Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 12, issue 7
Articles | Volume 12, issue 7
https://doi.org/10.5194/amt-12-3885-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-12-3885-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 7
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

Viewed

Total article views: 2,985 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,958 938 89 2,985 191 91 85
  • HTML: 1,958
  • PDF: 938
  • XML: 89
  • Total: 2,985
  • Supplement: 191
  • BibTeX: 91
  • EndNote: 85
Views and downloads (calculated since 22 Mar 2019)
Cumulative views and downloads (calculated since 22 Mar 2019)

Viewed (geographical distribution)

Total article views: 2,985 (including HTML, PDF, and XML) Thereof 2,787 with geography defined and 198 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 05 Oct 2024
Download
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.
Read more