Abstract View
Matrix-Based Inversion of Humidified Tandem DMA Data
MARKUS PETTERS, North Carolina State University
Abstract Number: 337
Working Group: Instrumentation and Methods
Abstract
Humidified tandem DMAs (HTDMAs) select a single particle mobility diameter, pass this quasi-monodisperse aerosol through a humidification system, and then measure the humidified mobility response function using a second DMA. The humidified mobility response function is influenced by the particle size distribution, aerosol charge distribution, and growth factor frequency distribution function of the upstream aerosol. Inversion from the humidified mobility response function to the growth factor frequency distribution is an ill-posed problem. Matrix-based inversion approaches are desirable because they make no prior assumption about the shape of the growth factor frequency distribution. However, prior attempts of matrix-based approaches sometimes produced oscillatory and negative solutions, thus limiting the utility of this approach. This work shows that constrained Tikhonov regularization is suitable to find the growth factor frequency distribution from raw HTDMA data, while also accounting for multiply charged particles. The proposed new methods are distributed via two freely available software packages: RegularizationTools.jl, a general-purpose software package to apply Tikhonov regularization to data and DifferentialMobilityAnalyzers.jl, which applies the regularization methods to the inversion of DMA and tandem DMA data. The inversion is applied to a multi-week HTDMA dataset collected at the U.S. Department of Energy observatory located at the Southern Great Plains site in Oklahoma, U.S.A. Results show that the proposed approaches are suitable for unsupervised, nonparametric inversion of large-scale datasets. The included software implementation of Tikhonov regularization is general and domain-independent, and thus can be applied to many other inverse problems arising in atmospheric measurement techniques and beyond.