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The differential mobility particle sizer (DMPS) is designed for measurementsof particle number size distributions. It performs a number of measurementswhile scanning over different particle sizes. A standard assumption in thedata-processing (inversion) algorithm is that the size distribution remainsthe same throughout each scan. For a DMPS deployed in an urban area thisassumption is likely to be violated most of the time, and the resulting sizedistribution data are unreliable. To improve the reliability, we developed anew algorithm using a statistical model in which the problematic assumptionwas replaced with more realistic smoothness assumptions, which were expressedthrough Gaussian process prior probabilities. We tested the model with datafrom a twin DMPS located at an urban background site in Helsinki and foundthat it provides size distribution data which are much more realistic.Furthermore, particle number concentrations extracted from the DMPS data werecompared with data from a condensation particle counter. At 10 minresolution, the correlation for a period of 10 days was 0.984 with the newalgorithm and 0.967 with the old one. Moreover, the time resolution wasimproved, and at 30 s resolution we obtained positive correlationsfor 89 % of the scans. Thus, the quality of the inverted data was clearlyimproved

Notably improved inversion of differential mobility particle sizer data obtained under conditions of fluctuating particle number concentrations