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Cooperative simultaneous inversion of satellite-based real-time PM2.5 and ozone levels using an improved deep learning model with attention mechanism.
Yan, Xing; Zuo, Chen; Li, Zhanqing; Chen, Hans W; Jiang, Yize; He, Bin; Liu, Huiming; Chen, Jiayi; Shi, Wenzhong.
Afiliación
  • Yan X; State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China.
  • Zuo C; State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China.
  • Li Z; Department of Atmospheric and Oceanic Science and ESSIC, University of Maryland, College Park, MD, 20740, USA.
  • Chen HW; Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden; Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, 41296, Sweden. Electronic address: hans.chen@chalmers.se.
  • Jiang Y; State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China.
  • He B; College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China.
  • Liu H; Satellite Environment Center, Ministry of Environmental Protection, Beijing, 100094, China.
  • Chen J; State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China.
  • Shi W; Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
Environ Pollut ; 327: 121509, 2023 Jun 15.
Article en En | MEDLINE | ID: mdl-36967005
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Ozono / Contaminantes Atmosféricos / Contaminación del Aire / Aprendizaje Profundo Tipo de estudio: Prognostic_studies País/Región como asunto: Asia Idioma: En Revista: Environ Pollut Asunto de la revista: SAUDE AMBIENTAL Año: 2023 Tipo del documento: Article País de afiliación: China
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Ozono / Contaminantes Atmosféricos / Contaminación del Aire / Aprendizaje Profundo Tipo de estudio: Prognostic_studies País/Región como asunto: Asia Idioma: En Revista: Environ Pollut Asunto de la revista: SAUDE AMBIENTAL Año: 2023 Tipo del documento: Article País de afiliación: China