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Exploring Global Land Coarse-Mode Aerosol Changes from 2001-2021 Using a New Spatiotemporal Coaction Deep-Learning Model.
Zang, Zhou; Zhang, Yue; Zuo, Chen; Chen, Jiayi; He, Bin; Luo, Nana; Zou, Junxiao; Zhao, Wenji; Shi, Wenzhong; Yan, Xing.
Afiliação
  • Zang Z; State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
  • Zhang Y; State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
  • Zuo C; State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
  • Chen J; State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
  • He B; State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
  • Luo N; School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102612, China.
  • Zou J; State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
  • Zhao W; College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China.
  • Shi W; Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
  • Yan X; State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
Environ Sci Technol ; 57(48): 19881-19890, 2023 Dec 05.
Article em En | MEDLINE | ID: mdl-37962866
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Poluentes Atmosféricos / Aprendizado Profundo Idioma: En Ano de publicação: 2023 Tipo de documento: Article
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Poluentes Atmosféricos / Aprendizado Profundo Idioma: En Ano de publicação: 2023 Tipo de documento: Article