Reactive transport modeling of thorium in a cloud computing environment

• Cloud computing provides a powerful tool for reactive transport modeling.
• Contaminants from the Bayan the Obo tailings site pose a threat to Yellow River.

Despite the long standing interest in modeling the fate and environmental impacts of radionuclides, simulations addressing the fate and transport of rare earth elements (REEs) and thorium (Th) have received comparably little attention. This study presents an architecture that enables reactive transport modeling and parameter sensitivity analysis on cloud computing platforms. We adapted an existing groundwater modeling framework to perform some of the computationally most expensive steps within a cloud environment based on Microsoft Windows Azure. The cloud computing architecture was evaluated and validated through the development of a schematic, cross sectional model along a transect across a tailings impoundment at a REE mine tailings site in northwest China. The model framework employs a suite of flow, solute transport and reactive transport simulation tools, i.e., MODFLOW, MT3DMS, and PHT3D. On the basis of our model simulations, the collection-trench for the impoundment constructed above the ground surface appears to collect a substantial portion of the leachate fluxes, but the remainder will bypass the trench and migrate downstream. Those bypassed leachate fluxes will subsequently interact with downstream fluviolacustrine aquifers and eventually discharge into the Yellow River south of the study site under the idealized simulation environment. Further investigations of the hydraulic parameters of the aquifer system and the impoundment dam, and other geochemical characteristics are needed to elucidate the fate and transport of thorium and improve the reliability of the numerical model. Although the discussion and analysis of this study is tailored to thorium reactive transport modeling of a REE tailings impoundment, such a framework can also be applied to deploy different types of scientific modeling applications on Azure Cloud.

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