Groundwater Monitoring and Remediation
The objective of in situ thermal treatment is typically to reduce the contaminant mass or average soil concentration below a specified value. Evaluation of whether the objective has been met is usually made by averaging soil concentrations from a limited number of soil samples.Results from several ﬁeld sites indicate large performance uncertainty using this approach, even when the number of samples is large. We propose a method to estimate average soil concentration by ﬁ tting a log normal probability model to thermal mass recovery data. A statistical approach is presented for making termination decisions from mass recovery data, soil sample data, or both for an entire treatment volume or for subregions that explicitly considers estimation uncertainty which is coupled to a stochastic optimization algorithm to identify monitoring strategies to meet objectives with minimum expected cost. Early termination of heating in regions that reach cleanup targets sooner enables operating costs to be reduced while ensuring a high likelihood of meeting remediation objectives. Results for an example problem demonstrate that signiﬁcant performance improvement and cost reductions can be achieved using this approach.
Parker, J. C., Kim, U., Fortune, A., Griepke, S., Galligan, J. P., and Bonarrigo, A. (2017). "Data Analysis and Modeling to Optimize Thermal Treatment Cost and Performance." Groundwater Monitoring & Remediation, 37(1), 51-66.
This is the accepted version of the following article: Parker Jack, C., Kim, U., Fortune, A., Griepke, S., Galligan James, P., and Bonarrigo, A. (2017). "Data Analysis and Modeling to Optimize Thermal Treatment Cost and Performance." Groundwater Monitoring & Remediation, 37(1), 51-66., which has been published in final form at: http://onlinelibrary.wiley.com/doi/10.1111/gwmr.12199/full
This research was conducted with funding from the U.S. Department of Defense Strategic Environmental Research and Development Program (SERDP) Environmental Restoration Program managed by Dr. Andrea Leeson under project ER-2310.