Satellite-based PM2.5 estimation directly from reflectance at the top of the atmosphere using a machine learning algorithm

Atmospheric particulate matter (PM) that have particle diameter less than 2.5 μm (PM2.5) are hazardous to public health whose concentration has been either measured on the ground or inferred from satellite-retrieved aerosol optical depth (AOD). The latter is subject to numerous sources of errors, ma...

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Vydané v:	Atmospheric environment (1994) Ročník 208; s. 113 - 122
Hlavní autori:	Liu, Jianjun, Weng, Fuzhong, Li, Zhanqing
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.07.2019
Predmet:	aerosols algorithms artificial intelligence atmospheric chemistry China Machine learning meteorological parameters monitoring particle size particulates PM2.5 concentration pollution public health reflectance satellites seasonal variation TOA reflectances China PM2.5 concentration TOA reflectances Machine learning
ISSN:	1352-2310, 1873-2844
On-line prístup:	Získať plný text
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