Machine learning assists in increasing the time resolution of X-ray computed tomography applied to mineral precipitation in porous media.

Many subsurface engineering technologies or natural processes cause porous medium properties, such as porosity or permeability, to evolve in time. Studying and understanding such processes on the pore scale is strongly aided by visualizing the details of geometric and morphological changes in the pores. For realistic 3D porous media, X-Ray Computed Tomography (XRCT) is the method of choice for visualization. However, the necessary high spatial resolution requires either access to limited high-energy synchrotron facilities or data acquisition times which are considerably longer (e.g. hours) than the time scales of the processes causing the pore geometry change (e.g. minutes). Thus, so far, conventional benchtop XRCT technologies are often too slow to allow for studying dynamic processes. Interrupting experiments for performing XRCT scans is also in many instances no viable approach. We propose a novel workflow for investigating dynamic precipitation processes in porous media systems in 3D using a conventional XRCT technology. Our workflow is based on limiting the data acquisition time by reducing the number of projections and enhancing the lower-quality reconstructed images using machine-learning algorithms trained on images reconstructed from high-quality initial- and final-stage scans. We apply the proposed workflow to induced carbonate precipitation within a porous-media sample of sintered glass-beads. So we were able to increase the temporal resolution sufficiently to study the temporal evolution of the precipitate accumulation using an available benchtop XRCT device.

Anthropogenic Trace Compounds (ATCs) in aquatic habitats - research needs on sources, fate, detection and toxicity to ensure timely elimination strategies and risk management.

Anthropogenic Trace Compounds (ATCs) that continuously grow in numbers and concentrations are an emerging issue for water quality in both natural and technical environments. The complex web of exposure pathways as well as the variety in the chemical structure and potency of ATCs represents immense challenges for future research and policy initiatives. This review summarizes current trends and identifies knowledge gaps in innovative, effective monitoring and management strategies while addressing the research questions concerning ATC occurrence, fate, detection and toxicity. We highlight the progressing sensitivity of chemical analytics and the challenges in harmonization of sampling protocols and methods, as well as the need for ATC indicator substances to enable cross-national valid monitoring routine. Secondly, the status quo in ecotoxicology is described to advocate for a better implementation of long-term tests, to address toxicity on community and environmental as well as on human-health levels, and to adapt various test levels and endpoints. Moreover, we discuss potential sources of ATCs and the current removal efficiency of wastewater treatment plants (WWTPs) to indicate the most effective places and elimination strategies. Knowledge gaps in transport and/or detainment of ATCs through their passage in surface waters and groundwaters are further emphasized in relation to their physico-chemical properties, abiotic conditions and biological interactions in order to highlight fundamental research needs. Finally, we demonstrate the importance and remaining challenges of an appropriate ATC risk assessment since this will greatly assist in identifying the most urgent calls for action, in selecting the most promising measures, and in evaluating the success of implemented management strategies.

Estimation of effective parameters for a two-phase flow problem in non-Gaussian heterogeneous porous media.

In this paper we discuss estimates of effective parameters for an upscaled model for buoyant counter flow of DNAPL and water in a closed box filled with heterogeneous porous material. The upscaling procedure is based on the assumption that the flow is dominated by capillary forces on the small scale and that the fluids are segregated. The upscaled model has the same form as the usual two-phase flow model with an effective capillary pressure function and an effective mobility function Λ. Effective parameters are then estimated in two different ways. Stochastic theory can be applied to calculate the effective parameters to first order in the parameter fluctuations. This approach does not take into account that different parameter ranges of the heterogeneous field may be connected or isolated, yielding very different macroscopic residual saturations. Therefore, the second estimate of effective parameters takes connectivity of parameter ranges into account. In this case, the univariate parameter distribution of the heterogeneous field and the values that mark connected materials are the only information about heterogeneity that is used. Effective parameters are then estimated using mean field theory (the Maxwell approach). The upscaled model and the estimation of effective parameters are applied to a numerical test case. Buoyant counter flow in heterogeneous parameter fields with different structures is simulated numerically and compared to the solutions of the quasi-1d upscaled model with differently estimated parameters. It is demonstrated that connectivity of the different parameter ranges is an important information that determines typical time scales for the flow process and the macroscopic residual saturation. Even simple estimates of effective parameters based on little information may capture the typical time scales, provided that information about connected parameter ranges is taken into account.