Articles | Volume 11, issue 10
https://doi.org/10.5194/amt-11-5687-2018
https://doi.org/10.5194/amt-11-5687-2018
Research article
 | 
18 Oct 2018
Research article |  | 18 Oct 2018

A machine learning approach to aerosol classification for single-particle mass spectrometry

Costa D. Christopoulos, Sarvesh Garimella, Maria A. Zawadowicz, Ottmar Möhler, and Daniel J. Cziczo

Related authors

Ice-nucleating particles active below −24 °C in a Finnish boreal forest and their relationship to bioaerosols
Franziska Vogel, Michael P. Adams, Larissa Lacher, Polly B. Foster, Grace C. E. Porter, Barbara Bertozzi, Kristina Höhler, Julia Schneider, Tobias Schorr, Nsikanabasi S. Umo, Jens Nadolny, Zoé Brasseur, Paavo Heikkilä, Erik S. Thomson, Nicole Büttner, Martin I. Daily, Romy Fösig, Alexander D. Harrison, Jorma Keskinen, Ulrike Proske, Jonathan Duplissy, Markku Kulmala, Tuukka Petäjä, Ottmar Möhler, and Benjamin J. Murray
Atmos. Chem. Phys., 24, 11737–11757, https://doi.org/10.5194/acp-24-11737-2024,https://doi.org/10.5194/acp-24-11737-2024, 2024
Short summary
Vertical distribution of ice nucleating particles over the boreal forest of Hyytiälä, Finland
Zoé Brasseur, Julia Schneider, Janne Lampilahti, Ville Vakkari, Victoria A. Sinclair, Christina J. Williamson, Carlton Xavier, Dmitri Moisseev, Markus Hartmann, Pyry Poutanen, Markus Lampimäki, Markku Kulmala, Tuukka Petäjä, Katrianne Lehtipalo, Erik S. Thomson, Kristina Höhler, Ottmar Möhler, and Jonathan Duplissy
Atmos. Chem. Phys., 24, 11305–11332, https://doi.org/10.5194/acp-24-11305-2024,https://doi.org/10.5194/acp-24-11305-2024, 2024
Short summary
Measurement report: The Fifth International Workshop on Ice Nucleation phase 1 (FIN-01): intercomparison of single-particle mass spectrometers
Xiaoli Shen, David M. Bell, Hugh Coe, Naruki Hiranuma, Fabian Mahrt, Nicholas A. Marsden, Claudia Mohr, Daniel M. Murphy, Harald Saathoff, Johannes Schneider, Jacqueline Wilson, Maria A. Zawadowicz, Alla Zelenyuk, Paul J. DeMott, Ottmar Möhler, and Daniel J. Cziczo
Atmos. Chem. Phys., 24, 10869–10891, https://doi.org/10.5194/acp-24-10869-2024,https://doi.org/10.5194/acp-24-10869-2024, 2024
Short summary
Biological and dust aerosols as sources of ice-nucleating particles in the eastern Mediterranean: source apportionment, atmospheric processing and parameterization
Kunfeng Gao, Franziska Vogel, Romanos Foskinis, Stergios Vratolis, Maria I. Gini, Konstantinos Granakis, Anne-Claire Billault-Roux, Paraskevi Georgakaki, Olga Zografou, Prodromos Fetfatzis, Alexis Berne, Alexandros Papayannis, Konstantinos Eleftheridadis, Ottmar Möhler, and Athanasios Nenes
Atmos. Chem. Phys., 24, 9939–9974, https://doi.org/10.5194/acp-24-9939-2024,https://doi.org/10.5194/acp-24-9939-2024, 2024
Short summary
A novel aerosol filter sampler for measuring the vertical distribution of ice-nucleating particles via fixed-wing uncrewed aerial vehicles
Alexander Julian Böhmländer, Larissa Lacher, David Brus, Konstantinos-Matthaios Doulgeris, Zoé Brasseur, Matthew Boyer, Joel Kuula, Thomas Leisner, and Ottmar Möhler
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-120,https://doi.org/10.5194/amt-2024-120, 2024
Preprint under review for AMT
Short summary

Related subject area

Subject: Aerosols | Technique: Laboratory Measurement | Topic: Data Processing and Information Retrieval
Estimating errors in vehicle secondary aerosol production factors due to oxidation flow reactor response time
Pauli Simonen, Miikka Dal Maso, Pinja Prauda, Anniina Hoilijoki, Anette Karppinen, Pekka Matilainen, Panu Karjalainen, and Jorma Keskinen
Atmos. Meas. Tech., 17, 3219–3236, https://doi.org/10.5194/amt-17-3219-2024,https://doi.org/10.5194/amt-17-3219-2024, 2024
Short summary
Quantifying functional group compositions of household fuel-burning emissions
Emily Y. Li, Amir Yazdani, Ann M. Dillner, Guofeng Shen, Wyatt M. Champion, James J. Jetter, William T. Preston, Lynn M. Russell, Michael D. Hays, and Satoshi Takahama
Atmos. Meas. Tech., 17, 2401–2413, https://doi.org/10.5194/amt-17-2401-2024,https://doi.org/10.5194/amt-17-2401-2024, 2024
Short summary
A new software toolkit for optical apportionment of carbonaceous aerosol
Tommaso Isolabella, Vera Bernardoni, Alessandro Bigi, Marco Brunoldi, Federico Mazzei, Franco Parodi, Paolo Prati, Virginia Vernocchi, and Dario Massabò
Atmos. Meas. Tech., 17, 1363–1373, https://doi.org/10.5194/amt-17-1363-2024,https://doi.org/10.5194/amt-17-1363-2024, 2024
Short summary
Theoretical derivation of aerosol lidar ratio using Mie theory for CALIOP-CALIPSO and OPAC aerosol models
Radhika A. Chipade and Mehul R. Pandya
Atmos. Meas. Tech., 16, 5443–5459, https://doi.org/10.5194/amt-16-5443-2023,https://doi.org/10.5194/amt-16-5443-2023, 2023
Short summary
An extraction method for nitrogen isotope measurement of ammonium in a low-concentration environment
Alexis Lamothe, Joel Savarino, Patrick Ginot, Lison Soussaintjean, Elsa Gautier, Pete D. Akers, Nicolas Caillon, and Joseph Erbland
Atmos. Meas. Tech., 16, 4015–4030, https://doi.org/10.5194/amt-16-4015-2023,https://doi.org/10.5194/amt-16-4015-2023, 2023
Short summary

Cited articles

Andreae, M. and Rosenfeld, D.: Aerosol–cloud–precipitation interactions. Part 1. The nature and sources of cloud-active aerosols, Earth-Sci. Rev., 89, 13–41, https://doi.org/10.1016/j.earscirev.2008.03.001, 2008. 
Atkinson, J., Murray, B., Woodhouse, M., Whale, T., Baustian, K., and Carslaw, K., Dobbie, S., O'Sullivan, D., and Malkin, T. L: The importance of feldspar for ice nucleation by mineral dust in mixed-phase clouds, Nature, 498, 355–358, https://doi.org/10.1038/nature12278, 2013. 
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.-M. , Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S.K., Sherwood, S., Stevens B., and Zhang, X. Y.: Clouds and Aerosols, Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 5, Cambridge University Press, Cambridge, UK and New York, NY, USA, 571–657, 2013. 
Breiman, L.: Bagging Predictors, Mach. Learn., 24, 123–140, 1996. 
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, 2001. 
Download
Short summary
Compositional analysis of atmospheric and laboratory aerosols is often conducted with mass spectrometry. In this study, machine learning is used to automatically differentiate particles on the basis of chemistry and size. The ability of the machine learning algorithm was then tested on a data set for which the particles were not initially known to judge its ability.