Two-dimensional gas chromatography/mass spectrometry, physical property modeling and automated production of component maps to assess the weathering of pollutants

• Published in: Journal of Chromatography A 2014-10, Vol.1364, p.223-233
• Main Authors: Antle, Patrick M., Zeigler, Christian D., Livitz, Dimitri G., Robbat, Albert
• Format: Article
• Language: English
• Subjects: Analytical chemistry; Applied sciences; Chemistry; Chromatographic methods and physical methods associated with chromatography; Component maps; Environmental forensics; Exact sciences and technology; Gas chromatographic methods; GC × GC/MS; PAH; Pollution; Pollution sources. Measurement results; Retention index; Soil and sediments pollution; Weathering
• ISSN: 0021-9673
• DOI: 10.1016/j.chroma.2014.08.033

•We estimated VP, SW, and KOW for every point in a GC×GC chromatogram.•The model is simple, based on isovolatility curves, RI, and LFER.•The model provides excellent data quality and was encoded into a software program.•We created component maps to discern the progression of site weathering. Local conditions influence how pollutants will weather in subsurface environments and sediment, and many of the processes that comprise environmental weathering are dependent upon these substances’ physical and chemical properties. For example, the effects of dissolution, evaporation, and organic phase partitioning can be related to the aqueous solubility (SW), vapor pressure (VP), and octanol–water partition coefficient (KOW), respectively. This study outlines a novel approach for estimating these physical properties from comprehensive two-dimensional gas chromatography–mass spectrometry (GC×GC/MS) retention index-based polyparameter linear free energy relationships (LFERs). Key to robust correlation between GC measurements and physical properties is the accurate and precise generation of retention indices. Our model, which employs isovolatility curves to calculate retention indices, provides improved retention measurement accuracy for families of homologous compounds and leads to better estimates of their physical properties. Results indicate that the physical property estimates produced from this approach have the same error on a logarithmic-linear scale as previous researchers’ log–log estimates, yielding a markedly improved model. The model was embedded into a new software program, allowing for automated determination of these properties from a single GC×GC analysis with minimal model training and parameter input. This process produces component maps that can be used to discern the mechanism and progression of how a particular site weathers due to dissolution, organic phase partitioning, and evaporation into the surrounding environment.