Abstract. Kinetic multi-layer models of aerosols and films have become the state-of-the-art method of describing complex aerosol processes at the particle and film level. We present MultilayerPy: an open-source framework for building, running and optimising kinetic multi-layer models – namely the kinetic multi-layer model of aerosol surface and bulk chemistry (KM-SUB) and the kinetic multi-layer model of gas–particle interactions in aerosols and clouds (KM-GAP). The modular nature of this package allows the user to iterate through various reaction schemes, diffusion regimes and experimental conditions in a systematic way. In this way, models can be customised and the raw model code itself, produced in a readable way by MultilayerPy, is fully customisable. Optimisation to experimental data using local or global optimisation algorithms is included in the package along with the option to carry out statistical sampling and Bayesian inference of model parameters with a Markov chain Monte Carlo (MCMC) sampler (via the emcee Python package). MultilayerPy abstracts the model building process into separate building blocks, increasing the reproducibility of results and minimising human error. This paper describes the general functionality of MultilayerPy and demonstrates this with use cases based on the oleic- acid–ozone heterogeneous reaction system. The tutorials in the source code (written as Jupyter notebooks) and the documentation aim to encourage users to take advantage of this tool, which is intended to be developed in conjunction with the user base.

Abstract. Kinetic multi-layer models of aerosols and films have become the state-of-the-art method of describing complex aerosol processes at particle and film level. We present MultilayerPy: an open-source framework for building, running and optimising kinetic multi-layer models – namely the kinetic multi-layer model of aerosol surface and bulk chemistry (KM-SUB), and the kinetic multi-layer model of gas-particle interactions in aerosols and clouds (KM-GAP). The modular nature of this package allows the user to iterate through various reaction schemes, diffusion regimes and experimental conditions in a systematic way. In this way, models can be customised and the raw model code itself, produced in a readable way by MultilayerPy, is fully customisable. Optimisation to experimental data using local or global optimisation algorithms is included in the package along with the option to carry out statistical sampling and Bayesian inference of model parameters with a Markov Chain Monte Carlo (MCMC) sampler (via the emcee Python package). MultilayerPy abstracts the model building process into separate building blocks, increasing the reproducibility of results and minimising human error. This paper describes the general functionality of MultilayerPy and demonstrates this with use cases based on the oleic acid-ozone heterogeneous reaction system. The tutorials in the source code (written as Jupyter notebooks) and the documentation aim to encourage users to take advantage of this tool, which is intended to be developed in conjunction with the user base.

<p>Heterogeneous processes such as aerosol-gas chemical reactions and vapour uptake are key to understanding the behaviour of aerosols in our environment. They contribute to their ability to take up water to form cloud droplets and determine the persistence of harmful particle-bound compounds, impacting the climate and human health.</p> <p>Kinetic multi-layer models such as the kinetic multi-layer model for aerosol surface and bulk chemistry (KM-SUB) and gas-particle interactions (KM-GAP) are state-of-the-art models used to describe these processes on the particle and film level (Shiraiwa et al., 2010, 2012). KM-SUB and KM-GAP-based models have been used to determine the oxidative potential of particulate matter, the impact of surfactant self-organisation on aerosol chemical lifetimes, and the impact of aerosol phase state on the long-range transport of toxic chemicals. These models are useful but cumbersome to write and there is a need for an open-source tool to assist researchers in creating and optimising them.</p> <p>We have developed MultilayerPy (Milsom et al., 2022), an open-source Python package which facilitates the creation and optimisation of kinetic multi-layer models. This software is written such that the user uses building blocks (i.e. reaction scheme, bulk diffusion parameterisations, and model components) to automatically generate model code which can then be ran and the output presented in a reproducible manner. This reduces the time needed to develop model descriptions of aerosol processes and allows the user to focus on the scientific issues rather than coding the models. I will present recent use cases of the software looking at the chemical lifetime of real aerosol material in the atmosphere, along with ongoing work extending the base package.</p> <p>References:</p> <p>Milsom, A., Lees, A., Squires, A. M. and Pfrang, C.: MultilayerPy (v1.0): a Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and films, Geosci. Model Dev., 15(18), 7139–7151, doi:10.5194/gmd-15-7139-2022, 2022.</p> <p>Shiraiwa, M., Pfrang, C. and Pöschl, U.: Kinetic multi-layer model of aerosol surface and bulk chemistry (KM-SUB): The influence of interfacial transport and bulk diffusion on the oxidation of oleic acid by ozone, Atmos. Chem. Phys., 10, 3673–3691, doi:10.5194/acp-10-3673-2010, 2010.</p> <p>Shiraiwa, M., Pfrang, C., Koop, T. and Pöschl, U.: Kinetic multi-layer model of gas-particle interactions in aerosols and clouds (KM-GAP): Linking condensation, evaporation and chemical reactions of organics, oxidants and water, Atmos. Chem. Phys., 12(5), 2777–2794, doi:10.5194/acp-12-2777-2012, 2012.</p>

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