Articles | Volume 13, issue 10
https://doi.org/10.5194/amt-13-5369-2020
https://doi.org/10.5194/amt-13-5369-2020
Research article
 | 
09 Oct 2020
Research article |  | 09 Oct 2020

Quantification of toxic metals using machine learning techniques and spark emission spectroscopy

Seyyed Ali Davari and Anthony S. Wexler

Viewed

Total article views: 1,875 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,276 528 71 1,875 76 80
  • HTML: 1,276
  • PDF: 528
  • XML: 71
  • Total: 1,875
  • BibTeX: 76
  • EndNote: 80
Views and downloads (calculated since 23 Oct 2019)
Cumulative views and downloads (calculated since 23 Oct 2019)

Viewed (geographical distribution)

Total article views: 1,875 (including HTML, PDF, and XML) Thereof 1,633 with geography defined and 242 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jun 2024
Download
Short summary
Traditional instruments for detection and quantification of toxic metals in the atmosphere are expensive. In this study, we have designed, fabricated, and tested a low-cost instrument, which employs cheap components to detect and quantify toxic metals. Advanced machine learning (ML) techniques have been used to improve the instrument's performance. This study demonstrates how the combination of low-cost sensors with ML can address problems that traditionally have been too expensive to be solved.