Development of a Novel Oil Spill Severity Index (OSSI) for the Niger Delta

Abstract Oil spills remain a primary issue of environmental and socio-spatial consequence in the Niger Delta. Typical oil spill assessment methods are based on specific and singular measures, such as volume of spill and the dimension of the contaminated area, with due consideration not given to the spatial proximity of spill to water resources and communities. This work developed a novel Oil Spill Severity Index (OSSI), a composite measure designed to rank spill sites according to urgency for remediation. The OSSI integrates four indicators: spatial extent of contamination, volume of oil released, proximity to residential communities, and closeness to essential water sources. GIS were used to derive the two proximity variables from the spatial location of incidents; while the indicators were normalized and weight aggregated to develop a single dimensionless index of spill severity (0-100). Four weighting scenarios were used in analyses to compare results obtained when Equal Weighting, Social Priority and Ecological Priority schemes are applied. Model validity was evaluated through Monte Carlo simulation, and bootstrap resampling and differences between severity class were assessed using ANOVA while variations in classifications under alternate weightings were assessed using Chi-square. Sensitivity analysis results show that distance to communities is the most significant factor influencing sensitivity coefficient (SC), with a SC value of 0.41, followed by distance to watercourses (SC = 0.29), Spill volume (SC = 0.18), and Contaminated Area (SC = 0.12). Additionally, the weighting analysis found that the social priority management perspective, compared to the equal and ecological priorities, increases number of high and very high categories cases. This study presents a pioneering effort to combine spill magnitude, settlement proximity, and ecological exposure within a unified severity model. This study enhances environmental risk classification, spill prioritization, and policy-based decision-making by identifying critical spill hotspots that conventional approaches may fail to detect.