In-silico mechanistic analysis of adsorption of Iodinated Contrast Media agents on graphene surface.

The study aims at assessing the potential of graphene-based adsorbents to reduce environmental impacts of Iodinated Contrast Media Agents (ICMs). We analyze an extensive collection of ICMs. A modeling approach resting on molecular docking and Density Functional Theory simulations is employed to examine the adsorption process at the molecular level. The study also relies on a Quantitative Structure-Activity Relationship (QSAR) modeling framework to correlate molecular properties with the adsorption energy (Ead) of ICMs, thus enabling identification of the key mechanisms underpinning adsorption and of the key factors contributing to it. A collection of distinct QSAR-based models is developed upon relying on Multiple Linear Regression and a standard genetic algorithm method. Having at our disposal multiple models enables us to take into account the uncertainty associated with model formulation. Maximum Likelihood and formal model identification/discrimination criteria (such as Bayesian and/or information theoretic criteria) are then employed to complement the traditional QSAR modeling phase. This has the advantage of (a) providing a rigorous ranking of the alternative models included in the selected set and (b) quantifying the relative degree of likelihood of each of these models through a weight or posterior probability. The resulting workflow of analysis enables one to seamlessly embed DFT and QSAR studies within a theoretical framework of analysis that explicitly takes into account model and parameter uncertainty. Our results suggest that graphene-based surfaces constitute a promising adsorbent for ICMs removal, π-π stacking being the primary mechanism behind ICM adsorption. Furthermore, our findings offer valuable insights into the potential of graphene-based adsorbent materials for effectively removing ICMs from water systems. They contribute to ascertain the significance of various factors (such as, e.g., the distribution of atomic van der Waals volumes, overall molecular complexity, the presence and arrangement of Iodine atoms, and the presence of polar functional groups) on the adsorption process.

Probabilistic assessment of failure of infiltration structures under model and parametric uncertainty.

We focus on the quantification of the probability of failure (PF) of an infiltration structure, of the kind that is typically employed for the implementation of low impact development strategies in urban settings. Our approach embeds various sources of uncertainty. These include (a) the mathematical models rendering key hydrological traits of the system and the ensuing model parametrization as well as (b) design variables related to the drainage structure. As such, we leverage on a rigorous multi-model Global Sensitivity Analysis framework. We consider a collection of commonly used alternative models to represent our knowledge about the conceptualization of the system functioning. Each model is characterized by a set of uncertain parameters. As an original aspect, the sensitivity metrics we consider are related to a single- and a multi-model context. The former provides information about the relative importance that model parameters conditional to the choice of a given model can have on PF. The latter yields the importance that the selection of a given model has on PF and enables one to consider at the same time all of the alternative models analyzed. We demonstrate our approach through an exemplary application focused on the preliminary design phase of infiltration structures serving a region in the northern part of Italy. Results stemming from a multi-model context suggest that the contribution arising from the adoption of a given model is key to the quantification of the degree of importance associated with each uncertain parameter.

A Combined Raman Spectroscopy and Atomic Force Microscopy System for In Situ and Real-Time Measures in Electrochemical Cells.

An innovative and versatile set-up for in situ and real time measures in an electrochemical cell is described. An original coupling between micro-Raman spectroscopy and atomic force microscopy enables one to collect data on opaque electrodes. This system allows for the correlation of topographic images with chemical maps during the charge exchange occurring in oxidation/reduction processes. The proposed set-up plays a crucial role when reactions, both reversible and non-reversible, are studied step by step during electrochemical reactions and/or when local chemical analysis is required.

Formulation and probabilistic assessment of reversible biodegradation pathway of Diclofenac in groundwater.

We present a conceptual and mathematical framework leading to the development of a biodegradation model capable to interpret the observed reversibility of the Pharmaceutical Sodium Diclofenac along its biological degradation pathway in groundwater. Diclofenac occurrence in water bodies poses major concerns due to its persistent (and bioactive) nature and its detection in surface waters and aquifer systems. Despite some evidences of its biodegradability at given reducing conditions, Diclofenac attenuation is often interpreted with models which are too streamlined, thus potentially hampering appropriate quantification of its fate. In this context, we propose a modeling framework based on the conceptualization of the molecular mechanisms of Diclofenac biodegradation which we then embed in a stochastic context, thus enabling one to quantify predictive uncertainty. We consider reference environmental conditions (biotic and denitrifying) associated with a set of batch experiments that evidence the occurrence of a reversible biotransformation pathway, a feature that is fully captured by our model. The latter is then calibrated in the context of a Bayesian modeling framework through an Acceptance-Rejection Sampling approach. By doing so, we quantify the uncertainty associated with model parameters and predicted Diclofenac concentrations. We discuss the probabilistic nature of uncertain model parameters and the challenges posed by their calibration with the available data. Our results are consistent with the recalcitrant behavior exhibited by Diclofenac in groundwater and documented through experimental data and support the observation that unbiased estimates of the hazard posed by Diclofenac to water resources should be assessed through a modeling strategy which fully embeds uncertainty quantification.

Natural springs protection and probabilistic risk assessment under uncertain conditions.

The study introduces a comprehensive framework for natural springs' protection and probabilistic risk assessment in the presence of uncertainty associated with the characterization of the groundwater system. The methodology is applied to a regional-scale hydrogeological setting, located in Northern Italy and characterized by the presence of high-quality natural springs forming a unique system whose preservation is of critical importance for the region. Diverse risk pathways are presented to constitute a fault tree model enabling identification of all basic events, each associated with uncertainty and contributing to an undesired system failure. The latter is quantified in terms of hydraulic head falling below a given threshold value for at least one amongst all active springs. The workflow explicitly includes the impact of model parameter uncertainty on the evaluation of the overall probability of system failure due to alternative groundwater extraction strategies. To cope with conceptual model uncertainty, two models based on different reconstructions of the aquifer geological structure are considered. In each conceptual model, hydraulic conductivities related to the geomaterials composing the aquifer are affected by uncertainty. It is found that (a) the type of conceptual model employed to characterize the aquifer structure strongly affects the probability of system failure and (b) uncertainties associated with the ensuing conductivity fields, even as constrained through model calibration, lead to different impacts on the variability of hydraulic head levels at the springs depending on the conceptual model adopted. The results of the study demonstrate that the proposed approach enables one to (i) quantify the risk associated with springs depletion due to alternative strategies of aquifer exploitation; (ii) quantify the way diverse sources of uncertainty (i.e., model and parameter uncertainty) affect the probability of system failure; (iii) determine the optimal exploitation strategy ensuring system functioning; and (iv) identify the most vulnerable springs, where depletion first occurs.

Pore-scale velocities in three-dimensional porous materials with trapped immiscible fluid.

We study and document the influence of wetting and nonwetting trapped immiscible fluid on the probability distribution of pore-scale velocities of the flowing fluid phase. We focus on drainage and imbibition processes within a three-dimensional microcomputed tomographic image of a real rock sample. The probability distribution of velocity magnitude displays a heavier tail for trapped nonwetting than wetting fluid. This behavior is a signature of marked changes in the distribution and strength of preferential flow paths promoted by the wettability property of the trapped fluid. When the latter is wetting the host solid matrix, high-velocity areas initially present during single-phase flow conditions are mainly characterized by increased or decreased velocity magnitudes, and the velocity field remains correlated with its counterpart associated with the single-phase case. Otherwise, when the trapped fluid is nonwetting, features that are observed to prevail are appearance and disappearance of high-velocity areas and a velocity field that is less correlated to the one obtained under single-phase conditions.

Space-time mesh adaptation for solute transport in randomly heterogeneous porous media.

We assess the impact of an anisotropic space and time grid adaptation technique on our ability to solve numerically solute transport in heterogeneous porous media. Heterogeneity is characterized in terms of the spatial distribution of hydraulic conductivity, whose natural logarithm, Y, is treated as a second-order stationary random process. We consider nonreactive transport of dissolved chemicals to be governed by an Advection Dispersion Equation at the continuum scale. The flow field, which provides the advective component of transport, is obtained through the numerical solution of Darcy's law. A suitable recovery-based error estimator is analyzed to guide the adaptive discretization. We investigate two diverse strategies guiding the (space-time) anisotropic mesh adaptation. These are respectively grounded on the definition of the guiding error estimator through the spatial gradients of: (i) the concentration field only; (ii) both concentration and velocity components. We test the approach for two-dimensional computational scenarios with moderate and high levels of heterogeneity, the latter being expressed in terms of the variance of Y. As quantities of interest, we key our analysis towards the time evolution of section-averaged and point-wise solute breakthrough curves, second centered spatial moment of concentration, and scalar dissipation rate. As a reference against which we test our results, we consider corresponding solutions associated with uniform space-time grids whose level of refinement is established through a detailed convergence study. We find a satisfactory comparison between results for the adaptive methodologies and such reference solutions, our adaptive technique being associated with a markedly reduced computational cost. Comparison of the two adaptive strategies tested suggests that: (i) defining the error estimator relying solely on concentration fields yields some advantages in grasping the key features of solute transport taking place within low velocity regions, where diffusion-dispersion mechanisms are dominant; and (ii) embedding the velocity field in the error estimator guiding strategy yields an improved characterization of the forward fringe of solute fronts which propagate through high velocity regions.

Benchmarking numerical codes for tracer transport with the aid of laboratory-scale experiments in 2D heterogeneous porous media.

We present a combined experimental and numerical modeling study that addresses two principal questions: (i) is any particular Eulerian-based method used to solve the classical advection-dispersion equation (ADE) clearly superior (relative to the others), in terms of yielding solutions that reproduce BTCs of the kind that are typically sampled at the outlet of a laboratory cell? and (ii) in the presence of matches of comparable quality against such BTCs, do any of these methods render different (or similar) numerical BTCs at locations within the domain? To address these questions, we obtained measurements from carefully controlled laboratory experiments, and employ them as a reference against which numerical results are benchmarked and compared. The experiments measure solute transport breakthrough curves (BTCs) through a square domain containing various configurations of coarse, medium, and fine quartz sand. The approaches to solve the ADE involve Eulerian-Lagrangian and Eulerian (finite volume, finite elements, mixed and discontinuous finite elements) numerical methods. Model calibration is not examined; permeability and porosity of each sand were determined previously through separate, standard laboratory tests, while dispersivities are assigned values proportional to mean grain size. We find that the spatial discretization of the flow field is of critical importance, due to the non-uniformity of the domain. Although simulated BTCs at the system outlet are observed to be very similar for these various numerical methods, computed local (point-wise, inside the domain) BTCs can be very different. We find that none of the numerical methods is able to fully reproduce the measured BTCs. The impact of model parameter uncertainty on the calculated BTCs is characterized through a set of numerical Monte Carlo simulations; in cases where the impact is significant, assessment of simulation matches to the experimental data can be ambiguous.

Solute concentration at a well in non-Gaussian aquifers under constant and time-varying pumping schedule.

Our study is keyed to the analysis of the interplay between engineering factors (i.e., transient pumping rates versus less realistic but commonly analyzed uniform extraction rates) and the heterogeneous structure of the aquifer (as expressed by the probability distribution characterizing transmissivity) on contaminant transport. We explore the joint influence of diverse (a) groundwater pumping schedules (constant and variable in time) and (b) representations of the stochastic heterogeneous transmissivity (T) field on temporal histories of solute concentrations observed at an extraction well. The stochastic nature of T is rendered by modeling its natural logarithm, Y=lnT, through a typical Gaussian representation and the recently introduced Generalized sub-Gaussian (GSG) model. The latter has the unique property to embed scale-dependent non-Gaussian features of the main statistics of Y and its (spatial) increments, which have been documented in a variety of studies. We rely on numerical Monte Carlo simulations and compute the temporal evolution at the well of low order moments of the solute concentration (C), as well as statistics of the peak concentration (Cp), identified as the environmental performance metric of interest in this study. We show that the pumping schedule strongly affects the pattern of the temporal evolution of the first two statistical moments of C, regardless the nature (Gaussian or non-Gaussian) of the underlying Y field, whereas the latter quantitatively influences their magnitude. Our results show that uncertainty associated with C and Cp estimates is larger when operating under a transient extraction scheme than under the action of a uniform withdrawal schedule. The probability density function (PDF) of Cp displays a long positive tail in the presence of time-varying pumping schedule. All these aspects are magnified in the presence of non-Gaussian Y fields. Additionally, the PDF of Cp displays a bimodal shape for all types of pumping schemes analyzed, independent of the type of heterogeneity considered.

Automatic method for estimation of in situ effective contact angle from X-ray micro tomography images of two-phase flow in porous media.

Multiphase flow in porous media is strongly influenced by the wettability of the system, which affects the arrangement of the interfaces of different phases residing in the pores. We present a method for estimating the effective contact angle, which quantifies the wettability and controls the local capillary pressure within the complex pore space of natural rock samples, based on the physical constraint of constant curvature of the interface between two fluids. This algorithm is able to extract a large number of measurements from a single rock core, resulting in a characteristic distribution of effective in situ contact angle for the system, that is modelled as a truncated Gaussian probability density distribution. The method is first validated on synthetic images, where the exact angle is known analytically; then the results obtained from measurements within the pore space of rock samples imaged at a resolution of a few microns are compared to direct manual assessment. Finally the method is applied to X-ray micro computed tomography (micro-CT) scans of two Ketton cores after waterflooding, that display water-wet and mixed-wet behaviour. The resulting distribution of in situ contact angles is characterized in terms of a mixture of truncated Gaussian densities.

Natural background levels and threshold values of chemical species in three large-scale groundwater bodies in Northern Italy.

We analyze natural background levels (NBLs) and threshold values (TVs) of spatially distributed chemical species (NH(4), B and As) which may be a potential pressure and concern in three large scale alluvial and fluvio-deltaic aquifers at different depths of the Apennines and Po river plains in Emilia-Romagna, Northern Italy. Our results are based on statistical methodologies designed to separate the natural and anthropogenic contributions in monitored concentrations by modeling the empirical distribution of the detected concentration with a mixture of probability density functions. Available chemical observations are taken over a 20 years period and are associated with different depths and cover planar investigation scales of the order of hundreds of kilometers. High concentration values detected for NH(4) and B appear to be related to high natural background levels. Due to interaction with the host rock in different geochemical environments we observed that concentration vary in time and space (including in depth) consistently with the hydrogeochemical features and the occurrence of natural attenuation mechanisms in the analyzed reservoirs. Conversely, estimated As NBLs are not consistent with the conceptual model of the hydrogeochemical behavior of the systems analyzed and experimental evidences of As content in aquifer cores. This is due to the inability of these techniques to incorporate the complex dynamics of the processes associated with the specific hydrogeochemical setting. Statistical analyses performed upon aggregating the concentration data according to different time observation windows allow identifying temporal dynamics of NBLs and TVs of target compounds within the observation time frame. Our results highlight the benefit of a dynamic monitoring process and analysis of well demarcated groundwater bodies to update the associated NBLs as a function of the temporal dependence of natural processes occurring in the subsurface. Monitoring protocols could also include the detailed evaluation of the geochemistry (redox) of the aquifers.

Experimental and modeling investigation of multicomponent reactive transport in porous media.

We present an experimental and modeling study of solute transport in porous media in the presence of mixing-induced precipitation of a solid phase. Conservative and reactive transport experiments were performed in a quasi-two-dimensional laboratory flow cell, filled with homogeneous and heterogeneous porous media. Conservative experiments were performed by injecting solutions containing sodium chloride and calcium chloride into the domain. In reactive transport experiments, inlet solutions of calcium chloride and sodium carbonate were injected in parallel, resulting in calcium carbonate precipitation where the solutions mix. Experimental results were used as a benchmark to examine the performance of a reactive transport numerical model. Good agreement between model predictions and experimental results was obtained for the conservative transport experiments. The reactive transport experiments featured the formation of a calcium carbonate mineral phase within the mixing zone between the two solutions, which controlled the spatial evolution of calcium carbonate in the domain. Numerical simulations performed on high resolution grids for both the homogeneous and heterogeneous porous systems underestimated clogging of the system. Although qualitative agreement between model results and experimental observations was obtained, accurate model predictions of the spatial evolution of calcium concentrations at sample points within the flow cell could not be achieved.

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.