4.7 Article

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

期刊

SCIENCE OF THE TOTAL ENVIRONMENT
卷 669, 期 -, 页码 739-745

出版社

ELSEVIER
DOI: 10.1016/j.scitotenv.2019.03.157

关键词

Comprehensive two-dimensional gas chromatography; Property estimation; Complex mixture; Environmental fate and behavior; Risk assessment; Vehicle engine oil

资金

  1. Japan Society for the Promotion of Science (JSPS) KAKENHI [15H05340]
  2. Grants-in-Aid for Scientific Research [15H05340] Funding Source: KAKEN

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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 x GC). A GC x 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 x 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 x GC model, which links mass spectral information for structural identification. The GC x 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. (C) 2019 Elsevier B.V. All rights reserved.

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