Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees

Fine particulate matter with aerodynamic diameters

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Bibliographische Detailangaben
Veröffentlicht in:	Atmospheric chemistry and physics 2020-03, Vol.20 (6), p.3273-3289
Hauptverfasser:	Wei, Jing, Li, Zhanqing, Cribb, Maureen, Huang, Wei, Xue, Wenhao, Sun, Lin, Guo, Jianping, Peng, Yiran, Li, Jing, Lyapustin, Alexei, Liu, Lei, Wu, Hao, Song, Yimeng
Format:	Artikel
Sprache:	eng
Schlagworte:	Aerosols Air pollution Algorithms Artificial intelligence Atmospheric correction Atmospheric models Deep learning Environmental health Environmental Sciences Environmental Sciences & Ecology Errors High resolution Humidity Land pollution Land use Life Sciences & Biomedicine Meteorology & Atmospheric Sciences Metropolitan areas Particulate emissions Particulate matter Physical Sciences Pollutants Pollution levels Pollution studies Randomization Resolution Satellite observation Satellites Science & Technology Spatial discrimination Spatial resolution Spectroradiometers Suspended particulate matter Topography Trees Urban areas
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Beschreibung
Zusammenfassung:	Fine particulate matter with aerodynamic diameters
ISSN:	1680-7316 1680-7324
DOI:	10.5194/acp-20-3273-2020