A comprehensive probabilistic approach for integrating and separating natural variability and parametric uncertainty in the prediction of distribution coefficient of radionuclides in rivers

• Published in: Journal of environmental radioactivity 2020-12, Vol.225, p.106371, Article 106371
• Main Author: P, Ciffroy
• Format: Article
• Language: English
• Subjects: Carbonates; EFAST; Geochemical model; Morris; Radiation Monitoring - methods; Radiation Monitoring - statistics & numerical data; Radioisotopes; Radionuclides; Rivers; Sensitivity analysis; Speciation; Uncertainty; Uncertainty analysis; Water Pollution, Radioactive - statistics & numerical data
• ISSN: 0265-931X; 1879-1700
• DOI: 10.1016/j.jenvrad.2020.106371

A geochemical speciation model was developed to predict Distribution coefficients (Kds) of radionuclides (RNs) in rivers. The model takes into account complexation of RNs with inorganic ligands, sorption of RNs with hydrous ferric oxides, complexation of RNs with dissolved and particulate organic carbon (DOC and POC) and sorption and/or co-precipitation of RNs to carbonates. A sorption model of Cs onto clay was also integrated. The tool is also designed to conduct uncertainty and sensitivity analysis. Sensitivity analysis follows a stepwise structured approach, starting from computationally ‘inexpensive’ Morris method to most costly variance-based EFAST method. A nested Monte Carlo approach was also implemented to separate natural variability and lack of knowledge in global uncertainty assessment. As case studies, Kd distributions were estimated for Co, Mn, Ag and Cs in seven French rivers. Uncertainty analysis allowed to quantify Kd ranges that can be expected when considering all the sensitive parameters together. •A geochemical speciation model was developed to predict radionuclides Kds in rivers.•An uncertainty and sensitivity analysis was performed.•Natural variability and lack of knowledge were separated in uncertainty assessment.•Kd values were estimated for Co, Mn, Ag and Cs in seven French rivers.