Articles | Volume 17, issue 1
https://doi.org/10.5194/amt-17-299-2024
© Author(s) 2024. 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-17-299-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Machine learning approaches for automatic classification of single-particle mass spectrometry data
Guanzhong Wang
Department of Aerospace Engineering, Institute for Applied Physics and Measurement Technology, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Department of Aerospace Engineering, Institute for Applied Physics and Measurement Technology, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Julian Schade
Department of Mechanical Engineering, Institute of Chemistry and Environmental Engineering, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Joint Mass Spectrometry Centre, Institute of Chemistry, Division of Analytical and Technical Chemistry, University of Rostock, 18059 Rostock, Germany
Johannes Passig
Joint Mass Spectrometry Centre, Institute of Chemistry, Division of Analytical and Technical Chemistry, University of Rostock, 18059 Rostock, Germany
Joint Mass Spectrometry Centre, Helmholtz Zentrum München, 85764 Neuherberg, Germany
Department of Life, Light and Matter, Faculty of Interdisciplinary Faculty, University of Rostock, 18059 Rostock, Germany
Thomas Adam
Department of Mechanical Engineering, Institute of Chemistry and Environmental Engineering, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Joint Mass Spectrometry Centre, Helmholtz Zentrum München, 85764 Neuherberg, Germany
Günther Dollinger
Department of Aerospace Engineering, Institute for Applied Physics and Measurement Technology, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Ralf Zimmermann
Joint Mass Spectrometry Centre, Institute of Chemistry, Division of Analytical and Technical Chemistry, University of Rostock, 18059 Rostock, Germany
Joint Mass Spectrometry Centre, Helmholtz Zentrum München, 85764 Neuherberg, Germany
Department of Life, Light and Matter, Faculty of Interdisciplinary Faculty, University of Rostock, 18059 Rostock, Germany
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Atmos. Chem. Phys., 25, 9275–9294, https://doi.org/10.5194/acp-25-9275-2025, https://doi.org/10.5194/acp-25-9275-2025, 2025
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Marco Schmidt, Haseeb Hakkim, Lukas Anders, Aleksandrs Kalamašņikovs, Thomas Kröger-Badge, Robert Irsig, Norbert Graf, Reinhard Kelnberger, Johannes Passig, and Ralf Zimmermann
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Laser desorption of individual particles prior to ionization is the key to reveal their organic composition. The CO2 lasers required are bulky and maintenance-intensive, limiting their use in the field. We have developed a compact solid-state IR laser that is easily aligned with the particle beam. Mass spectra and hit rates are similar to those of the CO2 laser. For combined characterization of organic and inorganic particle compositions, both lasers are superior to conventional single UV pulses.
Satish Basnet, Anni Hartikainen, Aki Virkkula, Pasi Yli-Pirilä, Miika Kortelainen, Heikki Suhonen, Laura Kilpeläinen, Mika Ihalainen, Sampsa Väätäinen, Juho Louhisalmi, Markus Somero, Jarkko Tissari, Gert Jakobi, Ralf Zimmermann, Antti Kilpeläinen, and Olli Sippula
Atmos. Chem. Phys., 24, 3197–3215, https://doi.org/10.5194/acp-24-3197-2024, https://doi.org/10.5194/acp-24-3197-2024, 2024
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Zhi-Hui Zhang, Elena Hartner, Battist Utinger, Benjamin Gfeller, Andreas Paul, Martin Sklorz, Hendryk Czech, Bin Xia Yang, Xin Yi Su, Gert Jakobi, Jürgen Orasche, Jürgen Schnelle-Kreis, Seongho Jeong, Thomas Gröger, Michal Pardo, Thorsten Hohaus, Thomas Adam, Astrid Kiendler-Scharr, Yinon Rudich, Ralf Zimmermann, and Markus Kalberer
Atmos. Chem. Phys., 22, 1793–1809, https://doi.org/10.5194/acp-22-1793-2022, https://doi.org/10.5194/acp-22-1793-2022, 2022
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Johannes Passig, Julian Schade, Robert Irsig, Thomas Kröger-Badge, Hendryk Czech, Thomas Adam, Henrik Fallgren, Jana Moldanova, Martin Sklorz, Thorsten Streibel, and Ralf Zimmermann
Atmos. Chem. Phys., 22, 1495–1514, https://doi.org/10.5194/acp-22-1495-2022, https://doi.org/10.5194/acp-22-1495-2022, 2022
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The single-particle distribution of health-relevant polycyclic aromatic hydrocarbons (PAHs) was studied at the Swedish coast in autumn. We found PAHs bound to long-range transported particles from eastern and central Europe and also from ship emissions and local sources. This is the first field study using a new technology revealing single-particle data from both inorganic components and PAHs. We discuss PAH profiles that are indicative of several sources and atmospheric aging processes.
Xiansheng Liu, Hadiatullah Hadiatullah, Xun Zhang, L. Drew Hill, Andrew H. A. White, Jürgen Schnelle-Kreis, Jan Bendl, Gert Jakobi, Brigitte Schloter-Hai, and Ralf Zimmermann
Atmos. Meas. Tech., 14, 5139–5151, https://doi.org/10.5194/amt-14-5139-2021, https://doi.org/10.5194/amt-14-5139-2021, 2021
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A monitoring campaign was conducted in Augsburg to determine a suitable noise reduction algorithm for the MA200 Aethalometer. Results showed that centred moving average (CMA) post-processing effectively removed spurious negative concentrations without major bias and reliably highlighted effects from local sources, effectively increasing spatio-temporal resolution in mobile measurements. Evaluation of each method on peak sample reduction and background correction further supports the reliability.
Johannes Passig, Julian Schade, Robert Irsig, Lei Li, Xue Li, Zhen Zhou, Thomas Adam, and Ralf Zimmermann
Atmos. Meas. Tech., 14, 4171–4185, https://doi.org/10.5194/amt-14-4171-2021, https://doi.org/10.5194/amt-14-4171-2021, 2021
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Dac-Loc Nguyen, Hendryk Czech, Simone M. Pieber, Jürgen Schnelle-Kreis, Martin Steinbacher, Jürgen Orasche, Stephan Henne, Olga B. Popovicheva, Gülcin Abbaszade, Guenter Engling, Nicolas Bukowiecki, Nhat-Anh Nguyen, Xuan-Anh Nguyen, and Ralf Zimmermann
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Southeast Asia is well-known for emission-intense and recurring wildfires and after-harvest crop residue burning during the pre-monsoon season from February to April. We describe a biomass burning (BB) plume arriving at remote Pha Din meteorological station, outline its carbonaceous particulate matter (PM) constituents based on more than 50 target compounds and discuss possible BB sources. This study adds valuable information on chemical PM composition for a region with scarce data availability.
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Short summary
This research aims to develop a novel warning system for the real-time monitoring of pollutants in the atmosphere. The system is capable of sampling and investigating airborne aerosol particles on-site, utilizing artificial intelligence to learn their chemical signatures and to classify them in real time. We applied single-particle mass spectrometry for analyzing the chemical composition of aerosol particles and suggest several supervised algorithms for highly reliable automatic classification.
This research aims to develop a novel warning system for the real-time monitoring of pollutants...