Observation and Inversion of Aerosol Particle Size Distribution over Yinchuan Area
Open Access
- 31 July 2021
- journal article
- research article
- Published by MDPI AG in Atmosphere
- Vol. 12 (8), 992
- https://doi.org/10.3390/atmos12080992
Abstract
Particle size distribution is one of the important microphysical parameters to characterize the aerosol properties. The aerosol optical depth is used as the function of wavelength to study the particle size distribution of whole atmospheric column. However, the inversion equation of the particle size distribution from the aerosol optical depth belongs to the Fredholm integral equation of the first kind, which is usually ill-conditioned. To overcome this drawback, the integral equation is first discretized directly by using the complex trapezoid formula. Then, the corresponding parameters are selected by the L curve method. Finally the truncated singular value decomposition regularization method is employed to regularize the discrete equation and retrieve the particle size distribution. To verify the feasibility of the algorithm, the aerosol optical depths taken by a sun photometer CE318 over Yinchuan area in four seasons, as well as hazy, sunny, floating dusty and blowing dusty days, were used to retrieve the particle size distribution. In order to verify the effect of truncated singular value decomposition algorithm, the Tikhonov regularization algorithm was also adopted to retrieve the aerosol PSD. By comparing the errors of the two regularizations, the truncated singular value decomposition regularization algorithm has a better retrieval effect. Moreover, to understand intuitively the sources of aerosol particles, the backward trajectory was used to track the source. The experiment results show that the truncated singular value decomposition regularization method is an effective method to retrieve the particle size distribution from aerosol optical depth.Funding Information
- National Natural Science Foundation of China (61765001, 61565001)
- Natural Science Foundation of Ningxia Province (2021AAC02021)
- Leading Talents of Scientific and Technological Innovation of Ningxia Province (no, no, no, no, NXYLXK2017A07)
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