Predicting DNAPL mass discharge and contaminated site longevity probabilities: Conceptual model and high‐resolution stochastic simulation

Abstract

Improper storage and disposal of nonaqueous-phase liquids (NAPLs) has resulted in widespread contamination of the subsurface, threatening the quality of groundwater as a freshwater resource. The high frequency of contaminated sites and the difficulties of remediation efforts demand rational decisions based on a sound risk assessment. Due to sparse data and natural heterogeneities, this risk assessment needs to be supported by appropriate predictive models with quantified uncertainty. This study proposes a physically and stochastically coherent model concept to simulate and predict crucial impact metrics for DNAPL contaminated sites, such as contaminant mass discharge and DNAPL source longevity. To this end, aquifer parameters and the contaminant source architecture are conceptualized as random space functions. The governing processes are simulated in a three-dimensional, highly resolved, stochastic, and coupled model that can predict probability density functions of mass discharge and source depletion times. While it is not possible to determine whether the presented model framework is sufficiently complex or not, we can investigate whether and to which degree the desired model predictions are sensitive to simplifications often found in the literature. By testing four commonly made simplifications, we identified aquifer heterogeneity, groundwater flow irregularity, uncertain and physically based contaminant source zones, and their mutual interlinkages as indispensable components of a sound model framework.

Identification of contaminant source architectures—A statistical inversion that emulates multiphase physics in a computationally practicable manner

“…Second, we generate corresponding random CSAs for each conditional permeability field via a fast stochastic invasion percolation algorithm (SIP) based on Ewing and Berkowitz [] as described in Koch and Nowak []. Considering that DNAPL percolation is unstable process with strong dependency on pore‐scale properties of the aquifer and that we generate only Darcy‐scale permeability fields, the CSA formation in each conditional permeability field possess an intrinsic remaining stochasticity that requires a stochastic simulation approach.…”

“…This has been demonstrated in various studies, e.g., for mass discharge and source depletion time by Lemke and Abriola [], Christ et al . [], and Koch and Nowak []. Hence, it is a major concern to increase the prediction confidence of such uncertain simulations via assimilating field data with appropriate inverse methods.…”

“…CSA inference is a nonunique, nonlinear, and high‐dimensional inverse problem. It is high‐dimensional because the involved unknowns are the entire three‐dimensional distribution of DNAPL saturation within the source zone, and finely resolved three‐dimensional fields of aquifer parameters need to be included as indispensable covariates [ Koch and Nowak , ]. The data available for inversion may comprise a variety of data types such as soil properties, hydraulic heads, groundwater velocities, locations of known pure‐phase contaminant residence, and dissolved‐phase concentration values.…”

“…However, these studies assume statistical independence between the proposed source functions and aquifer properties, i.e., they neglect the link between multiphase physics and aquifer heterogeneity. The strongly negative effect of this and other commonly applied simplifications of the source zone geometry on mass discharge and depletion time predictions has been demonstrated in Koch and Nowak [].…”

Entropy-Based Experimental Design for Optimal Model Discrimination in the Geosciences

“…Still, TCE is soluble in water to a small extent. Therefore, the pool releases dissolved TCE at small concentrations over a long time, and this is a source of contamination to the surrounding groundwater [88,89].…”