Predicting soil arsenic pools by visible near infrared diffuse reflectance spectroscopy

Somsubhra Chakraborty, Bin Li, Shovik Deb, Sathi Paul, David C. Weindorf, Bhabani S. Das

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Rapid and cost-effective analysis of soil solid As phases would be an invaluable tool in studying polluted soils and predicting soil As mobility. Analysis of soil solid As phases has commonly used sequential extraction; however, the approach is time consuming, destructive, and costly. Several studies have established the viability of using visible near infrared diffuse reflectance spectroscopy (VisNIR DRS) for elemental data analysis of soil, sediment, and other matrices. This pilot study used VisNIR DRS spectral data for rapidly predicting total As and five different solid As phases (Mg, PO4, Ox, HCl and org pools). A total of 200 surface soil (0–15 cm) samples were collected from arable lands surrounding a polluted landfill site and scanned via VisNIR DRS. The raw reflectance spectra were preprocessed using three spectral transformations for predicting soil total As and five extracted pools using partial least squares regression (PLSR). Quantitatively, better accuracy was produced by PO4 (Validation R2 = 0.72, RPIQ = 3.39) and org (Validation R2 = 0.93, RPIQ = 4.81) pools along with total As (Validation R2 = 0.88, RPIQ = 3.54) using the first derivative of original reflectance values. Both qualitative spectral analysis and PLSR coefficients indicated that prediction of soil As and its phases were dependent on their close association with spectrally active soil organic matter, clay minerals and Fe/Al-oxides.

Original languageEnglish
Pages (from-to)30-37
Number of pages8
StatePublished - Jun 15 2017


  • Diffuse reflectance spectroscopy
  • Landfill
  • Partial least squares regression
  • Soil arsenic solid phases
  • Visible near infrared


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