Abstract: Uranium mining activities produce the main element used in nuclear energy production. However, it can also negatively affect the environment including groundwater by release of residues or effluent containing radioactive elements. The study investigated the concentration and radiological hazard of uranium in groundwater and seepage water from the tailings of a uranium mine in Namibia. Inductively Coupled Plasma Mass Spectrometry (ICP-MS) was used to assess the concentration of uranium in the groundwater and seepage water and the radiological hazards were determined. The radiological hazard indices Radium equivalent activity (Raeq), Absorbed dose (D), Annual Effective Dose equivalent (AEDE), External hazard index (Hex) and Internal hazard index (Hin) were determined and compared to limits recommended by United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). The calculated average value of D and Hin of groundwater is 108.11nGyh−1 and 1.26, respectively and are above the UNSCEAR values (55 nGyh−1 and 1). Further, the average values of Raeq, AEDE and Hex were below the recommended values. The isotopic ratio of uranium radionuclides in groundwater indicates that the uranium in the sampled groundwater is below 1 suggesting it is not natural uranium present but a possible contamination from the mine seepage. The radiological hazard parameters of the seepage water were above the recommended values and thus pose a radiation risk to human and environment.

Abstract: This paper describes the processes leading to uranium distribution in the groundwater of five boreholes near a restored uranium mine (dug in granite), and the environmental impact of restoration work in the discharge area. The groundwater uranium content varied from \textless1 μg/L in reduced water far from the area of influence of the uranium ore-containing dyke, to 104 μg/L in a borehole hydraulically connected to the mine. These values, however, fail to reflect a chemical equilibrium between the water and the pure mineral phases. A model for the mobilization of uranium in this groundwater is therefore proposed. This involves the percolation of oxidized waters through the fractured granite, leading to the oxidation of pyrite and arsenopyrite and the precipitation of iron oxyhydroxides. This in turn leads to the dissolution of the primary pitchblende and, subsequently, the release of U(VI) species to the groundwater. These U(VI) species are retained by iron hydroxides. Secondary uranium species are eventually formed as reducing conditions are re-established due to water–rock interactions.

Abstract: To effectively manage sustainably groundwater bodies, it is essential to establish what the naturally occurring ranges of chemical concentrations in groundwaters are and how they change over time. We defined baseline trends for New Zealand groundwaters using: 1) pattern recognition techniques to deal with inconsistent monitoring suites between the national (110 sites) and the denser regional network (\textgreater1000 sites), and 2) multivariate statistics to identify and remove impacted sites from the enhanced dataset. Rates of changes were calculated for 13 parameters between January 2005 and December 2014 at more than 1000 groundwater quality monitoring sites. The resulting dataset included 262 complete cases (CC), which was enhanced using Machine-Learning (ML) techniques to a total of 607 sites. Hierarchical cluster analysis was used to identify trend clusters that were consistent between the CC, ML-enhanced datasets and a 2006 study based on solely on the national network. The largest cluster (WR) consisted of low magnitude changes across all parameters and was attributed to water-rock interaction processes. The second largest cluster (I) exhibited fast changes particularly for parameters linked to human-induced impact. The third largest cluster (D) comprised decreases of all parameters and was associated with dilution processes. Trend clusters were further refined using groundwater quality state information, enabling the identification of impacted sites outside of Cluster I in the ML-enhanced and CC datasets. Corresponding trend baselines were subsequently derived at unimpacted sites using univariate quantile distribution (5th and 95th percentile thresholds). Finally, we developed classifications combining baselines (state and trend) and natural variability to enhance state of the environment reporting. This allowed the new identification of deteriorating trends at sites where groundwater quality state is not yet affected in addition to trend reversals. These classifications can be adapted to incorporate new knowledge or align with surface water quality reporting.