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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Bibliographic Details
Published in:	Atmospheric environment (1994) Vol. 208; pp. 113 - 122
Main Authors:	Liu, Jianjun, Weng, Fuzhong, Li, Zhanqing
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.07.2019
Subjects:	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
Online Access:	Get full text
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