NIST Technical Note 1636 

Development and Demonstration of a Method to 

Evaluate Bio-Sampling Strategies using Building 

Simulation and Sample Planning Software 

W. Stuart Dols 

Brett D. Matzke 

Landon H. Sego 

Jayne B. Morrow 

Lisa L. Nuffer 

Brent A. Pulsipher 

Andrew K. Persily

NIST Technical Note 1636 

Development and Demonstration of a Method to 

Evaluate Bio-Sampling Strategies using Building 

Simulation and Sample Planning Software 

W. Stuart Dols 

Andrew K. Persily 

Building Environment Division 

Jayne B. Morrow 

Biochemical Science Division 

National Institute of Standards and Technology 

Brett D. Matzke 

Landon H. Sego 

Lisa L. Nuffer 

Brent A. Pulsipher 

Statistics and Sensor Analytics 

Pacific Northwest National Laboratory

June 2009 

U.S. Department of Commerce 

Gary Locke, Secretary 

National Institute of Standards and Technology 

Patrick D. Gallagher, Deputy Director
Certain commercial entities, equipment, or materials may be identified in this document in 

order to describe an experimental procedure or concept adequately. Such identification is not 

intended to imply recommendation or endorsement by the National Institute of Standards and 

Technology, nor is it intended to imply that the entities, materials, or equipment are necessarily 

the best available for the purpose. 

National Institute of Standards and Technology Technical Note 1636 

Natl. Inst. Stand. Technol. Tech. Note 1636, 56 pages (June 2009) 

CODEN: NSPUE2 
iii 

Abstract 

In an effort to validate and demonstrate response and recovery sampling approaches and 

technologies, the U.S. Department of Homeland Security (DHS), along with several other 

agencies, have simulated a biothreat agent release within a facility at Idaho National Laboratory 

(INL) on two separate occasions in the fall of 2007 and the fall of 2008. Because these events 

constitute only two realizations of many possible scenarios, increased understanding of 

sampling strategies can be obtained by virtually examining a wide variety of release and 

dispersion scenarios using computer simulations. This research effort demonstrates the use of 

two software tools, CONTAM, developed by the National Institute of Standards and Technology 

(NIST), and Visual Sample Plan (VSP), developed by Pacific Northwest National Laboratory 

(PNNL). The CONTAM modeling software was used to virtually contaminate a model of the INL 

test building under various release and dissemination scenarios as well as a range of building 

design and operation parameters. The results of these CONTAM simulations were then used to 

investigate the relevance and performance of various sampling strategies using VSP. One of the 

fundamental outcomes of this project was the demonstration of how CONTAM and VSP can be 

used together to effectively develop sampling plans to support the various stages of response 

to an airborne chemical, biological, radiological, or nuclear event. Following such an event (or 

prior to an event), incident details and the conceptual site model could be used to create an 

ensemble of CONTAM simulations which model contaminant dispersion within a building. 

These predictions could then be used to identify priority area zones within the building and 

then sampling designs and strategies could be developed based on those zones. 

Key Words: agent; biological; modeling; planning; response; sampling 
iv 

TABLE OF CONTENTS 

Abstract ........................................................................................................................................... iii

Executive Summary ......................................................................................................................... 1

1 Introduction .............................................................................................................................. 3

2 Description of Modeling Tools .................................................................................................. 5

2.1 CONTAM ......................................................................................................................................... 5 

2.2 VSP .................................................................................................................................................. 6 

2.3 CONtoVSP ....................................................................................................................................... 7 

3 Contaminant Transport Simulation Using CONTAM ................................................................ 8

3.1 Building Description ....................................................................................................................... 8 

3.2 CONTAM Model ........................................................................................................................... 10 

3.3 Scenarios Modeled ....................................................................................................................... 13 

4 Analysis of Simulations and Sampling Strategies Using VSP .................................................. 15

4.1 Identifying Patterns of Contaminant Dispersion .......................................................................... 16 

4.1.1 Visual Analysis ................................................................................................................... 16 

4.1.2 Cluster Analysis ................................................................................................................. 17 

4.1.3 Visual Analysis of Clusters ................................................................................................. 20 

4.2 Zone Classifications ...................................................................................................................... 26 

4.2.1 Class 1 Zones ..................................................................................................................... 26 

4.2.2 Class 2 Zones ..................................................................................................................... 27 

4.2.3 Class 3 Zones ..................................................................................................................... 27 

4.2.4 Class 4 Zones ..................................................................................................................... 28 

4.3 Developing Zoning Strategies ....................................................................................................... 28 

4.3.1 Cluster 1 Zoning: 1st Floor Releases (Lobby and Office) with Supply Fans OFF ............... 28 

4.3.2 Cluster 2 Zoning: 1st Floor Releases (Lobby and Office) with Supply Fans ON ................ 28 

4.3.3 Cluster 3 Zoning: 2nd Floor Office Releases with Supply Fans OFF ................................... 29 

4.3.4 Cluster 4 Zoning: 2nd Floor Office Releases with Supply Fans ON .................................... 29 

4.3.5 Cluster 5 Zoning: Outdoor Releases.................................................................................. 29 

4.4 Comparing Sampling Strategies ................................................................................................... 31 

4.4.1 Cluster 1 – 1st Floor Release with Supply Fan OFF ............................................................ 33 

4.4.2 Cluster 2 – 1st Floor Release with Supply Fan ON ............................................................. 40 

4.4.3 Cluster 3 – 2nd Floor Release with Supply Fan OFF ........................................................... 41 

4.4.4 Cluster 4 – 2nd Floor Release with Supply Fan ON ............................................................ 42 

4.4.5 Cluster 5 – Outdoor Release ............................................................................................. 43 

4.4.6 Summary of Sampling Strategy Comparisons ................................................................... 44 

5 Conclusions ............................................................................................................................. 45

6 References .............................................................................................................................. 47

Acknowledgements ....................................................................................................................... 48
v 

FIGURES 

Figure 1 – INL Building PBF-632 .................................................................................................................... 8 

Figure 2 – 2nd Floor Plan ................................................................................................................................ 9 

Figure 3 – 1st Floor Plan ................................................................................................................................. 9 

Figure 4 – CONTAM representation of 1st Floor Plenum ............................................................................ 11 

Figure 5 – CONTAM representation of 1st Floor ......................................................................................... 11 

Figure 6 – CONTAM representation of 2nd Floor Plenum ........................................................................... 12 

Figure 7 – CONTAM representation of 2nd Floor......................................................................................... 12 

Figure 8 – Illustration of CONTAM-VSP integrated sample design process................................................ 15 

Figure 9 – Partial Least Squares (PLS) Bi-Plot of the 384 CONTAM simulations. ........................................ 18 

Figure 10 – Cluster 1: 1st Floor (Office or Lobby) Release, Supply Fan OFF – 1st Floor Log scale ................ 21 

Figure 11 – Cluster 1: 1st Floor (Office or Lobby) Release, Supply Fan OFF – 2nd Floor Log scale ............... 21 

Figure 12 – Cluster 2: 1st Floor (Office or Lobby) Release, Supply Fan ON – 1st Floor Log scale ................. 22 

Figure 13 – Cluster 2: 1st Floor (Office or Lobby) Release, Supply Fan ON – 2nd Floor Log scale ................ 22 

Figure 14 – Cluster 3: 2nd Floor Office Release, Supply Fan OFF – 1st Floor Log scale ................................ 23 

Figure 15 – Cluster 3: 2nd Floor Office Release, Supply Fan OFF – 2nd Floor Log scale ................................ 23 

Figure 16 – Cluster 4: 2nd Floor Office Release, Supply Fan ON – 1st Floor Log scale ................................. 24 

Figure 17 – Cluster 4: 2nd Floor Office Release, Supply Fan ON – 2nd Floor Log scale ................................. 24 

Figure 18 – Cluster 5: Outdoor Release – 1st Floor Log scale ...................................................................... 25 

Figure 19 – Cluster 5: Outdoor Release – 2nd Floor Log scale ..................................................................... 25 

Figure 20 – Cluster 1 Zoning strategy of 1st Floor and 2nd Floor for scenarios with 

1st Floor Office Release and Supply Fans OFF .......................................................................... 30 

Figure 21 – Cluster 2 Zoning strategy for 1st and 2nd Floors for scenarios with 

1st Floor Office Release and Supply Fans ON ........................................................................... 30 

Figure 22 – Cluster 3 Zoning strategy for 1st and 2nd Floor for scenarios with 

2nd Floor Office Release and Supply Fans OFF ......................................................................... 30 

Figure 23 – Cluster 4 Zoning strategy of 1st and 2nd Floor for scenarios with 

2nd Floor Office Release and Supply Fans ON .......................................................................... 31 

Figure 24 – Cluster 5 Zoning strategy for 1st and 2nd Floor for scenarios with 

Outdoor Release ...................................................................................................................... 31 

Figure 25 – Purely Judgmental sample locations on 1st Floor..................................................................... 36 

Figure 26 – Purely Judgmental sample locations on 2nd Floor .................................................................... 36 

Figure 27 – Purely Probabilistic sample locations on 1st Floor ................................................................... 37 

Figure 28 – Purely Probabilistic sample locations on 2nd Floor ................................................................... 37 

Figure 29 – Purely Probabilistic with Zoning sample locations on 1st Floor................................................ 38 

Figure 30 – Purely Probabilistic with Zoning sample locations on 2nd Floor ............................................... 38 

Figure 31 – CJR with Zoning sample locations on 1st Floor ......................................................................... 39 

Figure 32 – CJR with Zoning sample locations on 2nd Floor ........................................................................ 39 
vi 

TABLES 

Table 1 – Factors and Levels for simulated scenarios ................................................................................. 14 

Table 2 – Comparison of sampling strategies for Cluster 1 (1st Floor Release with Supply Fans ON) ........ 35 

Table 3 – Comparison of sampling strategies for Cluster 2 (1st Floor Release with Supply Fans ON) ........ 40 

Table 4 – Comparison of sampling strategies for Cluster 3 (2nd Floor Release with Supply Fans OFF) ...... 41 

Table 5 – Comparison of sampling strategies for Cluster 4 (2nd Floor Release with Supply Fans ON) ....... 42 

Table 6 – Comparison of sampling strategies for Cluster 5 (Outdoor Release) .......................................... 43
1 

Executive Summary 

In an effort to validate and demonstrate response and recovery sampling approaches and 

technologies, the U.S. Department of Homeland Security (DHS), along with several other 

agencies, have simulated a biothreat agent release within a facility at Idaho National Laboratory 

(INL) on two separate occasions in the fall of 2007 [1] and the fall of 2008. Because these 

events constitute only two realizations of many possible scenarios, increased understanding of 

sampling strategies can be obtained by virtually examining a wide variety of release and 

dispersion scenarios using computer simulations. 

This research effort demonstrates the use of two software tools, CONTAM [2], developed by 

the National Institute of Standards and Technology (NIST), and Visual Sample Plan (VSP), 

developed by Pacific Northwest National Laboratory (PNNL) [3]. CONTAM is a computer 

program that is used to simulate building airflow and contaminant transport on a wholebuilding scale. VSP is a software tool that employs statistically defensible approaches in 

developing sampling designs and analyzing data. CONTAM and VSP were used to 1) virtually 

examine a wide range of biological contaminant release and dispersion scenarios at the INL 

facility and 2) develop a process for creating and evaluating sampling strategies for a given 

scenario. 

The CONTAM modeling software was used to virtually contaminate a model of the INL test 

building under various release and dissemination scenarios. Simulations were based on a range 

of building design and operation parameters, thereby providing information not constrained by 

experimental parameters as in the real-world testing. The results of these numerous CONTAM 

simulations were then used to investigate the relevance and performance of various sampling 

strategies using VSP. 

This research effort addressed three key research objectives: 

 1. Demonstrate the process of using CONTAM simulations to develop priority area zoning 

strategies and sampling designs in VSP 

 2. By examining hundreds of CONTAM simulations, explore whether there are consistent 

patterns of contaminant dispersion that would help in pre-planning zoning strategies 

within the building 

 3. Compare the performance of various sampling approaches across numerous simulated 

scenarios 

One of the fundamental outcomes of this project was the demonstration of how CONTAM and 

VSP can be used together to effectively develop sampling plans to support the various stages of 

response to an airborne chemical, biological, radiological, or nuclear (CBRN) event. Following 

such an event (or prior to an event), incident details and the conceptual site model could be 

used to create an ensemble of CONTAM simulations of contaminant dispersion within a 

building. These predictions could then be used to identify priority area zones within the 

building, and then sampling designs and strategies could be developed based on those zones. 
2 

CONTAM and VSP were also used in this project to compare sampling strategies over a range of 

scenarios. Ideally, this approach could be used to demonstrate that applying separate sampling 

designs within each zone can lead to some areas of the building being cleared when otherwise 

they would have been decontaminated or required further sampling—potentially incurring 

greater costs in terms of both time and money. Additional work could be pursued to show that 

relatively few sampling approaches or zoning schemes could potentially cover the broad range 

of possible contamination scenarios. 

This work demonstrates that CONTAM and VSP can be used to evaluate multiple sampling 

strategies as applied to buildings under a wide range of contaminant release, building 

configuration and operating scenarios to perform virtual experiments to supplement actual 

experiments. The strength of this approach lies in the ability to perform multiple simulations of 

a wide range of scenarios in relatively little time as compared to performing actual testing. VSP 

provides the capability to explore and employ a wide range of sampling strategies and has 

functionality specifically designed for the sampling of buildings. In combination, VSP and 

CONTAM provide a powerful set of tools with which investigators can study potential buildingspecific sampling strategies or obtain general knowledge related to building sample designs. 

By developing additional, more seamless integration, CONTAM and VSP may provide a valuable 

tool for teams who plan and perform response and restoration activities following a CBRN 

event. A CONTAM-VSP integration could also leverage another DHS investment: the integration 

of VSP with the Building Restoration Operations Optimization Model (BROOM) developed by 

Sandia National Laboratories. An integrated CONTAM-VSP-BROOM tool would provide users 

with start-to-finish sample design and collection capability. CONTAM would provide simulated 

predictions to guide zoning strategies in order to create optimal sampling designs in VSP. These 

sampling designs would then be passed to BROOM to facilitate data acquisition and 

management. Data analysis of laboratory results could then be conducted by BROOM and VSP. 

Such a tool, when properly used, could reduce the time and cost of response and restoration by 

facilitating the rapid design and execution of validated, statistically defensible sampling plans.
3 

1 Introduction 

The DHS Science and Technology Directorate (S&T) is tasked with researching and organizing 

the scientific, engineering, and technological resources of the United States, leveraging these 

existing resources into technological tools to help protect the homeland and supporting 

technological tools with standards. Standards development is critical to the deployment and 

performance evaluation of detection technologies, but the ability to detect contamination is 

equally dependent on the ability to find and recover contamination through sampling. 

In the event of a bio-terror attack like the contaminated letter incidents of 2001, a building is 

first sampled to assess the extent of contamination and determine the appropriate 

decontamination method. Effective surface decontamination is critical to reducing the risk of 

physical transport and reaerosolization of contaminant materials. Post-decontamination 

sampling is used to determine efficacy of the decontamination effort and to assess the ability to 

clear the building of any remaining contamination. In order to increase the confidence in our 

ability to detect potential biological contamination events, the performance of sampling 

strategies and uncertainties associated with sampling methodologies must be characterized. In 

response to a 2005 report written by the Government Accountability Office (GAO-05-251) DHS 

organized a working group, the Validated Sampling Plan Working Group (VSPWG), composed of 

multiple federal agencies and national laboratories to ensure that the overall process of 

sampling activities has been validated. In response to the VSPWG efforts to develop 

performance characteristics for surface sampling methodologies, agency efforts include 

evaluating the impact of methodology limitations and sampling locations on the overall 

confidence of detecting surface contamination. The generation of a sampling plan based on 

probabilistic methods to choose the sampling locations provides a level of confidence in the 

overall ability to detect low amounts of surface contamination. 

Over the past two years, a facility at Idaho National Laboratory served as the location for a 

multi-agency effort to experimentally evaluate the performance of sampling strategies to 

characterize and clear a building after a bio-contamination event. Both time and cost are 

significant constraints in large-scale field tests such as those conducted in Idaho. Modeling 

provides a means to expand our understanding of the distribution and transport of biological 

agents in a building beyond what can be explored by the physical and resource limitations of 

experimental testing. Model parameters can be adjusted to study the impact of variations in 

physical parameters that cannot be addressed experimentally in either a practical or economic 

manner. 

The purpose of this project was to integrate and coordinate the use of CONTAM [2], an airflow 

and contaminant dispersal simulation program, with the Visual Sample Plan (VSP) [3] computer 

program to provide individuals and agencies responding to a biological contamination event 

with a software-based approach to generating sample plans. The objectives of this project 

were to demonstrate the process of using CONTAM simulations to develop zoning strategies 

and sampling designs in VSP and to explore whether there are consistent patterns of 

contaminant dispersion that would help in pre-planning zoning strategies within the building. 

Bio-threat release scenarios were generated for a single office building of moderate complexity 
4 

using the CONTAM program (developed by and maintained at NIST). Release amounts, 

locations and circumstances including outdoor weather, ventilation system operation and other 

relevant building parameters were utilized to generate nearly four hundred release scenarios. 

Results of the NIST generated bio-threat dispersion and deposition scenarios were imported 

into the VSP software. VSP was developed at the Pacific Northwest National Laboratory (PNNL) 

to generate sampling plans that can include probabilistic sample site determinations and user 

selected judgmental sampling locations. PNNL visually analyzed the simulation results 

generated by NIST with CONTAM, looking for patterns, gradients and uncontaminated areas. In 

order to simplify the task of analyzing so many different scenarios, PNNL then performed a 

cluster analysis to group together scenarios that resulted in similar contamination patterns. 

Incident details, facility layout and CONTAM results were then utilized to classify the building 

spaces into four zoning categories according to their likelihood of being contaminated. The 

zoning procedure utilized the framework established by the Environmental Protection Agency 

(EPA) and Centers for Disease Control (CDC) through the VSPWG [4] to classify an area as a 

range from definitely contaminated to highly unlikely to be contaminated. Sampling 

approaches were generated for each zone classification. 

Sampling plans generated utilizing CONTAM and VSP may provide individuals and agencies 

responding to a biological contamination event with a tool for evaluating the effects of building 

parameters, explore zoning strategies that cover most scenarios, and establishing sampling 

strategies for various scenarios. The computer tools can provide a means of assessing sampling 

strategies generated under different release scenarios prior to an actual contamination event 

and can aide in pre-planning and response plan generation by the first responder community 

for a biological attack. 

A discussion of this effort, as well as a detailed description of the CONTAM and VSP tools, are 

provided in Section 2. The INL facility and the development of the CONTAM simulations are 

discussed in Section 3. Detailed results which address each of the three research objectives are 

presented in Section 4. Conclusions are given in Section 5. 
5 

2 Description of Modeling Tools 

Two main software tools were employed in this project: CONTAM [2] and Visual Sampling Plan 

(VSP) [3]. CONTAM was used to simulate a set of contaminant release scenarios, and VSP was 

used to perform the statistical analysis of the simulation results. CONTAM provided results in 

the form of mass of contaminant deposited within each room of the simulated building. A third 

program was developed to convert the CONTAM results into a format that VSP could import. 

The following sub-sections describe each of these tools in more detail, with specifics on how 

these tools were used in this project provided in Section 3. 

2.1 CONTAM 

CONTAM is a computer program that is used to simulate building airflow and contaminant 

transport on a whole-building scale. The program was developed by the National Institute of 

Standards and Technology (NIST). CONTAM is referred to as a multi-zone or nodal network 

model, because the air volumes that make up a CONTAM representation of a building are 

treated as a set of nodes interconnected by airflow links. These nodes or zones are 

characterized by a single pressure, temperature and contaminant concentration, though 

pressure does vary hydrostatically within each zone. 

For the purposes of analysis, a building is idealized by subdividing it into a set of zones, typically 

including rooms, major plenums between floors, and inter-floor shafts and chases. Zones are 

then interconnected by both intentional airflow paths, e.g., doors, windows, louvers and 

unintentional paths such as cracks that occur along the junctions between building partitions. 

Airflows occur between zones due to pressures arising from various driving forces including 

wind, temperature differences (stack effect) and mechanical systems. CONTAM allows the 

definition of mechanical ventilation systems either via a simplified representation of an air 

handling system or a fully detailed duct model. Contaminant concentrations within the building 

zones are affected by the location and strength of contaminant sources and removal 

mechanisms, e.g., material sinks and filters, as well as intra- and inter-zonal airflows. Detailed 

descriptions of CONTAM and multi-zone models in general, are available from several 

references [5-7] and the NIST website [8]. 

One important aspect of CONTAM that is pertinent to this project is the contaminant element 

used to simulate the deposition of the biological agent. Particle deposition is typically 

characterized by a deposition velocity [9], and CONTAM provides the ability to model 

contaminant removal by deposition. The CONTAM deposition velocity model determines the 

mass of contaminant deposited on a given surface during a simulation based on the following 

equation: 
6 

 = mass deposited [kg] 

Uair = density of air [kg/m3] 

Q = deposition velocity [m/s] 

 = deposition surface area [m2] 

ሺሻ = mass fraction of contaminant [kg/kg] 

t = time [s], (integration period was the same for all simulations) 

CONTAM allows the definition of as many deposition surfaces in a zone as desired, using 

different deposition velocities for each as appropriate. However, all deposition surfaces must 

be placed within a CONTAM zone. This means that each of the deposition surfaces will see the 

same zone concentration, represented by C(t) in the previous equation, within each zone. 

2.2 VSP 

VSP is a software tool that employs statistically defensible approaches in developing sampling 

designs and analyzing data. It was developed by the Pacific Northwest National Laboratory to 

support the Data Quality Objectives (DQO) process [10]. It ensures the right type, quality, and 

quantity of data are gathered to support confident decisions and helps in evaluating tradeoffs 

between increased confidence in decisions and costs or number of samples required. 

VSP allows the user to draw or import floor plans of buildings and generate and apply sampling 

designs to those floor plans. Sampling can be applied to the floor of a room or set of rooms, 

but can also be expanded to a 3D representation to include walls, ceilings, and furniture. 

Sample types (swab, wipe, vacuum) and points of interest (HVAC system, release point, 

sampling zones, etc.) can be user-specified. Many statistical sampling designs are available 

including random, systematic, sequential, adaptive cluster, collaborative, stratified, transect, 

multi-increment, combined judgmental and random, rank set, and judgmental sampling. A 

number of statistical analysis modules are available to complement the various sampling 

approaches. Statistical evaluations of the data are provided with decision recommendations, 

and most modules provide an automated report in text form. 

For a particular release scenario, each room may have its own sampling design or be combined 

with other rooms to form a zone (which is different from the definition of zone used in 

CONTAM). Sampling designs can be generated for each room or zone, and results can be 

imported and analyzed using built-in statistical tests, visual analysis, and geostatistical mapping. 

VSP has been developed with support from the U.S. Department of Energy (DOE), EPA, the 

Department of Defense, the Department of Homeland Security (DHS), CDC, and the United 

Kingdom Atomic Weapons Establishment. It is freely available at http://vsp.pnl.gov. In addition 

to supporting sampling within buildings, VSP has many sampling design and statistical analysis 

modules focusing on soils, sediments, surface water, streams, groundwater, and unexploded 
7 

ordnance sites. VSP is recommended by many regulators for defensible sampling design and 

statistical analysis. 

2.3 CONtoVSP 

CONTAM and VSP were developed independently of each other. Therefore, their inputs and 

outputs are not immediately compatible. A process was developed to convert the CONTAM 

results into a format that VSP could import. This process involved defining a cross-reference 

file format that would map the space definitions of VSP into the deposition surfaces of the 

CONTAM building representation, modifying CONTAM results files to include information 

needed to facilitate the mapping, and developing a computer program to perform the 

conversion and generate the input files for VSP. The conversion program is referred to as 

CONtoVSP. 

Typically, the VSP representation of a building surface would be subdivided into a set of grid 

cells that can take on numerical values, which in this case would be the mass deposited within 

the area of the cell. CONtoVSP obtains the mass deposited on each deposition surface from a 

CONTAM contaminant summary file (.csm) and apportions the total mass into a set of VSP grid 

cells that correspond to the deposition surface of CONTAM. The number of VSP cells associated 

with a room or sampling area is defined within VSP via the map file (.vsp). The number of cells 

that correspond to a CONTAM zone must also be set in the cross-reference file (.crf) in order for 

CONtoVSP to generate a properly formatted input file for VSP which requires a value for each 

sample cell defined in the VSP map file. This import file format can be viewed with VSP via the 

View – Coordinates menu command. 

CONtoVSP enables the mass deposited on each CONTAM deposition surface to be distributed 

to the VSP grid cells either uniformly or randomly with a normal distribution. If the mass is to 

be distributed uniformly, then each cell will contain the same amount of mass, equal to the 

total mass deposited on the CONTAM deposition surface divided by the number of cells. If 

distributed normally, then a random set of values will be generated using a normal probability 

distribution function with a mean and standard deviation which are specified in the crossreference file. CONtoVSP prevents negative values from being generated and normalizes the 

results by the total mass deposited on each CONTAM deposition surface.
8 

3 Contaminant Transport Simulation Using CONTAM 

This section describes the details of the modeling effort. It includes a description of the 

building, the CONTAM model, and the scenarios that were modeled in this study. 

3.1 Building Description 

The building used for this study is PBF-632 located at the Idaho National Laboratory (INL) and 

shown in Figure 1. Floor plans for the two floors of PBF-632 are shown in Figure 2 and Figure 3. 

Each floor is approximately 24.4 m x 15.2 m (80 ft x 50 ft) for a total of 372 m2 (4 000 ft2) per 

floor. As designed, each floor contains a constant volume air handler located within a 

mechanical room of the floor it serves. Outdoor air is brought in through an intake duct on the 

return air side of each air handler. Supply air ducts are located above suspended ceilings on the 

floor which they serve, and the space above the suspended ceilings also serves as a return air 

plenum. Return ducts draw air out of these plenums from just above each of the two 

mechanical equipment rooms. Supply air is provided to all occupied spaces except the 

restrooms and janitorial closet. Two return air grilles are located in the ceilings along the main 

hallway of each floor. Dedicated exhaust fans serve the four restrooms (two on each floor) and 

each of the mechanical rooms. Total design supply airflows for the 1st and 2nd floors are 

1,200 L/s (2,550 cfm) and 1,180 L/s (2,500 cfm) respectively. Design restroom exhaust flows 

are 94 L/s (200 cfm) for each restroom and 47 L/s (100 cfm) for each mechanical room. The 

design outdoor air intake rate is unavailable. 

Figure 1 – INL Building PBF-632 
9 

Figure 2 – 2nd Floor Plan 

Figure 3 – 1st Floor Plan 

1

2

3

4

5

6

7

8

9

10

11

12

13

13 R @ 9 15/64" = 10'- 0"12 T @ 12" = 12'-0"

Janitor

Men

Office Office Office Office Women

Office Office Office Office Office

Office

Office

Office

Office

Storage

Office Office

Mechanical Room

1

2

3

4

5

6

7

8

9

10

11

12

13

13 R @ 9 15/64" = 10'-0 "12 T @ 12" = 12'-0"

Lobby

Office Office Office

Office Office Office

Office

Office

Office

Office

Office Mechanical Room

Women

Men
10 

3.2 CONTAM Model 

The CONTAM model of the building was based on the building as designed. Design information 

was obtained from mechanical system drawings and electronic floor plans (AutoCAD format 

files, i.e., dwg files). Initially, the dwg files were imported into a 3D architectural Computer 

Aided Design (CAD) program that was then used to convert the main building geometry into an 

IFC-compliant (Industry Foundation Class) file. This file was then converted, via an in-house IFC 

to CONTAM converter, to an initial version of a CONTAM project file (.prj file) that included the 

building zones (rooms), doors and windows. Once the basic file was obtained it was modified 

to include building envelope and inter-zone leakages, plenums and a full duct system. For the 

purposes of this study, no leakage measurements were performed, so the envelope and interzone leakage values were assumed based on data in the literature [11, 12]. It is important to 

note that the purpose of this model was not to represent the actual building as-built, but to 

provide a realistic virtual test bed within which multiple release scenarios and building 

configurations could easily be applied and upon which the sampling strategies could be 

exercised. 

Each level of the building is represented in CONTAM by a schematic of the floor plan via the 

CONTAM sketchpad. The model used in this study consists of four levels – the 1st and 2nd floors 

and their respective plenums that contain the ductwork. These four schematics are shown in 

Figure 4 through Figure 7. As seen in the figures, the building is represented by various icons 

which include zones, airflow paths between zones, ducts and the deposition surfaces. Zones 

are delineated by the solid black lines. Airflow paths are represented by the diamond-shaped 

icons located along the walls and on the floors of all but the bottom level. The double blue lines 

and icons represent the duct system. Each zone has a deposition surface icon associated with it. 

The CONTAM colors and icons are defined as follows:

CONTAM Color Key 

 Office space 

 Release location 

 SIP room 

 Hallway and plenum 

 Mechanical room 

 Men’s room

 Women’s room

 Storage and other use 

 Stairs 

CONTAM Icon Key 

 Operable window or door 

 General leakage 

 Deposition or Release 

 Zone information icon 

 Downward-facing duct terminal 

 Horizontal duct terminal 

The release agent was modeled as a particle having an equivalent diameter of 1.06 Pm. The 

deposition surfaces were assumed to consist of only the floor area of each zone. A constant 

deposition velocity of 3.78 x 10-5 m/s [13] was assumed for all deposition surfaces within the 

CONTAM model. These surfaces were subdivided into 61 cm x 61 cm (2 ft x 2 ft) grid cells 

within VSP. 
11 

Figure 4 – CONTAM representation of 1st Floor Plenum 

Figure 5 – CONTAM representation of 1st Floor 
12 

Figure 6 – CONTAM representation of 2nd Floor Plenum 

Figure 7 – CONTAM representation of 2nd Floor 
13 

3.3 Scenarios Modeled 

The main benefit to using simulations to evaluate sampling strategies is the ability to perform 

multiple release scenarios under varying building operating and environmental conditions. 

CONTAM allows for releases to be simulated from within any of the building zones with varying 

amounts and time profiles. It also allows for releases external to the building by either 

targeting specific building openings or assuming a concentration profile around the building. 

CONTAM also allows the simulation of various building configurations including variations in 

door and window opening status, variations in fan on/off status and ventilation flow rates, as 

well as variations in weather conditions. 

Table 1 provides a list of the factors that were considered in the scenarios simulated and their 

associated levels. This is by no means an exhaustive list of potential variables and variations, 

but it was considered to be the most ambitious set of scenarios allowed under the resources 

available for this project. This set of factors and levels yields a total combination of 384 

scenarios. A project file generation tool was developed to create the 384 test cases and batch 

files were used to run CONTAM and the CONtoVSP conversion tool. In this way the full set of 

simulations could be performed more quickly and reliably. 

The release zones are shown in orange in Figure 5 and Figure 7 (near the bottom-right corners 

of the schematics) and the zones having the dedicated fans are shown in dark green (top-left 

corners of the schematics). In the case of the outdoor release, the contaminant is simulated to 

be uniformly distributed around the building. All simulations were for 24 h periods with 1 min 

time steps. All releases were modeled to occur for 1 min starting 1 h into the simulation. 

When the dedicated outdoor air fan was turned on, the duct that served the zone into which 

the outdoor air was provided was closed off in order to prevent the agent from being recirculated into the zone. Thus, when the release was from an interior source, these zones 

would serve as a form of shelter-in-place (SIP) zones and should have very little to no 

contamination. This was necessary to support one of the objectives of this research effort, i.e., 

the development of zoning strategies. 
14 

Table 1 – Factors and Levels for simulated scenarios 

Factors Levels Number 

of Levels 

Contaminant Release

Location

Outdoor Remote 

1st floor Lobby 

1st floor Office 

2nd floor Office 

4 

Amount

Low (1.0 mg) 

Medium (500 mg)

High (1.0 g) 

3 

HVAC Operation

1

st and 2nd floor supply fans 

(and Outdoor air intake rate) 

Both On (10 % outdoor air) 

Both Off (no outdoor air) 2 

1

st and 2nd floor exhaust fans On 

Off 2 

Dedicated outdoor air fan Inactive 

Active on 1st and 2nd floor offices 2 

Weather

Outdoor temperature 0 °C 

20 °C 2 

Wind speed 1 m/s 

5 m/s 2 

TOTAL Simulations 384 
15 

4 Analysis of Simulations and Sampling Strategies Using VSP 

One of the fundamental outcomes of this project was a demonstration of how CONTAM and 

VSP can be used together to effectively develop sampling plans to support the various stages of 

response to an airborne chemical, biological, radiological, or nuclear (CBRN) event. Following a 

CBRN event, incident details and the conceptual site model (CSM) could be used to create 

CONTAM predictions of contaminant dispersion within a building. These predictions would 

then be used to identify priority area zones within the building, and then sampling designs and 

strategies could be developed based on those zones. As discussed in Sections 2.1 and 3.2, a 

sizeable number of input parameters must be specified in order to execute the CONTAM 

model. It is plausible that reasonable estimates could be made for some of the CONTAM 

parameters using the incident details and the CSM. However, some of the CONTAM input 

parameters will remain unknown or at least less well known. Consequently, a group (or 

ensemble) of CONTAM dispersion predictions could be made by holding constant the input 

parameters which are known and varying the input parameters that are unknown. Visual 

analysis of the ensemble of predictions could then be used to develop a priority zoning strategy 

within the building. Sampling plans, in turn, would be developed in VSP to match the sampling 

objectives and characteristics of each of the zones. In short, CONTAM predictions can be used 

to create more efficient sampling designs in VSP. The integrated CONTAM-VSP process is 

illustrated in Figure 8. 

Figure 8 – Illustration of CONTAM-VSP integrated sample design process 

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VSP sampling design based on 

zoning strategy

Incident details 

Conceptual Site Model

CONTAM ensemble predictions 

(known input parameters fixed, 

unknown parameters varied)

Zoning strategy developed in VSP, based 

on CONTAM ensemble predictions
16 

PNNL used VSP to analyze a large number of simulated scenarios generated by CONTAM. Five 

zoning strategies were developed based on typical patterns observed in the CONTAM 

simulation results. For each zoning strategy, four sampling approaches were evaluated for the 

various scenarios: judgmental, purely probabilistic, purely probabilistic with zoning, and 

combined judgmental and random (CJR) with zoning. 

Section 4.1 discusses the process of using visual and cluster analysis to explore whether there 

are consistent patterns of contaminant dispersion that would help in pre-planning the zoning 

strategies within the building. The definitions and characteristics of the various zones are 

presented in Section 4.2. The demonstration of how the results from Section 4.1 are used to 

develop zoning strategies is presented in Section 4.3. Section 4.4 presents comparisons of the 

performance of the various sampling approaches across five typical scenarios identified by the 

visual and cluster analysis shown in Section 4.1. 

4.1 Identifying Patterns of Contaminant Dispersion 

4.1.1 Visual Analysis 

The results of the 384 CONTAM simulations were converted by CONtoVSP (Section 2.3) into the 

file format used for VSP files. In this format, each floor has its own file containing the response 

values (contaminant levels) for each grid cell organized by rooms. Files were provided using 

both the uniform and normal distribution methods for distributing contaminant to grid cells 

within each room (Section 2.3). All numerical analyses used data from the uniform distribution 

method. The methods produced similar images when viewed in VSP, and either set of data 

(uniform or normal) could be used for visual analyses. However, data from the normal 

distribution method have more texture instead of giving rooms solid colors, and this makes 

visual comparisons of adjacent rooms somewhat easier. Therefore, all images in Section 4 of 

this report are from data using the normal distribution method. To better account for both 

linear and non-linear patterns on the floors, PNNL created a second set of files that use base 10 

logarithms of the raw values, so that both the raw and log-scaled data could be analyzed. This 

was done for both floors in every simulation, resulting in four VSP files for each of a scenario’s 

sets of data (uniform or normal), the raw and log of the 1st floor values, and raw and log of the 

2nd floor values. Visual analysis was performed using the Color By Values feature of VSP [3]. 

This feature maps the data values in each cell to a continuous color scale, making it easy to 

visualize the location and intensity of contamination. A total of 3,072 (2 × 2 × 2 x 384) images 

were produced and analyzed visually with the objective of identifying scenarios that: 

 1) Showed contamination gradients or trends on individual floors 

 2) Showed floors with little or no contamination 

 3) Showed other interesting patterns including unique patterns isolated to only a handful 

of simulations, or scenarios that summarized a larger number of scenarios with similar 

dispersion of contaminant 

Using VSP’s menu commands to open and view each of the 3,072 files would clearly be a slow 

process. So, a “scenario viewer” was developed using a Python script to automate the process. 
17 

Scenarios are called up one-at-a-time, allowing the user to look at the 1st floor, and then the 

2nd, by hitting a “Next” button. If a scenario fit one of the three categories, a “Save” button 

allowed the scenario to be stored for future use. If both files have been viewed, or a scenario 

was saved, the viewer proceeds to the next scenario. The raw and log values were analyzed in 

separate runs of the viewer for both the uniform and normally distributed sets of data. 

Approximately 50 % of the scenarios fell into one of the three categories listed above. 

Examples of scenarios not included in this 50 % include cases in which most, if not all, of the 

rooms were contaminated with no discernible gradient, or contamination was uniform across 

all or most of the rooms. 

4.1.2 Cluster Analysis 

The visual analysis revealed many different patterns across the different scenarios. The next 

step was to perform a numerical analysis of the data that would help put the results of the 

visual analysis into perspective. It was decided that a cluster analysis was well-suited for 

summarizing the numerical results. 

We chose to use the rooms as the individual units for the cluster analysis. The numerical values 

used in the analysis were the average contamination levels of the grid cells in each room. This 

results in having the cluster analysis generally group together scenarios with similar levels of 

contamination in the same rooms so that scenarios with similar dispersion patterns and similar 

contaminant levels in rooms will tend to group together. 

Results of a partial least squares (PLS) regression [14] bi-plot are shown in Figure 9 generated 

using The Unscrambler software [15]. In PLS, each room has a variable stating the scenarios’ 

levels of contamination, and these rooms are simultaneously modeled using the experimental 

variables previously outlined in Table 1. Unlike traditional regression methods, all rooms are 

modeled together taking into account their correlations and the data structure of both the 

contamination levels of the rooms and the experimental variables. Clusters of scenarios can be 

shown graphically with the regression variables overlayed on the plot to show which variables 

are positively or negatively correlated with a cluster. Variables on the outside of the plot and 

near a cluster will tend to be positively correlated with that cluster. Variables on the outside of 

the plot and far away from a cluster will tend to be negatively correlated with that cluster. In 

Figure 9, clusters near the “High Contamination” are those with the highest levels of 

contamination, and likewise “Low Contamination” indicates clusters with lower levels of 

contamination. 

PLS was used largely because in addition to identifying clusters, it also attempts to identify how 

the combinations of the various CONTAM factors may influence the way in which the scenarios 

are clustered. The results show approximately 15 distinguishable groups of scenarios. Groups 

were largely determined by three of the explanatory variables that were significant in the PLS 

model: release point, the amount of contaminant released, and whether the supply fans were 

on or off. In order to reduce the large number of CONTAM simulations into smaller groups for 

analysis, a 3 × 3 × 2 factorial structure was used to create 18 clusters by using release point (1st
18 

floor office and 1st floor lobby combined, 2nd floor, or outdoor), the amount of contaminant 

released (low, medium, or high), and supply fan status (on or off). 

Figure 9 – Partial Least Squares (PLS) Bi-Plot of the 384 CONTAM simulations. 
19 

The resulting clusters of scenarios were defined as follows: 

 1) 1st floor release (lobby or office), low amount, supply fan on 

 2) 1st floor release (lobby or office), medium amount, supply fan on 

 3) 1st floor release (lobby or office), high amount, supply fan on 

 4) 1st floor release (lobby or office), low amount, supply fan off 

 5) 1st floor release (lobby or office), medium amount, supply fan off 

 6) 1st floor release (lobby or office), high amount, supply fan off 

 7) 2nd floor office release, low amount, supply fan on 

 8) 2nd floor office release, medium amount, supply fan on 

 9) 2nd floor office release, high amount, supply fan on 

 10) 2nd floor office release, low amount, supply fan off 

 11) 2nd floor office release, medium amount, supply fan off 

 12) 2nd floor office release, high amount, supply fan off 

 13) Outdoor release, low amount, supply fan on 

 14) Outdoor release, medium amount, supply fan on 

 15) Outdoor release, high amount, supply fan on 

 16) Outdoor release, low amount, supply fan off 

 17) Outdoor release, medium amount, supply fan off 

 18) Outdoor release, high amount, supply fan off 

Clusters involving 2nd floor and outdoor release points (7-18) each consisted of 16 CONTAM 

simulations. Clusters involving 1st floor release points contained 32 CONTAM simulations 

because the 1st floor office and 1st floor lobby release points were combined since they resulted 

in similar dispersion patterns and levels throughout most of the building. 

The visual analysis of the clusters also revealed patterns regarding the variables used for 

choosing these initial clusters. First, the supply fan appeared to be a large factor in determining 

how much of the building was contaminated. When the supply fan was on, all or most of the 

building was contaminated. When the supply fan was off, often only a smaller portion of the 

building around the release point was contaminated. 

The amount of contaminant released affected the amount of contaminant in each of the rooms, 

but did not appear to have a large effect on dispersion patterns. For instance, the simulations 

with the same factors other than having low, medium, or high amounts of contaminant tended 

to have the same rooms contaminated regardless of the amount of contaminant. This is to be 

expected, because the contaminant has no effect on airflow patterns and deposition rates as 

modeled by CONTAM. 
20 

4.1.3 Visual Analysis of Clusters 

Following the initial visual analysis and the cluster analysis, a new visual analysis of the 

scenarios in each individual cluster was then performed. In the original visual analysis of all 384 

CONTAM simulations, it was learned that the log-scaled data was much more intuitive for 

finding patterns using VSP. Therefore, the remaining analysis was performed using only the 

log-scaled data. Also, in order to maintain consistency across scenarios during the visual 

analysis, the color scales were fixed in VSP as opposed to each scenario being auto-scaled based 

on its individual range of values. Images were produced of maps of the 1st and 2nd floors for 

each scenario and their levels of contamination shown by a color scale as in the original visual 

analysis. This time, color scales were kept fixed and the images were organized by cluster. 

As a result of this analysis, the set of clusters were reduced even further from 18 down to 5. 

After examining images within each cluster, it became more apparent that the amount of 

contamination had very little influence on which rooms were contaminated, and therefore the 

amount of contaminant would not be a major factor when determining zoning strategies. 

Clusters with the same release point and supply fan setting were combined as were those with 

outdoor releases. For outdoor releases, it was obvious that a single zoning strategy was 

sufficient since the outdoor release scenario (a plume surrounding the building) always 

contaminated each room in the building to at least a small extent. This resulted in the following 

five clusters, which from this point forward will be referred to as Clusters 1-5: 

 1) 1st Floor (Office or Lobby) Release, Supply Fan OFF (96 CONTAM scenarios) 

 2) 1st Floor (Office or Lobby) Release, Supply Fan ON (96 CONTAM scenarios) 

 3) 2nd Floor Office Release, Supply Fan OFF (48 CONTAM scenarios) 

 4) 2nd Floor Office Release, Supply Fan ON (48 CONTAM scenarios) 

 5) Outdoor Release (96 CONTAM scenarios) 

Images from one of the scenarios in each of Clusters 1-5 are shown in Figure 10 through Figure 

19 as examples of contaminant deposition patterns for the scenarios in each cluster. 
21 

Figure 10 – Cluster 1: 1st Floor (Office or Lobby) Release, Supply Fan OFF – 1st Floor Log scale 

Figure 11 – Cluster 1: 1st Floor (Office or Lobby) Release, Supply Fan OFF – 2nd Floor Log scale 

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Figure 12 – Cluster 2: 1st Floor (Office or Lobby) Release, Supply Fan ON – 1st Floor Log scale 

Figure 13 – Cluster 2: 1st Floor (Office or Lobby) Release, Supply Fan ON – 2nd Floor Log scale 

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Figure 14 – Cluster 3: 2nd Floor Office Release, Supply Fan OFF – 1st Floor Log scale 

Figure 15 – Cluster 3: 2nd Floor Office Release, Supply Fan OFF – 2nd Floor Log scale 

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Figure 16 – Cluster 4: 2nd Floor Office Release, Supply Fan ON – 1st Floor Log scale 

Figure 17 – Cluster 4: 2nd Floor Office Release, Supply Fan ON – 2nd Floor Log scale 

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Figure 18 – Cluster 5: Outdoor Release – 1st Floor Log scale 

Figure 19 – Cluster 5: Outdoor Release – 2nd Floor Log scale 

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4.2 Zone Classifications 

The concept of dividing the building into classes is consistent with the Multi-Agency Radiation 

Survey and Site Investigation Manual (MARSSIM) developed by EPA, Nuclear Regulatory 

Commission (NRC), and DOE [16]. According to the MARSSIM, during the characterization 

phase of a release event, the rooms of the building should be divided into one of four possible 

priority areas or zones. These zones are not to be confused with CONTAM zones. The following 

is the set of classification criteria [17] into which the building areas were categorized for this 

study: 

Class 1 Zone: Definitely Contaminated or Assumed to Be Contaminated 

(i.e., Extremely High Likelihood of Being Contaminated). 

Class 2 Zone: High Likelihood of Being Contaminated. 

Class 3 Zone: Low Likelihood of Being Contaminated. 

Class 4 Zone: Extremely Low Likelihood of Being Contaminated [18]. 

Based on the scenarios analyzed, the building used in this case can be zoned into Class 1, 2, or 3 

zones. A separate zoning strategy was developed for each of the five clusters of scenarios, and 

these will be shown later. The relatively small size of the building, both floor area and number 

of floors, as well as the tendency of the ventilation system to mix the air throughout each floor, 

leads to reduced expectancy for Class 4 zones. Therefore, all rooms would be classified as at 

least Class 3 zones. Class 4 zones are more likely to occur in situations where a release would 

be isolated to a relatively small area of a large facility, either due to the nature of the release 

(i.e., location, amount or mechanism), building configuration, or system operation. 

The sampling approaches for each zone class are different because the sampling objectives and 

decision criteria can be based on the pre-event information developed for each part of the 

building through, for example, the visual and cluster analysis carried out herein. For example, 

during the characterization phase of a release event, Class 1 and Class 2 zones might warrant 

the use of a sampling approach that is geared towards determining the extent of 

contamination. While Class 3 or 4 zones can be handled with a clearance sampling approach 

where enough samples are taken to determine with high confidence that little or no 

contamination is present in the area, and decontamination may not be necessary. The 

recommended sampling strategies for each phase of the response and each zone are presented 

below. 

4.2.1 Class 1 Zones 

Class 1 zones are known or assumed to be contaminated, such as the location of the release 

point of the contaminant, or rooms that would almost certainly be contaminated (provided the 

release point is known). These rooms will presumably need to be decontaminated. If desired, 

judgment samples can be taken using the sampling expert’s best professional judgment, which 

may include choosing locations in rooms most likely to be contaminated and surfaces from 

which an accurate sample can be most easily extracted. The red area in Figure 20 shows a 
27 

Class 1 area, which in this case is the known release point. In some cases where a very large 

area is expected to be contaminated (usually in a much larger building), a hotspot sampling 

design may be applied to delineate areas of contamination. 

4.2.2 Class 2 Zones 

Class 2 zones are assumed to have a high probability of being contaminated, but there is no 

obvious evidence that points to it conclusively being contaminated. Initially, judgmental 

samples may be taken in the most likely areas of contamination. In Figure 20 the yellow areas 

are Class 2 Zones. These are rooms surrounding the suspected release point (the red Class 1 

Zone). If unacceptable levels of contamination are present, then the area can be reclassified as 

a Class 1 zone and can be scheduled for decontamination. If judgmental samples are clean, 

then a more extensive statistical sampling design can be applied to determine a course of 

action for the zone. A hotspot sampling design could be implemented to delineate possible 

areas of contamination, or the initial assessment of the zone being Class 2 may be 

reconsidered. A clearance design might be applied to determine with statistical confidence that 

a high percentage of the area is free of contamination. 

4.2.3 Class 3 Zones 

Class 3 zones are assumed to have a fairly low probability of being contaminated. In Figure 20, 

the blue areas are Class 3 Zones. On the 1st floor these rooms are not directly adjacent to the 

room in which the release occurred and tended to be “upstream” from the source in terms of 

airflow direction for the cases simulated. On the 2nd floor, the Class 3 rooms show that most of 

the 2nd floor has a high probability of not being contaminated. The sampling objective for Class 

3 zones is to take enough samples to either confidently conclude that the zone is not 

contaminated above the acceptable contamination level (ACL) or detect contamination if it 

exists. It is recommended that enough samples be taken to make a confidence statement 

about the zone. This statement is referred to as an X %/Y % upper percentile tolerance 

statement [19, 20] and is stated in the form “there is X % confidence that at least Y % of the 

surface area is uncontaminated (or below the ACL)”.

Two sampling strategies are recommended for sampling Class 3 zones: a purely probabilistic 

sampling approach and a combined judgmental and random (probabilistic) approach (CJR). In a 

purely probabilistic sampling approach, random samples are taken, all samples are expected to 

have an equal likelihood of being contaminated, and no prior assumption is made about the 

probability of the zone being contaminated. In the CJR approach [21], randomly located 

(probabilistic) samples are used to augment judgmental samples in order to make the desired 

X %/Y % confidence statement. In the CJR method, judgmental samples can be assumed to 

have a greater likelihood of containing detectable contamination than probabilistic samples. 

Also, before any sampling takes place, a Bayesian prior distribution is determined by the a priori

probability that a judgmental sample will contain detectable contamination. This is helpful for 

situations in which experts feel there is some evidence that a zone may be clean, but they wish 

to take samples to achieve a high level of confidence. The CJR method, when appropriate, can 

be advantageous because, it requires fewer total samples than a purely probabilistic approach. 
28 

4.2.4 Class 4 Zones 

Class 4 zones have an extremely low likelihood of being contaminated. These are zones for 

which there is sufficient evidence to conclude that no contaminant could have entered them. 

In general, sampling is not required for these zones—however, clearance sampling may be 

required by stakeholders, in which case a CJR design would be recommended. If there is any 

non-negligible chance that an area could be contaminated, the zone should not be classified as 

Class 4. 

4.3 Developing Zoning Strategies 

The scenarios within each cluster tend to have similar patterns of dispersion. A visual analysis 

was conducted on each of the five clusters to determine contamination patterns on each floor 

and to determine which rooms tended to be contaminated either all of the time, most of the 

time, rarely, or never. These results were then used to classify rooms as Class 1, Class 2, 

Class 3, or Class 4 zones. The following sections describe the zoning process and strategy for 

each of the five clusters. 

4.3.1 Cluster 1 Zoning: 1st Floor Releases (Lobby and Office) with Supply Fans OFF 

The zoning strategy for Cluster 1, (all scenarios with a 1st floor release with Supply fans OFF) is 

shown in Figure 20. The strategy for Cluster 1 as illustrated in Figure 20 reflects a 1st floor office 

release, but the cluster also contains 1st floor lobby release scenarios. These release points 

were combined into the same cluster because the spread of contamination tended to be very 

similar for the two different release points. Therefore, these scenarios were zoned the same 

with the exception that the 1st floor office and lobby were zoned as either Class 1 or Class 2 

depending on which was the release point. 

1st floor releases with Supply fans OFF resulted in the point of release always being 

contaminated (the red room in Figure 20). Therefore, the release room is always labeled Class 

1. The surrounding rooms, hallway, and mechanical room and bathrooms were usually 

contaminated and are labeled Class 2 zones (yellow rooms). Offices on the opposite side of the 

floor (left end of building on floor plans) were occasionally contaminated and are Class 3 zones 

(blue rooms). 

The 2nd floor was usually contaminated in the area above the mechanical room and bathrooms 

of the 1st floor. These are labeled Class 2 zones (yellow rooms in Figure 20). The remaining 

rooms on the 2nd floor were occasionally contaminated and are labeled Class 3 zones (blue 

rooms). 

4.3.2 Cluster 2 Zoning: 1st Floor Releases (Lobby and Office) with Supply Fans ON 

Figure 21 shows the zoning strategy for Cluster 2 (all scenarios with a 1st floor release and 

Supply Fans ON). On the 1st floor, the release point (office), a neighboring room, and the 

hallways were always contaminated and are Class 1 zones (red rooms in Figure 21). The rest of 

the floor, with the exception of one corner office, was usually contaminated and classified as 
29 

Class 2 zones (yellow rooms). The previously mentioned corner office was occasionally 

contaminated and classified as a Class 3 zone (blue room). 

The entire 2nd floor was usually contaminated with the exception of one corner office (which 

corresponds to the Class 3 office on the 1st floor), and these rooms were classified as Class 2 

zones (yellow rooms in Figure 21). The previously mentioned corner office was classified as a 

Class 3 zone (blue room). These two corner offices on the 1st and 2nd floors labeled as Class 3 

zones were the so-called SIP zones mentioned in section 3.3. 

4.3.3 Cluster 3 Zoning: 2nd Floor Office Releases with Supply Fans OFF 

Figure 22 shows the zoning strategy for Cluster 3 (all scenarios with a 2nd floor office release 

and Supply Fans OFF). On the 2nd floor, the release location and a neighboring office were 

always contaminated and are categorized as Class 1 zones (red rooms in Figure 22). The other 

offices around the release point and on the right half of the floor were usually contaminated, 

and are categorized as Class 2 zones (yellow rooms). The remaining rooms on the other half of 

the building were only occasionally contaminated and categorized as Class 3 zones (blue 

rooms). 

The entire 1st floor was usually not contaminated, and therefore the entire floor is categorized 

as Class 3 (the entire floor is blue in Figure 22). 

4.3.4 Cluster 4 Zoning: 2nd Floor Office Releases with Supply Fans ON 

Figure 23 shows the zoning strategy for Cluster 4 (all scenarios with a 2nd floor office release 

and Supply Fans ON). On the 2nd floor, the release location and a neighboring office were 

always contaminated and are categorized as Class 1 zones (red rooms in Figure 23). Most of 

the other areas on the 2nd floor were usually contaminated and are categorized as Class 2 zones 

(yellow rooms). Two corner offices near the SIP room were occasionally contaminated and are 

categorized as Class 3 zones (blue rooms). 

Most of the offices and areas on the 1st floor were usually contaminated and are categorized as 

Class 2 zones (yellow areas in Figure 23). Four offices in one corner of the floor near the SIP 

room were occasionally contaminated and categorized as Class 3 zones (blue rooms). 

4.3.5 Cluster 5 Zoning: Outdoor Releases 

Figure 24 shows the zoning strategy for Cluster 5 (all scenarios with an outdoor release). All 

rooms of both the 1st and 2nd floor were always contaminated. This was most likely due to the 

type of outdoor release simulated in this exercise, which was a plume passing over the building, 

and presumably contaminant making it into enough areas of the building that it spread to all 

parts either by ventilation system mixing or airflows induced by wind and stack effect. Since 

there is no release point within the building, all areas of the building were classified as Class 2 

zones indicating contamination is highly likely. 
30 

 

Figure 20 – Cluster 1 Zoning strategy of 1st Floor and 2nd Floor for 

scenarios with 1st Floor Office Release and Supply Fans OFF 

(Red = Class 1, Yellow = Class 2, Blue = Class 3) 

 

Figure 21 – Cluster 2 Zoning strategy for 1st and 2nd Floors for 

scenarios with 1st Floor Office Release and Supply Fans ON 

(Red = Class 1, Yellow = Class 2, Blue = Class 3) 

 

Figure 22 – Cluster 3 Zoning strategy for 1st and 2nd Floor for 

scenarios with 2nd Floor Office Release and Supply Fans OFF 

(Red = Class 1, Yellow = Class 2, Blue = Class 3) 
31 

 

Figure 23 – Cluster 4 Zoning strategy of 1st and 2nd Floor for 

scenarios with 2nd Floor Office Release and Supply Fans ON 

(Yellow = Class 2, Blue = Class 3) 

 

Figure 24 – Cluster 5 Zoning strategy for 1st and 2nd Floor for 

scenarios with Outdoor Release 

(Yellow = Class 2) 

4.4 Comparing Sampling Strategies 

Building airflow and contaminant transport modeling can be used to compare different 

sampling strategies (as a supplement to experimental efforts). The single zone (uniform 

concentration) assumption of the multi-zone model resulted in entire rooms being 

contaminated. Therefore, hotspot designs are not applicable to these circumstances. Hotspot 

sampling designs are more suited to scenarios that result in portions of rooms being 

contaminated. While hotspot scenarios were not addressed in this study, CONTAM does 

provide the ability to simulate more detailed contaminant distribution on a sub-room scale, 

e.g., using one-dimensional convection/diffusion and computational fluid dynamics (CFD) 

within selected zones. Examples of the types of sampling strategies that could be deployed 

under the uniform concentration assumption of CONTAM include the following: 
32 

 1. Purely Judgmental – The sampling team uses only their professional opinion to choose 

sampling locations. 

 2. Purely Probabilistic – Sample locations are chosen randomly or using a randomly placed 

grid. 

 3. Purely Probabilistic given a Zoning Approach – Depending on the sampling objective, 

either hotspot sampling is performed, or a number of samples are taken to make an 

X %/Y % confidence statement. 

 4. Combined Judgmental/Random (CJR) with Zoning – Both judgmental and probabilistic 

samples can be taken to make X %/Y % confidence statements. Judgment samples 

potentially carry more weight, and a Bayesian prior is applied that factors in existing, but 

not sufficient, evidence that the area may be clean. Random samples are also taken to 

supplement judgmental samples and to establish a sufficient number of samples to 

achieve the X %/Y % confidence statement. 

The following example is presented to show how different sampling strategies can be 

compared. In this example, the four sampling strategies mentioned above are applied to 

Cluster 1. VSP was used to generate the probabilistic sample locations. Because entire rooms 

were contaminated in the methodology used, locations of judgment samples were not critical 

for this exercise and do not affect the results. Therefore, judgmental sample locations were 

selected by PNNL based loosely on locations from previous live tests [22]. However, during an 

actual contaminant release, judgment sample locations would be chosen by contamination 

sampling experts depending on the amount of information known about the release [4]. If 

judgment sample locations were to influence the results, such as scenarios where rooms were 

only partially contaminated, the assistance of a sampling expert should be used to choose 

judgment sample locations. 

The zoning strategy shown previously in Figure 20 is designed for scenarios from Cluster 1 (1st

Floor Releases with Supply Fan OFF). First, the four sampling strategies for this case will be 

applied by showing sample locations, summarizing the strategies in a table, and overlaying the 

sampling strategy onto the ground truth data based on the specific scenario shown in Figure 10 

and Figure 11. The strategies will be compared to show how they differ with respect to the 

conclusions that can be drawn by applying them. Summaries and conclusions for the remaining 

four clusters will be provided in table form with respect to the zoning strategies applied on 

each floor. 

Sample sizes for the purely judgmental and purely probabilistic approaches are the same for 

Clusters 2-5 as in Cluster 1. For the two strategies that include zoning, samples sizes within the 

zones are also the same in Clusters 2-5 as in Cluster 1, but it should be noted that some zoning 

strategies have fewer zones resulting in fewer samples. For instance, the 1st floor of Cluster 3 

consists entirely of a Class 3 zone, so the conclusions are made without taking the additional 

samples needed for a separate Class 2 zone. Additionally, Cluster 5 has only Class 2 zones on 

both floors. Therefore, Cluster 5 scenarios do not need the additional samples needed for 

separate Class 1 and Class 3 zones. 
33 

It is important to note that the following comparisons of the four sampling strategies are based 

on single realizations of various sampling plans which have been applied to the data generated 

by CONTAM simulations which in this case are being taken as the ground truth. Consequently, 

the conclusions which can be drawn from a particular sampling design are relevant only for 

1) the particular set of judgmental and/or probabilistic sample locations selected in that design 

and 2) the particular results generated by CONTAM for that simulation. For this reason, a 

rigorous statistical evaluation of the four sampling strategies would only be possible with 

thousands of realizations of the various sampling designs to verify the precision of their 

statistical confidence statements. Nonetheless, the application of the four sampling strategies 

to the five cluster scenarios below provides a valuable illustration of the strengths and 

weaknesses of each of the sampling strategies. A detailed report of a rigorous statistical 

validation of the sampling designs for buildings in VSP is currently in preparation (as part of the 

DHS funded Chemical Operation Technology Demonstration). 

4.4.1 Cluster 1 – 1st Floor Release with Supply Fan OFF 

Table 2 shows the four sampling methods and number of samples required for the Cluster 1 

scenarios, i.e., 1st floor releases with the supply fans OFF. Sample locations for the purely 

judgmental approach are shown in Figure 25 and Figure 26. Sample locations for the purely 

probabilistic approach are shown in Figure 27 and Figure 28. Sample locations for the purely 

probabilistic with zoning approach are shown in Figure 29 and Figure 30. Sample locations for 

the CJR with zoning approach are shown in Figure 31 and Figure 32. 

The purely judgmental approach proves useful in identifying some rooms as being 

contaminated. Based on the ground truth overlay (Figure 10 and Figure 11), many rooms were 

sampled once or twice with no samples exceeding the ACL level. This is not enough samples to 

state with a high degree of statistical confidence that the room is clean. Additional sampling 

would be required to confidently identify these rooms as clean. This shows that judgmental 

sampling is effective at identifying rooms as contaminated when all grid cells are contaminated. 

The purely probabilistic approach also detected contaminated areas. However, because the 

two purely probabilistic sampling designs were applied to the entire 1st floor and the entire 2nd

floor, the possible cleanliness of smaller areas on those floors could not be readily quantified 

with X %/Y % confidence statements. This is the case in the evaluation of the simulated 

scenario. If the samples are considered on an individual basis, many of the same conclusions 

drawn from the purely judgmental sampling approach can be made, i.e., some rooms can be 

identified as being contaminated. The purely probabilistic approach has the potential 

advantage of making X %/Y % confidence statements about an entire floor if none of the 

samples are above the ACL. 

The purely probabilistic with zoning approach has a potential economic advantage over nonzoning approaches since the floors are sub-divided. This can lead to cost savings associated 

with eliminating portions of a floor as candidates for decontamination. When this sampling 

strategy is applied to the simulated scenario, the result is that the Class 3 zones on the 1st and 

2nd floor are deemed 95 % confident that at least 95 % of the grid cells are below the ACL. This 
34 

is more than half of the total surface area which could reduce decontamination costs if the 

building configuration and decontamination methods were amenable to such savings. It is 

important to note a 95 %/95 % was used in this exercise because of the tendency for rooms to 

be either entirely clean or entirely contaminated. In other types of release scenarios where 

there is the potential for very small portions of rooms to be contaminated, it is recommended 

that a stronger confidence statement be used such as 95 %/98 % or 95 %/99 %. 

The CJR with zoning approach takes advantage of prior information to effectively make 

conclusions about some portions of the building while requiring fewer samples. The CJR 

method yields similar conclusions to the purely probabilistic approach, but has the added 

advantages provided by both Bayesian prior and judgmental sampling. The Bayesian prior 

accounts for the probability that contamination is not present, which can be a reasonable 

assumption for Class 3 zones. This accounts for the sampling experts’ ability to identify likely 

areas of contamination, and results in much fewer samples as shown in Table 2, i.e., 145 for CJR 

with zoning versus 238 for purely probabilistic with zoning. It should be noted that the 

Bayesian prior used in this exercise was very conservative, and a less conservative (lower) 

Bayesian prior would result in fewer random samples being required. 
35 

Table 2 – Comparison of sampling strategies for Cluster 1 (1st Floor Release with Supply Fans ON). 

Comparison based on scenario shown in Figure 10 and Figure 11. 

Sampling 

Strategy Floor Zone # Judg. 

Samples 

# Prob. 

Samples Total 

Conclusion if no samples 

above the Acceptable 

Contamination Level (ACL) 

Comparison to Simulated Scenario 

Purely 

Judgmental 

1 n/a 30 0 30 

No contamination found, 

but cannot state statistical 

confidence 

Several rooms contaminated, others 

possibly clean but no statistical 

confidence 

2 n/a 24 0 24 

No contamination found, 

but cannot state statistical 

confidence 

Several rooms contaminated, others 

possibly clean but no statistical 

confidence 

Total 54 0 54 

Purely 

Probabilistic 

1 n/a 0 58 58 95 % confident that 95 % of 

the floor is below the ACL 

Some rooms contaminated 

2 n/a 0 58 58 95 % confident that 95 % of 

the floor is below the ACL 

Some rooms contaminated 

Total 0 116 116 

Purely 

Probabilistic 

with Zoning 

1 Class 1 6 0 6

No contamination found, 

but cannot state statistical 

confidence 

The room is contaminated 

1 Class 2 0 58 58 

95 % confident that 95 % of 

the Zone is below the ACL 

The rooms sampled are 

contaminated, and become class 1 

zones. 

1 Class 3 0 58 58 

95 % confident that 95 % of 

the floor is below the ACL 

No contamination found. We are 

95 % confident that at least 95 % of 

the zone is clean or below the ACL 

2 Class 2 0 58 58 

95 % confident that 95 % of 

the floor is below the ACL 

The rooms sampled are 

contaminated, and become Class 1 

zones.

2 Class 3 0 58 58 

95 % confident that 95 % of 

the floor is below the ACL 

No contamination found. We are 

95 % confident that at least 95 % of 

the zone is clean or below the ACL 

Total 6 232 238 

CJR with 

Zoning

1 Class 1 6 0 6

No contamination found, 

but cannot state statistical 

confidence 

The room is contaminated 

1 Class 2* 16 24 40 

95 % confident that 95 % of 

the floor is below the ACL 

The rooms sampled are 

contaminated, and become class 1 

zones. 

1 Class 3* 8 23 31 

95 % confident that 95 % of 

the floor is below the ACL 

No contamination found. We are 

95 % confident that at least 95 % of 

the zone is clean or below the ACL 

2 Class 2* 7 39 46 

95 % confident that 95 % of 

the floor is below the ACL 

The rooms sampled are 

contaminated, and become class 1 

zones. 

2 Class 3* 17 5 22 

95 % confident that 95 % of 

the floor is below the ACL 

No contamination found. We are 

95 % confident that at least 95 % of 

the zone is clean or below the ACL 

Total 54 91 145 

*Parameter inputs for CJR method: 50 % probability that judgment sample location contains detectable contamination and 

are twice as likely to contain detectable contamination as random sample locations. 
36 

Figure 25 – Purely Judgmental sample locations on 1st Floor 

Figure 26 – Purely Judgmental sample locations on 2nd Floor 
37 

Figure 27 – Purely Probabilistic sample locations on 1st Floor 

Figure 28 – Purely Probabilistic sample locations on 2nd Floor 
38 

Figure 29 – Purely Probabilistic with Zoning sample locations on 1st Floor 

(Blue = random, Pink = judgment) 

a

Figure 30 – Purely Probabilistic with Zoning sample locations on 2nd Floor 
39 

Figure 31 – CJR with Zoning sample locations on 1st Floor 

(Blue = random, Pink = judgment) 

Figure 32 – CJR with Zoning sample locations on 2nd Floor 

(Blue = random, Pink = judgment) 
40 

4.4.2 Cluster 2 – 1st Floor Release with Supply Fan ON 

Conclusions from comparing the simulated scenario to the different sampling approaches for 

Cluster 2 are shown in Table 3. Because most rooms were contaminated, judgment sampling 

worked well at detecting contaminated areas. Purely probabilistic sampling also identified 

contaminated areas. However, because the purely probabilistic sampling design was applied to 

the entire 1st floor, the possible cleanliness of smaller areas on that floor could not be readily 

quantified with an X %/Y % confidence statement. Both of the sampling strategies using a 

zoning approach were able to categorize the Class 3 zones as uncontaminated with 95 % 

confidence that at least 95 % of the area was below the ACL, but the CJR with zoning approach 

required fewer samples. 

Table 3 – Comparison of sampling strategies for Cluster 2 (1st Floor Release with Supply Fans ON). 

Comparison based on scenario shown in Figure 12 and Figure 13. 

Sampling 

Approach Floor Class 1 Zones Class 2 Zones Class 3 Zones 

Purely 

Judgmental 

AND 

Purely 

Probabilistic 

1 All but one room contaminated. For this room, there is not a high level of confidence that 

the room is clean due to insufficient sample size. 

2 All but two rooms contaminated. For these rooms, there is not a high level of confidence 

that the room is clean due to insufficient sample size. 

Purely 

Probabilistic 

with Zoning 

1 Conclude the zone is 

contaminated

Conclude the zone is 

contaminated

Conclude with 95 % confidence that at 

least 95 % of the zone does not contain 

detectable contamination 2 N/A

CJR with 

Zoning

1 Conclude the zone is 

contaminated

Conclude with 95 % confidence that at 

least 95 % of the zone does not contain 

detectable contamination, but with 

fewer samples than purely 

probabilistic. 

2 N/A
41 

4.4.3 Cluster 3 – 2nd Floor Release with Supply Fan OFF 

Conclusions from comparing the simulated scenario to the different sampling approaches for 

Cluster 3 are shown in Table 4. Judgment sampling worked well at detecting contaminated 

areas but was not able to clear the first floor as being uncontaminated due to insufficient 

sample size. Purely probabilistic sampling identified contaminated areas on the 2nd floor 

resulting in 95 % confidence that at least 95 % of the 1st floor was below the ACL. Both of the 

sampling strategies using a zoning approach were able to categorize the Class 3 zones (all of the 

1st floor and part of the 2nd floor) as uncontaminated with 95 % confidence that at least 95 % of 

the area was below the ACL, but the CJR with zoning approach required fewer samples. 

Table 4 – Comparison of sampling strategies for Cluster 3 (2nd Floor Release with Supply Fans OFF). 

Comparison base on scenario shown in Figure 14 and Figure 15. 

Sampling 

Approach Floor Class 1 Zones Class 2 Zones Class 3 Zones 

Purely 

Judgmental 

1 No contamination was found, but a statistical confidence statement cannot be made due 

to insufficient sample size and non-random sampling. 

2 

Contamination was found in slightly more than half of the rooms. For the 

uncontaminated rooms, there is not a high level of confidence that the rooms are clean 

due to insufficient sample size. 

Purely 

Probabilistic 

1 No contamination was found. There is 95 % confidence that at least 95 % of the floor 

does not contain detectable contamination.

2 

Contamination was found in slightly more than half of the rooms, thus it is concluded the 

floor contains detectable contamination. For several uncontaminated rooms, there is not 

a high level of confidence that the rooms are clean due to insufficient sample size. 

Purely 

Probabilistic 

with Zoning 

1 N/A

Conclude with 95 % confidence that at 

least 95 % of the floor does not contain 

detectable contamination. 

2 Conclude the zone is contaminated

Conclude with 95 % confidence that at 

least 95 % of the zone does not contain 

detectable contamination. 

CJR with 

Zoning

1 N/A

Conclude with 95 % confidence that at 

least 95 % of the floor does not contain 

detectable contamination, but with fewer 

samples than purely probabilistic. 

2 Conclude the zone is contaminated

Conclude with 95 % confidence that at 

least 95 % of the zone does not contain 

detectable contamination, but with fewer 

samples than purely probabilistic. 
42 

4.4.4 Cluster 4 – 2nd Floor Release with Supply Fan ON 

Conclusions from comparing the simulated scenario to the different sampling approaches for 

Cluster 4 are shown in Table 5. Because most rooms were contaminated, judgment sampling 

worked well at detecting contaminated areas. Purely probabilistic sampling also identified 

contaminated areas. However, because the two purely probabilistic sampling designs were 

applied to the entire 1st floor and the entire 2nd floor, the possible cleanliness of smaller areas 

on those floors could not be readily quantified with X %/Y % confidence statements. Both of 

the sampling strategies using a zoning approach were able to categorize the Class 3 zone on the 

2nd floor as uncontaminated with 95 % confidence that at least 95 % of the area was below the 

ACL, but the CJR approach required fewer samples. Both of the strategies with zoning led to 

the reclassification of the 1st floor Class 3 zone as a Class 1 zone, because one of the rooms 

contained detectable contamination. 

Table 5 – Comparison of sampling strategies for Cluster 4 (2nd Floor Release with Supply Fans ON). 

Comparison based on scenario shown in Figure 16 and Figure 17. 

Sampling 

Approach Floor Class 1 Zones Class 2 Zones Class 3 Zones 

Purely 

Judgmental 

1 Most rooms are contaminated. For three rooms not contaminated, there is not a high 

level of confidence that the room is clean due to insufficient sample size. 2 

Purely 

Probabilistic 

1 Contamination was found in most, and thus it is concluded the floor contains detectable 

contamination. For three uncontaminated rooms, there is not a high level of confidence 

that the rooms are clean due to insufficient sample size. 2 

Purely 

Probabilistic 

with Zoning 

AND 

CJR with 

Zoning

1 N/A

Conclude the zone 

is contaminated

Low levels of contamination were found in 

one room in the zone, and therefore the zone 

is reclassified as Class 1. 

2 Conclude the zone 

is contaminated

Conclude with 95 % confidence that at least 

95 % of the zone does not contain detectable 

contamination, but with fewer samples than 

purely probabilistic. 
43 

4.4.5 Cluster 5 – Outdoor Release 

Conclusions from comparing the simulated scenario to the different sampling approaches for 

Cluster 5 are shown in Table 6. Because all of the rooms were contaminated on both floors, all 

of the methods conclude both floors are entirely contaminated. 

Table 6 – Comparison of sampling strategies for Cluster 5 (Outdoor Release). Comparison based on 

scenario shown in Figure 18 and Figure 19. 

Sampling 

Approach Floor Class 1 Zones Class 2 Zones Class 3 Zones 

Purely 

Judgmental

AND 

Purely 

Probabilistic 

1 

Conclude the entire floor is contaminated. All samples were contaminated. 

2 

Purely 

Probabilistic 

with Zoning

AND 

CJR with 

Zoning

1 

N/A 

Conclude the entire floor is 

contaminated. All samples were 

contaminated.

N/A 

2 
44 

4.4.6 Summary of Sampling Strategy Comparisons 

The scenarios examined have rooms which were either entirely clean or entirely contaminated. 

The number of samples for each method was sufficient enough so that each room was sampled 

at least once. This results in all sampling strategies being very effective at identifying the 

contaminated areas for these particular scenarios. Where the strategies differ is in the 

statements that can be made about areas where no contamination was found, and in the 

number of samples needed to make conclusions. 

One key difference between the sampling strategies is their ability to make X %/Y % statements 

about areas where no contamination was found. The scenarios examined for Clusters 1 

through 4 had rooms on at least one floor that were not contaminated. For each of these 

scenarios, the two strategies which used zoning were able to identify parts of the building 

where it was concluded with high confidence (95 %) that most (95 %) of these areas were 

uncontaminated, and for three of the four cases were able to identify such areas on both floors. 

The purely probabilistic strategy was able make an X %/Y % statement about the 1st floor of the 

scenario examined for Cluster 3. The purely judgmental strategy could not make an acceptable 

X %/Y % statement about any areas. In general, the methods using zoning were most effective 

at making X %/Y % statements and identifying areas which did not require decontamination. 

The CJR strategy with zoning required fewer samples while making the same conclusions as the 

purely probabilistic with zoning strategy. 

Scenarios where an entire floor was contaminated were identified by all sampling strategies 

with the purely judgmental strategy requiring the fewest samples. 

The results of this exercise support the expected strengths of the different methods. Purely 

judgmental sampling requires fewer samples for confirming contamination in areas known or 

highly likely to be contaminated. If areas can be classified as Class 3, applying a probabilistic or 

CJR sampling method allows the sampling team to make an X %/Y % about these areas if no 

contamination is found, and decontamination may not be required. When sampling experts 

have sufficient prior information about the release event, the CJR method allows X %/Y % 

statements to be made with fewer samples. 
45 

5 Conclusions 

CONTAM and VSP have been shown to be useful for evaluating multiple sampling strategies as 

applied to buildings under a wide range of contaminant release, building configuration and 

operating scenarios. CONTAM can be used to perform virtual experiments to supplement 

actual experiments. Its strength lies in its ability to perform multiple simulations of a wide 

range of scenarios in relatively little time when compared to actual testing. VSP provides the 

capability to explore and employ a wide range of sampling strategies and has functionality 

specifically designed for the sampling of buildings. In combination, VSP and CONTAM provide a 

powerful set of tools with which investigators can study potential building-specific sampling 

strategies or obtain general knowledge related to building sample designs. 

One of the fundamental outcomes of this project was the demonstration of how CONTAM and 

VSP can be used together to effectively develop sampling plans to support the various stages of 

response to a CBRN event. Following such an event, incident details and the conceptual site 

model could be used to create CONTAM predictions of contaminant dispersion within a 

building. These predictions would then be used to identify priority area zones within the 

building and then sampling designs and strategies could be developed based on those zones. 

Furthermore, CONTAM and VSP could potentially be used in preplanning exercises for a CBRN 

event. CONTAM and VSP can be used to explore a priori how one might best delineate priority 

sampling zones within a building in a manner applicable to a wide range of plausible scenarios. 

Viewing a large number of scenarios can allow the classification of an event based on a reduced 

set of parameters. This could allow sampling experts to initially choose better sampling 

approaches for different portions of the building, lead to clearing uncontaminated areas more 

quickly, provide a more accurate means of determining the number of samples required to 

meet specific sampling goals, and even improve targeting of decontamination efforts. 

CONTAM and VSP can be used to evaluate the effectiveness of various sampling plans given 

varying ground truth scenarios. It was demonstrated that applying separate sampling designs 

within each zone can lead to some areas of the building being cleared when otherwise they 

would have been decontaminated or required further sampling. Together these tools can 

provide insight into the potential trade-offs between the number of samples, sampling 

locations, and associated uncertainty statements of the various sample designs that could be 

generated for a given set of scenarios. 

CONTAM and VSP can be used to identify ground truth scenarios that yield similar 

contamination patterns, and thus could use the same sampling plan. Cluster analysis was used 

to show that contaminant dispersion patterns were similar across multiple scenarios, and 

therefore the same sampling plan could be used for such cases. This revealed the potential to 

reduce the number of sampling approaches or zoning schemes required to successfully cover a 

range of possible contamination scenarios. 

This study was based on the uniform concentration assumption of CONTAM and empirically 

determined deposition rates from other studies. Consequently, hotspot scenarios were not 

addressed and variation in deposition due to other factors were not accounted for, e.g., surface 
46 

roughness and orientation, airflow, temperature and humidity. CONTAM does provide the 

ability to simulate more detailed contaminant distribution on a sub-room scale, e.g., using onedimensional convection/diffusion and computational fluid dynamics (CFD) within selected 

zones. This is another possible area to follow-up to utilize and improve upon the simulation of 

transport phenomenon within modeling tools in order to address a broader range of scenarios 

and to further evaluate the combined effectiveness of integrating sample planning tools. 

The CONTAM-VSP tool could be used to leverage the integration of VSP with the Building 

Restoration Operations Optimization Model (BROOM) [23] developed by Sandia National 

Laboratories which is another DHS investment. An integrated CONTAM-VSP-BROOM tool 

would provide users with start-to-finish sample design and collection capability. CONTAM 

would provide simulated predictions to guide zoning strategies in order to create optimal 

sampling designs in VSP. These sampling designs would then be passed to BROOM to facilitate 

data acquisition and management. Data analysis of laboratory results could then be conducted 

by BROOM and VSP. Such a tool, when properly used, could reduce the time and cost of 

response and restoration by facilitating the rapid design and execution of validated, statistically 

defensible sampling plans. 
47 

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48 

Acknowledgements 

This report was developed with the support of Dr. Bert Coursey, Director of the Office of 

Standards, Department of Homeland Security and the NIST Office of Law Enforcement 

Standards. The authors would like to express their appreciation to Dr. Barbara Jones for her 

role in supporting the initiation of this project and to acknowledge the support of the members 

of the Validated Sampling Plan Working Group led by Dr. Randy Long of the Department of 

Homeland Security.