Elizabeth May Pontedeiro1, Jirka Simunek1, M. Th. Van Genuchten2, and Renato Cotta3. (1) Department of Environmental Sciences, University of California, Riverside, CA 92507, (2) U.S. Salinity Laboratory, USDA-ARS, 450 W. Big Springs Rd., Riverside, CA 92507-4617, (3) Department of Mechanical Engineering, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
The mineral industry in Brazil (e.g., in Amazonia) and elsewhere uses many different types of ores that contain naturally occurring radioactive materials (U-238 and Th-232 series) called NORMs. These materials are generally produced in very large quantities with relatively low specific activities. The concentration of radionuclides during processing often results in relatively high dose rates, also later during decommissioning of the mining sites. Contaminated material is then typically disposed of in an industrial landfill on top of a base of earth materials in order to ensure integrity of the deposit over relatively large geologic times (thousand of years). Brazilian regulations require a performance assessment of the disposal facility using a leaching and off-site transport scenario. We used for this purpose analytical models within the STANMOD computer software package and semi-analytical solutions using the Generalised Integral Transform Technique (GITT). The long-term water flux was calculated using Hydrus-1D. The leaching or small farm scenario was modeled by assuming that rainfall percolated vertically through the disposal landfill, the liner and the unsaturated zone into the aquifer. Radionuclides subsequently transported laterally by groundwater were next intercepted by a nearby well or discharged into a stream. The scenario further assumed that the well is the only source of water available to a resident farmer, that all fish consumed in the area originates from the stream, and that all contaminated water somehow will be used in the biosphere. The combined quasi-analytical and numerical approach was found to be very useful for long-term performance assessment.