Automatic real-time interpolation of radiation hazards: a prototype and system architecture considerations

Detecting and monitoring the development of radioactive releases in the atmosphere is important. In many European countries monitoring networks have been established to perform this task. In the Netherlands the National Radioactivity Monitoring network (NRM) was installed. Currently, point maps are used to interpret the data from the NRM. Automatically generating maps in realtime would improve the interpretation of the data by giving the user a clear overview of the present radiological situation and provide an estimate of the radioactivity level at unmeasured locations. In this paper we present a prototype system that automatically generates real-time maps of radioactivity levels and presents results in an interoperable way through a Web Map Service. The system defines a first step towards a emergency management system and is suited primarily for data without large outliers. The automatic interpolation is done using universal kriging in combination with an automatic variogram fitting procedure. The focus is on mathematical and operational issues and on architectural considerations on how to improve the interoperability and portability of the prototype system.

[1]  B. J. de Haan,et al.  Mesoscale air pollution dispersion modelling , 1983 .

[2]  R. Smetsers,et al.  Source-Dependent Probability Densities Explaining Frequency Distributions of Ambient Dose Rate in the Netherlands , 1997 .

[3]  F. de Vries,et al.  Bodemkaart van Nederland 1:250.000 : beknopte beschrijving van de kaarteenheden , 1985 .

[4]  Edzer Pebesma,et al.  Mapping Radioactivity from Monitoring Data , 2005 .

[5]  Grégoire Dubois,et al.  Mapping Radioactivity in the Environment. Spatial Interpolation Comparison 97. , 2003 .

[6]  G. vanRossum Python reference manual , 1995 .

[7]  N. V. Egmond,et al.  Mesoscale air pollution dispersion models—II. Lagrangian puff model and comparison with Eulerian GRID model , 1983 .

[8]  D. Gregoire Automatic Mapping Algorithms for Routine and Emergency Monitoring Data , 2005 .

[9]  W. A. Jennings Quantities and units in radiation protection dosimetry , 1994 .

[10]  Massimo Craglia,et al.  Introduction to the International Journal of Spatial Data Infrastructures Research , 2006, Int. J. Spatial Data Infrastructures Res..

[11]  Grégoire Dubois,et al.  Introduction to the Spatial Interpolation Comparison (SIC) 2004 Exercise and Presentation of the Datasets , 2005 .

[12]  J. Chilès,et al.  Geostatistics: Modeling Spatial Uncertainty , 1999 .

[13]  Edzer J. Pebesma,et al.  Multivariable geostatistics in S: the gstat package , 2004, Comput. Geosci..

[14]  Guido Rossum,et al.  Python Reference Manual , 2000 .

[15]  R. Christensen Linear Models for Multivariate, Time Series, and Spatial Data , 1997 .

[16]  Robert Haining,et al.  Statistics for spatial data: by Noel Cressie, 1991, John Wiley & Sons, New York, 900 p., ISBN 0-471-84336-9, US $89.95 , 1993 .

[17]  Shashi Shekhar,et al.  UMN-MapServer: A High-Performance, Interoperable, and Open Source Web Mapping and Geo-spatial Analysis System , 2006, GIScience.

[18]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .

[19]  Felix Famoye,et al.  Plane Answers to Complex Questions: Theory of Linear Models , 2003, Technometrics.

[20]  Ronald Christensen,et al.  Plane Answers to Complex Questions: The Theory of Linear Models , 1989 .