Spectral Indices to Assess Pollution Level in Soils: Case-Adaptive and Universal Detection Models for Multiple Heavy Metal Pollution under Laboratory Conditions

Document Type

Article

Publication Date

1-1-2023

Abstract

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 a large number of samples (600 s) 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.

Publication Source (Journal or Book title)

IEEE Transactions on Geoscience and Remote Sensing

Number

730

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