Predicting total petroleum hydrocarbons in field soils with Vis-NIR models developed on laboratory-constructed samples

Abstract

Accurate quantification of petroleum hydrocarbons (PHCs) is required for optimizing remedial efforts at oil spill sites. While evaluating total petroleum hydrocarbons (TPH) in soils is often conducted using costly and time-consuming laboratory methods, visible and near-infrared reflectance spectroscopy (Vis-NIR) has been proven to be a rapid and cost-effective field-based method for soil TPH quantification. This study investigated whether Vis-NIR models calibrated from laboratory-constructed PHC soil samples could be used to accurately estimate TPH concentration of field samples. To evaluate this, a laboratory sample set was constructed by mixing crude oil with uncontaminated soil samples, and two field sample sets (F1 and F2) were collected from three PHC-impacted sites. The Vis-NIR TPH models were calibrated with four different techniques (partial least squares regression, random forest, artificial neural network, and support vector regression), and two model improvement methods (spiking and spiking with extra weight) were compared. Results showed that laboratory-based Vis-NIR models could predict TPH in field sample set F1 with moderate accuracy (R² > .53) but failed to predict TPH in field sample set F2 (R² < .13). Both spiking and spiking with extra weight improved the prediction of TPH in both field sample sets (R² ranged from .63 to .88, respectively); the improvement was most pronounced for F2. This study suggests that Vis-NIR models developed from laboratory-constructed PHC soil samples, spiked by a small number of field sample analyses, can be used to estimate TPH concentrations more efficiently and cost effectively compared with generating site-specific calibrations.