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Rapid Identification of Oil‐Contaminated Soils Using Visible Near‐Infrared Diffuse Reflectance Spectroscopy
Author(s) -
Chakraborty Somsubhra,
Weindorf David C.,
Morgan Cristine L.S.,
Ge Yufeng,
Galbraith John M.,
Li Bin,
Kahlon Charanjit S.
Publication year - 2010
Publication title -
journal of environmental quality
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.888
H-Index - 171
eISSN - 1537-2537
pISSN - 0047-2425
DOI - 10.2134/jeq2010.0183
Subject(s) - diffuse reflectance infrared fourier transform , diffuse reflection , contamination , soil water , environmental science , infrared , reflectivity , environmental chemistry , spectroscopy , chemistry , soil science , optics , biology , physics , ecology , photocatalysis , biochemistry , catalysis , quantum mechanics
In the United States, petroleum extraction, refinement, and transportation present countless opportunities for spillage mishaps. A method for rapid field appraisal and mapping of petroleum hydrocarbon–contaminated soils for environmental cleanup purposes would be useful. Visible near‐infrared (VisNIR, 350–2500 nm) diffuse reflectance spectroscopy (DRS) is a rapid, nondestructive, proximal‐sensing technique that has proven adept at quantifying soil properties in situ. The objective of this study was to determine the prediction accuracy of VisNIR DRS in quantifying petroleum hydrocarbons in contaminated soils. Forty‐six soil samples (including both contaminated and reference samples) were collected from six different parishes in Louisiana. Each soil sample was scanned using VisNIR DRS at three combinations of moisture content and pretreatment: (i) field‐moist intact aggregates, (ii) air‐dried intact aggregates, (iii) and air‐dried ground soil (sieved through a 2‐mm sieve). The VisNIR spectra of soil samples were used to predict total petroleum hydrocarbon (TPH) content in the soil using partial least squares (PLS) regression and boosted regression tree (BRT) models. Each model was validated with 30% of the samples that were randomly selected and not used in the calibration model. The field‐moist intact scan proved best for predicting TPH content with a validation r 2 of 0.64 and relative percent difference (RPD) of 1.70. Because VisNIR DRS was promising for rapidly predicting soil petroleum hydrocarbon content, future research is warranted to evaluate the methodology for identifying petroleum contaminated soils.