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 defi ned 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 prioritize 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.
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