A framework for the evaluation and selection of shoreline surface washing agents in oil spill response

The shorelines frequently suffer adverse impacts from oil spill accidents. As one important technique of shoreline cleanup, the application of surface washing agents (SWAs) can help achieve high oil removal from shoreline substrates with less damage to affected zone. In this study, a framework for evaluation and selection of SWAs in oil spill incidents was constructed to better understand and apply this technique. A decision tree was firstly developed to illustrate all possible scenarios which are appropriate to use SWAs in consideration of oil collectability, shoreline character, types and amount of stranded oil, and cleanup requirement. Based on literature review, theoretical modeling, and experts? suggestions, an integrated multi-criteria decision analysis (MCDA) method was then come up to select the most preferred SWA from five aspects of toxicity, effectiveness, minimal dispersion, demonstrated field test, and cost. Its suitability and rationality were proved by a hypothetical case. In addition, sensitivity analysis was performed by changing the weight of each criterion independently to check the priority rank of alternatives, and it also verified the robustness and stability of this model. The presented framework has significant implications for future research and application of SWAs in the shoreline cleanup.

Automated summary

The study established a framework for evaluation and selection of surface washing agents (SWAs) in oil spill incidents, utilizing decision tree and multi-criteria decision analysis method. Sensitivity analysis was conducted to verify the robustness of the model.