Abstract: Krypton-85 and other radioactive noble gases are widely used for groundwater dating purposes. 85Kr analysis require large volumes of water to reach the analytical requirements. Conventionally, this water is pumped to the surface to be degassed with a gas extraction system. The large pumping rate may disturb the natural flow field and requires substantial field logistics. Hence, we propose a new in-situ degassing method, in which membrane contactors are used to degas the groundwater directly in the well and gas is collected at the surface. This way, field work is facilitated, groundwater system disturbance is minimized, and the gas sample is collected at a specific depth. We demonstrate the tightness of the system regarding atmospheric air contamination for a collection times of 24 h, which is sufficient for both low-level counting and laser-based counting methods for 85Kr. The minimal borehole diameter is 7.5 cm for the prototype presented in this research but can easily be reduced to smaller diameters. In a case study, we compare the results obtained with the new passive method with those from a conventional packer setup sampling. Additionally, 3H/3He samples were collected for both sampling regimes and the dating results were compared with those from 85Kr. A good agreement between tracer ages is demonstrated and the age stratigraphy is consistent with the expected age distribution for a porous unconfined aquifer. In addition, our study emphasizes the differences between the age information sampled with various methods. In conclusion, we demonstrate that the new in situ quasi-passive method provides a more representative age stratigraphy with depth in most cases.

Abstract: Environmental hazards are caused by uranium mining legacies and enhanced radioactivity in utilized groundwater and surface water resources. Knowledge of uranium speciation in these waters is essential for predicting radionuclide migration and for installing effective water purification technology. The validity of the thermodynamic data for the environmental media affected by uranium mining legacies is of utmost importance. Therefore, a comprehensive and consistent database was established according to current knowledge. The uranium data included in the database is based on the NEA TDB (Guillaumont et al., 2003) and is modified or supplemented as necessary e.g. for calcium and magnesium uranyl carbonates. The specific ion interaction theory (Brönsted, 1922) is used to estimate activity constants, which is sufficient for the considered low ionic strengths. The success of this approach was evaluated by comparative experimental investigations and model calculations (PHREEQC (Parkhurst and Appelo, 1999)) for several model systems. The waters differ in pH (2.7–9.8), uranium concentration (10−9-10−4 mol/L) and ionic strength (0.002–0.2 mol/L). We used chemical extraction experiments, ESI-Orbitrap-MS and time-resolved laser-induced fluorescence spectroscopy (TRLFS) to measure the uranium speciation. The latter method is nonintrusive and therefore does not change the chemical composition of the investigated waters. This is very important, because any change of the system under study may also change the speciation.

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.