Groundwater Modeling with Nonlinear Uncertainty Analyses to Enhance Remediation Design Confidence |
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| Authors: | Martinus Brouwers Paul J Martin Daron G Abbey Jeremy White |
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| Affiliation: | 1. Matrix Solutions Inc., Guelph, ON, Canada;2. GNS Science, Taupo, New Zealand |
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| Abstract: | Soil and groundwater contamination are often managed by establishing on‐site cleanup targets within the context of risk assessment or risk management measures. Decision‐makers rely on modeling tools to provide insight; however, it is recognized that all models are subject to uncertainty. This case study compares suggested remediation requirements using a site‐specific numerical model and a standardized analytical tool to evaluate risk to a downgradient wetland receptor posed by on‐site chloride impacts. The base case model, calibrated to observed non‐pumping and pumping conditions, predicts a peak concentration well above regulatory criteria. Remediation scenarios are iteratively evaluated to determine a remediation design that adheres to practical site constraints, while minimizing the potential for risk to the downgradient receptor. A nonlinear uncertainty analysis is applied to each remediation scenario to stochastically evaluate the risk and find a solution that meets the site‐owner risk tolerance, which in this case required a risk‐averse solution. This approach, which couples nonlinear uncertainty analysis with a site‐specific numerical model provides an enhanced level of knowledge to foster informed decision‐making (i.e., risk‐of‐success) and also increases stakeholder confidence in the remediation design. |
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