Articles | Volume 15, issue 8
https://doi.org/10.5194/amt-15-2579-2022
https://doi.org/10.5194/amt-15-2579-2022
Research article
 | 
28 Apr 2022
Research article |  | 28 Apr 2022

Regularized inversion of aerosol hygroscopic growth factor probability density function: application to humidity-controlled fast integrated mobility spectrometer measurements

Jiaoshi Zhang, Yang Wang, Steven Spielman, Susanne Hering, and Jian Wang

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Cited articles

Collins, D. R., Flagan, R. C., and Seinfeld, J. H.: Improved Inversion of Scanning DMA Data, Aerosol Sci. Tech., 36, 1–9, https://doi.org/10.1080/027868202753339032, 2002. 
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Gysel, M., McFiggans, G. B., and Coe, H.: Inversion of tandem differential mobility analyser (TDMA) measurements, J. Aerosol Sci., 40, 134–151, https://doi.org/10.1016/j.jaerosci.2008.07.013, 2009. 
Hanke, M. and Raus, T.: A General Heuristic for Choosing the Regularization Parameter in Ill-Posed Problems, SIAM J. Sci. Comput., 17, 956–972, https://doi.org/10.1137/0917062, 1996. 
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Short summary
New nonparametric, regularized methods are developed to invert the growth factor probability density function (GF-PDF) from humidity-controlled fast integrated mobility spectrometer measurements. These algorithms are computationally efficient, require no prior assumptions of the GF-PDF distribution, and reduce the error in inverted GF-PDF. They can be applied to humidified tandem differential mobility analyzer data. Among all algorithms, Twomey’s method retrieves GF-PDF with the smallest error.