Environmental Remediation and Water Resources

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SBIR Phase II: Predictive Assimilation Framework for Subsurface Process Prediction

DOE-SC-Biological and Environmental Research

In the 21st century, society will need to address and resolve a large number of challenges related to terrestrial subsurface systems. One of the most important of these challenges is the remediation and long-term management of contaminated sites.

The Small Business Innovation Research (SBIR) project concentrates on the creation of a predictive assimilation framework (PAF) for contaminated sites. This PAF would autonomously assimilate different site-related data streams into numerical models, and provide information on current (and future) site and system behavior to site stakeholders. The technical and scientific capabilities of the PAF are developed and tested by incorporating (into adequate numerical models) a variety of hydrological, geophysical and biogeochemical datasets from a highly instrumented site (the DOE Rifle Subsurface Biogeochemistry Field Observatory in Rifle, Colorado).

The overall objective of Phase II, beginning in 2015, is to develop the PAF to the point where it is commercially viable for the initial target community—transitioning it from a demonstration to a full-fledged product.  This initial target community of PAF consists of environmental consulting and site management firms who work on investigating, remediating, and monitoring contaminated sites within the DOE complex. This target community will be expanded in Phase III to include contaminated sites owned by other federal agencies and industry.

There are three parts to this effort:

  • Subsurface Insights: Environmental Data Assimilation for Predictive Understanding
  • Bridger Photonics: Lidar-Based High-Resolution 3D Imager and Remote Gas Sensor: A New Paradigm for Terrestrial Environmental Monitoring
  • Dissolved Oxygen Sensor System for Real-Time, In Situ Subsurface Monitoring: Geospatial Mapping of DOE Field Sites

Subsurface Insights: Environmental Data Assimilation for Predictive Understanding

The objective of this proposal is to further develop and implement an environmental data assimilation tool that links streaming data from environmental field observatories with models to advance near real-time predictive understanding of environmental systems relevant to environmental remediation and carbon cycling. Specifically, we will integrate DOE- and academy-laboratory-developed capabilities in data analysis and modeling with state-of-the-art capabilities for data acquisition and management into an automated and autonomous framework. This framework, which will provide data-driven multiscale hydrogeologic-biogeochemical process monitoring and understanding, will be implemented as a server-deployed web application that will be accessible using any standard browser.

Bridger Photonics: Lidar-Based High-Resolution 3D Imager and Remote Gas Sensor: A New Paradigm for Terrestrial Environmental Monitoring

LBNL-EESA will lead three key tasks of this project. The first task will focus on developing network architecture to enable co-monitoring of above- and belowground processes, including CO2 fluxes, using the remote gas sensor under development. The second task will focus on algorithms to detect terrestrial environment hot spots and hot moments, and on data-fusion approaches to enable temporal and spatial assimilation of multiple datasets. The third task will consist of comparing the novel remote gas sensor with existing CO2 management approaches and of investigating controls on CO2 fluxes using geophysical data, spectral imagery and point-scale measurements.

Dissolved Oxygen Sensor System for Real-Time, In Situ Subsurface Monitoring: Geospatial Mapping of DOE Field Sites

LBNL will be tasked with providing access to DOE’s Rifle Subsurface Biogeochemistry Field Site, which it oversees as a base of operations for its Sustainable Systems Science Focus Area (SFA) research program. LBNL personnel will oversee all research activities at the site used to validate the sensor platform including assistance with sensor deployment, data uploading to the LBNL database for visualization activities, and routine maintenance, as well as providing access to complementary data used to evaluate the sensors success (e.g. colorimetric assessment of groundwater dissolved oxygen).