TY - JOUR
T1 - Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils
AU - Chakraborty, Somsubhra
AU - Weindorf, David C.
AU - Li, Bin
AU - Ali Aldabaa, Abdalsamad Abdalsatar
AU - Ghosh, Rakesh Kumar
AU - Paul, Sathi
AU - Nasim Ali, Md
N1 - Publisher Copyright:
© 2015 Elsevier B.V.
PY - 2015/5/1
Y1 - 2015/5/1
N2 - 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.
AB - 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.
KW - Penalized spline model
KW - Portable X-ray fluorescence spectrometry
KW - Random forest
KW - Soil petroleum contamination
KW - Visible near-infrared diffuse reflectance spectroscopy
UR - http://www.scopus.com/inward/record.url?scp=84922570264&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2015.01.087
DO - 10.1016/j.scitotenv.2015.01.087
M3 - Article
C2 - 25681776
AN - SCOPUS:84922570264
SN - 0048-9697
VL - 514
SP - 399
EP - 408
JO - Science of the Total Environment
JF - Science of the Total Environment
ER -