Dep. of Biological Systems Engineering, Univ. of Nebraska-Lincoln, 158 Chase Hall, East Campus, Lincoln, NE, 68583, USA.
Dep. of Biological Systems Engineering, Univ. of Nebraska-Lincoln, Chase Hall, East Campus, Lincoln, NE, 68583, USA.
J Environ Qual. 2020 Jul;49(4):847-857. doi: 10.1002/jeq2.20102. Epub 2020 Jun 13.
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 (R > .53) but failed to predict TPH in field sample set F2 (R < .13). Both spiking and spiking with extra weight improved the prediction of TPH in both field sample sets (R 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.
准确量化石油烃 (PHC) 对于优化溢油现场的补救措施至关重要。虽然使用昂贵且耗时的实验室方法通常可以评估土壤中的总石油烃 (TPH)，但可见近红外反射光谱 (Vis-NIR) 已被证明是一种快速且具有成本效益的现场土壤 TPH 定量方法。本研究探讨了是否可以使用从实验室构建的 PHC 土壤样本校准的 Vis-NIR 模型来准确估计现场样本的 TPH 浓度。为此，通过将原油与未受污染的土壤样本混合构建了实验室样本集，并从三个 PHC 污染场地收集了两个现场样本集 (F1 和 F2)。使用四种不同技术（偏最小二乘回归、随机森林、人工神经网络和支持向量回归）校准 Vis-NIR TPH 模型，并比较了两种模型改进方法（加标和加标加权）。结果表明，基于实验室的 Vis-NIR 模型可以中等精度（R>.53）预测现场样本集 F1 中的 TPH，但无法预测现场样本集 F2 中的 TPH（R<.13）。加标和加标加权均可改善两个现场样本集的 TPH 预测（R 分别为.63 至.88）；对 F2 的改善最为显著。本研究表明，与生成特定于站点的校准相比，使用从实验室构建的 PHC 土壤样本和少量现场样本分析加标构建的 Vis-NIR 模型可以更高效、更具成本效益地估计 TPH 浓度。
