A machine learning approach to aerosol classification for single-particle mass spectrometry
Author(s)Christopoulos, Costa (Costa D.); Garimella, Sarvesh; Zawadowicz, Maria Anna; Cziczo, Daniel James
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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 model was then applied to a 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.
DepartmentMassachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences; Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Atmospheric Measurement Techniques
Christopoulos, Costa D. et al. “A machine learning approach to aerosol classification for single-particle mass spectrometry.” Atmospheric Measurement Techniques 11 (2018): 5687-5699 © 2018 The Author(s)
Final published version