PYDIAS

(Python tools for inversion of aerosol size distributions)

The Python code encompasses a suite of utilities for inverting aerosol characteristics from classifier data. Code includes tools to evaluate transfer functions of a range of instruments (tfer), tools to perform the more traditional unidimensional inversion (e.g., of SMPS data, odiad), and tools for bidimensional inversion (from tandem measurements, bidias).

The bidimensional inversion program was originally released in Matlab with Sipkens et al. (2020a) and is designed to invert tandem measurements of aerosol size distributions. Initially, this code was developed for inversion of particle mass analyzer-differential mobility analyzer (PMA-DMA) data to find the two-dimensional mass-mobility distribution. However, the code has since been generalized to other applications, e.g., the inversion of PMA-SP2 data as in Naseri et al. (2021, 2022).

References

Buckley, D. T., Kimoto, S., Lee, M. H., Fukushima, N., & Hogan Jr, C. J. (2017). Technical note: A corrected two dimensional data inversion routine for tandem mobility-mass measurements. J. Aerosol Sci. 114, 157-168.

Cultrera, A., & Callegaro, L. (2016). A simple algorithm to find the L-curve corner in the regularization of inverse problems. arXiv preprint arXiv:1608.04571.

Naseri, A., Sipkens, T. A., Rogak, S. N., Olfert, J. S. (2021). An improved inversion method for determining two-dimensional mass distributions of non-refractory materials on refractory black carbon. Aerosol Sci. Technol. 55, 104-118.

Naseri, A., Sipkens, T. A., Rogak, S. N., Olfert, J. S. (2022). Optimized instrument configurations for tandem particle mass analyzer and single particle-soot photometer experiments. J. Aerosol Sci. 160, 105897.

Rawat, V. K., Buckley, D. T., Kimoto, S., Lee, M. H., Fukushima, N., & Hogan Jr, C. J. (2016). Two dimensional size–mass distribution function inversion from differential mobility analyzer–aerosol particle mass analyzer (DMA–APM) measurements. J. Aerosol Sci., 92, 70-82.

Sipkens, T. A., Olfert, J. S., & Rogak, S. N. (2019). MATLAB tools for PMA transfer function evaluation (mat-tfer-pma).

Sipkens, T. A., Hadwin, P. J., Grauer, S. J., & Daun, K. J. (2017). General error model for analysis of laser-induced incandescence signals. Appl. Opt. 56, 8436-8445.

Sipkens, T. A., Olfert, J. S., & Rogak, S. N. (2020a). Inversion methods to determine two-dimensional aerosol mass-mobility distributions: A critical comparison of established methods. J. Aerosol Sci. 140, 105484.

Sipkens, T. A., Olfert, J. S., & Rogak, S. N. (2020b). New approaches to calculate the transfer function of particle mass analyzers. Aerosol Sci. Technol. 54, 111-127.

Sipkens, T. A., Olfert, J. S., & Rogak, S. N. (2020c). Inversion methods to determine two-dimensional aerosol mass-mobility distributions II: Existing and novel Bayesian methods. J. Aerosol Sci. 146, 105565

Stolzenburg, M. R. (2018). A review of transfer theory and characterization of measured performance for differential mobility analyzers. Aerosol Sci. Technol. 52, 1194-1218.