Modeling the Desorption of Organic Contaminants from Long-Term Contaminated Soil Using Distributed Mass Transfer Rates

• Published in: Environmental science & technology 1997-06, Vol.31 (6), p.1581-1588
• Main Authors: Culver, Teresa B, Hallisey, Stephen P, Sahoo, Dipak, Deitsch, James J, Smith, James A
• Format: Article
• Language: English
• Subjects: Applied sciences; Biological and physicochemical properties of pollutants. Interaction in the soil; Chemistry; Contamination; environmental degradation; Exact sciences and technology; mathematics and statistics; Pollution; Soil and sediments pollution; soil chemistry; soil physics; soil science; Soils; waste management; Water
• ISSN: 0013-936X; 1520-5851
• DOI: 10.1021/es9600946

Simulation models for the fate and transport of groundwater contaminants are important tools for testing our understanding of transport phenomena at long-term contaminated sites and for designing remedial action plans. A finite difference formulation for contaminant transport including a distribution of contaminant mass-transfer rates between the water and soil is developed. Optimal model simulations using both log-normal and γ distributions of mass transfer rates are compared to the two-site equilibrium/kinetic model. In all cases, optimal sorption parameters were determined by best fit to laboratory data. For desorption of trichloroethene from long-term contaminated soils, the distributed mass-transfer rate model provided significantly improved simulations of aqueous concentrations, as compared to the two-site model, for both batch and soil column experiments. However, use of an apparent partition coefficient demonstrated that the performance of the two-site model was very sensitive to the value of the partition coefficient, while the performances of the distributed models were robust over a wide range of partition coefficients. Desorption studies in continuous-flow stirred tank reactors with laboratory-contaminated soils demonstrated that as the length of the contamination period increases, the simulation capability of the two-site model decreases.