Evaluation of intensive remediation using simulation-optimization modeling based on long-term monitoring at a DNAPL contaminated site.

Simulation-optimization modeling is extensively used to identify optimal remediation designs. However, verifying these optimal solutions often remains unclear. In this study, we determine optimal groundwater remediation strategies using simulation-optimization modeling and assess the effectiveness of previous remediation efforts by validating optimized results through 14 years of long-term monitoring of trichloroethylene (TCE) contamination. The study site is the Road Administrative Office (RAO) in Wonju, Korea, where significant TCE contamination has occurred, and long-term in-situ remediation and monitoring have been conducted. We employ MODFLOW for simulating groundwater flow and MT3D for modeling dissolved TCE concentration distribution. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied to derive optimal groundwater remediation designs. Initial simulation results effectively predicted long-term TCE contamination trends and the impact of short-term in-situ remediation. Our evaluation involved comparing these optimal designs with field test outcomes, leading to the integration of continuous intensive pump-and-treat with in-situ remediation strategies. By comparing various modeling scenarios against long-term TCE contamination trends, we confirmed the effectiveness of previous remediation efforts and demonstrated that the optimal remediation design substantially minimized TCE concentrations at the main source zone. This study highlights successful strategies in historical contamination and remediation trend assessments, proposing an optimal design for pump-and-treat with reduced pumping stress to manage remaining TCE contamination at the site effectively.