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Characterization and Remediation of Fractured Rock

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1 Introduction
1 Introduction Overview
1.1 Characterizing Fractured Rock
1.2 Comparing Unconsolidated Porous Media CSMs and Fractured Rock CSMs
1.3 21- Compartment Model
1.4 Value of Investigation
2 Geology
2 Geology Overview
2.1 Elements of Terrane Analysis
2.2 Benefits of Terrane Information for the Initial CSM
2.3 Terrane Analysis Case Study
2.4 Terrane Analysis Summary
3 Hydrology
3 Hydrology: Fluid Flow Overview
3.1 Fractured Rock Characteristics
3.2 Fluid Dynamics
3.3 Vapors in Fractured Rock
3.4 Role of Scale in Fractured Rock Fluid Flow
4 Chemistry
4 Chemistry: Fate and Transport Overview
4.1 Fate and Transport Mechanisms
4.2 Contaminant Properties Affecting Fate and Transport
5 Site Characterization
5 Site Characterization Overview
5.1 Review and Refine Existing CSM
5.2 Define the Problem
5.3 Identify Significant Data Gaps
5.4 Define Data Collection Objectives and Design Data Collection Process
5.5 Select Investigation Tools
5.6 Develop and Implement Work Plan
5.7 Manage, Interpret, and Present Data
5.8 Lessons Learned
6 Remediation Design
7 Monitoring
8 Modeling Fractured Rock
9 Stakeholder Perspectives
10 Regulatory Challenges
11 Case Studies
11 Case Studies Overview
11.1 Former Industrial Site, Greenville, South Carolina
11.2 Solvents Recovery Service of New England, Inc., Superfund Site, Southington, Connecticut
11.3 Characterization of Fractured Bedrock, United Kingdom
Appendix A. Karst Terranes
Appendix B. Bedrock Types
Appendix C. Drilling
Appendix D. The 21-Compartment Model
Additional Information
Glossary
References
Acronyms
Acknowledgments
Team Contacts
Document Feedback

 

Characterization and Remediation in Fractured Rocks
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5.3 Identify Significant Data Gaps

Data gaps in CSMs occur throughout the characterization process. Data gaps are normal in CSMs because the models rely on working hypotheses in various phases of completion and on incomplete information. To maximize efficiency and cost-effectiveness, consider the influence of physical, hydrologic, and chemical characteristics on fate and transport. At each stage, investigators must identify significant data gaps, as well as which data needs should be addressed (data collection objectives) and which can be ignored.

Table 5-1 notes several examples of data gaps:

  • vertical and horizontal extent of contamination
  • the direction of contaminant movement
  • the rate of contaminant movement

Each of these data gaps can easily be transformed into one or more specific characterization objectives. For example, the second data gap becomes the objective: determine the direction of contaminant movement. The resulting data collection objective to resolve this data gap might be to use borings to provide site-specific data on VOC concentrations at various depths. Locations of these borings can be determined with the help of the desktop evaluation. While all data gaps are assessed when confirming or refuting the CSM hypotheses, only significant data gaps should be considered for further investigation. The data gap in this example is significant because any deep migration of VOCs threatens the existing water supply wells.

One way to identify data gaps is using the 21-Compartment Model, completed during assessment of the initial CSM. After supplying the known information, partially filled or empty compartments may identify significant data gaps.

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