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Rapid transmission of contaminants in groundwater can occur in alluvial gravel aquifers that are permeated by highly conductive small-scale open framework gravels (OFGs). This open framework gravel structure and the associated distribution of hydraulic properties is complex, and so assessments of contamination risks in these aquifers are highly uncertain. Geostatistical models, based on lithological data, can be used to quantitatively characterize this structure. These models can then be used to support analyses of the risks of contamination in groundwater systems. However, these geostatistical models are themselves accompanied by significant uncertainty. This is seldom considered when assessing risks to groundwater systems. Geostatistical model uncertainty can be reduced by assimilating information from hydraulic system response data, but this process can be computationally challenging. We developed a sequential conditioning method designed to address these challenges. This method is demonstrated on a transition probability based geostatistical simulation model (TP), which has been shown to be superior for representing the connectivity of high permeability pathways, such as OFGs. The results demonstrate that the common modelling practice of adopting a single geostatistical model may result in realistic predictions being overlooked, and significantly underestimate the uncertainties of groundwater transport predictions. This has important repercussions for uncertainty quantification in general. It also has repercussions if using ensemble?based methods for history matching, since it also relies on geostatistical models to generate prior parameter distributions. This work highlights the need to explore the uncertainty of geostatistical models in the context of the predictions being made.

The nitrogen cycle plays a major role in aquatic nitrogen transformations, including in the terrestrial subsurface. However, the variety of transformations remains understudied. To determine how nitrogen cycling microorganisms respond to different aquifer chemistries, we sampled groundwater with varying nutrient and oxygen contents. Genes and transcripts involved in major nitrogen-cycling pathways were quantified from 55 and 26 sites, respectively, and metagenomes and metatranscriptomes were analyzed from a subset of oxic and dysoxic sites (0.3-1.1 mg/L bulk dissolved oxygen). Nitrogen-cycling mechanisms (e.g. ammonia oxidation, denitrification, dissimilatory nitrate reduction to ammonium) were prevalent and highly redundant, regardless of site?specific physicochemistry or nitrate availability, and present in 40% of reconstructed genomes, suggesting that nitrogen cycling is a core function of aquifer communities. Transcriptional activity for nitrification, denitrification, nitrite-dependent anaerobic methane oxidation and anaerobic ammonia oxidation (anammox) occurred simultaneously in oxic and dysoxic groundwater, indicating the availability of oxic-anoxic interfaces. Concurrent activity by these microorganisms indicates potential synergisms through metabolite exchange across these interfaces (e.g. nitrite and oxygen). Fragmented denitrification pathway encoding and transcription was widespread among groundwater bacteria, although a considerable proportion of associated transcriptional activity was driven by complete denitrifiers, especially under dysoxic conditions. Despite large differences in transcription, the capacity for the final steps of denitrification was largely invariant to aquifer conditions, and most genes and transcripts encoding N2O reductases were the atypical Sec-dependant type, suggesting energy-efficiency prioritization. Results provide insights into the capacity for cooperative relationships in groundwater communities, and the richness and complexity of metabolic mechanisms leading to the loss of fixed nitrogen.

A total of 165 wells were sampled as part of the 2014 national survey of pesticides in groundwater, a survey that is conducted on a four-yearly basis. The survey aims were to update the national overview of pesticides in New Zealand’s groundwater systems, to investigate temporal variation in pesticide concentrations, and to identify environmental factors associated with pesticide contamination. Samples were analysed for around 80 pesticides using gas chromatography with mass spectrometry detection. Pesticides were detected in 28 wells (17%), with two or more pesticides detected in 10 wells (6%). Pesticides were detected in wells from six of the 13 regions sampled. One well contained a pesticide (dieldrin) at a concentration greater than the maximum acceptable value (MAV) for drinking water. Twenty-one different pesticides were detected, with most concentrations being less than 0.1 mg m-3. Only four of the 51 pesticide detections exceeded a con centration of 1 mg m-3. Comparisons with earlier surveys indicate similar levels of pesticide detections in groundwater over the last 12 years, with higher levels of detections before that time. The majority of wells sampled in each national survey have not had pesticides detected; where detected, the concentrations were mostly less than 0.1 mg m-3 and, with the exception of dieldrin and terbuthylazine, less than 5% of MAVs.

In 2018 ESR coordinated a survey of pesticides in groundwater throughout New Zealand. The survey has been completed every four years since 1990 with 2018 being the eighth consecutive survey. Regional and Unitary Authorities carried out the well sampling and the 2018 survey was the first time that glyphosate, glufosinate and their metabolites, and a suite of Emerging Organic Contaminants (EOCs) were included. The pesticide and glyphosate analyses were carried out by AsureQuality and samples were analysed for acidic herbicides and a suite of organochlorine, organophosphorus and organonitrogen pesticides, and for glyphosate and three of its metabolites. The EOCs were analysed by Northcott Research Consultants Ltd. ESR’s role was to coordinate the survey, advise on well selection as needed, collate and interpret the results and provide a national summary report.