Articles | Volume 16, issue 7
https://doi.org/10.5194/acp-16-4401-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-16-4401-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Technical note
| 
11 Apr 2016
Technical note |
| 11 Apr 2016
Technical Note: Development of chemoinformatic tools to enumerate functional groups in molecules for organic aerosol characterization
Giulia Ruggeri
ENAC/IIE Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
Satoshi Takahama
CORRESPONDING AUTHOR
ENAC/IIE Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
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Cited
19 citations as recorded by crossref.
- Nontargeted Tandem Mass Spectrometry Analysis Reveals Diversity and Variability in Aerosol Functional Groups across Multiple Sites, Seasons, and Times of Day J. Ditto et al. 10.1021/acs.estlett.9b00702
- Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: method development for probabilistic modeling of organic carbon and organic matter concentrations C. Bürki et al. 10.5194/amt-13-1517-2020
- Analysis of reduced and oxidized nitrogen-containing organic compounds at a coastal site in summer and winter J. Ditto et al. 10.5194/acp-22-3045-2022
- Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: systematic intercomparison of calibration methods for US measurement network samples M. Reggente et al. 10.5194/amt-12-2287-2019
- Integrated microalgae culture with food processing waste for wastewater remediation and enhanced biomass productivity A. Peter et al. 10.1016/j.cclet.2022.08.001
- Impact of organic molecular structure on the estimation of atmospherically relevant physicochemical parameters G. Isaacman-VanWertz & B. Aumont 10.5194/acp-21-6541-2021
- ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction K. Felton et al. 10.1016/j.cej.2024.151999
- Real-world observations of reduced nitrogen and ultrafine particles in commercial cooking organic aerosol emissions S. Kim et al. 10.5194/acp-24-1281-2024
- Facile in situ generation of bismuth tungstate nanosheet-multiwalled carbon nanotube composite as unconventional affinity material for quartz crystal microbalance detection of antibiotics A. Munawar et al. 10.1016/j.jhazmat.2019.03.054
- Probing Individual Particles Generated at the Freshwater–Seawater Interface through Combined Raman, Photothermal Infrared, and X-ray Spectroscopic Characterization J. Mirrielees et al. 10.1021/acsmeasuresciau.2c00041
- Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules V. Besel et al. 10.1038/s41597-023-02366-x
- Effects of Molecular-Level Compositional Variability in Organic Aerosol on Phase State and Thermodynamic Mixing Behavior J. Ditto et al. 10.1021/acs.est.9b02664
- Technical note: Relating functional group measurements to carbon types for improved model–measurement comparisons of organic aerosol composition S. Takahama & G. Ruggeri 10.5194/acp-17-4433-2017
- The search for sparse data in molecular datasets: Application of active learning to identify extremely low volatile organic compounds V. Besel et al. 10.1016/j.jaerosci.2024.106375
- Model–measurement comparison of functional group abundance in α-pinene and 1,3,5-trimethylbenzene secondary organic aerosol formation G. Ruggeri et al. 10.5194/acp-16-8729-2016
- Quantifying functional group compositions of household fuel-burning emissions E. Li et al. 10.5194/amt-17-2401-2024
- Functional Factors of Biomass Burning Contribution to Spring Aerosol Composition in a Megacity: Combined FTIR-PCA Analyses O. Popovicheva et al. 10.3390/atmos11040319
- Similarity-based analysis of atmospheric organic compounds for machine learning applications H. Sandström & P. Rinke 10.5194/gmd-18-2701-2025
- UManSysProp v1.0: an online and open-source facility for molecular property prediction and atmospheric aerosol calculations D. Topping et al. 10.5194/gmd-9-899-2016
18 citations as recorded by crossref.
- Nontargeted Tandem Mass Spectrometry Analysis Reveals Diversity and Variability in Aerosol Functional Groups across Multiple Sites, Seasons, and Times of Day J. Ditto et al. 10.1021/acs.estlett.9b00702
- Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: method development for probabilistic modeling of organic carbon and organic matter concentrations C. Bürki et al. 10.5194/amt-13-1517-2020
- Analysis of reduced and oxidized nitrogen-containing organic compounds at a coastal site in summer and winter J. Ditto et al. 10.5194/acp-22-3045-2022
- Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: systematic intercomparison of calibration methods for US measurement network samples M. Reggente et al. 10.5194/amt-12-2287-2019
- Integrated microalgae culture with food processing waste for wastewater remediation and enhanced biomass productivity A. Peter et al. 10.1016/j.cclet.2022.08.001
- Impact of organic molecular structure on the estimation of atmospherically relevant physicochemical parameters G. Isaacman-VanWertz & B. Aumont 10.5194/acp-21-6541-2021
- ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction K. Felton et al. 10.1016/j.cej.2024.151999
- Real-world observations of reduced nitrogen and ultrafine particles in commercial cooking organic aerosol emissions S. Kim et al. 10.5194/acp-24-1281-2024
- Facile in situ generation of bismuth tungstate nanosheet-multiwalled carbon nanotube composite as unconventional affinity material for quartz crystal microbalance detection of antibiotics A. Munawar et al. 10.1016/j.jhazmat.2019.03.054
- Probing Individual Particles Generated at the Freshwater–Seawater Interface through Combined Raman, Photothermal Infrared, and X-ray Spectroscopic Characterization J. Mirrielees et al. 10.1021/acsmeasuresciau.2c00041
- Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules V. Besel et al. 10.1038/s41597-023-02366-x
- Effects of Molecular-Level Compositional Variability in Organic Aerosol on Phase State and Thermodynamic Mixing Behavior J. Ditto et al. 10.1021/acs.est.9b02664
- Technical note: Relating functional group measurements to carbon types for improved model–measurement comparisons of organic aerosol composition S. Takahama & G. Ruggeri 10.5194/acp-17-4433-2017
- The search for sparse data in molecular datasets: Application of active learning to identify extremely low volatile organic compounds V. Besel et al. 10.1016/j.jaerosci.2024.106375
- Model–measurement comparison of functional group abundance in α-pinene and 1,3,5-trimethylbenzene secondary organic aerosol formation G. Ruggeri et al. 10.5194/acp-16-8729-2016
- Quantifying functional group compositions of household fuel-burning emissions E. Li et al. 10.5194/amt-17-2401-2024
- Functional Factors of Biomass Burning Contribution to Spring Aerosol Composition in a Megacity: Combined FTIR-PCA Analyses O. Popovicheva et al. 10.3390/atmos11040319
- Similarity-based analysis of atmospheric organic compounds for machine learning applications H. Sandström & P. Rinke 10.5194/gmd-18-2701-2025
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Latest update: 02 Jul 2025
Short summary
We present a set of tools for mapping molecular information to functional group composition. This allows us to reduce the complexity of representing the organic aerosol composition, as it consists of hundreds of thousands of different compounds. We describe the tools and methods for validation, and demonstrate several applications in which this tool can facilitate measurement intercomparisons and chemical modeling of aerosol chemistry.
We present a set of tools for mapping molecular information to functional group composition....
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