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

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Ali Davari on behalf of the Authors (23 Feb 2020)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (04 Mar 2020) by Francis Pope
RR by Anonymous Referee #1 (15 Mar 2020)
RR by Anonymous Referee #2 (16 Mar 2020)
ED: Reconsider after major revisions (09 Apr 2020) by Francis Pope
AR by Ali Davari on behalf of the Authors (21 May 2020)  Author's response   Manuscript 
ED: Publish as is (29 Jul 2020) by Francis Pope
AR by Ali Davari on behalf of the Authors (07 Aug 2020)  Manuscript 
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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.