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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 7, issue 1
Atmos. Meas. Tech., 7, 95–105, 2014
https://doi.org/10.5194/amt-7-95-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 7, 95–105, 2014
https://doi.org/10.5194/amt-7-95-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 14 Jan 2014

Research article | 14 Jan 2014

A fast and easy-to-implement inversion algorithm for mobility particle size spectrometers considering particle number size distribution information outside of the detection range

S. Pfeifer1, W. Birmili1, A. Schladitz1,2, T. Müller1, A. Nowak1,3, and A. Wiedensohler1 S. Pfeifer et al.
  • 1Leibniz Institute for Tropospheric Research, Permoserstraße 15, 04318 Leipzig, Germany
  • 2Saxon State Office for Environment, Agriculture and Geology, Pillnitzer Platz 3, 01326 Dresden, Germany
  • 3Physikalisch-Technische Bundesanstalt, Bundesallee 100, 38116 Braunschweig, Germany

Abstract. Multiple-charge inversion is an essential procedure to convert the raw mobility distributions recorded by mobility particle size spectrometers, such as the DMPS or SMPS (differential or scanning mobility particle sizers), into true particle number size distributions. In this work, we present a fast and easy-to-implement multiple-charge inversion algorithm with sufficient precision for atmospheric conditions, but extended functionality. The algorithm can incorporate size distribution information from sensors that measure beyond the upper sizing limit of the mobility spectrometer, such as an aerodynamic particle sizer (APS) or an optical particle counter (OPC). This feature can considerably improve the multiple-charge inversion result in the upper size range of the mobility spectrometer, for example, when substantial numbers of coarse particles are present. The program also yields a continuous size distribution from both sensors as an output. The algorithm is able to calculate the propagation of measurement errors, such as those based on counting statistics, into on the final particle number size distribution. As an additional aspect, the algorithm can perform all inversion steps under the assumption of non-spherical particle shape, including constant or size-dependent shape factors.

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