Quantifying charger-related uncertainty in electrical mobility analysis of aerosols with MPSS-UQ 1.0

Charging probability is the greatest source of uncertainty in electrical mobility-based measurements of aerosol size distributions, but its influence on the results is rarely quantified and reported. In bipolar charging, the charge distribution is almost universally modeled using the Wiedensohler approximation, although it has been shown to deviate significantly from the true charge distribution under many measurement conditions. The charge distribution depends, among other factors, on the mobilities of the charger ions, which are typically not precisely known. Ignoring this uncertainty can lead to biased size distribution estimates and severe underestimation of their uncertainty. In this work, we quantify the uncertainty that imprecisely known charger ion mobilities introduce into the charged particle fractions, and then propagate this uncertainty into the estimates of the particle size distributions using a modular Bayesian approach, in which the ion mobilities are treated as nuisance parameters and marginalized over their plausible range. The inversion method, which is available as the open-source Python package MPSS-UQ, is implemented with particular emphasis on computational efficiency and tested with both synthetic and real mobility particle size spectrometer data. For a month-long field dataset, marginalizing over the uncertain ion mobilities widened the posterior credible intervals of the estimated size distributions on average by a factor of 2.4, with factors up to 10 in some size classes, compared to conditioning on fixed mobility values. The wider intervals provide a more realistic assessment of uncertainties in the inferred size distributions, and quantifying the structure of this uncertainty helps identify where improvements in the characterization of charger ion properties or in the measurement setup would be most effective.