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A review on global spatial distribution,sources and toxicity of perfluoroalkyl acid and prospect in the cryosphere

Journal of Mountain Science 2025年第1期22卷 167-183页

作者： LI Bowen Du Wentao KANG Shichang CHEN Jizu JIANG Youyan YIN Lichen LU Changsheng LIU Hongyi State Key Laboratory of Cryospheric Science and frozen soil engineering Northwest Institute of Eco-Environment and ResourcesChinese Academy of Sciences(CAS)Lanzhou 730000China University of Chinese Academy of Sciences Beijing 100049China Gansu Provincial Industry Technology Center of Intelligent Equipment and Big Data for Disaster Prevention Northwest Institute of Eco-Environment and ResourcesChinese Academy of Sciences(CAS)Lanzhou 730000China Lanzhou Regional Climate Center Lanzhou 730000China School of Earth Sciences and Spatial Information Engineering Hunan University of Science and TechnologyXiangtan 411201China School of Energy and Power Engineering Lanzhou University of TechnologyLanzhou 730000China Zhangye Meteorological Service Zhangye 734000China

Perfluoroalkyl acid analogs(PFAAs)are a class of chemically stable environmentally persistent organic pollutants(POPs)that are difficult to degrade and have a strong capacity to accumulate in the human *** have been found to be biotoxic to humans and have been detected in various environmental media,especially in the cryosphere at trace *** cryosphere,sensitively responds to climate change,plays a crucial role in the global water,carbon and energy ***,researches on cryosphere PFAAs especially in Tibetan Plateau(TP)is ***,we summarize the physicochemical properties,physiological toxicity,spatiotemporal distribution,sources,diffusion and migration pathways,as well as analysis and removal methods of PFAAs in the cryosphere *** results show that PFAAs pollutants are mainly produced and distributed in the more economically developed countries in Europe and the United States,as well as in East Asia,and PFAAs can be transported by atmospheric circulation and water cycle to remote regions including cryosphere *** current detection methods for PFAAs in cryosphere need to be further refined for increased accuracy and *** is also a need to develop more effective removal methods that will reduce the environmental and human threats posed by these ***,we propose key scientific questions for future research in cryosphere including PFAAs redistribution influenced by cryosphere changes,human activities,and the interaction of other spheres.

关键词： Perfluoroalkyl acid Cryosphere Physiological toxicity Analytical method Removal method

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Provincial anthropogenic emission inventory of carbonyl sulfide (COS) in China from 2015 to 2021

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Journal of geographical Sciences 2025年第5期35卷 923-940页

作者： Jiang, Yunhao Chen, Bin Wang, Shaoqiang Li, Tingyu Chen, Shiliang Wang, Lunche Wang, Lizhe School of Future Technology China University of Geosciences Wuhan China Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information Engineering China University of Geosciences Wuhan China Engineering Technology Innovation Center for Intelligent Monitoring and Spatial Regulation of Land Carbon Sinks Ministry of Natural Resources Wuhan China Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research CAS Beijing China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan China

Carbonyl sulfide (COS) is an effective tracer for estimating Gross Primary Productivity (GPP) in the carbon cycle. As the largest contribution to the atmosphere, anthropogenic COS emissions must be accurately quantified. In this study, an anthropogenic COS emission inventory from 2015 to 2021 was constructed by applying the bottom-up approach based on activity data from emission sources. China’s anthropogenic COS emissions increased from approximately 171 to 198 Gg S yr−1 from 2015–2021, differing from the trends of other pollutants. Despite an initial decline in COS emissions across sectors during the early stage of the COVID-19 pandemic, a rapid rebound in emissions occurred following the resumption of economic activities. In 2021, industrial sources, coal combustion, agriculture and vehicle exhaust accounted for 76.8%, 12.3%, 10.5% and 0.4% of total COS emissions, respectively. The aluminum industry was the primary COS emitter among industrial sources, contributing 40.7% of total emissions. Shandong, Shanxi, and Zhejiang were the top three provinces in terms of anthropogenic COS emissions, reaching 39, 21 and 17 Gg S yr−1, respectively. Provincial-level regions (hereafter province) with high COS emissions are observed mainly in the eastern and coastal regions of China, which, together with the wind direction, helps explain the pattern of high COS concentrations in the Western Pacific Ocean in winter. The Green Contribution Coefficient of COS (GCCCOS) was used to assess the relationship between GDP and COS emissions, highlighting the disparity between GDP and COS contributions to green development. As part of this analysis, relevant recommendations are proposed to address this disparity. The COS emission inventory in our study can be used as input for the Sulfur Transport and Deposition Model (STEM), reducing uncertainties in the atmospheric COS source–sink budget and promoting understanding of the atmosphere sulfur cycle.

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Monitoring Changes to Small-Sized Lakes Using Medium spatial and Temporal Satellite Imagery in the Badain Jaran Desert from 2015 to 2020

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International Journal of Crowd Science 2025年第2期9卷 96-109页

作者： Qinyu Zhao Luyan Ji Yonggang Su Kai Yu Yongchao Zhao College of Electronic and Information Engineering Hebei University Baoding China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial information Processing and Application System Beijing China Key Laboratory of Target Cognition and Application Technology (TCAT) Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

The Badain Jaran Desert is the second-largest desert in China, and its lakes, which are generally small-sized and highly dynamic, play a significant role for plants and animals in this arid region. Therefore, long-term monitoring of the distribution of lakes in the Badain Jaran Desert with high spatial and temporal resolution is of great importance. However, due to the tradeoff between pixel size and swath width, currently no single satellite sensor can provide such a time series. Thereby, in this study, we focus on applying the deep learning based spatiotemporal fusion method (super-resolution based spatial fusion with Generative Adversarial Network (GAN)) to a low spatial yet high temporal resolution data (i.e., MODIS 250 m daily reflectance time series) and a high spatial yet low temporal resolution data (i.e., Landsat 30 m 16-day reflectance time series) to generate a daily 30 m time series for 37 selected lakes in the Badain Jaran Desert. Then, an automatic water extraction algorithm is proposed, and a daily 30 m water mapping production is generated for our study area from 2015 to 2020. The overall accuracy can reach 0.92, while the average error of lake areas is less than 9.21%, which is much higher than that derived from the MODIS time series. Moreover, based on our daily high spatial resolution results, it is possible to analyze the water phenology for all sizes of lakes in the Badain Jaran Desert. We have performed a detailed analysis of interannual variability and seasonal changes for the selected 37 lakes in the Badain Jaran Desert. The results show that from 2015 to 2020, the shrinkage of the small lakes (<0.5 km 2 ) is more severe than lakes with a larger size. As for seasonal changes, the lake area can be divided into four stages: quick increase due to ice melting from winter to spring, slow decrease due to evaporation from spring to summer, moderate recovery due to the arrival of the rainy season from summer to autumn, and quick decrease due to lake

关键词： Reflectivity Heuristic algorithms Time series analysis Landsat Lakes Deserts Spatiotemporal phenomena spatial resolution MODIS Monitoring

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Investigation on the damage accumulation mechanisms of landslides in earthquake-prone area: Role of loading-unloading cycles

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geohazard Mechanics 2025年第1期3卷 59-72页

作者： Zhu, Ling Luo, Luguang Cui, Shenghua He, Zhihao Wang, Hui Zhang, Liangxi Kong, Decai School of Geosciences and Info-Physics Central South University Changsha 410083 China Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring Central South University Ministry of Education Changsha 410083 China School of Earth Sciences and Spatial Information Engineering Hunan University of Science and Technology Xiangtan 411201 China Faculty of Geo-Information Science and Earth Observation (ITC) University of Twente Enschede 7500 AE Netherlands State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology Chengdu 610059 China School of Emergency Management Xihua University Chengdu 610039 China The 4th Geological Brigade of Sichuan Chengdu 611130 China

Investigating rock damage behavior is crucial for understanding the formation mechanisms of fractured slopes in earthquake-prone areas. However, the current understanding of the nonlinear damage processes and mechanisms of rocks under cyclic loading is insufficient. This study investigated the damage behaviors of metamorphic sandstone, granite, and phyllite under cyclic loading using acoustic emission (AE), infrared thermal imaging, and digital image correlation (DIC) techniques. The experimental results demonstrated that the damage variables based on AE counts, infrared radiation temperature variance (IRTV), and surface deformation variance (SDV) increased with increasing cycles and stress levels. The temperature variation was influenced by lithology and the types of original pores and microcracks. The lag ratio and average lag time of the SDV effectively evaluated the progressive damage process. Specific damage mechanisms were identified, including the “compaction-embedment effect” in metamorphic sandstone, the “crystal incompatible deformation-fracture effect” in granite, and the “defective fracture effect” in phyllite. © 2024 The Author(s)

关键词： AE Fractured slopes Infrared radiation Rock damage Surface deformation

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Using total Kalman filter to extract time-varying signals in PWV time series

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Advances in Space Research 2025年

作者： Zhu, Dantong Zhang, Kefei Sun, Peng Wu, Suqin Hu, Qingfeng He, Peipei Yin, Weibo Wang, Yafei Liu, Junguo College of Surveying and Geo-Informatics North China University of Water Resources and Electric Power Zhengzhou450000 China School of Environment Science and Spatial Informatics China University of Mining and Technology Xuzhou361000 China Henan Provincial Key Laboratory of Hydrosphere and Watershed Water Security Zhengzhou450000 China

There has been considerable research in the literature focused on the extraction and analysis of trends and periodic signals in historical precipitable water vapor (PWV) time series. The conventional approach uses an ordinary least-square estimator (OLS) to resolve a time-constant harmonic model with constant trend, amplitude, and phase. However, due to various climatic factors, PWV signals may contain time-varying fluctuations, which should also be contained in signals. In this paper, we propose a novel time-varying harmonic model accounting for fluctuations in both trend and periodic signals. For this, the novel model is expressed in a state-space form and resolved by the Total Kalman Filter (TKF), along with an estimation of the noise parameters. This approach is referred to as the TKF-based approach. The performance of the novel TKF-based approach is demonstrated using homogeneous PWV time series from 2000 to 2018 over 91 GNSS stations and compared with a time-varying model resolved by Kalman smoother (KS) and the time-constant model resolved by OLS. Results show that the newly proposed TKF-based approach can effectively identify time-varying signals, yielding Gaussian-like modeling residuals. Furthermore, the mean standard deviations of the residuals are 4.27 and 3.92 mm for OLS-based and KS-based approaches. In contrast, the value of the new approach is 3.04 mm, indicating a 29 % and 22 % reduction relative to the aforementioned approaches. Finally, for climatical interpretations, an analysis of the correlation between El Niño-Southern Oscillation (ENSO) and time-varying signals over TOW2 suggests ENSO as one contributor to time-varying signals. © 2025 COSPAR

关键词： Image coding

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Bell State Preparation Based on Switching Between Quantum System Models

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Journal of Systems Science & Complexity 2017年第2期30卷 347-356页

作者： ZHOU Juan KUANG Sen CONG Shuang Department of Automation University of Science and Technology of ChinaHefei 230027China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science

For the preparation of any target Bell state under continuous quantum measurement, this paper proposes a method which achieves the control objective by switching between two different models or by switching between two control channels under one model. Proper control Hamiltonians are selected for the two system models, a switching strategy between the two models is designed, and the stability of the whole switching system is proved in theory. For a given target Bell state, the effectiveness of the proposed switching control strategy between different models is illustrated through simulation experiments.

关键词： Bell state model switching quantum systems switching strategy.

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Provincial anthropogenic emission inventory of carbonyl sulfide (COS) in China from 2015 to 2021

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Journal of geographical Sciences 2025年第05期 923-945页

作者： JIANG Yunhao CHEN Bin WANG Shaoqiang LI Tingyu CHEN Shiliang WANG Lunche WANG Lizhe School of Future Technology China University of Geosciences Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information EngineeringChina University of Geosciences Engineering Technology Innovation Center for Intelligent Monitoring and Spatial Regulation of Land Carbon Sinks Ministry of Natural Resources Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchCAS State Key Laboratory of Biogeology and Environmental Geology China University of Geosciences Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences

Carbonyl sulfide （COS） is an effective tracer for estimating Gross Primary Productivity （GPP） in the carbon *** the largest contribution to the atmosphere,anthropogenic COS emissions must be accurately *** this study,an anthropogenic COS emission inventory from 2015 to 2021 was constructed by applying the bottom-up approach based on activity data from emission ***’s anthropogenic COS emissions increased from approximately 171 to 198 Gg S yr-1from 2015–2021,differing from the trends of other *** an initial decline in COS emissions across sectors during the early stage of the COVID-19 pandemic,a rapid rebound in emissions occurred following the resumption of economic *** 2021,industrial sources,coal combustion,agriculture and vehicle exhaust accounted for 76.8%,12.3%,10.5%and 0.4%of total COS emissions,*** aluminum industry was the primary COS emitter among industrial sources,contributing40.7%of total ***,Shanxi,and Zhejiang were the top three provinces in terms of anthropogenic COS emissions,reaching 39,21 and 17 Gg S yr-1,***-level regions （hereafter province） with high COS emissions are observed mainly in the eastern and coastal regions of China,which,together with the wind direction,helps explain the pattern of high COS concentrations in the Western Pacific Ocean in *** Green Contribution Coefficient of COS (GCCCOS) was used to assess the relationship between GDP and COS emissions,highlighting the disparity between GDP and COS contributions to green *** part of this analysis,relevant recommendations are proposed to address this *** COS emission inventory in our study can be used as input for the Sulfur Transport and Deposition Model （STEM）,reducing uncertainties in the atmospheric COS source?sink budget and promoting understanding of the atmosphere sulfur cycle.

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Advancing diurnal analysis of vegetation responses to drought events in the Yangtze River Basin using next-generation satellite data

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Science of the Total Environment 2025年 959卷 178269页

作者： Li, Tingyu Wang, Shaoqiang Deng, Zhuoying Chen, Jinghua Chen, Bin Liang, Zhewei Chen, Xuan Jiang, Yunhao Gu, Peng Sun, Leigang Hubei Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information Engineering China University of Geosciences Hubei Wuhan China Engineering Technology Innovation Center for Intelligent Monitoring and Spatial Regulation of Land Carbon Sinks Ministry of Natural Resources Hubei Wuhan China Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research CAS Beijing China Hebei Academy of Sciences Institute of Geographical Sciences Hebei Shijiazhuang China

Extreme climate events, particularly droughts, pose significant threats to vegetation, severely impacting ecosystem functionality and resilience. However, the limited temporal resolution of current satellite data hinders accurate monitoring of vegetation's diurnal responses to these events. To address this challenge, we leveraged the advanced satellite ECOSTRESS, combining its high-resolution evapotranspiration (ET) data with a LightGBM model to generate the hourly continuous ECOSTRESS-based ET (HC-ETECO) for the middle and lower reaches of the Yangtze River Basin (YRB) from 2015 to 2022. This dataset showed strong agreement with both ground-based and satellite observations. Utilizing the SPEI, we identified the significant drought period: September to November 2019 and August to September 2022. By integrating hourly Solar-Induced Chlorophyll Fluorescence (SIF) data, we observed that during drought period, the typical afternoon peak in SIF was absent. In contrast to non-drought period, morning photosynthesis and SIF-based Water Use Efficiency (WUESIF) anomalies were primarily driven by high Vapor Pressure Deficit (VPD), while the afternoon reductions were influenced by both high VPD and low Soil Moisture (SM) as the drought progressed. Our simulated HC-ETECO data revealed that ET in the middle and lower reaches of the YRB was consistently lower than normal during drought period. Attribution analysis indicated that this reduction was primarily driven by midday temperature increases and high VPD, suggesting that vegetation in the region copes with drought stress predominantly by limiting water loss. These findings highlight the utility of the generated high-resolution ET dataset in advancing our understanding of vegetation dynamics under drought climate conditions. This work provides critical insights for enhancing climate adaptation strategies and enhancing ecosystem management practices in the face of increasing climate variability. © 2024 Elsevier B.V.

关键词： Anthropogenic

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RFID Cardinality Estimation Method for Intelligent Warehouse 35

RFID Cardinality Estimation Method for Intelligent Warehouse

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第35届中国控制会议

作者： SHEN Jingjing TAN Xiaobin WU Fan YAN Pei CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

ISBN: (纸本)9781509009107

With the rapid development of RFID technologies,RFID has been introduced into applications such as supply chain management,inventory control,sampling inspection,3-D positioning and object ***,the reader accesses all the tags in its interrogation region while some applications may only need to identify the tags in a specified area which is smaller than the reader's interrogation *** paper concerns the essential problem of estimating cardinality of tags in the specified *** key novelty of our solution builds on an estimation synopsis that can capture key counting information by moving the reader as well as a simple *** the help of this data structure,a BS can be obtained which only contains the target *** computing the number of 1 in the BS,we can easily get cardinality |E| of the tags in the specified *** conduct extensive experiments to examine this design and the results shows that our solution achieves high *** it not requires any modification of tags and can be implemented with only one reader and some passive RFID tags,the proposed method is easy to deploy in a practical system.

关键词： Cardinality estimation RFID tags algorithm design

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China's poverty assessment and analysis under the framework of the UN SDGs based on multisource remote sensing data

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地球空间信息科学学报（英文版） 2024年第1期27卷 111-131页

作者： Mengjie Wang Yanjun Wang Fei Teng Shaochun Li Yunhao Lin Hengfan Cai Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying Mapping and Remote SensingHunan University of Science and TechnologyXiangtanChina National-Local Joint Engineering Laboratory of Geo-spatial Information Technology Hunan University of Science and TechnologyXiangtanChina School of Earth Sciences and Spatial Information Engineering Hunan University of Science and TechnologyXiangtanChina Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying Mapping and Remote SensingHunan University of Science and TechnologyXiangtanChina National-Local Joint Engineering Laboratory of Geo-spatial Information Technology Hunan University of Science and TechnologyXiangtanChina

Poverty has always been a global concern that has restricted human *** first goal(SDG 1)of the United Nations Sustainable Development Goals(SDGs)is to eliminate all forms of poverty all over the *** establishment of a scientific and effective localized SDG 1 evaluation and monitoring method is the key to achieving SDG *** paper proposes SDG 1 China district and county-level localization evaluation method based on multi-source remote sensing data for the United Nations Sustainable Development *** temporal and spatial distribution characteristics of China's poverty areas and their SDG 1 evaluation values in 2012,2014,2016,and 2018 have been *** on the SDGs global indicator framework,this paper first constructed SDG 1 China's district and county localization indicator system and then extracted multidimensional feature factors from nighttime light images,land cover data,and digital elevation model ***,we establish SDG 1 China's localized partial least squares estimation model and SDG 1 China's localized machine learning estimation ***,we analyze and verify the spatiotemporal distribution characteristics of China's poverty areas and counties and their SDG 1 evaluation *** results show that SDG 1 China's district and county localization indicator system proposed in this study and SDG 1 China's localized partial least squares estimation model can better reflect the poverty level of China's districts and *** estimated model R2 is 0.65,which can identify 72.77%of China's national poverty *** 2012 to 2018,the spatial distribution pattern of SDG evaluation values in China's districts and counties is that the SDG evaluation values gradually increase from western China to eastern *** addition,the average SDG 1 evaluation value of China's districts and counties increased by 23%from 2012 to *** paper is oriented to the United Nations SDGs frame-work,explores the SDG 1 localized evalu

关键词： Multisource remote sensing data Sustainable Development Goals(SDGs) poverty indicator system partial least squares machine learning

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