Geoscience frontiers最新文献

Green environmental technologies, renewable energy and globalization are interconnected pillars that impact economies and societies. By effectively fostering these resources, environmental policies can help achieve economic prosperity, sustainable development and environmental protection. The current study seeks to address environmental and economic predicaments by empirically examining the role of green technology and renewable energy in influencing the load capacity factor and ecological footprint with the highest ecological impact. Given that these nations are also significant players in the global economy, we also examine the impact of Globalization and economic growth within econometric investigation. The current study uses moments quantile regression (MMQR) as an econometric strategy to report that while innovations in green technology and renewable energy positively influence load factor capacity and help reduce ecological footprint, certain facets of globalization worsen the ecological footprint, thereby unsettling its load factor capacity. These findings underscore the pressing need for policymakers to prioritize integrating environmental and trade policy agreements to ensure progress towards long-term environmental goals.

A reconciliation of the disagreement on whether financial globalization (FG) affects ecological footprint through the scale, technique and composition effects cannot be achieved without an explicit understanding of the direct and indirect interactions of FG with environmental sustainability. Hence, the novel perspective of this study lies in the investigation of how green innovations moderate the non-linear tendencies in the FG-environmental sustainability link among western African states given the abundance of natural resources and the prevailing pace of economic growth. The core findings are obtained from robust analysis based on cross-sectional autoregressive distributed lag (CS-ARDL) technique, the augmented mean group (AMG) technique, and the common correlated effects mean group (CCEMG) advanced estimators. Firstly, the beneficial ecological impacts of green innovations were observed. As per direct impact, enhanced financial globalization (FG) exhibits non-linear detrimental ecological effects. However, green innovations cushion the observed adverse ecological effects of FG. Furthermore, resource rents reduce ecological footprint within the moderating framework of green innovation as the environmental Kuznets curve (EKC) is validated among the states. Additionally, a bidirectional causal link between financial globalization, green innovations, economic growth, natural resources, and ecological footprint was observed. Thus, the significant policy implication is for the West African states to decisively increase their investments in green innovations while strategically encouraging the share of ecologically friendly resources in total resource utilization to guarantee a more sustainable environment.

Extreme fractionation of elements and isotopes in mafic igneous rocks is abnormal in deciphering the source nature and melting conditions of mafic magmatism. Especially, identification of geochemically ultra-depleted mafic melts and their mantle sources has great bearing on the property of crust-mantle differentiation at plate margins. This is illustrated by extreme Hf-Nd isotope fractionation in ultrahigh-pressure eclogites from the Sulu orogen in east-central China. In addition to the previous finding of ultrahigh ε_Nd(0) values, we report here new data of whole-rock trace elements and Lu-Hf isotopes in eclogites and related rocks from the same region. The present results show extremely high Lu/Hf ratios and abnormally high ε_Hf(0) values of up to 576 for the eclogites, significantly different from the garnet amphibolites and other eclogite-facies metamorphic rocks in the same orogen. This feature is coupled with the ultrahigh ε_Nd(0) values as well as the severe depletion of light rare earth elements (LREE) and high field strength elements (HFSE). Because HFSE and LREE are immobile in aqueous solutions and the effect of melt extraction is insignificant during the continental deep subduction, the extreme fractionation of Lu/Hf and Sm/Nd indicate their origination from a geochemically ultra-depleted mantle source. These eclogites have the depleted mantle Hf model ages of 1.27 Ga to 1.61 Ga, similar to the depleted mantle Nd model ages of 1.39 Ga to 1.67 Ga as previously reported. This suggests that the protolith of the extremely high ε_Hf-ε_Nd eclogites was a kind of mafic igneous rocks derived from fractional crystallization of geochemically ultra-depleted mafic melts, which were produced by partial melting of the highly refractory lithospheric mantle during a series of seafloor spreading initiation-failure cycles at a divergent plate margin after the breakup of supercontinent Columbia in the Early Mesoproterozoic. The mafic igneous rocks were located in a passive continental margin in the Late Paleozoic and experienced deep subduction and exhumation in the Triassic, giving rise to the presently studied eclogites. The ancient geochemical signatures were retained without considerable influence by mantle convection, providing insights into the nature of crust-mantle differentiation during the tectonic transition from supercontinental breakup to seafloor spreading beneath the sub-ridge lithospheric mantle.

The Permian Fengcheng Formation of the Western Junggar region in the Southwestern Central Asian Orogenic Belt (CAOB) represents one of Earth’s oldest alkali lake deposits. Here, we present a comprehensive study of the stratigraphy, petrography, two-dimensional seismic data, U–Pb geochronology, and Hf isotope analysis of detrital zircons of this deposit. The results, in conjunction with published data, reflect the tectonic evolution of southwestern CAOB. The ages of detrital zircons indicate that the Fengcheng Formation deposition is inferred to have concluded the early Permian Kungurian. The Hf isotopes of detrital zircons indicate that the detritus for the Fengcheng Formation was derived from upper crustal magmatic sources. The West Junggar Basin preserves the records of three Paleozoic tectonic stages. The first stage occurred in the Early Paleozoic and involved intraoceanic subduction and arc-continent collision. The second stage involved the Carboniferous closure of the Junggar Ocean following successive filling of oceanic basins. The final stage occurred in the Early Permian and was related to intracontinental rifting and tectonic inversion. The results of comparing the comprehensive data of U–Pb ages and Hf isotopes of 2537 zircons from West Junggar, Tianshan and Altay show that the orogenic belts to the south of the CAOB experienced similar plate kinematics and vertical crustal growth in the Paleozoic.

Flood disasters pose serious threats to human life and property worldwide. Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts. This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability, while considering spatial heterogeneity. In this framework, the Optimal Parameter-based Geographic Detector (OPGD), Recursive Feature Estimation (RFE), and Light Gradient Boosting Machine (LGBM) models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters. The SHapley Additive ExPlanation (SHAP) interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters. Yunnan Province, a typical mountainous and plateau area in Southwest China, was selected to implement the proposed framework and conduct a case study. For this purpose, a flood disaster inventory of 7332 historical events was prepared, and 22 potential driving factors related to precipitation, surface environment, and human activity were initially selected. Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity, with geomorphic zoning accounting for 66.1% of the spatial variation in historical flood disasters. The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts. Moreover, the simulation performance shows a slight improvement (a 6% average decrease in RMSE and an average increase of 1% in R²) even with reduced factor data. Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions; nevertheless, precipitation-related factors, such as precipitation intensity index (SDII), wet days (R10MM), and 5-day maximum precipitation (RX5day), were the main driving factors controlling flood disasters. This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity, offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.

Landslide susceptibility assessment is crucial in predicting landslide occurrence and potential risks. However, traditional methods usually emphasize on larger regions of landsliding and rely on relatively static environmental conditions, which exposes the hysteresis of landslide susceptibility assessment in refined-scale and temporal dynamic changes. This study presents an improved landslide susceptibility assessment approach by integrating machine learning models based on random forest (RF), logical regression (LR), and gradient boosting decision tree (GBDT) with interferometric synthetic aperture radar (InSAR) technology and comparing them to their respective original models. The results demonstrated that the combined approach improves prediction accuracy and reduces the false negative and false positive errors. The LR-InSAR model showed the best performance in dynamic landslide susceptibility assessment at both regional and smaller scale, particularly when identifying areas of high and very high susceptibility. Modeling results were verified using data from field investigations including unmanned aerial vehicle (UAV) flights. This study is of great significance to accurately assess dynamic landslide susceptibility and to help reduce and prevent landslide risk.

High arsenic (As) groundwater is a global problem primarily originating from As-enriched sediments. The provenance (source) and release mechanisms (sinks) of high As sediment have been identified, but the source-sink transfer is poorly understood, especially the influence of geological and surface processes. In this study, we explore the roles of tectonic movement and Yellow River evolution in provenance formation processes and evaluate the combined effects of provenance and sediment age on the As content of aquifer sediments in the northern Hetao Basin of Inner Mongolia. Based on optically stimulated luminescence (OSL) and ¹⁴C dating and detrital zircon U-Pb, As content, and lithological analyses of a 400 m core, we reconstructed As changes over the last 160 ka. Our results show clay deposited in a paleo-lake during the Gonghe movement period in the late Pleistocene (∼100 ka B.P.) is enriched in As (31.8 μg/g) due to significant provenance contributions of the As-bearing Langshan Group under tectonic uplift and mountain erosion. In contrast, clay deposited in the middle Pleistocene (∼160 ka B.P.) has lower As content (7.3 μg/g) due to the Yellow River as the primary provenance. Accordingly, the provenance of basin As forced by tectonic uplift and Yellow River evolution determines the background As of aquifer sediments. After deposition, sediment As content decays over time, with higher decay rates in coarse-grained sands than fine-grained. Overall, both provenance formation and sediment age, representing initial and dynamic states of solid phase As, jointly determine the As content of aquifer sediments. More solid phase As provided by younger sediments from the proximal orogenic provenance and reducing conditions due to frequent river–lake transitions, jointly lead to higher As concentrations in shallow groundwater. The study highlights the potential for using a combined analysis of the tectonic movement-surface processes-environment system to improve understanding of geogenic high As groundwater over global large sedimentary basins in the proximity of young orogenic belts.

Landslide inventory is an indispensable output variable of landslide susceptibility prediction (LSP) modelling. However, the influence of landslide inventory incompleteness on LSP and the transfer rules of LSP resulting error in the model have not been explored. Adopting Xunwu County, China, as an example, the existing landslide inventory is first obtained and assumed to contain all landslide inventory samples under ideal conditions, after which different landslide inventory sample missing conditions are simulated by random sampling. It includes the condition that the landslide inventory samples in the whole study area are missing randomly at the proportions of 10%, 20%, 30%, 40% and 50%, as well as the condition that the landslide inventory samples in the south of Xunwu County are missing in aggregation. Then, five machine learning models, namely, Random Forest (RF), and Support Vector Machine (SVM), are used to perform LSP. Finally, the LSP results are evaluated to analyze the LSP uncertainties under various conditions. In addition, this study introduces various interpretability methods of machine learning model to explore the changes in the decision basis of the RF model under various conditions. Results show that (1) randomly missing landslide inventory samples at certain proportions (10%–50%) may affect the LSP results for local areas. (2) Aggregation of missing landslide inventory samples may cause significant biases in LSP, particularly in areas where samples are missing. (3) When 50% of landslide samples are missing (either randomly or aggregated), the changes in the decision basis of the RF model are mainly manifested in two aspects: first, the importance ranking of environmental factors slightly differs; second, in regard to LSP modelling in the same test grid unit, the weights of individual model factors may drastically vary.

The Huoshaoyun deposit in the Karakorum area of NW China is the world’s largest zinc-lead carbonate ore deposit. Here we investigate the genesis of the mineralization based on multiproxy investigations. The deposit contains zinc-lead carbonate and sulfide minerals, with smithsonite (Smt), cerussite (Cer), and sulfides accounting for 85%, 10%, and 5% of the total lead and zinc resources, respectively. Three ore-forming stages, involving Smt, Cer, and sulfides ores were summarized. The Smt mineralization is characterized by veined, massive, and stratified Smt forming horizontal sedimentary layered ore and vertical feeder veins similar to the SEDEX-type deposits. The sulfide and Cer veins display typical hydrothermal characteristics and are superimposed on the massive Smt ores. The Smt ores show high Li, Be, Cr, Y, Ba, Nd, Yb, and Zr contents, whereas the Cer veins have extremely high Sr contents (up to 3814–9174 ppm) and low Zr contents (less than 0.01 ppm). Galena and sphalerite show higher Cd concentrations compared to Smt and Cer ores.

The Smt ores differ with different spatial locations, with Smt ores formed at the vent have δ⁶⁶Zn values of +0.15‰ to +0.21‰, the massive Smt formed close to the vent show a value of +0.13‰, and those formed away from the vent show a value of 0.05‰, all values being close to 0. The sulfides have δ⁶⁶Zn values of −0.09‰ to +0.04‰. The C-O isotopes of Smt ores are similar to both altered and unaltered host limestone, suggesting that the limestone was a potential source for carbon and oxygen. Quartz with veined Smt shows magmatic signatures with δ¹⁸O_VSMOW of +1.14‰ to +2.23‰, high Pb (115–401 ppm) and Zn concentrations (390–997 ppm), whereas quartz associated with sulfide has meteoric fluid signature with the lowest δ¹⁸O_VSMOW (−14‰ to −10.7‰), low Pb (11.6–49.0 ppm) and Zn (18.1–72.8 ppm) concentrations. The temperature of equilibration computed based on oxygen isotope fractionation between Smt and coeval quartz indicate a dual source with that of quartz derived from an aqueous fluid, whereas the source for Smt might involve CO₂ or HCO₃⁻.

We trace multiple metallogenic stages for this deposit including exhalation, hydrothermal deposition, and fault-controlled sulfide vein formation. The largest orebody (III-1) preserves a 16 Mt reserve of Zn and was formed by crust-mantle interaction at ca. 195 Ma in the early development of the Linjitang post-arc rift system. Fluid convection, zinc enrichment driven by granitic magma, volcanic activity, and karst alteration induced by acid rain in a lagoonal environment promoted ore enrichment.