Spectral Responses of Heavy Metal Contaminated Soils in the Vicinity of a Hydrothermal Ore Deposit: A Case Study of Boksu Mine, South Korea

et al. 2020

Remote Sensing

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This paper illustrates a spectroscopic analysis of heavy metal concentration in mine soils with the consideration of mineral assemblages originated by weathering and mineralization processes. The mine soils were classified into two groups based on the mineral composition: silicate clay mineral group (Group A) and silicate–carbonate–skarn–clay mineral group (Group B). Both soil groups are contaminated with Cu, Zn, As, and Pb, while the contamination level was higher for Group A. The two groups exhibit different geochemical behaviors with different heavy metal contamination. The spectral variation associated with heavy metal was highly correlated with absorption features of clay and iron oxide minerals for Group A, and the absorption features of skarn minerals, iron oxides, and clay minerals for Group B. It indicates that the geochemical adsorption of heavy metal elements mainly occurs with clay minerals and iron oxides from weathering, and of skarn minerals, iron oxides, and clay minerals from mineralization. Therefore, soils from different secondary mineral production processes should be analyzed with different spectral models. We constructed spectral models for predicting Cu, Zn, As, and Pb in soil group A and Zn and Pb in soil group B using corresponding absorptions. Both models were statistically significant with sufficient accuracy.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Variations in Spectral Signals of Heavy Metal Contamination in Mine Soils Controlled by Mineral Assemblages

Kim

et al. 2020

Remote Sensing

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“…Furthermore, this study is based on fresh samples with controlled dry conditions. The weathering process and wet surface would complicate the spectral signals associated with hydrolysis and water components [38,79]. Adding controlled moisture and weathered samples would allow us to better understand the uncertainty in mapping magnesite in a natural environment.…”

Section: Discussion and Limitations Of The Present Workmentioning

confidence: 99%

“…The mixture of those minerals would make complications in the spectral signatures. The spectral characteristics of talc had distinctive absorptions at 1400 nm, caused by hydrolysis components and doublet absorptions at 2300 nm of the MgOH component [38]. Due to the common absorptions and variations in the absorption positions of magnesite and gangue minerals, the natural occurrence with the mixture of those minerals would increase the complexity of its spectral classification.…”

Section: Introductionmentioning

confidence: 99%

Detection of Magnesite and Associated Gangue Minerals using Hyperspectral Remote Sensing—A Laboratory Approach

Chung

et al. 2020

Remote Sensing

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This study introduced a detection method for magnesite and associated gangue minerals, including dolomite, calcite, and talc, based on mineralogical, chemical, and hyperspectral analyses using hand samples from thirteen different source locations and Specim hyperspectral short wave infrared (SWIR) hyperspectral images. Band ratio methods and logistic regression models were developed based on the spectral bands selected by the random forest algorithm. The mineralogical analysis revealed the heterogeneity of mineral composition for naturally occurring samples, showing various carbonate and silicate minerals as accessory minerals. The Mg and Ca composition of magnesite and dolomite varied significantly, inferring the mixture of minerals. The spectral characteristics of magnesite and associated gangue minerals showed major absorption features of the target minerals mixed with the absorption features of accessory carbonate minerals and talc affected by mineral composition. The spectral characteristics of magnesite and dolomite showed a systematic shift of the Mg-OH absorption features toward a shorter wavelength with an increased Mg content. The spectral bands identified by the random forest algorithm for detecting magnesite and gangue minerals were mainly associated with spectral features manifested by Mg-OH, CO3, and OH. A two-step band ratio classification method achieved an overall accuracy of 92% and 55.2%. The classification models developed by logistic regression models showed a significantly higher accuracy of 98~99.9% for training samples and 82–99.8% for validation samples. Because the samples were collected from heterogeneous sites all over the world, we believe that the results and the approach to band selection and logistic regression developed in this study can be generalized to other case studies of magnesite exploration.

“…The soils near the Boksu mine are heavily contaminated with cadmium (Cd), lead (Pb), arsenic (As), nickel (Ni), zinc (Zn), and copper (Cu). The contamination levels of these elements are very high based on the metal and metalloid contamination standard in soil [5,29]. For mine reclamation, phytoremediation approaches using P. densiflora have been carried out on re-cultivated soil.…”

Section: Study Areamentioning

confidence: 99%

Investigation of Spectral Variation of Pine Needles as an Indicator of Arsenic Content in Soils

Shin

et al. 2019

Minerals

The spectral response to arsenic (As) stress of pine needles (Pinus densiflora Siebold and Zucc.) from an abandoned lead (Pb)–zinc (Zn) mine was investigated based on chemical and spectroscopic analyses. The correlation analysis between the content of As in needle samples and that of soils and spectral parameters of the needle samples were conducted. The results showed very high correlation between As content in pine needles and soils. The major spectral response of pine needles to the As stress were characterized by the increase in the green and red color reflectance, the decrease in the first derivatives at 1648 nm, and the shrink in the red absorption feature. These changes were caused by the pigment content loss and the structural changes of phenolic compounds in the pine needles due to the As content. The linear regression analysis with the stepwise method showed the first derivatives at 668 nm and 1648 nm were the most useful variables in the regression model for As content prediction in pine needles. The As index of pine needles could be used to detect As content in soils associated with As and heavy metals contamination and/or mineralization in coniferous forests.