Sound uncertainty-based strategy for oil spill source identification

Abstract

Oil spills are frequent and a major environmental threat, whether they are massive or small. Therefore, authorities and experts have developed analytical chemistry tools to identify spill sources and address these illegal acts by comparing oil patterns obtained by Gas Chromatography-Mass Spectrometry analysis of the spill (Sp) and suspected sources (SS) samples. Several methodologies have proposed different balances between data processing complexity and reliability. Supported by the accessibility and validity of Microsoft Excel spreadsheets, an alternative, accurate, and user-friendly tool was developed for spill source identification based on Monte Carlo Method (MCM) simulation of correlated oil components expressed by abundance ratio (DR). However, the statistical control of various DR and the degree of similarity of samples' compositions, at defined confidence levels, impact the probability of true and false composition equivalence claim of Sp and SS becoming a challenge to recognise the offender. This work not only compares the MCM and the conventional approaches allowing to highlight the limitations that result in evidence with greater uncertainty, but also offers a statistically sound strategy that manages the probabilities of a compositional equivalence claim assessing the ability to distinguish competing spill sources and reporting the most likely polluting source with reduced uncertainty. A decision chart proposed, based on objective and statistically sound criteria, indicates the performance of consecutive DR comparison trials if necessary. The target values established for the probability of compositional equivalence claim of the Sp and the first and second most likely SS (≥95.0 % and ≤0.50 %, respectively) provide to forensic experts’ sound evidence to be presented in court (likelihood ratio ≥190). This work represents a significant breakthrough in comparing complex chemical oil patterns.