The overarching goal of this project is to develop and demonstrate an advanced, comprehensive decision support platform—referred to as PFAS-LEACH—that can predict per- and polyfluoroalkyl substance (PFAS) leaching in source zones. Designed to predict source attenuation, spatial mass distribution, and long-term mass discharge to groundwater at PFAS-impacted source zones, it is anticipated that PFAS-LEACH will improve risk assessment and long-term site management, and will be useful for developing remedial action objectives (RAOs) and for evaluating anticipated impacts of different site remediation approaches at many Department of Defense (DoD) and other PFAS-impacted sites. The project has the following specific technical objectives:

1. Prepare and instrument novel pilot-scale PFAS “first-release” and leaching experiments;
2. Generate high-resolution pilot-scale PFAS “first-release” and leaching data;
3. Test and validate PFAS-LEACH-COMP at the pilot- and field-scales;
4. Develop and demonstrate a comprehensive decision support platform PFAS-LEACH for predicting PFAS leaching in source zones; and 
5. Develop and communicate materials to facilitate technology transition.

PFAS-LEACH represents the first decision support platform designed specifically to determine PFAS leaching in source zones. It is comprised of three tiers of simulators decreasing in model complexity, and a comprehensive parameter selection module to support the simulators.

• The Tier 1 simulator is a state-of-the-art numerical code that incorporates a comprehensive representation of flow, transport, and transformation processes, referred to as PFAS-LEACH-COMP.
• The Tier 2 simulator [PFAS-LEACH-HYDRUS] will incorporate a set of simplifications to focus on one-dimensional leaching along the vertical direction and will be implemented within a modified version of the widely used industry-standard software, HYDRUS1D.
• The Tier 3 simulator [PFAS-LEACH-Screening], implemented in Excel, will incorporate additional simplifications such that flow and transport can be solved either with a set of analytical solutions or a compartment model that employs simple mass-balance calculations.

Spanning a wide range of complexity, the simulators are expected to be used at different stages of site management depending on the availability of data and parameters at the sites. In this project, a set of unique data will be generated that represents the “first-release” scenarios of PFAS impact at the pilot-scale in well-instrumented long soil cores and large soil monolith under highly controlled initial and boundary conditions. These highly controlled pilot-scale experiments will be combined with the field-scale leaching data from several PFAS-impacted sites to test and validate the efficacy of PFAS-LEACH in providing accurate prediction of the spatial and temporal dynamics of PFAS leaching in source zones and the mass flux discharging to groundwater.

PFAS-LEACH will provide capability to predict spatial distributions of PFAS in the vadose zone and mass discharge to groundwater. These quantitative predictions will enable more accurate site characterizations, risk assessments, and development of RAOs, which will reduce risks, improve engineering design of remediation and mitigation efforts, as well as reduce site-management costs for many DoD and other PFAS-impacted sites. Additionally, the unique datasets generated by the highly controlled pilot-scale transport experiments in this project can be used to rigorously test and validate PFAS transport models that are being or will be developed in other projects supported by SERDP and ESTCP. (Anticipated Project Completion - 2024)

Brusseau, M.L. and B. Guo. 2022. PFAS Concentrations in Soil versus Soil Porewater: Mass Distributions and the Impact of Adsorption at Air-Water Interfaces. Chemosphere, 302:134938. doi.org/10.1016/j.chemosphere.2022.134938.

Brusseau, M.L. and B. Guo. 2021. Air-Water Interfacial Areas Relevant for Transport of Per- and Poly-Fluoroalkyl Substances. Water Research, 207:117785. doi.org/10.1016/j.watres.2021.117785.

Brusseau, M.L. and B. Guo. 2023. Revising the EPA Dilution-attenuation Soil Screening Model for PFAS. Journal of Hazardous Materials Letters, 4, p.100077. (Editor’s Choice)

Brusseau, M.L., B. Guo, D. Huang, N. Yan, and Y. Lyu. 2021. Ideal Versus Nonideal Transport of PFAS in Unsaturated Porous Media. Water Research, 202:117405. doi.org/10.1016/j.watres.2021.117405.Guo, B., J. Zeng, M.L. Brusseau, and Y. Zhang. 2022. A Screening Model for Quantifying PFAS Leaching in the Vadose Zone and Mass Discharge to Groundwater. Advances in Water Resources, 160:104102. doi.org/10.1016/j.advwatres.2021.104102.

Guo, B., H. Saleem, M.L. and Brusseau. 2023. Predicting Interfacial Tension and Adsorption at Fluid–fluid Interfaces for Mixtures of PFAS and/or Hydrocarbon Surfactants. Environmental Science & Technology. doi.org/10.1021/acs.est.2c08601. 

Huang, D., H. Saleem, B. Guo, and M.L. Brusseau. 2022. The Impact of Multiple-Component PFAS Solutions on Fluid-Fluid Interfacial Adsorption and Transport of PFOS in Unsaturated Porous Media. Science of The Total Environment, 806(2):150595. doi.org/10.1016/j.scitotenv.2021.150595.

Lyu, Y., B. Wang, X. Du, B. Guo, and M.L. Brusseau. 2022. Air-water Interfacial Adsorption of C4-C10 Perfluorocarboxylic Acids during Transport in Unsaturated Porous Media. Science of The Total Environment, 831, p.154905.

Zeng, J. and B. Guo. 2021. Multidimensional Simulation of PFAS Transport and Leaching in the Vadose Zone: Impact of Surfactant-Induced Flow and Subsurface Heterogeneities. Advances in Water Resources, 155:104015. doi.org/10.1016/j.advwatres.2021.104015.

Zeng, J., M.L. Brusseau, and B. Guo. 2021. Model Validation and Analyses of Parameter Sensitivity and Uncertainty for Modeling Long-Term Retention and Leaching of PFAS in the Vadose Zone. Journal of Hydrology, 603(D):127172. doi.org/10.1016/j.jhydrol.2021.127172. 

Zeng, J. and B. Guo. 2023. Reduced Accessible Air–water Interfacial Area Accelerates PFAS Leaching in Heterogeneous Vadose Zones. Geophysical Research Letters, 50, e2022GL102655.