Articles | Volume 15, issue 12
Atmos. Meas. Tech., 15, 3779–3803, 2022
https://doi.org/10.5194/amt-15-3779-2022
Atmos. Meas. Tech., 15, 3779–3803, 2022
https://doi.org/10.5194/amt-15-3779-2022
Research article
24 Jun 2022
Research article | 24 Jun 2022

Ch3MS-RF: a random forest model for chemical characterization and improved quantification of unidentified atmospheric organics detected by chromatography–mass spectrometry techniques

Emily B. Franklin et al.

Model code and software

ebarnesey/Ch3MS-RF: Pre-Publication Main Release (v1.1.1) Emily B. Franklin https://doi.org/10.5281/zenodo.6320122

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Short summary
The composition of atmospheric aerosols are extremely complex, containing hundreds of thousands of estimated individual compounds. The majority of these compounds have never been catalogued in widely used databases, making them extremely difficult for atmospheric chemists to identify and analyze. In this work, we present Ch3MS-RF, a machine-learning-based model to enable characterization of complex mixtures and prediction of structure-specific properties of unidentifiable organic compounds.