Acesulfame allows the tracing of multiple sources of wastewater and riverbank filtration.

Aquifers providing drinking water are increasingly threatened by emerging contaminants due to wastewater inputs from multiple sources. These inputs have to be identified, differentiated, and characterized to allow an accurate risk assessment and thus ensure the safety of drinking water through appropriate management. We hypothesize, that in climates with seasonal temperature variations, the sweetener acesulfame potassium (ACE) provides new pathways to study wastewater inputs to aquifers. Specifically, this study investigates the temperature-driven seasonal oscillation of ACE to assess multiple sources of wastewater inputs at a riverbank filtration site. ACE concentrations in the river water varied from 0.2 to 1 µg L-1 in the cold season (T < 10 °C) to 0-0.1 µg L-1 in the warm season (T > 10 °C), due to temperature-dependent biodegradation during wastewater treatment. This oscillating signal could be traced throughout the aquifer over distances up to 3250 m from two different infiltration sources. A transient numerical model of groundwater flow and ACE transport was calibrated over hydraulic heads and ACE concentrations, allowing the accurate calculation of mixing ratios, travel times, and flow-path directions for each of the two infiltration sources. The calculated travel time from the distant infiltration source was of 67 days, while that from the near source was of 20 days. The difference in travel times leads to different potential degradation of contaminants flowing into the aquifer from the river, thus demonstrating the importance of individually assessing the locations of riverbank infiltration. The calibrated ACE transport model allowed calculating transient mixing ratios, which confirmed the impact of river stage and groundwater levels on the mixing ratio of the original groundwater and the bank filtrate. Therefore, continuous monitoring of ACE concentrations can help to optimize the management of the water works with the aim to avoid collection of water with very short travel times, which has important regulative aspects. Our findings demonstrate the suitability of ACE as a transient tracer for identifying multiple sources of wastewater, including riverbank filtration sites affected by wastewater treatment plant effluents. ACE seasonal oscillation tracking thus provides a new tool to be used in climates with pronounced seasonal temperature variations to assess the origins of contamination in aquifers, with time and cost advantages over multi-tracer approaches.

Seasonal biodegradation of the artificial sweetener acesulfame enhances its use as a transient wastewater tracer.

The persistence of the artificial sweetener acesulfame potassium (ACE) during wastewater treatment and subsequently in the aquatic environment has made it a widely used tracer of wastewater inputs to both surface water and groundwater. However, the recently observed biodegradation of ACE during wastewater treatment has questioned the validity of this application. In this study, we assessed the use of ACE not only as a marker of wastewater, but also as a transient wastewater tracer that allows both the calculation of mixing ratios and travel times through the aquifer as well as the calibration of transient groundwater flow and mass transport models. Our analysis was based on data obtained in a nearly 8-year river water and groundwater sampling campaign along a confirmed wastewater-receiving riverbank filtration site located close to a drinking water supply system. We provide evidence that temperature controls ACE concentration and thus its seasonal oscillation. River water data showed that ACE loads decreased from 1.5-4 mg·s-1 in the cold season (December to June; T<10 °C) to 0-0.5 mg·s-1 in the warm season (July to November; T>10 °C). This seasonal variability of >600% was detectable in the aquifer and preserved >3 km, with ACE concentrations oscillating between

Stochastic modelling of solute mass discharge to identify potential source zones of groundwater diffuse pollution.

In heavily urbanised areas, groundwater diffuse pollution is recognised as one of the most insidious threats to groundwater quality. Diffuse pollution originates from multiple small sources releasing a low contaminant mass over a relatively large area; the lack of a defined plume in groundwater, the limited leaked mass, and the fact that leakage may have occurred in the past and be now ceased, make these sources difficult to locate and characterise. In addressing this environmental issue, an inverse approach based on the Null space Monte Carlo stochastic method has been applied in the framework of an innovative methodology with the aim to locate potential source areas distributed in a large (120 km2) urban area. To simplify the problem and better understand the limitations and effectiveness of the proposed methodology, the analysis has been performed using a groundwater model with fixed (i.e., determined by a previous calibration) hydraulic conductivity and flow boundary conditions. The only source of uncertainty considered in the study is the PCE mass discharge from all model cells of the topmost layer. After implementing and calibrating a deterministic solute transport model, multiple random realisations of mass discharge fields were generated, all of which are history-match constrained and hydrogeologically plausible. The obtained stochastic parameter sets were used to investigate the statistical distribution of the solute mass discharge and map the areas that are more likely to host unknown sources of PCE. Although the application of the NSMC stochastic method on the synthetic case study has provided promising results, it has also highlighted that multiple sources of uncertainty (e.g., continuity and duration of each source, attenuation processes) could adversely affect the reliability of the results in a real-world context, in which the effect of other uncertain parameters (hydraulic conductivity amongst all) would need to be considered in addition. This study offers new insights to the problem of aquifer diffuse pollution by providing key information on the potential source zones and on the areas that urgently need to be prioritised for further investigations.

Null-space Monte Carlo particle tracking to assess groundwater PCE (Tetrachloroethene) diffuse pollution in north-eastern Milan functional urban area.

The Lombardy Region in Italy is one of the most urbanized and industrialized areas in Europe. The presence of countless sources of groundwater pollution is therefore a matter of environmental concern. The sources of groundwater contamination can be classified into two different categories: 1) Point Sources (PS), which correspond to areas releasing plumes of high concentrations (i.e. hot-spots) and 2) Multiple-Point Sources (MPS) consisting in a series of unidentifiable small sources clustered within large areas, generating an anthropogenic diffuse contamination. The latter category frequently predominates in European Functional Urban Areas (FUA) and cannot be managed through standard remediation techniques, mainly because detecting the many different source areas releasing small contaminant mass in groundwater is unfeasible. A specific legislative action has been recently enacted at Regional level (DGR IX/3510-2012), in order to identify areas prone to anthropogenic diffuse pollution and their level of contamination. With a view to defining a management plan, it is necessary to find where MPS are most likely positioned. This paper describes a methodology devised to identify the areas with the highest likelihood to host potential MPS. A groundwater flow model was implemented for a pilot area located in the Milan FUA and through the PEST code, a Null-Space Monte Carlo method was applied in order to generate a suite of several hundred hydraulic conductivity field realizations, each maintaining the model in a calibrated state and each consistent with the modelers' expert-knowledge. Thereafter, the MODPATH code was applied to generate back-traced advective flowpaths for each of the models built using the conductivity field realizations. Maps were then created displaying the number of backtracked particles that crossed each model cell in each stochastic calibrated model. The result is considered to be representative of the FUAs areas with the highest likelihood to host MPS responsible for diffuse contamination.