Advanced Long-term Environmental Monitoring Systems (ALTEMIS) for Sustainable Remediation - 22041
Creators
- Wainwright, Haruko1
- Xu, Zexuan1
- Dafflon, Baptiste1
- Uhlemann, Sebastian1
- Praveen, Satyarsh1
- Vetter, Kai1
- Quiter, Brian1
- Gonzalez Raymat, Hansell2
- Danielson, Tom2
- Meray, Aurelien3
- Siddiquee, Masudur3
- Upadhyay, Himanshu3
- Eddy-Dilek, Carol3
- Johnson, Tim4
- Denham, Miles5
- WM Symposia, Inc., PO Box 27646, 85285-7646 Tempe, AZ (United States)
- 1. Lawrence Berkeley National Laboratory (United States)
- 2. Savannah River National Laboratory (United States)
- 3. Applied Research Center - FIU (United States)
- 4. Pacific Northwest National Laboratory (United States)
- 5. Panoramic Environmental Consulting, LLC (United States)
Description
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 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 artificial intelligence (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, gamma-ray mapping 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. (authors)
Availability note (English)
Available from: WM Symposia, Inc., PO Box 27646, 85285-7646 Tempe, AZ (US)Additional details
Identifiers
Publishing Information
- Imprint Pagination
- 43 p.
- Report number
- INIS-US--24-WM-22041
Conference
- Title
- WM2022 - 48. Annual Waste Management Conference
- Dates
- 6-10 Mar 2022
- Place
- Phoenix - Arizona (United States)
INIS
- Country of Publication
- United States
- Country of Input or Organization
- France
- INIS RN
- 55069521
- Subject category
- S12: MANAGEMENT OF RADIOACTIVE WASTES, AND NON-RADIOACTIVE WASTES FROM NUCLEAR FACILITIES;
- Resource subtype / Literary indicator
- Conference, Non-conventional Literature
- Descriptors DEI
- GROUND WATER; INTERFACES; INTERPOLATION; MAPPING; MOBILITY; MONITORING; OPACITY; OPTIMIZATION; PYTHON; REMEDIAL ACTION; SATELLITES; SENSORS; SIMULATION; SOILS; TRANSPORT; VULNERABILITY
- Descriptors DEC
- HYDROGEN COMPOUNDS; MATHEMATICAL SOLUTIONS; NUMERICAL SOLUTION; OPTICAL PROPERTIES; OXYGEN COMPOUNDS; PHYSICAL PROPERTIES; PROGRAMMING LANGUAGES; WATER
Optional Information
- Notes
- 14 refs.; available online at: https://www.xcdsystem.com/wmsym/2022/sessions.cfm