Insight into unresolved complex mixtures of aromatic hydrocarbons in heavy oil via two-dimensional gas chromatography coupled with time-of-flight mass spectrometry analysis

Recommendations for the advancement of oil-in-water media and source oil characterization in aquatic toxicity test studies

During toxicity testing, chemical analyses of oil and exposure media samples are needed to allow comparison of results between different tests as well as to assist with identification of the drivers and mechanisms for the toxic effects observed. However, to maximize the ability to compare results between different laboratories and biota, it has long been recognized that guidelines for standard protocols were needed. In 2005, the Chemical Response to Oil Spills: Ecological Effects Research Forum (CROSERF) protocol was developed with existing common analytical methods that described a standard method for reproducible preparation of exposure media as well as recommended specific analytical methods and analyte lists for comparative toxicity testing. At the time, the primary purpose for the data collected was to inform oil spill response and contingency planning. Since then, with improvements in both analytical equipment and methods, the use of toxicity data has expanded to include their integration into fate and effect models that aim to extend the applicability of lab-based study results to make predictions for field system-level impacts. This paper focuses on providing a summary of current chemical analyses for characterization of oil and exposure media used during aquatic toxicity testing and makes recommendations for the minimum analyses needed to allow for interpretation and modeling purposes.

Analytical chemistry solutions to hazard evaluation of petroleum refining products

Products of petroleum refining are substances that are both complex and variable. These substances are produced and distributed in high volumes; therefore, they are heavily scrutinized in terms of their potential hazards and risks. Because of inherent compositional complexity and variability, unique challenges exist in terms of their registration and evaluation. Continued dialogue between the industry and the decision-makers has revolved around the most appropriate approach to fill data gaps and ensure safe use of these substances. One of the challenging topics has been the extent of chemical compositional characterization of products of petroleum refining that may be necessary for substance identification and hazard evaluation. There are several novel analytical methods that can be used for comprehensive characterization of petroleum substances and identification of most abundant constituents. However, translation of the advances in analytical chemistry to regulatory decision-making has not been as evident. Therefore, the goal of this review is to bridge the divide between the science of chemical characterization of petroleum and the needs and expectations of the decision-makers. Collectively, mutual appreciation of the regulatory guidance and the realities of what information these new methods can deliver should facilitate the path forward in ensuring safety of the products of petroleum refining.

Petroleum hydrocarbon pollution in sediments from the Gulf and Omani waters: Status and review

This review presents the spatio-temporal distribution of petroleum hydrocarbons including total petroleum hydrocarbon (TPH), total organic carbon (TOC), total aliphatics, unresolved complex mixture (UCM), polycyclic aromatic hydrocarbons (PAHs), and total aromatic hydrocarbons in marine sediments of the Gulf (Iraq, Iran, Kuwait, Saudi Arabia, Bahrain, Qatar, United Arab Emirates and Oman). The TPH ranged between 0.134 and 48,018 μg g^-1 dw where 10-15 μg g^-1 dw was considered as a background concentration. The TOC levels were between 0.04 and 14.96% with a mean concentration of 1.154 ± 0.523%. Total aliphatic hydrocarbon levels were reported between 0.1 and 76 μg g^-1, the 2005 levels that had the largest spatial coverage were between 0.1 and 4.4 μg g^-1. The unresolved complex mixture was very variable post Gulf War but by 2005 most of the Gulf War artifacts had decreased and the levels were between 1.5 and 73.5 μg g^-1. The ƩPAHs in bottom sediments by 2005 were between 0.3 and 3450 ng g^-1. The total aromatics were limited in spatial extent and varied between 1.0 and 14,000 μg g^-1. Most of the locations with elevated contamination levels were near point sources, e.g. oil facilities and ports, and these sites could be categorized as chronically contaminated by oil. This review highlights the paucity of the data both in terms of the spatial extent and temporal coverage, and with several Gulf states undergoing large-scale coastal developments and offshore oil exploration, it will be prudent to undertake regular monitoring of the petroleum hydrocarbons to ensure effective ecosystem functioning as well as seafood and drinking water safety in the Gulf region. The spatial distribution also highlights the lack of uniformity in assessments and the need to support marine pollution assessments in the Gulf countries.

Compositional Analysis of Heavy Petroleum Distillates by Comprehensive Two-dimensional Gas Chromatography, Field Ionization and High-resolution Mass Spectrometry

We report recent progresses of combining comprehensive two-dimensional gas chromatography (2DGC or GC × GC) separation, field ionization (FI), and time-of-flight mass spectrometry (TOF MS) for the detailed analysis of vacuum gas oil distillation (VGO) cuts. 2DGC separates petroleum molecules by the combination of boiling point and polarity. FI generates molecule ions-only mass spectra. TOF MS allows accurate mass analysis of hydrocarbon molecules. A new data analysis strategy is implemented for compositional analysis. First, all masses were separated into nominal mass classes. Since petroleum homologues have unique Kendrick mass defects (KMD), KMD plots were generated for easy recognition of homologues series within each nominal mass class. Finally, KMD windows were imposed for complete resolution of petroleum molecules. Using this approach, a total of 16 hydrocarbon types, 14 sulfur types, and their carbon number distributions were determined in the three VGO distillation cuts. Two series of geological biomarkers were also revealed by the analysis.

A non-toxic microbial surfactant from Marinobacter hydrocarbonoclasticus SdK644 for crude oil solubilization enhancement

This study aims to investigate the ability of a biosurfactant produced by Marinobacter hydrocarbonoclasticus strain SdK644 isolated from hydrocarbon contaminated sediment to enhance the solubilization rate of crude oil contaminated seawater. Phylogenetic analysis shows that strain SdK644 was very closely related to M. hydrocarbonoclasticus with 16S rRNA gene sequence similarity of 97.44%. Using waste frying oil as inducer carbon source, the producing biosurfactant by strain SdK644 was applied to improve crude oil solubilization in seawater. The preliminary characterization of the produced biosurfactant by FT-IR analysis indicates its possible classification in a glycolipids group. Results from crude oil solubilization assay showed that SdK644 strain biosurfactant was 2-fold greater than Tween 80 surfactant in crude oil solubilization and 12-fold higher than seawater control, as shown by GC-MS analysis of aliphatic compounds. Furthermore, this bioactive compound was shown to be nontoxic against Artemia larvae in short-term acute toxicity bioassay. Generally, the results showed the possible use of M. hydrocarbonoclasticus strain SdK644 biosurfactant in bioremediation processes of the marine environments.

Oil source recognition technology using concentration-synchronous-matrix-fluorescence spectroscopy combined with 2D wavelet packet and probabilistic neural network

Developing an accurate, rapid and economic oil source recognition method is essential for water recourses protection. Concentration-synchronous-matrix-fluorescence (CSMF) spectroscopy combined with 2D wavelet packet and probabilistic neural network (PNN) was proposed for source recognition of crude oil and petroleum products samples in this study. 2D wavelet packet was used to extract wavelet packet coefficients as the feature vectors from CSMF contour image and four algorithms, Back-propagation (BP) neural network, Radial based function neural network (RBFNN), Support vector Machine (SVM) and probabilistic neural network (PNN) were carried out for pattern recognition. With the introduction of interference factors such as weathering and sea water adulteration to the three samples from Bohai bay territory of China, the comparison about accuracy and recognition time of the four methods was discussed and the results showed that PNN network maintain the highest recognition accuracy and speed. These findings may offer potential application for oil spill recognition for unconventional oil.

Semi-Targeted Analysis of Complex Matrices by ESI FT-ICR MS or How an Experimental Bias may be Used as an Analytical Tool

Ammonia is well suited to favor deprotonation process in electrospray ionization mass spectrometry (ESI-MS) to increase the formation of [M - H]^-. Nevertheless, NH₃ may react with carbonyl compounds (aldehyde, ketone) and bias the composition description of the investigated sample. This is of significant importance in the study of complex mixture such as oil or bio-oil. To assess the ability of primary amines to form imines with carbonyl compounds during the ESI-MS process, two aldehydes (vanillin and cinnamaldehyde) and two ketones (butyrophenone and trihydroxyacetophenone) have been infused in an ESI source with ammonia and two different amines (aniline and 3-chloronaniline). The (+) ESI-MS analyses have demonstrated the formation of imine whatever the considered carbonyl compound and the used primary amine, the structure of which was extensively studied by tandem mass spectrometry. Thus, it has been established that the addition of ammonia, in the solution infused in an ESI source, may alter the composition description of a complex mixture and leads to misinterpretations due to the formation of imines. Nevertheless, this experimental bias can be used to identify the carbonyl compounds in a pyrolysis bio-oil. As we demonstrated, infusion of the bio-oil with 3-chloroaniline in ESI source leads to specifically derivatized carbonyl compounds. Thanks to their chlorine isotopic pattern and the high mass measurement accuracy, (+) ESI Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) unambiguously highlighted them from the numerous C_xH_yO_z bio-oil components. These results offer a new perspective into the detailed molecular structure of complex mixtures such as bio-oils. Graphical Abstract ᅟ.

Assessing bioavailability of complex chemical mixtures in contaminated soils: Progress made and research needs

Understanding the distribution, behaviour and interactions of complex chemical mixtures is key for providing the evidence necessary to make informed decisions and implement robust remediation strategies. Much of the current risk assessment frameworks applied to manage land contamination are based on total contaminant concentrations and the exposure assessments embedded within them do not explicitly address the partitioning and bioavailability of chemical mixtures. These oversights may contribute to an overestimation of both the eco-toxicological effects of the fractions and the mobility of contaminants. In turn, this may limit the efficacy of risk frameworks to inform targeted and proportionate remediation strategies. In this review we analyse the science surrounding bioavailability, its regulatory inclusion and the challenges of incorporating bioavailability in decision making process. While a number of physical and chemical techniques have proven to be valuable tools for estimating bioavailability of organic and inorganic contaminants in soils, doubts have been cast on its implementation into risk management soil frameworks mainly due to a general disagreement on the interchangeable use of bioavailability and bioaccessibility, and the associated methods which are still not standardised. This review focuses on the role of biotic and abiotic factors affecting bioavailability along with soil physicochemical properties and contaminant composition. We also included advantages and disadvantages of different extraction techniques and their implications for bioavailability quantitative estimation. In order to move forward the integration of bioavailability into site-specific risk assessments we should (1) account for soil and contaminant physicochemical characteristics and their effect on bioavailability; (2) evaluate receptor's potential exposure and uptake based on mild-extraction; (3) adopt a combined approach where chemical-techniques are used along with biological methods; (4) consider a simplified and cost-effective methodology to apply at regulatory and industry setting; (5) use single-contaminant exposure assessments to inform and predict complex chemical mixture behaviour and bioavailability.

Unsupervised classification of petroleum Certified Reference Materials and other fuels by chemometric analysis of gas chromatography-mass spectrometry data

As feedstocks transition from conventional oil to unconventional petroleum sources and biomass, it will be necessary to determine whether a particular fuel or fuel blend is suitable for use in engines. Certifying a fuel as safe for use is time-consuming and expensive and must be performed for each new fuel. In principle, suitability of a fuel should be completely determined by its chemical composition. This composition can be probed through use of detailed analytical techniques such as gas chromatography-mass spectroscopy (GC-MS). In traditional analysis, chromatograms would be used to determine the details of the composition. In the approach taken in this paper, the chromatogram is assumed to be entirely representative of the composition of a fuel, and is used directly as the input to an algorithm in order to develop a model that is predictive of a fuel's suitability. When a new fuel is proposed for service, its suitability for any application could then be ascertained by using this model to compare its chromatogram with those of the fuels already known to be suitable for that application. In this paper, we lay the mathematical and informatics groundwork for a predictive model of hydrocarbon properties. The objective of this work was to develop a reliable model for unsupervised classification of the hydrocarbons as a prelude to developing a predictive model of their engine-relevant physical and chemical properties. A set of hydrocarbons including biodiesel fuels, gasoline, highway and marine diesel fuels, and crude oils was collected and GC-MS profiles obtained. These profiles were then analyzed using multi-way principal components analysis (MPCA), principal factors analysis (PARAFAC), and a self-organizing map (SOM), which is a kind of artificial neural network. It was found that, while MPCA and PARAFAC were able to recover descriptive models of the fuels, their linear nature obscured some of the finer physical details due to the widely varying composition of the fuels. The SOM was able to find a descriptive classification model which has the potential for practical recognition and perhaps prediction of fuel properties.