<i>In Situ</i> Monitoring of Groundwater Contamination Using the Kalman Filter

This study presents a Kalman filter-based framework to establish a real-time <i>in situ</i> monitoring system for groundwater contamination based on <i>in situ</i> measurable water quality variables, such as specific conductance (SC) and pH. First, this framework uses principal component analysis (PCA) to identify correlations between the contaminant concentrations of interest and <i>in situ</i> measurable variables. It then applies the Kalman filter to estimate contaminant concentrations continuously and in real-time by coupling data-driven concentration-decay models with the previously identified data correlations. We demonstrate our approach with historical groundwater data from the Savannah River Site F-Area: We use SC and pH data to estimate tritium and uranium concentrations over time. Results show that the developed method can estimate these contaminant concentrations based on <i>in situ</i> measurable variables. The estimates remain reliable with less frequent or no direct measurements of the contaminant concentrations, while capturing the dynamics of short- and long-term contaminant concentration changes. In addition, we show that data mining, such as PCA, is useful to understand correlations in groundwater data and to design long-term monitoring systems. The developed <i>in situ</i> monitoring methodology is expected to improve long-term groundwater monitoring by continuously confirming the contaminant plume’s stability and by providing an early warning system for unexpected changes in the plume’s migration