Rapid Screening of Complex Matrices: Utilizing Kendrick Mass Defect To Enhance Knowledge-Based Group Type Evaluation of Multidimensional Gas Chromatography-High-Resolution Time-of-Flight Mass Spectrometry Data.

Organic compound characterization of highly complex matrices involves scientific challenges, such as the diversity of "true" unknowns, the concentration ranges of various compound classes, and limited available amounts of sample. Therefore, discovery-based multidimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC×GC-HRToFMS) is increasingly applied. Nevertheless, most studies focus on target analysis and tend to disregard important details of the sample composition. The increased peak or separation capacity of GC×GC-ToFMS allows for in-depth chemical analysis of the molecular composition. However, high amounts of data, containing several thousands of compounds per experiment, are generally acquired during such analyses. Coupling GC×GC to high-resolution mass spectrometry further increases the amount of data and therefore requires advanced data reduction and mining techniques. Commonly, the main approach for the evaluation of GC×GC-HRToFMS data sets either focuses on the chromatographic separation (e.g., group type analysis), or utilizes exact mass data applying Kendrick mass defect analysis or van Krevelen plots. The presented approach integrates the accurate mass data and the chromatographic information by combining Kendrick mass defect information and knowledge-based rules. This combination allows for fast, visual data screening as well as quantitative estimation of the sample's composition. Moreover, the resulting sample classification significantly reduces the number of variables, allowing distinct chemometric analysis in nontargeted studies, such as detailed hydrocarbon analyses and environmental and forensic investigations.

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.

A unique data analysis framework and open source benchmark data set for the analysis of comprehensive two-dimensional gas chromatography software.

Comprehensive two-dimensional gas chromatography (GC × GC) is amongst the most powerful separation technologies currently existing. Since its advent in early 1990, it has become an established method which is readily available. However, one of its most challenging aspects, especially in hyphenation with mass spectrometry is the high amount of chemical information it provides for each measurement. The GC × GC community agrees that there, the highest demand for action is found. In response, the number of software packages allowing for in-depth data processing of GC × GC data has risen over the last couple of years. These packages provide sophisticated tools and algorithms allowing for more streamlined data evaluation. However, these tools/algorithms and their respective specific functionalities differ drastically within the available software packages and might result in various levels of findings if not appropriately implemented by the end users. This study focuses on two main objectives. First, to propose a data analysis framework and second to propose an open-source dataset for benchmarking software options and their specificities. Thus, allowing for an unanimous and comprehensive evaluation of GC × GC software. Thereby, the benchmark data includes a set of standard compound measurements and a set of chocolate aroma profiles. On this foundation, eight readily available GC × GC software packages were anonymously investigated for fundamental and advanced functionalities such as retention and detection device derived parameters, revealing differences in the determination of e.g. retention times and mass spectra.

Detection and removal of biologically active organic micropollutants from hospital wastewater.

The presence of contaminants of emerging concern (CECs), such as antibiotics, antimicrobial disinfectants, nonprescription drugs, personal care products, pharmaceuticals, and steroids, in water resources can impact aquatic and human health. A large portion of the CECs entering regional wastewater treatment plants originate from hospitals. The purposes of this study were to conduct exploratory analytical work to characterize two hospital wastewaters and to evaluate treatment of CECs at hospitals before dilution with domestic wastewater. A 24-h batch reaction with biogenic manganese oxides coated onto coir fiber was used to treat the wastewaters. Organic contaminants in the wastewaters were concentrated by both liquid-liquid extraction (LLE) and solid-phase extraction (SPE). LLE extracts were analyzed by Comprehensive Two-Dimensional Gas Chromatography/Time-of-Flight Mass Spectrometry (GC × GC-TOFMS) while SPE extracts were analyzedby UltraHigh Performance Liquid Chromatography/Time-of-Flight Mass Spectrometry (UHPLC-TOFMS). Fifty-two organic micropollutants were detected (26 by GC × GC-TOFMS, 25 by UHPLC-TOFMS, 1 by both) in the wastewaters, while 29 were removed by >90% and six were degraded by <50% after treatment. Control experiments revealed that sorption to coir fiber and oxidation by manganese oxides were the primary contaminant removal mechanisms. Both the LLE and SPE extracts were used to evaluate potential human toxicity of the hospital wastewaters before and after treatment. Twenty-eight human cell-based bioreceptor assays were used to screen the wastewaters, and secondary tests were run to quantify toxicity equivalents to activated receptors. The wastewaters initially contained organic micropollutants that strongly activated the Androgen Receptor, Estrogen Receptor ß, and the Mineralocorticoid Receptor but no bioactive compounds were detected after treatment. Point-of-entry treatment of hospital wastewater should reduce bioactive compounds from entering the environment.

Aerosol emissions of a ship diesel engine operated with diesel fuel or heavy fuel oil.

Gaseous and particulate emissions from a ship diesel research engine were elaborately analysed by a large assembly of measurement techniques. Applied methods comprised of offline and online approaches, yielding averaged chemical and physical data as well as time-resolved trends of combustion by-products. The engine was driven by two different fuels, a commonly used heavy fuel oil (HFO) and a standardised diesel fuel (DF). It was operated in a standardised cycle with a duration of 2 h. Chemical characterisation of organic species and elements revealed higher concentrations as well as a larger number of detected compounds for HFO operation for both gas phase and particulate matter. A noteworthy exception was the concentration of elemental carbon, which was higher in DF exhaust aerosol. This may prove crucial for the assessment and interpretation of biological response and impact via the exposure of human lung cell cultures, which was carried out in parallel to this study. Offline and online data hinted at the fact that most organic species in the aerosol are transferred from the fuel as unburned material. This is especially distinctive at low power operation of HFO, where low volatility structures are converted to the particulate phase. The results of this study give rise to the conclusion that a mere switching to sulphur-free fuel is not sufficient as remediation measure to reduce health and environmental effects of ship emissions.

Particulate matter from both heavy fuel oil and diesel fuel shipping emissions show strong biological effects on human lung cells at realistic and comparable in vitro exposure conditions.

BACKGROUND: Ship engine emissions are important with regard to lung and cardiovascular diseases especially in coastal regions worldwide. Known cellular responses to combustion particles include oxidative stress and inflammatory signalling. OBJECTIVES: To provide a molecular link between the chemical and physical characteristics of ship emission particles and the cellular responses they elicit and to identify potentially harmful fractions in shipping emission aerosols. METHODS: Through an air-liquid interface exposure system, we exposed human lung cells under realistic in vitro conditions to exhaust fumes from a ship engine running on either common heavy fuel oil (HFO) or cleaner-burning diesel fuel (DF). Advanced chemical analyses of the exhaust aerosols were combined with transcriptional, proteomic and metabolomic profiling including isotope labelling methods to characterise the lung cell responses. RESULTS: The HFO emissions contained high concentrations of toxic compounds such as metals and polycyclic aromatic hydrocarbon, and were higher in particle mass. These compounds were lower in DF emissions, which in turn had higher concentrations of elemental carbon ("soot"). Common cellular reactions included cellular stress responses and endocytosis. Reactions to HFO emissions were dominated by oxidative stress and inflammatory responses, whereas DF emissions induced generally a broader biological response than HFO emissions and affected essential cellular pathways such as energy metabolism, protein synthesis, and chromatin modification. CONCLUSIONS: Despite a lower content of known toxic compounds, combustion particles from the clean shipping fuel DF influenced several essential pathways of lung cell metabolism more strongly than particles from the unrefined fuel HFO. This might be attributable to a higher soot content in DF. Thus the role of diesel soot, which is a known carcinogen in acute air pollution-induced health effects should be further investigated. For the use of HFO and DF we recommend a reduction of carbonaceous soot in the ship emissions by implementation of filtration devices.

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.