Abstract: 4He concentrations in excess of the solubility equilibrium with the atmosphere by up to two to three orders of magnitude are observed in the Carrizo Aquifer in Texas, the Ojo Alamo and Nacimiento aquifers in the San Juan Basin, New Mexico, and the Auob Sandstone Aquifer in Namibia. A simple 4He accumulation model is applied to explain these excess 4He concentrations in terms of both in situ production and a crustal flux across the bottom layer of the aquifer. Results from the model simulations suggest variability in the 4He fluxes, ranging from 6×10−6 cm3 STP cm−2 yr−1 for the Auob Sandstone Aquifer to 3.6×10−7 cm3 STP cm−2 yr−1 for the Carrizo aquifer. For the Ojo Alamo and Nacimiento aquifers an intermediate value of 3×10−6 cm3 STP cm−2 yr−1 was estimated. The contribution of in-situ produced 4He to the measured concentrations was also estimated. This contribution is negligible for the Auob Sandstone Aquifer as compared with both the concentrations measured at the top and bottom of the aquifer for most of the pathway. In the Carrizo aquifer, in-situ produced 4He contributes 27.5% and 15.4%, to the total 4He observed at the top and bottom of the aquifer, respectively. For both aquifers of the San Juan Basin in-situ production almost entirely dominates the 4He concentrations at the top of the aquifer for most of the pathway. In contrast, the internal production is negligible as compared with the measured concentrations at the bottom of these aquifers, reaching, at most, 1.1%. The model simulations require an exponential decrease in the horizontal velocity of the water with increasing recharge distance to reproduce the distribution of 4He in these aquifers. For the Auob Sandstone Aquifer the highest range in the velocity values is obtained (25 to 0.4 m yr−1). The simulations for the Carrizo aquifer and both aquifers located in the San Juan Basin require velocities varying from 4 to 0.1 m yr−1, and from 2 to 0.3 m yr−1, respectively. For each aquifer, average permeability values were also estimated. They are generally in agreement with results obtained from pumping tests, hydrodynamic modeling and previous 14C measurements. On the basis of the results obtained by calibrating the model with the measured 4He concentrations, the mean water residence times were estimated. They agree reasonably well with 14C ages. When applied as chronologies for noble gas temperatures in the same aquifers, the calculated 4He ages allow the identification of three different climate periods similar to those previously identified using 14C ages: (1) the Holocene period (0–10 Ka BP), (2) the Last Glacial Maximum (≈18 Ka BP), and (3) the preceeding period (30–150 Ka BP).

Abstract: The Zoovch Ovoo uranium roll-front-type deposit is hosted in the Sainshand Formation, a Late Cretaceous siliciclastic reservoir, which constitutes the upper part of the post-rift infilling of the Mesozoic East Gobi Basin in SE Mongolia. The Sainshand Formation consists of unconsolidated medium-grained sand, silt and clay intervals deposited in fluvial-lacustrine settings. The uranium deposit is confined within a 60–80 m thick siliciclastic sequence inside aquifer-driven systems. The overall system experienced shallow burial and was never subjected to temperatures higher than 40 °C. This study proposes a comprehensive metallogenic model for this uranium deposit. Sedimentological and mineralogical observations from drill core samples to the microscopic scale (optical and Scanning Electron Microscopy) together with in situ geochemistry of late-formed phases (Laser Ablation–Inductively Coupled Plasma Mass Spectrometry, Electron Probe Microanalysis, Fourier Transform–Infrared Spectroscopy) were considered for the reconstruction of the main stages of U trapping. In the mineralized zone, the uranium ore is expressed as Ca–enriched uraninite (UO2) and less commonly as Ca–enriched phospho-coffinite (U, P)SiO4. Trapping mechanisms include i) complexation (i.e. uranyl-carboxyl complexes), ii) adsorption on organic or clay particles) and iii) reduction by pyrite and by bacterial activity to amorphous uraninite. In all cases, the organic matter plays either the role of trap for uranium or nutrient for bacteria that can trap uranium through their metabolism. The shallow burial diagenesis conditions do not allow direct reduction of U(VI) by organic carbon. The δ34S values of the iron disulfide are very diverse, fluctuating in extreme cases between −50 to + 50‰, with an average δ34S value for framboidal pyrite at 2‰, and −20‰ for euhedral pyrite. The positive and negative values reflect close versus open fractionation systems, while bacterial sulphate reduction (BSR) is active during the whole diagenetic history of the deposit as an essential source of reduced sulfur. Therefore, using detrital organic matter as a carbon source, microorganisms play a significant role in uranium trapping, either as a direct reducing agent for uranium or pyrite formation, which will trap uranium through redox driven epigenetic processes.

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.