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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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重返月球-月球探测新视角（英文）

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Science Bulletin 2024年第13期 2136-2148页

作者：林杨挺杨蔚张辉惠鹤九胡森肖龙刘建忠肖智勇岳宗玉张金海刘洋杨晶林红磊张爱铖郭弟均芶盛徐琳何雨旸张贤国秦礼萍凌宗成李雄耀杜爱民贺怀宇张鹏曹晋滨李献华 Key Laboratory of earth and Planetary Physics Institute of Geology and Geophysics Chinese Academy of Sciences State Key Laboratory for Mineral Deposits Research and school of earth sciences and Engineering Nanjing University State Key Laboratory of Geological Processes and Mineral Resources School of Earth Sciences China University of Geosciences institute of Geochemistry Chinese Academy of Sciences Planetary environmental and Astrobiological Research Laboratory School of Atmospheric Sciences Sun Yat-sen University National space Science Center Chinese Academy of Sciences Deep space Exploration Laboratory/CAS Key Laboratory of Crust-Mantle Materials and environments University of Science and Technology of China Shandong Key Laboratory of Optical Astronomy and Solar-Terrestrial environment Institute of Space Sciences Shandong University Technology and Engineering Center for space Utilization Chinese Academy of Sciences school of space and environment Beihang University

Lunar exploration is deemed crucial for uncovering the origins of the earth-Moon system and is the first step for advancing humanity's exploration of deep space. Over the past decade, the Chinese Lunar Exploration Program(CLEP), also known as the Chang'e(CE) Project, has achieved remarkable *** has successfully developed and demonstrated the engineering capability required to reach and return from the lunar surface. Notably, the CE Project has made historic firsts with the landing and on-site exploration of the far side of the Moon, along with the collection of the youngest volcanic samples from the Procellarum KREEP Terrane. These achievements have significantly enhanced our understanding of lunar evolution. Building on this success, China has proposed an ambitious crewed lunar exploration strategy,aiming to return to the Moon for scientific exploration and utilization. This plan encompasses two primary phases: the first crewed lunar landing and exploration, followed by a thousand-kilometer scale scientific expedition to construct a geological cross-section across the lunar surface. Recognizing the limitations of current lunar exploration efforts and China's engineering and technical capabilities, this paper explores the benefits of crewed lunar exploration while leveraging synergies with robotic exploration. The study refines fundamental lunar scientific questions that could lead to significant breakthroughs, considering the respective engineering and technological requirements. This research lays a crucial foundation for defining the objectives of future lunar exploration, emphasizing the importance of crewed missions and offering insights into potential advancements in lunar science.

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A new implementation of FLEXPART with Enviro-HIRLAM meteorological input,and a case study during a heavy air pollution event

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Big earth Data 2024年第2期8卷 397-434页

作者： Benjamin Foreback Alexander Mahura Petri Clusius Carlton Xavier Metin Baykara Putian Zhou Tuomo Nieminen Victoria Sinclair Veli-Matti Kerminen Tom V.Kokkonen Simo Hakala Diego Aliaga Risto Makkonen Alexander Baklanov Roman Nuterman Men Xia Chenjie Hua Yongchun Liu Markku Kulmala Pauli Paasonen Michael Boy Aerosol and Haze Laboratory Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijingChina institute for Atmospheric and earth System Research/Physics Faculty of ScienceUniversity of HelsinkiHelsinkiFinland Division of Nuclear Physics Department of PhysicsLund UniversityLundSweden Swedish Meteorological and Hydrological institute(SMHI) Research DepartmentUnit of Meteorology/Environment and ClimateNorrköpingSweden Climate and Marine sciences Department Eurasia Institute of Earth SciencesIstanbul Technical UniversityIstanbulTurkey Department of Physics Faculty of ScienceUniversity of HelsinkiHelsinkiFinland Department of Meteorology(MISU)and Bolin Centre for Climate Research Stockholm UniversityStockholmSweden Climate System Research Unit Finnish Meteorological InstituteHelsinkiFinland Science and Innovation Department World Meteorological Organization(WMO)GenevaSwitzerland Niels Bohr institute University of CopenhagenCopenhagenDenmark Joint International Research Laboratory of Atmospheric and earth System sciences School of Atmospheric SciencesNanjing UniversityNanjingChina school of Engineering Science Lappeenranta-Lahti University of Technology(LUT)LappeenrantaFinland Atmospheric Modelling Centre Lahti Lahti University CampusLahtiFinland

We integrated Enviro-HIRLAM(environment-High Resolution Limited Area Model)meteorological output into FLEXPART(FLEXible PARTicle dispersion model).A FLEXPART simulation requires meteorological input from a numerical weather prediction(NWP)*** publicly available version of FLEXPART can utilize either ECMWF(European Centre for Medium-range Weather Forecasts)Integrated Forecast System(IFS)forecast or reanalysis NWP data,or NCEP(*** Center for environmental Prediction)Global Forecast System(GFS)forecast or reanalysis NWP *** primary benefits of using Enviro-HIRLAM are that it runs at a higher resolution and accounts for aerosol effects in meteorological *** compared backward trajectories gener-ated with FLEXPART using Enviro-HIRLAM(both with and without aerosol effects)to trajectories generated using NCEP GFS and ECMWF IFS meteorological inputs,for a case study of a heavy haze event which occurred in Beijing,China in November *** found that results from FLEXPART were considerably different when using different meteorological *** aerosol effects were included in the NWP,there was a small but noticeable differ-ence in calculated ***,when looking at potential emission sensitivity instead of simply expressing trajectories as lines,additional information,which may have been missed when looking only at trajectories as lines,can be inferred.

关键词： Atmospheric and chemical transport modelling trajectory and particle dispersion modelling severe air pollution episode FLEXPART Enviro-HIRLAM

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HiFAST:An H?data calibration and imaging pipeline for FAST

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Science China(Physics,Mechanics & Astronomy) 2024年第5期Mechanics & Astronomy) .卷 186-203页

作者： Yingjie Jing Jie Wang Chen Xu Ziming Liu Qingze Chen Tiantian Liang Jinlong Xu Yixian Cao Jing Wang Huijie Hu Chuan-Peng Zhang Qi Guo Liang Gao Mei Ai Hengqian Gan Xuyang Gao Jinlin Han Ligang Hou Zhipeng Hou Peng Jiang Xu Kong Fujia Li Zerui Liu Li Shao Hengxing Pan Jun Pan Lei Qian Jinghai Sun Ningyu Tang Qingliang Yang Bo Zhang Zhiyu Zhang Ming Zhu National Astronomical Observatories Chinese Academy of Sciences institute for Frontiers in Astronomy and Astrophysics Beijing Normal University school of Astronomy and space Science University of Chinese Academy of Sciences Guizhou Radio Astronomical Observatory Guizhou University Max Planck institute for Extraterrestrial Physics Kavli institute for Astronomy and Astrophysics Peking University institute of Computational Cosmology Department of PhysicsUniversity of Durham CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of Sciences Deep space Exploration Laboratory/Department of Astronomy University of Science and Technology of China school of Astronomy and space Science University of Science and Technology of China Astrophysics Department of PhysicsUniversity of Oxford College of earth sciences Guilin University of Technology Department of Physics Anhui Normal University school of Astronomy and space Science Nanjing University Key Laboratory of Modern Astronomy and Astrophysics Nanjing University

The Five-hundred-meter Aperture Spherical radio Telescope（FAST） has the largest aperture and a 19-beam L-band receiver,making it powerful for investigating the neutral hydrogen atomic gas（H?） in the *** present HiFAST（https://***）,a dedicated,modular,and self-contained calibration and imaging pipeline for processing the H?data of *** pipeline consists of frequency-dependent noise diode calibration,baseline fitting,standing wave removal using an FFT-based method,flux density calibration,stray radiation correction,and gridding to produce data *** modules can be combined as needed to process the data from most FAST observation modes:tracking,drift scanning,On-The-Fly mapping,and most of their *** HiFAST,the root-mean-square（RMS） noises of the calibrated spectra from all 19 beams were only slightly（～5%） higher than the theoretical *** results for the extended source M33 and the point sources are consistent with the results from *** moment maps（0,1 and 2） of M33 agree well with the results from the Arecibo Galaxy environment Survey（AGES） with a fractional difference of less than 10%.For a common sample of 221 sources with signal-to-noise ratio S/N>10 from the Arecibo Legacy Fast ALFA（ALFALFA） survey,the mean value of fractional difference in the integrated flux density,Sint,between the two datasets is approximately 0.005%,with a dispersion of 15.4%.Further checks on the integrated flux density of 23 sources with seven observations indicate that the variance in the flux density of the source with luminous objects(Sint>2.5 Jy km s-1) is less than 5%.Our tests suggest that the FAST telescope,with the efficient,precise,and user-friendly pipeline HiFAST,will yield numerous significant scientific findings in the investigation of the H? in the universe.

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Trends and drivers of anthropogenic NOx emissions in China since 2020

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environmental Science and Ecotechnology 2024年第5期21卷 287-293页

作者： Hui Li Bo Zheng Yu Lei Didier Hauglustaine Cuihong Chen Xin Lin Yi Zhang Qiang Zhang Kebin He Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration Institute of Environment and EcologyTsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China State environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing 100084China State environmental Protection Key Laboratory of environmental Planning and Policy Simulation and Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental PlanningBeijing 100041China Laboratoire des sciences du Climat et de l’Environnement LSCE/IPSLCEA-CNRS-UVSQUniversite Paris-SaclayGif-sur-YvetteFrance Center for Satellite Application on Ecology and environment Ministry of Ecology and Environment of ChinaBeijing 100094China institute of Future Human Habitats Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China Ministry of Education Key Laboratory for earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084China State Key Joint Laboratory of environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China

Nitrogen oxides(NOx),significant contributors to air pollution and climate change,form aerosols and ozone in the ***,timely,and transparent information on NOx emissions is essential for decision-making to mitigate both haze and ozone ***,a comprehensive understanding of the trends and drivers behind anthropogenic NOx emissions from Chinadthe world's largest emitterdhas been lacking since 2020 due to delays in emissions *** we show a consistent decline in China's NOx emissions from 2020 to 2022,despite increased fossil fuel consumption,utilizing satellite observations as constraints for NOx emission estimates through atmospheric *** reduction is corroborated by data from two independent spaceborne instruments:the TROPOspheric Monitoring Instrument(TROPOMI)and the Ozone Monitoring Instrument(OMI).Notably,a reduction in transport emissions,largely due to the COVID-19 lockdowns,slightly decreased China's NOx emissions in *** subsequent years,2021 and 2022,reductions in NOx emissions were driven by the industry and transport sectors,influenced by stringent air pollution *** satellite-based inversion system developed in this study represents a significant advancement in the real-time monitoring of regional air pollution emissions from space.

关键词： China's NOx emissions Pollution control Socio-economic drivers Atmospheric inversion

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微塑料和纳米塑料威胁青藏高原环境

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Science Bulletin 2024年第5期69卷 589-592页

作者：张玉兰康世昌罗犀 Tanuj Shukla 高坛光 Deonie Allen Steve Allen Melanie Bergmann State Key Laboratory of Cryospheric Science Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhou 730000China College of earth and environmental sciences Lanzhou UniversityLanzhou 730000China school of Geography Earth and Environmental SciencesUniversity of BirminghamBirmingham 200003UK Ocean Frontiers institute Dalhousie UniversityHalifax B3H 4R2Canada Alfred-Wegener-Institut Helmholtz-Zentrum für polar-und Meeresforschung Bremerhaven D-27515Germany University of Chinese Academy of sciences Beijing 100049China

Plastic pollution is a planetary level threat which affects earth’s environment and *** the poles to deep ocean basins,the growth of plastic waste has already exceeded its *** projected increase of plastic production and waste generation over the coming years makes the situation even more *** after stagnation in 2020 due to the COVID-19 pandemic,the global plastic production has also increased from 335 to 391 million metric tons(Mt)between 2016 and 2021(Fig.S1 online);yet currently post-consumer recycled and bio-based/attributed plastics only accounts~9%of the world’s plastic production[1].Statistically.

关键词：微塑料 attributed plastics

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Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval

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Acta Oceanologica Sinica 2024年第7期43卷 93-111页

作者： Tianyu Zhang Mohammed Shokr Zhida Zhang Fengming Hui Xiao Cheng Zhilun Zhang Jiechen Zhao Chunlei Mi school of Geospatial Engineering and Science Sun Yat-Sen University/Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519082China Key Laboratory of Comprehensive Observation of polar environment(Sun Yat-Sen University) Ministry of EducationZhuhai 519082China State Key Laboratory of Remote Sensing Science College of Global Change and Earth System ScienceBeijing Normal UniversityBeijing 100875China Science and Technology Branch Environment and Climate Change CanadaToronto M3H5T4Canada Qingdao Innovation and Development Base(Centre) Harbin Engineering UniversityQingdao 266500China State Key Laboratory of Resources and environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China University of Chinese Academy of sciences Beijing 100049China

Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input *** article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice ***,it estimates the uncertainty of the output in response to the uncertainties of the input *** parameterized independent variables include atmospheric longwave emissivity,air density,specific heat of air,latent heat of ice,conductivity of ice,snow depth,and snow *** input parameters include air temperature,ice surface temperature,and wind *** the independent variables,the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth,followed ice *** sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity,atmospheric emissivity,and snow conductivity and *** retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved *** in situ measurements,the uncertainties of the measured air temperature and surface temperature are found to be *** resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement *** results show that the overall uncertainty of TIT to air temperature,surface temperature,and wind speed uncertainty is around 0.09 m,0.049 m,and−0.005 m,respectively.

关键词： Arctic sea ice 1-D thermodynamic ice model thin-ice thickness sea ice parameterization

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research institute Chinese Academy of SciencesBeijing 100094China school of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China institute of Computing Technology Chinese Academy of SciencesBeijing 100190China school of Geographical sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric environment Monitoring and Pollution Control Nanjing 210044China school of environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of earth’s complexities and further advance geoscience exploration.

关键词： earth utilizing landscape

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全球湖泊水生植被高分辨率遥感制图研究（英文）

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Science Bulletin 2024年第19期 3115-3126页

作者：侯雪姣刘金英黄华兵张运林刘冲宫鹏 International Research Center of Big Data for Sustainable Development Goals school of Geospatial Engineering and Science Sun Yat-sen University Key Laboratory of Comprehensive Observation of polar environment (Sun Yat-sen University) Ministry of Education Taihu Laboratory for Lake Ecosystem Research State Key Laboratory of Lake Science and Environment Nanjing Institute of Geography and Limnology Chinese Academy of Sciences University of Chinese Academy of sciences University of Chinese Academy of sciences Nanjing Department of Geography and Department of Earth Sciences The University of Hong Kong

Aquatic vegetation is crucial for improving water quality,supporting fisheries and preserving biodiversity in *** the spatiotemporal dynamics of aquatic vegetation is indispensable for the assessment and protection of lake ***,a comprehensive global assessment of lacustrine aquatic vegetation is *** study introduces an automatic identification algorithm (with a total accuracy of 94.4%) for Sentinel-2 MSI,enabling the first-ever global mapping of aquatic vegetation distribution in 1.4 million lakes using 14.8 million images from 2019 to *** show that aquatic vegetation occurred in 81,116 lakes across six continents over the past four years,covering a cumulative maximum aquatic vegetation area (MVA) of 16,111.8 *** global median aquatic vegetation occurrence (VO,in%) is 3.0%,with notable higher values observed in South America (7.4%) and Africa (4.1%)compared with Asia (2.7%) and North America (2.4%).High VO is also observed in lakes near major rivers such as the Yangtze,Ob,and Paraná*** historical data with our calculated MVA,the aquatic vegetation changes in 170 lakes worldwide were *** shows that 72.4%(123/170) of lakes experienced a decline in aquatic vegetation from the early 1980s to 2022,encompassing both submerged and overall aquatic *** most substantial decrease is observed in Asia and *** findings suggest that,beyond lake algal blooms and temperature,the physical characteristics of the lakes and their surrounding environments could also influence aquatic vegetation *** research provides valuable information for the conservation and restoration of lacustrine aquatic vegetation.

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Change of Organic δ^(13)C in Ornithogenic Sediments of the Xisha Archipelago,South China Sea and its Implication for Ecological Development

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Acta Geologica Sinica(English Edition) 2017年第3期91卷 1109-1119页

作者： WU Libin LIU Xiaodong XU Liqiang institute of polar environment School of Earth and Space SciencesUniversity of Science and Technology of China school of Resources and environmental Engineering Hefei University of Technology

Abstract： Organic carbon isotopes in sediments have been frequently used to identify the source of organic matter. Here we present a study of organic δ13C on two sediment profiles influenced by guano from Guangjin and Jinqing islands in the Xisha Archipelago, South China Sea. Organic matter from ornithogenic coral sand sediments has two main sources, guano pellets and plant residues, and their organic δ13C （δ13CoM） are significantly different. Organic carbon δ13Cguano is much higher than δ13Cplant, and δ13CoM of bulk samples is intermediate. Based on a two-end-member mixing model, the proportions of guano- and plant-derived organic matter in the bulk samples were reconstructed quantitatively. The results showed that seabirds began to inhabit the islands around approximately 1200-1400 AD, and that guano pellets have been an important source of soil organic matter since then. With the accumulation of guano-derived nutrients, plants began to develop prosperously on the islands in the last 200 years, which is reflected by the significant increase of plant-derived organic matter in the upper sediment layer. However, guano-derived organic matter decreased greatly in recent decades, indicating a rapid decrease in seabird population. Our results show that organic 6~3C can be effectively used to quantitatively determine different source contributions of OM to bulk ornithogenic coral sand sediments.

关键词： organic matter δ13C guano plant end-member analysis

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