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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 22 Mar 2019

Research article | 22 Mar 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Classification of iron oxide aerosols by a single particle soot photometer using supervised machine learning

Kara D. Lamb1,2 Kara D. Lamb
  • 1Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA
  • 2NOAA Earth System Research Laboratory Chemical Sciences Division, Boulder, CO, USA

Abstract. Single particle soot photometers (SP2) use laser-induced incandescence to detect aerosols on a single particle basis. Both refractory black carbon (rBC) and other light absorbing metallic aerosols, including iron oxides (FeOx), have been characterized by the SP2, but single particles cannot be unambiguously identified from their incandescent peak height (a function of particle mass) and color ratio (a measure of blackbody temperature) alone. Machine learning offers a promising approach to improving the classification of these aerosols. Here we explore the advantages and limitations of classifying single particle signals obtained with the SP2 using a supervised learning algorithm. Laboratory samples of different aerosols that incandesce in the SP2 (fullerene soot, mineral dust, volcanic ash, coal fly ash, Fe2O3, and Fe3O4) were used to train a random forest algorithm. The trained algorithm was then applied to test data sets of laboratory samples and atmospheric aerosols. This method provides a systematic approach for classifying incandescent aerosols by providing a score, or conditional probability, that a particle is likely to belong to a particular aerosol class (rBC, FeOx, etc.) given its observed single-particle features. We consider two alternative approaches for identifying aerosols in mixed populations: one with specific class labels for each species sampled, and one with three broader classes for aerosols with similar properties. While the specific class approach performs well for rBC and Fe3O4 (> = 99 % of these aerosols are correctly identified), its classification of other aerosol types is significantly worse (only 47–66 % of other particles are correctly identified). Using the broader class approach, we find a classification accuracy of 99 % for FeOx samples measured in the laboratory. The method allows for classification of FeOx as anthropogenic or dust-like for aerosols with effective spherical diameters from 170 to > 1200 nm. The misidentification of both dust-like aerosols and rBC as anthropogenic FeOx is small, with < 3 % of the dust-like aerosols and < 0.1 % of rBC misidentified as FeOx for the broader class case. When applying this method to atmospheric observations taken in Boulder, CO, a clear mode consistent with FeOx was observed, distinct from dust-like aerosols.

Kara D. Lamb
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Status: open (until 17 May 2019)
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Kara D. Lamb
Kara D. Lamb
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Publications Copernicus
Short summary
Recent atmospheric observations have indicated emissions of iron-oxide containing aerosols from anthropogenic sources could be 8× higher than previous estimates, leading models to underestimate their climate impact. Previous studies have shown the single particle soot photometer (SP2) can quantify the atmospheric abundance of these aerosols. Here, I explore a machine learning approach to improve SP2 detection, significantly reducing misclassifications of other aerosols as iron oxide aerosols.
Recent atmospheric observations have indicated emissions of iron-oxide containing aerosols from...