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Tutorial: Dynamic organic growth modeling with a volatility basis set

Stolzenburg, Dominik and Wang, Mingyi and Schervish, Meredith and Donahue, Neil M. (2022) Tutorial: Dynamic organic growth modeling with a volatility basis set. Journal of Aerosol Science, 166 . Art. No. 106063. ISSN 0021-8502. doi:10.1016/j.jaerosci.2022.106063. https://resolver.caltech.edu/CaltechAUTHORS:20221014-968413500.4

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Abstract

Organic aerosols are ubiquitous in the atmosphere and oxygenated organics are a major driver of aerosol growth. The volatility basis set (VBS) as introduced by Donahue et al. (2006, 2011) is often used to simplify the partitioning behavior of the huge variety of atmospheric organics. Recently, the VBS was used to dynamically model aerosol growth from the smallest sizes onwards. This tutorial is intended to equip the reader with the necessary tools to facilitate organic growth modelling based on gas-phase measurements of oxygenated organics using a 2-dimensional VBS. We start with a contextualization of the VBS in partitioning theory and point out the need for dynamic modeling. We provide an overview on the most common methods to estimate the volatility of oxygenated organics and give detailed instruction on how to construct the binned VBS. We then explain the dynamic condensation model including solution and curvature effects. Furthermore, we provide a python package for VBS growth calculations and show with two examples from ambient and chamber measurements how growth rates can be calculated. Last, we summarize the limitation of this approach and outline necessary future developments.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.jaerosci.2022.106063DOIArticle
ORCID:
AuthorORCID
Stolzenburg, Dominik0000-0003-1014-1360
Wang, Mingyi0000-0001-5782-2513
Donahue, Neil M.0000-0003-3054-2364
Additional Information:We acknowledge Wayne Chuang for his contribution to the first dynamic VBS growth modelling tool, which was the basis for the code developed here. We also thank Nina Sarnela, Martin Heinritzi and Mario Simon for the mass spectrometric analysis of the two presented example cases and acknowledge Loic Gonzales-Carracedo for the deployment of the DMA-train in Hyytiälä. This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 895875 ("NPF-PANDA"). We acknowledge the funding from the US National Science Foundation AGS2132089.
Funders:
Funding AgencyGrant Number
Marie Curie Fellowship895875
NSFAGS-2132089
DOI:10.1016/j.jaerosci.2022.106063
Record Number:CaltechAUTHORS:20221014-968413500.4
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20221014-968413500.4
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117414
Collection:CaltechAUTHORS
Deposited By: Research Services Depository
Deposited On:18 Oct 2022 22:09
Last Modified:18 Oct 2022 22:09

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