Search Results (7,935)

Estimating the impacts of climate change on streamflow in the Xiaoxingkai Lake Basin is vital for ensuring sustainable water resource management and transboundary cooperation across the entire Xingkai Lake Basin, a transboundary lake system shared between China and Russia. In this study, 11 Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways (SSP245 and SSP585) were used to drive the Soil and Water Assessment Tool Plus (SWAT+) model. Streamflow projections were made for two future periods: the 2040s (2021–2060) and the 2080s (2061–2100). To correct for systematic biases in the GCM outputs, we applied the Delta Change method, which significantly reduced root mean square error (RMSE) in both precipitation and temperature by 3–35%, thereby improving the accuracy of SWAT+ simulations. To better capture inter-model variability and enhance the robustness of streamflow projections, we used the Bayesian Model Averaging (BMA) technique to generate a weighted ensemble, which outperformed the simple arithmetic mean by reducing uncertainty across models. Our results indicated that under SSP245, greater increases were projected in annual streamflow as well as in wet and normal-flow seasons (e.g., streamflow in normal-flow season in the 2080s increased by 13.0% under SSP245, compared to 7.0% under SSP585). However, SSP585 produced a much larger relative amplification in the dry season, with percentage changes relative to the historical baseline reaching up to +171.7% in the 2080s, although the corresponding absolute increases remained limited due to the low baseline flow. These findings quantify climate-driven hydrological changes in a cool temperate lake basin by integrating climate projections, hydrological modeling, and ensemble techniques, and highlight their implications for understanding hydrological sustainability under future climate scenarios, providing a critical scientific foundation for developing adaptive, cross-border water management strategies, and for further studies on water resource resilience in transboundary basins. Full article

Supercritical CO₂ (ScCO₂) injection has emerged as a promising method to enhance shale gas recovery while simultaneously achieving CO₂ sequestration. This research investigates how ScCO₂ interacts with water and shale rock, altering the pore structure characteristics of shale reservoirs. The study examines shale samples from three marine shale formations in southern China under varying thermal and pressure regimes simulating burial conditions at 1000 m (45 °C and 10 MPa) and 2000 m (80 °C and 20 MPa). The research employs multiple analytical techniques including XRD for mineral composition analysis, MICP, N₂GA, and CO₂GA for comprehensive pore characterization, FE–SEM for visual observation of mineral and pore changes, and multifractal theory to analyze pore structure heterogeneity and connectivity. Key findings indicate that ScCO₂–water–shale interactions lead to dissolution of minerals such as kaolinite, calcite, dolomite, and chlorite, and as the reaction proceeds, substantial secondary mineral precipitation occurs, with these changes being more pronounced under 2000 m simulation conditions. Mineral dissolution and precipitation cause changes in pore structure parameters of different pore sizes, with macropores showing increased PV and decreased SSA, mesopores showing decreased PV and SSA, and micropores showing insignificant changes. Moreover, mineral precipitation effects are stronger than dissolution effects. These changes in pore structure parameters lead to alterations in multifractal parameters, with mineral precipitation reducing pore connectivity and consequently enhancing pore heterogeneity. Correlation analysis further revealed that H and D₋₁₀−D₁₀ exhibit a significant negative correlation, confirming that reduced connectivity corresponds to stronger heterogeneity, while mineral composition strongly controls the multifractal responses of macropores and mesopores, with micropores mainly undergoing morphological changes. However, these changes in micropores are mainly manifested as modifications of internal space. Siliceous shale samples exhibit stronger structural stability compared to argillaceous shale, which is attributed to the mechanical strength of the quartz framework. By integrating multifractal theory with multi–scale pore characterization, this study achieves a unified quantification of shale pore heterogeneity and connectivity under ScCO₂–water interactions at reservoir–representative pressure–temperature conditions. This novelty not only advances the methodological framework but also provides critical support for understanding CO₂–enhanced shale gas recovery mechanisms and CO₂ geological sequestration in depleted shale gas reservoirs, highlighting the complex coupling between geochemical reactions and pore structure evolution. Full article

Temperature and precipitation are the primary factors restricting litter decomposition in desert ecosystems. The desert ecosystems in Central Asia are ecologically fragile regions, and the climate shows a trend of “warm and wet” due to the regional climate change. However, the influencing mechanisms of warming and winter snow changes on litter decomposition are still poorly understood in desert ecosystems. Furthermore, the litter decomposition rate cannot be directly compared due to the large variations in litter quality across different ecosystems. Here, we simulated warming and altered winter snow changes in the field, continuously monitored litter decomposition rates of standard litter bags (i.e., red tea and green tea) and a dominant plant species (i.e., Erodium oxyrrhynchum) during a snow-cover and non-snow-cover period over five months. We found that warming and increased snow cover increased the litter decomposition rate of red tea, green tea, and Erodium oxyrhinchum, and had significant synergistic effects on litter decomposition. The effects of warming and winter snow changes on litter decomposition were more pronounced in April, when the hydrothermal conditions were the best. The decomposition rates of all three litter types belowground were higher than those on the soil surface, highlighting the important roles of soil microbes in accelerating litter decomposition. Furthermore, we found that warming and winter snow changes altered litter decomposition by influencing soil enzyme activities related to soil carbon cycling during the snow-cover period, while influencing soil enzyme activities related to soil phosphorus cycling during the non-snow-cover period. And, notably, decreased snow cover promoted soil enzyme activities during the snow-cover period. More interestingly, our results indicated that the decomposition rate (k) was the lowest, but the stability factor (S) was the highest in the Gurbantünggüt Desert based on the cross-ecosystem comparison using the “Tea Bag Index” method. Overall, our results highlighted the critical roles of warming and winter snow changes on litter decomposition. In future research, the consideration of relationships between litter decomposition and soil carbon sequestration will advance our understanding of soil carbon cycling under climate change in desert ecosystems. Full article

This study explores the catalytic performance of V³⁺-modified Mg/Al hydrotalcite-derived materials in the oxidative dehydrogenation (ODH) of n-butane, compared with catalysts derived from pyrovanadate and decavanadate precursors. Different methods for preparing hydrotalcite-like materials were applied to obtain vanadium-containing Mg-Al mixed oxide catalysts for n-butane ODH. The hydrotalcite-like precursors were doped with vanadates (V⁵⁺) via ion exchange or co-precipitation or with V³⁺ cations incorporated into brucite-like layers. During calcination in air or argon flow, different vanadium-containing phases were obtained. Our findings demonstrate that V³⁺-doped hydrotalcites exhibit superior activity and selectivity toward the total C₄H₈ products, with enhanced selectivity for 1,3-butadiene. The highest n-butane conversion was observed for catalysts with an MgO structure and vanadium dispersed in the oxide matrix. A similar conversion level (~44%) was obtained for a spinel-like Mg₂VO₄ catalyst, but only a 15% level was found for the highly crystalline α-Mg₂V₂O₇ catalyst. In contrast, the highest selectivities toward dehydrogenated products were observed for V³⁺-containing and α-Mg₂V₂O₇ catalysts. NH₃- and CO₂-temperature programmed desorption (TPD) analyses showed that high basicity combined with low acidity favors the formation of butene isomers and 1,3-butadiene. This work highlights the strategic potential of tailoring vanadium speciation and hydrotalcite-based catalyst design for low-carbon chemical manufacturing, supporting the transition toward a circular economy. Full article

Identifying the impact of climate change and changes in underlying surface conditions on river runoff changes is critical for sustainable water resource use and watershed management in arid regions. The Kriya River is not only a key support for water resources in the arid environment of the Tarim Basin, but also a solid foundation for the survival and development of agricultural oases. In this study, the Kriya River Basin in Xinjiang, China, was taken as the research object, and the Mann–Kendall, Sen’s Slope, Cumulative Sum, and other methods were used to systematically analyze the temporal evolution law and multi-modal characteristics of runoff in the basin. Based on the Budyko hydrothermal coupling equilibrium equation, the contribution of temperature, evaporation, and the underlying surface to runoff variation was quantitatively interpreted. The study found that the annual runoff depth of the Kriya River Basin has shown a significant positive evolution trend in the past 60 years, with an increase rate of 0.5189 mm/a (p ≤ 0.01). Through the identification of mutation points, the runoff time series of the Kriya River was divided into the base period 1957–1999 and the change period 2000–2015. Without considering the supply of snowmelt runoff, the contribution rate of precipitation to runoff change was 75.23%, followed by the change in underlying surface (23.08%), and the potential evapotranspiration was only 1.69%. The results of this study provide a good scientific reference for water resources management and environmental governance in the Kriya River Basin. Full article

A novel composite material comprising titanium dioxide and layered double hydroxides (TiO₂/LDHs) was innovatively proposed and prepared using the co-precipitation method to overcome the shortcomings of titanium dioxide, such as low efficiency in separating electron–hole pairs induced by light and a low utilization rate of visible light. This material was used to study the visible-light-driven photocatalytic degradation of methylene blue. The experimental results show that by constructing efficient heterojunction structures through the alignment of interface band energies and regulating the interface charge transfer pathways, the recombination rate of photogenerated electron–hole pairs is significantly reduced, and the photocatalytic activity is greatly enhanced. Among the tested samples, the TiO₂/LDHs composite material with an aluminum-to-titanium molar ratio of 1:1 (AT11) demonstrated the best photocatalytic performance. Within 70 min of simulated sunlight exposure, the degradation rate of methylene blue reached 98.2%, and the optimal concentration of the catalyst was 1 g/L. The photocatalytic process follows a first-order kinetic model. After four cycles of use, the degradation efficiency of methylene blue by the AT11 composite material was 78.93%, demonstrating good stability. The free radical capture experiments indicated that the main active substances for the photocatalytic degradation of methylene blue were h⁺ and ·OH. The constructed TiO₂/LDHs heterostructure system significantly enhanced the photocatalytic performance of TiO₂ materials, which was conducive to the efficient utilization of solar energy. Full article

Prussian blue (PB) and its analogs (PBAs) are considered ideal cathode materials for sodium-ion batteries (SIBs) due to the following merits, including high redox potential, simple synthesis methods, and excellent structural stability. Herein, we synthesized a high-entropy PB cathode material, Na_1.20Mn_0.38Fe_0.15Ni_0.14Co_0.15Cu_0.16[Fe(CN)₆]_0.82□_0.18·0.38H₂O (HE-HCF), through a facile co-precipitation method. The five transition metals in HE-HCF have similar atomic sizes and electronegativity, collectively occupying the high-spin Fe-HS sites. The manganese-based system design reduces the preparation cost, and the high-entropy doping approach further decreases the content of crystalline water in the structure. Benefiting from the synergistic effects of the multiple component elements, HE-HCF demonstrates a capacity retention rate of 72.7% at 0.1 A g⁻¹. Moreover, it even maintains 85.3% of its initial capacity after 1000 cycles at 1 A g⁻¹. Electrochemical impedance spectroscopy (EIS) and galvanostatic intermittent titration technique (GITT) analyses further confirm that HE-HCF exhibits low charge transfer resistance and a small reaction activation energy. Full article

This study proposes a novel quality control method combining fuzzy logic and threshold discrimination for processing X-band dual-polarization radar data from Beijing. The method effectively eliminates non-precipitation echoes, including electromagnetic interference, clear-air echoes, and ground clutter through five key steps: (1) Identifying electromagnetic interference using continuity of reflectivity across adjacent elevation angles, radial mean correlation coefficient, and differential reflectivity; (2) Preserving precipitation data in ground clutter-mixed regions by jointly utilizing the difference in reflectivity before and after clutter suppression by the signal processor, and characteristic value proportions; (3) Developing a fuzzy logic algorithm with six parameters (e.g., reflectivity texture, depolarization ratio) for ground clutter and clear-air echoes removal; (4) Filtering echoes with missing dual-polarization variables using cross-elevation mean reflectivity, mean correlation coefficient, and valid range bin proportion; (5) Removing residual noise via radial/azimuthal reflectivity continuity analysis. Validation with 635 PPI scans demonstrates high identification accuracy across echo types: 93.5% for electromagnetic interference, 98.4% for ground clutter, 97.7% for clear-air echoes, and 98.2% for precipitation echoes. Full article

This study reports the successful synthesis of pure cobalt-substituted calcium molybdate powders (Co-doped CaMoO₄) through a co-precipitation method conducted at room temperature, without the use of surfactants or hazardous organic solvents. The formation of solid solutions with x values ranging from 0.00 to 0.08 was confirmed by X-ray diffraction, Rietveld refinement, and Raman spectroscopy analyses. Elemental analysis using energy-dispersive X-ray spectroscopy showed a strong correlation between the experimental and nominal stoichiometries. The synthesized molybdate powders consist of micrometer-sized particles exhibiting diverse morphologies, including microspheres, flower-like architectures, and dumbbell-shaped particles. These agglomerates are composed of primary particles smaller than 43 nm. The specific surface area increased from 3.59 m²/g for the undoped CaMoO₄ to 10.74 m²/g for the 6% Co-doped CaMoO₄. These nanostructured powders represent promising host materials for 4f ions, making them potential candidates for solid-state lighting applications. Full article

Stochastic weather generators are commonly employed to create synthetic sequences of daily weather variables across diverse fields, including hydrological, ecological, and agricultural studies. Realistic precipitation sequences, in particular, serve as essential inputs in numerous modeling frameworks. Generalized linear models (GLMs) that incorporate covariates to capture seasonality and teleconnections represent one effective approach for stochastic weather generation. However, these models often underestimate the interannual variability of seasonally aggregated variables, notably precipitation intensity during wet seasons. Recent methods developed to mitigate the issue of overdispersion have nevertheless struggled to adequately replicate observed precipitation intensities in wet seasons. To overcome this limitation, we propose integrating a modified hybrid gamma and generalized Pareto distribution into the GLM-based weather generator. This enhanced method was evaluated using daily precipitation data from Seoul, Korea, and successfully reproduced realistic precipitation intensities while effectively addressing the overdispersion issue. Full article

Revealing the trade-offs, synergies, and driving mechanisms among land use functions is essential for mitigating conflicts between functions, optimizing territorial spatial patterns, and providing policy support for regional sustainable development. Taking the Central Yunnan Urban Agglomeration as a case study, this study adopts a grid-based evaluation unit and employs a multi-model fusion approach to systematically analyze the interaction mechanisms among land use functions. By integrating the Pearson correlation method and root mean square deviation (RMSD) model, the trade-off and synergy relationships and their spatiotemporal evolution were quantitatively assessed. The XGBoost–SHAP model and optimized parameter-based geographical detector (OPGD) were introduced to identify the nonlinear characteristics and interaction effects of influencing factors on land use function trade-offs and synergies. In addition, a geographically weighted regression (GWR) model was used to explore spatial heterogeneity in these effects. The results indicate that (1) from 2010 to 2020, the overall synergy between production and ecological functions (PF&EF) in the urban agglomeration was enhanced, while trade-offs between production and living functions (PF&LF) intensified, and the trade-off intensity between living and ecological functions (LF&EF) decreased. Significant spatial heterogeneity exists among land use function interactions: PF&EF and PF&LF trade-offs are concentrated in the central and eastern parts of the urban agglomeration, while LF&EF trade-offs are more scattered, mainly occurring in highly urbanized and ecologically sensitive areas; (2) the dominant factors influencing land use function trade-offs and synergies include precipitation, slope, land use intensity, elevation, NDVI, Shannon diversity index (SHDI), distance to county centers, and distance to expressways; (3) these dominant factors exhibit strong nonlinear effects and significant threshold responses in shaping trade-offs and synergies among land use functions; and that (4) compared with the OLS model, the GWR model demonstrated higher fitting accuracy. This reveals that the impacts of natural, socio-economic, and landscape pattern factors on land use function interactions are characterized by pronounced spatial heterogeneity. Full article

Climate change significantly affects hydrological processes in forest ecosystems, particularly in sensitive coastal areas such as the Croatan National Forest (CNF) in North Carolina. Accurate projections of future water yield are essential for managing agriculture, forestry, and natural ecosystems. This study investigates the potential impacts of climate change on water yield using a combination of statistical downscaling and machine learning. Two downscaling methods, a Statistical DownScaling Model (SDSM) and Multivariate Adaptive Constructed Analogs (MACA), were evaluated, with the SDSM providing superior performance for local climate conditions. To improve precipitation input accuracy, twenty ensemble scenarios were generated using the SDSM, and various machine learning algorithms were applied to identify the optimal ensemble. Among these, the Extreme Gradient Boosting (XGBoost) algorithm exhibited the lowest error and was selected for producing high-quality precipitation time series. This methodology is integrated into the MIDAS (Machine Learning-Based Integration of Downscaled Projections for Accurate Simulation) approach, which leverages machine learning to enhance climate input precision and reduce uncertainty in hydrological modeling. Water yield was simulated over the period 1961–2060, combining observed and projected climate data to capture both historical trends and future changes. The results show that combining statistical downscaling with machine learning algorithms can help improve the accuracy of water yield projections under climate change and be useful for water resource planning, forest management, and climate adaptation. Full article

The trade-offs and synergies among ecosystem services can provide clues for understanding the mechanisms of regional ecological evolution. Previous studies have mainly concentrated on administrative divisions to characterize ecosystem services trade-offs and synergies within specific regions. However, ambiguity persists regarding the spatial diversity and scale dependency of regional ecosystem services, along with the degree to which human activity and climatic variation influence the relationships of multiscale ecosystem services. This study focuses on the Yangtze River Delta Urban Agglomeration in China. Based on grid, county-level, and city-level scales, it analyzes five ecosystem services, namely habitat quality, carbon storage, food production, soil conservation, and water yield, from 2000 to 2020. By using correlation analysis and spatial autocorrelation methods, this study explores the intensity of the trade-offs and synergies among ecosystem services and their spatial patterns. Then, combined with the Optimal Parameters-based Geographical Detector, it identifies the dominant driving factors, quantifies their degree of contribution, and reveals the multiscale differentiation of ecosystem service relationships and their causes. The results show that the five ecosystem services all exhibit significant spatiotemporal heterogeneity. At the grid scale, there is a trade-off relationship between food production and the other four services, while a strong synergistic effect exists among the remaining four services. At the county scale, the synergistic association between habitat quality and carbon storage is the most significant, with the highest contributions from the average annual precipitation and average annual temperature (q-values 0.893 and 0.782, respectively). At the prefecture-level city scale, the intensity of the ecosystem services trade-offs and synergies shows an increasing trend, and the impact of interactions between socio-ecological elements is significantly higher than that at the grid and county scales. This research provides an evidence-based foundation for decision makers to devise suitable strategies that support the coordinated advancement of ecology and the economy across various spatial scales. It is crucial for promoting precise ecosystem regulation and the sustainability of the Yangtze River Delta Urban Agglomeration in China. Full article

Identifying reliable negative control compounds is essential for determining the sensitivity and specificity of screening assays. However, well-characterized negative controls for developmental neurotoxicity behavioral assays in larval zebrafish (Danio rerio) are lacking. This study evaluated nine chemicals with no reported evidence of mammalian developmental neurotoxicity, and a positive control (fluoxetine) for developmental and neurodevelopmental (i.e., behavioral) toxicity in zebrafish. Embryos were exposed to each chemical (≤100 µM) during development, 0–5 days post-fertilization (dpf), then assessed as larvae (6 dpf) using a locomotor behavior light–dark transition test. Behavior was analyzed using two methods: (1) the traditional method, comparing the average total distance moved, and (2) a 13-endpoint approach analyzing 13 aspects of the locomotor profile. Results showed that ibuprofen, omeprazole, and fluoxetine induced developmental toxicity (teratogenesis), with fluoxetine also causing behavioral neurotoxicity. Behavioral effects of developmental exposure to selegiline hydrochloride depended on the analysis method. Exposure to the other six chemicals (D-mannitol, glycerol, L-ascorbic acid, metformin hydrochloride, saccharin, and sodium benzoate), as well as ibuprofen or omeprazole, did not produce behavioral effects using either analysis method. Identifying negative control chemicals is essential for evaluating behavioral alterations precipitated by unknown substances and will assist with screening new chemicals for neurodevelopmental toxicity. Full article

The article presents an analysis of current (during the period 1985–2022) and projected (during the period 2025–2099) changes in the hydrological regime of the Buktyrma, Yesil, and Zhaiyk river basins in Kazakhstan under the conditions of global climate change. This study is based on the integration of data from General Circulation Models (GCMs) of the sixth phase of the CMIP6 project, socio-economic development scenarios SSP2-4.5 and SSP5-8.5, as well as the results of hydrological modelling using the SWIM model. The studies were carried out with an integrated approach to hydrological change assessment, taking into account scenario modelling, uncertainty analysis and the use of bias correction methods for climate data. A calculation method was used to analyse the intra-annual distribution of runoff, taking into account climate change. Detailed forecasts of changes in runoff and intra-annual water distribution up to the end of the 21st century for key water bodies in Kazakhstan were obtained. While the projections of river flow and hydrological parameters under CMIP6 scenarios are actively pursued worldwide, few studies have explicitly focused on forecasting intra-annual flow distribution in Central Asia, calculated using a methodology appropriate for this region and using CMIP6 ensemble scenarios. There have been studies on changes in the intra-annual distribution of runoff for individual river basins or local areas, but for the historical period, there have also been studies on modelling runoff forecasts using CMIP6 climate models, but have been very few systematic publications on the distribution of predicted intra-annual runoff in Central Asia, and this issue has not been fully studied. The projections suggest an intensification of flow seasonality (1), earlier flood peaks (2), reduced summer discharges (3) and an increased likelihood of extreme hydrological events under future climatic conditions. Changes in the seasonal structure of river flow in Central Asia are caused by both climatic factors—temperature, precipitation and glacier degradation—and significant anthropogenic influences, including irrigation and water management structures. These changes directly affect the risks of flooding and water shortages, as well as the adaptive capacity of water management systems. Given the high level of water management challenges and interregional conflicts over water use, the intra-annual distribution of runoff is important for long-term planning, the development of adaptation measures, and the formulation of public policy on sustainable water management in the face of growing climate challenges. This is critically important for water, agricultural, energy, and environmental planning in a region that already faces annual water management challenges and conflicts due to the uneven seasonal distribution of resources. Full article