ALBERT

Description: 〈h3〉Abstract〈/h3〉 〈p〉A case study is presented that implements two numerical models for simulating a 30-year pump-and-treat (PAT) operation conducted at a large contaminated site for which high-resolution data sets are available. A Markov chain based stochastic method is used to conditionally generate the realizations with random distribution of heterogeneity for the Tucson International Airport Area (TIAA) federal Superfund site in the USA. The fields were conditioned to data collected for 245 boreholes drilled at the site. Both MT3DMS and the advanced random-walk particle method (RWhet) were used to simulate the PAT-based mass removal process. The results show that both MT3DMS and RWhet represent the measured data reasonably, with root mean square error (RMSE) less than 0.03. The use of fine grids and the total-variation-diminishing method (TVD) limited the effects of numerical dispersion for MT3DMS. However, the effects of numerical dispersion were observed when compared to the simulations produced with RWhet using a larger number of particles, which provided more accurate results with RMSE diminishing from 0.027 to 0.024 to 0.020 for simulations with 1, 20, and 50 particles, respectively. The computational time increased with more particles used in the model, but was still much less than the time required for MT3DMS, which is an advantage of RWhet. By showing the results using both methods, this study provides guidance for simulating long-term PAT systems. This work will improve understanding of contaminant transport and plume persistence, and in turn will enhance site characterization and site management for contaminated sites with large plumes.〈/p〉