Factors Influencing Microbial Contamination of Groundwater: A Systematic Review of Field-Scale Studies.

Pathogenic microorganisms released onto the soil from point or diffuse sources represent a public health concern. They can be transported by rainwater that infiltrates into subsoil and reach the groundwater where they can survive for a long time and contaminate drinking water sources. As part of the SCA.Re.S. (Evaluation of Health Risk Related to the Discharge of Wastewater on the Soil) project, we reviewed a selection of field-scale studies that investigated the factors that influenced the fate of microorganisms that were transported from the ground surface to the groundwater. A total of 24 studies published between 2003 and 2022 were included in the review. These studies were selected from the PubMed and Web of Science databases. Microbial contamination of groundwater depends on complex interactions between human activities responsible for the release of contaminants onto the soil, and a range of environmental and biological factors, including the geological, hydraulic, and moisture characteristics of the media traversed by the water, and the characteristics and the viability of the microorganisms, which in turn depend on the environmental conditions and presence of predatory species. Enterococci appeared to be more resistant in the underground environment than thermotolerant coliforms and were suggested as a better indicator for detecting microbial contamination of groundwater.

Frequency domain electromagnetic induction imaging: An effective method to see inside a capped landfill.

The frequency-domain electromagnetic (FDEM) methods are a powerful tool for evaluating the impact caused on natural environments by anthropic facilities such as landfills. Noninvasive FDEM rapidly investigates large areas with no impact on the system. This is essential in case of capped landfills, as the impermeable liner represents a strong limitation for the use of all others direct and indirect investigation methods. This technique allows the propagation of the EM fields and collection of subsurface response below the liner thus representing the only effective solution both for static imaging and time-lapse monitoring of the processes that take place into the waste deposits. Traditionally, electromagnetic data are visualized as apparent electrical conductivity (ECa) maps that give practically no information about the variation of the conductivity with depth because ECa is only the equivalent conductivity of a homogeneous soil that would give the same measured response along depth. More recent approaches allow for an inversion of data thus providing clear information on the thickness of the investigated subsurface layers. The need for building a 3D electromagnetic model is crucial in the context of the urban waste landfill characterization, where leachate induces strong anomalies in electrical conductivity, which in turn causes a nonlinear model of the EMI response. A rigorous EMI inversion approach has been tested at a closed landfill in Southern Italy. The inverted model provided detailed information unattainable with other methods, by corroborating the assumption that electromagnetic measurements represent the best technique to characterize closed systems such as capped landfills.

Evidence of Preferential Flow Activation in the Vadose Zone via Geophysical Monitoring.

Preferential pathways allow rapid and non-uniform water movement in the subsurface due to strong heterogeneity of texture, composition, and hydraulic properties. Understanding the importance of preferential pathways is crucial, because they have strong impact on flow and transport hydrodynamics in the unsaturated zone. Particularly, improving knowledge of the water dynamics is essential for estimating travel time through soil to quantify hazards for groundwater, assess aquifer recharge rates, improve agricultural water management, and prevent surface stormflow and flooding hazards. Small scale field heterogeneities cannot be always captured by the limited number of point scale measurements collected. In order to overcome these limitations, noninvasive geophysical techniques have been widely used in the last decade to predict hydrodynamic processes, due to their capability to spatialize hydrogeophysical properties with high resolution. In the test site located in Bari, Southern Italy, the geophysical approach, based on electrical resistivity tomography (ERT) monitoring, has been implemented to detect preferential pathways triggered by an artificial rainfall event. ERT-derived soil moisture estimations were obtained in order to quantitatively predict the water storage (m3m-3), water velocity (ms-1), and spread (m2) through preferential pathways by using spatial moments analysis.

Geophysical and hydrological data assimilation to monitor water content dynamics in the rocky unsaturated zone.

In recent years, geophysics is increasingly used to study the flow and transport processes in the vadose zone. Particularly, when the vadose zone is made up of rocks, it is difficult to install sensors in the subsurface to measure hydrological state variables directly. In these cases, the electrical resistivity tomography (ERT) represents a useful tool to monitor the hydrodynamics of the infiltration and to estimate hydraulic parameters and state variables, such as hydraulic conductivity and water content. We propose an integrated approach aimed at predicting water content dynamics in calcarenite, a sedimentary carbonatic porous rock. The uncoupled hydrogeophysical approach proposed consists in 4D ERT monitoring conducted during an infiltrometer test under falling head conditions. Capacitance probes were installed to measure water content at different depths to validate the estimations derived from ERT. A numerical procedure, based on a data assimilation technique, was accomplished by combining the model (i.e., Richards' equation) with the observations in order to provide reliable water content estimations. We have used a new data assimilation method that is easy to implement, based on the ensemble Kalman filter coupled with Brownian bridges. This approach is particularly suitable for strongly non-linear models, such as Richards' equation, in order to take into account both the model uncertainty and the observation errors. The proposed data assimilation approach was tested for the first time on field data. A reasonable agreement was found between observations and predictions confirming the ability of the integrated approach to predict water content dynamics in the rocky subsoil.