Christensen Jan H, Hansen Asger B, Tomasi Giorgio, Mortensen John, Andersen Ole
Department of Environmental Chemistry and Microbiology, National Environmental Research Institute, Frederiksborgvej 399, PO Box 358, 4000 Roskilde, Denmark.
Environ Sci Technol. 2004 May 15;38(10):2912-8. doi: 10.1021/es035261y.
A new integrated methodology for forensic oil spill identification is presented. It consists of GC-MS analysis, chromatographic data processing, variable-outlier detection, multivariate data analysis, estimation of uncertainties, and statistical evaluation. The methodology was tested on four groups of diagnostic ratios composed of petroleum biomarkers and ratios within homologous PAH categories. Principal component analysis (PCA) was employed and enabled the simultaneous analysis of many diagnostic ratios. Weathering was taken into account by considering the sampling uncertainties estimated from replicate spill samples. Statistical evaluation ensured an objective matching of oil spill samples with suspected source oils as well as classification into positive match, probable match, and nonmatch. The data analysis is further refined if two or more source oils are classified as probable match by using weighted least squares fitting of the principal components, local PCA models, and additional information relevant to the spill case. The methodology correctly identified the source of two spill samples (i.e., crude oils from Oseberg East and Oseberg Field Centre) and distinguished them from closely related source oils.
本文提出了一种用于法医溢油鉴定的新综合方法。它包括气相色谱 - 质谱分析、色谱数据处理、可变异常值检测、多元数据分析、不确定度估计和统计评估。该方法在由石油生物标志物和同系多环芳烃类别内的比率组成的四组诊断比率上进行了测试。采用主成分分析（PCA），能够同时分析许多诊断比率。通过考虑从重复溢油样本估计的采样不确定度来考虑风化因素。统计评估确保了溢油样本与疑似源油的客观匹配，并将其分类为正匹配、可能匹配和不匹配。如果通过使用主成分的加权最小二乘法拟合、局部PCA模型以及与溢油案例相关的附加信息，将两种或更多种源油分类为可能匹配，则进一步完善数据分析。该方法正确识别了两个溢油样本的来源（即来自奥塞贝格东部和奥塞贝格油田中心的原油），并将它们与密切相关的源油区分开来。
