Search Results (1,629)

There are many controversies over the material sources of the Late Paleozoic strata in the Lower Yangtze region, and there is a lack of consensus on the basin source–sink system, which hinders the reconstruction of Late Paleozoic paleogeography and exploration of energy and mineral resources in the area. This study aimed to clarify the sedimentary provenance and tectonic background of the Upper Permian Longtan Formation in the Chizhou area of southern Anhui Province. The key objectives were to: (i) analyze the geochemical characteristics of sandstones using major, trace, and rare earth elements; (ii) determine the tectonic setting of the sediment source region based on discrimination diagrams; and (iii) integrate geochemical, sedimentological, and paleocurrent data to reconstruct the source-to-sink system. The geochemical data suggest that the sandstone samples exhibit relatively high SiO₂, Fe₂O₃, MgO, and Na₂O content and relatively low TiO₂, Al₂O₃, and K₂O content, consistent with average values of post-Archean Australian shale (PAAS) and the upper continental crust (UCC). The chondrite-normalized rare earth element patterns resemble PAAS, with enrichment in light REEs and depletion in heavy REEs. Tectonic discrimination diagrams indicate a provenance from active continental margins and continental island arcs, with minor input from passive continental margins. Combined with regional tectonic context and paleocurrent measurements, the results suggest that the Longtan Formation sediments primarily originated from the Neoproterozoic Jiangnan orogenic belt and the Cathaysia Block, notably the Wuyi terrane. These research results not only provide new geological data for further clarifying the provenance of Late Paleozoic sedimentary basins in the Lower Yangtze region but also establish the foundation for constructing the Late Paleozoic tectonic paleogeographic pattern in South China. Full article

South Dongting Lake is an essential aquatic ecosystem that receives substantial water inflows from the Xiangjiang and Zishui Rivers. However, it is significantly impacted by human activities, including mining, smelting, and farming. These activities have led to serious contamination of the lake’s sediments with heavy metals (HMs). This study investigated the distribution, mobility, and influencing factors of HMs at the sediment–water interface. To this end, sediment samples were analyzed from three key regions (Xiangjiang River estuary, Zishui River estuary, and northeastern South Dongting Lake) using traditional sampling methods and Diffusive Gradients in Thin Films (DGT) technology. Analysis of fifteen HMs (Pb, Bi, Ni, As, Se, Cd, Sb, Mn, Zn, V, Cr, Cu, Tl, Co, and Fe) revealed significant spatial heterogeneity. The results showed that Cr, Cu, Pb, Bi, Ni, As, Se, Cd, Sb, Mn, Zn, and Fe exhibited high variability (CV > 0.20), whereas V, Tl, and Co demonstrated stable concentrations (CV < 0.20). Concentrations were found to exceed background values of the upper continental crust of eastern China (UCC), Yangtze River sediments (YZ), and Dongting Lake sediments (DT), particularly at the Xiangjiang estuary (XE) and in the northeastern regions. Speciation analysis revealed that V, Cr, Cu, Ni, and As were predominantly found in the residual fraction (F4), while Pb and Co were concentrated in the oxidizable fraction (F3), Mn and Zn appeared primarily in the exchangeable fractions (F1 and F2), and Cd was notably dominant in the exchangeable fraction (F1), suggesting a high potential for mobility. Additionally, DGT results confirmed a significant potential for the release of Pb, Zn, and Cd. Contamination assessment using the Pollution Load Index (PLI) and Geoaccumulation Index (Igeo) identified Pb, Bi, Ni, As, Se, Cd, and Sb as major pollutants. Among these, Bi and Cd were found to pose the highest risks. Furthermore, the Risk Assessment Code (RAC) and the Potential Ecological Risk Index (PERI) highlighted Cd as the primary ecological risk contributor, especially in the XE. The study identified sediment grain size, pH, electrical conductivity, and nutrient levels as the primary influencing factors. The PMF modeling revealed HM sources as mixed smelting/natural inputs, agricultural activities, natural weathering, and mining/smelting operations, suggesting that remediation should prioritize Cd control in the XE with emphasis on external inputs. Full article

Magnetic parameters of river sediments are commonly used as end-members for source tracing in the coasts and shelves. The eastern continental shelf area of China, with multiple sources of input, is a key region for discussing sediment sources. However, magnetic parameters are influenced by grain size, and the nature of this influence remains unclear. In this study, the Jiulong River was selected as a case to analyze the magnetic parameters and mineral characteristics for both the coarse (>63 μm) and fine-grained (<63 μm) fractions. Results show that the magnetic minerals mainly contain detrital-sourced magnetite and hematite. In the North River, a tributary of the Jiulong River, the content of coarse-grained magnetic minerals increases from upstream to downstream, contrary to fine-grained magnetic minerals, suggesting the influence of hydrodynamic forces. Some samples with abnormally high magnetic susceptibility may result from the combined influence of the parent rock and human activities. In the scatter diagrams of magnetic parameters for provenance tracing, samples of the <63 μm fractions have a more concentrated distribution than that of the >63 μm fractions. Hence, magnetic parameters for the <63 μm fraction are more useful in provenance identification. Full article

We need to educate students and the public about addressing natural resource challenges to maintain civilization moving into a sustainable future. Because US mineral and energy resources are found in its continental crust and sedimentary basins, introductory geology students need to be well-informed about US crust and basins. We think that creating effective videos about these topics is the best way to engage students to want to learn more. In preparation for making these videos, we researched what introductory geology students are taught and what they learn about these topics. Student interviews informed us about learned curriculum, and taught curriculum was analyzed using a novel keyword-counting method applied to textbook indices. We found that geophysics is stressed twice as much as geology, radiometric dating, and sedimentary basins. We expected that students would have learned more about geophysics and less about the other topics; however, this was not the case. Students knew more about geology, and less about geophysics, radiometric dating, and sedimentary basins. To make effective videos on these topics, we need to explain the following threshold concepts: seismic refraction to scaffold student understanding of crustal geophysics, as well as radiometric dating and deep time to understand crustal geology and the economic importance of sedimentary basins. Full article

Atmospheric warming results in increase in temperatures for the mean, the coldest, and the hottest day of the year, season, or month. Global warming leads to a large increase in the atmospheric water vapor content and to changes in the hydrological cycle, which include an intensification of precipitation extremes. Using the GISS-E2.1 climate model, we present the future changes in the coldest and hottest daily temperatures as well as in extreme precipitation indices (under four main Shared Socioeconomic Pathways (SSPs)). The increase in the wet-day precipitation ranges between 6% and 15% per 1 °C global surface temperature warming. Scaling of the 95th percentile versus the total precipitation showed that the sensitivity for the extreme precipitation to the warming is about 10 times stronger than that for the mean total precipitation. For six precipitation extreme indices (Total Precipitation, R95p, RX5day, R10mm, SDII, and CDD), the histograms of probability density functions become flatter, with reduced peaks and increased spread for the global mean compared to the historical period of 1850–2014. The mean values shift to the right end (toward larger precipitation and intensity). The higher the GHG emission of the SSP scenario, the more significant the increase in the index change. We found an intensification of precipitation over the globe but large uncertainties remained regionally and at different scales, especially for extremes. Over land, there is a strong increase in precipitation for the wettest day in all seasons over the mid and high latitudes of the Northern Hemisphere. There is an enlargement of the drying patterns in the subtropics including over large regions around Mediterranean, southern Africa, and western Eurasia. For the continental averages, the reduction in total precipitation was found for South America, Europe, Africa, and Australia, and there is an increase in total precipitation over North America, Asia, and the continental Russian Arctic. Over the continental Russian Arctic, there is an increase in all precipitation extremes and a consistent decrease in CDD for all SSP scenarios, with the maximum increase of more than 90% for R95p and R10 mm observed under SSP5–8.5. Full article

The Copernicus European Regional Reanalysis (CERRA) was released in August 2022, providing a continental atmospheric reanalysis, and, in addition, the New European Wind Atlas (NEWA) is a recently released hindcast product that can be used to create a high temporal and spatial resolution wind resource atlas of Europe. In order to demonstrate the suitability of the NEWA and CERRA wind datasets for offshore wind energy applications, the accuracy of these datasets was assessed for the Mediterranean Sea, a basin with a high potential for the development of offshore wind projects. Long-term in situ measurements from 13 offshore locations along the basin were used in order to assess the performance of the CERRA and NEWA wind speed datasets in the hourly and seasonal time scales by using a variety of different evaluation tools. The results revealed that the CERRA dataset outperforms NEWA and is a reliable source for offshore wind energy assessment studies in the examined areas, although special attention should be paid to extreme value analysis of the wind speed. Full article

Heating, ventilation, and air-conditioning (HVAC) systems account for the largest share of energy consumption in European Union (EU) buildings, representing approximately 40% of the final energy use and contributing significantly to carbon emissions. Latent thermal energy storage (LTES) using phase change materials (PCMs) has emerged as a promising strategy to enhance HVAC efficiency. This review systematically examines the role of latent thermal energy storage using phase change materials (PCMs) in optimizing HVAC performance to align with EU climate targets, including the Energy Performance of Buildings Directive (EPBD) and the Energy Efficiency Directive (EED). By analyzing advancements in PCM-enhanced HVAC systems across residential and commercial sectors, this study identifies critical pathways for reducing energy demand, enhancing grid flexibility, and accelerating the transition to nearly zero-energy buildings (NZEBs). The review categorizes PCM technologies into organic, inorganic, and eutectic systems, evaluating their integration into thermal storage tanks, airside free cooling units, heat pumps, and building envelopes. Empirical data from case studies demonstrate consistent energy savings of 10–30% and peak load reductions of 20–50%, with Mediterranean climates achieving superior cooling load management through paraffin-based PCMs (melting range: 18–28 °C) compared to continental regions. Policy-driven initiatives, such as Germany’s renewable integration mandates for public buildings, are shown to amplify PCM adoption rates by 40% compared to regions lacking regulatory incentives. Despite these benefits, barriers persist, including fragmented EU standards, life cycle cost uncertainties, and insufficient training. This work bridges critical gaps between PCM research and EU policy implementation, offering a roadmap for scalable deployment. By contextualizing technical improvement within regulatory and economic landscapes, the review provides strategic recommendations to achieve the EU’s 2030 emissions reduction targets and 2050 climate neutrality goals. Full article

The present study provides a detailed compilation and analysis of the bulk density and magnetic susceptibility of the rocks from the northwest Himalayas, Pakistan. The area is tectonically extremely complex and comprises sedimentary, metamorphic, and igneous rocks. These rocks range in age from Early Proterozoic to Recent. During the fieldwork, 476 rock samples were collected for density measurements and 410 for magnetic susceptibility measurements from the major rock units exposed in the study area. The measured physical parameters reveal a significant difference in the density and susceptibility of the rocks present in the investigated area. The sedimentary rock units belonging to the Indian Plate show the lowest mean values for bulk density, followed by metasedimentary rocks, Early Proterozoic rocks, igneous and metaigneous rock units of the Indian Plate, Indus Suture Melange Zone, and Kohistan Island Arc rocks, respectively. The magnetic susceptibility of sedimentary rock units of the Indian Plate has the lowest mean values, followed by metasedimentary rocks of the Indian Plate, igneous and metaigneous rock units of the Indian Plate, Early Proterozoic rocks of the Indian Plate, Kohistan Island Arc rocks, and Indus Suture Melange Zone. In brief, the sedimentary rocks of the Indian Plate have the lowest bulk density and magnetic susceptibility values, whereas the Kohistan Island Arc rocks have the highest values. Overall, the bulk density and magnetic susceptibility of rock units in the study area follow those predicted for different types of rocks. These measurements can be used to develop possible potential field models of the northwest Himalayas to better understand the tectonics of the ongoing continental-to-continental collision, as well as for many other geological analyses. Full article

Members of the genus Nemipterus are economically important fish species distributed in the tropical and subtropical Indo-West Pacific region. The majority of species in this genus inhabit waters with sandy–muddy substrates on the continental shelf, although different species are found at slightly varying water depths. In this study, we sequenced seven species within the genus Nemipterus after identifying the specimens using complementary morphological analysis and DNA barcoding. Each species yielded over 40,000,000 clean reads, totaling over 300,000,000 clean reads across the seven species. A total of 276,389 unigenes were obtained after de novo assembly and a total of 168,010 (60.79%) unigenes were annotated in the protein database. The comprehensive functional annotation based on the KOG, GO, and KEGG databases revealed that these unigenes are mainly associated with numerous physiological, metabolic, and molecular processes, and that the seven species exhibit similarity in these aspects. By constructing a phylogenetic tree and conducting divergence time analysis, we found that N. bathybius and N. virgatus diverged most recently, approximately during the Neogene Period (14.9 Mya). Compared with other species, N. bathybius and N. virgatus are distributed in deeper water layers. Therefore, we conducted selection pressure analysis using these two species as the foreground branches and identified several environmental-responsive genes. The results indicate that genes such as aqp1, arrdc3, ISP2, Hip, ndufa1, ndufa3, pcyt1a, ctsk, col6a2, casp2 exhibit faster evolutionary rates during long-term adaptation to deep-water environments. Specifically, these genes are considered to be associated with adaptation to aquatic osmoregulation, temperature fluctuations, and skeletal development. This comprehensive analysis provides valuable insights into the evolutionary biology and environmental adaptability of threadfin breams, contributing to the conservation and sustainable management of these species. Full article

Artificial reservoirs in arid regions provide unique ecological environments for studying the spatial and functional dynamics of plankton communities under the combined stressors of climate change and anthropogenic activities. This study conducted a systematic investigation of the phytoplankton community structure and its environmental drivers in 17 artificial reservoirs in the Ili region of Xinjiang in August and October 2024. The Ili region is located in the temperate continental arid zone of northwestern China. A total of 209 phytoplankton species were identified, with Bacillariophyta, Chlorophyta, and Cyanobacteria comprising over 92% of the community, indicating an oligarchic dominance pattern. The decoupling between numerical dominance (diatoms) and biomass dominance (cyanobacteria) revealed functional differentiation and ecological complementarity among major taxa. Through multivariate analyses, including Mantel tests, principal component analysis (PCA), and redundancy analysis (RDA), we found that phytoplankton community structures at different ecological levels responded distinctly to environmental gradients. Oxidation-reduction potential (ORP), dissolved oxygen (DO), and mineralization parameters (EC, TDS) were key drivers of morphological operational taxonomic unit (MOTU). In contrast, dominant species (SP) were more responsive to salinity and pH. A seasonal analysis demonstrated significant shifts in correlation structures between summer and autumn, reflecting the regulatory influence of the climate on redox conditions and nutrient solubility. Machine learning using the random forest model effectively identified core taxa (e.g., MOTU1 and SP1) with strong discriminatory power, confirming their potential as bioindicators for water quality assessments and the early warning of ecological shifts. These core taxa exhibited wide spatial distribution and stable dominance, while localized dominant species showed high sensitivity to site-specific environmental conditions. Our findings underscore the need to integrate taxonomic resolution with functional and spatial analyses to reveal ecological response mechanisms in arid-zone reservoirs. This study provides a scientific foundation for environmental monitoring, water resource management, and resilience assessments in climate-sensitive freshwater ecosystems. Full article

The Chagai porphyry copper belt is a major component of the Tethyan metallogenic domain, which spans approximately 300 km and hosts several giant porphyry copper deposits. The tectonic setting, whether subduction-related or post-collisional, and the deep dynamic processes governing the formation of these giant deposits remain poorly understood. Mafic microgranular enclaves (MMEs), mafic dikes, and multiple porphyries have been documented in the Saindak mining area. This work examines both the ore-rich and non-ore intrusions in the Saindak porphyry Cu-Au deposit, using methods like molybdenite Re-Os dating, U-Pb zircon ages, Hf isotopes, and bulk-rock geochemical data. Geochronological results indicate that ore-fertile and barren porphyries yield ages of 22.15 ± 0.22 Ma and 22.21 ± 0.33 Ma, respectively. Both MMEs and mafic dikes have zircons with nearly identical ²⁰⁶Pb/²³⁸U weighted mean ages (21.21 ± 0.18 Ma and 21.21 ± 0.16 Ma, respectively), corresponding to the age of the host rock. Geochemical and Sr–Nd–Hf isotopic evidence indicates that the Saindak adakites were generated by the subduction of the Arabian oceanic lithosphere under the Eurasian plate, rather than through continental collision. The adakites were mainly formed by the partial melting of a metasomatized mantle wedge, induced by fluids from the dehydrating subducting slab, with minor input from subducted sediments and later crust–mantle interactions during magma ascent. We conclude that shallow subduction of the Arabian plate during the Oligocene–Miocene may have increased the flow of subducted fluids into the sub-arc mantle source of the Chagai arc. This process may have facilitated the widespread deposition of porphyry copper and copper–gold mineralization in the region. Full article

The Life Cycle Cost Assessment (LCCA) methodology is widely used to determine the optimal thickness of thermal insulation for walls and roofs. The results depend on several factors, such as the degree day calculations method, the ambient or sol–air temperature, base temperature variations, and the heat capacity of the thermal envelope elements. This study aims to analyze the impact of solar radiation on mass walls with different orientations in five cities in northern Chile, which have severe solar climates. The goal is to determine the optimal thickness of expanded polystyrene insulation using the LCCA method, considering solar radiation, a varying base temperature, and validating results by analyzing the energy demand for heating and cooling of a typical house. The findings show that excluding solar radiation in the LCCA methodology can lead to an underestimation of the optimal insulation thickness by 21–39% for walls in northern Chile. It was also found that using variable monthly threshold temperatures for heating and cooling based on the adaptive thermal comfort model results in a slight underestimation (1–3%) of the optimal thickness compared to a constant annual temperature. An energy simulation of a typical house in five cities in northern Chile showed that neglecting the effect of solar radiation when determining the thermal insulation thickness for the studied wall can lead to a minor increase in heating and cooling energy demand, ranging from approximately 1% to 9%. However, this study emphasizes the importance of applying optimal insulation thickness for cities with more continental climates like Santiago and Calama, where the heating demand is higher than cooling. Full article

Oceanic mesoscale activity influences the atmosphere in the southwestern and southern sectors of the Atlantic Ocean. However, the influence of high latitudes, specifically sea ice, on mid-latitudes and a better understanding of mesoscale ocean–atmosphere thermodynamic interactions still require further study. To quantify the effects of oceanic mesoscale activity during the periods of maximum and minimum Antarctic sea ice extent (September 2019 and February 2020), numerical experiments were conducted using a coupled regional model and an online two-dimensional spatial filter to remove high-frequency sea surface temperature (SST) oscillations. The largest SST anomalies were observed in the Brazil–Malvinas Confluence and along oceanic fronts in September, with maximum SST anomalies reaching 4.23 °C and −3.71 °C. In February, the anomalies were 2.18 °C and −3.06 °C. The influence of oceanic mesoscale activity was evident in surface atmospheric variables, with larger anomalies also observed in September. This influence led to changes in the vertical structure of the atmosphere, affecting the development of the marine atmospheric boundary layer (MABL) and influencing the free atmosphere above the MABL. Modulations in precipitation patterns were observed, not only in oceanic regions, but also in adjacent continental areas. This research provides a novel perspective on ocean–atmosphere thermodynamic coupling, highlighting the mesoscale role and importance of its representation in the study region. Full article

Abies nephrolepis and Picea jezoensis are native Pinaceae trees distributed in high mountainous regions of Northeast Asia (typically above ~1000 m a.s.l. on the Korean peninsula, northeastern China, Sakhalin, and the Russian Far East) and southern boreal forests, vulnerable to climate change and human disturbances, necessitating accurate habitat identification for effective conservation. While protected areas (PAs) are essential, merely expanding existing ones often fail to protect populations under human pressure and climate change. Using species distribution models with current and projected climate data, we mapped potential habitats across Northeast Asia. Spatial clustering analyses integrated with PA and land cover data helped identify optimal sites and priorities for new conservation areas. Ensemble species distribution models indicated extensive suitable habitats, especially in southern Sikhote-Alin, influenced by maritime-continental climates. Specific climate variables strongly affected habitat suitability for both species. The Kamchatka peninsula consistently emerged as an optimal habitat under future climate scenarios. Our study highlights essential environmental characteristics shaping the habitats of these species, reinforcing the importance of strategically enhancing existing PAs, and establishing new ones. These insights inform proactive conservation strategies for current and future challenges, by focusing on climate refugia and future habitat stability. Full article

Accurate crop yield prediction is essential for stabilizing food supply chains and reducing the uncertainties in financial risks related to agricultural production. Yet, it is even more essential to understand how crop yield models make predictions depending on their relationship to Earth Observation (EO) indicators. This study presents a state-of-the-art explainable artificial intelligence (XAI) method to estimate corn yield prediction over the Corn Belt in the continental United States (CONUS). We utilize the recently introduced Kolmogorov–Arnold Network (KAN) architecture, which offers an interpretable alternative to the traditional Multi-Layer Perceptron (MLP) approach by utilizing learnable spline-based activation functions instead of fixed ones. By including a KAN in our crop yield prediction framework, we are able to achieve high prediction accuracy and identify the temporal drivers behind crop yield variability. We create a multi-source dataset that includes biophysical parameters along the crop phenology, as well as meteorological, topographic, and soil parameters to perform end-of-season and in-season predictions of county-level corn yields between 2016–2023. The performance of the KAN model is compared with the commonly used traditional machine learning (ML) models and its architecture-wise equivalent MLP. The KAN-based crop yield model outperforms the other models, achieving an R² of 0.85, an RMSE of 0.84 t/ha, and an MAE of 0.62 t/ha (compared to MLP: R² = 0.81, RMSE = 0.95 t/ha, and MAE = 0.71 t/ha). In addition to end-of-season predictions, the KAN model also proves effective for in-season yield forecasting. Notably, even three months prior to harvest, the KAN model demonstrates strong performance in in-season yield forecasting, achieving an R² of 0.82, an MAE of 0.74 t/ha, and an RMSE of 0.98 t/ha. These results indicate that the model maintains a high level of explanatory power relative to its final performance. Overall, these findings highlight the potential of the KAN model as a reliable tool for early yield estimation, offering valuable insights for agricultural planning and decision-making. Full article