1. Managing Environmental Data for Conceptual Site Models
Managing Environmental Data for Conceptual Site Models
Dr. David W. Rich
Indianapolis, IN
February 26, 2014
2:45 – 3:30

2. Summary of Presentation
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Investigation and remediation projects have complex requirements
Automated tools can help the process
Previously the main tool for characterization was lab analyses
Now higher resolution field data is becoming more important
Use of field data is evolving from screening only, to directly impacting decision making
Data management and displays must adjust to this
So must site interpretation models
The result should be better site understanding and project decisions

3. Overview of the Process
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Site issues
Identify concerns
Prepare a preliminary model with available data
Identify gaps in the data
Data management issues
Gather data for detailed site characterization
Discrete data
Continuous data
Import, QC and manage data
Generate useful output
Modeling issues
Scale of model vs. scale of data
Integration of geology and chemistry
Making and implementing decisions

4. Site Issues Identify concerns
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Identify concerns
What are the matrices - soil, water, air, etc.?
Determine constituents of concern
Identify impacts - type, severity
Prepare a preliminary model with available data
Gather and organize existing data - geology, chemistry, GIS data
Present data so it can be analyzed
Identify gaps in the data
Where is the site well characterized, and where not?
What new information is needed where?
Fill the gaps and revise the model

5. Preliminary Model - Geology
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6. Managing and Displaying Site Geology
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You might want to assign geology and lithology to each physical sample
This makes it easy to tie the geology to field and lab data
Or store formation “tops” by location independent of the samples
This probably better represents the actual site geology

7. Introduction

8. Crosstab Callouts From the Database

9. Soil Borings with Values from the Database
Benzene (mg/kg) Lithology
Benzene (ppm)

10. Voxler Example
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11. Data Management Issues
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High level view of the process
Modern systems can manage more of the process
Planning for sample events
Gathering field data and taking samples
Obtaining lab and field data
Importing and storing data
Discrete data
Continuous data
Addressing data issues - duplicates, non-detects, flagged data, dilutions, etc.
Locations of software and data
Data selection and formatting of results
Output content and formats

12. High Level View of the Data Management Process
Plan your sample events
Store in a robust repository
Manage field and lab activities
Analyze and display data
Manage data and quality
It’s all in one location

13. Planning Your Sample Events
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Entering stations

14. Container Labels
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15. Planning the sample event

16. Gathering Field Results
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17. Gathering Soil Samples
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18. Gathering Boring Log Data
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Cloud-based log data entry
Images courtesy of LogItEasy.com

19. Gathering Continuous Data
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Gathering continuous downhole data
Images courtesy of

20. Environmental Data and the “Cloud”
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21. Environmental Data and the “Cloud”
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22. Specifying Import Options
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Import Wizard

23. Quality Control - Consistency Checking
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24. Quality Control - Validation
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25. Quality Control - Validation Summary
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Quality Control - Validation Summary

26. 26/44
Bulk Data

27. 27/44
Selection and Display

28. Display Options Determine How Your Results Are Displayed
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Example options:
Regulatory limits
Values and flags
Unit conversion
Date display
Calculated parameters
Non-detects
Significant figures
Graph display options
Custom queries

29. Generating Output

30. Typical Data Presentation
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31. Modeling Issues Be sure to clearly state the problem to be solved
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Be sure to clearly state the problem to be solved
Gather and organize all the different data components
Maps and other GIS data
Geology, hydrogeology
Discrete data such as lab data
Continuous data, such as direct push data
We want to characterize three main things: rock properties, fluid properties, and concentrations
Have a good understanding of your tools
Use an appropriate process for the specific problem
Present results clearly and succinctly

32. Modeling Issues www.geotech.com
Source: Mapping Research at the USGS Toxic Substances Hydrology Program Research Site to investigate Recalcitrant Contamination in Fractured Bedrock, by Pierre Lacomb and Rachel Dearden

33. Modeling Issues
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34. Modeling Issues

35. Modeling Issues

36. Modeling Issues

37. Modeling Issues 37/44 www.geotech.com
Source: Environmental Visualization: Applications to Site Characterization, Remedial Programs, and Litigation Support, Meng Ling and Jian Chen, Workshop on Visualisation in Environmental Sciences (EnvirVis) (2013)
Source: Techniques for 3D Geological and Hydrogeological Modeling. A Case Study of Conawapa Generating Station, Sharif, S., Mann, J.D., & Smith, J.B., KGS Acres, Winnipeg, Manitoba, Canada, and Cook, G.N. , Manitoba Hydro, Winnipeg, Manitoba, Canada.

38. Traditional conceptual site model
Characterizing a Site
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Traditional conceptual site model

39. Gather contaminant data
Characterizing a Site
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Gather contaminant data
Grid and model
Traditional result

40. Build a hi-res geological model Grid and model each unit
Characterizing a Site
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Here’s a different way
Build a hi-res geological model
Grid and model each unit

41. The results can be very different
Characterizing a Site
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Stack the models
Traditional result
The results can be very different

42. Making and Implementing Decisions
Gather as much data as practical
Use the right tools to manage and present the data
Present it clearly and succinctly
Tailor the presentation to the problem to be solved
Determine alternative solutions
Enumerate advantages and disadvantages
Select from among these and implement
Review results regularly

43. Conclusions
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Investigation and remediation projects have complex requirements
Automated tools can help the process
The industry is seeing more use of field data relative to lab data
Data management and displays must adjust to this
So must site interpretation models
Use of high-resolution geology and concentration can lead to better site models
The result should be better site understanding and project decisions

44. Managing Environmental Data for Conceptual Site Models
Managing Environmental Data for Conceptual Site Models
Dr. David W. Rich
Indianapolis, IN
February 26, 2014
2:45 – 3:30