Elucidating Environmental Fingerprinting Mechanisms of Unconventional Gas Development through Hydrocarbon Analysis

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 × 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 × GC separations showed variance in cyclic hydrocarbons and polyaromatic hydrocarbons among the four wells. Additionally, generating plots that combine GC × GC separation with KMD established a novel data-rich visualization technique that further differentiated the samples.

Untargeted Identification of Wood Type-Specific Markers in Particulate Matter from Wood Combustion

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.

Advanced scripting for the automated profiling of two-dimensional gas chromatography-time-of-flight mass spectrometry data from combustion aerosol

Multidimensional gas chromatography is an appropriate tool for the non-targeted and comprehensive characterisation of complex samples generated from combustion processes. Particulate matter (PM) emission is composed of a large number of compounds, including condensed semi-volatile organic compounds (SVOCs). However, the complex amount of information gained from such comprehensive techniques is associated with difficult and time-consuming data analysis. Because of this obstacle, two-dimensional gas chromatography still receives relatively little use in aerosol science [1-4]. To remedy this problem, advanced scripting algorithms based on knowledge-based rules (KBRs) were developed in-house and applied to GCxGC-TOFMS data. Previously reported KBRs and newer findings were considered for the development of these algorithms. The novelty of the presented advanced scripting tools is a notably selective search criterion for data screening, which is primarily based on fragmentation patterns and the presence of specific fragments. Combined with "classical" approaches based on retention times, a fast, accurate and automated data evaluation method was developed, which was evaluated qualitatively and quantitatively for type 1 and type 2 errors. The method's applicability was further tested for PM filter samples obtained from ship fuel combustion. Major substance classes, including polycyclic aromatic hydrocarbons (PAH), alkanes, benzenes, esters and ethers, can be targeted. This approach allows the classification of approximately 75% of the peaks of interest within real PM samples. Various conditions of combustion, such as fuel composition and engine load, could be clearly characterised and differentiated.