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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Jun 2022
Research article |
| 24 Jun 2022
| 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
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, University of
California Berkeley, Berkeley 94720, USA
Lindsay D. Yee
Department of Environmental Science, Policy and Management, University of California Berkeley, Berkeley 94720, USA
Bernard Aumont
Université Paris-Est Créteil and Université de Paris, CNRS, LISA, 94010 Créteil, France
Robert J. Weber
Department of Environmental Science, Policy and Management, University of California Berkeley, Berkeley 94720, USA
Paul Grigas
Department of Industrial Engineering and Operations Research,
University of California Berkeley, Berkeley 94720, USA
Department of Civil and Environmental Engineering, University of
California Berkeley, Berkeley 94720, USA
Department of Environmental Science, Policy and Management, University of California Berkeley, Berkeley 94720, USA
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Cited
14 citations as recorded by crossref.
- Linking Precursors and Volatility of Ambient Oxygenated Organic Aerosols Using Thermal Desorption Measurement and Machine Learning X. Wang et al. 10.1021/acsestair.4c00076
- Anthropogenic and Biogenic Contributions to the Organic Composition of Coastal Submicron Sea Spray Aerosol E. Franklin et al. 10.1021/acs.est.2c04848
- Systematic characterization of unknown compounds via dimensionality reduction of time series S. Kim et al. 10.1080/02786826.2024.2445634
- Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography—A review M. Gaida et al. 10.1016/j.chroma.2023.464467
- Electrochemical deposition of HSA on Ag electrode for its quantitative determination using SERS and machine learning I. Boginskaya et al. 10.1016/j.sna.2024.115700
- A mathematical model for project cost prediction combining multiple algorithms R. Zhang 10.1680/jsmic.23.00061
- ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis Z. Zheng et al. 10.1021/jacs.3c05819
- Data‐Driven Compound Identification in Atmospheric Mass Spectrometry H. Sandström et al. 10.1002/advs.202306235
- Characterizing PM2.5 Emissions and Temporal Evolution of Organic Composition from Incense Burning in a California Residence J. Ofodile et al. 10.1021/acs.est.3c08904
- Technical note: Towards atmospheric compound identification in chemical ionization mass spectrometry with pesticide standards and machine learning F. Bortolussi et al. 10.5194/acp-25-685-2025
- Visualization and Synthesis of LCCC Space Based on RF-SVM Optimization S. Wang et al. 10.1109/ACCESS.2023.3327137
- Characterisation of atmospheric organic aerosols with one- and multidimensional liquid chromatography and mass spectrometry: State of the art and future perspectives S. Hildmann & T. Hoffmann 10.1016/j.trac.2024.117698
- A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra M. Jabed et al. 10.1021/acssuschemeng.3c01821
- Similarity-based analysis of atmospheric organic compounds for machine learning applications H. Sandström & P. Rinke 10.5194/gmd-18-2701-2025
14 citations as recorded by crossref.
- Linking Precursors and Volatility of Ambient Oxygenated Organic Aerosols Using Thermal Desorption Measurement and Machine Learning X. Wang et al. 10.1021/acsestair.4c00076
- Anthropogenic and Biogenic Contributions to the Organic Composition of Coastal Submicron Sea Spray Aerosol E. Franklin et al. 10.1021/acs.est.2c04848
- Systematic characterization of unknown compounds via dimensionality reduction of time series S. Kim et al. 10.1080/02786826.2024.2445634
- Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography—A review M. Gaida et al. 10.1016/j.chroma.2023.464467
- Electrochemical deposition of HSA on Ag electrode for its quantitative determination using SERS and machine learning I. Boginskaya et al. 10.1016/j.sna.2024.115700
- A mathematical model for project cost prediction combining multiple algorithms R. Zhang 10.1680/jsmic.23.00061
- ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis Z. Zheng et al. 10.1021/jacs.3c05819
- Data‐Driven Compound Identification in Atmospheric Mass Spectrometry H. Sandström et al. 10.1002/advs.202306235
- Characterizing PM2.5 Emissions and Temporal Evolution of Organic Composition from Incense Burning in a California Residence J. Ofodile et al. 10.1021/acs.est.3c08904
- Technical note: Towards atmospheric compound identification in chemical ionization mass spectrometry with pesticide standards and machine learning F. Bortolussi et al. 10.5194/acp-25-685-2025
- Visualization and Synthesis of LCCC Space Based on RF-SVM Optimization S. Wang et al. 10.1109/ACCESS.2023.3327137
- Characterisation of atmospheric organic aerosols with one- and multidimensional liquid chromatography and mass spectrometry: State of the art and future perspectives S. Hildmann & T. Hoffmann 10.1016/j.trac.2024.117698
- A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra M. Jabed et al. 10.1021/acssuschemeng.3c01821
- Similarity-based analysis of atmospheric organic compounds for machine learning applications H. Sandström & P. Rinke 10.5194/gmd-18-2701-2025
Latest update: 30 May 2025
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.
The composition of atmospheric aerosols are extremely complex, containing hundreds of thousands...
