Analysis of petroleum-contaminated soils by diffuse reflectance spectroscopy and sequential ultrasonic solvent extraction-gas chromatography

In this study, we demonstrate that partial least-squares regression analysis with full cross-validation of spectral reflectance data estimates the amount of polycyclic aromatic hydrocarbons in petroleumcontaminated tropical rainforest soils. We applied the approach to 137 field-moist intact soil samples collected from three oil spill sites in Ogoniland in the Niger Delta province (5.317N, 6.467E), Nigeria. We used sequential ultrasonic solvent extractionegas chromatography as the reference chemical method. We took soil diffuse reflectance spectra with a mobile fibre-optic visible and near-infrared spectrophotometer (350e2500 nm). Independent validation of combined data from studied sites showed reasonable prediction precision (root-mean-square error of prediction ¼ 1.16e1.95 mg kg1, ratio of prediction deviation ¼ 1.86e3.12, and validation r2 ¼ 0.77e0.89). This suggests that the methodology may be useful for rapid assessment of the spatial variability of polycyclic aromatic hydrocarbons in petroleum-contaminated soils in the Niger Delta to inform risk assessment and remediation.