07/01/2018 - 06/30/2020
- Matthew Ginder-Vogel, UW-Madison, Dept. of Civil and Environmental Engineering
- Madeline Gotkowitz, Montana Bureau of Mines and Geology (previously affiliated with the Wisconsin Geological and Natural History Survey)
- Florence Udenby, University of Toronto, Dept. of Civil and Mineral Engineering (previously affiliated with the Wisconsin Department of Natural Resources)
Groundwater, an important source of drinking water in Wisconsin, is susceptible to contamination by naturally occurring metals and radionuclides (e.g., radium). Many wells open to the Cambrian-Ordovician aquifer system (COAS) in Wisconsin source water containing radium (Ra) levels measuring at or above the maximum contaminant level (MCL) of the combined activity of 226Ra and 228Ra. Regional groundwater quality trends are useful to predict Ra occurrence across the Midwestern US; however, complex contaminant-solid phase associations make it difficult to use these trends at the state or municipal level. This study aims to develop a conceptual understanding of the major sources of Ra to groundwater throughout the COAS in Wisconsin, using sequential extractions examining Ra-solid phase associations as well as temporal and spatial analysis of longterm datasets.
This study develops a combined geochemical and hydrologic conceptual model describing Ra release from, and potential sequestration in, in the COAS in Wisconsin in order to provide a scientific basis for strategies to minimize Ra in drinking water sourced from groundwater. Particularly, we demonstrate the influence of local factors on Ra mobilization in the COAS in Wisconsin, where it depends on both reactive solid-phases present in bedrock stratigraphy and on local geochemical conditions influencing solid-phase-Ra interactions. This suggests that changes in geochemical conditions (e.g., competitive ion exchange) impacts Ra partitioning from solidphase to the aqueous system differently across stratigraphic units. We also display the value of using water quality compliance datasets for investigating contaminant trends both spatially and temporally; it is important to keep in mind geographic scale and context in examining Ra trends.