Inversion algorithm of black carbon mixing state based on machine learning

The radiative properties of black carbon (BC) are significantly influenced by its mixing state. The single-particle soot photometer (SP2) is a widely recognized instrument for quantifying BC mixing state. However, the derivation of BC mixing state from SP2 is quite challenging. Since the SP2 records...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Atmospheric measurement techniques Jg. 18; H. 5; S. 1149 - 1162
Hauptverfasser:	Tian, Zeyuan, Wang, Jiandong, Wang, Jiaping, Liu, Chao, Xing, Jia, Wang, Jinbo, Zhang, Zhouyang, Jin, Yuzhi, Shen, Sunan, Wang, Bin, Nie, Wei, Huang, Xin, Ding, Aijun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Katlenburg-Lindau Copernicus GmbH 06.03.2025 Copernicus Publications
Schlagworte:	Aerosols Algorithms Analysis Black carbon Carbon Data analysis Data mining Data processing Decision making Ensemble learning Lasers Leading edges Learning algorithms Light Machine learning Optical properties Particle size Photometers Physical properties Protective coatings Radiation Real time Sensors Signal to noise ratio
ISSN:	1867-8548, 1867-1381, 1867-8548
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!