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Machine learning-based estimation of ground-level NO2 concentrations over China.
Chi, Yulei; Fan, Meng; Zhao, Chuanfeng; Yang, Yikun; Fan, Hao; Yang, Xingchuan; Yang, Jie; Tao, Jinhua.
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  • Chi Y; State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 10
  • Fan M; State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
  • Zhao C; State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China. Electronic address: czhao@bnu.edu.cn.
  • Yang Y; State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China.
  • Fan H; State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China.
  • Yang X; State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China.
  • Yang J; State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China.
  • Tao J; State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
Sci Total Environ ; 807(Pt 1): 150721, 2022 Feb 10.
Article en En | MEDLINE | ID: mdl-34619217
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Contaminantes Atmosféricos / Contaminación del Aire Tipo de estudio: Prognostic_studies Límite: Humans País/Región como asunto: Asia Idioma: En Revista: Sci Total Environ Año: 2022 Tipo del documento: Article
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Contaminantes Atmosféricos / Contaminación del Aire Tipo de estudio: Prognostic_studies Límite: Humans País/Región como asunto: Asia Idioma: En Revista: Sci Total Environ Año: 2022 Tipo del documento: Article