Quantifying the relative weathering of toxic hydrocarbons from the Deepwater Horizon oil spill using peak-cognizant raw GC-MS signal processing

Tonmoy Biswas; Fabian MullerDahlberg; Bernice Kubicek; Alexandra Zelenski; Lokeshwari Potluri; Allison Flores; Trevor Smith; Diego Diaz; Ananya Sen Gupta; Laura M Basirico; Kevin L Armbrust; Edward Overton

doi:10.1109/ACCESS.2026.3690058

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Quantifying the relative weathering of toxic hydrocarbons from the Deepwater Horizon oil spill using peak-cognizant raw GC-MS signal processing

Journal article

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Quantifying the relative weathering of toxic hydrocarbons from the Deepwater Horizon oil spill using peak-cognizant raw GC-MS signal processing

Tonmoy Biswas, Fabian MullerDahlberg, Bernice Kubicek, Alexandra Zelenski, Lokeshwari Potluri, Allison Flores, Trevor Smith, Diego Diaz, Ananya Sen Gupta, Laura M Basirico, …

IEEE access, Vol.14, pp.70975-70995

05/04/2026

DOI: 10.1109/ACCESS.2026.3690058

Appears in UI Libraries Support Open Access

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Abstract

In this work, we propose a new graph-based data science method to perform peak-cognizant automated analysis of the raw signal from gas chromatography mass spectrometry (GC-MS) data. The application of interest is the environmental weathering of complex mixtures, e.g. crude oil from the Deepwater Horizon oil spill in April 2010. Specifically, we introduce a graph-based representation of the different peaks, target and non-target, in the raw GC-MS signal, which are interpreted as analytes by environmental chemists. Such representation enables us to (i) autonomously interpret the rich complexity of analytes present in the raw GC-MS signal, as well as (ii) autonomously quantify how each analyte degrades across different samples taken from the same source at different times. Accordingly, we present our findings in the form of persistent graphs based on a portfolio of weathered crude oil samples from the Deepwater Horizon 2010 spill, collected across multiple sites across the Gulf of Mexico. The results show considerable variability across different classes of analytes, as expected based on their well-known resilience to environmental weathering. Specifically, we consider analytes such as pyrenes, phenanthrenes, etc. that degrade under environmental weathering processes, and compare their persistence graphs against analytes like hopanes, which are well-known to be recalcitrant against environmental degradation. The value proposition of this work study hydrocarbon persistence autonomously from the raw instrument signal in a peak-cognizant way, which will significantly reduce lab personnel time to manually interpret the weathering profile of these individual analytes. Our work is distinct from statistical chemometric studies which study such analytes in the aggregate, and where such peak-cognizant quantification and interpretation is not possible. In particular, the autonomous peak-cognizant analysis method proposed allows the discovery of persistent non-target analytes in the raw instrument signal, which have never been studied by experts, and buried in the aggregate statistics of chemometric approaches.We also posit the potential application to other complex mixtures, such as soil, and related weathering of soil contaminants, based on this proposed method.

Circuits and Systems

Antennas

Broadcast technology

Broadcasting

Deepwater Horizon oil spill

Feeds

Filtering

Filters

Gas chromatography-mass spectrometry (GC-MS)

Oil weathering

Peak processing

Radio access networks

Regional area networks

Retention Time Alignment

Sonar

UIOWA OA Agreement

Details

Title: Subtitle: Quantifying the relative weathering of toxic hydrocarbons from the Deepwater Horizon oil spill using peak-cognizant raw GC-MS signal processing
Creators: Tonmoy Biswas - University of Iowa
Fabian MullerDahlberg - University of Iowa
Bernice Kubicek - University of Iowa
Alexandra Zelenski - University of Iowa
Lokeshwari Potluri - University of Iowa
Allison Flores - University of Iowa
Trevor Smith - University of Iowa
Diego Diaz - University of Iowa
Ananya Sen Gupta - University of Iowa, Electrical and Computer Engineering
Laura M Basirico - Louisiana State University
Kevin L Armbrust - Louisiana State University
Edward Overton - Department of Environmental Sciences, Louisiana State University, Baton Rouge, Louisiana, USA
Resource Type: Journal article
Publication Details: IEEE access, Vol.14, pp.70975-70995
DOI: 10.1109/ACCESS.2026.3690058
ISSN: 2169-3536
eISSN: 2169-3536
Publisher: IEEE
Number of pages: 1
Grant note: W911NF-22-1-0272 / US Department of Defense Army Research Office (ARO) 1808463 / National Science Foundation (10.13039/100000001)
Language: English
Date published: 05/04/2026
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9985160645002771

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