NISTIR 7035

Comparison of Measured and Predicted

Tracer Gas Concentrations in a

Townhouse

 Steven J. Emmerich

 Steven J. Nabinger

Arpita Gupte

Cynthia Howard-Reed

Lance Wallace
NISTIR 7035

Comparison of Measured and Predicted

Tracer Gas Concentrations in a

Townhouse

Steven J. Emmerich

 Steven J. Nabinger

Arpita Gupte

Cynthia Howard-Reed

Building Environment Division

Building and Fire Research Laboratory

 National Institute of Standards and Technology

Lance Wallace

National Exposure Research Laboratory

U.S. Environmental Protection Agency

August 2003

U.S. Department of Commerce

Donald L. Evans, Secretary

Technology Administration

Phillip J. Bond, Undersecretary of Commerce for Technology

National Institute of Standards and Technology

Arden L. Bement, Jr., Director
ABSTRACT

To provide additional validation data for the multizone airflow and contaminant modeling 

approach (i.e., as implemented in the CONTAMW simulation program), a series of tracer gas 

tests were performed in an occupied three-story townhouse in Reston, Va. Tests simulated with 

CONTAMW consisted of short-term release of 1500 mL of tracer gas, sulfur hexaflouride (SF6), 

within one room of the house and the measurement of SF6 concentration in 10 indoor locations. 

In four of the ten main test cases, the heating and air-conditioning system fan was operating. In 

an eleventh test case, an attic fan was operating. The location of the burst source of tracer gas 

included the recreation room (basement level), the kitchen/dining room (main level) and the 

master bedroom (upstairs level). Experiments were conducted between May 2000 and June 2001. 

Local ambient conditions that ranged from a low outdoor temperature of 5 °C to a high of 29 °C 

and wind conditions that ranged from calm to moderate, with an average wind speed of 4 m/s, 

were measured and used as model inputs. 

A statistical comparison of measurements and predictions was performed per ASTM Guide 

D5157 “Standard Guide for Statistical Evaluation of Indoor Air Quality Models” (ASTM 1997) 

for all cases. Guide D5157 provides statistical parameters to assess both agreement and bias and 

suggested criteria that model predictions should meet. Predicted and measured transient 

concentrations for individual zone and time-averaged zone concentrations for the whole house 

were compared for each test. 

The results for zone average concentrations were very good with most cases meeting most or all 

of the D5157 criteria. For example, 9 of 10 cases met the normalized mean square error (NMSE) 

criteria and all cases met the normalized fractional bias (FB) criteria. Three cases did not meet 

the D5157 criteria for both correlation coefficient and line of regression but the discrepancy was 

due almost entirely to poor prediction in a single zone – the main floor bathroom. Excluding that 

zone resulted in these cases meeting or coming very close to meeting the D5157 criteria. For all 

cases, the predicted SF6 concentration averaged linearly over all zones was within 25 % of the 

average measured concentration. 

Predictions of individual zone transient concentrations were less reliable. While many cases met 

all the criteria, nearly half of the cases failed to meet two or more of the D5157 criteria. One 

factor affecting the comparison of transient concentrations was significant differences between 

measured and predicted peak concentrations. This difficulty is not surprising as neither the 

simulations nor the experiments were designed to adequately account for very short-term peaks. 

The report also compares predicted and measured whole house air change rates and examines the 

effects of weather on those rates. 

Key Words: airflow modeling, building technology, computer simulation, infiltration, 

multizone, residential buildings, and validation. 

 iii
 iv
TABLE OF CONTENTS

Abstract iii

List of Tables vii

List of Figures ix

Introduction 1

Experimental Method 2

Test House 2

Instrumentation 3

Procedures 5

Simulation Method 6

CONTAMW Model 6

Statistical Evaluation of Model Predictions 9

Results 10

Air Change Rates 10

Comparison of Predicted and Measured Tracer Gas Concentrations 12

Summary 29

Attic Fan Case 31

Discussion 33

Conclusion 35

Acknowledgements 36

References 37

Use of Non-SI Units in a NIST Publication 

The policy of the National Institute of Standards and Technology is to use the International 

System of Units (metric units) in all its publications. However, in North America in the 

construction and heating, ventilation and air-conditioning (HVAC) industries, certain non-SI 

units are so widely used instead of SI units that it is more practical and less confusing to include 

measurement values for customary units only in the figures and tables of this report. 

 v
 vi
LIST OF TABLES

1 HAC System Flows 4 

2 Air Leakage Values 8 

3 Summary of SF6 Injection Test Cases 13 

4 Statistical Parameters for Case #1 – May 24, 2000 14 

5 Statistical Parameters for Case #2 – October 2, 2000 17 

6 Statistical Parameters for Case #3 – March 9, 2001 18 

7 Statistical Parameters for Case #4 – January 19, 2001 19 

8 Statistical Parameters for Case #5 – March 16, 2001 21 

9 Statistical Parameters for Case #6 – July 24, 2000 22 

10 Statistical Parameters for Case #7 – February 16, 2001 24 

11 Statistical Parameters for Case #8 – March 30, 2001 26 

12 Statistical Parameters for Case #9 – June 22, 2001 27 

13 Statistical Parameters for Case #10 – February 27, 2001 28 

14 Summary of Statistical Parameters 30 

15 Statistical parameters for attic fan test case 32 

 vii
 viii
LIST OF FIGURES

1 Front View of Townhouse 2 

2 Schematic Floorplan of Townhouse 3 

3 CONTAMW Sketchpad Representation of Townhouse 6 

4 Measured and Simulated Blower Door Test Results 7 

5 Measured and Predicted Townhouse Air Change Rates From Year 10 

6 Regression of Overnight Air Change Rates vs. Absolute Indoor – Outdoor 

Temperature Difference (January to December 2001) 

11 

7 Overnight Air Change Rates vs. Wind Speeds Measured at the Townhouse 

(January to December 2001) 

12 

8 Predicted and Measured SF6 Concentrations for Case #1 – May 24, 2000 14 

9 Average Infiltration by Zone for Case #1 15 

10 Predicted and Measured SF6 Concentrations for Case # 2 – October 2, 2000 16 

11 Predicted and Measured SF6 Concentrations for Case # 3 – March 9, 2001 18 

12 Predicted and Measured SF6 Concentrations for Case # 4 – January 19, 2001 19 

13 Measured and Predicted Concentrations in the HAC Return for Case # 4 20 

14 Predicted and Measured SF6 Concentrations for Case # 5 – March 16, 2001 21 

15 Predicted and Measured SF6 Concentrations for Case # 6 – July 24, 2000 22 

16 Measured and Predicted Concentrations in the HAC Return for Case # 6 23 

17 Predicted and Measured SF6 Concentrations for Case # 7 – February 16, 2001 24 

18 Measured and Predicted Concentrations in the HAC Return for Case # 7 25 

19 Predicted and Measured SF6 Concentrations for Case # 8 – March 30, 2001 25 

20 Predicted and Measured SF6 Concentrations for Case # 9 – June 22, 2001 27 

21 Predicted and Measured SF6 Concentrations for Case # 10 – February 27, 2001 28 

22 Measured and Predicted Concentrations in the HAC Return for Case # 10 29 

23 Comparison of Predicted and Measured Zone Average Concentrations for All 

Cases 

31 

24 Predicted (with and without attic fan) and measured SF6 conc. For attic fan case 32 

 ix
INTRODUCTION

There are two general types of computer simulation techniques for studying airflow and 

contaminant transport in buildings – zonal modeling and multizone modeling. Zonal (or 

room airflow) modeling takes a microscopic view by applying a computational fluid 

dynamics (CFD) program to examine the detailed flow fields and pollutant concentration 

distributions within a room or rooms. Multizone airflow and pollutant transport modeling 

takes a macroscopic view of indoor air quality by evaluating average pollutant 

concentrations in the different zones of a building as contaminants are transported 

through the building and its heating and air-conditioning (HAC) system. Each approach 

has strengths and limitations for studying different aspects of building ventilation and 

indoor air quality (IAQ). 

The multizone approach is implemented by constructing a network of elements 

describing the flow paths (HAC ducts, doors, windows, cracks, etc.) between the zones of 

a building. The network nodes represent the zones, which are modeled at a uniform

pressure, temperature, and pollutant concentration. After calculating the airflow between 

zones, including the ambient, zonal pollutant concentrations are calculated by applying 

mass balance equations to the zones, which may contain pollutant sources and/or sinks. 

Feustel and Dieris (1992) describes a survey of multizone airflow models. One multizone 

model is the CONTAM model developed in the Building and Fire Research Laboratory 

(BFRL) at the National Institute of Standards and Technology (NIST). The newest 

publicly available version of CONTAM is CONTAMW 2.0 (Dols et al. 2002). 

Multizone IAQ modeling has been available as a research and analysis tool for over 20 

years. However, due to improvements in such modeling programs (particularly the 

development of user friendly graphic interfaces), the availability of inexpensive 

computing power, and more complex building design requirements, the application of 

such programs has greatly increased and is moving from the research world to building 

designers, fire safety engineers, and other practitioners. This has, in turn, increased the 

need for establishing the validity of these models. 

While a number of studies have examined the issue of validation as reviewed in 

Emmerich (2001), there is a need for further validation work - particularly in simulating 

the impact of IAQ control technologies (such as mechanical ventilation or improved air 

cleaners). This study builds on earlier work in which measurements and CONTAM 

predictions in a single-zone test house were compared (Emmerich and Nabinger 2001). 

This report describes experiments and simulations performed to evaluate the capability to 

accurately simulate tracer gas concentrations with a multizone airflow and indoor air 

quality (IAQ) model, in this case CONTAM. Measurements of tracer gas concentrations, 

building air change rates, and interzone airflow patterns were performed in a multizone 

townhouse. 

1 
EXPERIMENTAL METHOD

Test House 

The test house in this study is a three-story, four-bedroom, four-bathroom end-unit 

townhouse with a floor area of approximately 50 m2 per level (see Figures 1 and 2) and 

an overall volume of approximately 400 m3

. The 30-year old townhouse is located in 

Reston, VA, approximately 35 km northwest of Washington, D.C. Tall trees on two sides 

(the property backs onto a 1.2 x 105

 m2 forested park) and nearby townhouse rows shield 

the townhouse from wind. The townhouse is built on a slope such that there is a partial 

basement consisting of a utility room, pantry, bathroom, and recreation room with 

walkout patio. The middle level consists of a kitchen, dining room, living room, and 

bathroom. The top level contains four bedrooms and two bathrooms. Features of the 

house include sliding glass doors in the basement and living room, a furnace with no 

outdoor air intake, a fan that ventilates the attic with outdoor air when the attic 

temperature is elevated, a fireplace (flue always closed), exhaust fans in all four 

bathrooms, and a clothes dryer that exhausts to the outside. The townhouse’s heating and 

air-conditioning (HAC) system uses 100 % recirculated air and its ductwork does not 

enter the attic, resulting in no direct duct leakage to or from outside. Two blower door 

tests (ASTM E779) were performed (May 1999 and July 2000) to assess the envelope 

leakage of the house. The closed house air change rate at 50 Pa averaged 14.2 h-1 with an 

effective leakage area of 1120 cm2

 at 4 Pa. By opening the doors of the adjacent 

townhouse during the first test and seeing no significant change in pressure, it was 

determined there was negligible influence of the townhouse that shares a common wall. 

Figure 1 Front View of Townhouse 

2 
Instrumentation 

Building air change rates and interzone airflow patterns were assessed using an 

automated tracer gas measurement system consisting of a PC-based data acquisition and 

control system and a gas chromatograph (GC) with an electron capture detector (ECD). 

The GC-ECD was used to measure sulfur hexafluoride (SF6) concentrations over a range 

of about 5 μg/m3

 to 175 μg/m3 (1 ppb(v) to 30 ppb(v)) with an accuracy of approximately 

2 %. The tracer gas system uses a ten-port sample valve to sample air at ten indoor 

locations every 10 min. Thus, six measurements were taken in each location every hour. 

Indoor and outdoor temperatures were measured with thermistors having an uncertainty 

of about 0.4 °C. Wind speed and direction were measured using a sonic anemometer 

installed on the townhouse roof about 2 m (6.6 ft) above the crest of the roof. This is 

below the manufacturers recommendation of 10 m (33 ft) above the roof but was 

necessary due to constraints about appearance. Indoor and outdoor relative humidity was 

measured with bulk polymer resistance sensors with an accuracy of 3 % of the reading. 

First Floor Second Floor Third Floor 

PATIO

RECREATION ROOM

UTILITY

ROOM

Furnace

T RH

T

T

T RH

RH T

T

T

T

T

T

T

T

RH

RH

T

RH

Legend:

= Supply

= Return

= Exhaust Fan = Temperature

= Relative Humidity

SF6

SF6

SF6

SF6

SF6

SF6

SF6

SF6

SF6

SF6

= SF6 SF6 sample point for GC/ECD

PATIO

RECREATION ROOM

UTILITY

ROOM

Furnace

T RH

T

T

T RH

RH T

T

T

T

T

T

T

T

RH

RH

T

RH

Legend:

= Supply

= Return

= Exhaust Fan = Temperature

= Relative Humidity

SF6

SF6

SF6

SF6

SF6

SF6

SF6

SF6

SF6

SF6

= SF6 SF6 sample point for GC/ECD

Figure 2 Schematic Floorplan of Townhouse 

For most of the tests, there were ten indoor sampling locations for tracer gas and air 

temperature. The rooms included the utility and recreation rooms on the basement level, 

kitchen, bathroom and living room on the main level, master bedroom and two offices on 

3 
the upstairs level, the attic, and the central return of the HAC system as seen in Figure 2. 

Most single point sample probes were located near the wall and many near the floor. 

Relative humidity was measured in one location on each level of the house. Early 

measurements found no SF6 in the outdoor air; the outdoor concentration of SF6 was 

assumed to be 0 μg/m3

 for the remainder of the effort. 

Table 1 HAC System Flows 

Supply location Test 1 (L/s) Test 2 (L/s) Model (L/s) 

Front Office 33 33.0 33 

Second bedroom 35 45 40 

Bathroom, upstairs hall * 24 24 

Master Bedroom 144 102 123 

Master Bath 43 * 43 

Back Office 24 31 27 

Living Room supply 1 38 50 44 

Living Room supply2 31 12 21 

Kitchen/Dining Room supply 1 12 59 35 

Kitchen/Dining Room supply 2 31 52 41 

1st floor bath * * 24 

1st floor hallway 21 * 21 

Basement Bathroom 9.4 4.7 7 

Rec supply 1 40 52 46 

Rec supply 2 * 28 28 

Sum 461 491 576

* Supply either not measurable or missed during test. Total model supply flow includes a 

supply leak of 19 L/s into the basement utility room. 

The supply airflow rate through the HAC system was measured by performing a velocity 

traverse conducted in the supply air duct with a hot wire anemometer (HWA) to find a 

point representative of the average velocity in the return duct. A HWA was then mounted 

at that point to monitor duct airflow velocity during the experiments. The HWA has an 

uncertainty of 2.5 % of the indicated reading. The measurements were performed in 

accordance with ASHRAE Standard 111 (ASHRAE 1988), which is estimated to result in 

an uncertainty of 5 % to 10 % under field conditions. The traverse test on the system

supply was repeated several times and a similar traverse was performed on the system

return to better characterize the system flows. The average supply flow was 530 L/s and 

the average return flow was 675 L/s. While the HAC system was operating in fan 

constant mode, a balometer with an estimated accuracy of 10 % was used to measure the 

individual HAC system supply flows with the results shown in Table 1. After accounting 

4 
for not measurable flows the sum of measured supplies agreed reasonably well with the 

results of the traverse test. Attempts were made to measure the system return flows with 

the balometer also, however, accurate measurements were not possible due to the 

locations and magnitudes of those flows. A system duct leakage test confirmed the 

presence of significant supply and return leakage. 

Procedures 

Two types of tests were conducted in the townhouse, both using the tracer gas 

measurement system. The first type was used for measuring air change rates every four 

hours throughout the year. These tests involved automatic injections of SF6 with the HAC 

fan on continuously as opposed to being controlled by the system thermostat. Once 

injected, the SF6 decay rate was automatically calculated from the sampling point in the 

HAC system return for approximately 3.5 h. This test was performed continuously 

throughout the year and was only interrupted by breakdowns, calibrations, or short-term

tests (such as the manual injections described below). 

The second type of test was measurements of SF6 transport from a single zone to the rest 

of the house for the purpose of comparison to CONTAMW model predictions (described 

later). These tests were performed by manually injecting 1500 mL of 1 % SF6 (in air) into 

a single room of the house. During the manual injections, the automatic injection system

was turned off. Six cases were done with the HAC fan on. In order to also test the models 

mixing assumptions for the manual injections, no attempts to directly mix SF6 in the 

individual zones were made.

5 
SIMULATION METHOD

CONTAMW Model 

This section of the report describes the CONTAMW model of the test house. 

CONTAMW is a multizone airflow and pollutant transport model with a graphic 

interface used to create and edit a description of a building’s features relating to airflow 

and the generation and removal of contaminants (Dols and Walton 2002). Multizone 

airflow and pollutant transport modeling takes a macroscopic view of air movement and 

IAQ by calculating average pollutant concentrations in the different zones of a building 

as contaminants are transported through the building and its HVAC system. The 

multizone approach is implemented by constructing a network of elements describing the 

flow paths (doors, windows, cracks, fan flows, etc.) between the zones of a building and 

between the building and ambient zone. The network nodes represent the zones that are 

modeled at a uniform pressure (varying with height), temperature, and pollutant 

concentration. After calculating the airflow between zones and ambient, zonal pollutant 

concentrations are calculated by applying mass balance equations to the zones, based on 

any modeled pollutant sources or sinks. The model uses ambient weather conditions and 

outdoor pollutant contaminant data to calculate these airflows and concentrations. 

The graphical representations of the three main floors of the test house as they appear in 

CONTAMW are shown in Figure 3. The townhouse attic was included in the model as a 

single zone but is not shown. The layout of the townhouse within CONTAMW and the 

division of the zones (including those not labeled) were defined to represent the actual 

floorplan of the townhouse as seen in Figure 2, therefore each zone represents one room. 

The zones labeled in Figure 3 correspond to the zones in which the injections took place 

and/or locations of measured concentrations. 

BATH

FOFC

BOFC

MBR 

LR

KDR

REC 

UTIL 

First Floor Second Floor Third Floor

Figure 3 CONTAMW Sketchpad Representation of Townhouse (Note: The zone abbreviations 

correspond to the room descriptions given in Figure 2.) 

6 
Ideally, the air leakage model of the house could be created by performing leakage tests 

on all individual airflow paths (wall joints, windows, etc.). However, due to the difficulty 

in isolating each individual path, this approach is impractical. An alternative approach 

was implemented where individual air leakage elements were first created in the 

CONTAMW model based on best estimate values from Table 25-3 of the ASHRAE 

Handbook of Fundamentals (ASHRAE 1997). This step ensures a more realistic 

distribution of appropriate airflow elements compared to uniform distribution of leakage 

on the exterior envelope. Then, the individual leakage elements were adjusted such that 

the resulting total flows from a simulated blower door test matched those from the 

measured blower door test. A comparison of the simulated and measured blower door test 

results is shown in Figure 4, and the resulting airflow leakage elements are summarized 

in Table 2. The leakage elements in Table 2 are based on a reference pressure difference 

of 4 Pa and a discharge of 1.0 with a flow exponent of 0.63. 

y = 0.1139x

0.6502

R2 = 0.9978

y = 0.1134x

0.6535

R2 = 0.9994

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 10 20 30 40 50 6

Pressure (Pa)

Flow (

m3/s)

0

Measured

Simulated

Power (Measured)

Power (Simulated)

Figure 4 Measured and Simulated Blower Door Test Results 

7 
Table 2 Air Leakage Values 

Airflow element description Effective Leakage Area 

at 4 Pa 

Wall-wall joint 1.5 cm2/m

Ceiling-wall joint 1.5 cm2/m

Floor-wall joint 4.0 cm2/m

Window 1.1 cm

2

/m

Ceiling 1.8 cm

2

/m2

Door/window frame 1.7 cm2/m2

Front door 21 cm2

Sliding glass door 60 cm2

Wall penetrations 6 cm2

Ceiling penetration 6 cm2

Attic vents (17 total) 0.00226 m2

Attic fan opening 0.152 m2

Open interior doors (as two-way flow, one-opening elements) Approx. measured size 

To account for the effect of wind on a building, CONTAMW requires user inputs for a 

wind pressure modifier to account for local terrain effects and a wind pressure profile to 

account for relative wind direction (see Dols et al. 2000 for details on modeling wind 

effects in CONTAMW). Since the townhouse is almost surrounded by tall trees, the 

CONTAMW wind pressure modifiers for an urban location were used. This decision was 

supported by the observation that wind has a minimal impact on infiltration in the house 

(Wallace 2002). The wind pressure profile (i.e., wind pressure coefficients as a function 

of wind direction) was based on Figure 25.8 of the ASHRAE Handbook of Fundamentals 

(ASHRAE 1997). 

The air handling system flows listed in Table 1 above were included in the house model 

using the CONTAMW simple air handling system model. The supply flows used in the 

house model were generally the average of the two measurements. In a few cases, the 

supply was not measured during a test. In these instances the one measured value was 

used. Since the flow for the 1st floor bathroom could not be measured, the value for the 

2nd floor bathroom was substituted. Lacking accurate measurements for the return flows, 

the total modeled system supply flow of 580 L/s was distributed equally between the 

three system returns. Since measurements could not accurately determine the locations or 

amounts of leakage, the only duct leakage included in the model was a 19 L/s supply leak 

into the utility room representing leakage at the system itself. 

Although this section summarizes the key model inputs, the complete CONTAMW

project files are too large for inclusion here and will be made available on the NIST 

Multizone Modeling Website at www.bfrl.nist.gov/IAQanalysis. 

8 
Statistical Evaluation of Model Predictions

The predicted tracer gas concentrations were compared with measured values using 

ASTM D5157 Standard Guide for Statistical Evaluation of Indoor Air Quality Models. 

This standard provides quantitative and qualitative tools for evaluation of IAQ models 

(ASTM 1997). It provides guidance in choosing data sets for model evaluation and 

focuses on evaluating the accuracy of indoor concentrations predicted by a model. As 

part of the comparison of CONTAMW predictions to measurements of tracer gas 

concentrations, the ASTM D5157 suggested criteria were applied. The data sets collected 

during this study meet the ASTM D5157 criteria for model evaluation, as they are 

entirely independent of the data used to develop the model and to estimate model inputs. 

Also, the data are of sufficient temporal and spatial detail to evaluate the CONTAMW

predictions of individual zonal tracer gas concentrations. 

ASTM D5157 provides three statistical tools for evaluating the accuracy of IAQ model 

predictions and two additional statistical tools for assessing bias. Values for these 

statistical criteria are provided to indicate whether the model performance is adequate. 

The tools for assessing agreement between predictions and measurements include: 

1) The correlation coefficient of predictions and measurements should be 0.9 or greater. 

2) The line of regression between the predictions and measurements should have a slope 

between 0.75 and 1.25 and an intercept less than 25 % of the average measured 

concentration. 

3) The normalized mean square error (NMSE) should be less than 0.25. The NMSE is 

calculated as: 

∑=

= −

N

i

NMSE Cpi Coi CoCp

1

2 ( ) / 2 

 

where Cp is the predicted concentration and Co is the observed concentration, and the 

over-bar represents an average over the N data points during the test period for each test 

case. 

ASTM D5157 also provides two statistical measures of bias with values for judging 

adequate model performance. These measures of bias include: 

1) Normalized fractional bias (FB) of the mean concentrations. Fractional bias should be 

0.25 or smaller and is calculated as:

( FB = Cp −Co Cp +Co 2( ) / )

)2 σ o

 

 

2) Fractional bias based on the variance (FS) which should be 0.5 or smaller. FS is 

calculated as: 

( 2 2 2 FS = − 2(σ p σ o )/ σ p +

where σp is the standard deviation of the predicted concentrations and σo is the standard 

deviation of the observed concentrations. 

 

9 
RESULTS 

Air Change Rates 

The townhouse air change rate was measured using the tracer gas decay method (ASTM 

2000) using the automated SF6 injection system previously described. Measurements 

were taken beginning approximately 25 min after the tracer gas release and ending prior 

to the next injection approximately 3 h and 30 min later. Figure 5 shows air change rates 

vs. indoor – outdoor temperature differences for most 4-hour periods from the year 2001. 

Also shown on Figure 5 are air change rates predicted by the CONTAMW model of the 

townhouse for wind speeds of 0 m/s and 6 m/s for the average of the four wind directions. 

The measured 4-hour average wind speed was nearly always less than 4 m/s and typically 

ranged from 1 m/s to 3 m/s. No formal comparison was made between these measured 

and predicted values as there was no attempt to control occupant activity at all times (i.e., 

window opening, fan operation, etc.). However, given this lack of control the predicted 

air change rates appear reasonably close to the measured values with the main 

discrepancy being over-prediction of air change rates at large temperature differences. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-10 -5 0 5 10 15 20 25

Indoor - outdoor temperature difference (°C)

Air change rate (h-1)

Measured

Predicted; wind speed = 0 m/s

Predicted; wind speed = 6 m/s 

30

Figure 5 Measured and predicted townhouse air change rates 

 

Figure 6 shows a regression of the overnight air change rates against the absolute value of

the indoor – outdoor temperature differences that allows comparison to an earlier study of 

air change rates in the same townhouse (see Figure 8 of Wallace et al. 2002). Figure 6 is 

limited to overnight periods to minimize the potential for window opening or other 

occupant impacts on the air change rates. Compared to the Wallace’s et.al. earlier work, 

Figure 5 shows a larger slope (0.019 compared to 0.016) and intercept (0.24 compared to 

0.18) with a lower correlation coefficient (0.76 compared to 0.91). Given the broader 

10 
seasonal range of Figure 6 and the inevitable operating variability of an occupied house, 

these two studies agree well. 

y = 0.0188x + 0.2411

R2

 = 0.5865

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30

Absolute indoor-outdoor temperature difference (°C)

Air change rate (h-1) Figure 6 Regression of overnight air change rate vs. absolute indoor-outdoor temperature difference 

(January to December 2001) 

The 2001 overnight air change data also confirm Wallace’s report of little to no impact of 

measured wind conditions on air change rates in the townhouse (Wallace et al. 2002). 

Figure 7 shows the measured overnight air change rate vs. average measured wind speed 

for those time periods with an indoor-outdoor temperature difference of less than 10 °C. 

For comparison, the figure also shows the predicted air change rate over the same range 

of wind speeds for indoor – outdoor temperature differences from 0 °C to 10 °C. While 

the data show no obvious trend, the predictions bracket the majority of the measurements. 

The few measured values significantly above the predictions may have been occasions 

when windows were left open at night. The minimal effect of wind is likely due to the 

townhouse’s location in a heavily wooded area that surrounds two of the three exposed 

walls. 

11 
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.5 1 1.5 2 2.533.5 4 4.5

Wind speed (m/s)

Air change rate (h-1)

Measured

Predicted; Delta T = 0 °C

Predicted; delta T = 5 °C

Predicted; Delta T = 10 °C

Figure 7 Overnight air change rates vs. wind speeds (January to December 2001) 

Comparison of Predicted and Measured Tracer Gas Concentrations 

A total of ten experiments were conducted and simulations performed under a variety of 

conditions for test dates between May 2000 and June 2001 for the purpose of comparing 

predicted and measured tracer gas concentrations. The tests consisted of injecting tracer 

gas (1% SF6) and measuring the concentrations for 2 h to 6 h. The test cases are 

summarized in Table 3 including the date, injection zone, HAC fan status, average indoor 

and outdoor temperature, and average wind speed. The HAC system was on in four cases. 

The zones in which injections took place included the recreation room (REC) on the 1st

floor, the kitchen/dining room (KDR) on the 2nd floor, and the master bedroom (MBR) on 

the 3rd floor. 

12 
Table 3 Summary of SF6 injection test cases 

Case Test Date Injection

Zone

HAC Fan Avg 

Tin (°C) 

Avg 

Tout (°C) 

Avg 

Vwind (m/s) 

1 May 24, 2000 KDR Off 26.4 29.4 3.6 

2 Oct. 2, 2000 KDR Off 24.5 24.4 1.3 

3 March 9, 2001 KDR Off 18.8 9.3 1.7 

4 January 19, 2001 KDR On 22.4 5.2 0.4 

5 March 16, 2001 REC Off 20.6 10.6 1.0 

6 July 24, 2000 REC On 26.2 19.5 2.6 

7 Feb. 16, 2001 REC On 18.6 6.7 2.4 

8 March 30, 2001 MBR Off 21.4 14.5 2.3 

9 June 22, 2001 MBR Off 26.9 26.3 1.2

10 Feb. 27, 2001 MBR On 20.6 21.6 2.5 

Case #1 

The first test case was an injection in the KDR zone with the HAC system off. The 

measured and predicted SF6 concentrations for the individual zones are shown in Figure 

8. Table 4 shows average and standard deviation of both the observed and predicted 

concentrations along with the ASTM D5157 statistical parameters (described previously) 

calculated for both the individual zones – REC, KDR, MBR, utility room (UTIL), living 

room (LR), back office (BOFC), front office (FOFC), main floor bathroom (BATH), and 

attic (ATC) – and for the overall zone average. The suggested ASTM D5157 statistical 

criteria were evaluated for both individual zone transient concentrations and overall zone 

average concentrations for the entire testing period for all cases. The columns of Table 4 

(and subsequent tables) include the zone, average observed concentration (Co) in ppb(v), 

average predicted concentration (Cp) in ppb(v), standard deviation of observed 

concentrations (σo), standard deviation of predicted concentrations (σp), correlation 

coefficient (R), regression slope (m), regression intercept divided by the average 

observed concentration (b/Co), normalized mean square error (NMSE), fractional bias of 

the mean concentrations (FB), and fractional bias based on the variance (FS). The 

average concentration in the bottom row of the tables is a linear average of the nine zone 

concentrations in ppb(v). 

13 
Measured

0

50

100

150

200

250

15:30 16:00 16:30 17:00 17:30

Concentration (ppb(v))

BOffice

Utility

Recr

Kitchen

Living

Bath

Master

FOffice

Attic

Predicted

0

50

100

150

200

250

15:30 16:00 16:30 17:00 17:30

Concentration (ppb(v))

BOffice

Utility

Recr

Kitchen

Living

Bath

Master

FOffice

Attic

Figure 8 Predicted and measured SF6 concentrations for Case #1 

Table 4 Statistical parameters for Case #1 (concentrations in ppb(v)) 

Zone Co Cp σo σp R m b/Co (%) NMSE FB FS 

REC 22.9 21.9 10 8.9 0.97 0.85 10 0.01 -0.05 -0.27

UTIL 20.0 21.4 10 8.7 0.90 0.77 30 0.05 0.07 -0.29

LR 44.5 28.0 18 13 0.94 0.69 -6.2 0.25 -0.45 -0.60

KDR (inj. zone) 56.3 47.3 35 53 0.67 1.0 -17 0.58 -0.17 0.78 

MBR 11.3 21.8 8.1 9.1 0.35 0.40 150 0.81 0.64 0.23 

BOFC 21.5 22.8 10 9.3 0.62 0.55 51 0.15 0.06 -0.25

FOFC 12.0 17.0 8.2 7.5 0.56 0.51 90 0.37 0.34 -0.18 

BATH 49.3 2.5 24 1.3 0.63 0.03 1.7 22 -1.8 -2.0

ATC 1.8 6.0 1.0 3.0 0.96 2.7 56 1.9 1.0 1.5

Average 

Concentrations

26.6 21.0 19 13 0.46 0.31 48 0.54 -0.24 -0.73 

The bold values in the table are those that met the ASTM suggested criteria. Based on 

most of the statistical parameters, this case resulted in the poorest agreement of all the 

cases between measurements and predictions. Specifically, the values for R, m, b/Co, 

NMSE, and FS calculated for the comparison of average zone concentrations all fall 

outside the ASTM D5157 suggested limits with most of them being worse than for any 

other test case. However, much of the discrepancy is due to a single zone, the BATH, 

which is discussed later as being a particularly difficult zone in the simulations. 

Excluding the BATH zone from consideration increases R to 0.93 for the average 

concentrations and also brings all the other statistical parameters very close to the ASTM 

suggested limits. The MBR zone also suffered from poor agreement as the model overpredicted the amount of SF6 that would reach this zone on the third floor. On average, the 

model under-predicts the zone average concentrations by about 20 % and, as Figure 8 

shows, predicts mixing throughout the house to occur more quickly than measured. 

14 
Several conditions likely contributed to Case # 1 having the poorest agreement between 

measured and predicted values. These conditions included the HAC system being off, the 

outdoor temperature being the highest of any case with an average of 29.4°C, and the 

average wind speed being the highest of any case at 3.6 m/s. Although wind speed had 

minimal impact on the air change rate in the townhouse as discussed above, it has been 

noted in other validation studies that high wind speed conditions commonly presents a 

challenge to multizone airflow models. This may be because the detailed leakage 

distribution may have a larger effect on the flow patterns that depend on wind direction 

(Emmerich 2001). For example, Bassett (1990) found that calculated airflows were more 

sensitive to wind direction than measured airflows. In this case, the wind direction played 

an important role as the primarily southwest wind during the simulation helped pressurize 

the bath zone and prevented tracer gas from entering that room. Test simulations with 

reduced wind speed or different wind directions showed far more SF6 entering the BATH 

zone, although not nearly as much as measured. 

Two other factors likely contributed to the severe under-prediction of SF6 concentration 

in the BATH. First, the BATH zone is likely a bit too leaky in the model. Figure 9 below 

shows that the infiltration directly to the BATH is far higher than any other zone. 

Although the BATH zone has more outside wall area (relative to zone volume) than any 

other zone and thus may be expected to have somewhat higher infiltration, the difference 

seems larger than it should be. However, no measurements were made to characterize the 

leakiness of individual zones. Finally, the airflow element connecting the BATH zone to 

the adjacent hallway in the model is a two-way airflow element that estimates mixing 

between the zones based on the temperature difference between the zones. For this case, 

the average temperature difference between the zones is only 0.3 °C, reducing the extent 

of air and tracer gas transport in and out of the BATH zone relative to what 

measurements indicate actually occurred. 

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

ATC MBR BOFC FOFC LR KDR BATH REC UTIL

Zone

Infiltration (h-1 )

Figure 9 Average infiltration by zone for Case #1 

15 
Case # 2 

As with Case # 1, Case # 2 involved an injection in the KDR zone with the HAC system

fan off. The individual zones’ measured and predicted concentrations are shown in Figure 

10 and the calculated D5157 statistical parameters are shown in Table 5. The outdoor 

temperature was warm at 24.4 °C but several degrees cooler than Case # 1, and the 

average wind speed was much lower at 1.3 m/s. Overall, the agreement between 

measurements and predictions was much better with the comparison for the average 

concentrations meeting or coming close to most of the ASTM D5157 suggested limits. 

The correlation coefficient and the slope were a bit low at 0.81 and 0.57 respectively. 

Most of the predicted zone concentrations agreed very well with the measurements, with 

only the BATH and ATC zones exhibiting poor agreement. The BATH prediction was 

significantly lower than the measurement but the agreement was much better than the 

first case. With the BATH zone excluded, the overall correlation coefficient improves to 

0.9. The main discrepancy between measured and predicted concentrations was caused 

by an overall predicted average concentration that was about 20 % below the average 

measured concentration, which is only partially due to the large under-prediction in the 

BATH zone. 

Also, despite the injection occurring in the KDR zone, the measured peak concentration 

in the LR zone was higher than in the KDR zone. This result is not too surprising as the 

measurement locations for the two zones (near the east wall of the LR and the west wall 

of the KDR) were nearly the same distance from the injection location in the middle of

the kitchen space of the KDR zone. 

Predicted

0

25

50

75

100

125

150

14:00 15:00 16:00 17:00

Concentration (ppb(v))

Recr

Utility

Living

Kitchen

Master

BOffice

Bath

FOffice

Attic

Measured

0

25

50

75

100

125

150

14:00 15:00 16:00 17:00

Concentration (ppb(v))

Recr

Utility

Living

Kitchen

Master

BOffice

Bath

FOffice

Attic

Figure 10 Predicted and measured SF6 concentrations for Case # 2 

16 
Table 5 Statistical parameters for Case # 2 (concentrations in ppb(v)) 

Zone Co Cp σo σp R m b/Co (%) NMSE FB FS 

REC 28.2 24.7 8.8 8.7 0.97 0.95 -7.7 0.02 -0.13 -0.03

UTIL 20.6 25.0 5.9 8.4 0.99 1.4 -19 0.05 0.19 0.66 

LR 50.6 32.6 30 14 0.98 0.47 17 0.33 -0.43 -1.2

KDR (inj. zone) 48.7 37.6 24 29 0.81 0.98 -21 0.21 -0.26 0.39

MBR 31.2 26.1 10 7.3 0.96 0.67 17 0.05 -0.17 -0.69 

BOFC 32.1 26.5 10 7.4 0.95 0.67 15 0.05 -0.19 -0.67 

FOFC 29.4 26.4 11 7.4 0.85 0.58 32 0.05 -0.11 -0.73 

BATH 34.8 18.0 13 7.8 0.73 0.43 8.2 0.56 -0.64 -0.96

ATC 13.9 8.0 3.4 1.8 0.64 0.34 24 0.34 -0.52 -1.1

Average 

Concentrations

32.2 25.0 12 8.4 0.81 0.57 20 0.12 -0.25 -0.67 

 

Case # 3 

As with the previous 2 cases, the third test involved an injection in the Kitchen with the 

HAC system off. The individual zones’ measured and predicted transient concentrations 

are shown in Figure 11, and the D5157 statistical parameters are shown in Table 6. The 

ambient conditions were quite different from Cases 1 and 2, with the average outdoor 

temperature only 9.3 °C and the average wind speed of 1.7 m/s. The comparison between 

measurements and simulations fell between the two cases with the agreement being better 

than the first case but worse than the second case. Once again, the biggest difference 

between the predictions and measurements was in the BATH, with substantially lower 

predicted concentration than measured. As before, the comparison of average 

concentrations improves greatly (R=0.93) with the BATH zone excluded. Also, transient 

predictions for most zones compare very well to the measurements with the ASTM 

criteria being met for many zones. The overall average concentration is under-predicted 

by 13 %, in this case due entirely to the under-prediction in the BATH. 

17 
Measured

0

25

50

75

100

125

150

11:00 12:00 13:00 14:00 15:00

Concentration (ppb(v))

Attic

FOffice

Master

BOffice

Kitchen

Living

Bath

Recr

Utility

Predicted

0

25

50

75

100

125

150

11:00 12:00 13:00 14:00 15:00

Concentration (ppb(v))

Attic

FOffice

Master

BOffice

Kitchen

Living

Bath

Recr

Utility

Figure 11 Predicted and measured SF6 concentrations for Case #3 

Table 6 Statistical parameters for Case # 3 (concentrations in ppb(v)) 

Zone Co Cp σo σp R m b/Co (%) NMSE FB FS 

REC 11.3 9.5 5.7 4.5 0.94 0.74 9.4 0.07 -0.18 -0.47

UTIL 9.3 9.4 4.1 4.3 0.98 1.0 -4.0 0.006 0.003 0.11

LR 30.2 22.8 28 17 0.93 0.58 17 0.33 -0.28 -0.88

KDR (inj. zone) 34.1 29.6 31 37 0.91 1.1 -21 0.24 -0.14 0.33

MBR 15.4 19.5 7.0 9.7 0.72 0.99 27 0.20 0.23 0.62 

BOFC 16.1 20.0 7.7 11 0.52 0.72 53 0.30 0.22 0.62 

FOFC 14.2 19.3 5.9 9.7 0.70 1.2 20 0.26 0.30 0.92

BATH 31.8 8.7 23 5.7 0.99 0.25 2.8 2.9 -1.1 -1.8

ATC 7.5 8.7 3.3 4.0 0.99 1.2 -3.6 0.03 0.15 0.37 

Average 

Concentrations

18.9 16.4 10 7.6 0.56 0.41 45 0.24 -0.14 -0.52

Case # 4 

 

Case # 4 also involved an injection in the KDR zone but with the HAC fan on. In this 

case, and in others, when the HAC system was on, the KDR zone was excluded from

statistical analysis because the measuring point in this zone was located directly above 

the supply vent. With the system operating, the measured value was not indicative of 

zone concentrations and therefore excluded. The ambient conditions were cold (average 

Tout of 5.2 °C) and calm (average wind speed of 0.4 m/s). As seen in Figure 12, the HAC 

system operation results in significant mixing between zones within one hour after the 

injection. As shown in Table 7, the agreement between measurements and predictions is 

very good for both individual zone transient concentrations and zone average 

concentrations, with nearly all parameters meeting the D5157 criteria. 

18 
Predicted

0

25

50

75

100

15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00

Concentration (ppb(v))

Attic

FOffice

Master

BOffice

Living

Bath

Recr

Utility

Measured

0

25

50

75

100

15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00

Concentration (ppb(v))

Attic

FOffice

Master

BOffice

Living

Bath

Recr

Utility

Figure 12 Predicted and measured SF6 concentrations for Case # 4 

Table 7 Statistical parameters for Case # 4 (concentrations in ppb(v)) 

Zone Co Cp σo σp R m b/Co (%) NMSE FB FS 

REC 13.1 11.4 8.8 8.1 0.95 0.87 -0.2 0.07 -0.14 -0.16

UTIL 11.3 10.2 6.5 7.0 0.92 0.97 -7.1 0.07 -0.10 0.12

LR 17.9 15.4 18 15 0.98 0.83 3.2 0.08 -0.15 -0.34

KDR (inj. zone) 13.8 18.2 * * * * * * * *

MBR 14.2 13.4 7.5 9.6 0.96 1.2 -28 0.06 -0.05 0.49

BOFC 14.2 13.5 8.0 8.6 0.97 1.0 -9.3 0.02 -0.04 0.15

FOFC 14.3 13.6 7.8 9.5 0.99 1.21 -25 0.02 -0.05 0.38

BATH 14.1 11.6 8.4 8.5 1.0 1.0 -19 0.04 -0.19 0.04

ATC 12.1 8.6 4.6 3.6 0.97 0.76 -4.7 0.13 -0.33 -0.48

Average 

Concentrations

13.9 12.2 2.0 2.2 0.88 0.96 -8.2 0.018 -0.13 0.19

 *KDR zone excluded from statistical analysis 

For all cases with the HAC system operating, the predicted and measured concentrations 

in the central return duct were compared as a surrogate indicator of overall agreement for 

whole house average concentrations. As shown in Figure 13, there is very good 

agreement between the measured and predicted concentrations in the central return duct 

of the HAC system for case 4 with all parameters meeting the D5157 criteria. The overall 

average concentration is under predicted by about 10 %. 

19 
0

5

10

15

20

25

30

35

40

15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00

Concentration (ppb(v))

Predicted

Measured

Figure 13 Measured and predicted concentrations in the HAC return for Case # 4 

Case # 5 

The test for Case # 5 involved an injection in the basement REC zone with the HAC 

system off. The ambient conditions were cold (10.6 °C) and calm (1.0 m/s). The zones’ 

measured and predicted transient concentrations, shown in Figure 14, compared 

reasonably well, with the BATH zone again showing the poorest comparison. The D5157 

statistical parameters are presented in Table 8. Although the BATH zone concentration 

was under predicted, the difference was less for this case compared to the KDR injection 

cases. The model also over-predicted the concentration in the UTIL zone on the same

floor as the injection zone. The correlation coefficient between zone average 

concentrations was fair at 0.75 and the overall average predicted concentration was 

within 10 % of the measured.

20 
Predicted

0

25

50

75

100

125

150

175

12:00 13:00 14:00 15:00 16:00

Concentration (ppb(v))

Attic

FOffice

Master

BOffice

Kitchen

Living

Bath

Recr

Utility

Measured

0

25

50

75

100

125

150

175

12:00 13:00 14:00 15:00 16:00

Concentration (ppb(v))

Attic

FOffice

Master

BOffice

Kitchen

Living

Bath

Recr

Utility

Figure 14 Predicted and measured SF6 concentrations for Case # 5 

Table 8 Statistical parameters for Case # 5 (concentrations in ppb(v)) 

Zone Co Cp σo σp R m b/Co (%) NMSE FB FS 

REC (inj. zone) 31.1 30.5 40 40 1.0 0.99 -0.7 0.001 -0.02 -0.02

UTIL 13.5 21.8 7.6 18.9 0.98 2.4 -81 0.67 0.47 1.44

LR 19.0 16.0 7.8 7.5 0.91 0.88 -4.2 0.06 -0.17 -0.06

KDR 16.8 12.1 6.6 5.0 0.88 0.67 4.8 0.16 -0.33 -0.53

MBR 17.6 17.8 6.4 8.9 0.90 1.2 -23 0.05 0.02 0.63 

BOFC 16.3 17.9 6.6 9.1 0.59 0.81 28 0.19 0.09 0.63 

FOFC 19.4 18.1 6.4 9.0 0.98 1.4 -43 0.03 -0.07 0.65 

BATH 15.5 6.0 6.9 3.6 0.61 0.32 6.8 1.2 -0.87 -1.1

ATC 10.4 8.8 3.5 3.7 0.98 1.0 -19 0.03 -0.17 0.10

Average 

Concentrations

17.7 16.6 5.7 7.2 0.75 0.95 -1.5 0.07 -0.07 0.46

Case # 6 

Case # 6 involved an injection in the REC room with the central HAC system on during 

warm ambient conditions (19.5 °C). The measured and predicted transient zone 

concentrations are shown in Figure 15. As with Case # 4 with the HAC system operating, 

the agreement between measurements and predictions was excellent for both individual 

zone transient predictions and for zone average predictions. Nearly every parameter met 

the D5157 criteria (see Table 9). 

21 
Predicted

0

25

50

75

100

125

150

13:30 14:00 14:30 15:00 15:30 16:00

Concentration (ppb(v))

BOffice

Utility

Recr

CentRet

Kitchen

Living

Bath

Master

FOffice

Attic

Measured

0

25

50

75

100

125

150

13:30 14:00 14:30 15:00 15:30 16:00

Concentration (ppb(v))

BOffice_

M

Utility_

M

Recr_M

CentRet

_M

Kitchen_

M

Living_

M

Figure 15 Predicted and measured SF6 concentrations for Case # 6 

Table 9 Statistical parameters for Case # 6 (concentrations in ppb(v)) 

Zone Co Cp σo σp R m b/Co (%) NMSE FB FS 

REC (inj. zone) 20.0 21.0 13 23 0.98 1.7 -64 0.24 0.04 0.99 

UTIL 16.0 14.9 5.7 6.4 0.97 1.1 -18 0.02 -0.07 0.26

LR 18.1 17.0 7.0 7.5 0.92 0.98 -4.0 0.03 -0.06 0.13

KDR 17.8 17.0 * * * * * * * *

MBR 18.9 18.2 7.4 8.2 0.98 1.1 -13 0.01 -0.04 0.22

BOFC 18.9 18.0 6.9 8.0 0.98 1.1 -18 0.01 -0.05 0.30

FOFC 20.0 18.0 7.6 7.9 0.94 0.98 -7.7 0.03 -0.10 0.09

BATH 19.0 17.0 8.0 8.7 0.99 1.1 -18 0.02 -0.11 0.16

ATC 12.0 9.2 4.4 2.8 0.80 0.51 25 0.14 -0.27 -0.84

Average 

Concentrations

17.9 16.7 2.5 3.2 0.96 1.2 -32 0.01 -0.07 0.51 

 *KDR zone excluded from statistical analysis 

As with Figure 13 for Case # 4, Figure 16 shows the measured and predicted 

concentrations in the central return of the HAC system for case # 6. The agreement is 

excellent with R=0.98 and the remaining parameters also meeting the D5157 criteria. The 

overall average predicted concentration was within 10 % of the measured. 

22 
0

10

20

30

40

50

60

13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00

Concentration (ppb(v))

Predicted

Measured

Figure 16 Measured and predicted concentrations in the HAC system return for Case # 6 

Case # 7 

Case # 7 involved an injection in the REC zone with the HAC system operating during 

cold ambient conditions (6.7 °C). The measured and predicted transient zone 

concentrations are shown in Figure 17, and the D5157 statistical parameters are presented 

in Table 10. As for the other cases with the HAC system on, there was very good 

agreement between predictions and measurements for both individual zone transient 

concentrations and zone average concentrations. Just a few parameters fell outside the 

D5157 criteria and mostly not by much. As with Cases 4 and 6, Figure 18 shows that the 

predicted and measured concentrations in the HAC central return agreed very well, with a 

correlation coefficient of 1.0 and overall average predicted concentration exceeding 

measured by less than 10 %. 

23 
Predicted

0

20

40

60

80

100

120

12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00

Concentration (ppb(v))

BOffice

Utility

Recr

Living

Bath

Master

FOffice

Attic

Measured

0

20

40

60

80

100

120

12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00

Concentration (ppb(v))

BOffice

Utility

Recr

Living

Bath

Master

FOffice

Attic

Figure 17 Predicted and measured SF6 concentrations for Case # 7 

Table 10 Statistical parameters for Case # 7 (concentrations in ppb(v)) 

Zone Co Cp σo σp R m b/Co (%) NMSE FB FS 

REC (inj. zone) 15.3 18.9 13 25 0.98 1.8 -60 0.52 0.21 1.1 

UTIL 11.8 11.2 5.5 5.8 0.87 0.91 4.3 0.06 -0.05 0.09

LR 13.8 14.1 7.6 8.0 0.96 1.0 0 0.02 0.02 0.12

KDR 12.1 14.3 * * * * * * * *

MBR 14.5 16.3 6.7 10 0.94 1.4 -32 0.10 0.12 0.82 

BOFC 13.9 16.0 6.3 9.0 0.94 1.4 -20 0.08 0.14 0.69 

FOFC 15.2 16.0 8.3 9.6 0.96 1.1 -4.8 0.04 0.05 0.28

BATH 15.7 14.4 9.5 10 0.99 1.0 -12 0.01 -0.08 0.09

ATC 9.34 8.34 3.9 3.5 0.94 0.83 5.9 0.04 -0.11 -0.23

Average 

Concentrations

13.5 14.5 2.1 3.1 0.86 1.3 -21 0.02 0.06 0.74 

*KDR zone excluded from statistical analysis 

24 
0

10

20

30

40

50

60

12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00

Concentration (ppb(v))

Predicted

Measured

Figure 18 Measured and predicted concentrations in the HAC return for Case # 7 

Case # 8 

Case # 8 involved an injection of SF6 in the MBR zone on the top floor of the townhouse 

with the HAC system fan off. The ambient conditions were mild with an average 

temperature of 14.5 °C and an average wind speed of 2.3 m/s. Overall, the agreement 

between measured and predicted concentrations, shown in Figure 19 and Table 11, was 

good with a correlation coefficient between zone average concentrations of 0.96. Many of 

the statistical parameters for both transient and average concentrations met the D5157 

criteria, but concentrations were under-predicted in several zones with the predicted 

overall average concentration about 20 % lower than the measured. 

Measured

0

25

50

75

100

125

150

175

200

225

250

13:00 14:00 15:00 16:00 17:00

Concentration (ppb(v))

Recr

Utility

Living

Kitchen

Master

BOffice

Bath

FOffice

Attic

Predicted

0

25

50

75

100

125

150

175

200

225

250

13:00 14:00 15:00 16:00 17:00

Concentration (ppb(v))

Recr

Utility

Living

Kitchen

Master

BOffice

Bath

FOffice

Attic

Figure 19 Predicted and measured SF6 concentrations for Case # 8 

25 
Table 11 Statistical parameters for Case # 8 (concentrations in ppb(v)) 

Zone Co Cp σo σp R m b/Co (%) NMSE FB FS 

REC 2.0 4.0 0.67 1.8 0.85 2.2 -15 0.76 0.69 1.5

UTIL 2.1 4.0 0.74 1.7 0.71 1.6 28 0.68 0.64 1.4

LR 8.0 9.5 2.7 5.3 0.80 1.6 -42 0.20 0.17 1.2 

KDR 8.1 8.8 3.5 4.9 0.93 1.3 -18 0.07 0.09 0.62 

MBR (inj. zone) 48.0 42.6 57 61 0.99 1.1 -18 0.05 -0.12 0.15

BOFC 17.2 14.0 10.3 9.3 0.56 0.50 31 0.40 -0.21 -0.20

FOFC 24.8 13.7 14 9.2 0.94 0.60 -4.5 0.50 -0.57 -0.85

BATH 7.2 3.5 3.1 1.9 0.87 0.53 -4.1 0.68 -0.69 -0.93

ATC 13.1 9.2 7.5 4.9 0.90 0.58 13 0.25 -0.35 -0.82

Average 

Concentrations

14.5 12.1 14 12 0.96 0.80 3.9 0.13 -0.17 -0.36

Case # 9 

Case # 9 also involved an injection in the MBR with the HAC system off but during very 

warm ambient conditions (26.3 °C and 1.2 m/s). The measured and predicted transient 

zone concentrations are shown in Figure 20, and the D5157 statistical parameters are 

presented in Table 12. This case had very good agreement in terms of average

concentrations as it met all D5157 criteria with the exception of a slightly high intercept. 

Case 9 was one of the few cases with higher overall average predicted concentration than 

measured. Once again, predicted concentrations in the BATH were low but the transient 

prediction correlated well with the measurement. Other individual zone transient 

concentration predictions ranged from fair to very good with the main issue being 

moderate over-prediction of the amount of SF6 moving down from the 3rd floor to 2nd and 

1st floors.

26 
Predicted

0

25

50

75

100

125

150

175

200

225

250

11:00 12:00 13:00 14:00 15:00

Concentration (ppb(v))

BOffice

Utility

Recr

Kitchen

Living

Bath

Master

FOffice

Attic

Measured

0

25

50

75

100

125

150

175

200

225

250

11:00 12:00 13:00 14:00 15:00

Concentration (ppb(v))

BOffice

Utility

Recr

Kitchen

Living

Bath

Master

FOffice

Attic

Figure 20 Predicted and measured SF6 concentrations for Case # 9 

Table 12 Statistical parameters for Case # 9 (concentrations in ppb(v)) 

Zone Co Cp σo σp R m b/Co (%) NMSE FB FS 

REC 10.1 15.7 4.0 9.3 0.94 1.6 0.8 0.27 0.44 1.4

UTIL 9.6 15.8 4.1 6.7 0.90 1.5 16 0.34 0.48 0.91

LR 13.2 22.3 2.7 7.2 0.60 1.6 8.9 0.42 0.52 1.5

KDR 11.9 21.2 4.0 7.2 0.65 1.2 64 0.48 0.56 1.0

MBR (inj. zone) 56.8 53.1 49 57 0.84 0.98 -4.6 0.31 -0.07 0.30

BOFC 36.1 27.1 23 10 0.76 0.34 41 0.36 -0.28 -1.3

FOFC 24.5 28.8 12 12 0.89 0.90 27 0.07 0.16 0.02

BATH 9.6 3.6 4.4 0.9 0.94 0.19 18 1.5 -0.92 -1.8

ATC 12.5 11.6 5.6 4.0 0.90 0.64 29 0.05 -0.07 -0.66 

Average 

Concentrations

20.5 22.1 16 14 0.91 0.78 30 0.10 0.11 -0.30

Case # 10 

The final test case was an injection in the MBR with the HAC system operating. Ambient 

conditions were warm (21.6 °C) with moderate wind (2.5 m/s). The measured and 

predicted transient concentrations for each zone are shown in Figure 21. As seen in Table 

13, this case showed good agreement for average zone concentrations with most D5157 

criteria met (the slope and FS criteria were exceeded slightly). This case showed the 

greatest over prediction - about 25 % on average. The length of this test was cut short due 

to the attic fan coming on at 15:00 (see below for a test case with the attic fan operating). 

Generally, a longer test will lead to better correlation for transient conditions. Also, the 

stack driving forces were minimal for this case, with only an average of 1°C difference 

between indoor and outdoor temperatures. With that in mind, individual zone transient 

27 
predictions compared fairly well to the measurements with R ranging from 0.66 to 0.96. 

However, most zones had one or more parameter failing to meet the D5157 criteria. 

Predicted

0

25

50

75

100

125

150

13:00 13:30 14:00 14:30 15:00

Concentration (ppb(v))

Attic

FOffice

Master

BOffice

Kitchen

Living

Bath

Recr

Utility

Measured

0

25

50

75

100

125

150

13:00 13:30 14:00 14:30 15:00

Concentration (ppb(v))

Attic

FOffice

Master

BOffice

Kitchen

Living

Bath

Recr

Utility

Figure 21 Predicted and measured SF6 concentrations for Case # 10 

Table 13 Statistical parameters for Case # 10 (concentrations in ppb(v)) 

Zone Co Cp σo σp R m b/Co (%) NMSE FB FS 

REC 19.9 25.2 7.6 10 0.85 1.1 12 0.11 0.24 0.60 

UTIL 16.2 23.3 5.8 11 0.75 1.4 -1.8 0.28 0.36 1.1

LR 22.1 28.0 7.7 9.6 0.78 0.98 28 0.11 0.23 0.45

KDR 22.6 27.4 * * * * * * * *

MBR (inj. zone) 32.6 41.3 24 36 0.96 1.5 -21 0.21 0.24 0.81 

BOFC 21.1 30.6 10 12 0.66 0.75 70 0.25 0.36 0.24 

FOFC 24.2 31.7 12 13 0.74 0.78 52 0.17 0.27 0.11 

BATH 23.4 27.3 9.7 9.1 0.78 0.73 44 0.08 0.15 -0.14

ATC 8.3 5.0 4.8 1.6 0.68 0.22 38 0.60 -0.50 -1.6

Average 

Concentrations

20.9 26.5 6.9 10 0.96 1.4 -16 0.08 0.23 0.74 

Note: For this case, the initial concentration for 3 zones was not measured prior to the 

injection. The average initial concentration of all other zones was used instead. 

*KDR zone excluded from statistical analysis 

Although the transient measured and predicted concentrations in the HAC central return 

showed good agreement (as shown in Figure 22), the agreement was not as good as the 

other HAC system on cases (4, 6, and 7). However, the correlation coefficient was 0.88 

and all parameters met the D5157 criteria. 

28 
0

10

20

30

40

50

13:00 13:30 14:00 14:30 15:00

Concentration (ppb(v))

Predicted

Measured

Figure 22 Measured and predicted concentrations in the HAC return for Case # 10 

Summary 

Table 14 summarizes the statistical parameters calculated for all the cases based on the 

average zone concentrations. As seen in the table, the correlation coefficients range from

0.46 to 0.96, with 6 of 10 cases at 0.9 or higher. As discussed for the individual cases, 

agreement for the first two cases suffers largely from significant under-prediction in the 

BATH zone. As expected, the agreement was consistently better for the cases with the 

HAC system operating, as the HAC flows are a significant driving factor in determining 

the zone concentrations. It was also no surprise that the case with the highest wind speed 

(Case # 1) had the poorest correlation between measurements and predictions since past 

studies have reported poor agreement between measurements and predictions under high 

wind speed conditions as discussed earlier. 

29 
Table 14 Summary of statistical parameters from comparison of average measured and predicted 

zone concentrations for all cases (concentrations in ppb(v)) 

Case HAC Co Cp σo σp R m B/Co 

(%)

NMSE FB FS

1 Off 26.6 21.0 19 13 0.46 0.31 48 0.54 -0.24 -0.73 

2 Off 32.2 25.0 12 8.4 0.81 0.57 20 0.12 -0.25 -0.67 

3 Off 18.9 16.4 10 7.6 0.56 0.41 45 0.24 -0.14 -0.52

4 On 13.9 12.2 2.0 2.2 0.88 0.96 -8.2 0.018 -0.13 0.19

5 Off 17.7 16.6 5.7 7.2 0.75 0.95 -1.5 0.07 -0.07 0.46

6 On 17.9 16.7 2.5 3.2 0.96 1.2 -32 0.01 -0.07 0.51 

7 On 13.5 14.5 2.1 3.1 0.86 1.3 -21 0.02 0.06 0.74 

8 Off 14.5 12.1 14 12 0.96 0.80 3.9 0.13 -0.17 -0.36

9 Off 20.5 22.1 16 14 0.91 0.78 30 0.10 0.11 -0.30

10 On 20.9 26.5 6.9 10 0.96 1.4 -16 0.08 0.23 0.74 

The individual zone average concentrations from all cases are also plotted in Figure 23 to 

summarize the comparison of predictions and measurements (excluding the bath zone). 

The statistical parameters comparing measurements and predictions for this entire set of 

data do not quite meet the D5157 criteria as the correlation coefficient and slope are low 

and the intercept is slightly high. However, excluding the bathroom zone increases the 

correlation coefficient to 0.9 and brings all other parameters within the suggested values. 

In fact, excluding the single possible outlier point of the BATH zone from the first case 

increases the correlation coefficient to 0.85 and brings all other parameters except the 

slope (0.72) within the D5157 suggested criteria. 

The predicted SF6 concentration averaged over all zones and cases (not weighted by zone 

volume) was within 10 % of the average measured concentration. Excluding the 

bathroom zone, the overall average predicted concentration of 115 μg/m3

 (19.3 ppb(v)) 

was essentially identical to the overall average measured concentration of 116 μg/m3

(19.5 ppb(v)). 

30 
y = 0.74x + 4.8

R2

 = 0.78

0

10

20

30

40

50

60

0 10 20 30 40 50 6

Observed zone average concentration (ppb(v))

Predicte

d zo

n

e a

vera

g

e co

ncentr

atio

n (

p

p

b(v))

0

Figure 23 Comparison of predicted and measured zone average concentrations for all cases 

Attic Fan Case 

A common use of models like CONTAMW is to evaluate the impact of a change from a 

baseline case to another operating condition, such as the operation of a fan. As an 

additional challenge to the model, a test case was conducted during which an exhaust fan 

in the attic was operated. The attic fan exhausts air drawn from the attic to the outside. 

The fan flow was measured with the balometer to be 390 L/s. Other test conditions were 

similar to validation Case # 5 described above: 1500 mL of 1 % SF6 was manually 

injected into the REC zone with the house HAC system off. Ambient conditions included 

an average temperature of 25 °C and wind speed of 1.0 m/s. The measured and predicted 

(both without and with the attic fan in the model) transient zone concentrations are shown 

in Figure 24. The statistical parameters comparing the predictions (with attic fan) and 

measurements for both individual zone transient concentrations and overall zone average 

concentrations are presented in Table 15. As with several of the validation test cases, the 

agreement between predictions and measurements is good but not perfect as several 

zones fail to meet the ASTM criteria. However, if the modeler is interested in the relative 

impact of the attic fan (compared to the baseline case without it), the model does very 

31 
well at predicting the impact on the concentrations with the attic fan operating over the 4 

h test period (as shown in Figure 24). 

Measured with Fan

0

25

50

75

100

125

150

175

200

10:00 11:00 12:00 13:00 14:00

Concentration (ppb(v))

BOffice

Utility

Recr

CentRet

Kitchen

Living

Bath

Master

FOffice

Attic

Predicted with Fan

0

25

50

75

100

125

150

175

200

10:00 11:00 12:00 13:00 14:00

Concentration (ppb(v))

BOffice

Utility

Recr

Kitchen

Living

Bath

Master

FOffice

attc

Predicted without Attic Fan

0

25

50

75

100

125

150

175

200

10:00 11:00 12:00 13:00 14:00

Concentration (ppb(v))

BOffice

Utility

Recr

Kitchen

Living

Bath

Master

FOffice

attc

Figure 24 Predicted (with and without attic fan) and measured SF6 conc. for attic fan case 

Table 15 Statistical parameters for attic fan test case 

Zone Co Cp σo σp R m b/Co (%) NMSE FB FS 

REC (inj. zone) 16.5 27.0 35.1 49.4 0.98 1.4 26 0.89 0.48 0.66

UTIL 3.7 8.0 4.9 9.0 0.73 1.4 81 2.0 0.73 1.09

LR 9.9 5.4 13.4 6.3 0.76 0.36 19 2.1 -0.58 -1.28

KDR 4.9 3.6 4.8 3.8 0.99 0.80 -6.4 0.16 -0.30 -0.41 

MBR 8.4 5.2 8.3 5.5 0.96 0.64 -2.3 0.52 -0.48 -0.77

BOFC 8.8 5.8 9.4 6.6 0.98 0.68 -3.3 0.41 -0.42 -0.69

FOFC 10.2 5.7 11.5 6.6 0.85 0.48 7.8 1.2 -1.0 

BATH 8.6 0 NA NA NA NA NA NA NA NA

ATC 2.5 2.7 2.2 2.8 0.93 1.2 -10 0.19 0.06 0.46

Average 

Concentrations

8.2 7.0 4.2 7.8 0.72 1.3 -48 0.51 -0.15 1.11 

-0.56

32 
DISCUSSION

It would be ideal if all the analyzed cases met all of the calculated statistical criteria for 

all variables. Conversely, it would be disappointing but straightforward if the model 

failed miserably when compared to measurements. However, even with either of these 

seemingly straightforward outcomes, the experimental validation results would still 

require interpretation. As discussed previously (Emmerich 2001), an absolute validation 

of a complex model, such as CONTAMW, is impossible as there are infinite possible 

building models that the user could create. If all the cases presented above met all the 

criteria, it could even lull users into a false sense of security of the model’s capabilities 

since the success of any individual modeling effort depends largely on the user’s 

knowledge of both the model and the building. 

However, there are still important reasons to perform experimental validation such as to 

identify and eliminate large errors. For the situations modeled in this effort, no large 

errors in the CONTAMW model were identified. It is also important to remember that 

ASTM D5157 is a guideline not an ultimate arbiter of model accuracy. Rather than the 

specific parameters and criteria, its most valuable aspect may be to move model 

validation beyond the all too common and oversimplified analysis of “the measurements 

and predictions differed by X %” and toward useful statistical analysis of model 

validation results. 

Additionally, some of the discrepancies between model predictions and experimental 

measurements are due to experimental limitations instead of model deficiencies. For 

example, this effort involved a fairly rich data set in terms of number of variables 

monitored and spatial and temporal detail. Still, after completing the simulation effort, 

one can identify additional measurements that would have been desirable. Specifically, 

the halls and stairs of the townhouse provide the prime pathways for contaminant 

transport for cases without the HAC system operating – having temperature and 

concentration measurements in these important zones would have been valuable. Also, as 

previously discussed, inaccuracies in experimental measurements include much more 

than simply the instrument accuracy. All measurements reported here were a single point 

(occasionally chosen for reasons other than experimental accuracy) that was used to 

represent an average room concentration. The ability of this single point measurement to 

represent the room is certainly questionable shortly after a major system perturbation 

(i.e., a short duration tracer gas injection) or in the presence of continuous local 

disruption (i.e., location of a room sampling point in the path of ventilation supply air). 

Conduction of the experimental effort in an occupied home (although not occupied 

during the injection tests) presented the challenge of possessing less than complete 

control of the ‘laboratory’. 

The model, while quite detailed, could have been more so. The simple air-handling 

system option of CONTAMW was used rather than the detailed duct model option. While 

it is unlikely this modeling choice affected the HAC system-on cases, it may have 

33 
affected the HAC system-off cases as the idle ducts can act as an important airflow 

pathways between zones that otherwise have little or no direct communication. 

Additionally, some of the cases may have been affected by the CONTAMW 1.0 

limitation of constant zone temperatures. While the interior temperature for most zones 

was stable during most tests, there were situations such as the second test case when the 

temperature difference between the 3rd floor and the attic ranged from 2°C to 5 °C during 

the test. It would be interesting to repeat simulations for selected cases with the newest 

version of CONTAMW 2.0, which includes the capability of varying zone temperatures. 

Finally, it is not necessary for this model evaluation to stand entirely on its own. Rather, 

it should be considered in the context of the existing body of work validating 

CONTAMW and similar models. Emmerich (2001) reviewed ten such efforts that have 

been reported in the literature. This effort differs from others reported primarily in 

number of variables monitored, number of cases analyzed, and execution of experimental 

effort in an occupied home. In the end, the results reported here echo those summarized 

in the earlier review: a knowledgeable user can expect to make reasonable predictions of 

air change rates, interzonal flows, and contaminant concentrations for residential-scale 

buildings dominated by stack-driven or ventilation flows with inert pollutants. Areas 

identified previously as needing more work, such as large buildings, wind-driven flows, 

reactive contaminants, small time scales, and non-trace contaminants were not addressed 

in this study. Some of these needs are being addressed by ongoing research at NIST. 

34 
CONCLUSION

The primary objective of this effort was to evaluate the capability of the multizone IAQ 

model CONTAMW to simulate tracer gas concentrations in a real multizone building 

subject to real ambient conditions. Predictions of SF6 concentrations were compared to 

measurements for eleven test injection cases with statistical analysis performed per 

ASTM Guide D5157. Overall, the comparison between predictions and measurements of 

average zone concentrations was good implying the adequacy of CONTAMW to predict 

average contaminant concentrations in residential buildings under real world conditions. 

A single zone – the main floor bathroom – accounted for most significant discrepancies 

between measurements and predictions of zone average concentrations. Several potential 

causes of the consistent under-prediction of tracer gas transport into this zone were 

identified including wind speed and direction, envelope leakiness, and small temperature 

differences relative to the adjacent zone. However, with the removal of this zone, the 

predicted values fell well within the suggested guidelines. In most cases, the average 

zone concentrations were under-predicted, but remained within 10 % of the measured 

values. Thus, the poor predictions for this particular zone did not affect the accuracy of 

the prediction concentrations throughout the rest of the house. 

Although individual zone transient concentration comparisons were not always good, the 

majority had correlation coefficients of at least 0.8 and many were much higher. Again, 

the bathroom zone was the only zone with frequent discrepancies in transient 

concentration predictions while other zone predictions experienced only sporadic 

discrepancies. 

A secondary objective of the validation effort was to identify any large shortcomings in 

the model, which may be overcome through modifications to the model. However, this 

validation effort did not identify any needed modifications to the CONTAMW model. 

35 
ACKNOWLEDGEMENTS

The authors wish to acknowledge the efforts of Stuart Dols and Kevin Blank in support 

of this project. 

36 
REFERENCES

ASHRAE. Practices for Measurement, Testing, Adjusting and Balancing of Building 

Heating, Ventilation, Air-Conditioning and Refrigeration Systems ASHRAE Standard 

111 (1988) American Society of Heating, Refrigerating and Air-Conditioning Engineers, 

Inc. 

ASHRAE. Handbook of Fundamentals. (1997) American Society of Heating, 

Refrigerating and Air-Conditioning Engineers, Inc. 

ASTM. Standard Guide for Statistical Evaluation of Indoor Air Quality Models. D5157-

91 (1997) American Society for Testing and Materials. 

ASTM. Standard Test Method for Determining Air Change in a Single Zone by Means of 

a Tracer Gas Dilution. E741-95 (2000) American Society for Testing and Materials. 

ASTM. Standard Test Method for Determining Air Leakage Rate by Fan Pressurization. 

E779-99 (1999) American Society for Testing and Materials. 

Bassett M. Infiltration and leakage paths in single family houses - a multizone infiltration 

case study. (1990) AIVC Technical Note 27, Air Infiltration and Ventilation Centre. 

Dols WS, Walton GN, and KR Denton. CONTAMW 1.0 User Manual (2000) NISTIR 

6476, National Institute of Standards and Technology. 

Dols WS and Walton GN. CONTAMW 2.0 User Manual (2002) NISTIR 6921, National 

Institute of Standards and Technology. 

Emmerich SJ and Nabinger SJ. Measurement and Simulation of the IAQ Impact of 

Particle Air Cleaners in a Single-Zone Building. (2001) International Journal of 

HVAC&R Research Vol. 1, No. 7, ASHRAE. 

Emmerich SJ. “Validation of Multizone IAQ Modeling of Residential-scale Buildings: A 

Review” (2001) ASHRAE Transactions Vol. 107.2. 

Wallace LA, Emmerich SJ, and Howard-Reed C. “Continuous measurements of air 

change rates in an occupied home for 1 year: The effect of temperature, wind, fans, and 

windows” (2002) Journal of Exposure Analysis and Environmental Epidemiology Vol. 

12.

37