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A general framework and practical procedure for improving pxrf measurement accuracy with integrating moisture content and organic matter content parameters
Rapid, accurate detection of heavy-metal content is extremely important for precise risk control and targeted remediation. Herein, a general modeling method and process based on the relationship between Pxrf measured values and site parameters are explored to construct a Pxrf correction model suitab...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7955097/ https://www.ncbi.nlm.nih.gov/pubmed/33712638 http://dx.doi.org/10.1038/s41598-021-85045-4 |
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author | Chen, Zengsiche Xu, Ya Lei, Guoyuan Liu, Yuqiang Liu, Jingcai Yao, Guangyuan Huang, Qifei |
author_facet | Chen, Zengsiche Xu, Ya Lei, Guoyuan Liu, Yuqiang Liu, Jingcai Yao, Guangyuan Huang, Qifei |
author_sort | Chen, Zengsiche |
collection | PubMed |
description | Rapid, accurate detection of heavy-metal content is extremely important for precise risk control and targeted remediation. Herein, a general modeling method and process based on the relationship between Pxrf measured values and site parameters are explored to construct a Pxrf correction model suitable to improve each site’s measurement accuracy. Results show a significant correlation between Pb, Mn, and Zn Pxrf measured values and actual concentrations, with correlation coefficients between 0.8 and 0.93. Through the correlation analysis, the correlation coefficient between the water content and the measured value of pxrf is in the range of 0.2–0.5. Pxrf measurement of all heavy metals was weakly affected by soil organic matter content, with correlation coefficients all lower than 0.5. Model transformation effectively improved the correlation between measured Pxrf value and actual concentration, and transformation increased the correlations of Sr, Mn, and Cu by around 0.11. Model verification results showed that the Pb, Zn, Fe, and Mn models can be used to improve Pxrf method detection accuracy. |
format | Online Article Text |
id | pubmed-7955097 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-79550972021-03-15 A general framework and practical procedure for improving pxrf measurement accuracy with integrating moisture content and organic matter content parameters Chen, Zengsiche Xu, Ya Lei, Guoyuan Liu, Yuqiang Liu, Jingcai Yao, Guangyuan Huang, Qifei Sci Rep Article Rapid, accurate detection of heavy-metal content is extremely important for precise risk control and targeted remediation. Herein, a general modeling method and process based on the relationship between Pxrf measured values and site parameters are explored to construct a Pxrf correction model suitable to improve each site’s measurement accuracy. Results show a significant correlation between Pb, Mn, and Zn Pxrf measured values and actual concentrations, with correlation coefficients between 0.8 and 0.93. Through the correlation analysis, the correlation coefficient between the water content and the measured value of pxrf is in the range of 0.2–0.5. Pxrf measurement of all heavy metals was weakly affected by soil organic matter content, with correlation coefficients all lower than 0.5. Model transformation effectively improved the correlation between measured Pxrf value and actual concentration, and transformation increased the correlations of Sr, Mn, and Cu by around 0.11. Model verification results showed that the Pb, Zn, Fe, and Mn models can be used to improve Pxrf method detection accuracy. Nature Publishing Group UK 2021-03-12 /pmc/articles/PMC7955097/ /pubmed/33712638 http://dx.doi.org/10.1038/s41598-021-85045-4 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Chen, Zengsiche Xu, Ya Lei, Guoyuan Liu, Yuqiang Liu, Jingcai Yao, Guangyuan Huang, Qifei A general framework and practical procedure for improving pxrf measurement accuracy with integrating moisture content and organic matter content parameters |
title | A general framework and practical procedure for improving pxrf measurement accuracy with integrating moisture content and organic matter content parameters |
title_full | A general framework and practical procedure for improving pxrf measurement accuracy with integrating moisture content and organic matter content parameters |
title_fullStr | A general framework and practical procedure for improving pxrf measurement accuracy with integrating moisture content and organic matter content parameters |
title_full_unstemmed | A general framework and practical procedure for improving pxrf measurement accuracy with integrating moisture content and organic matter content parameters |
title_short | A general framework and practical procedure for improving pxrf measurement accuracy with integrating moisture content and organic matter content parameters |
title_sort | general framework and practical procedure for improving pxrf measurement accuracy with integrating moisture content and organic matter content parameters |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7955097/ https://www.ncbi.nlm.nih.gov/pubmed/33712638 http://dx.doi.org/10.1038/s41598-021-85045-4 |
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