In Situ Monitoring of Groundwater
Contamination Using the Kalman Filter
Posted on 2018-06-22 - 14:56
This
study presents a Kalman filter-based framework to establish
a real-time in situ monitoring system for groundwater
contamination based on in situ 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 in situ 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 in situ 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 in situ 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.
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Schmidt, Franziska; Wainwright, Haruko M.; Faybishenko, Boris; Denham, Miles; Eddy-Dilek, Carol (2018). In Situ Monitoring of Groundwater
Contamination Using the Kalman Filter. ACS Publications. Collection. https://doi.org/10.1021/acs.est.8b00017
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AUTHORS (5)
FS
Franziska Schmidt
HW
Haruko M. Wainwright
BF
Boris Faybishenko
MD
Miles Denham
CE
Carol Eddy-Dilek