Document Type : Original Research Paper
Authors
1 School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 College of Environment, Department of Environment (DOE), Tehran, Iran
Abstract
Environmental degradation, particularly in marine ecosystems, has become a critical issue, due to industrial activities. Offshore areas are significantly impacted by the deep sea mining operations, leading to pollution and ecological imbalances. The existing environmental risk assessment models often fail to integrate the qualitative and quantitative data effectively, highlighting a significant research work gap. This work aims to address this gap by developing a comprehensive framework using the Bayesian Networks (BN), and the NETICA software to evaluate the risks associated with the installation of three-legged deep sea mining structures. The major goals are to systematically identify and prioritize the risks, and to develop effective mitigation strategies. The novelty of this work lies in its innovative use of the Bayesian modeling to combine the expert knowledge with the empirical data, providing a detailed categorization of risks into the low, medium, and high levels. The output parameters focus on the severity, likelihood, and detectability of risks. The results indicate that 40% of the habitat destruction risks are low, 46% fall within the ALARP region, and 14% are high, while the species destruction risks are 31% low, 50% ALARP, and 19% high. These findings guide the targeted mitigation measures to ensure effective protection of the offshore marine environment. Also the work concludes with a set of recommendations aimed at mitigating identified risks, and minimizing the environmental impacts. These include the implementation of advanced monitoring technologies, adoption of best management practices, and enforcement of stricter regulatory frameworks.
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