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Deep carbon recycling viewed from global plate tectonics

National Science Review 2024年第6期11卷 37-55页

作者： Maoliang Zhang Sheng Xu Yuji Sano School of earth System Science Tianjin University Marine Core Research institute Kochi University Atmosphere and Ocean Research institute The University of Tokyo

Plate tectonics plays an essential role in the redistribution of life-essential volatile elements between earth's interior and surface, whereby our planet has been well tuned to maintain enduring habitability over much of its history. Here we present an overview of deep carbon recycling in the regime of modern plate tectonics,with a special focus on convergent plate margins for assessing global carbon mass balance. The up-to-date flux compilation implies an approximate balance between deep carbon outflux and subduction carbon influx within uncertainty but remarkably limited return of carbon to convecting mantle. If correct, carbon would gradually accumulate in the lithosphere over time by（i） massive subsurface carbon storage occurring primarily in continental lithosphere from convergent margins to continental interior and（ii） persistent surface carbon sinks to seafloors sustained by high-flux deep CO2emissions to the atmosphere. Further assessment of global carbon mass balance requires updates on fluxes of subduction-driven carbon recycling paths and reduction in uncertainty of deep carbon outflux. From a global plate tectonics point of view, we particularly emphasize that continental reworking is an important mechanism for remobilizing geologically sequestered carbon in continental crust and sub-continental lithospheric mantle. In light of recent advances,future research is suggested to focus on a better understanding of the reservoirs, fluxes, mechanisms, and climatic effects of deep carbon recycling following an integrated methodology of observation, experiment,and numerical modeling, with the aim of decoding the self-regulating earth system and its habitability from the deep carbon recycling perspective.

关键词： deep carbon recycling convergent plate margins subduction continental reworking global carbon mass balance

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Key issues on magnetic reconnection at Mercury

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earth and Planetary Physics 2024年第5期8卷 673-685页

作者： Jun Zhong Key Laboratory of earth and Planetary Physics Institute of Geology and Geophysics Chinese Academy of Sciences

Magnetic reconnection processes and their impact on planetary magnetospheric dynamics exhibit significant differences due to differences in upstream solar wind conditions and internal planetary environments. Current understanding of reconnection phenomena at Mercury is rooted in the MESSENGER mission. However, direct detection of reconnection remains rare. Here, we aim to assess the limitations of MESSENGER in detecting reconnection in Mercury's space and to discuss key issues of reconnection that will be addressed by BepiColombo, including the dynamics of magnetic flux ropes, particle acceleration, density asymmetric reconnection,IMF-driven near-tail structures, and potential modes of magnetospheric convection.

关键词： magnetic reconnection Mercury MESSENGER BepiColombo

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Spatio-temporal variability in microfossil and geochemical records of Cenomanian-Turonian oceanic anoxic event-2: a review

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Journal of Palaeogeography 2024年第4期13卷 646-674页

作者： C.P.Sooraj Shweta Gupta Jahnavi Punekar Department of earth Sciences Indian Institute of Technology Bombay

The Cenomanian-Turonian boundary oceanic anoxic event(OAE-2; Bonarelli event; ～94 Ma) is associated with a species turnover in the marine calcareous microfossil record, widespread marine anoxia,black shale deposition and positive δ13C excursions. This study reviews 141 CTBE sites globally, aiming to understand micropaleontological, geochemical, and sedimentological expressions of OAE-2. There is a clear palaeogeographic and palaeobathymetric heterogeneity in the development of OAE-2 marine anoxia. A majority of the documented OAE-2 sites are from deep marine environments. The calcareous nannoplankton and benthic foraminifera record a diversity decline, while planktic foraminifera shows community level shifts and no major mass extinction. The variability of total organic carbon in OAE-2 sediments across sites(10 wt.%) and the δ13C profiles(diachronous) have been attributed to different mechanisms of anoxia *** increased primary “productivity model” gains support from productivity proxies(e.g., Ba, P, Cu, Ni),redox-sensitive elements(e.g., Mn, Mo, U, V, As), and eutrophic genera(e.g., benthic foraminifera Gabonita spp., calcareous nannofossils Biscutum spp. and Zeugrhabdotus erectus). The enhanced organic carbon“preservation model” in stratified(semi) restricted basins gains support from sites with lower enrichment of redox-sensitive elements and oligotrophic flora and fauna in the OAE-2 records. geochronology of the Caribbean and the High Atlantic Large Igneous Province events(CLIP and HALIP) mark them as likely triggers of the OAE-2 related global perturbation of marine biogeochemistry. Volcanic triggers may have caused climate warming, altered hydrological cycles, enhanced continental weathering, shifts in ocean circulation, and nutrient flows. Addressing knowledge gaps, further research is urged, utilizing innovative proxies, and exploring underrepresented depositional systems to comprehensively understand OAE-2 onset and biotic crisis.

关键词： Oceanic Anoxic Event-2 Global organic carbon distribution geochemical proxies Marine calcifiers Palaeoceanography

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Multiscale spatiotemporal meteorological drought prediction:A deep learning approach

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Advances in Climate Change Research 2024年第2期15卷 211-221页

作者： Jia-Li ZHANG Xiao-Meng HUANG Yu-Ze SUN Department of earth System Science Ministry of Education Key Laboratory for Earth System ModellingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China

Reliable monitoring and thorough spatiotemporal prediction of meteorological drought are crucial for early warning and decision-making regarding drought-related *** utilisation of multiscale methods is effective for a comprehensive evaluation of drought occurrence and progression,given the complex nature of meteorological ***,the nonlinear spatiotemporal features of meteorological droughts,influenced by various climatological,physical and environmental factors,pose significant challenges to integrated prediction that considers multiple indicators and time *** address these constraints,we introduce an innovative deep learning framework based on the shifted window transformer,designed for executing spatiotemporal prediction of meteorological drought across multiple *** formulate four prediction indicators using the standardized precipitation index and the standard precipitation evaporation index as core methods for drought definition using the ERA5 reanalysis *** indicators span time scales of approximately 30 d and one ***-term indicators capture more anomalous variations,whereas long-term indicators attain comparatively higher accuracy in predicting future *** focus on the East Asian region,notable for its diverse climate conditions and intricate terrains,to validate the model's efficacy in addressing the complexities of nonlinear spatiotemporal *** model's performance is evaluated from diverse spatiotemporal viewpoints,and practical application values are analysed by representative drought *** results substantiate the effectiveness of our proposed model in providing accurate multiscale predictions and capturing the spatiotemporal evolution characteristics of *** of the four drought indicators accurately delineates specific facets of the meteorological drought ***,three representative drought events,namely flash drought,sustained drought and severe drought,

关键词： Meteorological drought Spatiotemporal prediction Multiscale Swim transformer Deep learning

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Fabrication and application prospects of Pr-Fe-B/Alnico nanocomposite alloys

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Journal of Rare earths 2024年第9期42卷 1717-1723,I0003页

作者： Hui Liang Sajjad Ur Rehman Ruibiao Zhang Yongqiang Deng Haihua Liu Munan Yang National Rare earth Functional Materials Innovation Center College of Rare EarthsJiangxi University of Science and TechnologyGanzhou341000China Zhejiang Tiantong Electronic information Materials Research institute Co. Ltd.Haining314400China

The potentials of rare earth-based nanocomposite alloys have never been realized due to strict microstructural *** to the easy demagnetization it is challenging to increase the soft magnetic phase *** avoid the easy demagnetization,Pr-Fe-B/Alnico magnets were fabricated and reported in this *** content of the Alnico phase is increased from 0 to 25 wt%,while the content of Pr element is reduced to below the sub-stoichiometry of the 2:14:1 main *** maximum magnetic energy product,which is the figure-of-merit for permanent magnets,is increased from 122 kJ/m^(3) for the standard alloy to 146 kJ/m^(3) for the alloy with 15 wt% Alnico which shows a significant improvement considering the fact that the Curie point of the magnet is also increased by~66 *** special microstructure contains distinctly and heterogeneously distributed 2:14:1 and Alnico *** dimensions of neither the 2:14:1 nor the Alnico phases meet the dimensional requirements of the nanocomposite magnets,but still the smooth demagnetization curves are noted for the *** behavior of effective anisotropy,the performance of the magnets in applied magnetic field and the magnetic interactions among the various constituent grains were quantitatively studied by reversible susceptibility,irreversible susceptibility and re coil loop *** study may provide some guiding principles for the development of nanocomposite magnetic alloys with excellent magnetic properties by using much less RE elements.

关键词： Composite alloys High performance magnets Magnetic properties Alnico alloys Rare earth magnets

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Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques

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Journal of Mountain Science 2024年第2期21卷 397-412页

作者： ZHAO Haijun WEI Aihua MA Fengshan DAI Fenggang JIANG Yongbing LI Hui Key Laboratory of Shale Gas and geoengineering Institute of Geology and GeophysicsChinese Academy of SciencesBeijing 100029China Innovation Academy for earth Science Chinese Academy of SciencesBeijing 100029China Hebei Province Key Laboratory of Sustained Utilization&Development of Water Recourse Hebei GEO UniversityShijiazhuang 050031China School of Water Resources and Environment Hebei GEO UniversityShijiazhuang 050031China The Sixth geological Brigade of geological Mineral Exploration and Development Bureau of Hebei Province Shijiazhuang 050085China

The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility *** this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning *** total,133 historical debris flow records and 16 related factors were *** support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step ***,the particle swarm optimization(PSO)algorithm was employed to select the SVM model ***,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,*** statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the *** results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM ***,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing *** was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment ***,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning *** high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intens

关键词： Chengde Feature selection Support vector machine Particle swarm optimization Principal component analysis Debris flow susceptibility

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Unraveling adaptive changes in electric vehicle charging behavior toward the postpandemic era by federated meta-learning

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The Innovation 2024年第2期5卷 21-23页

作者： Linlin You Rui Zhu Mei-Po Kwan Min Chen Fan Zhang Bisheng Yang Man Sing Wong Zheng Qin School of Intelligent Systems Engineering Sun Yat-Sen UniversityShenzhen 518107China institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis WaySingapore 138632Republic of Singapore institute of Space and earth information Science Chinese University of Hong KongHong KongChina Key Laboratory of Virtual geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China institute of Remote Sensing and geographical information System School of Earth and Space SciencesPeking UniversityBeijing 100871China State Key Laboratory of information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhan 430079China Department of Land Surveying and geo-Informatics The Hong Kong Polytechnic UniversityHong KongChina

Dear Editor,Electric vehicle(EV)sales have significantly grown over the years to fulfill growing demands for economic travel and greenhouse gas mitigation.1 However,the surge in the number of EVs has led to charging anxiety as users struggle to find an available charging station before running out of electricity,resulting in longer reserve and waiting times.2 Moreover,severe mobility restrictions caused by infectious diseases,such as coronavirus disease 2019(COVID-19),have greatly affected people’s travel behavior3,4 and hindered their willingness to use EVs,given that charging in public spaces consumes time and increases the risk of contracting the virus.5 This implies that in the postpandemic era,in which individuals coexist with the virus,the interplay between the two important trends,namely vehicle electrification and mobility restrictions,can extensively affect people’s daily commuting by using EVs.

关键词： travel behavior charging

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Assessment of International GNSS Service Global Ionosphere Map products over China region based on measurements from the Crustal Movement Observation Network of China

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earth and Planetary Physics 2024年第2期8卷 400-407页

作者： Jin Hu HaiBing Ruan FuQing Huang ShengYang Gu XianKang Dou institute of Space Weather Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Space Weather Chinese Academy of Sciences(CAS)Beijing 100190China School of Electronic information Wuhan UniversityWuhan 430072China Wuxi Research institute Nanjing University of Information Science and TechnologyWuxi Jiangsu 214000China CAS Key Laboratory of geospace Environment School of Earth and Space SciencesUniversity of Science and Technology of ChinaHefei 230026China

The global ionosphere maps(GIM)provided by the International GNSS Service(IGS)are extensively utilized for ionospheric morphology monitoring,scientific research,and practical *** the credibility of GIM products in data-sparse regions is of paramount *** this study,measurements from the Crustal Movement Observation Network of China(CMONOC)are leveraged to evaluate the suitability of IGS-GIM products over China region in *** indices of mean error(ME),root mean square error(RMSE),and normalized RMSE(NRMSE)are then utilized to quantify the accuracy of IGS-GIM *** revealed distinct local time and latitudinal dependencies in IGS-GIM errors,with substantially high errors at nighttime(NRMSE:39%)and above 40°latitude(NRMSE:49%).Seasonal differences also emerged,with larger equinoctial deviations(NRMSE:33.5%)compared with summer(20%).A preliminary analysis implied that the irregular assimilation of sparse IGS observations,compounded by China’s distinct geomagnetic topology,may manifest as error *** results suggest that modeling based solely on IGS-GIM observations engenders inadequate representations across China and that a thorough examination would proffer the necessary foundation for advancing regional total electron content(TEC)constructions.

关键词： International GNSS Service(IGS)Global Ionosphere Maps(GIM) Crustal Movement Observation Network of China(CMONOC) total electron content(TEC) data assessment

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Planet wind: Planetary evolution dominated by electromagnetic forces

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Science China earth Sciences 2024年第10期67卷 3331-3336页

作者： Yong WEI Key Laboratory of earth and Planetary Physics Institute of Geology and GeophysicsChinese Academy of SciencesBeijing 100029China College of earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100049China

1. Introduction Over the five centuries since Copernicus, the focus of planetary science has transitioned from the visible realm to the invisible. In the more than 60 years following the advent of the space age, our a... 详细信息

关键词： evolution dominated planetary

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Inversion of maize leaf nitrogen using UAV hyperspectral imagery in breeding fields

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International Journal of Agricultural and Biological Engineering 2024年第3期17卷 144-155页

作者： Qiwen Cheng Bingsun Wu Huichun Ye Yongyi Liang Yingpu Che Anting Guo Zixuan Wang Zhiqiang Tao Wenwei Li Jingjing Wang School of Tropical Agriculture and Forestry Hainan UniversityHaikou 570228China Rubber Research institute Chinese Academy of Tropical Agricultural SciencesHaikou 571101China Key Laboratory of earth Observation of Hainan Province Hainan Aerospace Information Research InstituteSanya 572029HainanChina Key Laboratory of Digital earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China institute of Crop Science Chinese Academy of Agricultural SciencesBeijing 100081China

Nitrogen(N)as a pivotal factor in influencing the growth,development,and yield of *** the N status of maize rapidly and non-destructive and real-time is meaningful in fertilization management of agriculture,based on unmanned aerial vehicle(UAV)remote sensing *** this study,the hyperspectral images were acquired by UAV and the leaf nitrogen content(LNC)and leaf nitrogen accumulation(LNA)were measured to estimate the N nutrition status of maize.24 vegetation indices(VIs)were constructed using hyperspectral images,and four prediction models were used to estimate the LNC and LNA of *** models include a single linear regression model,multivariable linear regression(MLR)model,random forest regression(RFR)model,and support vector regression(SVR)***,the model with the highest prediction accuracy was applied to invert the LNC and LNA of maize in breeding *** results of the single linear regression model with 24 VIs showed that normalized difference chlorophyll(NDchl)had the highest prediction accuracy for LNC(R^(2),RMSE,and RE were 0.72,0.21,and 12.19%,respectively)and LNA(R^(2),RMSE,and RE were 0.77,0.26,and 14.34%,respectively).And then,24 VIs were divided into 13 important VIs and 11 unimportant *** prediction models for LNC and LNA were constructed using 13 important VIs,and the results showed that RFR and SVR models significantly enhanced the prediction accuracy of LNC and LNA compared to the multivariable linear regression model,in which RFR model had the highest prediction accuracy for the validation dataset of LNC(R^(2),RMSE,and RE were 0.78,0.16,and 8.83%,respectively)and LNA(R^(2),RMSE,and RE were 0.85,0.19,and 9.88%,respectively).This study provides a theoretical basis for N diagnosis and precise management of crop production based on hyperspectral remote sensing in precision agriculture.

关键词： maize nitrogen hyperspectral imagery vegetation index UAV random forest regression support vector regression

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