• Evaluation of Geostatistical Approaches for better Estimation of Polluted Soil Volume with Uncertainty Evaluation
  • Kim, Ho-Rim;Kim, Kyoung-Ho;Yun, Seong-Taek;Hwang, Sang-Il;Kim, Hyeong-Don;Lee, Gun-Taek;Kim, Young-Ju;
  • KU-KIST Green School (Graduate School of Energy and Environment) and Department of Earth and Environmental Sciences, Korea University;KU-KIST Green School (Graduate School of Energy and Environment) and Department of Earth and Environmental Sciences, Korea University;KU-KIST Green School (Graduate School of Energy and Environment) and Department of Earth and Environmental Sciences, Korea University;Korea Environment Institute (KEI);National Instrumentation Center for Environmental Management College of Agriculture and Life Sciences, Seoul National University;National Instrumentation Center for Environmental Management College of Agriculture and Life Sciences, Seoul National University;Korea Environment Institute (KEI);
  • 지구통계 기법을 활용한 토양 오염범위 산정 및 불확실성 평가
  • 김호림;김경호;윤성택;황상일;김형돈;이군택;김영주;
  • 고려대학교 그린스쿨대학원(KU-KIST) 및 지구환경과학과;고려대학교 그린스쿨대학원(KU-KIST) 및 지구환경과학과;고려대학교 그린스쿨대학원(KU-KIST) 및 지구환경과학과;한국환경정책.평가연구원;서울대학교 농생명과학공동기기원(NICEM);서울대학교 농생명과학공동기기원(NICEM);한국환경정책.평가연구원;
Abstract
Diverse geostatistical tools such as kriging have been used to estimate the volume and spatial coverage of contaminated soil needed for remediation. However, many approaches frequently yield estimation errors, due to inherent geostatistical uncertainties. Such errors may yield over- or under-estimation of the amounts of polluted soils, which cause an over-estimation of remediation cost as well as an incomplete clean-up of a contaminated land. Therefore, it is very important to use a better estimation tool considering uncertainties arising from incomplete field investigation (i.e., contamination survey) and mathematical spatial estimation. In the current work, as better estimation tools we propose stochastic simulation approaches which allow the remediation volume to be assessed more accurately along with uncertainty estimation. To test the efficiency of proposed methods, heavy metals (esp., Pb) contaminated soil of a shooting range area was selected. In addition, we suggest a quantitative method to delineate the confident interval of estimated volume (and spatial extent) of polluted soil based on the spatial aspect of uncertainty. The methods proposed in this work can improve a better decision making on soil remediation.

Keywords: Remediation of polluted soils;Heavy metals;Geostatistical approaches;Estimation of spatial extent and volume of polluted soil;Uncertainty estimation;

References
  • 1. Broos, M.J., Aarts, L., van Tooren, C.F., and Stein, A., 1999, Quantification of the effects of spatially varying environmental contaminants into a cost model for soil remediation, Journal of Environmental Management, 56(2), 133-145.
  •  
  • 2. Choe, J.G., 2007, Geostatistics, Sigma-press. 386p.
  •  
  • 3. Deutsch, C.V. and Journel, A.G., 1998, GSLIB: Geostatistical Software Library and Useris Guide, Oxford University Press. 369 p.
  •  
  • 4. Delbari, M. Afrasiaba, P., and Loiskandlb, W., 2009, Using sequential Gaussian simulation to assess the field-scale spatial uncertainty of soil water content, Catena, 79(2), 163-169.
  •  
  • 5. Demougeot-Renard H, 2004, Geostatistical approach for assessing soil volumes requiring remediation: Validation using leadpolluted soils underlying a former smelting works, Environmental science technology, 38(19), 5120-5126.
  •  
  • 6. D'Or, D., Demougeot-Renard, H., and Garcia, M., 2009, An Integrated Geostatistical Approach for Contaminated Site and Soil Characterization, Mathematical Geosciences, 41(3), 307-322.
  •  
  • 7. Flatman, G., 1984, Geostatistical strategy for soil sampling: The survey and the census, Environmental Monitoring and Assessment, 4(4), 335-349.
  •  
  • 8. Goovaerts, P. and Journel, A.G., 1995, Integrating soil map information in modelling the spatial variation of continuous soil properties, European Journal of Soil Science, 46, 397-414.
  •  
  • 9. Goovaerts, P., 1997, Geostatistics for Natural Resources Evaluation, Oxford University Press, New York. 483 p.
  •  
  • 10. Goovaerts, P., 1999, Geostatistics in soil science: state-of-the-art and perspectives, Geoderma, 89(1-2), 1-45.
  •  
  • 11. Goovaerts, P., 2001, Geostatistical modelling of uncertainty in soil science, Geoderma, 103(1-2), 3-26.
  •  
  • 12. Isaaks, E.H. and Srivastava, R.M., 1989, An Introduction to Applied Geostatistics. Oxford University Press, New York, 560 p.
  •  
  • 13. Juang, K.W., Chen, Y.S., and Lee, D.Y., 2004, Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils, Environmental Pollution, 127, 229-238.
  •  
  • 14. Jung, H,S., Yun, S.T., Choi, B.Y., Kim, H., Jung, M.C., Kim, S.O., and Kim, K.H., 2010, Geochemical studies on the contamination and dispersion of trace metals in intertidal sediments around a military air weapons shooting range, Journal of Soils and Sediments 10, 1142-1158.
  •  
  • 15. Lee, G.T., Kim, H.D., Kang, J.Y., Han, H.D., Choi, C.I., and Kim, Y.H., 2005, Investigation report of soil contamination [00 shooting range].
  •  
  • 16. McKenna, S.A., 1998, Geostatistical approach for managing uncertainty in environmental remediation of contaminated soils: case study, Environmental and Engineering Geoscience, 4, 175-184.
  •  
  • 17. Oliver, M.A. and Webster, R., 1986, Semi-variograms for modelling the spatial pattern of landformand soil properties, Earth Surface Processes and Landforms, 11, 491-504.
  •  
  • 18. Papritz, A., Herzig, C., Borer, F., and Bono, R., 2005, Modelling the spatial distribution of copper in the soils around a metal smelter in northwestern Switzerland, Geostatistics for Environmental Applications, Springer-Verlag, Berlin Heidelberg (2005), pp. 343-354.
  •  
  • 19. Park, N.U., 2010, Application of Indicator Geostatistics for Probabilistic Uncertainty and Risk Analyses of Geochemical Data, The Journal of The Korean Earth Science Society, 31(4), 301-312.
  •  
  • 20. Rautman, C.A., 1997, Geostatistics and cost-effective environmental remediation. In: Baafi, E.Y., Schofield, N.A. (Eds.), Geostatistics Wollongong '96. Kluwer Academic Publishers, Dordrecht, pp. 941-950.
  •  
  • 21. Remy, N., Boucher, A., and Wu, J., 2009, Applied Geostatistics with SGeMS: A User's Guide, Cambridge University Press. 286 p.
  •  
  • 22. Steiger, B.V., Webster, R., Schulin, R., and Lehmann, R., 1996, Mapping heavy metals in polluted soil by disjunctive kriging, Environmental Pollution, 94(2), 205-215.
  •  
  • 23. Stewart, R.N. and Purucker, S.T., 2011, An environmental decision support system for spatial assessment and selective remediation, Environ Model Software, 26, 751-760.
  •  
  • 24. Tanskanen, H., Kukkonen, I., and Kaija, J., 1991, Heavy metal pollution in the environment of a shooting range. Geological Survey of Finland, Special Paper 12, 187-193.
  •  
  • 25. Webster, R. and Oliver, M.A., 2007, Geostatistics for environmental scientists, Wiley. 315p.
  •  

This Article

  • 2012; 17(6): 69-81

    Published on Dec 31, 2012

  • 10.7857/JSGE.2012.17.6.069
  • Received on Nov 9, 2012
  • Revised on Dec 3, 2012
  • Accepted on Dec 3, 2012