Estimativa de impactos sobre a infraestrutura crítica local decorrentes de um evento radiológico

Abstract

This dissertation aims to estimate the initial impacts on critical urban infrastructure resulting from the atmospheric dispersion of radioactive material following a Radiological Dispersal Device (RDD) event, commonly referred to as a “dirty bomb.” The study focuses on the first 100 hours after the event, evaluating different radiation dose levels and their influence on essential facilities, including hospitals, schools, power substations, highways, fire stations, police departments, and emergency medical service (SAMU) units. Simulations were conducted using HotSpot Health Physics Codes v3.1.2, developed by the Lawrence Livermore National Laboratory (LLNL), which applies a Gaussian model to simulate the dispersion of radioactive contaminants in the atmosphere. Three scenarios were analyzed, considering doses of 700 mSv, 100 mSv, and 50 mSv, corresponding to deterministic and stochastic health effects. The simulations also incorporated different Pasquill-Gifford atmospheric stability classes, which significantly influence the behavior of the radioactive plume in urban environments. The resulting plumes were imported into QGIS, where they were spatially overlaid onto georeferenced urban infrastructure maps. This integration allowed the identification of overlap between contaminated zones and critical infrastructure, enabling a detailed spatial analysis. Five indicators were employed to quantify exposure and vulnerability: the Risk Factor (RF), Total Impact Factor (FIT), Vulnerability Index (VI), Standard Deviation of Risk Factors (SDRF), and Median of Risk Factors (MRF), providing both statistical and operational insight into the severity of the impacts. Results indicate that stable atmospheric conditions tend to concentrate radioactive doses in narrower areas, while unstable conditions promote broader dispersion with lower concentrations. Schools and hospitals consistently emerged as the most vulnerable structures across all scenarios. Furthermore, the spatial distribution of infrastructure plays a decisive role in the extent of radiological impacts, highlighting the importance of localized emergency planning. It is concluded that the integration of atmospheric modeling with geospatial analysis is an effective tool to support urban planning and radiological emergency management. The proposed methodology proves to be suitable for anticipating risks and guiding decision-making, thus contributing to urban resilience and the development of public policies aimed at protecting populations and essential services.