Estimation of Potentially Toxic Elements Contamination in Anthropogenic Soils on a Brown Coal Mining Dumpsite by Reflectance Spectroscopy: A Case Study
Identifieur interne : 000023 ( Pmc/Curation ); précédent : 000022; suivant : 000024Estimation of Potentially Toxic Elements Contamination in Anthropogenic Soils on a Brown Coal Mining Dumpsite by Reflectance Spectroscopy: A Case Study
Auteurs : Asa Gholizadeh [République tchèque] ; Luboš Borůvka [République tchèque] ; Radim Vašát [République tchèque] ; Mohammadmehdi Saberioon [République tchèque] ; Aleš Klement [République tchèque] ; Josef Kratina [République tchèque] ; Václav Tejneck [République tchèque] ; Ond Ej Drábek [République tchèque]Source :
- PLoS ONE [ 1932-6203 ] ; 2015.
Abstract
In order to monitor Potentially Toxic Elements (PTEs) in anthropogenic soils on brown coal mining dumpsites, a large number of samples and cumbersome, time-consuming laboratory measurements are required. Due to its rapidity, convenience and accuracy, reflectance spectroscopy within the Visible-Near Infrared (Vis-NIR) region has been used to predict soil constituents. This study evaluated the suitability of Vis-NIR (350–2500 nm) reflectance spectroscopy for predicting PTEs concentration, using samples collected on large brown coal mining dumpsites in the Czech Republic. Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR) with cross-validation were used to relate PTEs data to the reflectance spectral data by applying different preprocessing strategies. According to the criteria of minimal Root Mean Square Error of Prediction of Cross Validation (RMSEPcv) and maximal coefficient of determination (R2cv) and Residual Prediction Deviation (RPD), the SVMR models with the first derivative pretreatment provided the most accurate prediction for As (R2cv) = 0.89, RMSEPcv = 1.89, RPD = 2.63). Less accurate, but acceptable prediction for screening purposes for Cd and Cu (0.66 ˂ R2cv) ˂ 0.81, RMSEPcv = 0.0.8 and 4.08 respectively, 2.0 ˂ RPD ˂ 2.5) were obtained. The PLSR model for predicting Mn (R2cv) = 0.44, RMSEPcv = 116.43, RPD = 1.45) presented an inadequate model. Overall, SVMR models for the Vis-NIR spectra could be used indirectly for an accurate assessment of PTEs’ concentrations.
Url:
DOI: 10.1371/journal.pone.0117457
PubMed: 25692671
PubMed Central: 4333826
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<author><name sortKey="Saberioon, Mohammadmehdi" sort="Saberioon, Mohammadmehdi" uniqKey="Saberioon M" first="Mohammadmehdi" last="Saberioon">Mohammadmehdi Saberioon</name>
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<author><name sortKey="Klement, Ales" sort="Klement, Ales" uniqKey="Klement A" first="Aleš" last="Klement">Aleš Klement</name>
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<front><div type="abstract" xml:lang="en"><p>In order to monitor Potentially Toxic Elements (PTEs) in anthropogenic soils on brown coal mining dumpsites, a large number of samples and cumbersome, time-consuming laboratory measurements are required. Due to its rapidity, convenience and accuracy, reflectance spectroscopy within the Visible-Near Infrared (Vis-NIR) region has been used to predict soil constituents. This study evaluated the suitability of Vis-NIR (350–2500 nm) reflectance spectroscopy for predicting PTEs concentration, using samples collected on large brown coal mining dumpsites in the Czech Republic. Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR) with cross-validation were used to relate PTEs data to the reflectance spectral data by applying different preprocessing strategies. According to the criteria of minimal Root Mean Square Error of Prediction of Cross Validation (RMSEP<sub>cv</sub>
) and maximal coefficient of determination (R<sup>2</sup>
<sub>cv</sub>
) and Residual Prediction Deviation (RPD), the SVMR models with the first derivative pretreatment provided the most accurate prediction for As (R<sup>2</sup>
<sub>cv</sub>
) = 0.89, RMSEP<sub>cv</sub>
= 1.89, RPD = 2.63). Less accurate, but acceptable prediction for screening purposes for Cd and Cu (0.66 ˂ R<sup>2</sup>
<sub>cv</sub>
) ˂ 0.81, RMSEP<sub>cv</sub>
= 0.0.8 and 4.08 respectively, 2.0 ˂ RPD ˂ 2.5) were obtained. The PLSR model for predicting Mn (R<sup>2</sup>
<sub>cv</sub>
) = 0.44, RMSEP<sub>cv</sub>
= 116.43, RPD = 1.45) presented an inadequate model. Overall, SVMR models for the Vis-NIR spectra could be used indirectly for an accurate assessment of PTEs’ concentrations.</p>
</div>
</front>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id>
<journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id>
<journal-id journal-id-type="publisher-id">plos</journal-id>
<journal-id journal-id-type="pmc">plosone</journal-id>
<journal-title-group><journal-title>PLoS ONE</journal-title>
</journal-title-group>
<issn pub-type="epub">1932-6203</issn>
<publisher><publisher-name>Public Library of Science</publisher-name>
<publisher-loc>San Francisco, CA USA</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">25692671</article-id>
<article-id pub-id-type="pmc">4333826</article-id>
<article-id pub-id-type="doi">10.1371/journal.pone.0117457</article-id>
<article-id pub-id-type="publisher-id">PONE-D-14-29688</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Estimation of Potentially Toxic Elements Contamination in Anthropogenic Soils on a Brown Coal Mining Dumpsite by Reflectance Spectroscopy: A Case Study</article-title>
<alt-title alt-title-type="running-head">Potentially Toxic Element Estimation in Man-Made Soils by Spectroscopy</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Gholizadeh</surname>
<given-names>Asa</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref rid="cor001" ref-type="corresp">*</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Borůvka</surname>
<given-names>Luboš</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Vašát</surname>
<given-names>Radim</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Saberioon</surname>
<given-names>Mohammadmehdi</given-names>
</name>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Klement</surname>
<given-names>Aleš</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Kratina</surname>
<given-names>Josef</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Tejnecký</surname>
<given-names>Václav</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Drábek</surname>
<given-names>Ondřej</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff001"><label>1</label>
<addr-line>Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Science, Kamýcká 129, 165 21- Suchdol, Praha 6- Prague, Czech Republic</addr-line>
</aff>
<aff id="aff002"><label>2</label>
<addr-line>Laboratory of Image and Signal Processing, Institute of Complex Systems, Faculty of Fisheries and Protection of Waters, University of South Bohemia in České Budějovice, Zámek 136 37 333- Nové Hrady, Czech Republic</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Shah</surname>
<given-names>Vishal</given-names>
</name>
<role>Academic Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1"><addr-line>Dowling College, UNITED STATES</addr-line>
</aff>
<author-notes><fn fn-type="conflict" id="coi001"><p><bold>Competing Interests: </bold>
The authors have declared that no competing interests exist.</p>
</fn>
<fn fn-type="con" id="contrib001"><p>Conceived and designed the experiments: AG MMS. Performed the experiments: AK JK. Analyzed the data: AG RV. Contributed reagents/materials/analysis tools: AG LB OD VT. Wrote the paper: AG MMS LB.</p>
</fn>
<corresp id="cor001">* E-mail: <email>gholizadeh@af.czu.cz</email>
</corresp>
</author-notes>
<pub-date pub-type="epub"><day>18</day>
<month>2</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="collection"><year>2015</year>
</pub-date>
<volume>10</volume>
<issue>2</issue>
<elocation-id>e0117457</elocation-id>
<history><date date-type="received"><day>7</day>
<month>7</month>
<year>2014</year>
</date>
<date date-type="accepted"><day>26</day>
<month>12</month>
<year>2014</year>
</date>
</history>
<permissions><copyright-year>2015</copyright-year>
<copyright-holder>Gholizadeh et al</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>
, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited</license-p>
</license>
</permissions>
<self-uri content-type="pdf" xlink:type="simple" xlink:href="pone.0117457.pdf"></self-uri>
<abstract><p>In order to monitor Potentially Toxic Elements (PTEs) in anthropogenic soils on brown coal mining dumpsites, a large number of samples and cumbersome, time-consuming laboratory measurements are required. Due to its rapidity, convenience and accuracy, reflectance spectroscopy within the Visible-Near Infrared (Vis-NIR) region has been used to predict soil constituents. This study evaluated the suitability of Vis-NIR (350–2500 nm) reflectance spectroscopy for predicting PTEs concentration, using samples collected on large brown coal mining dumpsites in the Czech Republic. Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR) with cross-validation were used to relate PTEs data to the reflectance spectral data by applying different preprocessing strategies. According to the criteria of minimal Root Mean Square Error of Prediction of Cross Validation (RMSEP<sub>cv</sub>
) and maximal coefficient of determination (R<sup>2</sup>
<sub>cv</sub>
) and Residual Prediction Deviation (RPD), the SVMR models with the first derivative pretreatment provided the most accurate prediction for As (R<sup>2</sup>
<sub>cv</sub>
) = 0.89, RMSEP<sub>cv</sub>
= 1.89, RPD = 2.63). Less accurate, but acceptable prediction for screening purposes for Cd and Cu (0.66 ˂ R<sup>2</sup>
<sub>cv</sub>
) ˂ 0.81, RMSEP<sub>cv</sub>
= 0.0.8 and 4.08 respectively, 2.0 ˂ RPD ˂ 2.5) were obtained. The PLSR model for predicting Mn (R<sup>2</sup>
<sub>cv</sub>
) = 0.44, RMSEP<sub>cv</sub>
= 116.43, RPD = 1.45) presented an inadequate model. Overall, SVMR models for the Vis-NIR spectra could be used indirectly for an accurate assessment of PTEs’ concentrations.</p>
</abstract>
<funding-group><funding-statement>This research was financially supported by the EC Operational Program (Project No.: ESF/MEYS CZ.1.07/2.3.00/30.0040) and Czech University of Life Sciences Prague. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement>
</funding-group>
<counts><fig-count count="3"></fig-count>
<table-count count="2"></table-count>
<page-count count="14"></page-count>
</counts>
<custom-meta-group><custom-meta id="data-availability"><meta-name>Data Availability</meta-name>
<meta-value>All relevant data are within the paper.</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
<notes><title>Data Availability</title>
<p>All relevant data are within the paper.</p>
</notes>
</front>
</pmc>
</record>
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