Quantifying weathering profiles of environmental contaminants from marine and coastal oil spills using signal processing techniques

Complex mixtures such as crude oil consist of hundreds of contaminants, which manifest as peaks in the raw instrument signals from analytical technology such as gas chromatography (GC), mass spectrometry (MS) and combinations thereof. Many of these contaminants, such as alkyl naphthalenes, are well-known for their toxicity to aquatic animals and humans. This poses a risk to public and environmental health as these types of compounds can persist in marine and coastal environments in varying degrees after a major oil spill such as the Deepwater Horizon disaster in the Gulf of Mexico, in April 2010. We propose a general approach towards peak feature extraction from the raw instrument signal for identifying well-known (target) and unknown (non-target) toxic contaminants in crude petroleum. Specifically, we present computational methods for determination of weathering profiles of different chemical contaminants commonly found in the ocean in the aftermath of oil spills. Proposed autonomous quantification of large-scale weathering profiles across a wide variety of marine pollutants will enable apportioning the long-term impact of off-shore drilling and oil spills to public and environmental health and safety.