Articles | Volume 15, issue 23
https://doi.org/10.5194/amt-15-7137-2022
© 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-15-7137-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Dec 2022
Research article |
| 12 Dec 2022
| 12 Dec 2022
Mass spectrometry-based Aerosolomics: a new approach to resolve sources, composition, and partitioning of secondary organic aerosol
Institute for Atmospheric and Environmental Sciences, Goethe-University Frankfurt, 60438 Frankfurt am Main, Germany
Franziska Bachmeier
Institute for Atmospheric and Environmental Sciences, Goethe-University Frankfurt, 60438 Frankfurt am Main, Germany
Felix Leonard Gottwald
Institute for Atmospheric and Environmental Sciences, Goethe-University Frankfurt, 60438 Frankfurt am Main, Germany
Mario Simon
Institute for Atmospheric and Environmental Sciences, Goethe-University Frankfurt, 60438 Frankfurt am Main, Germany
Alexander Lucas Vogel
CORRESPONDING AUTHOR
Institute for Atmospheric and Environmental Sciences, Goethe-University Frankfurt, 60438 Frankfurt am Main, Germany
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Cited
13 citations as recorded by crossref.
- Prioritizing molecular formulae identified by non-target analysis through high-throughput modelling: application to identify compounds with high human accumulation potential from house dust Z. Zhang et al. 10.1039/D3EM00317E
- 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
- Chemical Characterization of Rural Organic Aerosol in the North China Plain Using Ultrahigh-Resolution Mass Spectrometry Y. Zhang et al. 10.3390/atmos14111636
- Regional differences in molecular characteristics of atmospheric water-soluble organic carbon over northern China: Comparison of remote, rural, and urban environments H. Wen et al. 10.1016/j.scitotenv.2024.174170
- Assessment of air quality around the thermal power plant area, Chandrapur, Maharashtra, India V. Manik & S. Gudadhe 10.36953/ECJ.26772653
- 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
- Seasonal investigation of ultrafine-particle organic composition in an eastern Amazonian rainforest A. Thomas et al. 10.5194/acp-25-959-2025
- Seasonal analysis of organic aerosol composition resolves anthropogenic and biogenic sources at a rural background station in central Europe M. Thoma et al. 10.1039/D4EA00163J
- Unveiling a large fraction of hidden organosulfates in ambient organic aerosol J. Ma et al. 10.1038/s41467-025-59420-y
- Similarity-based analysis of atmospheric organic compounds for machine learning applications H. Sandström & P. Rinke 10.5194/gmd-18-2701-2025
- Obtaining accurate non-methane hydrocarbon data for ambient air in urban areas: comparison of non-methane hydrocarbon data between indirect and direct methods S. Gao et al. 10.5194/amt-16-5709-2023
- Measuring pesticides in the atmosphere: current status, emerging trends and future perspectives M. Brüggemann et al. 10.1186/s12302-024-00870-4
- Mass spectrometry-based Aerosolomics: a new approach to resolve sources, composition, and partitioning of secondary organic aerosol M. Thoma et al. 10.5194/amt-15-7137-2022
12 citations as recorded by crossref.
- Prioritizing molecular formulae identified by non-target analysis through high-throughput modelling: application to identify compounds with high human accumulation potential from house dust Z. Zhang et al. 10.1039/D3EM00317E
- 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
- Chemical Characterization of Rural Organic Aerosol in the North China Plain Using Ultrahigh-Resolution Mass Spectrometry Y. Zhang et al. 10.3390/atmos14111636
- Regional differences in molecular characteristics of atmospheric water-soluble organic carbon over northern China: Comparison of remote, rural, and urban environments H. Wen et al. 10.1016/j.scitotenv.2024.174170
- Assessment of air quality around the thermal power plant area, Chandrapur, Maharashtra, India V. Manik & S. Gudadhe 10.36953/ECJ.26772653
- 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
- Seasonal investigation of ultrafine-particle organic composition in an eastern Amazonian rainforest A. Thomas et al. 10.5194/acp-25-959-2025
- Seasonal analysis of organic aerosol composition resolves anthropogenic and biogenic sources at a rural background station in central Europe M. Thoma et al. 10.1039/D4EA00163J
- Unveiling a large fraction of hidden organosulfates in ambient organic aerosol J. Ma et al. 10.1038/s41467-025-59420-y
- Similarity-based analysis of atmospheric organic compounds for machine learning applications H. Sandström & P. Rinke 10.5194/gmd-18-2701-2025
- Obtaining accurate non-methane hydrocarbon data for ambient air in urban areas: comparison of non-methane hydrocarbon data between indirect and direct methods S. Gao et al. 10.5194/amt-16-5709-2023
- Measuring pesticides in the atmosphere: current status, emerging trends and future perspectives M. Brüggemann et al. 10.1186/s12302-024-00870-4
Latest update: 06 Jun 2025
Short summary
We introduce the aerosolomics database and apply it to particulate matter samples. Nine VOCs were oxidized under various conditions in an oxidation flow reactor, and the formed SOA was measured using liquid chromatography mass spectrometry. With the database, an unambiguous top-down attribution of atmospheric oxidation products to their parent VOCs is now possible. Combining the database with hierarchical clustering enables a better understanding of sources, formation, and partitioning of SOA.
We introduce the aerosolomics database and apply it to particulate matter samples. Nine VOCs...
