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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 10 | Copyright
Atmos. Meas. Tech., 11, 5687-5699, 2018
https://doi.org/10.5194/amt-11-5687-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 18 Oct 2018

Research article | 18 Oct 2018

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

Costa D. Christopoulos1, Sarvesh Garimella1,a, Maria A. Zawadowicz1,b, Ottmar Möhler2, and Daniel J. Cziczo1,3 Costa D. Christopoulos et al.
  • 1Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
  • 2Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
  • 3Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
  • anow at: ACME AtronOmatic, LLC, Portland, OR, USA
  • bnow at: Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA

Abstract. Compositional analysis of atmospheric and laboratory aerosols is often conducted via single-particle mass spectrometry (SPMS), an in situ and real-time analytical technique that produces mass spectra on a single-particle basis. In this study, classifiers are created using a data set of SPMS spectra to automatically differentiate particles on the basis of chemistry and size. Machine learning algorithms build a predictive model from a training set for which the aerosol type associated with each mass spectrum is known a priori. Our primary focus surrounds the growing of random forests using feature selection to reduce dimensionality and the evaluation of trained models with confusion matrices. In addition to classifying  ∼ 20 unique, but chemically similar, aerosol types, models were also created to differentiate aerosol within four broader categories: fertile soils, mineral/metallic particles, biological particles, and all other aerosols. Differentiation was accomplished using  ∼ 40 positive and negative spectral features. For the broad categorization, machine learning resulted in a classification accuracy of  ∼ 93%. Classification of aerosols by specific type resulted in a classification accuracy of  ∼ 87%. The trained model was then applied to a blind mixture of aerosols which was known to be a subset of the training set. Model agreement was found on the presence of secondary organic aerosol, coated and uncoated mineral dust, and fertile soil.

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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.
Compositional analysis of atmospheric and laboratory aerosols is often conducted with mass...
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