Using Ramped Pyrolysis – Gas Chromatography – Mass Spectrometry to Evaluate Petroleum Hydrocarbons Following the Deepwater Horizon Oil Spill

In summer of 2010, the Deepwater Horizon oil spill polluted hundreds of miles of coastline along the Gulf of Mexico. A combination of human-mediated and natural weathering processes then altered the chemical composition (i.e. toxicity) of this spilled crude oil over time and space. One of the most important, yet challenging, aspects of oil spill science is to quantify these chemical changes in natural environments. In this study, we develop ramped pyrolysis – gas chromatography – mass spectrometry (Py-GC-MS) to address this challenge. In this technique, ~0.1mg of freeze-dried sample is pyrolyzed over a gradual temperature ramp (50-650°C). The eluded gas is cold-trapped over different thermal ranges (a.k.a. thermal slicing) and each range is individually analyzed via GC-MS, yielding quantifiable, compound-specific results. Py-GC-MS with thermal slicing has never been used for petroleum hydrocarbon analysis, but it has many advantages - it uses minimal sample, is time efficient and does not require sample preparation (minimizing compound loss and increasing the analytical window). During development of this method, we analyzed oiled sediments and tar collected on Grand Isle, Louisiana from 2010-2012. We quantified n-alkane (C₁₀-C₃₈), polycyclic aromatic hydrocarbon (PAH) and hopane content and confirmed these results with traditional solvent extraction, silica gel fractionation and mass spectrometry. Overall, we found rapid depletion of n-alkanes and PAHs (>90% depletion) in all samples within one year of Deepwater Horizon. After this, n-alkanes were almost 100% depleted by 2012, while PAH degradation continued to a maximum total degradation of 99% and 98% in sediment and tar, respectively. This not only describes the fate of petroleum compounds in salt marshes and beach deposits over time, but also complements previous radiocarbon studies of the same samples showing different rates of degradation in different micro-environments. In addition, the results presented here highlight the analytical power and efficiency of Py-GC-MS as an analytical approach for petroleum hydrocarbon analysis.