Journal of Atmospheric and Environmental Optics, Volume. 18, Issue 3, 191(2023)

Research on aerosol recognition and particle size distribution inversion based on scattering matrix

CHEN Minwang1,2, QIU Zhenwei2、*, and HONG Jin2
Author Affiliations
  • 1School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei 230026, China
  • 2Key Laboratory of Optical Calibration and Characterization, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
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    Scattering matrix is an important parameter to describe the scattering characteristics of medium, which is sensitive to the physical and chemical properties of the medium. In order to investigate the feasibility of using this parameter to identify aerosols and obtain their physicochemical properties, an experimental measurement was designed to obtain the scattering matrices of the two aerosol samples of poly-alpha-olefin and sodium chloride, and the angular distribution of the two matrix elements was discussed. Furthermore, based on Mie scattering theory, the particle size distribution of poly-alpha-olefin aerosols was inversed using template matching method and the measurement results. The results show that based on the angular distribution of matrix elements, the two kinds of aerosols can be effectively identified and distinguished, and the physicochemical properties of aerosols can also be obtained by combining the correlation scattering model and inversion method. This research provides a method reference for aerosol identification and acquisition of physicochemical properties.

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    Minwang CHEN, Zhenwei QIU, Jin HONG. Research on aerosol recognition and particle size distribution inversion based on scattering matrix[J]. Journal of Atmospheric and Environmental Optics, 2023, 18(3): 191

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    Paper Information

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    Received: Mar. 11, 2021

    Accepted: --

    Published Online: Jun. 29, 2023

    The Author Email: QIU Zhenwei (zwqiu@aiofm.ac.cn)

    DOI:10.3969/j.issn.1673-6141.2023.03.001

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