Using Comprehensive Two-Dimensional Gas Chromatography Retention Indices To Estimate Environmental Partitioning Properties for a Complete Set of Diesel Fuel Hydrocarbons

Screening Disinfection Byproducts in Arid-Coastal Wastewater: A Workflow Using GC×GC-TOFMS, Passive Sampling, and NMF Deconvolution Algorithm

Disinfection during tertiary municipal wastewater treatment is a necessary step to control the spread of pathogens; unfortunately, it also gives rise to numerous disinfection byproducts (DBPs), only a few of which are regulated because of the analytical challenges associated with the vast number of potential DBPs. This study utilized polydimethylsiloxane (PDMS) passive samplers, comprehensive two-dimensional gas chromatography (GC×GC) coupled with time-of-flight mass spectrometry (TOFMS), and non-negative matrix factorization (NMF) spectral deconvolution for suspect screening of DBPs in treated wastewater. PDMS samplers were deployed upstream and downstream of the chlorination unit in a municipal wastewater treatment plant located in Abu Dhabi, and their extracts were analyzed using GC×GC-TOFMS. A workflow incorporating a multi-tiered, eight-filter screening process was developed, which successfully enabled the reliable isolation of 22 candidate DBPs from thousands of peaks. The NMF spectral deconvolution improved the match factor score of unknown mass spectra to the reference mass spectra available in the NIST library by 17% and facilitated the identification of seven additional DBPs. The close match of the first-dimension retention index data and the GC×GC elution patterns of DBPs, both predicted using the Abraham solvation model, with their respective experimental counterparts-with the measured data available in the NIST WebBook and the GC×GC elution patterns being those observed for the candidate peaks-significantly enhanced the accuracy of peak assignment. Isotopic pattern analysis revealed a close correspondence for 11 DBPs with clearly visible isotopologues in reference spectra, thereby further strengthening the confidence in the peak assignment of these DBPs. Brominated analogues were prevalent among the detected DBPs, possibly due to seawater intrusion. The fate, behavior, persistence, and toxicity of tentatively identified DBPs were assessed using EPI Suite™ and the CompTox Chemicals Dashboard. This revealed their significant toxicity to aquatic organisms, including developmental, mutagenic, and endocrine-disrupting effects in certain DBPs. Some DBPs also showed activity in various CompTox bioassays, implicating them in adverse molecular pathways. Additionally, 11 DBPs demonstrated high environmental persistence and resistance to biodegradation. This combined approach offers a powerful tool for future research and environmental monitoring, enabling accurate identification and assessment of DBPs and their potential risks.

Novel Models for Accurate Estimation of Air-Blood Partitioning: Applications to Individual Compounds and Complex Mixtures of Neutral Organic Compounds

The air-blood partition coefficient (Kab) is extensively employed in human health risk assessment for chemical exposure. However, current Kab estimation approaches either require an extensive number of parameters or lack precision. In this study, we present two novel and parsimonious models to accurately estimate Kab values for individual neutral organic compounds, as well as their complex mixtures. The first model, termed the GC×GC model, was developed based on the retention times of nonpolar chemical analytes on comprehensive two-dimensional gas chromatography (GC×GC). This model is unique in its ability to estimate the Kab values for complex mixtures of nonpolar organic chemicals. The GC×GC model successfully accounted for the Kab variance (R2 = 0.97) and demonstrated strong prediction power (RMSE = 0.31 log unit) for an independent set of nonpolar chemical analytes. Overall, the GC×GC model can be used to estimate Kab values for complex mixtures of neutral organic compounds. The second model, termed the partition model (PM), is based on two types of partition coefficients: octanol to water (Kow) and air to water (Kaw). The PM was able to effectively account for the variability in Kab data (n = 344), yielding an R2 value of 0.93 and root-mean-square error (RMSE) of 0.34 log unit. The predictive power and explanatory performance of the PM were found to be comparable to those of the parameter-intensive Abraham solvation models (ASMs). Additionally, the PM can be integrated into the software EPI Suite, which is widely used in chemical risk assessment for initial screening. The PM provides quick and reliable estimation of Kab compared to ASMs, while the GC×GC model is uniquely suited for estimating Kab values for complex mixtures of neutral organic compounds. In summary, our study introduces two novel and parsimonious models for the accurate estimation of Kab values for both individual compounds and complex mixtures.

Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography-A review

In recent years, comprehensive two-dimensional gas chromatography (GC × GC) has been gradually gaining prominence as a preferred method for the analysis of complex samples due to its higher peak capacity and resolution power compared to conventional gas chromatography (GC). Nonetheless, to fully benefit from the capabilities of GC × GC, a holistic approach to method development and data processing is essential for a successful and informative analysis. Method development enables the fine-tuning of the chromatographic separation, resulting in high-quality data. While generating such data is pivotal, it does not necessarily guarantee that meaningful information will be extracted from it. To this end, the first part of this manuscript reviews the importance of theoretical modeling in achieving good optimization of the separation conditions, ultimately improving the quality of the chromatographic separation. Multiple theoretical modeling approaches are discussed, with a special focus on thermodynamic-based modeling. The second part of this review highlights the importance of establishing robust data processing workflows, with a special emphasis on the use of advanced data processing tools such as, Machine Learning (ML) algorithms. Three widely used ML algorithms are discussed: Random Forest (RF), Support Vector Machine (SVM), and Partial Least Square-Discriminate Analysis (PLS-DA), highlighting their role in discovery-based analysis.

Comprehensive Two-Dimensional Gas Chromatography in Petroleum Derived Samples: A Review on Advances in Source and Weathering Studies of Spilled Oil

Since its introduction comprehensive two-dimensional gas chromatography (GC × GC) has been widely applied to analyze complex samples due to its enhanced peak capacity and selectivity, thereby increasing the number of identifiable peaks and improving coelution issues. Even though it is still undergoing development, GC × GC provides many advantages in the analysis of petroleum-derived samples, whether in reservoir geochemistry applications or in environmental studies associated with spilled oils. In general, it facilitates more thorough fingerprinting and compositional evaluation. In environmental studies, it helps enhance understanding of weathering processes and the environmental behavior of hydrocarbons, as its chromatographic retention indices can robustly estimate liquid vapor pressures, aqueous solubility and other physical chemical properties. This review presents a brief history of GC × GC instrumentation, discussing recent and significant advances in petroleum applications, from data handling techniques to reservoir geochemistry and environmental forensics, as well as some specific advantages achieved and certain limitations that continue to be encountered.

Modeling the GCxGC Elution Patterns of a Hydrocarbon Structure Library To Innovate Environmental Risk Assessments of Petroleum Substances

Comprehensive two-dimensional gas chromatography (GCxGC) offers unrivaled separation of petroleum substances, which can contain thousands of constituents or more. However, interpreting substance compositions from GCxGC data is costly and requires expertise. To facilitate environmental risk assessments, industries provide aggregated compositional information known as "hydrocarbon blocks" (HCBs), but these proprietary methods do not transparently associate the HCBs with GCxGC chromatogram data. These obstacles frustrate efforts to study the environmental risks of petroleum substances and associated environmental samples. To address this problem, we developed a GCxGC elution model for user-defined petroleum substance compositions. We calibrated the elution model to experimental GCxGC retention times of 56 known hydrocarbons by fitting three tunable model parameters to two candidate instrument methods. With the calibrated model, we simulated retention times for a library of 15,447-15,455 hydrocarbon structures (plus 40-48 predicted as chromatographically unretained) spanning 11 classes of petroleum substance constituents in the C₁₀-C₃₀ range. The resulting simulation data reveal that GCxGC retention times are quantitatively associated with hydrocarbon class and carbon number information throughout the GCxGC chromatogram. These innovations enable the development of transparent and efficient technical methods to investigate the chemical compositions and environmental properties of petroleum substances, including in environmental and lab-weathered samples.

Advances in Chemical Analysis of Oil Spills Since the Deepwater Horizon Disaster

Analytical techniques for chemical analysis of oil, oil photochemical and biological transformation products, and dispersants and their biodegradation products benefited significantly from research following the 2010 Deepwater Horizon (DWH) disaster. Crude oil and weathered-oil matrix reference materials were developed based on the Macondo well oil and characterized for polycyclic aromatic hydrocarbons, hopanes, and steranes for use to assure and improve the quality of analytical measurements in oil spill research. Advanced gas chromatography (GC) techniques such as comprehensive two-dimensional GC (GC × GC), pyrolysis GC with mass spectrometry (MS), and GC with tandem MS (GC-MS/MS) provide a greater understanding at the molecular level of composition and complexity of oil and weathering changes. The capabilities of high-resolution MS (HRMS) were utilized to extend the analytical characterization window beyond conventional GC-based methods to include polar and high molecular mass components (>400 Da) and to provide new opportunities for discovery, characterization, and investigation of photooxidation and biotransformation products. Novel separation approaches to reduce the complexity of the oil and weathered oil prior to high-resolution MS and advanced fluorescence spectrometry have increased the information available on spilled oil and transformation products. HRMS methods were developed to achieve the required precision and sensitivity for detection of dispersants and to provide molecular-level characterization of the complex surfactants. Overall, research funding following the DWH oil spill significantly advanced and expanded the use of analytical techniques for chemical analysis to support petroleum and dispersant characterization and investigations of fate and effects of not only the DWH oil spill but future spills.

Methods for Interpreting the Partitioning and Fate of Petroleum Hydrocarbons in a Sea Ice Environment

Decreases in Arctic Sea ice extent and thickness have led to more open ice conditions, encouraging both shipping traffic and oil exploration within the northern Arctic. As a result, the increased potential for accidental releases of crude oil or fuel into the Arctic environment threatens the pristine marine environment, its ecosystem, and local inhabitants. Thus, there is a need to develop a better understanding of oil behavior in a sea ice environment on a microscopic level. Computational quantum chemistry was used to simulate the effects of evaporation, dissolution, and partitioning within sea ice. Vapor pressures, solubilities, octanol-water partition coefficients, and molecular volumes were calculated using quantum chemistry and thermodynamics for pure liquid solutes (oil constituents) of interest. These calculations incorporated experimentally measured temperatures and salinities taken throughout an oil-in-ice mesocosm experiment conducted at the University of Manitoba in 2017. Their potential for interpreting the relative movements of oil constituents was assessed. Our results suggest that the relative movement of oil constituents is influenced by differences in physical properties. Lighter molecules showed a greater tendency to be controlled by brine advection processes due to their greater solubility. Molecules which are more hydrophobic were found to concentrate in areas of lower salt concentration.

Investigation of partition coefficients and fingerprints of atmospheric gas- and particle-phase intermediate volatility and semi-volatile organic compounds using pixel-based approaches

Ambient gas- and particle-phase intermediate volatility and semi-volatile organic compounds (I/SVOCs) of Beijing were analyzed by a thermal desorption comprehensive two-dimensional gas chromatography quadrupole mass spectrometry (TD-GC × GC-qMS). A pixel-based scheme combing the integration-based approach was applied for partition coefficients estimation and fingerprints identification. Blob-by-blob recognition was firstly utilized to characterize I/SVOCs from the molecular level. 412 blobs in gas-phase and 460 blobs in particle-phase were resolved, covering a total response of 47.5% and 43.5%. A large pool of I/SVOCs was found with a large diversity of chemical classes in both gas- and particle-phase. Acids (8.5%), b-alkanes (5.8%), n-alkanes (C8-C25, 5.3%), and aromatics (4.4%) were dominant in gas-phase while esters (7.0%, including volatile chemical product compounds, VCPs), n-alkanes (C9-C34, 5.7%), acids (4.6%), and siloxanes (3.6%) were abundant in particle-phase. Air pollutants were then evaluated by a two-parameter linear free energy relationship (LFER) model, which could be further implemented in the two-dimensional volatility basis set (2D-VBS) model. Multiway principal component analysis (MPCA) and partial least squares-discriminant analysis (PLS-DA) implied that naphthalenes, phenol, propyl-benzene isomers, and oxygenated volatile organic compounds (OVOCs) were key components in the gas-phase under different pollution levels. This work gives more insight into property estimation and fingerprints identification for complex ambient samples.

Development of retention time indices for comprehensive multidimensional gas chromatography and application to ignitable liquid residue mapping in wildfire investigations

In this study, we introduce a simple three-step workflow for a universally applicable RI system, to be used in GC×GC analysis of ignitable liquid residue (ILR) for arson investigations. The proposed RI system applies a combination of two well-established GC RI systems: non-isothermal Kovats (K) index in the first dimension and Lee (L) index in the second dimension. The proposed KLI RI system showed very good correlations when compared with predicted values and existing RI systems (r² = 0.97 in first dimension, r² = 0.99 in second dimension) and was valid for a wide range of analyte concentrations and operational settings (coefficient of variance (CV) < 1% in first dimension, < 10% in second dimension). Using the KLI RI, an ILR classification contour map was created to assist with the identification of ILR types within ASTM E1618. The contour map was successfully applied to neat fuels and a fire scene sample, highlighting the application to wildfire investigation. Standardizing the RI process and establishing acceptable error margins allows the exploration and comparison of comprehensive data generated from GC×GC analysis of ILRs regardless of location, time, or system, further enhancing comprehensive and tenable chemometric analyses of samples. Overall, the KLI workflow was inexpensive, quick to apply, and user-friendly with its simple 3-step design.

Measurements of relative distribution constants of vaporized hydrocarbons between air and polydimethylsiloxane via capillary columns for the calculation of headspace hydrocarbon compositions

This study focused on the measurements and validity of relative distribution constants of vaporized hydrocarbons between air and polydimethylsiloxane (PDMS) using commercially available capillary columns. Capillary column gas chromatography (CCGC) measurements, using two columns containing a PDMS stationary phase with different film thicknesses, were conducted to determine the relative distribution constants of n-heptane, toluene, n-octane, p-xylene, n-nonane, and 1,2,4-trimethylbenzene between air and PDMS at 90 and 120 °C. To validate the accuracy of the relative distribution constants via CCGC, the compositions of three headspace samples containing different amounts of hydrocarbons were calculated using the relative distribution constants via CCGC and extracted amounts via PDMS solid phase microextraction (SPME) at 90 and 120 °C. It was found that calculated hydrocarbon compositions of headspace samples were comparable to true headspace hydrocarbon compositions via direct vapor analysis, with an average absolute relative error of 3.2%. Our results indicate that CCGC is an alternative method that can provide a reliable and convenient method to determine the relative distribution constants of various hydrocarbons between air and PDMS for quantitative chemical analysis of headspace.

Waste Containment Ponds Are a Major Source of Secondary Organic Aerosol Precursors from Oil Sands Operations

The surface mining and bitumen extraction of oil sands (OS) generates over one million barrels of heavy oil each day in the Alberta Oil Sands Region of Canada. Recent observations suggest that emissions from OS development contribute to secondary organic aerosol (SOA) formation, but the chemical composition, mass fluxes, and sources of those emissions are poorly delineated. Here, we simulated OS extraction and used comprehensive two-dimensional gas chromatography to quantify and characterize direct air emissions, bitumen froth, residual wastewater, and tailings components, ultimately enabling fate modeling of over 1500 chromatographic features simultaneously. During the non-ice cover season, tailings ponds emissions contributed 15 000-72 000 metric tonnes of hydrocarbon SOA precursors, translating to 3000-13 000 tonnes of SOA, whereas direct emissions during the extraction process itself were notably smaller (960 ± 500 tonnes SOA yr^-1). These results suggest that tailings pond waste management practices should be targeted to reduce environmental emissions.

Recent applications of chemometrics in one- and two-dimensional chromatography

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

Mixture Touch: A Web Platform for the Evaluation of Complex Chemical Mixtures

Risk assessment of chemical mixtures isRisk assessment of chemical mixtures is challenging because information about the chemical structure, concentration, properties, and toxicity, down to the individual compounds, is generally not readily accessible. To cope with this challenge, we think Mixture Touch- a web platform that offers a one-window solution, for free, for the risk assessment of complex mixtures that are analyzed with comprehensive two-dimensional gas chromatography (GC × GC). GC × GC is a powerful analytical technique for target and nontarget analysis of complex mixtures. Our web platform allows users to visualize the GC × GC data, conduct spectral identification, estimate properties, and analyze potential risks based on established methods. For illustration purpose, we show how to assess the aquatic bioaccumulation potential of short-chain chlorinated paraffin (SCCP), which is an industrially manufactured mixture. The platform readily demonstrated that most of the SCCP congeners did not have the tendency to accumulate in aquatic organisms but in humans. The platform can bridge the gap between the GC × GC experts, GC × GC users, analytical experts, and risk assessors. It could enhance the level of risk assessments of mixtures utilizing the high performance of the state-of-the-art analytical instruments.

The effect of column and temperature variation on the determination of the dead time in gas chromatographic systems using indirect methods

The precise determination of the dead time is essential in a chromatography system as it is a primary parameter for the determination of other secondary parameters such as the adjusted retention time, relative retention time, retention factor and retention index. Several of the indirect methods used for the determination of the dead time in this study were iteration, nonlinear, spreadsheet and statistics methods, which were implemented using the ANSI C programming language. The calculation of each method was tested with temperature and column variations for measuring the retention time of a n-alkane homologous series and accuracy analysis of each mathematical method (indirect method) to the marker substance (direct method). Changes in the temperature and column variations (type, polarity and column length) affected the calculation of the dead time values but did not affect its accuracy. The value of the dead time generated by the non-linear method was relatively high, with errors above 10%, while the other methods utilized are quite good with errors below 8% regardless of the column and temperature variations.

Comprehensive two-dimensional gas-chromatography-based property estimation to assess the fate and behavior of complex mixtures: A case study of vehicle engine oil

A method was developed to estimate the properties and assess the potential environmental risk of analytes in a complex mixture by comprehensive two-dimensional gas chromatography (GC × GC). A GC × GC-based estimation model was calibrated for 12 physicochemical properties that were relevant to the environment or to biological organisms, including human beings. Vehicle engine oil that had been contaminated by numerous compounds during its use was investigated as a case study to which the GC × GC model could be applied. Engine-oil samples were collected from a vehicle at intervals over a distance of 11407 km. The carbon and nitrogen contents in the oil remained unchanged at 83%-84% and 2%-5%, respectively, during the run; however, in excess of 100 compounds were present in the oil upon completion of the run. Post analyses of the studied mixture samples were performed with the developed GC × GC model, which links mass spectral information for structural identification. The GC × GC model allows us to classify the detected analytes in complex mixtures in terms of their properties, such as their aquatic bioaccumulation potential. The application of the model showed that the analyzed engine oil contained in excess of 100 compounds that could accumulate in aquatic biota and reach the arctic via long-range transport, which suggests that the components in the complex mixture of engine oil could pose a risk. The newly developed model that was derived in this study shows great potential for use in the mixture assessment.

Weathering of Oil in a Surficial Aquifer

The composition of crude oil in a surficial aquifer was determined in two locations at the Bemidji, MN, spill site. The abundances of 71 individual hydrocarbons varied within 16 locations sampled. Little depletion of these hydrocarbons (relative to the pipeline oil) occurred in the first 10 years after the spill, whereas losses of 25% to 85% of the total measured hydrocarbons occurred after 30 years. The C_6-30 n-alkanes, toluene, and o-xylene were the most depleted hydrocarbons. Some hydrocarbons, such as the n-C_10-24 cyclohexanes, tri- and tetra- methylbenzenes, acyclic isoprenoids, and naphthalenes were the least depleted. Benzene was detected at every sampling location 30 years after the spill. Degradation of the oil led to increases in the percent organic carbon and in the δ ¹³ C of the oil. Another method of determining hydrocarbon loss was by normalizing the total measured hydrocarbon concentrations to that of the most conservative analytes. This method indicated that the total measured hydrocarbons were depleted by 47% to 77% and loss of the oil mass over 30 years was 18% to 31%. Differences in hydrocarbon depletion were related to the depth of the oil in the aquifer, local topography, amount of recharge reaching the oil, availability of electron acceptors, and the presence of less permeable soils above the oil. The results from this study indicate that once crude oil has been in the subsurface for a number of years there is no longer a "starting oil concentration" that can be used to understand processes that affect its fate and the transport of hydrocarbons in groundwater.

Technical basis for using passive sampling as a biomimetic extraction procedure to assess bioavailability and predict toxicity of petroleum substances

Solid-phase microextraction fibers coated with polydimethylsiloxane (PDMS) provide a convenient passive sampling format to characterize bioavailability of petroleum substances. Hydrocarbons absorb onto PDMS in proportion to both freely dissolved concentrations and partitioning properties of the individual constituents, which parallels the mechanistic basis used to predict aquatic toxicity in the PETROTOX model. When deployed in a non-depletive manner, combining SPME with thermal desorption and quantification using gas chromatography-flame ionization creates a biomimetic extraction (BE) procedure that has the potential to simplify aquatic hazard assessments of petroleum substances since the total moles of all hydrocarbons sorbed to the fiber can be related to toxic thresholds in target lipid of aquatic organisms. The objective of this work is to describe the technical basis for applying BE measurements to predict toxicity of petroleum substances. Critical BE-based PDMS concentrations corresponding to adverse effects were empirically derived from toxicity tests on different petroleum substances with multiple test species. The resulting species sensitivity distribution (SSD) of PDMS effect concentrations was then compared and found consistent with the previously reported target lipid-based SSD. Further, BE data collected on samples of aqueous media dosed with a wide range of petroleum substances were highly correlated to predicted toxic units derived using the PETROTOX model. These findings provide justification for applying BE in environmental hazard and risk evaluations of petroleum substances and related mixtures.

Predicting Partitioning and Diffusion Properties of Nonpolar Chemicals in Biotic Media and Passive Sampler Phases by GC × GC

The chemical parameters needed to explain and predict bioavailability, biodynamics, and baseline toxicity are not readily available for most nonpolar chemicals detected in the environment. Here, we demonstrate that comprehensive two-dimensional gas chromatography (GC × GC) retention times can be used to predict 26 relevant properties for nonpolar chemicals, specifically: partition coefficients for diverse biotic media and passive sampler phases; aquatic baseline toxicity; and relevant diffusion coefficients. The considered biotic and passive sampler phases include membrane and storage lipids, serum and muscle proteins, carbohydrates, algae, mussels, polydimethylsiloxane, polyethylene, polyoxymethylene, polyacrylate, polyurethane, and semipermeable membrane devices. GC × GC-based chemical property predictions are validated with a compilation of 1038 experimental property data collected from the literature. As an example application, we overlay a map of baseline toxicity to fathead minnows onto the separated analyte signal of a polychlorinated alkanes (chlorinated paraffins) technical mixture that contains 7820 congeners. In a second application, GC × GC-estimated properties are used to parametrize multiphase partitioning models for mammalian tissues and organs. In a third example, we estimate chemical depuration kinetics for mussels. Finally, we illustrate an approach to screen the GC × GC chromatogram for nonpolar chemicals of potentially high concern, defined based on their GC × GC-estimated biopartitioning properties, diffusion properties, and baseline toxicity.

Interpreting comprehensive two-dimensional gas chromatography using peak topography maps with application to petroleum forensics

Background

Comprehensive two-dimensional gas chromatography \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) provides high-resolution separations across hundreds of compounds in a complex mixture, thus unlocking unprecedented information for intricate quantitative interpretation. We exploit this compound diversity across the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) topography to provide quantitative compound-cognizant interpretation beyond target compound analysis with petroleum forensics as a practical application. We focus on the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) topography of biomarker hydrocarbons, hopanes and steranes, as they are generally recalcitrant to weathering. We introduce peak topography maps (PTM) and topography partitioning techniques that consider a notably broader and more diverse range of target and non-target biomarker compounds compared to traditional approaches that consider approximately 20 biomarker ratios. Specifically, we consider a range of 33–154 target and non-target biomarkers with highest-to-lowest peak ratio within an injection ranging from 4.86 to 19.6 (precise numbers depend on biomarker diversity of individual injections). We also provide a robust quantitative measure for directly determining “match” between samples, without necessitating training data sets.

Results

We validate our methods across 34 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) injections from a diverse portfolio of petroleum sources, and provide quantitative comparison of performance against established statistical methods such as principal components analysis (PCA). Our data set includes a wide range of samples collected following the 2010 DeepwaterHorizon disaster that released approximately 160 million gallons of crude oil from the Macondo well (MW). Samples that were clearly collected following this disaster exhibit statistically significant match \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(99.23 \pm 1.66 )\,\%$$\end{document}(99.23±1.66)% using PTM-based interpretation against other closely related sources. PTM-based interpretation also provides higher differentiation between closely correlated but distinct sources than obtained using PCA-based statistical comparisons. In addition to results based on this experimental field data, we also provide extentive perturbation analysis of the PTM method over numerical simulations that introduce random variability of peak locations over the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) biomarker ROI image of the MW pre-spill sample (sample \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\#1$$\end{document}#1 in Additional file 4: Table S1). We compare the robustness of the cross-PTM score against peak location variability in both dimensions and compare the results against PCA analysis over the same set of simulated images. Detailed description of the simulation experiment and discussion of results are provided in Additional file 1: Section S8.

Conclusions

We provide a peak-cognizant informational framework for quantitative interpretation of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) topography. Proposed topographic analysis enables \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) forensic interpretation across target petroleum biomarkers, while including the nuances of lesser-known non-target biomarkers clustered around the target peaks. This allows potential discovery of hitherto unknown connections between target and non-target biomarkers.

Electronic supplementary material

The online version of this article (doi:10.1186/s13065-016-0211-y) contains supplementary material, which is available to authorized users.

Simulating Gas-Liquid-Water Partitioning and Fluid Properties of Petroleum under Pressure: Implications for Deep-Sea Blowouts

With the expansion of offshore petroleum extraction, validated models are needed to simulate the behaviors of petroleum compounds released in deep (>100 m) waters. We present a thermodynamic model of the densities, viscosities, and gas-liquid-water partitioning of petroleum mixtures with varying pressure, temperature, and composition based on the Peng-Robinson equation-of-state and the modified Henry's law (Krychevsky-Kasarnovsky equation). The model is applied to Macondo reservoir fluid released during the Deepwater Horizon disaster, represented with 279-280 pseudocomponents, including 131-132 individual compounds. We define >n-C8 pseudocomponents based on comprehensive two-dimensional gas chromatography (GC × GC) measurements, which enable the modeling of aqueous partitioning for n-C8 to n-C26 fractions not quantified individually. Thermodynamic model predictions are tested against available laboratory data on petroleum liquid densities, gas/liquid volume fractions, and liquid viscosities. We find that the emitted petroleum mixture was ∼29-44% gas and ∼56-71% liquid, after cooling to local conditions near the broken Macondo riser stub (∼153 atm and 4.3 °C). High pressure conditions dramatically favor the aqueous dissolution of C1-C4 hydrocarbons and also influence the buoyancies of bubbles and droplets. Additionally, the simulated densities of emitted petroleum fluids affect previous estimates of the volumetric flow rate of dead oil from the emission source.

A One-Pot/Single-Analysis Approach to Substrate Scope Investigations Using Comprehensive Two-Dimensional Gas Chromatography (GC×GC)

A representative substrate scope investigation for an enantioselective catalytic ketone-reduction has been performed as a single reaction on a mixture containing equimolar amounts of nine (9) prototypical compounds. The resulting analyte pool containing 18 potential products from nine different reactions could all be completely resolved in a single chromatographic injection using comprehensive two-dimensional gas chromatography (GC×GC) with time-of-flight mass spectrometry, allowing for simultaneous determination of percent conversion and enantiomeric excess for each substrate. The results obtained for an enantioselective iron-catalyzed asymmetric transfer hydrogenation using this one-pot/single-analysis approach were similar to those reported for the individualized reactions, demonstrating the utility of this strategy for streamlining substrate scope investigations. Moreover, for this particular catalyst, activity and selectivity were not greatly affected by the presence of other ketones or enantioenriched reduced products. This approach allows for faster and greener analyses that are central to new reaction development, as well as an opportunity to gain further insights into other established transformations.

Guidance for improving comparability and relevance of oil toxicity tests

The complex nature and limited aqueous solubility of petroleum substances pose challenges for consistently characterizing exposures in aquatic life hazard assessments. This paper reviews important considerations for the design, conduct and interpretation of laboratory toxicity tests with physically and chemically dispersed oils based on an understanding of the behavior and toxicity of the hydrocarbons that comprise these substances. Guiding principles are provided that emphasize the critical need to understand and, when possible, characterize dissolved hydrocarbon exposures that dictate observed toxicity in these tests. These principles provide a consistent framework for interpreting toxicity studies performed using different substances and test methods by allowing varying dissolved exposures to be expressed in terms of a common metric based on toxic units (TUs). The use of passive sampling methods is also advocated since such analyses provide an analytical surrogate for TUs. The proposed guidance is translated into a series of questions that can be used in evaluating existing data and in guiding design of future studies. Application of these questions to a number of recent publications indicates such considerations are often ignored, thus perpetuating the difficulty of interpreting and comparing results between studies and limiting data use in objective hazard assessment. Greater attention to these principles will increase the comparability and utility of oil toxicity data in decision-making.

First day of an oil spill on the open sea: early mass transfers of hydrocarbons to air and water

During the first hours after release of petroleum at sea, crude oil hydrocarbons partition rapidly into air and water. However, limited information is available about very early evaporation and dissolution processes. We report on the composition of the oil slick during the first day after a permitted, unrestrained 4.3 m(3) oil release conducted on the North Sea. Rapid mass transfers of volatile and soluble hydrocarbons were observed, with >50% of ≤C17 hydrocarbons disappearing within 25 h from this oil slick of <10 km(2) area and <10 μm thickness. For oil sheen, >50% losses of ≤C16 hydrocarbons were observed after 1 h. We developed a mass transfer model to describe the evolution of oil slick chemical composition and water column hydrocarbon concentrations. The model was parametrized based on environmental conditions and hydrocarbon partitioning properties estimated from comprehensive two-dimensional gas chromatography (GC×GC) retention data. The model correctly predicted the observed fractionation of petroleum hydrocarbons in the oil slick resulting from evaporation and dissolution. This is the first report on the broad-spectrum compositional changes in oil during the first day of a spill at the sea surface. Expected outcomes under other environmental conditions are discussed, as well as comparisons to other models.

Mapping environmental partitioning properties of nonpolar complex mixtures by use of GC × GC

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.

Resolving biodegradation patterns of persistent saturated hydrocarbons in weathered oil samples from the Deepwater Horizon disaster

Biodegradation plays a major role in the natural attenuation of oil spills. However, limited information is available about biodegradation of different saturated hydrocarbon classes in surface environments, despite that oils are composed mostly of saturates, due to the limited ability of conventional gas chromatography (GC) to resolve this compound group. We studied eight weathered oil samples collected from four Gulf of Mexico beaches 12-19 months after the Deepwater Horizon disaster. Using comprehensive two-dimensional gas chromatography (GC × GC), we successfully separated, identified, and quantified several distinct saturates classes in these samples. We find that saturated hydrocarbons eluting after n-C22 dominate the GC-amenable fraction of these weathered samples. This compound group represented 8-10%, or 38-68 thousand metric tons, of the oil originally released from Macondo well. Saturates in the n-C22 to n-C29 elution range were found to be partly biodegraded, but to different relative extents, with ease of biodegradation decreasing in the following order: n-alkanes > methylalkanes and alkylcyclopentanes+alkylcyclohexanes > cyclic and acyclic isoprenoids. We developed a new quantitative index designed to characterize biodegradation of >n-C22 saturates. These results shed new light onto the environmental fate of these persistent, hydrophobic, and mostly overlooked compounds in the unresolved complex mixtures (UCM) of weathered oils.

PETROTOX: an aquatic toxicity model for petroleum substances

A spreadsheet model (PETROTOX) is described that predicts the aquatic toxicity of complex petroleum substances from petroleum substance composition. Substance composition is characterized by specifying mass fractions in constituent hydrocarbon blocks (HBs) based on available analytical information. The HBs are defined by their mass fractions within a defined carbon number range or boiling point interval. Physicochemical properties of the HBs are approximated by assigning representative hydrocarbons from a database of individual hydrocarbons with associated physicochemical properties. A three-phase fate model is used to simulate the distribution of each structure among the water-, air-, and oil-phase liquid in the laboratory test system. Toxicity is then computed based on the predicted aqueous concentrations and aquatic toxicity of each structure and the target lipid model. The toxicity of the complex substance is computed assuming additivity of the contribution of the individual assigned hydrocarbons. Model performance was evaluated by using direct comparisons with measured toxicity data for petroleum substances with sufficient analytical characterization to run the model. Indirect evaluations were made by comparing predicted toxicity distributions using analytical data on petroleum substances from different product categories with independent, empirical distributions of toxicity data available for the same categories. Predictions compared favorably with measured aquatic toxicity data across different petroleum substance categories. These findings demonstrate the utility of PETROTOX for assessing environmental hazards of petroleum substances given knowledge of substance composition.

Robust algorithm for aligning two-dimensional chromatograms

Comprehensive two-dimensional gas chromatography (GC × GC) chromatograms typically exhibit run-to-run retention time variability. Chromatogram alignment is often a desirable step prior to further analysis of the data, for example, in studies of environmental forensics or weathering of complex mixtures. We present a new algorithm for aligning whole GC × GC chromatograms. This technique is based on alignment points that have locations indicated by the user both in a target chromatogram and in a reference chromatogram. We applied the algorithm to two sets of samples. First, we aligned the chromatograms of twelve compositionally distinct oil spill samples, all analyzed using the same instrument parameters. Second, we applied the algorithm to two compositionally distinct wastewater extracts analyzed using two different instrument temperature programs, thus involving larger retention time shifts than the first sample set. For both sample sets, the new algorithm performed favorably compared to two other available alignment algorithms: that of Pierce, K. M.; Wood, Lianna F.; Wright, B. W.; Synovec, R. E. Anal. Chem.2005, 77, 7735-7743 and 2-D COW from Zhang, D.; Huang, X.; Regnier, F. E.; Zhang, M. Anal. Chem.2008, 80, 2664-2671. The new algorithm achieves the best matches of retention times for test analytes, avoids some artifacts which result from the other alignment algorithms, and incurs the least modification of quantitative signal information.

Estimating phospholipid membrane-water partition coefficients using comprehensive two-dimensional gas chromatography

Recent studies have shown that membrane-water partition coefficients of organic chemicals can be used to predict bioaccumulation and type I narcosis toxicity more accurately than the traditional K(OW)-based approach. In this paper, we demonstrate how comprehensive two-dimensional gas chromatography (GC × GC) can be used to estimate such membrane-water partition coefficients (K(PLW)s), focusing in particular on phosphatidyl choline based lipids. This method performed well for a set of 38 compounds, including polycyclic aromatic hydrocarbons, polychlorinated benzenes and biphenyls, and substituted benzenes including some phenols and anilines. The average difference between the estimated and the measured log K(PLW) values of 0.47 log units is smaller than in the case of a log K(OW) correlation approach but larger than seen using a polyparameter linear free energy relationship based approach. However, the GC × GC based method presents the advantage that it can be applied to mixtures of chemicals that are not completely identified, such as petroleum hydrocarbon mixtures. At the same time, our application of the GC × GC method suffered larger errors when applied to certain hydrogen bonding compounds due to the inability of the GC × GC capillary columns phases that we used to interact with analytes via hydrogen bond donation/electron acceptance.

Selective extraction of emerging contaminants from water samples by dispersive liquid-liquid microextraction using functionalized ionic liquids

Functionalized ionic liquids containing the tris(pentafluoroethyl)trifluorophosphate (FAP) anion were used as extraction solvents in dispersive liquid-liquid microextraction (DLLME) for the extraction of 14 emerging contaminants from water samples. The extraction efficiencies and selectivities were compared to those of an in situ IL DLLME method which uses an in situ metathesis reaction to exchange 1-butyl-3-methylimidazolium chloride (BMIM-Cl) to 1-butyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide (BMIM-NTf(2)). Compounds containing tertiary amine functionality were extracted with high selectivity and sensitivity by the 1-(6-amino-hexyl)-1-methylpyrrolidinium tris(pentafluoroethyl)trifluorophosphate (HNH(2)MPL-FAP) IL compared to other FAP-based ILs and the BMIM-NTf(2) IL. On the other hand, polar or acidic compounds without amine groups exhibited higher enrichment factors using the BMIM-NTf(2) IL. The detection limits for the studied analytes varied from 0.1 to 55.1 μg/L using the traditional IL DLLME method with the HNH(2)MPL-FAP IL as extraction solvent, and from 0.1 to 55.8 μg/L using in situ IL DLLME method with BMIM-Cl+LiNTf(2) as extraction solvent. A 93-fold decrease in the detection limit of caffeine was observed when using the HNH(2)MPL-FAP IL compared to that obtained using in situ IL DLLME method. Real water samples including tap water and creek water were analyzed with both IL DLLME methods and yielded recoveries ranging from 91% to 110%.

The M/V Cosco Busan spill: source identification and short-term fate

Understanding the fate of heavy fuel oils (HFOs) in the environment is critical for sound decisions regarding its usage and spill cleanup. To study weathering of HFOs, we examined the M/V Cosco Busan spill (November 2007; San Francisco Bay, CA, USA). In this baseline report, we identified which ruptured tank (port tank 3 or 4) was the source of the spilled oil and characterized changes in the oil composition across location and time. Samples from three impacted shorelines, collected within 80 days of the spill, were analyzed using one- and two-dimensional gas chromatography (GC and GC × GC, respectively). Weathering varied across sites, but compounds with GC retention times less than n-C(16) were generally lost by evaporation and dissolution. Changes in n-C(18)/phytane and benz[a]anthracene/chrysene ratios indicated some biodegradation and photodegradation, respectively.

Chemically selective analysis of molecular monolayers by nonlinear optical stokes ellipsometry

The application of nonlinear optical Stokes ellipsometry (NOSE) coupled with principal component analysis (PCA) is demonstrated for the chemically selective analysis of molecular monolayers. NOSE allows for rapid polarization measurements of nonlinear optical materials and thin surface films, which in turn benefits from comparably fast data analysis approaches. PCA combined with linear curve fitting techniques greatly reduce the analysis time relative to nonlinear curve fitting. NOSE-PCA is first validated with studies of z-cut quartz, followed by analysis of four thin dye films with similar nonlinear optical properties. The high precision of NOSE measurements combined with the rapid analysis time enabled chemical discrimination between different dyes and the practical realization of NOSE microscopy.