AeroMix v1.0.1: a Python package for modeling aerosol optical properties and mixing states

Abstract

Abstract. Assessing aerosol mixing states, which primarily depend on aerosol chemical compositions, is indispensable to estimate direct and indirect effects of aerosols. The limitations of the direct measurements of aerosol chemical composition and mixing states necessitate modeling approaches to infer the aerosol mixing states. The Optical Properties of Aerosols and Clouds (OPAC) model has been extensively utilized to construct optically equivalent aerosol chemical compositions from measured aerosol optical properties using Mie inversion. However, the representation of real atmospheric aerosol mixing scenarios in OPAC has perennially been challenged by the exclusive assumption of external mixing. A Python successor to the aerosol module of the OPAC model is developed, named AeroMix, with novel capabilities to (1) model externally and core–shell mixed aerosols, (2) simulate optical properties of aerosol mixtures constituted by any number of aerosol components, and (3) define aerosol composition and relative humidity in up to six vertical layers. Designed as a versatile open-source aerosol optical model framework, AeroMix is tailored for sophisticated inversion algorithms aimed at modeling aerosol mixing states and also their physical and chemical properties. AeroMix's performance is demonstrated by modeling the probable aerosol mixing states over Kanpur (urban) and the Bay of Bengal (marine) in south Asia. The modeled mixing states are consistent with independent measurements using a single-particle soot photometer (SP2) and transmission electron microscopy (TEM), substantiating the potential capability of AeroMix to model complex aerosol mixing scenarios involving multiple internally mixed components in diverse environments. This work contributes a valuable tool for modeling aerosol mixing states to assess their impact on cloud-nucleating properties and radiation budget.