Heavy metal pollution at mine sites estimated from reflectance spectroscopy following correction for skewed data

Weichao Sun, Andrew K. Skidmore, Tiejun Wang, Xia Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The heavy metal concentration of soil samples often exhibits a skewed distribution, especially for soil samples from mining areas with an extremely high concentration of heavy metals. In this study, to model soil contamination in mining areas using reflectance spectroscopy, the skewed distribution was corrected and heavy metal concentration estimated. In total, 46 soil samples from a mining area, along with corresponding field soil spectra, were collected. Laboratory spectra of the soil samples and the field spectra were used to estimate copper (Cu) concentration in the mining area. A logarithmic transformation was used to correct the skewed distribution, and based on the sorption of Cu on spectrally active soil constituents, the spectral bands associated with iron oxides were extracted from the visible and near-infrared (VNIR) region and used in the estimation. A genetic algorithm was adopted for band selection, and partial least squares regression was used to calibrate the estimation model. After transforming the distribution of Cu concentration, the accuracies (R2) of the estimation of Cu concentration using laboratory and field spectra separately were 0.94 and 0.96. The results indicate that Cu concentration in the mining area can be estimated using reflectance spectroscopy following correction of skewed distribution. Capsule: Reflectance spectroscopy of soil could be an alternative to investigate heavy metal concentration in mining areas.

Original languageEnglish
Pages (from-to)1117-1124
Number of pages8
JournalEnvironmental Pollution
Volume252
DOIs
Publication statusPublished - Sep 2019
Externally publishedYes

Keywords

  • Soil heavy metal
  • Visible and near infrared spectroscopy
  • Band selection
  • Skewed distribution
  • Logarithmic transformation

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