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

[Image: see text] 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 environmen...

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Autores principales: Arey, J. Samuel, Martin Aparicio, Alberto, Vaiopoulou, Eleni, Forbes, Stuart, Lyon, Delina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775207/
https://www.ncbi.nlm.nih.gov/pubmed/36475671
http://dx.doi.org/10.1021/acs.est.2c06922
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author Arey, J. Samuel
Martin Aparicio, Alberto
Vaiopoulou, Eleni
Forbes, Stuart
Lyon, Delina
author_facet Arey, J. Samuel
Martin Aparicio, Alberto
Vaiopoulou, Eleni
Forbes, Stuart
Lyon, Delina
author_sort Arey, J. Samuel
collection PubMed
description [Image: see text] 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 C(10)–C(30) 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.
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spelling pubmed-97752072022-12-23 Modeling the GCxGC Elution Patterns of a Hydrocarbon Structure Library To Innovate Environmental Risk Assessments of Petroleum Substances Arey, J. Samuel Martin Aparicio, Alberto Vaiopoulou, Eleni Forbes, Stuart Lyon, Delina Environ Sci Technol [Image: see text] 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 C(10)–C(30) 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. American Chemical Society 2022-12-07 2022-12-20 /pmc/articles/PMC9775207/ /pubmed/36475671 http://dx.doi.org/10.1021/acs.est.2c06922 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Arey, J. Samuel
Martin Aparicio, Alberto
Vaiopoulou, Eleni
Forbes, Stuart
Lyon, Delina
Modeling the GCxGC Elution Patterns of a Hydrocarbon Structure Library To Innovate Environmental Risk Assessments of Petroleum Substances
title Modeling the GCxGC Elution Patterns of a Hydrocarbon Structure Library To Innovate Environmental Risk Assessments of Petroleum Substances
title_full Modeling the GCxGC Elution Patterns of a Hydrocarbon Structure Library To Innovate Environmental Risk Assessments of Petroleum Substances
title_fullStr Modeling the GCxGC Elution Patterns of a Hydrocarbon Structure Library To Innovate Environmental Risk Assessments of Petroleum Substances
title_full_unstemmed Modeling the GCxGC Elution Patterns of a Hydrocarbon Structure Library To Innovate Environmental Risk Assessments of Petroleum Substances
title_short Modeling the GCxGC Elution Patterns of a Hydrocarbon Structure Library To Innovate Environmental Risk Assessments of Petroleum Substances
title_sort modeling the gcxgc elution patterns of a hydrocarbon structure library to innovate environmental risk assessments of petroleum substances
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775207/
https://www.ncbi.nlm.nih.gov/pubmed/36475671
http://dx.doi.org/10.1021/acs.est.2c06922
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