Notably improved inversion of Differential Mobility Particle Sizer data obtained under conditions of fluctuating particle number concentrations

Abstract

Abstract. 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 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 30 s resolution, and the overall agreement was good. Thus, the quality of the inverted data was clearly improved.