Probabilistic risk assessment and decision support tools for the evaluation of oil transport in the gulf of Finland, North-eastern Baltic sea

The maritime traffic in the Gulf of Finland (GoF), North-eastern Baltic Sea, is predicted to rapidly grow in the near future, which increases the environmental risks through both direct environmental effects and by increasing the risk of severe oil accident. A multidisciplinary group of researchers has developed a prototype of risk assessment and decision support model, applying Bayesian Networks (BNs), for the evaluation of environmental risks arising from the oil transport. It consists of sub-models on tanker collisions, causation probability (human factor), the resulting leak size, and the efficacy of open sea oil recovery. This meta-model is based on three alternative growth scenarios concerning the maritime traffic of the GoF in 2015 and the probability of a major oil accident given these conditions within four selected areas. The model can be used to compare the effectiveness of some preventive management actions and oil recovery against the accident risk. The multidisciplinary approach developed helps in comparing the risks in different parts of the oil accident cause - effect chain when current knowledge and uncertainty are taken into account. In addition, a user interface for the model has been created and tested for the analysis of spatial ecological risk arising from the oil transport in the GoF. A simplified version of the risk assessment meta-model is used to calculate probabilistic oil accident scenarios. The resulting probability distributions are used as an input in Geographic Information System (GIS) -environment, where probabilistic oil drifting maps are calculated accordingly. In the end, these drift calculations are evaluated against information on the known occurrences of endangered species on the Finnish coastline and conservation value indexes related to them. This allows us to calculate and compare the total risk for endangered species given the conditions selected in the risk assessment meta-model. This approach provides an interesting, alternative viewpoint concerning the decisions on how and where the available risk management resources should be directed.