SC IEN CE

United States

Environmental Protection

Agency

EPA/600/R-08/052 | December 2008 | www.epa.gov/ord

Theoretical and Experimental 

Analysis of Important Parameters 

for Determining the Impact of a 

Biological Attack on a Building

FINAL REPORT

Office of Research and Development

National Homeland Security Research Center
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EPA/600/R-08/052 | December 2008 | www.epa.gov/ord

FINAL REPORT ON

Theoretical and Experimental Analysis of 

Important Parameters for Determining the 

Impact of a Biological Attack on a Building

Contract No. GS-10F-0275K

Task Order 1105

Prepared for

Joseph Wood and Les Sparks, Project Officers

U.S. ENVIRONMENTAL PROTECTION AGENCY

Research Triangle Park, NC

Prepared by

Brian E. Hawkins, Ph.D. and Kent C. Hofacre

“WARNING – This document may contain technical data whose export is restricted 

by U.S. law. Violators of export control laws may be subject to severe legal penalties. 

Do not disseminate this document outside the United States or disclose its contents to 

non-U.S. persons except in accordance with applicable laws and regulations and after 

obtaining any required authorizations.”

BATTELLE COLUMBUS OPERATIONS

505 King Avenue

Columbus, Ohio 43201-2693 

Office of Research and Development

National Homeland Security Research Center, Decontamination and Consequence Management Division
ii

Disclaimer

This report is a work prepared for the United States government by Battelle. In no event 

shall either the United States government or Battelle have any responsibility or liability 

for any consequences of any use, misuse, inability to use, or reliance on the information 

contained herein, nor does either warrant or otherwise represent in any way the accuracy, 

adequacy, efficacy, or applicability of the contents hereof.
iii

Table of Contents

Glossary ........................................................................................................................................................................vii

Executive Summary.......................................................................................................................................................ix

1.0 Introduction...............................................................................................................................................................1

2.0 Objective ...................................................................................................................................................................1

3.0 Scope.........................................................................................................................................................................1

4.0 Model Development..................................................................................................................................................3

4.1 General Approach ...........................................................................................................................................3

4.2 Simplified Model Development......................................................................................................................3

5.0 Experimental Methods..............................................................................................................................................7

5.1 Test Design .....................................................................................................................................................7

5.2 Test Building Description ...............................................................................................................................8

5.3 Release Methods...........................................................................................................................................12

5.4 Sampling Methods........................................................................................................................................13

6.0 Preliminary Efforts..................................................................................................................................................15

6.1 Airflow Measurements..................................................................................................................................15

6.2 Leakage Tests................................................................................................................................................20

7.0 Experimental Results..............................................................................................................................................21

8.0 Discussion of Experimental Results .......................................................................................................................41

9.0 Model Impact Analysis...........................................................................................................................................43

9.1 Impact Analysis Approach............................................................................................................................43

9.2 Impact Analysis Limitations.........................................................................................................................47

9.3 Preliminary Impact Analysis Results............................................................................................................48

9.4 Parameter Space Map Impact Analysis ........................................................................................................50

9.4.1 “Large” Notional Building Parameter Space Map Results ................................................................51

9.4.2 “Small” Notional Building Parameter Space Map Results ................................................................54

9.5 Functional Analysis Guidelines....................................................................................................................59

9.5.1 Contaminant Transport Dominated by HVAC Mechanisms ..............................................................60

9.5.2 Contaminant Transport Dominated by Interzonal Leakage................................................................60

9.5.3 Perfect Filtration.................................................................................................................................61

10.0 In-Room Air Cleaners...........................................................................................................................................63

11.0 Conclusions and Recommendations .....................................................................................................................65

12.0 References.............................................................................................................................................................67

Appendix A. SF6 Experimental Methods and Results ................................................................................................A-1

Appendix B. Preliminary Simulation Results.............................................................................................................B-1

Appendix C. Data Quality...........................................................................................................................................C-1
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Lists of Figures

Figure 1. Well-Mixed Zone Model Diagram...............................................................................................................4

Figure 2. Test Building HVAC Region Diagram.........................................................................................................9

Figure 3. Alterations to the Upper Floor (3rd Floor) of the Test Volume...................................................................10

Figure 4. Alterations to the Lower Floor (2nd Floor) of the Test Volume ...............................................................11

Figure 5. Photograph of the Particulate Eductor Release Mechanism ......................................................................12

Figure 6. Particle Size Distribution of Visolite® Aerosolized with an Eductor Release Mechanism 

Operating at a Gas Flow Rate of 100 lpm as Measured by an Aerosizer®.................................................13

Figure 7. Photograph of MetOne Handheld Optical Particle Counter ......................................................................13

Figure 8. Typical Particulate Data Gathered by a MetOne During Testing...............................................................14

Figure 9. Photographs of Flow Measurement Using a.) a Balometer and b.) an Anemometer.................................15

Figure 10. Sampling Scheme for “Large” Notional Building .....................................................................................24

Figure 11. Sampling Scheme for “Small” Notional Building .....................................................................................25

Figure 12. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with 

Moderate Recirculation, Low Leakage, Moderate-Low Filtration, Standard Makeup Air, and 

Standard Infiltration ...................................................................................................................................27

Figure 13. Demonstration of Repeatability by Comparison of Experimental and Model-Predicted Data 

for “Large” Notional Building with Moderate Recirculation, Low Leakage, Moderate-Low Filtration, 

Standard Makeup Air, and Standard Infiltration ........................................................................................28

Figure 14. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building 

with Moderate Recirculation, Low Leakage, Moderate Filtration, Standard Makeup Air, 

and Standard Infiltration.............................................................................................................................28

Figure 15. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building

with Moderate Recirculation, Low Leakage, Low Filtration, Standard Makeup Air, 

and Standard Infiltration.............................................................................................................................29

Figure 16. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building

with Moderate Recirculation, Low Leakage, High Filtration, Standard Makeup Air, 

and Standard Infiltration.............................................................................................................................29

Figure 17. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building 

with Moderate Recirculation, High Leakage, Moderate Filtration, Standard Makeup Air, 

and Standard Infiltration.............................................................................................................................30

Figure 18. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building

with Moderate Recirculation, High Leakage, Low Filtration, Standard Makeup Air, 

and Standard Infiltration.............................................................................................................................30

Figure 19. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building

with Moderate Recirculation, High Leakage, High Filtration, Standard Makeup Air, 

and Standard Infiltration.............................................................................................................................31

Figure 20. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building

with Moderate Recirculation, Low Leakage, Moderate Filtration, Standard Makeup Air, 

and Standard Infiltration.............................................................................................................................31

Figure 21. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building 

with Moderate Recirculation, High Leakage, Moderate Filtration, Standard Makeup Air, 

and Standard Infiltration..............................................................................................................................32

Figure 22. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building with Moderate 

Recirculation, Low Leakage, High Filtration, Standard Makeup Air, and Standard Infiltration ...............33

Figure 23. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building with Moderate

Recirculation, High Leakage, High Filtration, Standard Makeup Air, and Standard Infiltration ..............33

Figure 24. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building with Moderate

Recirculation, High Leakage, Low Filtration, Standard Makeup Air, and Standard Infiltration ................34
v

Lists of Figures (Continued)

Figure 25. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building with Moderate

Recirculation, Low Leakage, Low Filtration, Standard Makeup Air, and Standard Infiltration................34

Figure 26. Photographs of Various Door Positions for the “Large” Notional Building (i.e., the door between 

B113 and B114) and Recirculation Rates (i.e., low or moderate) with Corresponding Interzonal 

Leakage Rates............................................................................................................................................35

Figure 27. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with

Low Recirculation, Very Low Leakage, Moderate Filtration, Standard Makeup Air, and 

Standard Infiltration ...................................................................................................................................36

Figure 28. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with Low

Recirculation, Low Leakage, Moderate Filtration, Standard Makeup Air, and Standard Infiltration........36

Figure 29. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with Low

Recirculation, High Leakage, Moderate Filtration, Standard Makeup Air, and Standard Infiltration .......37

Figure 30. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with Low

Recirculation, Low Leakage, Low Filtration, Standard Makeup Air, and Standard Infiltration................37

Figure 31. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with Low

Recirculation, Low Leakage, High Filtration, Standard Makeup Air, and Standard Infiltration ...............38

Figure 32. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building with 

Low Recirculation, Moderate Leakage, Moderate Filtration, Standard Makeup Air, 

and Standard Infiltration.............................................................................................................................39

Figure 33. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building with Low

Recirculation, Moderate Leakage, Low Filtration, Standard Makeup Air, and Standard Infiltration........39

Figure 34. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building with Low

Recirculation, Moderate Leakage, High Filtration, Standard Makeup Air, and Standard Infiltration .......40

Figure 35. Illustration of Exposure-Based Metric Inadequacies for a Hypothetical Case of Various Filtration 

Efficiencies, Equal Zone Volumes, Makeup Air, Moderate Recirculation, Infiltration,

and High Leakage ......................................................................................................................................44

Figure 36. Normalized Exposure at 30 Minutes Versus Model Input Parameters for a “Large” 

Notional Building.......................................................................................................................................45

Figure 37. Normalized Time to Critical Exposure Versus Model Input Parameters for a “Large” |

Notional Building.......................................................................................................................................45

Figure 38. Normalized Exposure at 30 MinutesVersus Model Input Parameters for a “Large” Notional Building ...48

Figure 39. Impact Analysis Results for Various Zone 3 Volumes Under 1 ACH Makeup Air, 5 ACH Recirculation,

 0.3 ACH Infiltration, 30% Filtration, and 1 ACH Interzonal Leakage .....................................................50

Figure 40. “Large” Building (14,600 m3) Parameter Mapping Results for a Recirculation Rate of 7 ACH...............53

Figure 41. “Large” Building (14,600 m3) Parameter Mapping Results for a Recirculation Rate of 5 ACH...............53

Figure 42. “Large” Building (14,600 m3) Parameter Mapping Results for a Recirculation Rate of 3 ACH...............53

Figure 43. “Small” Building (1,000 m3) Parameter Mapping Results for a Recirculation Rate of 7 ACH.................57

Figure 44. “Small” Building (1,000 m3) Parameter Mapping Results for a Recirculation Rate of 5 ACH.................57

Figure 45. “Small” Building (1,000 m3) Parameter Mapping Results for a Recirculation Rate of 3 ACH..................57

Figure 46. Parameter Space Map of the Dominant Parameter, or Parameter with the Highest Impact Score,

for a “Small” Building (1,000 m3) .............................................................................................................58

Figure 47. Photograph of In-Room Air Cleaner Used During Testing (Whirlpool Model AP4503H0) .....................63

Figure 48. Comparison of Experimental Data for an In-Room Air Cleaner for a “Large” Notional Building

with Moderate Recirculation, Low Leakage, Moderate Filtration, Standard Makeup Air,

and Standard Infiltration.............................................................................................................................64
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List of Tables

Table 1. Moderate Recirculation Test Matrices .........................................................................................................7

Table 2. Low Recirculation Test Matrices .................................................................................................................8

Table 3. Lower Floor Airflow Measurements Under Moderate Recirculation ........................................................16

Table 4. Upper Floor Airflow Measurements Under Moderate Recirculation.........................................................17

Table 5. Moderate Recirculation Airflow Summary ................................................................................................17

Table 6. Lower Floor Airflow Measurements Under Low Recirculation ................................................................18

Table 7. Upper Floor Airflow Measurements Under Low Recirculation.................................................................19

Table 8. Low Recirculation Airflow Summary ........................................................................................................19

Table 9. Moderate Recirculation Test Matrix Results Key......................................................................................22

Table 10. Low Recirculation Test Matrix Results Key..............................................................................................23

Table 11. Comparison of Experimental and Predicted Performance Metrics for the Zone of Interest for the

“Moderate” Recirculation Condition .........................................................................................................42

Table 12. Comparison of Experimental and Predicted Performance Metrics for the Zone of Interest for the 

“Low” Recirculation Condition .................................................................................................................42

Table 13. Typical Change in Parameters for Use in Calculating Scale Factors.........................................................46

Table 14. Normalized Impact Scores for the Base Case for a “Large” Notional Building........................................47

Table 15. Impact Analysis Results for Various Zone 3 Volumes Under 1 ACH Makeup Air, 5 ACH Recirculation,

0.3 ACH Infiltration, 30% Filtration, and 1 ACH Interzonal Leakage ......................................................49

Table 16. Typical Parameter Values and Model Parameter Ranges...........................................................................49

Table 17. Impact Analysis Results for the “Large” Building (14,600 m3) Parameter Space Map.............................52

Table 18. Impact Analysis Results for the “Small” Building (1,000 m3) Parameter Space Map...............................56

Table 19. Comparison of Experimental Performance Metrics for the Zone of Interest for the “Moderate”

Recirculation Condition With and Without an In-Room Air Cleaner........................................................64
vii

Glossary

ACH Air change rate expressed as airflow in units of volume per hour divided by the zone 

volume in identical volume units (ASHRAE, 1991).

Filtration or Filtration Efficiency The nominal efficiency with which a filter removes particles from an air stream.

Infiltration Uncontrolled inward leakage of outdoor air into a building through cracks and interstices 

in any building element. Normally caused by pressure effects or differences in air density 

(ASHRAE, 1991). Also known as building air infiltration.

Leakage Uncontrolled and unplanned flow between two zones of a building. Normally caused 

by slight pressure differences between the zones. Also know as interzonal leakage or 

room leakage.

Makeup Air Air brought into a building from outside to replace that exhausted. Air intentionally 

brought into a building that was not previously circulated through the building 

(ASHRAE, 1991). Also known as outdoor air.

Recirculation Air taken from a zone and returned to the zone after being passed through conditioning 

elements (ASHRAE, 1991). Also known as recirculated air.

Zone 1 Zone of release

Zone 2 Zone of interest

Zone 3 The lumped zone, which represents the bulk of the building, or the rest of the building.
viii

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ix

Executive Summary

Although it is intuitive that building and heating, ventilation, 

and air conditioning (HVAC) system parameters play a major 

role in determining how an agent disperses after an indoor 

biological attack, how and to what extent those parameters affect 

the transport of contaminants is not well understood. A better 

understanding of how building and HVAC parameters affect 

the transport of an agent will improve the ability to mitigate 

the effects of a biological attack and aid in determining where 

resources in building protection are best directed. Therefore, the 

objective of this project was to theoretically and experimentally 

evaluate the effects that modifications to HVAC and building 

design and operation would have on the spread of biological 

agents (in aerosol form) within a building.

To this end, a three-zone model representation of a building 

was developed to determine which HVAC and building 

parameters are most important, and how accurately they 

need to be known, in determining the impact of an indoor 

bio-agent attack. The three-zone model consists of a zone 

of release and zone of interest, which are adjacent and of 

equal size, as well as a “lumped” zone, which represents 

the remainder of the building. All three zones are serviced 

by a single HVAC system with common recirculation.

The potential impact of an attack was quantified in terms of 

two performance metrics: the cumulative exposure to the agent 

after 30 minutes and the time to reach a critical exposure level 

(referred to as Ct) for that agent. The “critical exposure level” 

was arbitrarily selected. Other values of critical exposure may 

have an effect on the outcomes estimated in this report. A 

sensitivity analysis method was also developed for determining 

the required level of accuracy for each building and HVAC 

operating parameter, i.e., to determine how well each parameter 

must be known to accurately estimate the two performance 

metrics. The analysis method allows for investigation of the 

relative impact of each parameter, i.e., the change in the two 

performance metrics as a result of a change in a parameter.

Field tests were then performed to gather data for the validation 

of both the three-zone modeling concept and the sensitivity 

analysis method for determining parameter impact. To cover the 

broad range of parameters and conditions needed for testing, most 

of the tests were performed without duplicates. The field tests 

consisted of a series of tracer experiments under varying HVAC 

conditions in a test building. Visolite®, a fluorescent-tagged, 

calcium carbonate aerosol, was chosen as the tracer material. The 

aerosol was released using a custom-built eductor. The venue 

for these tests was a three-story building, located in Anniston, 

Alabama, that contained three separate air handling units 

(AHUs), each possessing its own supply and return ductwork 

and each servicing a different region of the building. After a few 

initial building alterations, the test venue was able to represent 

both a large and a small notional building, depending on which 

zones were chosen as the zone of release and zone of interest.

An initial analysis using model simulations revealed that makeup 

air and infiltration rates had little impact during an internal release 

scenario and system parameters were of greater importance 

than single-zone parameters (i.e., the recirculation rate of the 

entire building was more important than the recirculation rate 

of the zone of interest). The initial analysis work also identified 

building size, interzonal leakage rate, recirculation rate, and 

filter efficiency as the key parameters affecting the selected 

performance metrics. The simulation results showed that as the 

building size increased, the filtration efficiency went from being 

a potentially dominant factor to a lesser factor compared to the 

interzonal leakage rate. The system recirculation was found 

to be of secondary importance but had a strong effect on the 

importance of the filtration efficiency for smaller buildings (i.e., 

for building volumes less than five times the zone of interest).

On the whole, the excellent agreement observed between 

experimental and model-predicted data validated the use of 

the three-zone model to approximate contaminant spread in 

a building. Excellent agreement between the lumped third 

zone in the model and multiple rooms throughout the test 

building validated the use of a three-zone model to predict 

contaminant levels in real buildings with more than three 

zones. Modeling, as supported by the experimental data, 

provides a useful tool for estimating and assessing which 

parameters significantly affect the spread of contaminant in a 

building. Buildings of varying size and HVAC performance 

can be assessed. It was demonstrated that changes in HVAC 

filter efficiency, air exchange, and building “size” could be 

varied with a predictable impact on contaminant spread.

Ancillary experiments suggest that the use of an in-room 

air cleaner can greatly reduce the particulate matter level in 

the room. The magnitude of this reduction will vary greatly 

depending on the volume of the room, as well as the throughput 

and efficiency of the in-room air cleaner.

From this project it is clear that there is no one universal answer 

regarding which building or HVAC parameter is most important 

or how accurately it needs to be known. It depends on the release 

location, HVAC characteristics, building characteristics, and 

proximity of the zone of interest to the release location. This 

implies that the usefulness of mitigation strategies to protect 

buildings (or more precisely its occupants) must be considered on 

a case-by-case basis. The sensitivity analysis method discussed 

herein provides some indication of the general trends and 

identifies the most important parameters impacting contaminant 

spread for various combinations of HVAC parameters and 

building volumes relative to the zone of interest. The modeling 

approach developed here could be used to assess various 

scenarios and buildings of specific interest, without the need for 

extensive knowledge of HVAC and building parameters. Using 

the modeling tool developed for initial analysis may be useful in 

determining the merits of modifying the building to enhance the 

protection of occupants or to mitigate the spread of contaminants.
x

Recommendations for future study focus on two aspects of the 

well-mixed model: usability and applicability. The usability 

of the model could be improved by developing a user-friendly 

graphical user interface (GUI), which would allow casual users 

(e.g., building operators) to rapidly perform simple impact 

analyses for a building of interest given limited building 

information (e.g., HVAC settings, building and room volumes). 

The development of a GUI would increase the utility and impact 

of this effort by making the model available to more people. Also 

recommended is an enhancement to expand the applicability of 

the tool by developing and verifying an analogous model for a 

building with a more complex HVAC ductwork scheme. The 

present model is applicable only to a building with one common 

return ductwork system. Developing an analogous model that 

effectively represents buildings with multiple return ductwork 

systems (i.e., multiple air handling units) and performing an 

experimental verification, similar to this work, would aid in 

making a model more applicable to large buildings with more 

complex air handling schemes.
1

1.0

Introduction

In general, the spread of a contaminant following the release 

of a biological agent within a building is affected by the 

building characteristics (e.g., building volume, air infiltration 

rates, or room-to-room leakage) and parameters of the HVAC 

system. However, a quantitative understanding of the relative 

impacts that each of the building and HVAC parameters has 

on contaminant dispersion is needed. A better understanding 

of these parameters’ impacts will improve the ability to 

protect buildings from a biological attack. Therefore, a 

project was undertaken in which theoretical and experimental 

analyses were conducted to determine the impact that HVAC 

and building modifications have on the spread of particulate 

agents within a building. The results of the theoretical 

analyses would guide the design of experiments, and the 

experiments would help to validate the model.

A building test bed in Anniston, Alabama, was selected 

for the experimental program, as it allowed for access 

and control of most of the parameters of interest. 

The size of the building was also shown to influence 

which controllable building or HVAC parameter was 

important regarding the control of contaminant spread. 

Although the size of the building is not a controllable 

parameter in actual applications, the test bed facility did 

allow manipulation of room sizes so that “large” and 

“small” building configurations could be assessed.

2.0

Objective

The objective of this project was to evaluate the impact that 

modifications to HVAC and building design and operation 

have on the spread of biological (aerosolized) agents within 

a building. In the course of conducting this project—

specifically the theoretical analysis of important parameters 

for determining the impact of an attack on buildings —it 

became apparent that another objective would also be to 

experimentally demonstrate the application of the three-zone, 

well-mixed model.

3.0

Scope

The approach and development of a three-zone, well-mixed 

model is described. The experimental methods used during 

this study are detailed. A series of experiments at a test bed 

were used to experimentally confirm the usefulness of a 

three-zone, well-mixed volume model in simulating a “real” 

building. The results are then used to analyze what parameter 

changes had the largest impact on potential building 

performance, and conclusions that can be made from this 

work are stated.
2

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3

4.0

Model Development

4.1 General Approach

The modeling approach taken was to develop an algorithm 

to estimate contaminant transport in a notional building as 

a function of time. The model allowed building parameters 

(e.g., HVAC airflow, filter efficiency) to be investigated. The 

model was used to generate time-dependent contaminant 

concentration profiles in zones of interest, which were then 

used as performance metrics for estimating exposures. Model 

parameters were then varied over a range of conditions so 

that the changes in performance metrics could be assessed. 

These changes in performance metrics were then used to 

determine the most important parameters and how well 

they need to be known. (In a separate but related project, a 

more mechanistic model of contaminant transport based on 

aerosol dynamics is being developed. That model considers 

contaminant concentrations in zones of a notional building 

and how HVAC flow, air exchange rates, infiltration rates, 

and other parameters impact those concentrations. That 

model has been leveraged for consistency with this simplified 

approach as briefly discussed below. The simplified model 

approach, input parameters of interest, performance metrics, 

and data interpretation/analysis process are discussed in 

Sections 4.2 through 4.5 with a few illustrative examples.)

The model approach used for this analysis of building and HVAC 

parameters and their effect on hazard exposure is purposely 

general to allow consideration for a range of building types and 

sizes. The analysis is based on relative changes in exposure in a 

zone of interest with changes in building or HVAC parameters. 

The absolute determination of whether a change in HVAC or 

building parameter is important (whether a resulting exposure 

was reduced below a lethal concentration, for example) is not 

determined by this analysis. Absolute determination of those 

effects is dependent on scenario attack parameters such as agent 

release, building occupant exposure durations, and agent type. 

So, for example, in some scenarios it may have significantly 

different impacts on occupants if a 90% efficient filter is used and 

in other scenarios it may not make a difference. That is not to say 

that the model presented here is not valid for estimating resulting 

aerosol and other contaminant concentrations as a function 

of time and zone for specific release and building operating 

scenarios, just that the approach presented here for determining 

the effects of various parameters is not an absolute approach. The 

fact that the model developed here is generalized and based on 

relative change in exposure does not diminish its usefulness. The 

model allows for quick assessment and sensitivity analysis, with 

little specific building information. Thus, it is very applicable for 

building engineers and planners.

The HVAC Aerosol Dynamics Model, HVACADM, developed 

under a separate project, was leveraged here for investigating the 

effects of different contaminant behavior mechanisms. (Although 

the primary focus of the modeling and experimental program is 

for agents in particulate form, the approach could also be adapted 

for chemical agents as well.) To leverage the HVACADM model 

to address the objectives of this task, a simplified model that 

focuses on mass balances around three zones was developed. This 

simplified model allows the parameters affecting contaminant 

transport to be assessed without the use of mechanistic models.

The goal of the project is two-fold: (1) determine which 

parameters are most important for predicting the impact of a CB 

attack on a building and (2) determine the degree of accuracy to 

which these parameters needs to be known for practical purposes 

(i.e., at what point the level of exposure becomes insensitive to

a change in the parameters controlling the contaminant spread 

and exposure).

The nomenclature used to define building and HVAC parameters 

in this report follows as closely as possible that used by the 

American Society of Heating, Refrigerating and Air-Conditioning 

Engineers (ASHRAE). In this fashion, consistent use of terms is 

intended to aid with an understanding of the model approach and 

results. Definitions of the key terms are provided in the Glossary 

section of this report.

4.2 Simplified Model Development

The simplified model is based on the well-mixed zone 

methodology, which implies instantaneous, perfect mixing within 

a zone and thus a uniform concentration throughout the zone 

volume. A mass balance was applied to each well-mixed zone to 

derive the governing equations for the model (see Equation 1 and 

Figure 1).

[Accumulation] = [Input] – [Output] + [Generation] (1)

The accumulation term accounts for changes in the concentration 

of agent within the zone volume, the input term accounts for 

agent that is added to the zone via the input streams (makeup 

air, recirculation, infiltration, and interzonal leakage entering the 

zone), the generation term accounts for a release within the zone, 

and the output term accounts for agent removed from the zone via 

the output streams (exhaust, recirculation, and interzonal leakage 

leaving the zone). In this model, a zone could be an individual 

room or a group of rooms served by a common HVAC system.
4

Figure 1. Well-Mixed Zone Model Diagram

The mass balance can be expressed in terms of particle 

concentration by dividing both sides of the equation by the 

zone volume; explicitly writing out the terms for a balance 

around Zone 1 yields Equation 2.

(2)

Where

C1(t) is the concentration of contaminant in both the 

well-mixed Zone 1 volume and the streams exiting Zone 

1 (an unknown function of time), 

t is the time,

ηfilter is the fractional efficiency of the particulate filter 

used in the HVAC system for general makeup air and 

recirculation,

η1 is the fractional efficiency of an additional particulate 

filter used for the makeup air and recirculation of Zone 1,

Qi,1 is the makeup airflow rate of fresh air into Zone 1,

V 1 is the volume of Zone 1,

Cvent(t) is the concentration of the contaminant in the 

makeup air stream prior to filtration,

Qinf,1 is the infiltration flow rate into Zone 1,

Cinf(t) is the concentration of the contaminant in the 

infiltration stream,

Q21 is the interzonal leakage from Zone 2 to Zone 1,

C2(t) is the concentration of the contaminant in both the 

well-mixed Zone 2 and the streams exiting Zone 2 (an 

unknown function of time),

Q31 is the interzonal leakage from Zone 3 to Zone 1,

C3(t) is the concentration of the contaminant in both the 

well-mixed Zone 3 and the streams exiting Zone 3 (an 

unknown function of time),

QR,1 is the recirculation flow rate for Zone 1,

Q12 is the interzonal leakage from Zone 1 to Zone 2,

Q13 is the interzonal leakage from Zone 1 to Zone 3, and

G1 is the generation rate of agent per unit time in Zone 1.

Next, Euler’s Method, a numerical solution approach, is 

applied by converting the derivative to a simple difference 

(dt → ∆t, dC1 → ∆C1 = C1,t+∆t-C1,t), and solving for C1,t+∆t 

yields Equation 3.

(3)

Where

C1,t+∆t

 is the concentration in Zone 1 at the next time 

step (t+∆t),

C1,t is the concentration in Zone 1 at the current time 

step,

C2,t is the concentration in Zone 2 at the current time 

step,

C3,t is the concentration in Zone 3 at the current time 

step, and

∆t is the size of the time increment.
5

It is important to note that this is a simplified numeric 

solution and thus requires a small time increment 

(≤ 0.10 min). Similar equations are then written for the

other zones.

The concentration of the contaminant in the HVAC stream 

prior to filtration (Cvent) can easily be calculated as a 

weighted average of its constituents (see Equation 4).

(4)

While this effort will not specifically consider the effects 

of local exhaust, the effects of local exhaust could be 

incorporated into the model through the adjustments of Ci,t, 

which denotes the concentration in the makeup air (i.e., the 

HVAC air intake). Using the model, it would be possible

to study local exhaust effects by quantifying the results of 

varying the makeup air concentration as a function of the 

exhaust concentration.

Once Euler’s Method has been used to obtain the 

concentration within each zone as a function of time, the 

cumulative exposure within each zone can be calculated by 

numerical integration; e.g., for Zone 1 (see Equation 5):

(5)

Where E1 denotes the cumulative exposure in Zone 1.

The mass balance approach described above allows for 

time- and location-dependent concentrations to be predicted 

for each zone of the building. Those time-dependent 

concentration estimates are then used in Section 7.0, when 

the model results are compared to the measured aerosol 

concentration for the various release scenarios, building 

configurations, and HVAC operating conditions.
6

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7

5.0

Experimental Methods

5.1 Test Design

Introductory note: A Quality Assurance Project Plan (QAPP) 

was developed for the experimental program discussed in 

this chapter and was approved by the EPA project lead. The 

QAPP was not approved by the EPA QA officer; however, 

this final report does address the key QA concerns of the 

QA manager.

The test design for this experimental work was guided by the 

effort in developing the simple three-zone, well-mixed model 

described in Section 4.0. This effort identified the filtration 

efficiency, leakage, system recirculation, and building 

size relative to the room of release as the key parameters 

in assessing the impact of an indoor CB attack. Other 

parameters (e.g., the infiltration and makeup air) were found 

to have a lesser impact on the resulting concentrations and 

exposures in comparison to the aforementioned parameters 

of filtration efficiency, leakage, and system recirculation. 

This initial modeling analysis was used as a basis to design 

the experimental field study. Filtration efficiencies ranging 

from no filtration (i.e., the natural loss of aerosol in ductwork 

and other AHU components) to a high-efficiency filter (i.e., 

a 95% dioctyl phthalate [DOP] rated filter) were selected 

to cover a range of possible filtration efficiencies. Natural 

losses of aerosol were treated as a filtration efficiency to 

represent the lower possible range of filtration efficiencies. 

Recirculation rates of 3, 5, and 7 air changes per hour 

(ACH) were initially planned to cover the range of likely 

system recirculation rates; however, due to limitations of the 

HVAC system, it was not possible to achieve recirculation 

rates above 6 ACH. Therefore, it was decided to encompass 

only the low (3 ACH) and moderate (5 ACH) recirculation 

conditions. Two leakage conditions were chosen as goals for 

the study: low leakage (<0.4 ACH) and high leakage (>0.6 

ACH). These selections were made due to the desire for a 

natural leakage path as would occur in any common building 

(as opposed to a forced leakage with a small fan) and the 

resulting lack of control over the leakage rates. The high and 

low leakage values were chosen to approximate “loose” and 

“tight” building construction, respectively.

A tiered test matrix was initially designed to provide a 

planned approach of study for numerous possible test 

outcomes and contingencies. This matrix provided adequate 

coverage of the range of parameters of interest to demonstrate 

that the model findings were accurate with respect to 

estimating the effects of building/HVAC parameters and 

contaminant concentration. Rapid analysis and reduction 

of results were then used to decide which tests in the tiered 

matrix would be performed. The final test matrices are shown 

in Tables 1 and 2 for moderate and low recirculation.

Table 1. Moderate Recirculation Test Matrices Recirculation Building Size Leakage

Filtration

“Moderate” (5 ACH)

“Large”

“Low”

(0.175 ACH)

“Low” (10%)

“Moderate-Low” (25%)

“Moderate-Low” (25%)

“Moderate” (50%)

“High” (90%)

“High”

(1.025 ACH)

“Low” (10%)

“Moderate” (50%)

“High” (90%)

“Small”

“Low”

(0.125 ACH)

“Low” (10%)

“Moderate” (50%)

“High” (90%)

“High”

(1.175 ACH)

“Low” (10%)

“Moderate” (50%)

“High” (90%)

“Large” notional building / “Low” leakage denotes a closed door position

“Large” notional building / “High” leakage denotes a door ajar position

“Small” notional building / “Low” leakage denotes a door open 20 cm

“Small” notional building / “High” leakage denotes a door fully open
8

Table 2. Low Recirculation Test Matrices Recirculation Building Size Leakage

Filtration

“Low” (3 ACH)

“Large”

“Very Low”

(0.050 ACH)

“Moderate” (10%)

“Low”

(0.200 

ACH)

“Low” (10%)

“Moderate” (50%)

“High” (90%)

“Small”

“High”

(0.950 ACH)

“Moderate” (50%)

“Moderate”

(0.725 ACH)

“Low” (10%)

“Moderate” (50%)

“High” (90%)

“Large” notional building / “Very Low” leakage denotes a closed door position

“Large” notional building / “Low” leakage denotes a door partially ajar

“Large” notional building / “High” leakage denotes a door open about 1 cm

“Small” notional building / “Moderate” leakage denotes a door open 70 cm

5.2 Test Building Description

The venue for these tests was a three-story building, located 

in Anniston, Alabama. The building’s HVAC system 

consisted of three separate air handling units (AHUs), each 

servicing a specific region of the building (see Figure 2). 

The 1st floor of the building did not possess an active HVAC 

system (shaded in white in Figure 2). Air handler 1 (AHU1) 

serviced approximately 2/3 of the 3rd floor of the building 

(shaded in blue in Figure 2). Air handler 2 (AHU2) serviced 

approximately 2/3 of the 2nd floor of the building (shaded 

in red in Figure 2). Air handler 3 (AHU3) serviced a multifloor region of the building consisting of approximately 1/3 

of the 2nd and 3rd floors (shaded in green in Figure 2). Each 

of the AHUs possessed its own supply and return ductwork. 

Since the simple well-mixed model considers only a simple 

HVAC system with a common supply and return ductwork, a 

subsection of the test building, defined as the HVAC region 

serviced by AHU3, was selected for subsequent study. The 

HVAC region serviced by AHU3 was of particular interest 

for this study because it spanned multiple floors and would 

require the least number of building modifications to provide 

a suitable test venue.

Each supply vent and return duct was equipped with a simple, 

manually operated damper that controlled the fractioning 

of supply and return flows using “path of least resistance” 

principles. While this simple construction prevented the 

creation of significant pressure differences between zones, it 

is believed to be representative of typical HVAC design. The 

fraction of total airflow recirculated (and thus the fraction 

of fresh air) was adjusted via variable flow controllers and a 

central computer control system. In addition, the total airflow 

delivered by the air handling unit was also controlled via the 

computer control system.
9

Figure 2. Test Building HVAC Region Diagram

AHU1

AHU2

Legend 

3rd Floor 

1st Floor 

2nd Floor 

AHU3

No AHU

Some initial building alterations were required prior to testing 

to allow for the desired test conditions. These alterations 

involved the extension of existing walls to create independent 

zones, the addition of new walls to create independent zones, 

and the creation of a number of return inlets within the newly 

created zones. The alterations made to the building were 

performed by professional contractors and are described in 

detail below, as well as illustrated in Figures 3 and 4.

The test bed contains three independent air handling units 

that service three independent HVAC regions. Throughout 

this document the terms “test volume” and “building” will 

be used interchangeably to refer to the HVAC region that 

constitutes the easternmost portion of the top two floors of 

the building (i.e., the portion shaded in green in Figure 2). 

The building construction is a standard commercial design 

in which a limited number of walls extend beyond the drop 

ceiling to the slab. For the purposes of modeling the building, 

it is important to note that only walls that extend to the slab 

define a zone. Prior to alterations, the HVAC region contained 

only four independent zones of approximately the same size 

(i.e., two on each floor). As discussed previously, this effort 

required both large and small notional building scenarios. 

To achieve this, walls were either added or extended to 

define new zones. Again, it is important to note that “large” 

and “small” refer to the size of the building relative to 

the zone of interest. For example, a 3,000 m3

 building 

composed of 100 rooms that are each 30 m3 in volume is 

equivalent to a 50,000 m3 building composed of 100 rooms 

that are each 500 m3 in volume. The building size relative 

to the zone of interest for both of these examples would be 

100, making each of them a “large” notional building.

On the upper floor (3rd floor), a portion of an existing wall 

was extended to the slab and a new slab-to-slab wall was 

constructed to split Room B209 into two independent 

zones. The newly created zones were approximately equal 

in volume with each representing approximately one-tenth 

(1/10) of the test volume. These alterations created the 

zone of interest and zone of release for the “small” notional 

building and are illustrated in Figure 3. In addition, a door 

was incorporated into the new wall to provide a variable leak 

path between the zone of release and zone of interest.

On the lower floor (2nd floor), the existing walls that defined 

a series of small rooms were extended to the slab to create 

four independent zones. Each of the newly created zones was 

approximately one-seventy-fifth (1/75) of the test volume. 

Two of the zones, B113 and B114, were used as the zone of 

interest and the zone of release, respectively, for the “large” 

notional building. These alterations are illustrated in Figure 4. 

Doors were also added to the walls between each of the 

newly created zones to provide a variable leak path between 

the zones.

While each of these newly created zones already possessed 

a makeup air inlet (i.e., a fresh air supply vent), most lacked 

a return inlet (i.e., a recirculated air intake). For this reason, 

return inlets were added to each of the newly created zones 

(see Figures 3 and 4). Each return inlet was equipped with 

a damper to adjust the recirculated airflow taken from each 

zone. These building alterations produced an experimental 

test volume suitable for the planned experiments. The 

test volume is considered to represent exceptionally tight 

construction. All doors possessed high quality seals and 

sweeps. All walls were well caulked and sealed. For this 

reason, the leakage observed under various door positions 

during this study should not necessarily be taken as standard 

leakage rates for other buildings.
10

Figure 3. Alterations to the Upper Floor (3rd Floor) of the Test Volume

Existing Wall Extended to SlabSlab-to-Slab Wall Drop Ceiling Wall

New DoorNew Return Vent New Slab-to-Slab Wall 

Legend 
11

Figure 4. Alterations to the Lower Floor (2nd Floor) of the Test Volume 

Existing Wall Extended to SlabSlab-to-Slab WallDrop Ceiling Wall 

New DoorNew Return Vent

Legend 
12

5.3 Release Methods

Visolite®, a fluorescent-tagged, calcium carbonate (chalk) 

dust, was used as the innocuous aerosol tracer. A particulate 

aerosol was chosen as the tracer for this effort because of 

the advantages of existing filtration systems and real-time 

measurement systems, such as optical particle counters. It 

is important to note that while a particulate aerosol was

used for this experimental effort, the model and analysis 

presented here is valid for any contaminant that does 

not appreciably deposit or adsorb on surfaces during the 

timeframe of interest.

The amount of Visolite® to be released was estimated based 

on the results of previous indoor air quality field studies 

performed by Battelle. These previous results, combined with 

past experience, suggested that a release of 1 to 20 grams 

of Visolite® would be sufficient to produce the desired test 

aerosol. The main elements of the release mechanism are 

an air supply, an eductor, a grooved turntable, and a rotary 

motor (see Figure 5). Air flowing through the eductor creates 

a suction that pulls the Visolite® powder from the grooved 

turntable. Provided the powder is evenly distributed within 

the groove of the turntable, the speed at which the motor 

rotates the turntable defines the release rate of the aerosol.

In previous work this eductor type mechanism has proven 

its effectiveness in dispersing similar quantities of a powder 

aerosol. The particle size distribution of Visolite® aerosolized 

via this eductor mechanism is shown in Figure 6. Since it 

is possible that the solid aerosol dissemination will produce 

nuisance dust levels that are temporarily above suggested 

safety limits, the release will be triggered by an electronic 

timer to avoid any potential exposure of test personnel.

Figure 5. Photograph of the Particulate Eductor Release Mechanism
13

Figure 6. Particle Size Distribution of Visolite® Aerosolized with an Eductor Release 

Mechanism Operating at a Gas Flow Rate of 100 lpm as Measured by an Aerosizer®

5.4 Sampling Methods

During the testing period, five handheld particle counters 

(MetOne model HHPC-6, manufactured by Hach Ultra 

Analytics, see Figure 7) were used to monitor aerosol levels 

at selected locations within the area of interest. The MetOne 

particle counter uses optical particle counting techniques 

(i.e., measuring the scattered light as particles pass through 

a laser) to provide data on six particle size ranges from 0.3 

to 20 μm (0.5 to 0.7 μm, 0.7 to 1.0 μm, 1.0 to 2.0 μm, 2.0 

to 5.0 μm, 5.0 to 10.0 μm, and 10.0 to 20 μm). Since the 

Visolite® particles are being used only as a tracer or indicator, 

the particle size distributions were not of interest, only 

particle concentration. For this effort, particle concentrations 

from the MetOne channel ranging from 2.0 to 5.0 μm were 

used as the indicator of the Visolite® concentration. Figure 

8 shows a typical example of MetOne data gathered in the 

zone of interest during testing. Typical background levels 

can be observed in the time period prior to release (i.e., time 

less than zero). The Data Quality Objectives (DQOs) and an 

electronic archive of the data gathered during this work can 

be found in Appendices C and D, respectively.

The MetOne particle counter is lightweight and automated, 

making it easy to deploy/recover, which facilitates rapid 

turnaround. Pretest procedures involve turning the instrument 

on and pressing the start button. MetOne particle counters 

were factory calibrated prior to use in this study. In addition, 

the outputs from the multiple MetOne units used in this 

study were compared against each other to ensure sizing 

consistency throughout the study. When not in use, MetOne 

particle counters were run continuously on HEPA-filtered air 

to flush and clean the devices.

Figure 7. Photograph of MetOne Handheld 

Optical Particle Counter
14

Figure 8. Typical Particulate Data Gathered by a MetOne During Testing

The apparent discrepancy between the particle size 

distribution in comparison to Figure 6 is attributed to a 

difference in the operating principle of the particle sizer 

(optical versus time of flight) and a potential error in the 

sizing of a fluorescent particle using optical means. This 

does not significantly impact this work since the 2.0 to 

5.0 μm channel is used as a tracer (i.e., the exact size range 

of particles measured by a given channel is not critical to this 

study as long as it is constant throughout experimentation). 
15

6.0

Preliminary Efforts

Prior to conducting the experimental study, a number of 

preliminary efforts were required to ensure relevant, usable 

data. These preliminary efforts consisted of initial building 

alterations, airflow measurements, and initial leakage tests.

Since the main goal of the experimental work was 

comparison with a model, the actual values of HVAC 

parameters were of critical importance. To this end, a brief 

series of HVAC parameter quantification tests was performed 

to experimentally determine, or in some cases verify, the 

actual values of key HVAC parameters. These tests can 

be divided into airflow measurements and leakage tests. 

Airflow measurements consisted of measuring the volumetric 

flow rate or face velocity at all ductwork throughout the 

test bed. Leakage tests were initially intended to provide a 

direct measure of the interzonal leakage under operational 

conditions. Unfortunately, due to leakage across a return 

damper, the intended method was no longer viable and a 

combinatorial approach was adopted.

6.1 Airflow Measurements

The first portion of the preliminary characterization 

tests consisted of measuring the airflow using various 

anemometers, adjusting the blower and Phoenix valve 

settings that control the HVAC flows, and subsequently 

remeasuring the airflows. A balometer (ALNOR model 

number APM 150) and an anemometer (Davis model 

number LCA30 VT) were used to measure flow (see 

Figure 9 for photograph). These methods were sufficient 

for experimentally measuring the system recirculation and 

makeup air rates for each zone.

The low, moderate, and high recirculation rates initially 

planned for this study were intended to cover a range of 

recirculation rates from 3 to 7 ACH. However, due to 

limitations of the HVAC system, it was not possible to 

achieve recirculation rates greater than 6 ACH. Therefore, it 

was decided to reduce the scope of the study to encompass 

only the low and moderate recirculation conditions. In 

addition, the use of four mixing fans each in both the zone of 

interest and zone of release of the “small” notional building 

were agreed to enhance mixing. Tables 3 and 4 contain the 

results for moderate recirculation flow measurements made 

on the lower and upper floors, respectively, while Table 5 

contains summary information in the form of air exchange 

rates for the moderate recirculation condition (nominally 5 

ACH). Similarly, Tables 6 and 7 contain the results for low 

recirculation flow measurements made on the lower and 

upper floors, respectively, while Table 8 contains summary 

information in the form of air exchange rates for the low 

recirculation condition (nominally 3 ACH).

Figure 9. Photographs of Flow Measurement Using a.) a Balometer and b.) an Anemometer
16

Table 3. Lower Floor Airflow Measurements Under Moderate Recirculation

Zone ID Room Number Room Volume [ft3] Vent ID Type Measured Flow [cfm]

L1 B113 561

S1 Supply 56

R1 Return 54

L2 B114 561

S2 Supply 55

R2 Return 58

L3 B115 561

S3 Supply 56

R3 Return 57

L4 B116 557

S4 Supply 57

R4 Return 56

L5

B110E 7,417

S5 Supply 140

S6 Supply 144

S7 Supply 128

S8 Supply 120

S9 Supply 142

S10 Supply 132

R5 Return 860

B110D 381 N/A

L6

B110 7,709

S11 Supply 151

S12 Supply 170

S13 Supply 145

S14 Supply 148

S15 Supply 153

B111 484 S16 Supply 53

B112 421 S17 Supply 56

B110B 1,092 N/A

B117 1,790

S18 Supply 209

R6 Return 1,280

B118 1,826 S19 Supply 194

N/A is defined as not applicable.

Bold room numbers are those rooms that nominally represent the zone and are referred to as nominal room numbers in other tables.
17

Table 4. Upper Floor Airflow Measurements Under Moderate Recirculation

Zone ID Room Number Room Volume [ft3] Vent ID Type Measured Flow [cfm]

U1

B209A 3,989

S20 Supply 114

S21 Supply 110

S22 Supply 101

S24 Supply 110

R7 Return 499

B211 579 S23 Supply 66

U2

B209B 3,420

S25 Supply 171

S26 Supply 187

R8 Return 445

B210 512 S27 Supply 106

B229A 379 N/A

U3

B229 3,125 S28 Supply 190

B203 513 S29 Supply 137

B204 380 N/A

B205 99 N/A

B206 477 S30 Supply 61

B207 745 S31 Supply 67

B208 3,175

S32 Supply 141

S33 Supply 113

B213 252 S34 Supply 90

B214 621

S35 Supply 171

R9 Return 1,240

B215 490 S36 Supply 75

B216 1,030 S37 Supply 110

B217 508 S38 Supply 116

N/A is defined as not applicable.

Bold room numbers are those rooms that nominally represent the zone and are referred to as nominal room numbers in other tables.

Table 5. Moderate Recirculation Airflow Summary

Zone ID Nominal Room Number Zone Volume [ft3]

Measured Makeup Air 

[ACH]

Measured Recirculation 

[ACH]

L1 B113 561 1.0 5.0

L2 B114 561 1.0 4.9

L3 B115 561 1.0 5.0

L4 B116 557 1.0 5.1

L5 B110E 7,798 1.0 5.1

L6 B110 13,322 1.0 4.8

U1 B209A 4,568 1.1 5.5

U2 B209B 4,311 1.1 5.4

U3 B229 11,415 1.1 5.6
18

Table 6. Lower Floor Airflow Measurements Under Low Recirculation

Zone ID Room Number Room Volume [ft3] Vent ID Type Measured Flow [cfm]

L1 B113 561

S1 Supply 37

R1 Return 32

L2 B114 561

S2 Supply 38

R2 Return 39

L3 B115 561

S3 Supply 38

R3 Return 39

L4 B116 557

S4 Supply 36

R4 Return 38

L5

B110E 7,417

S5 Supply 86

S6 Supply 93

S7 Supply 71

S8 Supply 85

S9 Supply 84

S10 Supply 88

R5 Return 510

B110D 381 N/A

L6

B110 7,709

S11 Supply 108

S12 Supply 130

S13 Supply 116

S14 Supply 114

S15 Supply 126

B111 484 S16 Supply 38

B112 421 S17 Supply 36

B110B 1,092 N/A

B117 1,790

S18 Supply 170

R6 Return 998

B118 1,826 S19 Supply 160

N/A is defined as not applicable.

Bold room numbers are those rooms that nominally represent the zone and are referred to as nominal room numbers in other tables.
19

Table 7. Upper Floor Airflow Measurements Under Low Recirculation

Zone ID Room Number Room Volume [ft3] Vent ID Type Measured Flow [cfm]

U1

B209A 3,989

S20 Supply 63

S21 Supply 80

S22 Supply 68

S24 Supply 73

R7 Return 341

B211 579 S23 Supply 58

U2

B209B 3420

S25 Supply 97

S26 Supply 115

R8 Return 270

B210 512 S27 Supply 63

B229A 379 N/A

U3

B229 3,125 S28 Supply 133

B203 513 S29 Supply 80

B204 380 N/A

B205 99 N/A

B206 477 S30 Supply 30

B207 745 S31 Supply 30

B208 3,175

S32 Supply 95

S33 Supply 69

B213 252 S34 Supply 38

B214 621

S35 Supply 125

R9 Return 780

B215 490 S36 Supply 38

B216 1,030 S37 Supply 67

B217 508 S38 Supply 80

N/A is defined as not applicable.

Bold room numbers are those rooms that nominally represent the zone and are referred to as nominal room numbers in other tables.

Table 8. Low Recirculation Airflow Summary

Zone ID

Nominal Room 

Number Zone Volume [ft3]

Measured Makeup Air 

[ACH]

Measured Recirculation 

[ACH]

L1 B113 561 1.0 2.9

L2 B114 561 1.0 3.1

L3 B115 561 1.0 3.1

L4 B116 557 1.0 2.9

L5 B110E 7,798 1.0 2.9

L6 B110 13,322 1.1 3.4

U1 B209A 4,568 1.1 3.4

U2 B209B 4,311 1.0 2.9

U3 B229 11,415 1.0 3.1
20

6.2 Leakage Tests

The second portion of the preliminary characterization 

tests consisted of tests designed to determine the interzonal 

leakage under normal operating conditions. According to the 

results from the theoretical simulations, the quantification 

of the interzonal leakage rate represents a key measurement 

for this study. The interzonal leakage was to be measured by 

setting the HVAC system to run with 100% fresh air (i.e., 

no recirculation, with a greatly increased fresh air makeup 

air rate) and performing tracer gas tests with SF6. Assuming 

there was no cross-contamination between the building 

exhaust and fresh air inlet, any SF6 that entered the zone of 

interest must be due to interzonal leakage. By performing this 

leakage test at the various HVAC conditions planned for this 

study, an experimental measure of a key parameter could be 

determined. Other leakage measurement methods involve the 

overpressurization of the zone of interest and do not reflect 

normal building operation (ASTM, 2003).

The results of the initial leakage test indicated that 

significant recirculation was occurring (see Appendix 

A). Upon further investigation, it became apparent that 

significant leakage was occurring across the recirculation 

damper and that reducing that leakage to zero was not 

feasible given the materials and time constraints of 

the study. While it was possible to estimate the actual 

recirculation using the theoretical model, this represented 

an indirect method of estimating the interzonal leakage, 

which depended heavily on the model-predicted value of 

the recirculated airflow. For this reason, a combinatorial 

approach to determining the leakage was adopted.

Under a combinatorial approach, the leakage rates for a given 

building configuration (i.e., door position and recirculation 

rate) are estimated using the results of a single experiment. 

A leakage estimate was obtained by varying the leakage 

parameter within the model to find a value that resulted 

in good agreement between experimentally measured 

and model-predicted results for a single experiment (i.e., 

a minimum in the square of the error between modelpredicted and experimentally measured values). The resulting 

estimated leakage rates are then used for all tests under that 

building configuration (i.e., door position and recirculated air 

rate). The resulting leakage estimate should be valid for any 

filtration efficiency since the filtration efficiency impacts the 

concentration in the zone, not the leakage rate (i.e., the flow 

rate across the leak path). In this fashion, a combinatorial 

approach effectively used a portion of the experimental 

data to directly estimate the interzonal leakage rate. The 

application of this combinatorial method is further clarified in 

the Results section of this report.
21

7.0

Experimental Results

The test approach, which was adopted on-site, required 

a reorganization of the originally planned test matrices. 

Furthermore, since manual adjustments were needed for 

each recirculation condition, test matrices were organized by 

recirculation rate for efficiency reasons. Initially, all possible 

combinations of three filtration levels and two leakage levels 

were performed for two notional buildings for the moderate 

recirculation rate. This provided a solid basis for comparison 

of the experimental and model-predicted results, as well 

as the large body of data required for the combinatorial 

approach. Based on the interim results, it was agreed that a 

reduced test matrix was appropriate for the low recirculation 

condition given the excellent agreement of experimental 

and model results observed for the moderate recirculation 

condition (Sparks, 2005). For this reason, a limited matrix 

using three filtration levels and three leakage levels for the 

two notional buildings was executed for the low recirculation 

condition. Tables 9 and 10 contain the moderate and low 

recirculation test matrices, respectively.

The “large” notional building can be simplified in terms of 

three well-mixed zones and is shown in Figure 10. Room 

B113, which had a volume of 560 ft3

, was the zone of 

release (shaded blue in Figure 10). Room B114, which had 

a volume of 560 ft3, was the zone of interest (shaded in cyan 

in Figure 10). Several rooms constituted the lumped zone, 

which represented the rest of the building in the model. The 

lumped zone had a volume of approximately 42,480 ft3. 

Room B110, which had a volume of approximately 7,710 ft3, 

was selected as representative of the lumped zone for the rest 

of the building (shaded in green in Figure 10). With the entire 

test volume constituting approximately 43,600 ft3, the zone of 

interest and zone of release each represented approximately 

one-seventy-fifth (1/75) of the test volume.

The “small” notional building can be simplified in terms of 

three well-mixed zones and is shown in Figure 11. Room 

B209B, which had a volume of 4,310 ft3

, was the zone of 

release (shaded blue in Figure 11). Room B209A, which had 

a volume of 4,570 ft3, was the zone of interest (shaded in 

cyan in Figure 11). Due to the size of both the zone of release 

and the zone of interest, four standard house fans were used 

in each of these zones to promote mixing. Room B110, which 

had a volume of approximately 7,710 ft3, was selected as the 

room that would represent the lumped zone for the rest of the 

building (shaded in green in Figure 11). With the entire test 

volume constituting approximately 43,600 ft3, the zone of 

interest and zone of release each represented approximately 

one-tenth (1/10 ) of the test volume.

In addition to the aforementioned zones, Room B115, 

which had a volume of 560 ft3, was selected as an additional 

sampling location for the “large” notional building tests 

(shaded gold in Figure 10). By comparing the “large” 

notional building experimental test data from Rooms 

B115 and B110, the validity of using a lumped zone 

to approximate the majority of the test volume can be 

established. Experimental data gathered during this study 

indicate that the concentrations measured in Room B115 

were indistinguishable from those in Room B110, thus 

showing the validity of the lumped zone approach (lumping 

multiple rooms into a single zone for modeling purposes). 

This agreement is shown graphically in Figure 12. After 

establishing the validity of this assumption, data from Room 

B110 and Room B115 were averaged to produce the lumped 

zone concentration curves for subsequent “large” notional 

building tests.
22

Table 9. Moderate Recirculation Test Matrix Results Key

Tracer

Recirculation

Building Size

Leakage

Filtration Results Shown in 

Figure #

Particulate

“Moderate” (5 ACH)

“Large”

“Low”

(0.175 ACH)

“Low” (10%) 15

Particulate “Moderate-Low” (25%) 12

Particulate “Moderate-Low” (25%) 13

Particulate “Moderate” (50%) 14

Particulate “High” (90%) 16

Particulate

“High”

(1.025 ACH)

“Low” (10%) 18

Particulate “Moderate” (50%) 17

Particulate “High” (90%) 19

Particulate

“Small”

“Low”

(0.125 ACH)

“Low” (10%) 25

Particulate “Moderate” (50%) 20

Particulate “High” (90%) 22

Particulate

“High”

(1.175 ACH)

“Low” (10%) 24

Particulate “Moderate” (50%) 21

Particulate “High” (90%) 23

“Large” notional building / “Low” leakage denotes a closed door position

“Large” notional building / “High” leakage denotes a door ajar position

“Small” notional building / “Low” leakage denotes a door open 20 cm

“Small” notional building / “High” leakage denotes a door fully open
23

Table 10. Low Recirculation Test Matrix Results Key

Tracer

Recirculation

Building Size

Leakage

Filtration Results Shown in Figure #

Particulate

“Low”

(3 ACH)

“Large”

“Very Low”

(0.050 ACH)

“Moderate” (50%) 27

Particulate

“Low”

(0.200 ACH)

“Low” (10%) 30

Particulate “Moderate” (50%) 28

Particulate “High” (90%) 31

Particulate

“High”

(0.950 ACH)

“Moderate” (50%) 29

Particulate

“Small”

 “Moderate”

(0.725 ACH)

“Low” (10%) 33

Particulate “Moderate” (50%) 32

Particulate “High” (90%) 34

“Large” notional building / “Very Low” leakage denotes a closed door position

“Large” notional building / “Low” leakage denotes a door partially ajar

“Large” notional building / “High” leakage denotes a door open about 1 cm

“Small” notional building / “Moderate” leakage denotes a door open 70 cm
24

Lower Floor (2nd Floor) 

Rest of Building I 

B110

Primary Leakage Path

MetOne Sampling LocaonRelease Locaon

Slab-to-Slab Wall

Legend 

Zone of Release

B113

Zone of Interest

B114

Rest of Building II

B115*

Drop Ceiling Wall

Figure 10. Sampling Scheme for “Large” Notional Building

*B115 is the second room that comprises the lumped zone.
25

Figure 11. Sampling Scheme for “Small” Notional Building

Primary Leakage Path

MetOne Sampling LocaonRelease Locaon

Slab-to-Slab Wall 

Legend 

Zone of Release 

B209B

Zone of Interest

B209A 

Rest of Building I 

B110

Upper Floor (3rd Floor) 

Lower Floor (2nd Floor) 

Drop Ceiling Wall
26

The initial set of tests focused on the “large” notional 

building under moderate recirculation (5 ACH, see Tables 3 

through 5 for further details). Figures 12 through 16 contain 

graphical comparisons of experimental and model-predicted 

data for four filtration levels at conditions of “low” leakage 

(i.e., the door between B113 and B114 closed). Both 

experimental and model-predicted data are presented as 

concentration in particles per liter (ppl) as a function of time 

with a release occurring at time zero. Experimental data 

presented are from the 2.0 to 5.0 μm channel of MetOne 

particle counters. Under the combinatorial approach, this 

set of experimental curves was used to estimate the “low” 

leakage rate, as well as the filtration efficiencies. Estimates 

were obtained by varying a parameter within the model 

to find a value that resulted in good agreement between 

experimentally measured and model-predicted results for 

a single experiment (i.e., a minimum in the square of the 

error between model-predicted and experimentally measured 

values). The resulting parameters were then used for 

subsequent tests under the appropriate conditions. Figure 12 

illustrates the validity of the lumped zone assumption by 

comparing two of the constituent zones (B110 in green and 

B115 in gold). The good agreement exhibited in Figure 

12 is indicative of all the “large” notional building results. 

As a result of this agreement, the lumped zone curves in 

other “large” notional building graphical comparisons will 

represent an average of these two zones. This validates 

the use of the lumped zone assumption and illustrates 

how a three-zone model can be used to provide useful 

information on real buildings with more than three zones. 

In addition, good agreement was observed between the 

two samplers placed in the zone of interest, supporting the 

well-mixed assumption in the small, 6′ x10′ zone of interest 

(see Figure 13). As a result of this good agreement, data 

from the two samplers located in the zone of interest were 

numerically averaged to represent this zone. A comparison of 

Figures 12 and 13 also provides a limited demonstration of 

the repeatability of these results.

In general, all experimental data gathered over the course of 

this study exhibit imperfect mixing in the form of a mixing 

lag. The mixing lags observed seemed to correlate well 

with zone volume, meaning that smaller zones exhibited 

smaller mixing lags (see Figures 14 and 20). A mixing lag is 

a delay in the rise of a zone’s experimental concentration in 

comparison to the perfect mixing exhibited by a theoretical 

model-predicted curve (see Figure 12). In addition to this, the 

experimentally observed release zone data collected during 

“large” notional building tests exhibit a plateau during the 

initial peak. This plateau was due to the saturation limit of the 

MetOne handheld particle counters used in this study. While 

a reduction in release mass could eliminate this sampling 

artifact, the release mass was nominally held constant to 

provide sufficient particulate levels in the lumped zone (i.e., 

the rest of the building) during high filtration tests.

The estimates of the effective filtration efficiency gleaned 

from this series of tests provide valuable insight. The results 

of this study indicate that even with no filter installed, the 

basic HVAC system used in the test bed is 10% efficient in 

removing particulate in the 2.0 to 5.0 μm range (see Figure 

15). The data indicate that the MERV 7 and MERV 8 filters 

used during this test performed below the rated efficiency 

of their respective MERV ratings (25% estimated during 

this study versus a specification of roughly 50 to 70% for 

the MERV 7, 50% estimated during this study versus a 

specification of 70% for the MERV 8). The MERV 7 results 

are consistent with those of a related project (referred to 

here as Task 2; Hecker, 2006), while no data from Task 2 

were available for the MERV 8 filter. The 95% DOP filter 

used during testing performed slightly below the rated 

efficiencies (90% estimated during this study versus a 

specification of 95%+ for the 95% DOP). Task 2 data on 

the 95% DOP filter indicate a filtration efficiency greater 

than 99% over the applicable particle size range (Hecker, 

2006). It is possible that the discrepancy between the 

results of this study and the rated efficiencies for the MERV 

8 and 95% DOP filter are due to these estimates being 

made in an operating building rather than in a specially 

designed test fixture as used to obtain a MERV rating 

(i.e., leakage around the filters or leaks in ducting could 

be responsible for the reduced filtration efficiencies).

Figure 17 contains a graphical comparison of experimental 

and model-predicted data for the “high” leakage condition 

at moderate filtration (i.e., MERV 8 filter). Under the 

combinatorial approach these data were used to estimate the 

leakage rate associated with the “high” leakage condition 

(i.e., door between B113 and B114 ajar). Having successfully 

estimated both leakage rates (Figures 12 and 17) as well as 

effective filtration efficiencies (Figures 12 through 16), the 

remaining tests performed on the “large” notional building 

under moderate recirculation illustrate direct comparisons 

between experimental data and model predictions. Figures 

18 and 19 graphically illustrate comparisons between 

experimental data and model predictions in which the 

value of all the HVAC parameters have previously 

been determined. Although the agreement between the 

experimental and model-predicted lumped zone (i.e., the 

rest of the building) suggests a slight error in the filtration 

efficiency estimates, on the whole, the agreement between 

experimental and model-predicted data for the “large” 

notional building under moderate recirculation is excellent.
27

Given the promising results of the initial test set, a second 

set of tests was undertaken focusing on the “small” notional 

building under moderate recirculation conditions (5 ACH, see 

Tables 3 through 5 for further HVAC flow details). Figures 21 

and 22 contain graphical comparisons of the experimental 

and model-predicted data for two leakage conditions with 

a moderate filtration level (i.e., a MERV 8 filter). Under 

the combinatorial approach previously adopted, these two 

comparisons were used to estimate the leakage rates for 

the “small” notional building under moderate recirculation. 

Again, leakage estimates were obtained by varying the 

leakage parameter within the model to find a value that 

resulted in good agreement between experimentally measured 

and model-predicted results for a single experiment (i.e., a 

minimum in the square of the error between model-predicted 

and experimentally measured values). The resulting leakage 

parameter was then used for all tests performed under the 

given combination of door position and recirculated air rate. 

Figure 12. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with Moderate 

Recirculation (5 ACH), Low Leakage (0.175 ACH), Moderate-Low Filtration (25% / MERV 7 Filter), 

Standard Makeup Air (1 ACH), and Standard Infiltration (0.5 ACH). Note that the Standard Infiltration 

(0.5 ACH), Low Leakage (0.175 ACH), and Moderate-Low Filtration (25%) parameter was fit in this 

comparison by minimizing the sum of the square of the residuals between the experimentally observed 

and model-predicted data.
28

Figure 13. Demonstration of Repeatability by Comparison of Experimental and Model-Predicted Data for “Large” 

Notional Building with Moderate Recirculation (5 ACH), Low Leakage (0.175 ACH), Moderate-Low 

Filtration (25% / MERV 7 Filter), Standard Makeup Air (1 ACH), and Standard Infiltration (0.5 ACH). 

Note that all HVAC parameters for the model-predicted data were taken from previous comparisons with 

experimental data.

Figure 14. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with Moderate 

Recirculation (5 ACH), Low Leakage (0.175 ACH), Moderate Filtration (50% / MERV 8 Filter), Standard 

Makeup Air (1 ACH), and Standard Infiltration (0.5 ACH). Note that the Moderate Filtration (50%) 

parameter was fit in this comparison by minimizing the sum of the square of the residuals between the 

experimentally observed and model-predicted data.
29

Figure 15. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with Moderate 

Recirculation (5 ACH), Low Leakage (0.175 ACH), Low Filtration (10% / No Filter), Standard Makeup Air 

(1 ACH), and Standard Infiltration (0.5 ACH). Note that the Low Filtration (10%) parameter was fit in this 

comparison by minimizing the sum of the square of the residuals between the experimentally observed 

and model-predicted data.

Figure 16. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with Moderate 

Recirculation (5 ACH), Low Leakage (0.175 ACH), High Filtration (90% / 95% DOP), Standard Makeup 

Air (1 ACH), and Standard Infiltration (0.5 ACH). Note that the High Filtration (90%) parameter was 

fit in this comparison by minimizing the sum of the square of the residuals between the experimentally 

observed and model-predicted data.
30

Figure 17. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with Moderate 

Recirculation (5 ACH), High Leakage (1.025 ACH), Moderate Filtration (50% / MERV 8), Standard 

Makeup Air (1 ACH), and Standard Infiltration (0.5 ACH). Note that the High Leakage (1.025 ACH) 

parameter was fit in this comparison by minimizing the sum of the square of the residuals between the 

experimentally observed and model-predicted data.

Figure 18. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with Moderate 

Recirculation (5 ACH), High Leakage (1.025 ACH), Low Filtration (10% / No filter), Standard Makeup Air 

(1 ACH), and Standard Infiltration (0.5 ACH). Note that no HVAC parameters were fit in this comparison. 

This comparison is a direct indication of agreement between experimental data and model predictions.
31

Figure 19. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with Moderate 

Recirculation (5 ACH), High Leakage (1.025 ACH), High Filtration (90% / 95% DOP), Standard 

Makeup Air (1 ACH), and Standard Infiltration (0.5 ACH). Note that no HVAC parameters were fit in this 

comparison. This comparison is a direct indication of agreement between experimental data and model 

predictions.

Figure 20. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building with Moderate 

Recirculation (5 ACH), Low Leakage (0.125 ACH), Moderate Filtration (50% / MERV 8), Standard 

Makeup Air (1 ACH), and Standard Infiltration (0.5 ACH). Note that the Low Leakage (0.125 ACH) 

parameter was fit in this comparison by minimizing the sum of the square of the residuals between the 

experimentally observed and model-predicted data.
32

Throughout the tests for the “small” notional building, a 

mixing lag was observed within both the zone of release 

and the zone of interest. This mixing lag is evident when 

comparing samplers located at different areas of the two 

zones (for the zone of release, compare the dark blue and 

light blue lines in Figures 21 and 22; for the zone of interest, 

compare the cyan and red lines in Figures 21 and 22). Note 

that locations labeled “remote” were located farther from 

the source of particles (e.g., the release source in the zone of 

release or the primary leakage path in the zone of interest). 

From Figures 21 and 22, it is clear that, even with the 

assisted mixing of the house fans, there was a finite lag in the 

transport and mixing of Visolite® across the zones. However, 

it is also clear that, after that finite mixing lag, the volume 

appears to have been well mixed and subsequently agrees 

very well with model predictions.

Since the filtration efficiencies have already been estimated 

from the initial “large” notional building tests, the remaining 

four tests illustrated direct comparisons between experimental 

and model-predicted data. Figures 22 through 25 graphically 

illustrate the excellent agreement between experimental data 

and model predictions. The slight discrepancies observed can 

easily be attributed to an initial mixing lag and a combination 

of experimental error in measured and estimated parameters. 

This excellent agreement was a clear indication that the 

three-zone, well-mixed model is accurate in simulating the 

effects of changes in filtration efficiency and interzonal 

leakage rates for a notional building. Thus, after a cursory 

analysis of the data, a subsequent series of tests was focused 

on the effects of changing the recirculation.

Figure 21. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building with Moderate 

Recirculation (5 ACH), High Leakage (1.175 ACH), Moderate Filtration (50% / MERV 8), Standard 

Makeup Air (1 ACH), and Standard Infiltration (0.5 ACH). Note that the High Leakage (1.175 ACH) 

parameter was fit in this comparison by minimizing the sum of the square of the residuals between the 

experimentally observed and model-predicted data.
33

Figure 22. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building with Moderate 

Recirculation (5 ACH), Low Leakage (0.125 ACH), High Filtration (90% / 95% DOP), Standard Makeup 

Air (1 ACH), and Standard Infiltration (0.5 ACH). Note that no HVAC parameters were fit in this 

comparison. This comparison is a direct indication of agreement between experimental data and model 

predictions.

Figure 23. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building with Moderate 

Recirculation (5 ACH), High Leakage (1.175 ACH), High Filtration (90% / 95% DOP), Standard 

Makeup Air (1 ACH), and Standard Infiltration (0.5 ACH). Note that no HVAC parameters were fit in this 

comparison. This comparison is a direct indication of agreement between experimental data and model 

predictions.
34

Figure 24. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building with Moderate 

Recirculation (5 ACH), High Leakage (1.175 ACH), Low Filtration (10% / No filter), Standard Makeup Air 

(1 ACH), and Standard Infiltration (0.5 ACH). Note that no HVAC parameters were fit in this comparison. 

This comparison is a direct indication of agreement between experimental data and model predictions.

Figure 25. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building with Moderate 

Recirculation (5 ACH), Low Leakage (0.125 ACH), Low Filtration (10% / No filter), Standard Makeup Air 

(1 ACH), and Standard Infiltration (0.5 ACH). Note that no HVAC parameters were fit in this comparison. 

This comparison is a direct indication of agreement between experimental data and model predictions.
35

After establishing the validity of the model in terms of 

changing the filtration efficiency and interzonal leakage 

for both a “large” and “small” notional building in the first 

two test sets, the third set of tests focused on verifying the 

effects of changing the recirculation rate. This process was 

complicated by a lack of precise control over the interzonal 

leakage rate. That is, a change in the recirculation rate or the 

door position can and will result in a significantly altered 

leakage value. Figure 26 illustrates this through photographs 

of the various door positions that produce various interzonal 

leakage rates at moderate and low recirculation. It is of 

particular interest that after altering the air handling unit, 

Phoenix valves, and dampers to produce the low recirculation 

condition (see Tables 6 through 8 for details), the interzonal 

leakage rate changed from approximately 0.175 ACH to 

0.05 ACH for an identical door position (i.e., a closed door). 

This variation indicates that the interzonal leakage rate can 

be extremely sensitive to the HVAC operating conditions. 

In addition, it is important to note the large variation in 

interzonal leakage that results from a slight change in the 

door position. This sensitivity of the interzonal leakage rate 

to changes in HVAC operating conditions and door position 

strongly support the decision to use a combinatorial approach 

to estimate the leakage under normal operating conditions 

rather than use a more standard blower door test method 

that estimates the leakage under increased pressures and 

extrapolates to the standard operating pressure.

Thus, due to the lack of precise control over the interzonal 

leakage rate, an initial set of tests at low recirculation was 

necessary to estimate the door position that would produce 

a leakage level within an acceptable range. Figures 27 

through 29 contain graphical comparisons of experimental 

and model-predicted data for three leakage levels (i.e., 

varying door positions) for the “large” notional building 

under low recirculation (3 ACH, see Tables 6 through 8 for 

further details) and moderate filtration (i.e., MERV 8 filter). 

Under the combinatorial approach, these experiments were 

used to estimate the “very low,” “low,” and “high” leakage 

rates under varying conditions (i.e., door position between 

B113 and B114). Experimental data and model predictions 

based on previously estimated HVAC parameters are 

graphically displayed in Figures 30 and 31.

Door Closed 

Recirculation Leakage

Moderate (5 ACH) 0.175 ACH

Low (3 ACH) 0.050 ACH

Door Partially Ajar 

Recirculation Leakage

Moderate (5 ACH) Not Run

Low (3 ACH) 0.200 ACH

Door Ajar 

Recirculation Leakage

Moderate (5 ACH) 1.025 ACH

Low (3 ACH) Not Run

Figure 26. Photographs of Various Door Positions for the “Large” Notional Building (i.e., the door between B113 and 

B114) and Recirculation Rates (i.e., low or moderate) with Corresponding Interzonal Leakage Rates
36

Figure 27. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with Low 

Recirculation (3 ACH), Very Low Leakage (0.05 ACH), Moderate Filtration (50% / MERV 8 filter), 

Standard Makeup Air (1 ACH), and Standard Infiltration (0.5 ACH). Note that the Very Low Leakage (0.05 

ACH) parameter was fit in this comparison by minimizing the sum of the square of the residuals between 

the experimentally observed and model-predicted data.

Figure 28. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with Low 

Recirculation (3 ACH), Low Leakage (0.200 ACH), Moderate Filtration (50% / MERV 8 filter), Standard 

Makeup Air (1 ACH), and Standard Infiltration (0.5 ACH). Note that the Low Leakage (0.200 ACH) 

parameter was fit in this comparison by minimizing the sum of the square of the residuals between the 

experimentally observed and model-predicted data.
37

Figure 29. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with Low 

Recirculation (3 ACH), High Leakage (0.950 ACH), Moderate Filtration (50% / MERV 8 filter), Standard 

Makeup Air (1 ACH), and Standard Infiltration (0.5 ACH). Note that the High Leakage (0.950 ACH) 

parameter was fit in this comparison by minimizing the sum of the square of the residuals between the 

experimentally observed and model-predicted data.

Figure 30. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with Low 

Recirculation (3 ACH), Low Leakage (0.200 ACH), Low Filtration (10% / No filter), Standard Makeup Air 

(1 ACH), and Standard Infiltration (0.5 ACH). Note that no HVAC parameters were fit in this comparison. 

This comparison is a direct indication of agreement between experimental data and model predictions.
38

Figure 31. Comparison of Experimental and Model-Predicted Data for “Large” Notional Building with Low 

Recirculation (3 ACH), Low Leakage (0.200 ACH), High Filtration (90% / 95% DOP filter), Standard 

Makeup Air (1 ACH), and Standard Infiltration (0.5 ACH). Note that no HVAC parameters were fit in this 

comparison. This comparison is a direct indication of agreement between experimental data and model 

predictions.

As with previous comparisons, the agreement between 

experiment and model for the “large” notional building under 

low recirculation was generally quite good.

Given the excellent agreement between model and 

experimental data, a reduced number of runs, consisting of 

three levels of filtration efficiency at one leakage condition, 

were executed for the “small” notional building under low 

recirculation. The leakage rate was estimated from the 

moderate filtration test (see Figure 32), leaving the remaining 

two tests as a direct comparison of model-predicted and 

experimentally measured data (see Figures 33 and 34). 

Again, the agreement observed between experimental and 

model curves for a “small” notional building under “low” 

recirculation is rather favorable.

The excellent agreement observed between model-predicted 

and experimental data for changing recirculation rates 

indicates that the three-zone, well-mixed model is accurate 

in simulating the effects of changes in the recirculation rate 

for a notional building. The consistent nature and excellent 

quality of the model agreement with experimental data 

indicates that, in addition to accurately simulating the effects 

of changes in filtration efficiency and interzonal leakage 

rates, the model can accurately predict the effects of changing 

recirculation rates for a notional building.
39

Figure 32. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building with Low 

Recirculation (3 ACH), Moderate Leakage (0.725 ACH), Moderate Filtration (50% / MERV 8 filter), 

Standard Makeup Air (1 ACH), and Standard Infiltration (0.5 ACH). Note that the Moderate Leakage 

(0.725 ACH) parameter was fit in this comparison by minimizing the sum of the square of the residuals 

between the experimentally observed and model-predicted data.

Figure 33. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building with Low 

Recirculation (3 ACH), Moderate Leakage (0.725 ACH), Low Filtration (10% / No filter), Standard 

Makeup Air (1 ACH), and Standard Infiltration (0.5 ACH). Note that no HVAC parameters were fit in this 

comparison. This comparison is a direct indication of agreement between experimental data and model 

predictions.
40

Figure 34. Comparison of Experimental and Model-Predicted Data for “Small” Notional Building with Low 

Recirculation (3 ACH), Moderate Leakage (0.725 ACH), High Filtration (90% / 95% DOP filter), Standard 

Makeup Air (1 ACH), and Standard Infiltration (0.5 ACH). Note that no HVAC parameters were fit in this 

comparison. This comparison is a direct indication of agreement between experimental data and model 

predictions.
41

8.0

Discussion of Experimental Results

There was excellent agreement between experimentally 

observed and model-predicted data. For this comparison, 

the time to reach a critical exposure (Ct) of 50,000 particles 

per liter (ppl)* minutes, and the cumulative exposure at 

30 minutes, were selected as the performance metrics 

to compare model estimates with measured data. These 

performance metrics were selected to allow comparison 

of the different operating conditions and hence the effect 

of varying building/HVAC operating variables of interest. 

Tables 11 and 12 contain the tabulated performance 

metrics for experimental and model-predicted data for 

moderate and low recirculation conditions, respectively. 

Given that the average percentage errors are 9% for 

the time to reach the Ct and 13% for the exposure at 30 

minutes, it is clear that the excellent agreement, which 

has been displayed graphically in Figures 12 through 34, 

is also present in the calculated performance metrics. 

That is to say that the model-predicted performance 

metrics agree well with the experimentally determined 

performance metrics within the experimental variability.

Ideally, a stringent analysis of parameter impact would be 

conducted using the experimental data. This type of analysis 

would compare two experiments in which only a single 

parameter of interest varied and would quantify the change 

in performance metrics due to the change in the parameter 

of interest. A slight variation in the mass released and a lack 

of precise control over the leakage preclude this type of 

stringent analysis. Due to the formation of small deposits 

within the eductor nozzle, the mass released during each test 

varied by approximately +

/- 0.6 grams from the mass loaded 

into the release mechanism. For this reason, comparisons 

between experimental results convolute the effects of 

changing both a release variable (i.e., the release mass) and 

key system variables (i.e., an HVAC parameter). In addition, 

it was not possible to “dial” in the leakage value when 

considering different notional buildings (i.e., different rooms) 

or differing recirculation flow rates. This lack of precise 

control over the leakage is due to the realistic nature of the 

leakage path and driving force used in this study. The natural 

leakage path of a door and the natural driving force of slight 

HVAC flow imbalances resulted in realistic results but did not 

provide precise control of the leakage itself. Because of the 

excellent agreement of model estimates with experimental 

results, the model allows for this type of stringent analysis to 

be made with confidence.

While the variable release mass and a lack of precise control 

over the leakage preclude a stringent analysis of parameter 

impact from the experimental results, some generalizations 

concerning parameter impact can be made from the 

experimental data gathered during this study. For “large” 

notional buildings, the significant changes in performance 

metrics between high and low leakage rates suggest that 

the leakage is the dominant parameter (see Table 11). 

In comparison, it appears that changes in the filtration 

efficiency and system recirculation rate have lesser impacts 

on the performance metrics for a “large” notional building 

scenario (see Tables 11 and 12). In contrast to the “large” 

notional building results, the results for a “small” notional 

building imply that the filtration efficiency is the dominant 

parameter while the leakage may play a secondary role (See 

Table 11). These generalities represent the trends observed 

in the experimental data. Given the excellent agreement 

between modeled and experimental data, the following 

section (Section 9.0) develops an approach to determining the 

relative impact of key parameters and to finding out how well 

a parameter must be known to assess an attack.
42

Table 11. Comparison of Experimental and Predicted Performance Metrics for the Zone of Interest for the “Moderate” 

Recirculation Condition

Recirculation

Building Size

Leakage

Filtration

Experimentally Measured Model-Predicted

Time to Ct 

of 50,000 

ppl*min [min]

Exposure @ 30 

min [ppl*min]

Time to Ct 

of 50,000 

ppl*min [min]

Exposure @ 

30 min 

[ppl*min]

“Moderate” (5 ACH)

“Large”

“Low” 

(0.175 ACH)

“Low” (10%) 9.1 1.8x105 9.25 1.9x105

“Moderate-Low” (25%) 14.5 1.0x105 12.75 1.1x105

“Moderate-Low” (25%) 12.0 1.2x105 11.5 1.3x105

“Moderate” (50%) 11.2 1.3x105 10.25 1.4x105

“High” (90%) 15.6 8.1x104 12.75 1.1x105

“High”

(1.025 ACH)

“Low” (10%) 6.1 3.1x105 5.5 3.8x105

“Moderate” (50%) 3.4 6.5x105 4.75 7.1x105

“High” (90%) 4.8 4.0x105 5.00 4.1x105

“Small”

“Low” 

(0.125 ACH)

“Low” (10%) 10.6 2.0x105 10.25 2.1x105

“Moderate” (50%) 18.7 7.7x104 17.75 8.1x104

“High” (90%) N/R 3.1x104 N/R 4.7x104

“High”

1.175 ACH)

“Low” (10%) 7.4 3.0x105 7.0 3.1x105

“Moderate” (50%) 7.5 2.5x105 7.25 2.4x105

“High” (90%) 8.6 1.7x105 9.25 1.5x105

N/R denotes that the specified cumulative Ct was not reached.

Table 12. Comparison of Experimental and Predicted Performance Metrics for the Zone of Interest for the “Low” 

Recirculation Condition

Recirculation

Building Size

Leakage

Filtration

Experimentally Measured Model-Predicted

Time to Ct 

of 50,000 

ppl*min [min]

Exposure @ 30 

min [ppl*min]

Time to Ct of 

50,000 ppl*min 

[min]

Exposure @ 30 

min [ppl*min]

“Low” (3 ACH)

“Large”

“Very Low”

(0.050 ACH)

“Low” (10%) 27.8 5.4x104 24.0 6.3x104

“Low”

(0.200 ACH)

“Moderate-Low” (25%) 4.7 4.3x105 5.3 5.5x105

“Moderate” (50%) 8.8 2.1x105 8.0 2.9x105

“High” (90%) 8.1 2.5x105 7.5 3.3x105

“High”

(0.950 ACH)

“Low” (10%) 10.2 1.75x105 12.0 1.5x105

“Small”

“Low” 

(0.125 ACH)

“Low” (10%) 10.9 1.9x105 10.0 2.0x105

“Moderate” (50%) 10.8 1.7x105 12.0 1.4x105

“High” (90%) 10.4 2.2x105 9.3 2.4x105
43

9.0

Model Impact Analysis

While it is neither feasible nor realistic to conduct 

experiments under all possible conditions and configurations, 

the model represents a tool that can easily and reasonably 

be used to analyze a tremendous number of possible 

conditions and building configurations. This section 

focuses on developing an approach to determining 

which parameter has the largest impact and subsequently 

applying that approach to a number of combinations 

of system parameters and building configurations.

9.1 Impact Analysis Approach

In evaluating the impacts of changing various system 

variables, it is necessary to choose some type of performance 

metrics for quantifying the protection of the building 

occupants. Two performance metrics were considered in 

this study: (1) the level of exposure in the zone of interest 

(Zone 2) over the first 30 minutes (E30,2), and (2) the time to 

reach an effective dosage Ct of interest (tCt). These metrics 

represent the severity and speed of the exposure, respectively, 

and provide an accurate description of the impact of HVAC 

parameters on exposure in the zone of interest. The exposure 

metric (E30,2) alone can yield valuable information about 

the performance of the system to reduce total exposure. The 

examination of only the time to reach an effective Ct (tCt) 

gives an indication useful for response time analysis. While 

the specific details of the chosen performance metrics (e.g., 

30 minutes as the timeframe for the exposure metric) relate 

to acute hazards, which likely occur in the unsteady state 

immediately following a release, alternative details could be 

chosen to apply this approach to longer timeframes. In this 

respect, this impact analysis approach is very flexible and 

could be applied to numerous different scenarios through the 

choice of appropriate performance metrics.

The combination of these exposure metrics summarizes 

valuable information about the performance of the system, 

while conclusions based on only one metric may ignore 

critical performance details. For example, while timebased metrics are important for evacuation and protection 

strategies, they do not directly correlate to the total exposure 

for a given scenario. In a similar fashion, exposure-based 

metrics do not provide any indication of how quickly a 

critical exposure value is realized within a zone.
44

Figure 35. Illustration of Exposure-Based Metric Inadequacies for a Hypothetical Case of Various 

Filtration Efficiencies (80, 90, and 99.99), Equal Zone Volumes (V1=V2=V3), Makeup Air 

1 ACH), Moderate Recirculation (5 ACH), Infiltration (0.5 ACH), and High Leakage (1 ACH)

For illustration, Figure 35 contains plots of cumulative 

exposure for Zone 2, for a hypothetical case similar to some 

of the experimental conditions used during the field study 

except that all three-zone volumes are equal. By plotting an 

arbitrary critical exposure value (e.g., 10 CFU/m3 *min), the 

importance of both types of metrics are graphically illustrated 

in Figure 35. (Note that CFU stands for colony-forming 

unit.) The arbitrary critical exposure value is reached at 6.1 

and 8.8 minutes for filtration efficiencies of 80% and 90%, 

respectively, while the cumulative exposure for a filtration 

efficiency of 99.99% never reaches the arbitrary critical 

value. Thus, considering a time-based metric, one might 

conclude that changing the filtration efficiency from 80% to 

90% has relatively little impact on the system performance. 

However, the total cumulative exposure for these two cases 

varies by 50% (17.2 CFU/m3

 *min for 80% versus 11.8 CFU/

m3 *min for 90%), which may be significant to occupant 

survival. For these reasons, both a time-based metric and an 

exposure-based metric are needed to assess performance.

The goal of this impact analysis is to determine the relative 

impact of the various HVAC and building parameters on 

the performance metrics (i.e., the parameter that has the 

largest impact on the protection offered by the building). 

To accomplish this, a series of runs was performed varying 

parameters one at a time over their particular ranges of 

variability. To graphically display the sensitivity of the 

performance metrics to changes in various parameters, a 

normalization procedure was applied to eliminate any bias 

towards parameters with inherently small magnitudes and/

or limited ranges. For example, filter collection efficiency 

may realistically range over five logs (i.e., ~0 to 99.99%), 

but infiltration rates, only one (i.e., 0.1 to 1). To this end, 

both the performance metrics and the parameters were 

normalized by baseline values and the results plotted 

(for examples, see Figures 36 and 37). In Figure 36, the 

normalized performance metric (E30/E30 base) is plotted versus 

the normalized study parameter (P/Pbase). By comparing 

the gradient of the plot at the analysis baseline condition 

(i.e., the slope of the curves at P/Pbase=1), the effects of a 

relative change in the model parameter on the performance 

metric can be compared. As one would intuitively expect, 

a larger gradient (i.e., a steeper slope) indicates that a 

small relative change in the model parameter will produce 

a large change in the performance metric. For example, 

a smaller slope for the filter efficiency curve (shown in 

dark blue in Figures 36 and 37) and a larger slope for the 

leakage rate (shown in bright red in Figures 36 and 37) 
45

indicate that the leakage rate between rooms has a larger 

relative impact on the exposure metric for the hypothetical 

building than the filter efficiency. Please note that the 

filtration efficiencies were subjected to a log transform 

according to Equation 6. This log transform frames filtration 

efficiencies such that they may vary from zero to infinity.

(6)

Figure 36. Normalized Exposure at 30 Minutes (E30,2) Versus Model Input Parameters for a “Large” 

Notional Building (V3=100V1) Under 1 ACH Makeup Air, 5 ACH Recirculation, 0.3 ACH 

Infiltration, 30% Filtration, and 1 ACH Interzonal Leakage Note that P/Pbase=1 signifies the 

analysis baseline.

Figure 37. Normalized Time to Critical Exposure (tCt) Versus Model Input Parameters for a “Large” 

Notional Building (V3=100V1) Under 1 ACH Makeup Air, 5 ACH Recirculation, 0.3 ACH 

Infiltration, 30% Filtration, and 1 ACH Interzonal Leakage Note that P/Pbase=1 signifies the 

analysis baseline.
46

)

Table 13. Typical Change in Parameters for Use in Calculating Scale Factors

Model Parameter Set Change(a) Baseline Value(b) Scale Factor(c)

Filter Efficiency [%] 1⁄4 PFilter Efficiency (d) 30 % 0.25

Makeup Air Rate [ACH] 0.25 ACH 1 ACH 0.25

Recirculation Rate [ACH] 0.5 ACH 5 ACH 0.10

Infiltration Rate [ACH] 0.1 ACH 0.3 ACH 0.33

Room Leakage [ACH] 0.25 ACH 1 ACH 0.25

(a) denoted ∆PSet Change in Equations 8 and 9

(b) denoted Pbase in Equations 8 and 9

(c) denoted as the quantity (∆PSet Change/Pbase) in Equations 8 and 9

(d) 1⁄4 PFilter Efficiency for a filter change depends on the filter efficiency. For a 10% efficient filter, 1⁄4 of PFilter Efficiency is equal to 18.0%, while for a 

99% efficient filter, 1⁄4 of PFilter Efficiency is equal to 0.2%.

The sum of the absolute value of the gradients of the two 

normalized performance metric curves associated with 

a model parameter (i.e., the quantity |E′30,P | + | t′Ct,P |), 

evaluated at P/Pbase=1, are an indication of how much impact 

a parameter has. Equation 7 describes this mathematically 

where E′30,P is the slope of the normalized exposure metric 

curve at P/Pbase=1 for parameter P (with P denoting any of the 

five parameters from the legend of Figures 36 or 37), t′Ct,P is 

the slope of the normalized time to critical exposure metric 

curve at P/Pbase=1 for parameter P (with P denoting any of the 

five parameters from the legend of Figures 36 or 37), Pbase

is the base case parameter value (i.e., the notional building 

being considered), ∆P is the change in parameter P being 

considered (i.e., a reasonable change in the parameter), and 

the quantity (∆E30/E30,base + ∆tCt/tCt,base) is taken to be the 

cumulative change in performance metrics possible for a 

potential change in parameter P of ∆P.

(7)

Equation 7 provides an indication of the impact of a 

reasonable change in a parameter; however, it must be 

adjusted so that (1) any dependence on the baseline parameter 

is eliminated and (2) once the baseline parameter dependence 

has been eliminated, a factor to account for the magnitude 

of each parameter is included to allow for comparisons 

between parameters (a detail that normalization by the 

baseline parameter was previously used to accomplish). To 

accomplish both of these items, a scale factor defined as the 

ratio of a set change in the parameter (e.g., changes in filter 

efficiencies are likely on the order of a quarter of a log while 

changes in leakage rates are likely on the order of 0.25 ACH) 

to the baseline parameter was introduced. An illustration is 

shown in Table 13, which lists the assumed values for typical 

changes in a parameter, the baseline parameter values, and 

the calculated scale factors.

The absolute value of the gradients of the two curves 

associated with a model parameter, that is the quantity

|E′30,P | + | t′Ct,P | where E'30,P and t'Ct,P denote the slopes for 

each of the performance metrics of the curve for a given 

parameter (P) evaluated at P/Pbase=1, can then be multiplied 

by the resulting scale factors to produce an impact score for 

each parameter (see Equations 8 and 9). In Equations 8 and 

9, IP is the impact score, ∆PSet Change is the set change in a 

parameter, and the quantity (∆E30/E30,base + ∆tCt/tCt,base) is 

taken to be the cumulative change in performance metrics 

possible for a potential change in parameter P of ∆P. In this 

way, the relative impact of each parameter on the model can 

be quantified.

(8)

(

Base

SetChange

P P Ct P P

P

tEI

∆ = ′ + ′ ∗ ,,30

(9)

For the sake of simplicity, the impact scores are then 

renormalized by the maximum value. This sets the range 

of possible impact scores to between zero and unity, which 

provides a set scale of relative impact (i.e., an impact score 

of unity indicates the most important model parameter). In 

this way, the resulting normalized impact score indicates 

the relative impact of simulation parameters on model 

performance (i.e., the higher the normalized impact score, 

the stronger the effect on model performance).

Table 14 illustrates the results from this analysis. The 

normalized impact scores indicate that leakage is the 

dominant factor in determining model performance with 

recirculation, filtration, makeup air, and infiltration all 

playing lesser roles. By noting the sign (i.e., positive 

or negative) of the slope (see Figures 36 and 37 for the 

illustrative case), the nature of the dominant mechanism 

associated with the parameter (i.e., delivery or removal 
47

mechanism) can be ascertained. Thus, for the illustrative 

case, it can be concluded that the delivery of contaminant 

is limited by the leakage rate while removal of contaminant 

is limited by the combination of the recirculation, filtration, 

and makeup air that dilute and filter the air in the simplified 

HVAC system. Using this approach, it is possible to conclude 

that reducing the interzonal leakage is easily identifiable as 

the key parameter in improving the protection offered by this 

building. This is not to say that other parameters do not affect 

the exposure metrics, but rather that the interzonal leakage 

has the largest impact for parameters scrutinized for the set 

change used in the analysis. The impact analysis does not 

identify the magnitude of the impact; it only identifies the 

relative impact of each parameter.

9.2 Impact Analysis Limitations

As with any analysis, it is important to understand the 

limitations of the method outlined above as well as to 

understand the applicability of the results. To illustrate these 

two concepts, an alternative set of baseline parameters with 

a greatly reduced leakage (i.e., all parameters are the same as 

the previously detailed baseline case except for an interzonal 

leakage of 0.01 ACH) was used for comparison with the 

illustrative results of the previous two sections. Comparisons 

between the alternative baseline results and the illustrative 

results from Sections 9.1 are intended to demonstrate 

that: (1) the results and discussion stated in this work are 

applicable only to cases described by the parameter set used 

in the analysis; (2) inferences made based on the gradients 

of the normalized plots (e.g., Figures 36 and 37) are valid 

only in the parameter space represented by the baseline case 

(i.e., at or near P/PBase=1); and that (3) while this analysis 

was designed for typical buildings under standard daytime 

operation (e.g., the baseline parameters outlined in Table 13), 

it is valid for atypical buildings under nonstandard operating 

conditions (e.g., an alternative baseline with a greatly 

reduced leakage rate of 0.01 ACH). Thus, while the specific 

analysis results presented in this document may be valid only 

for notional buildings under standard daytime operation, the 

analysis approach used here is valid for virtually all buildings 

under any operating conditions.

Figure 38 is the analog to Figure 36 for the alternative 

baseline case (i.e., original baseline case with a greatly 

reduced leakage rate of 0.01 ACH). Note that in Figure 38, 

the gradient of the infiltration rate curve (shown in green) 

seems small in comparison to that of the filter efficiency 

curve (shown in blue), while in Figure 36, the two curves 

are very similar (i.e., they exhibit approximately the same 

slope at P/PBase=1 in Figure 36). This difference indicates 

that the relative importance of both the filtration efficiency 

and the infiltration rate depend on other parameters. This 

illustrates that the results of an analysis are valid only when 

the parameters used in the analysis describe the situation of 

interest. No set of parameters is universally applicable. Some 

situations are not represented by the parameter set used in 

this illustration (e.g., a building equipped with a HEPA filter).

It is also essential to note that comparing the slopes of the 

curves at P/PBase ≠ 1 is not valid since those parameter spaces 

represent the simultaneous variation of multiple parameters. 

For example, comparing the slopes of the curves from 

Figure 38 for filter efficiency and leakage rate at P/PBase = 2 

is comparing the impact of a relative change in room leakage 

for a leakage of 2 ACH and 30% filter efficiency to the 

impact of a relative change in filter efficiency for a leakage 

of 1 ACH and 65% filter efficiency. While this information 

is interesting, no conclusions can be drawn without further 

exploring and defining the parameter space surrounding those 

points (i.e., repeat the analysis of the previous two sections 

for an alternative set of baseline conditions corresponding to 

the parameter space represented by each of those points).

It is also important to note that the impact analysis outlined 

here is focused on zones directly linked via leakage to the 

zone of release. An analogous approach could be used to 

determine the impact of various parameters on the rest of the 

building (i.e., zones not directly linked to the zone of release 

via leakage). This could be done by treating the exposure and 

time to peak within the third, lumped zone in an identical 

manner as the exposure and time to peak for the zone of 

interest were treated in the impact analysis (see Section 9.1).

Table 14. Normalized Impact Scores for the Base Case for a “Large” Notional Building (V3=100V1) Under 1 ACH 

Makeup Air, 5 ACH Recirculation, 0.3 ACH Infiltration, 30% Filtration, and 1 ACH Interzonal Leakage

Zone 2 Parameter

Sum of Normalized 

Gradients(a) Scale Factor(b) Impact Score(c)

Renormalized

Impact Score

Filter Efficiency 0.075 0.25 0.019 0.057

Makeup Air Rate 0.245 0.25 0.061 0.18

Interzonal Leakage 1.32 0.25 0.33 1.00

Infiltration Rate 0.060 0.33 0.020 0.061

Recirculation Rate 1.16 0.10 0.116 0.35

(a) denoted |E′30,P | + | t′Ct,P | in Equations 8 and 9

(b) denoted as the quantity (∆PSet Change/Pbase) in Equations 8 and 9 

(c) denoted IP in Equations 8 and 9
48

Figure 38. Normalized Exposure at 30 Minutes (E30,2) Versus Model Input Parameters 

for a “Large” Notional Building (V3=100V1) Under 1 ACH Makeup Air, 5 ACH 

Recirculation, 0.3 ACH Infiltration, 30% Filtration, and 0.01 ACH Interzonal 

Leakage. Note that P/Pbase=1 signifies condition is identical to the previously 

described base case with the exception of a greatly reduced leakage rate (0.01 ACH).

9.3 Preliminary Impact Analysis Results

In order to identify the key parameters of interest, a 

preliminary set of model simulations were performed. 

These simulations examined the impact of the makeup air, 

recirculation, infiltration, leakage, and filtration efficiency 

for notional buildings of varying sizes for a typical set 

of building parameters (i.e., 1 ACH Makeup Air, 5 ACH 

Recirculation, 0.3 ACH Infiltration, 1 ACH Leakage, and 

30% Filtration).

To examine the effects of building size, the volume of Zone 

3 was set to 1, 5, 10, 25, 50, and 100 times the volume of 

a standard 142-m3

 room, yielding Zone 3 volumes of 142, 

710, 1,420; 3,550; 7,100; and 14,200 m3, respectively. Next, 

each of the HVAC parameters was individually varied from 

the typical set of building parameters. Table 15 contains 

the impact analysis results for the typical set of building 

parameters for a Zone 1 release for various Zone 3 volumes. 

These results can also be viewed graphically as displayed in 

Figure 39. Note that the curves connecting the data points 

are merely to help illustrate the dependency. For reference, 

Table 16 contains the typical set of building parameters used 

and the ranges used in the analysis. The detailed results, in 

the form of graphs of normalized performance metrics versus 

normalized parameters, can be found in Appendix B.

The low normalized impact scores for the makeup air 

and infiltration indicate that over the range of building 

volumes modeled, for the typical building parameters and 

a release scenario within a zone (Zone 1), the makeup 

air and infiltration have little effect on the performance 

metrics. Thus, the makeup air and infiltration rates would 

not be parameters of interest in attempting to improve the 

performance of a building described by the typical building 

parameters against a similar release scenario. From a 

theoretical standpoint, it is interesting to note the shape of the 

system makeup air impact curve, which exhibits a maximum 

near a building volume of ~1,000 m3

 (which corresponds to 

a Zone 3 volume of five times that of Zone 2). This region 

indicates that for low volumes (i.e., V3 < 5V2), described 

by the base case conditions, the dilution provided by the 

system makeup airflow rate of 1 ACH is of slightly greater 

importance than the system recirculation rate of 5 ACH. 

As building volume (i.e., the Zone 3 volume) is increased 

beyond ~1,000 m3, the dilution effects of the system 

recirculation surpass those of the system makeup air.
49

Table 15. Impact Analysis Results for Various Zone 3 Volumes Under 1 ACH Makeup Air, 5 ACH Recirculation, 0.3 

ACH Infiltration, 30% Filtration, and 1 ACH Interzonal Leakage

Zone 2 Parameter Normalized 

Impact Score Zone 2 Parameter Normalized Impact Score

V3 = 1V2

Filtration Efficiency 1.00

V3 = 25V2

Filtration Efficiency 0.42

Leakage 0.21 Leakage 1.00

System Makeup Air 0.16 System Makeup Air 0.28

System Infiltration 0.06 System Infiltration 0.17

System Recirculation 0.07 System Recirculation 0.41

V3 = 5V2

Filtration Efficiency 1.00

V3 = 50V2

Filtration Efficiency 0.12

Leakage 0.87 Leakage 1.00

System Makeup Air 0.26 System Makeup Air 0.21

System Infiltration 0.10 System Infiltration 0.07

System Recirculation 0.20 System Recirculation 0.37

V3 = 10V2

Filtration Efficiency 0.76

V3 = 100V2

Filtration Efficiency 0.06

Leakage 1.00 Leakage 1.00

System Makeup Air 0.33 System Makeup Air 0.21

System Infiltration 0.10 System Infiltration 0.07

System Recirculation 0.32 System Recirculation 0.38

Table 16. Typical Parameter Values and Model Parameter Ranges

Variable Min Base case Max

Filter Efficiency [%] 0 30 99.9999

Makeup Air Rate [ACH] 0 1 15

Recirculation Rate [ACH] 0 5 15

Infiltration Rate [ACH] 0 0.3 2

Room Leakage [ACH]

Zone 1 Volume [m3]

Zone 2 Volume [m3]

Zone 3 Volume [m3]

0

N/A

N/A

143

1

143

143

14,300

2

N/A

N/A

14,300

N/A is defined as not applicable.
50

Figure 39. Impact Analysis Results for Various Zone 3 Volumes Under 1 ACH Makeup Air, 5 ACH 

Recirculation, 0.3 ACH Infiltration, 30% Filtration, and 1 ACH Interzonal Leakage

The results of the filtration efficiency, recirculation rate, 

and leakage rate are more interesting. The analysis shows 

that the most important parameter affecting building 

performance (with respect to changing the exposure or time 

for a critical exposure to occur) changes as the building 

size —Zone 3— changes. For building volumes less than 

~3,800 m3 (which corresponds to a Zone 3 volume of 25 

times that of Zone 2), the dominant factor is clearly the 

removal of agent from the combined recirculation stream 

via filtration. Figure 39 indicates that as the building 

volume increases beyond ~1,000 m3 (i.e., V3=5V2), the 

dominant factors are the leakage rate, the recirculation 

rate, and, to a lesser extent, the filtration efficiency. This 

trend suggests that as the building size increases, or more 

specifically as the volume of recirculated air increases, 

the main mitigation mechanism becomes dilution, while 

the filter efficiency becomes of lesser importance. As this 

occurs, the interzonal leakage, which acts as a delivery 

mechanism, becomes of increased importance and assumes 

the role of the dominant factor determining performance.

The above discussion regarding the use of impact scores to 

assess which parameters are most important and how well 

the parameters need to be known illustrates that a single, 

global generalization cannot necessarily be made. That is, the 

effect of parameters on the spread of contaminant depends 

on numerous other parameters and on the system HVAC 

configuration/operation. Consequently, in assessing factors 

that affect building performance, a good understanding of the 

building operation is needed. It would then be best to assess 

the building of interest using the simplified model approach 

presented here. The usefulness of the simplified model 

approach is that detailed modeling and input data sets would 

not be required for an initial assessment.

9.4 Parameter Space Map Impact Analysis

Based on preliminary modeling results, a map of the 

parameter space defined by varying interzonal leakage rates, 

system recirculation rates, and general filtration efficiencies 

was constructed. For “large” and “small” notional building 

volumes, simulations exploring these three key variables 

were performed. Interzonal leakage rates of 1.0, 0.7, 0.4, 

and 0.1 ACH were used to examine the effects of various 

leakage rates. These values were chosen to approximate the 

range over which the interzonal leakage can realistically be 

controlled. General filtration efficiencies of 10, 30, 60, 90, 

and 99% were used to further map out the parameter space of 

interest. Again, these values were chosen to approximate the 

range over which the filter efficiency could realistically vary.

For each combination of filtration efficiencies and leakage 

rates, further simulations were performed at system 

recirculation rates of 3, 5, and 7 ACH. These values were 

chosen based on rough estimates of the maximum system 
51

recirculation rates achievable by conventional HVAC 

systems. Generally speaking, there are two main types of air 

handling systems used in typical commercial office buildings: 

(1) fixed volume systems and (2) variable air volume systems 

(Bell, 2000). Fixed volume systems typically operate at full 

capacity (100%), and thus no further increase in the system 

recirculation rate is possible (Bell, 2000). Variable air volume 

systems are used in higher-end office buildings and operate 

at 50 to 75% of full capacity (Bell, 2000). Based on this 

information, a maximum system recirculation rate of 7 ACH 

was determined to be appropriate for this work. In order to 

provide insight into the impact of the system recirculation 

rate, a recirculation rate of 3 ACH was also added to the 

simulation matrix.

Also based on preliminary results, building volumes of 

5 and 100 times that of the zone of interest were chosen 

to represent “small” and “large” notional buildings, 

respectively. For “large” and “small” notional building 

volumes, each combination of these three parameters (i.e., 

filtration efficiency, leakage rate, and recirculation rate) was 

used as an analysis baseline case. Thus, for each possible 

combination of the aforementioned values, the procedure 

described in Section 9.1 (illustrated in Figures 36 and 37, as 

well as Table 14) was performed.

9.4.1 “Large” Notional Building Parameter Space Map 

Results

A “large” notional building was defined for this effort as 

having a volume of 14,600 m3. An example of the “large” 

building, with a nominal ceiling height of 3 m, might include 

a 1,200-m3 lobby (20 m x 20 m), ten 75-m3 reception areas 

(5 m x 5 m), twelve 150-m3 conference rooms (10 m x 

5 m), one hundred twenty 75-m3 offices (5 m x 5 m), and 

approximately 1,850 m3 of hallways, stairwells, elevators, 

and restrooms. This “large” notional building would have a 

standard occupancy of 134 to 148, based on estimates of one 

person per office, four to eight persons in the lobby, and one 

to two persons in the reception areas.

The impact analysis results for the parameter space of 

interest for a Zone 1 release in a “large” building are listed 

in Table 17. The parameter space map is also illustrated 

by a series of three-dimensional surface plots in Figures 

40 through 42. Note that the three-dimensional plots use a 

surface spline to connect the actual data points contained in 

Table 17 and are intended as an illustrative tool to graphically 

present the results. 
52

Table 17. Impact Analysis Results for the “Large” Building (14,600 m3) Parameter Space Map 

(Note that Recirculation has been abbreviated Recirc in the Table Interior.)

Leakage [ACH]

0.1 0.4 0.7 1

Recirculation 

[ACH]

Filtration

[%]

Recirc

Impact

Leakage

Impact

Filtration

Imapct

Recirc

Impact

Leakage

Impact

Filtration

Imapct

Recirc

Impact

Leakage

Impact

Filtration

Imapct

Recirc

Impact

Leakage

Impact

Filtration

Imapct

7

10 0.02 1.00 0.40 0.10 1.00 0.44 0.20 1.00 0.39 0.27 1.00 0.59

30 0.03 1.00 0.22 0.12 1.00 0.20 0.21 1.00 0.32 0.35 1.00 0.2460 0.03 1.00 0.08 0.13 1.00 0.09 0.21 1.00 0.13 0.29 1.00 0.0790 0.05 1.00 0.02 0.13 1.00 0.01 0.20 1.00 0.02 0.32 1.00 0.0199 0.06 1.00 0.00 0.13 1.00 0.00 0.23 1.00 0.00 0.30 1.00 0.00

Leakage [ACH]

0.1 0.4 0.7 1

Recirculation 

[ACH]

Filtration|

[%]

Recirc

Impact

Leakage

Impact

Filtration

Imapct

Recirc

Impact

Leakage

Impact

Filtration

Imapct

Recirc

Impact

Leakage

Impact

Filtration

Imapct

Recirc

Impact

Leakage

Impact

Filtration

Imapct

5

10 0.02 1.00 0.22 0.12 1.00 0.26 0.24 1.00 0.21 0.31 1.00 0.19

30 0.03 1.00 0.13 0.13 1.00 0.17 0.23 1.00 0.12 0.36 1.00 0.1360 0.03 1.00 0.05 0.13 1.00 0.04 0.23 1.00 0.10 0.36 1.00 0.0590 0.03 1.00 0.01 0.14 1.00 0.01 0.25 1.00 0.01 0.33 1.00 0.0299 0.03 1.00 0.00 0.14 1.00 0.00 0.25 1.00 0.00 0.33 1.00 0.00

Leakage [ACH]

0.1 0.4 0.7 1

Recirculation 

[ACH]

Filtration

[%]

Recirc

Impact

Leakage

Impact

Filtration

Imapct

Recirc

Impact

Leakage

Impact

Filtration

Imapct

Recirc

Impact

Leakage

Impact

Filtration

Imapct

Recirc

Impact

Leakage

Impact

Filtration

Imapct

3

10 0.02 1.00 0.09 0.13 1.00 0.14 0.26 1.00 0.08 0.39 1.00 0.08

30 0.03 1.00 0.06 0.15 1.00 0.05 0.27 1.00 0.06 0.40 1.00 0.0660 0.03 1.00 0.03 0.15 1.00 0.02 0.26 1.00 0.02 0.40 1.00 0.0390 0.04 1.00 0.00 0.16 1.00 0.01 0.28 1.00 0.00 0.41 1.00 0.0099 0.04 1.00 0.00 0.16 1.00 0.00 0.28 1.00 0.00 0.41 1.00 0.00
53

Figure 40. “Large” Building (14,600 m3) Parameter Mapping Results for a Recirculation Rate of 7 ACH

Figure 41. “Large” Building (14,600 m3) Parameter Mapping Results for a Recirculation Rate of 5 ACH

Figure 42. “Large” Building (14,600 m3) Parameter Mapping Results for a Recirculation Rate of 3 ACH
54

The parameter space map data for a notional “large” building 

indicate that under most conditions the leakage rate between 

zones has the largest impact on building performance for an 

interior release. Even for extreme conditions not sampled in 

this study where another parameter may have a larger impact, 

the leakage still would have a large impact (i.e., greater than 

0.6 on a scale normalized to unity). This strongly suggests 

that for a “large” building, the interzonal leakage rate is the 

key parameter in improving the performance of the building 

against an internal release.

In understanding these results, it is paramount to consider 

that the impact analysis indicates the parameter for which 

a reasonable change (i.e., the Set Changes in Table 13) will 

produce the largest change in building performance (i.e., the 

sum of the performance metrics described in Section 9.1). 

To further elucidate this trend, let us consider our three key 

parameters (recirculation, leakage, and filtration) in terms of 

a rough mass balance around the zone of interest. Leakage 

from the release zone and the common HVAC recirculation 

represent the two potential routes by which contaminant may 

enter the zone of interest at any given instant in time. In the 

parameter space studied, leakage from the release zone to 

the zone of interest represents a flow path of 0.1-1 ACH with 

a concentration equal to that of the release zone. Similarly, 

in the parameter space studied, the system recirculation 

represents a flow path of 3–7 ACH with a concentration equal 

to roughly 1/100 of the concentration in the release zone, 

based on a building volume of 100 times that of the zone of 

interest. Considering these estimates, it is clear that leakage 

from the release zone is the dominant delivery mechanism.

Similarly, leakage from the zone of interest, system 

recirculation, and filtration represent the potential routes 

by which contamination may be removed from the zone of 

interest at any given instant in time. In the parameter space 

studied, the leakage from the zone of interest represents a 

removal rate of 0.1-1 ACH (for the purposes of this crude 

analysis, the makeup air and infiltration will be neglected). 

The system recirculation represents a removal rate of 

3–7 ACH in the parameter space of this study. Filtration 

represents a reduction in the concentration delivered by the 

system recirculation. At first glance, filtration may appear 

to be the dominant removal mechanism; however, it is 

important to consider that the reduction in the recirculated 

concentration due to filtration is in addition to the reduction 

in the recirculated concentration due to dilution. For the 

“large” building, the concentration in the system recirculation 

has already been diluted by a factor of 100 due to the size 

of the building. Thus, the impact of a 90% efficient filter on 

the performance metrics can be approximated as the impact 

of changing the concentration of the recirculated flow from 

roughly 1/100 of the release zone concentration to roughly 

1/1,000 of the release zone concentration. Clearly, the impact 

of this change in the recirculated concentration is much less 

than the impact of the leakage, which can be approximated 

as the impact of changing the interzonal leakage rate, which 

possesses a contaminant concentration equal to that in the 

zone of release. In this manner, the impact of filtration on the 

performance metrics is marginalized by dilution in the system 

recirculation in a “large” building under the conditions 

studied here.

These results imply an interesting trend in addition to the 

importance of the interzonal leakage. As illustrated in 

Figures 40 through 42, the filtration impact tends to decrease 

as filtration efficiency increases. The slope of this trend is 

increased at higher recirculation rates and minimal at lower 

recirculation rates, confirming the coupled nature of filtration 

and recirculation. Thus, although the recirculation rate has 

a minimal impact score in and of itself, it has a significant 

effect on the filtration impact.

9.4.2 “Small” Notional Building Parameter Space 

Map Results

A “small” notional building was defined for this effort as 

having a volume of 1,000 m3. A notional example of the 

“small” building, with a nominal ceiling height of 3 m, 

might include a 300-m3 lobby (10 m x 10 m), a 150-m3

conference room (10 m x 5 m), six 75-m3 offices (5 m x 5 m), 

and approximately 100 m3 of hallways and restrooms. This 

“small” notional building would have a standard occupancy 

of approximately seven to eight persons based on estimates 

of one person per office and one to two people in the lobby.

The impact analysis results for the parameter space of interest 

for a Zone 1 release in a “small” building are listed in Table 

18. The parameter space map is also illustrated by a series 

of three-dimensional surface plots in Figures 43 through 45. 

Note that the three-dimensional plots use a surface spline 

to connect the actual data points contained in Table 18 and 

are intended as an illustrative tool to graphically present the 

results. In order to further reduce the data, a parameter space 

map indicating the approximate parameter with the highest 

impact is given in Figure 46.

The impact analysis results for the notional “small” building 

are more complex and “feature rich” than their “large” 

building counterparts. Similar to the “large” building results, 

the parameter space maps for the “small” building indicate 

that at high filtration efficiencies the interzonal leakage has 

the largest impact on building performance. However, at 

lower filtration efficiencies the filtration efficiency has the 

largest impact. The division between these two regimes 

appears to be linked to recirculation, varying from roughly 

75% at a recirculation rate of 7 ACH to approximately 30% 

at a recirculation rate of 3 ACH. Thus, while recirculation 

is not a dominant parameter in terms of its direct impact, it 

does play a determining role in what is the dominant impact 

parameter. These results indicate that, generally speaking, for 

the notional “small” building, removal of contaminant via 

filtration and delivery of contaminant via interzonal leakage 

have the largest impacts on building performance for an 

interior release.
55

Which of these parameters dominates appears to depend 

mainly on the filtration efficiency with interzonal leakage 

dominating at high filtration efficiencies and filtration 

efficiency dominating at low filtration efficiencies. As 

described above, recirculation rate plays a secondary role 

by altering the division between the filtration-dominant 

region and the leakage-dominant region. Thus, the critical 

filtration efficiency value will depend on both the building 

size (relative to the zone of interest) and the system 

recirculation rate. For example, the critical filtration 

efficiency is approximately 50% for a building volume of 

1,000 m3 (relative to a zone of interest volume of 143 m3) 

and a recirculation rate of 5 ACH, while the critical filtration 

efficiency is roughly 75% for a building volume of 1,000 m3

(relative to a zone of interest volume of 143 m3) and a 

recirculation rate of 7 ACH. Again, as in previous sections, 

the estimated accuracy requirements follow the impact 

analysis results in that high impact parameters have tight 

estimated accuracy requirements.

The marked differences between the “small” and “large” 

building results imply that for a “small” building the delivery 

of contaminant via interzonal leakage and the removal 

of contaminant via filtration of the recirculated air are of 

comparable magnitudes. The reason for this is the large 

reduction in the dilution of contaminant in the recirculated 

stream (a factor of ~5 versus a factor of ~100). With the 

reduction in dilution, contaminant removal via filtration of 

the recirculated air becomes the dominant mechanism for the 

removal of contaminant from the zone of interest. Thus, the 

reduction in dilution has greatly increased the importance 

(i.e., the impact) of filtration and created regions in the 

parameter space where a change in the filtration efficiency 

will produce the largest change in building performance (i.e., 

the performance metrics).
56

Table 18. Impact Analysis Results for the “Small” Building (1,000 m3) Parameter Space Map 

(Note that Recirculation has been abbreviated Recirc in the Table Interior.)

Interzonal Leakage [ACH]

0.1 0.4 0.7 1

Recirculation

[ACH]

Filtration

[%]

Recirc

Impact

Leakage

Impact

Filtration

Impact

Recirc

Impact

Leakage

Impact

Filtration

Impact

Recirc

Impact

Leakage

Impact

Filtration

Impact

Recirc

Impact

Leakage

Impact

Filtration

Impact

7

10 0.03 0.17 1.00 0.02 0.19 1.00 0.02 0.16 1.00 0.03 0.16 1.00

30 0.02 0.30 1.00 0.04 0.30 1.00 0.05 0.30 1.00 0.06 0.31 1.0060 0.05 0.80 1.00 0.10 0.72 1.00 0.16 0.78 1.00 0.20 0.75 1.0090 0.03 1.00 0.17 0.12 1.00 0.18 0.21 1.00 0.18 0.33 1.00 0.1999 0.04 1.00 0.01 0.13 1.00 0.01 0.20 1.00 0.01 0.35 1.00 0.01

Interzonal Leakage [ACH]

0.1 0.4 0.7 1

Recirculation

[ACH]

Filtration

[%]

Recir

Impact

Leakage

Impact

Filtration

Impact

Recirc

Impact

Leakage

Impact

Filtration

Impact

Recirc

Impact

Leakage

Impact

Filtration

Impact

Recirc

Impact

Leakage

Impact

Filtration

Impact

5

10 0.06 0.31 1.00 0.03 0.29 1.00 0.04 0.34 1.00 0.05 0.27 1.00

30 0.05 0.48 1.00 0.05 0.52 1.00 0.09 0.46 1.00 0.13 0.42 1.0060 0.04 1.00 0.85 0.12 1.00 0.85 0.21 1.00 0.94 0.29 1.00 0.9590 0.03 1.00 0.12 0.13 1.00 0.12 0.25 1.00 0.14 0.36 1.00 0.1499 0.03 1.00 0.01 0.14 1.00 0.01 0.25 1.00 0.01 0.33 1.00 0.01

Interzonal Leakage [ACH]

0.1 0.4 0.7 1

Recirculation

[ACH]

Filtration

[%]

Recirc

Impact

Leakage

Impact

Filtration

Impact

Recirc

Impact

Leakage

Impact

Filtration

Impact

Recirc

Impact

Leakage

Impact

Filtration

Impact

Recirc

Impact

Leakage

Impact

Filtration

Impact

3

10 0.15 0.68 1.00 0.08 0.74 1.00 0.07 0.67 1.00 0.12 0.55 1.00

30 0.13 1.00 0.99 0.06 0.98 1.00 0.14 1.00 0.89 0.17 0.83 1.0060 0.03 1.00 0.44 0.10 1.00 0.44 0.20 1.00 0.43 0.31 1.00 0.3590 0.03 1.00 0.07 0.14 1.00 0.07 0.27 1.00 0.07 0.39 1.00 0.0899 0.04 1.00 0.00 0.15 1.00 0.00 0.28 1.00 0.00 0.43 1.00 0.00
57

Figure 43. “Small” Building (1,000 m3) Parameter Mapping Results for a Recirculation Rate of 7 ACH

Figure 44. “Small” Building (1,000 m3) Parameter Mapping Results for a Recirculation Rate of 5 ACH

Figure 45. “Small” Building (1,000 m3) Parameter Mapping Results for a Recirculation Rate of 3 ACH
58

Figure 46. Parameter Space Map of the Dominant Parameter, or Parameter with the Highest Impact Score, 

for a “Small” Building (1,000 m3) (Note that grey regions are intended to indicate a region where 

two or more parameters are dominant.)
59

9.5 Functional Analysis Guidelines

Although it is not possible to make blanket statements about 

dominant parameters, a simple functional analysis can be 

used to develop some general guidelines regarding which 

HVAC parameters are important for a range of situations. 

This simple functional analysis will generalize the key 

delivery and removal mechanisms for the zone of interest 

(i.e., Zone 2) in terms of simple functional forms.

The simplified analysis begins with combining the makeup 

airflow rate and the recirculation flow rate into a lumped 

HVAC flow rate (see Equation 10).

(10)

Where QI,i , QR,i, and QHVAC,i denote the makeup airflow rate, 

the recirculated airflow rate, and the lumped HVAC flow rate 

for Zone i.

Furthermore, since both the makeup air and the recirculated 

airflow rate are nominally functions of the zone volume, 

the lumped HVAC flow rate can be rewritten as an integer 

function of the zone volume (see Equation 11).

(11)

Where Vi denotes the volume of Zone i and N is an integer 

frequency, nominally on the order of 3–8 hr-1.

Neglecting infiltration (i.e., assuming there is no infiltration 

or that the magnitude of infiltration is so small that it can be 

ignored), there are two delivery mechanisms for the zone of 

interest (i.e., Zone 2). One delivery mechanism is interzonal 

leakage from the zone of release (i.e., Zone 1), which is 

functionally dependent on the product of the leakage flow 

rate, denoted QLeak, and the concentration within the zone of 

release, denoted C1(t). The other delivery mechanism is the 

simple HVAC system, where the delivery of contaminant 

is functionally dependent on the product of the lumped 

HVAC flow (QHVAC,2), the concentration in the common 

ductwork (CHVAC(t)), and a filtration efficiency term (1-η). 

It is important to note that both the concentration within the 

zone of release (C1(t)) and the concentration in the common 

HVAC ductwork (CHVAC(t)) are functions of time under 

non-steady-state conditions. Despite the time dependency of 

the concentrations, a general functional form for the delivery 

term is obtained (see Equation 12).

 (12)

This delivery mechanism functional form can be further 

reduced by approximating the concentration in the common 

HVAC ductwork (CHVAC(t)) in terms of the HVAC flows 

and concentrations of each of the contributing zones 

(Equation 13). It is important to note that the resulting 

expression (i.e., Equation 13) is an approximation and 

assumes that the lumped HVAC flow rate for any given zone 

is much larger than the interzonal leakage entering or exiting 

that zone. Assuming that the concentration within the zone of 

release is the highest of the three zones, this approximation 

represents an overestimation of the contributions of the 

HVAC flow to contaminant delivery. While this approach 

is not exact, it is more than sufficient for a simple, rough 

analysis such as this.

(13)

Assuming that the recirculation and makeup air are systemwide variables (i.e., each zone has the same recirculation 

and makeup air when expressed in units of air changes 

per hour), this expression can be further reduced using the 

previously derived expression for the lumped HVAC flows 

as a function of zone volume (i.e., Equation 11) to a volume 

weighted average of the concentrations within each zone (see 

Equation 14). The resulting expression is quite helpful in 

estimating the contributions of common HVAC system to the 

delivery of contaminant.

(14)

Similarly, when infiltration is neglected, there are two 

mechanisms for the removal of contaminant from the zone of 

interest (i.e., Zone 2). One removal mechanism is interzonal 

leakage from the zone of interest to the bulk of the building 

(i.e., Zone 3), which is functionally dependent on the product 

of the leakage flow rate, denoted QLeak, and the concentration 

within the zone of interest, denoted C2(t). The other removal 

pathway is the simple HVAC system, where the removal 

of contaminant is functionally dependent on the product of 

the lumped HVAC flow (QHVAC,2) and the concentration in 

the zone of interest (C2(t)). Again, the concentration within 

the zone of interest (C2(t)) is a function of time under nonsteady-state conditions. Following this approach, a general 

functional form for the removal of contaminant is obtained 

(see Equation 15).

(15)

Through consideration of the functional forms of the 

delivery mechanisms (Equation 12), removal mechanisms 

(Equation 15), and the contaminant concentration in the 

common HVAC ductwork (Equation 14) a rough, back-ofthe-envelope analysis of a given situation can be performed 

to provide an initial indication as to which parameters are 

most important and which parameters, if any, can potentially 

be neglected or ignored.
60

9.5.1 Contaminant Transport Dominated by HVAC 

Mechanisms

One potential condition is that the delivery and removal 

of contaminant is dominated by the HVAC mechanisms. 

This would mean that the HVAC terms in the delivery and 

removal functional forms dominate (i.e., are much larger 

than their leakage counterparts). This dominance can be 

mathematically expressed using the individual terms from 

Equations 12 and 15 (see Equations 16 and 17 for the 

delivery and removal, respectively).

(16)

(17)

Dividing the equation obtained from manipulating 

the removal functional form (i.e., Equation 17) by the 

concentration within Zone 2 yields a mathematical statement 

which, intuitively, must be true for HVAC mechanisms to 

dominate (see Equation 18).

(18)

Assuming that the lumped HVAC flow for the zone of 

interest is much larger than the interzonal leakage flow rate, 

the derived expression for the concentration in the common 

HVAC ductwork as a volume weighted average of the zone 

concentrations can be substituted into the delivery functional 

form (see Equation 19).

(19)

Assuming that the mass of contaminant in the zone of release 

is significantly larger than the mass of contaminant in the 

rest of the building (i.e., V1C1(t) >> V2C2(t)+ V3C3(t)), and 

dividing both sides of the equation by the concentration in the 

release zone and the lumped HVAC flow for Zone 2, yields a 

time-independent statement (see Equation 20).

(20)

By comparing the reduced delivery and removal forms 

(Equations 20 and 18, respectively), it is apparent that 

the delivery condition is more severe (i.e., if the delivery 

condition stated in Equation 20 is met, the removal 

condition in Equation 18 will be satisfied). In this fashion, 

the conditions whereby the common HVAC system will 

dominate can be identified (i.e., Equation 20).

For example, under conditions of little or no filtration (i.e., 

(1-η) ≈ 0), Zone 1 must represent a volume fraction of the 

building smaller than the ratio of the interzonal leakage flow 

rate to the lumped HVAC flow for Zone 2 (see Equation 21).

(21)

Furthermore, if the interzonal leakage was equivalent 

to 1 ACH and the lumped HVAC flow for Zone 2 was 

equal to 6 ACH (e.g., 1 ACH of makeup air and 5 ACH of 

recirculation), Zone 1 would need to be significantly larger 

than 1/6 of the building volume (see Equation 22).

(22)

Thus, for the HVAC flows to dominate the delivery of 

contaminant, Zone 1 must represent a very large fraction of 

the building. Effectively, this will happen when the building, 

or rather volume served by the common HVAC ductwork, 

is small. In addition, any filtration will further increase the 

required size of Zone 1 in relation to the rest of the building.

9.5.2 Contaminant Transport Dominated by Interzonal 

Leakage

Another potential condition is that the delivery and removal 

of contaminant is dominated by the interzonal leakage. In 

this case, the leakage terms in the delivery and removal 

functional forms would dominate (i.e., would be much larger 

than their HVAC counterparts). Again, this dominance can 

be mathematically expressed using the individual terms 

from Equations 12 and 15 (see Equations 23 and 24 for the 

delivery and removal, respectively).

(23)

(24)

Dividing the equation obtained from manipulating 

the removal functional form (i.e., Equation 24) by the 

concentration within Zone 2 yields a mathematical statement 

which, intuitively, must be true for leakage mechanisms to 

dominate (see Equation 25).

(25)
61

Given that the filtration term is, by definition, less than unity 

(i.e., (1-η) < 1) and the concentration in the zone of release is 

larger than the concentration in the common HVAC ductwork 

under all but trivial conditions (i.e., C1(t) > CHVAC(t)), the 

removal condition is more severe than the delivery condition 

(i.e., if the removal condition stated in Equation 25 is met, 

the delivery condition in Equation 23 will be true). Thus, a 

very intuitive statement regarding the conditions under which 

the interzonal leakages dominate the removal and delivery of 

contaminant is derived (i.e., Equation 25).

9.5.3 Perfect Filtration

Using this approach, it is also possible to examine the 

effects of perfect filtration (i.e., a filtration efficiency of 

100%). Under perfect filtration, the filtration term in the 

delivery mechanism goes to zero (i.e., (1-η) → 0) and the 

leakage clearly dominates the delivery of contaminant (see 

Equation 26). However, the filtration has no impact on the 

removal functional form (see Equation 27).

(26)

(27)

Thus, while perfect filtration eliminates the delivery of 

contaminant via the common HVAC system, it does not 

affect the removal functional form. Under the nominally 

“common” conditions of 1 ACH of leakage and 6 ACH of 

lumped HVAC flow, perfect filtration would result in leakage 

dominating the delivery of contaminant and HVAC flows 

dominating the removal of contaminant.
62

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63

10.0

In-Room Air Cleaners

The effects of an in-room air cleaner were also briefly 

investigated. During the in-room air cleaner test, a 

commercially available air cleaner (Whirlpool Model 

AP4503H0, see Figure 47) was operated in the zone of 

interest (B114), as well as in one of the small rooms, which 

were lumped together with the rest of the building (B115). 

Table 19 contains a comparison of the performance metrics 

for the zone of interest for tests performed under moderate 

recirculation (5 ACH) and moderate filtration (50%) with 

and without the in-room air cleaner operating. The results 

indicated that the in-room air cleaner successfully reduced 

the concentration at 30 minutes by roughly one order of 

magnitude and prevented the Ct from reaching the critical 

Ct value selected for this study. These results are shown 

graphically in Figure 48, where the impact of an in-room 

air cleaner on the zone of interest (B114) can be viewed as 

the difference between the purple and magenta data plots 

and the impact of an in-room air cleaner on a small room 

not adjacent to the zone of release (B115) can be viewed as 

the difference between the neon green and turquoise data 

plots. In contrast to the significant reduction in concentration 

observed in rooms with in-room air cleaners, rooms without 

an in-room air cleaner displayed a negligible reduction in 

concentration during the in-room air cleaner tests (denoted 

by the difference between the neon green and dark green data 

plots in Figure 48).

These plots clearly illustrate that whether the in-room air 

cleaner is operating in a room near the release or far from the 

release, it can have a large impact on the concentration within 

that particular zone/room. This result is not surprising given 

the high throughput (~440 cfm) and HEPA-grade filtration 

efficiency of the in-room air cleaner used.

Figure 47. Photograph of In-Room Air Cleaner Used During 

Testing (Whirlpool Model AP4503H0)
64

Table 19. Comparison of Experimental Performance Metrics for the Zone of Interest for the “Moderate” Recirculation 

Condition With and Without an In-Room Air Cleaner

Recirculation

Building Size

Leakage

Filtration

Test ID

(Figure 48)

Experimentally Measured

Time to Ct of 50,000 

ppl*min [min]

Exposure @ 30 min 

[ppl*min]

“Moderate” (5 ACH)

“Large”

“Low” (0.175 ACH)

“Low” (10%) N/A 9.1 1.8x105

“Moderate” (50%) B1 11.2 1.3x105

“Moderate” (50%) 

with IAC IAC1 N/R 2.9x104

“High” (90%) N/A 15.6 8.1x104

N/R denotes that specified cumulative Ct was not reached.

N/A is defined as not applicable.

Figure 48. Comparison of Experimental Data for an In-Room Air Cleaner for a “Large” Notional Building with 

Moderate Recirculation (5 ACH), Low Leakage (0.175 ACH), Moderate Filtration (50% MERV 8 Filter), 

Standard Makeup Air (1 ACH), and Standard Infiltration (0.5 ACH). Test IAC1 was conducted with inroom air cleaners in selected zones denoted as (IAC). Zones without in-room air cleaners during test IAC1 

are denoted as (No IAC). Test B1 was conducted with no in-room air cleaners. 
65

11.0

Conclusions and Recommendations

A three-zone model of a building was developed to determine 

which HVAC and building operating parameters are most 

important, and how accurately they need to be known, to 

determine the impact of an indoor bio-agent attack. The 

three-zone model consists of a zone of release and zone 

of interest that are adjacent and of equal size, as well as a 

“lumped” zone that consists of the remainder of the building. 

All three zones are serviced by a single HVAC system with 

common recirculation.

An experimental study was then conducted to verify the 

effectiveness and validity of using the three-zone model 

to approximate contaminant spread in a building. In the 

experimental study in the test building, modifications were 

made to the building and HVAC system to assess the impact 

those changes had on the spread of the contaminant. The 

building was modified through the addition of new slab-toslab walls, the extension of existing partial walls, and the 

addition of several return vents to create a test volume that 

resembled the model.

Two performance metrics were used to compare the model 

and experimental results: the cumulative exposure to the 

particulate agent for the first 30 minutes following a release 

and the time to reach a critical exposure value (referred to as 

Ct). On the whole, the excellent agreement observed between 

experimental and model-predicted data validated the use of 

the three-zone model to approximate contaminant spread 

in a representative building. Excellent agreement between 

the lumped third zone in the model and multiple rooms 

throughout the test building validated the use of a three-zone 

model to provide useful information on real buildings with 

more than three zones.

A sensitivity analysis method was then developed for the 

three-zone model to provide an estimate of how well a 

parameter must be known to assess the impact of an attack, as 

well as to determine which parameter has the largest impact. 

An initial analysis using the model simulation revealed that 

building makeup air and infiltration rates were noncritical 

parameters for an internal release scenario and that system 

parameters were of greater importance than single-zone 

parameters (i.e., the recirculation rate of the entire building 

was more important than the recirculation rate of the zone 

of interest). The initial analysis also identified the building 

size, interzonal leakage rate, recirculation rate, and filter 

efficiency as the key parameters affecting the two selected 

performance metrics. The simulation results also showed 

that the most important parameter to consider depended on 

building size relative to the fixed volume zone of interest 

(e.g., a 3,000-m3

 building composed of 100 rooms that are 

each 30 m3 in volume is equivalent to a 50,000-m3 building 

composed of 100 rooms that are each 500 m3 in volume). 

As the building size increased, the filtration efficiency went 

from being a potentially dominant parameter to a lesser 

factor compared to the interzonal leakage. The system 

recirculation was found to be of secondary importance in 

and of itself but had a strong effect on the importance of the 

filtration efficiency for smaller buildings (i.e., for building 

volumes less than five times the zone of interest). With 

all parameters studied, as the importance of the parameter 

increased, so did the accuracy with which it needs to be 

known for reliable estimates of performance metrics. 

The results of the experimental study corroborated the 

findings and trends identified by the model simulations.

The most important conclusion to be made from the 

model simulation results, and the supporting experimental 

measurements of the spread of a contaminant, is that 

there is not a dominant parameter, nor a single value of 

how accurately it needs to be known, to accurately assess 

the impact of an indoor release in a building. The model 

approach, as supported by the experimental data, provides 

a useful and easy tool for estimating and assessing which 

parameters most impact the spread of contaminant in a 

building. Buildings of varying size and HVAC performance 

can be assessed by varying the corresponding model 

parameters accordingly.

The analysis method provided does indicate the general 

trends and identifies key parameters for specific combinations 

of HVAC parameters and building volumes relative to 

the zone of interest as discussed above. The simplified 

modeling approach developed here could be used to assess 

various scenarios and buildings of specific interest, without 

the need for extensive knowledge of HVAC and building 

parameters. Using the modeling tool may have merit for 

rapidly identifying the value of modifying the building to 

enhance the protection of occupants or to mitigate the spread 

of contaminants.

Ancillary experiments suggest that the use of an in-room 

air cleaner can greatly reduce the particulate matter level 

in the room. The magnitude of this reduction will vary 

greatly depending on the volume of the room, as well as the 

throughput and efficiency of the in-room air cleaner.

Recommendations for future study focus on two aspects 

of the well-mixed model: usability and applicability. The 

usability of the model could be improved by developing 

a user-friendly graphical user interface (GUI) that would 

allow casual users (e.g., building operators) to rapidly 

perform simple impact analyses for a building of interest 

given limited building information (e.g., HVAC settings, 

building and room volumes). The development of a GUI 
66

would increase the utility and impact of this effort by making 

the model available to more people. Also recommended is 

an enhancement to expand the applicability of the tool by 

developing and verifying an analogous model for a building 

with a more complex HVAC ductwork scheme. The present 

model is only applicable to a building with one commonreturn ductwork system. Developing an analogous model 

that effectively represents buildings with multiple-return 

ductwork systems (i.e., multiple air handling units) and 

performing an experimental verification, similar to this 

work, would aid in making a model more applicable to large 

buildings with more complex air handling schemes.
67

12.0

References

American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). ASHRAE 

Terminology of Heating, Ventilation, Air Conditioning, & Refrigeration. (2nd edition). Atlanta, Georgia: 

ASHRAE, 1991.

American Society for Testing and Materials (ASTM). ASTM Standard E 779-03: Standard Test Method for 

Determining Air Leakage Rate by Fan Pressurization. West Conshohocken, Pennsylvania: ASTM, 2003.

Bell, A.A. HVAC Equations, Data and Rules of Thumb. New York: McGraw Hill, 2000.

Hawkins and Hofacre, 2006.

Hecker, R.T. “Development of Performance Information for Common Ventilation Filters.” Battelle Report 

to U.S. Environmental Protection Agency, Draft Final Report GS-10F-0275K, Task Order 1105, Task 2

(in progress).

Sparks, Leslie, Ph.D. U.S. Environmental Protection Agency, Personal Communication, 2005.
A-68

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A-1

Appendix A

SF6 Experimental Methods and Results

SF6 Experimental Methods

Release Methods. The tracer gas used was SF6. SF6 is an 

inert, nonhazardous tracer gas and thus yields an accurate 

indication of the air distribution without being affected 

by surface interactions (e.g., reactions or adsorption). It 

is also safe to use in indoor applications with occupants 

present and can be detected at trace (tens of parts per 

trillion) concentrations. The amount of SF6 released was 

calculated by considering the maximum safe concentration 

of SF6 according to established safety limits. The maximum 

concentration in any zone should not exceed the time 

weighted average (TWA) of the permissible exposure limit 

(PEL) for the tracer gas used, which for SF6, is equivalent 

to 1,000 ppm (MSDS, 2004). For the purposes of this 

calculation, a release zone volume of 125 m3

 was assumed 

(roughly 22 x 20 x 10). Since the maximum observed 

concentration will be in the release zone, the maximum 

release mass will be 0.30 kg of SF6 (50 liters at STP). 

This amount of SF6 (0.30 kg) will produce a maximum 

concentration of 400 ppm in a release zone with a volume of 

125 m3. If this release amount of SF6 is safe for a small room, 

it will be safe for release in a larger, better ventilated hallway, 

and will certainly be safe for release into an HVAC system 

with even larger makeup air rates, serving an even larger 

area. Based on a rough calculation of the entire building as a 

single volume with one air change per hour, this release mass 

should also produce measurable levels of SF6 throughout 

large portions of the building and, thus, will be more than 

adequate for this field study.

To facilitate model comparisons, the release duration 

of the SF6 will be adjusted to correspond to release 

characteristics used in model simulations (i.e., a 

10-minute release duration). This will be achieved 

through the use of an orifice (or other flow restriction) 

to effectively meter the flow of SF6 from a cylinder.

Sampling Methods. Operation of all air samplers will be 

conducted per standard operating procedures (SOPs). All 

personnel that will operate the samplers were trained on their 

operation and demonstrated proficiency of use during dry 

runs. Operators signed the SOP acknowledgement sheet, and 

the test leader confirmed that the personnel were proficient 

with their operation.

During the testing period, an automated air sampler 

developed by Battelle will be used to collect air samples at 

selected locations within the area of interest. The functional 

components of the automated sampler are composed of 

one diaphragm pump, 12 three-way solenoid valves, 12 

Tedlar® sampling bags, a flow restriction, and a customdesigned electronics control board. Up to 100 samplers 

will be used. A pyramidal sampling control concept will 

be used to centralize control of the sampling parameters. 

Thus, a single computer will dictate the sampling parameters 

for each serialized sampler in a text file. The text file 

containing the sampling parameters for each of the 100 

serialized samplers will then be distributed via ten handheld 

PODs (handheld program transmitter), which also serve 

to retrieve the data log from the samplers and record the 

barcodes of sample bags (see Figure A-1). The PODs 

are roughly the size and shape of a standard handheld 

transmitter (e.g., a palm pilot). This sampling concept will 

allow for centralized control of the sampling parameters, 

while retaining the convenience of having multiple staff 

members distribute the sampling parameters. In addition, 

staff members will not be required to be present to collect 

samples, and thus, will not interfere with the local airflow 

and transport of tracer. In this approach, the test leader 

will direct the sample coordinator regarding the final input 

and verification of the sampling event to be executed.

The automated sampler will use a serial configuration of 

three-way valves to minimize any carryover concerns and 

will offer flexibility in dictating various sample parameters 

such as purge time, sample time, and delay time between 

samples. The samples collected will then be analyzed using 

a GC/ECD to determine the SF6 concentration of each 

sample. A schematic diagram of the sampler’s pneumatic 

configuration when used to collect air samples for SF6

analysis is illustrated in Figure A-2. Photos depicting 

the internal and external view of the samplers in this 

configuration are provided in Figure A-3.
A-2

Figure A-1. Pyramidal Sampling Control Concept
A-3

Figure A-2. Automated Sampler Pneumatic Configuration
A-4

Figure A-3. Internal and External Pictures of the Automated Sample
A-5

Each of the automated samplers operate at a fixed flow, 

ranging from 20 to 500 cc/min. (within ± 10%), through 

the use of a flow restriction (e.g., a capillary tube). Exact 

flow rates for each test will be determined by the task leader 

so that the desired concentration versus time profile for 

the specific tests can be obtained. Operating with 500 cc 

Tedlar® sampling bags, sampling durations of 1 to 25 

minutes are possible. The delay between samples can be 

controlled for each sample to allow for any desired sampling 

schedule. Sampling duration can be controlled in 10-second 

increments. The sampler is designed so that the sampling 

pump operates for at least 1 minute to flush the sampling 

lines prior to sample collection. (This occurs only when there 

is a delay between samples of at least 1 minute; otherwise, 

the sampler pump operates continuously and does not 

require a flush period.) This operating feature will ensure 

that there is no artifact associated with carryover from the 

previous sample, which would be most significant if the room 

concentration decreased rapidly. The sampler will be operated 

so that a minimum of 250 cc will be collected, which will 

allow for repeat analyses to be performed, if needed.

Sampler dimensions are 30 x 30 x 65 cm (w x h x l) and the 

sampler weighs 12 kg. The samplers are intended to sit on 

the floor or desktop. The sample inlet tube can be secured 

with a support rod and may be placed as high as 3 m above 

the sampler. Indicator lights (LEDs) are visible on the top 

of the control box. The LEDs indicate whether the pump 

is operating and which sampling bag is being filled. These 

provide the operator assurance of proper operation without 

disturbing operation.

Since the tests may rely heavily on the automated sampler, a 

rigorous battery of pretest checks will be performed. These 

pretest checks are designed to verify that everything is in 

working order prior to execution of the tracer gas test. Each 

serialized automated sampler will execute a preprogrammed 

sampling sequence without any sample bags attached. The 

samplers will be monitored during the pretest to verify that 

the pump is running and the valves are operating according 

to the program. This monitoring will occur in the form of 

checking the status of the LEDs, which indicate whether the 

pump is running and which valves are actuated. In addition, 

the flow rate for all sampling lines will be verified.

After pretest checks are performed on all of the automated 

samplers, the samplers will be loaded with sample cartridges 

containing twelve 500-cc sampling bags and then deployed to 

the sampling locations.

SF6 Leakage Test Results

The results of the initial leakage tests suggested that 

imperfect mixing would be one major discrepancy 

between the experimental results and the model-predicted 

concentration curves (see Figures A-4 and A-5). The results 

of the initial leakage tests also indicated that significant 

recirculation was occurring (see Figure A-4). Upon further 

investigation it became apparent that significant leakage was 

occurring across the recirculation damper and that reducing 

that leakage to zero was not feasible given the materials 

and time constraints of the project. While it was possible to 

estimate the actual recirculation using the theoretical model 

(see Figure A-5), this represented an indirect method of 

estimating the interzonal leakage, which depended heavily 

on the model-predicted value of the recirculated airflow. 

For example, estimating the interzonal leakage while 

neglecting the recirculation (as in Figure A-4) leads to an 

interzonal leakage estimate of 0.4 ACH, while estimating 

the recirculation (as in Figure A-5) leads to an interzonal 

leakage estimate of 0.2 ACH. Given this strong dependence 

of the estimated leakage rate on the estimated recirculation 

rate, it was clear that this did not represent a direct measure 

of the leakage, as planned. For this reason, a combinatorial 

approach to determining the leakage was adopted.
A-6

Figure A-4. Comparison of Model-Predicted and Experimental Data for an Initial Leakage Test Assuming 

No Recirculation. Experimental data were gathered for the “small” notional building under 

a 6 ACH of makeup air. Model-predicted data are for the “small” notional building under 6 

ACH of makeup air with 0.4 ACH interzonal leakage.

Figure A-5. Comparison of Model-Predicted and Experimental Data for an Initial Leakage Test Assuming 

Limited Recirculation. Experimental data were gathered for the “small” notional building 

under a 6 ACH of makeup air. Model-predicted data are for the “small” notional building 

under 5 ACH of makeup air and 1.5 ACH recirculation with 0.2 ACH interzonal leakage.
B-1

Appendix B

Preliminary Simulation Results

Figure B-1. Normalized Exposure at 30 Minutes (E30,2) Versus Model Input Parameters for a Zone 1 

Release with a Zone 2 Parameter Scheme for a Building (V3=1V1). Note that P/Pbase=1 

signifies the analysis baseline.

Figure B-2. Normalized Time to Critical Exposure (tCt) Versus Model Input Parameters for a Zone 1 

 Release with a Zone 2 Parameter Scheme for a Building (V3=1V1). Note that P/Pbase=1

 signifies the analysis baseline.
B-2

Figure B-3. Normalized Exposure at 30 Minutes (E30,2) Versus Model Input Parameters for a Zone 1 

Release with a System Parameter Scheme for a Building (V3=1V1). Note that P/Pbase=1 

signifies the analysis baseline.

Figure B-4. Normalized Time to Critical Exposure (tCt) Versus Model Input Parameters for a Zone 1 

Release with a System Parameter Scheme for a Building (V3=1V1). Note that P/Pbase=1 

signifies the analysis baseline.
B-3

Figure B-5. Normalized Exposure at 30 Minutes (E30,2) Versus Model Input Parameters for a Zone 1 

Release with a Zone 2 Parameter Scheme for a Building (V3=5V1). Note that P/Pbase=1 

signifies the analysis baseline.

Figure B-6. Normalized Time to Critical Exposure (tCt) Versus Model Input Parameters for a Zone 1 

Release with a Zone 2 Parameter Scheme for a Building (V3=5V1). Note that P/Pbase=1 

signifies the analysis baseline.
B-4

Figure B-7. Normalized Exposure at 30 Minutes (E30,2) Versus Model Input Parameters for a Zone 1 

Release with a System Parameter Scheme for a Building (V3=5V1). Note that P/Pbase=1 

signifies the analysis baseline.

Figure B-8. Normalized Time to Critical Exposure (tCt) Versus Model Input Parameters for a Zone 1 

Release with a System Parameter Scheme for a Building (V3=5V1). Note that P/Pbase=1 

signifies the analysis baseline.
B-5

Figure B-9. Normalized Exposure at 30 Minutes (E30,2) Versus Model Input Parameters for a Zone 1 

Release with a Zone 2 Parameter Scheme for a Building (V3=10V1). Note that P/Pbase=1 

signifies the analysis baseline.

Figure B-10. Normalized Time to Critical Exposure (tCt) Versus Model Input Parameters for a Zone 1 

Release with a Zone 2 Parameter Scheme for a Building (V3=10V1). Note that P/Pbase=1 

signifies the analysis baseline.
B-6

Figure B-11. Normalized Exposure at 30 Minutes (E30,2) Versus Model Input Parameters for a Zone 1 

Release with a System Parameter Scheme for a Building (V3=10V1). Note that P/Pbase=1 

signifies the analysis baseline.

Figure B-12. Normalized Time to Critical Exposure (tCt) Versus Model Input Parameters for a Zone 1 

Release with a System Parameter Scheme for a Building (V3=10V1). Note that P/Pbase=1 

signifies the analysis baseline.
B-7

Figure B-13. Normalized Exposure at 30 Minutes (E30,2) Versus Model Input Parameters for a Zone 1 

Release with a Zone 2 Parameter Scheme for a Building (V3=25V1). Note that P/Pbase=1 

signifies the analysis baseline.

Figure B-14. Normalized Time to Critical Exposure (tCt) Versus Model Input Parameters for a Zone 1 

Release with a Zone 2 Parameter Scheme for a Building (V3=25V1). Note that P/Pbase=1 

signifies the analysis baseline.
B-8

Figure B-15. Normalized Exposure at 30 Minutes (E30,2) Versus Model Input Parameters for a Zone 1 

Release with a System Parameter Scheme for a Building (V3=25V1). Note that P/Pbase=1 

signifies the analysis baseline.

Figure B-16. Normalized Time to Critical Exposure (tCt) Versus Model Input Parameters for a Zone 1 

Release with a System Parameter Scheme for a Building (V3=25V1). Note that P/Pbase=1 

signifies the analysis baseline.
B-9

Figure B-17. Normalized Exposure at 30 Minutes (E30,2) Versus Model Input Parameters for a Zone 1 

Release with a Zone 2 Parameter Scheme for a Building (V3=50V1). Note that P/Pbase=1 

signifies the analysis baseline.

Figure B-18. Normalized Time to Critical Exposure (tCt) Versus Model Input Parameters for a Zone 1 

Release with a Zone 2 Parameter Scheme for a Building (V3=50V1). Note that P/Pbase=1 

signifies the analysis baseline.
B-10

Figure B-19. Normalized Exposure at 30 Minutes (E30,2) Versus Model Input Parameters for a 

Zone 1 Release with a System Parameter Scheme for a Building (V3=50V1). 

Note that P/Pbase=1 signifies the analysis baseline.

Figure B-20. Normalized Time to Critical Exposure (tCt) Versus Model Input Parameters for a 

Zone 1 Release with a System Parameter Scheme for a Building (V3=50V1). Note 

that P/Pbase=1 signifies the analysis baseline.
B-11

Figure B-21. Normalized Exposure at 30 Minutes (E30,2) Versus Model Input Parameters for a Zone 1 

Release with a Zone 2 Parameter Scheme for a Building (V3=1V1). Note that P/Pbase=1 

signifies the analysis baseline.

Figure B-22. Normalized Time to Critical Exposure (tCt) Versus Model Input Parameters for a Zone 1 

Release with a Zone 2 Parameter Scheme for a Building (V3=1V1). Note that P/Pbase=1 

signifies the analysis baseline.
B-12

Figure B-23. Normalized Exposure at 30 Minutes (E30,2) Versus Model Input Parameters for a Zone 1 

Release with a System Parameter Scheme for a Building (V3=1V1). Note that P/Pbase=1 

signifies the analysis baseline.

Figure B-24. Normalized Time to Critical Exposure (tCt) Versus Model Input Parameters for a Zone 1 

Release with a System Parameter Scheme for a Building (V3=1V1). Note that P/Pbase=1 

signifies the analysis baseline. 
C-1

Appendix C

Data Quality

Data Quality

Data Quality Objectives (DQOs) are qualitative and 

quantitative statements designed to ensure that the type, 

quality, and quantity of data used are appropriate for the 

intended application. As discussed in the body of the report 

(Sections 1, 2, 3, and 5), the experiments performed had 

two purposes. One purpose of the experiments was to 

evaluate the effect of HVAC, architectural, and operating 

procedure modifications on the spread of gases and aerosols 

through a building. The other purpose was to experimentally 

demonstrate the validity of the three-zone, well-mixed model 

developed in Task 7. In an effort to collect a wide range of 

data, replicate testing was not performed under this project; 

therefore, it was not possible to quantitatively state the 

required agreement between replicates.

The type of data being collected during these tests, particulate 

concentrations, are appropriate since they can be compared 

to one another to determine the effect of the modification 

on the spread of gases and aerosols. A qualitative analysis 

of the collected data was performed by comparing these 

data to those collected on similar projects and to the results 

of the Task 7 theoretical analyses to confirm the trends 

noted in the Task 7 results. For example, a short-term or 

instantaneous release in a well-mixed volume zone serviced 

by a typical HVAC system would be expected to produce 

a rapid increase in contaminant concentration, followed by 

a logarithmic decay in the concentration. The height of the 

peak is indicative of the ratio of the mass released to the 

zone volume, while the decay is indicative of the removal 

constant. The removal constant is an aggregation of many 

potential factors, including contaminant decay (if applicable), 

particle deposition (if applicable), filtration (if applicable), 

and exhaust (if applicable).

Engineering logic was also used to compare the data sets to 

each other to determine whether or not they made qualitative 

sense. (For example, one would expect a lower particulate 

concentration when a higher efficiency filter was used in the 

HVAC system.) Although some variation is expected, gross 

deviations from past data and logically expected results were 

immediately investigated as they were indicative of problems 

that arose. An excellent example of this was observed in the 

SF6 leakage test results discussed in Appendix A. The results 

of the SF6 leakage tests showed that the building HVAC 

system was unable to operate with 100% makeup air (i.e., no 

recirculated air). This prompted a physical inspection of air 

handling dampers in which a test technician climbed inside 

of the air handling unit, revealing that significant leakage 

was occurring across the recirculation damper. Another 

example of the use of engineering logic in achieving data 

quality objectives is apparent in the discussions of MetOne 

particle counter saturation in Section 7.0. In this example, 

experimental measurements indicated that the release mass 

used was sufficiently large to cause saturation of the MetOne 

optical particle counter in the zone of release. In this case, 

an engineering judgment was made not to reduce the release 

mass in order to maintain a sufficient particle concentration 

in zones far from the release. This judgment was deemed 

appropriate for two reasons. The first reason was that the 

concentration within the release zone exhibited the expected 

logarithmic decay from the saturation concentration within 

the test timeframe, indicating a high likelihood that the 

particle concentration within the release zone was behaving 

as expected. The second reason was that the concentration 

in zones far from the release was sufficiently above baseline 

noise to prove useful to model comparisons. Furthermore, 

given that subsequent planned tests would use higher 

efficiency filtration, it was the judgment of the test leader 

that reducing the release mass would result in concentrations 

below background in zones far from the release. In this 

manner, engineering logic was used in conjunction with 

qualitative analyses to scrutinize the quality of data sets, 

make data-based test decisions, and achieve the data quality 

objectives of this task.

Sample calculations are detailed throughout the body of the 

report, particular in Sections 7, 8, and 9.
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