Abstract: Groundwater remediation management does not end with the installation of the cleanup system and flipping the switch. Water quality samples from groundwater monitoring wells are used to verify that the remediation plan is working as planned, and to ensure that there are no unexpected impacts or risks. An important component of the adaptive long-term monitoring and operation system package aLTMOs is a data-driven Bayesian filter, a data assimilation technique that integrates monitoring data and phenomenological modeling results to provide insightful analyses on plume dynamics in both spatial and temporal dimensions. In this process, a model prediction is calculated using the flow and transport simulation. When new measurements are made, they are used systematically to adjust the model prediction. Irregular spatial and temporal data collection can be incorporated. Concentration estimates and their uncertainties are updated as monitoring data are collected. Based on uncertainty analysis in spatial and temporal dimensions and cleanup goals, an optimal monitoring plan is selected. This paper uses a real superfund site problem to demonstrate the application of the data-driven Bayesian modeling technique in uncertainty analysis and redundant sampling reduction optimization in groundwater long term monitoring design.