An assessment tool to improve rural groundwater access: Integrating hydrogeological modelling with socio-technical factors.

Sustainable exploitation of groundwater resources for drinking water provision in rural communities in sub-Sahara Africa remains elusive due to the limited knowledge of these hydrogeological systems. This is exacerbated by poor maintenance of existing infrastructure, limited technical capacity, the socio-economic characteristics of the area and poor governance. Assessing the likelihood of a given individual user experiencing water shortage calls for an interdisciplinary approach. After a preliminary multifactorial analysis incorporating a range of variables from technical to societal, it was found that most of the overall risk of water shortage for an individual household could be attributed to three factors; (1) Proximity, specified as the distance to the closest supply well (determined by geographical parameters), (2) Availability of good quality water in the wells (determined by hydrogeological understanding and modelling), and (3) Sustainability (determined by socio-technical and socio-economic parameters). In the latter case, a distinction was made between hardware functionality- the water point's performance considering a sufficient yield and reliability through time- and software functionality, based on a combination of socioeconomic data from surveys and analysed using Multiple Factor Analysis (MFA). All three factors are eventually mapped onto indicators in the range of [0-1] and then represented in a Geographical Information System based on the partition of the entire spatial domain (e.g., counties, villages, and neighbourhoods). The three indicators are then combined in a final index based on the product of the three factors, thus mapping time-dependent overall risk and allowing the assessment of temporal risk-evolution scenarios. The methodology is applied to Kwale County, Kenya, where community handpumps and groundwater points comprise the main water supply system. Apart from mapping the present situation, the methodology is finally used to assess the impact of future climate scenarios.

Exploring the biocapacitance in M3C-based biosensors for the assessment of microbial activity and organic matter.

Reliable monitoring of microbial and water quality parameters in freshwater ecosystems (either natural or human-made) is of capital importance for improving both the management of water resources and the assessment of microbially-driven bio-geo-chemical processes. In this context, bioelectrochemical systems (BES), such as microbial three-cell electrodes (M3C), are very promising devices for their use as biosensors. However, current experiences on the use of BES-based devices for biosensing purposes are almost exclusively limited to water-saturated environments. This limitation hampers the use of this technology for a wider range of applications where the biosensor may work discontinuously (such as discontinuously saturated ecosystems). Discontinuous operation of M3C-based biosensors creates an electric current peak immediately after the reconnection of the system due to electron accumulation, in a process known as biocapacitance. The present work aimed at quantifying the bioindication potential of biocapacitance for the assessment of key ecosystem parameters such as microbial metabolic activity and biomass, as well as organic matter concentration. Significant linear regression coefficients (R2 > 0.9) were found for all combinations of parameters tested. Moreover, for most of the ecological parameters assessed, an electric charge accumulation of 1-5 min (biocapacitance elapsed time) and discharge of 5 min was enough to get reliable information. In conclusion, we have demonstrated for the first time that biocapacitance in M3C-based biosensors can be used as a proxy parameter for the assessment of microbial activity, microbial biomass and organic matter concentration in a model nature-based ecosystem.

Power assisted MFC-based biosensor for continuous assessment of microbial activity and biomass in freshwater ecosystems.

Microbial activity and biomass are important factors that determine nutrient and carbon fluxes in freshwater ecosystems and, therefore are also related to both water quality and climate change induced stressors. This study aimed at assessing the feasibility of a power assisted Microbial Fuel Cell (MFC)-based biosensors for the continuous monitoring of microbial activity and biomass concentrations in saturated freshwater ecosystems. For this purpose, four lab-scale reactors were constructed and operated for 30 weeks. Reactors were fed with four different organic matter concentrations to promote a suite of microbial activity and biomass conditions. The reactors consisted of 3.8 L PVC vessels filled with 23 extractable gravel- sockets, used for microbial activity and biomass assessment, and 1 MFC granular-graphite socket, for biosensing assessment. Microbial activity was determined by the ATP content and the hydrolytic enzymatic activity, and the biomass content was assessed as the volatile solids attached to the gravel. Very significant linear relationships could be established between the parameters studied and the current density produced by the MFC with a very short detection time: 10 min for the ATP content (R2 = 0.88) and 1 h for the enzymatic activity (R2 = 0.78) and biomass (R2 = 0.74). Moreover, the power assisted MFC-based biosensing tool demonstrated to be functional after a long operation time and under a wide range of organic loading conditions. Overall, the results highlight the feasibility to develop a power assisted MFC-based biosensor for on-line monitoring of the microbial activity and biomass of a given ecosystem (either natural or artificial) even in remote locations.

A simple contaminant fate and transport modelling tool for management and risk assessment of groundwater pollution from contaminated sites.

Contaminated sites pose a significant threat to groundwater resources. The resources that can be allocated by water regulators for site investigation and cleanup are limited compared to the large number of contaminated sites. Numerical transport models of individual sites require large amounts of data and are labor intensive to set up, and thus they are likely to be too expensive to be useful in the management of thousands of contaminated sites. Therefore, simple tools based on analytical solutions of contaminant transport models are widely used to assess (at an early stage) whether a site might pose a threat to groundwater. We present a tool consisting of five different models, representing common geological settings, contaminant pathways, and transport processes. The tool employs a simplified approach for preliminary, conservative, fast and inexpensive estimation of the contamination levels of aquifers. This is useful for risk assessment applications or to select and prioritize the sites, which should be targeted for further investigation. The tool is based on steady-state semi-analytical models simulating different contaminant transport scenarios from the source to downstream groundwater, and includes both unsaturated and saturated transport processes. The models combine existing analytical solutions from the literature for vertical (from the source to the top of the aquifer) and horizontal (within the aquifer) transport. The effect of net recharge causing a downward migration and an increase of vertical dispersion and dilution of the plume is also considered. Finally, we illustrate the application of the tool for a preliminary assessment of two contaminated sites in Denmark and compare the model results with field data. The comparison shows that a first preliminary assessment with conservative, and often non-site specific parameter selection, is qualitatively consistent with broad trends in observations and provides a conservative estimate of contamination.

Dynamic interactions between hydrogeological and exposure parameters in daily dose prediction under uncertainty and temporal variability.

We study the time dependent interaction between hydrogeological and exposure parameters in daily dose predictions due to exposure of humans to groundwater contamination. Dose predictions are treated stochastically to account for an incomplete hydrogeological and geochemical field characterization, and an incomplete knowledge of the physiological response. We used a nested Monte Carlo framework to account for uncertainty and variability arising from both hydrogeological and exposure variables. Our interest is in the temporal dynamics of the total dose and their effects on parametric uncertainty reduction. We illustrate the approach to a HCH (lindane) pollution problem at the Ebro River, Spain. The temporal distribution of lindane in the river water can have a strong impact in the evaluation of risk. The total dose displays a non-linear effect on different population cohorts, indicating the need to account for population variability. We then expand the concept of Comparative Information Yield Curves developed earlier (see de Barros et al. [29]) to evaluate parametric uncertainty reduction under temporally variable exposure dose. Results show that the importance of parametric uncertainty reduction varies according to the temporal dynamics of the lindane plume. The approach could be used for any chemical to aid decision makers to better allocate resources towards reducing uncertainty.

Relative importance of geostatistical and transport models in describing heavily tailed breakthrough curves at the Lauswiesen site.

We analyze the relative importance of the selection of (1) the geostatistical model depicting the structural heterogeneity of an aquifer, and (2) the basic processes to be included in the conceptual model, to describe the main aspects of solute transport at an experimental site. We focus on the results of a forced-gradient tracer test performed at the "Lauswiesen" experimental site, near Tübingen, Germany. In the experiment, NaBr is injected into a well located 52 m from a pumping well. Multilevel breakthrough curves (BTCs) are measured in the latter. We conceptualize the aquifer as a three-dimensional, doubly stochastic composite medium, where distributions of geomaterials and attributes, e.g., hydraulic conductivity (K) and porosity (phi), can be uncertain. Several alternative transport processes are considered: advection, advection-dispersion and/or mass-transfer between mobile and immobile regions. Flow and transport are tackled within a stochastic Monte Carlo framework to describe key features of the experimental BTCs, such as temporal moments, peak time, and pronounced tailing. We find that, regardless the complexity of the conceptual transport model adopted, an adequate description of heterogeneity is crucial for generating alternative equally likely realizations of the system that are consistent with (a) the statistical description of the heterogeneous system, as inferred from the data, and (b) salient features of the depth-averaged breakthrough curve, including preferential paths, slow release of mass particles, and anomalous spreading. While the available geostatistical characterization of heterogeneity can explain most of the integrated behavior of transport (depth-averaged breakthrough curve), not all multilevel BTCs are described with equal success. This suggests that transport models simply based on integrated measurements may not ensure an accurate representation of many of the important features required in three-dimensional transport models.