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. Tikhonov regularization is a tool for reducing noise amplificationduring data inversion. This work introduces RegularizationTools.jl,a general-purpose software package for applying Tikhonov regularizationto data. The package implements well-established numerical algorithmsand is suitable for systems of up to ∼ 1000 equations.Included is an abstraction to systematically categorize specific inversionconfigurations and their associated hyperparameters. A generic interfacetranslates arbitrary linear forward models defined by a computer functioninto the corresponding design matrix. This obviates the need to explicitlywrite out and discretize the Fredholm integral equation, thus facilitatingfast prototyping of new regularization schemes associated with measurementtechniques. Example applications include the inversion involving datafrom scanning mobility particle sizers (SMPSs) and humidified tandemdifferential mobility analyzers (HTDMAs). Inversion of SMPS size distributionsreported in this work builds upon the freely available softwareDifferentialMobilityAnalyzers.jl. The speed of inversion isimproved by a factor of ∼ 200, now requiring between2 and 5 ms per SMPS scan when using 120 size bins. Previously reportedoccasional failure to converge to a valid solution is reduced by switchingfrom the L-curve method to generalized cross-validation as the metricto search for the optimal regularization parameter. Higher-order inversionsresulting in smooth, denoised reconstructions of size distributionsare now included in DifferentialMobilityAnalyzers.jl. Thiswork also demonstrates that an SMPS-style matrix-based inversion canbe applied to find the growth factor frequency distribution from rawHTDMA data while also accounting for multiply charged particles.The outcome of the aerosol-related inversion methods is showcasedby inverting multi-week SMPS and HTDMA datasets from ground-basedobservations, including SMPS data obtained at Bodega Marine Laboratoryduring the CalWater 2/ACAPEX campaign and co-located SMPS and HTDMAdata collected at the US Department of Energy observatory locatedat the Southern Great Plains site in Oklahoma, USA. Results showthat the proposed approaches are suitable for unsupervised, nonparametricinversion of large-scale datasets as well as inversion in real timeduring data acquisition on low-cost reduced-instruction-set architecturesused in single-board computers. The included software implementationof Tikhonov regularization is freely available, general, and domain-independentand thus can be applied to many other inverse problems arising inatmospheric measurement techniques and beyond.

Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data