(eng): The task of decision support in the case of a radiation accident is to provide up-to-date information on the radiation situation, prognosis of its future evolution and possible consequences. The reliability of predictions can be significantly improved using data assimilation, which refers to a group of mathematical methods for an efficient combination of observed data with a numerical model. This paper concerns application of advanced data assimilation methods in the off-site consequence assessment when radionuclides are released into the atmosphere. Our goal is to develop a decision support system with automated real-time assessment of radiation situation that does not underestimate its uncertainty. To achieve this goal we employ particle filtering − a Bayesian approach suitable for sequential processing of a stream of measurements incoming from a monitoring network.