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Sorption / Predictive modelling / Surface complexation models / Uranium / GraniteSummary. One widely accepted approach -component additivity (CA) -to describe the sorption of contaminants onto complex materials such as rocks or soils is based on the assumption that the surface of a complex mineral assemblage is composed of a mixture of mineral constituents whose surface properties are known from independent studies. An internally consistent SCM (surface complexation model) database can be developed that describes the adsorption reactions of solutes to each phase. Here, the capability of such a methodology was tested, using the code MINTEQA2 including thermodynamic data of the NEA-TDB, and literature data for SCM, namely the DDL model.The sorption characteristics of U(VI) on granite (from Eibenstock, Saxony, Germany, with the main components quartz, albite, orthoclase, and muscovite) was predicted and then compared to batch experiments. Granite plays an important role in the remediation of former uranium ore mining and milling sites, but is also one of the host rocks considered for final disposal of nuclear materials. Safety assessment requires a detailed understanding of this system and its retention potential with regard to hazardous components. Namely the sorption of uranium in this complex rock is not fully understood yet. The experiments thus also provided a better understanding of the far-field behaviour in granitic geological nuclear repositories.The robustness of the prediction was tested by variation of the granite composition and the variation of the specific surface area (SSA) -first all components were predicted with a uniform granite SSA, second with a distinct SSA for each granite component (determined on pure minerals for the same grain size fractions). Changes in compositions yielded only marginal differences in the prediction. Different approaches to SSA showed somewhat larger deviations.In conclusion, the CA methodology is a valid and robust approach to U(VI) sorption onto complex substrates such as granite, provided sufficient information on component phases is available. This should help to minimize experimental efforts and to increase confidence into complex SCM computations.

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