The paper presents a methodology for prompt mapping of radioactively contaminated areas. The efficiency of obtaining cartographic information is achieved by using correlation dependences between the characteristics of radioactive contamination obtained during various radiological surveys of contaminated areas. The method of spatial interpolation based on regression-kriging is used to formalize the data on radioactive contamination. This method allows combining the information resulting from direct measurements of density of territory contamination by radionuclides we are interested in with the information contained in other characteristics of radioactive contamination received at the surveyed area (for example, ER). Such an approach allows more accurate mapping of 137Cs deposition density and concentration of its activity in the root layer of soil, as well as significant reduction of the time and cost to survey the mapped area (sampling, sample preparation and measurement of samples). This, in turn, accelerates and reduces the cost for mapping of radioactively contaminated territories (fields, lands, sites). The methodology has been tested on the fields contaminated by radionuclides resulting from the Chornobyl accident in the Narodychi, Polissia and Ivanivka Districts and has proved its performance and efficiency. Comparison and analysis of the obtained maps of radioactive contamination of the territory show that in the conditions of limited amount of data on direct measurements and time limit, the use of correlation dependences between the characteristics of radioactive contamination of soil is often the only possible way to increase the information content and accuracy of the obtained cartographic information.
2. Labunska, I., Kashparov, V., Levchuk, S., Santillo, D., Johnston, P., Polishchuk, S., Lazarev, N., Khomutinin, Yu. (2018). Current radiological situation in areas of Ukraine contaminated by the Chornobyl accident: Part 1. Human dietary exposure to Caesium-137 and possible mitigation measures. Environment International, 117, 250-259. Retrieved from https://doi.org/10.1016/j.envint.2018.04.053.
3. Maltsev, K., Mukharamova, S. (2014). Creation of spatial variable models (using Surfer package). Kazan, Kazan University, 103.
4. Demianov, V., Savelieva, E. (2010). Geostatistics: Theory and Practice. Institute for Problems of Safe Development of Nuclear Energy, Russian Academy of Sciences, Moscow, Nauka, 327.
5. Hengl, T., Heuvelink, G., Stein, A. (2004). A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, 122 (1-2), 75-93. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.699.7306&rep=rep1&type=pdf
6. Hengl, T., Heuvelink, G., Rossiter, D. (2007). About regression-kriging: From equations to case studies. Computers & Geosciences, 33, 1301-1315. Retrieved from
7. Khomutinin, Yu., Kashparov, V., Zhebrovska, E. (2001). Optimization of sampling and sample measurement in radioecological monitoring. Kyiv, VIPOL, 160.
8. Khomutinin, Yu. (2003). Optimization of sampling in assessing density of radioactive fallout. Collection of Scientific Works of the Institute of Nuclear Research, 1(9), 145-155.
9. Khomutinin, Yu., Levchuk, S., Pavliuchenko, V. (2016). Optimization of soil sampling when mapping the density of radioactive fallout. Bulletin of Zhytomyr University, 3, 74-84.
10. Khomutinin, Yu., Glukhovsky, O., Protsak, V., Kashparov, V., Levchuk, S., Pavliuchenko, V. (2018). Mapping of Spots of Radioactive Contamination. Nuclear and Radiation Safety, 2(78), 35-40. Retrieved from https://www.sstc.com.ua/documents/journal/2018/2/Texts/8_2_2018_text.pdf