Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 13, issue 10
Articles | Volume 13, issue 10
https://doi.org/10.5194/amt-13-5369-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-5369-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 10
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: 1,785 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,217 505 63 1,785 64 55
  • HTML: 1,217
  • PDF: 505
  • XML: 63
  • Total: 1,785
  • BibTeX: 64
  • EndNote: 55
Views and downloads (calculated since 23 Oct 2019)
Cumulative views and downloads (calculated since 23 Oct 2019)

Viewed (geographical distribution)

Total article views: 1,785 (including HTML, PDF, and XML) Thereof 1,554 with geography defined and 231 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 25 Apr 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.
Read more