Abstract
The intent of minimizing the impact of the large amount of radioactive material potentially released into the atmosphere in a nuclear event implies preparedness activities. In the early phase and in absence of field observations, countermeasures would largely rely on a previous characterization of the transport and dispersion of radioactive particles and the potential levels of radioactive contamination. This study presents a methodology to estimate the atmospheric transport, dispersion and ground deposition patterns of radioactive particles. The methodology starts identifying the main airflow directions by means of the air mass trajectories calculated by the HYSPLIT model, and, secondly, the dispersion and the ground deposition characteristics associated with each airflow pattern by running the RIMPUFF atmospheric dispersion model. From the basis of these results, different products can be obtained, such as the most probable transport direction, spatial probability distribution of deposition and the geographical probability distribution of deposition above certain predefined threshold. The method is trained on the HYSPLIT trajectories and RIMPUFF simulations during five consecutive years (2012–2016) at the Almaraz Nuclear Power Plant, in Spain. 3644 forward air mass trajectories were calculated (at 00 and 12 UTC, and with duration of 36 h). Eight airflow patterns were identified, and within each pattern, the persistent days, i.e. those days in which trajectories at 00 and 12 UTC grouped into the same airflow pattern, were extracted to simulate the atmospheric dispersion and ground deposition following a hypothetical ISLOCA accident sequence of 35 h. In total, 833 simulations were carried out, in which ground contamination was estimated at cell level on a non-homogeneous geographical grid spacing up to 800 km from Almaraz. The corresponding outcomes show a large variability in the area covered and in deposition values between airflow patterns, which provide comprehensive and oriented information and resources to decision makers to emergency management.
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