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Variability of Major Aerosol Types in China Classified Using AERONET Measurements

Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(20), 2334;
Received: 25 August 2019 / Revised: 28 September 2019 / Accepted: 5 October 2019 / Published: 9 October 2019
(This article belongs to the Special Issue Urban Air Quality Monitoring using Remote Sensing)
Aerosol type is a critical piece of information in both aerosol forcing estimation and passive satellite remote sensing. However, the major aerosol types in China and their variability is still less understood. This work uses direct sun measurements and inversion derived parameters from 47 sites within the Aerosol Robotic Network (AERONET) in China, with more than 39,000 records obtained between April 1998 and January 2017, to identify dominant aerosol types using two independent methods, namely, K means and Self Organizing Map (SOM). In total, we define four aerosol types, namely, desert dust, scattering mixed, absorbing mixed and scattering fine, based on their optical and microphysical characteristics. Seasonally, dust aerosols mainly occur in the spring and over North and Northwest China; scattering mixed are more common in the spring and summer, whereas absorbing aerosols mostly occur in the autumn and winter during heating period, and scattering fine aerosols have their highest occurrence frequency in summer over East China. Based on their spatial and temporal distribution, we also generate seasonal aerosol type maps that can be used for passive satellite retrieval. Compared with the global models used in most satellite retrieval algorithms, the unique feature of East Asian aerosols is the curved single scattering albedo spectrum, which could be related to the mixing of black carbon with dust or organic aerosols. View Full-Text
Keywords: aerosol type classification; spatial and temporal variability aerosol type classification; spatial and temporal variability
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Zhang, L.; Li, J. Variability of Major Aerosol Types in China Classified Using AERONET Measurements. Remote Sens. 2019, 11, 2334.

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