Browsing by Author "Yatsenko, Yuliia"

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    An initial data-limited modeling of the environmental consequences: case-study of the vasylkiv fuel reservoir fire
    (Видавництво Львівської політехніки, 2023-02-28) Radomska, Margaryta; Stevens, Rodney; Semkiv, Marta; Yatsenko, Yuliia; Lysovenko, Serhii; National Aviation University; University of Gothenburg; Institute of Cell Biology, National Academy of Sciences of Ukraine; Taras Shevchenko National University of Kyiv; Institute of Superhard Materials, National Academy of Sciences of Ukraine
    The paper presents the application of the Multi-Criteria Evaluation of environmental damage under the conditions of limited available data. War actions often cause damage to industrial facilities, which in turn impacts the environment. At the same time, access to such sites and information about the development of specific events may be limited or fragmented. To support the decision-making process in such situations, the Multi-Criteria Evaluation offers structured and transparent utilization of the known quantitative and qualitative information. The Vasylkiv fuel depot fire in Kryachki village during the early days of the war was analyzed in terms of potential damage to soil, which is often omitted in the assessments of the environmental impacts of fire. The case-study analysis included a definition of the “fire-environment” system components and the factors affecting the final level of damage, the weighting of these factors and formulation of the trends describing the intensity of soil pollution as a product of particular factor values. The set dependencies were then used to model scenarios with variable meteorological conditions and varied infrastructural conditions of the reservoir park. The modelling results imply the need to account for meteorological parameters in the evaluation of environmental damage and the development of post-accident mitigation plans. The Multi-Criteria Evaluation is also recommended for preparing for potential accidents since it can compensate for the lack of data through theoretical knowledge and practical experience if a multidisciplinary team is involved.