Radioactive Contaminanant Transport in Subsurface Porous Environment

  • An Jin
  • Shoou-Yuh Chang
Conference paper


The deterministic model is developed based on the discrete advection-dispersion-adsorption equations with radioactive decay mechanism. The data assimilation scheme is designed to utilize the Kalman Filter (KF) to incorporate the knowledge of uncertainties in both the model and the measurement. Spatially correlated regional noise structures are proposed and integrated into a data assimilation scheme. This model demonstrates that the data assimilation scheme reduces the uncertainty and predicts more accurately than a deterministic model. Through absorbing information from observation, the predictive plumes of radioactive contamination from the assimilation system can follow the change of a randomized irregular plume shape in the real world more closely than a non-assimilation deterministic model.


Root Mean Square Error Kalman Filter Data Assimilation Deterministic Model Retardation Factor 
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  1. Brooker, P.I. (2001) “Modelling spatial variability using soil profiles in the Riverland of South Australia.” Environmental International, 27(2001), 121–126.CrossRefGoogle Scholar
  2. Chang, S.-Y. and Jin, A. (2005). “Kalman filtering with regional noise to improve accuracy of contaminant transport models." Journal of Environmental Engineering, 131(6), 971–982.CrossRefGoogle Scholar
  3. Conwell, P.M., Silliman, S.E. and Zheng, L. (1997). “Design of a piezometer network for estimation of the variogram of the hydraulic gradient: The role of the instrument.” Water Resources Research, 33(11), 2489–2492.CrossRefGoogle Scholar
  4. Ferreyra, R.A., Apezteguia, H.P., Sereno, R. and Jones, J.W. (2002) “Reduction of soil sampling density using scaled semivariograms and simulated annealing.” Geoderma, 110(2002), 265–289.CrossRefGoogle Scholar
  5. Goovaerts, P. (1999) “Geostatistics in soil science: state-of-the-art and perspectives.” Geoderma, 89(1999), 1–45.CrossRefGoogle Scholar
  6. Heuvelink, G.B.M. and Webster, R. (2001) “Modelling soil variation: past, present, and future.” Geoderma, 100(2001), 269–301.CrossRefGoogle Scholar
  7. Kalman, R.E., and Bucy, R.S. (1961) “New results in linear filtering and prediction theory.” Trans. ASME, Series D, Journal of Basic Engineering, 83, 95–108.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • An Jin
    • 1
  • Shoou-Yuh Chang
    • 1
  1. 1.North Carolina A&T State UniversityGreensboroUSA

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