Environmental Chemistry Modeling Laboratory, GR C2 544, Swiss Federal Institute of Technology at Lausanne (EPFL) , Station 2, CH-1015 Lausanne, Switzerland.
Environ Sci Technol. 2014 Jun 17;48(12):6814-26. doi: 10.1021/es501674p. Epub 2014 Jun 5.
Comprehensive two-dimensional gas chromatography (GC × 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 × 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 ≤402 °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 × GC instrument program. The analyst can thus map partitioning property estimates onto GC × 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.
全二维气相色谱（GC×GC）对于分离和定量复杂混合物中的非极性有机化合物非常有效。在这里，我们提出了一个基于 GC×GC 色谱保留时间信息来估算 11 种非极性分析物环境分配性质的模型。所考虑的分配性质跨越了几个相，包括纯液体、空气、水、辛醇、十六烷、颗粒天然有机质、溶解有机质和生物体脂质。模型的训练集和测试集基于 648 个单独实验分配性质数据的文献汇编。对于 50 种非极性环境污染物的测试集，预测的分配系数的均方根误差范围为 0.19 到 0.48 对数单位，优于针对相同化学物质集的 Abraham 型溶剂化模型。该方法适用于包含 C、H、F、Cl、Br 和 I、沸点≤402°C 的非极性有机化合物。所提出的模型经过校准，易于应用，仅要求用户识别一小部分适应 GC×GC 仪器程序的已知分析物。因此，分析人员可以将分配性质估算映射到复杂混合物的 GC×GC 色谱图上。例如，可以对分析的非极性化学物质进行长距离迁移潜力、水生生物累积潜力、北极污染潜力和其他特征分配行为的筛选。
