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Hydraulic fracturing is an increasingly common technique for the extraction of natural gas entrapped in shale formations. This technique has been highly criticized due to the possibility of environmental contamination, underscoring the need for method development to identify chemical factors that could be utilized in point-source identification of environmental contamination events. Here, we utilize comprehensive two-dimensional gas chromatography (GC x GC) coupled to high-resolution time-of-flight (HRT) mass spectrometry, which offers a unique instrumental combination allowing for petroleomics hydrocarbon fingerprinting. Four flowback fluids from Marcellus shale gas wells in geographic proximity were analyzed for differentiating factors that could be exploited in environmental forensics investigations of shale gas impacts. Kendrick mass defect (KMD) plots of these flowback fluids illustrated well-to-well differences in heteroatomic substituted hydrocarbons, while GC x GC separations showed variance in cyclic hydrocarbons and polyaromatic hydrocarbons among the four wells. Additionally, generating plots that combine GC x GC separation with KMD established a novel data-rich visualization technique that further differentiated the samples. [ABSTRACT FROM AUTHOR]

Residential wood combustion emissions are one of the major global sources of particulate and gaseous organic pollutants. However, the detailed chemical compositions of these emissions are poorly characterized due to their highly complex molecular compositions, nonideal combustion conditions, and sample preparation steps. In this study, the particulate organic emissions from a masonry heater using three types of wood logs, namely, beech, birch, and spruce, were chemically characterized using thermal desorption in situ derivatization coupled to a GCxGC-ToF/MS system. Untargeted data analyses were performed using the comprehensive measurements. Univariate and multivariate chemometric tools, such as analysis of variance (ANOVA), principal component analysis (PCA), and ANOVA simultaneous component analysis (ASCA), were used to reduce the data to highly significant and wood type-specific features. This study reveals substances not previously considered in the literature as meaningful markers for differentiation among wood types. [ABSTRACT FROM AUTHOR]