Skip to main content
Log in

Scaling model for prediction of radionuclide activity in contaminated soils using a regression triplet technique

  • Published:
Journal of Radioanalytical and Nuclear Chemistry Aims and scope Submit manuscript

Abstract

A scaling model was built to calculate the activity of alpha emitting radionuclides in contaminated soil in the lysimeter field. Linear regression can be applied for the evaluation of radioactivity measurement data. Activities of the radionuclides 241Am, 238Pu, 239,240Pu and 90Sr obtained by experiments from real contaminated soils of the experimental lysimeter placed in a nuclear power plant (NPP) in Slovakia were evaluated using linear regression models with the method of least squares. A suitable scaling model for monitoring the 241Am, 238Pu, 239,240Pu alpha radionuclide activity was built using the regression triplet analysis and regression diagnostics. A regular designed scaling model opens the possibilities of longtime activity monitoring of these radionuclides, thus decreasing the number of necessary radiochemical analyses. The Fisher-Snedecor test, however, confirmed that the regression model for 90Sr activity monitoring by 241Am, 239,240Pu activity determination in contaminated soils can not be recommended.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Strategy and Methodology for Radioactive Waste Characterization, IAEA-TECDOC-1537, ISBN 92-0-100207-6, IAEA, Vienna, 2007.

  2. IAEA Technical Report Series Nr. 462, Managing of Low Radioactivity Material from the Decommissioning of Nuclear Facilities, IAEA Vienna, 2008.

  3. X. Hou, P. Roos, Critical Comparison of Radiometric and Mass Spectrometric Methods for the Determination of Radionuclides in Environmental, Biological and Nuclear Waste Samples, Postprint, 2007, http://www.risoe.dk/rispubl/art/2007_333.pdf.

  4. K.S. Redus, et al., Scaling Ratios Produce Misleading Results in D&D Projects, WM’05 Conference, Tucson, February 27–March 3, 2004.

  5. ISO 21238:2007 Nuclear Energy-Nuclear Waste-Standard Guide for the Scaling Factor Method to Determine the Radioactivity of Low and Intermediate Level Radioactive Waste Packages Generated at Nuclear Power Plant (LWR), International Standard.

  6. Updated Scaling Factors in Low-Level Radwaste, EPRI NP-5077, Project 1557-6, Final Report, 1987.

  7. Á. Vincze, I. Gresits, S. Tölgyesi, E. Erdős, J. Solymosi P. Ormai, A. Fritz, Application of the Scaling Technique for The Characterisation of Different Radioactive Waste at NPP Paks, Radiation Protection in Neighbouring Countries of Central Europe, Prague, Czech Republic, September 8–12, 1997.

  8. E. Hertelendi, Z. Szücs, J. Csongor, J. Gulyás, É. Svingo, P. Ormai, A. Fritz, J. Solymosi, I. Gresits, N. Vajda, Zs. Molnár, Application of Scaling Technique for Estimation of Radionuclide Inventory in Radioactive Waste. 3rd Regional Meeting Nuclear Energy in Central Europe. Portoroz, Slovenia, 16–19 September, 1996.

  9. D. Harker, Scaling Factor for Waste Activities Measured by G-M Method, ER-WAG7-57, INEL - 95/020.

  10. M. Noé, W. Müller, R. Gens, M. Gili, A. Morales, A. Yates, P. Ormai, Development of methods to provide an inventory of radiologically relevant radionuclides. Analytical methods and correlation of data. Final report (Contracts FI2W-CT90-0034 and FI2W-CT91-0109) EUR 17978, 1998, Euroffice.

  11. M. Meloun, J. Militký, M. Forina, Chemometrics for Analytical Chemistry, Vol. 2. PC-Aided Regression and Related Methods, Horwood, Chichester, 1994.

  12. D. A. Belsey, E. Kuh, R. E. Welsch, Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, Wiley, New York, 1980.

    Google Scholar 

  13. R. D. Cook, S. Weisberg, Residuals and Influence in Regression, Chapman & Hall, London, 1982.

    Google Scholar 

  14. A. C. Atkinson, Plots, Transformations and Regression: An Introduction to Graphical Methods of Diagnostic Regression Analysis, Claredon Press, Oxford, 1985.

    Google Scholar 

  15. S. Chatterjee, A. S. Hadi, Sensitivity Analysis in Linear Regression, Wiley, New York, 1988.

    Book  Google Scholar 

  16. V. Barnett, T. Lewis, Outliers in Statistical Data, 2nd Ed., Wiley, New York, 1984.

    Google Scholar 

  17. R. E. Welsch, Linear Regression Diagnostics, Technical Report 923-77, Sloan School of Management, Massachusetts Institute of Technology, 1977.

  18. R. E. Welsch, S. C. Peters, Finding Influential Subsets of Data in Regression Models, Proc. Eleventh Interface Symp. on Computer Science and Statistics, A. R. Gallant, T. M. Gerig, (Eds), Raleigh: Institute of Statistics, North Carolina State University, 1978.

    Google Scholar 

  19. S. Weisberg, Applied Linear Regression, Wiley, New York, 1985.

    Google Scholar 

  20. P. J. Rousseeuw, A. M. Leroy, Robust Regression and Outlier Detection, Wiley, New York, 1987.

    Book  Google Scholar 

  21. K. A. Brownlee, Statistical Theory and Methodology in Science and Engineering, Wiley, New York, 1965.

    Google Scholar 

  22. M. Meloun, J. Militký, M. Hill, R. G. Brereton, The Analyst, 127 (2002) 433.

    Article  CAS  Google Scholar 

  23. M. Meloun, J. Militký, Anal. Chim. Acta. 439 (2001) 169.

    Article  CAS  Google Scholar 

  24. D. X. Williams, Applied Statistics, 22 (1973) 407.

    Google Scholar 

  25. J. B. Gray, Graphics for Regression Diagnostics, Proc. of the Statistical Computing Section, Amer. Statist. Assoc., 1985, pp.102–107.

  26. ADSTAT (English version), TriloByte Statistical Software, Pardubice, 1999.

  27. R. D. Cook, S. Weisberg, Diagnostics for Heteroscedasticity in Regression, Biometrika, 70, 1983.

  28. I. Matušek, Kovacs, Evaluation of the results of lysimetric experiment during the period 2004, Ecosur Report, 2005.

  29. L. Mátel, V. Mikulaj, P. Rajec, J. Radioanal. Nucl. Chem., 175 (1993) 41.

    Article  Google Scholar 

  30. C. W. Sill, Nucl.Chem.Waste Managem., 7 (1987) 201.

    Article  CAS  Google Scholar 

  31. F. D. Hindman, Anal.Chim., 55 (1983) 2460.

    Article  CAS  Google Scholar 

  32. V. Mikulaj, V. Švec, J. Radioanal. Nucl. Chem., 175 (1993) 317.

    Article  CAS  Google Scholar 

  33. M. Afsar, H. Schuttelkopf, Determination of Am-241, Cm-244 in Environmental Samples, KfK 4346, 1988.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Dulanská.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dulanská, S., Meloun, M. & Mátel, L. Scaling model for prediction of radionuclide activity in contaminated soils using a regression triplet technique. J Radioanal Nucl Chem 280, 519–531 (2009). https://doi.org/10.1007/s10967-008-7419-9

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10967-008-7419-9

Keywords

Navigation