Metric Identification and Protocol Development for Characterizing DNAPL Source Zone Architecture and Associated Plume Response

Abstract

The overarching goal of this research is to develop and demonstrate a comprehensive approach for field characterization of DNAPL source zones which quantifies the key features that control plume response. Here the intent is to integrate targeted (local-scale) in situ tests with transect-based observations of downstream contaminant flux or concentration and information on subsurface geologic variability. To address its primary goal, the research encompasses the following specific objectives: (1) identification of the most information rich metrics for linking NAPL architecture to plume response; (2) development and refinement of in situ test methods and modeling tools that can be used to quantify identified metrics in targeted regions of the source zone; (3) integration of these metrics and tools with current machine learning characterization methods for an overall source zone assessment protocol; and (4) development of simplified models for prediction of plume response. The research approach integrates batch, column and aquifer cell experiments with mathematical modeling and data processing tools to identify and quantify features of the DNAPL architecture controlling down gradient plume response.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2013
Accession Number
ADA602378

Entities

People

  • Andrew Ramsburg
  • Eric L. Miller
  • John A. Christ
  • Kurt D. Pennell
  • Linda Abriola

Organizations

  • Tufts University

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Alkanes
  • Chemical Properties
  • Chemical Synthesis
  • Chemistry
  • Computational Science
  • Databases
  • Dimensionality Reduction
  • Ecology
  • Groundwater
  • Image Processing
  • Information Processing
  • Information Science
  • Machine Learning
  • Organic Chemistry
  • Three Dimensional
  • Two Dimensional
  • Water Resources

Fields of Study

  • Environmental science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Groundwater Contamination Remediation.
  • Software Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference