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

Franklin, Emily B.; Yee, Lindsay D.; Aumont, Bernard; Weber, Robert J.; Grigas, Paul; Goldstein, Allen H.

doi:https://doi.org/10.5194/amt-15-3779-2022

Articles | Volume 15, issue 12

https://doi.org/10.5194/amt-15-3779-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-15-3779-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 12

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, Lindsay D. Yee, Bernard Aumont, Robert J. Weber, Paul Grigas, and Allen H. Goldstein

Supplement

https://doi.org/10.5194/amt-15-3779-2022-supplement

Model code and software

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

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.