Articles | Volume 12, issue 7
https://doi.org/10.5194/amt-12-3885-2019
https://doi.org/10.5194/amt-12-3885-2019
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: 2,528 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,575 893 60 2,528 170 59 60
  • HTML: 1,575
  • PDF: 893
  • XML: 60
  • Total: 2,528
  • Supplement: 170
  • BibTeX: 59
  • EndNote: 60
Views and downloads (calculated since 22 Mar 2019)
Cumulative views and downloads (calculated since 22 Mar 2019)

Viewed (geographical distribution)

Total article views: 2,528 (including HTML, PDF, and XML) Thereof 2,340 with geography defined and 188 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 17 Apr 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.