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

Andrade Edson R., Gomes Renato G., Stenders Ricardo, Brum Tercio, Lima Sergio X., Castro Mariana S. C., Silva Ademir X.

Nukleonika

2020

Vol. 65, No. 1

37--43

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.

Simulated nuclear contamination scenario, solid cancer risk assessment, and support to decision

Lima Sergio X., Costa Karolina P. S., Lima Zelmo R., Rother Fagner C., Araujo Olga M. O., Vital Helio C., Brum Tercio, Junior Wilson F. R. S., Amorim Jose Carlos C., Healy Matthew J. F., Andrade Edson R.

Nukleonika

2019

Vol. 64, No. 2

41--48

The detonation of an (hypothetical) improvised nuclear device (IND) can generate atmospheric release of radioactive material in the form of particles and dust that ultimately contaminate the soil. In this study, the detonation of an IND in an urban area was simulated, and its effects on humans were determined. The risk of solid cancer development due to radiation was calculated by taking into account prompt radiation and whole-body exposure of individuals near the detonation site up to 10 km. The excess relative risk (ERR) of developing solid cancer was evaluated by using the mathematical relationships from the Radiation Effects Research Foundation (RERF) studies and those from the HotSpot code. The methodology consists of using output data obtained from simulations performed with the HotSpot health physics code plugging in such numbers into a specific given equation used by RERF to evaluate the resulting impact. Such a preliminary procedure is expected to facilitate the decision-making process significantly.

Cost-effective approach to lung cancer risk for a radiological dispersal device (RDD) scenario

Costa Karolina P. S., Lima Sergio X., Brum Tercio, Lima Zelmo R., Amorim Jose C. C., Healy Matthew J. F., Vital Helio C., Prah Matjaž, Andrade Edson R.

Nukleonika

2019

Vol. 64, No. 4

123--129

A release of radioactive material into the environment can lead to hazardous exposure of the population and serious future concerns about health issues such as an increased incidence of cancer. In this context, a practical methodology capable of providing useful basic information from the scenario can be valuable for immediate decisions and future risk assessment. For this work, the simulation of a radiological dispersal device (RDD) filled with americium-241 was considered. The radiation dose simulated by the HotSpot code was used as an input to the epidemiological equations from BEIR V producing the data used to assess the risk of lung cancer development. The methodology could be useful in providing training for responders aimed to the initial support addressed to decision-making for emergency response at the early phase of an RDD scenario. The results from the simulation allow estimating (a) the size of the potentially affected population, (b) the type of protection action considering gender and location of the individuals, (c) the absorbed doses, (d) the matrix of lung cancer incidence predictions over a period of 5 years, and (e) the cost-effectiveness in the initial decision environment.