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首页> 外文期刊>Environmental Science & Technology >Compound-specific isotope analysis coupled with multivariate statistics to source-apportion hydrocarbon mixtures
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Compound-specific isotope analysis coupled with multivariate statistics to source-apportion hydrocarbon mixtures

机译：化合物特定的同位素分析与多元统计量相结合，用于烃源混合物

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摘要

Compound Specific Isotope Analysis (CSIA) has been shown to be a useful tool for assessing biodegradation, volatilization, and hydrocarbon degradation. One major advantage of this technique is that it does not rely on determining absolute or relative abundances of individual components of a hydrocarbon mixture which may change considerably during weathering processes. However, attempts to use isotopic values for linking sources to spilled or otherwise unknown hydrocarbons have been hampered by the lack of a robust and rigorous statistical method for testing the hypothesis that two samples are or are not the same. Univariate tests are prone to Type I and Type I I error, and current means of correcting error make hypothesis testing of CSIA source-apportionment data problematic. Multivariate statistical tests are more appropriate for use in CSIA data. However, many multivariate statistical tests require high numbers of replicate measurements. Due to the high precision of IRMS instruments and the high cost of CSIA analysis, it is impractical, and often unnecessary, to perform many replicate analyses. In this paper, a method is presented whereby triplicate CSIA information can be projected in a simplified data-space, enabling multivariate analysis of variance (MANOVA) and highly precise testing of hypotheses between unknowns and putative sources. The method relies on performing pairwise principal components analysis (PCA), then performing a MANOVA upon the principal component variables (for instance, three, using triplicate analyses) which capture most of the variability in the original data set. A probability value is obtained allowing the investigator to state whether there is a statistical difference between two individual samples. A protocol is also presented whereby results of the coupled pairwise PCA-MANOVA analysis are used to down-select putative sources for other analysis of variance methods (i.e., PICA on a subset of the original data) and hierarchical clustering to look for relationships among samples which are not significantly different. A Monte Carlo simulation of a 10 variable data set; tanks used to store, distribute, and offload fuels from Navy vessels; and a series of spilled oil samples and local tug boats from Norfolk, VA (U.S.A.) were subjected to CSIA and the statistical analyses described in this manuscript, and the results are presented. The analysis techniques described herein combined with traditional forensic analyses provide a collection of tools suitable for source-apportionment of hydrocarbons and any organic compound amenable to GC-combustion-IRMS.
机译：化合物特异性同位素分析（CSIA）已被证明是评估生物降解，挥发和碳氢化合物降解的有用工具。该技术的一个主要优点是它不依赖于确定碳氢化合物混合物各个成分的绝对或相对丰度，而这些绝对或相对丰度在风化过程中可能会发生很大变化。但是，由于缺乏一种可靠而严格的统计方法来检验两个样品是否相同的假设，阻碍了使用同位素值将烃源与泄漏或未知的烃类联系起来的尝试。单变量测试容易产生I型和I I型错误，而当前的纠正错误方法使CSIA源分配数据的假设测试成为问题。多元统计检验更适合用于CSIA数据。但是，许多多元统计检验都需要大量重复测量。由于IRMS仪器的高精度和CSIA分析的高成本，执行许多重复分析是不切实际的，而且通常是不必要的。在本文中，提出了一种方法，通过该方法可以在简化的数据空间中投影三次CSIA信息，从而可以进行多元方差分析（MANOVA），并可以高度精确地检验未知数和推定来源之间的假设。该方法依赖于执行成对的主成分分析（PCA），然后对捕获原始数据集中大部分可变性的主成分变量（例如，使用三次重复分析）执行MANOVA。获得概率值，使研究者可以陈述两个单独样本之间是否存在统计差异。还提出了一种协议，通过该协议，成对的PCA-MANOVA耦合分析的结果被用于选择推定的源，用于其他方差分析方法（例如，原始数据的子集上的PICA）和分层聚类，以寻找样本之间的关系没有明显的不同。 10个变量数据集的蒙特卡洛模拟；用于储存，分配和卸载海军舰船燃料的油箱；然后对来自弗吉尼亚州诺福克（美国）的一系列溢油样本和本地拖船进行了CSIA评估，并对本文进行了统计分析，并给出了结果。本文所述的分析技术与传统的法医分析相结合，提供了适用于烃类和适用于GC燃烧IRMS的任何有机化合物进行源分配的一系列工具。

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