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Byline: Gregoire Dubois, Edzer J. Pebesma, Peter Bossew In the case of a severe nuclear accident, radionuclides may be released into the atmosphere and contaminate large areas. Radiological maps are obtained after converting local measurements into continuous information in space. Ideally, the mapping process should be fully automatic and provide information in real time. This paper is presenting the results obtained from two statistical exercises that addressed the issue of automating the spatial interpolation step both in routine and emergency situations. The first exercise addressed mainly the current state-of-the-art of spatial interpolation and explored the impact of human factors on the results obtained. The second exercise was dealing specifically with the automation issue. To further address the response of these mapping algorithms in emergency situations, simulated data have also been used to explore the impact of extreme values on the process. It is shown that, independently of the choice of algorithms, many obstacles still remain before we can rely on fully automatic mapping systems in emergency situations, especially during the early and critical stages of an accident when measurements on the contamination are sparse.

Delineation of polluted zones with respect to regulatory standards, accounting at the same time for the uncertainty of the estimated concentrations, relies on classification criteria that can lead to significantly different pollution risk maps, which, in turn, can depend on the regulatory standard itself. This paper reviews four popular classification criteria related to the violation of a probability threshold or a physical threshold, using annual (1996-2000) nitrogen dioxide concentrations from 40 air monitoring stations in Milan. The relative advantages and practical limitations of each criterion are discussed, and it is shown that some of the criteria are more appropriate for the problem at hand and that the choice of the criterion can be supported by the statistical distribution of the data and/or the regulatory standard. Finally, the polluted area is estimated over the different years and concentration thresholds using the appropriate risk maps as an additional source of uncertainty. [ABSTRACT FROM AUTHOR]