油指纹多元统计分析在鉴别地表水石油类污染来源中的应用研究

采用油指纹多元统计方法,对嘉兴市杭州塘和长山河水系沿岸加油站9个不同来源的轻质柴油进行了分类鉴别,并考察了风化的影响.结果表明:仅基于GC-MS谱图,很难对不同轻质柴油的来源进行鉴别;但基于GC-MS谱图,提取正构烷烃及多环芳烃特征比值,进一步采用多元统计方法,可以对不同来源的轻质柴油进行区分.考虑实际水环境中油污染物会风化,向河水中投入两种轻质柴油,考察了不同初始浓度和不同风化时间下的油指纹变化,发现油污染物初始浓度为0.5 mg·L~(-1)时,所有特征比值在风化过程中均极不稳定,无法用于分类鉴别;油污染物初始浓度达到1.0 mg·L~(-1)及以上时,特征比值C_(17)/Pr、C_(18)/Ph在风化5 d内可用作油指纹的鉴别指标,5 d后无法用于鉴别;特征比值Pr/Ph、(C_(19)+C_(20))/(C_(21)+C_(22))、CPI受风化时间影响小,始终可以作为油指纹鉴别的特征指标.

Identifying the origin of petroleum pollutants in surface water through oil fingerprint and multivariate statistical analysis

Oil fingerprint and multivariate statistical analysis were used to identify the light diesel oils from nine waterborne bunker petrol stations along the Hangzhou Tang River and Changshan River in Jiaxing City, and the influence of weathering on the identification was investigated. The results show that the origin of the diesel oils was not identifiable based on the GC-MS spectra alone, but was successfully identified through calculating the characteristic ratios of n-alkanes and polycyclic aromatic hydrocarbons (PAHs) based on the GC-MS spectra and the subsequent multivariate statistical analysis. River water samples were dosed with two kinds of light diesel to investigate the influence of weathering on the oil fingerprints at different initial concentrations, and weathering time. With the initial oil concentration of 0.5 mg·L^-1, all characteristic ratios were unstable and unfeasible for the identification of oil origin. With the initial oil concentrations of 1.0 mg·L^-1 or higher, however, the characteristic ratios of C₁₇/Pr and C₁₈/Ph could be used for identifying oil origin when the weathering was within five days, while the characteristic ratios of Pr/Ph, (C₁₉+C₂₀)/(C₂₁+C₂₂), and CPI were less affected and could be used for oil fingerprint identification regardless of the weathering time.