What question did this study set out to answer?

Develop a real-time, in situ monitoring framework to continuously estimate subsurface contaminant concentrations using readily measurable water quality surrogates.

June 22, 2018Environmental Science & TechnologyOpen Access

In Situ Monitoring of Groundwater Contamination Using the Kalman Filter

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Authors

FSFranziska SchmidtLos Alamos National Laboratory HWHaruko WainwrightMassachusetts Institute of Technology BFBoris FaybishenkoLawrence Berkeley National Laboratory

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Overview

Modeling study demonstrates real-time estimation of tritium and uranium levels in groundwater using pH and specific conductance, highlighting a reliable early-warning tracking framework.

Key Points

Develop a real-time, in situ monitoring framework to continuously estimate subsurface contaminant concentrations using readily measurable water quality surrogates.
Applied principal component analysis (PCA) to historical groundwater records to identify statistical correlations between target contaminants and proxy variables (pH and specific conductance).
Coupled data-driven concentration-decay models with identified correlations inside a Kalman filter algorithm to continuously estimate contaminant levels over time.
Tested and evaluated the framework using historical field monitoring data of tritium and uranium from the Savannah River Site F-Area.
Accurately estimated tritium and uranium concentrations from surrogate specific conductance and pH measurements even with reduced or absent direct contaminant sampling.
Captured both short-term variations and long-term dynamics of contaminant concentration shifts, effectively confirming plume stability and providing an early-warning mechanism for migration.

Cite This Study

Schmidt et al. (2018) studied this question.

synapsesocial.com/papers/6a72d59fb0d581bd9f303c1f https://doi.org/10.1021/acs.est.8b00017

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June 22, 2018Environmental Science & TechnologyOpen Access

In Situ Monitoring of Groundwater Contamination Using the Kalman Filter

View Full Paper

Ask AI

Bookmark

Authors

FSFranziska SchmidtLos Alamos National Laboratory HWHaruko WainwrightMassachusetts Institute of Technology BFBoris FaybishenkoLawrence Berkeley National Laboratory

Discussion

Member takes

Overview

Modeling study demonstrates real-time estimation of tritium and uranium levels in groundwater using pH and specific conductance, highlighting a reliable early-warning tracking framework.

Key Points

Develop a real-time, in situ monitoring framework to continuously estimate subsurface contaminant concentrations using readily measurable water quality surrogates.
Applied principal component analysis (PCA) to historical groundwater records to identify statistical correlations between target contaminants and proxy variables (pH and specific conductance).
Coupled data-driven concentration-decay models with identified correlations inside a Kalman filter algorithm to continuously estimate contaminant levels over time.
Tested and evaluated the framework using historical field monitoring data of tritium and uranium from the Savannah River Site F-Area.
Accurately estimated tritium and uranium concentrations from surrogate specific conductance and pH measurements even with reduced or absent direct contaminant sampling.
Captured both short-term variations and long-term dynamics of contaminant concentration shifts, effectively confirming plume stability and providing an early-warning mechanism for migration.

Cite This Study

Schmidt et al. (2018) studied this question.

synapsesocial.com/papers/6a72d59fb0d581bd9f303c1f https://doi.org/10.1021/acs.est.8b00017

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