Transport and fate of PFAS in the subsurface

Per- and polyfluoroalkyl substances (PFAS) are a diverse group of fluorinated organic contaminants in the environment resulting from use at firefighting facilities, in consumer products, waste at manufacturing sites, and disposal in landfills.  Leaching of per- and polyfluoroalkyl substances (PFAS) currently retained in the shallow subsurface above the water table are a persistent source of groundwater contamination. This retention process is very complex because the timing and extent of PFAS mobility in the shallow subsurface are controlled by soil properties, climate and weather events, and site specific hydrogeologic conditions. Despite important advances in understanding the migration and retention behavior of PFAS, there remains a critical knowledge gap on how spatial variation in geologic properties—that have a strong impact on groundwater flow processes—impact how, when, and where PFAS contamination migrates in the subsurface.

The overall objective of this collaborative NSF-funded project is to utilize medical imaging technology and recent advances in numerical models to quantify dynamic, spatially variable PFAS adsorption in heterogeneous unsaturated geologic porous media. The specific aims are to measure in situ adsorption of two representative PFAS in saturated and partially saturated heterogeneous columns. These time-lapse imaging measurements will then be used to provide direct validation to advanced spatially-resolved numerical models and enable the development of upscaled models of PFAS transport under heterogeneous subsurface conditions. The project is anticipated to provide unprecedented in situ transport and adsorption measurements, upscaling approaches, and parameterization of a newly-developed numerical model used to describe and predict PFAS leaching through soils to groundwater aquifers.

This research is expected to advance our ability to quantitatively measure and model PFAS migration and retention under more realistic geologic conditions. This will result in the improved wellbeing of individuals in society through the application of mathematical models to better understand PFAS contamination at a site in rural Wisconsin. Furthermore, improved understanding of PFAS migration in the subsurface will enable future progress in PFAS modeling, prediction, and remediation strategy development.

Theoretical calculations of interfacial PFAS retardation due to adsorption on air-water interfaces at unsaturated conditions.

Measurement of bacterial transport and immobilization in variably saturated geologic materials of WI

The occurrence of bacteria and other microbial agents in private wells across the state of Wisconsin poses significant risks to human health. Discrepancies between bacteria measurements in drinking water wells compared with the expected near-surface immobilization from laboratory experiments in homogeneous geologic materials indicate an incomplete understanding of dynamic processes of bacteria transport and immobilization in realistic geologic systems. The overall objective of this collaborative project funded by the University of Wisconsin Water Resources Institute is to quantify preferential bacterial breakthrough and immobilization through heterogenous saturated and unsaturated columns representative of soils in the Central Sands Region of Wisconsin. To achieve this objective, we are using positron emission tomography (PET), an imaging modality capable of quantifying the migration of radiolabeled bacteria (E. coli) through columns of geologic materials. Imaging data will provide three-dimensional time-lapse observations of the role of geologic heterogeneity and fluid saturation conditions on the advection, dispersion, and immobilization of radiolabeled bacteria. The expected outcomes will be unprecedented experimental measurements and uniquely constrained analytical and numerical models of bacterial transport and immobilization in geologic porous media. These measurements and models will provide more accurate and geologically specific information about how bacteria and microbial agents contaminate groundwater sources. This information will complement existing contamination risk parameters such as depth-to-bedrock measurements, soil type, and climate and weather conditions to improve guidance, regulations, and risk assessments of bacterial contamination of drinking water wells in Wisconsin.

Statistical and Deep Learning Approaches for Permeability Inversion Using Massive Positron Emission Tomography Datasets

A positron emission tomography image at a single timestep often consists of over 10,000 concentration measurements of the radiolabeled substance present in a sediment column or geologic core. These massive time-lapse datasets are possible due to the millimeter-scale discretization, termed ‘voxels’, of positron emission tomography (PET) images. With PET imaging, it is now possible to acquire in situ measurements of a wide range of radiolabeled fluids, gases, and nanoparticles within a discretized 3D spatial domain. The imaging method enables us to generate massive volumes of data not typically available from traditional hydrogeologic laboratory or field approaches. What is not known is how to fully maximize the value of these massive time-lapse datasets for multi-scale model parameterization, as this requires a paradigm shift from data interpretation and inversion approaches designed for small sparse datasets. The objective of this project is to develop mathematical algorithms to calculate multi-scale permeability heterogeneity in geologic core samples using positron emission tomography datasets collected from experiments in a wide range of geologic samples. A preprint of our recent progress can be found here.

Predicted permeability field (left image) based on the inversion of numerically calculated solute transport information calculated on the original synthetic permeability field (right image).

Fluid channelization and fracture-matrix interaction

Understanding flow and transport behavior in fractures is important for geothermal energy recovery, geologic carbon storage security, and unconventional resource recovery. The focus of research projects in this area is to utilize positron emission tomography (PET) to quantify fracture connectivity, fluid channelization, and fracture-matrix interaction in complex fracture networks. An example of this type of imaging data is shown in the video below, highlighting how aqueous radiotracer transport occurs almost exclusively in pre-existing fractures.  This data is used to constrain a range of models addressing questions such as:

  • How does fracture roughness impact flow channelization and how does this change with mechanical displacement?
  • How does solute diffusion into the rock matrix change as a function of flow conditions and fracture alteration?

Time-lapse image of solute traveling through a naturally fractured shale core (from right to left) over the course of six hours. 

Spatial and temporal quantification of nanoparticle transport in geologic porous media

Silica nanoparticles exist in the environment due to increasing utilization in consumer products, lubricants, and production during certain manufacturing and combustion processes. In addition to their undesirable—and potentially hazardous presence in the environment—nanoparticles are rapidly emerging as a tool for fluid flow alteration in the subsurface, and for removal of organic pollutants, toxic metals, uranium, and phosphate from contaminated water. Nanoparticle transport and retention in simplified porous media has been well constrained and mechanistically described. However, we lack quantitative understanding and prediction in dynamic non-ideal systems where nanoparticles are influenced by a complex combination of inconsistent hydrodynamic forces and physical and chemical heterogeneity. The goal of this project is to use novel experimental methods to develop a better understanding of in situ nanoparticle transport in geologic media. Specifically, we aim to use in situ imaging of nanoparticle transport and retention to improve mechanistic understanding and modeling predictions of the flow and fate of nanoparticles in permeable geologic media.