Abstract
Abstract. Measuring particle size distribution accurately down to approximately 1 nm is needed for studying atmospheric new particle formation. The scanning particle size magnifier (PSM) using diethylene glycol as a working fluid has been used for measuring sub-3 nm atmospheric aerosol. A proper inversion method is required to recover the particle size distribution from PSM raw data. Similarly to other aerosol spectrometers and classifiers, PSM inversion can be deduced from a problem described by the Fredholm integral equation of the first kind. We tested the performance of the stepwise method, the kernel function method (Lehtipalo et al., 2014), the H&A linear inversion method (Hagen and Alofs, 1983), and the expectation–maximization (EM) algorithm. The stepwise method and the kernel function method were used in previous studies on PSM. The H&A method and the expectation–maximization algorithm were used in data inversion for the electrical mobility spectrometers and the diffusion batteries, respectively (Maher and Laird, 1985). In addition, Monte Carlo simulation and laboratory experiments were used to test the accuracy and precision of the particle size distributions recovered using four inversion methods. When all of the detected particles are larger than 3 nm, the stepwise method may report false sub-3 nm particle concentrations because an infinite resolution is assumed while the kernel function method and the H&A method occasionally report false sub-3 nm particles because of the unstable least squares method. The accuracy and precision of the recovered particle size distribution using the EM algorithm are the best among the tested four inversion methods. Compared to the kernel function method, the H&A method reduces the uncertainty while keeping a similar computational expense. The measuring uncertainties in the present scanning mode may contribute to the uncertainties of the recovered particle size distributions. We suggest using the EM algorithm to retrieve the particle size distributions using the particle number concentrations recorded by the PSM. Considering the relatively high computation expenses of the EM algorithm, the H&A method is recommended for preliminary data analysis. We also gave practical suggestions on PSM operation based on the inversion analysis.
Highlights
The particle size magnifier (PSM) using diethylene glycol as a working fluid (Vanhanen et al, 2011) is widely used in new particle formation studies (Kulmala et al, 2012, 2013; Kontkanen et al, 2017) and other industrial applications
When all of the detected particles are larger than 3 nm, the stepwise method may report false sub-3 nm particle concentrations because an infinite resolution is assumed while the kernel function method and the H&A method occasionally report false sub-3 nm particles because of the unstable least squares method
The inversion methods tested in this study, i.e. the stepwise method, the kernel function method, the H&A method, and the EM algorithm, are able to estimate the classified particle diameters when the PSM was measuring nearly monodisperse sub-3 nm particles
Summary
The particle size magnifier (PSM) using diethylene glycol as a working fluid (Vanhanen et al, 2011) is widely used in new particle formation studies (Kulmala et al, 2012, 2013; Kontkanen et al, 2017) and other industrial applications Particles in the PSM grow to larger sizes due to the condensation of supersaturated diethylene glycol, and after the initial growth these particles are detected using a downstream condensation particle counter (CPC). The PSM detection efficiency (the CPC is included if not specially mentioned) of particles with a certain diameter is a function of the supersaturation ratio of diethylene glycol. Increasing the flow rate passing through the chamber containing saturated diethylene glycol vapour, i.e. the saturator flow rate, can enhance the supersaturation ratio and the particle detection efficiencies. The total particle number concentration detected by the PSM varies with the varying saturator flow rate, and one can determine the particle size distribution according to the observed relationship between the particle number concentration and the saturator flow rate
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