Mapping Environmental Partitioning Properties of Nonpolar Complex Mixtures by Use of GC 脳 GC

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• 作者：Deedar Nabi ; Jonas Gros ; Petros Dimitriou-Christidis ; J. Samuel Arey
• 刊名：Environmental Science & Technology
• 出版年：2014
• 出版时间：June 17, 2014
• 年：2014
• 卷：48
• 期：12
• 页码：6814-6826
• 全文大小：473K
• 年卷期：v.48,no.12(June 17, 2014)
• ISSN：1520-5851

文摘

Comprehensive two-dimensional gas chromatography (GC 脳 GC) is effective for separating and quantifying nonpolar organic chemicals in complex mixtures. Here we present a model to estimate 11 environmental partitioning properties for nonpolar analytes based on GC 脳 GC chromatogram retention time information. The considered partitioning properties span several phases including pure liquid, air, water, octanol, hexadecane, particle natural organic matter, dissolved organic matter, and organism lipids. The model training set and test sets are based on a literature compilation of 648 individual experimental partitioning property data. For a test set of 50 nonpolar environmental contaminants, predicted partition coefficients exhibit root-mean-squared errors ranging from 0.19 to 0.48 log unit, outperforming Abraham-type solvation models for the same chemical set. The approach is applicable to nonpolar organic chemicals containing C, H, F, Cl, Br, and I, having boiling points 鈮?02 掳C. The presented model is calibrated, easy to apply, and requires the user only to identify a small set of known analytes that adapt the model to the GC 脳 GC instrument program. The analyst can thus map partitioning property estimates onto GC 脳 GC chromatograms of complex mixtures. For example, analyzed nonpolar chemicals can be screened for long-range transport potential, aquatic bioaccumulation potential, arctic contamination potential, and other characteristic partitioning behaviors.