Oil Identification Based on a Goodness-of-Fit Metric Applied to Hydrocarbon Analysis Results

Abstract

Assessment of environmental damage following accidental oil spills requires reliable oil identification methods. Results from hydrocarbon analyses of environmental samples are often difficult to interpret, because of the changes in oil composition (or weathering) that follows release into the environment, and because of confounding by hydrocarbons from other sources. To a first-order approximation, weathering proceeds according to simple first-order loss-rate (FOLR) kinetics for polycyclic aromatic hydrocarbons (PAH) based on molecular size. This relationship between relative weathering rate and molecular size can be exploited to infer the initial PAH composition of spilled oils, and this information can be combined with results for weathering-invariant analytes to substantially increase the precision and accuracy of hydrocarbon source recognition methods. The approach presented here evaluates a goodness-of-fit metric between the measured hydrocarbon composition of an environmental sample and a suspected source, after correcting for PAH weathering losses based on FOLR kinetics. Variability from analytical and sampling error may thus be accounted for, and source identifications can be expressed as objective probability statements. This approach is illustrated by application to four independent case studies.