Optimization of mobile radioactivity monitoring networks

Abstract

In case of a nuclear accident, decision makers rely on high resolution and accurate information about the spatial distribution of the radioactivity levels in the surroundings of the accident site. Static nuclear monitoring networks are therefore employed in many countries in Europe. However, these networks were designed to cover the whole country and are usually too course to reach a high density in the local environment around the accident site. Therefore a strategy is considered in which the measurement density is increased during emergencies by adding measurements from mobile measuring devices. This raises the question where the mobile devices should be placed. This paper proposes a geostatistical methodology to optimize the allocation of the mobile devices, such that the expected weighed sum of false negative and false positive areas, i.e., false classification into safe and unsafe zones is minimized. The radioactivity concentration was modelled as the sum of a deterministic trend and a zero-mean spatially correlated stochastic residual, whereby the deterministic trend was defined as the outcome of a spatially explicit physical atmospheric dispersion model (NPK-PUFF model). The NPK-PUFF model used meteorological data and the characteristics of the radioactive release as input. The residual was characterized by a semivariogram that was estimated from the differences between outputs from various NPK-PUFF runs with input settings reflecting the uncertainty in the NPK-PUFF inputs (e.g., wind speed, wind direction). Spatial simulated annealing was used to obtain the optimal monitoring design, whereby accessibility and openness of sampling sites was also included. The method was computationally demanding but results were promising and the computational speed may be considerably improved to compute the optimal monitoring network in nearly real-time.

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