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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/amt-2019-377
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-377
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Oct 2019

23 Oct 2019

Review status
A revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

Quantification of toxic metallic elements using machine learning techniques and spark emission spectroscopy

Seyyed Ali Davari1,2 and Anthony S. Wexler1,2 Seyyed Ali Davari and Anthony S. Wexler
  • 1Air Quality Research Center (AQRC), University of California, Davis, 95616, Davis, USA
  • 2Department of Mechanical and Aerospace Engineering, Civil and Environmental Engineering, and Land, Air and Water Resources, University of California, Davis, USA

Abstract. The United States Environmental Protection Agency (US EPA) list of Hazardous Air Pollutants (HAPs) includes metal elements suspected or associated with development of cancer. Traditional techniques for detecting and quantifying toxic metallic elements in the atmosphere are either not real time, hindering identification of sources, or limited by instrument costs. Spark emission spectroscopy is a promising and cost effective technique that can be used for analyzing toxic metallic elements in real time. Here, we have developed a cost-effective spark emission spectroscopy system to quantify the concentration of toxic metallic elements targeted by US EPA. Specifically, Cr, Cu, Ni, and Pb solutions were diluted and deposited on the ground electrode of the spark emission system. Least Absolute Shrinkage and Selection Operator (LASSO) was optimized and employed to detect useful features from the spark-generated plasma emissions. The optimized model was able to detect atomic emission lines along with other features to build a regression model that predicts the concentration of toxic metallic elements from the observed spectra. The limits of detections (LOD) were estimated using the detected features and compared to the traditional single-feature approach. LASSO is capable of detecting highly sensitive features in the input spectrum; however for some elements the single-feature LOD marginally outperforms LASSO LOD. The combination of low cost instruments with advanced machine learning techniques for data analysis could pave the path forward for data driven solutions to costly measurements.

Seyyed Ali Davari and Anthony S. Wexler

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Seyyed Ali Davari and Anthony S. Wexler

Seyyed Ali Davari and Anthony S. Wexler

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