Regularized inversion of aerosol hygroscopic growth factor probability density function: application to humidity-controlled fast integrated mobility spectrometer measurements
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Published:2022-04-28
Issue:8
Volume:15
Page:2579-2590
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ISSN:1867-8548
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Container-title:Atmospheric Measurement Techniques
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language:en
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Short-container-title:Atmos. Meas. Tech.
Author:
Zhang JiaoshiORCID, Wang YangORCID, Spielman Steven, Hering SusanneORCID, Wang JianORCID
Abstract
Abstract. Aerosol hygroscopic growth plays an important role in
atmospheric particle chemistry and the effects of aerosol on radiation and
hence climate. The hygroscopic growth is often characterized by a growth
factor probability density function (GF-PDF), where the growth factor is
defined as the ratio of the particle size at a specified relative humidity
to its dry size. Parametric, least-squares methods are the most widely used
algorithms for inverting the GF-PDF from measurements of the humidified tandem
differential mobility analyzer (HTDMA) and have been recently applied to
the GF-PDF inversion from measurements of the humidity-controlled fast
integrated mobility spectrometer (HFIMS). However, these least-squares
methods suffer from noise amplification due to the lack of regularization in
solving the ill-posed problem, resulting in significant fluctuations in the
retrieved GF-PDF and even occasional failures of convergence. In this study,
we introduce nonparametric, regularized methods to invert the aerosol GF-PDF and
apply them to HFIMS measurements. Based on the HFIMS kernel function, the
forward convolution is transformed into a matrix-based form, which
facilitates the application of the nonparametric inversion methods with
regularizations, including Tikhonov regularization and Twomey's iterative
regularization. Inversions of the GF-PDF using the nonparameteric methods
with regularization are demonstrated using HFIMS measurements simulated from
representative GF-PDFs of ambient aerosols. The characteristics of
reconstructed GF-PDFs resulting from different inversion methods, including
previously developed least-squares methods, are quantitatively compared. The
result shows that Twomey's method generally outperforms other inversion
methods. The capabilities of Twomey's method in reconstructing the
pre-defined GF-PDFs and recovering the mode parameters are validated.
Funder
Small Business Innovative Research and Small Business Technology Transfer
Publisher
Copernicus GmbH
Subject
Atmospheric Science
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