Abstract: The concentration of the bioavailable uranium fraction (Ubio) at the European scale was deduced by geochemical modelling considering several definitions found in the literature and the FOREGS European stream waters geochemical atlas dataset to produce a Ubio baseline. A sensitivity analysis was performed using three thermodynamic databases. We also investigated the link between total dissolved uranium (Uaq) concentrations, speciation and global stream water chemistry on the one hand, and the lithology and ages of the surrounding rocks on the other. The more U-enriched the stream sediments or rock type contexts are, which tends to be the case with rocks containing silicates (4.1 mg/kg), the less U-concentrated the stream waters are (0.15 μg/L). Sedimentary rocks lead to slightly higher Uaq concentrations (0.34 μg/L) even if the concentration in sediment (Used) is relatively low (1.6 mg/kg). This trend is reversed for Ubio, with higher concentrations in a crystalline context. The mean estimated Ubio value ranges from 1.5.10−3 to 65.3 ng/L and can fluctuate by 3 orders of magnitude depending on the considered definition as opposed to by 2 orders of magnitude accountable to differences between thermodynamic databases. The classification of the water in relation to the two surrounding rock lithologies makes it possible to reduce the mean variability for the Ubio concentrations. Irrespective of the definition of Ubio considered, in 59% of cases the Ubio fraction represents less than 1% of Uaq. Several threshold values relating to Ubio were proposed, assuming knowledge only of the aqueous concentrations of the major elements and Uaq.

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.