Automatic Mapping of Monitoring Data

This paper presents an approach, based on universal kriging, for automatic mapping of monitoring data. The performance of the mapping approach is tested on two datasets containing daily mean gamma dose rates in Germany reported by means of the national automatic monitoring network (IMIS). In the second dataset an accidental release of radioactivity in the environment was simulated in the South-Western corner of the monitored area. The approach has a tendency to smooth the actual data values, and therefore it underestimates extreme values, as seen in the second dataset. However, it is capable of identifying a release of radioactivity provided that the number of sampling locations is sufficiently high. Consequently, we believe that a combination of applying the presented mapping approach and the physical knowledge of the transport processes of radioactivity should be used to predict the extreme values.