[A-HW25] Near Surface Investigation and Modeling for Groundwater Resources Assessment and Conservation

[AHW25-01] Advanced Long-term Environmental Monitoring Systems
(ALTEMIS) for Sustainable Groundwater Remediation

★Invited Papers

*Haruko M Wainwright¹ (1.Massachusetts Institute of Technology)

Keywords:Groundwater contamination, machine learning, sensor technologies

Sustainable remediation has emerged as a key concept to address soil and groundwater contamination over the past decade, promoting the transition from intense soil removal and treatments towards more effective and sustainable approaches as well as passive remediation and monitored natural attenuation (MNA). Long-term monitoring is critical for such sites to confirm system stability and the continuing reduction of contaminant and hazard levels, and to detect changes or anomalies in contaminant mobility (if they occur). The Advanced Long-term Environmental Monitoring Systems (ALTEMIS) project funded by the US Department of Energy aims to establish the new paradigm of long-term monitoring based on state-of-art technologies – in situ groundwater sensors, geophysics, drone/satellite-based remote sensing, reactive transport modeling, and AI – that will improve effectiveness and robustness, while reducing the overall cost. In particular, we focus on (1) spatially integrative technologies for monitoring system vulnerabilities – surface cap systems and groundwater/surface water interfaces using geophysics, UAV and distributed sensors, and (2) in situ in-well sensor technologies for monitoring master variables that control or are associated with contaminant plume mobility and direction, (3) open-source machine learning framework, PyLEnM (Python for Long-term Environmental Monitoring) for spatiotemporal interpolations and monitoring design optimization, and (4) high-performance computing-based contaminant transport modeling for evaluating monitoring designs and climate vulnerability/resilience. This system transforms the monitoring paradigm from reactive monitoring – respond after plume anomalies are detected – to proactive monitoring – detect the changes associated with the plume mobility before concentration anomalies occur. In addition, through the open-source package, we aim to improve the transparency of data analytics at contaminated sites, empowering concerned citizens as well as improving public relationship. We envision that this type of monitoring methods can be applied to the existing and future nuclear facilities for detecting any anomalies and leakages quickly or for assuring the public about the safety of those facilities.