads and motion responses, along with calculation of motion response amplitude operators (RAOs) at various incident angles, showed the system avoids wave periods in the South China Sea (no resonance), ensuring structural safety for offshore operations. A 7-day marine test of the prototype was conducted in Yazhou Bay, Hainan Province, to monitor real-time temperature and strain data of the riser in the test sea area. The sensing submarine cable accurately responded to temperature changes at different depths with high stability and precision; using the Frenet-based 3D curve reconstruction algorithm, pipeline shape was inverted from the monitored strain data, enabling real-time pipeline monitoring. During the test, the buoy and fiber-optic sensing module operated stably. This marine test confirms the buoy observation system’s reasonable design parameters and feasible scheme, applicable to temperature and deformation monitoring of marine risers. Full article

Efficient soil moisture monitoring is fundamental to precision agriculture, enabling improved irrigation management, enhanced crop productivity, and sustainable water use. This review comprehensively evaluates soil moisture sensing technologies, classifying them into invasive and non-invasive approaches. The underlying operating principles, strengths, and limitations, as well as documented practical applications, are critically discussed for each technology. Invasive methods, including dielectric sensors, matric potential devices, heat-pulse sensors, and microstructured optical fibres, offer high-resolution data but require careful installation and calibration to account for environmental and soil-specific variables such as texture, salinity, and temperature. Non-invasive technologies—such as microwave remote sensing, electromagnetic induction, and ground-penetrating radar—enable large-scale monitoring without disturbing the soil profile; however, they face challenges in terms of resolution, cost, and data interpretation. Key performance factors across all sensor types include installation methodology, environmental sensitivity, spatial representativeness, and integration with decision-support systems. The review also addresses recent innovations such as biodegradable and Micro–Electro–Mechanical Systems sensors, the incorporation of Internet of Things platforms, and the application of artificial intelligence for enhanced data analytics and sensor calibration. While sensor deployment has demonstrated tangible benefits for irrigation efficiency and yield improvement, widespread adoption remains constrained by technical, economic, and infrastructural barriers, particularly for smallholder farmers. The analysis concludes by identifying research gaps and recommending strategies to facilitate the broader uptake of soil moisture sensors, with a focus on cost reduction, calibration standardisation, and integration into climate-resilient agricultural frameworks. Full article

Chlorophyll-a (Chl-a) concentration is a key parameter for assessing the degree of eutrophication and the algal bloom risk in water bodies. Accurate and robust monitoring of Chl-a is crucial for effective water quality management of inland and coastal optically complex Case-II waters. This study proposes a stratified integrated framework that combines optical water type (OWT) classification and multi-index blending models and evaluates the capability of ZY-1 02E hyperspectral imagery in the retrieval of Chl-a concentration in Case-II waters. This research is based on ZY-1 02E hyperspectral remote sensing images and ground synchronous measurement data from four typical water bodies in China (Dongpu Reservoir, Nanyi Lake, Tangdao Bay, and Moon-lake Reservoir). Using Fuzzy C-Means (FCM) clustering combined with spectral feature analysis, three different OWTs were identified, and the bands sensitive to Chl-a for each water type were recognized. Subsequently, the most suitable semi-empirical indices (BR, TBI) were selected, and a new suspended matter correction index (SMCI) was constructed by integrating spectral bands and TSM data specifically for high-turbidity waters to facilitate the retrieval of Chl-a concentration. The RMSE and MAPE of the model constructed based on the unclassified dataset were 3.1586 μg·L⁻¹ and 30.82%, respectively. When the stratified ensemble method based on optical water type classification was employed, the overall RMSE and MAPE were reduced to 1.5832 μg·L⁻¹ and 16.36%. The results demonstrate that this hierarchical ensemble framework significantly improved the retrieval accuracy of Chl-a concentration. An uncertainty assessment of the Chl-a retrieval model for highly turbid waters incorporating SMCI was conducted using the Monte Carlo method, revealing a mean coefficient of variation of 0.0567 and a coverage rate of 95.65% for the 95% confidence interval, indicating high predictive stability and reliability of the model. This study emphasizes the importance of the integrated framework strategy that combines OWTs classification and multi-index blending models for accurate and robust remote sensing estimation of Chl-a concentration under optically complex environmental conditions. It confirms the application potential of ZY-1 02E hyperspectral data in monitoring Chl-a in inland and near-coastal waters at medium and small scales. Full article

Reliable global turbidity monitoring is crucial for water resource management, yet existing satellite-based methods face limitations in accuracy, generalization, and scalability across diverse aquatic environments. This study presents a robust, globally applicable turbidity estimation model using Sentinel-2 imagery and a machine-learning approach, developed based on harmonized global open-source datasets (GLORIA and MAGEST; turbidity range: 0–2200 FNU) encompassing 68 lakes, 2 rivers, 2 estuaries, and 11 coastal oceans across 17 countries. Among the evaluated machine-learning models, gradient boosting regression demonstrated the best performance, achieving a high correlation (r: 0.95) with minimal bias (1.32 FNU) and robust generalization across all water types, outperforming existing turbidity models when evaluated on the same test dataset. Shapley Additive exPlanations-based model interpretability identified the Rrs865/Rrs560 ratio as the dominant predictor, with critical contributions from Rrs783, Rrs665, and Rrs865. The model’s performance is evaluated across various optical water types and aquatic systems in diverse geographical settings, showcasing its robustness in sediment-rich and highly turbid environments that underscores its suitability for reliable turbidity monitoring after severe storms or extreme precipitation. Additionally, innovative automated pipelines integrated within a scientific exploitation platform facilitate scalable and near-real-time operational monitoring. This methodological integration provides a significant advancement in satellite-based turbidity monitoring, enabling informed water quality management under diverse environmental and climatic conditions. Full article

The present study evaluates a surface dielectric barrier discharge (SDBD) plasma system utilizing porous metal electrodes to enhance the performance of non-thermal plasma (NTP)-based water treatment. A custom high-voltage, variable-frequency power driver was developed to operate SDBD reactors featuring novel porous electrode configurations aimed at enhancing plasma–liquid interaction. Three types of porous metal electrodes—copper (60 ppi), copper (20 ppi), and nickel (60 ppi)—were investigated as ground electrodes to evaluate their impact on discharge behavior and treatment performance. Electrical characterization via Lissajous plot analysis and optical emission spectroscopy (OES) was used to assess plasma power and reactive species generation. Ozone measurement and hydroxyterephthalic acid (HTA) dosimetry confirmed the formation of O₃ and hydroxyl radicals (·OH), while methylene blue (MB) removal experiments quantified pollutant removal percentage and energy yield. Among the tested electrodes, the copper (20 ppi) configuration achieved the highest MB removal percentage of 95.07%, followed by nickel (60 ppi) with 90.53%, and copper (60 ppi) with only 27.55%. Correspondingly, the energy yield ($EY$) reached 0.349 g/kWh for copper (20 ppi) at 15 min of plasma exposure, 0.19 g/kWh for nickel (60 ppi) at 20 min, and 0.049 g/kWh for copper (60 ppi) at 15 min. These results highlight the potential of porous metal electrodes as effective design choices for optimizing plasma–liquid interaction in SDBD systems. The findings support the development of compact, energy-efficient plasma water purification technologies using air-fed, surface DBD configurations. Full article

Aerosol–cloud interaction remains one of the largest sources of uncertainty in weather and climate modeling. This study investigates the impacts of aerosols on the macro- and microphysical properties of different cloud types over East Asia, based on nine years of joint satellite observations from CloudSat, CALIPSO, and MODIS, combined with ERA5 reanalysis data. Results reveal pronounced cloud-type dependence in aerosol effects on cloud fraction, cloud top height, and cloud thickness. Aerosols enhance the development of convective clouds while suppressing the vertical extent of stable stratiform clouds. For ice-phase structures, ice cloud fraction and ice water path significantly increase with aerosol optical depth (AOD) in deep convective and high-level clouds, whereas mid- to low-level clouds exhibit reduced ice crystal effective radius and ice water content, indicating an “ice crystal suppression effect.” Even after controlling for 14 meteorological variables, partial correlations between AOD and cloud properties remain significant, suggesting a degree of aerosol influence independent of meteorological conditions. Humidity and wind speed at different altitudes are identified as key modulating factors. These findings highlight the importance of accounting for cloud-type differences, moisture conditions, and dynamic processes when assessing aerosol–cloud–climate interactions and provide observational insights to improve the parameterization of aerosol indirect effects in climate models. Full article

Methane (CH₄) emissions are a major contributor to greenhouse gases and pose significant challenges to global climate mitigation efforts. The accurate determination of CH₄ concentrations via remote sensing is crucial for emission monitoring but remains impeded by surface spectral heterogeneity—notably albedo variations and land cover diversity. This study systematically assessed the sensitivity of three mainstream algorithms, namely, matched filter (MF), albedo-corrected reweighted-L1-matched filter (ACRWL1MF), and differential optical absorption spectroscopy (DOAS), to surface type, albedo, and emission rate through high-fidelity simulation experiments, and proposed a dynamic regularized adaptive matched filter (DRAMF) algorithm. The experiments simulated airborne hyperspectral imagery from the Airborne Visible/InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) with known CH₄ concentrations over diverse surfaces (including vegetation, soil, and water) and controlled variations in albedo through the large-eddy simulation (LES) mode of the Weather Research and Forecasting (WRF) model and the MODTRAN radiative transfer model. The results show the following: (1) MF and DOAS have higher true positive rates (TP > 90%) in high-reflectivity scenarios, but the problem of false positives is prominent (TN < 52%); ACRWL1MF significantly improves the true negative rate (TN = 95.9%) through albedo correction but lacks the ability to detect low concentrations of CH₄ (TP = 63.8%). (2) All algorithms perform better at high emission rates (1000 kg/h) than at low emission rates (500 kg/h), but ACRWL1MF performs more robustly in low-albedo scenarios. (3) The proposed DRAMF algorithm improves the F1 score (0.129) by about 180% compared to the MF and DOAS algorithms and improves TP value (81.4%) by about 128% compared to the ACRWL1MF algorithm through dynamic background updates and an iterative reweighting mechanism. In practical applications, the DRAMF algorithm can also effectively monitor plumes. This research indicates that algorithms should be selected considering the specific application scenario and provides a direction for technical improvements (e.g., deep learning model) for monitoring gas emission. Full article

Coastal wetlands are a critical buffer between land and water that are threatened by land use and climate change, necessitating improved monitoring for management and resilience planning. The recently launched NASA-ISRO L- and S-band SAR satellite (NISAR) will provide regular collections of fully polarimetric SAR imagery over the Great Lakes, allowing for unprecedented remote monitoring of the large expanses of coastal wetlands in the region. Prior research with polarimetric C-band SAR showed inconsistencies with common polarimetric analysis techniques, including the erroneous misattribution of double-bounce scattering in three-component scattering models. To prepare for NISAR and determine whether SAR-based coastal wetland analysis methods established with the C-band are applicable to the L- and S-bands, the NASA-ISRO airborne system (ASAR) collected imagery over western Lake Erie and Lake St. Clair coincident with a field data collection campaign. ASAR data were analyzed to identify common Great Lakes coastal wetland vegetation species, assess the extent of inundation, and derive biomass retrieval algorithms. Co-polarized phase difference histograms were also analyzed to assess the validity of three-component scattering decompositions. The L- and S-bands allowed for the production of wetland type maps with high accuracies (92%), comparable to those produced using a fusion of optical and SAR data. Both frequencies could assess the extent of flooded vegetation, with the S-band correctly identifying inundated vegetation at a slightly higher rate than the L-band (83% to 78%). Marsh vegetation biomass retrieval algorithms derived from L-band data had the best correlation with field data (R² = 0.71). Three component scattering models were found to misattribute double-bounce scattering at incidence angles shallower than 35°. The L- and S-band results were compared with satellite RADARSAT-2 imagery collected close to the ASAR acquisitions. This study provides an advanced understanding of polarimetric SAR for monitoring wetlands and provides a framework for utilizing forthcoming NISAR data for effective monitoring. Full article

Turbidity is a crucial and reliable indicator that is extensively utilized in water quality monitoring through remote sensing technology. The development of accurate and applicable models for turbidity estimation is essential. While many existing studies rely on uniform models based on statistical regression or traditional machine learning techniques, the application of deep learning models for turbidity estimation remains limited. This study proposed deep learning models for turbidity estimation based on optical classification of inland waters using Sentinel-2 data. Specifically, the fuzzy c-means (FCM) clustering method was employed to classify optical water types (OWTs) based on their spectral reflectance characteristics. A weighted sum of the turbidity prediction results was generated by the OWT-based convolutional neural network-random forest (CNN-RF) model, with weights derived from the FCM membership degrees. Turbidity for four typical waters was mapped by the proposed method using Sentinel-2 images. The FCM method efficiently classified waters into three OWTs. The OWT-based weighted CNN-RF model demonstrated strong robustness and generalization performance, achieving a high prediction accuracy (R² = 0.900, RMSE = 11.698 NTU). The turbidity maps preserved the spatial continuity of the turbidity distribution and accurately reflected water quality conditions. These findings facilitate the application of deep learning models based on optical classification in turbidity estimation and enhance the capabilities of remote sensing for water quality monitoring. Full article

Textile production contributes significantly to water pollution, making dye removal crucial for protecting water resources from toxic textile waste. The use of nano-adsorbents for water purification has emerged as a promising approach to removing pollutants from wastewater. Nickel Ferrite (NiFe₂O₄), Iron Oxide (Fe₂O₃), and Nickel Oxide (NiO) nanoparticles (NPs) were prepared via an auto-combustion sol–gel technique using chitosan as a capping and stabilizing agent. The prepared nanomaterials were characterized using various techniques such as XRD, UV-Vis DRS, FT-IR, Raman, EDX, SEM, and TEM to confirm their structure, particle size, morphology, functional groups on the surface, and optical properties. Subsequently, the adsorption of the methylene blue (MB) dye using the prepared nanomaterials was studied. NiFe₂O₄ NPs exhibited the best adsorption behavior compared to the mono-metal oxides. Moreover, all prepared nanomaterials were compatible with the pseudo-second-order model. Further investigations were conducted for NiFe₂O₄ NPs, showing that both the Freundlich and Langmuir isotherm models can explain the adsorption of the MB dye on the surface of NiFe₂O₄ NPs. Factors affecting MB dye adsorption were discussed, such as adsorbent dose, concentration of the MB dye, contact time, pH, and temperature. NiFe₂O₄ NPs exhibited a maximum removal efficiency of the MB dye, reaching 96.8% at pH 8. Different water sources were used to evaluate the ability of NiFe₂O₄ NPs to purify a wide range of water types. Full article

In this study, nanostructured ferberites (FeWO₄) were synthesized via hydrothermal routes in an acidic medium. It was then investigated as an efficient photocatalyst for degrading organic dye molecules, with methylene blue (MB) as a model pollutant. The formation mechanism of ferberite revealed that the physical form of the precursor, FeSO₄·7H₂O, acts as a decisive factor in morphological evolution. Depending on whether it is in a solid or dilute solution form, two distinct nanostructures are produced: nanoplatelets and self-organized microspheres. Both structures are composed of stoichiometric FeWO₄ (Fe: 49%, W: 51%) in a single monoclinic phase (space group P2/c:1) with high purity and crystallinity. The p-type semiconductor behavior was confirmed using Mott–Schottky model and the optical analysis, resulting in small band gap energies (≈1.7 eV) favoring visible absorption light. Photocatalytic tests under simulated solar irradiation revealed rapid and efficient degradation in less than 10 min under near-industrial conditions (pH 5). This was achieved using only a ferberite catalyst and a low concentration of H₂O₂ (4 mM) without additives, dopants, or artificial light sources. Advanced studies based on photocurrent measurements, trapping and stability tests were carried out to identify the main reactive species involved in the photocatalytic process and better understanding of photodegradation mechanisms. These results demonstrate the potential of nanostructured FeWO₄ as a sustainable and effective photocatalyst for water purification applications. Full article

Jefea lantanifolia (S. Schauer) Strother is traditionally used in Hidalgo, Mexico, to manage type 2 diabetes (T2D). The aerial parts are prepared as an infusion and consumed throughout the day. This study conducted a 2 h acute experiment under both fasting and postprandial conditions to evaluate the effects of the aqueous infusion (AE), the ethanol–water extract (EWE), and their isolated constituents in hyperglycemic rats. Structures were established using conventional spectroscopic methods. The absolute configuration was determined by optical rotation and calculated electronic circular dichroism (ECD) methods. Phytochemical analysis led to the isolation of six compounds: luteolin (1); 2β-hydroxy-dimerostemma brasiolide-1-O-(3-hydroxymethacrylate) (2); homoplantaginin (3); cynarin (4); luteolin-7-O-glucoside (5); and nepitrin (6). The extract was deemed safe at a dose of 2 g/kg b. w. in acute toxicity assays. In vivo experiments showed significant reductions in blood glucose levels during fasting, with compounds 2 and 3 achieving reductions of 42% and 40%, respectively, compared to 51% with glibenclamide. Postprandially, all treatments demonstrated effective glucose-lowering activity, particularly compound 3 and the EWE. These findings support the traditional use of J. lantanifolia and highlight its phytochemicals as promising candidates for further pharmacological investigation. Long-term studies and high-dose evaluations are warranted to validate therapeutic potential and establish safety profiles. Full article