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Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder.
Xing, Jia; Li, Siwei; Zheng, Shuxin; Liu, Chang; Wang, Xiaochun; Huang, Lin; Song, Ge; He, Yihan; Wang, Shuxiao; Sahu, Shovan Kumar; Zhang, Jia; Bian, Jiang; Zhu, Yun; Liu, Tie-Yan; Hao, Jiming.
Afiliación
  • Xing J; State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
  • Li S; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
  • Zheng S; School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
  • Liu C; Microsoft Research Asia, Beijing 100080, China.
  • Wang X; Microsoft Research Asia, Beijing 100080, China.
  • Huang L; State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
  • Song G; Microsoft Research Asia, Beijing 100080, China.
  • He Y; School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
  • Wang S; Mechanical Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States.
  • Sahu SK; State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
  • Zhang J; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
  • Bian J; State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
  • Zhu Y; Microsoft Research Asia, Beijing 100080, China.
  • Liu TY; Microsoft Research Asia, Beijing 100080, China.
  • Hao J; College of Environment and Energy, Guangzhou Higher Education Mega Center, South China University of Technology, Guangzhou 510006, China.
Environ Sci Technol ; 56(14): 9903-9914, 2022 07 19.
Article en En | MEDLINE | ID: mdl-35793558
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Contaminantes Atmosféricos / Contaminación del Aire Tipo de estudio: Prognostic_studies Idioma: En Revista: Environ Sci Technol Año: 2022 Tipo del documento: Article País de afiliación: China
Texto completo
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XML
PubMed Links
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Contaminantes Atmosféricos / Contaminación del Aire Tipo de estudio: Prognostic_studies Idioma: En Revista: Environ Sci Technol Año: 2022 Tipo del documento: Article País de afiliación: China