I I Zone 1 (Red) 1,000 uCi/m2
I | Zone 2 (Orange) 2 feat PAG 240 MCI/m2
~] Zone 3 (Yellowi 50 Yeat PAG 112 iiCwr
-------
United States
Environmental Protection
Agency
RDD Waste Estimation Support Tool Report
Version 1.2
National Homeland Security Research Center
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
Office of Research and Development
National Homeland Security Research Center
-------
Acknowledgments
The authors would like to acknowledge the support of Dan Schultheisz and Tom Peake of
EPA/ORIA, who provided partial funding for this effort. In addition, there are several
individuals whose input was particularly valuable in the development of the Waste Estimation
Support Tool, including Bill Steuteville of EPA/Region 3, Jim Michael and Mario lerardi of
EPA/ORCR, Cayce Parrish of EPA/OHS, Emily Snyder of EPA/ORD, and Paul Kudarauskas of
EPA/OEM.
Page i
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Disclaimer
The U.S. Environmental Protection Agency (EPA), through its Office of Research and
Development, funded and managed the research described here under EPA Contract Number EP-
C-07-015, Work Assignment Number 4-14, with Eastern Research Group and Interagency
Agreement DW89922983 with the Oak Ridge Institute for Science and Education. This
document has been subjected to the Agency's review and has been approved for publication.
Note that approval does not signify that the contents necessarily reflect the views of the Agency.
Mention of trade names or commercial products in this document or in the methods referenced in
this document does not constitute endorsement or recommendation for use. EPA does not
endorse the purchase or sale of any commercial products or services.
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Table of Contents
Acknowledgments i
Table of Contents iii
List of Figures v
List of Tables viii
Acronyms, Abbreviations, and Glossary ix
Executive Summary 1
1.0 Introduction 3
1.1 Background 5
1.2 Purpose 6
1.1.1. Liberty RadEx 8
2.0 Description 10
2.1 Approach 11
2.2 GIS Data Analysis Tools 12
2.3 Image Analysis Tool 14
2.3.1 Artificial Neural Networks 17
2.3.2 Ground Surface Estimation 18
2.4 Database Tool 20
2.5 Waste Estimation Spreadsheet Tool 22
3.0 System Requirements 25
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4.0 Instructions for Generating Waste Estimate 26
4.1 Instruction for Operating the RDD Waste Estimation Spreadsheet Tool 44
5.0 Results -Liberty RadEx Example 60
6.0 Conclusions 64
6.1 Looking Forward 64
7.0 References... ..66
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List of Figures
Figure 1. Data Aggregation Methodology 1
Figure 2. Graphical Depiction of Methodology 2
Figure 3. Liberty RadEx Plume Shapefiles 9
Figure 4. Preliminary Data Aggregation Methodology 10
Figure 5. Graphical Depiction of Methodology 12
Figure 6. Surface Color Palette 15
Figure 7. Surface Media Classification 16
Figure 8. Feed Forward Neural Network 18
Figure 9. Hazus-MH Database Tool Output 22
Figure 10. Example Inventory Relationship of Model Building Type and Occupancy Class [3] 24
Figure 11. Liberty RadEx Plume Zones 27
Figure 12. Hazus-MH Startup 28
Figure 13. Accessing WEST 29
Figure 14. Set Default Directory Button 29
Figure 15. Create Neural Network Training Set Button 30
Figure 16. Train Neural Network Button 31
Figure 17. Show/Hide ArcToolBox Window Button 31
Figure 18. Add WEST Toolbox 32
Figure 19. Unload Table To Text Properties 32
Figure 20. Clear Selected Features Button 33
Figure 21. Add Data Button 33
Figure 22. World Imagery Layer 33
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Figure 23. Repair Geometry Location 34
Figure 24. Rejuvenate 1 Script 34
Figure 25. Rejuvenate 2 Script 35
Figure 26. Zoom To Selected 36
Figure 27. Export TTF Menu 36
Figure 28. Image Zone 1 Script 37
Figure 29. Export BMP Menu 38
Figure 30. Intersect 1 Script 39
Figure 31. Intersect 2 Script 40
Figure 32. Convert Square Footages Button 40
Figure 33. Hazus Database Tool Button 41
Figure 34. Select Hazus Folder 41
Figure 35. Select Inventory 42
Figure 36. Hazus Database Tool 43
Figure 37. ID Surfaces Button 43
Figure 38. RDD Waste Estimation Spreadsheet Tool Main Screen 45
Figure 39. Security Alert-Macro Screen 45
Figure 40. Waste Estimation Spreadsheet Tool Main Screen Start Button 46
Figure 41. RDD Waste Estimation Spreadsheet Tool Home Screen 46
Figure 42. Scenario Basic Information Screen 48
Figure 43. File Import Status Screen 51
Figure 44. Partitioning and Remaining Activity Screen - Activity at Deposition 51
Figure 45. Partitioning and Remaining Activity Screen - Remaining Activity at Time t 52
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Figure 46. Source Partitioning Factors and Weathering Correction Factors Screens 53
Figure 47. Accessing Decon/Demo Parameters Screen from Partitioning and Remaining Activity
Screen 54
Figure 48. Decontamination/Demolition Parameters Screen 54
Figure 49. Accessing Default Parameter Screens 56
Figure 50. Accessing Waste Results from Decontamination/Demolition Parameters Screen 57
Figure 51. Waste Results Screen 57
Figure 52. Accessing Waste Graphs from Waste Results Screen 58
Figure 53. Waste Graphs Screen 58
Figure 54. Save Scenario Option 59
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List of Tables
Table 1. List of WEST GIS Scripts 13
Table 2. Media segregation parameters used in the Liberty RadEx Scenario 61
Table 3. Example Waste Quantity Estimation from Liberty RadEx Scenario 62
Table 4 Example Waste Activity Estimation from Liberty RadEx Scenario (|iCi/m3) 63
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Acronyms, Abbreviations, and Glossary
AI - Artificial Intelligence
ANN - Artificial Neural Network
ArcGIS - A complete software system, published by ESRI, for designing and managing
solutions through the application of geographic knowledge
Beta Test - A limited release testing phase
BP - Back Propagation
Bq - Becquerel (a measure of radioactivity)
C&D - Construction and Demolition
CBRN - Chemical, biological, radiological, and nuclear
Census Tract - Small relatively permanent geographical subdivisions of a county
Chernobyl disaster - A nuclear disaster that occurred at the Chernobyl Nuclear Power Plant in
Ukraine on April 26th, 1986
Ci - Curie(s) (a measure of radioactivity - 1 Ci = 37 billion Bq)
Comprehensive Data Management System - A complementary tool for Hazus-MH that
provides users with the capability to update and manage statewide datasets.
Contiguous Albers Equal Area Conic Projection - A conical equal area map projection that
uses two established parallels.
Cs-137 - Radioactive isotope of cesium with a half-life of 30 years
CSV - A file format based on comma-separated values or character-separated values
DHS -U.S. Department of Homeland Security
Dirty Bomb - A radiological weapon that combines explosives with radioactive material
DoD - U.S. Department of Defense
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DoE - U.S. Department of Energy
EPA - U.S. Environmental Protection Agency
ESRI - Geographic information systems mapping software company based in Redlands,
California
FEMA - Federal Emergency Management Agency
FLIR - Forward looking infrared
FRMAC - Federal Radiological Monitoring and Assessment Center
Fukushima Disaster - A nuclear disaster that occurred at the Fukushima Daiichi Nuclear Power
Plant in Japan on March 11th, 2011
G - Graphical Programming Language
GAO - U.S. Government Accountability Office
Geospatial - Relating to or denoting data that are associated with a particular location
GIS - Geographic Information System
GPS - Global Positioning System
GUI - Graphical User Interface
Hazus-MH Database Tool - A database tool used to query the Hazus-MH databases based on
census tract data.
Hazus-MH - Hazus-MH is a nationally applicable standardized methodology, published and
supported by FEMA, which is used to estimate potential losses from earthquakes, floods, and
hurricanes.
1C - Incident Commander
IND - Improvised Nuclear Device
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Lab VIEW - A graphical programming environment published by National Instruments
Corporation of Austin, Texas
Land Cover - Physical material located on the earth's surface
Landsat - a series of Earth-observing satellite missions jointly managed by NASA and the
USGS
Layer - Set of thematic data characterized and stored in a map library
Liberty RadEx - A national Tier 2 full-scale exercise conducted in April 2010, based on a
fictional terrorist attack involving a radiological dispersal device in the city of Philadelphia, PA.
LLRW - Low Level Radioactive Waste
MLP - Multi-Layer Perceptron
MSW - Municipal Solid Waste
NARAC -National Atmospheric Release Advisory Center
NASA -National Aeronautics and Space Administration
Neural Network - A data analysis and pattern-recognition tool that mimics the behavior of
neurons found in the nervous system
Neuro-fuzzy Method - A combination of artificial networks and fuzzy logic
NHSRC - EPA National Homeland Security Research Center
NI - National Instruments
NRC - United States Nuclear Regulatory Commission
NRF - National Response Framework
NSSIPC - National Security Staff Interagency Policy Coordination
OEM - EPA Office of Emergency Management
ORD - EPA Office of Research and Development
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ORIA - EPA Office of Radiation and Indoor Air
ORISE - Oak Ridge Institute for Science and Education
OSC - On-Scene Coordinator
PAGs - Protective Action Guides
PE - Processing Element
Python - General-purpose, high-level programming language
QA - Quality Assurance
QAPP - Quality Assurance Project Plan
QC - Quality Control
RDD - Radiological Dispersal Device
RGB - Red, Green, and Blue
Shapefile - A geospatial file format that stores non-topological geometry and attribute
information for the spatial features in a data set
SQL - Structured Query Language, an international standard database manipulation query
language
TAD - Threat Agent Disposal (workgroup)
Thematic Mapping - A map designed to portray a particular theme
TM - Thematic mapping
TXT - file format used to designate a text file
US - United States
USACE - U.S. Army Corps of Engineers
USGS-U.S. Geological Survey
VB - Visual Basic, an event driven programming language developed by Microsoft
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VI - Virtual Instruments
WARRP - Wide Area Recovery and Resiliency Program
WEST - Waste Estimation Support Tool
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Executive Summary
Historically, Radiological Dispersal Device (RDD) planning scenarios have primarily
focused on response efforts, giving little attention to the recovery and management of debris and
waste that would likely consume significant state and federal resources. The U.S. EPA's RDD
Waste Estimation Support Tool (WEST) is a planning tool for estimating the potential volume and
radioactivity levels of waste generated by a radiological incident and subsequent decontamination efforts.
WEST supports decision makers by generating a first-order estimate of the quantity and characteristics of
waste resulting from a radiological incident, and allows the user to evaluate various
decontamination/demolition strategies to examine the impact of those strategies on waste generation.
The WEST is composed of two processes, preliminary data aggregation and the
generation of waste inventories (separately known as the Waste Tool) as depicted in Figure 1. To
function properly, the waste tool requires three important inputs from the preliminary data
aggregation process: geographic information, surface media, and building stock (i.e., building
quantity, size, square footage, and construction materials).
Preliminary Data Aggregation
Geographic
Information (GI5)
Building Stock
(HAZUS)
Figure 1. Data Aggregation Methodology
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As shown in Figure 2, the WEST utilizes multiple platforms to assess the environmental
and structural composition of an area to estimate the quantity, characteristics, and activities of
waste and debris. The general approach to creating an RDD scenario begins by classifying
geographical areas by level of contamination (i.e., using a dispersion plume shapefile). Based on
the underlying building stock inventory and outdoor surface media, the waste tool calculates the
amount and characteristics of debris resulting from the initial RDD blast and waste/debris
resulting from building demolition and/or ground surface and selected decontamination
techniques, including estimates of wastewater. The resulting data support the development of
integrated response strategies that take different considerations into account (e.g., demolition,
decontamination, and disposal) within a relatively short period of time.
Methodology
Plume
and
Deposition
Maps
Sensitivity
Analysis
(Crystal Gall)
HAZU5
Database
Extraction Tool
fault Data
Surface
Deposition,
Mass, Area of
Materials, in
Impact Areas
Building Data
Processing
Script
Override
Default Data
(optional)
Waste
Estimates
-Mass
-Volume
-Activity
Demolition,
Decon
Decisions
Figure 2. Graphical Depiction of Methodology
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1.0 Introduction
Radioactive materials have a wide range of benevolent uses, especially in the purviews of
medicine, industry, and research. However, conventional radioactive materials can also be used
for sinister purposes such as radiological dispersal devices (RDDs) [1]. An RDD is a type of
chemical, biological, radiological, and nuclear (CBRN) weapon in which radioactive material is
combined with a dispersal device e.g., explosive. When detonated, the RDD, coupled with
atmospheric transport, has the potential to disperse radioactive material over a wide area,
contaminating exposed surfaces [1]. RDDs differ from traditional nuclear weapons: where
nuclear weapons are capable of instantly incinerating a measurable area, RDDs are typically
armed with a conventional explosive, resulting in a much smaller area of direct blast damage.
However, both are capable of spreading radioactive particulates over a large area. Casualties
from an RDD would initially remain relatively low [1]. Decontamination and remediation are the
most arduous tasks associated with detonation of an RDD. As the radioactive paniculate matter
settles, its behavior will be influenced by the type of surface material. Depending on the
radionuclide, permeable surfaces can act as a sponge, absorbing the radionuclide, making it
difficult to decontaminate [2]. Decontamination resulting from a RDD that uses cesium may be
financially exhaustive, potentially requiring extended recovery efforts. Efforts to mitigate the
risk arising from RDDs entail securing radioactive sources, developing and deploying detection
measures, and utilizing intelligence and counterterrorism resources [1]. Measures used to plan or
prepare for detonation of an RDD are complex in number and typically involve event modeling
in addition to response and recovery exercises. [1, 3, 4]
The modeling of atmospheric products generated by an RDD is described as one of the
key planning factors by the Planning Guidance for Response to a Nuclear Detonation Report
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developed by the National Security Staff Interagency Policy Coordination (NSSIPC)
Subcommittee [3]. The NSSIPC further recommends that, in uncertain situations where technical
information is limited, modeling should be used to the fullest possible extent [3]. Predictive
plume and deposition models, however, are limited and tend to use postulated environmental
inputs, which inhibit accuracy [4]. Accuracy in modeling, specifically in expedited response
scenarios, is typically forfeited. Exploring capabilities for autonomous prediction of
environmental inputs to aid in the generation of CBRN models addresses a major knowledge
gap.
Modeling the distribution of the radionuclides in the plume is only the beginning of the
remediation process. Contaminated areas are better defined through sampling and
characterization processes that eventually supersede the initial plume modeling. However, a
number of days or weeks may elapse before the affected area is fully characterized and, to
minimize remediation timelines, initial development of remediation strategies must start
immediately following the contamination event. This process includes identification of the
materials found in both the indoor and outdoor portions of the affected areas and developing
approaches for optimal cleanup of those surfaces and materials. Supplying the incident
commander (1C) with decision making tools to prioritize remediation processes as soon as
possible is a key element of a rapid, effective remediation that minimizes economic and health
impacts to the affected community.
The Waste Estimation Support Tool (WEST) supports this process by exploiting plume
models distributed by the National Atmospheric Release Advisory Committee (NARAC)
depicting deposition and concentration levels, land cover classification capabilities using feed-
forward neural network derived pattern recognition algorithms, and building stock values
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including square footage and building counts and composition estimates as generated by the
Federal Emergency Management Agency's (FEMA's) Hazus-MH software [5]. Using these
modules, researchers have developed a suite of applications for rapidly estimating waste
inventories and levels of radioactivity generated by detonation of an RDD as a function of user-
defined decontamination and demolition approaches. This report begins by describing the
background of the WEST and the need for estimating waste inventories generated by a RDD.
Furthermore, this report describes the WEST and its supporting applications.
1.1 Background
For emergency planners and federal responders to scope out the waste and debris
management issues resulting from a radiological response and recovery effort, it is critical to
understand not only the quantity, characteristics, and level of contamination of the waste and
debris but also the implications of response and cleanup approaches regarding waste generation.
Until recently, pre-operational efforts, considering a large scale radiological event, were focused
on the immediate response and early recovery phases, ignoring the issues associated with long
term recovery. Though the activities associated with disaster response are crucial, the resulting
recovery efforts tend to be the most arduous and time consuming in nature, especially in the
purview of remediation.
In response to these shortfalls, the EPA conducted a series of exercises focusing on the
longer-term recovery issues in addition to the formation of the Threat Agent Disposal (TAD)
workgroup which examined the disposal of chemical, biological, and radiological threats. One of
the most prominent national level exercises, Liberty RadEx, held in Philadelphia in April of
2010, was labeled as a drill to test the country's capability to clean up and help communities
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recover from a dirty bomb terrorist attack. The WEST was developed to generate first-order
estimates of a waste inventory for the RDD described in the exercise scenario.
1.2 Purpose
The purpose of this report is to describe the need for estimating the waste inventory
generated by an RDD event, how the WEST and its supporting applications function, and lastly
how to operate the tool itself. The recovery phase of an RDD event has largely been
underestimated, particularly in the realm of debris management. Without proper planning, the
management of debris would likely exhaust state and federal resources [6]. Further, waste
management decisions are often controversial and need to be supported with the best information
available. The WEST provides a better understanding of the recovery process by generating
qualitative and quantitative estimates of debris and waste resulting from an RDD.
The detonation of an RDD in an urban area by terrorists is one of the National Planning
Scenarios for which the U.S. Department of Homeland Security (DHS) is coordinating activities
of various government agencies with response preparation requirements [7]. A recent survey by
the Government Accountability Office (GAO) found that almost all city and state governments
would be overwhelmed by an RDD response and would request aid from the Federal government
[6]. Roles and responsibilities of the various government agencies during emergency response
activities are described in the National Response Framework (NRF) [6, 8]. Under the NRF, the
EPA is the lead agency for cleanup activities in the aftermath of an RDD event, including
decontamination and waste disposal. Other Federal agencies, including the U.S. Department of
Energy (DOE), U.S. Department of Defense (DoD) through the U.S. Army Corps of Engineers
(USAGE), and the U.S. Nuclear Regulatory Commission (NRC) also have major roles in an
RDD cleanup [9],
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Numerous exercises have been performed by agencies at the federal, state, and local level
to help prepare for an RDD incident. However, GAO noted that in spite of over 70 RDD and
improvised nuclear device (IND) exercises over the last several years prior to 2009, only three
have included interagency recovery discussions following the exercise, and none have directly
included activities related to the disposal of contaminated waste and debris in the exercise
activities [6],
An integrated RDD response will require inclusion of many competing considerations,
including risk to occupants and residents from post-cleanup radiation levels, prioritization of
cleanups, costs associated with cleanups, speed of cleanup, decisions to demolish/remove or
decontaminate, economic impacts created from denial of access to facilities and businesses,
waste/debris treatment, transportation, and disposal costs. Determination of waste characteristics
and whether the generated waste is considered to be construction and demolition (C&D) debris,
municipal solid waste (MSW), hazardous waste, mixed waste, or low level radioactive waste
(LLRW), and characterization of the wastewater that is generated from the incident or
subsequent cleanup activities will influence the cleanup costs and timelines. Selected
decontamination techniques to meet the cleanup level goals, whether they involve chemical
treatment, strippable coatings, abrasive removal, or aqueous washing, will also influence the
types and amounts of waste generated and associated cleanup costs and timelines. For emergency
planners and federal responders to scope out the waste and debris management issues resulting
from an RDD response and recovery effort, it is critical to understand not only the quantity,
characteristics, and level of contamination of the waste and debris, but also the implications of
response and cleanup approaches regarding waste generation. This lesson has been learned
during recent cleanups of naturally-occurring Bacillus anthracis resulting from contaminated
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animal hides. The best course of action in the cleanup was determined to be to produce as little
waste as possible during the response and recovery. As the waste management issues are raised
to a heightened degree of visibility from a planning standpoint, there is a critical need to scope
out the magnitude and characteristics of the waste and debris so that staging/storage areas and
treatment/disposal pathways can be identified. This report describes an effort to develop a first
order estimate of a waste inventory based on the RDD scenario and plume maps utilized in the
Liberty RadEx National Level Exercise from April 2010 [10].
1.1.1. Liberty RadEx
The Liberty RadEx drill, a national Tier 2 full-scale RDD exercise conducted in
Philadelphia in April of 2010, was the largest drill of its kind to test the country's capability to
clean up and help communities recover from a dirty bomb terrorist attack. The scenario involved
a large truck bomb carrying 2,300 curies (Ci) of Cs-137 in the form of cesium chloride that was
hypothetically detonated in downtown Philadelphia, with ensuing atmospheric transport and
deposition creating a large area of contamination. Some of the products developed by the 1C,
using the NARAC prior to and during the exercise, were the GIS shapefiles which described the
predicted deposition plume from the RDD as it moved downwind from the blast event. These
shapefiles included predictions of ground-level deposition of Cs-137 in terms of aerial activity,
or the activity of the ground surface following deposition in terms of microcuries per square
r\
meter (uCi/m ). The predicted deposition activities were segregated into three different levels
r\
designated high, medium, and low, reflecting the isopleths at 37, 8.8, and 4.1 MBq/m (1,000,
240, and 112 uCi/m2) predicted surface activities. These surface activities are designated in the
tables below as "Zone 1," "Zone 2," and "Zone 3," respectively, and are shown in Figure 3. The
outer two zones in Figure 3 are based on Protective Action Guides (PAGs) which represent
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radiation levels that help state and local authorities make radiation protection decisions, such as
evacuations.
Zone 1 (Red) 1,000 |jCi/m2
Zone 2 (Orange) 2 Year PAG 240 ±iC\/m2
Zone 3 (Yellow) 50 Year PAG 112 uCi/m2
Figure 3. Liberty RadEx Plume Shapefiles
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2.0 Description
The WEST is fundamentally composed of two processes, preliminary data aggregation
and the generation of waste inventories (separately known as the Waste Tool) as depicted in
Figure 4. To function properly, the waste tool requires three important inputs from the
preliminary data aggregation process: geographic information, surface media, and building stock.
Preliminary Data Aggregation
Geographic
Information (GIS)
Surface Media
Building Stock
(HAZUS)
Waste!
Figure 4. Preliminary Data Aggregation Methodology
The preliminary data aggregation process generates the following:
• Surface media statistics
• Building counts
• Building square footage
• Census tract/zone intersect percentages
• Zonal plume area
The Hazus-MH tool also produces supplemental infrastructure data, not used by the WEST
directly. The supplemental infrastructure data are potentially important to the user and may be
used in future versions of WEST to enhance the analysis of decontamination strategies.
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Each of the three processes requires a separate application to generate the required
output. The preliminary data aggregation processes in addition to the Waste Tool are explained
in greater detail further on in this section.
2.1 Approach
The general approach that was used for developing the WEST is as follows [11]:
• Define the geographical areas affected by the hypothetical radiological contamination
incident and subsequent radionuclide deposition using the geographic information system
(GIS) shapefiles created during exercise modeling efforts by the Federal Radiological
Monitoring and Assessment Center (FRMAC) supporting the Liberty RadEx exercise;
• Generate an inventory of building structures and other items within the affected
geographical areas using the Hazus-MH software developed by FEMA;
• Estimate the outdoor ground media (asphalt, concrete, vegetation/soils) surface area using
overhead satellite imagery;
• Based on the inventory of buildings, outdoor areas, and other items, use a database and
spreadsheet to calculate an estimate of the amount and characteristics of debris resulting
from the initial ROD blast and waste/debris resulting from building demolition and/or
ground surface and selected decontamination techniques, including estimates of
wastewater; and
• Since this approach uses MS Excel spreadsheets to perform the calculations of
decontamination and demolition decisions, Excel plug-ins like Crystal Ball (Oracle Inc.,
Santa Clara, CA) can be used to perform sensitivity analysis on the parameters to find out
which decontamination option had the largest influence on cost or waste quantities.
A comprehensive depiction of the methodology behind the tool is shown in Figure 5.
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Methodology
ft&
Satellite
Image
Processing
Tool
Sensitivity
Analysis
(Crystal Ball)
Default Data
Surface
Deposition,
Mass, Area of
Materials in
Impact Areas
Waste
Estimates
-Mass
-Volume
-Activity
Override
Default Data
(optional)
Demolition,
Decon
Decisions
Figure 5. Graphical Depiction of Methodology
2.2 GIS Data Analysis Tools
When working with wide area events, it is important to understand the infrastructure and
geographical qualities of a specific area. The entire process begins by generating a geographic
area that completely encompasses the event. This process is often referred to as geospatial
analysis and is systematically automated using a geographic information system such as ESRI's
ArcGIS (ESRI Inc., Redlands, CA). ArcGIS has various extensions available to extend its
functionality. One of the most acclaimed extensions for modeling potential loss of infrastructure
is FEMA's Hazus-MH. Formally used to model earthquakes, floods, and hurricanes, Hazus-MH
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operates using building stock databases that are applicable to other large scale disasters including
those that involve radiological events.
Due to the steep learning curve associated with Hazus-MH and ArcGIS, the process of
generating a scenario has been extensively automated by creating a script, otherwise known as a
Toolbox in ArcGIS, to generate the needed results quickly with minimal input from the end user.
The script can easily be installed as an add-on feature within ArcGIS by using the "Add
Toolbox" function. Two inputs are needed for the script to function, a census layer derived from
Hazus-MH and a plume layer.
The Toolbox houses seven scripts that perform three basic functions:
• Geometry and attribute modifications
• Census and geometry extraction
• Satellite imagery extraction
Table 1 provides a brief synopsis of each script within the Toolbox.
Table 1. List of WEST GIS Scripts
Script Name
Rejuvenate 1
Rejuvenate 2
Intersect 1
Intersect 2
1.
2.
1.
2.
1.
2.
3.
4.
1.
2.
3.
4.
5.
6.
7.
Summary
Merge three plume shape files into one.
Add new field titled "Line_ID" for identification.
Create Identifiers for the Line ID field.
Assign zones 001 - 003 for future reference.
Project both plume and census shapefiles.
Establish area of plume before and after intersect.
Establish area of census tract shapefile.
Area of plume is exported using the unload table to text
Create new field for recording duplicates.
script.
Execute "loCount" script to record the number of duplicates.
Delete duplicates.
Export all census tracts in the table as a .csv file.
Execute "iDup" script to record fields with equal areas.
Delete duplicates.
Export reaming (diverging) census tracts as a .txt file.
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Image Zone 1 1. Select and clip levelOOl from plume.
2. User selected satellite imagery is clipped using levelOOl plume.
3. Resulting image is saved at user's preference.
Image Zone 2 1. Select and clip Ievel002 from plume.
2. User selected satellite imagery is clipped using Ievel002 plume.
3. Resulting image is saved at user's preference.
Image Zone 3 1. Select and clip levelOOS from plume.
2. User selected satellite imagery is clipped using levelOOS plume.
3. Resulting image is saved at user's preference.
Although the GIS portion of the WEST is specific to the United States (US), since Hazus-
MH is used to generate the building stock data needed for the waste estimate, the waste
estimation spreadsheet itself is generic and applicable to a wide variety of situations. To use the
WEST on an area not in the US, the geospatial data needed for import would have to be
reproduced from whatever source the user has access to and reformatted to be suitable for import
into the waste estimation spreadsheet. The use of census tracts as the smallest unit area for
estimating waste is not hard coded into the WEST; other geographical units could be used.
2.3 Image Analysis Tool
A key component of estimating decontamination, demolition, and waste/debris disposal
options from a wide area radiological event is to be able to classify outdoor media in an
expedited manner. By analyzing satellite imagery of a selected area, the Image Analysis Tool
classifies outdoor surface areas by type, e.g., soil, asphalt, or foliage. As demonstrated in Figure
6, surface classification is achieved by measuring the range fluctuations between Red, Green,
and Blue (RGB) color codes for each individual pixel within a satellite image. These values
range from 0 to 255, providing 16,581,375 color variations for the satellite image processing tool
to examine.
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Color
Red
128
150
51
255
204
192
153
51
51
30
40
45
204
153
51
51
51
Green
128
150
51
204
192
128
153
12S
51
51
12S
255
153
2:o
204
204
102
204
102
51
51
Blue
128
150
51
204
204
153
192
128
102
204
153
51
255
204
204
128
102
153
Surface
ASPHALT
ASPHALT
ASPHALT
CON-CRETE
CONCRETE
CONCRETE
CONCRETE
CONCRETE
CONCRETE
VEGETATION
VEGETATION
VEGETATION
VEGETATION
VEGETATION
VEGETATION
VEGETATION
VEGETATION
VEGETATION
VEGETATION
WATER
WATER
WATER
WATER
WATER
WATER
WATER
WATER
WATER
WATER
WATER
Figure 6. Surface Color Palette
Using the GIS tools described above to generate the imagery, the user uploads the
resulting bitmap to the Image Analysis Tool. The tool then decodes the bitmap into an RGB
format. Once the color ranges have been segmented and calibrated, the image is then redrawn
according to surface type. The tool individually redraws each surface material that was pre-
trained or defined by the user. An example is shown in Figure 7.
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Carved
Satellite
Image
Segmented
Concrete
Figure 7. Surface Media Classification1
The Image Analysis Tool functions using a form of artificial intelligence called Artificial
Neural Networks (ANNs). ANNs are well known as pattern recognition algorithms.
Understanding the composition of surfaces within the contours of a plume resulting from an
RDD is essential when assessing the makeup of debris, establishing decontamination parameters,
and ultimately plays a key role in the remediation process. Though the classification of surfaces
is crucial when determining waste characteristics, the process of manually analyzing wide area
events is nearly impossible to accurately accomplish in a short period of time. However, by
harnessing machine vision utilizing an ANN, surfaces can quickly be analyzed and classified
based on color. By automating surface classification, the composition of surfaces over a wide
area can quickly be determined, providing a more accurate estimation of surface contamination.
1 The carved multispectral Landsat image depicts a plume covering Denver, Colorado. The segmented concrete
image is the result of processing the carved satellite image using the Image Analysis Tool. The black pixels in the
carved satellite image represent concrete, while the white pixels represent non-concrete surfaces.
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Both ANNs and the image classification process are explained in greater detail in the following
sections.
2.3.1 Artificial Neural Networks
Formally designed as a data analysis and pattern recognition tool that mimics the
behavior of neurons found in the nervous system, ANNs outperform all other traditional
classification methods and have been widely used in the realm of pattern recognition [12, 13]. By
means of computerized artificial intelligence (AI), ANNs offer pattern recognition similar to
human intelligence in addition to managing fallible data [14, 15]. Visually speaking, a neural
network can be depicted graphically using numerical values coupled with nodes that utilize
message-passing algorithms to identify patterns. The nodes within the graph act as input or
output sources, while the graph itself works as a medium for networking or linking the various
nodes. In essence, the architecture of a neural network mimics the architecture of a statistical
processor by making analytical conjectures about data, capable of computing various
relationships in a short duration with the convenience of decreased execution time compared to
other means [16].
Nature-inspired and modeled based on the human brain, neural networks are composed of
interconnecting nodes called processing elements (PEs) [14, 17]. Each PE exhibits a distinct
behavior based an assigned parameter and linkage [17]. The linkages between the PEs are
assigned weights that act as constants, which can be adjusted using a learning algorithm [13].
There are many types of ANNs; one of the simplest being the feed-forward network, which is
utilized by the Image Analysis Tool. The feed-forward network operates using one input layer,
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hidden layer(s) also known as weights, and one output layer as shown in Figure 8.
Figure 8. Feed Forward Neural Network
ANNs fundamentally learn by environmental exposure (comparison of known inputs and known
outputs) and adjustment of interneuron synaptic weights. There are various learning methods
available, each with its own specialty. The Image Analysis Tool for example, utilizes a
backpropagation learning algorithm.
2.3.2 Ground Surface Estimation
As previously mentioned, the ability of ANNs to classify surface composition is a crucial
component in the purviews of RDD waste estimation; however, the task of associating a group of
symmetrically related pixels retaining analogous colors is primarily a human concept, easily
expressed as linguistic terms and often a matter of postulation [18]. Significant improvements
have been made in the realm of machine vision and image processing technology in the past few
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years, reducing the amount of speculation and time associated with manually classifying imagery
[16].
A body of research exists on both the classification of satellite imagery using neural
networks and texture analysis based on pixel values. The ANN backed Image Analysis Tool
works specifically at the pixel level, detecting discrete changes in color values in satellite
imagery, often referred to as texture classification [15]. A generic term, texture classification is
defined as the process of segmenting images into homogeneous textured sections based on a
sequence of trained textures [19]. Texture has been described as having two separate dimensions:
one for describing primitive features and the other for representing interactions between those
features [15]. By applying dimensional separations, classification networks are able to segment
textures based on learned samples.
Neural Networks have been widely used in the classification of land cover for the
purposes of monitoring and planning for land use. One particular study focused on developing an
automated ANN classification system using a supervised Multi-Layer Perceptron (MLP) network
module, one of the most commonly used modules in land cover classification. The MLP-based
ANN was engineered to classify urban areas, forest, planted crop fields, grass, fallow areas,
transitional areas, wetlands, and water features from Landsat imagery. Training was completed
using the supervised back propagation (BP) algorithm, which consisted of one input layer, one or
more hidden layers, and one output layer that used a total of 3,360 training pixels and 360 testing
pixels. Upon completion, the MLP network module had a classification accuracy of 88.13%.
Researchers noted that the module was suitable for land cover mapping using remotely sensed
data, and would be particularly favorable when the distribution of the data are not normal [12,
16].
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The ability of ANNs to work with poorly organized datasets in an expedited manner
makes them perfect solutions for classifying land cover. Applying the proficiencies of ANNs to
resolve land cover to estimate debris resulting from CBRN weaponry is a relatively new concept.
One of the few studies that employed a neural network is the work of Kanvesky et al., (1996)
using an ANN to predict radioactive fallout resulting from the Chernobyl disaster. This study,
however, failed to address surface deposition. To date, models that address the deposition of
radioactive particles largely omit the specific identification of surface media found within a
study area, instead assigning generic environmental conditions for a specified area (e.g., wooded
or urban settings) [20]. ANNs have demonstrated obvious successes in identification of terrain
by means of remote sensing imagery, indicating an opportunity to apply ANN texture
classification capabilities to RDD deposition and debris modeling [18]. In the absence of
literature, vast research and development opportunities exist for the application of ANNs to
CBRN dispersion modeling.
2.4 Database Tool
Possibly the most arduous element of the waste estimation process is the acquisition and
analysis of building stock data (i.e., building quantity, size, square footage, and construction
materials) over a wide area. The Hazus-MH Database Tool confronts this issue by utilizing
FEMA's Hazus-MH building stock database. Hazus-MH, FEMA's loss estimation software, is
considered the leading entity for estimating building stock counts for rural and urban
environments. Originally designed to estimate the loss inflicted by floods, hurricanes, and
earthquakes, the Hazus-MH building stock data is used by WEST to generate square footage and
building count estimates. One of the unique functions of the Hazus-MH Database Tool is the
ability to extract building stock data directly from the Hazus-MH databases automatically
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without navigating Hazus-MH or FEMA's Comprehensive Data Management System. By
automating the building stock extraction process, the time required to produce the waste
estimates is greatly reduced, and the universe of potential users is expanded beyond those with
significant GIS expertise.
The Hazus-MH Database Tool functions as a standalone executable using the Lab VIEW
runtime engine. By applying census tracts derived from the previously mentioned GIS functions,
the Database Tool queries over sixty tables for matching census identifications located in various
databases (a complete listing of table and database names is found in Appendix B). Once
allocated and filtered, the results are uploaded to local tables for further examination by the user.
The Database Tool also queries a number of databases relating to infrastructure and
demographics. A complete listing of the exported data is shown in Figure 9. Upon execution of
the Database Tool, two general building stock databases are exported to the Waste Tool: a)
Square Footage by Building Type; and b) Building Count by Building Type. Additional
infrastructure and demographic information is provided for informative purposes only, and to
date, is not used by the tool, although this information may be used in the future.
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Essential
Facilities
High
Potential
Loss
Facilities
Transport
Systems
Lifeline
Utility
Systems
State
Boundaries
General
Building
Stock
Fire Station
Police Station
Hazardous
Materials
Facilities
Airport
Facilities
Bus Facilitie
Highway
Tunnels
Light Rail
Bridges
Light Rail
Facilities
Port Facilities
Railway
Bridges
Rail Facilities
Potable
Water
Distribution
Pipelines
Potable Water
Facilities
Waste Water
Facilities
Communication
Facilities
Waste Water
Distribution
Sewers
Electnc
Power
Facilities
Natural Fas
Distribution
Pipelines
Natural Gas
Facilities
Oil Facilities
Building
Count bv
Square
Footage by
Occupancy
Demographics
Square
Footage by
Building
Type
I
Building
Count by
Building
Type
Figure 9. Hazus-MH Database Tool Output
2.5 Waste Estimation Spreadsheet Tool
The RDD Waste Estimation Spreadsheet Tool (Waste Tool) is a Microsoft Excel 2007
application that was created to process the data generated using the GIS data analysis tools (see
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Section 2.2), the Image Analysis Tool (see Section 2.3), and the Hazus-MH Database Tool (see
Section 2.4). The Waste Tool will use those data to generate waste estimates. The Waste Tool
consists of two separate Microsoft Excel files, a calculation file and an application (user
interface) file. The Waste Tool provides a simple and intuitive interface for users to specify
various required inputs and to modify preprogrammed default parameters. The Waste Tool
performs numerous calculations based on the data described above and additional user inputs to
describe waste tradeoffs by:
• Total number and total square footage of all affected structures in each
contamination zone (Hazus-MH);
• Interior surface areas of buildings (Hazus-MH);
• Exterior surface areas of buildings (Image Analysis Tool);
• Quantities of structural and non-structural demolition debris (WEST);
• Quantities of waste resulting from decontamination activities (WEST); and
• Initial radionuclide activity on various building and ground surfaces (WEST) -
based on initial deposition estimates from NARAC.
The Waste Tool also contains reference tables consisting of default data and information
derived from Hazus-MH on:
• Descriptions of model building type and specific occupancy type as defined by
Hazus-MH. An example of available building and occupancy types is shown in
Figure 10;
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Residential
Commercial
Industria
Occupancy Class
Wood
Steel
Concrete
Masonry Model Building Type
Mobile Home ' *
Figure 10. Example Inventory Relationship of Model Building Type and Occupancy Class
[3]
• Debris factors;
• Typical building heights; and
• Typical number of stories for each model building type.
Default data for decontamination technologies, surface material densities, building geometry
calculations, and unit conversion factors are also included in the tool.
The Waste Tool performs the following calculations based on the total square footage
and building counts for each model building type (i.e., in terms of building use, occupancy class,
and structural system) located in each census tract that crosses one or more of the three
contamination zones:
• Average square footage per building;
• Roof area per building;
• Interior floor area per building;
• Exterior surface area per building, excluding roof area;
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• Interior surface area per building, excluding floors; and
• Structural and non-structural debris for each debris type (brick, wood, and
reinforced concrete and steel) and for each model building type.
Once the preliminary data have been generated and imported into the tool, users can specify
the type of decontamination technology to be used in each deposition zone, or can choose to
model the demolition of all buildings in any given zone. Once the demolition and/or
decontamination parameters have been specified, the Waste Tool estimates the amount of
contaminated waste that would be generated. The waste estimates include building materials and
ground surface materials, as well as the water that is generated during decontamination activities.
3.0 System Requirements
The following specifications are required in order to run the WEST and its supporting software.
Operating the WEST below the suggested user requirements may cause system errors or freezes.
Required software:
• Hazus-MHMR52.0
• ArcGIS 9.3
• Microsoft Office 2007 or later
• Lab VIEW 2010 runtime (included)
• Waste Estimation Support Tool (included)
• Google Earth (optional)
Suggested system requirements:
• Processor: Pentium 4/M or equivalent
• RAM: 2 GB
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• Screen Resolution: 1024 x 768 pixels
• Operating System: Windows XP
• Disk Space: 45 GB (includes required software)
4.0 Instructions for Generating Waste Estimate
The following text describes a general approach for generating preliminary datasets used by the
Waste Tool. The subsequent procedures should take approximately one hour.
1. Retrieve shapefiles from provided source.
2. Create Hazus-MH Scenario encompassing study region.
3. Set up the WEST folder.
4. Load the WEST Scripts into ArcGIS.
5. Combine plume shapefiles.
6. Generate satellite imagery of plume shapefiles.
7. Process study regions to generate square footage and building stock info.
8. Process satellite imagery to generate outdoor surface estimates.
9. Import building count and outdoor surface descriptor files into RDD Waste Estimation
Spreadsheet.
Plume Shapefiles
Retrieve shapefiles for the provided scenario (e.g., LRE or WARRP) from an appropriate
source, such as NARAC. The plume must be segmented into three zones (the maximum that can
be handled by the current version of WEST). If four or more zones exist within the plume, delete
the unused contours so that three remain. Figure 11 shows the plume used in the Liberty RadEx
scenario.
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ZoneS
Figure 11. Liberty RadEx Plume Zones
HAZUS Scenario
Before generating a scenario in Hazus-MH, be sure to extract the necessary state
inventory file from the Data Inventory DVD. To make the default inventory data accessible to
Hazus-MH (in order to create new study regions), do the following:
1. Navigate to the compressed state data file on your DVD.
2. Select the file (CA.exe for example) and double-click to uncompress it.
3. When prompted for the 'Extract to' folder, enter the path to the Hazus-MH Data Path
folder. By default, this folder is C:\Program Files\HAZUS-MH\Data Inventory.
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From the Hazus-MH startup menu, create an earthquake scenario at the county level that
encompasses the plume (tip: by selecting "Show map" you can view/select the preferred state
and county). By adding the plume shapefile using the add data button from the "Show map"
screen, you can view the underlying county(s). Be sure that the entire plume lies within the study
region. Figure 12 (below) depicts the necessary steps for creating a study region (be sure to
select "Earthquake" as hazard type).
Welcome to HAZUS-MH.
In order to use HAZUS-MH, you need to define the study region to be
Please select the desired option below, and a wzaid will guide yot
through the necessary steps.
a
' Open a region
'' Delete a region
!~ Duplicate a legion
Export/Backup a region
•"" import aiegion
u
o
'CiD
CL>
ai
>~
T3
Z3
+->
l/l
c
0)
Q.
o
HAZUS MH Startup
Please setect the desiied option below, and a wizard will guide you
through the necessary steps.
Create a new region
r Ddete a region
(" Duplicate a region
'""" Expat/Backup 3 region
''" import a region
Create New Region
Hazaid Type
The hazard lype controls the type and amoum of data lhat vdl be aggiegaied.
The hazard lype selected affect; the analysis options lhat wi be available
Your study region can include one or mote ol Ihe following hazards. Check below the
hazaid($) you ate interested in
F Flood
F Hurricane
Notes
1. Selection of hazards listed above depends upon the hazaid modules installed.
2 Once a study ieg>on is built with a given hazardfs). it cannot be modified later on, in
olher words, you cannot add another hazard to it. Alternatively, you may re-create a
similai legion with different haj*d[s)
In older to use HAZUS-MH, you reed to define the study region to be
used in the analysis.
Select Region
The study legion selection seis the rejon lhat will be opened
D
Select the study region you want to open from Ihe let ol study regions you have created
to to.
| Cieated
Liberty RadEx
-------
WEST. Graphical User Interface (GUI)
The WEST GUI functions as a "point and click" interface intended to guide the user through the
data aggregation process using a series of icons that function as application shortcuts. The WEST
GUI can be accessed by clicking "Start-^AH Programs-^WEST"
HxD Hex Editor
Adobe Reader X
Figure 13. Accessing WEST
WEST Folder
The following procedures describe the steps necessary to establish the WEST directory. The
directory will be used to store files associated with the WEST.
1. Create a folder to receive all the data - (e.g., Desktop\WEST).
2. To establish a WEST default directory/folder, click the Set Default Directory button.
Figure 14. Set Default Directory Button
3. Select the folder created in Step 1.
Copy/Train Neural Network
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The following procedures describe the steps necessary to copy or create a new neural network
training set. It is important to note that a pre-trained neural network is included with the WEST.
Step 2 only applies if you want to create a custom training set.
1. Copy the provided WESTTrainingSet.txt and WESTTrainingSet-Trained.txt files
(located in default WEST installation directory) into the WEST folder.
2. Alternatively, if the training sets are unavailable, you may create your own.
a. To create a neural network training set that will be used to train the neural
network, click the Create Neural Network Training Set button.
Figure 15. Create Neural Network Training Set Button
b. When prompted to select a surface color map click Default (alternatively you
may create your own color map by selecting Custom; however, this process tends
to be complex and tedious and is beyond the scope of this document).
c. Save the file as "WESTTrainingSet.txt" within the WEST installation directory
d. To train the neural network that will be used to classify the satellite imagery, click
the Train Neural Network button.
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Figure 16. Train Neural Network Button
e. The Train Neural Network application will open and begin training the neural
network by accessing the WESTTrainingSet.txt file created in the previous step.
Once the training has been completed, save the file as "WESTTrainingSet-
Trained.txt" within the WEST installation directory.
Load Scripts
The following procedures explain how to add the WEST Toolbox, a suite of ArcGIS scripts used
to automate the geospatial processes.
1. Open the scenario you just created in Hazus-MH.
2. Toggle the "Show/Hide ArcToolBox Window" button to display the ArcToolBox pane.
Figure 17. Show/Hide ArcToolBox Window Button
3. Right click ArcToolbox, select "Add Toolbox" and add the WEST Toolbox (in the
WEST Apps folder).
Page 31 of 68
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i ArcToo"
j& 3D New Toolbox
3h Anil
Add Toolbox,
+ & Cor Environments,.,
+ ^ Dat
:!£ ^ Hide Locked Tools
El ^ Gee
+ 3|| Get
Save Settings
+ ^ Lint !-oad Settings
+ ^ Mobile Tools
Figure 18. Add WEST Toolbox
4. In ArcToolbox, expand WEST Toolbox, right click "unload table to text'
5 Select "Properties"
6 Select Source Tab
7. Navigate to "unload table to textpy" script (in the WEST Apps folder).
Unload Table To Text Properties
? X
General Source | Parameters 1 Validation 1 Help j
Script File:
f~ bhow command window when executing script
p" Run Python script in process
Figure 19. Unload Table To Text Properties
8. Hit "Apply" then OK.
Combine Shapefiles
The subsequent procedures are used to intersect the plume and census shapefiles within Hazus-
MH. By combining the shapefiles, data within the shapefiles are merged for easier analysis.
Page 32 of 68
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1. Clear selected features by pressing the "Clear Selected Features" button. A grayed out
button indicates that no features are currently selected. Be sure that no features are
selected before and after each script is executed.
Figure 20. Clear Selected Features Button
2. Using the "Add Data" button, load the plume shapefile(s) (Shapefiles are not provided,
you must either create or own or retrieve them from an official source e.g., NARAC).
Figure 21. Add Data Button
3. Select "Add Data" and load the provided satellite imagery (Located in default WEST
installation directory or alternatively select "File-^Add Data From ArcGIS Online" if
you don't have it).
Worldjmagery.lyr
ArcGIS Layer
37KB
Figure 22. World Imagery Layer
4. Validate the integrity of the shapefiles.
a Run ArcGIS Script "Data Management Tools->Features->Repair Geometry"
on each shapefile.
Page 33 of 68
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+ i
+ i
+ i
+ i
+ i
+ i
- i
ArcToolbox
3D Analyst Tools
i; | Analysis Tools
Cartography Tools
Conversion Tools
Data Interoperability Tools
Data Management Tools
Data Comparison
Database
Disconnected Editing
Distributed Geodatabase
Domains
Feature Class
Features
f* Add XY Coordinates
jfr Adjust 3D Z
^ Check Geometry
f* Copy Features
^ Delete Features
Multipart To Singlepart
Figure 23. Repair Geometry Location
5. If the plume shapefiles you got from NARAC for Zones 1-3 are already combined into a
single shape file then skip to Step 8, otherwise continue with Step 6.
6 Run "WEST Toolbox -> Rejeuvenatel"
a. WEST Folder: the WEST folder you created.
b. Select plumes: corresponding to Zones 1, 2, and 3 - make sure Zone 3 is largest,
Zone 2 is second largest, and Zone 1 is smallest and click "OK".
•• Rejuvenate 1
j V'..'est Directory
I
, Zone 3
I
, Zone 2
I
, Zone 1
1
*l
n
i
1
OK Cancel Environments... «Hide Help
Figure 24. Rejuvenate 1 Script
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7. Select "Add Data" and import the newly created shape file that is in the WEST folder. It
will be named the same as the Zone 3 plume shape file with the word "merge" following.
8. Run Rej euvenate2.
a. WEST Folder: the WEST folder you created.
b. "Shapefile_Merge": the merged shapefile from Step 5.
Rejuvenate 2
_, V'/EST Directory
j Plume Shapeflle
Cancel | Environments,,, «Hide Help I
Figure 25. Rejuvenate 2 Script
9. A merged shapefile with all the plumes has now been created in the WEST directory. It
is named the same as the previous merged shape file.
Satellite Imagery
The next steps describe how to dissect satellite imagery from the zonal areas. The resulting
imagery will subsequently be classified according to surface type.
1. Confirm the merged shapefile created during Step 8 "Run Rejuvenate2" has been added.
2. Right click on the merged shapefile and select "Open Attribute Table"; Right click on
the left box on the line for Zone 1 (line ID = LevelOOl) and select "Zoom to Selected";
Close window.
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FID Shape LINEJD
C
1
Polygon LevelOOS
Polygon Level002
Polygon LevelQCH
••*•• Flash
(*i Zoom To
f) Pan To
O Identify1.,,
13 Select/Unselect
Zoom To Selected
ITI Clear Selected
HE) Copy Selected
X Delete Selected
Figure 26. Zoom To Selected
3. Hide all Layers except "Imagery"; you should now see the satellite image area
encompassing Zone 1.
4 Select Menu File-»"Export Map"; Export the image as a TIF file at 200 dpi
resolution, with the "Write GeoTIFF Tags" option checked on the "Format" tab; Name
the file "Zonel"; Save the file into the WEST folder.
«
IMy Netwo*
Places
General Format
Color Mode:
Compression:
Deflate Quality:
Background Colo
Filename: |Zonel| HI Save
Save as type: 1 TIFFr.tif; »|| Cancel
1
24-bit True Color _^J
|None _^J
r. Dl'j
|P Write GeoTIFF Tags]
Figure 27. Export TIF Menu
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5. Select "Add Data" and import the Zonel.tif file you just created.
6. Under the ArcToolbox, go to "WEST Toolbox-Mmage Zonel".
a. Plume: the merged shapefile.
b Satellite Image: Zonel tif
c. Save Image As: "Zonel .img" - use the file dialog to save that into your WEST
folder.
d. WEST directory: your WEST directory you created.
Image Zone 1
j Plume
% Satellite Image
j Save Image As [Use Extension .img e.g. Plume 1.img}
., WEST Directory
Ml
Ml
OK Cancel | Environments... | «Hide Help |
Figure 28. Image Zone 1 Script
7. Select "Add Data" and import the Zonel .img file that was created in your WEST folder.
8. Hide all layers except Zonel.img; — you should see the satellite imagery for the
irregularly shaped Zone 1.
9 Select Menu File->"Export Map"; Export the image as a BMP file at 200 dpi
resolution; Name the file "Zonel.bmp".
Page 37 of 68
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File name:
[Zonel
Save as type: | BMP f*.bmp)
Save
Cancel
|200
|1625
11740
pixels
I? Write World File
Figure 29. Export BMP Menu
10. Repeat this process for Zones 2 and 3 (Zone 2 line ID = Level002 and Zone 3 line ID =
LevelOOS).
Process Study Regions
The following procedures describe how to process the study regions using the intersect scripts.
Upon execution of the scripts, a statistical reference is derived for calculating the building stock.
1. Under the ArcToolbox, go to "WEST Toolbox-^ Intersect 1".
a. Census Data: Study Region Tract.
b. Plume Data: the merged shapefile.
c. WEST Folder: the WEST folder you created.
d. PLUMEAREA.CSV : save the file as "PLUMEAREA.csv" in your WEST folder.
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^ Census Data
» West Directory
..PIUMEAREA.csv gave As;
Cancel | Environments,,, | «Hide Help |
Figure 30. Intersect 1 Script
2. Intersectl created a new shapefile in the WEST Directory called
"hztract_Project_Intersect.shp". Add this file using the "Add Data" button.
3. Under the ArcToolbox, go to "WEST Toolbox-^ Intersect 2".
a. Intersected Shape File: hztract_Project_Intersect.
b. OUTPUT.csv: save the file as "OUTPUT.csv" in your WEST folder.
c. CENSUS.txt: save the file as "CENSUS.txt" in your WEST folder.
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__, Inte^ecte^hapefile
_, OUTPUT.csv rSave As}
j CENSU5.txt £ave As)
Cancel | Environments... | «Hide Help
Figure 31. Intersect 2 Script
Convert Zone Square Footages
The following procedures describe how to convert the square footages extracted during the GIS
portion.
1. To convert the zone square footages, click the Convert Zone Square Footages button.
Figure 32. Convert Square Footages Button
2. The Convert Zone Square Footages application automatically locates and converts the
required values. Click the Ok button to close the application.
Process Building Stock
The following procedures describe how to extract the building stock inventory using the Hazus
Database Tool.
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1. To process building stock inventories, click the Hazus Database Tool button.
Figure 33. Hazus Database Tool Button
2. Step 1: locate Hazus-MH directory.
a. Select the Hazus-MH Folder: default location: C:\Program Files\HAZUS-MH
(tip: to select the folder currently being viewed, select the "Select Current
Folder" button.)
b. If Hazus-MH folder is located outside of default directory, check box and select
your "HazusData" folder, otherwise click "OK" to continue.
Select the Hazus-MH Folder
C;'f rogram Files \HAZUS-MH
My Hazus-MH region subfolders are stored
outside of the Hazus-MH default directory
Select HazusData Folder
_l
Figure 34. Select Hazus Folder
3. Step 2: select state inventory and region.
a. Select State Data Inventory Database: the state of your study region.
b. Check Include General Building Stock.
c Check Export Data to WEST
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d. Select Region: the name of the study region you created in Step 2.
e. Click Execute.
Hazus-MH Version: MRS 2.0
Select State Data Inventory Database
|co v|
Indude General Building Stock (Requires Hazus-MH)
Export Data to WEST: T
Select Region
| WARRP Demo 10-24
H
Execute
J
Figure 35. Select Inventory
4. The required building stock files, i.e., building count and square footage values are
extracted to the pre-established working directory. Optionally, the Database Tool has
embedded functionality to further assist in site assessment:
a. Browse critical infrastructure and building stock.
b. Export infrastructure and building stock tables for separate analysis by clicking
the Export Table to Excel button
c. Plot infrastructure and building stock data in Google Earth by clicking the View
Features in Google Earth button (must have Google Earth installed).
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Home Essential Facilities High Potential Loss Facilities Transporta
bon Systems Lifeline Utility Systems State Boundaries
General Building Stock [Requires HAZUS-MH) |
Fire Station | Medical Police Station School
School
Tract
03031001101
03031001102
03031001500
03031001600
03031001702
03031001300
03031001900
03031002000
03031002403
03001003505
03001003507
03001003503
03001003524
OSOO 1003 525
03001003529
03001003530
0300 100353 1
03001003534
03031000201
03031000202
03031000401
03031000402
03031000600
03031003500
03001003901
03001003952
03001009001
08001009002
03001009003
Name
HORACE MANN MIDDLE SCHOOL
BRYAr-fT WEBSTER ELEMENTARY SCHOOL
GARDEN PLACE ELEMENTARY SCHOOL
INNER-CITY CHRISTIAN SCH PARTN
EMILY GRIFFITH OPPORTUNITY SCHOOL
DEL PUEBLO ELEMENTARY SCHOOL
GREENLEE/METRO LAB ELEMENTARY SCHOOL
P.S.1CHARTER SCHOOL
GILPIN ELEMBJTARY SCHOOL
HULSTROM ELEMENTARY SCHOOL
MALLEY DRIVE ELEMENTARY SCHOOL
WOODGLEN ELEMENTARY SCHOOL
BRIGHT HORIZONS PRE-KINDERGARTEN SCHOOL
STARGATE CHARTER SCHOOL
CHILDRENS WORLD
SHADOW RIDGE MIDDLE SCHOOL
CHERRY DRIVE ELEMENTARY SCHOOL
RIVERDALE aEMENTARY SCHOOL
BEACH COURT ELEMENTARY SCHOOL
REMINGTON ELEMENTARY SCHOOL
COLUMBIAN ELEMENTARY SCHOOL
CONTEMPORARY LEARNING ACADEMY HIGH SCHOO
FRED N THOMAS CAREER EDUCATION CENTER
SWANSEA ELEMENTARY SCHOOL
ADAMS CITY MIDDLE SCHOOL
MAPLETON PRESCHOOL
MONTEREY ELEMENTARY SCHOOL
CORONADO HILLS ELEMENTARY SCHOOL
SKYVIEW NEW TECHNOLOGY HIGH SCHOOL
Address
4130 NAVAJO STREET
36350JJIVASST
4425 LINCOLN STREET
2609 LAWRENCE STREET
12SO WaTON STREET
750 GALAPAGO
1150 LIP AN STREET
1030 DELAWARE STREET
2949 CALIFORNIA STREET
10604 GRANT DRIVE
1300 EAST MALLEY DRIVE
11717 NORTH MADISON STREET
5321 EAST 136TH AVENUE
39S1 CQTTONWQOD LAKES BOULEVAR
12290 PENNSYLVANIA ST
12551HOLLYSTREET
11500 CHERRY DRIVE
10 724 RM DRIVE
4950 BEACH COURT
473 5 PECOS STREET
2925 WEST40TH AVENUE
221 1 WEST 27TH AVENUE
2650 ELIOT STREET
4650 COLUMBINE STREET
445 1 EAST 72ND AVENUE
602 EAST 54TH AVENUE
2201 MC EL WAIN BOULEVARD
3 300 DOWNING DRIVE
1200 E 73TH AVENUE
City Zip code
DENVER 30211
DENVER 30211
DENVER 30216
DENVER 30205
DENVER 30204
DENVER 30204
DENVER 30204
DENVER 30204
DENVER 30205
NORTHGLENN 80233
NORTHGLENN 30233
THORNTON 30233
BRIGHTON 30601
THORNTON 30241
THORNTON 30241
THORNTON 30602
THORNTON 302 3
THORNTON 302 3
DENVER 302 1
DENVER 302 1
DENVER 302 1
DENVER 302 1
DENVER 302 1
DENVER 30216
COMMERCE CITY 30022
DENVER 30229
DENVER 80229
THORNTON 30229
DENVER 30229
State Contact
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
i
Figure 36. Hazus Database Tool
Process Satellite imagery
Once the satellite images have been dissected according to zone, the resulting imagery must now
be classified according to surface type.
1. To process satellite imagery, click the ID Surfaces button.
Figure 37. ID Surfaces Button
a. Select the Zonel .bmp file created during the Satellite Imagery process.
b. When it asks you if the study region is rectangular select the "No" option.
c. Repeat this process for Zones 2 and 3.
Import Files
Page 43 of 68
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Up to this point, all files used to create a scenario within the waste tool have been generated.
Before continuing, ensure the following files have been created in the WEST directory:
• Ground Surface Percentage Data = RDD Tool Ground Surface Data.csv
• Building Count Data = BldgsinCensusTract.csv
• Census Tract/Zone Percentage Data = TRACT_AREAS.CSV
• Building Square Footage Data = SqFtofBldgsinCensusTract.csv
• Zone Area Data = Plumearea. csv
TRACT_AREAS.csv, BldgsinCensusTract.csv, and SqFtofBldgsinCensusTract.csv should
contain the same number of entries. Four bitmap files have also been generated. Check images
for classification accuracy.
4.1 Instruction for Operating the RDD Waste Estimation Spreadsheet Tool
Once the generated files have been checked for compatibility (i.e., the number of records in the
import CSV files are the same, the data structures of the import CSV files are consistent with
what is needed by the spreadsheet), they are now ready to be uploaded to the waste tool. The
following steps explain how to install and operate the Waste Tool.
1. Install the two RDD Waste Estimation Spreadsheet Tool files into the same directory of your
choosing:
RDDToolApp 20120815 V1.2.xlsm
RDDToolData.xlsx
2. Open the RDDToolApp_20120815_V1.2.xlsm file. The following screen will appear.
Page 44 of 68
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Figure 38. RDD Waste Estimation Spreadsheet Tool Main Screen
3. Enable Macros. Click the Options... box. The Microsoft Office Security Options window
appears.
Microsoft Office Security Options
Security Alert - Macro
Macro
Macros have been d-satfed. Macros might contain viruses or other security hazards. Do
not enable tins content unless you trust the source of th*s file,
Warning: It is not possible to determine that this content came from a
trustworthy source. You should kave this content disabled unless the
content provides critical functionality and vou trust its source.
We Path: C:\...
O Help crotect me from unknown content (recommended)
Figure 39. Security Alert - Macro Screen
4. Click the Enable this content radio button, then click the OK button.
5. Click Start on the top left side of the Microsoft Excel toolbar ribbon.
Page 45 of 68
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Figure 40. Waste Estimation Spreadsheet Tool Main Screen Start Button
6. The RDD Waste Estimation Tool Home window appears.
jjj, Add Scenario \_g Edit Scenario ^ Copy Scenario [_£ Delete Scenario ^ Help @i About [«J Exit
t:;Tf?r!iive Decon
Ground Surface Percentage Data (RDD Tool Ground Surface Data.csv)
Building Count Data (BldgsinCensusTract.csv)
Census Tract/Zone Percentage Data (TRACT_AREA5.CSV)
Building Square Footage Data (SqFtoFBldgsinCensusTract.csv)
Zone Area Data (Plurnearea.csv)
|£j Open Scenario
Figure 41. RDD Waste Estimation Spreadsheet Tool Home Screen
Two pre-existing scenarios are provided in the Tool: Extensive Decon and Limited Decon. Any
other user created scenarios will also be listed in the Scenarios list on the Home window. The
two default scenarios cannot be deleted. From the Home window, you can:
Page 46 of 68
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• Add a new scenario.
• Edit an existing scenario.
• Copy an existing scenario.
• Delete an existing scenario.
• Open an existing scenario.
• Save an existing scenario.
You do not need to import data to add, edit, copy, or delete an existing scenario. You can
create multiple scenarios and import different data sets to apply to one or more of the scenarios.
When you Open or Save a scenario, you must import the five data files created from previous
steps in the methodology.
a. To add a new scenario from the Home window, click the Add Scenario button. The
Scenario Basic Information window appears.
Page 47 of 68
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ROD Waste Estimation Spreadsheet Tool
ROD Waste Estimation Spreadsheet Tool
Scenario Basic Information
Home -J Save "> Undo fy. Help
Version 1.2
New Scenario
Scenario Name
Comments
0 days
1
Time Elapsed Since Initial Deposition
Tntal Affected Area Scaling Factor |
Initial Ground Surface Activity at Deposition
Select area activity unit I Select - _lJ Per
Select..!
4fe
•->' Radionuclide Zone 1 Activity Zone 2 Activity Zone 3 Activity
Am-241 0
Ba-140/La-HO 0
Ce-141 | 0
|Ce-144/Pr-144/Pr-144m | 0
Cf-252 0
0 | 0
0 | 0
0 | 0
0 0
0 | 0
A Activity
- Includes Daughter
r
r
Figure 42. Scenario Basic Information Screen
i. Give the scenario a unique name.
ii. Enter the number of days that have elapsed since initial deposition.
iii. The default affected area scaling factor is 1. Changing this factor allows you to adjust
the total area of each affected zone by the factor specified. For example, if the total
affected area is 1,000,000 m2, entering a scaling factor of 1.5 will adjust the total
r\
affected area to 1,500,000 m . All subsequent calculations will be based on the
scaled area.
iv. Specify the areal activity units that will be associated with the activity values that will
be entered for each radionuclide.
Page 48 of 68
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v. Specify the radionuclides present at deposition by entering the ground surface activity
for those radionuclides for each zone. If the activity includes the contribution from
the daughter product(s), then check the Activity Includes Daughter box. If this box
is not checked, the tool will calculate the activity contribution from any daughter
products. See the Technical Documentation or click on the Help button for more
information on which daughters are included in the calculations.
vi. Once all of the information has been entered, click the Save button. To return to the
Home window, click the Home button.
b. To edit an existing scenario, highlight the scenario name in the Scenarios box and click
Edit Scenario. The Scenario Basic Information window will appear. Make any desired
changes to the scenario and then click the Save button. To return to the Home window,
click the Home button.
c. To copy an existing scenario, highlight the scenario name in the Scenarios box and click
Copy Scenario. A new scenario will automatically be created and will be listed in the
Scenarios box with the default scenario name "Copy of..." To change the name of the
copied scenario or to make any other changes to the scenario basic information, highlight
the copied scenario in the Scenario box and then click the Edit Scenario button. The
Scenario Basic Information window will appear. Make any desired changes to the
scenario and then click the Save button. To return to the Home window, click the Home
button.
d. To delete an existing scenario, highlight the scenario name in the Scenarios box and
click the Delete Scenario button. This action will permanently delete a scenario and
cannot be undone.
Page 49 of 68
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7. Open or Save a Scenario
a. To open or save a scenario, you must have at least one scenario created and available
in the Scenarios box. The procedure for opening or saving a scenario is as follows:
b. Highlight the scenario in the Scenarios box to which you want to apply the
geographic data.
c. The five geographic data files that must be imported and applied to the scenario were
created in previous steps and were saved in a directory. The files must be provided in
the following order:
• Ground Surface Percentage Data = RDD Tool Ground Surface Data.csv
• Building Count Data = BldgsinCensusTract.csv
• Census Tract/Zone Percentage Data = TRACT_AREAS.CSV
• Building Square Footage Data = SqFtofBldgsinCensusTract.csv
• Zone Area Data = Plumearea.csv
d. Import each of those five files by clicking on the respective Select File... button at
the bottom right of the Home window. A Select File dialogue box will appear.
Navigate to the location of the data file and select the data file to import. Then click
Open. The directory path and filename will appear on the Home window.
e. Once the five geographic data files have been selected, click the Open Scenario
button or the Save Scenario button. If you choose to save a scenario, a Save File
dialogue window opens where you can specify the location to export a copy of the
Microsoft Excel data file.
f. A status window will appear that will show the progress of the data import.
Page 50 of 68
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RDD Waste Estimation Spreadsheet Tool
Opening Limited Decon...
Status
Propagating Zone data.,.
Filling data for zone: Z3.S3
24%
Figure 43. File Import Status Screen
Once the data import is complete, the geographic data are applied to the scenario. If you chose
to save the scenario, the Microsoft Excel data file was exported to the directory that you
specified in Step 7.e above.
If you chose to open a scenario, the Partitioning and Remaining Activity window appears.
IS^S RDD Waste Estimation Spreadsheet Tool
|a*ij«g| Partitioning and Remaining Activity
'•r^Home II
<* Zone 1 r zone 2 f~ Zone 3
1 Decon/Demo Parameters
Limited Decon
<• Activity at Deposition C Remaining Activitv at t
Streets Streets
Radronuclide Asphalt Sidewalks /Concrete Soil Exterior Walls Roofs Interior Floors Interior Walls
|Cs-137 | 1.GOE-HJ3
j Ba-137m 9.46E+02
View or Modify Source Partitioning Factors
1.00E4Q3 | 1, DOE 403 j 5.00E402 | 1.00E-HJ3 ] 1.00E402 | 5.00E-K)1
9.46E402 9.46E402 j 4
View or Modify Weathering Correction Factors
73E402 | 9.46E402 j 9. -WE 401 | 4.73E401
Figure 44. Partitioning and Remaining Activity Screen - Activity at Deposition
This window presents the results for the activity at deposition and the activity remaining at the
number of days specified since initial deposition for all radionuclides for which an initial ground
surface activity was specified on the Scenario Basic Information window. The deposition and
Page 51 of 68
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remaining activities are calculated for all three zones and for various structural and non-
structural surface types.
S*SJ ROD Waste Estimation Spreadsheet Tool
IjSJiiJgJI Partitioning and Remaining Activity
rzooe
*• Zone 1 r Zone 2 <~ Zone 3
r"1™
f" ActivityatDepGSitn-.i, * TV ,-v,i,-i -.cb r. ;,i r
1 _
Streets Streets
Radtonucltde Asphalt Sidewalks/Concrete Soil Exterior Walls
Cs-137 | 8.60E402 |
View or Modify Source Partitioning Factors
3.60E+02 | 9.80E+02 | 4.73E+02 f
View of Modify Weathering Correction Factors
Limited Decon
Roofs Interior Floors Interior Walls
9.90E+02 3.60E+01 | 4.73E+01
Figure 45. Partitioning and Remaining Activity Screen - Remaining Activity at Time t
The activities are calculated based on the specified initial ground surface activity, source
partitioning factors, and weathering correction factors. For more information on these factors and
details on the activity calculations, please refer to Appendix D.
8. To view or modify the default source partitioning factors, click the View or Modify Source
Partitioning Factors button.
9. To view or modify the weathering correction factors, click the View or Modify Weathering
Correction Factors button.
Page 52 of 68
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ROD Waste Estimation Spreadsheet Tool
ROD Waste Estimation Spreadsheet Tool
Partitioning and Remaining Activity
Limited Decon
Partitioning & Remaining Activity
Decon/Demo Parameters
Zone
f* Zone 1
View
Zone 2
Zone 3
Activity at Deposition <~ Remaining Activity at t
Activity at Deposition
Cs-137
Streets
Asphalt
i nnF-nm
Streets
Sidewalks/Concrete Soil
i nnF-t-m i nnF-4-n
Exterior Walls Roofs Interior Floors Interior Walls
^ 1 =; mF4.n? 1 1 nnF*m i fff±fr> II s nflf-un
| Ba-137rn9.46E+02 | 9.46E+B2 9.46E+02 f 4.73E+OZ | 9.46E+02 | 9.46E+01 [ 4.73E+01
I View or Moctfy Source Partitioning Factors I I View or Modify Weathering Correction Factors I
\
\
ROD Wain tuinwlton Spfcadihoel Tool
Source Partitioning Factors
QOdseftCmitl ^ Kesun De«»* v*i«
ROD Waste Estimation Spreadsheet Tool
Weathering Correction Factors
,JCic«8
-------
ROD Waste Estimation Spreadsheet Tool
§
ramra ROD Waste Estimation Spreadsheet Tool
Kp^jj Partitioning and Remaining Activity Limited Decon
-------
clicking the desired radio button at the top left side of the screen. The data input screens for each
zone are exactly the same and are organized as follows:
A. Ground Surfaces.
a. Percent of Total Ground Area Comprised of Asphalt, Concrete, and
Soils.
b. Decontamination Parameters for Ground Surface Media.
i. Streets - Asphalt.
ii. Streets/Sidewalks - Concrete.
iii. Soil.
B. Buildings
a. Percentage of Buildings to Decontaminate.
b. Percentage of Buildings to Demolish.
c. Decontamination Parameters for Building Surfaces.
i. Exterior Walls.
ii. Roofs.
iii. Interior Floors.
iv. Interior Walls.
Certain parameters are global and apply to all zones. These parameters include:
• Surface material densities;
• Decontamination technique parameters;
• Building parameters; and
• Dust suppression technology parameters.
Page 55 of 68
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11. You can view or modify parameters for each of the above four categories by selecting the
appropriate button on the Decontamination/Demolition Parameters window. The View or
Modify Building Parameters and View or Modify Dust Suppression Technology
Parameters buttons are available only when entering data for buildings.
ROD Waste Estimation Spreadsheet Tool
ROD Waste Estimation Spreadsheet Tool
Decontamination/Demolition Parameters
Waste Results L^I Waste Graphs
Clow » Ssve 0 Cl™ a C«,™l ^r prSo,e Defa* v&jts y, m* and Rel
Zone
'"• Zone I '" Zone 2 <~ Zone 3
View v Modfy Surface Material Properties 11 View or Modify DecontamnaOon Tedwique Propert
I View or Mod*y Dust Suppression Technology Parametet
; Close & Sa,* 00M-(.C*xo( |g
I 0 Close»Cancel I ^ Restore Default Values I
0 CbseStCancd |g» Restore Defadt Vahios I r<
Quit Suppresston Technology Water Use
Rre Hose rx«st Suwession 1-9 m3/m3
Automated Pre«ure/Noirlf Spray System "
Figure 49. Accessing Default Parameter Screens
12. Once all of the parameters have been entered or modified, click Waste Results.
Page 56 of 68
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ROD Waste Estimation Spreadsheet Tool
Decontamination/Demolition Parameters
jf Home Partitioning & Remaining Activity
Limited Decon
r:
'•• Zone 1 <~ Zone 2 r Zone 3
View or Modify Surface Material Propertie-
View or Modify Decontamination Technique Propertie
Ground Surfaces I Buildings I
Asphalt | 51 %
Concrete [ 3cT %
Soils 1 18" %
•®f ff Streets - Asphalt
^ C Streets/Sidewalks - Concrete
0 ^Soil
Enter Data
Figure 50. Accessing Waste Results from Decontamination/Demolition Parameters Screen
Waste results are presented in three formats: summary, demolition detail, and decontamination
detail. Users can choose to view the results in various mass, volume, and activity units.
te Estimation Spr
RDD Waste Estimation Spreadsheet Tool
Waste Results
gg Home I Partitioning & Remaining Activity Oecon/Demo Parameters
Limited Decon
Show Mass in jshortton _^J Show Volume in (gallon _*j Show Activity |uCi _^J per |m3
Summary | Demolition Detail ] Decontamination Detail |
Mass Volume
( Liquid Waste
Figure 51. Waste Results Screen
Waste results can be graphically represented by clicking the Waste Graphs button.
Page 57 of 68
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ROD Waste Estimation Spreadsheet Tool
ROD Waste Estimation Spreadsheet Tool
Waste Results
I partitioning & Remaining Activity Decon/Demo Parameters
Limited Decon
jshortton _^J Show Volume in jgallon -»j Show Activity |
| Demolition Detail j Decontamination Detail |
[Decontamination Waste
| Solid Waste
| Liquid Waste
Demolition Waste
| Solid Waste
[ Liquid Waste
| Total
Mass
4.87E+06
] 1.61E-J-06
3.27E-KI6
| 1.32E+06
| 7.47E-1-05
5.73E+05
6.19E+06
Volume
1 .Q4E+Q9
2.60E+06
7.S2E+08
2.26E4-08
8.85E+07
1 .37E+08
1.27E+09
Figure 52. Accessing Waste Graphs from Waste Results Screen
Waste results are graphed based on four outputs: solid waste volume percentage, liquid waste
volume percentage, estimated solid waste activity, and estimated aqueous waste activity.
isle Estimation Spreadsheet Tool
ROD Waste Estimation Spreadsheet Tool
Waste Graphs
Limited Decon
[.) Close view I I***) Waste Voline Percentage • Al Zones
Liquid Waste Volume Percentage - All Zones
• Decontamination Liquid Waste
• Demolition Liquid Waste
Figure 53. Waste Graphs Screen
13. To return to the tool home screen, click Close to return to the Waste Results window and
click Home.
14. To export the graphs/spreadsheet for further analysis, click the Save Scenario button.
Page 58 of 68
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|_£ Add Scenario | [^ Edit Scenario [jfj Copy Scenario [jj Delete Scenario ty Help £$ About
Ground Surface Percentage Data (ROD Tool Ground Surface Data.csv)
r
Building Count Data (BldgsinCensusTract.csv)
Census Tract/Zone Percentage Data (TRACT_AREA5.C5V)
Building 5quare Footage Data (5qFtofBMgsinCensusTract.csv)
Zone Area Data (Plumearea.esv)
jrj Open Scenario I |.r^" Save Scenario I
Figure 54. Save Scenario Option
Page 59 of 68
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5.0 Results - Liberty RadEx Example
In the event of an RDD incident, several options for decontamination of building and
urban materials exist, including strippable coatings, chemical decontamination technologies,
washing and cleaning, and various abrasive techniques such as scabbling. Each of these
techniques removes the contaminated material and potentially some of the underlying substrate,
producing varying amounts of waste in solid and/or liquid form. There is a complex relationship
between decontamination method selection, waste generation rates, as well as technical,
regulatory, and political considerations that drive the selection of the remediation strategy that
results in the most cost-effective and rapid return to normalcy. The decision-making process for
the overall remediation effort will need to consider several issues, including human health risk,
effectiveness of the decontamination technology, cost of application of the decontamination
technology, rate at which materials can be decontaminated using that technology, and the
quantity of waste (and level of contamination) produced by that technology and associated
disposal costs. Some decontamination parameters may be defined by practical limits that occur
during operational activities (e.g., minimum amount of soil that could be removed is six inches
due to the degree of control operators have over the typical heavy equipment used for soil
excavation).
Based on several decontamination technologies that EPA has identified that are likely to be used
(the tool currently allows a user to select from strippable coatings, abrasive removal, washing, a
"no decontamination" option, as well as a user-defined decontamination technology option) for
various surface types, decontamination waste quantities and characteristics were estimated using
a combination of default and user-adjustable parameters in the spreadsheet tool [21]. The
estimates include:
Page 60 of 68
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• Contaminated material (e.g., the layer of radioactive material that must be removed from
structures, roads, soil, etc);
• Residues from the decontamination technologies (e.g., removed strippable coatings); and
• Wastewater and sludges from onsite decontamination efforts.
Based on the Liberty RadEx scenario, a number of "best guess" assumptions were made
for a hypothetical mitigation strategy for three affected geographical zones shown previously in
Figure 3, including the fraction of buildings to be demolished versus the fraction to be
decontaminated, as well as a potential mix of decontamination technologies that might be
deployed. This process is demonstrated below in Table 2. The decontamination and demolition
options selected in no way reflect EPA policy or even likely strategies that may be used in a real
ROD incident.
Table 2. Media segregation parameters used in the Liberty RadEx Scenario
Media
Asphalt
Concrete
Soil
External Walls
Roofs
Interior Walls
Zone 1:
90% demolition, 10%
decontamination
1" removal
1" removal
6" removal
1 mm removal
1 mm removal
1 mm removal
Zone 2:
10% demolition, 90%
decontamination
1" removal - 70%
Wash - 30%
1" removal - 70%
Wash - 30%
6" removal
1 mm removal - 20%
Wash - 80%
1 mm removal - 20%
Wash - 80%
1 mm removal - 20%
Wash - 30%
Strippable Coating - 50%
Zone 3
10% demolition, 90%
decontamination
1" removal - 70%
Wash - 30%
1" removal - 70%
Wash - 30%
6" removal
Wash
1 mm removal - 20%
Wash - 80%
1 mm removal - 20%
Wash - 30%
Strippable Coating - 50%
Page 61 of 68
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Floors
1" removal
1" removal
1" removal - 50%
Wash - 50%
Based on the assumptions and analyses described above and in Section 2, the waste estimation
spreadsheet produces an estimate of both waste quantity and activity. The results of the
estimated waste quantities from this example scenario are shown in Table 3, and estimates of
activity are shown in Table 4. Estimations of certain quantities (e.g., liquid wastes) make no
assumptions as to the availability of resources (e.g., wash water) necessary to produce those
quantities of wastes. In fact, one of the useful outputs of the tool is a gross indication of the
theoretical viability of certain strategies (e.g., where water supplies are limited, using washing as
a decontamination option may not be possible).
Table 3 demonstrates the amount of waste generated by demolition and decontamination
measures. Note the total waste produced (approximately 1.3 million metric tons) and the amount
of liquid waste generated as a result (approximately 41 billion liters).
Table 3. Example Waste Quantity Estimation from Liberty RadEx Scenario
Solid Waste
Demolition
Decontamination
Total
Liquid Waste
Demolition
Decontamination
Total
Zone 1
66,883
22,060
88,943
52,948,845
-
52,948,845
Zone 2
82,548
308,651
391,199
65,350,416
16,425,394,718
16,490,745,134
Zone 3
142,110
681,265
823,375
112,503,382
24,797,444,633
24,909,948,015
Total
291,540
1,011,976
1,303,516
230,802,643
41,222,839,351
41,453,641,994
Units
metric
tons
metric
tons
metric
tons
liters
liters
liters
Page 62 of 68
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Table 4 depicts the amount of waste radioactivity by media type. Overall, activity is
minimal, demonstrating the possible use of LLRW or MSW depositories.
Table 4 Example Waste Activity Estimation from Liberty RadEx Scenario (uCi/m3)
Media
Demolition
All Debris
Liquid Waste
Decontamination
Asphalt
Concrete
Soils
Exterior Walls - Porous
Exterior Walls - Nonporous
Roofs - Porous
Roofs - Nonporous
Interior Walls - Porous
Interior Walls - Nonporous
Interior Floors
Liquid Waste
Coating Waste
Zone 1
4.62E+01
5.62E+03
3.82E+04
3.82E+04
6.56E+03
4.98E+05
4.91E+05
9.98E+05
9.98E+05
4.98E+04
4.91E+04
3.82E+03
Zone 2
1.53E+01
1.87E+03
9.18E+03
9.18E+03
1.57E+03
1.19E+05
1.18E+05
2.40E+05
2.40E+05
1.19E+04
1.18E+04
9.18E+02
3.87E+01
4.41E+03
Zone 3
6.63E+00
8.10E+02
4.28E+03
4.28E+03
7.34E+02
1.12E+05
1.12E+05
5.58E+03
5.50E+03
4.28E+02
1.45E+01
2.06E+03
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6.0 Conclusions
The EPA has developed a GIS-based tool to estimate the quantity, characteristics, and
activities of waste and debris resulting from an RDD or other radiological release event. The tool
uses a combination of the Hazus-MH software, Microsoft Access, and a suite of internally
developed tools to produce the waste inventories. Adjustable parameters allow the user to
estimate the impacts on the waste streams of different demolition and decontamination strategies.
Characteristics of waste and wastewater generated from the incident or subsequent cleanup
activities will influence the cleanup costs and timelines. Federal responders and decision makers
using this tool may be better able to implement an integrated response by effective analysis of
many competing considerations, resulting in optimal decision making capabilities. Use of this
tool may be a useful task to include with cities' planning activities to accompany the background
radiation surveys that are being performed.
6.1 Looking Forward
Multiple avenues for future enhancements exist from aggregating the preliminary data to
calibrating the estimates produced by the tool. In addition to being a waste estimation apparatus
for radiological events, the underlying methodology remains the same relative to chemical and
biological events as well, ultimately enabling the WEST to function as a CBRN waste estimation
tool.
In light of recent events in Japan, the application of the WEST to incidents outside the
United States is pragmatically evident. The tool utilizes infrastructure databases that are
specifically attuned to the United States, so international compatibility is currently not possible.
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However, efforts are being made to resolve this issue. Additional future enhancements to the
tool include:
• Evaluation of available decontamination options per scenario.
• Costs and time associated with each decontamination method.
• Transportation cost, logistics, and time according to destination.
• Ability to update the image analysis pattern recognition algorithm for improved accuracy.
• Customizable surface detection for increased classification capacity.
• Automated building stock aggregation using remote sensing resources.
• Topographic outputs for geospatial interpretation.
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7.0 References
1. Medalia, I.E., Dirty Bombs: Background in Brief, in CRS Report for Congress,
R418912QII, Library of Congress. Congressional Research Service
2. Andersson, K.G., Airborne Radioactive Contamination in Inhabited Areas. 2009:
Elsevier Science.
3. National Security Staff Interagency Policy Coordination Subcommittee, Planning
Guidance for Response to a Nuclear Detonation, 2010.
4. Yu, C., et al., Preliminary Report on Operational Guidelines Developed for Use in
Emergency Preparedness and Response to a Radiological Dispersal Device Incident,
2009.
5. FEM A. The Federal Emergency Management Agency's (FEMA 's) Methodology for
Estimating Potential Losses from Disasters. 2009 [cited September 28], 2009; Available
from: http://www.fema.gov/protecting-our-communities/hazus.
6. Government Accountability Office, Combating Nuclear Terrorism: Preliminary
Observations on Preparedness to Recover from Possible Attacks Using Radiological or
Nuclear Materials, 2009: Washington, DC.
7. U.S. Department of Homeland Security. National Preparedness Guidelines. 2007 [cited
2009 October 27]; Available from:
http://www.dhs.gov/xlibrary/assets/National Preparedness Guidelines.pdf
8. U.S. Department of Homeland Security. National Response Framework. 2008 [cited
2009 October 27]; Available from: http://www.fema.gov/pdf/emergencv/nrf/nrf-core.pdf
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9. Demmer, R.L., Large Scale, Urban Decontamination; Developments, Historical
Examples and Lessons Learned, in Proceedings of the WM07 Conference2QQ7: Tucson,
AZ
10. U.S. EPA. Liberty RadEx. 2010 May 26, 2011]; Available from:
http ://www. epa.gov/libertyradex/.
11. Lemieux, P., et al. A First-Order Estimate of Debris and Waste Resulting from a
Hypothetical Radiological Dispersal Device Incident, in Proceedings of the WM2010
Conference. 2010. Phoenix, AZ.
12. Yuan, H., C. Van Der wiele, and S. Khorram, An Automated Artificial Neural Network
System for Land Use/Land Cover Classification from Landsat TM Imagery. Remote
Sensing, 2009. 1(3): p. 243-265.
13. Jordan, M.I. and C.M. Bishop, Neural Networks. ACM Computing Surveys, 1996. 28(1):
p. 73-75.
14. Mas, J.F. and JJ. Flores, The Application of Artificial Neural Networks to the Analysis of
Remotely Sensed Data. Int. J. Remote Sens., 2008. 29(3): p. 617-663.
15. Miller, D.M., EJ. Kaminsky, and S. Rana, Neural Network Classification of Remote-
Sensing Data. Comput. Geosci., 1995. 21(3): p. 377-386.
16. Yang, C.-C., et al., Application of Artificial Neural Networks in Image Recognition and
Classification of Crop and Weeds. Canadian Agricultural Engineering, 2000. 42(3): p.
147-152.
17. Aleksander, I. and H. Morton An Introduction to Neural Computing. 1995: International
Thomson Computer Press. 284.
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18. Stathakis, D. and A. Vasilakos, Satellite Image Classification Using Granular Neural
Networks,. International Journal of Remote Sensing, 2006. 27(18).
19. Greenspan, H. and R.M. Goodman, Remote Sensing Image Analysis via a Texture
Classification Neural Network, in Advances in Neural Information Processing Systems 5,
[NIPS Conference]1993, Morgan Kaufmann Publishers Inc. p. 425-432.
20. Hill, A., Using the Hazard Prediction and Assessment Capability (HPAC) Hazard
Assessment Program for Radiological Scenarios Relevant to the Australian Defence
Force, 2003, DSTO Platforms Sciences Laboratory: Victoria.
21. Drake, J., R. James, and R. Demmer. Performance Evaluation of Decontamination
Technologies for Dirty Bomb Cleanup, in Proceedings of the WM 2010 Conference.
2010. Phoenix, AZ.
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United States
Environmental Protection
Agency
PRESORTED STANDARD
POSTAGE & FEES PAID
EPA
PERMIT NO. G-35
Office of Research and Development (8101R)
Washington, DC 20460
Official Business
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