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The spatial dynamics of Ukraine air quality impacted by the war and pandemic

International Journal of Digital Earth 2023年第1期16卷 3680-3705页

作者： vizhi Qian Liu Theodore S.Trefonidesc Sina Hasheminassab Jennifer Smith Thomas Huang Kevin M.Marlis Joe T.Roberts Zifu Wang dexuan sha Ana Beatriz Moura Pereira Heramb Podar Jacob Cain Chaowei Yang Department of Geography and GeoInformation Science George Mason UniversityFairfaxVAUSA Jet Propulsion Laboratory California Institute of TechnologyPasaden1CAUSA Dartmouth College HanoverNHUSA Bayer WashingtonDCUSA Northern Virginia Community College AnnandaleVAUSA Indian Institute of Technology RoorkeeIndia

In recent years,our world has experienced significant disruptions due to the COviD-19 pandemic,and Russia's 2022 invasion of Ukraine,impacting human activities and the global *** paper explored air quality changes in Ukraine due to COVID-19,and Russia's invasion of Ukraine using on-demand with a what-you-see-is-what-you-get *** the cOVID-19 pandemic,strict quarantine policies in Ukraine led to a 2%reduction in tropospheric NO_(2) concentration before the lockdown and 4%during the lockdown *** like Kyiv,Donetsk,and Dnipro exhibited reductions of 5%,11%,and 16%,*** SO_(2) column concentration decreased by 6%before the lockdown and 2.5%during the lockdown period,except in high population density *** showed the highest reduction of 17%in SO_(2) concentration,while Donetsk and Dnipro exhibited an 11%***,during the Russian invasion,there was a significant increase in tropospheric NO_(2) concentration in heavily destroyed Kharkiv while most eastern regions experienced a *** total SO_(2) column was 48%higher before the war but reduced throughout the country after the war,except for in Kyiv and a few central *** findings can contribute to analyzing air pollution and building digital twin simulations for future reconstruction scenarios.

关键词： ukraine war air pollution pandemic climate change environment spatiotemporal computing digital twins

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Big Earth data analytics:a survey

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Big Earth Data 2019年第2期3卷 83-107页

作者： Chaowei Yang Manzhu Yu Yun Li Fei Hu Yongyao Jiang Qian Liu dexuan sha Mengchao Xu Juan Gu NSF Spatiotemporal Innovation Center George Mason UniversityFairfaxVA 22030USA Center for OpenSource Data and AI Technologies IBMSan FranciscoCAUSA Machine Learning at ESRI Ltd RedlandsCAUSA Department of fundamental Geoinformatics Beijing Institute of Surveying and MappingBeijngChina

Big Earth data are produced from satellite observations,Internet-ofThings,model simulations,and other *** data embed unprecedented insights and spatiotemporal stamps of relevant Earth phenomena for improving our understanding,responding,and addressing challenges of Earth sciences and *** the past years,new technologies(such as cloud computing,big data and artificial intelligence)have gained momentum in addressing the challenges of using big Earth data for scientific studies and geospatial applications historically *** paper reviews the big Earth data analytics from several aspects to capture the latest advancements in this fast-growing *** first introduce the concepts of big Earth *** architecture,various functionalities,and supporting modules are then reviewed from a generic methodology *** methods supporting the functionalities are surveyed and analyzed in the context of different *** driven questions are exemplified through cutting-edge Earth science researches and applications.A list of challenges and opportunities are proposed for different stakeholders to collaboratively advance big Earth data analytics in the near future.

关键词： Geospatial industry geospatial analytics policy makers data scientist data system spatiotemporal data

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A spatiotemporal data collection of viral cases for COVID-19 rapid response

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Big Earth Data 2021年第1期5卷 90-111页

作者： dexuan sha Yi Liu Qian Liu Yun Li Yifei Tian Fayez Beaini Cheng Zhong Tao Hu Zifu Wang Hai Lan You Zhou Zhiran Zhang Chaowei Yang NSF Spatiotemporal Innovation Center George Mason UniversityFairfaxVAUSA Department of Geography and GeoInformation Science George Mason UniversityFairfaxVAUSA Department of Aerospace and Mechanical Engineering University of Notre DameNotre DameINUSA Center for Geographic Analysis Harvard UniversityCambridgeMAUSA School of Resource and Environmental Sciences Wuhan UniversityWuhanHubeiChina

Under the global health crisis of COVID-19,timely,and accurate epi-demic data are important for observation,monitoring,analyzing,modeling,predicting,and mitigating *** case data can be jointly analyzed with relevant factors for various applications in the context of the *** COVID-19 case data are scattered across a variety of data sources which may consist of low data quality accompanied by inconsistent data *** address this short-coming,a multi-scale spatiotemporal data product is proposed as a public repository platform,based on a spatiotemporal cube,and allows the integration of different data sources by adopting various data *** the spatiotemporal cube,a comprehensive data processing workflow gathers disparate COVID-19 epidemic data-sets at the global,national,provincial/state,county,and city *** proposed framework is supported by an automatic update with a 2-h frequency and the crowdsourcing validation team to produce and update data on a daily time *** rapid-response dataset allows the integration of other relevant socio-economic and environ-mental factors for spatiotemporal *** data is available in Harvard Dataverse platform(https://***/***?persistentId=doi:10.7910/DVN/8HGECN)and GitHub open source repository(https://***/stccenter/COVID-19-Data).

关键词： COVID-19 pandemic public health semi-automatic validation spatiotemporal data set

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Taking the pulse of COVID-19:a spatiotemporal perspective

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International Journal of Digital Earth 2020年第10期13卷 1186-1211页

作者： Chaowei Yang dexuan sha Qian Liu Yun Li Hai Lan Weihe Wendy Guan Tao Hu Zhenlong Li Zhiran Zhang John Hoot Thompson Zifu Wang David Wong Shiyang Ruan Manzhu Yu Douglas Richardson Luyao Zhang Ruizhi Hou You Zhoua Cheng Zhong Yifei Tian Fayez Beaini Kyla Carte Colin Flynn Wei Liu Dieter Pfoser Shuming Bao Mei Li Haoyuan Zhang Chunbo Liu Jie Jiang Shihong Du Liang Zhao Mingyue Lu Lin Li Huan Zhou Andrew Ding NSF Spatiotemporal Innovation Center George Mason Univ.FairfaxVAUSA Department of Geography and GeoInformation Science George Mason Univ.FairfaxVAUSA Department of Geographical Sciences University of MarylandCollege ParkMDUSA Center for Geographic Analysis Harvard UniversityCambridgeMAUSA Geoinformation and Big Data Research Lab Department of GeographyUniversity of South CarolinaColumbiaSCUSA Chinese Academy of Surveying and Mapping BeijingPeople’s Republic of China School of Resource and Environmental Sciences Wuhan UniversityWuhanPeople’s Republic of China Cloud Computeand Storage OperationsInformation Technology ServicesGeorge Mason UniversityFairfaxVAUSA Department of Geography Pennsylvania State UniversityState CollegePAUSA School of Business Management East China Normal UniversityShanghaiPeople’s Republic of China School of Mathematical Sciences East China Normal UniversityShanghaiPeople’s Republic of China Department of Psychology George Mason UniversityFairfaxVAUSA mDepartment of Computer Science George Mason UniversityFairfaxVAUSA Department of Information Technology George Mason UniversityFairfaxVAUSA Department of Biology George Mason UniversityFairfaxVAUSA College of Land Science and Technology China Agricultural UniversityBeijingPeople’s Republic of China China Data Institute Ann ArborMIUSA Institute of Remote Sensing and GIS Peking UniversityBeijingPeople’s Republic of China Aerospace Information Research Institute Chinese Academy of SciencesBeijingPeople’s Republic of China School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and ArchitectureBeijingPeople’s Republic of China School of Geographical Sciences Nanjing University of Information Science&TechnologyNanjingPeople’s Republic of China School of Geodesy and Geomatics Wuhan UniversityWuhanPeople’s Republic of China wFaculty of Environment University of WaterlooWaterlooCanada

The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the *** transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and *** response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global *** perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies.

关键词： Big Data Earth system emergency geospatial sciences epidemics applications

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子宫腺肌病患者子宫肌层纤维化程度与痛经的相关性

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中华妇产科杂志 2018年第10期53卷 689-693页

作者：董千靖郑德璇沈雪汪沙首都医科大学附属北京妇产医院妇科微创中心 100006

目的探讨子宫腺肌病子宫肌层纤维化程度与痛经的相关性。方法选取2015年7月至2016年12月于首都医科大学附属北京妇产医院，因子宫腺肌病行子宫切除术的患者30例作为子宫腺肌病组，选择同期因子宫颈上皮内瘤变Ⅲ级行子宫切除术的患者28... 详细信息

目的探讨子宫腺肌病子宫肌层纤维化程度与痛经的相关性。方法选取2015年7月至2016年12月于首都医科大学附属北京妇产医院，因子宫腺肌病行子宫切除术的患者30例作为子宫腺肌病组，选择同期因子宫颈上皮内瘤变Ⅲ级行子宫切除术的患者28例作为对照组，收集两组患者术前痛经的视觉模拟评分法（VAS）评分，采用改良Masson染色法和免疫组化二步法分别检测两组患者子宫肌层中的胶原纤维面积比率和Ⅰ型胶原蛋白的表达水平。结果（1）纤维化程度分析：胶原纤维面积比率及Ⅰ型胶原蛋白的表达水平在子宫腺肌病组患者肌层中分别为（34．5±5．1）％、0．23±0．06，高于对照组[分别为（26．7±10．1）％、0．18±0．08]，分别比较，差异均有统计学意义（P均〈0．05）。（2）纤维化程度与痛经的相关性：胶原纤维面积比率及Ⅰ型胶原蛋白的表达水平在子宫腺肌病组中的重度痛经、中度痛经、无一轻度痛经患者分别为（35．3±4．3）％、0．25±0．05，（35．7±3．2）％、0．26±0．06，（25．0±2．9）％、0．15±0．03，无～轻度痛经者与中度痛经者、重度痛经者之间均有差异（P均〈0．01）；且胶原纤维面积比率及I型胶原蛋白表达水平与痛经程度均呈正相关，相关系数分别为r胶原纤维面积=0．50、rⅠ型胶原蛋白=0．50（P均〈0．05）。结论子宫腺肌病子宫肌层发生了明显纤维化，胶原纤维面积比率及I型胶原蛋白表达均高于正常，且与痛经VAS评分呈正相关；子宫腺肌病患者痛经的发生可能与肌层纤维化形成有关。

关键词：子宫腺肌病纤维化痛经胶原Ⅰ型

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