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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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AMT | Articles | Volume 13, issue 10
Atmos. Meas. Tech., 13, 5369–5377, 2020
https://doi.org/10.5194/amt-13-5369-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 13, 5369–5377, 2020
https://doi.org/10.5194/amt-13-5369-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

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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Interactive discussion

Status: closed
Status: closed
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
Publications Copernicus
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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.
Traditional instruments for detection and quantification of toxic metals in the atmosphere are...
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