Further to quantification of content, can reflectance spectroscopy determine the speciation of cobalt and nickel on a mine waste dump surface?

Vahid Khosravi; Asa Gholizadeh; Prince Chapman Agyeman; Faramarz Doulati Ardejani; Saeed Yousefi; Mohammadmehdi Saberioon

doi:10.1016/j.scitotenv.2023.161996

Further to quantification of content, can reflectance spectroscopy determine the speciation of cobalt and nickel on a mine waste dump surface?

Sci Total Environ. 2023 May 10:872:161996. doi: 10.1016/j.scitotenv.2023.161996. Epub 2023 Feb 10.

Authors

Vahid Khosravi¹, Asa Gholizadeh², Prince Chapman Agyeman², Faramarz Doulati Ardejani³, Saeed Yousefi⁴, Mohammadmehdi Saberioon⁵

Affiliations

¹ Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamycka 129, Suchdol, Prague 16500, Czech Republic. Electronic address: khosravi@af.czu.cz.
² Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamycka 129, Suchdol, Prague 16500, Czech Republic.
³ School of Mining, College of Engineering, University of Tehran, Tehran, Iran.
⁴ Department of Mining, Faculty of Engineering, University of Birjand, Birjand, Iran.
⁵ Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing and Geoinformatics, Telegrafenberg, Potsdam 14473, Germany.

PMID: 36775166
DOI: 10.1016/j.scitotenv.2023.161996

Abstract

Toxic elements released due to mining activities are of the most important environmental concerns, characterised not only by their concentration, but also by their distribution among different chemical species, known as speciation. These are conventionally determined using chemical analysis and sequential extraction, which are expensive and time-demanding. In this study, the possibility of using visible-near-infrared-shortwave infrared (VNIR-SWIR) reflectance spectroscopy was investigated as an alternative technique to quantify the contents of cobalt (Co) and nickel (Ni) in soil samples collected from Sarcheshmeh copper mine waste dump surface, in Iran. As a novel approach, the capability of VNIR-SWIR spectroscopy was also investigated in speciation of those elements. Three machine learning (ML) techniques (i.e., extreme gradient boosting (EGB), random forest (RF) and support vector regression (SVR)) were used to make relationships between soil spectral responses and Co and Ni contents of the samples. For all ML algorithms, the best prediction accuracies were obtained by the models developed on the first derivative (FD) spectra (for Co: RMSE_p values of 7.82, 8.03 and 9.22 mg·kg^-1, and for Ni: RMSE_p values of 9.88, 10.32 and 11.02 mg·kg^-1, using EGB, RF and SVR, respectively). Spatial variability maps of elements showed relatively similar patterns between observed and predicted values. Correlation and ML (EGB, RF, SVR)-based methods revealed that the most important wavelengths for Co and Ni prediction were those related to iron oxides/hydroxides and clay minerals, as two main soil properties responsible for controlling their speciation. This study demonstrated that the EGB technique was successful at indirect quantification and spatial variability mapping of Co and Ni on the mine waste dump surface. In addition, it provided an inspiration for implementation of the VNIR-SWIR reflectance spectroscopy as a potentially fast and cost-effective method for speciation studies of toxic elements, especially in heterogeneous soil environments.

Keywords: Extreme gradient boosting; Spatial distribution; Speciation; Toxic elements; VNIR–SWIR spectroscopy.