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• W2809010667 abstract "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." @default.
• W2809010667 created "2018-06-29" @default.
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• W2809010667 date "2018-06-22" @default.
• W2809010667 modified "2023-10-01" @default.
• W2809010667 title "<i>In Situ</i> Monitoring of Groundwater Contamination Using the Kalman Filter" @default.
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• W2809010667 doi "https://doi.org/10.1021/acs.est.8b00017" @default.
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• W2809010667 hasPublicationYear "2018" @default.
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