A simulation nonlinear multiple-regression methodology for estimating parameters that characterize the transport of contaminants is developed and demonstrated. Finite difference containment transport simulation is combined with a nonlinear weighted least squares multiple-regression procedure. The technique provides optimal parameter estimates and gives statistics for assessing the reliability of these estimates under certain general assumptions about the distributions of the random measurement errors. Monte Carlo analysis is used to estimate parameter reliability for a hypothetical homogeneous soil column for which concentration data contain large random measurement errors. The value of data collected spatially versus data collected temporally was investigated for estimation of velocity, dispersion coefficient, effective porosity, first-order decay rate, and zero-order production. The use of spatial data gave estimates that were 2-3 times more reliable than estimates based on temporal data for all parameters except velocity. (Estimated author abstract) Refs.
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STATISTICAL METHODOLOGY FOR ESTIMATING TRANSPORT PARAMETERS: THEORY AND APPLICATIONS TO ONE-DOMENSIONAL ADVECTIVE-DISPERSIVE SYSTEMS.