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

The differential mobility particle sizer (DMPS) is designed for measurements of particle number size distributions. It performs a number of measurements while scanning over different particle sizes. A standard assumption in the data-processing (inversion) algorithm is that the size distribution remains the same throughout each scan. For a DMPS deployed in an urban area this assumption is likely to be violated most of the time, and the resulting size distribution data are unreliable. To improve the reliability, we developed a new algorithm using a statistical model in which the problematic assumption was replaced with more realistic smoothness assumptions, which were expressed through Gaussian process prior probabilities. We tested the model with data from a twin DMPS located at an urban background site in Helsinki and found that it provides size distribution data which are much more realistic. Furthermore, particle number concentrations extracted from the DMPS data were compared with data from a condensation particle counter. At 10 min resolution, the correlation for a period of 10 days was 0.984 with the new algorithm and 0.967 with the old one. Moreover, the time resolution was improved, and at 30 s resolution we obtained positive correlations for 89 % of the scans. Thus, the quality of the inverted data was clearly improved.