We analyzed chemical composition, mineralogy, and spectral characteristics of the tailings of a hydrothermal gold mine in South Korea. We measured spectral responses of tailings to arsenic (As) and lead (Pb) concentration and developed and validated a prediction model for As and Pb in the tailings. The tailing was heavily contaminated with heavy metal elements and composed of rock forming minerals, gangue minerals and hydrothermal alteration minerals. The spectral features of the tailing were closely related to hydrothermal alteration minerals. The spectral responses associated with As and Pb concentrations were detected in shortwave infrared (SWIR) region at absorption positions of the hydrothermal alteration minerals. The prediction models were constructed using spectral bands of absorption features of the hydrothermal alteration minerals and were statistically significant. We found distinctive differences in spectral characteristics and spectral response to heavy metal contamination between the tailings and soils in the mining area. While the spectral signals to heavy metal concentration of tailings were associated with the hydrothermal alteration minerals, those of soils in mining area were manifested by clay minerals originated from weathering processes. This infers that geological processes associated with formation of soils and tailings are the major controlling factors of spectral responses to heavy metal contamination. This study provides a rare reference for the estimation of As and Pb concentration in the tailings with similar types of ore deposit and host rock.

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.

This study developed case-adaptive spectral indices for detecting heavy metal pollution in skarn soil, hydrothermal soil, and acid mine drainage (AMD) soil, and a universal index for general cases by merging data from all cases. Sequential analyses were conducted including heavy metal concentration, mineral identification, grain size, and spectral characteristics. A factor analysis was used to combine multiple heavy metal elements into representative factors and used for establishing the spectral indices using the stepwise multiple linear regression (SMLR), which is compared with random forest regression (RFR). The spectral indices of skarn soil predict Zn, As, and Pb pollution from moderate to ultrahigh levels using absorption bands of skarn and supergene minerals. The spectral index for hydrothermal soil predicts pollution of Cr and Ni from very low to very high using absorption bands of hydrothermal alteration or supergene minerals. The index for the AMD soil predicts Al, Fe, and As pollution from very low to very high levels by employing the absorption features of iron hydroxide. The universal index predicts Fe, Zn, and Pb pollution levels from very low to ultrahigh by utilizing the clay and iron oxide absorptions, commonly observed in general soils. All models showed statistical significance with R ² = 0.66-0.92 for SMLR and R ² = 0.66-0.95 for RFR. Because our models were derived from large number of samples (600s) for detecting the pollution levels of multiple heavy metal elements for the first time, the models could provide a decision-making tool for soil survey in heavy metal pollution.

This study investigates spectral characteristics of romaine lettuce at different uptake level of Zn. Also, spectral changes are analyzed to define spectral response of romaine lettuce to Zn uptake. The Zn concentration of control samples range from 0.009 to 0.030 %, whereas that of Zn contaminated samples shows a range of 0.014 to 0.119 %. The Zn concentration dramatically rises from 68th day of experiment. The reflectance of Zn containing romaine lettuce decreases in NIR region and increase in visible region as Zn concentration increases. Moreover, blue shift occurs at red edge point, as romaine lettuce contains more Zn. We think that it is very remarkable to detect Zn containing romaine lettuce in field in seconds.