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AMT | Articles | Volume 11, issue 7
Atmos. Meas. Tech., 11, 4477–4491, 2018
https://doi.org/10.5194/amt-11-4477-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 11, 4477–4491, 2018
https://doi.org/10.5194/amt-11-4477-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 26 Jul 2018

Research article | 26 Jul 2018

Data inversion methods to determine sub-3 nm aerosol size distributions using the particle size magnifier

Runlong Cai et al.
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Cited articles  
Ahonen, L. R., Kangasluoma, J., Lammi, J., Lehtipalo, K., Hämeri, K., Petäjä, T., and Kulmala, M.: First measurements of the number size distribution of 1–2 nm aerosol particles released from manufacturing processes in a cleanroom environment, Aerosol Sci. Tech., 51, 685–693, 2017. 
Buckley, D. T. and Hogan, C. J.: Determination of the transfer function of an atmospheric pressure drift tube ion mobility spectrometer for nanoparticle measurements, Analyst, 142, 1800–1812, 2017. 
Dempster, A. P., Laird, N. M., and Rubin, D. B.: Maximum likelihood from incomplete data via the EM algorithm, J. Roy. Stat. Soc. B Met., 39, 1–38, 1977. 
Do, C. B. and Batzoglou, S.: What is the expectation maximization algorithm?, Nat. Biotechnol., 26, 897–899, 2008. 
Ellis, S. P.: Instability of least square, least absolute deviation and least median of squares linear regression, Stat. Sci., 13, 337–350, 1998. 
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Short summary
We tested the performance of four inversion methods to recover sub-3 nm aerosol size distributions using the particle size magnifier (PSM). The PSM is widely used in new particle formation study; however, the inversion methods used in previous studies may report false particle concentrations. Due to the results, we suggest using the expectation–maximization algorithm to address the PSM inversion problem. We also gave practical suggestions on PSM operation based on the inversion analysis.
We tested the performance of four inversion methods to recover sub-3 nm aerosol size...
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