Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils

Somsubhra Chakraborty, David C. Weindorf, Bin Li, Abdalsamad Abdalsatar Ali Aldabaa, Rakesh Kumar Ghosh, Sathi Paul, Md Nasim Ali

Research output: Contribution to journalArticle

41 Scopus citations

Abstract

Using 108 petroleum contaminated soil samples, this pilot study proposed a new analytical approach of combining visible near-infrared diffuse reflectance spectroscopy (VisNIR DRS) and portable X-ray fluorescence spectrometry (PXRF) for rapid and improved quantification of soil petroleum contamination. Results indicated that an advanced fused model where VisNIR DRS spectra-based penalized spline regression (PSR) was used to predict total petroleum hydrocarbon followed by PXRF elemental data-based random forest regression was used to model the PSR residuals, it outperformed (R2=0.78, residual prediction deviation (RPD)=2.19) all other models tested, even producing better generalization than using VisNIR DRS alone (RPD's of 1.64, 1.86, and 1.96 for random forest, penalized spline regression, and partial least squares regression, respectively). Additionally, unsupervised principal component analysis using the PXRF+VisNIR DRS system qualitatively separated contaminated soils from control samples. Capsule: Fusion of PXRF elemental data and VisNIR derivative spectra produced an optimized model for total petroleum hydrocarbon quantification in soils.

Original languageEnglish
Pages (from-to)399-408
Number of pages10
JournalScience of the Total Environment
Volume514
DOIs
StatePublished - May 1 2015

Keywords

  • Penalized spline model
  • Portable X-ray fluorescence spectrometry
  • Random forest
  • Soil petroleum contamination
  • Visible near-infrared diffuse reflectance spectroscopy

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