Combining instrument inversions for sub-10 nm aerosol number size-distribution measurements

Abstract

Resolving aerosol dynamical processes in the sub-10 nm range is crucial for our understanding of the contribution of new particle formation to the global cloud condensation nuclei budget or air pollution. Accurate measurements of the particle size distribution in this size-range are challenging due to high diffusional losses and low charging and/or detection efficiencies. Several instruments have been developed in recent years in order to access the sub-10 nm particle size distribution; however, no single instrument can provide high counting statistics, low systematic uncertainties and high size-resolution at the same time. Here we compare several data inversion approaches that allow combining data from different sizing instruments during the inversion and provide python/Julia packages for free usage of the methods. We find that Tikhonov regularization using the L-curve method for optimal regularization parameter estimation gives very reliable results over a wide range of tested data sets and clearly improves standard inversion approaches. Kalman Filtering or regularization using a Poisson likelihood can be powerful tools, especially for well-defined chamber experiments or data from mobility spectrometers only, respectively. Nullspace optimization and non-linear iterative regression are clearly inferior compared to the aforementioned methods. We show that with regularization we can reconstruct the size-distribution measured by up to 4 different mobility particle size spectrometer systems and several particle counters for datasets from Hyytiälä and Helsinki, Finland, revealing the sub-10 nm aerosol dynamics in more detail compared to a single instrument assessment.