Radiation-induced cancer risk and decision-making in a simulated Cs-137 urban event

The triggering of a “dirty bomb” generates a complex scenario, with enormous challenges for the responders due to initial misinformation and the urgency to act quickly yet effectively. Normally, the first 100 h are decisive for perceiving the risk in a more realistic dimension, but the support of methodologies that rely on computational simulations can be valuable when making key decisions. This work seeks to provide support for the early decision-making process by using a Gaussian model for the distribution of a quantity of Cs-137 spread by a radiological dispersive device (RDD). By sequentially joining two independent programs, HotSpot Health Physics codes and RESidual RADiation (RESRAD)-RDD family of codes, we came up with results that suggest a segmented approach to the potentially affected population. These results advocate that (a) the atmospheric stability conditions represented by the Pasquill–Gifford classes and (b) the population subgroups defined by radiation exposure conditions strongly influence the postdetonation radiological effects. These variables should be taken into account in the elaboration of flexible strategies that include many climatic conditions and to priori-tize attention to different groups of public at risk. During the initial phases of such an event, it is believed that simulations using Gaussian models may be of value in anticipating the possible changes in key variables during the decision-making process. These variables may severely affect the effectiveness of the actions of responders and the general public’s safety.