Articles | Volume 25, issue 1
https://doi.org/10.5194/acp-25-685-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-685-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
17 Jan 2025
Technical note |
| 17 Jan 2025
| 17 Jan 2025
Technical note: Towards atmospheric compound identification in chemical ionization mass spectrometry with pesticide standards and machine learning
Federica Bortolussi
CORRESPONDING AUTHOR
Department of Chemistry, University of Helsinki, 00560 Helsinki, Finland
Hilda Sandström
Department of Applied Physics, Aalto University, Espoo, Finland
Fariba Partovi
Aerosol Physics Laboratory, Physics Unit, Tampere University, 33720 Tampere, Finland
Karsa Ltd., A. I. Virtasen aukio 1, 00560 Helsinki, Finland
Joona Mikkilä
Karsa Ltd., A. I. Virtasen aukio 1, 00560 Helsinki, Finland
Patrick Rinke
Department of Applied Physics, Aalto University, Espoo, Finland
Physics Department, TUM School of Natural Sciences, Technical University of Munich, Garching, Germany
Atomistic Modelling Center, Munich Data Science Institute, Technical University of Munich, Garching, Germany
Munich Center for Machine Learning (MCML), Munich, Germany
Matti Rissanen
Department of Chemistry, University of Helsinki, 00560 Helsinki, Finland
Aerosol Physics Laboratory, Physics Unit, Tampere University, 33720 Tampere, Finland
Related authors
No articles found.
Neil M. Donahue, Victoria Hofbauer, Henning Finkenzeller, Dominik Stolzenburg, Paulus S. Bauer, Randall Chiu, Lubna Dada, Jonathan Duplissy, Xu-Cheng He, Martin Heinritzi, Christopher R. Hoyle, Andreas Kürten, Aleksandr Kvashnin, Katrianne Lehtipalo, Naser Mahfouz, Vladimir Makhmutov, Roy L. Mauldin III, Ugo Molteni, Lauriane L. J. Quéléver, Matti Rissanen, Siegfried Schobesberger, Mario Simon, Andrea C. Wagner, Mingyi Wang, Chao Yan, Penglin Ye, Ilona Riipinen, Hamish Gordon, Joachim Curtius, Armin Hansel, Imad El Haddad, Markku Kulmala, Douglas R. Worsnop, Rainer Volkamer, Paul M. Winkler, Jasper Kirkby, and Richard Flagan
EGUsphere, https://doi.org/10.5194/egusphere-2025-2412, https://doi.org/10.5194/egusphere-2025-2412, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
We describe accurate measurement of particle formation and growth in the CERN CLOUD chamber, using a suite of gas- and particle-phase instruments. The interconnected measurements establish high accuracy in key particle properties and critically important gas-phase sulfuric acid. This is a template for accurate calibration of similar experiments and thus accurate determination of aerosol nucleation and growth rates, which are an important source of uncertainty in climate science.
Mojtaba Bezaatpour, Miikka Dal Maso, and Matti Rissanen
EGUsphere, https://doi.org/10.5194/egusphere-2025-2564, https://doi.org/10.5194/egusphere-2025-2564, 2025
Short summary
Short summary
We developed a computer program that can read chemical formulas and identify key features in thousands of organic compounds. This helps scientists estimate how easily these compounds evaporate, which is important for understanding air pollution and climate. We tested the program using real-world data and found it to be highly accurate. Our work makes it faster and easier to study the behavior of many complex chemicals in the atmosphere.
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025, https://doi.org/10.5194/gmd-18-2701-2025, 2025
Short summary
Short summary
Machine learning has the potential to aid the identification of organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning models in atmospheric sciences.
Henning Finkenzeller, Jyri Mikkilä, Cecilia Righi, Paxton Juuti, Mikko Sipilä, Matti Rissanen, Douglas Worsnop, Aleksei Shcherbinin, Nina Sarnela, and Juha Kangasluoma
Atmos. Meas. Tech., 17, 5989–6001, https://doi.org/10.5194/amt-17-5989-2024, https://doi.org/10.5194/amt-17-5989-2024, 2024
Short summary
Short summary
Chemical ionisation mass spectrometry is used in the atmospheric sciences to measure trace gas concentrations. Neutral gases require charging in inlets before the mass-to-charge ratio of the resulting ions can be analysed. This study uses multiphysics modelling to investigate how the MION2 and Eisele type inlets work and shows the effect of tuning parameters and their current limitations. The findings are helpful for inlet users and are expected to aid in developing improved inlets.
Alex Rowell, James Brean, David C. S. Beddows, Zongbo Shi, Avinash Kumar, Matti Rissanen, Miikka Dal Maso, Peter Mettke, Kay Weinhold, Maik Merkel, and Roy M. Harrison
Atmos. Chem. Phys., 24, 10349–10361, https://doi.org/10.5194/acp-24-10349-2024, https://doi.org/10.5194/acp-24-10349-2024, 2024
Short summary
Short summary
Ions enhance the formation and growth rates of new particles, affecting the Earth's radiation budget. Despite these effects, there is little published data exploring the sources of ions in the urban environment and their role in new particle formation (NPF). Here we show that natural ion sources dominate in urban environments, while traffic is a secondary source. Ions contribute up to 12.7 % of the formation rate of particles, indicating that they are important for forming urban PM.
Stéphanie Alage, Vincent Michoud, Sergio Harb, Bénédicte Picquet-Varrault, Manuela Cirtog, Avinash Kumar, Matti Rissanen, and Christopher Cantrell
Atmos. Meas. Tech., 17, 4709–4724, https://doi.org/10.5194/amt-17-4709-2024, https://doi.org/10.5194/amt-17-4709-2024, 2024
Short summary
Short summary
Calibration exercises are essential for determining the accuracy of instruments. We performed calibrations on a NO3¯ ToFCIMS instrument to determine its sensitivity and linearity for detecting various organic compounds. Our findings revealed significant variability, over several orders of magnitude, in the calibration factors obtained. The results suggest that relying on a single calibration factor from H2SO4 for the quantification of all compounds detected by this technique is not appropriate.
Romain Salignat, Matti Rissanen, Siddharth Iyer, Jean-Luc Baray, Pierre Tulet, Jean-Marc Metzger, Jérôme Brioude, Karine Sellegri, and Clémence Rose
Atmos. Chem. Phys., 24, 3785–3812, https://doi.org/10.5194/acp-24-3785-2024, https://doi.org/10.5194/acp-24-3785-2024, 2024
Short summary
Short summary
Using mass spectrometry data collected at the Maïdo Observatory (2160 m a.s.l., Réunion), we provide the first detailed analysis of molecular cluster chemical composition specifically in the marine free troposphere. The abundance of the identified species is related both to in situ meteorological parameters and air mass history, which also provide insight into their origin. Our work makes an important contribution to documenting the chemistry and physics of the marine free troposphere.
Xu-Cheng He, Jiali Shen, Siddharth Iyer, Paxton Juuti, Jiangyi Zhang, Mrisha Koirala, Mikko M. Kytökari, Douglas R. Worsnop, Matti Rissanen, Markku Kulmala, Norbert M. Maier, Jyri Mikkilä, Mikko Sipilä, and Juha Kangasluoma
Atmos. Meas. Tech., 16, 4461–4487, https://doi.org/10.5194/amt-16-4461-2023, https://doi.org/10.5194/amt-16-4461-2023, 2023
Short summary
Short summary
In this study, the upgraded multi-scheme chemical ionisation inlet 2 is presented. Sulfuric acid, hypoiodous acid, iodine, sulfur dioxide, and hydroperoxyl radicals are calibrated, and the improved ion optics allow us to detect sulfuric acid and iodine-containing molecules at as low as a few parts per quadrillion by volume. Additionally, we confirm the reliable detection of iodic acid using both the nitrate and bromide chemical ionisation methods under atmospherically relevant conditions.
Shawon Barua, Siddharth Iyer, Avinash Kumar, Prasenjit Seal, and Matti Rissanen
Atmos. Chem. Phys., 23, 10517–10532, https://doi.org/10.5194/acp-23-10517-2023, https://doi.org/10.5194/acp-23-10517-2023, 2023
Short summary
Short summary
This work illustrates how a common volatile hydrocarbon, hexanal, has the potential to undergo atmospheric autoxidation that leads to prompt formation of condensable material that subsequently contributes to aerosol formation, deteriorating the air quality of urban atmospheres. We used the combined state-of-the-art quantum chemical modeling and experimental flow reactor experiments under atmospheric conditions to resolve the autoxidation mechanism of hexanal initiated by a common oxidant.
Lukas Pichelstorfer, Pontus Roldin, Matti Rissanen, Noora Hyttinen, Olga Garmash, Carlton Xavier, Putian Zhou, Petri Clusius, Benjamin Foreback, Thomas Golin Almeida, Chenjuan Deng, Metin Baykara, Theo Kurten, and Michael Boy
EGUsphere, https://doi.org/10.5194/egusphere-2023-1415, https://doi.org/10.5194/egusphere-2023-1415, 2023
Preprint archived
Short summary
Short summary
Secondary organic aerosols (SOA) form effectively from gaseous precursors via a process called autoxidation. While key chemical reaction types seem to be known, no general description of autoxidation chemistry exists. In the present work, we present a method to create autoxidation chemistry schemes for any atmospherically relevant hydrocarbon. We exemplarily investigate benzene and its potential to form aerosols. We found that autoxidation, under some conditions, can dominate the SOA formation.
Melissa Meder, Otso Peräkylä, Jonathan G. Varelas, Jingyi Luo, Runlong Cai, Yanjun Zhang, Theo Kurtén, Matthieu Riva, Matti Rissanen, Franz M. Geiger, Regan J. Thomson, and Mikael Ehn
Atmos. Chem. Phys., 23, 4373–4390, https://doi.org/10.5194/acp-23-4373-2023, https://doi.org/10.5194/acp-23-4373-2023, 2023
Short summary
Short summary
We discuss and show the viability of a method where multiple isotopically labelled precursors are used for probing the formation pathways of highly oxygenated organic molecules (HOMs) from the oxidation of the monoterpene a-pinene. HOMs are very important for secondary organic aerosol (SOA) formation in forested regions, and monoterpenes are the single largest source of SOA globally. The fast reactions forming HOMs have thus far remained elusive despite considerable efforts over the last decade.
Miska Olin, Magdalena Okuljar, Matti P. Rissanen, Joni Kalliokoski, Jiali Shen, Lubna Dada, Markus Lampimäki, Yusheng Wu, Annalea Lohila, Jonathan Duplissy, Mikko Sipilä, Tuukka Petäjä, Markku Kulmala, and Miikka Dal Maso
Atmos. Chem. Phys., 22, 8097–8115, https://doi.org/10.5194/acp-22-8097-2022, https://doi.org/10.5194/acp-22-8097-2022, 2022
Short summary
Short summary
Atmospheric new particle formation is an important source of the total particle number concentration in the atmosphere. Several parameters for predicting new particle formation events have been suggested before, but the results have been inconclusive. This study proposes an another predicting parameter, related to a specific type of highly oxidized organic molecules, especially for similar locations to the measurement site in this study, which was a coastal agricultural site in Finland.
Dalrin Ampritta Amaladhasan, Claudia Heyn, Christopher R. Hoyle, Imad El Haddad, Miriam Elser, Simone M. Pieber, Jay G. Slowik, Antonio Amorim, Jonathan Duplissy, Sebastian Ehrhart, Vladimir Makhmutov, Ugo Molteni, Matti Rissanen, Yuri Stozhkov, Robert Wagner, Armin Hansel, Jasper Kirkby, Neil M. Donahue, Rainer Volkamer, Urs Baltensperger, Martin Gysel-Beer, and Andreas Zuend
Atmos. Chem. Phys., 22, 215–244, https://doi.org/10.5194/acp-22-215-2022, https://doi.org/10.5194/acp-22-215-2022, 2022
Short summary
Short summary
We use a combination of models for gas-phase chemical reactions and equilibrium gas–particle partitioning of isoprene-derived secondary organic aerosols (SOAs) informed by dark ozonolysis experiments conducted in the CLOUD chamber. Our predictions cover high to low relative humidities (RHs) and quantify how SOA mass yields are enhanced at high RH as well as the impact of inorganic seeds of distinct hygroscopicities and acidities on the coupled partitioning of water and semi-volatile organics.
Mao Xiao, Christopher R. Hoyle, Lubna Dada, Dominik Stolzenburg, Andreas Kürten, Mingyi Wang, Houssni Lamkaddam, Olga Garmash, Bernhard Mentler, Ugo Molteni, Andrea Baccarini, Mario Simon, Xu-Cheng He, Katrianne Lehtipalo, Lauri R. Ahonen, Rima Baalbaki, Paulus S. Bauer, Lisa Beck, David Bell, Federico Bianchi, Sophia Brilke, Dexian Chen, Randall Chiu, António Dias, Jonathan Duplissy, Henning Finkenzeller, Hamish Gordon, Victoria Hofbauer, Changhyuk Kim, Theodore K. Koenig, Janne Lampilahti, Chuan Ping Lee, Zijun Li, Huajun Mai, Vladimir Makhmutov, Hanna E. Manninen, Ruby Marten, Serge Mathot, Roy L. Mauldin, Wei Nie, Antti Onnela, Eva Partoll, Tuukka Petäjä, Joschka Pfeifer, Veronika Pospisilova, Lauriane L. J. Quéléver, Matti Rissanen, Siegfried Schobesberger, Simone Schuchmann, Yuri Stozhkov, Christian Tauber, Yee Jun Tham, António Tomé, Miguel Vazquez-Pufleau, Andrea C. Wagner, Robert Wagner, Yonghong Wang, Lena Weitz, Daniela Wimmer, Yusheng Wu, Chao Yan, Penglin Ye, Qing Ye, Qiaozhi Zha, Xueqin Zhou, Antonio Amorim, Ken Carslaw, Joachim Curtius, Armin Hansel, Rainer Volkamer, Paul M. Winkler, Richard C. Flagan, Markku Kulmala, Douglas R. Worsnop, Jasper Kirkby, Neil M. Donahue, Urs Baltensperger, Imad El Haddad, and Josef Dommen
Atmos. Chem. Phys., 21, 14275–14291, https://doi.org/10.5194/acp-21-14275-2021, https://doi.org/10.5194/acp-21-14275-2021, 2021
Short summary
Short summary
Experiments at CLOUD show that in polluted environments new particle formation (NPF) is largely driven by the formation of sulfuric acid–base clusters, stabilized by amines, high ammonia concentrations or lower temperatures. While oxidation products of aromatics can nucleate, they play a minor role in urban NPF. Our experiments span 4 orders of magnitude variation of observed NPF rates in ambient conditions. We provide a framework based on NPF and growth rates to interpret ambient observations.
Emma Lumiaro, Milica Todorović, Theo Kurten, Hanna Vehkamäki, and Patrick Rinke
Atmos. Chem. Phys., 21, 13227–13246, https://doi.org/10.5194/acp-21-13227-2021, https://doi.org/10.5194/acp-21-13227-2021, 2021
Short summary
Short summary
The study of climate change relies on climate models, which require an understanding of aerosol formation. We train a machine-learning model to predict the partitioning coefficients of atmospheric molecules, which govern condensation into aerosols. The model can make instant predictions based on molecular structures with accuracy surpassing that of standard computational methods. This will allow the screening of low-volatility molecules that contribute most to aerosol formation.
Mingyi Wang, Xu-Cheng He, Henning Finkenzeller, Siddharth Iyer, Dexian Chen, Jiali Shen, Mario Simon, Victoria Hofbauer, Jasper Kirkby, Joachim Curtius, Norbert Maier, Theo Kurtén, Douglas R. Worsnop, Markku Kulmala, Matti Rissanen, Rainer Volkamer, Yee Jun Tham, Neil M. Donahue, and Mikko Sipilä
Atmos. Meas. Tech., 14, 4187–4202, https://doi.org/10.5194/amt-14-4187-2021, https://doi.org/10.5194/amt-14-4187-2021, 2021
Short summary
Short summary
Atmospheric iodine species are often short-lived with low abundance and have thus been challenging to measure. We show that the bromide chemical ionization mass spectrometry, compatible with both the atmospheric pressure and reduced pressure interfaces, can simultaneously detect various gas-phase iodine species. Combining calibration experiments and quantum chemical calculations, we quantify detection sensitivities to HOI, HIO3, I2, and H2SO4, giving detection limits down to < 106 molec. cm-3.
Meri Räty, Otso Peräkylä, Matthieu Riva, Lauriane Quéléver, Olga Garmash, Matti Rissanen, and Mikael Ehn
Atmos. Chem. Phys., 21, 7357–7372, https://doi.org/10.5194/acp-21-7357-2021, https://doi.org/10.5194/acp-21-7357-2021, 2021
Short summary
Short summary
Cyclohexene resembles certain relatively complex compounds in the atmosphere that through oxidation produce vapours that take part in aerosol formation. We studied the highly oxygenated organic molecules (HOMs) formed in cyclohexene ozonolysis, the relationship between their chemical composition and their tendency to condense onto seed aerosol, as well as the effect of NOx pollutants on their signals. Two existing models were also tested for their ability to predict the volatility of the HOMs.
Clémence Rose, Matti P. Rissanen, Siddharth Iyer, Jonathan Duplissy, Chao Yan, John B. Nowak, Aurélie Colomb, Régis Dupuy, Xu-Cheng He, Janne Lampilahti, Yee Jun Tham, Daniela Wimmer, Jean-Marc Metzger, Pierre Tulet, Jérôme Brioude, Céline Planche, Markku Kulmala, and Karine Sellegri
Atmos. Chem. Phys., 21, 4541–4560, https://doi.org/10.5194/acp-21-4541-2021, https://doi.org/10.5194/acp-21-4541-2021, 2021
Short summary
Short summary
Sulfuric acid (H2SO4) is commonly accepted as a key precursor for atmospheric new particle formation. However, direct measurements of [H2SO4] remain challenging, motivating the development of proxies. Using data collected in two different volcanic plumes, we show, under these specific conditions, the good performance of a proxy from the literature and also highlight the benefit of the newly developed proxies for the prediction of the highest [H2SO4] values.
Martin Heinritzi, Lubna Dada, Mario Simon, Dominik Stolzenburg, Andrea C. Wagner, Lukas Fischer, Lauri R. Ahonen, Stavros Amanatidis, Rima Baalbaki, Andrea Baccarini, Paulus S. Bauer, Bernhard Baumgartner, Federico Bianchi, Sophia Brilke, Dexian Chen, Randall Chiu, Antonio Dias, Josef Dommen, Jonathan Duplissy, Henning Finkenzeller, Carla Frege, Claudia Fuchs, Olga Garmash, Hamish Gordon, Manuel Granzin, Imad El Haddad, Xucheng He, Johanna Helm, Victoria Hofbauer, Christopher R. Hoyle, Juha Kangasluoma, Timo Keber, Changhyuk Kim, Andreas Kürten, Houssni Lamkaddam, Tiia M. Laurila, Janne Lampilahti, Chuan Ping Lee, Katrianne Lehtipalo, Markus Leiminger, Huajun Mai, Vladimir Makhmutov, Hanna Elina Manninen, Ruby Marten, Serge Mathot, Roy Lee Mauldin, Bernhard Mentler, Ugo Molteni, Tatjana Müller, Wei Nie, Tuomo Nieminen, Antti Onnela, Eva Partoll, Monica Passananti, Tuukka Petäjä, Joschka Pfeifer, Veronika Pospisilova, Lauriane L. J. Quéléver, Matti P. Rissanen, Clémence Rose, Siegfried Schobesberger, Wiebke Scholz, Kay Scholze, Mikko Sipilä, Gerhard Steiner, Yuri Stozhkov, Christian Tauber, Yee Jun Tham, Miguel Vazquez-Pufleau, Annele Virtanen, Alexander L. Vogel, Rainer Volkamer, Robert Wagner, Mingyi Wang, Lena Weitz, Daniela Wimmer, Mao Xiao, Chao Yan, Penglin Ye, Qiaozhi Zha, Xueqin Zhou, Antonio Amorim, Urs Baltensperger, Armin Hansel, Markku Kulmala, António Tomé, Paul M. Winkler, Douglas R. Worsnop, Neil M. Donahue, Jasper Kirkby, and Joachim Curtius
Atmos. Chem. Phys., 20, 11809–11821, https://doi.org/10.5194/acp-20-11809-2020, https://doi.org/10.5194/acp-20-11809-2020, 2020
Short summary
Short summary
With experiments performed at CLOUD, we show how isoprene interferes in monoterpene oxidation via RO2 termination at atmospherically relevant concentrations. This interference shifts the distribution of highly oxygenated organic molecules (HOMs) away from C20 class dimers towards C15 class dimers, which subsequently reduces both biogenic nucleation and early growth rates. Our results may help to understand the absence of new-particle formation in isoprene-rich environments.
Cited articles
Besel, V., Todorović, M., Kurtén, T., Rinke, P., and Vehkamäki, H.: Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules, Scientific data, 10, 450, https://doi.org/10.1038/s41597-023-02366-x, 2023. a
Besel, V., Todorović, M., Kurtén, T., Vehkamäki, H., and Rinke, P.: The search for sparse data in molecular datasets: Application of active learning to identify extremely low volatile organic compounds, J. Aerosol Sci., 179, 106375, https://doi.org/10.1016/j.jaerosci.2024.106375, 2024. a
Bortolussi, F., Partovi, F., Joona, M., and Matti, R.: Organic pesticide database with 716 molecules analyzed with chemical ionization mass spectrometry. Reagent ions: bromide, protonated acetone, hydronium ion, dioxide, Zenodo [data set], https://doi.org/10.5281/zenodo.11208543, 2024a. a
Bortolussi, F., Sandström, H., and Rinke, P.: PesticidesMS, Gitlab [code], https://gitlab.com/cest-group/pesticidesms (last access: 15 January 2025), 2024b. a
Breitenlechner, M., Fischer, L., Hainer, M., Heinritzi, M., Curtius, J., and Hansel, A.: PTR3: An Instrument for Studying the Lifecycle of Reactive Organic Carbon in the Atmosphere, Anal. Chem., 89, 5824–5831, https://doi.org/10.1021/acs.analchem.6b05110, 2017. a
Brouard, C., Shen, H., Dührkop, K., D'Alché-Buc, F., Böcker, S., and Rousu, J.: Fast metabolite identification with Input Output Kernel Regression, Bioinformatics, 32, i28–i36, https://doi.org/10.1093/bioinformatics/btw246, 2016. a, b
Brüggemann, M., Mayer, S., Brown, D., Terry, A., Rüdiger, J., and Hoffmann, T.: Measuring pesticides in the atmosphere: current status, emerging trends, and future perspectives, Environmental Sciences Europe, 36, 39, https://doi.org/10.1186/s12302-024-00870-4, 2024. a
de Gouw, J. and Warneke, C.: Measurements of volatile organic compounds in the earth's atmosphere using proton-transfer-reaction mass spectrometry, Mass Spectrom. Rev., 26, 223–257, https://doi.org/10.1002/mas.20119, 2007. a
Dührkop, K., Shen, H., Meusel, M., Rousu, J., and Böcker, S.: Searching molecular structure databases with tandem mass spectra using CSI:FingerID, P. Natl. Acad. Sci. USA, 112, 12580–12585, https://doi.org/10.1073/pnas.1509788112, 2015. a, b
Eisele, F. L. and Tanner, D. J.: Measurement of the gas phase concentration of H2SO4 and estimates of H2SO4 production and loss in the atmosphere, J. Geophys. Res.-Atmos., 98, 9001–9010, https://doi.org/10.1029/93JD00031, 1993. a
Erban, A., Fehrle, I., Martinez-Seidel, F., Brigante, F., Más, A. L., Baroni, V., Wunderlin, D., and Kopka, J.: Discovery of food identity markers by metabolomics and machine learning technology, Sci. Rep., 9, 9697, https://doi.org/10.1038/s41598-019-46113-y, 2019. a
Ertl, P., Rohde, B., and Selzer, P.: Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties, J. Med. Chem., 43, 3714–3717, https://doi.org/10.1021/jm000942e, 2000. a, b
Franklin, E. B., Yee, L. D., Aumont, B., Weber, R. J., Grigas, P., and Goldstein, A. H.: Ch3MS-RF: a random forest model for chemical characterization and improved quantification of unidentified atmospheric organics detected by chromatography–mass spectrometry techniques, Atmos. Meas. Tech., 15, 3779–3803, https://doi.org/10.5194/amt-15-3779-2022, 2022. a
GALAB: Galab Laboratories, Hamburg, Germany, https://www.galab.com/, last access: 15 January 2025. a
Géron, A.: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 1st edn., O'Reilly Media, ISBN 9781492032649, 2022. a
Griffiths, J. R. and de Hoffmann, E.: Mass Spectrometry: Principles and Applications, 3rd edn., John Wiley & Sons, ISBN 9780470033104, 2007. a
Hall, L. H. and Kier, L. B.: The Molecular Connectivity Chi Indexes and Kappa Shape Indexes in Structure-Property Modeling, Chap. 9, Wiley-VCH, Inc., 367–422, https://doi.org/10.1002/9780470125793.ch9, 1991. a
Heinonen, M., Shen, H., Zamboni, N., and Rousu, J.: Metabolite identification and molecular fingerprint prediction through machine learning, Bioinformatics, 28, 2333–2341, https://doi.org/10.1093/bioinformatics/bts437, 2012. a, b
Himanen, L., Jäger, M. O., Morooka, E. V., Canova, F. F., Ranawat, Y. S., Gao, D. Z., Rinke, P., and Foster, A. S.: DScribe: Library of descriptors for machine learning in materials science, Comput. Phys. Commun., 247, 106949, https://doi.org/10.1016/j.cpc.2019.106949, 2020. a, b, c, d
Hoerl, A. E. and Kennard, R. W.: Ridge Regression: Biased Estimation for Nonorthogonal Problems, Technometrics, 12, 55–67, https://doi.org/10.1080/00401706.1970.10488634, 1970. a
Houde, M., Wang, X., Colson, T.-L. L., Gagnon, P., Ferguson, S. H., Ikonomou, M. G., Dubetz, C., Addison, R. F., and Muir, D. C. G.: Trends of persistent organic pollutants in ringed seals (Phoca hispida) from the Canadian Arctic, Sci. Total Environ., 665, 1135–1146, https://doi.org/10.1016/j.scitotenv.2019.02.138, 2019. a
Huey, L. G.: Measurement of trace atmospheric species by chemical ionization mass spectrometry: Speciation of reactive nitrogen and future directions, Mass Spectrom. Rev., 26, 166–184, https://doi.org/10.1002/mas.20118, 2007. a
Huo, H. and Rupp, M.: Unified representation of molecules and crystals for machine learning, Machine Learning: Science and Technology, 3, 4, https://doi.org/10.1088/2632-2153/aca005, 2022. a, b, c, d
Hyttinen, N., Otkjær, R. V., Iyer, S., Kjaergaard, H. G., Rissanen, M. P., Wennberg, P. O., and Kurtén, T.: Computational Comparison of Different Reagent Ions in the Chemical Ionization of Oxidized Multifunctional Compounds, J. Phys. Chem. A, 122, 269–279, https://doi.org/10.1021/acs.jpca.7b10015, 2018. a, b
Hyttinen, N., Pihlajamäki, A., and Häkkinen, H.: Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions, J. Phys. Chem. Lett., 13, 9928–9933, https://doi.org/10.1021/acs.jpclett.2c02612, 2022. a
Hyttinen, N., Li, L., Hallquist, M., and Wu, C.: Machine Learning Model to Predict Saturation Vapor Pressures of Atmospheric Aerosol Constituents, ACS ES&T Air, 1, 1156–1163, https://doi.org/10.1021/acsestair.4c00113, 2024. a
Iyer, S., Lopez-Hilfiker, F., Lee, B. H., Thornton, J. A., and Kurtén, T.: Modeling the Detection of Organic and Inorganic Compounds Using Iodide-Based Chemical Ionization, J. Phys. Chem. A, 120, 576–587, https://doi.org/10.1021/acs.jpca.5b09837, 2016. a, b
Krishnamurthy, R., Newsom, R. K., Berg, L. K., Xiao, H., Ma, P.-L., and Turner, D. D.: On the estimation of boundary layer heights: a machine learning approach, Atmos. Meas. Tech., 14, 4403–4424, https://doi.org/10.5194/amt-14-4403-2021, 2021. a
Laakso, J., Himanen, L., Homm, H., Morooka, E. V., Jäger, M. O., Todorović, M., and Rinke, P.: Updates to the DScribe library: New descriptors and derivatives, J. Chem. Phys., 158, 234802, https://doi.org/10.1063/5.0151031, 2023. a
Langer, M. F., Goeßmann, A., and Rupp, M.: Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning, npj Computational Materials, 8, 41, https://doi.org/10.1038/s41524-022-00721-x, 2022. a
Laskin, J., Laskin, A., and Nizkorodov, S. A.: Mass Spectrometry Analysis in Atmospheric Chemistry, Anal. Chem., 90, 166–189, https://doi.org/10.1021/acs.analchem.7b04249, 2018. a
Lee, B. H., Lopez-Hilfiker, F. D., Mohr, C., Kurtén, T., Worsnop, D. R., and Thornton, J. A.: An Iodide-Adduct High-Resolution Time-of-Flight Chemical-Ionization Mass Spectrometer: Application to Atmospheric Inorganic and Organic Compounds, Environ. Sci. Technol., 48, 6309–6317, https://doi.org/10.1021/es500362a, 2014. a
Lumiaro, E., Todorović, M., Kurten, T., Vehkamäki, H., and Rinke, P.: Predicting gas–particle partitioning coefficients of atmospheric molecules with machine learning, Atmos. Chem. Phys., 21, 13227–13246, https://doi.org/10.5194/acp-21-13227-2021, 2021. a, b, c
Munson, B.: Chemical Ionization Mass Spectrometry, Anal. Chem., 43, 28–37, https://doi.org/10.1021/ac60307a723, 1971. a
Munson, B.: Chemical Ionization Mass Spectrometry: Theory and Applications, in: Encyclopedia of Analytical Chemistry, John Wiley & Sons, Ltd, 1–18, https://doi.org/10.1002/9780470027318.a6004, 2006. a
Munson, M. S. B. and Field, F. H.: Chemical Ionization Mass Spectrometry: I. General Introduction, J. Am. Chem. Soc., 88, 2621–2630, https://doi.org/10.1021/ja00964a001, 1966. a
Nguyen, D. H., Nguyen, C. H., and Mamitsuka, H.: SIMPLE: Sparse Interaction Model over Peaks of moLEcules for fast, interpretable metabolite identification from tandem mass spectra, Bioinformatics, 34, i323–i332, https://doi.org/10.1093/bioinformatics/bty252, 2018. a, b
Nguyen, D. H., Nguyen, C. H., and Mamitsuka, H.: Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches, Briefings in Bioinformatics, 20, 2028–2043, https://doi.org/10.1093/bib/bby066, 2019. a, b
Partovi, F., Mikkilä, J., Iyer, S., Mikkilä, J., Kontro, J., Ojanperä, S., Juuti, P., Kangasluoma, J., Shcherbinin, A., and Rissanen, M.: Pesticide Residue Fast Screening Using Thermal Desorption Multi-Scheme Chemical Ionization Mass Spectrometry (TD-MION MS) with Selective Chemical Ionization, ACS Omega, 8, 25749–25757, https://doi.org/10.1021/acsomega.3c00385, 2023. a, b, c, d
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a
Rissanen, M. P., Kurtén, T., Sipilä, M., Thornton, J. A., Kangasluoma, J., Sarnela, N., Junninen, H., Jørgensen, S., Schallhart, S., Kajos, M. K., Taipale, R., Springer, M., Mentel, T. F., Ruuskanen, T., Petäjä, T., Worsnop, D. R., Kjaergaard, H. G., and Ehn, M.: The Formation of Highly Oxidized Multifunctional Products in the Ozonolysis of Cyclohexene, J. Am. Chem. Soc., 136, 15596–15606, https://doi.org/10.1021/ja507146s, 2014. a
Rissanen, M. P., Mikkilä, J., Iyer, S., and Hakala, J.: Multi-scheme chemical ionization inlet (MION) for fast switching of reagent ion chemistry in atmospheric pressure chemical ionization mass spectrometry (CIMS) applications, Atmos. Meas. Tech., 12, 6635–6646, https://doi.org/10.5194/amt-12-6635-2019, 2019. a
Riva, M., Rantala, P., Krechmer, J. E., Peräkylä, O., Zhang, Y., Heikkinen, L., Garmash, O., Yan, C., Kulmala, M., Worsnop, D., and Ehn, M.: Evaluating the performance of five different chemical ionization techniques for detecting gaseous oxygenated organic species, Atmos. Meas. Tech., 12, 2403–2421, https://doi.org/10.5194/amt-12-2403-2019, 2019. a
Rupp, M.: Machine Learning for Quantum Mechanics in a Nutshell, Int. J. Quantum Chem., 115, 1058–1073, https://doi.org/10.1002/qua.24954, 2015. a, b, c
Rupp, M., Tkatchenko, A., Müller, K.-R., and von Lilienfeld, O. A.: Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning, Phys. Rev. Lett., 108, 1–5, https://doi.org/10.1103/PhysRevLett.108.058301, 2012. a, b, c
Sandström, H., Rissanen, M., Rousu, J., and Rinke, P.: Data-Driven Compound Identification in Atmospheric Mass Spectrometry, Adv. Sci., 11, 8, https://doi.org/10.1002/advs.202306235, 2024. a, b, c
Siomos, N., Fountoulakis, I., Natsis, A., Drosoglou, T., and Bais, A.: Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering, Remote Sens., 12, 965, https://doi.org/10.3390/rs12060965, 2020. a
Sipilä, M., Sarnela, N., Jokinen, T., Henschel, H., Junninen, H., Kontkanen, J., Richters, S., Kangasluoma, J., Franchin, A., Peräkylä, O., Rissanen, M. P., Ehn, M., Vehkamäki, H., Kurten, T., Berndt, T., Petäjä, T., Worsnop, D., Ceburnis, D., Kerminen, V.-M., Kulmala, M., and O'Dowd, C.: Molecular-scale evidence of aerosol particle formation via sequential addition of HIO3, Nature, 537, 532–534, https://doi.org/10.1038/nature19314, 2016. a
Stuke, A., Todorović, M., Rupp, M., Kunkel, C., Ghosh, K., Himanen, L., and Rinke, P.: Chemical diversity in molecular orbital energy predictions with kernel ridge regression, J. Chem. Phys., 150, 204121, https://doi.org/10.1063/1.5086105, 2019. a, b
Stuke, A., Rinke, P., and Todorović, M.: Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization, Machine Learning: Science and Technology, 2, 035022, https://doi.org/10.1088/2632-2153/abee59, 2021. a
Su, P., Joutsensaari, J., Dada, L., Zaidan, M. A., Nieminen, T., Li, X., Wu, Y., Decesari, S., Tarkoma, S., Petäjä, T., Kulmala, M., and Pellikka, P.: New particle formation event detection with Mask R-CNN, Atmos. Chem. Phys., 22, 1293–1309, https://doi.org/10.5194/acp-22-1293-2022, 2022. a
Thoma, M., Bachmeier, F., Gottwald, F. L., Simon, M., and Vogel, A. L.: Mass spectrometry-based Aerosolomics: a new approach to resolve sources, composition, and partitioning of secondary organic aerosol, Atmos. Meas. Tech., 15, 7137–7154, https://doi.org/10.5194/amt-15-7137-2022, 2022. a
van der Maaten, L. and Hinton, G.: Visualizing Data using t-SNE, J. Mach. Learn. Res., 9, 2579–2605, http://jmlr.org/papers/v9/vandermaaten08a.html (last access: 15 January 2025), 2008. a
Wildman, S. A. and Crippen, G. M.: Prediction of Physicochemical Parameters by Atomic Contributions, J. Chem. Inf. Comp. Sci., 39, 868–873, https://doi.org/10.1021/ci990307l, 1999. a
Xue, L. and Bajorath, J.: Molecular Descriptors in Chemoinformatics, Computational Combinatorial Chemistry, and Virtual Screening, Comb. Chem. High T. Scr., 3, 5, https://doi.org/10.2174/1386207003331454, 2000. a
Short summary
Chemical ionization mass spectrometry (CIMS) is widely used in atmospheric chemistry studies. We still have a limited understanding of the complex functioning of the instrument; therefore, we applied machine learning to provide insights from CIMS analyses. We were able to predict both detection and signal intensity with a fair error, and we found out the most important structural fragments for negative ionization schemes (NH and OH) and positive ones (nitrogen-containing groups).
Chemical ionization mass spectrometry (CIMS) is widely used in atmospheric chemistry studies. We...
Altmetrics
Final-revised paper
Preprint