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Discovering Changes in the Global Liquefied Natural Gas Marine Supply Network During the Russian-Ukrainian Crisis with Big Data

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Chinese Geographical Science 2024年

作者： XIAO Renrong ZHAO Pengjun ZHANG Mengzhu KANG Tingting MA Tianyu College of Resources and Environment Lanzhou University key laboratory of earth surface system and human-earth relations Ministry of Natural Resources of China School of Urban Planning and Design Peking University

The recent Russian-Ukrainian crisis has had a substantial impact on the global natural gas supply landscape and brought major challenges to liquified natural gas(LNG) supply chain safety in many countries. However, academic research in this field remains scarce. This study aims to analyze the global LNG marine supply network changes during the recent Russian-Ukrainian crisis. Big data analytics are applied to the global vessel trajectory information recorded by automatic identification system(AIS). The weighted degree index indicates that global LNG marine supply is still primarily led by a few exporting countries, and the Middle East Port of Ras Laffan now occupies the central position. In 2022, the betweenness centrality and other indicators of the LNG shipping network showed a high middle and low end, indicating that the network has a certain degree of resilience. After the Russian-Ukrainian crisis happened,LNG exports from Qatar, the United States, and Russia increased. Resource flows have reshaped the competitive standing of *** European LNG market witnessed a significant intensification in the export competition between Russia and the United States. Despite the ongoing crisis, Russia's LNG export competitiveness remained resilient and displayed no indications of weakening. The results show new evidence for the changes in global natural resources marine supply influenced by the geopolitical events among *** has important implications for policymaking of LNG supply safety to the countries that rely heavily on imports.

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Quick and automatic detection of co-seismic landslides with multifeature deep learning model

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Science China earth Sciences 2024年第7期67卷 2311-2325页

作者： Wenchao HUANGFU Haijun QIU Peng CUI Dongdong YANG Ya LIU Bingzhe TANG Zijing LIU Mohib ULLAH College of Urban and Environmental Sciences Northwest UniversityXi'an 710127China Institute of Mountain Hazards and Environment Chinese Academy of SciencesChengdu 610041China Shaanxi key laboratory of earth surface system and Environmental Carrying Capacity Northwest UniversityXi'an 710127China Institute of earth surface system and Hazards Northwest UniversityXi'an 710127China

Co-seismic landslide detection is essential for post-disaster rescue and risk assessment after an earthquake ***,a variety of ground objects,including roads and bare land,have spectral characteristics similar to those of co-seismic landslides,making it difficult to gather information and assess their impact rapidly and ***,an automatic detection method based on a deep learning model,named ENVINet5,with multiple features(ENVINet5_MF)was proposed to solve this problem and improve the detection accuracy of co-seismic *** ENVINet5_MF method is advantageous for co-seismic landslide detection because it features a landslide gain index(LGI)that effectively eliminates the spectral interference of bare land and *** conducted two experiments using multi-temporal PlanetScope images acquired in Hokkaido,Japan,and Mainling,*** accuracy evaluation and rationality analysis show that ENVINet5_MF performed better than comparative methods and that the co-seismic landslide areas detected by ENVINet5_MF were the most consistent with ground reference *** findings of this study suggest that ENVINet5_MF can provide an efficient and accurate method for coseismic landslide detection to ensure a rapid response to co-seismic landslide disasters.

关键词： Co-seismic landslide Automatic detection Deep learning Landslide gain index PlanetScope images

More Catastrophic Flow Events may Follow the Chamoli Rock and Ice Avalanche under Climate Change

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Journal of earth Science 2024年第4期35卷 1382-1384页

作者： Haijun Qiu Ya Liu Bingzhe Tang Ninglian Wang Jiading Wang Shaanxi key laboratory of earth surface system and Environmental Carrying Capacity College of Urban and Environmental SciencesNorthwest UniversityXi’an 710127China Institute of earth surface system and Hazards Northwest UniversityXi’an 710127China State key laboratory of Continental Dynamics Department of GeologyNorthwest UniversityXi’an 710069China

0 INTRODUCTION Global warming not only exacerbates the instability of the climate system,causing more weather extremes but also has far-reaching impacts on surface systems,changing disaster environments(Qiu et al.,2024;Ye et al.,2024).The key medium between climate warming and the mountain environment is the cryosphere,whose recession increases the occurrence of chain disasters such as rock-ice avalanches and other flow events(Yang et al.,2023).

关键词： warming disaster climate

Machine learning assisted adsorption performance evaluation of biochar on heavy metal

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Frontiers of Environmental Science & Engineering 2024年第5期18卷 29-42页

作者： Qiannan Duan Pengwei Yan Yichen Feng Qianru Wan Xiaoli Zhu Shaanxi key laboratory of earth surface system and Environmental Carrying Capacity College of Urban and Environmental SciencesNorthwest UniversityXi’an710127China Department of Environment Science Shaanxi Normal UniversityXi’an710062China

Heavy metals(HMs)represent pervasive and highly toxic environmental pollutants,known for their long latency periods and high toxicity levels,which pose significant challenges for their removal and ***,the removal of heavy metals from the environment is crucial to ensure the water *** materials,known for their intricate pore structures and abundant oxygen-containing functional groups,are frequently harnessed for their effectiveness in mitigating heavy metal ***,conventional tests for optimizing biochar synthesis and assessing their heavy metal adsorption capabilities can be both costly and *** address this challenge,this paper proposes a data-driven machine learning(ML)approach to identify the optimal biochar preparation and adsorption reaction conditions,with the ultimate goal of maximizing their adsorption *** utilizing a data set comprising 476 instances of heavy metal absorption by biochar,seven classical integrated models and one stacking model were trained to rapidly predict the efficiency of heavy metal adsorption by *** predictions were based on diverse physicochemical properties of biochar and the specific adsorption reaction *** results demonstrate that the stacking model,which integrates multiple algorithms,allows for training with fewer samples to achieve higher prediction accuracy and improved generalization ability.

关键词： Machine learning Biochar Heavy metal Adsorption efficiency

Impacts of land surface darkening on frozen ground and ecosystems over the Tibetan Plateau

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Science China earth Sciences 2024年第10期67卷 3213-3225页

作者： Shuchang TANG Tao WANG Dan LIU Tandong YAO Shilong PIAO Sino-French Institute for earth system Science College of Urban and Environmental Sciences Peking University State key laboratory of Tibetan Plateau earth system Resources and Environment Institute of Tibetan Plateau Research Chinese Academy of Sciences

Tibetan Plateau（TP） is known as the “Third Pole” of the earth. Any changes in land surface processes on the TP can have an unneglectable impact on regional and global climate. With the warming and wetting climate, the land surface of the TP saw a darkening trend featured by decreasing surface albedo over the past decades, primarily due to the melting of glaciers, snow,and greening vegetation. Recent studies have investigated the effects of the TP land surface darkening on the field of climate, but these assessments only address one aspect of the feedback loop. How do these darkening-induced climate changes affect the frozen ground and ecosystems on the TP? In this study, we investigated the impact of TP land surface darkening on regional frozen ground and ecosystems using the state-of-the-art land surface model ORCHIDEE-MICT. Our model results show that darkening-induced climate changes on the TP will lead to a reduction in the area of regional frozen ground by 1.1×10～4±0.019×10～4km2, a deepening of the regional permafrost active layer by 0.06±0.0004 m, and a decrease in the maximum freezing depth of regional seasonal frozen ground by 0.06±0.0016 m compared to the scenario without TP land surface ***, the darkening-induced climate change on the TP will result in an increase in the regional leaf area index and an enhancement in the regional gross primary productivity, ultimately leading to an increase in regional terrestrial carbon stock by0.81±0.001 PgC. This study addresses the remaining piece of the puzzle in the feedback loop of TP land surface darkening, and improves our understanding of interactions across multiple spheres on the TP. The exacerbated regional permafrost degradation and increasing regional terrestrial carbon stock induced by TP land surface darkening should be considered in the development of national ecological security barrier.

关键词： Tibetan Plateau Albedo Land surface darkening Frozen ground Ecosystem

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Analysis of amino acid enantiomers in ambient aerosols:Effects and removal of coexistent aerosol matrix

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Journal of Environmental Sciences 2024年第3期137卷 732-740页

作者： Ying Li Xiaoying Li Libin Wu Luhan Shi ShanWang Pingqing Fu Yingyi Zhang Senchao Lai The key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters Ministry of EducationSchool of Environment and EnergySouth China University of TechnologyGuangzhou 510006China Institute of surface-earth system Science School of Earth System ScienceTianjin UniversityTianjin 300072China now at Hong Kong University of Science and Technology Hong Kong 00852China

Amino acids(AAs)including D-and L-enantiomers are a group of organic nitrogen species in ambient *** to the low abundances of AAs(level of ng/m^(3))and the matrix effects by coexistent components,it is challenging to quantify AA enantiomers in ambient aerosols especially under pollution *** this study,we present an optimized method for analyzing AA enantiomers in atmospheric aerosol samples including a pretreatment process and the detection by high performance liquid chromatography coupled to a fuorescence detector(HPLC-FLD).Matrix effects caused by coexistent chemicals on AA enantiomers analysis in ambient aerosol samples were investigated especially for those collected in pollution *** results revealed that the determination of AA enantiomers is significantly affected by the coexistent organic carbon(as a proxy of organic matter)and water-soluble ion of NH_(4)^(+).To remove the matrix effects,we applied a pretreatment using the solid phase extraction column coupled with alkaline adjustment to sample *** pretreatment,18 AAs including 6 pairs of D-and L-enantiomers(i.e.,leucine,isoleucine,valine,alanine,serine,and aspartic acid)can be successfully separated and quantified in aerosol samples by *** recoveries are in the range of 67%-106%.This method was successfully applied to the urban aerosol samples from pollution and non-pollution periods for AA enantiomers *** suggest that the concentrations of D-AAs and the ratio of D-AA/L-AA are indicative of the contribution of bacterial sources and the infuence of biomass burning.

关键词： Amino acid enantiomers Atmospheric aerosol Organic carbon Ammonia Chiral ratio

Characteristics and influencing factors of CO_(2) emission from inland waters in China

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Science China earth Sciences 2024年第6期67卷 2034-2055页

作者： Ting ZHOU Xiaofeng WANG Zuolin XIAO Zhaoyin QING Xianxiang LI Jilong WANG Ziyi QUE Chongqing key laboratory of Carbon Cycling and Carbon Regulation in Mountainous Ecosystems Chongqing 401331China School of Geography and Tourism Chongqing Normal UniversityChongqing 401331China Chongqing Field Observation and Research Station of earth surface Ecological Processes in Three Gorges Reservoir Area Chongqing 405400China School of earth system Science Tianjin UniversityTianjin 300072China

Inland water bodies,being the most active biogeochemical cycle reactors,play a pivotal role in the global carbon cycle and CO_(2) *** study integrates existing observation dataset of CO_(2) flux(fCO_(2))in rivers,lakes and reservoirs in China,to elucidate their spatial-temporal patterns and key influencing factors and to reappraise the significance for regional carbon *** showed that,the fCO_(2) in rivers,lakes and reservoirs in China presented significant variability with large range of-379.3-4947.6,-160.1-785.0 and -74.0-1603.1 mg CO_(2)m-2h^(-1),*** median of the fCO_(2) in rivers was of 228.5 mg CO_(2)m^(-2)h^(-1),observably higher than these in lakes and reservoirs(26.0 and 28.3 mg CO_(2)m^(-2)h^(-1),respectively).The fCO_(2)in rivers and reservoirs exhibited similar decreasing trend from south to north as a result of universal climate restraint,the averaged fCO_(2) in Pearl River and Yangtze River basins showed much higher than that in Northeastern ***,the averaged fCO_(2) in the Mongolia-Xinjiang lake district and the Northeast lake district were higher than other lake districts,followed by the Qinghai-Xizang lake district,and the Eastern and Yungui lake district were generally low,contradicting the climatic *** water primary production enhancement resulting from human activities was the main driver of spatial variation in the fCO_(2) in ***,the fCO_(2) in rivers presented seasonal pattern with higher wet season than dry season,while opposite patterns were found in lakes and *** temperature,precipitation and water primary production were main ***,it showed pH was a key factor indicating the variability of the fCO_(2) levels either in rivers,lakes or in *** lakes,the fCO_(2) is closely linked to chlorophyll a(Chl-a)and dissolved oxygen(DO),whereas,the fCO_(2) in rivers is primarily associated with organic carbon(OC)and total nitrogen(TN),highlighting the dive

关键词： Rivers/lakes/reservoirs CO_(2) emission Spatio-temporal variability Influence factors Regional contribution

Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models

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Advances in Atmospheric Sciences 2024年第7期41卷 1326-1341页

作者： Lu LI Yongjiu DAI Zhongwang WEI Wei SHANGGUAN Nan WEI Yonggen ZHANG Qingliang LI Xian-Xiang LI School of Atmospheric Sciences Sun Yat-sen UniversityGuangzhou 510275China Southern Marine Science and Engineering Guangdong laboratory(Zhuhai) Guangzhou 510275China Guangdong Province key laboratory for Climate Change and Natural Disaster Studies Guangzhou 510275China Institute of surface-earth system Science School of Earth System ScienceTianjin UniversityTianjin 300072China College of Computer Science and Technology Changchun Normal UniversityChangchun 130123China

Accurate soil moisture(SM)prediction is critical for understanding hydrological ***-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface *** addition to PB models,deep learning(DL)models have been widely used in SM predictions ***,few pure DL models have notably high success rates due to lacking physical ***,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM *** this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast system to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM *** performances of the proposed hybrid models were investigated and compared with two existing hybrid *** results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid ***,the ensemble model performed best among all hybrid models at all forecast time scales and different soil *** is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day *** findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.

关键词： soil moisture forecasting hybrid model deep learning ConvLSTM attention mechanism

Interpreting Larix principis-rupprechtii radial growth in northern China based on the Vaganov-Shashkin model

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Journal of Forestry Research 2024年第3期35卷 99-110页

作者： Jiachuan Wang Shuheng Li Qiang Li Yili Guo Yijie Han Qi Liu Yiqi Zhao College of Urban and Environmental Sciences Northwest UniversityXi'an 710127People's Republic of China Institute of Global Environmental Change Xi'an Jiaotong UniversityXi'an 710049People's Republic of China Shaanxi key laboratory of earth surface system and Environmental Carrying Capacity Northwest UniversityXi'an 710127People's Republic of China State key laboratory of Loess and Quaternary Geology Institute of Earth EnvironmentChinese Academy of SciencesXi'an 710061People's Republic of China

Changes in annual radial growth is an important indication of climate change. Dendroclimatology studies in northern China have focused on linear statistical analysis,but lacking studies based on the process of ring formation to clarify the radial growth of trees. Tree-ring width standard chronology(STD) was established using samples of Larix principis-rupprechtii collected at 2303 m altitude on Luya Mountain. Using the Vaganov-Shashkin(VS) model to simulate growth and development, the internal physiological mechanism of radial growth is identified. It was concluded that:(1) the growing season of L. principis-rupprechtii was May to September;(2) soil moisture was a significant factor in the early and late growing seasons, and temperature was the dominant factor in its main growth period;and(3) formation of narrow ring widths was closely related to drought stress, the development of wide ring widths will be restricted by increasing future temperatures. The VS model is applicable for radial growth simulation of subalpine coniferous forests and for guiding the cultivation of local tree species in the future.

关键词： Vaganov-Shashkin model Larix principis-rupprechti Tree-ring width Annual growth