Per- and polyfluoroalkyl substances (PFAS) fate and transport across a groundwater-surface water interface

Per- and polyfluoroalkyl substances (PFAS) are emerging contaminants of concern whose fate and transport in environmental media are incompletely understood. In the 1960s, PFAS were dumped in the House Street Disposal Site, an unlined landfill on the crest of a glacial end moraine near Rockford, Michigan, USA. In 2017, PFAS were discovered in groundwater and subsequently, a network of monitoring wells delineated a 2 mi (3 km) PFAS plume migrating downgradient toward the Rogue River. Today, the Michigan Department of Natural Resources (MDNR) operates fish-rearing ponds in the area where the plume intersects the groundwater-surface water interface (GSI). Each year, the MDNR fills these man-made ponds using water from a nearby creek. Springs in the ponds prevent them from draining completely at the end of fish-rearing each fall. We sampled surface water and modeled groundwater flow to investigate PFAS transport across the GSI. Numerical models constructed with and without the fishponds did not substantially change MODFLOW model calibration curves or predicted MODPATH flow lines, indicating that PFAS transport is dominated by the regional flow system with limited influence from semiannual changes to boundary conditions at the GSI. Surface water samples collected from five locations within and adjacent to the fishponds were analyzed using EPA Draft Method 1633. PFAS were detected at all locations with the highest total PFAS >60 ng/L in the fishponds. Mixing models based on total PFAS indicate that approximately 10 % of the fishpond water is sourced by groundwater. However, similar analyses with perfluoroalkyl carboxylic acids (PFCA) and perfluoroalkyl sulfonic acids (PFSA) imply that groundwater comprises as much as 30 % of water in the ponds, suggesting differential movement of individual PFAS across the groundwater-surface water interface. Additional investigation of PFAS within the pond sediments is needed to better understand partitioning and differential transport behavior across the GSI.