Heavy Metal Contamination Index Using Spectral Variables for White Precipitates Induced by Acid Mine Drainage: A Case Study of Soro Creek, South Korea

Abstract

We analyzed heavy metal contamination, mineral composition, and spectral characteristics of white aluminum precipitates from an acid mine drainage in Taebaek, South Korea. We introduced a single index for prediction of overall heavy metal contamination level in white precipitates using spectral variables. The white precipitates were severely contaminated with heavy metal elements and consisted of primary and secondary minerals. Due to the contamination in the environment, the precipitation pH values ranged from 4.76 to 7.80. The spectral characteristics of white precipitates are dominated by secondary minerals. The spectral reflectance of white precipitates decreased in all wavelengths, and the absorption depth related with OH, H2O, and Al-OH decreased with increase in heavy metal contamination. We found the distinctive differences at ferric iron and Al-OH absorptions between the white and reddish-brown precipitates. Based on these observations, we developed heavy metal contamination index and built prediction models using the index. The validation tests showed the index, and the regression model can accurately predict the amount of heavy metal from the spectral readings. Given the fact that the stream precipitates resulted by acid mine drainage is typically similar for each type, we expect that the heavy metal contamination index can be used to make reliable estimates of heavy metal contamination in the white precipitates by remote sensing applications.