EGU2020-1194, updated on 12 Jun 2020
https://doi.org/10.5194/egusphere-egu2020-1194
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Influence of distribution co-efficient on radionuclide transport modelling of uranium from a tailings pond in northern Karnataka, India

Bhavya Ravinder and Elango Lakshmanan
Bhavya Ravinder and Elango Lakshmanan
  • Anna University, College of Engineering, Department of Geology, India (elango@annauniv.edu)

A well-designed environmental monitoring plan is essential for safety of uranium mining and processing operations. Evaluating the possible uncertainties in a numerical model helps in enhancing the model output and also increases the reliability over the model results. For a radionuclide transport model, distribution co-efficient is a sensitive parameter and major source of uncertainty in results. In this study, an approach to quantify input source of uncertainty of distribution co-efficient in an engineered tailings pond in Northern Karnataka, India has been carried out. Probabilistic analysis such as Response Surface Method and Monte Carlo Simulation are used to propagate uncertainty. This study considers uncertainty associated with intrinsic heterogeneity of natural systems and estimates the probability that dose rate value through drinking water pathway around the tailings pond exceeds the WHO guidelines for drinking water. The radionuclides considered in this study are 238U, 234U, 230Th and 226Ra. This study can be used to study the impact of distribution co-efficient on the radionuclide transport model.

Key words: Numerical modelling, Tailings pond area, Uranium mining, Uncertainty,  Distribution coefficient

 

How to cite: Ravinder, B. and Lakshmanan, E.: Influence of distribution co-efficient on radionuclide transport modelling of uranium from a tailings pond in northern Karnataka, India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1194, https://doi.org/10.5194/egusphere-egu2020-1194, 2019

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