Abstract: Groundwater overexploitation has resulted in huge scarcity and increase in the demand for water and food security in India. Groundwater in India has been observed to have experienced various water quality issues like arsenic, fluoride, and Uranium (U) contamination, leading to risk in human health. Markedly, the health risk of higher U in drinking water, as well as its chemical toxicity in groundwater have adverse effects on human. This study has reported occurrence of U as an emerging and widespread phenomenon in South Indian groundwater. Data on U in groundwater were generated from 284 samples along the Cretaceous Tertiary boundary within 4 seasons viz. pre-monsoon (PRM), southwest monsoon (SWM), northeast monsoon (NEM), and post-monsoon (POM). High U concentrations (74 μgL−1) showed to be above the World Health Organization’s provisional guideline value of 30 μgL−1. The geochemical, stable isotope and geophysical studies suggested that U in groundwater could vary with respect to season and was noted to be highest during NEM. The bicarbonate (HCO3) released by weathering process during monsoon could affect the saturation index (SI)Calcite and carbonate species of U. However, the primary source of U was found to be due to geogenic factors, like weathering, dissolution, and groundwater level fluctuation, and that, U mobilization could be enhanced due to anthropogenic activities. The findings further indicated that groundwater in the study area has reached the alarming stage of chemical toxicity. Hence, it is urgent and imperative that workable management strategies for sustainable drinking water source be developed and preventive measures be undertaken, relative to these water quality concerns to mitigate their disconcerting effect on human health.

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.