Estimating heavy metal concentrations in Technosols with reflectance spectroscopy

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • Friederike Kästner
  • Magdalena Sut-Lohmann
  • Shaghayegh Ramezany
  • Thomas Raab
  • Hannes Feilhauer
  • Sabine Chabrillat

Organisationseinheiten

Externe Organisationen

  • Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum (GFZ)
  • Brandenburgische Technische Universität Cottbus - Senftenberg (BTU)
  • Universität Leipzig
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Aufsatznummer115512
FachzeitschriftGEODERMA
Jahrgang406
Frühes Online-Datum18 Okt. 2021
PublikationsstatusVeröffentlicht - 15 Jan. 2022

Abstract

Reflectance spectroscopy in the visible-infrared and shortwave infrared (450–2500 nm) wavelength region is a rapid, cost-effective and non-destructive method that can be used to monitor heavy metal (PTE, potential toxic elements) contaminated areas. Due to the PTE pollution that has accumulated in the course of wastewater treatment, the existence of Technosols presents an environmental problem, a potential source for PTE uptake by vegetation, or even the release of PTEs into groundwater. In this study, multivariate procedures using Partial Least Squares Regression (PLSR) and Random Forest Regression (RFR) are applied to quantify relationships between soil heavy metal concentration (Cr, Cu, Ni, Zn) and reflectance data of highly contaminated Technosols from a former sewage farm near Berlin, Germany. Laboratory measurements of 110 soil samples in four different preparation steps were acquired with HySpex hyperspectral cameras. The impact of the different preparation steps, namely “oven-dried”, “sieved”, “ground”, “LOI”, was evaluated for its potential to enhance the method performance or to reduce the time-consuming soil sample preparation. Furthermore, different spectral pre-processing methods were evaluated regarding improvements of spectral modelling performance and their ability to minimise noise and multiple scattering effects. Considering the optimal coefficient of determination (R2), PLSR shows an improving performance and accuracy with increasing preparation steps such as ground or LOI for all metals of interest (R2_Cr: 0.52–0.78; R2_Cu: 0.36–0.73; R2_Ni: 0.19–0.42 and R2_Zn: 0.41–0.74). RFR shows a weaker estimation performance for all metals, even when using higher sample preparation levels (R2_Cr: 0.36–0.62; R2_Cu: 0.17–0.72; R2_Ni: 0.20–0.35 and R2_Zn: 0.26–0.67). The results show that an application of methods such as PLSR for the prediction of PTE concentration in Technosols is still a challenge but provides more robust estimations than the user-friendly RFR method. Additionally, this study shows that PTE estimation performance in heterogeneous soil samples can be improved by increased laboratory soil preparation steps and further spectral pre-processing steps.

ASJC Scopus Sachgebiete

Zitieren

Estimating heavy metal concentrations in Technosols with reflectance spectroscopy. / Kästner, Friederike; Sut-Lohmann, Magdalena; Ramezany, Shaghayegh et al.
in: GEODERMA, Jahrgang 406, 115512, 15.01.2022.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Kästner, F, Sut-Lohmann, M, Ramezany, S, Raab, T, Feilhauer, H & Chabrillat, S 2022, 'Estimating heavy metal concentrations in Technosols with reflectance spectroscopy', GEODERMA, Jg. 406, 115512. https://doi.org/10.1016/j.geoderma.2021.115512
Kästner, F., Sut-Lohmann, M., Ramezany, S., Raab, T., Feilhauer, H., & Chabrillat, S. (2022). Estimating heavy metal concentrations in Technosols with reflectance spectroscopy. GEODERMA, 406, Artikel 115512. https://doi.org/10.1016/j.geoderma.2021.115512
Kästner F, Sut-Lohmann M, Ramezany S, Raab T, Feilhauer H, Chabrillat S. Estimating heavy metal concentrations in Technosols with reflectance spectroscopy. GEODERMA. 2022 Jan 15;406:115512. Epub 2021 Okt 18. doi: 10.1016/j.geoderma.2021.115512
Kästner, Friederike ; Sut-Lohmann, Magdalena ; Ramezany, Shaghayegh et al. / Estimating heavy metal concentrations in Technosols with reflectance spectroscopy. in: GEODERMA. 2022 ; Jahrgang 406.
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title = "Estimating heavy metal concentrations in Technosols with reflectance spectroscopy",
abstract = "Reflectance spectroscopy in the visible-infrared and shortwave infrared (450–2500 nm) wavelength region is a rapid, cost-effective and non-destructive method that can be used to monitor heavy metal (PTE, potential toxic elements) contaminated areas. Due to the PTE pollution that has accumulated in the course of wastewater treatment, the existence of Technosols presents an environmental problem, a potential source for PTE uptake by vegetation, or even the release of PTEs into groundwater. In this study, multivariate procedures using Partial Least Squares Regression (PLSR) and Random Forest Regression (RFR) are applied to quantify relationships between soil heavy metal concentration (Cr, Cu, Ni, Zn) and reflectance data of highly contaminated Technosols from a former sewage farm near Berlin, Germany. Laboratory measurements of 110 soil samples in four different preparation steps were acquired with HySpex hyperspectral cameras. The impact of the different preparation steps, namely “oven-dried”, “sieved”, “ground”, “LOI”, was evaluated for its potential to enhance the method performance or to reduce the time-consuming soil sample preparation. Furthermore, different spectral pre-processing methods were evaluated regarding improvements of spectral modelling performance and their ability to minimise noise and multiple scattering effects. Considering the optimal coefficient of determination (R2), PLSR shows an improving performance and accuracy with increasing preparation steps such as ground or LOI for all metals of interest (R2_Cr: 0.52–0.78; R2_Cu: 0.36–0.73; R2_Ni: 0.19–0.42 and R2_Zn: 0.41–0.74). RFR shows a weaker estimation performance for all metals, even when using higher sample preparation levels (R2_Cr: 0.36–0.62; R2_Cu: 0.17–0.72; R2_Ni: 0.20–0.35 and R2_Zn: 0.26–0.67). The results show that an application of methods such as PLSR for the prediction of PTE concentration in Technosols is still a challenge but provides more robust estimations than the user-friendly RFR method. Additionally, this study shows that PTE estimation performance in heterogeneous soil samples can be improved by increased laboratory soil preparation steps and further spectral pre-processing steps.",
keywords = "Partial Least Squares (PLS) regression, PTE, Random Forest regression, Reflectance Spectroscopy, Sewage farm, Soil environment monitoring",
author = "Friederike K{\"a}stner and Magdalena Sut-Lohmann and Shaghayegh Ramezany and Thomas Raab and Hannes Feilhauer and Sabine Chabrillat",
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language = "English",
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TY - JOUR

T1 - Estimating heavy metal concentrations in Technosols with reflectance spectroscopy

AU - Kästner, Friederike

AU - Sut-Lohmann, Magdalena

AU - Ramezany, Shaghayegh

AU - Raab, Thomas

AU - Feilhauer, Hannes

AU - Chabrillat, Sabine

N1 - Funding Information: The authors acknowledge the financial support of the “Central Innovation Programme for small and medium-sized enterprises (SMEs)” of the Federal Ministry for Economic Affairs and Energy. The assistance of Constantin Hildebrand, Martina Heinrich, Dr. Florian Hirsch and Gbenga Fawehinmi for helping in the field and doing the chemical analysis, as well as Dr. Robert Milewski for checking the code and results during the model analysis. We thank the anonymous reviewers for their constructive comments that helped to improve this manuscript.

PY - 2022/1/15

Y1 - 2022/1/15

N2 - Reflectance spectroscopy in the visible-infrared and shortwave infrared (450–2500 nm) wavelength region is a rapid, cost-effective and non-destructive method that can be used to monitor heavy metal (PTE, potential toxic elements) contaminated areas. Due to the PTE pollution that has accumulated in the course of wastewater treatment, the existence of Technosols presents an environmental problem, a potential source for PTE uptake by vegetation, or even the release of PTEs into groundwater. In this study, multivariate procedures using Partial Least Squares Regression (PLSR) and Random Forest Regression (RFR) are applied to quantify relationships between soil heavy metal concentration (Cr, Cu, Ni, Zn) and reflectance data of highly contaminated Technosols from a former sewage farm near Berlin, Germany. Laboratory measurements of 110 soil samples in four different preparation steps were acquired with HySpex hyperspectral cameras. The impact of the different preparation steps, namely “oven-dried”, “sieved”, “ground”, “LOI”, was evaluated for its potential to enhance the method performance or to reduce the time-consuming soil sample preparation. Furthermore, different spectral pre-processing methods were evaluated regarding improvements of spectral modelling performance and their ability to minimise noise and multiple scattering effects. Considering the optimal coefficient of determination (R2), PLSR shows an improving performance and accuracy with increasing preparation steps such as ground or LOI for all metals of interest (R2_Cr: 0.52–0.78; R2_Cu: 0.36–0.73; R2_Ni: 0.19–0.42 and R2_Zn: 0.41–0.74). RFR shows a weaker estimation performance for all metals, even when using higher sample preparation levels (R2_Cr: 0.36–0.62; R2_Cu: 0.17–0.72; R2_Ni: 0.20–0.35 and R2_Zn: 0.26–0.67). The results show that an application of methods such as PLSR for the prediction of PTE concentration in Technosols is still a challenge but provides more robust estimations than the user-friendly RFR method. Additionally, this study shows that PTE estimation performance in heterogeneous soil samples can be improved by increased laboratory soil preparation steps and further spectral pre-processing steps.

AB - Reflectance spectroscopy in the visible-infrared and shortwave infrared (450–2500 nm) wavelength region is a rapid, cost-effective and non-destructive method that can be used to monitor heavy metal (PTE, potential toxic elements) contaminated areas. Due to the PTE pollution that has accumulated in the course of wastewater treatment, the existence of Technosols presents an environmental problem, a potential source for PTE uptake by vegetation, or even the release of PTEs into groundwater. In this study, multivariate procedures using Partial Least Squares Regression (PLSR) and Random Forest Regression (RFR) are applied to quantify relationships between soil heavy metal concentration (Cr, Cu, Ni, Zn) and reflectance data of highly contaminated Technosols from a former sewage farm near Berlin, Germany. Laboratory measurements of 110 soil samples in four different preparation steps were acquired with HySpex hyperspectral cameras. The impact of the different preparation steps, namely “oven-dried”, “sieved”, “ground”, “LOI”, was evaluated for its potential to enhance the method performance or to reduce the time-consuming soil sample preparation. Furthermore, different spectral pre-processing methods were evaluated regarding improvements of spectral modelling performance and their ability to minimise noise and multiple scattering effects. Considering the optimal coefficient of determination (R2), PLSR shows an improving performance and accuracy with increasing preparation steps such as ground or LOI for all metals of interest (R2_Cr: 0.52–0.78; R2_Cu: 0.36–0.73; R2_Ni: 0.19–0.42 and R2_Zn: 0.41–0.74). RFR shows a weaker estimation performance for all metals, even when using higher sample preparation levels (R2_Cr: 0.36–0.62; R2_Cu: 0.17–0.72; R2_Ni: 0.20–0.35 and R2_Zn: 0.26–0.67). The results show that an application of methods such as PLSR for the prediction of PTE concentration in Technosols is still a challenge but provides more robust estimations than the user-friendly RFR method. Additionally, this study shows that PTE estimation performance in heterogeneous soil samples can be improved by increased laboratory soil preparation steps and further spectral pre-processing steps.

KW - Partial Least Squares (PLS) regression

KW - PTE

KW - Random Forest regression

KW - Reflectance Spectroscopy

KW - Sewage farm

KW - Soil environment monitoring

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U2 - 10.1016/j.geoderma.2021.115512

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VL - 406

JO - GEODERMA

JF - GEODERMA

SN - 0016-7061

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