Using particle-resolved aerosol model simulations to guide the interpretations of cloud condensation nuclei experimental data

Abstract

Ambient aerosol particles can undergo dynamic mixing processes as they coagulate with particles from other air masses and emission sources. Therefore, aerosols exist in a spectrum, from externally mixed to internally mixed. The mixing state of aerosols can affect their ability to uptake water (hygroscopicity) and their cloud condensation nuclei (CCN) activity, modifying their contribution to the planet’s total radiative budget. However, current water-uptake measurement methods may not be able to capture the complex mixing state. In this research, the dynamic mixing process was simulated by the particle-resolved aerosol model PartMC and also created by experiments in a laminar flow mixing tube. The mixing evolution of ammonium sulfate and sucrose binary mixtures were observed along with the changes in their water uptake properties expressed as the single hygroscopicity parameter, κ. The use of a mixing simulation in conjunction with experiments allow for better identification of the particle mixing state and the particle water uptake and show that no one kappa value can capture the complexity of mixing across the mixed particle size distribution. In other words, the PartMC simulations can be used as a guiding tool to interpret a system’s mixing state based on its experimental droplet activation spectra. This work demonstrates the importance of the integration and use of mixing models to aid in mixing state determination and hygroscopicity measurements of mixed systems.

Copyright © 2023 American Association for Aerosol Research

Graphical abstract

Editor:

• Kihong Park

Author contribution

AAA, PNR, and KAM designed and conducted the mixing tube water-uptake experiments. KG performed the PyCat Data analysis. NR and AD conducted the Part MC simulations. PNR conducted analysis across the datasets and prepared the manuscript with input from all coauthors.

Disclosure statement

The authors declare that they have no conflict of interest.

Data availability

The data is available upon request from the corresponding author as stated.

Additional information

Funding

PNR, KAM, KG, and AAA acknowledge support from the National Science Foundation: AGS-1723920 and AGS-2124489. NR acknowledges support from AGS-1941110.