Skip to main content
Log in

A Stochastic Advection-Diffusion Model for the Rocky Flats Soil Plutonium Data

  • Published:
Annals of the Institute of Statistical Mathematics Aims and scope Submit manuscript

Abstract

An advection-diffusion equation with time and space dependent random coefficients is derived as a model for the plutonium concentration changes in the surface soil around the Rocky Flats Plant northwest of Denver, Colorado. The equation is used to fit a set of temporal-spatial data sampled annually over a 23 year period from 71 sites around the plant. The coefficients of the advection-diffusion equation are derived from the estimated covariance function of the observed random field using a combination of maximum likelihood and quasi-likelihood techniques. Goodness-of-fit of the model to the data is also assessed. Finally we interpret the model in terms of the advection-diffusion mechanism.

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

  • Anderson, T. W. (1958). An Introduction to Multivariate Statistical Analysis, Willey, New York.

    Google Scholar 

  • Box, G. E. P. and Jenkins, G. M. (1970). Time Series Analysis, Forecasting and Control, Holden Day, San Francisco.

    Google Scholar 

  • Farrell, D. A., Woodbury, A. D., Sudicky, E. A. and Rivett, M. O. (1994). Stochastic and deterministic analysis of dispersion in unsteady flow at the Borden Tracer-Test site, Journal of Contaminant Hydrology, 15, 159–185.

    Google Scholar 

  • Grenander, U. and Rosenblatt, M. (1984). Statistical Analysis of Stationary Time Series, Chelsea, New York.

    Google Scholar 

  • Guttorp, P. (1994). Comment to a paper by Handcock M. S. and Wallis J. R., J. Amer. Statist. Assoc., 89, 382–384.

  • Heyde, C. C. and Gay, R. (1993). Smoothed periodogram asymptotics and estimation for processes and fields with possible long-range dependence, Stochastic Process. Appl., 45, 169–182.

    Google Scholar 

  • Huebner, M. and Rozovskii, B. L. (1995). On asymptotic properties of maximum likelihood estimators for parabolic stochastic PDE's, Probab. Theory Related Fields, 103, 143–163.

    Google Scholar 

  • Itô, K. (1984). Foundations of Stochastic Differential Equations in Infinite Dimensional Spaces, Society for Industrial and Applied Mathematics, Philadelphia, Pennsylvania.

    Google Scholar 

  • Itô, K. and Kunisch, K. (1990). The augmented Lagrangian method for parameter estimation in elliptic systems, SIAM J. Control Optim., 28, 113–136.

    Google Scholar 

  • Jones, R. H. and Vecchia, A. V. (1993). Fitting Continuous ARMA models to unequally spaced spatial data, J. Amer. Statist. Assoc., 88, 947–954.

    Google Scholar 

  • Jones, R. H. and Zhang, Y. (1994). Spatial and temporal analysis of the Rocky Flats Soil Plutonium Data, Report, Colorado Department of Public Health and Environment, Colorado.

    Google Scholar 

  • Jones, R. H. and Zhang, Y. (1997). Models for continuous stationary space-time processes, Modeling Longitudinal and Spatially Correlated Data: Methods, Applications and Future Directions (eds. T. G. Gregoire, P. Bickel, P. Diggle, S. Fienberg, K. Krickeberg, I. Olkin, N. Wermuth and S. Zeger), Lecture Notes in Statist., 122, 289–298, Springer, New York.

    Google Scholar 

  • Kallianpur, G. and Xiong, J. (1995). Stochastic Differential Equations in Infinite Dimensional Spaces, IMS Lecture Notes—Monograph Series, Vol. 26, Hayward, California.

    Google Scholar 

  • Karatzas, I. and Shreve, S. E. (1988). Brownian Motion and Stochastic Calculus, Springer, New York.

    Google Scholar 

  • Kwakernaak, H. (1974). Filtering for systems excited by Poisson white noise, Lecture Notes in Econom. and Math. Systems, 107, 468–492.

  • Lamm, P. (1992). Parameter estimation for distributed equations in parameter-dependent state spaces: Application to shape identification, SIAM J. Control Optim., 30, 894–925.

    Google Scholar 

  • Lindsay, B. G. (1988). Composite likelihood methods, Contemp. Math., 80, 221–239.

    Google Scholar 

  • Matérn, B. (1986). Spatial Variation, 2nd ed., Lecture Notes in Statist., No. 36, Springer, Berlin.

    Google Scholar 

  • Mohapl, J. (1994). Maximum likelihood estimation in linear infinite dimensional models, Comm. Statist. Stochastic Models, 10, 781–794.

    Google Scholar 

  • Mohapl, J. (1998). Discrete sample estimation for Gaussian random fields generated by stochastic partial differential equations, Comm. Statist. Stochastic Models, 14, 883–903.

    Google Scholar 

  • Mohapl, J. (1999). On estimation in random fields generated by linear stochastic partial differential equations, Mathematica Slovaca, 49, 95–115.

    Google Scholar 

  • Omatu, S. (1984). Estimation theory in Hilbert spaces and its applications, Advances in Probability and Related Topics, Vol. 7 (ed. M. Pinsky), 367–410, Marcel Dekker, Inc., New York.

    Google Scholar 

  • Piterbarg, L. I. and Ostrovskii, G. A. (1997). Advection and Diffusion in Random Media, Implications for Sea Surface Temperature Anomalies, Kluwer, Dordrecht.

    Google Scholar 

  • Press, W. H., Flannery, B. P., Teukolsky, S. A. and Vetterling, W. T. (1986). Numerical Recipes in FORTRAN: The Art of Scientific Computing, 1st ed., Cambridge University Press.

  • Sudicky, E. A., Cherry, J. A. and Frind, E. O. (1983). Migration of contaminants in groundwater at a landfill, A case study, Journal of Hydrology, 63, 81–108.

    Google Scholar 

  • Unny, T. E. (1988). Stochastic partial differential equations in groundwater hydrology, Stochastic Structural Dynamics, Proceedings of the Symposium held at the University of Illinois at Urbana Champaign October 30–November 1, 1988 (eds. N. S. Namachchivaya, H. H. Hilton and Y. K. Wen).

  • Vomvoris, E. G. and Gelhar, L. W. (1986). Stochastic prediction of dispersive contaminant transport, Tech. Report, No. EPA/600/2-86/114 of the Civil Engineering Department, Massachusetts Institute of Technology, Cambridge (reproduced by the U. S. Department of Commerce, National Technical Information Service).

    Google Scholar 

  • Walsh, J. B. (1986). An introduction to stochastic partial differential equations, Lecture Notes in Math., 1180, 266–437.

  • Warnes, J. J. and Ripley, B. D. (1987). Problems with likelihood estimation of covariance functions of spatial Gaussian processes, Biometrika, 74, 640–642.

    Google Scholar 

  • Whittle, P. (1954). On stationary processes in the plane, Biometrika, 41, 434–439.

    Google Scholar 

  • Whittle, P. (1962). Topographic correlation, power law covariance functions and diffusion, Biometrika, 49, 305–314.

    Google Scholar 

  • Zhang, Y. (1995). Autoregressive models for continuous stationary space-time processes, Ph.D. Thesis, Department of Preventive Medicine and Biometrics, Graduate School of the University of Colorado.

Download references

Author information

Authors and Affiliations

Authors

About this article

Cite this article

Mohapl, J. A Stochastic Advection-Diffusion Model for the Rocky Flats Soil Plutonium Data. Annals of the Institute of Statistical Mathematics 52, 84–107 (2000). https://doi.org/10.1023/A:1004137016101

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1004137016101

Navigation