Visible and near-infrared (VisNIR) spectroscopy is becoming recognised by soil scientists as a rapid and cost-effective measurement method for hydrocarbons in petroleum-contaminated soils. This study investigated the potential application of VisNIR spectroscopy (350–2500 nm) for the prediction of phenanthrene, a polycyclic aromatic hydrocarbon (PAH), in soils. A total of 150 diesel-contaminated soil samples were used in the investigation. Partial least-squares (PLS) regression analysis with full cross-validation was used to develop models to predict the PAH compound. Results showed that the PAH compound was predicted well with residual prediction deviation of 2.0–2.32, root-mean-square error of prediction of 0.21–0.25 mg kg−1, and coefficient of determination (r 2) of 0.75–0.83. The mechanism of prediction was attributed to covariation of the PAH with clay and soil organic carbon. Overall, the results demonstrated that the methodology may be used for predicting phenanthrene in soils utilizing the interrelationship between clay and soil organic carbon.
展开▼
机译：可见和近红外（VisNIR）光谱已被土壤科学家认可为一种快速且经济高效的石油污染土壤中碳氢化合物的测量方法。这项研究调查了VisNIR光谱（350-2500 nm）在预测土壤中菲（一种多环芳烃（PAH））上的潜在应用。总共使用了150个被柴油污染的土壤样品。使用具有完全交叉验证的偏最小二乘（PLS）回归分析来开发模型来预测PAH化合物。结果表明，对PAH化合物的预测良好，残留预测偏差为2.0–2.32，预测的均方根误差为0.21–0.25 mg kg -1 ，测定系数为（r 2 ）为0.75-0.83。预测机制归因于PAH与粘土和土壤有机碳的协变。总体而言，结果表明该方法可用于利用粘土与土壤有机碳之间的相互关系预测土壤中的菲。
展开▼
