G.R. Hundley, W.S. Charlton, K. Childress, “Determining Acquisition Pathways for a Radiological Dispersal Device”, 51st Annual Meeting for the Institute of Nuclear Materials Management, Baltimore, Maryland, July 11-15, 2010.
It remains unlikely that a terrorist organization could produce or procure an actual nuclear weapon; however, the construction of a radiological dispersal device (RDD) from commercially produced radioactive sources and conventional explosives could inflict moderate human casualties and significant economic damage. The vast availability of radioactive sources and the nearly limitless methods of dispersing them demand an inclusive study of the acquisition pathways for an RDD. A complete network depicting the possible acquisition pathways for an RDD could be subjected to predictive modeling in order to determine the most likely pathway an adversary might take. This tool could also allow for more focused intelligence collection as well as a better understanding of an adversary’s activities from signatures identified in the network. The goal of this research was to create a complete visual network that detailed all the possible pathways that an adversary might take to achieve the dispersal of radioactive material in a terrorist attack. This network is split into four different sections of pathways: adversary motivations, radioactive material acquisition, weaponization, and assembly and dispersal. Radioactive material acquisition considers sources present in industrial and medical fields, as well as the possibility of an organization legitimately purchasing a commercially available source or interdicting one during transportation. Weaponization pathways characterize sources as externally or internally dangerous, and both physical and chemical means of source processing are considered. Assembly and dispersal pathways include explosive type, as well as delivery and detonation methods. Future work will perform predictive modeling and allow user adjustment of node importance to provide a practical tool that evolves with real-time intelligence.