United States Office of Research and EPA/600/R-00/047
Environmental Protection Development March 2000
Agency Washington, D.C. 20460
vvEPA Environmental Technology
Verification Report
Environmental Decision
Support Software
C Tech Development
Corporation
Environmental Visualization
System Pro (EVS-PRO)
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THE ENVIRONMENTAL TECHNOLOGY VERIFICATION
PROGRAM.
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ETV Joint Verification Statement
TECHNOLOGY TYPE: ENVIRONMENTAL DECISION SUPPORT SOFTWARE
APPLICATION: VISUALIZATION, SAMPLE OPTIMIZATION, AND COST-
BENEFIT ANALYSIS OF ENVIRONMENTAL DATA SETS
TECHNOLOGY NAME: Environmental Visualization System Pro (EVS-PRO)
COMPANY: C Tech Development Corporation
ADDRESS: 16091 Santa Barbara Lane PHONE: 800-NOW-4-EVS
Huntington Beach, CA 92649 FAX: (714) 840-2778
WEBSITE: www.ctech.com
E-MAIL: evs-info@ctech.com
The U.S. Environmental Protection Agency (EPA) has created the Environmental Technology Verification
Program (ETV) to facilitate the deployment of innovative or improved environmental technologies through
performance verification and dissemination of information. The goal of the ETV Program is to further
environmental protection by substantially accelerating the acceptance and use of improved and cost-effective
technologies. ETV seeks to achieve this goal by providing high-quality, peer-reviewed data on technology
performance to those involved in the design, distribution, financing, permitting, purchase, and use of
environmental technologies.
ETV works in partnership with recognized standards and testing organizations and stakeholder groups
consisting of regulators, buyers, and vendor organizations, with the full participation of individual technology
developers. The program evaluates the performance of innovative technologies by developing test plans that
are responsive to the needs of stakeholders, conducting field or laboratory tests (as appropriate), collecting and
analyzing data, and preparing peer-reviewed reports. All evaluations are conducted in accordance with
rigorous quality assurance protocols to ensure that data of known and adequate quality are generated and that
the results are defensible.
The Site Characterization and Monitoring Technologies Pilot (SCMT), one of 12 technology areas under
ETV, is administered by EPA's National Exposure Research Laboratory (NERL). With the support of the U.S.
Department of Energy's (DOE's) Environmental Management program, NERL selected a team from
Brookhaven National Laboratory (BNL) and Oak Ridge National Laboratory (ORNL) to perform the
verification of environmental decision support software. This verification statement provides a summary of
the test results of a demonstration of C Tech Development Corporation's Environmental Visualization System
Pro (EVS-PRO) decision support software (DSS) product.
DEMONSTRATION DESCRIPTION
In September 1998, the performance of five DSS products were evaluated at the New Mexico Engineering
Research Institute, located in Albuquerque, New Mexico. In October 1998, a sixth DSS product was tested at
EPA-VS-SCM-30 The accompanying notice is an integral part of this verification statement. March 2000
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BNL in Upton, New York. Each technology was independently evaluated by comparing its analysis results
with measured field data and, in some cases, known analytical solutions to the problem.
Depending on the software, each was assessed for its ability to evaluate one or more of the following
endpoints of environmental contamination problems: visualization, sample optimization, and cost-benefit
analysis. The capabilities of the DSS were evaluated in the following areas: (1) the effectiveness of integrating
data and models to produce information that supports the decision, and (2) the information and approach used
to support the analysis. Secondary evaluation objectives were to examine DSS for its reliability, resource
requirements, range of applicability, and ease of operation. The verification study focused on the developers'
analysis of multiple test problems with different levels of complexity. Each developer analyzed a minimum of
three test problems. These test problems, generated mostly from actual environmental data from six real
remediation sites, were identified as Sites A, B, D, N, S, and T. The use of real data challenged the software
systems because of the variability in natural systems. The technical evaluation team performed a complete
baseline analysis for each problem. These results, along with the data were used as a baseline for comparison
with the DSS results.
C Tech Development Corporation staff chose to use EVS-PRO to perform the visualization endpoint on
selected data from each of the six sites. In addition, sample optimization was performed for the Site B, N, and
S problems, making use of the geostatistical algorithms in EVS-PRO. Cost-benefit analysis (estimates of
contaminated volume as a function of cleanup level) was also performed on these three problems and for the
Site A cost-benefit problem and the Site D sample optimization problem.
EVS-PRO was used to generate several different types of output as appropriate to the problem under study.
Output included three-dimensional (3-D) maps of the regions of contamination above specified threshold
concentrations as a function of the probability of exceeding the threshold value. A scale of coordinates and
surface features were included on the maps to provide a frame of reference. Where aerial photographs were
provided, EVS-PRO superimposed the site maps over the photograph to improve visual understanding of the
extent of the problem. For the Site A cost-benefit problem, EVS-PRO also generated an animation that
provided a 3-D depiction of the extent of contamination. For the Site T groundwater contamination problem,
EVS-PRO generated an animation depicting subsurface soil stratrigraphy. These animations rotated the view
through 360° to provide the analyst with a more complete view of the data. For Sites B and S, C Tech also
provided files generated by EVS-PRO using virtual reality modeling language (VRML) that could be viewed
and navigated. Navigation permits the viewer to rotate the image to any angle to gain a better understanding
of the extent of contamination. The data from Sites A, B, D, and S were used to generate a cost-benefit
analysis of the volume contaminated above the specified contaminant-specific cleanup threshold as a function
of probability. For the Site N sample optimization problem, EVS-PRO produced maps of uncertainty as a
function of the number of samples collected. This information was used to illustrate the reduction in
uncertainty obtained with increased sampling and to highlight regions of high uncertainty that may require
further sampling. Several hundred visualizations were produced as part of the demonstration.
Details of the demonstration, including an evaluation of the software's performance, may be found in the
report entitled Environmental Technology Verification Report: Environmental Decision Support Software—
C Tech Development Corporation, Environmental Visualization System Pro (EVS-PRO), EPA/600/R-00/047.
TECHNOLOGY DESCRIPTION
C Tech's EVS-PRO unites interpolation, geostatistical analysis, and fully 3-D visualization tools into a
software system developed to address, among other things, sample optimization and cost-benefit analysis.
EVS-PRO's capabilities can be used to provide 3-D maps of geologic structure, subsurface contamination,
and regions containing contamination above specified threshold concentrations at a fixed probability level.
EVS-PRO can also perform geostatistical analyses that optimize sample locations for site characterization and
can estimate volumes and mass of contaminated media for use in cost-benefit analysis. EVS-PRO can
quantify the statistical variation in the contaminant volume and mass estimates resulting from the current
level of characterization
EPA-VS-SCM-30 The accompanying notice is an integral part of this verification statement. March 2000
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VERIFICATION OF PERFORMANCE
The following performance characteristics of EVS-PRO were observed:
Decision Support: EVS-PRO provides decision support through 3-D visualization of environmental data such
as contaminant concentration contours, quantification of uncertainties in interpolation predictions,
recommendation of additional sample location to reduce uncertainties, and providing statistical information
about the extent of contamination (e.g., volume contaminated as a function of probability).
Documentation of the EVS-PRO Analysis: For each problem, C Tech provided a detailed description of the
steps necessary to import the data into EVS-PRO and perform the desired analysis. The steps proceeded
logically, and manipulations to format the data into the EVS format were relatively simple. Numerous files—
including visualizations, input files, and output files—were provided for review.
Comparison with Baseline Analysis and Data: EVS-PRO produced visualizations from six different sites.
All visualizations produced by EVS-PRO were consistent with the baseline data. Visualizations included 3-D
representations of geologic structure, hydraulic head, concentration contours above threshold values, and
uncertainty maps. The visualizations accurately incorporated surface features (maps of roads, buildings, water
bodies) and aerial photographs when available. Visualizations often provided well and sample locations as a
function of elevation. Sample locations were accurately color-coded to match the measured data.
Sample optimization was performed for Sites B, N, and S. The analyses for Site B and S adequately
characterized the plume with an acceptable number of additional samples. For the Site N problem, in which
the number of samples was limited, the software inadequately characterized the extent of contamination.
EVS-PRO was used to provide cost-benefit analysis of the volume of contamination as a function of threshold
concentration and probability level for Sites A, B, N, and S. Its volume estimates were often a poor match to
the baseline analysis.
EVS-PRO can perform sample optimization analysis to recommend sampling locations and cost-benefit
analysis of the volume of contaminated media as a function of probability. To assist the analyst, the software
calculates values for the essential parameters used in these analyses based on the data. While the use of these
calculated default values makes it easier for the analyst, the values were not always optimal for the sample
optimization or cost-benefit analysis. For the Site N sample optimization problem, in particular,
approximately a third of the site remained unsampled due to the approach used in EVS-PRO and the limit on
the number of samples. For the cost-benefit problems, the estimates of contaminated volumes were often a
poor match to the baseline analysis. This was especially true in estimates of volume above the threshold
concentration with a low probability of exceeding the threshold. In these situations, the default parameters
selected by EVS-PRO often caused predictions of contamination in regions upgradient from the main plume
that did not contain data. Operator intervention to optimize model parameters would have led to better, more
accurate analyses. The problems identified are a function of the operator and not the software and emphasize
the need to have qualified analysts operate the software and for the analyst to examine the model outputs for
consistency with the data.
Multiple Lines of Reasoning: EVS-PRO provides a number of different approaches to analyzing and
visualizing the data, including control over essential modeling parameters. This permits multiple analyses of
the data. The software generates statistical and geostatistical information about the extent of contamination,
thus providing multiple evaluations to assist in data interpretation. The use of EVS-PRO to generate multiple
lines of reasoning assists the analyst in conducting a thorough evaluation of the data.
EPA-VS-SCM-30 The accompanying notice is an integral part of this verification statement. March 2000
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In addition to performance criteria, the following secondary criteria were evaluated:
Ease of Use: EVS-PRO is a sophisticated software product with over 150 computational or visualization
modules. The use of visual programming to link the modules makes EVS-PRO fairly easy to use. Most
environmental analysts would be able to use the major features of EVS-PRO after two days of training.
Advanced features such as use of the scripting language would require more training. An inconvenience of
EVS-PRO is its requirement of a fixed-format data field for input files. However, this limitation has been
removed in the most recent version of EVS-PRO.
Efficiency and Range of Applicability: EVS-PRO efficiently imported, analyzed, and visualized
environmental data sets. In the demonstration, the software analyzed four complete problems (three sample
optimization/cost-benefit problems and one cost-benefit problem) and two partial problems (perform
visualization) with eight person-days of effort. Of these, approximately four days were spent analyzing the
data and four days were spent preparing the report.
Operator Skill Base: For efficient use of the basic features in EVS-PRO, the operator must have knowledge
about contouring environmental data sets and managing database files. To use the advanced geostatistical and
statistical features, the operator should be knowledgeable in these areas.
Platform: During the demonstration, EVS-PRO was run on a Windows 95 operating system. The computer
used for the demonstration was a Pentium II 400 with a Titan II graphics card, 128 MB of RAM, a 4 GB-hard
drive, and a 20X CD-ROM.
Training and Technical Support: C Tech provides an extensive users' manual documenting code operation
and use. Self-paced training modules are available as part of the software package. Technical support is
supplied over the Internet and through e-mail. Training courses are available throughout the year.
Cost: For a single user EVS-PRO sells for $9995. The EVS pricing structure depends on the product and
number of licenses sold to the customer. Discounts are available to educational institutions.
Overall Evaluation: The main strengths of EVS-PRO are its outstanding 3-D visualization capabilities and its
capability to rapidly process, analyze, and visualize data. The range of visualization output formats and their
quality define EVS-PRO as a premier, state-of-the-art visualization system. Its ability to sort and query the
data and write scripts to automate repetitive tasks permits EVS-PRO to examine large amounts of data and
quickly generate visualizations of the data in many depiction and animation formats. EVS-PRO's object-
oriented programming structure allows the many modules to be easily linked together to perform a complex
analysis. EVS-PRO is a mature software program that does not have any major limitations.
A credible computer analysis of environmental problems requires good data, reliable and appropriate
software, adequate conceptualization of the site, and a technically defensible problem analysis. The results of
the demonstration showed that the EVS-PRO software can be used to generate reliable and useful analyses for
evaluating environmental contamination problems. This is the component of a credible analysis that can be
addressed by the software; other components, such as proper conceptualization and use of the code, depend
on the analyst's skills. The results of an EVS-PRO analysis can support decision-making. EVS-PRO has been
employed in a variety of environmental applications. Although the EVS-PRO software has been demonstrated
to have the capability to produce reliable and useful analyses, improper use of the software can cause the
results of the analysis to be misleading or inconsistent with the data. As with any complex environmental DSS
product, the quality of the output is directly dependent on the skill of the operator.
EPA-VS-SCM-30 The accompanying notice is an integral part of this verification statement. March 2000
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As with any technology selection, the user must determine if this technology is appropriate for the application
and the project data quality objectives. For more information on this and other verified technologies visit the
ETV Web site at http://www.epa.gov/etv.
Gary J. Foley, Ph.D. David E. Reichle
Director ORNL Associate Laboratory Director
National Exposure Research Laboratory Life Sciences and Environmental Technologies
Office of Research and Development
NOTICE: EPA verifications are based on evaluations of technology performance under specific, predetermined
criteria and appropriate quality assurance procedures. EPA, ORNL, and BNL make no expressed or implied
warranties as to the performance of the technology and do not certify that a technology will always operate as
verified. The end user is solely responsible for complying with any and all applicable federal, state, and local
requirements. Mention of commercial product names does not imply endorsement.
EPA-VS-SCM-30 The accompanying notice is an integral part of this verification statement. March 2000
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EPA/600/R-00/047
March 2000
Environmental Technology
Verification Report
Environmental Decision Support
Software
C Tech Development Corporation
Environmental Visualization System
Pro (EVS-PRO)
By
Terry Sullivan
Brookhaven National Laboratory
Upton, New York 11983
Anthony Q. Armstrong
Amy B. Dindal
Roger A. Jenkins
Oak Ridge National Laboratory
Oak Ridge, Tennessee 37831
JeffOsleeb
Hunter College
New York, New York 10021
Eric N. Koglin
U.S. Environmental Protection Agency
Environmental Sciences Division
National Exposure Research Laboratory
Las Vegas, Nevada 89193-3478
oml
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Notice
The U.S. Environmental Protection Agency (EPA), through its Office of Research and Development (ORD),
and the U.S. Department of Energy's Environmental Management Program through the National Analytical
Management Program (NAMP), funded and managed, through Interagency Agreement No. DW89937854
with Oak Ridge National Laboratory, the verification effort described herein. This report has been peer and
administratively reviewed and has been approved for publication as an EPA document. Mention of trade
names or commercial products does not constitute endorsement or recommendation for use of a specific
product.
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Table of Contents
List of Figures v
List of Tables vii
Foreword k
Acknowledgments xi
Abbreviations and Acronyms xiii
1 INTRODUCTION 1
Background 1
Demonstration Overview 2
Summary of Analysis Performed by EVS-PRO 2
2 ENVIRONMENTAL VISUALIZATION SYSTEM CAPABILITIES 5
EVS-PRO Features 5
Overview of C Tech Environmental Software 6
3 DEMONSTRATION PROCESS AND DESIGN 8
Introduction 8
Development of Test Problems 8
Test Problem Definition 8
Summary of Test Problems 8
Analysis of Test Problems 9
Preparation of Demonstration Plan 11
Summary of Demonstration Activities 11
Evaluation Criteria 12
Criteria for Assessing Decision Support 12
Documentation of the Analysis and Evaluation of the Technical Approach 13
Comparison of Projected Results with the Data and Baseline Analysis 13
Use of Multiple Lines of Reasoning. 13
Secondary Evaluation Criteria 13
Documentation of Software 13
Training and Technical Support 14
Ease of Use 14
Efficiency and Range of Applicability 14
4 EVS-PRO EVALUATION 15
EVS-PRO Technical Approach 15
Description of Test Problems 16
Site A Cost-Benefit Problem 16
SiteB Sample Optimization and Cost-Benefit Problem 16
Site D Sample Optimization Problem 17
Site N Sample Optimization Problem 17
Site S Sample Optimization Problem 17
Site T Cost-Benefit Problem 18
Evaluation of EVS-PRO 18
Decision Support 18
Documentation of the EVS-PRO Analysis and Evaluation of the
Technical Approach 18
Comparison of EVS-PRO Results with the Baseline Analysis and Data 18
Site A Cost-Benefit Problem 18
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SiteB Sample Optimization and Cost-Benefit Problem 24
Site D Sample Optimization Problem 32
SiteN Sample Optimization Problem 34
Site S Sample Optimization and Cost-Benefit Problem 40
Site T Geology Interpretation 45
Multiple Lines of Reasoning 47
Secondary Evaluation Criteria 47
Ease of Use 47
Efficiency and Range of Applicability 49
Training and Technical Support 49
Additional Information about the EPS-PRO Software 49
Summary of Performance 49
5 ENVIRONMENTAL VISUALIZATION SYSTEM UPDATE AND REPRESENTATIVE
APPLICATIONS 52
Objective 52
Technology Update 52
New Interfaces 52
Enhanced Modules 52
New Modules 52
Representative Applications 53
6 REFERENCES 57
Appendix A—Summary of Test Problems 59
Appendix B—Description of Interpolation Methods 65
IV
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List of Figures
1 EVS-PRO representation of the Site A lOO-^g/L PCE plume at the 50% probability level 19
2 Baseline representation of the Site A nominal PCE plume at 210-220 ft above MSL obtained
using Surfer 20
3 Baseline representation of the Site A nominal PCE plume at 180-190 ft above MSL obtained
using Surfer 21
4 EVS-PRO representations of plumes of PCE at the 500-|jg/L threshold concentration
at 90%, 50%, and 10% probability levels 22
5 EVS-PRO representations of plumes of TCA at the 50-|jg/L threshold concentration
at 90%, 50%, and 10% probability levels 23
6 EVS-PRO representation of Site B water levels 26
7 EVS-PRO representation of the Site B 50% probability level Tc-99 plume above
the 10,000 pCi/L threshold after completion of sample optimization 27
8 Baseline representation of the Site B Tc-99 plume obtained using GSLIB and the
same data set as that obtained by C Tech after sample optimization 28
9 EVS-PRO representation of the Site B 25% probability level (maximum plume volume)
Tc-99 plume above the 40,000 pCi/L threshold after completion of sample optimization 29
10 Baseline representation of the Site B 25% probability level Tc-99 plume at 40,000 pCi/L 30
11 EVS-PRO-generated visualization of the Site B 50% probability TCE plume above
the 50- ng/L threshold 31
12 EVS-PRO representation of the Site D nominal TCE contamination above the 50-|jg/L
threshold, based on third quarter 1991sampling data 32
13 EVS-PRO 3-D representation of the Site D nominal TCE contamination above the 50-|jg/L
threshold, based on third quarter 1991sampling data 33
14 EVS-PRO 3-D exploded view representation of the Site D nominal TCE contamination
above the 50- |jg/L threshold, based on third quarter 1991 sampling data 33
15 Baseline representation of the Site N arsenic contours at the 125- and 500-mg/kg
thresholds obtained using Surfer and the data provided to the analyst for conducting
the sample optimization analysis 35
16 EVS-PRO representation of the Site N nominal arsenic contamination above the 500-mg/kg
threshold after completion of the sample optimization analysis 36
17 Surfer representation of the Site N nominal arsenic contamination above the 125- and
500-mg/kg thresholds using the same data set as the C Tech analyst after completion
of the sample optimization analysis 37
18 Baseline analysis of the Site N nominal arsenic contamination above the 125- and
500-mg/kg thresholds using the entire data set (4187 points) 38
19 EVS-PRO-generate d uncertainty maps for the Site N sample optimization problem 39
20 EVS-PRO 2-D representation of the Site S 75% (minimum), 50% (nominal), and 25%
(maximum) probability CTC plumes above the 5-|Jg/L threshold 41
21 Baseline analysis of the Site S nominal CTC plume at threshold concentrations of 5
and 500 |jg/L 42
22 EVS-PRO 3-D visualization of the Site S nominal CTC plume above the 5- ng/L threshold 43
23 EVS-PRO 3-D exploded view of the Site T subsurface stratigraphy 46
24 EVS-PRO depiction of Site T subsurface stratigraphy based on indicator kriging modeling
of soil layers 47
25 An example of the EVS network editor illustrating connection of different modules 48
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VI
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List of Tables
1 Pricing structure for floating-license version of EVS-PRO 6
2 Summary of test problems 9
3 Data supplied for the test problems 9
4 SiteB groundwater contamination problem threshold concentrations 16
5 Site N soil contamination threshold concentrations for the sample optimization problem 17
6 EVS-PRO and baseline estimates of the volume of PCE and TCA contamination at Site A
as a function of probability 25
7 EVS-PRO and baseline estimates of the volume of Tc-99 and TCE contamination at Site B
as a function of probability 31
8 EVS-PRO, baseline, and analytical estimates of the area of CTC contamination at Site S
as a function of probability 44
9 EVS-PRO, baseline, and analytical estimates of the volume of CTC contamination at Site S
as a function of probability 44
10 EVS-PRO performance summary 50
vn
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Foreword
The U.S. Environmental Protection Agency (EPA) is charged by Congress with protecting the nation's natural
resources. The National Exposure Research Laboratory (NERL) is EPA's center for the investigation of
technical and management approaches for identifying and quantifying risks to human health and the
environment. NERL's research goals are to (1) develop and evaluate technologies for the characterization and
monitoring of air, soil, and water; (2) support regulatory and policy decisions; and (3) provide the science
support needed to ensure effective implementation of environmental regulations and strategies.
EPA created the Environmental Technology Verification (ETV) Program to facilitate the deployment of
innovative technologies through performance verification and information dissemination. The goal of the
ETV Program is to further environmental protection by substantially accelerating the acceptance and use of
improved and cost-effective technologies. The ETV Program is intended to assist and inform those involved
in the design, distribution, permitting, and purchase of environmental technologies. This program is
administered by NERL's Environmental Sciences Division in Las Vegas, Nevada.
The U.S. Department of Energy's (DOE's) Environmental Management (EM) program has entered into active
partnership with EPA, providing cooperative technical management and funding support. DOE EM realizes
that its goals for rapid and cost-effective cleanup hinge on the deployment of innovative environmental
characterization and monitoring technologies. To this end, DOE EM shares the goals and objectives of the
ETV.
Candidate technologies for these programs originate from the private sector and must be commercially ready.
Through the ETV Program, developers are given the opportunity to conduct rigorous demonstrations of their
technologies under realistic field conditions. By completing the evaluation and distributing the results, EPA
establishes a baseline for acceptance and use of these technologies.
Gary J. Foley, Ph.D.
Director
National Exposure Research Laboratory
Office of Research and Development
IX
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Acknowledgments
The authors wish to acknowledge the support of all those who helped plan and conduct the demonstration,
analyze the data, and prepare this report. In particular, we recognize the technical expertise of Jeff VanEe
(EPA NERL) and Budhendra Bhaduri (ORNL), who were peer reviewers of this report. For internal peer
review, we thank Marlon Mezquita (EPA Region 9); for technical and logistical support during the
demonstration, Dennis Morrison (NMERI); for evaluation of training during the demonstration, Marlon
Mezquita and Gary Hartman (DOE's Oak Ridge Operations Office); for computer and network support,
Leslie Bloom (ORNL); and for technical guidance and project management of the demonstration, David
Garden, and Regina Chung (DOE Oak Ridge Operations Office), David Bottrell (DOE Headquarters), Stan
Morton (DOE Idaho Operations Office), Deana Crumbling (EPA's Technology Innovation Office), and
Stephen Billets (EPA NERL). The authors also acknowledge the participation of Reed Copsey of C Tech
Development Corporation, who performed the analyses during the demonstration.
For more information on the Decision Support Software Technology Demonstration, contact
Eric N. Koglin
Project Technical Leader
Environmental Protection Agency
Environmental Sciences Division
National Exposure Research Laboratory
P.O. Box 93478
Las Vegas, Nevada 89193-3478
(702) 798-2432
For more information on the C Tech Development Corporation Environmental Visualization System product,
contact
Reed Copsey
C Tech Development Corporation
16091 Santa Barbara Lane
Huntington Beach, CA 92649
reed@ctech.com
www.ctech.com
XI
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Xll
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Abbreviations and Acronyms
As
.avi
.bmp
BNL
CAD
CTC
Cd
CD
CD-ROM
Cr
DBCP
.dbf
DCA
DCE
DCP
DOE
DSS
.dxf
EDB
EM
EPA
ESRI
EVS
EVS-PRO
ETV
FTP
GB
Geo-EAS
GIS
GSLIB
GUI
.hav
IDW
JPg
L
MAS
MB
MHz
MSL
MVS
.mpg
NAMP
NAPL
NERL
NMERI
ODBC
ORD
ORNL
PCE
ppb
arsenic
file format for animation visualizations in Windows
bitmap (file format)
Brookhaven National Laboratory
computer-aided design
carbon tetrachloride
cadmium
compact disk
compact disk—read-only memory
chromium
dibromochloropropane
database file
dichloroethane
dichloroethene
dichloropropane
U.S. Department of Energy
decision support software
data exchange format (file)
ethylene dibromide
Environmental Management
U.S. Environmental Protection Agency
Environmental Systems Research Institute
Environmental Visualization System
Environmental Visualization System Pro
Environmental Technology Verification Program
file transfer protocol
gigabyte
Geostatistical Environmental Assessment Software
geographical information system
Geostatistical Software Library Version 2.0
graphical user interface
file format for animation visualizations
inverse distance weighting
JPEG file interchange format
liter
Modeling Animation System
megabyte
megahertz (used to define the clock speed on computer processors)
mean sea level
Mining Visualization System
file format for animation visualizations
National Analytical Management Program (DOE)
nonaqueous phase liquid
National Exposure Research Laboratory (EPA)
New Mexico Engineering Research Institute
open database connectivity
Office of Research and Development
Oak Ridge National Laboratory
perchloroethene or tetrachloroethene
parts per billion
Xlll
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ppm parts per million
QA quality assurance
QC quality control
RAM random access memory
ROM read only memory
SADA Spatial Analysis and Decision Assistance
SCMT Site Characterization and Monitoring Technology
.shp Shape file
TCA trichloroethane
TCE trichloroethene
Tc-99 technetium-99
VC vinyl chloride
VOC volatile organic compound
VRML virtual reality modeling language
.wrl file extension for files created using VRML
2-D two-dimensional
3-D three-dimensional
xiv
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Section 1 — Introduction
Background
The U.S. Environmental Protection Agency (EPA)
has created the Environmental Technology
Verification Program (ETV) to facilitate the
deployment of innovative or improved environ-
mental technologies through performance verifi-
cation and dissemination of information. The goal
of the ETV Program is to further environmental
protection by substantially accelerating the
acceptance and use of improved and cost-effective
technologies. ETV seeks to achieve this goal by
providing high-quality, peer-reviewed data on
technology performance to those involved in the
design, distribution, financing, permitting, purchase,
and use of environmental technologies.
ETV works in partnership with recognized standards
and testing organizations and stakeholder groups
consisting of regulators, buyers, and vendor
organizations, with the full participation of
individual technology developers. The program
evaluates the performance of innovative tech-
nologies by developing test plans that are responsive
to the needs of stakeholders, conducting field or
laboratory tests (as appropriate), collecting and
analyzing data, and preparing peer-reviewed reports.
All evaluations are conducted in accordance with
rigorous quality assurance (QA) protocols to ensure
that data of known and adequate quality are
generated and that the results are defensible.
ETV is a voluntary program that seeks to provide
objective performance information to all of the
actors in the environmental marketplace and to assist
them in making informed technology decisions.
ETV does not rank technologies or compare their
performance, label or list technologies as acceptable
or unacceptable, seek to determine "best available
technology," nor approve or disapprove
technologies. The program does not evaluate
technologies at the bench or pilot scale and does not
conduct or support research.
The program now operates 12 pilots covering a
broad range of environmental areas. ETV has begun
with a 5-year pilot phase (1995-2000) to test a wide
range of partner and procedural alternatives in
various pilot areas, as well as the true market
demand for and response to such a program. In these
pilots, EPA utilizes the expertise of partner
"verification organizations" to design efficient
processes for conducting performance tests of
innovative technologies. These expert partners are
both public and private organizations, including
federal laboratories, states, industry consortia, and
private sector facilities. Verification organizations
oversee and report verification activities based on
testing and QA protocols developed with input from
all major stakeholder/customer groups associated
with the technology area. The demonstration
described in this report was administered by the Site
Characterization and Monitoring Technology
(SCMT) Pilot. (To learn more about ETV, visit
ETV's Web site at http://www.epa.gov/etv.)
The SCMT pilot is administered by EPA's National
Exposure Research Laboratory (NERL). With the
support of the U.S. Department of Energy's (DOE's)
Environmental Management (EM) program, NERL
selected a team from Brookhaven National
Laboratory (BNL) and Oak Ridge National
Laboratory (ORNL) to perform the verification of
environmental decision support software. Decision
support software (DSS) is designed to integrate
measured or modeled data (such as soil or
groundwater contamination levels) into a framework
that can be used for decision-making purposes.
There are many potential ways to use such software,
including visualization of the nature and extent of
contamination, locating optimum future samples,
assessing costs of cleanup versus benefits obtained,
or estimating human health or ecological risks. The
primary objective of this demonstration was to
conduct an independent evaluation of each
software's capability to evaluate three common
endpoints of environmental remediation problems:
visualization, sample optimization, and cost-benefit
analysis. These endpoints were defined as follows.
• Visualization — using the software to organize
and display site and contamination data in ways
that promote understanding of current
conditions, problems, potential solutions, and
eventual cleanup choices;
• Sample optimization — selecting the minimum
number of samples needed to define a
contaminated area within a predetermined
statistical confidence;
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• Cost-benefit analysis — assessment of either the
size of the zone to be remediated according to
cleanup goals, or estimation of human health
risks due to the contaminants. These can be
related to costs of cleanup.
The developers were permitted to select the
endpoints that they wished to demonstrate because
each piece of software had unique features and
focused on different aspects of the three endpoints.
Some focused entirely on visualization and did not
attempt sample optimization or cost-benefit analysis,
while others focused on the technical aspects of
generating cost-benefit or sample-optimization
analysis, with a minor emphasis on visualization.
The evaluation of the DSS focused only on the
analyses conducted during the demonstration. No
penalty was assessed for performing only part of the
problem (e.g., performing only visualization).
Evaluation of a software package that is used for
complex environmental problems is by necessity
primarily qualitative in nature. It is not meaningful
to quantitatively evaluate how well predictions
match at locations where data has not been collected.
(This is discussed in more detail in Appendix B.) In
addition, the selection of a software product for a
particular application relies heavily on the user's
background, personal preferences (for instance,
some people prefer Microsoft Word, while others
prefer Corel WordPerfect for word processing), and
the intended use of the software (for example,
spreadsheets can be used for managing data;
however, programs specifically designed for
database management would be a better choice for
this type of application). The objective of these
reports is to provide sufficient information to judge
whether the DSS product has the analysis
capabilities and features that will be useful for the
types of problems typically encountered by the
reader.
Demonstration Overview
In September 1998, a demonstration was conducted
to verify the performance of five environmental
software programs: Environmental Visualizations
System (C Tech Development Corp.), Arc View and
associated software extenders [Environmental
Systems Research Institute (ESRI)], GroundwaterEY"
(Decision/^ Corp.), Sampling/^ (DecisionEY"
Corp.), and SitePro (Environmental Software Corp.).
In October, a sixth software package from the
University of Tennessee Research Corporation,
Spatial Analysis and Decision Assistance (SADA),
was tested. This report contains the evaluation for
Environmental Visualization System (EVS-PRO).
Each developer was asked to use its own software to
address a minimum of three test problems. In
preparation for the demonstration, ten sites were
identified as having data sets that might provide
useful test cases for the demonstration. All of this
data received a quality control review to screen out
sites that did not have adequate data sets. After the
review, ten test problems were developed from field
data at six different sites. Each site was given a
unique identifier (Sites A, B, D, N, S, and T). Each
test problem focused on different aspects of
environmental remediation problems. From the
complete data sets, test problems that were subsets
of the entire data set were prepared. The
demonstration technical team performed an
independent analysis of each of the ten test problems
to ensure that the data sets were complete.
All developers were required to choose either Site S
or Site N as one of their three problems because
these sites had the most data available for
developing a quantitative evaluation of DSS
performance.
Each DSS was evaluated on its own merits based on
the evaluation criteria presented in Section 3.
Because of the inherent variability in soil and
subsurface contamination, most of the evaluation
criteria are qualitative. Even when a direct
comparison is made between the developer's
analysis and the baseline analysis, different
numerical algorithms and assumptions used to
interpolate data between measured values at known
locations make it almost impossible to make a
quantitative judgement as to which technical
approach is superior. The comparisons, however, do
permit an evaluation of whether the analysis is
consistent with the data supplied for the analysis and
therefore useful in supporting remediation decisions.
Summary of Analysis Performed by
EVS-PRO
C Tech's Environmental Visualization System
(EVS-PRO) unites interpolation, geostatistical
analysis, and fully three-dimensional (3-D)
visualization tools into a software system developed
to address, among other things, sample optimization,
and cost-benefit analysis. EVS-PRO's capabilities
can be used to provide 3-D maps of geologic
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structure, subsurface contamination, and regions
containing contamination above specified threshold
levels at a fixed probability level. EVS-PRO can
also perform geostatistical analyses that suggest
optimal sample locations for site characterization
and can estimate volumes and mass of contaminated
media for use in cost-benefit analysis. EVS-PRO can
quantify the statistical variation in the contaminant
volume and mass estimates resulting from the
current extent of characterization
C Tech Development Corporation staff chose to use
EVS-PRO to perform the visualization endpoint on
selected data from each of the six sites. In addition,
sample optimization was performed for the Site B,
N, and S problems, making use of the geostatistical
algorithms in EVS-PRO. Cost-benefit analysis
(estimates of contaminated volume as a function of
cleanup level) was also performed on these three
problems and for the Site A cost-benefit problem
and the Site D sample optimization problem.
The Site A problem was a 3-D groundwater
contamination cost-benefit problem. The data
supplied included maps of buildings, roads, and
water bodies; groundwater contamination concen-
trations for more than 50 wells, with data supplied at
5-ft vertical intervals for each well; the hydraulic
head in each well; and information on the elevation
of the ground surface and bedrock at each well. The
contaminants of concern were perchloroethene
(PCE) and trichloroethane (TCA). EVS-PRO
generated 3-D maps of the regions of contamination
above two threshold concentrations at three prob-
ability levels. A scale of coordinates and surface
features were included on the maps to provide a
frame of reference. EVS-PRO also generated a 3-D
animation depicting the extent of contamination. The
animation rotated the viewing angle of the contami-
nation through 360° to provide the analyst with a
more complete view of the contamination. In
addition, EVS-PRO was used to estimate the volume
of contamination at the three probability levels and
two threshold concentrations.
The Site B sample optimization problem involved
groundwater contamination in two spatial
dimensions. The data supplied included an aerial
photograph of the site; maps of buildings, roads, and
water bodies; groundwater contamination
concentrations at 25 well locations; and the
hydraulic head in each well. The C Tech analyst
used the geostatistical algorithms in EVS-PRO to
identify 23 additional locations for further sampling
to define the extent of contamination for technetium-
99 (Tc-99). On the basis of the final data set, the
analyst used EVS-PRO to generate maps of the
plume at the two threshold concentrations at three
probability levels. The software depicted measured
concentrations at the correct spatial locations with
color-coded spheres; the color represented the
concentration value. A scale of coordinates and
surface features was included on the maps to provide
a frame of reference. Some maps superimposed the
aerial photograph to provide a frame of reference. A
similar analysis was performed for the other two
contaminants, trichloroethene (TCE) and vinyl
chloride (VC), using the original data set (i.e.,
sample optimization was not performed). The
analyst also provided EVS-PRO-generated virtual
reality modeling language (VRML) files that could
be viewed and navigated with free downloadable
plug-ins for an Internet browser. (C Tech
recommends the Cosmo Viewer that can be obtained
from www.karmanaut.com/cosmo/player.)
Navigation permits the viewer to rotate the drawing
to any angle to better understand the extent of
contamination. The data were also used to generate a
cost-benefit analysis of the volume contaminated vs
cleanup threshold for all three contaminants.
The Site D sample optimization problem involved
groundwater contamination from four organic
compounds—dichloroethane (DCA), dichloroethene
(DCE), PCE, and TCE. The data supplied included
maps of buildings, roads, and water bodies; boring
data providing geologic structure; groundwater
contamination concentrations at 33 well locations for
five sampling periods; and the hydraulic head in
each well during one sampling period. The EVS-
PRO scripting language was used to create a routine
to query the data file, select the data for a single
contaminant and sampling time, and visualize the
contaminant data, producing 2-D and 3-D maps of
contamination. The process was repeated
automatically for each contaminant at each sampling
period. The 2-D maps provided atop view of the
areal extent of contamination with a site map
containing buildings and roads. The 3-D maps
showed the contamination as a function of depth,
with solid and exploded views. The exploded views
helped make clear the extent of contamination in
different geologic layers. EVS-PRO also provided
estimates of the volume of contaminated water
above the specific threshold concentration for each
contaminant.
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The Site N sample optimization problem involved
soil contamination from three heavy metals—arsenic
(As), cadmium (Cd), and chromium (Cr). EVS-PRO
was used to perform sample optimization for arsenic
contamination. The analysts used the software in an
iterative fashion to select a few sample locations for
further data collection. This new information was
used to generate the next set of sample locations, and
the process continued until the maximum number of
allowed sample locations (80 in this problem) had
been specified. With the final data set, EVS-PRO
generated arsenic concentration contour maps based
on contaminant threshold concentrations and the
degree of confidence in the interpolation results.
These maps were overlain with site features (roads
and waterways). Maps of uncertainty as a function
of the number of samples were also provided to
illustrate the reduction in uncertainty obtained with
increased sampling.
The Site S sample optimization problem is a 3-D
groundwater contamination problem for a single
contaminant [carbon tetrachloride (CTC)]. Initially,
concentration data were supplied for 19 well
locations at 5-ft vertical intervals within each well
and for another 5 well locations at 40-ft vertical
intervals. Using the geostatistical routines in EVS-
PRO to select sample locations, the C Tech analyst
requested additional data at 15 locations to further
define the plume. EVS-PRO was then used to
generate 2-D maps of the concentration distribution
based on the maximum concentration in each well
and the probability of exceeding the two threshold
concentrations for CTC. Three-dimensional
visualizations were also provided in VRML format
to allow the user to navigate around the plume. The
data were also used to generate a cost-benefit
analysis of the contaminated volume vs the cleanup
threshold.
The Site T problem was a groundwater
contamination problem. The data supplied for
analysis of this problem included maps of buildings
and roads, soil and groundwater contamination data
for four organic contaminants, and geologic boring
data representing the location of different soil layers
(e.g., clay, sand, silt). The C Tech analyst chose to
demonstrate the capability of the software to
visualize the 3-D subsurface soil layer structure. A
3-D animation that rotated the viewing angle of the
soil structure through 360° was provided to permit a
more complete view of the layers.
Section 2 of this report contains a brief description
of the capabilities of EVS-PRO. Section 3 outlines
the process followed in conducting the demon-
stration. This section discusses the approach used to
develop the test problems, the ten test problems, the
baseline analyses that were used for comparison
with the developers' analyses, and the evaluation
criteria. More detailed descriptions of the test
problems can be found in Appendix A. Section 4
presents the technical review of the analyses
performed by EVS-PRO. It includes a more detailed
discussion of the problems attempted, comparisons
of the EVS-PRO analyses and the baseline results,
and an evaluation of EVS-PRO against the criteria
established in Section 3. Section 5 presents an
update on the EVS technology and provides
examples of representative applications of EVS in
environmental problem-solving.
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Section 2 — Environmental Visualization System Capabilities
This section provides a general overview of the
capabilities of the products in C Tech's Environ-
mental Visualization System (EVS). The infor-
mation was supplied by C Tech.
EVS-PRO Features
C Tech's EVS-PRO unites interpolation, geologic
modeling, geostatistical analysis, and fully 3-D
visualization tools into a software system developed
to address mining and environmental contamination
issues. EVS-PRO can be used to analyze all types of
analytes and geophysical data in any environment
(soil, groundwater, surface water, air, etc.). One of
EVS-PRO's greatest strengths is its integrated
geostatistical analysis, which provides quantitative
assessment of the quality of a site assessment ("Min-
Max Plume" technology); as a part of the
geostatistical analysis additional sample locations
requiring investigation are identified. The tools that
are part of the software can improve site assessment
and enhance the capability to analyze and present
data for assessments, remediation planning,
litigation support, regulatory reporting, and public
relations.
EVS-PRO was developed to meet the needs of the
geologist, the environmental engineer, and the
environmental program manager as they relate to the
following areas:
• Site assessment: Determination of optimal
locations for collecting data in order to best
determine the spatial extent of contamination at
the lowest possible cost.
• Site evaluation: Determination of the spatial
extent of contamination. EVS-PRO's "Min-Max
Plume" technology quantifies the statistical
variation in the volume and mass estimates
resulting from the current level of
characterization.
• Geology: Creation of a 3-D model of the
geology of a site and determination of the
relationship between the geology and the
contaminant plumes. This information allows for
better-targeted remediation plans that consider
the effect of geology on the migration and
capture of contamination. EVS-PRO can also
compute plume volumes and masses on a
(geologic) layer basis.
• Communication: Visual presentation of site
geology and contamination is critical for
effective communication. EVS-PRO can
integrate geologic information, environmental
contamination data, site maps (showing
buildings, roads, and other features), and aerial
photographs into a single visualization. EVS-
PRO provides both still and animated 3-D
visualization.
EVS-PRO is a modular software system designed to
address the wide range of problems encountered by
the environmental community. It can be customized
for the most demanding application while preserving
an ease of use that provides immediate productivity.
EVS-PRO can deal with virtually all types of data
and environments. It is designed to be easy enough
for use (at a rudimentary level) by nonspecialist
personnel. However, the modular software
architecture and the breadth of its tools provide
comprehensive capabilities that can meet the needs
of scientists and researchers. For example, at a site
with a history of multiyear sampling, a script can be
generated to sort and query the data and plot
contamination levels from each year, and ultimately
to display all of the data in the form of an animation.
EVS-PRO can be used on Windows 95, 98, 2000,
and NT systems. It does not require any supple-
mentary software; however, it can interface with
many popular software packages. EVS-PRO can
read and write AutoCAD .dxf files and ESRI
shapefiles and can directly access ODBC-compliant
databases, provided ODBC 32 is installed on the
client's computer. Environmental database and data
management software products such as ESRI's
Arc View GIS, Integrate's TerraBase, EarthSoft's
EQuIS, and GIS Solution's GIS/Key also support
output to EVS-PRO. EVS-PRO can perform 3-D
postprocessing and animation for groundwater and
solute transport modeling packages such as
MODFLOW and MT3D by using a recently released
translation module that converts standard
MODFLOW and MT3D output files to EVS-PRO
format.
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Overview of C Tech Environmental
Software
C Tech Development Corporation has five major
products. EVS-PRO was used in this demonstration.
These products, the single-license price, and a brief
description of each software follow.
• EVS for Arc View (Environmental Visualization
System for ArcView) — $2,495
EVS for ArcView is a subset of EVS Standard,
described below. It is tightly integrated with
ArcView through a powerful ArcView
extension. It has 15 applications that include
geology only, chemistry only and geology and
chemistry applications. It is fully 3-D and has
the same viewer as C Tech's other products.
EVS for ArcView is a low-cost way to add more
3-D capability to an office: EVS for ArcView
seats may be combined with EVS-PRO or MVS
seats that are reserved for the more expert users.
EVS for ArcView was designed to be used by
nearly anyone with a background in
environmental problems, including project
managers and principals.
• MAS (Modeling Animation System) — $2,495
MAS has a limited subset of the animation
capabilities of EVS-PRO and a full suite of
visualization modules. It does not include any
geostatistics modules or any of the geologic
modeling or gridding capabilities of EVS-PRO.
MAS was developed to perform 3-D
postprocessing and animation for groundwater
and solute transport modeling packages such as
MODFLOW, MT3D, and CFEST.
• EVS Standard (Environmental Visualization
System Standard) — $4,995
EVS Standard is C Tech's baseline customizable
3-D analysis and visualization system. EVS
Standard includes all of the capabilities of EVS
for ArcView (including ArcView GIS
integration) and adds a modular, customizable
environment for geologists and environmental
engineers.
• EVS-PRO (Environmental Visualization
System Pro) — $9,995
EVS-PRO is C Tech's most popular product for
state-of-the art analysis, visualization, and
animation. EVS-PRO builds upon all of the
capabilities of EVS Standard and MAS and adds
advanced gridding, model building, output
options, geostatistics capabilities, animation, and
GIS functions to accommodate litigation
support, public relations and the more
demanding requirements of earth science
professionals.
• MVS (Mining Visualization System) — $24,995
Mining Visualization System (MVS) is C Tech's
flagship product for state-of-the art analysis and
visualization. MVS builds upon all of the
capabilities of EVS-PRO and adds powerful new
features targeted to the needs of mining
engineers and planners, or the geologist or
environmental engineer with the most
demanding requirements.
In addition to the fixed license product, EVS-PRO
and MVS can be bought with a floating license. The
floating license versions have steep discounts with
quantity and a more complex pricing structure. The
floating license uses a hardware key on the machine
that serves licenses, but any machine on the network
can run the software until the available seats are
used up. Table 1 provides prices for six nominal
Table 1. Pricing structure for floating-license version of EVS-PRO
Tier
class
A
B
C
D
E
F
Nominal
seats
1
3
8
17
34
100
Price for
first seat
$13,500
$16,000
$23,000
$41,800
$51,500
$101,000
Additional
seats
$6,250
$4,000
$3,000
$1,800
$1,500
$1,000
Price for nominal
number of seats
$ 13, 500 for 1 seat
$24,000 for 3 seats
$44,000 for 8 seats
$70,600 for 17 seats
$10 1,000 for 34 seats
$200,000 for 100 seats
Effective seat price
for nominal seats
$13,500
$8,000
$5,500
$4,153
$2,971
$2,000
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configurations; prices for other configurations are purchase. Additional years are currently 12% of
available on request. software's list price. Training courses are available,
and extensive documentation is available online,
One year of technical support, with software through the Web, and in a users' manual. Tutorials
maintenance and upgrades, is included in the are provided with the software to help train users.
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Section 3 — Demonstration Process and Design
Introduction
The objective of this demonstration was to conduct
an independent evaluation of the capabilities of
several DSSs in the following areas: (1) effective-
ness in integrating data and models to produce
information that supports decisions pertaining to
environmental contamination problems, and (2) the
information and approach used to support the
analysis. Specifically, three endpoints were
evaluated:
• Visualization — Visualization software was
evaluated in terms of its ability to integrate site
and contamination data in a coherent and
accurate fashion that aids in understanding the
contamination problem. Tools used in
visualization can range from data display in
graphical or contour form to integrating site
maps and aerial photos into the results.
• Sample optimization — Sample optimization
was evaluated for soil and groundwater
contamination problems in terms of the
software's ability to select the minimum number
of samples needed to define a contaminated
region with a specified level of confidence.
• Cost-benefit analysis — Cost-benefit analysis
involved either defining the size of remediation
zone as a function of the cleanup goal or
evaluating the potential human health risk. For
problems that defined the contamination zone,
the cost could be evaluated in terms of the size
of the zone, and cost-benefit analysis could be
performed for different cleanup levels or
different statistical confidence levels. For
problems that calculated human health risk, the
cost-benefit calculation would require
computing the cost to remediate the
contamination as a function of reduction in
health risk.
Secondary evaluation objectives for this
demonstration were to examine the reliability,
resource requirements, range of applicability, and
ease of operation of the DSS. The developers
participated in this demonstration in order to
highlight the range and utility of their software in
addressing the three endpoints discussed above.
Actual users might achieve results that are less
reliable, as reliable, or more reliable than those
achieved in this demonstration, depending on their
expertise in using the software to solve
environmental problems.
Development of Test Problems
Test Problem Definition
A problem development team was formed to collect,
prepare, and conduct the baseline analysis of the
data. A large effort was initiated to collect data sets
from actual sites with an extensive data collection
history. Through literature review and contact with
different government agencies (EPA field offices,
DOE, the U.S. Department of Defense, and the U.S.
Geological Survey) the team identified ten sites
throughout the United States that had the potential
for developing test problems for the demonstration.
The data from these ten sites were screened for
completeness of data, range of environmental
conditions covered, and potential for developing
challenging and defensible test problems for the
three endpoints of the demonstration. The objective
of the screening was to obtain a set of problems that
covered a wide range of contaminants (metals,
organics, and radionuclides), site conditions, and
source conditions (spills, continual slow release, and
multiple releases over time). On the basis of this
screening, six sites were selected for development of
test problems. Of these six sites, four had sufficient
information to provide multiple test problems. This
provided a total often test problems for use in the
demonstration.
Summary of Test Problems
A detailed description of the ten test problems was
supplied to the developers as part of the
demonstration (Sullivan, Armstrong, and Osleeb
1998). A general description of each of the problems
can be found in Appendix A. This description
includes the operating history of the site, the
contaminants of concern, and the objectives of the
test problem (e.g., define the volume over which the
contaminant concentration exceeds 100 |Jg/L). The
test problems analyzed by C Tech are discussed in
Section 4 as part of the evaluation of the
performance of EVS-PRO.
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Table 2 summarizes the ten problems by site iden-
tifier, location of contamination (soil or ground-
water), problem endpoints, and contaminants of
concern. The visualization endpoint could be per-
formed on all ten problems. In addition, there were
four sample optimization problems, four cost-benefit
problems, and two problems that combined sample
optimization and cost-benefit issues. The
contaminants included metals, volatile organic
compounds (VOCs), and radionuclides. The
environmental conditions included 2-D and 3-D soil
and groundwater contamination problems over
varying geologic, hydrologic, and environmental
settings. Table 3 provides a summary of the types of
data supplied with each problem.
Analysis of Test Problems
Prior to the demonstration, the demonstration
technical team performed a quality control
examination of all data sets and test problems. This
involved reviewing database files for improper data
(e.g., negative concentrations), removing
information that was not necessary for the
demonstration (e.g., site descriptors), and limiting
the data to the contaminants, the region of the site,
and the time frame covered by the test problems
(e.g., only data from one year for three
contaminants). For sample optimization problems, a
limited data set was prepared for the developers as a
starting point for the analysis. The remainder of the
Table 2. Summary of test problems
Site identifier
A
A
B
D
N
N
S
S
T
T
Media
Groundwater
Groundwater
Groundwater
Groundwater
Soil
Soil
Groundwater
Groundwater
Soil
Groundwater
Problem endpoints
Visualization, sample optimization
Visualization, cost-benefit
Visualization, sample optimization,
cost-benefit
Visualization, sample optimization,
cost-benefit
Visualization, sample optimization
Visualization, cost-benefit
Visualization, sample optimization
Visualization, cost-benefit
Visualization, sample optimization
Visualization, cost-benefit
Contaminants
Dichloroethene, trichloroethene
Perchloroethene, trichloroethane
Trichloroethene, vinyl-chloride,
technetium-99
Dichloroethene, dichloroethane,
trichloroethene, perchloroethene
Arsenic, cadmium, chromium
Arsenic, cadmium, chromium
Carbon tetrachloride
Chlordane
Ethylene dibromide,
dibromochloropropane,
dichloropropane, carbon tetrachloride
Ethylene dibromide,
dibromochloropropane,
dichloropropane, carbon tetrachloride
Table 3. Data supplied for the test problems
Site history
Surface structure
Sample locations
Contaminants
Geology
Hydrogeology
Transport parameters
Human health risk
Industrial operations, environmental settings, site descriptions
Road and building locations, topography, aerial photos
x, y, z coordinates for
soil surface samples
soil borings
groundwater wells
Concentration data as a function of time and location (x, y, and z) for metals,
inorganics, organics, radioactive contaminants
Soil boring profiles, bedrock stratigraphy
Hydraulic conductivities in each stratigraphic unit; hydraulic head measurements
and locations
Sorption coefficient (Kd), biodegradation rates, dispersion coefficients, porosity,
bulk density
Exposure pathways and parameters, receptor location
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data were reserved to provide input concentrations to
developers for their sample optimization analysis.
For cost-benefit problems, the analysts were
provided with an extensive data set for each test
problem with a few data points reserved for
checking the DSS analysis. The data quality review
also involved importing all graphics files (e.g., .dxf
and .bmp) that contained information on surface
structures such as buildings, roads, and water bodies
to ensure that they were readable and useful for
problem development. Many of the drawing files
were prepared as ESRI shape files compatible with
ArcView™. ArcView was also used to examine the
graphics files.
Once the quality control evaluation was completed,
the test problems were developed. The test problems
were designed to be manageable within the time
frame of the demonstration and were often a subset
of the total data set. For example, in some cases, test
problems were developed for a selected region of the
site. In other cases, the database could have
contained information for tens of contaminants,
while the test problems themselves were limited to
the three or four principal contaminants. At some
sites, data were available over time periods
exceeding 10 years. For the DSS test problems, the
analysts were typically supplied chemical and
hydrologic data for a few sampling periods.
Once the test problems were developed, the
demonstration technical team conducted a complete
analysis of each test problem. These analyses served
as the baseline for evaluating results from the
developers. Each analysis consisted of taking the
entire data set and obtaining an estimate of the
plume boundaries for the specified threshold
contaminant concentrations and estimating the area
of contamination above the specified thresholds for
each contaminant.
The independent data analysis was performed using
Surfer™ (Golden Software 1996). Surfer was
selected for the task because it is a widely used,
commercially available software package with the
functional capabilities necessary to examine the
data. This functional capability includes the ability
to import drawing files to use as layers in the map,
and the ability to interpolate data in two dimensions.
Surfer has eight different interpolation methods,
each of which can be customized by changing model
parameters, to generate contours. These different
contouring options were used to generate multiple
views of the interpolated regions of contamination
and hydrologic information. The best fit to the data
was used as the baseline analysis. For 3-D problems,
the data were grouped by elevation to provide a
series of 2-D slices of the problem. The distance
between slices ranged between 5 and 10 ft
depending on the availability of data. Compilation of
vertical slices generated 3-D depictions of the data
sets. Comparisons of the baseline analysis to the
EVS-PRO results are presented in Section 4.
In addition to Surfer, two other software packages
were used to provide an independent analysis of the
data and to provide an alternative representation for
comparison with the Surfer results. The
Geostatistical Software Library Version 2.0 (GSLIB)
and Geostatistical Environmental Assessment
Software Version 1.1 (Geo-EAS) were selected
because both provide enhanced geostatistical
routines that assist in data exploration and selection
of modeling parameters to provide extensive
evaluations of the data from a spatial context
(Deutsch and Journel 1992; Englund and Sparks
1991). These three analyses provide multiple lines of
reasoning, particularly for the test problems that
involved geostatistics. The results from Surfer,
GSLIB, and Geo-EAS were compared and
contrasted to determine the best fit of the data, thus
providing a more robust baseline analysis for
comparison to the developers' results.
Under actual site conditions, uncertainties and
natural variability make it impossible to define
plume boundaries exactly. In these case studies, the
baseline analyses serve as a guideline for evaluating
the accuracy of the analyses prepared by the
developers. Reasonable agreement should be
obtained between the baseline and the developer's
results. A discussion of the technical approaches and
limitations to estimating physical properties at
locations that are between data collection points is
provided in Appendix B.
To minimize problems in evaluating the software
associated with uncertainties in the data, the
developers were required to perform an analysis of
one problem from either Site N or Site S. For Site N,
with over 4000 soil contamination data points, the
baseline analysis reflected the actual site conditions
closely; and if the developers performed an accurate
analysis, the correlation between the two should be
high. For Site S, the test problems used actual
contamination data as the basis for developing a
problem with a known solution. In both Site S
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problems, the data were modified to simulate a
constant source term to the aquifer in which the
movement of the contaminant can be described by
the classic advective-dispersive transport equation.
Transport parameters were based on the actual data.
These assumptions permitted release to the aquifer
and subsequent transport to be represented by a
partial differential equation that was solved
analytically. This analytical solution could be used
to determine the concentration at any point in the
aquifer at any time. Therefore, the developer's
results can be compared against calculated
concentrations with known accuracy.
After completion of the development of the ten test
problems, a predemonstration test was conducted. In
the predemonstration, the developers were supplied
with a problem taken from Site D that was similar to
test problems for the demonstration. The objective of
the predemonstration was to provide the developers
with a sample problem with the level of complexity
envisioned for the demonstration. In addition, the
predemonstration allowed the developers to process
data from a typical problem in advance of the
demonstration and allowed the demonstration tech-
nical team to determine if any problems occurred
during data transfer or because of problem defini-
tion. The results of the predemonstration were used
to refine the problems used in the demonstration.
Preparation of Demonstration Plan
In conjunction with the development of the test
problems, a demonstration plan (Sullivan and
Armstrong 1998) was prepared to ensure that all
aspects of the demonstration were documented and
scientifically sound and that operational procedures
were conducted within quality assurance
(QA)/quality control (QC) specifications. The
demonstration plan covered
• the roles and responsibilities of demonstration
participants;
• the procedures governing demonstration
activities such as data collection to define test
problems and data preparation, analysis, and
interpretation;
• the experimental design of the demonstration;
• the evaluation criteria against which the DSS
would be judged; and
• QA and QC procedures for conducting the
demonstration and for assessing the quality of
the information generated from the
demonstration.
All parties involved with implementation of the plan
approved and signed the demonstration plan prior to
the start of the demonstration.
Summary of Demonstration
Activities
On September 14-25, 1998, the Site Character-
ization and Monitoring Technology Pilot, in
cooperation with DOE's National Analytical
Management Program, conducted a demonstration to
verify the performance of five environmental DSS
packages. The demonstration was conducted at the
New Mexico Engineering Research Institute,
Albuquerque, New Mexico. An additional software
package was tested on October 26-29, 1998, at
Brookhaven National Laboratory, Upton, New York.
The first morning of the demonstration was devoted
to a brief presentation of the ten test problems, a
discussion of the output requirements to be provided
from the developers for evaluation, and transferring
the data to the developers. The data from all ten test
problems—along with a narrative that provided a
description of the each site, the problems to be
solved, the names of data files, structure of the data
files, and a list of output requirements—were given
to the developers. The developers were asked to
address a minimum of three test problems for each
software product.
Upon completion of the review of the ten test
problems and the discussion of the outputs required
from the developers, the developers received data
sets for the problems by file transfer protocol (FTP)
from a remote server or on a high-capacity
removable disk. Developers downloaded the data
sets to their own personal computers, which they had
supplied for the demonstration. Once the data
transfers of the test problems were complete and the
technical team had verified that each developer had
received the data sets intact, the developers were
allowed to proceed with the analysis at their own
pace. During the demonstration, the technical team
observed the developers, answered questions, and
provided data as requested by the developers for the
sample optimization test problems. The developers
were given 2 weeks to complete the analysis for the
test problems that they selected.
The third day of the demonstration was visitors' day,
an open house during which people interested in
DSS could learn about the various products being
tested. During the morning of visitors' day,
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presenters from EPA, DOE, and the demonstration
technical team outlined the format and content of the
demonstration. This was followed by a presentation
from the developers on the capabilities of their
respective software products. In the afternoon,
attendees were free to meet with the developers for a
demonstration of the software products and further
discussion.
Prior to leaving the test facility, the developers were
required to provide the demonstration technical team
with the final output files generated by their
software. These output files were transferred by FTP
to an anonymous server or copied to a zip drive or
compact disk-read only memory (CD-ROM). The
technical team verified that all files generated by the
developers during the demonstration were provided
and intact. The developers were given a 10-day
period after the demonstration to provide a written
narrative of the work that was performed and a
discussion of their results.
Evaluation Criteria
One important objective of DSS is to integrate data
and models to produce information that supports an
environmental decision. Therefore, the overriding
performance goal in this demonstration was to
provide a credible analysis. The credibility of a
software and computer analysis is built on four
components:
• good data,
• adequate and reliable software,
• adequate conceptualization of the site, and
• well-executed problem analysis (van der Heijde
and Kanzer 1997).
In this demonstration, substantial efforts were taken
to evaluate the data and remove data of poor quality
prior to presenting it to the developers. Therefore,
the developers were directed to assume that the data
were of good quality. The technical team provided
the developers with detailed site maps and test
problem instructions on the requested analysis and
assisted in site conceptualization. Thus, the
demonstration was primarily to test the adequacy of
the software and the skills of the analyst. The
developers operated their own software on their own
computers throughout the demonstration.
Attempting to define and measure credibility makes
this demonstration far different from most
demonstrations in the ETV program in which
measurement devices are evaluated. In the typical
ETV demonstrations, quality can be measured in a
quantitative and statistical manner. This is not true
for DSS. While there are some quantitative
measures, there are also many qualitative measures.
The criteria for evaluating the DSS's ability to
support a credible analysis are discussed below. In
addition a number of secondary objectives, also
discussed below, were used to evaluate the software.
These included documentation of software, training
and technical support, ease of use of the software,
efficiency, and range of applicability.
Criteria for Assessing Decision
Support
The developers were asked to use their software to
answer questions pertaining to environmental
contamination problems. For visualization tools,
integration of geologic data, contaminant data, and
site maps to define the contamination region at
specified concentrations was requested. For software
tools that address sample optimization questions, the
developers were asked to suggest optimum sampling
locations, subject to constraints on the number of
samples or on the confidence with which
contamination concentrations were known. For
software tools that address cost-benefit problems,
the developers were asked either to define the
volume (or area) of contamination and, if possible,
supply the statistical confidence with which the
estimate was made, or to estimate human health
risks resulting from exposure to the contamination.
The criterion for evaluation was the credibility of the
analyses to support the decision. This evaluation was
based on several points, including
• documentation of the use of the models, input
parameters, and assumptions;
• presentation of the results in a clear and
consistent manner;
• comparison of model results with the data and
baseline analyses;
• evaluation of the use of the models; and
• use of multiple lines of reasoning to support the
decision.
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The following sections provide more detail on each
of these topics.
Documentation of the Analysis and
Evaluation of the Technical Approach
The developers were requested to supply a concise
description of the objectives of the analysis, the
procedures used in the analysis, the conclusions of
the analysis with technical justification of the
conclusions, and a graphical display of the results of
the analysis. Documentation of key input parameters
and modeling assumptions was also requested.
Guidance was provided on the quantity and type of
information requested to perform the evaluation.
Based on observations obtained during the
demonstration and the documentation supplied by
the developers, the use of the models was evaluated
and compared to standard practices. Issues in proper
use of the models include selection of appropriate
contouring parameters, spatial and temporal
discretization, solution techniques, and parameter
selection.
This evaluation was performed as a QA check to
determine if standard practices were followed. This
evaluation was useful in determining whether the
cause of discrepancies between model projections
and the data resulted from operator actions or from
the model itself and was instrumental in
understanding the role of the operator in obtaining
quality results.
Comparison of Projected Results with the
Data and Baseline Analysis
Quantitative comparisons between DSS-generated
predictions and the data or baseline analyses were
performed and evaluated. In addition, DSS-
generated estimates of the mass and volume of
contamination were compared to the baseline
analyses to evaluate the ability of the software to
determine the extent of contamination. For
visualization and cost-benefit problems, developers
were given a detailed data set for the test problem
with only a few data points held back for checking
the consistency of the analysis. For sample optimi-
zation problems, the developers were given a limited
data set to begin the problem. In this case, the data
not supplied to the developers were used for
checking the accuracy of the sample optimization
analysis. However, because of the inherent vari-
ability in environmental systems and the choice of
different models and parameters by the analysts,
quantitative measures of the accuracy of the analysis
are difficult to obtain and defend. Therefore,
qualitative evaluations of how well the model
projections reproduced the trends in the data were
also performed.
A major component of the analysis of environmental
data sets involves predicting physical or chemical
properties (contaminant concentrations, hydraulic
head, thickness of a geologic layer, etc.) at locations
between measured data. This process, called
interpolation, is often critical in developing an
understanding of the nature and extent of the
environmental problem. The premise of interpolation
is that the estimated value of a parameter is a
weighted average of measured values around it.
Different interpolation routines use different criteria
to select the weights. Due to the importance of
obtaining estimates of data between measured data
points in many fields of science, a wide number of
interpolation routines exist. Three classes of
interpolation routines commonly used in
environmental analysis are nearest neighbor, inverse
distance, and kriging. These three classes of
interpolation, and their strengths and limitations, are
discussed in detail in Appendix B.
Use of Multiple Lines of Reasoning
Environmental decisions are often made with
uncertainties because of an incomplete
understanding of the problem and lack of
information, time, and/or resources. Therefore,
multiple lines of reasoning are valuable in obtaining
a credible analysis. Multiple lines of reasoning may
incorporate statistical analyses, which in addition to
providing an answer, provide an estimate of the
probability that the answer is correct. Multiple lines
of reasoning may also incorporate alternative
conceptual models or multiple simulations with
different parameter sets. The DSS packages were
evaluated on their capabilities to provide multiple
lines of reasoning.
Secondary Evaluation Criteria
Documentation of Software
The software was evaluated in terms of its
documentation. Complete documentation includes
detailed instructions on how to use the software
package, examples of verification tests performed
with the software package, a discussion of all output
files generated by the software package, a discussion
of how the output files may be used by other
programs (e.g., ability to be directly imported into an
Excel spreadsheet), and an explanation of the theory
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behind the technical approach used in the software
package.
Training and Technical Support
The developers were asked to list the necessary
background knowledge necessary to successfully
operate the software package (i.e., basic under-
standing of hydrology, geology, geostatistics, etc.)
and the auxiliary software used by the software
package (e.g., Excel). In addition, the operating
systems (e.g., Unix, Windows NT) under which the
DSS can be used was requested. A discussion of
training, software documentation, and technical
support provided by the developers was also
required.
Ease of Use
Ease of use is one of the most important factors to
users of computer software. Ease of use was
evaluated by an examination of the software
package's operation and on the basis of adequate on-
line help, the availability of technical support, the
flexibility to change input parameters and databases
used by the software package, and the time required
for an experienced user to set up the model and
prepare the analysis (that is, input preparation time,
time required to run the simulation, and time
required to prepare graphical output). The
demonstration technical team observed the operation
of each software product during the demonstration to
assist in determining the ease of use. These
observations documented operation and the technical
skills required for operation. In addition, several
members of the technical team were given a 4-hour
tutorial by each developer on their respective
software to gain an understanding of the training
level required for software operation as well as the
functional capabilities of each software.
Efficiency and Range of Applicability
Efficiency was evaluated on the basis of the resource
requirements used to evaluate the test problems. This
was assessed through the number of problems
completed as a function of time required for the
analysis and computing capabilities.
Range of applicability is defined as a measure of the
software's ability to represent a wide range of
environmental conditions and was evaluated through
the range of conditions over which the software was
tested and the number of problems analyzed.
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Section 4 — EVS-PRO Evaluation
EVS-PRO Technical Approach
For sample optimization and quantification of
uncertainties in predicted values, EVS-PRO uses a
geostatistical approach. Geostatistical methods are
based on the premise that measured variables located
close to each other will have similar values, while
variables far apart will have little correlation
between their corresponding values. A statistical
measure for this interrelationship is summarized by
the correlation between measured variables
measured at different points in space. This measure
or related measures such as the variogram and
covariance form the central idea around which linear
estimation methods in geostatistics operate. The use
of correlation measures also separates this estimation
method from other interpolation algorithms such as
inverse distance, linear interpolation, splines, and
quadrature methods. Using a statistical estimator
allows the estimation error to be calculated along
with the estimate. Thus, a geostatistical method
provides both the most likely value and an estimate
of the range of other possible values for a given
location. This is important information because the
spatial variability present in most parameters is such
that values predicted prior to actual data
measurement are unlikely to exactly match the
"newly" measured value. In fact, often there are
many possible solutions to the estimation problem
that agree with the measurements (Appendix B).
Kriging, which is used in EVS-PRO, is one of the
more common geostatistical methods used to
provide smoothed estimates of variables. The
kriging model used in EVS-PRO matches the data at
all measured locations.
EVS-PRO imports measured data, defines a grid
(i.e., divides the volume of concern into a number of
3-D hexahedral blocks), automatically calculates the
spatial correlation of the data in three dimensions
(i.e., generates a variogram), and from the variogram
estimates the parameters necessary for kriging
interpolation of the data. The kriging process
provides an estimate of the most likely value of the
variable and a statistical measure of the variability
expected at that location. Default values for
calculating the spatial correlation can be changed by
the user of EVS-PRO.
In performing estimates of the volume of soil that
contains contamination above the cleanup
concentration as a function of probability levels,
EVS-PRO uses "Min-Max Plume" technology. In
this approach, EVS-PRO determines the minimum
and maximum plume volume by using kriging
interpolation to calculate the nominal value and
associated standard deviation at every location in the
model. The predicted nominal value and standard
deviation are used to estimate the "minimum" and
"maximum" values as a function of probability level.
For the case of the maximum plume and a 75%
probability level, a "maximum" value is determined
at each model location such that if a measurement
were collected at any location, the hypothetically
measured value could be expected to be less than the
"maximum" value 75% of the time. Using
interpolation and the "maximum" value at each
model location, the volume of contamination is
defined at the 75% "maximum" level. The
"minimum" value is defined as the value at which
the hypothetically measured value is expected to be
greater than the "minimum" value 75% of the time,
and the 75% "minimum" plume is defined similarly.
The 75% "minimum" plume represents the region of
contamination in which the analysis shows that there
is a 75% probability that the contamination volume
is at least that large. This approach of estimating
concentration as a function of probability is
consistent with the EPA data quality objectives
guidance (EPA 1994).
The objective in performing sample optimization is
to collect samples at the locations that will provide
the maximum amount of information to define the
extent of contamination. This is accomplished in
EVS-PRO with a measure called uncertainty. For
determining sample locations, EVS-PRO first
calculates the confidence in the predicted values at
each model location. Confidence is a measure of
how well the predicted value represents the actual
value and answers questions such as, "What is the
probability that the predicted value will be within a
factor of 10 of the measured value?" Uncertainty, as
used in EVS-PRO, is a concentration-weighted
inverse of the confidence. This weighting gives more
importance in selecting sample locations to regions
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of high concentration and low confidence. High
uncertainty indicates a high probability of poorly
characterized contamination. The weighting function
used to compute uncertainty has been optimized
with the intent of minimizing the number of new
sampling locations.
EVS-PRO permits the user to focus the uncertainty
estimates on a specific range of concentration values
by setting floor and ceiling values for calculating
uncertainty. Values below the floor level are
considered unimportant (or more specifically, they
are set to the floor level). Similarly, values above the
ceiling level are set to the ceiling value. Setting the
ceiling at the concentration value of most interest,
such as the threshold level, will cause the selection
of new sampling locations to converge most rapidly
on defining the plume boundary at the ceiling
concentration. This will, however, sacrifice the
accurate characterization of the most contaminated
regions in the domain. This limitation is generally
acceptable if the primary goal is determination of the
extent (rather than specific distribution) of
contamination.
Description of Test Problems
C Tech staff chose to use EVS-PRO to perform the
visualization endpoint on selected data from each of
the six sites. In addition, the software performed
sample optimization for Sites B, N, and S. Cost-
benefit analysis (estimates of contaminated volume
as a function of cleanup concentrations) was also
performed on these three problems and for the Site
A cost-benefit problem and the Site D sample
optimization problem. As part of the demonstration,
several hundred visualization outputs were
generated. These included 3-D depictions of plume
boundaries with site features superimposed,
animations providing multiple perspectives on
contamination plumes and subsurface stratigraphy,
and interactive 3-D visualizations that allow the
viewer to rotate the figure to any viewing angle. A
few examples that display the range of capabilities
and features in EVS-PRO are included in this report.
A general description of each test problem and the
analysis performed using EVS-PRO follows.
Detailed descriptions of all test problems are
provided in Appendix A and in Sullivan, Armstrong,
and Osleeb (1998).
Site A Cost-Benefit Problem
The Site A cost-benefit problem was a 3-D
groundwater contamination problem for two
contaminants, PCE and TCA. Contamination had
migrated more than a mile towards nearby well
fields. The objective of this test problem was to
define the location, depth, and volume of the plume
at PCE concentrations of 100 and 500 |jg/L and
TCA concentrations of 5 and 50 |jg/L at probability
levels of 10, 50, and 90%. The 10% probability
region is the region in which there is at least a 10%
chance that the contamination will exceed the
threshold concentration. Therefore, the 10%
probability region predicts the maximum volume of
contamination and the 90% probability region
predicts the minimum. In the terminology of EVS,
the 10% probability plume corresponds to the 90%
"maximum" plume and the 90% probability region
corresponds to the 90% "minimum" plume. The
probability of exceeding a threshold concentration is
used in a cost-benefit analysis of cleanup goals vs
the cost of remediation. C Tech used EVS-PRO to
accomplish the problem objectives.
The data supplied for the analysis of Site A included
maps of roads, buildings, and water bodies; data on
the concentrations of the two contaminants at
different depths and locations in more than 80
groundwater wells; hydraulic head data; and data on
geologic structure. Chemical analysis data were
collected at 5-ft intervals from each well.
Site B Sample Optimization and Cost-
Benefit Problem
The Site B problem was a 2-D groundwater
contamination problem. Initial sampling attempted
to define the central region of the contaminant
plume, which extends more than a mile and
approaches a nearby river. The objective of the
sample optimization problem was to develop a
sampling strategy to define the region in which the
groundwater contamination exceeds specified
threshold concentrations (Table 4) at probability
levels of 10, 50, and 90%. In addition, the analyst
was asked to calculate the health risks associated
Table 4. Site B groundwater contamination
problem threshold concentrations
Contaminant
TCE
VC
Tc-99
Threshold concentrations
50, 500 (ng/L)
50, 250 (jig/L)
10000, 40000 (pCi/L)
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with drinking 2 L of contaminated groundwater per
day from two exposure points in the plume based on
current conditions and at 5 years in the future. One
exposure point was near the centerline of the plume,
while the other was on the edge of the plume. This
information could be used in a cost-benefit analysis
of the reduction of human health risk as a function of
remediation.
The data supplied for analysis of Site B included an
aerial photograph of the site; maps of buildings,
roads, and water bodies; hydraulic head data; and
concentration data for three contaminants (TCE, VC,
and Tc-99) in groundwater wells at 25 locations
during one year of sampling.
C Tech staff chose to demonstrate the visualization,
sample optimization, and cost-benefit analysis
capabilities of EVS-PRO. Health risk analysis was
not performed. For sample optimization the analyst
chose to demonstrate EVS-PRO's capabilities using
the Tc-99 contamination.
Site D Sample Optimization Problem
The Site D problem was a 3-D groundwater sample
optimization problem. The objective of this test
problem was to test the software's capabilities to
select sample locations that accurately define the
extent of contamination and then to use the infor-
mation to estimate the contaminated volume of
groundwater as a function of probability.
The data supplied for the analysis of Site D included
maps of buildings, roads, and water bodies; concen-
tration data on four contaminants (PCE, DCE, TCE,
and TCA) at different depths and locations in 33
groundwater wells for five consecutive sampling
periods; hydraulic head data; and geologic boring
data. The C Tech analyst decided to use this infor-
mation to visualize the original data set and did not
perform sample optimization. C Tech's objective
was to demonstrate the power of EVS-PRO in
automating data visualization.
Site N Sample Optimization Problem
This test problem was a surface soil contamination
problem for three contaminants (As, Cd, and Cr).
The test problem was designed to assess the
accuracy with which the software can be used to
predict sample locations to define the extent of
surface soil contamination above certain
predetermined threshold concentrations. The
threshold concentrations for each contaminant are
shown in Table 5. Budgetary restraints limited the
number of additional sample locations to 80.
Because of the limited number of samples, the
analyst was asked to supply estimates of the extent
of contamination based on the confidence in the
results.
The analyst was given an extensive data set for the
three contaminants over a small highly contaminated
area of the site (<10 acres). The problem required
the analyst to develop a sample optimization scheme
to define the extent of contamination for the entire
site (125 acres). Site maps with roads, buildings, and
water bodies were also provided. The C Tech analyst
used arsenic contamination values to make sample
optimization decisions for this problem.
Site S Sample Optimization Problem
The Site S sample optimization problem focused on
a 3-D groundwater contamination problem for a
single contaminant, CTC. The objectives of this
problem were to develop a sampling strategy to
define the 3-D region of the plume at threshold
concentrations of 5 and 500 |jg/L and confidence
levels of 10, 50, and 90%; to estimate the volume of
contaminated groundwater at the defined thresholds;
and to calculate human health risks to support cost-
benefit decisions. The C Tech analyst performed the
problem and estimated the plume location and
Table 5. Site N soil contamination threshold concentrations (mg/kg) for the
sample optimization problem
Contaminant
Arsenic (As)
Cadmium (Cd)
Chromium (Cr)
Minimum threshold
concentration
125
70
370
Maximum threshold
concentration
500
700
3700
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volume as a function of three probability levels (25,
50, and 75%).
The data supplied for analysis of this problem
included geologic cross-section data, hydraulic head
data, hydrologic and transport parameters, and
contaminant concentration data from 24 monitoring
wells. Data for 19 of these wells had been collected
at 5-ft vertical intervals; data for the other 5 wells
had been collected on 40-ft vertical intervals. A total
of 434 contaminant sample locations and values
were provided to the analyst. To focus only on the
accuracy of the analysis, the problem was simplified.
Information regarding surface structures (e.g.,
buildings and roads) was not supplied to the
analysts. In addition, the data set was developed
such that the contaminant concentrations were
known exactly at each point (i.e., release and
transport parameters were specified and
concentrations could be determined from an
analytical solution). This analytical solution
permitted a reliable benchmark for evaluating the
accuracy of the software's predictions.
Site T Cost-Benefit Problem
The Site T problem was a 3-D groundwater
contamination cost-benefit problem. The data
supplied for analysis of this problem included maps
of buildings and roads, soil boring data for 23 wells,
and contamination data for four organic
contaminants [ethylene dibromide (EDB),
dibromochloroproprane (DBCP), dichloropropane
(DCP), and CTC]. This site was characterized by a
complicated subsurface structure. The C Tech
analyst decided to demonstrate EVS-PRO's
capabilities in representing the subsurface
stratigraphy of the site in 3-D rather than perform
another cost-benefit analysis.
Evaluation of EVS-PRO
Decision Support
EVS-PRO provides decision support through 3-D
visualization of environmental data such as
contaminant concentration contours, quantifying
uncertainties in interpolation predictions,
recommending additional sample locations to reduce
uncertainties, and providing statistical information
about the extent of contamination. In the
demonstration, C Tech used EVS-PRO to import
data on contaminant concentrations, hydraulic heads,
and geologic structure from ASCII text files and to
import visual data such as aerial photographs and
maps of buildings, roads, and water bodies from
jpg, .dxf, and .shp files. EVS-PRO was used to
integrate this information on a single platform and
place the information in a 3-D visual context. EVS-
PRO was used to generate 3-D maps of concen-
tration contours and estimates of the volume of
contaminated media as a function of the probability
of exceeding threshold concentrations. Maps of
uncertainty were generated to highlight the regions
of the sites that would require additional sampling to
further refine the estimate of location and size of the
contaminated area. The accuracy of the analyses is
discussed in the section comparing results from
EVS-PRO with baseline data and analysis.
Documentation of the EVS-PRO Analysis
and Evaluation of the Technical Approach
For each problem, C Tech provided a detailed
description of the steps necessary to import the data
into EVS-PRO and perform the desired analysis.
The steps proceeded logically, and manipulations to
arrange the data in the EVS-PRO data structure were
relatively simple: Data files were supplied to the
analyst in .dbf format. These files were then im-
ported into a program such as Microsoft Excel,
reformatted in the structure required by EVS-PRO,
and saved in comma-delimited ASCII text file
format.
C Tech also provided the parameters for contouring
in the output files and problem documentation. The
technical approach used by C Tech followed stan-
dard practices. However, the analyst often relied on
default parameters supplied by EVS-PRO software
for performing geostatistical analysis and inter-
polation. Selection of these parameters on a
problem-specific basis would have improved the
accuracy of the EVS-PRO analyses. This is
discussed in more detail in the evaluation of the Site
N sample optimization problem and the Site B and
the Site S contaminated volume estimates.
Comparison of EVS-PRO Results with the
Baseline Analysis and Data
Site A Cost-Benefit Problem
C Tech used EVS-PRO to analyze the distribution of
PCE and TCA contamination at Site A. To illustrate
the software's capabilities in generating 3-D
visualization of the data, the analyst generated 3-D
maps of the regions of contamination above two
threshold concentrations at three probability levels.
A scale of coordinates and surface features was
included on the maps to provide a frame of
reference. These files were generated using targa
(.tga) formatting in two resolutions. EVS-PRO also
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generated four animations depicting the 3-D extent
of contamination. The animations rotated the
viewing angle of the contamination through 360° to
provide a more complete view of the contamination.
In addition, EVS-PRO was used to estimate the
volume of contamination at the three probability
levels and two threshold concentrations.
Figure 1 shows the EVS-PRO representation of the
100-ng/L PCE plume at the 50% probability level
(nominal plume). This visualization integrates a
number of different pieces of information. The
volume predicted to be contaminated above the
threshold with 50% probability is represented by the
solid region. Dimensions (elevation, easting, and
northing) are provided on the figure as a frame of
reference. The ground surface is represented as the
sloping colored plane at the top of the figure. The
elevation of the ground surface was determined from
the data supplied as part of the problem. The ground
elevation contour key is at the bottom right of the
figure. Site features such as the local river and
buildings are draped over the ground-surface contour
map. The river can be seen as the blue line on the
northern part of the map. Buildings are difficult to
see from this perspective; however, a residential
community can be seen at the southeast corner as the
series of small markings. In the subsurface region of
the visualization, lines with a series of circular
markers represent well locations and data collection
points as a function of elevation. The circles are
color-coded to match the measured value at that
point. The concentration key, at the top left of the
figure, indicates blue as the lowest concentration and
red as the highest. The figure also integrates the data
on the bedrock elevation at the site and constrains
the predicted plume boundary to be above the
bedrock at all locations. The monitoring wells were
sampled at 5-ft intervals until bedrock was reached.
Therefore, an approximate idea of the location of the
bedrock can be obtained from the deepest sample
location in each well. The red regions in the plume
lying just above the bedrock at the southwest corner
of the plume indicate high predicted concentrations;
it can be inferred from the visualization that the
contaminant has migrated downward to the bottom
of the aquifer. This emphasizes the value of a 3-D
representation of the data.
iood
10000 ppt.
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PCE Plume at 1 CIO ppb 243 251 259 267 275
Figure 1. EVS-PRO representation of the Site A 100-|_ig/L PCE plume at the 50% probability level.
19
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The review of the EVS-PRO visualization output
demonstrated that the locations of all features (river,
buildings, wells, etc.) were accurately mapped. A
comparison of the ground surface elevation (top
colored surface in Figure 1) with a similar analysis
performed by the evaluation test team using Surfer
indicated general agreement between the two
approaches. Because of the perspective in Figure 1,
it was not possible to evaluate the differences
between the two approaches quantitatively. A
comparison of the EVS-PRO depiction of
concentration (the color-coded circles in Figure 1)
with the data showed that the data were accurately
represented. The use of this feature permits the
analyst to see how well the contour (solid surface)
matches the measured data.
The technical team performed a baseline analysis
with the same data set using the 2-D interpolation
routines in Surfer. The analysis was performed by
dividing the subsurface into ten 2-D slices as a
function of sample elevation. Most slices were 10 ft
thick, but the top slice was only 5 ft thick [260-
265 ft above mean sea level (MSL)], and the bottom
slice was 15 ft thick (165-180 ft above MSL). If
more than one data point was measured in the
vertical slice, the maximum value was used to
determine the extent of contamination. The baseline
analysis was performed for two contaminants, PCE
and TCA. Figure 2 represents the nominal PCE
plume at 100 |jg/L (blue) and 500 |j,g/L (red) for data
collected at elevations between 210 and 220 ft above
MSL. The figure provides a top view with the river
and the easting and northing scales providing points
of reference. Sample locations are marked with a
filled circle. Similar figures were developed for each
of the ten layers for both contaminants.
The technical team compared the predicted
contamination zones at the threshold concentrations
for the two contaminants (100 and 500 |jg/L for
PCE; 5 and 50 ng/L for TCA). The analyst was
supplied with hydraulic head data that indicated
groundwater flow was from west to east in a
direction that was essentially parallel to the river.
There was general agreement between the baseline
analysis and the EVS-PRO results in the
downstream region of contamination. The
differences in approach (2-D vs 3-D analysis and
different interpolation parameters) made a
quantitative comparison between the baseline and
EVS-PRO results impossible. The conclusion of this
review is that, in general, EVS-PRO generated
acceptable 3-D depictions of the groundwater
contamination for this problem at the 50%
probability level.
In two areas, however, there were large
discrepancies between the baseline and EVS-PRO
analyses. The EVS-PRO approach predicted large
amounts of contamination at the northwest corner of
the site, north of the river, at locations where there
were no contamination data (Figure 1). This
prediction was caused by the high concentrations of
contaminant at nearby wells. However, groundwater
flow data indicated that this region was upstream
from the source area. The analysis performed by C
Tech on Site A did not utilize the groundwater flow
data provided. Rather, the analysis was conducted
strictly as geostatistical estimation. This resulted in
1270001
05 126000-
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fc 125000-1
124000-
974000 976000 978000 980000 982000
Easting (ft)
Figure 2. Baseline representation of the Site A nominal PCE plume at 210-220 ft above MSL obtained
using Surfer. The red contour represents the 500-|jg/L threshold and the blue contour represents
the 100- ng/L threshold.
20
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unrealistic estimates upstream of the source area.
The C Tech analyst should have bounded the plume
upstream and perpendicular to the source to ensure
that contamination would not be extrapolated in
these directions. In contrast, the baseline approach
did not depict this contamination because its
interpolation parameters were optimized to minimize
this effect and exclude regions upstream from the
source area in its analysis. An example of the
baseline analysis for PCE contamination at
elevations between 210 and 220 ft above MSL is
provided in Figure 2. In this figure, blue indicates
the 100-|jg/L contour and red indicates regions
above 500 |Jg/L. In the baseline analysis,
contamination is not predicted north of the river.
The other region in which there were major
differences between the baseline and EVS-PRO
analyses was at deeper subsurface elevations (160-
200 ft above MSL). This is the region in which the
top of the bedrock is typically found. The baseline
analysis did not constrain the predicted zones of
contamination to those regions that were above the
bedrock and therefore tended to overpredict the
spreading of contamination at these depths. This is
illustrated in Figure 3, which shows predicted PCE
contours at elevations between 180 and 190 ft above
MSL. Figure 3 also shows the contour of the
bedrock elevation at 190 ft above MSL. Regions
within the brown contour line have bedrock depths
lower than 190 ft. As can be seen from the figure,
the sample locations marked with the filled circles
are all within the elevation contour boundaries, as
expected. However, the predicted spread of
contamination includes many regions in which the
bedrock elevation is higher than 190 ft above MSL.
This is clearly incorrect and illustrates the
limitations of using the 2-D approach. In practice,
the 2-D analysis could have been repeated, limiting
the concentration interpolations to only the regions
in which bedrock did not exist at that elevation, but
this would have required considerable effort. The
EVS-PRO model correctly depicts the contamination
as a function of depth. In Figure 1, it can be seen that
contamination regions at elevations of 180-190 ft
above MSL are confined to the regions above the
bedrock. For complex systems, the 3-D approach
used by EVS-PRO is superior to a 2-D approach.
The C Tech analyst used EVS-PRO to estimate the
plume boundary as a function of probability and
provided 3-D plume maps of the contaminated
region at three probability levels. Figure 4 presents
the 10% (maximum), 50% (nominal), and 90%
(minimum) probability plume maps for PCE at the
500-|j,g/L contour. Figure 5 presents the 10, 50, and
90% probability plume maps for TCA at 50 |Jg/L.
The depictions are similar to Figure 1 and contain
the same types of information (e.g., ground
elevation, surface features, well and sample
D)
C
127000-
126000-
125000-
124000-
974000 976000 978000 980000 982000
Easting (ft)
Figure 3. Baseline representation of the Site A nominal PCE plume at 180-190 ft above MSL obtained using
Surfer. The red contour is the 500-|jg/L threshold, and the blue contour is the 100-|jg/L threshold.
The brown contour lines represent bedrock elevations. Regions inside the brown contours are the
only sections of the site with a confining bedrock layer below 190 ft.
21
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Figure 4. E VS-PRO representations of plumes of PCE at the 500-|jg/L threshold
concentration at 90% (top), 50% (middle), and 10% (bottom) probability
levels.
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Figure 5. EVS-PRO representations of plumes of TCA at the 50 (ig/L threshold
concentration at 90% (top), 50% (middle) and 10% (bottom) probability levels.
23
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locations with color-coded representations of
measured concentration data, and contour regions).
Figure 4 shows that the 90% 500-|jg/L plume of
PCE contamination (minimum volume, 90%
certainty that the plume exists) is located primarily
at the northwest corner of the map and has not
spread far. The 50% 500-|ag/L PCE plume is
considerably larger than the 90% plume, and there is
an indication of a hot spot directly below the source
just above the confining bedrock layer (since the red
area on the map indicates a concentration
>10,000 ng/L). The 10% 500-|ag/L plume is shown
to have spread throughout the site, including areas
north of the river in regions for which measured data
do not exist. Several hot spots can be seen just above
the confining bedrock layer. The difference in plume
estimates at the three different probability levels
reflects the choice of contouring parameters and
amount of knowledge about the contamination.
Figure 5 shows the main region of the 90%
probability 50-|jg/L TCA plume at the southwest
corner of the map, in a different location than the
PCE plume. However, there is an indication of TCA
contamination in the northwest corner of the site in a
location similar to the hot spot of the PCE plume.
The 50% 50-|jg/L TCA plume shows large amounts
of contamination (the red zone on the map) at the
southwest corner of the site. The lower probability
(i.e., less confidence in estimated TCA
concentrations) has the effect of spreading the
predicted zone of contamination downstream and to
the northwest corner of the site, which does not have
measured data. In fact, most of the volume of
predicted contamination occurs in regions without
measured data. The 10% 50-|jg/L TCA plume
indicates that the complete western section of the
site may be contaminated above the threshold
concentration. Much of this region is upstream from
the source. These predictions are the result of the
high measured values near the boundary of the
modeled region. More information—e.g., additional
data on contamination, groundwater flow, or source
locations—is required to bound the plume in this
region.
In addition to the single-perspective visualizations of
the plume shown in Figures 1, 4, and 5, EVS-PRO
also generated animations for the 50% plumes for
both contaminants at both threshold concentrations
in .avi format. These files rotate the perspective 360°
around the outside edge of the site and permit the
analyst to gain a much better understanding on the
location of contamination. Click here for an example
of this type of visualization for the nominal PCE
plume at 500 |Jg/L.
The C Tech analyst also provided estimates of the
contaminated volume as a function of threshold
concentration and probability level (Table 6). To
establish a basis for comparison, the baseline
analysis was performed by calculating the
contaminated volume in each of the ten vertical 2-D
slices and summing them to obtain the total volume.
The baseline volume estimates were constrained by
the elevation of the bedrock (i.e., if bedrock was
present, it was assumed to be uncontaminated). As
Table 6 shows, there is relatively good agreement
between the EVS-PRO and baseline nominal
estimates of the PCE plume volume, with the EVS-
PRO estimates being 22% lower at the 100-ng/L
threshold and 37% lower at 500 |Jg/L. Comparison
of the nominal estimates of the TCA plume volume
shows that the EVS-PRO estimates are considerably
greater than the baseline estimates, with the EVS-
PRO estimate being almost twice as large as the
baseline estimate at 5 |jg/L and almost eight times
larger at 50 |jg/L. The cause for this large
discrepancy is the EVS-PRO prediction of the plume
to the west and north of the site in regions where
measured data are not available. Figure 5 shows that
most of the predicted volume of the nominal plume
occurs in this region. The 90% probability plume
shown in Figure 5 (top) is similar to the baseline
analysis nominal plume at the 50% probability level,
and the predicted volumes are also similar. The
differences in approaches (2-D vs 3-D) and the use
of nonoptimal interpolation parameters in EVS-PRO
led the technical team to conclude that performing a
complete geostatistical analysis would not aid in
understanding the performance of EVS-PRO at this
site. However, the EVS-PRO geostatistical
approaches and analyses performed for Sites B and S
are evaluated in other sections of this report.
Site B Sample Optimization and Cost-Benefit
Problem
The C Tech analyst chose to demonstrate the
visualization, sample optimization, and cost-benefit
analysis capabilities of EVS-PRO on the Site B
problem. For sample optimization, the analyst chose
to demonstrate EVS-PRO's capabilities using the
Tc-99 contamination data. Starting with the initial
24
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Table 6. EVS-PRO and baseline estimates of the volume of PCE and
TCA contamination (ft3) at Site A as a function of probability
Analysis
90% probability
(minimum)
50% probability
(nominal)
10% probability
(maximum)
PCEatlOOu>/L
EVS-PRO
Baseline
7.97E7
—
2.55E8
3.28E8
5.39E8
—
PCE at 500/^/L
EVS-PRO
Baseline
1.06E7
—
8.05E7
1.28E8
3.00E8
—
TCAatS/^/L
EVS-PRO
Baseline
2.36E8
—
5.98E8
3.13E8
1.03E9
—
TCA at 50 ^/L
EVS-PRO
Baseline
9.49E6
—
7.89E7
1.06E7
2.95E8
—
25 samples, the analyst selected several additional
sample locations and requested data at these
locations to further define the extent of Tc-99
contamination. The information was provided, and
the process was repeated until a total of 23
additional locations had been selected. The technical
team concluded that the number of samples for
defining the plume using geostatistics was slightly
larger than anticipated. Although the EVS-PRO
software selects default parameters for modeling
spatial correlation, optimization of these parameters
would have resulted in approximately 15-20
additional sample locations to define the Tc-99
plume at the specified thresholds of 10,000 and
40,000 pCi/L. EVS took 5-8 more samples than
would have been required had the analysis routines
been optimized. Requiring these additional sample
locations would lead to additional project costs.
Incorporation of information about groundwater
flow into the sample optimization process would
also help reduce the number of additional samples.
Using the final data set of 48 sample locations, EVS-
PRO generated maps of the Tc-99 plume at the two
threshold concentrations at three probability levels:
25% (maximum), 50% (nominal), and 75%
(minimum). Color-coded circles represented the
Tc-99 concentrations. The maps provided an outline
of the region containing Tc-99 above the threshold
concentration at a fixed probability level. Surface
features and a scale of coordinates were included on
the maps to provide a frame of reference. An aerial
photograph was also geo-referenced on some maps.
A total of six maps were provided for Tc-99.
A similar analysis was performed for the other two
contaminants, TCE and VC, using the original data
set (sample optimization was not performed).
C Tech provided an animated file containing the
hydraulic head data representing the depth of the top
of the water table and an aerial photograph of the
site. C Tech also provided files generated by EVS-
PRO using virtual reality modeling language
(VRML) which could be viewed and navigated with
the free downloadable software plug-ins. (C Tech
recommends the Cosmo Viewer that can be obtained
from www.karmanaut.com/cosmo/player.)
Navigation permits the viewer to rotate the drawing
to any angle to examine the extent of contamination.
The data were also used to generate a cost-benefit
analysis of the volume contaminated vs cleanup
threshold for all three contaminants.
Figure 6 is the EVS-PRO representation of the
hydraulic head data with the aerial photograph and
the site map draped on the ground surface. Review
of this figure demonstrated that the aerial photo-
graph, which was in a jpg file format that was
correctly mapped to the site. This can be seen in the
match between the photograph and site features such
as buildings and rivers supplied on drawing
exchange files (.dxf). Comparison of the hydraulic
head contour map with the data and a separate
contour map developed using Surfer showed that the
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MSC Tech Viewer
Exit Editors RNC Az El
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382.0
3S2.9
383.7
384.5
385.4
Select Object... Top
Left Button Rotate
Figure 6. EVS-PRO representation of Site B water levels. Water levels are represented through contours
and changes in elevation at the lower surface by the color key at the bottom of the figure. The
top surface contains an aerial photograph overlaid with a map of site features (buildings, roads,
and water bodies).
EVS-PRO map accurately represented the data.
Click here to view an animation generated by EVS-
PRO.
Figure 7 is the EVS-PRO representation of the
10,000-pCi/L Tc-99 plume at the 50% probability
level (nominal plume) after sample optimization had
been completed. This problem is effectively a 2-D
problem because the aquifer had a uniform thickness
of approximately 25 ft throughout the problem
domain and data were therefore not collected as a
function of depth in the aquifer. The region
predicted to be contaminated above 10,000 pCi/L
with 50% probability is represented by the solid
surface, with the height of the surface representing
the thickness of the aquifer. The solid surface is
color-coded to represent contaminant concentrations,
with yellow representing the threshold value and red
representing the highest concentrations
(>100,000 pCi/L). Dimensions (elevation, easting,
and northing) are provided on the figure as a frame
of reference. The ground surface is represented by
use of the aerial photograph, and surface features
obtained from drawing files are highlighted. In the
subsurface region of the visualization, lines with a
series of circular markers represent well locations
and data collection points. The circles are color-
coded, using the concentration key, to match the
measured value at a given point.
The EVS-PRO visualization of plume location as a
function of threshold concentration (Table 4) and
probability level was reviewed for each contaminant
(Tc-99, TCE, and VC). The review confirmed that
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C Tech Viewer
Exit Editors RNC Az_EI
$92000
225000
IFzeono
227006
Nominal Tc99 Plume at 1 O.OOO pCf/l
1 pCi/l
1O pCi/l
_L
100 pCi/l
1OOQ pCi/l 10000 pCf/l 10OOQQ
Select Object... j Top
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Figure 7. EVS-PRO representation of the Site B 50% probability level Tc-99 plume above the
10,OOOpCi/L threshold after completion of sample optimization. The top surface contains an
aerial photograph of the site and maps of surface features. Color-coded circles represent data
measurements.
all features (river, buildings, wells, etc.) were
accurately mapped. Comparison of the depiction of
concentration (color-coded circles in Figure 7) with
the data showed that the data were accurately
represented. The use of this feature permits the
analyst to see how well the contour matches the
measured data.
The technical team performed a baseline analysis
with Surfer using the same data set to obtain one
estimate of the plume location and volume. In
addition to Surfer, two other software packages—
GSLIB and Geo-EAS—were used to provide
independent analyses of the data and alternative
representations for comparison with the Surfer
results. These baseline analyses utilized three
interpolation routines (IDW, ordinary kriging, and
indicator kriging) with varying parameters to
produce the "best fit" of estimated concentrations to
the baseline data. Figure 8 presents the baseline
analysis obtained using GSLIB and indicator
kriging, which is comparable to the EVS-PRO
nominal plume for Tc-99. Comparisons of the
nominal plume locations were performed for Tc-99,
TCE, and VC using the same data set that was used
by the C Tech analyst. There was general agreement
between the two approaches in the downstream
region of contamination. In general, EVS-PRO
generated accurate depictions of the groundwater
contamination for the nominal estimates (50%
probability level) of the Tc-99, TCE, and VC
plumes.
The EVS-PRO plume maps at the 25% probability
level for both Tc-99 and TCE indicated the potential
for contamination in regions at the edge of the
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50000.00
WOOD.00
20000.00
0000.00
1000.00
100.00
Figure 8. Baseline representation of the Site B Tc-99 plume (concentrations in pCi/L)
obtained using GSLIB and the same data set as that obtained by C Tech after
sample optimization. Site features such as buildings and waterways are also
shown on the map.
modeled domain that did not contain data. This
prediction is a reflection of the modeling parameters
used for estimating concentrations as a function of
probability and the lack of data near these regions. In
these regions, EVS-PRO's default contouring
parameters predict a large influence from high
measured values of concentration in the central
region of the plume even though low values are
measured closer to the model boundary around the
edge of the plume. For example, Figure 9 shows the
25% probability plume (75% maximum plume in
EVS-PRO terminology) for Tc-99 at the
40,000-pCi/L threshold level. The map depicts the
region containing high measured values of Tc-99
(easting measurements between 227,000 and
229,000, northing measurements between 593,000
and 594,000). The sample optimization procedure
has apparently bounded the plume on all sides.
(Sample locations are marked on the map and color-
coded to indicate concentration.) However, the EVS-
PRO prediction as shown in Figure 9 indicates that
contamination may be present (with a 25%
probability) to the north/northwest and to the
southeast of the measured plume. While the
prediction is consistent with the statistical structure
of the data and the parameters used for interpolation,
this does not make physical sense. The region to the
north/northwest of the site is not in the direction of
groundwater flow; therefore, one would not expect
contamination unless another source of
contamination is present. If another source is
present, use of the statistical properties of the
existing plume does not make sense. The area to the
southeast of the plume is downstream from the
source region. However, there are several wells with
measured data below the 40,000-pCi/L threshold
value between the region above the threshold and the
region without data predicted to be above the
threshold. This distribution of contaminants would
require an intermittent source. The baseline
approach did not depict contamination to the north
of the plume because interpolation parameters were
optimized to minimize spreading of the plume in
directions perpendicular to the groundwater flow
(Figure 10). The C Tech analysis performed on Site
B did not utilize the groundwater flow data
provided. Rather, the analysis was conducted strictly
as geostatistical estimation. This resulted in
unrealistic estimates upstream of the source area.
The C Tech analyst should have bounded the plume
upstream and perpendicular to the source to ensure
that contamination would not be erroneously
extrapolated in these directions. The evaluation
team concluded that the EVS-PRO analysis could
have been improved if the operator had overridden
the default values for geostatistical analysis supplied
by EVS-PRO and optimized the geostatistical
parameters.
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MsC Tech Viewer
Exit Editors fiNC Az_EI
75% Maximum Tc99 Plume at 40,OOO pCi/l
10 pci/i
100 pci/i IOOQ pci/i 10000 pcr/i 100000
Select Object... | Top
Left Button Rotate
Figure 9. E VS-PRO representation of the Site B 25% probability level (maximum plume
volume) Tc-99 plume above the 40,000 pCi/L threshold after completion of
sample optimization. The top surface contains an aerial photograph of the site
overlaid with maps of surface features. Color-coded circles represent data
measurements.
29
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Figure 10. Baseline representation of the Site B 25% probability level Tc-99 plume at
40,000 pCi/L (gray areas). The analysis was obtained using GSLIB and the same
data set as that obtained by C Tech after sample optimization. Site features such
as buildings and waterways are also shown on the map.
Viewing the information from several different
perspectives can enhance understanding of the
plume location. EVS-PRO generated several
VRML files (.wrl extension) that permitted the
reviewers to navigate around the TCE plume as
well as an animation that automatically rotated the
plume. Click here to view an example of a 50-
|jg/L TCE nominal plume generated by EVS-PRO.
Figure 11 shows the image that can be rotated in
the VRML viewer. In the figure, all wells are
labeled and measured values are posted next to the
wells. The solid region represents the
contaminated volume; coordinates provide a frame
of reference. The isolated solid region in the
southeast corner is a modeling artifact due to the
choice of interpolation parameters. There are no
data in this region and it is unlikely that the
predicted contamination is real.
The C Tech analyst also provided estimates of the
contaminated volume as a function of threshold
concentration and probability level for Tc-99 and
TCE; these are shown in Table 7 along with
baseline estimates. Comparison of the nominal
(50% probability) Tc-99 plume volume estimates
indicates that there is fairly consistent agreement
between the EVS-PRO and baseline estimates at
the 10,000-pCi/L threshold, with the EVS-PRO
estimates being 47% lower. At the 40,000-pCi/L
threshold, the EVS-PRO volume estimate for the
nominal plume is approximately 32% lower than
the baseline estimate.
The C Tech analyst used the default contouring
parameters selected by EVS-PRO. These
parameters are not optimized to obtain volume
estimates and tend to overestimate the
contamination at low probabilities and
underestimate the contamination at high
probabilities. This leads to a wide variation in the
predicted volume of the plume as a function of
probability. In most cases, the EVS-PRO low-
probability estimates of the plume (maximum
volume) exceed the estimates of the baseline
analysis. Conversely, the EVS-PRO high-
probability estimates of the plume (minimum
volume) are less than the baseline estimates. For
example, the plume volume predicted by EVS-
PRO for Tc-99 at the 25% probability level at the
40,000 pCi/L threshold is an order of magnitude
greater than the volume predicted by the baseline
estimate. An examination of Figure 9, which
depicts the EVS-PRO prediction for contaminated
volume at 25% probability, clearly indicates that
most of the predicted volume occurs at the edges
of the domain where data are not available.
30
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Figure 11. EVS-PRO-generated visualization of the Site B 50% probability TCE
plume above the 50- (ig/L threshold. Measured concentrations are posted to
the map. This is one representation from the .wrl file, which can be rotated
to obtain different viewing perspectives.
Table 7. EVS-PRO and baseline estimates of the volume of Tc-99 and TCE
contamination at Site B (ft3) as a function of probability
Analysis
75% probability
(min-plume)
50% probability
(nominal)
25% probability
(max-plume)
Tc-99 at 10,000 pd/L
EVS-PRO
Baseline
1.3E7
6.9E7
4.5E7
8.5E7
4.2E8
1.1E8
Tc-99 at 40,000 pd/L
EVS-PRO
Baseline
5.3E4
1.1E7
9.4E6
1.4E7
1.3E8
1.5E7
TCE at 50/^/L
EVS-PRO
Baseline
4.0E7
7.8E7
8.9E7
1.2E8
4.03E8
2.07E8
TCE at 500 w/L
EVS-PRO
Baseline
2.8E6
6.4E6
1.2E7
3.1E7
4.9E7
8.6E7
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A comparison of the nominal TCE plume volume
estimates (Table 7) shows that the EVS-PRO
estimates are less than the baseline estimates. The
EVS-PRO estimate is 25% lower than the baseline
analysis at 50 |Jg/L, which is a consistent match, and
61% lower at 500 |Jg/L, a poor match. Comments
similar to those for the Tc-99 plume apply to the low
and high probability volume estimates for TCE.
Site D Sample Optimization Problem
The data for Site D included information on four
contaminants at five different sampling periods in 33
wells. C Tech did not perform sample optimization
for the Site D problem. Because the sample
optimization capabilities of EVS-PRO had already
been demonstrated with three other problems (those
for Sites B, N, and S), C Tech chose, instead, to
demonstrate the power of its scripting language in
rapidly generating visualizations of existing data.
EVS-PRO used its scripting language to write a
procedure to query the data file to select a single
contaminant and all measurements in a single
sampling period. This information was used to
visualize the original contaminant data and to
produce 2-D and 3-D maps of contamination. The
software automatically repeated this process for each
of the four contaminants at each of the five sampling
periods.
The 2-D maps generated provide a top view of the
areal extent of contamination with a site map
containing buildings and roads. The 3-D maps show
the contamination as a function of depth, with solid
and exploded views. The exploded views help in
understanding the extent of contamination in
different geologic layers. Figures 12, 13, and 14
show the TCE contamination in the third quarter of
1991 in the three views provided. EVS-PRO
produced 120 visualizations, showing four
contaminants, five sampling periods, three views,
and two resolutions of each view; it also provided
estimates of the contaminated volume of water
above the specific threshold concentration for each
contaminant. The entire process was done
automatically without operator intervention by use
of the script file. The ability to automate data
processing and to quickly generate multiple views of
the data based on sorting criteria (e.g., contaminant,
'CE :>qy i
Figure 12. EVS-PRO representation of the Site D nominal TCE contamination above the
50-|jg/L threshold, based on third quarter 1991 sampling data. Site feature such as
roads and buildings are evident on the map. The scale on the map should be in
ppb instead of ppm.
32
-------
me rCE
Figure 13. EVS-PRO 3-D representation of the Site D nominal TCE contamination above the 50-|jg/L
threshold, based on third quarter 1991 sampling data. Site feature such as roads and buildings
are evident on the map. The scale on the map should be in ppb instead of ppm.
ug/kg plume: TCE3q"o'l
0. I ppm
1 PP-TT
I 0 ppm
Figure 14. EVS-PRO 3-D exploded view representation of the Site D nominal TCE contamination
above the 50-|jg/L threshold, based on third quarter 1991 sampling data. Site feature
such as roads and buildings are evident on the map. The scale on the map should be in
ppb instead of ppm.
33
-------
time, etc.) is a powerful tool for understanding the
existing data. After the script file was completed, the
generation of the 120 different views of the data
required about ten minutes of computer time.
The technical team reviewed all the visualizations
for consistency with the data. The review confirmed
that all features (buildings, roads, wells, etc.) were
accurately mapped. However, the legends in Figures
11-14 were incorrectly labeled. Data were provided
in units of parts per billion; however, they are
presented in the visualizations in units of parts per
million. This labeling error was attributed to the
analyst incorrectly setting the units in the software.
With the exception this labeling error, the data were
accurately depicted on the maps.
The original data at the different sampling periods
were often spatially unbounded (i.e., high measured
values were obtained without surrounding low
measured values). The intent of the problem was for
the analyst to use sample optimization techniques to
define sample locations to bound the plume and
thereby define the nature and extent of the
contamination. Since this was not done, the technical
review team did not perform a quantitative
evaluation of the EVS-PRO results. The technical
team concluded that the plumes were not defined
with enough accuracy to obtain meaningful
estimates for comparison.
Site N Sample Optimization Problem
For the Site N problem, initial contamination data
were provided for a small region of the site, and the
analyst was asked to define the concentrations of the
contaminant for the entire site using only 80
additional samples. Figure 15 presents the site map
generated by the technical team; the initial sample
locations are marked with the symbol + and arsenic
concentration contours at the two threshold
concentrations are also displayed. The map also
indicates the locations of roads, ponds, and creeks.
The C Tech analyst used EVS-PRO to select sample
locations to define the extent of contamination for
the entire site. Of the three contaminants present at
the site, the analyst selected arsenic, which had the
highest measured concentrations, as the reference
contaminant for sample optimization decisions. This
is an acceptable approach because in practice, it is
unlikely that a different sample optimization scheme
would be developed for each contaminant. The
analyst used the geostatistics routines in EVS-PRO
to automatically select sample locations to bound the
area of contamination in the small region
encompassing the original data. This new
information was used to generate the next set of
sample locations, and the process continued until the
maximum number of allowed sample locations (80)
had been specified. With the final data set, EVS-
PRO generated arsenic concentration contour maps
based on contaminant threshold concentrations and
the degree of confidence in the interpolation results.
Maps of uncertainty as a function of the number of
samples were also provided to illustrate the
reduction in uncertainty achieved with increased
sampling.
Figure 16 shows the final EVS-PRO arsenic
contamination map based on the data after sample
optimization was completed. This map represents
the 50% probability contamination zone and
contains site features (roads and waterways) along
with the contours. According to the contour key, red
represents the highest concentrations and dark blue
the lowest. Unfortunately, the key was determined
from EVS-generated values because the changes in
color did not match the threshold values (125 and
500 mg/kg) of the problem. This makes
interpretation difficult. The contamination depicted
apparently represents contamination levels above
500 mg/kg. Color-coded circles mark the sample
locations. Examination of the sample locations
shows that the contamination was bounded in the
southwest corner of the site; however, there are large
areas that do not have any samples. This indicates
the sample optimization procedure did not cover the
entire site.
While performing the Site N sample optimization,
the C Tech analyst incorrectly assumed that the
contamination across the entire site was correlated to
the plume in the initial small-region data set. This
caused the software to identify new locations for
sampling that were adjacent to the known
contamination and resulted in poor coverage in the
unexplored region. A more appropriate approach to
this problem would be to sample the unmeasured
areas using an equal-area approach and distribute a
portion of the 80 samples throughout the site. Based
on the additional information, geostatistical analysis
could be used to further define the contamination
zones.
To place the EVS-PRO results on the contamination
scale requested in the test problem, the technical
34
-------
23000-
22500-
22000-
0)
e
21500-
0
z
21000-
20500-
20000-
30000
30500
31000
31500
32000
Easting (ft)
Figure 15. Baseline representation of the Site N arsenic contours at the 125- (blue) and
500- (red) mg/kg thresholds obtained using Surfer and the data provided to the
analyst for conducting the sample optimization analysis. Site features such as
roads and waterways are provided on the map.
35
-------
,
—
- — I
•^rr"
-
4 .—
' '
*
. • . ' '
' ' * t
. ' '
.• .:!LL1
• * t
» A •--*— —*—
• * —._*—i ii i i . M
4JPr *
vK iV> * *
4*fc* • * *
* • **.^t"* ^ * *
• * & .
•-ff--'
* '.i * ~H *G^t •Ci u-i -LM u u. x
>_Q :j^ ; ! | i_j \_ -J -* —' -^
§— ,, 71 ix — Q| c°
ooooooao
.minol Arsenic Concentrations mg/'kq
Figure 16. EVS-PRO representation of the Site N nominal arsenic contamination above the
500-mg/kg threshold after completion of the sample optimization analysis. Sample
locations are depicted on the map as color-coded circles.
team took the data set used by EVS-PRO (original
data plus 80 additional data points) and generated an
arsenic contour map at the 125-mg/kg (blue) and
500 mg/kg (red) thresholds. This map is shown in
Figure 17. Sample locations on this figure are
marked by a diamond. Figure 18 presents the
baseline analysis obtained using the entire data set
(4187 points). The results shown in Figures 17 and
18 were generated using the Surfer software package
and kriging for data interpolation. A comparison of
Figures 16, 17, and 18 indicates that the EVS-PRO
sample optimization procedure did a poor job of
locating contamination at the site. EVS-PRO found
only one additional region above the 500 mg/kg
threshold (the region at the southwest corner was
part of the original data). The data, as depicted in
Figure 18, indicate that several such regions exist.
One region with high arsenic concentrations in the
central part of the site that was missed by the EVS-
PRO sample optimization process was several acres
in area. EVS-PRO found about half of the regions
with contamination above the 125 mg/kg threshold.
The poor performance of the sample optimization
analysis was caused by the analyst's assumption that
the contamination for the entire site was correlated
to the data initially provided for a small section of
the site and use of the default geostatistical
parameters. EVS-PRO bounded the contamination in
the southwest corner of the site and then selected
36
-------
23000-
22500-
22000-
0)
e
21500-
0
z
21000-
20500^>
20000-
30000
30500
31000
31500
32000
Easting (ft)
Figure 17. Surfer representation of the Site N nominal arsenic contamination above the
125- (blue) and 500- (red)mg/kg thresholds using the same data set as the
C Tech analyst after completion of the sample optimization analysis.
37
-------
23000-
22500-
22000-
01
=
I
0
z
21500-
21000-
20500-
20000-
29500
30000
30500
31000
31500
32000
Easting (ft)
Figure 18. Baseline analysis of the Site N nominal arsenic contamination above the 125- (blue)
and 500- (red) mg/kg thresholds using the entire data set (4187 points).
samples by moving a short distance from the
measured data. The process was repeated in steps
until the limit of 80 additional samples was reached.
This meant that the analysis had very little data for
large areas of the site. The result of this process is
reflected in Figure 19, which shows two maps
generated by EVS-PRO representing uncertainty at
the start and at the finish of the sample optimization
process. Uncertainty as used in EVS-PRO is a
measure of the confidence in the predicted
concentrations. Regions of high uncertainty
generally require more data before the region of
contamination can be defined accurately. In
Figure 19 red represents high uncertainty and blue
represents low uncertainty. The top map represents
the uncertainty based on the initial data plus the first
set of 12 samples. The map indicates that there is
large uncertainty throughout most of the site. The
bottom map represents uncertainty after 80
additional samples and shows that the uncertainty
has been reduced in the southwest corner of the site
but still remains large in the northeast of the site. If
more samples had been allowed, it is likely that
adequate characterization would have been achieved.
38
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22800 -
22300 • •
21800
2 I 550 -
21050 -
Unce rtai n ty
EVS-PRO uncertainty maps
Red = region of highest
uncertainty
Blue = region of least
uncertainty
* 0
m-^n *
'
fans Uncertafnty
Figure 19. EVS-PRO-generated uncertainty maps for the Site N sample optimization problem. Top,
uncertainty after the first round of sampling. Bottom, uncertainly after completion of a sample
optimization limited to 80 additional data points.
39
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EVS-PRO allows the user to override the default
parameters to optimize the search strategy for
locating additional samples based on the site-specific
problem. Had this been done, it is likely that a better
sample optimization scheme would have been
obtained using only 80 samples.
The C Tech analyst also provided maps of the
arsenic contamination at the 10% and 90%
probability levels. They appeared to be almost
identical to the 50% probability level map. This was
surprising because of the high uncertainty levels
represented in Figure 19. The similarity between the
maps may have been due to the concentration scale
used by the C Tech analyst, which did not focus on
the thresholds specified in the problem of 125 and
500 mg/kg for arsenic.
Site S Sample Optimization and Cost-Benefit
Problem
The Site S test problem involved CTC groundwater
contamination. Initial data were provided for 24
locations as a function of depth. The analyst was
also given hydraulic head data indicating that the
flow in this region was approximately due south. In
addition, a small vertical hydraulic gradient
indicated that the water was sinking deeper as it
moved from north to south. Maps of site features
such as roads and buildings were not provided in this
test case.
The C Tech analyst chose to perform the sample
optimization in two dimensions. The analyst
reasoned that if a well was drilled, data would be
collected at all depths, thus providing a vertical
profile of contaminant concentrations. Therefore, the
analyst judged it pointless to attempt a 3-D
optimization. The technical team agrees that this is a
reasonable approach. Using the geostatistical
routines in EVS-PRO to select sample locations, the
C Tech analyst requested additional data at 15
locations to further define the plume. The technical
team concluded that this was a reasonable number of
samples for defining the plume using only
geostatistics. However, optimization of the
contouring parameters and use of the information
about groundwater flow could have resulted in
essentially the same information with fewer samples
(10-12) than were used during the sample
optimization process.
Using the final data set, EVS-PRO generated 2-D
maps of the concentration distribution based on the
maximum in each well and the probability of
exceeding the two threshold concentrations for CTC.
Two-dimensional maps of the maximum (25%
probability level), nominal (50% probability level)
and minimum plume (75% probability level) were
provided. Three-dimensional visualizations were
also provided in VRML format to allow the user to
navigate around the plume. The data were also used
to generate a cost-benefit analysis of the
contaminated area (2-D) and volume (3-D) vs
cleanup threshold at the three probability levels.
The EVS-PRO 2-D 5-^g/L contour map based on
the maximum concentration in each well at the three
different probability levels is shown in Figure 20.
The 5-|jg/L concentration level is denoted by green;
areas in red have concentrations above 100 |Jg/L.
Sample locations are marked with a color-coded
circle indicating the measured value at that point.
This illustration highlights the differences in
predicted concentration as a function of probability.
The 75% probability plume (minimum) indicates
much lower concentrations than does the 50%
probability plume. However, the area of
contamination in these two cases appears similar.
The 25% plume (maximum) shows extensive
spreading of the plume around the edges of the
modeled domain where there are no data. This is a
result of the kriging parameters used to determine
the probability levels. The regions at the edge of the
domain are not downstream from the source, and
therefore, it is not probable that the contamination
would occur in these regions.
Figure 21 presents the technical team's 2-D analysis
of the CTC contamination at the 5-|jg/L (blue) and
500-|j,g/L (red) levels for the maximum concen-
tration in each well. This map was generated using
the data set obtained by the C Tech analyst through
the sample optimization process. Surfer was used to
interpolate the data using kriging with an anisotropy
ratio of 0.3. This value for anisotropy was based on
the observed spreading of contamination in the
direction perpendicular to flow and was optimized to
provide the best match with the observed data
through repeated kriging analysis. Comparison of
the baseline analysis with the EVS-PRO nominal
plume shows excellent agreement. The baseline
analysis predicts that the plume migrates slightly
further to the south (northing of 25000-25500 on
the map). The slightly greater predicted area of the
contamination is due to the value chosen for the
anisotropy ratio, which spreads the predicted
contamination along the direction of flow.
40
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""" z**-
300 ppn
113 jpn
I ppn
I].!' I.;.'
IT [pn
O.CO p(ir
Q.OI ppm
B I e
£ i :
g s i
a a
75% Plume
50% Plume
25% Plume
Figure 20. EVS-PRO 2-D representation of the Site S 75% (minimum), 50% (nominal), and 25%
(maximum) probability CTC plumes above the 5-p.g/L threshold. Visualization is based
on the maximum measured value (independent of elevation) in each well after
completion of the sample optimization process.
41
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r
0
z
255000-
254500-
254000-
253500-
253000-
252500-
252000-
251500-
251000-
250500-
250000-
+1 +
1296500
1297500
Easting (ft)
Figure 21. Baseline analysis of the Site S nominal CTC plume
at threshold concentrations of 5 (ig/L (blue) and
500 |ig/L (red). The analysis was conducted using
Surfer and the data set used by C Tech in
generating its plume maps. The maximum value in
each well was used for determination of the extent
of contamination. Sample locations are marked
with a +.
42
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Figure 22. EVS-PRO 3-D visualization of the Site S nominal CTC plume above the 5-p.g/L
threshold. Color-coded circles represent all measured data.
The EVS-PRO 3-D visualization of the nominal
plume at 5 |jg/L is shown in Figure 22. The map
indicates that the plume migrates deeper as it travels
from north to south. This is consistent with the data.
In most wells, data were collected at 5-ft intervals,
as represented by the color-coded circles at each
well location. Comparison of the color-coded
representations of concentration with the measured
data showed agreement between the two. This 3-D
perspective adds further insight on the structure of
the plume as compared to the 2-D view in Figure 20.
The depth of contamination and the thickness of the
plume are evident in this view. Click here to view a
VRML file of the plume. Figure 22 is one view that
can be obtained from the VRML file. The VRML
file allowed the technical team to rotate the 3-D
image and see the CTC contamination from different
perspectives.
The C Tech analyst also provided estimates of the
contaminated area and volume as a function of
threshold concentration and probability level for
CTC. The technical team used the same data as
obtained by C Tech through sample optimization
and, using Surfer, estimated the contaminated area
and volumes above the two threshold concentrations.
Area and volume estimates as a function of
probability were also obtained using GSLIB.
Finally, the data for this test problem were
developed from the analytical solution of a differen-
tial equation that represented contaminant transport
in the aquifer subject to a constant CTC source. The
analytical solution was used to estimate the actual
area and volume of the contamination above the
threshold concentrations. The use of Surfer, GSLIB,
and the analytical solution provide a thorough
baseline analysis for comparison.
While agreement between the EVS-PRO and
baseline areal contaminant maps was good,
comparison of the initially provided nominal (50%
probability) area and volume estimates for the CTC
plume provided by C Tech were in poor agreement
at both threshold concentrations. The C Tech analyst
was questioned about the differences and given the
43
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opportunity to reexamine the calculations. Two
separate operator errors were found in the calcu-
lations. For the volume calculations, the C Tech
analyst indicated that the vertical axis was scaled by
a factor of 5 for visualization. Volume calculations
were performed on the scaled axis and therefore
were a factor of 5 larger than they should have been.
For the area calculations, the C Tech analyst stated
that the parameters set to enhance sample optimi-
zation decisions led to poor estimates of the area. In
this case, a "ceiling" was set such that all measured
values greater than the threshold were set to the
threshold value. This focuses the selection of the
next sample locations to the regions on the outer
edge of the plume and helps to define the plume
boundaries with fewer samples. The initial area
estimates were made using the ceiling limited values
and not the actual values. This caused an under-
estimation of the actual plume size. After correcting
for the operator mistakes in calculating the area and
volume, C Tech supplied revised estimates for the
area (Table 8) and volume (Table 9). The technical
team estimates based on Surfer, GSLIB, and the
analytical solution are also provided in these tables.
The revised EVS-PRO area estimates show very
good agreement with the baseline analyses
performed using GSLIB and Surfer and the
analytical solution. For the 500-|j,g/L threshold, the
EVS-PRO area estimate is 45% less than the
baseline GSLIB estimate and 25% less than the
estimate based on the analytical solution. For the
5-ug/L threshold, the EVS-PRO estimate is 12% less
than the baseline GSLIB estimate and 15% less than
the analytical solution estimates.
Table 8. EVS-PRO, baseline, and analytical estimates of the area of CTC
contamination (ft2) at Site S as a function of probability
Analysis
75% probability
(min-plume)
50% probability
(nominal)
25% probability
(max-plume)
CTC atSj^/L
EVS-PRO
Baseline Surfer
Baseline GSLIB
Analytical
1.6E6
—
1.2E6
—
2.3E6
2.8E6
2.6E6
2.7E6
3.6E6
—
4.1E6
—
CTC at 500/^/L
EVS-PRO
Baseline Surfer
Baseline GSLIB
Analytical
1.4E5
—
9.8E5
—
9.3E5
1.4E6
1.7E6
1.2E6
1.7E6
—
3.1E6
—
Table 9. EVS-PRO, baseline, and analytical estimates of the volume of CTC
contamination (ft3) at Site S as a function of probability
Analysis
75% probability
(min-plume)
50% probability
(nominal)
25% probability
(max-plume)
CTC atSj^/L
EVS-PRO
Baseline Surfer
Baseline GSLIB
Analytical
9.8E7
—
9.4E7
—
1.5E8
4.3E8
2.2E8
1.6E8
2.2E8
—
6.1E8
—
CTC at 500/^/L
EVS-PRO
Baseline Surfer
Baseline GSLIB
Analytical
1.6E5
—
2.9E7
—
2.2E7
5.5E7
5.2E7
3.9E7
6.0E7
—
7.3E7
—
44
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The technical team obtained volume estimates by
generating 2-D plume maps as a function of depth at
10-ft intervals using the data obtained by C Tech
through sample optimization. The area above the
threshold was calculated for each of these vertical
slices and converted to a volume by multiplying the
area by the thickness of the layer. The volume of
each layer was summed to obtain the total volume.
This can be an acceptable approach for Site S
because, unlike Site A, the site has no confining
bedrock layer. Baseline volume estimates were
obtained using Surfer and GSLIB. The baseline
results, the analytically calculated volume, and the
EVS-PRO estimates are presented in Table 9. The
EVS-PRO estimates show excellent agreement with
the analytical solution and the Surfer and GSLIB
baseline estimates for the nominal plume volume at
both threshold concentrations. For the nominal
plume (50% probability) the EVS-PRO volume
estimates are 6% lower than the analytical estimate
at the 5-|jg/L threshold and 43% lower than the
analytical estimate at the 500-|jg/L threshold.
For the 5-|jg/L CTC threshold concentration, the
EVS-PRO 25, 50, and 75% probability level volume
estimates show reasonable agreement with the base-
line GSLIB estimates. Estimates between the two
approaches match almost exactly for the minimum
plume (75% probability) and differ by a factor of 3
for the maximum plume (25% probability). The
range in EVS-PRO plume volumes, from the mini-
mum (1x10s ft3) to the maximum (2.2x 10s ft3), in-
cludes the baseline analytical volume of 1.6x10s ft3.
For the 500-|ag/L CTC threshold value, the EVS-
PRO volume estimates at the 25 and 50% prob-
ability levels are less than the baseline GSLIB
estimates by factors of 0.8 and 0.4, respectively. At
the 75% probability level, the EVS-PRO estimate is
1/180 of the baseline GSLIB estimate. The 75%
probability level is the volume over which there is at
least a 75% chance that the contamination will
exceed the threshold concentration. The 75% prob-
ability level EVS-PRO estimate for the 500-|jg/L
CTC threshold value is extremely low and incon-
sistent with the baseline data, the baseline analysis,
and the analytical solution. Upon review of the
volume estimates, C Tech also provided a secondary
method of estimating contaminated volumes. In this
case, the EVS-PRO adaptive gridding model was
used. This model places more computational
elements in regions where concentrations are
changing most rapidly. This should lead to a better
volume estimate. The use of adaptive gridding did
not lead to major changes in the nominal plume
(50% probability) or the maximum plume (25%
probability) volume estimates. It did, however,
substantially increase the estimate for the minimum
plume (75% probability) to a value of 8.1 x 106 ft3.
This volume is a factor of 4 lower than the baseline
GSLIB estimate and more consistent with the data.
Based on the comparison of the final EVS-PRO area
and volume estimates with the analytical solution
and baseline estimates, the technical team concluded
that EVS-PRO accurately calculated these quantities
for this problem. However, initial EVS-PRO
estimates provided by C Tech were not a good
match to the baseline estimates. The cause of the
discrepancy was determined to be two separate
operator errors. This indicates that the EVS-PRO
analyst must be familiar with the operation of the
software to obtain the proper area and volume of
contamination estimates.
Site T Geology Interpretation
For the Site T problem, rather than perform another
analysis of the extent of contamination (similar to
the analysis performed for Sites A, B, D, and S),
C Tech used EVS-PRO to analyze subsurface
stratigraphy. The subsurface stratigraphy at Site T is
characterized by many thin layers of different soil
types, including clays, silt, silty-sand, sand, and
gravel. The soil boring data were used to generate a
3-D animation of the subsurface stratigraphy. The
animation rotated the viewing angle of the soil
structure through 360° to provide a more complete
view of the layers. The animation also provided an
exploded view (layers separated slightly) to allow
visualization of regions where the different layers
pinch out (i.e., where the layer has zero thickness).
Figure 23 provides an exploded view of one per-
spective from the animation and contains the various
subsurface layers as identified by the key. Pinching
out of the different layers is clearly shown in the
figure. Coordinate data, including elevation, are
provided as a frame of reference. Click here to view
an animation generated using EVS-PRO. The tech-
nical team evaluated the animation as well as
supporting visualizations provided by C Tech. The
boring well locations were marked on some of the
supporting visualizations, and this helped in
checking the visualizations. The review demon-
strated that the location of the geologic layers
matched the data at sample location points. Each
layer in the C Tech animation was consistent with
the data provided for the analysis.
45
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2085000
Silt'Sajid
Silt
Clay
Figure 23. EVS-PRO 3-D exploded view of the Site T subsurface stratigraphy. Soil types are identified
in the key at the left of the figure.
In addition to the animation of subsurface
stratigraphy in Figure 23, C Tech demonstrated
EVS-PRO's ability to generate another depiction of
the Site T subsurface using 3-D indicator kriging of
the subsurface data. The geologic indicator kriging
feature of EVS-PRO is a powerful geospatial tool
that provides multiple lines of reasoning for inter-
preting subsurface soil or geologic information.
Figure 24 presents the results from the 3-D geologic
indicator kriging for Site T. Click here to view the 3-
D animation. The result of the 3-D kriging of layers
allows for better representation of the subsurface
layers at unsampled locations and provides a better
representation of regions that transition from layer to
layer. The technical team evaluated the kriging
parameters and the results presented in Figure 24
and concluded that the subsurface data is adequately
represented and that the EVS-PRO depiction is
consistent with the data provided.
46
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File Editors RNC Az_EI Instance
Viz Left Button (Rotate _*J
Figure 24. EVS-PRO depiction of Site T subsurface stratigraphy based on indicator kriging modeling of soil layers.
Soil types are identified in the key at the left of the figure.
Multiple Lines of Reasoning
The C Tech analyst used EVS-PRO to perform
geostatistical analysis with the data. This
information provided a quantitative measure of the
probability of exceeding threshold concentrations
and allows the decision maker to judge the effects of
uncertainty on the decision. Although EVS-PRO
automates selection of contouring parameters and
sample optimization locations, the operator is able to
override the default values to optimize these
functions for the problem under study. Selection of a
particular scheme depends on the objectives of the
analysis and the amount of data. EVS-PRO provides
the capability to examine subsurface stratigraphy by
use of different interpolation algorithms, as
demonstrated on the Site T problem. In addition,
EVS-PRO provides multiple visualization options
that assist in understanding the nature and extent of
contamination problems.
Secondary Evaluation Criteria
Ease of Use
EVS-PRO is a complex software package containing
over 150 modules. To assist the user, EVS-PRO
contains a graphical user interface (GUI) that
accesses all of the features and modules. The EVS-
PRO network editor, which is part of the GUI, uses
object-oriented programming and allows the user to
select and link all of the modules necessary for the
simulation. The GUI and the drag-and-drop features
of the network editor make EVS-PRO easy to use.
An example of a network application is presented in
47
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Figure 25. Modules at the top of the screen in this
figure can be dragged into the lower part of the
screen and connected to other modules through
mouse operations.
The GUI provides a platform for addressing
problems efficiently and for tailoring the analysis to
the problem under study (e.g., contours at certain
threshold concentrations). EVS-PRO stores
alphanumeric data using an open database
connectivity (ODBC) protocol. This database
structure permits queries on any field (e.g., chemical
name, date, concentration, and well identifiers) and
also permits filtering (e.g., to include only data
within a range of elevations or to include selected
data points).
EVS-PRO can import and export text and image
files in a number of formats. Image files can be
imported in drawing exchange format, ESRI shape
file format, bitmap, or jpg format. Output files can
be produced in all of these formats and as animation
(.avi) and VRML (.wrl) files. One limitation of
EVS-PRO is that it requires alphanumeric data to be
provided in a fixed order. Consequently, in the
demonstration, the analyst imported the data into
ile E_dit jDbject Journal Options jHelp
Figure 25. An example of the EVS network editor illustrating connection of different modules.
48
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Excel, sorted the data into the structure required by
EVS-PRO, and saved the file in ASCII comma-
delimited format (.csv) before using it in EVS-PRO.
Reorganizing the data was a major task in the
demonstration. This limitation has been removed in
subsequent versions of EVS-PRO.
During the demonstration, several members of the
technical team received a 4-hour introduction to
EVS-PRO. The reviewers found that EVS-PRO was
a large, feature-rich software program that has an
extensive on-line manual with case studies to guide
the novice user through the system and applications.
The reviewers concluded that with one or two days
of training, they would be able to use the funda-
mental features found in EVS-PRO. However, it is
clear that more training and regular use of the
product would be needed to use all of the features
found in the product efficiently. In particular, a
larger investment of time would be required to learn
to use the scripting language that permits automation
of repetitive processes. The reviewers were im-
pressed with the object-oriented structure of the
code, which permitted linking of the various
modules for an analysis.
Efficiency and Range of Applicability
EVS-PRO was used for four complete problems
(three sample optimization/cost-benefit problems
and one cost-benefit problem) and two partial
problems (visualization of the initial data for one
sample optimization problem and one geology
interpretation problem) with 8 person-days of
effort. Approximately four days were spent
analyzing the data and another four days preparing
the report. EVS-PRO processed a large amount of
data and produced a large number of visualizations
in a wide range of formats in a very short time. This
was made possible primarily because of the auto-
mation and scripting features available in EVS-
PRO. EVS-PRO provides the flexibility to address
problems efficiently and to tailor the analysis to the
problem under study. Databases can be queried and
information processed by any field in the database
(e.g., plot only TCE contamination over one samp-
ling period). Although default parameters are
available for most operations, the user has control
over the choice of the parameters that control the
geostatistical simulations. In addition, a wide range
of environmental conditions (e.g., multiple con-
taminants, different media such as groundwater or
soil, complex subsurface stratigraphy) can be
evaluated. EVS-PRO should be applicable to
almost any soil or subsurface contamination
problem.
Training and Technical Support
C Tech provides an extensive users' manual
documenting code operation and use. The manual
discusses the general framework used by EVS-PRO,
construction of models using the object-oriented
network approach, input parameters for each of the
models, and examples of model applications. Self-
paced training modules are available as part of the
software package. Technical support is supplied by
telephone and through e-mail. Training courses are
available throughout the year. Software updates are
available over the Internet.
Additional Information about the
EVS-PRO Software
To make efficient use of the basic features in EVS-
PRO, the operator must be familiar with contouring
environmental data sets and managing database files.
To use the advanced geostatistical features, the
operator should also be knowledgeable in this area.
During the demonstration, EVS-PRO was run on a
Windows 95 operating system. The computer used
for the demonstration was a Pentium II 400 with a
Titan II graphics card, 128 MB of RAM, a 4 GB-
hard drive, and a 20X CD (read only).
EVS-PRO, the C Tech product used in the demon-
stration, sells for $9995 for a single license. The
pricing structure for EVS depends on the product
selected and the number of licenses purchased. A
detailed description of software products and prices
is provided in Section 2 of this report.
Summary of Performance
EVS-PRO's performance is summarized in Table
10. The technical team concluded that the main
strengths of EVS-PRO are its outstanding 3-D
visualization capabilities and its capability to rapidly
process, analyze, and visualize data. The capability
to produce true 3-D data analyses and visualizations
under conditions of complex subsurface geological
characteristics and distribution of contaminants is
important. The range of visualization output formats
and their quality define EVS-PRO as a premier,
state-of-the-art visualization system. The ability to
sort and query the data and write scripts to automate
repetitive tasks permits EVS-PRO to examine large
amounts of data and quickly generate analyses and
visualizations of the data. EVS-PRO's object-
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Table 10. EVS-PRO performance summary
Feature/parameter
Decision support
Documentation of
analysis
Comparison with
baseline analysis
and data
Multiple lines of
reasoning
Ease of use
Efficiency
Range of
applicability
Training and
technical support
Operator skill base
Platform
Cost
Performance summary
EVS-PRO provides decision support through 3-D visualization of environmental data such as
contaminant concentration contours, quantifying uncertainties in interpolation predictions,
recommending additional sample locations to reduce uncertainties, and providing statistical
information about the extent of contamination.
A detailed report documented the technical approach, assumptions, and parameters used in the
analysis.
EVS-PRO produced analyses and visualizations from six different sites. Visualizations
included 3-D representations of geologic structure, hydraulic head, concentration contours
above threshold values, and uncertainty maps. All the visualizations were consistent with the
data. The visualizations accurately incorporated maps of surface features (roads, buildings,
water bodies) and aerial photographs when available. Visualizations often provided well and
sample locations as a function of elevation. Sample locations were accurately color-coded to
match the measured data. Sample optimization was performed for Sites B, N, and S. The
analyses for Site B and S adequately characterized the plume with an acceptable number of
additional samples. The Site N analysis, which limited the number of samples, inadequately
characterized the extent of contamination, a result of the use of the software's default
parameters for spatial correlation modeling. Cost -benefit analysis of the volume of
contamination as a function of threshold concentration and probability were provided for
Sites A, B, N, and S. Volume estimates were often a poor match to the baseline analysis.
Once again, this is due to the use of EVS-PRO 's calculated default values for interpolation
of data and selection of boundary conditions for spatial modeling. Volume estimates for
plume extent at low-probability levels were typically greater than the baseline estimate by a
factor of 3 or more.
EVS-PRO provides a number of different approaches for visualizing and examining the data,
including control over essential modeling parameters. This flexibility permits multiple
analyses of the data. EVS-PRO generates statistical information about the extent of
contamination that assists in data evaluation.
EVS-PRO is a sophisticated software product with over 150 modules. The use of visual
programming to link the modules makes use of EVS-PRO fairly easy. Most environmental
analysts would be able to use the major features of EVS-PRO after two days of training.
Advanced features such as use of the 3-D kriging of data sets and use of scripting language
would require more training. An ease-of-use inconvenience is EVS-PRO 's requirement of a
fixed data field format. Enhancements to current versions of EVS-PRO have removed this
limitation.
EVS-PRO efficiently imported, analyzed, and visualized environmental data sets. The
program was used to analyze four complete problems (three sample optimization/cost-
benefit problems and one cost-benefit problem) and two partial problems (visualization and
geology interpretation) with 8 person-days of effort.
EVS-PRO is a flexible tool in which the operator can define the modeling parameters so as to
tailor the analysis and visualization to almost any problem involving contamination in soils
or groundwater.
Users' manual
On-line help with guidance on parameter selection
Technical support and training courses available for a fee
Free Web-based support, including tutorials and documentation
To efficiently use the basic features of EVS-PRO, the operator must be familiar with
contouring environmental data sets and managing database files. To use the advanced
geostatistical and statistical features, the operator should be knowledgeable in these areas.
Windows 95, 98, NT
$9995 for a single user. The EVS pricing structure depends on the product and number of
licenses sold to the customer. Discounts are available to educational institutions.
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oriented programming structure allows the many
modules to be easily linked together to perform a
complex analysis.
EVS-PRO is a mature software system that does not
have any major limitations. A minor ease-of-use
limitation of EVS-PRO is the need to structure the
data in a fixed order. This often requires the analyst
to take an existing database and reformat it. Current
versions of EVS-PRO allow the user to directly
query ODBC compatible databases from within
EVS-PRO for the purpose of creating input files in
EVS format representing both geology and
chemistry data.
EVS-PRO can perform sample optimization analysis
recommending sampling locations and cost-benefit
analysis of contaminated volume as a function of
probability. To assist the analyst, EVS-PRO calcu-
lates values for the essential parameters used in these
analyses based on the data. While the use of these
calculated default values makes it easier for the
analyst, it was observed that the values were not
always optimal for the sample optimization or cost-
benefit analysis. In particular, for the Site N sample
optimization problem approximately a third of the
site remained unsampled because of the approach
used in EVS-PRO and the limit on the number of
samples. For the cost-benefit problems, the estimates
of contaminated volumes were often a poor match to
the baseline analysis. This was especially true for the
low-probability plume volume estimates, where use
of the default parameters often caused the program
to predict contamination in regions upstream from
the main plume that did not contain data. The test
team concluded that operator intervention to opti-
mize geostatistical model parameters would have led
to better, more accurate analyses. The problems
identified are a function of the operator, not the
software, and emphasize the need to have qualified
analysts operate the software and for the analyst to
examine the model outputs for consistency with the
data. The C Tech analyst effectively provided a first
iteration to conducting an analysis, thus emphasizing
the analysis and visualization capabilities of the
EVS-PRO software.
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Section 5 — Environmental Visualization System Update
and Representative Applications
Objective
The purpose of this section is to allow C Tech
Development Corporation to provide information
regarding new developments with EVS-PRO since
the demonstration activities. In addition, C Tech
Development Corporation has provided a list of
representative applications in which its technology
has been or is currently being used.
Technology Update
The EVS suite of products is continually evolving
and improving. Since the demonstration, a number
of changes have been made to EVS products. The
following lists the changes with a brief description
of their function. For complete information contact
C Tech atwww.ctech.com.
New Interfaces
EVS for ArcView extension (which requires ESRI's
ArcView Version 3.1 or later) has been developed.
This ArcView extension is available for use (at no
cost) to all C Tech customers. It provides an
environment to create EVS chemistry and geology
files from within ArcView and to launch any version
of C Tech software from within ArcView. This
extension was developed to provide a more user-
friendly environment for casual (or less experienced)
users and to support the newest version of EVS,
EVS for ArcView. This interface removes the
limitations of fixed-format input identified in this
report.
Enhanced Modules
The following enhancements are now available:
• New input and output ports to all modules that
read ASCII geology or chemistry input files.
• New features for the Viewer, including a new
pull-down menu called "Instances," which
automatically connect a few frequently used
modules.
• An enhanced Light Editor that improves surface
topography visualizations.
• Dramatic enhancements to the animator to allow
control of virtually any EVS/MVS parameter
without editing of the animation script file.
• Spport in many modules for input of exponential
values.
• Modifications to the Volume_Render module for
better default values for software rendering.
• Enhancement of 3D_Geology_Map module to
perform automatic distribution of cells into
model layers based on the average thickness of
each layer.
• Modifications to the "cut" module to allow for
the cutting to be based on an externally input
slice plane. The user can displace the cutting
surface any distance from this external plane.
With two cuts, the user can create a region of
any width that is centered around an external
slice. Using slice_horizontal, slice_easting, or
slice_northing (all of which can also be rotated),
as input, the operator can have much more
control over cutting. In addition, the rightmost
output port of "cut" now outputs the "other half
of the model. This is useful for displaying a
solid model on one side of the cut model and a
plume on the other.
• True 3-D text which utilizes any of the True
Type fonts installed on a computer. Three-
dimensional text objects are filled polygons with
no thickness or true 3-D solid objects (with
optional beveled edges). The new fonts will be
available in the Titles module, Colorjegend,
Map_Spheres, and Generate_Axes.
• The ability to assign specific user-defined colors
to individual objects (like geologic layers).
• Enhanced single and multi-range datamap
editing.
• Nonlinear interpolation in the Animator.
• Multi-range data maps that are saved with
applications.
New Modules
EVS and MVS have been upgraded by the addition
of a number of new modules to perform specific
tasks during data processing, analysis, and
visualization. Two of the more important modules
are as follows:
• MVS now includes C Tech's version of the
Stanford GSLIB 3-D kriging routine, KT3D,
which has enhanced functionality. The MVS
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gridding capability combined with a user-
friendly Windows interface is a major
improvement over the Stanford GSLIB version.
• Four new modules (Animate_Field,
Animate_UCD, Animate_netCDF, and
Animate_netCDF_Explode) are the foundation
for the newest product, MAS. These modules—
in MAS, EVS-PRO, and MVS—are complex
macro modules that incorporate the functionality
of several modules and integrate a customized
looping function that allows for creating
interpolated time sequences of frames to
produce animations.
In addition, modules have been written to
• create bitmap files containing spatial reference
information; this module can be used to prepare
a georeferenced image that can be imported by
ArcView and other GIS applications;
• create a top view of the site;
• import and display image files (e.g., bitmap
files);
• provide a simple means to add the numerical
output from up to four input ports, with the
capability of editing this module's expression to
perform subtraction or other math operations;
• subdivide triangular and quadrilateral cells until
none of the sides of the output triangles exceed a
user-specified length;
• create the fundamental geologic grid information
to a file format that Ground Water Vistas can
read, including x,y origin, rotation, and x-y
resolutions in addition to descriptive header
lines;
• optimize output for Open_GL rendering;
• create slices in the vertical, easting, or northing
planes and manipulate their positions
interactively;
• create 3-D buildings directly in EVS/MVS
without using computer-aided design (CAD)
programs;
• cut a cylindrical cross-section tunnel along a 3-D
polyline path;
• configure and set default values for most key
modules and default data paths, allowing the
user to add project- or user-specific settings in
EVS, EVS-PRO, and MVS; and
• create additional animation files (including AVI,
MPG, and HAV).
Representative Applications
The following companies have provided brief
descriptions of the work that they have performed
using C Tech's visualization software (EVS-PRO
and MVS). A short discussion of each project
follows. For more information contact C Tech or the
user company at the web address provided.
Research Triangle Institute (RTI)
www.rti.org
RTI used EVS-PRO to develop
• animated visualizations of the migration of a
chlorinated solvent plume for an industrial
facility in the southeastern United States. This
visualization supported the development of a
monitored natural attenuation corrective action,
saving the client hundreds of thousands of
dollars in potential cleanup costs.
• visualizations of the geology and water levels
within heavily used aquifers in the Atlantic
coastal plain. RTI showed that water-level
declines in many areas exceed the natural
recharge of the water-supply aquifers,
potentially leading to problems with decreasing
groundwater supplies, saltwater encroachment,
and land subsidence.
• animated visualizations of the advance of an
open-pit mine in the Atlantic coastal plain. The
complex geologic data included 19 geologic
units in addition to the ore. The animations
showed the projected development of the mine
through ten years. The visualizations effectively
demonstrated technical mining geologic data to
upper-level management and to other
stakeholders in the mining operation.
• analyses of ore-quality data using 3-D
geostatistics to determine the distribution of a
series of ore-quality parameters and also to
project the variability in the ore quality
associated with future mine development.
U.S. Department of Energy, Grand Junction
(Colorado) Office (DOE-GJO)
DOE-GJO used EVS-PRO to
• model and map contaminant plumes and tank
structures in the high-level radioactive waste
tank farms at the Hanford facility;
• map surface and subsurface topography and
geologic structure for the Uranium Mill Tailings
53
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Remedial Action (UMTRA) Ground Water
project; and
• provide visual representation of various surface
and subsurface geophysical surveys done by
DOE-GJO.
Walden Associates, Inc.
www.walden-assoc.com
Using EVS software, Walden Associates created a
3-D animation that illustrated subsurface conditions
at an airport below the runway deck and adjacent
taxi way. The intent of the animation was to illustrate
the depth and number of required structural pilings
proposed for construction. The 3-D model was
rotated and probed to show subsurface conditions
throughout the proposed construction area. The
animation was finalized with aerial photographs, a
3-D pile driver, and aircraft for location and scale.
The final movie file was presented to the port
authority on a VCR tape for in-house presentations.
URS Greiner Woodward Clyde (URS)
www.urscorp.com
URS used EVS-PRO to
• support a case for natural attenuation of a
groundwater plume at a former manufactured
gas plant site in southeastern Pennsylvania. The
presence of nonaqueous phase liquids (NAPLs)
and unremediated source areas threatened the
viability of this approach. An animated video
demonstrating plume stability was created in
EVS-PRO from the actual results of eight rounds
of quarterly monitoring. After the video was
presented to the regulators, subsequent site
closure discussions focused on the positive
aspects of plume stability, rather than on the
negatives of undefined NAPLs or unremediated
sources. The results achieved with the full-
motion video could not have been achieved with
conventional tabular or graphic output.
• depict the proposed configuration and
construction of a new subway in a major U.S.
city. The animation produced in EVS-PRO was
an effective means of showing existing and
developing subsurface information, existing
facilities, and proposed design and construction
concepts to the management team and to outside
third parties interested in or affected by the
project.
• generate an animation that illustrates the
monitoring well network associated with a
groundwater pump and treat system and the
effectiveness of the remediation over time. The
animation begins by displaying the original
extent of the groundwater plume and then shows
how the plume regressed over time in response
to the pumping.
• generate an animation depicting the results of a
preliminary geotechnical investigation for a
power generation facility. The animation shows
proposed structures and the layout of the
property to enhance an understanding of how the
geologic conditions beneath the site may
influence the type of foundations needed for
each structure. Based on the locations of the
structures and the underlying thickness of the
clay unit, locations for additional geotechnical
borings were proposed.
• generate an animation to present the natural
attenuation remediation strategy over a 20-year
period at a chemical facility in Pennsylvania.
The extent of the groundwater plume in the
lower water-bearing unit was depicted from a
combination of actual groundwater quality data
(1990-95) and modeled predictions (from
Modflow/MT3D) to project the extent of the
plume until the year 2009.
• generate an animation to illustrate the potential
cost savings of drilling piles into a shallower
sand unit rather than the proposed deeper sand
unit. The animation shows the location of the
proposed buildings, the stratigraphic units, and
representative piles drilled into the deeper sand
unit.
• generate an animation for a Superfund site in
New Jersey to portray the vertical and horizontal
extent of two adjacent but non-intersecting
sources. The animation displayed the well
locations with the sample concentrations
represented by colored spheres and made readily
identifiable the regions where the plumes may
need to be remediated to meet regulatory
standards.
Environ Corporation
www. environcorp. com
Environ used EVS-PRO to
• analyze the relative impact of two sources of
contamination to an aquifer used as a municipal
drinking water supply and allocate remedial
costs accordingly. EVS-PRO was also used to
display the results of MODFLOW and MT3D
simulations that calculated the relative impact of
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the sources at downgradient drinking water
wells.
• investigate transport pathways to calculate and
visualize the 3-D boundaries of soil
contamination. Comparison of the vertical
contaminant profile with the known geologic
layers enabled the identification of preferential
transport pathways (in this case, sand lenses).
This information was key in selecting an
appropriate and cost-effective remedy.
Conestoga-Rovers & Associates (CRA)
www.rovers.com
CRA has used EVS and MVS in the visualization of
more than 30 sites in the United States in the last
two years. A few typical tasks were as follows:
• to demonstrate the nature and extent of
contamination in a multi-aquifer system
underneath an active manufacturing facility and
on-site landfill. Visualization presentations were
given to company officials, community groups,
and the Ohio Environmental Protection Agency.
The use of EVS and MVS allowed for a clearer
focus on the sources and extent of contami-
nation. It also resulted in a greater understanding
of the complex chemical and hydrogeological
issues at the site, for all parties involved.
• to develop a model of aquifer and aquitard
materials using indicator kriging. The resulting
model challenged previous assumptions of a
two-aquifer system divided by a clay aquitard
and suggested potential pathways between the
aquifers. The new model showed that much of
the persistent contamination at the site was the
result of dense NAPLs in the subsurface on the
upgradient side of the site. The model was also
used to aid in selection of both the location and
type of remedial design.
• to visualize in three dimensions the results of
MODFLOW and MT3D simulations of the spill
of VOCs in an aquifer system. The site had a
complex pumping history, with flow changing
directions with time and also as a function of
depth. The use of EVS allowed for a simple
demonstration of how contaminants had
migrated vertically and horizontally over the
decades.
• to visualize the site, extent of contamination, and
effect of remedial action for settlement
negotiations for cost-recovery litigation in
respect to a landfill, a former Superfund site, in
New Jersey. Although the remedial action was
completed several years ago, cost-recovery had
been ongoing.
The IT Group
www.theitgroup. com
IT used EVS-PRO to
• visualize TCE data to guide investigation and to
aid in the determination of possible multiple
sources at an active naval air station;
• visualize native geology in relation to fill and
cut areas at a former treatment, storage, and
disposal facility being closed under the Resource
Conservation and Recovery Act (RCRA). EVS-
PRO produced visualizations of removal of soil
with heavy metal concentrations exceeding
regulatory thresholds.
Hong Kong Geological Survey (HKGS)
HKGS used MVS for visualization and
interpretation of complex geological conditions
beneath reclaimed land at Tung Chung New Town,
Lantau, Hong Kong. MVS was used to investigate,
model, and visualize geological conditions,
including the occurrence of marble xenoliths within
a granite intrusion, development of karst with
sinkholes, and extensive decomposition (weathering)
of country rock. Site investigation data included
about 1000 drill holes together with seismic
reflection and microgravity surveys.
Frontline Environmental Management, Inc.
www. onthefrontlines. com
Frontline used EVS-PRO to
• delineate the extent of metal and polyaromatic
hydrocarbon contamination of soil at a former
starch plant. Statistical analysis provided the
confidence needed to secure funding investment
for redevelopment.
• visualize the results of groundwater flow
modeling at a vinyl manufacturing facility
located adjacent to one of Canada's "Heritage
Rivers" and across from a municipal wellfield.
The animations were used as a public
communications tool.
• evaluate the contamination levels in soil
adjacent to a former vinyl manufacturing
facility, where site redevelopment involves
realignment of a creek that is under the
jurisdiction of the Canadian Department of
Fisheries and Oceans.
55
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visualize the distribution of gasoline components Geosismica y Ambiente, Ltd., Bogota, Columbia
(benzene, toluene, ethyl-benzene, and xylene) in Geosismica is using MVS in a coal mine to calculate
soil and groundwater in three dimensions. EVS ±e volume of coal md ^e correlation of the coal-
animation was used to demonstrate the changes bearmg md mterstitial geologic layers and to design
in the volume of impacted subsurface materials ,, , ..,, • , , .. .,
., . ,, .f. , . . , the coal pit for economical exploitation.
that would occur with changes in required
cleanup guideline concentrations.
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Section 6 — References
Deutsch, C. V., and A. Journel. 1992. Geostatistical Software Library Version 2.0 and User's Guide for
GSLIB 2.0. Oxford Press.
Englund, E. I, and A. R. Sparks. 1991. Geo-EAS (Geostatistical Environmental Assessment Software) and
User's Guide, Version 1.1. EPA 600/4-88/033.
EPA (U.S. Environmental Protection Agency). 1994. Guidance for the Data Quality Objective Process,
QA/G-4. EPA/600/R-96/055. U.S. Environmental Protection Agency, Washington, D.C.
Golden Software. 1996. Surfer Version 6.04, June 24. Golden Software Inc., Golden, CO.
Sullivan, T. M., and A. Q. Armstrong. 1998. "Decision Support Software Technology Demonstration Plan."
Environmental & Waste Technology Center, Brookhaven National Laboratory, Upton, N.Y., September.
Sullivan, T. M., A. Q. Armstrong, and J. P. Osleeb. 1998. "Problem Descriptions for the Decision Support
Software Demonstration." Environmental & Waste Technology Center, Brookhaven National Laboratory,
Upton, N.Y., September.
van der Heijde, P. K. M., and D. A. Kanzer. 1997. Ground-Water Model Testing: Systematic Evaluation and
Testing of Code Functionality and Performance. EPA/600/R-97/007. National Risk Management, Research
Laboratory, U.S. Environmental Protection Agency, Cincinnati, OH.
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Appendix A— Summary of Test Problems
Site A: Sample Optimization Problem
Site A has been in operation since the late 1940s as an industrial machine plant that used solvents and
degreasing agents. It overlies an important aquifer that supplies more than 2.7 million gal of water per day for
industrial, commercial, and residential use. Site characterization and monitoring activities were initiated in the
early 1980s, and it was determined that agricultural and industrial activities were sources of contamination.
The industrial plant was shut down in 1985. The primary concern is volatile organic compounds (VOCs) in
the aquifer and their potential migration to public water supplies. Source control is considered an important
remediation objective to prevent further spreading of contamination.
The objective of this Site A problem was to challenge the software's capabilities as a sample optimization
tool. The Site A test problem presents a 3-D (3-D) groundwater contamination scenario where two VOCs,
dichloroethene (DCE) and trichloroethene (TCE), are present. The data that were supplied to the analysts
included information on hydraulic head, subsurface geologic structure, and chemical concentrations from
seven wells that covered an approximately 1000-ft square. Chemical analysis data were collected at 5-ft
intervals from each well.
The design objective of this test problem was for the analyst to predict the optimum sample locations to
define the depth and location of the plume at contamination levels exceeding the threshold concentration
(either 10 or 100 |J,g/L). Because of the limited data set provided to the analysts and the variability found in
natural systems, the analysts were asked to estimate the plume size and shape as well as the confidence in
their prediction. A high level of confidence indicates that there is a high probability that the contaminant
exceeds the threshold at that location. For example, at the 10-|jg/L threshold, the 90% confidence level plume
is defined as the region in which there is greater than a 90% chance that the contaminant concentration
exceeds 10 |Jg/L. The analysts were asked to define the plume for three confidence levels—10% (maximum
plume, low certainty, and larger region), 50% (nominal plume), and 90% (minimum plume, high certainty,
and smaller region). The initial data set provided to the analyst was a subset of the available baseline data and
intended to be insufficient for fully defining the extent of contamination in any dimension. The analyst used
the initial data set to make a preliminary estimate of the dimensions of the plume and the level of confidence
in the prediction. In order to improve the confidence and better define the plume boundaries, the analyst
needed to determine where the next sample should be collected. The analyst conveyed this information to the
demonstration technical team, which then provided the analyst with the contamination data from the specified
location or locations. This iterative process continued until the analyst reached the test problem design
objective.
Site A: Cost-Benefit Problem
The objectives of the Site A cost-benefit problem were (1) to determine the accuracy with which the software
predicts plume boundaries to define the extent of a 3-D groundwater contamination problem on a large scale
(the problem domain is approximately 1 square mile) and (2) to evaluate human health risk estimates resulting
from exposure to contaminated groundwater. The VOC contaminants of concern for the cost-benefit problem
were perchloroethene (PCE) and trichloroethane (TCA).
In this test problem analysts were to define the location and depth of the PCE plume at concentrations of 100
and 500 |jg/L and TCA concentrations of 5 and 50 |jg/L at confidence levels of 10 (maximum plume),
50 (nominal plume), and 90% (minimum plume). This information could be used in a cost-benefit analysis of
remediation goals versus cost of remediation. The analysts were provided with geological information,
borehole logs, hydraulic data, and an extensive chemical analysis data set consisting of more than 80 wells.
Chemical analysis data were collected at 5-ft intervals from each well. Data from a few wells were withheld
from the analysts to provide a reference to check interpolation routines. Once the analysts defined the PCE
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and TCA plumes, they were asked to calculate the human health risks associated with drinking 2 L/d of
contaminated groundwater at two defined exposure points over the next 5 years. One exposure point was in
the central region of the plume and one was at the outer edge. This information could be used in a cost-benefit
analysis of reduction of human health risk as a function of remediation.
Site B: Sample Optimization and Cost-Benefit Problem
Site B is located in a sparsely populated area of the southern United States on a 1350-acre site about 3 miles
south of a large river. The site is typical of many metal fabrication or industrial facilities because it has
numerous potential sources of contamination (e.g., material storage areas, process activity areas, service
facilities, and waste management areas). As with many large manufacturing facilities, accidental releases
from laboratory activities and cleaning operations introduced solvents and other organic chemicals into the
environment, contaminating soil, groundwater, and surface waters.
The objective of the Site B test problem was to challenge the software's capabilities as a sample optimization
and cost-benefit tool. The test problem presents a two-dimensional (2-D) groundwater contamination scenario
with three contaminants—vinyl chloride (VC), TCE, and technetium-99 (Tc-99). Chemical analysis data were
collected at a series of groundwater monitoring wells on quarterly basis for more than 10 years along the
direction of flow near the centerline of the plume. The analysts were supplied with data from one sampling
period.
There were two design objectives for this test problem. First, the analyst was to predict the optimum sample
location to define the depth and location of the plume at specified contaminant threshold concentrations with
confidence levels of 50, 75, and 90%. The initial data set provided to the analyst was a subset of the available
baseline data and was intended to be insufficient for fully defining the extent of contamination in two
dimensions. The analyst used the initial data set to make a preliminary estimate of the dimensions of the
plume and the level of confidence in the prediction. In order to improve the confidence in defining the plume
boundaries, the analyst needed to determine the location for collecting the next sample. The analyst conveyed
this information to the demonstration technical team, who then provided the analyst with the contamination
data from the specified location or locations. This iterative process continued until the analyst reached the
design objective.
Once the location and depth of the plume was defined, the second design objective was addressed. The second
design objective was to estimate the volume of contamination at the specified threshold concentrations at
confidence levels of 50, 75, and 90%. This information could be used in a cost-benefit analysis of remediation
goals versus cost of remediation. Also, if possible, the analyst was asked to calculate health risks associated
with drinking 2 L/d of contaminated groundwater from two exposure points in the plume. One exposure point
was near the centerline of the plume, while the other was on the edge of the plume. This information could be
used in a cost-benefit analysis of reduction of human health risk as a function of remediation.
Site D: Sample Optimization and Cost-Benefit Problem
Site D is located in the western United States and consists of about 3000 acres of land bounded by municipal
areas on the west and southwest and unincorporated areas on northwest and east. The site has been an active
industrial facility since it began operation in 1936. Operations have included maintenance and repair of
aircraft and, recently, the maintenance and repair of communications equipment and electronics. The aquifer
beneath the site is several hundred ft thick and consists of three or four different layers of sand or silty sand.
The primary concern is VOC contamination of soil and groundwater as well as contamination of soil with
metals.
The objective of the Site D problem was to test the software's capability as a tool for sample optimization and
cost-benefit problems. This test problem was a 3-D groundwater sample optimization problem for four VOC
contaminants—PCE, DCE, TCE, and trichloroethane (TCA). The test problem required the developer to
predict the optimum sample locations to define the region of the contamination that exceeded threshold
concentrations for each contaminant. Contaminant data were supplied for a series of wells screened at
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different depths for four quarters in a 1-year time frame. This initial data set was insufficient to fully define
the extent of contamination. The analyst used the initial data set to make a preliminary estimate of the
dimensions of the plume and the level of confidence in the prediction. In order to improve the confidence in
the prediction of the plume boundaries, the analyst needed to determine the location for collecting the next
sample. The analyst conveyed this information to the demonstration technical team, who then provided the
analyst with the contamination data from the specified location or locations. This iterative process was
continued until the analyst determined that the data could support definition of the location and depth of the
plume exceeding the threshold concentrations with confidence levels of 10, 50, and 90% for each
contaminant.
After the analyst was satisfied that the sample optimization problem was complete and the plume was defined,
he or she was given the option to continue and perform a cost-benefit analysis. At Site D, the cost-benefit
problem required estimation of the volume of contamination at specified threshold concentrations with
confidence levels of 10, 50, and 90%. This information could then be used in a cost-benefit analysis of
remediation goals versus cost of remediation.
Site N: Sample Optimization Problem
Site N is located in a sparsely populated area of the southern United States and is typical of many metal
fabrication or industrial facilities in that it has numerous potential sources of contamination (e.g., material
storage areas, process activity areas, service facilities, and waste management areas). Industrial operations
include feed and withdrawal of material from the primary process; recovery of heavy metals from various
waste materials and treatment of industrial wastes. The primary concern is contamination of the surface soils
by heavy metals.
The objective of the Site N sample optimization problem was to challenge the software's capability as a
sample optimization tool to define the areal extent of contamination. The Site N data set contains the most
extensive and reliable data for evaluating the accuracy of the analysis for a soil contamination problem. To
focus only on the accuracy of the soil sample optimization analysis, the problem was simplified by removing
information regarding groundwater contamination at this site, and it was limited to three contaminants. The
Site N test problem involves surface soil contamination (a 2-D problem) for three contaminants—arsenic
(As), cadmium (Cd), and chromium (Cr). Initial sampling indicated a small contaminated region on the site;
however, the initial sampling was limited to only a small area (less than 5% of the site area).
The design objective of this test problem was for the analyst to develop a sampling plan that defines the
extent of contamination on the 150-acre site based on exceedence of the specified threshold concentrations
with confidence levels of 10, 50% and 90%. Budgetary constraints limited the total expenditure for sampling
to $96,000. Sample costs were $1200 per sample, which included collecting and analyzing the surface soil
sample for all three contaminants. Therefore, the number of additional samples had to be less than 80. The
analyst used the initial data to define the areas of contamination and predict the location of additional
samples. The analyst was then provided with additional data at these locations and could perform the sample
optimization process again until the areal extent of contamination was defined or the maximum number of
samples (80) was attained. If the analyst determined that 80 samples was insufficient to adequately
characterize the entire 150-acre site, the analyst was asked to use the software to select the regions with the
highest probability of containing contaminated soil.
Site N: Cost-Benefit Problem
The objective of the Site N cost-benefit problem was to challenge the software's ability to perform cost-
benefit analysis as defined in terms of area of contaminated soil above threshold concentrations and/or
estimates of human health risk from exposure to contaminated soil. This test problem considers surface soil
contamination (2-D) for three contaminants—As, Cd, and Cr. The analysts were given an extensive data set
for a small region of the site and asked to conduct a cost-benefit analysis to evaluate the cost for remediation
to achieve specified threshold concentrations. If possible, an estimate of the confidence in the projected
remediation areas was provided at the 50 and 90% confidence limits. For human health risk analysis, two
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scenarios were considered. The first was the case of an on-site worker who was assumed to have consumed
500 mg/d of soil for one year during excavation activities. The worker would have worked in all areas of the
site during the excavation process. The second scenario considered a resident who was assumed to live on a
200- by 100-ft area at a specified location on the site and to have consumed 100 mg/d of soil for 30 years.
This information could be used in a cost-benefit (i.e., reduction of human health risk) analysis as a function of
remediation.
Site S: Sample Optimization Problem
Site S has been in operation since 1966. It was an industrial fertilizer plant producing pesticides and fertilizer
and used industrial solvents such as carbon tetrachloride (CTC) to clean equipment. Recently, it was
determined that routine process operations were causing a release of CTC onto the ground; the CTC was then
leaching into the subsurface. Measurements of the CTC concentration in groundwater have been as high as
80 ppm a few hundred ft down-gradient from the source area. The site boundary is approximately 5000 ft
from the facility where the release occurred. Sentinel wells at the boundary are not contaminated with CTC.
The objective of the Site S sample optimization problem was to challenge the software's capability as a
sample optimization tool. The test problem involved a 3-D groundwater contamination scenario for a single
contaminant, CTC. To focus only on the accuracy of the analysis, the problem was simplified. Information
regarding surface structures (e.g., buildings and roads) was not supplied to the analysts. In addition, the data
set was modified such that the contaminant concentrations were known exactly at each point (i.e., release and
transport parameters were specified, and concentrations could be determined from an analytical solution).
This analytical solution permitted a reliable benchmark for evaluating the accuracy of the software's
predictions.
The design objective of this test problem was for the analyst to define the location and depth of the plume at
CTC concentrations exceeding 5 and 500 |jg/L with confidence levels of 10, 50, and 90%. The initial data set
provided to the analysts was insufficient to define the plume accurately. The analyst used the initial data to
make a preliminary estimate of the dimensions of the plume and the level of confidence in the prediction. In
order to improve the confidence in the predicted plume boundaries, the analyst needed to determine where the
next sample should be collected. The analyst conveyed this information to the demonstration technical team,
who then provided the analyst with the contamination data from the specified location or locations. This
iterative process continued until the analyst reached the design objective.
Site S: Cost-Benefit Problem
The objective of the Site S cost-benefit problem was to challenge the software's capability as a cost-benefit
tool. The test problem involved a 3-D groundwater cost-benefit problem for a single contaminant, chlordane.
Analysts were given an extensive data set consisting of data from 34 wells over an area that was 2000 ft long
and 1000 ft wide. Vertical chlordane contamination concentrations were provided at 5-ft intervals from the
water table to beneath the deepest observed contamination.
This test problem had three design objectives. The first was to define the region, mass, and volume of the
plume at chlordane concentrations of 5 and 500 |J,g/L. The second objective was to extend the analysis to
define the plume volumes as a function of three confidence levels—10, 50, and 90%. This information could
be used in a cost-benefit analysis of remediation goals versus cost of remediation. The third objective was to
evaluate the human health risk at three drinking-water wells near the site, assuming that a resident drinks
2 L/d of water from a well screened over a 10-ft interval across the maximum chlordane concentration in the
plume. The analysts were asked to estimate the health risks at two locations at times of 1, 5, and 10 years in
the future. For the health risk analysis, the analysts were told to assume source control preventing further
release of chlordane to the aquifer. This information could be used in a cost-benefit analysis of reduction of
human health risk as a function of remediation.
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Site T: Sample Optimization Problem
Site T was developed in the 1950s as an area to store agricultural equipment as well as fertilizers, pesticides,
herbicides, and insecticides. The site consists of 18 acres in an undeveloped area of the western United States,
with the nearest residence being approximately 0.5 miles north of the site. Mixing operations (fertilizers and
pesticides or herbicides and insecticides) were discontinued or replaced in the 1980s when concentrations of
pesticides and herbicides in soil and wastewater were determined to be of concern.
The objective of the Site T sample optimization problem was to challenge the software's capability as a
sample optimization tool. The test problem presents a surface and subsurface soil contamination scenario for
four VOCs: ethylene dibromide (EDB), dichloropropane (DCP), dibromochloropropane (DBCP), and CTC.
This sample optimization problem had two stages. In the first stage, the analysts were asked to prepare a
sampling strategy to define the areal extent of surface soil contamination that exceeded the threshold
concentrations listed in Table A-l with confidence levels of 10, 50, and 90% on a 50- by 50-ft grid. This was
done in an iterative fashion in which the analysts would request data at additional locations and repeat the
analysis until they could determine, with the aid of their software, that the plume was adequately defined.
The stage two design objective addressed subsurface contamination. After defining the region of surface
contamination, the analysts were asked to define subsurface contamination in the regions found to have
surface contamination above the 90% confidence limit. In stage two, the analysts were asked to suggest
subsurface sampling locations on a 10-ft vertical scale to fully characterize the soil contamination at depths
from 0 to 30 ft below ground surface (the approximate location of the aquifer).
Table A-l. Site T soil contamination threshold
concentrations
Contaminant
Ethylene dibromide (EDB)
Dichloropropane (DCP)
Dibromochloropropane (DBCP)
Carbon tetrachloride (CTC)
Threshold concentration
(Hg/kg)
21
500
50
5
Site T: Cost-Benefit Problem
The objective of the Site T cost-benefit problem was to challenge the software's capability as a cost-benefit
tool. The test problem involved a 3-D groundwater contamination scenario with four VOCs (EDB, DCB,
DBCP, and CTC). The analysts were given an extensive data set and asked to estimate the volume, mass, and
location of the plumes at specified threshold concentrations for each VOC. If possible, the analysts were
asked to estimate the 50 and 90% confidence plumes at the specified concentrations. This information could
be used in a cost-benefit analysis of various remediation goals versus the cost of remediation. For health risk
cost-benefit analysis, the analysts were asked to evaluate the risks to a residential receptor (with location and
well screen depth specified) and an on-site receptor over the next 10 years. For the residential receptor,
consumption of 2 L/d of groundwater was the exposure pathway. For the on-site receptor, groundwater
consumption of 1 L/d was the exposure pathway. For both human health risk estimates, the analysts were told
to assume removal of any and all future sources that may impact the groundwater. This information could be
used in a cost-benefit analysis of various remediation goals versus the cost of remediation.
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Appendix B — Description of Interpolation Methods
A major component of the analysis of environmental data sets involves predicting physical or chemical
properties (contaminant concentrations, hydraulic head, thickness of a geologic layer, etc.) at locations
between measured data. This process, called interpolation, is often critical in developing an understanding of
the nature and extent of the environmental problem. The premise of interpolation is that the estimated value of
a parameter is a weighted average of measured values around it. Different interpolation routines use different
criteria to select the weights. Because of the importance of obtaining estimates of parameters between
measured data points in many fields of science, a wide number of interpolation routines exist.
Three classes of interpolation routines commonly used in environmental analysis are nearest neighbor, inverse
distance, and kriging. These three classes cover the range found in the software used in the demonstration and
use increasingly complex models to select their weighting functions.
Nearest neighbor is the simplest interpolation routine. In this approach, the estimated value of a parameter is
set to the value of the spatially nearest neighbor. This routine is most useful when the analyst has a lot of data
and is estimating parameters at only a few locations. Another simple interpolation scheme is averaging of
nearby data points. This scheme is an extension of the nearest neighbor approach and interpolates parameter
values as an average of the measured values within the neighborhood (specified distance). The weights for
averaging interpolation are all equal to \ln, where n is the number of data points used in the average. The
nearest neighbor and averaging interpolation routines do not use any information about the location of the
data values.
Inverse distance weighting (IDW) interpolation is another simple interpolation routine that is widely used. It
does account for the spatial distance between data values and the interpolation location. Estimates of the
parameter are obtained from a weighted average of neighboring measured values. The weights of IDW
interpolation are proportional to the inverse of these distances raised to a power. The assigned weights are
fractions that are normalized such that the sum of all the weights is equal to 1.0. In environmental problems,
contaminant concentrations typically vary by several orders of magnitude. For example, the concentration
may be a few thousand micrograms per liter near the source and tens of micrograms per liter away from the
source. With IDW, the extremely high concentrations tend to have influence over large distances, causing
smearing of the estimated area of contamination. For example, for a location that is 100 m from a measured
value of 5 |jg/L and 1000 m from a measured value of 5000 |Jg/L, using a distance weighting factor of 1 in
IDW yields a weight of 5000/1000 for the high-concentration data point and 5/100 for the low-concentration
data point. Thus, the predicted value is much more heavily influenced by the large measured value that is
physically farther from the location at which an estimate is desired. To minimize this problem, the inverted
distance weight can be increased to further reduce the effect of data points located farther away. IDW does
not directly account for spatial correlation that often exists in the data. The choice of the power used to obtain
the interpolation weights is dependent on the skills of the analyst and is often obtained through trial and error.
The third class of interpolation schemes is kriging. Kriging attempts to develop an estimate of the spatial
correlation in the data to assist in interpolation. Spatial correlation represents the correlation between two
measurements as a function of the distance and direction between their locations. Ordinary kriging
interpolation methods assume that the spatial correlation function is based on the assumption that the
measured data points are normally distributed. This kriging method is often used in environmental
contamination problems and was used by some DSS products in the demonstration and in the baseline
analysis. If the data are neither lognormal nor normally distributed, interpolations can be handled with
indicator kriging. Some of the DSS products in this demonstration used this approach. Indicator kriging
differs from ordinary kriging in that it makes no assumption on the distribution of data and is essentially a
nonparametric counterpart to ordinary kriging.
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Both kriging approaches involve two steps. In the first step, the measured data are examined to determine the
spatial correlation structure that exists in the data. The parameters that describe the correlation structure are
calculated as a variogram. The variogram merely describes the spatial relationship between data points.
Fitting a model to the variogram is the most important and technically challenging step. In the second step,
the kriging process interpolates data values at unsampled locations by a moving-average technique that uses
the results from the variogram to calculate the weighting factors. In kriging, the spatial correlation structure is
quantitatively evaluated and used to calculate the interpolation weights.
Although geostatistical-based interpolation approaches are more mathematically rigorous than the simple
interpolation approaches using nearest neighbor or IDW, they are not necessarily better representations of the
data. Statistical and geostatistical approaches attempt to minimize a mathematical constraint, similar to a least
squares minimization used in curve-fitting of data. While the solution provided is the "best" answer within the
mathematical constraints applied to the problem, it is not necessarily the best fit of the data. There are two
reasons for this.
First, in most environmental problems, the data are insufficient to determine the optimum model to use to
assess the data. Typically, there are several different models that can provide a defensible assessment of the
spatial correlation in the data. Each of these models has its own strengths and limitations, and the model
choice is subjective. In principle, selection of a geostatistical model is equivalent to picking the functional
form of the equation when curve-fitting. For example, given three pairs of data points, (1,1), (2,4), and (3,9),
the analyst may choose to determine the best-fit line. Doing so gives the expression y = 4x - 3.33, wherey is
the dependent variable and x is the independent variable. This has a goodness of fit correlation of 0.97, which
most would consider to be a good fit of the data. This equation is the "best" linear fit of the data constrained
to minimization of the sum of the squares of the residuals (difference between measured value and predicted
value at the locations of measured values). Other functional forms (e.g., exponential, trigonometric, and
polynomial) could be used to assess the data. Each of these would give a different "best" estimate for
interpolation of the data. In this example, the data match exactly with y = x2, and this is the best match of this
data. However, that this is the best match cannot be known with any high degree of confidence.
This conundrum leads to the second reason for the difficulty, if not impossibility, of finding the most
appropriate model to use for interpolation—which is that unless the analyst is extremely fortunate, the
measured data will not conform to the mathematical model used to represent the data. This difficulty is often
attributed to the variability found in natural systems, but is in fact a measure of the difference between the
model and the real-world data. To continue with the previous example, assume that another data point is
collected atx = 2.5 and the value is.y = 6.67. This latest value falls on the previous linear best-fit line, and the
correlation coefficient increases to 0.98. Further, it does not fall on the curve y = x2. The best-fit 2nd-order
polynomial now changes fmmy =x2to become>> = 0.85x2 + 0.67x - 0.55. The one data point dramatically
changed the "best"-fit parameters for the polynomial and therefore the estimated value at locations that do not
have measured values.
Lack of any clear basis for choosing one mathematical model over another and the fact that the data are not
distributed in a manner consistent with the simple mathematical functions in the model also apply to the
statistical and geostatistical approaches, albeit in a more complicated manner. In natural systems, the
complexity increases over the above example because of the multidimensional spatial characteristics of
environmental problems. This example highlighted the difficulty in concluding that one data representation is
better than another. At best, the interpolation can be reviewed to determine if it is consistent with the data.
The example also highlights the need for multiple lines of reasoning when assessing environmental data sets.
Examining the data through use of different contouring algorithms and model parameters often helps lead to a
more consistent understanding of the data and helps eliminate poor choices for interpolation parameters.
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