August 17, 2018
In Situ monitoring of groundwater contamination using the Kalman filter.
A real-time, in situ, "smart" monitoring and early warning system for migrating and reacting contaminant plumes was developed using a Kalman filter-based framework and successfully tested at a contaminant plume at the Savannah River Site.
Because this framework enables easy integration of networked, autonomous, and inexpensive wellbore measurements with cloud computing, the approach is expected to reduce groundwater monitoring cost, increase confidence in the efficacy of monitored natural attenuation, and ensure early response if needed.
A Kalman filter method was used to estimate contaminant concentrations continuously and in real-time by coupling data-driven concentration decay models with data correlations. The approach was successfully demonstrated using historical groundwater data from the uranium- and tritium-contaminated F-Area of the Savannah River Site. Specific conductance and pH were used as proxy variables to estimate tritium and uranium concentrations over time. Results show that the developed method can estimate contaminant concentrations based on in situ, easily measured variables
BER Program Manager
Lawrence Berkeley National Laboratory
This material is based on work supported as part of the Lawrence Berkeley National Laboratory Science Focus Area, which is funded by the Office of Biological and Environmental Research, within the U.S. Department of Energy Office of Science, and as part of the Advanced Simulation Capability for Environmental Management (ASCEM) project, which is funded by the U.S. Department of Energy Office of Environmental Management. Both efforts are under Award Number DE-AC02- 05CH11231 to Lawrence Berkeley National Laboratory. Franziska Schmidt was supported by the Jane Lewis Fellowship at University of California, Berkeley.
Schmidt, F., Wainwright, H. M., Faybishenko, B., Denham, M., & Eddy-Dilek, C. "In Situ Monitoring of Groundwater Contamination Using the Kalman Filter." Environmental Science & Technology 52(13), 7418–25 (2018). DOI: 10.1021/acs.est.8b00017.
Berkeley Lab Watershed Function SFA