Search Results (109)

Coastal wetlands play a crucial role in carbon sequestration, yet the invasion of Spartina alterniflora (SA) significantly alters the cycling and sequestration of soil organic carbon (SOC) in coastal wetlands. Nevertheless, the potential underlying mechanisms governing the dynamics of SOC in coastal wetlands following SA invasion remain poorly understood. Here, we investigated the impacts of SA invasion on the dynamics and potential sequestration mechanisms of SOC in the Hangzhou Bay Estuary Wetland, China. Compared to the bare flat (BF), SOC and its fractions in 0–20 cm increased by 1.37–2.24 times after 8 years of SA invasion. Variance partitioning analysis indicated that the combined effects of soil physicochemical properties, soil carbon cycle-related enzymes, and vegetation type were the primary drivers of SOC and its fractions. Redundancy analysis revealed significant positive correlations between SOC and key soil physicochemical properties and enzymes, including sucrase, clay particles, total nitrogen, ammonium nitrogen, and β-glucosidase. Structural equation modeling demonstrated that SA invasion was associated with significant alterations in soil physicochemical properties and positively correlated with both stable and labile carbon fractions, or indirectly linked to these fractions through carbon cycle-related enzymes, thereby substantially positively contributing to SOC. This study supports the hypothesis that the invasion of SA affects the linkage pathway of SOC sequestration and offers valuable guidance for carbon sequestration strategies of coastal wetlands. Full article

Shale gas wastewater from hydraulic fracturing poses significant environmental risks due to its high salinity and complex inorganic composition. This study investigates the behavior of major and trace inorganic constituents across a full-scale treatment train in the Sichuan Basin, China. Despite multi-stage processes including equalization, flocculation, flotation, biological reactors, membrane filtration, and clarification, key inorganic species such as Cl, Na, Br, Sr, Li, and B remained largely persistent in the final effluent with values of 13,760, 8811, 70, 95.9, 26.6, and 60.2 mg/L, respectively. Geochemical tracers including Br/Cl (average: 0.0022 mM/mM), Na/Br (average: 125 mg/mg), and Sr/Ca (average: 0.15 mM/mM) ratios, combined with halide endmember mixing models, revealed that salinity primarily originated from highly evaporated formation brines, with limited evidence for halite dissolution or external contamination. Elevated Sr (average: 89.3 mg/L) and Ca (average: 274 mg/L) levels relative to Mg (average: 32 mg/L) suggest significant water–rock interaction. Environmental risk assessments showed that concentrations of several elements in treated effluent greatly exceeded national and international discharge or reuse standards. These findings underscore the limitations of conventional treatment technologies and highlight the urgent need for advanced processes and regulatory frameworks that address the unique challenges of high-TDS (total dissolved solids) unconventional wastewater. Full article

In recent years, remarkable advancements have been achieved in hyperspectral unmixing (HU). Sparse unmixing, in which models mix pixels as linear combinations of endmembers and their corresponding fractional abundances, has become a dominant paradigm in hyperspectral image analysis. To address the inherent limitations of spectral-only approaches, spatial contextual information has been integrated into unmixing. In this article, a superpixel collaborative sparse unmixing algorithm with graph differential operator (SCSU–GDO), is proposed, which effectively integrates superpixel-based local collaboration with graph differential spatial regularization. The proposed algorithm contains three key steps. First, superpixel segmentation partitions the hyperspectral image into homogeneous regions, leveraging boundary information to preserve structural coherence. Subsequently, a local collaborative weighted sparse regression model is formulated to jointly enforce data fidelity and sparsity constraints on abundance estimation. Finally, to enhance spatial consistency, the Laplacian matrix derived from graph learning is decomposed into a graph differential operator, adaptively capturing local smoothness and structural discontinuities within the image. Comprehensive experiments on three datasets prove the accuracy, robustness, and practical efficacy of the proposed method. Full article

A managed aquifer recharge (MAR) project utilizing reclaimed water has been operated for over 10 years in northeastern Beijing, China, with the goal of restoring the long-dried Chaobai River and replenishing the region’s depleted groundwater resources. To ensure the safe implementation of the project, we quantitatively assessed the impact of river water recharge on the multi-layered groundwater system by investigating the hydrochemical compositions of the reclaimed water, river water, and groundwater. Results show that river water is characterized by higher concentrations of Na⁺, Cl⁻, and SO₄²⁻ than found in groundwater, and that river water recharge has altered the groundwater types in the 30 m-depth unconfined layer, changing them from Ca-Mg-HCO₃ and Ca-HCO₃ types to Na-Ca-HCO₃-Cl and Ca-Mg-Na-HCO₃ types. End-member mixing analyses of river water samples indicate that three end-members are needed to represent the seasonal and spatial variations in river water. A five-end-member mixing model is then developed to quantify fractions of river water (f_R) in different aquifer layers. The estimated f_R values vary from 18.4% to 100%, with an average of 67.6% in the 30 m-depth layer, while f_R values in the 80 m-depth confined layer are mainly below 30%, with an average of 13.3%, which corresponds well to the known site geology. Overall, combining hydrochemical analysis with the end-member mixing approach is useful for assessing the impact of river recharge on groundwater. This study also highlights the need for high-resolution characterization of subsurface heterogeneity in MAR sites. Full article

Water source identification and dynamic assessment are critical for mining safety, particularly in mines governed by complex geological structures. The hydrochemical mixing model demonstrates a natural advantage for early warning of water intrusion compared to geophysical monitoring techniques. This study discusses core issues related to the mixing model, including the conceptual framework, selection of end-members, and choice of tracers, and formulates principles for general applicability. In this study, three sources were identified using the conceptual model and hydrochemical analysis: water in F7 (main fault), shallow fracture water, and river water. A correlation analysis and variability analysis were applied to determine the tracers, and the ¹⁸O, D, Cl, B, and Li were determined. The end-members of the three sources are time-dependent in July and September, especially the shallow fracture water’s end-members. The dynamics of the mixing ratios of the three sources suggest that river water contributes only to the inrush (1–4%), with this being especially low in September, as the increasing hydraulic gradient from south to north prevents recharge. The water in F7 accounts for at least 70% of the inrush water. Shallow fracture water accounts for the rest and increases slightly in September as the precipitation increases in mining-disturbed areas. Finally, this work makes the later water control work more targeted. Full article

Background: While the mHealth and school-based scale-up intervention for salt reduction (EduSaltS) effectively reduced salt intake and blood pressure among adults living with participating schoolchildren, the sustainability of these effects remains uncertain. This study aimed to evaluate whether these effects persisted one year post intervention. Methods: A one-year follow-up of a cluster randomized controlled trial was conducted, involving 524 children and their 524 adult family members from 20 primary schools. At 24 months, 509 children (97.1%) and 486 adults (92.7%) completed the assessment. Mixed linear models were used to analyze the difference in changes in salt intake between the intervention and control groups at 24 months, compared to baseline and 12 months, as measured by consecutive 24 h urinary sodium excretions. Secondary outcomes included the differences in changes in blood pressure and salt-related knowledge, attitudes, and practices (KAP) scores. Results: The adjusted mean difference in changes in salt intake between groups was −0.34 g/24 h (95% CI: −0.94 to 0.26, p = 0.265) for children and −0.72 g/24 h (95% CI: −1.48 to 0.05, p = 0.065) for adults at 24 months versus baseline. The corresponding differences from 12 to 24 months were −0.09 g/24 h (95% CI: −0.69 to 0.51, p = 0.775) for children and 0.29 g/24 h (95% CI: −0.50 to 1.08, p = 0.468) for adults. The adjusted difference in changes in adult blood pressure showed a slight, nonsignificant rebound at 24 months. The intervention group maintained significantly higher KAP scores than the control group at both 12 and 24 months. Conclusions: The effects of EduSaltS on reducing salt intake and blood pressure in adults diminished slightly one year after the intervention ended. However, sustained improvements in salt-related KAP were observed in both children and adults. Ongoing support is vital to sustain long-term salt-reduction behaviors. Full article

Zoharite (IMA 2017-049), (Ba,K)₆ (Fe,Cu,Ni)₂₅S₂₇, and gmalimite (IMA 2019-007), ideally K₆□Fe²⁺₂₄S₂₇, are two new sulfides of the djerfisherite group. They were discovered in an unusual gehlenite–wollastonite paralava with pyrrhotite nodules located in the Hatrurim pyrometamorphic complex, Negev Desert, Israel. Zoharite and gmalimite build grained aggregates confined to the peripheric parts of pyrrhotite nodules, where they associate with pentlandite, chalcopyrite, chalcocite, digenite, covellite, millerite, heazlewoodite, pyrite and rudashevskyite. The occurrence and associated minerals indicate that zoharite and gmalimite were formed at temperatures below 800 °C, when sulfides formed on external zones of the nodules have been reacting with residual silicate melt (paralava) locally enriched in Ba and K. Macroscopically, both minerals are bronze in color and have a dark-gray streak and metallic luster. They are brittle and have a conchoidal fracture. In reflected light, both minerals are optically isotropic and exhibit gray color with an olive tinge. The reflectance values for zoharite and gmalimite, respectively, at the standard COM wavelengths are: 22.2% and 21.5% at 470 nm, 25.1% and 24.6% at 546 nm, 26.3% and 25.9% at 589 nm, as well as 27.7% and 26.3% at 650 nm. The average hardness for zoharite and for gmalimite is approximately 3.5 of the Mohs hardness. Both minerals are isostructural with owensite, (Ba,Pb)₆(Cu,Fe,Ni)₂₅S₂₇. They crystallize in cubic space group Pm$\overline{3}$m with the unit-cell parameters a = 10.3137(1) Å for zoharite and a = 10.3486(1) Å for gmalimite. The calculated densities are 4.49 g·cm⁻³ for the zoharite and 3.79 g·cm⁻³ for the gmalimite. The primary structural units of these minerals are M₈S₁₄ clusters, composed of MS₄ tetrahedra surrounding a central MS₆ octahedron. The M site is occupied by transition metals such as Fe, Cu, and Ni. These clusters are further connected via the edges of the MS₄ tetrahedra, forming a close-packed cubic framework. The channels within this framework are filled by anion-centered polyhedra: SBa₉ in zoharite and SK₉ in gmalimite, respectively. In the M₈S₁₄ clusters, the M atoms are positioned so closely that their d orbitals can overlap, allowing the formation of metal–metal bonds. As a result, the transition metals in these clusters often adopt electron configurations that reflect additional electron density from their local bonding environment, similar to what is observed in pentlandite. Due to the presence of shared electrons in these metal–metal bonds, assigning fixed oxidation states—such as Fe²⁺/Fe³⁺ or Cu⁺/Cu²⁺—becomes challenging. Moreover, modeling the distribution of mixed-valence cations (Fe²⁺/³⁺, Cu⁺/²⁺, and Ni²⁺) across the two distinct M sites—one located in the MS₆ octahedron and the other in the MS₄ tetrahedra—often results in ambiguous outcomes. Consequently, it is difficult to define an idealized end-member formula for these minerals. Full article

In satellite remote sensing, mixed pixels commonly arise in medium- and low-resolution imagery, where surface reflectance is a combination of various land cover types. The widely adopted linear mixing model enables the decomposition of mixed pixels into constituent endmembers, effectively bridging spectral resolution gaps by retrieving the spectral properties of individual land cover types. This study introduces a method to enhance multispectral surface reflectance data by reconstructing additional spectral information, particularly in the visible spectral range, using the TROPOMI BRDF product generated by the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm. Employing non-negative matrix factorization (NMF), the approach extracts spectral basis vectors from reference spectral libraries and reconstructs key spectral features using a limited number of wavelength bands. The comprehensive test results show that this method is particularly effective in supplementing surface reflectance information for specific wavelengths where gas absorption is strong or atmospheric correction errors are significant, demonstrating its applicability not only within the 400–800 nm range but also across the broader spectral range of 400–2400 nm. While not a substitute for hyperspectral observations, this approach provides a cost-effective means to address spectral resolution gaps in multispectral datasets, facilitating improved surface characterization and environmental monitoring. Future research will focus on refining spectral libraries, improving reconstruction accuracy, and expanding the spectral range to enhance the applicability and robustness of the method for diverse remote sensing applications. Full article

Phosphorus is the primary contributor to eutrophication in water bodies, and identifying phosphorus sources in rivers is crucial for controlling phosphorus pollution and subsequent eutrophication. Although phosphate oxygen isotopes (δ¹⁸O_P) have the capacity to trace phosphorus sources and cycling in water and sediments, they have not been used in small- to medium-sized watersheds, such as the Xiaodongjiang River (XDJ), which is located in an agricultural watershed, source–complex region of southern China. This study employed phosphate oxygen isotope techniques in combination with a land-use-based mixed end-member model and the MixSIAR Bayesian mixing model to quantitatively determine potential phosphorus sources in surface water and sediments. The δ¹⁸O_P values of the surface water ranged from 5.72‰ to 15.02‰, while those of sediment ranged from 10.41‰ to 16.80‰. In the downstream section, the δ¹⁸O_P values of the surface water and sediment were similar, suggesting that phosphate in the downstream water was primarily influenced by endogenous sediment control. The results of the land-use–source mixing model and Bayesian model framework demonstrated that controlling phosphorus inputs from fertilizers is essential for reducing phosphorus emissions in the XDJ watershed. Furthermore, ongoing rural sewage treatment, manure management, and the resource utilization of aquaculture substrates contributed to reduced phosphorus pollution. This study showed that isotope techniques, combined with multi-model approaches, effectively assessed phosphorus sources in complex watersheds, offering a theoretical basis for phosphorus pollution management to prevent eutrophication. Full article

We explore a new approach for the parsimonious, generalizable, efficient, and potentially automatable characterization of spectral diversity of sparse targets in spectroscopic imagery. The approach focuses on pixels which are not well modeled by linear subpixel mixing of the Substrate, Vegetation and Dark (S, V, and D) endmember spectra which dominate spectral variance for most of Earth’s land surface. We illustrate the approach using AVIRIS-3 imagery of anthropogenic surfaces (primarily hydrocarbon extraction infrastructure) embedded in a background of Arctic tundra near Prudhoe Bay, Alaska. Computational experiments further explore sensitivity to spatial and spectral resolution. Analysis involves two stages: first, computing the mixture residual of a generalized linear spectral mixture model; and second, nonlinear dimensionality reduction via manifold learning. Anthropogenic targets and lakeshore sediments are successfully isolated from the Arctic tundra background. Dependence on spatial resolution is observed, with substantial degradation of manifold topology as images are blurred from 5 m native ground sampling distance to simulated 30 m ground projected instantaneous field of view of a hypothetical spaceborne sensor. Degrading spectral resolution to mimicking the Sentinel-2A MultiSpectral Imager (MSI) also results in loss of information but is less severe than spatial blurring. These results inform spectroscopic characterization of sparse targets using spectroscopic images of varying spatial and spectral resolution. Full article

Hydrous minerals are significant indicators of the ancient aqueous environment on Mars, and orbital hyperspectral data are one of the most effective tools for obtaining information about the distribution of hydrous minerals on the Martian surface. However, prolonged weathering, erosion, and other external forces result in complex mixing effects, often weakening the spectral absorption features of individual minerals. This study proposes a quantitative inversion method for Martian hydrous minerals by integrating a radiative transfer model with a deep learning network. Based on the physics of the Hapke radiative transfer model, the single-scattering albedo spectra of mineral end members were obtained. Additionally, the Linear Spectral Mixture Model was employed to generate a large number of fully constrained mineral mixture samples, providing theoretical support for experimental data. An LSTM-1DCNN model was trained to establish a data-driven quantitative inversion framework. CRISM data were applied to the Eberswalde Crater region to retrieve the abundances of 21 hydrous minerals, including tremolite, opal, and serpentine. The average abundance of hydrous minerals was calculated to be 0.018, with a total area proportion of approximately 8%. Additionally, by analyzing the distribution areas of hydrous silicates, hydrous sulfates, and hydrous hydroxides, the water activity history of the region was inferred. The results align with findings from related studies and mineral spectral index results. By incorporating deep learning into traditional mixing models, this study identifies the distribution of various low-abundance hydrous minerals, enhancing the accuracy of Martian hydrous mineral inversion. It is expected to provide valuable references for the selection of landing sites for Tianwen-3 and support the smooth implementation of China’s Mars exploration mission. Full article

Non-photosynthetic vegetation (NPV) significantly impacts ecosystem degradation, drought, and wildfire risk due to its flammable and persistent litter. Yet, the accurate estimation of NPV in heterogeneous landscapes, such as dry–hot valleys, has been limited. This study utilized multi-source time-series remote sensing data from Sentinel-2 and GF-2, along with field surveys, to develop an NDVI-DFI ternary linear mixed model for quantifying NPV coverage (f_NPV) in a typical dry–hot valley region in 2023. The results indicated the following: (1) The NDVI-DFI ternary linear mixed model effectively estimates photosynthetic vegetation coverage (f_PV) and f_NPV, aligning well with the conceptual framework and meeting key assumptions, demonstrating its applicability and reliability. (2) The RGB color composite image derived using the minimum inclusion endmember feature method (MVE) exhibited darker tones, suggesting that MVE tends to overestimate the vegetation fraction when distinguishing vegetation types from bare soil. On the other hand, the pure pixel index (PPI) method showed higher accuracy in estimation due to its higher spectral purity and better recognition of endmembers, making it more suitable for studying dry–hot valley areas. (3) Estimates based on the NDVI-DFI ternary linear mixed model revealed significant seasonal shifts between PV and NPV, especially in valleys and lowlands. From the rainy to the dry season, the proportion of NPV increased from 23.37% to 35.52%, covering an additional 502.96 km². In summary, these findings underscore the substantial seasonal variations in f_PV and f_NPV, particularly in low-altitude regions along the valley, highlighting the dynamic nature of vegetation in dry–hot environments. Full article

Numerical simulation of sediment transport and subsequent morphological evolution rely on accurate parameterizations of sediment characteristics. However, these data are often not available or are spatially and/or temporally limited. This study approaches the problem of limited sediment grain-size data with a series of simulations assessing model sensitivity to sediment parameters and initial bed composition configurations in Delft3D, leading to improved modeling practices. A previously validated Delft3D sediment transport and morphology model for Dauphin Island, Alabama, USA, is used as the benchmark case. A method for the generation of representative sediment grain sizes and their spatially varying distributions is presented via end-member analysis of in situ surficial sediment samples. Derived sediment classes and their spatial distributions are applied to two sensitivity case simulations with increasing bed composition complexity. First, multiple sediment classes are applied in a single fully mixed layer, regardless of sediment type. Second, multiple sediment classes are applied in a thin, fully mixed transport layer with underlayers containing only the non-cohesive sediment classes below. Simulations were carried out in a probabilistic, Delft3D MorMerge configuration to capture long-term morphology change for 10 years. We found there is sensitivity to the inclusion of additional sediment classes and sediment distribution made evident in bed level and morphology change. Inclusion of highly mobile fine sediments altered model results in each sensitivity case. The model was also found to be sensitive to initial bed composition in terms of bed level and morphology change, with notable differences between sensitivity cases on decadal timescales, indicating an armoring effect in the second sensitivity case, which used the transport and underlayer bed configuration. The results of this study offer guidance for numerical modelers concerned with sediment behavior in coastal and estuarine environments. Full article

We introduce a new model for non-linear endmember extraction and spectral unmixing of hyperspectral imagery called Generative Simplex Mapping (GSM). The model represents endmember mixing using a latent space of points sampled within a $(n-1)$-simplex corresponding to n unique sources. Barycentric coordinates within this simplex are naturally interpreted as relative endmember abundances satisfying both the abundance sum-to-one and abundance non-negativity constraints. Points in this latent space are mapped to reflectance spectra via a flexible function combining linear and non-linear mixing. Due to the probabilistic formulation of the GSM, spectral variability is also estimated by a precision parameter describing the distribution of observed spectra. Model parameters are determined using a generalized expectation-maximization algorithm, which guarantees non-negativity for extracted endmembers. We first compare the GSM against three varieties of non-negative matrix factorization (NMF) on a synthetic data set of linearly mixed spectra from the USGS spectral database. Here, the GSM performed favorably for both endmember accuracy and abundance estimation with all non-linear contributions driven to zero by the fitting procedure. In a second experiment, we apply the GTM to model non-linear mixing in real hyperspectral imagery captured over a pond in North Texas. The model accurately identified spectral signatures corresponding to near-shore algae, water, and rhodamine tracer dye introduced into the pond to simulate water contamination by a localized source. Abundance maps generated using the GSM accurately track the evolution of the dye plume as it mixes into the surrounding water. Full article

Surface water fraction mapping is an essential preprocessing step for the subpixel mapping (SPM) of surface water, providing valuable prior knowledge about surface water distribution at the subpixel level. In recent years, spectral mixture analysis (SMA) has been extensively applied to estimate surface water fractions in multispectral images by decomposing each mixed pixel into endmembers and their corresponding fractions using linear or nonlinear spectral mixture models. However, challenges emerge when introducing existing surface water fraction mapping methods to hyperspectral images (HSIs) due to insufficient exploration of spectral information. Additionally, inaccurate extraction of endmembers can result in unsatisfactory water fraction estimations. To address these issues, this paper proposes an adaptive unmixing method based on iterative multi-objective optimization for surface water fraction mapping (IMOSWFM) using Zhuhai-1 HSIs. In IMOSWFM, a modified normalized difference water fraction index (MNDWFI) was developed to fully exploit the spectral information. Furthermore, an iterative unmixing framework was adopted to dynamically extract high-quality endmembers and estimate their corresponding water fractions. Experimental results on the Zhuhai-1 HSIs from three test sites around Nanyi Lake indicate that water fraction maps obtained by IMOSWFM are closest to the reference maps compared with the other three SMA-based surface water fraction estimation methods, with the highest overall accuracy (OA) of 91.74%, 93.12%, and 89.73% in terms of pure water extraction and the lowest root-mean-square errors (RMSE) of 0.2506, 0.2403, and 0.2265 in terms of water fraction estimation. This research provides a reference for adapting existing surface water fraction mapping methods to HSIs. Full article