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Retrieving zinc concentrations in topsoil with reflectance spectroscopy at Opencast Coal Mine sites
Heavy metals contaminations in mining areas aroused wide concerns globally. Efficient evaluation of its pollution status is a basis for further soil reclamation. Visible and near–infrared reflectance (Vis–NIR) spectroscopy has been diffusely used for retrieving heavy metals concentrations. However,...
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/PMC8497582/ https://www.ncbi.nlm.nih.gov/pubmed/34620914 http://dx.doi.org/10.1038/s41598-021-99106-1 |
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author | Guo, Bin Zhang, Bo Su, Yi Zhang, Dingming Wang, Yan Bian, Yi Suo, Liang Guo, Xianan Bai, Haorui |
author_facet | Guo, Bin Zhang, Bo Su, Yi Zhang, Dingming Wang, Yan Bian, Yi Suo, Liang Guo, Xianan Bai, Haorui |
author_sort | Guo, Bin |
collection | PubMed |
description | Heavy metals contaminations in mining areas aroused wide concerns globally. Efficient evaluation of its pollution status is a basis for further soil reclamation. Visible and near–infrared reflectance (Vis–NIR) spectroscopy has been diffusely used for retrieving heavy metals concentrations. However, the reliability and feasibility of calibrated models were still doubtful. The present study estimated zinc (Zn) concentrations via the random forest (RF) and partial least squares regression (PLSR) using ground in-situ Zn concentrations as well as soil spectral reflectance at an Opencast Coal Mine of Ordos, China in February 2020. The coefficient of determination (R(2)), root mean square error (RMSE), mean absolute error (MAE), and the ratio of performance to deviation (RPD) were selected to assess the robustness of the methods in estimating Zn contents. Moreover, the characteristic bands were chosen by Pearson correlation analysis and Boruta Algorithm. Finally, the comparison between RF and PLSR combined with eight spectral reflectance transformation methods was conducted for four concentration groups to determine the optimal model. The results indicated that: (1) Zn contents represented a skewed distribution (coefficient of variation (CV) = 33%); (2) the spectral reflectance tended to decrease with the increase of Zn contents during 580–1850 nm based on Savitzky–Golay smoothing (SG); (3) the continuous wavelet transform (CWT) demonstrated higher effectiveness than other spectral reflectance transformation methods in enhancing spectral responses, the R(2) between Zn contents and the soil spectral reflectance achieved the highest (R(2) = 0.71) by using CWT; (4) the RF combined with CWT exhibited the best performance than other methods in the current study (R(2) = 0.97, RPD = 3.39, RMSE = 1.05 mg kg(−1), MAE = 0.79 mg kg(−1)). The current study supplied a scientific scheme and theoretical support for predicting heavy metals concentrations via the Vis–NIR spectral method in possible contaminated areas such as coal mines and metallic mineral deposit areas. |
format | Online Article Text |
id | pubmed-8497582 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-84975822021-10-12 Retrieving zinc concentrations in topsoil with reflectance spectroscopy at Opencast Coal Mine sites Guo, Bin Zhang, Bo Su, Yi Zhang, Dingming Wang, Yan Bian, Yi Suo, Liang Guo, Xianan Bai, Haorui Sci Rep Article Heavy metals contaminations in mining areas aroused wide concerns globally. Efficient evaluation of its pollution status is a basis for further soil reclamation. Visible and near–infrared reflectance (Vis–NIR) spectroscopy has been diffusely used for retrieving heavy metals concentrations. However, the reliability and feasibility of calibrated models were still doubtful. The present study estimated zinc (Zn) concentrations via the random forest (RF) and partial least squares regression (PLSR) using ground in-situ Zn concentrations as well as soil spectral reflectance at an Opencast Coal Mine of Ordos, China in February 2020. The coefficient of determination (R(2)), root mean square error (RMSE), mean absolute error (MAE), and the ratio of performance to deviation (RPD) were selected to assess the robustness of the methods in estimating Zn contents. Moreover, the characteristic bands were chosen by Pearson correlation analysis and Boruta Algorithm. Finally, the comparison between RF and PLSR combined with eight spectral reflectance transformation methods was conducted for four concentration groups to determine the optimal model. The results indicated that: (1) Zn contents represented a skewed distribution (coefficient of variation (CV) = 33%); (2) the spectral reflectance tended to decrease with the increase of Zn contents during 580–1850 nm based on Savitzky–Golay smoothing (SG); (3) the continuous wavelet transform (CWT) demonstrated higher effectiveness than other spectral reflectance transformation methods in enhancing spectral responses, the R(2) between Zn contents and the soil spectral reflectance achieved the highest (R(2) = 0.71) by using CWT; (4) the RF combined with CWT exhibited the best performance than other methods in the current study (R(2) = 0.97, RPD = 3.39, RMSE = 1.05 mg kg(−1), MAE = 0.79 mg kg(−1)). The current study supplied a scientific scheme and theoretical support for predicting heavy metals concentrations via the Vis–NIR spectral method in possible contaminated areas such as coal mines and metallic mineral deposit areas. Nature Publishing Group UK 2021-10-07 /pmc/articles/PMC8497582/ /pubmed/34620914 http://dx.doi.org/10.1038/s41598-021-99106-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Guo, Bin Zhang, Bo Su, Yi Zhang, Dingming Wang, Yan Bian, Yi Suo, Liang Guo, Xianan Bai, Haorui Retrieving zinc concentrations in topsoil with reflectance spectroscopy at Opencast Coal Mine sites |
title | Retrieving zinc concentrations in topsoil with reflectance spectroscopy at Opencast Coal Mine sites |
title_full | Retrieving zinc concentrations in topsoil with reflectance spectroscopy at Opencast Coal Mine sites |
title_fullStr | Retrieving zinc concentrations in topsoil with reflectance spectroscopy at Opencast Coal Mine sites |
title_full_unstemmed | Retrieving zinc concentrations in topsoil with reflectance spectroscopy at Opencast Coal Mine sites |
title_short | Retrieving zinc concentrations in topsoil with reflectance spectroscopy at Opencast Coal Mine sites |
title_sort | retrieving zinc concentrations in topsoil with reflectance spectroscopy at opencast coal mine sites |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8497582/ https://www.ncbi.nlm.nih.gov/pubmed/34620914 http://dx.doi.org/10.1038/s41598-021-99106-1 |
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