Search Results (5,562)

This study investigates the geological, mineralogical, and geochemical features of the Alday ore occurrence (Central Kalba, East Kazakhstan) and aims to identify indicators of rare-metal mineralization, with lithium considered to be one of its principal components. In this study, the structural–stratigraphic position of the occurrence is refined; three series of albite–spodumene pegmatites are identified; the compositions of the ore-bearing schists and the granitoids of the Kunush and Kalba complexes are compared; and the role of metasomatic alteration in the concentration of Li, Ta, Nb, Be, and Sn is established. The plagiogranites and dikes of the Kunush complex are characterized by Li anomalies (up to 306 g/t), Ta (up to 64 g/t), and a fractionated REE spectrum (La/Yb up to 108). In addition, the following predictive criteria are formulated: the presence of tectonically disrupted dikes in the Kunush complex with Na₂O/K₂O > 4, the presence of albite and muscovite alteration zones, and the presence of ladder-type spodumene-bearing pegmatites controlled by northwest-trending faults. The ⁴⁰Ar/³⁹Ar muscovite age of the Alday pegmatites (~292 Ma) aligns with the age range of the Kalba granite complex. Based on the main principles of rare-metal pegmatite generation, it is determined that the Tochka pegmatites were formed during the fluid–magmatic fractionation of magma in large granitic reservoirs of the Kalba complex. The Karagoin–Saryozek zone—located between several large granite massifs of the Kalba complex, where host rocks function as a roof—may be promising for investigating rare-metal pegmatite mineralization. Full article

Ecosystem vulnerability assessment is paramount for local environmental stability and lasting economic progress. This study selects Hubei Province as the research area, applying multi-source spatiotemporal datasets spanning the period 2000–2023. A pressure–state–response (PSR) framework, incorporating 14 distinct indicators, was developed. The selection criteria for these indicators adhered to principles of scientific rigor, all-encompassing scope, statistical representativeness, and practical applicability. The chosen indicators effectively encompass natural, anthropogenic, and socio-economic drivers, aligning with the specific ecological attributes and key vulnerability factors pertinent to Hubei Province. The analytic network process (ANP) method and entropy weighting (EW) method were integrated to ascertain comprehensive weights, thereby computing the ecological vulnerability index (EVI). In the meantime, we analyzed temporal and spatial EVI shifts. Spatial autocorrelation analysis, the geodetic detector, the Theil–Sen median, the Mann–Kendall trend test, and the Grey–Markov model were employed to elucidate spatial distribution, driving factors, and future trends. Results indicate that Hubei Province exhibited mild ecological vulnerability from 2000 to 2023, but with a notable deteriorating trend: extreme vulnerability areas expanded from 0.34% to 0.94%, while moderate and severe vulnerability zones also increased. Eastern regions demonstrate elevated vulnerability, but they were lower in the west, correlating with human activity intensity. The global Moran’s I index ranged from 0.8579 to 0.8725, signifying a significant positive spatial correlation of ecological vulnerability, with the highly vulnerable areas concentrated in regions with intense human activities, while the less vulnerable areas are located in ecologically intact areas. Habitat quality index and carbon sinks emerged as key drivers, possibly stemming from the forest–wetland composite ecosystem’s high dependence on water conservation, biodiversity maintenance, and carbon storage functions. Future projections based on Grey–Markov models indicate that ecological fragility in Hubei Province will exhibit an upward trend, with ecological conservation pressures continuing to intensify. This research offers a preliminary reference basis of grounds for ecological zoning, as well as sustainable regional development in Hubei Province, while also providing a theoretical and practical framework for constructing an ecological security pattern within the Yangtze River Economic Belt (YREB) and facilitating ecological governance in analogous river basins globally, thereby contributing to regional sustainable development goals. Full article

The Linfen–Yuncheng Basin is located on the southern edge of the Fenwei Fault Zone, influenced by intense tectonic activity, thick Quaternary sedimentation, and anthropogenic disturbance, it exhibits prominent characteristics of ground subsidence and fissure development. However, uncertainties still exist regarding the primary controlling factors of subsidence. This study employs multi-temporal InSAR data, combined with small baseline subset (SBAS–InSAR) technology to invert the high-precision ground line of sight deformation fields, and conducts time-series decomposition analysis using the Seasonal Trend Decomposition (STL) method. The results show that from 2017 to 2025, subsidence was mainly concentrated in the central and southern regions of the basin, with a maximum cumulative subsidence exceeding 200 mm and an average annual subsidence rate of −40 mm/year. Its spatial distribution is highly consistent with major structural zones such as the Zhongtiao Mountain Front Fault and the Linyi Fault, indicating that fault activity exerts a significant controlling effect on subsidence patterns. Groundwater level fluctuations are positively correlated with overall ground subsidence, and the response rate of different monitoring points is constrained by differences in aquifer depth and permeability. Groundwater aquifer points exhibit rapid and reversible subsidence response, while confined aquifer points are affected by low-permeability or compressible layers, showing a significant lag effect. The research results indicate that time-series analysis based on InSAR can not only effectively reveal the subsidence evolution process at different scales, but also provide a scientific basis for groundwater resource regulation, geological disaster prevention and control, and sustainable regional land utilization. Full article

The prevention and control of desertification in northern China is currently in a critical stage of transitioning from large-scale governance to precise adaptation. Identifying potential risk areas during the ecological restoration process is a scientific prerequisite for achieving long-term governance. This study focuses on the typical ecologically fragile area of Ordos City, where high-resolution grazing pressure grid data and a night-time light index were innovatively integrated into the assessment system to develop a desertification sensitivity evaluation framework that couples climatic, vegetative, soil, and human activity (CVSH) factors. Compared to linear models, the CVSH framework enhances dynamic assessment accuracy by coupling human activity indicators, particularly addressing the policy lag effect inherent in PSR models. The study systematically tracked the temporal and spatial differentiation process of desertification sensitivity from 2000 to 2024, finding that the spatial pattern shows a significant “the west is high while the east is low” concentration, and the time series has experienced a phased turning point of “first suppression then growth”. Mechanism analysis indicates that climate aridification and vegetation degradation are the dominant stress factors, while intense human activities have significantly exacerbated the vulnerability of local ecosystems through nonlinear interactions, leading to the re-expansion of high-sensitivity zones after 2018, with their area proportion increasing sharply from 15.52% to 30.07%. This study reveals the fragility of ecological engineering effectiveness and the complexity of risk evolution under the combined influence of climate fluctuations and human interference, providing a direct scientific picture and decision support for achieving differentiated ecological risk management and sustainable land management in different regions. Full article

Rapid urbanization and persistent air pollution threaten the functional resilience of megacities in Southeast Asia, particularly Bangkok, where PM_2.5 concentrations consistently exceed World Health Organization (WHO) guidelines. To strengthen urban adaptive capacity, this study investigates the role of Nature-based Solutions (NbS), particularly urban green spaces, as resilience-oriented infrastructure for air quality management. Using data from 32 monitoring stations across the Bangkok Metropolitan Administration (BMA) and surrounding areas from 2021 to 2023, spatial and temporal trends in PM_2.5 concentrations were analyzed through geostatistical modeling and inferential statistics. Although all sites exceeded the WHO PM_2.5 guideline of 5 µg/m³, larger and more connected green spaces consistently exhibited better air-quality than the surrounding non-green urban mosaic. Areas with extensive vegetation, greater canopy cover, and more compact park geometries (lower perimeter-to-area ratios) demonstrated improved pollution attenuation capacity, while fragmented parks are more exposed to surrounding emissions. Integration of Local Climate Zone (LCZ) classification further indicated that compact high-rise zones and high-traffic corridors exhibited higher PM_2.5 levels due to reduced airflow and structural confinement. The study underscores the need to embed NbS within resilience-based urban planning to promote long-term environmental stability and public health recovery in rapidly urbanizing megacities like Bangkok. Full article

Polar sea ice is undergoing rapid change, with recent record-low extents in both hemispheres, raising the demand for skillful predictions from days to seasons for navigation, ecosystem management, and climate risk assessment. Accurate sea ice prediction is essential for understanding coupled climate processes, supporting safe polar operations, and informing adaptation strategies. Physics-based numerical models remain the backbone of operational forecasting, but their skill is limited by uncertainties in coupled ocean–ice–atmosphere processes, parameterizations, and sparse observations, especially in the marginal ice zone and during melt seasons. Statistical and empirical models can provide useful baselines for low-dimensional indices or short lead times, yet they often struggle to represent high-dimensional, nonlinear interactions and regime shifts. This review synthesizes recent progress of DL for key sea ice prediction targets, including sea ice concentration/extent, thickness, and motion, and organizes methods into (i) sequential architectures (e.g., LSTM/GRU and temporal Transformers) for temporal dependencies, (ii) image-to-image and vision models (e.g., CNN/U-Net, vision Transformers, and diffusion or GAN-based generators) for spatial structures and downscaling, and (iii) spatiotemporal fusion frameworks that jointly model space–time dynamics. We further summarize hybrid strategies that integrate DL with numerical models through post-processing, emulation, and data assimilation, as well as physics-informed learning that embeds conservation laws or dynamical constraints. Despite rapid advances, challenges remain in generalization under non-stationary climate conditions, dataset shift, and physical consistency (e.g., mass/energy conservation), interpretability, and fair evaluation across regions and lead times. We conclude with practical recommendations for future research, including standardized benchmarks, uncertainty-aware probabilistic forecasting, physics-guided training and neural operators for long-range dynamics, and foundation models that leverage self-supervised pretraining on large-scale Earth observation archives. Full article

Estimating how crop yield responds to site-specific nitrogen (N) fertilization is essential for maximizing yield potential under variable field conditions. However, classical Machine Learning (ML) approaches applied to observational farm data primarily focus on yield prediction and often fail to recover causal N response due to confounding arising from non-random fertilizer application. In this study, we develop and evaluate a Causal Machine Learning (CML) framework to estimate heterogeneous N treatment effects under real commercial rice-farming conditions in the Axios River Plain, Greece. The proposed approach combines Double Machine Learning (DML) with remote sensing, soil, climatic, and management data to adjust for confounding and identify causal relationships between N inputs, Leaf Nitrogen Concentration (LNC), and yield. A doubly robust (DR) learner is used to estimate yield sensitivity to N at key agronomic thresholds, while a Causal Forest model leverages LNC to assess crop physiological N status. Results demonstrate that the CML-based framework outperforms conventional XGBoost predictive models in identifying field plots that are responsive to additional N. By integrating causal effect estimation with plant-status information, the proposed decision support system identifies zones where yield gains can be achieved through targeted N increases while avoiding overfertilization in non-responsive areas. Full article

With the increase in mining intensity and scale, the damage to groundwater resources and surface ecology caused by coal mining has become the main problem facing coal development. Coal mining can cause a redistribution of stress field and stress concentration in local areas of overlying rock, resulting in varying degrees of movement and damage to the overlying rock. Quantitative analysis of the degree of migration and damage in different areas of overlying rock and zoning control is crucial for achieving loss reduction and green mining. In this paper, the overburden damage is divided into regions according to the different causes of formation, regional characteristics of severity, and other factors, and the specific calculation method is given. UDEC7.0 numerical simulation software is used to simulate the overlying rock damage, and the best mining parameters are provided through the area changes in different zones. The research conclusions are as follows: according to the different damage states of overburden rock, the damage of overburden rock can be divided into four parts: I, caving fracture zone, II, fracture development zone, III, sliding failure zone, and IV, slight failure zone. In the four zones, the damage in zones II and IV is relatively light. During the mining process, attention should be given to controlling the development of Zone I to prevent it from abnormally enlarging; for Zone II, hydraulic fracturing can be used when there is a thick, hard key layer that poses a water inrush risk; for Zone III, the focus should be on preventing surface step fractures caused by it. For example, when a thick, hard key layer is present in Zone II, hydraulic fracturing can be applied to avoid large area hanging roofs and severe rock pressure. When the mining height is low, it mainly affects the proportion of regions I and III. With the increase in mining height, the main affected region becomes the II region. The larger the mining height is, the larger the proportion of the II region. With the increase in propulsion speed, the impact range on the surface increases, but the area with severe damage is relatively reduced. With the increase in mining width, the proportion of relatively seriously damaged areas increased. On-site measurements have shown that when the speeds of 120,401 and 22,207 working faces are slow, the rock layer pressure shows a dense state, the overburden fracture is more fully developed, and the area proportion of I and II zones is increased, which reflects the phenomenon of dense surface fracture development on the surface. When the advancing speed is large, the area proportions of zones III and IV increase, and the damage scope decreases. The on-site testing verified the conclusions drawn from theoretical analysis and numerical simulation, which can guide other mines under similar conditions to achieve safe and green production. Full article

Partitioning is an important characteristic of the biomass gasification process in a downdraft fixed-bed gasifier, so simulating the partition characteristics has practical guiding significance for revealing the essence of gasification reactions. In this paper, based on the real gasifier, a fluid-solid interfacial reaction method was proposed to simulate heterogeneous reactions based on a holistic gasification model. The partition characteristics, such as the boundary, position, step points, and area of zones, were explored and defined through analyzing the species concentration field, kinetic rate field of reactions, and temperature field. The results indicate that the partition characteristics of kinetic rate distributions are the root cause for zoning in fixed-bed gasification. On the center line of the fixed-bed gasifier, the change nodes of CO concentration tend to be consistent with the nodes of the high kinetic rate of reactions. These results provide a theoretical foundation for the online monitoring of and intervention in biomass gasification. Full article

To address the difficulty of controlling surrounding rock subjected to repeated repair-induced disturbances, the characteristics of the roadway surrounding rock and its deformation–failure mechanisms were examined. An experimental scheme for surrounding-rock control was formulated, and a three-dimensional numerical model was established. Four support schemes were evaluated to identify a rational support method and corresponding parameters: (a) rock bolts and cable bolts; (b) rock bolts, cable bolts, and floor cable bolts; (c) rock bolts, cable bolts, floor cable bolts, and U-shaped closed steel sets; and (d) rock bolts, cable bolts, floor cable bolts, U-shaped closed steel sets, and grouting. Comparative analyses were conducted in terms of plastic-zone evolution, stress-field distribution, surrounding-rock displacement, and the mechanical response of the support structures. The results indicate that, in roadways experiencing multiple repair disturbances and supported only by rock bolts and cable bolts, distinct stress-concentration zones develop within the supported surrounding rock, suggesting that reliance solely on bolts and cables is unfavorable for effective rock-mass control. Grouting improves the overall integrity and self-bearing capacity of the surrounding rock. Both the U-shaped closed support and the combined U-shaped closed support with grouting effectively restrain surrounding-rock deformation, and the corresponding stress distribution shows no pronounced stress-concentration zones. Based on the analyses of surrounding-rock displacement, support-structure loading, and incremental shear strain, the effectiveness of the support schemes in mitigating roof and floor displacement ranks, in descending order, as (d), (c), (b), and (a). Engineering practice further demonstrates that the combined support system consisting of 29U-type sets, grouted bolts, and bundle-type grouted cable bolts provides effective control over the deformation and failure of the roadway surrounding rock. Full article

The rise in seasonal lifestyle tourism, characterized by winter-escape health and wellness stays and long-term leisure residence, has intensified peak–off-peak imbalances and pressures on the allocation of tourism service supply in tropical island destinations. However, existing research lacks a systematic comparison of seasonal fluctuations and long-term evolution for this subgroup at the city/county level. Therefore, this study aims to characterize the seasonal pattern, long-term trend features, and typological differentiation of seasonal lifestyle tourism at the county level, and to compare differences across types. Using monthly data on seasonal lifestyle tourism for 18 cities/counties in Hainan from 2021 to 2024, we apply TRAMO/SEATS decomposition to identify seasonal structures and measure seasonal amplitude and employ the Hodrick–Prescott (HP) filter to extract trend components and determine their directions of change. We further construct five development types by integrating trend categories and changes in seasonal amplitude and test between-type differences using one-way analysis of variance (ANOVA). Results show that Hainan exhibits a stable “winter–spring peak and summer–autumn trough” pattern (peaks concentrated in January–March and December, with the off-season typically spanning May–October), with strong seasonality and pronounced spatial heterogeneity. The four-year mean seasonal range at the county level is 215.01, with high values clustered in southern Hainan; Haikou remains relatively low, while Wenchang shows an upward trend. Long-term trends are clearly differentiated: 13 counties show sustained growth, 2 show decline, and 3 display a U-shaped recovery (decline followed by rebound). Growth rates also vary substantially, with Qionghai increasing at roughly 27 times the rate of Qiongzhong. Integrating seasonal and trend characteristics yields five types, of which the Robust Development type accounts for the largest share (50%). Between-type differences are mainly reflected in tourism service supply capacity: the number of star-rated hotels (p = 0.033, η² = 0.530) and overnight visitors (p = 0.004, η² = 0.676) differ significantly across types, whereas differences in natural-environment conditions are not significant. This study provides a scientific basis for zoning management and optimizing low-season strategies in Hainan. Full article

As lakes contribute significant amounts of methane (CH₄) to the atmosphere, they account for a significant share of the global greenhouse gases (GHGs) budget. Since lakes are ecosystems where physical and biological processes influencing CH₄ formation are concentrated, the study focuses on atmospheric CH₄ column concentrations over lake areas. This study aims to analyze the temporal variation in atmospheric CH₄ column concentrations (XCH₄) over Lake Eğirdir and Lake Burdur in Türkiye in 2023 and 2025 as well as the relationship between XCH₄ and environmental parameters such as Water Surface Temperature (WST), Normalized Difference Chlorophyll Index (NDCI), and Floating Algae Index (FAI). The temporal variability of XCH₄ observed over both lakes showed statistically significant positive correlations with lake-area-averaged WST, NDCI, and FAI (Pearson r = 0.49–0.65, p < 0.01). This outcome indicates consistent temporal patterns between XCH₄ and environmental conditions at the lake scale. Furthermore, time-series graphs show that monthly average XCH₄ values in both lakes reached their highest levels during the summer and autumn months. During these periods, XCH₄ concentrations exceeded 1860 ppb in Lake Eğirdir and 1900 ppb in Lake Burdur. The areas of land use/land cover (LULC) classes surrounding the lakes were evaluated together with XCH₄, and relatively higher XCH₄ values were observed over agricultural areas, which constitute the dominant class in the basins of both lakes. The distribution of XCH₄ throughout the lake depth showed higher values in the shallow and mid-depth zones and lower values in the deeper areas beyond 20 m, indicating that the distribution of XCH₄ varies throughout lake depth. The results obtained underline the importance of remote sensing data in monitoring XCH₄ in lake ecosystems. Full article

Inland depression irrigation districts in the arid regions of Xinjiang, owing to the absence of natural drainage conditions, exhibit unique groundwater-salt dynamics and face prominent risks of soil salinization, thus necessitating clarification of their water-salt transport mechanisms to ensure sustainable agricultural development. This study takes the Karamay Agricultural Comprehensive Development Zone as the research subject. The study examines the distribution characteristics of soil salinity, groundwater depth, and Total Dissolved Solids (TDS) of groundwater across diverse soil textures, elucidates the correlative relationships between groundwater dynamics and soil salinity, and forecasts the evolutionary trajectory of groundwater levels within the irrigation district. The findings reveal that groundwater depth in silty soil regions (3.24–3.11 m) substantially exceeds that in silty clay regions (2.43–2.61 m), whereas TDS of groundwater demonstrates marginally elevated concentrations in silty clay areas (19.05–16.78 g L⁻¹) compared to silty soil zones (18.18–16.29 g L⁻¹). Soil salinity exhibits pronounced surface accumulation phenomena and considerable inter-annual seasonal variations: manifesting a “spring-peak, summer-trough” pattern in 2023, which inversely transitioned to a “summer-peak, spring-trough” configuration in 2024, with salinity hotspots predominantly concentrated in silty clay distribution zones. A significant sigmoid functional relationship emerges between soil salinity and groundwater depth (R² = 0.73–0.77), establishing critical depth thresholds of 2.44 m for silty soil and 2.72 m for silty clay, beneath which the risk of secondary salinization escalates dramatically. The XGBoost model demonstrates robust predictive capability for groundwater levels (R² = 0.8545, MAE = 0.4428, RMSE = 0.5174), with feature importance analysis identifying agricultural irrigation as the predominant influencing factor. Model projections indicate that mean groundwater depths across the irrigation district will decline to 2.91 m, 2.76 m, 2.62 m, and 2.36 m over the ensuing 1, 3, 5, and 10 years, respectively. Within a decade, 73.33% of silty soil regions and 92.31% of silty clay regions will experience groundwater levels below critical thresholds, subjecting the irrigation district to severe secondary salinization threats. Consequently, comprehensive mitigation strategies encompassing precision irrigation management and enhanced drainage infrastructure are imperative. Full article

The construction of reservoirs has undeniably provided numerous conveniences and benefits to human societies. However, it has also markedly altered downstream flow regimes, leading to essential fish habitat loss that directly undermines the ecosystem services provided by fish populations, thereby jeopardizing the long-term sustainability of fishery resources. Existing assessments of spawning suitability largely concentrate on static characteristics of available spawning grounds, while the dynamics of habitat suitability migration and contraction in response to changing environmental flows remain poorly understood. To address this gap, we classified hydrological years into wet, flat, and dry categories to reflect the varying environmental flow requirements during the fish-spawning period. Using the Mike21 hydraulic model together with a spatial suitability analysis for spawning habitats, we quantified spawning ground suitability from both temporal and spatial perspectives. Taking the four major Chinese carps (FMCC) and the Dongta spawning ground in the Pearl River as a case study, our findings reveal that the proportion of highly suitable habitats closely tracks the environmental-flow trajectories. Throughout the FMCC spawning period, the spatial pattern of high suitability zones undergoes a marked migration in response to flow variations across wet, flat, and dry years, consistently shifting upstream. Specifically, as discharge rises from low-flow to high-flow events, the most suitable areas move from downstream deep-pool sections toward upstream shallow riffle zones, which is crucial for the sustainable development of fishery resources. Full article

The stress distribution after excavation becomes highly complex in large-span bifurcated tunnel sections commonly found in urban underground interchanges. This study investigates the stress evolution induced by the excavation of large-span and bifurcated tunnel, focusing on the 32.17 m maximum-span section of the Shenzhen Baopeng–Shahe Underground Interchange. The results show that stress concentration near the tunnel walls of large-span sections is greater than that in sections with bifurcated tunnels. Adjusting the burial depth of the large-span tunnel, the influence of stiff layer thickness on the redistribution of surrounding rock stress was analyzed. When the tunnel is buried at a shallow depth and the stiff layer thickness is small, the maximum tangential stress of the surrounding rock occurs at the stiff layer boundary, and the surrounding rock remains entirely elastic. In large-span tunnels, as the thickness of the stiff layer increases from 5 m to 20 m, the stress relaxation zone grows from 0 m to 8 m, and the stress-bearing zone expands from 10 m to 27 m. As the burial depth increases and the stiff layer thickness grows, the maximum tangential stress shifts to within the stiff layer. In this case, the tangential stress distribution at the stiff layer boundary becomes non-smooth. Therefore, an appropriate stiff layer thickness must be selected to prevent the surrounding rock from entering a plastic state. The findings provide theoretical guidance and technical support for the design of large-scale underground interchange bifurcated tunnels, advancing the intelligent and scientific development of urban underground transportation facilities and offering significant practical and social benefits. Full article