Contaminant discharge and uncertainty estimates from passive flux meter measurements

Abstract

The passive flux meter (PFM) measures local cumulative water and contaminant fluxes at an observation well. Conditional stochastic simulation accounting for both spatial correlation and data skewness is introduced to interpret passive flux meter observations in terms of probability distributions of discharges across control planes (transects) of wells. An estimator of the effective number of independent data is defined and applied in the development of two significantly simpler approximate methods for estimating discharge distributions. One method uses a transformation of the t statistic to account for data skewness and the other method is closely related to the classic bootstrap. The approaches are demonstrated with passive flux meter data from two field sites (a trichloroethylene [TCE] plume at Ft. Lewis, WA, and a uranium plume at Rifle, CO). All methods require that the flux heterogeneity is sufficiently represented by the data and maximum differences in discharge quantile estimates between methods are ∼7%.