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.

中文翻译：

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使用在实验室构建的样品上开发的 VisNIR 模型预测田间土壤中的总石油烃

石油碳氢化合物 (PHC) 的准确量化是优化溢油现场补救措施所必需的。虽然评估土壤中的总石油烃 (TPH) 通常使用昂贵且耗时的实验室方法进行，但可见光和近红外反射光谱 (Vis-NIR) 已被证明是一种快速且具有成本效益的基于现场的方法土壤 TPH 定量。本研究调查了从实验室构建的 PHC 土壤样品校准的 Vis-NIR 模型是否可用于准确估计现场样品的 TPH 浓度。为了对此进行评估，通过将原油与未受污染的土壤样本混合构建了一个实验室样本集，并从三个受 PHC 影响的地点收集了两个现场样本集（F1 和 F2）。Vis-NIR TPH 模型使用四种不同的技术（偏最小二乘回归、随机森林、人工神经网络和支持向量回归）进行校准，并比较了两种模型改进方法（尖峰和带额外权重的尖峰）。结果表明，基于实验室的 Vis-NIR 模型可以以中等准确度 (R2 > .53) 预测现场样本集 F1 中的 TPH，但无法预测现场样本集 F2 (R2 < .13) 中的 TPH。尖峰和带额外权重的尖峰都提高了两个现场样本集的 TPH 预测（R2 分别从 0.63 到 0.88）；F2 的改进最为明显。这项研究表明，Vis-NIR 模型是根据实验室构建的 PHC 土壤样品开发的，并通过少量现场样品分析进行了加标，