Issue 1, 2023

Precursor apportionment of atmospheric oxygenated organic molecules using a machine learning method

Abstract

Gas-phase oxygenated organic molecules (OOMs) can contribute significantly to both atmospheric new particle growth and secondary organic aerosol formation. Precursor apportionment of atmospheric OOMs connects them with volatile organic compounds (VOCs). Since atmospheric OOMs are often highly functionalized products of multistep reactions, it is challenging to reveal the complete mapping relationships between OOMs and their precursors. In this study, we demonstrate that the machine learning method is useful in attributing atmospheric OOMs to their precursors using several chemical indicators, such as O/C ratio and H/C ratio. The model is trained and tested using data acquired in controlled laboratory experiments, covering the oxidation products of four main types of VOCs (isoprene, monoterpenes, aliphatics, and aromatics). Then, the model is used for analyzing atmospheric OOMs measured in both urban Beijing and a boreal forest environment in southern Finland. The results suggest that atmospheric OOMs in these two environments can be reasonably assigned to their precursors. Beijing is an anthropogenic VOC dominated environment with ∼64% aromatic and aliphatic OOMs, and the other boreal forested area has ∼76% monoterpene OOMs. This pilot study shows that machine learning can be a promising tool in atmospheric chemistry for connecting the dots.

Graphical abstract: Precursor apportionment of atmospheric oxygenated organic molecules using a machine learning method

Supplementary files

Article information

Article type
Paper
Submitted
03 Oct 2022
Accepted
30 Nov 2022
First published
03 Dec 2022
This article is Open Access
Creative Commons BY-NC license

Environ. Sci.: Atmos., 2023,3, 230-237

Precursor apportionment of atmospheric oxygenated organic molecules using a machine learning method

X. Qiao, X. Li, C. Yan, N. Sarnela, R. Yin, Y. Guo, L. Yao, W. Nie, D. Huang, Z. Wang, F. Bianchi, Y. Liu, N. M. Donahue, M. Kulmala and J. Jiang, Environ. Sci.: Atmos., 2023, 3, 230 DOI: 10.1039/D2EA00128D

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