Search Results (12,457)

The rising global temperature and changing climate patterns have increased the frequency and intensity of extreme heat events, droughts, and heavy precipitation, significantly affecting agriculture, water resources, and ecosystems. Solar radiation management (SRM) has been proposed as a geoengineering strategy to mitigate these effects by reducing incoming solar radiation. This study evaluated future trends and variability in rainfall and temperature extremes in the Philippines under GeoMIP (G6Solar and G6Sulfur) and ScenarioMIP (SSP2-4.5 and SSP5-8.5) projections. Using five General Circulation Models (GCMs) and a suite of 10 climate indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), changes in extreme precipitation and temperature across different climate zones in the Philippines were assessed. Climate projections for the future (2041–2070) scenario were analyzed using bias correction, downscaling, and spatial interpolation techniques. Trend analysis was evaluated using the Mann–Kendall test and Sen’s slope estimator, while variability was assessed through statistical methods. The results show widespread warming and increased extreme precipitation, but these trends vary significantly across regions. Non-uniform responses emerge across scenarios, with some northern regions experiencing decreases in specific precipitation indices despite the broader warming trend under SRM and non-SRM conditions. These findings provide critical insights into the potential impacts of SRM on future climate extremes in the Philippines and guidance on climate policy recommendations for decision-makers and stakeholders. Full article

Tajikistan is highly vulnerable to climate change and depends heavily on agriculture, making soil moisture dynamics critical for water and food security. This study provides a comprehensive assessment of soil moisture variability across four depth layers (0–10 cm, 10–40 cm, 40–100 cm, and 100–200 cm) from 2000 to 2021 using NASA’s GLDAS-2 model and remote sensing data for land-air temperature, precipitation, and vegetation to identify key nexus of soil moisture change. Moisture data were converted to volumetric water content (m³/m³) to enable valid cross-layer comparisons. Our findings show that volumetric soil moisture increases with depth, from 0.219 m³/m³ at the surface to 0.293 m³/m³ in the deepest layer. Eastern Tajikistan exhibits higher moisture levels than the west, likely due to differing precipitation patterns. Seasonally, spring replenishes the soil with the highest moisture (0.270 m³/m³ at 0–10 cm), while summer strips it away (0.194 m³/m³ at 0–10 cm), potentially reflecting evapotranspiration losses. A significant warming trend is evident, with mean annual temperature peaking at 4.32 °C in 2016. Precipitation strongly influences upper-layer moisture (correlation: 0.49 at 0–10 cm; 0.44 at 10–40 cm). While annual averages remain stable, seasonal trends reveal significant winter wetting (+0.00043 m³/m³ per year, p < 0.001) and summer drying in the deepest layer, indicating intensifying seasonal contrasts. Vegetation follows a parallel pattern, declining from 2000 to 2010 and recovering thereafter. Greening is observed in 16.74% of the area, concentrated in the western mountains and northern highlands, while only 2.98% shows decline, mostly in small, fragmented patches. These findings highlight the substantial connection between climate, soil moisture, and vegetation in Tajikistan. They also suggest the need for depth-specific and seasonally aware water management strategies in this climate-sensitive region. Managing water here means looking beyond surface averages and thinking in layers, seasons, and geography. Full article

22Cr12NiMoWV martensitic heat-resistant steel serves as a candidate material for underground coiler sector plates, whereas the coupling relationship between partial austenitization, precipitate/carbide evolution, martensitic interfaces and mechanical response under medium-temperature quenching–tempering conditions is still ambiguous. This work systematically explores four key heat treatment variables to clarify the microstructure–property correlation and strengthening rebalance mechanism. In the 790–830 °C partial austenitization interval, the austenite fraction increases from 36.49 wt.% to 72.32 wt.% with a concurrent decline of M₂₃C₆ carbides from 5.34 wt.% to 4.92 wt.%, demonstrating competitive evolution between austenite generation and carbide retention. Specimens quenched at 810 °C for 2 h deliver a yield strength of 1015.4 ± 13.8 MPa and tensile strength of 1192.9 ± 17.8 MPa, 24.9% and 19.9% higher than conventional QT samples, owing to synergistic reinforcement from α′ martensite matrix, orientation interfaces and Cr-Mo-W-V-rich precipitates. After 400 °C × 4 h tempering, the steel still maintains superior strength, and its average misorientation falls from 40.41° to 31.17°. Though its engineering ductility is inferior to the quenched state, the mixed dimple–quasi-cleavage fracture mode suggests a partial recovery of ductile fracture characteristics compared with over-treated samples. The uncovered strengthening mechanism provides microstructural theoretical support for process optimization. Compared with the conventional quenching and tempering process, the optimized medium-temperature process (810 °C × 2 h quenching + 400 °C × 4 h tempering) reduces energy consumption and the production cycle and provides solid theoretical and experimental data for a green and low-cost industrial heat treatment of coil plates. Full article

Mountain sandy patches are typical indicators of aeolian degradation in arid and semi-arid zones; however, few studies have systematically analyzed their static spatial morphological features and statistical correlations with environmental variables. Taking the Sarbulak River Basin in the Ili River Valley of Xinjiang as the study area, this study extracts multiple morphological metrics of mountain sandy patches from high-resolution UAV orthophotos and adopts the XGBoost-SHAP framework combined with correlation analysis to quantitatively analyze patch morphological traits and their statistical links with environmental predictors. The main results are as follows: (1) Elongated geometry dominates mountain sandy patches with diverse auxiliary shapes, and the average major axis of all patches reaches 16 m. Every pair of morphological indicators shows significant positive correlations at p < 0.01 level. (2) The model’s relative predictive importance varies markedly across predictors. Wind speed ranks first with a normalized SHAP contribution of 34.7%, followed by precipitation (18.7%), NDVI (13.0%), and grazing intensity (9.0%). The four predictors jointly account for over 75% of total predictive signals and constitute a wind–water–vegetation–grazing statistical association system. All predictors show obvious nonlinear responses to mountain sandy patch occurrence with distinct statistical thresholds. (3) Strong combined statistical correlations exist between wind speed, precipitation, NDVI, temperature, elevation, and grazing intensity, and multi-variable combinations correspond to a higher probability of large-scale sandy patches. This paper summarizes key threshold intervals derived from SHAP dependence curves: patches tend to expand when wind speed ranges from 2.10 to 2.15 m/s; precipitation below 219.7 mm presents negative correlations with patch distribution; NDVI within 0.17–0.29 corresponds to positive marginal associations with sandy patch occurrence; grazing intensity exceeding 3.60 SU/ha matches frequent patch enlargement; and areas above 645.9 m elevation display higher patch prevalence. Full article

The Galinge deposit in East Kunlun, China is a large Fe-polymetallic skarn system with a significant by-product, Co, but the respective roles of magmatism, skarn evolution, and wall rock interaction in Co enrichment remain incompletely understood. We integrate zircon and garnet U–Pb geochronology, zircon Lu–Hf isotopes and trace elements, whole-rock geochemistry, and SEM-EDS and EPMA mineral chemistry. Granodiorite and diorite porphyry yield zircon U–Pb ages of 230.09 ± 0.91 Ma and 229.4 ± 1.3 Ma, respectively, whereas skarn garnet yields 224.4 ± 9.3 Ma, placing intrusion and skarn formation within a Late Triassic magmatic–hydrothermal system. Both suites are metaluminous, LREE-enriched, and Nb–Ta–Ti-depleted; zircon ε_Hf(t) values of −9.4 to −1.8 indicate the predominant reworking of older crustal material with variable input from a more radiogenic component. Strictly screened Ti-in-zircon temperatures and lattice strain Ce anomalies yield median apparent ΔFMQ values of +3.36 for granodiorite and +3.04 for diorite porphyry, indicating comparably oxidized magmatic conditions. The analyzed intrusions contain 2.12–13.4 ppm Co, whereas cobaltite and Co-bearing arsenopyrite contain 32.83–34.14 wt% and 0.38–4.53 wt% Co, respectively. Spatial and paragenetic relations place Co enrichment after magnetite deposition, during an early sulfide-stage hydrothermal sulfarsenide event within the skarn system. We infer that Late Triassic intrusions supplied heat, fluids, and ligands, whereas structural focusing and cooling, coupled with carbonate wall rock reactions and a reduction in carbonaceous or Fe²⁺-bearing domains, promoted As–S-rich Co precipitation; the leaching of intermediate–mafic wall rocks may have supplemented the Co inventory. Full article

The Ordovician strata in Northern Tarim host extensively developed carbonate karst reservoirs that contain abundant hydrocarbon resources. However, owing to extreme burial depths, pronounced heterogeneity, and limited seismic resolution within the Tarim Basin, the diagenetic fluid types and their specific influences on reservoir development remain poorly understood. Consequently, this study integrates core observation, thin-section identification (TSI), cathodoluminescence (CL), scanning electron microscopy (SEM), X-ray diffraction (XRD), stable isotopes (C, O, Sr), trace and rare earth elements (REE), fluid inclusion analysis (FIA), and in situ laser U-Pb dating (U-Pb). This multifaceted petrographic and geochemical approach characterizes the macro- and microscopic geological features of these karst reservoirs. By elucidating the types, timing, and phases of diagenetic fluids, this research evaluates fluid-driven impacts on reservoir quality and establishes a comprehensive genetic model for reservoir evolution. Results identify five distinct tectonic fracturing phases. Phases 1, 2, and 4 involved calcite infilling precipitated from seawater and meteoric freshwater, with fluid inclusion homogenization temperatures of 62–87 °C, 57–91 °C, and 94–126 °C, formed during the Caledonian–Hercynian, Early Hercynian, and Indosinian–Yanshanian periods, respectively. Phase 3 featured hydrothermal dolomite infilling during the Hercynian, with fluid inclusion homogenization temperatures ranging from 128 to 163 °C, whereas Phase 5 remained unfilled during the Himalayan. Constrained by the U–Pb age interval of 445.2–436.5 Ma acquired from vug-filling calcite together with cross-cutting petrographic relationships, multi-stage meteoric freshwater dissolution mainly occurred from Middle Caledonian Episode III (447–443.7 Ma) to the Early Hercynian (460–359 Ma). Reservoirs within the Penglaiba–Lower Yingshan Formations underwent a complex evolution comprising syngenetic-to-early diagenetic pore development, Middle Caledonian–Early Hercynian weathering crust karstification and dedolomitization, and Late Hercynian hydrothermal dissolution-infilling, ultimately resulting in the formation of tectonic-karst composite reservoirs. Full article

The dynamics of aboveground biomass (AGB) in rubber plantations (RPs) provide an important basis for evaluating carbon stocks and environmental adaptability in tropical plantations. However, continuous monitoring of AGB of RPs at the regional scale is lacking, and its nonlinear responses to hydrothermal conditions remain insufficiently understood. This study focused on RPs in western Hainan Island (WHI), including Danzhou, Baisha, Lingao, and Chengmai, and integrated field plot data with multi-source remote sensing datasets. A framework for mapping RPs combining rule-based constraints and phenology-based random forest (RF) classification was developed. After key variable screening, extreme gradient boosting (XGBoost), Shapley additive explanations (SHAP), and generalized additive model (GAM) were used for AGB estimation and identification of climatic responses. The results showed that mapping of RPs achieved an overall accuracy of 92.89% and a Kappa coefficient of 0.854. The XGBoost-derived estimates showed that AGB of RPs in the study area increased by approximately 1.43 × 10⁶ Mg from 2017 to 2025, with growth areas mainly concentrated in the Danzhou–Baisha and western Chengmai. AGB exhibited significant nonlinear responses to climatic factors. Specifically, the effect of precipitation (PRE) shifted to negative after approximately 1945 mm yr⁻¹, whereas annual mean maximum temperature (TMAX) shifted to a positive effect after about 29.72 °C, although this effect gradually weakened as temperature continued to rise. Combinations such as PRE × annual mean temperature (PRE × TMP), PRE × TMAX, and PRE × potential evapotranspiration (PRE × PET) exhibited significant nonlinear interactions, indicating that the direction and magnitude of the effect of PRE shifted with changes in temperature and PET levels. These findings link the spatiotemporal changes in AGB of RPs in WHI with hydrothermal thresholds and their interacting effects, deepening our understanding of the climatic response characteristics of AGB in RPs in this region. They also provide a scientific basis for RP monitoring, carbon stock assessment, and climate-adaptive management in WHI. Full article

Municipal sewage sludge contains considerable amounts of protein, making protein recovery a viable route for sludge valorization. In this work, sludge disintegration was achieved by cyclone cutting coupled with ozone oxidation, and the mixed liquor of foam standing liquid and supernatant was used as the feedstock for protein recovery via isoelectric point precipitation. Pretreatment tests showed that under 60 mg/L ozone concentration, 10 °C and 60 min, alkaline conditions enhanced sludge lysis; the mixed liquor suspended solids (MLSS) removal rate reached 87.65% at pH 9, and the protein concentration in the foam layer reached 1530.14 mg/L at pH 11, yielding a protein-rich feedstock suitable for subsequent extraction. In the isoelectric point precipitation stage, single-factor and L9(3⁴) orthogonal experiments were conducted to examine the effects of pH, temperature and centrifugal speed on extraction rate, and four ensemble learning algorithms (GBR, RF, XGBoost and CatBoost) were employed to build prediction models. The results showed that the factor influence order was pH > centrifugal speed > temperature, with pH being extremely significant (p < 0.01). Under leave-one-out cross-validation, the XGBoost model performed best (R² = 0.9243, MAE = 2.78%, RMSE = 3.52%). Response surface analysis determined the optimal parameters as pH 4.0, 5 °C and 3500 r/min, with both predicted and measured precipitation-stage extraction rates of 86.19%. Amino acid analysis indicated that essential amino acids accounted for 39.9% of the extracted protein, with good rehydration and foaming stability. Ensemble learning algorithms can reveal the multi-factor nonlinear coupling in isoelectric point precipitation, providing data support for process optimization of sludge protein recovery. Full article

Constructing Schottky heterojunctions with plasmonic components offers a promising route to enhance photocatalytic performance, yet the synergistic roles of the Schottky barrier and localized surface plasmon resonance (LSPR) in pollutant degradation remain insufficiently elucidated. Herein, a series of Ti₃C₂/Ag-Ag₂NCN (TAN) composites with varied Ag loadings was prepared via an in situ precipitation–chemical reduction method. The pseudo-first-order rate constant of the TAN-30 heterojunction reached roughly 7.0 times the value of bare Ag₂NCN, while its tetracycline degradation efficiency under visible light reached 87.0% at 240 min. Moreover, the heterojunction exhibited outstanding reusability over five successive runs. Comprehensive characterizations reveal that the Schottky barrier at the Ti₃C₂/Ag₂NCN interface effectively suppresses photogenerated carrier recombination, while the LSPR effect of metallic Ag⁰ broadens the light absorption range and elevates the local surface temperature, synergistically accelerating charge migration. The dominance of h⁺ and •O₂⁻ among the reactive species was established by both radical trapping assays and ESR spectroscopic analysis. This work provides mechanistic insights into LSPR-enhanced Schottky heterojunctions and offers a rational design strategy for MXene-based photocatalysts toward efficient antibiotic wastewater treatment. Full article

Climate change is becoming an increasingly important source of economic and health vulnerability in developing countries. This study examines the dynamic relationship between climate change and poverty across 22 countries in the Middle East and North Africa (MENA) region from 2000 to 2023. Poverty is captured through two indicators: household consumption expenditure, the monetary dimension, and infant mortality, the non-monetary dimension. Methodologically, the analysis relies on a panel autoregressive distributed lag (panel ARDL) model, estimated using the Pooled Mean Group (PMG) and Mean Group (MG) approaches. The results reveal a long-run relationship among climatic variables, macroeconomic factors, and poverty-related indicators. In the long run, precipitation is associated with a decline in household consumption expenditure, while temperature is associated with higher infant mortality, indicating a deterioration in both monetary and health-related well-being under changing climatic conditions. In the short run, rising temperatures are also associated with lower household consumption expenditure, revealing the immediate vulnerability of living standards to climate shocks. In addition, GDP per capita is associated with higher household consumption and lower infant mortality, while education is associated with lower health-related poverty. Inflation appears to exacerbate poverty, whereas the positive association between health expenditure and infant mortality suggests reverse causality or inefficiencies in the allocation of health resources. These findings highlight the need to articulate climate adaptation strategy, macroeconomic stability, education investment, and improved efficiency of health spending in order to achieve sustainable reduction in poverty in the MENA region. Full article

Araucaria angustifolia (Bertol.) Kuntze, a critically endangered tree species, plays an irreplaceable ecological role in its native habitats. Under global climate change, identifying the drivers governing its geographic distribution and assessing climate-related threats can provide scientific guidance for the long-term conservation and habitat restoration of this species. In this study, a total of 287 valid occurrence records from 27 countries were compiled. Combined with 14 screened environmental variables, an optimized Maximum Entropy (MaxEnt) model was used to predict the potential suitable habitats of A. angustifolia under historical climate conditions (1970–2000), as well as under low-emission (SSP126) and high-emission (SSP585) scenarios for the future periods of 2050, 2070, and 2090. Under historical climatic conditions, the average training AUC value from 10 replicate model runs was 0.979, indicating excellent and reliable model performance. Globally, the species has 1.91 × 10⁶ km² of moderately suitable habitat and 0.95 × 10⁶ km² of highly suitable habitat, with a total suitable habitat area of 2.86 × 10⁶ km², accounting for only 1.92% of the global terrestrial area. Mean annual temperature (bio1), mean temperature of the coldest quarter (bio11), and annual temperature range (bio7) are the dominant environmental variables shaping the distribution of A. angustifolia, followed by annual precipitation (bio12). Under future climate scenarios, the overall suitable habitats of A. angustifolia exhibit a slight contracting trend, whereas their spatial distribution patterns remain relatively stable. Full article

Land subsidence in soft-soil urban transport hubs arises from the complex coupling of natural geology and intensive anthropogenic activities, yet its spatial differentiation mechanisms and seasonal drivers remain poorly understood in high-development core areas. This study develops a progressive analytical framework integrating Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR), GeoDetector, Gaussian Mixture Model (GMM), and Singular Spectrum Analysis (SSA) to investigate spatiotemporal deformation patterns and driving mechanisms in the Shanghai Hongqiao Transport Hub Core Area from 2015 to 2024 using 209 Sentinel-1A images. Validation against official subsidence contours yields a Pearson correlation coefficient of 0.697 (p < 0.001) and an RMSE of 4.23 mm, confirming good spatial pattern agreement. Urban functional zones and construction stages are identified as the dominant influencing factors, with their interaction exhibiting notable bi-factor enhancement. Six distinct deformation response types are delineated via GMM, and two opposing seasonal signals are distinguished: near-instantaneous precipitation-driven surface loading on shallow soft soil and temperature-driven thermoelastic expansion of built structures. These findings may inform differentiated subsidence management and offer a transferable workflow for analogous soft-soil urban areas. Full article

The Younger Dryas (YD) terminated the last glaciation and initiated the Holocene, which was accompanied by widespread glacial advances. Traditional scaling models, production rates, and outlier screening approaches for ¹⁰Be exposure dating often yield inconsistent formation ages for the same moraine, hindering a comprehensive understanding of spatiotemporal patterns and climatic controls of YD glaciation across the Tibetan Plateau. In this study, we reprocessed ¹⁰Be exposure ages using the Probabilistic Cosmogenic Age Analysis Tool 2.2 (P-CAAT) to constrain YD moraine chronologies. Three glacial events were dated to 12.5 ka, 11.9 ka, and 11.5 ka. Glacier-climate simulations showcased cold dry conditions in the monsoon domain and cold-wet conditions in the westerly domain during the YD. The modelled temperature reductions matched global climatic records. Glacial advances were primarily forced by cooling in monsoon areas, whereas joint cooling and increased precipitation dominated in westerly regions. This study clarifies the temporal characteristics and driving mechanisms of YD glaciation on the plateau, though additional dating information is required for further verification. Full article

As a globally climate-sensitive region and ecological security barrier, the spatiotemporal dynamics of net ecosystem productivity (NEP) on the Qinghai–Tibetan Plateau are of great significance for understanding carbon cycling in alpine ecosystems. However, due to insufficient representation of parameter heterogeneity in models and unclear nonlinear attribution of complex environmental factors, substantial uncertainties remain in the spatiotemporal patterns and driving mechanisms of NEP in this region. Therefore, this study first employed an improved Carnegie–Ames–Stanford Approach (CASA) model to assess NEP on the Qinghai–Tibetan Plateau from 2000 to 2022 and characterize its spatiotemporal evolution, and subsequently applied the optimal multivariate-stratification geographical detector (OMGD) to quantify the independent and synergistic driving effects of hydrothermal conditions, extreme climate events, and human activities on NEP variations. The results indicate that: (1) from 2000 to 2022, vegetation NEP on the Qinghai–Tibetan Plateau exhibited a southeast-to-northwest decreasing spatial heterogeneity pattern, with a multi-year mean value of 219.61 g C·m⁻²; (2) during the study period, NEP showed an overall increasing trend (at a rate of 1.596 g C·m⁻²·yr⁻¹), with 52.5% of the region experiencing significant increases, primarily concentrated in the central–eastern humid regions and alpine meadow areas; and (3) among individual factors, the growing season length was the primary driver of NEP, in addition to temperature and precipitation, while human activities exerted negligible influence; under interaction effects, the hydrothermal synergistic enhancement (0.79 < q < 0.89) exhibited the highest explanatory power. These results show that carbon sequestration in alpine ecosystems is governed by nonlinear hydrothermal interactions and provide a scientific basis for assessing carbon sink resilience in the “Asian Water Tower” under global warming. Full article

Quantifying the impacts of climate change and extreme climatic events on crop yields is essential for safeguarding global food security. The rapid growth of data availability and advances in computational capacity have established machine learning (ML) as a critical tool for unraveling the complex, nonlinear relationships between climatic factors and agricultural productivity. This systematic review synthesizes evidence from 137 peer-reviewed studies published between 2015 and 2025 that applied ML models to assess the effects of both long-term climate trends and discrete extreme events on crop yields worldwide. Bibliometric and thematic analyses reveal a rapidly evolving field, with over 85% of studies published since 2020, and a strong concentration on staple cereals—wheat, maize, and rice—in major agricultural regions including China, the United States, and India. Random Forest (RF) was the most commonly used algorithm; ensemble and deep-learning models achieved high predictive accuracy within well-resourced study contexts. Temperature and precipitation extremes emerged as the most frequently examined stressors, with distinct methodological patterns: studies focusing on climate change trends predominantly employed RF and LSTM models, whereas those investigating extreme events increasingly adopted hybrid approaches that integrate ML with process-based crop models. This review highlights the transformative potential of ML while identifying persistent challenges, such as geographical imbalances in research coverage, the need for enhanced interpretability in extreme event attribution, and the critical importance of modeling compound extremes. Future research should prioritize the development of explainable, causally informed, and transferable ML frameworks to support equitable climate adaptation strategies in global agriculture. Full article