United States Office of Research and EPA/600/R-99/094
Environmental Protection Development October 1999
Agency Washington, D.C. 20460
v>EPA Environmental Technology
Verification Report
Environmental Decision
Support Software
Environmental Systems
Research Institute, Inc.
ArcView GIS Version 3.1 using
ArcView Spatial Analyst and
ArcView 3D Analyst
extensions
-------
-------
THE ENVIRONMENTAL TECHNOLOGY VERIFICATION PROGRAM
vvEPA
1 miit me i ii
-------
DEMONSTRATION DESCRIPTION
In September 1998, the performance of five DSS products was evaluated at the New Mexico Engineering
Research Institute located in Albuquerque, New Mexico. In October 1998, a sixth DSS product was
tested at 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 the 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.
ESRI staff used ArcView GIS Version 3.1 and its Spatial Analyst and 3D Analyst extensions to perform
the visualization endpoint using data from Sites A, B, and N. The Site A test problem, a three-
dimensional groundwater cost-benefit problem, required an analysis of remediation volume as a function
of cleanup levels for two volatile organic compounds (perchloroethene and trichloroethane). Data were
supplied at a series of wells for one representative period. Within each well, data were collected on a 5-ft
vertical spacing from the top of the water table to the confining bedrock. The Site B test problem was a
two-dimensional groundwater contamination sample optimization problem for three contaminants
(trichloroethene, vinyl chloride, and technetium-99). Developers were provided with a series of wells
containing contaminant concentrations and were asked to specify additional locations in which to collect
more data to better define the nature and extent of contamination. The Site N test problem was a two-
dimensional soil contamination cost-benefit problem. This problem included three heavy metal
contaminants (arsenic, cadmium, and chromium). The objective was to define the cost (area) of
remediation as a function of two cleanup levels for each contaminant.
The intent of the ArcView analyses was to demonstrate the capability to integrate large quantities of data
into a visual framework to assist in understanding a site's contamination problem. For the Site N
analysis, ArcView was used to estimate the area and costs associated with cleanup to different threshold
levels. Sample optimization components of the test problems were not performed.
Details of the demonstration, including an evaluation of the software's performance, may be found in the
report entitled Environmental Technology Verification Report: Environmental Systems Research
Institute, ArcView GIS Version 3.1 using ArcView Spatial Analyst and ArcView 3D Analyst Extensions,
EPA/600/R-99/094.
TECHNOLOGY DESCRIPTION
ArcView GIS version 3.1 is a geographic information system (GIS). One function of the software is to
help environmental professionals quickly and comprehensively characterize, manage, and visualize
information relevant to understanding environmental contamination problems. The ArcView GIS
integrates common database operations, such as query and statistical analysis, with the visualization and
geographic analysis benefits offered by maps. The Spatial Analyst extension was developed to solve
problems requiring that distance or other continuous surface modeling information be considered as part
of the analysis. The 3D Analyst extension permits the creation of three-dimensional surface models and
EPA-VS-SCM-35 The accompanying notice is an integral part of this verification statement October 1999
-------
assists users with three primary tasks—surface model construction, analysis, and display. Arc View and
its extensions operate on Windows 95, 98, and NT platforms.
VERIFICATION OF PERFORMANCE
The following performance characteristics of ArcView GIS Version 3.1 and its extensions Spatial
Analyst and 3D Analyst were observed:
Decision Support: ArcView GIS version 3.1 was able to quickly import data on contaminant
concentrations, geologic structure, and surface structure from a variety of sources with different formats
and integrate this information on a single platform. It was able to place the information in a visual
context that supports data interpretation.
Documentation of the ArcView Analysis: ArcView generated reports that provided an adequate
explanation of the process and parameters used to analyze each problem. Documentation of data transfer,
manipulation of the data (e.g., how to treat contamination data as a function of depth in a well), and
analyses were included. Model selection and parameters for contouring were also provided in the
exportable documentation. ArcView generated graphical output in . jpg format and incorporated this
directly into a Microsoft Word file.
Comparison with Baseline Analysis and Data: ArcView generated hydraulic head, ground surface
elevation, bedrock elevation, and contaminant concentration maps. The maps ranged from posting of a
marker at each data location, in which the size was proportional to the value of the parameter being
represented (e.g., contamination level), to generation of concentration contours. Comparison of the
contours of concentration and hydraulic head with the data and the baseline analysis showed that
ArcView results were consistent with the measured values. ArcView accurately mapped wells, buildings,
and site features. It accurately posted data to sample locations and hot-linked data to well locations. The
Site N cost-benefit analysis performed using ArcView estimated the volume of contamination and the
cost of remediation and was found to be consistent with the data and baseline analysis.
Multiple Lines of Reasoning: ESRI staff used ArcView, Spatial Analyst, and 3D Analyst to provide
multiple interpretations of the data with different contouring algorithms and contouring parameters. The
best fit to the data was provided for review. The multiple representations of the data permitted a better
understanding of the extent of the contamination problem.
In addition to performance criteria, the following secondary criteria were evaluated.
Ease of Use: The demonstration showed that the basic features in ArcView were easy to use. An analyst
with a background in environmental problems and a basic knowledge of database and GIS operations can
use ArcView after one to two days of training. The ArcView platform has a graphical user interface with
a logical menu structure to permit use of the options in the software package. ArcView supports data
queries that permit evaluation of the data based on user-defined criteria, for example, using only
trichloroethene data collected in 1999 for contouring. This query capability is a powerful data analysis
tool. ArcView was demonstrated to accept a wide range of formats when importing data (e.g., database
files, drawing files in .shp and jpg formats) and can export files using a large number of formats. Use of
advanced features, such as the Avenue scripting language, would require additional training and regular
use.
Efficiency and Representativeness: ESRI staff completed three visualization problems and generated the
report documenting the analysis with 12 person-days of effort. ArcView has a flexible database structure
that supports multiple data input formats. This provides a platform that addresses problems efficiently
and can be tailored to the problem under study. ArcView permits queries on any field (e.g., chemical
EPA-VS-SCM-35 The accompanying notice is an integral part of this verification statement October 1999
-------
name, date, concentration, well identifiers) and also permits filtering (e.g., include only data between
certain dates, maximum concentration at a location over a range of sample dates). The software has the
capability to evaluate a wide range of environmental conditions (e.g., contaminant in groundwater, soil,
multiple contaminants on a single site).
Training and Technical Support: Arc View offers several options for training and technical support. A
detailed on-line help system is supplied with the software package, and a user's manual is available to
assist in operation of the software. A step-by-step tutorial that covers the major features is provided with
the software package. A one-day training course is available if desired. Technical support is available for
a yearly maintenance fee.
Operator Skill Base: To use ArcView efficiently, the operator should have a basic understanding of the
use of computer software in analyzing environmental problems. This includes fundamental knowledge
about GIS and relational database files. In addition, knowledge about contouring environmental data sets
is beneficial.
Platform: ArcView was demonstrated on a Windows NT 4.0 operating system. It requires a minimum of
128 megabytes (MB) of random access memory (RAM). During the demonstration, two machines were
used. For Sites B and N, a 233-MHz Pentium II laptop with 128 MB of RAM, a 5-gigabyte hard drive
and standard 1024x768 video monitor was used. The laptop was equipped with an internal CD drive, a
1-gigabyte Jazz drive, and a PCMCIA network adapter. For the Site A analysis, the computer contained a
300-MHz Pentium II processor with 128 MB of RAM and an Elsa Gloria XLM graphics card with 16
MB of video RAM and an Open GL chipset. This computer was equipped with an internal CD drive, a
1-gigabyte Jazz drive, an internal network adapter, and a 19-in. monitor.
Cost: Pricing varies for single stand-alone systems through enterprise-wide systems. Currently, the
government price for the Windows version of a single stand-alone system of ArcView GIS Version 3.1 is
$996; for Spatial Analyst and 3D Analyst, the Government Services Administration price is $2342 each.
Prices for these products for private industry or for use on a UNIX-based operating system are slightly
higher.
Overall Evaluations: The main strength of ArcView, Spatial Analyst, and 3D Analyst is their ability to
easily integrate data and maps in a single platform to allow spatial visualization of the data. The
visualization output was clear and easy to understand. The ability to sort and query data makes
examination of a subset of the data easy to perform. Arc View's ability to manage data files from a wide
range of sources makes it suitable for managing complex environmental contamination problems. The
ease of use makes ArcView and its extensions accessible for the occasional user who wants to view the
spatial correlation between data. For the more advanced user, the scripting language, Avenue, makes the
ArcView products extremely flexible and customizable for problem-specific applications. ArcView is a
mature product with a large customer base.
The technical team concluded that for visualization of environmental data sets, there were no major
limitations in the ArcView set of programs. Minor problems noticed by the technical team included the
inability to open some of the project files provided at the demonstration and, for a new user, the need to
learn the terminology to understand the operation of ArcView (e.g., "scenes", "themes", "program
files").
The credibility of a computer analysis of environmental problems depends on good data, reliable and
appropriate software, adequate conceptualization of the site, and a technically defensible problem
analysis. The results of the demonstration show that the ArcView software can be used to generate
reliable and useful analyses for evaluating environmental contamination problems. This is the only
component of a credible analysis that can be addressed by the software. The results of an ArcView
EPA-VS-SCM-35 The accompanying notice is an integral part of this verification statement October 1999
-------
analysis can support decision-making. Arc View has been employed in a variety of environmental
applications. Although Arc View 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.
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
Director
National Exposure Research Laboratory
Office of Research and Development
David E. Reichle
ORNL Associate Laboratory Director
Life Sciences and Environmental Technologies
NOTICE: EPA verifications are based on evaluations of technology performance under specific, predetermined
criteria and appropriate quality assurance procedures. EPA, OPJSIL, 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-35 The accompanying notice is an integral part of this verification statement
October 1999
-------
-------
EPA/600/R-99/094
October 1999
Environmental Technology
Verification Report
Environmental Decision Support
Software
Environmental Systems Research
Institute, Inc.
ArcView GIS Version 3.1 using
ArcView Spatial Analyst and
ArcView 3D Analyst extensions
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
Eric N. Koglin
U.S. Environmental Protection Agency
Environmental Sciences Division
National Exposure Research Laboratory
Las Vegas, Nevada 89193-3478
orivl
-------
Notice
The U.S. Environmental Protection Agency (EPA), through its Office of Research and Development, and the
U.S. Department of Energy's Environmental Management Program through the National Analytical
Management Program, 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.
-------
Table of Contents
List of Figures v
List of Tables vii
Foreword ix
Acknowledgments xi
Abbreviations and Acronyms xiii
1 INTRODUCTION 1
Background 1
Demonstration Overview 2
Summary of Analysis Performed by Arc View GIS Version 3.1 and
Its Extensions 3
2 ARCVIEW VERSION 3.1, SPATIAL ANALYST, AND 3D ANALYST
DESCRIPTION 5
3 DEMONSTRATION PROCESS AND DESIGN 7
Introduction 7
Development of Test Problems 7
Test Problem Definition 7
Summary of Test Problems 7
Analysis of Test Problems 8
Preparation of Demonstration Plan 10
Summary of Demonstration Activities 10
Evaluation Criteria 11
Criteria for Assessing Decision Support 11
Secondary Evaluation Criteria 12
4 EVALUATION OF EVALUATION OF ARCVIEW VERSION 3.1, SPATIAL ANALYST,
AND 3D ANALYST 14
Description of Test Problems 14
SiteB 14
Site N Cost-Benefit Problem 14
Site A 15
Evaluation of Arc View Version 3.1 with Its Extensions 15
Decision Support 15
Documentation of the Arc View Analysis and Evaluation of the
Technical Approach 15
Comparison of Arc View Results with the Baseline Analysis and Data 16
Multiple Lines of Reasoning 31
Secondary Evaluation Criteria 31
Ease of Use 31
Efficiency and Representativness 32
Training and Technical Support 32
Additional Information about the ArcView Software 32
Summary of Performance 33
in
-------
5 ARCVIEW GIS VERSION 3.1, SPATIAL ANALYST AND 3D ANALYST UPDATE
AND REPRESENTATIVE APPLICATIONS 35
Objective 35
Technology Update 35
Representative Applications 35
6 REFERENCES 37
Appendix A: Summary of Test Problems 39
Site A: Sample Optimization Problem 39
Site A: Cost-Benefit Problem 39
Site B: Sample Optimization and Cost-Benefit Problem 40
Site D: Sample Optimization and Cost-Benefit Problem 40
SiteN: Sample Optimization Problem 41
SiteN: Cost-Benefit Problem 41
Site S: Sample Optimization Problem 42
Site S: Cost-Benefit Problem 42
Site T: Sample Optimization Problem 42
SiteT: Cost-Benefit Problem 43
Appendix B: Description of Interpolation methods 45
IV
-------
List of Figures
1 Site B map integrating surface features (roads, streams, railroad, and lakes)
with monitoring well locations (red dots) 17
2 Site B with aerial photo overlaid on the map of buildings, railroads, and streets 17
3 Site B map with demonstration of database query capabilities 18
4 ArcView with Spatial Analyst arsenic contamination map at 75 and 500 mg/kg thresholds 19
5 ArcView with Spatial Analyst cadmium contamination map at 75 and 500 mg/kg thresholds.... 20
6 ArcView with Spatial Analyst chromium contamination map at 370 and 3700 mg/kg
thresholds 20
7 Baseline analysis contamination map for arsenic at 75 (blue) and 500 (red) mg/kg thresholds
generated by DSS technical team using Surfer 21
8 ArcView Site A view of bedrock, groundwater levels, and measured contaminant
concentrations 23
9 ArcView representation of the bedrock surface compared with the measured bedrock depth at
fixed locations 24
10 ArcView and 3D Analyst overview of the Site A TCA contamination problem 25
11 ArcView and 3D Analyst TCA concentration contours in the stratum defined by
-7 to -17 feet below ground surface 26
12 ArcView and 3D Analyst top view, containing surface features, of the TCA contours in
the stratum defined by -7 to -17 feet below ground surface containing surface features 27
13 ArcView and 3D Analyst side view of the regions with TCA contamination levels greater
than 20 (lg/l.in the strata containing data from -7 to -57 feet below ground surface 28
14 ArcView and 3D Analyst top view of the region with TCA contamination levels greater
than 20 (ig/1 at depths between-7 and-57 feet below ground surface 29
15 ArcView and 3D Analyst top view of the region with PCE contamination contours for
the strata between -7 and-57 ft below ground surface surface 30
16 Site A TCA 20-(ig/L contour, comparison between IDW and kriging 31
17 Screen capture of Spatial Analyst's ModelBuilder 35
-------
-------
List of Tables
1 Summary of test problems 8
2 Data supplied forthe test problems 9
3 Site N soil contamination threshold values 14
4 Comparison of area estimates based on kriging and IDW interpolation routines 21
5 Performance summary for Arc View version 3.1 with Spatial Analyst and
3D Analyst extensions 34
vn
-------
-------
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
-------
-------
-------
-------
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 Randy Breeden
and Mike Gansecki (EPA Region 8) and Budhendra Bhudari (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 (New Mexico Engineering Institute); 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 National Exposure Research Laboratory). The authors also
acknowledge the participation of Mark Long and Tom Gross of Environmental Systems Research Institute
(ESRI), who performed the analyses during the demonstration, and Jennifer Harar and Dennis Smith who
provided logistical support and assisted in presenting the capabilities of all the ESRI products during Visitors
Day.
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 Environmental Software SitePro products, contact
Jennifer Harar
ESRI
8620 Westwood Center Drive
Vienna, VA 22180
703 506-9515 extension 8055
JHarar@ESRI.com
XI
-------
-------
-------
-------
Abbreviations and Acronyms
2-D
3-D
3D Analyst
As
.bmp
BNL
CTC
Cd
CD
COSIMA
Cr
DBCP
.dbf
DCA
DCE
DCP
DOE
DSS
.dxf
EDB
EPA
ESRI
ETV
ETVR
EVS
FTP
GEO-AS
GIS
GUI
GSA
GSLIB
GW
IDW
•JPg
MB
msl
NERL
ORNL
PCE
ppb
ppm
QA
QC
RAM
ROM
SCMT
.shp
Spatial Analyst
TCA
TCE
two dimensional
three dimensional
Arc View 3D Analyst
arsenic
bitmap file
Brookhaven National Laboratory
carbon tetrachloride
cadmium
compact disk
Contaminated Sites Management
chromium
dibromochloroproprane
database file
dichloroethane
dichloroethene
dichloropropane
U.S. Department of Energy
Decision Support Software
data exchange format file
ethylene dibromide
U.S. Environmental Protection Agency
Environmental Systems Research Institute, Inc.
Environmental Technology Verification Program
environmental technology verification report
Environmental Visualization System
file transfer protocol
Geostatistical Environmental Assessment Software
geographical information system
graphical user interface
Government Services Administration
Geostatistical Software Library
Groundwater
inverse distance weighting
JPEG file format
megabyte
mean sea level
National Exposure Research Laboratory
Oak Ridge National Laboratory
perchloroethene or tetrachloroethene
parts per billion
parts per million
quality assurance
quality control
random access memory
read-only memory
Site Characterization and Monitoring Technology
Shape file format
Arc View Spatial Analyst
trichloroethane
trichloroethene
Xlll
-------
Tc-99 technetium-99
VC vinyl chloride
VOC volatile organic compound
xiv
-------
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
environmental technologies through performance
verification and dissemination of information. The
goal of ETV 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 (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 for their
consideration 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 uses the expertise of partner
"verification organizations" to design efficient
processes for testing the performance 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)
National Analytical 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 (DSS).
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. There are many potential ways to
use such software, including visualizing the nature
and extent of contamination, locating optimum
future samples, assessing costs of cleanup versus
benefits obtained, or estimating the 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
• Cost-benefit analysis—either assessing the size
of the zone to be remediated according to
cleanup goals, or estimating 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, while
others focused on the technical aspects of generating
cost-benefit or sample optimization analysis, with a
minor emphasis on visualization. Because the
software products were not required to address all
three endpoints, partial analysis of a test problem
was permitted and the review of each DSS was
based only on the parts of the problem to which it
was applied.
The capabilities of each DSS were evaluated to
determine its effectiveness in integrating data and
models to produce information that supports
remedial action decisions pertaining to soil and
groundwater contamination problems. Secondary
evaluation objectives for this demonstration were the
reliability, resource requirements, ease of use, and
availability of training and technical support of each
DSS.
Evaluation of a software used for complex
environmental problems is by necessity primarily
qualitative in nature. It is not meaningful to evaluate
quantitatively how well predictions match at
locations where data have not been collected. (This
issue is discussed in more detail in Appendix B.) In
addition, the selection of a software product for a
particular application relies heavily on the users'
backgrounds, personal preferences (e.g., some
people prefer Microsoft Word, while others prefer
Corel WordPerfect for word processing), and
intended use of the software (e.g., spreadsheets can
be used for managing data; however programs
specifically designed for database management
would be a better choice for such an application).
The objective of these reports is to provide sufficient
information to judge whether the DSS product has
the analysis capabilities and features to be useful on
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 Corporation),
Arc View and associated software extenders
[Environmental Systems Research Institute (ESRI)],
GroundwaterEY" (DecisionKY), Sampling^"
(Decision^7, Inc.), and SitePro (Environmental
Software Corporation). In October, a sixth software
package from the University of Tennessee Research
Corporation, Spatial Analysis and Decision
Assistance, was tested. This report contains the
evaluation for Arc View GIS Version 3.1 and its
extensions Spatial Analyst and 3D Analyst.
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 these
data received a quality control (QC) 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
ArcView GIS Version 3.1 and Its
Extensions
ArcView GIS version 3.1 is a computer-based tool
for mapping and analyzing processes and events that
are related by their location. Geographic information
systems (GIS) technology integrates common
database operations, such as query and statistical
analysis, with the visualization and geographic
analysis benefits offered by maps. ArcView GIS
version 3.1 provides environmental decision support
through its integration of data from multiple sources
(i.e., spreadsheet, drawing, and database files) into a
platform that supports query operations, data
manipulation and visualization. ArcView can
generate two-dimensional maps of data and surface
features. The 3D Analyst extension provides the
capability to layer two-dimensional maps to provide
a quasi-three-dimensional representation of site
features (e.g., geologic layers, contamination).
ArcView GIS version 3.1 allows analysts to manage
and share their site data using a project file that
integrates the different data and visualization files.
ESRI staff chose to use ArcView to perform the
visualization endpoint for data from Sites A, B, and
N. The intent of the ArcView analyses was to
demonstrate the capability to integrate large
quantities of data into a visual framework for
assistance in understanding a site's contamination
problem. ESRI staff chose to apply three different
levels of ArcView visualization functionality. On
Site B, they used the standard ArcView product. On
Site N, they added the Spatial Analyst extension to
perform and display contoured surfaces. On Site A,
they added the 3D Analyst extension and three other
extensions available free from the ESRI website to
develop and display three-dimensional surfaces and
data. These extensions are discussed in more detail
in Sections 2 and 4.
The Site B problem involved groundwater
contamination in two-dimensions. The data supplied
for analysis of Site B included surface maps of
buildings, roads, and water bodies; concentration
data on three contaminants (trichloroethene (TCE),
vinyl chloride (VC), and technetium-99 (Tc-99)) in
groundwater wells and hydraulic head data.
ArcView was used to generate maps containing
color-coded well locations, buildings, roads,
railroads, and water bodies. The color coding was
used to show the location of high-concentration
regions in the mapped domain. ESRI staff
demonstrated ArcView's capabilities to integrate the
data from a wide range of sources (aerial
photographs, database files, and drawing files) to
assist in the understanding of the problem.
The Site N problem analyzed by ESRI was a two-
dimensional soil contamination cost-benefit analysis.
The data supplied for analysis of Site N included
concentration data on three contaminants, arsenic
(As), cadmium (Cd), and chromium (Cr), at 524
locations. In addition, drawing files containing roads
and surface water bodies were supplied. The
objective of this problem was to analyze the data and
supply an estimate of the contaminated area based
on two different cleanup levels for each
contaminant. The information could then be used in
a cost-benefit analysis. ESRI used ArcView with the
Spatial Analyst extension to generate maps for each
contaminant at the two cleanup levels. ESRI then
combined the maps for all three contaminants and
provided an estimate of the contaminated area and
costs for remediation based on cleanup level.
The Site A problem was a three-dimensional
groundwater contamination cost-benefit analysis.
The data supplied included surface drawings of
buildings, roads, and water bodies, and groundwater
contamination concentrations at more than 50 wells
with data supplied on a 5-ft vertical spacing in each
well. The contaminants of concern were
perchloroethene (PCE) and trichloroethane (TCA).
ESRI demonstrated ArcView's capability to query
the data and select data for contouring as a function
of elevation and contaminant type. ArcView
generated contour maps of contaminant
concentrations on a 10-ft spacing from the water
table to the bedrock (nine layers). These maps were
used to generate a quasi three-dimensional
representation of the contamination above certain
specified threshold values. Buildings and surface
features were included on the map to provide a
frame of reference. In addition, ArcView 3D Analyst
was used to generate a three-dimensional
representation of the bedrock elevation and a two-
dimensional representation of water levels at the site.
Section 2 contains a brief description of the
capabilities of ArcView, Spatial Analyst and 3D
Analyst. Section 3 outlines the process followed in
conducting the demonstration. This includes the
approach used to develop the test problems, a
summary description of the ten test problems, the
-------
approach used to perform the baseline analyses for Arc View analyses and the baseline results, and an
comparison with the developers' analyses, and the evaluation of Arc View against the criteria
evaluation criteria. (More detailed descriptions of established in Section 3. Section 5 presents an
the test problems can be found in Appendix A.) update on the Arc View technology and provides
Section 4 presents the technical review of the examples of representative applications of Arc View
analyses performed by Arc View, Spatial Analyst, in environmental problem solving.
and 3D Analyst. This includes a detailed discussion
of the problems attempted, comparisons of the
-------
Section 2 — ArcView Version 3.1, Spatial Analyst, and 3D Analyst
Description
The following section provides a general overview
of the capabilities of ESRI's ArcView GIS version
3.1 and its extensions Spatial Analyst and 3D
Analyst. The information was supplied by ESRI.
ArcView GIS version 3.1 is a computer-based tool
for mapping and analyzing processes and events that
are related by their location. GIS technology
integrates common database operations such as
query and statistical analysis with the unique
visualization and geographic analysis benefits
offered by maps. These abilities distinguish GIS
from other information systems and make it valuable
to a wide range of public and private enterprises for
explaining events, predicting outcomes, and
planning strategies.
ArcView GIS version 3.1 was used to demonstrate
database connectivity, geographic display and
mapping functionality, and model interfaces, which
are vital tools for site characterization, risk
assessment, and groundwater remediation analysis.
ArcView GIS can take environmental/facility site
data, aerial photo and satellite imagery, waste site
location data, natural resource data, well and boring
log data, and project impact data and integrate them
in a single software platform. Users can produce
tailored products by analyzing data layers to
determine patterns, relationships and trends. The
extensible software architecture of ArcView GIS
delivers a scaleable platform for GIS computing.
This new architecture has enabled ESRI to develop a
series of "plug-in" modules for ArcView that extend
its functional capabilities. Two of these extensions,
Spatial Analyst and 3D Analyst, were used in the
demonstration.
ArcView Spatial Analyst version 1.1 introduces a
broad range of new spatial modeling and analysis
features previously not available to desktop users. It
allows a user to create, query, map, and analyze
spatially continuous data (cell-based raster data) and
perform integrated raster-vector analysis. For
example, Spatial Analyst can take contaminant
concentration data and form an interpolated spatially
continuous surface for the data. It can then be used
to define the area of the map in which the
concentration exceeds a specified value. Spatial
Analyst can work with
spatially continuous data (including overlaying,
querying, and displaying multiple themes) and
perform integrated analysis. This analysis could
include a task such as aggregating properties of
continuous data (contaminant concentrations) based
on an overlaid discrete data theme (locations of
buildings and roads).
Spatial Analyst provides solutions to problems that
require consideration of distance or other continuous
surface modeling information as part of the analysis.
For example, site suitability analysis often requires
combining information about slope [information best
represented as a continuous interpolated surface
(raster data)] and the locations of roads and property
boundaries [information best represented as lines
(vector data) on the map] to arrive at the best
location for a new facility. Spatial Analyst not only
can generate the appropriate surface representation
of information from a variety of existing data
sources, but also can derive new information from
the overlay of multiple surface maps (e.g., roads,
buildings, property lines, surface slope). The results
can then be used to suggest possible solutions to the
original problem.
The 3D Analyst allows for the viewing and analysis
of three-dimensional data in a new ArcView
document type called a "scene." The 3D Analyst
provides functionality to assist users with three
primary tasks—surface model construction, analysis,
and display. Three-dimensional surfaces can be
edited directly in 3D Analyst. This capability helps
define high-quality three-dimensional surfaces and
permits the user to make changes due to changes in
data (e.g., new roads or buildings) without re-
creating the entire representation. The 3D Analyst
goes beyond common forms of surface analysis,
such as contouring and slope/aspect derivation, by
providing attribute support, low-level navigation
tools, and iterators. Numeric values representing
user-defined attributes can be assigned to triangle
nodes (point features) and facets (areal features).
Thus for any location on a modeled surface, the user
can access not only the surface geometry, but also
-------
other thematic characteristics such as land cover.
The navigation tools and iterators are useful to
applications that need to walk through the
triangulation or run through a collection of triangles
that satisfy some criterion. For example, the iterator
can be used to define all modeled regions (triangles)
that contribute to the water flow to a point location.
Interactive perspective viewing of the three-
dimensional surfaces is possible.
Customization for site-specific applications is
possible using the Arc View program language,
Avenue. In preparation for the demonstration, ESRI
employees wrote three additional extensions using
Avenue. One extension called "Scene Text" handles
the user-defined properties and placement of text
that can be added to three-dimensional scenes when
3D Analyst is used. A second extension, "3D Scene
Axes," uses Scene Text and adds functionality for
making and labeling the three-dimensional
coordinate axes in three-dimensional scenes. The
third extension, "Interpolate Multi Z-Value Data,"
handles the stratification, interpolation, and display
of the three-dimensional well sample data. It
manages user input for changing the properties of
the interpolation that will be used on the stratified
data points. The result is a contour surface of
contamination for each stratum. This extension also
handles display properties for the generated contour
surfaces. These additional extensions are available
free at www.esri.com. The Web page contains links
to many extensions of the Arc View GIS product.
ESRI customers often supply these extensions, and
ESRI does not provide technical support for any of
them.
Arc View GIS can be used as a stand-alone project
system or extended into an entire department,
division, or organization. It can be used to access
and view ARC/INFO® databases, including personal
computer ARC/INFO data. Arc View can also
directly use raster image data (continuous surface
map) in a wide variety of formats. Users can access
and visualize geographic data stored either locally or
remotely on a network.
ESRI offers training courses in the use of its
products at the ESRI headquarters in Redlands,
California, at ESRI regional offices, and through
ESRI authorized instructors. A "virtual campus" also
offers access to training classes over the Internet at
www.esri.com. ESRI has prepared several tutorials
to train users on the application of various Arc View
features and concepts. On-line help is available for
Arc View and its extensions, and ESRI provides a
technical support hotline to assist users in
implementing the software during the original
warranty period. There is a 60-day complimentary
technical support period for Arc View and its
optional extensions. Additional technical support
services are available from ESRI through software
maintenance and support programs.
-------
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) effectiveness 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 a given 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. Literature review and contact with different
government agencies (EPA field offices, DOE, the
U.S. Department of Defense, and the United States
Geological Survey) identified ten different 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 overtime). 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 ng/L). The
test problems analyzed by ESRI are discussed in
Section 4 as part of the evaluation of the
performance of Arc View and its extensions Spatial
Analyst and 3D Analyst.
-------
Table 1 summarizes the ten problems by site
identifier, location of contamination (soil or
groundwater), problem endpoints, and contaminants
of concern. The visualization endpoint could be
performed 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
range of contaminants considered included metals,
volatile organic compounds (VOCs), and
radionuclides. The range of environmental
conditions included two- and three-dimensional soil
and groundwater contamination problems over
varying geologic, hydrologic, and environmental
settings. Table 2 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
data were reserved to provide input concentrations to
developers for their sample optimization analysis.
Table 1. Summary of test problems
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
Arc View. Arc View 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
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, dichlorethane,
trichloroethene, perchloroethene
Arsenic, cadmium, chromium
Arsenic, cadmium, chromium
Carbon tetrachloride
Chlordane
Ethylene dibromide,
dibromochloropropane, dichloropropane,
carbon tetrachloride
Ethylene dibromide,
dibromochloropropane, dichloropropane,
carbon tetrachloride
-------
Table 2. 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
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™. Surfer was selected for the task because it
is a widely used, commercially available software
package with the functionality necessary to examine
the data. This functionality 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 three-
dimensional problems, the data were grouped by
elevation to provide a series of two-dimensional
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 three-dimensional depictions of the data
sets. Comparisons of the baseline analysis to the
results from ArcView and its extensions 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. 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 5,000 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
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
technical team to determine if any problems
occurred during data transfer or because of problem
definition. 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 QA/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
Characterization 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
BNL, 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,
10
-------
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
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 concentration levels 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.
The following sections provide more detail on each
of these topics.
11
-------
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
optimization problems, the developers were
provided with 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 variability 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
behind the technical approach used in the software
package.
12
-------
Training and Technical Support
The developers were asked to list the necessary
background knowledge necessary to successfully
operate the software package (i.e., basic
understanding 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 functionalities 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.
13
-------
Section 4 — Evaluation of ArcView Version 3.1, Spatial Analyst,
and 3D Analyst
not perform the sample optimization/cost benefit
Description of Test Problems
ESRI's ArcView is a data integration and
visualization tool. ArcView and its extensions
Spatial Analyst and 3D Analyst assimilate site,
well, and contaminant data and can generate two-
and three-dimensional representations of the
information. In the DSS demonstration, ESRI staff
selected problems for Sites B, N, and A. For
Site B, ESRI used the standard ArcView GIS
version 3.1 software. For Site N, ESRI added the
Spatial Analyst extension to generate and display
contoured surfaces. For Site A, ESRI added the 3D
Analyst extension to generate and display three-
dimensional surfaces and data. As part of the
demonstration, several dozen visualization outputs
were generated. A few examples that display the
range of Arc View's capabilities and features are
included in this report. A general description of
each test problem and the analysis performed
using ArcView follows. Detailed descriptions of
all test problems are provided in Sullivan,
Armstrong, and Osleeb (1998).
SiteB
The objective of this test problem was to challenge
the software's capabilities as a sample
optimization and cost-benefit tool. The test
problem presents a two-dimensional groundwater
contamination scenario with three contaminants,
VC, TCE, and Tc-99. Other contaminants were
supplied in the database but were not part of the
original problem. 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 year.
ESRI staff chose to demonstrate the basic
capabilities of ArcView GIS version 3.1 and did
analysis requested in the problem description.
ArcView was used to generate the following
output for this problem:
• Map with buildings, roads, railroads, water
bodies, and well locations.
• Map with an aerial photo overlain on previous
map.
• Maps based on queries of the database. For
example, a map containing roads, buildings,
and water bodies was produced that
highlighted all wells with measured neptunium
concentrations greater than zero.
Site N Cost-Benefit Problem
The objective of this test problem was to challenge
the ability of the software to perform cost-benefit
analysis as defined in terms of area of
contaminated soil above two threshold
concentrations. The Site N data set contained the
most extensive and reliable data set for evaluating
the accuracy of the analysis for a soil
contamination problem. To focus only on the
accuracy of the soil cost-benefit analysis, the
problem was simplified by removing information
regarding groundwater contamination at this site,
and it was limited to three contaminants.
This test problem considers surface soil
contamination (two-dimensional) for 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 area and cost
for remediation to achieve specified threshold
concentrations provided in Table 3.
ArcView estimated the areal extent of the soil
contamination by using Spatial Analyst to generate
contours at the specified threshold concentrations
for each contaminant. The following output was
generated for this problem:
Table 3. Site N soil contamination threshold concentrations
Contaminant
Arsenic
Cadmium
Chromium
Minimum threshold
concentration (mg/kg)
75
70
370
Maximum threshold
concentration (mg/kg)
500
700
3700
14
-------
• For each contaminant (As, Cd, and Cr), a map
with roads and water bodies overlain with
concentration contours at the specified
threshold concentrations.
• An estimate of the area of contamination
above the respective minimum threshold
concentration for each contaminant.
Site A
The objective of this test problem was to
determine the accuracy with which the software
predicts plume boundaries that define the extent of
a three-dimensional groundwater contamination
problem on a large scale (the problem domain is
approximately 1 mile2). The VOC contaminants of
concern for the cost-benefit problem were PCE
and TCA.
The design objective of this test problem was for
the analyst to define the location and depth of the
plume at PCE concentrations of 100 and 500 ppb
and TCA concentrations of 5 and 50 ppb at
confidence levels of 10% (maximum plume), 50%
(nominal plume), and 90% (minimum plume). 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.
ESRI used ArcView GIS version 3.1 with the 3D
Analyst extension to generate the contours of the
contaminant concentration data as a function of
depth below ground surface. ESRI used ArcView
to query the data and divided the data into 10-ft-
thick sections from the top of the water table to the
bedrock. The data were supplied on 5-ft spacings,
so each layer had two data points. The maximum
contaminant concentration in each layer was used
to generate the two-dimensional contour for each
layer. Output from the ESRI analysis included the
following:
• Three-dimensional surface maps of
contaminant concentrations in monitoring
wells as a function of elevation.
Contamination was displayed using markers
(circles) that increased in size with increasing
concentration.
• A three-dimensional surface map of the
bedrock layer with a semi-transparent ground
layer containing buildings and wells.
• A three-dimensional surface map of the
interpolated bedrock surface with well depths
shown visually as extruded lines.
• A three-dimensional surface map of the
bedrock layer with semi-transparent water
level contour map.
• Two-dimensional contour maps of the bedrock
surface and ground surface elevation.
• Two-dimensional water level maps with
buildings and surface water bodies.
• Two-dimensional concentration contour maps
for each of the ten groundwater layers for PCE
and TCA (20 maps total).
• A layered view of a three-dimensional surface
map of concentration contours in selected
layers for TCA.
• A layered view of a three-dimensional surface
map of regions where the TCE concentrations
exceeded 600 |og/L, with the bedrock and
water levels incorporated on the map. The data
for this problem were taken from the sample
optimization test problem for Site A.
Additional analysis of the sample optimization
problem was not presented; however, ESRI
staff decided to demonstrate the visualization
capabilities of the 3D Analyst extension of
ArcView.
Evaluation of ArcView GIS Version
3.1 with Its Extensions
Decision Support
During the demonstration, it was observed that
ArcView provides a platform that can quickly
import data on contaminant concentrations,
geologic structure, and surface structure from a
variety of sources with different formats and
integrate the information on a single platform.
ArcView and its extensions Spatial Analyst and
3D Analyst were used to place this information in
a visual context that supports data interpretation.
Multiple queries and views of the data could be
generated to assist in data interpretation. The
accuracy of the analysis is discussed in the section
on comparison of ArcView results with baseline
analysis and data.
Documentation of the ArcView
Analysis and Evaluation of the
Technical Approach
For each analysis, ESRI staff provided a step-by-
step description of the manipulations necessary to
import the data provided into ArcView and
perform the desired analysis. The steps proceeded
15
-------
logically and in a straightforward manner.
Manipulations to format the data within the
Arc View architecture were relatively simple. For
example, a Site B data file (.dbf) containing
sample locations and measured contaminant
concentrations, and a drawing file (.shp)
containing site maps were imported into the
Arc View data management system. The Arc View
database provided an integrated structure and was
coupled with the Arc View analysis tools (e.g.,
contouring/mapping, graphing, and reporting). In
addition, Site B data were hot-linked to the Site B
map that was generated from the drawing files by
Arc View according to the sample locations. These
hot links enabled the user to view the site map and
click on the sample location to access the database
information. Another useful feature of the
software was direct export of the output into
standard commercially available word processing
software. Graphical images were generated in jpg
format and imported directly into commercially
available software (Microsoft Word).
Documentation of data transfer and manipulation
(for example, how to treat contamination data as a
function of depth in a well) and analyses were
included. Model selection and parameters for
contouring were also provided in the test problem
documentation.
The technical approach used by ESRI staff did not
always conform to standard practices, nor did the
staff address the test problems as it was posed. In
particular, for Site A, the information supplied for
some wells at some elevations contained null
values (blanks); ESRI staff decided to treat the
null values as zero. In general, assuming values is
not recommended. This approach was an operator
choice. The software has the capability to exclude
null values from further use in the analysis. For
Site B, ESRI did not follow the test problem
directions. ESRI staff did not evaluate any of the
three contaminants (TCE, VC, Tc-99) requested in
the problem description. However, ESRI did
evaluate contaminant data for neptunium-237.
While the deviation from the requested problem
did not impact ESRI's ability to demonstrate the
capabilities of its software products, it did make
the evaluation of technical accuracy more difficult.
Comparison of ArcView Results with
the Baseline Analysis and Data
SiteB
ESRI staff used ArcView GIS version 3.1 to
import drawing files containing information on
roads, railroads, surface water bodies, and
buildings. Likewise, database files containing well
locations and contaminant concentrations were
imported and integrated into a single map with the
drawing files (Figure 1). All figures provided by
ESRI as a result of this demonstration are screen
captures from ArcView. Each screen capture is
composed of two regions. On the left is a list of
the files used to create the visualization. Only files
that are checked are used to create the view. In this
case, all files (well locations, geologic samples,
streets, railroads, streams/rivers, and lake) are
activated for creating Figure 1. Changing the files
that are activated can create multiple views of the
data. The right of the screen capture contains the
ArcView visualization. ArcView hot-links
database information to the map. Color coding is
used to distinguish between the different features
(e.g., railroads are displayed in yellow). Moving
the pointer to a well and clicking on the well
allows database information to be accessible for
viewing. ESRI staff also demonstrated that
ArcView had the capability to import and view
aerial photos (supplied in jpg format) as an
overlay to the map. ESRI staff imported the jpg
file and registered the file location to locations on
the map to create Figure 2. The capability to query
the database and highlight monitoring well
locations that passed the query criteria was also
demonstrated (Figure 3). In Figure 3, the database
was queried and all well locations that had positive
measurement for the radionuclide neptunium-237
were highlighted in yellow. An example of the
query is presented in the lower left-hand corner of
Figure 3. The technical evaluation team examined
each of the output figures and determined that the
mapping of surface features and posting of the
well locations was consistent with the baseline
data.
-------
Figure 1. Site B map integrating surface features (roads, streams, railroad, and lakes) with
monitoring well locations (red dots).
Figure 2. Site B with aerial photo overlaid on the map of buildings, railroads, and streets.
17
-------
V w«ULc.M*«rs -i.
•
j (MolegnSimpKi
•
V itnrt
w
^' n*iiAj4i
V' 3lT4aRfJRlV4ilS
A/
y DulMin^c
y^ Lik»
? 3m4lif 1»f
Figure 3. Site B map with demonstration of database query capabilities.
Site N Cost-Benefit Problem
Arc View GIS version 3.1 and the Spatial Analyst
extension were used to evaluate the surface soil
contamination data for three contaminants, As, Cr,
and Cd, at Site N. Drawing files containing the
locations of roads and surface water bodies were
imported and incorporated into maps with
contours generated by Spatial Analyst from the
contaminant data using an inverse distance
weighting (IDW) interpolation routine. Sampled
locations are marked with a small green circle on
these maps. The circles are color coded so that
darker green corresponds to higher concentrations.
Contour maps (Figures 4, 5, and 6) were generated
for each contaminant at the threshold
concentrations requested in the test problem
definition (Table 3). In these figures, the yellow
shaded area is the region in which the interpolated
concentration is above the minimum threshold in
Table 3, and the red shaded area is the region
above the maximum threshold. Using the
contoured profiles, a query was performed to
select all points in which the concentration
exceeded the minimum threshold concentration for
the contaminant. This information was used to
generate a map that highlighted the area on the site
in which any contaminant exceeded the minimum
threshold concentration. This map was used by
ESRI staff to calculate the area, volume, and cost
for remediation using the Data Calculator tool in
ArcView. In the test problem definition, the
developers were instructed to clean the top foot of
soil for all contaminated regions on Site N.
For comparison with the ESRI results, the DSS
technical team generated a baseline analysis for
the three contaminants at the two threshold
concentrations, using Surfer software and using
kriging as the interpolation routine. A visual
comparison between the baseline analysis and the
ArcView Spatial Analyst results (Figures 4, 5, and
6) showed that the two approaches gave similar
results. Figure 7 provides the baseline analysis
generated by the technical team using Surfer and
the arsenic data, which can be compared directly
with Figure 4. In Figure 7, the sampling points are
marked with a "+," the blue shaded area represents
the region in which the interpolated concentration
exceeds the minimum threshold for arsenic, and
the red shaded area is the region above the
maximum threshold. The major difference
between the two analyses resulted from the data
analysis approach taken by the two groups. In the
Site N test problem, the data were provided on a
limited portion of the site, thus requiring both the
technical team and ESRI analysts to define a
boundary
-------
Vi Streets
VJ Surface Water
A/
yT Arsenic (mg/kg)
0- 1
1 - 10
10- 100
• 100 - 1000
• 1000- 10000
yj Surface from Arsenic
| | 75-500 mg/kg
f Above 500
Interpolation Bounds
^•^T^X
Figure 4. Arc View with Spatial Analyst arsenic contamination map at 75 and 500 mg/kg thresholds.
around the data. In both cases, this was done by
drawing a boundary around the sampled locations.
In Arc View, boundaries were drawn as rectangles,
causing a slightly larger area (40%) to be used for
the ESRI analysis. In Surfer, a polygon can be
used to circumscribe the data locations. The ESRI
analyst could have used a polygon and obtained a
boundary identical to those of the technical test
teams. An examination of Figures 4 and 5 shows
large areas near the boundary of the domain that
do not contain sampled locations, yet the Arc View
Spatial Analyst interpolation routine suggests that
contamination concentrations exceeded the
threshold concentration (i.e., yellow areas that do
not contain green circles that represent sampled
locations). These areas were not present in the
baseline analysis because of the closer match
between the boundary and the outermost data
points (Figure 7).
To obtain a more quantitative comparison between
the ESRI and technical team results, the surface
area in which the estimated contamination
exceeded the minimum threshold concentration
was evaluated. The ESRI analysis combined the
areas for the three contaminants to determine the
total site area requiring remediation and calculated
that a surface area of 498,300 ft2 contained
contamination above the minimum threshold
concentration. This was 50% larger than the area
calculated in the baseline analysis generated by the
technical team (330,217 ft2). Two reasons were
found for the difference. First, as previously
discussed, the boundary defined in the ESRI
analysis was 40% larger than that in the baseline
analysis. This fact accounted for most of the
difference between the two analyses. Second, the
technical team confirmed that the IDW
interpolations used by ESRI predicted a larger area
of contamination than kriging. The technical team
attempted to reproduce the ESRI analysis using
IDW interpolation and the boundary defined by
the technical team. In this case, the area estimate
obtained using IDW was 381,000 ft2. Next, the
technical team performed a comparison of kriging
and IDW for each contaminant at each threshold
concentration using Surfer and concluded that
IDW consistently predicts a larger area of
contamination. Table 4 lists the area estimates and
the percentage difference between the two
interpolation routines for each contaminant and
threshold concentration. For the higher
1Q
-------
HBE3
^j Streets
f V
Vf Surface Water
A/
I|FJ Cadmium (mg/kg)
0- 1
1 - 10
10- 100
100 - 1000
* 1000- 10000
V^ Surface frcm C admium (m
| | 70-700 mg/kg
[ | At"!".'* 700 mg/kg
Interpolation Bounds
Figure 5. Arc View with Spatial Analyst cadmium contamination map at 70 and 700 mg/kg thresholds.
Figure 6. Arc View with Spatial Analyst chromium contamination map at 370 and 3700 mg/kg thresholds.
-------
22600
30200 30400 30600 30800 31000 31200 31400
Figure 7. Baseline analysis contamination map for arsenic at 75 (blue) and 500 (red) mg/kg thresholds
generated by DSS technical team using Surfer.
Table 4. Comparison of area estimates based on kriging and IDW interpolation routines
Contaminant
As
As
Cd
Cd
Cr
Cr
Threshold
concentration
(mg/kg)
75
500
70
700
370
3700
Kriging area
estimate (ft2)
330217
56981
270876
18207
37095
0
IDW area estimate
(ft2)
381452
58894
319023
18513
39301
0
Difference
(%)
-15.5
-3.4
-17.8
-1.7
-6.0
0
71
-------
threshold concentration of each contaminant, area
estimates are within 5%. For the lower threshold
concentration, area estimates differed by as much
as 17.8%. The variations between the area esti-
mate generated using kriging and the area estimate
using IDW are the result of the different contour-
ing algorithms. For this test problem, both ap-
proaches were consistent with the data, and one
cannot make a scientific judgement as to which
approach is more nearly correct. To check the
IDW interpolation routines used in Arc View
Spatial Analyst, ESRI staff supplied data at six
arbitrary interpolation points for each con-
taminant for the Site N test problem. The technical
team compared these predicted values with those
generated by other interpolation routines (kriging)
and the measured data (nearest neighbors) and
found consistency among all interpretations of the
data. In most instances, the difference between any
two estimates was within 50%. This is expected
due to the variability in the measured data. At lo-
cations with more than 50% variation, large
changes in measured concentrations occurred
around the interpolation point. For example, the
ESRI prediction for chromium at one sample loca-
tion was 1031 mg/kg, while the nearest measured
concentration, which was 41 ft from the ESRI lo-
cation, was 198 mg/kg. However, the next-nearest
point, which was 44 ft away in another direction,
had a measured concentration of 2613 mg/kg.
Therefore, the estimate generated by the Spatial
Analyst extension of Arc View was consistent with
the data.
Site A Cost-Benefit Problem
ESRI staff used ArcView with the 3D Analyst
extension to analyze groundwater contamination
due to PCE and TCA at this site. To illustrate the
software's capabilities in generating three-
dimensional visualization of the data, ESRI staff
generated a number of output files showing
various aspects of the site and the contamination.
The three-dimensional maps shown in this
document (Figures 8-15) are a small subset of all
of the views generated during the demonstration
and are meant to provide an overview of the types
of capabilities in ArcView and 3D Analyst.
Site A Bedrock and Groundwater Level
Analysis
The initial analyses performed by ESRI staff in-
volved integrating the surface feature data with
information on bedrock location, surface eleva-
tion, and groundwater level. Figure 8 displays the
Site A bedrock surface (brown region at the
bottom of the figure) overlaid with a map of
the groundwater levels (blue and green regions at
the top of the figure). The water level contour key
is found in the left part of the figure. In the
foreground, the axis represents the northing for the
site. The wells had contaminant concentrations
measured every 5 ft from the water table to the
bedrock. The measured contaminant
concentrations at various wells are represented in
the figure by circles. Note that the diameter of
each circle is a function of contamination
concentration, providing a visual reference for
contaminant concentrations. Other figures
demonstrated the capability to include surface
features such as water bodies and buildings
directly on the map. At the demonstration, it was
shown that this view could be rotated to any angle
to obtain a different perspective of the data. This is
an important and powerful feature for interpreting
the data. Figure 9 shows the interpolated bedrock
surface (reddish-brown region at the bottom of the
figure) with a direct comparison with the
measured data. The depths to the bedrock are
represented as lines extending from the surface to
their termination depth, which is denoted by a
circle at the bottom. The elevation scale is on the
left of the diagram. Buildings on the surface are
shown as extruded boxes. The bedrock surface
between measured data was interpolated using
kriging. Examination of the figure shows that most
points on the interpolated surface are within a few
feet of the measured surface. However, some
points are separated from the measured bedrock
data by several feet. In these instances, the
interpolation routines had difficulty because of a
rapid change in bedrock elevation over a short
distance. Based on the figure, approximately half
of the measured bedrock elevations are above the
interpolated surface, and half are below. This
capability permits the analyst to visually judge the
quality of the interpolated surface compared with
the measured data.
The technical team evaluated the accuracy of the
interpolated bedrock surface and groundwater
levels by comparing the ArcView and 3D Analyst
results with the measured data and with
interpolated surfaces generated using Surfer. The
evaluation indicated that the surfaces generated
using ArcView and 3D Analyst were consistent
with the measured data and baseline analysis.
Differences that occurred between the baseline
analysis and the ArcView analysis were attributed
to the
77
-------
jTl^TT-l^k-^'. ni;. ,,-mmm r
si
ii]!Ki a
• 2W8 IMS53
•
25TS1S- :JBMJ
aw 3i -
3MJ073 ZM.fl
2B1 a&- 302^33
3M333-2W.K?
3S3 IK-T • 2B3 M
»*9»3- 209.347
] 313 U?T- 2«a BP-l
• 2M 5T<
^]3M5J4-2eS310
I 3M 310- ZOB D61
H;
_
Figure 8. Arc View Site A view of bedrock, groundwater levels, and measured contaminant
concentrations.
differences in contouring algorithms. The
technical team attempted to reproduce the 3D
Analyst results using Surfer and the same
contouring algorithms used by ESRI; they
generated results similar to those ESRI obtained
using 3D Analyst.
Site A Contaminant Analysis
Arc View and 3D Analyst were used to visualize
the concentration data for the two contaminants
(TCA and PCE) in the cost-benefit test problem
for Site A. Because this is a three-dimensional
groundwater contamination problem, ESRI staff
approached the problem by using their product's
query capabilities to divide the contaminant data
into vertical strata 10 ft thick. Within a vertical
stratum, if more than one measured contaminant
concentration was present in a well, the maximum
value was used to generate interpolated surfaces.
The test problem asked that the region of
contamination be defined at two threshold
concentrations for each contaminant. For TCA, the
values were 5 and 50 |o,g/L; for PCE, the values
were 100 and 500
Figure 10 shows an overview of the TCA
contamination in groundwater generated using
ArcView and 3D Analyst. In this figure, the
73
-------
m
VI .
VI B v^c-l rttaurs fhf
VI
J
--•
20 >;
» *,
* 62
Ji 404
MO. MO
Figure 9. Arc View representation of the bedrock surface compared with the measured bedrock depth at fixed
locations.
buildings, the river, and the well locations were
included on the ground surface as points of
reference. The ground surface corresponds to the
elevation data supplied with the test problem and
accurately slopes downward from west to east.
The ground elevation contour key is found to the
left of the map, with brown representing the
highest and green the lowest elevation. Vertical
exaggeration was used to highlight this feature. A
brown circle was used to represent groundwater
sample locations below the ground surface. The
diameter of each circle corresponds to the
magnitude of the TCA concentration. When 3D
Analyst is used, this view can be rotated to obtain
other perspectives on the measured data.
ESRI staff began interpolation of contaminant data
during the DSS demonstration by applying trend
and spline interpolation methods. However, both
of those were rejected because the wide range in
concentration values in neighboring wells caused
both of these methods to over- and underestimate
interpolated values by large margins. Kriging
interpolators were investigated next but were not
used because of the great variance in contaminant
concentrations among data points close together.
Initial IDW interpolators were tested using an
exponent of 2 in the IDW interpolator. These
initial studies were rejected because this approach
tended to expand the area of the plume to regions
with no measured data. To overcome this problem,
74
-------
£*' jDScsno T
m
V A,:IJ..>PI
. 0 TCA
. 1-5
. 6- 10
• 10 - X
• 20- 30
• 30-40
. 40-60
50-60
200-1000
1CCO-1I25
2-iS B6B
J» DSS
25.34-13
5S6 fi2fi
ISO 215
363.60!
•;os saa
27D STB
356 BK
260.215
:« 602
366969
270.316
2^3.762
Figure 10. Arc View and 3D Analyst overview of the Site A TCA contamination problem.
several exponents were tried in the IDW
interpolator before the exponent was selected for
the final analysis (7 for the TCA and 9 for the PCE
analysis). Also, the scale of the analysis was
varied by ESRI analysts (i.e.. small sampling
radius and a small number of neighboring points)
to better refine the interpolations. Each of these
parameter choices helped to define the location of
contamination more accurately. The use of
multiple interpolation schemes and multiple lines
of reasoning provides various views of the data,
thereby assisting the analyst in data interpretation.
Upon selection of the IDW interpolation routine
with an exponent of 7, the TCA contaminant
analysis proceeded. ESRI staff generated
interpolations of TCA data for vertical strata that
were 10 ft thick. The technical team compared the
Arc View outputs with the measured TCA
concentrations. Figure 11 shows an example of
TCA interpolations for the stratum defined
between -7 and -17 ft below ground surface.
Arc View was used to depict the well and
groundwater sample locations as circles. For wells
with a maximum concentration of less than 5
|og/L, the circle is light blue; for concentrations of
greater than 5 |og/L, the circle is red. The diameter
of the circle corresponds to the magnitude of the
TCA concentration at that sample location. The
technical team verified that all wells were labeled
correctly in terms of their location and of having a
TCA concentration greater than 5|o,g/L. From the
visualization, it was not possible to determine if
the size of the circle corresponded exactly with the
TCA concentrations. However, wells with high
concentrations were displayed with larger circles
than wells with lower concentrations. Although it
is not shown
-------
5-20
20-50
50-80
all r?i
B330 -405
405 • =00
• 500 - 805
60S - 720
M 730-345
-tut
-1126
I IMn"
TCA.P7
J TCA.D6
: TCA D5
90-90
- 100
100-200
Figure 11. Arc View and 3D Analyst TCA concentration contours in the stratum defined by -1 to -17 ft below ground
surface.
in Figure 11, Arc View has the capability to post
the well identifier on the image. Also, with
Arc View it is possible to select a well using the
computer mouse and obtain all of the data for that
well. These Arc View features assist the analyst in
data interpretation and analysis. Figure 12 shows
the same contour information as Figure 11 from a
top view, with the ground surface and surface
features overlaid on the map. In Figure 12, wells
with a TCA concentration of greater than 5 |o,g/L
are color coded in orange with the size
proportional to concentration. Figure 13 shows a
three-dimensional layered view of the TCA
contamination for the five layers between -7 and -
57 ft and TCA concentrations above 20 |o,g/L.
Figure 14 shows the top view from Figure 13 with
buildings and the river overlaid on the
contamination contour to provide a spatial frame
of reference.
ESRI staff performed a similar analysis for PCE
contamination at Site A. Figure 15 shows atop
view of the PCE contours generated for samples
between -7 and - 57 ft below ground surface. The
blue region around the edge of the contours
represents the region in which the concentration is
less than the lower threshold of Table 3 (100
|og/L). The purple region defines the region in
which the concentration exceeds the 100 |o,g/L
threshold level. Regions above the maximum
threshold of 500 |o,g/L cannot be determined from
this map. The map also contains buildings, the
river, and ground surface elevation contours. A
-------
T. *_(*) sro
rf Ta_dB slip
a
1-5
- in
10-33
Figure 12. Arc View and 3D Analyst top view, containing surface features, of the TCA contours in the
stratum defined by -7 to -17 ft below ground surface containing surface features.
comparison of Figure 14, the TCA plume, and
Figure 15, the PCE plume, shows that the PCE
plume originates from a different area than the
main TCA plume. ESRI staff also provided maps
of PCE concentrations for each 10-ft stratum.
The technical team compared the Arc View and 3D
Analyst interpolations of the TCA- and PCE -
contaminated regions with the baseline analysis.
The Surfer baseline analysis, generated by the
technical team, also segregated the data into ten-ft
intervals and used the same vertical discretization
and data treatment (maximum value in the stratum
for each well) as the ArcView analysis. However,
the Surfer analysis used kriging with an anisotropy
ratio of 0.3 and a direction of -70 degrees with
respect to vertical for the TCA contours and -80
degrees with respect to vertical for the PCE
contours. These parameters were selected by the
technical team based on the direction of
groundwater flow and the ratio of the width to the
length of the plume. Several different sets of
parameters (anisotropy ratio and angle) were
evaluated by the technical team for each stratum to
define the best fit for that stratum.
Comparing the kriging baseline analysis of the
TCA threshold concentration contours with the
ArcView and 3D Analyst results was difficult. As
previously noted, Figure 11 provides an example
of the ArcView output received for each vertical
stratum. The slight change in colors between TCA
contour levels does not allow an accurate analysis
of the location of the 50-|o,g/L TCA threshold
contour. The lower TCA threshold contour, 5
|og/L, can be discerned from the figure as the
outermost outline of the contours. Similar color
figures were provided for each of the ten strata for
TCA and PCE contours. Figure 12 shows atop
view of Figure 11 with buildings and rivers
overlaid on the map. From Figure 12, the extent of
the 5-|o,g/L TCA contour can be clearly seen;
however, the 50-|o,g/L contour is difficult to
determine. In all of the top views provided by
ESRI, the 5-|o,g/L TCA contour corresponded with
the baseline analysis. The location of the 50-|o,g/L
contour was difficult to establish because of the
color scheme chosen to represent the contours.
Similarly for PCE, the minimum threshold, 100
|o,g/L could
77
-------
Ela Ed* JO Suf» TJiyiYM ^uta^ Qropiitcj Scaia la-.'
® is a @®@ s® o
w-
126-ieo
ISO •
245-030
-.105
Figure 13. Arc View and 3D Analyst side view of the regions with TCA contamination levels greater than 20 (o,g/L
in the strata containing data from -1 to -57 ft below ground.
be determined with reasonable accuracy from the
maps supplied; however, the maximum threshold,
500 |og/L, could not. For this reason, the
agreement between the ESRI and the baseline
analysis for the maximum threshold level for TCA
and PCE could not be evaluated.
The technical team took two approaches to
determine the accuracy with which the 3D Analyst
contaminant concentration contours matched the
measured contaminant data and the baseline
analysis generated by Surfer. First, for each
stratum, a visual comparison was made between
the 3D Analyst and the Surfer-generated contours.
The comparison for these 20 contours showed
reasonable agreement at the minimum threshold
concentrations for both TCA and PCE. As
expected, agreement was greatest in the vicinity of
sampled locations. Any disagreement between the
analyses occurred in the regions between sample
locations. Comparison at the maximum threshold
concentrations was difficult because of the color
coding of the contours selected by ESRI.
The second approach to determine accuracy was to
repeat the Surfer baseline analysis using the
interpolation routines selected by ESRI staff for
use in 3D Analyst (i.e., IDW with an exponent of
7 for TCA). The Surfer IDW contours were
visually compared with the 3D Analyst contours;
the results were similar, but it was not possible to
determine if they matched exactly.
Finally, to illustrate the difference between the
kriging and IDW contouring algorithms, the
technical team used Surfer to generate 20-ng/L
TCA contours using the maximum measured value
in all wells. This example illustrates a number of
the difficulties in contouring measured data,
highlights the differences between the two
contouring approaches, and is representative of the
findings in the ESRI results for each stratum. As a
starting
-------
Aimels.shp
. 0
. 1-5
- 1- ID
. 10-20
• 20- 3D
• 30--40
• 40-90
• a-60
• so-TO
TO- BO
90-90
40-100
100-200
Figure 14. Arc View and 3D Analyst top view of the region with TCA contamination levels greater than 20 |J.g/L
at depths between -7 and -57 ft below ground surface.
point, the ESRI analysis using Arc View and 3D
Analyst and the measured TCA concentrations
between -7 and -57 ft (presented in Figure 14)
was repeated by the technical team using Surfer
and IDW interpolations with a search radius of
1298 ft and a weight of 7 (the same parameters as
used in the ESRI analysis). Also, for comparison,
kriging using an anisotropy ratio of 0.3 and an
angle of 80° with respect to the vertical was
performed on the same TCA data using Surfer.
Surfer generated a map containing the 20-|o,g/L
contour for TCA using the maximum measured
TCA concentration in each well and a base map
including the river, buildings (irregularly shaped
outlines), and well locations (black circles). Figure
16 shows that both IDW (cross-hatched region)
and kriging (solid line) contours give essentially
the same results. Both identify one plume
originating from the building just south of the river
(975000 easting, 124800 northing) and a second
major plume originating from a building to the
southeast from that point. As expected, both
contouring algorithms agree closely at the location
of the wells (sample locations) and differ slightly
between wells.
Further examination of Figure 16 indicates two
other isolated areas of contamination on the map.
One appears south of the main plume at an easting
of 978000. The other appears near the river at an
easting of 979000. In both of these cases, the
plume arises from one well with a measured TCA
concentration slightly greater than the contour
level of 20 |o,g/L. For example, near the river, the
measured TCA concentration is 21 |o,g/L. In both
of these isolated areas, the IDW contoured area is
larger than the kriged area, indicating a larger zone
of influence from that data point. This can also be
seen in the main plume, where, between the rows
of wells, the contaminated areas estimate obtained
using the IDW method tends to spread wider than
the kriging method. The main difference between
the contours in terms of enclosing wells within the
contours occurs at the series of wells that run
79
-------
1
.-. ..•.•„ w . ' V-i, m . I
.131.1
a m & ^sm HHI
2B0215-2G3G03
•«5 MB - ?66 SUB
295.9B9 - 2TO 376
170.376 -173 762
ttBO-sam
HOD-6000
«no-7ooo
7010-9000
HMD-9000
9QQD-10000
10000-20000
20000 -295 68
V A/:(H«IS arrd.aip
«
V water.sip
V Suld.shp
Figure 15. Arc View and 3D Analyst top view of the region with PCE contamination contours for the strata between
-1 and -57 ft below ground surface.
primarily north to south just east of the large
L-shaped building (easting 977000). In this series
of wells, IDW places only one well inside the
contour, while kriging places three inside. The
measured TCA concentrations for these three
wells are 11, 125, and 17 |o,g/L. Therefore, the
kriging approach included two wells with
measured TCA concentrations slightly less than
the 20-|o,g/L contour level. However, overall, both
contouring methods give a reasonable
representation of the data that is suitable to assist
in understanding the extent of contamination.
From a technical perspective, there is no basis for
claiming one approach is superior to the other. In
fact, there is excellent agreement between the two
approaches. This is due in part to having an
adequately characterized site. However, it is also
due to the fact that in each interpolation approach,
kriging and IDW, the model parameters were
optimized through examining many sets of
parameters to obtain the best fit to the data. The
Arc View and 3D Analyst software permit the
analyst to conduct such a study; however,
ultimately it is up to the analyst to optimize the
treatment of the data.
For both the technical team and the ESRI staff
analyses, the three-dimensional data were
analyzed as a series of two-dimensional slices.
This approach does not account for changes in
bedrock elevations and can lead to incorrect
contouring. A better technical approach would
have been to contour only in regions that were
above the bedrock. This can be done by drawing
exclusion zones around the region, as
demonstrated by
-------
127000
126000
en
125000-
124000
974000 975000 976000 977000 978000 979000 980000
Easting (ft)
Site A TCA 20 ug/1 contour. Contours generated from the maximum concentration in
the well at elevations between -7 and - 57 feet. Comparison of IDW (hatched region)
and kriging (solid line) interpolation routines.
Figure 16. Site A TCA 20-ug/L contour. Comparison between IDW (cross-hatched) and kriging (solid line).
ESRI staff on the Site N test problem. However, it
would have required considerably more time and
effort on the part of the analyst.
Multiple Lines of Reasoning
ESRI staff used ArcView, Spatial Analyst, and 3D
Analyst to provide multiple interpretations of the
data with different contouring algorithms and
contouring parameters. The best fit to the data was
provided for review. This flexibility permitted a
better understanding of the extent of the
contamination problem.
Secondary Evaluation Criteria
Ease of Use
During the demonstration, it was observed that
ArcView and its extensions were easy to use.
ArcView has a graphical user interface (GUI) with
pull-down menus to permit use of the options in
the software. ArcView imports database files with
any user-defined structure, an important feature
that removes the need to reformat data. ArcView
also demonstrated the capability to import a wide
range of image files (.dxf, .shp, and jpg) and
integrate them into the visualization of the
problem. For example, during the demonstration,
it was able to incorporate an aerial photograph
(jpg file) containing surface features and .dbf files
containing data on contamination and hydrology.
The GUI provided a platform to address problems
efficiently and to tailor the analysis to the problem
under study (for example, contours can be defined
at any value; the number of layers in a three-
dimensional analysis is user-defined; and, for
multiple measurement values at a single location,
ArcView can take the maximum, minimum,
minimum non-zero, or average value for the
analysis). The database structure permitted queries
on any field (e.g., chemical name, date,
concentration, well identifiers) and permitted
filtering (e.g., include only data within a range of
elevations, maximum concentration at a location
over a range of dates).
ArcView can export text and graphics directly to
standard word processing softwares. ArcView
-------
generated jpg and text files that can be read by a
large number of software products. It also was
able to generate project files that contain
information on all of the visualization and data
files used in a single project. Thus the entire
project can be moved to another machine with
Arc View software. However, the technical review
team using Arc View could not open the project
files provided from the demonstration. The cause
is believed to be that not all the files referenced by
the project file were provided by ESRI.
During the demonstration, several members of the
technical team received a 4-hour introduction to
Arc View. The reviewers observed that Arc View
was a large, feature-rich software program that had
several tutorials to guide the novice user through
the system and applications. The reviewers felt
that with 1 or 2 days of training, they would be
able to use the fundamental features found in
Arc View. However, some of the reviewers were
confused by the terminology used by Arc View
(e.g., "scenes," "views," "themes," "project
files"). In addition, they all felt that regular use of
the product would be needed to efficiently use all
of the features found in the product. For example,
Arc View contains a scripting language, Avenue,
that permits automation of routine tasks, database
manipulation, and customization of the pull-down
menus. Learning to use this feature effectively
would require much more extensive training.
Efficiency and Representativeness
During the demonstration, ESRI provided two
technical staff members for 1 week and two
marketing staff members for 1 day. Additional
time was required to prepare the reports of the
analyses. The marketing staff members were
present for Visitors Day and handled the
presentation for this meeting and some of the
individual demonstration. ESRI estimated that the
level of staff effort required to prepare the data,
conduct the analysis, and write the report was
approximately 1 day for the Site B and Site N
problems and 10 days for the Site A problem.
Therefore, a total of 12 person-days were needed
to complete the three visualization problems along
with the documentation, but one problem took
substantially longer than the other two.
Approximately half of the time was spent
conducting the analyses, and half was spent
preparing the report.
The software was able to handle a wide range of
environmental contaminants and conditions. Based
on the capabilities demonstrated, the technical
team concludes that the software may be
representative for a wide range of environmental
problems. The capability to sort and query the
database files permits efficient focusing of the
analysis to the problem. Multiple contaminants
can be evaluated in a single analysis. The
capability to tailor the output to the threshold
concentrations makes data interpretation easier.
The capability to write instructions to Arc View
through its Avenue scripting language permits the
analysis to be very flexible.
Training and Technical Support
ESRI provides a number of options for ArcView
training and technical support:
• There is an extensive on-line help manual.
• Tutorial case studies are provided with
ArcView and are available at www.esri.com.
• Training courses are available at the ESRI
headquarters, at regional ESRI offices, and at
the customer's site.
• Technical support is provided for 60 days with
the purchase of any ESRI product. Additional
technical support can be purchased.
ArcView GIS version 3.1, Spatial Analyst, and 3D
Analyst each has a user manual that provides
detailed instructions on how to operate the
software.
Additional Information about the
ArcView Software
To use ArcView efficiently, the operator should
have a basic understanding of the use of computer
software to analyze environmental problems. This
understanding includes fundamental knowledge
about GIS and relational database structures and
knowledge of contouring environmental data sets.
ArcView was demonstrated on a Windows NT 4.0
operating system. It requires a minimum of 128
MB of RAM. During the demonstration, two
machines were used. For Sites B and N, a 233-
MHz Pentium II laptop with 128 MB of RAM, a
5-gigabyte hard drive, and standard 1024 x 768
video was used. The laptop was equipped with an
internal CD drive, a 1-gigabyte Jazz drive, and a
PCMCIA network adapter. The computer used for
the Site A analysis contained a 300-MHz Pentium
II processor with 128 MB of RAM and an Elsa
Gloria XLM graphics card with 16 MB of video
RAM and an Open GL chipset. This computer was
equipped with an internal CD drive, a 1-gigabyte
37
-------
Jazz drive, an internal network adapter, and a 19-
inch monitor.
The price varies for single stand-alone systems
through enterprise-wide systems. ESRI
representatives assist customers in choosing the
appropriate system configuration for their needs,
and the software is available for purchase directly
from ESRI or through authorized resellers. Several
existing contracts also make purchasing software
easy for the federal government, including the
ESRI Government Services Administration (GSA)
Schedule #GS-35F-5086H. Currently, the GSA
price for the Windows version of a single stand-
alone system of ArcView GIS version 3.1 is $996.
For Spatial Analyst and 3D Analyst, the GSA
price is $2342 each. Prices for these products for
private industry or for use on Unix systems are
slightly higher.
Summary of Performance
A summary of Arc View's, Spatial Analyst's, and
3D Analyst's performance is presented in Table 5.
Overall, the main strength of ArcView GIS
version 3.1 and its extensions is their ability to
integrate data and maps easily in a single platform
to allow spatial visualization of the data. The
visualization output was clear and easy to
understand. The GUI platform appeared to be easy
to use and had pull-down menus and on-line help.
ArcView supports a wide range of formats for
importing and exporting data including computer-
aided-design files (.dxf), GIS files (.shp), and data
files (.dbf, ASCII text). The ability to sort and
query data makes examination of a subset of the
data easy to perform. ArcView's ability to manage
data files from a wide range of sources make it
suitable for managing complex environmental
contamination problems. The ease of use makes
ArcView and its extensions accessible for the
occasional user who wants to view the spatial
correlation between data. For the more advanced
user, the scripting language, Avenue, makes the
ArcView products extremely flexible and
customizable for problem-specific applications.
ArcView is a mature product with a large
customer base.
The technical team concluded that for
visualization of environmental data sets, there are
no major limitations in the ArcView set of
programs. Minor problems noticed by the
technical team included the inability to open some
of the project files provided at the demonstration
and, for a new user, the need to learn the
terminology to understand the operation of
Arc View, (e.g., "scenes," "themes," "project
files").
33
-------
Table 5. Performance summary for Arc View version 3.1 with Spatial Analyst and 3D Analyst
extensions
Decision support
Documentation of analysis
Comparison with baseline
analysis and data
Multiple lines of reasoning
Ease of use
Efficiency
Representativeness
Training and technical
support
Operator skill base
Platform
Cost
Arc View integrated data, aerial photos, and surface features into two-and
three-dimensional spatial representations of the data. Query and sort
capabilities permitted investigation of the data against threshold
concentrations. Contour maps of contaminant concentration placed
contamination regions in visual context.
Documentation of the process and parameters was provided and assumptions
explained. Model parameters, queries, and maps were exported to word
processing files to document the analysis. Graphical output was prepared in
jpg format and incorporated directly into a MircroSoft Word file.
Two-dimensional contaminant concentration and hydraulic head contours
were consistent with the measured data.
Accurately mapped wells, buildings, and site features.
Accurately posted data to sample locations.
Hot-linked data to well locations.
Contour map of bedrock surface was consistent with the data.
Quasi-three-dimensional layered maps of contaminant concentration were
consistent with the data.
Data contoured with different model parameters. Best fit to the data presented
for visualization of the data.
Many features promote ease of use, including logical layout of pull-down
menus, query capabilities, tutorials to guide novice users, and input and
output in a wide range of formats. One or two days of training are needed to
become familiar with the basic features of the software. More training is
required to become proficient in the Avenue scripting language.
Three visualization problems were completed and documented with 12
person-days of effort.
Arc View GIS version 3 . 1 contains a database architecture that permits
incorporation of a wide range of data sources into the analysis. Query
capabilities permit flexibility in the analysis to handle a wide range of
conditions. Avenue scripting language permits tailoring the analysis to the
application.
User manual
On-line help
Web-based help
Many tutorials to teach different software features
Training courses available through ESPJ
Technical support provided free for 60 days after purchase; may be purchased
for longer times.
Basic knowledge about environmental data and GIS, database files, and
contouring
Windows NT 4.0. Minimum of 128 MB RAM, 233 or 300 MHz Pentium II
processor and an Open GL video card for three-dimensional representations.
GSA costs for products that use Windows operating systems:
Arc View GIS version 3.1 — $996
ArcView Spatial Analyst — $2342
ArcView 3D Analyst — $2342
Prices for commercial customers or for UNIX -based operating systems are
slightly higher.
34
-------
Section 5 — ArcView GIS Version 3.1, Spatial Analyst, and 3D
Analyst Update and Representative Applications
Objective
The purpose of this section is to allow ESRI to
provide information regarding new developments
with its technology since the demonstration
activities. In addition, the developer has provided a
list of representative applications in which its
technology has been or is currently being used.
Technology Update
Version 3.1 for ArcView was released in July 1998
and was used during the ETV demonstration period.
Version 3.1 contains a large number of
enhancements to Version 3.0. These include
improved report generation capability, support for
more input/output formats, and map annotation and
presentation capabilities. A white paper detailing the
enhancements is located at http://www.esri.com/
library/whitepapers/pdfs/arcview.pdf
ArcView 3.2 was released in September 1999
ArcView GIS 3.2 provides numerous quality
improvements as well as new features, including a
projection utility for shapefiles; enhanced Spatial
Database Engine and Open DataBase Connectivity
database access; an update for the Report Writer
extension, including Crystal Reports Version 7; new
data readers and converters; and new and updated
data for the ESRI Data & Maps CDs.
The 3D Analyst extension used in the demonstration
was first released in mid-1998. A white paper
detailing the functionality of the extension was
released in December 1998. The white paper can be
found at http://www.esri.com/library/whitepapers/
pdfs/3 danalys.pdf
The Spatial Analyst extension has not received any
major updates since the demonstration in September
1998. Spatial Analyst 2 is expected to be released in
late 1999. ArcView Spatial Analyst 2 software will
include the new ModelBuilder that enables users to
quickly build and interact with spatial models. Users
can construct models using process wizards or by
dragging icons representing data (grid themes) and
functions (such as slope, buffer, and overlay) into
the model document and connecting them with lines
to show how the data is processed.
The ModelBuilder provides both beginning and
advanced users with a set of easy-to-use tools for
building various types of spatial models within
ArcView Spatial Analyst. The flow diagrams created
in the model are a convenient way to build spatial
models and are an excellent way to document and
present one's models to others.
These new tools can be used to construct spatial
models in any application area. For example,
organizations can use Spatial Analyst's
ModelBuilder to build land use suitability models,
environmental sensitivity models, hazardous risk
models, and social impact models. The user can also
build models in which all of these spatial
assessments are included in a single larger model.
Figure 17 Screen capture of Spatial Analyst's
ModelBuilder
Also in late 1999, Arclnfo 8 will be the most
significant release of Arclnfo, ESRI's professional
GIS. Arclnfo 8 has been completely redesigned and
engineered to be an easy-to-use, fast, modern, and
powerful GIS. A key feature of Arclnfo 8 is that it
makes sophisticated GIS more usable. New
applications like ArcMap and ArcCatalog
accomplish this goal by approaching GIS from a
new perspective. While the depth of functionability
in Arclnfo is tremendous, new user interfaces and
wizards make it easy by presenting users with what
they need when they need it.
35
-------
The Geostatistical Analyst—an extension to Arclnfo
Version 8—is aimed at an emerging advanced
spatial modeling audience. These tools were
developed specifically for surface generation using
geostatistical tools and analyzing the error of the
resulting estimation (surface).
The generation of predictive surfaces, their accuracy,
and their estimation of error are critical to modeling
and analysis. The Geostatistical Analyst will help
spatial scientists understand and use kriging and
other advanced mathematical methods used for
surface generation. It will provide control over the
surface generation process and provide advanced
tools for analysis of resulting surfaces.
Representative Applications
ESRI has many examples of Arc View technology
used on a wide range of environmental-related
projects in both the public and private sectors. Here
are some examples.
• EPA's Superfund application, Fields, is an
Arc View application from Region 5 in Chicago.
Fields can be seen at http://www.epa.gov/
rSwater/fields/FIELDSITE/SHARED/PAGES/
FLDHOME/HTM. The Fields program involves
contamination of a stream by pesticides and is
similar in nature to the problems solved in the
ETV DSS project.
• ESRI, the Department of the Interior's Minerals
Management Service, the state of Florida, and
Louisiana State University collaborated to
develop an Arc View Marine Spill Analysis
System for the Gulf of Mexico coastal states.
Although developed as an oil spill contingency
planning tool, the database compiled can be used
for other environmental and planning
applications.
• The state of Florida's Department of
Environmental Protection uses ArcView to
analyze the environmental impact of issuing a
permit for a project of any kind (e.g., building,
destruction) anywhere in Florida. This generic
permit analysis application will report many
types of information about a site that would
affect a decision about whether to issue a permit,
including environmental sensitivity, cultural
value, and environmental risk factors.
• New Jersey's Office of Water Monitoring
Management uses Arc/Info and ArcView to
analyze the water quality of New Jersey's 1200
lakes and 6000 miles of streams and rivers. One
use of the system is to show impairment ratings
for stream segments. The impairment rating is a
measure of the health hazard posed to fish and
human swimmers by high concentrations of
nutrients, organics, and/or metals.
The New York City Mayor's Office of
Environmental Coordination uses ArcView in its
efforts to redevelop land and revitalize local
economies by reclaiming brownfields.
Chevron Nigeria Ltd. has implemented ArcView
GIS technology to locate oil in the Niger Delta
and work with the Nigerian government on long-
term agreements for oil extraction and resource
protection. GIS technology is used to assess oil
drilling and processing operations with the least
possible disruption to Nigeria's plants and
animals.
Additional examples can be found at
www.esri.com/partners/gissolutions/.
In addition to ArcView, ESRI has developed other
software tools for addressing environmental
contamination issues. For example, see EPA's
EnviroMapper (ESRI software MapObjects
Internet mapping application for Superfund Sites)
at http://maps.epa.gov: 10008/enviro/html/mod/
enviromapper/index.html. EnviroMapper is an
extremely successful tool for delivering
environmental data to the public; it received more
than 200,000 Web hits last year. Also, ERSI-
Germany created an Arc/Info application used
across Europe for remedial action. COSIMA
(Contaminated Sites Management) is currently
being translated into English for a broader
distribution.
36
-------
Section 6 — References
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, NY, September.
Sullivan, T. M., and A. Q. Armstrong, 1998. "Decision Support Software Technology Demonstration Plan."
Environmental & Waste Technology Center, Brookhaven National Laboratory, Upton, NY, 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
37
-------
-------
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 three-dimensional (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 |o,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-|a,g/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 |o,g/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 |o,g/L and TCA concentrations of 5 and 50 |o,g/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
and TCA plumes, they were asked to calculate the human health risks associated with drinking 2 L/day of
39
-------
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/day 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 feet 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 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 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
40
-------
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
scenarios were considered. The first was the case of an on-site worker who was assumed to have consumed
500 mg/day 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/day of soil for 30 years.
41
-------
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 feet 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 |o,g/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 |og/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/day 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.
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.
42
-------
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
Dichloropropane
Dibromochloropropane
Carbon tetrachloride
Threshold concentration
(•g/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/day of groundwater was the exposure pathway. For the on-site receptor, groundwater
consumption of 1 L/day 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.
43
-------
-------
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 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 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 lln, 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 |o,g/L and 1000 m from a measured value of 5000 |o,g/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 decision support software (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.
45
-------
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 expressiony = 4x- 3.33, where y 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 at x = 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 from y = x2to become y = 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.
46
------- |