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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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AMT | Articles | Volume 12, issue 7
Atmos. Meas. Tech., 12, 3885–3906, 2019
https://doi.org/10.5194/amt-12-3885-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 12, 3885–3906, 2019
https://doi.org/10.5194/amt-12-3885-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 15 Jul 2019

Research article | 15 Jul 2019

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

Kara D. Lamb

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Kara Lamb on behalf of the Authors (20 Jun 2019)  Author's response    Manuscript
ED: Publish as is (21 Jun 2019) by Mingjin Tang
Publications Copernicus
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Short summary
Recent atmospheric observations have indicated emissions of iron-oxide-containing aerosols from anthropogenic sources could be 8x 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...
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