NISTIR 7212

Modeling the IAQ Impact of HHI

Interventions in Inner-city Housing

 Steven J. Emmerich

Cynthia Howard-Reed

Arpita Gupte
NISTIR 7212

October 2002

Modeling the IAQ Impact of HHI

Interventions in Inner-city Housing

Steven J. Emmerich

Cynthia Howard-Reed

Arpita Gupte

Building Environment Division

Building and Fire Research Laboratory

National Institute of Standards and Technology

Prepared for:

U.S. Department of Housing and Urban Development

Office of Healthy Homes and Lead Hazard Control

March 2005

U.S. Department of Commerce

Carlos M. Gutierrez, Secretary

Technology Administration

Phillip J. Bond, Under Secretary of Commerce for Technology

National Institute of Standards and Technology

Hratch Semerjian, Acting Director
ABSTRACT

The U.S. Department of Housing and Urban Development (HUD) has identified a need to 

improve urban housing conditions to protect children’s health through its Healthy Homes 

Initiative (HHI). One critical area within this program is indoor air quality (IAQ), for example, 

inadequate ventilation, moisture, combustion by-products, etc. and the identification of effective 

intervention strategies to address these issues. To evaluate the impact of different interventions 

on indoor contaminant concentrations and occupant exposures, a simulation study was conducted 

using the multizone airflow and contaminant dispersal model CONTAM. This study modeled the 

exposures of a family of five to concentrations of carbon dioxide, carbon monoxide, nitrogen 

dioxide, water vapor, 0.3 μm to 10 μm particles, radon, and volatile organic compounds in a 

three-story townhouse. To investigate the impacts of environmental conditions, the townhouse 

was modeled with weather conditions from all four seasons in Boston, MA, Miami, FL, and 

Seattle, WA. The model included outdoor and indoor sources of the contaminants as well as 

adsorption and deposition loss mechanisms. CONTAM predicted ventilation rates, contaminant 

concentrations and occupant exposures for a baseline case and eight different interventions in 

each city/season combination. The interventions included venting an otherwise unvented space 

heater, replacement of a faulty stove, upgrading a furnace filter, installation of air conditioning, 

operation of kitchen and bathroom exhaust fans, ceasing the practice of using a gas oven to heat 

the house, tightening the house’s envelope, and installation of mechanical ventilation. 

Intervention strategies were compared on an individual basis and a small sub-set of interventions 

were considered to demonstrate an intervention ranking system for identifying the best strategy. 

The key parameter utilized in evaluating the interventions is the concentration relative to the 

baseline rather than comparison of absolute concentrations to guideline values. Overall, a 

combination of mechanical ventilation, local exhaust, and an improved air filter was most 

effective for reducing the largest number of contaminants in the study. 

Key Words: exposure, indoor air quality, modeling, residential building, ventilation, air 

cleaning, contaminants. 

 iii
 iv
TABLE OF CONTENTS

Abstract iii

List of Tables vii

List of Figures viii

Executive Summary 1

Introduction 6

Simulation Method 6

Simulation Results 19

Summary and Discussion 39

Acknowledgements 42

References 43

Appendix A Flow Elements 46

Appendix B Occupancy Schedule Tables 48

Appendix C Intervention Results – Contaminant Concentrations 51

Appendix D Impact of Interventions on Occupant Exposure 81

 v
 vi
LIST OF TABLES

1 CONTAM Model Zones 9

2 Baseline Simulation Cases 10

3 Occupant generation rates of CO2 11

4 Occupant generation rates of H2O 11

5 H2O generation rates and schedules for bathing, cooking, and dishwashing 12

6 Sources of CO and NO2 12

7 Particle generation rates and schedules for cooking 13

8 Particle generation rates and schedules for changing kitty litter. 13

9 Location and schedule for burst VOC sources. 14

10 Outdoor Concentrations of CO, NO2, CO2, VOCs, and Particles 15

11 Boundary Layer Diffusion Controlled Model Parameters 15

12 Deposition Rates for NO2 and Particles 15

13 Baseline and Intervention CO and NO2 emission rates for a gas stove 16

14 Comparison of Removal Efficiencies of Typical Furnace Filter and 

Intervention Mechanical Air Cleaner 

17

15 Factorial design for four intervention combinations 18

16 Air change rates for baseline cases 20

17 Boston Baseline Results – Living area averages 22

18 Miami Baseline Results – Living area averages 22

19 Seattle Baseline Results – Living area averages 23

20 Intervention air change rates (h-1). 30

21 Percentage of hours during one week period of each season with air change 

rates < 0.35 h-1

30

22 Change in Family Exposure for Factorial Simulation Design 37

23 Rank of Interventions with Positive Overall Impact on Average Percent 

Change in Family Exposure. 

38

vii 
LIST OF FIGURES

1 Floor plan of house in CONTAM 8

2 24 h CO concentrations in Boston in fall. 24

3 24 h CO concentrations in Boston in winter. 24

4 24 h CO concentration in Boston in spring. 25

5 24 h CO concentration in Boston in summer. 25

6 24 h Radon concentration in Seattle in fall. 26

7 24 h Radon concentration in Seattle in summer. 26

8 24 h Particle 4 concentration in Boston in spring 27

9 24 h Particle 4 concentration in Miami in winter 28

10 24 h Particle 4 concentration in Seattle in spring 28

11 24 h Particle 4 concentration in Seattle in winter. 29

12 24 h H2O concentration in Miami in winter. 29

13 Impact of air change rate on CO concentration in kitchen and bedrooms. 33

14 Impact of air change rate on P4 concentration in kitchen and bedrooms. 35

viii 
EXECUTIVE SUMMARY 

The U.S. Department of Housing and Urban Development’s Healthy Homes Initiative (HHI) is 

addressing a wide range of indoor air quality (IAQ) concerns to improve urban housing 

conditions and protect the health of children. Residential indoor air pollutants of concern include 

combustion by-products, volatile organic compounds, radon, and bioaerosols (Tobin et al., 1992; 

Sherman, 1999). Many of these contaminants are often measured at higher concentrations in 

lower income urban housing (Laquatra et al., 2002; Brugge et al., 2002), and these residences are 

typically in the greatest need of remediation. As such, the U.S. Department of Housing and 

Urban Development (HUD) has made it a priority to identify wide-reaching intervention 

strategies that can be feasibly implemented to improve IAQ in lower income homes, and the HHI 

program is funding several demonstration projects to implement interventions that correct many 

of the IAQ problems found in these homes. Due to the costs of fieldwork, however, the 

demonstration projects are only able to implement a limited number of interventions in a small 

number of homes. A more feasible way to prioritize the intervention strategies is through 

modeling. A modeling approach allows the evaluation of many potential interventions under a 

wider variety of conditions to help provide a knowledge base for recommending the most 

effective strategies. Such modeling can also be used to evaluate the interventions for possible 

unintended negative impacts. Thus, model results have the potential to provide valuable insight 

toward the improvement of IAQ in urban housing with multiple deficiencies. 

To evaluate the impact of potential interventions on indoor contaminant concentrations and 

occupant exposures, a simulation study was conducted using the multi-zone IAQ model 

CONTAM. A CONTAM model of an actual townhouse was used as a baseline building 

(Emmerich et al., 2002), with modifications to make it more representative of lower-income 

urban housing. Since ventilation and IAQ performance vary by climate, the house was modeled 

in Boston, MA, Seattle, WA, and Miami, FL for one week of each season. To account for 

occupant-generated contaminants and to account for occupant exposure to indoor contaminants, 

a family of five was assumed to occupy the townhouse. The occupants of the house included an 

adult male, adult female, and three children, with a schedule for each family member that 

specifies the time spent in each room of the house. 

The model was used to predict air change rates, contaminant concentrations of carbon dioxide 

(CO2), water vapor (H2O), carbon monoxide (CO), nitrogen dioxide (NO2), airborne particles in 

five size ranges (P1: 0.3 μm to 0.5 μm, P2: 0.5 μm to 1.0 μm, P3: 1.0 μm to 2.5 μm, P4: 2.5 μm 

to 5.0 μm, and P5: 5.0 μm to 10 μm), volatile organic compounds (VOCs) and radon (Rn), and 

occupant exposures to these contaminants. The modeled sources included occupant respiration 

(CO2 and H2O), bathing (H2O), cooking (H20, CO, NO2, and particles), dishwashing (H2O), 

changing kitty litter (particles), building materials (VOC1), cleaning (VOC2), unvented 

combustion appliances (CO and NO2), the soil (Rn), and outdoor air (all contaminants). These 

modeled sources were not intended to be comprehensive, but rather representative of typical 

residential sources to provide insight into each of the individual contaminants. Also, some of the 

sources produce additional contaminants beyond the ones considered, e.g., unvented combustion 

appliances also produce CO2, H2O, and particles but were not included as a source of these 

contaminants in this study. 

1
The loss of contaminants due to adsorption, deposition and decay were also included in the 

models. Reversible sink effects for H2O and VOCs were modeled with sink elements based on 

the boundary layer diffusion controlled (BLDC) model available in CONTAM. Particles were 

also removed in the baseline cases by a typical furnace filter in the air handler system (AHS). 

The HUD HHI interventions and the requirements of ASHRAE’s new residential ventilation and 

IAQ standard 62.2-2004 were considered in selecting the eight interventions that were modeled. 

The interventions can be categorized as dilution ventilation (continuous mechanical whole-house 

ventilation and tightening the building envelope), local ventilation (intermittent operation of

kitchen and bathroom exhaust fans), air cleaning (upgrading filter in the heating and airconditioning (HAC) system and dehumidification through the operation of an air-conditioner), or 

source control (removal of unvented space heater, upgrading gas stove, and ceasing use of gas 

oven to heat home). Obviously, there are many other interventions that could be considered, 

including a host of possible source control actions. 

This report presents detailed simulation results including air change rates, contaminant 

concentrations and occupant exposures for the baseline cases. Each modeled intervention was 

then evaluated based on its impact relative to the baseline case. Based on these evaluations, a 

summary of each intervention strategy is provided below: 

 

Upgrading gas stove: Upgrading the gas stove results in lower NO2 and CO concentrations year 

round. In fact, it was the single most effective intervention at reducing these contaminants in all 

climates. In general, older combustion appliances have lower efficiencies and tend to emit more 

pollutants. Although a gas stove was used to illustrate this point in this project, the intervention 

would be effective for reducing contaminants for any unvented combustion appliance. 

Operation of gas oven to heat home: Educating occupants of the potential dangers of using a gas 

oven for heat is the least expensive intervention strategy examined. Operating a gas oven to heat 

a house can elevate concentrations of CO and NO2 to unhealthful, even fatal, levels (see warning 

at http://www.epa.gov/iaq/pubs/combust.html). For this project, using the gas oven to heat the 

house resulted in the second highest average winter concentration of CO, assuming a properly 

operating oven was used. If a faulty oven had been used, the occupants’ exposure to CO would 

have been much higher and may have exceeded fatal levels. Unfortunately, this practice does 

persist in lower income housing when the residents do not understand the risks involved. 

Stopping this practice is a source control option that would prevent excessive exposure to CO 

and NO2 at little to no cost to the resident (unless faulty heating equipment needs to be replaced, 

which could entail significant cost). 

Removal of unvented space heater: Using only properly vented space heaters also reduces indoor 

air concentrations of CO and NO2. Venting the combustion products from space heaters to the 

outside would essentially remove these pollutants from the living space, thereby significantly 

reducing occupants’ exposure to CO and NO2 in cold climates. This intervention would involve 

both education of the occupants and the installation of an exhaust vent, if needed. 

Enhanced particle air cleaner: Air cleaning reduces contaminants originating both indoors and 

outdoors with no negative impact on other contaminant concentrations. Most homeowners, 

2
however, generally have access only to air cleaners that remove particles, limiting the scope of 

the intervention. While an effective intervention for removing particles, such an air filter only 

works when the HAC system is operating. For this project, the HAC system was operated for 

cold seasons in Boston and Seattle and hot seasons in Miami. In the other seasons, the HAC 

system was off and the air filter was not removing any particles. As a result, this intervention 

was only useful in cold or hot climates. This intervention would also be effective in more 

temperate seasons; however, it is important to consider the balance between first and operating 

costs and benefit from reducing particle concentrations. Portable air cleaners are also an option 

but were not evaluated in this study. 

Installation of air-conditioner: Despite a relatively short operation time assumed in the modeling 

effort, the air-conditioner significantly decreased average indoor humidity levels. In fact, the 

humidity reduction from operating an air-conditioner for one season in Boston and Seattle 

outweighed the impacts of other interventions operated year-round. Again, there is a need to 

consider both first and operation costs along with the benefits obtained by limiting relative 

humidity to levels low enough to prevent mold, allergens, and other indoor air problems. 

Kitchen and bathroom exhaust fans: The exhaust fan was the most effective intervention strategy 

to reduce peak concentrations associated with cooking and showering. This reduction in 

concentration during source events had a significant impact on the occupants’ exposure to CO, 

NO2, P2, P3 and H2O. It was the single most effective intervention for exposure to P2 and P3 in 

most climates. The increased air change rate due to exhaust fan operation also reduced the 

concentrations of contaminants from sources in other parts of the house (e.g., VOC1 and radon). 

The downside of this intervention was the increase in concentrations of contaminants originating 

outdoors. This negative impact was significant for P4, P5, and H2O in Miami. The benefits of 

using an exhaust fan during source events, however, far outweighed the negative impacts. In fact, 

project results showed that using the exhaust fan during more source events (e.g., cleaning in 

kitchen or bathroom, or dishwasher operation) would have reduced concentrations and exposures 

even more. If there is no exhaust fan installed or if the current fan does not vent to the outside, 

there will be an installation cost in addition to an operating cost associated with this intervention. 

However, in some cases, it is a matter of educating the occupants to turn on the fan during source 

events. 

Mechanical whole-house ventilation: Continuous mechanical ventilation is another intervention 

that affects all indoor air contaminants, but not always positively. Mechanical ventilation was 

most effective at reducing contaminants primarily originating indoors via a continuous source 

(e.g., CO2, Rn, and VOC1). Mechanical ventilation also diluted concentrations from shorter term

sources (e.g., CO, NO2, and VOC2). Contaminants originating primarily outdoors were 

negatively impacted (e.g., H2O in Miami and particles). The negative impacts of mechanical 

ventilation tended to be greater than those of the exhaust fan intervention, since the mechanical 

ventilation occurred continuously and the exhaust fan only operated during source events. 

Effective filtration could mitigate this impact. Adding mechanical ventilation using an exhaust 

fan is the least expensive option, whereas adding outdoor air supply is likely to be more 

expensive. There is also an incremental cost associated with cooling or heating the added 

outdoor air. 

3
Tightening the envelope: Tightening the building envelope has long been recommended for 

improving energy efficiency, but the resulting reduction in air change rate has dramatic effects 

on pollutants originating indoors. In fact, it was the single worst intervention in terms of 

increasing the concentrations of CO, CO2, NO2, P2, Rn, VOC1, and VOC2. Although it was 

most effective at reducing H2O in Miami, P1, and P4, tightening should not be implemented 

without considering the need for supplementary outdoor air. 

In addition to investigating the impact of each intervention individually, a methodology was 

developed to rank combinations of interventions. The methodology involves a full factorial 

simulation design (Box et al., 1978), which tests the significance of individual interventions as 

well as ranks combinations of interventions. A full factorial design also has the advantage of 

being able to detect when variables do not act additively on a specific response. To demonstrate 

the value of the ranking system, a factorial simulation design was developed for the following 

four interventions: envelope tightening, exhaust fans, mechanical ventilation, and enhanced 

filtration. The fall season in Boston was chosen as the model season and city, respectively. The 

impact of each combination of interventions was assessed based on the sum of individual 

exposures of the five occupants living in the house. 

Factorial results were also compared across all contaminants by calculating the average percent 

change in exposure. Based on this analysis, intervention combinations were ranked as shown in 

Table ES1. An ANOVA analysis on these results showed tightening the house to have the most 

significant impact on contaminant concentrations followed by using mechanical ventilation and 

exhaust fans. Using a more efficient air filter did not have a significant individual impact on the 

results, but becomes more significant when used in combination with other interventions (see 

discussion below). Although tightening the house was found to have the most significant impact, 

it is in the direction of increasing occupant exposures. The most effective individual 

interventions at reducing all contaminant concentrations are the use of mechanical ventilation 

and exhaust fans. 

Table ES1. Rank of interventions with positive overall impact. 

Rank Intervention

Combination 

Average Reduction Over All 

Contaminants (%) 

Negative Impact on Exposure 

to Contaminants Below:

1 exfan, filter, mv 15 P4 

2 exfan, filter 13

3 filter, mv 9.8

4 exfan, mv 9.7 P1, P3, P4, P5 

5 exfan 7.7 P1, P3, P4, P5 

6 exfan, filter, mv, 

tight 

7.4 CO2, P5, Rn, VOC1, VOC2 

7 filter 5.8

8 mv 4.3 P1, P3, P4, P5 

9 exfan, mv, tight 2.0 CO2, P4, P5, Rn, VOC1, VOC2 

exfan: exhaust fan 

filter: improved particle filter 

mv: mechanical ventilation

tight: envelope tightening 

4
For the ranking evaluation, the combination with the largest reduction in exposure across all 

contaminants was operating exhaust fans, installing mechanical ventilation, and adding a more 

efficient air filter, without tightening the house. This strategy, however, did have a negative 

impact on the concentration of P4. The most effective intervention strategy across all 

contaminants with no negative impacts was operating exhaust fans and adding a more efficient 

air filter, followed by the installation of mechanical ventilation and a more efficient air filter. 

Individually, the interventions of exhaust fan and mechanical ventilation led to an overall 

reduction in contaminant concentrations, but both also led to increased exposures to particles. 

This result emphasizes the importance of considering combinations of interventions to achieve 

the most effective strategy. Another intervention combination, which has been recommended by 

ASHRAE (2001a) and others, is tightening the envelope and adding filtered mechanical 

ventilation. For this project, the combination of tightening, mechanical ventilation via exhaust 

fans, and adding a more efficient air filter resulted in an overall increase in occupant exposure. 

Thus, depending on the relative changes, tightening the envelope could overwhelm additional 

mechanical ventilation, which should be considered when implementing an intervention strategy.

5
INTRODUCTION 

Several air contaminants measured indoors have been shown to have negative impacts on human 

health (Berglund et al., 1992; Samet, 1993; Roberts and Dickey, 1995; Jones, 1999). Residential 

indoor air pollutants of concern include: combustion byproducts, volatile organic compounds, 

radon, and bioaerosols (Tobin et al., 1992; Haghighat and Bellis, 1993; Sherman, 1999). Many of 

these contaminants are often measured at higher concentrations in lower income urban housing 

(Rotko et al., 2001; Rotko et al., 2000; Laquatra et al., 2002; Brugge et al., 2002). Although these 

residences are typically in the greatest need of remediation, they are the least likely to be fixed. 

As such, the U.S. Department of Housing and Urban Development (HUD) has made it a priority 

to identify wide-reaching intervention strategies that can be feasibly implemented to improve the 

indoor air quality (IAQ) in lower income homes. As part of this effort, the HHI program is 

funding several demonstration projects to implement interventions that correct many of the IAQ 

problems found in lower income urban homes. Due to the costs of fieldwork, however, the 

demonstration projects are only able to implement a limited number of interventions in a small 

number of homes. A more feasible way to prioritize the hazards and identify the farthestreaching intervention strategies is through modeling. A modeling approach allows the evaluation 

of many potential interventions under a wider variety of conditions to help provide a knowledge 

base for recommending the most effective strategies. Such modeling can also be used to evaluate 

the interventions for possible unintended negative impacts. Thus, model results have the 

potential to provide tremendous insight toward the improvement of IAQ in urban housing with 

multiple deficiencies. 

Previous experimental and simulation intervention studies have investigated the impacts of 

source reduction (Shaw et al., 1999), ventilation strategies (Emmerich and Persily, 1996; Kruger 

and Kraenzmer, 1996; Persily, 1998; Shaw et al., 1999; Takaro et al., 2002), and air cleaning 

(Emmerich and Nabinger, 2001; Howard-Reed et al., 2004) on indoor air concentrations of 

pollutants. This project expands on these studies by considering all three types of interventions 

for a variety of contaminants and sources. A methodology was developed to rank interventions 

individually and in different combinations on the basis of: 1) impact on room contaminant 

concentrations, 2) impact on occupant exposure, 3) number of contaminants impacted, and 4) 

any negative impacts. The key parameter utilized in evaluating the interventions is the 

concentration relative to the baseline rather than comparison of absolute concentrations to 

guideline values. Based on this analysis, this report identifies interventions with the most 

significant positive impact for reducing contaminants in lower income urban housing and 

provides a tool for future intervention evaluation. 

SIMULATION METHOD 

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 of IAQ by applying a computational fluid dynamics 

(CFD) program to predict the detailed flow fields and pollutant concentration distributions within 

a room or rooms. Multizone airflow and pollutant transport modeling takes a macroscopic view 

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

contaminants are transported through the building and its heating, ventilating and airconditioning (HVAC) system. To identify the impact of an intervention on the whole house, a 

multizone model was selected for this study. 

6
The multizone approach is implemented by constructing a building model as a network of

elements describing the flow paths (HVAC 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 

and the outdoors, zone pollutant concentrations are calculated by applying mass balance 

equations to the zones, which may contain pollutant sources and/or sinks. Feustel and Dieris 

(1992) described a survey of multizone airflow models, including the CONTAM model 

developed in the Building and Fire Research Laboratory (BFRL) at the National Institute of

Standards and Technology (NIST). The most recent publicly available version of CONTAM is 

CONTAM 2.1 (Walton and Dols, 2003), which was used in this study. 

The CONTAM simulations were conducted in three phases. The first phase involved the 

modification of an existing CONTAM model, populating the building with contaminant sources 

and occupants, and performing week-long baseline simulations for each of the three cities and 

four seasons (12 total simulations). The second phase involved adding one intervention at a time 

to the building model and re-running the simulation for each of the city and season combinations, 

with a total of eight interventions simulated. Finally, a third phase included a factorial analysis of 

four of the interventions in order to analyze the impact of combining interventions. 

Baseline Building Model 

A well-studied CONTAM model of a townhouse was used as a baseline building for this project 

(Emmerich et al., 2002). Most recently, the townhouse model was validated for its ability to 

predict air change rates and SF6 concentrations based on measured data (Emmerich et al., 2002). 

Although the original model was based on a house not considered typical of lower income urban 

housing, it was modified to be more representative of this housing type. The model townhouse is 

a three-story, three bedroom, three bathroom end-unit townhouse with a floor area of 

approximately 35 m2

 per level and an approximate living space volume of 250 m3. The 

unfinished basement is three-quarters underground with no outside access doors. The basement 

contains a gas furnace, gas hot water heater and dryer, all vented to the outside. The second level 

consists of a kitchen, living room, and bathroom. There is a sliding glass balcony door and 

fireplace in the living room. The third level includes three bedrooms, two bathrooms, and several 

closets. The fourth level is an attic, with a volume of 50 m3

. A floor plan of the house as entered 

into CONTAM is shown in Figure 1 with the features of each zone provided in Table 1. 

To characterize the airflow between zones and the outdoors, CONTAM uses different types of 

flow elements. A summary of the flow elements used in this model is provided in Appendix A. 

As shown in Appendix A, most of the model flow elements use leakage area data from the 

literature for different types of openings (e.g., wall-to-wall joints, electrical outlets, window 

frames, etc.). Other flow element types used in this model include orifice area data for the attic 

vents and two-opening data for the open doorways between zones. Leakage area elements were 

also used for airflow between zones at interior walls and ceilings and floors. 

A variable wind pressure coefficient was applied to the exterior envelope leakage elements. 

Wind pressure coefficients characterize the relationship between wind and surface pressures and 

depend on the wind direction, the building shape, the position on the building surface, and the 

7
presence of shielding near the building. Equations provided in the Fundamentals Handbook 

(2001a) were used to construct a wind pressure profile for the model house. 

Figure 1. CONTAM floor plan of house. 

Level 1: Basement Level 2: Main 

Level 3: Bedrooms Level 4: Attic 

Utility Room 

Living Room 

Kitchen 

Bathroom 1 

 Staircase 

Fireplace

Basement 

Stairs 

Fireplace 

Hall 

Closet 

Master Bedroom 

Master 

Bath 

Bedroom 2 Bedroom 3 

Hall 

Bath2 

Basement 

Stairs 

Closet 3 

Closet 2 

 Linen Closet 

 Staircase 

 Master 

Bedroom 

Closet

Fireplace 

Figure 1. Floor plan of house in CONTAM. 

8
A simple recirculating air handling system (AHS) was added to the model but was “operated” 

only during some weather conditions. The system ductwork does not enter the attic, resulting in 

insignificant duct leakage to the outside. The system was modeled as operating with a total 

volumetric airflow of 0.35 m3

/s. For baseline cases, the air handling system used a typical 

furnace filter that removed 7.5 % to 20 % of particles in a single pass, depending on particle size. 

Table 1. CONTAM Model Zones. 

Zone: Description

Floor Area 

(m2

) 

Volume 

(m3) 

LEVEL: Basement (1) 

util Utility Room 20 55

basestr Staircase 3.4 9.4

 Level Total 23 64 

LEVEL: Main (2) 

lrdr Living Room 17 47

kit Kitchen 11 30

bath1 Bathroom 0.73 2.0

hcls Hall Closet 0.36 0.99

fp Fireplace 0.083 0.23 

str2 Staircase 3.3 9.0

 Level Total 32 89 

LEVEL: Bedrooms (3) 

mbr Master Bedroom 11 30

mcl1 Master Bedroom Closet 0.78 2.1 

mbth Master Bathroom 2.5 6.9

bath2 Bathroom 1.5 4.1

hall Hall 2.5 6.9

lncl Linen Closet 0.37 1.0

br2 Bedroom 6.5 18

br2cls Bedroom Closet 0.90 2.5

br3 Bedroom 8 22

br3cls Bedroom Closet 0.99 2.7

Level Total 35 105 

LEVEL: Attic (4) 

attc Attic 13 50

Level Total 13 50 

Since ventilation and IAQ performance vary by climate, the house was modeled in Boston, MA, 

Seattle, WA, and Miami, FL for one week of each season. While not covering all possible U.S. 

climates, these locations are representative of much of the climatic range of the United States. 

For each city-season combination, transient simulations were performed using TMY2 weather 

data (Marion and Urban, 1995). Since the three cities had a wide range of average ambient 

temperatures, different indoor heating and cooling schemes were used. Table 2 shows a summary 

of the baseline simulation files. For average weekly outdoor temperatures below 10 °C, which 

9
included Boston and Seattle winter and spring seasons, the central air heating system was 

assumed to operate for the first ten minutes of each hour. For average outdoor temperatures 

above 25 °C, which included Miami spring, summer, and fall, the air conditioning system was 

also assumed to operate for the first ten minutes of each hour. For all other cases where the 

average temperatures fell between 10 °C and 25 °C the AHS was assumed to be off. 

Table 2. Baseline Simulation Cases 

City Season 

Average 

Weekly 

Outdoor 

Temp. 

(°C) 

Average 

Weekly 

Wind Speed 

(m/s) 

Average 

Weekly 

Relative 

Humidity1

 

(% RH) 

Average Indoor 

Temp. 

by Level (°C) 

 1 2 3 4 

Air 

Handler

Status 

Boston Spring 10 4.2 72 20 20 20 20 None

Boston Summer 19 4.5 69 22 24 26 34 None

Boston Fall 7 5.6 72 20 20 20 20 Heat

Boston Winter 1 6.0 59 18 20 22 12 Heat

Miami Spring 25 2.8 60 22 22 22 36 A/C

Miami Summer 27 4.0 75 22 24 26 36 A/C

Miami Fall 26 3.1 75 22 22 22 36 A/C

Miami Winter 19 3.9 72 22 24 26 34 None

Seattle Spring 12 4.5 67 20 20 20 20 None

Seattle Summer 17 3.6 74 22 24 26 34 None

Seattle Fall 8 4.6 76 20 20 20 20 Heat

Seattle Winter 6 3.8 72 18 20 22 12 Heat

1

Based on average temperature and average humidity ratio. 

Occupants 

To account for occupant-generated contaminants and to determine the exposure of building 

occupants to indoor contaminants, a family of five was assumed to occupy the townhouse. The 

occupants of the house included an adult male, adult female, and three children of ages 4, 10, and 

13 years. A weekend (Saturday and Sunday) and weekday (Monday – Friday) schedule was 

created for each family member that specifies the time spent in each room of the house, as well 

as time outside of the house (see Appendix B). During the time spent outside of the house, the 

occupant exposure was assumed to be zero. Based on these schedules CONTAM accounts for 

the contaminant generated by each individual in the room where they are located at a given time 

and keeps track of the contaminant concentrations to which they are exposed. CONTAM then 

calculates the mean concentration over a given period of time as a measure of exposure. 

Contaminants and Sources 

The contaminants that were considered in the simulations include carbon dioxide (CO2), water 

vapor (H2O), carbon monoxide (CO), nitrogen dioxide (NO2), airborne particles (in 5 sizes 

ranging from 0.3 μm to 10 μm), volatile organic compounds (VOCs) and radon (Rn). The 

sources of these contaminants were not intended to be comprehensive, but rather representative 

of some typical residential sources and to provide insight into each of the individual 

contaminants. Also, some of the sources produce additional contaminants beyond the ones 

considered (e.g., unvented combustion appliances produce CO2, H2O, and particles but were not 

included as a source of these contaminants in this study). 

10
Carbon Dioxide 

In general, concentrations of carbon dioxide (CO2) do not reach harmful levels indoors; however, 

these concentrations have often been used as an indicator of ventilation. The only indoor source 

of CO2 considered for this study was the respiration of the occupants. The generation rate of CO2

from a person is a function of body size and physical activity. Table 3 shows the occupant CO2

generation rates used for awake and sleeping time periods based on ASHRAE Fundamentals 

Handbook (2001a). The locations of these CO2 sources depend on the occupant schedules 

discussed above (see Appendix B). Another important source of CO2 was the outdoor air which 

was assumed to have a constant concentration of 630 mg/m3 (all outdoor concentrations are 

presented in a later section in Table 10). 

Table 3. Occupant generation rates of CO2

Occupant 

Weight 

(kg) 

CO2 generation rate – 

awake (mg/s) 

CO2 generation rate – 

sleeping (mg/s) 

Adult Male 81 11 6.6 

Adult Female 67 9.8 6.2 

Child #1 (13 years old) 50 8.6 5.2 

Child #2 (10 years old) 36 6.8 4.1 

Child #3 (4 years old) 17 3.8 2.3 

Water Vapor 

Water itself is not considered a harmful contaminant, but, rather, it is the microorganisms (e.g., 

mold, dust mites, etc.) that can grow at higher relatively humidity that are of interest. There are 

numerous potential sources of water vapor in homes. A representative subset of water vapor 

sources was chosen for this project and includes respiration and perspiration of occupants, 

bathing, cooking, and dishwashing. As with occupant generation of CO2, water vapor generation 

rates depend on body size and activity level. Table 4 shows the occupant water vapor generation 

rates used for awake and sleeping time periods based on Trechsel (1994). The locations of these 

H2O sources depend on the occupant schedules discussed above (see Appendix B). 

Table 4. Occupant generation rates of H2O 

Occupant H2O generation rate – 

awake (mg/s) 

H2O generation rate – 

sleeping (mg/s) 

Adult Male 15.3 9.2 

Adult Female 15.3 9.2 

Child #1 (13 years old) 12.5 7.5 

Child #2 (10 years old) 11.1 6.7 

Child #3 (4 years old) 6.0 3.6 

The water vapor generation rates and schedules for bathing, cooking and dishwashing are based 

on an earlier NIST study (Persily, 1998). Table 5 shows the rates, locations and schedules of 

these sources. In order to prevent the relative humidity from exceeding 100 %, CONTAM’s 

cutoff concentration model was used. This model used the constant coefficient generation rates 

in Table 5 until the room concentration reached 100 % relative humidity, i.e., a saturation point 

of 14.8 g/kg to 21.5 g/kg depending on the room temperature. At the saturation point, the source 

would stop emitting H2O until the concentration fell below the saturation point again. 

11
Table 5. H2O generation rates and schedules for bathing, cooking, and dishwashing. 

Activity H2O generation 

rate (g/s) 

Location Weekday

schedule 

Weekend 

schedule 

Adult male 

shower 

0.67 Master Bathroom 6:00 a.m. – 

6:10 a.m.

9:00 a.m. – 

9:10 a.m.

Adult female 

shower 

0.67 Master Bathroom 6:30 a.m. – 

6:40 a.m.

9:10 a.m. – 

9:20 a.m.

Child #1 

shower 

0.67 Bathroom #2 7:00 a.m. – 

7:10 a.m.

10:00 a.m. – 

10:10 a.m.

Child #2 

shower 

0.67 Bathroom #2 7:20 a.m. – 

7:30 a.m.

9:10 a.m. – 

9:20 a.m.

Child #3 

shower 

0.67 Bathroom #2 7:10 a.m. – 

7:20 a.m.

9:40 a.m. – 

9:50 a.m.

Cooking – 

breakfast 

0.14 Kitchen 6:30 a.m. – 

7:00 a.m.

9:30 a.m. – 

10:00 a.m.

Cooking – 

lunch 

0.42 Kitchen 12:00 p.m. – 

12:30 p.m. 

12:00 p.m. – 

12:30 p.m. 

Cooking – 

dinner 

0.28 Kitchen 5:00 p.m. – 

6:00 p.m. 

5:00 p.m. – 

6:10 p.m. 

Dishwashing - 

breakfast 

0.085 Kitchen 7:40 a.m. – 

8:00 a.m.

10:20 a.m. – 

10:40 a.m.

Dishwashing - 

dinner 

0.17 Kitchen 7:00 pm – 

7:30 pm

7:00 pm – 

7:30 pm

Carbon Monoxide and Nitrogen Dioxide 

The indoor sources of CO and NO2 included a gas stove and an unvented space heater. Table 6 

shows the generation rates and schedules for the gas stove (Persily 1998) and unvented space 

heater (Emmerich and Persily 1996), which are based on values in the literature. The unvented 

space heater was only operated during the winter seasons in Boston and Seattle.

Table 6. Sources of CO and NO2

Source CO generation

rate (mg/s) 

NO2 generation 

rate (mg/s) 

Location Weekday 

schedule 

Weekend 

schedule 

Gas stove – 

breakfast 

0.21 0.028 Kitchen 6:30 a.m. – 

7:00 a.m.

9:30 a.m. – 

10:00 a.m.

Gas stove - 

lunch 

0.42 0.056 Kitchen 12:00 p.m. – 

12:30 p.m. 

12:00 p.m. – 

12:30 p.m. 

Gas stove - 

dinner 

0.42 0.056 Kitchen 5:00 p.m. – 

5:30 p.m. 

5:00 p.m. – 

5:30 p.m. 

Gas stove – 

dinner 

0.83 0.11 Kitchen 5:30 p.m. – 

6:00 p.m. 

5:30 p.m. – 

6:00 p.m. 

Unvented 

space heater 

0.28 0.070 Living

Room

8:00 a.m. – 

10:00 a.m.

7:00 p.m. – 

10:40 p.m. 

10:40 a.m. – 

12:00 p.m. 

7:00 p.m. – 

11:40 p.m. 

12
Airborne Particles 

Combustion and mechanical processes generate airborne particles of many different sizes and 

composition. Although many different properties of airborne particles can impact human health, 

this study only addressed particle size as it impacts generation rates and removal. The model 

included 5 particle size ranges (0.3 μm to 0.5 μm, 0.5 μm to 1.0 μm, 1.0 μm to 2.5 μm, 2.5 μm 

to 5.0 μm, 5.0 μm to 10 μm), which correspond to size ranges commonly measured in the field 

(Wallace and Howard-Reed, 2002; Howard-Reed et al., 2003; Wallace et al., 2004). Indoor 

particle sources included cooking (for generation of smaller particles) and changing of kitty litter 

twice a week (for generation of larger particles). As with the other contaminants, many other 

potential sources of particles may exist in any given residence. These sources were chosen as 

examples based on the availability of source strength data. The source strengths for these events 

were based on measurements in previous studies (Wallace et al., 2004; Howard-Reed et al., 

2003) and are summarized in Tables 7 and 8. 

Table 7. Particle generation rates and schedules for cooking. 

Source 

Particle Generation Rate 

(number per hour) 

0.3-0.5 μm 0.5-1.0 μm 1.0-2.5 μm 

Location

Weekday 

Schedule 

Weekend 

Schedule 

Cooking – 

Breakfast 

6.4 x 1010 1.6 x 1010 8.0 x 109 Kitchen 6:30 a.m. – 

6:40 a.m.

9:30 a.m. – 

9:40 a.m.

Cooking – 

Lunch 

4.0 x 1010 1.0 x 1010 5.0 x 109 Kitchen 12:00 p.m. – 

12:10 p.m. 

12:00 p.m. – 

12:10 p.m. 

Cooking – 

Dinner 

8.0 x 1010 2.0 x 1010 1.0 x 109 Kitchen 5:00 p.m. – 

5:10 p.m. 

5:00 p.m. – 

5:10 p.m. 

Table 8. Particle generation rates and schedules for changing kitty litter. 

Source 

Particle Burst Amount 

(number of particles) Location

Weekday 

Schedule 

Weekend 

Schedule 

Kitty Litter: 0.5 – 1.0 μm 5.6 x 108

Kitty Litter: 1.0 – 2.5 μm 5.0 x 108

Kitty Litter: 2.5 – 5.0 μm 6.8 x 108

Kitty Litter: 5.0 – 10 μm 7.9 x 108

Living 

Room

Wednesday 

@ 9:20 a.m.

Saturday 

@ 9:20 a.m.

Volatile Organic Compounds 

Volatile organic compounds (VOCs) include a broad class of compounds with wide variations in 

physical and chemical properties, health impacts, and sources. The study includes two 

nonspecific VOCs as surrogates for two general classes of sources. The first VOC (VOC1) was 

generated in each room of the house with a generation rate proportional to the floor area. A 

continuous generation rate of 0.2 mg/h·m2

 was used, based on an average of approximately 50 

published flooring emission rates for toluene (U.S. EPA, 1999). The second VOC was generated 

by a burst source in different rooms of the house. Based on an emission rate for floor cleaning, a 

mass of 0.08 g was used for the burst (Wallace, 1987). The release location and schedule of the 

burst VOC source is shown in Table 9. 

13
Table 9. Location and schedule for burst VOC sources. 

Location Day of Week Time 

Kitchen Monday - Friday 7:30 a.m.

Saturday and Sunday 10:30 a.m.

Bathroom #1 Saturday 11:00 a.m.

Living Room Saturday 11:20 a.m.

Master Bedroom Saturday 11:30 a.m.

Master Bathroom Saturday 11:40 a.m.

Hall Saturday 3:40 p.m.

Bedroom #2 Saturday 3:50 p.m. 

Bedroom #3 Saturday 4:00 p.m. 

Bathroom #2 Saturday 4:40 p.m. 

Radon 

A pressure dependent radon source described in an earlier NIST report (Fang and Persily, 1995) 

was included in the basement zone of the model. The source equation is as follows: 

S = G∆Pn (1) 

where: 

S = contaminant source strength (Bq/s·m2

) 

G = generation rate coefficient (Bq/s·m2

·Pa) 

∆P = pressure difference (Pa) 

n = pressure exponent 

With little information available in the literature for model inputs, the value of G was determined 

based on it yielding reasonable concentrations in the house. Exact values were not critical, since 

analysis for this project is based on relative concentrations. Based on these trial simulations a 

generation rate of 0.004 Bq/s·m2

·Pa and a pressure exponent of 1 were chosen. 

Outdoor Concentrations 

Outdoor concentrations of water vapor were based on the humidity ratios in the TMY2 weather 

data. Outdoor concentrations of CO, NO2, CO2, and VOCs were based on those used in earlier 

NIST studies (Persily 1998 and Emmerich and Persily 1995) and are presented in Table10.

Outdoor particle concentrations were based on average concentrations measured outside a 

research townhouse in Reston, VA (Wallace and Howard-Reed, 2002). It should be noted that 

Reston is considered a suburban location where outdoor pollutant levels may not be as high as an 

urban location. Nonetheless, a constant value for each particle size range was used (see Table 

10). 

14
Table 10. Outdoor Concentrations of CO, NO2, CO2, VOCs, and Particles. 

Time 

Contaminant 

12:00 a.m. 

– 7:00 a.m. 

7:00 a.m. – 

9:00 a.m. 

9:00 a.m. – 

5:00 p.m. 

5:00 p.m. – 

7:00 p.m. 

7:00 p.m. – 

12:00 a.m. 

CO (mg/m3) 1.1 2.3 1.7 3.4 1.7

NO2 (mg/m3) 0.038 0.075 0.038 0.075 0.038

CO2 (mg/m3) 630 630 630 630 630

VOC (mg/m3) 0.10 0.10 0.10 0.10 0.10

P1: 0.3 – 0.5 μm (#/cm3) 64 64 64 64 64

P2: 0.5 – 1.0 μm (#/cm3) 3.7 3.7 3.7 3.7 3.7

P3: 1.0 – 2.5 μm (#/cm3) 0.39 0.39 0.39 0.39 0.39

P4: 2.5 – 5.0 μm (#/cm3) 0.11 0.11 0.11 0.11 0.11

P5: 5.0 – 10 μm (#/cm3) 0.020 0.020 0.020 0.020 0.020

Removal Mechanisms 

Contaminant Sinks 

The loss of contaminants due to adsorption, deposition and decay were also included in the 

models as follows. Reversible sink effects for H2O and VOCs were modeled with sink elements 

based on the boundary layer diffusion controlled (BLDC) model. The BLDC adsorption model is 

described in detail by Axley (1990). The parameters required for this sink model are the film

mass transfer coefficient, the adsorbent mass and the partition coefficient. Sink values for H2O 

and VOCs are given in Table 11. It was assumed that these values would apply to all living areas 

of the house, except for closets and stairways. 

Table 11. Boundary Layer Diffusion Controlled Model Parameters 

Parameter Sink: H2O VOC

Film Transfer Coefficient 0.25 m/h 0.08 m/h 

Film Density 1.2 kg/m3 1.2 kg/m3

Surface Mass 550 kg 550 kg 

Partition Coefficient 1.5 5 

Nitrogen dioxide decay and particle deposition were modeled as single-reactant first order 

reactions with a single, constant value in all rooms of the houses. The kinetic rate coefficient 

used for NO2 decay was 0.86 h-1 and is based on the average of measurements in a contemporary 

research house reported by Leslie et al. (1988). Particle deposition rates were measured in a 

research townhouse and reported elsewhere (Howard-Reed et al., 2003). A summary of the 

deposition rates is given in Table 12. 

Table 12. Deposition Rates for NO2 and Particles 

Contaminant Deposition Rate (h-1) 

NO2 0.86 

P1: 0.3 μm to 0.5 μm 0.30 

P2: 0.5 μm to 1.0 μm 0.42 

P3: 1.0 μm to 2.5 μm 0.78 

P4: 2.5 μm to 5.0 μm 1.4 

P5: 5.0 μm to 10 μm 2.7 

15
The half-life of Radon-222 is 3.8 days, which corresponds to a decay rate of 0.0076 h-1. 

Air Cleaning 

Particles were also removed in the baseline cases with a central HAC system by a typical furnace 

filter. Removal rates were based on experimental results of a previous NIST study (Emmerich 

and Nabinger, 2001). The specific removal rates for each particle size were as follows: 7.5 % for 

0.3 μm to 0.5 μm, 14 % for 0.5 μm to 1.0 μm, 20 % for 1.0 μm to 2.5 μm, 20 % for 2.5 μm to

5.0 μm, and 20 % for 5.0 μm to 10 μm. Note that these removal rates also account for losses 

through deposition to the ductwork. 

Scenarios/Interventions 

Baseline models were created in order to provide a benchmark for determining the impact of 

various housing repairs/interventions and other scenarios of interest. The housing 

repairs/interventions included some being performed by HUD’s NOFA grant awardees, 

recommendations of ASHRAE 62.2-2004, and additional alternative IAQ strategies. Other 

possible interventions such as dehumidification were not modeled. The interventions/scenarios 

were as follows: 

- Removal of space heater or properly venting it to the outside. 

For winter baseline simulations in Boston and Seattle, an unvented space heater was used 

in the living room and constituted a source of NO2 and CO (see Table 6). The 

intervention of removing the space heater emissions was modeled by not including the 

space heater in the winter simulations. 

- Replacement of the faulty stove. 

A gas stove releases NO2 and CO with an emission rate dependent on several 

characteristics of the stove. For the baseline case, a relatively high emission rate was 

assumed based on values in a previous report (Emmerich and Persily, 1995). In this 

intervention, lower stove emission rates associated with a more efficiently operating 

stove was modeled for each city and season and compared with the baseline values (from

Persily 1998). A comparison of the stove emission rates is provided in Table 13. 

 Table 13. Baseline and Intervention CO and NO2 emission rates for a gas stove. 

Source 

Baseline Generation Rates 

(mg/s) 

 CO NO2

Reduced Generation Rates 

(mg/s) 

 CO NO2

Gas stove – breakfast 0.21 0.028 0.0070 0.0043 

Gas stove - lunch 0.42 0.056 0.014 0.0085 

Gas stove - dinner 0.42 0.056 0.014 0.0085 

Gas stove – dinner high 0.83 0.11 0.028 0.017 

- Replace typical furnace filter with enhanced particle air filter. 

For the baseline simulations, a typical furnace filter was used in the HAC system (see 

Table 2). For the intervention, the furnace filter was replaced with a higher efficiency air 

filter. The particle removal efficiencies for the improved air filter were based on a study 

by Emmerich and Nabinger (2001) and are given in Table 14. 

16
Table 14. Comparison of Removal Efficiencies of Typical Furnace Filter and 

Intervention Air Filter. 

Particle Size Typical Furnace Filter 

Removal Efficiency (%) 

Enhanced Air Filter Removal 

Efficiency (%) 

0.3 – 0.5 0.075 0.36 

0.5 – 1.0 0.135 0.49 

1.0 – 2.5 0.20 0.62 

2.5 – 5.0 0.20 0.62 

5.0 – 10 0.20 0.62 

- Installation of air conditioner. 

The impact on humidity of adding an air conditioner was investigated for the summer 

simulations in Boston and Seattle. Due to high outdoor temperatures (average 

temperature > 25 °C) in Miami, an air conditioner was included in the Miami baseline 

cases in spring, summer and fall. As a result, Miami was not included in this intervention. 

For the Boston and Seattle summer simulations, an air conditioner was added to the 

model and operated the first 10 min of every hour. While operating, the air conditioner 

removed moisture at a rate of 17 % (based on the value used in Persily 1998) and 

particles at a rate equivalent to the typical furnace filter values in Table 14. 

- Inclusion of kitchen and bathroom exhaust fans. 

All the baseline cases did not include local exhaust fans. This intervention involved the 

inclusion of intermittent kitchen and bathroom exhaust fans that meet the requirements of

ASHRAE Standard 62.2 (2004). The kitchen exhaust fan has airflow of 47 L/s and was 

operated during cooking events (see Table 6). The bathroom exhaust fans each had 

airflow of 24 L/s and were operated during showers. 

- Operation of a gas oven to heat the house. 

In addition to using gas ovens for cooking, some residents use them as a heat source in 

the winter (Brugge et al. 2002). This practice can result in elevated concentrations of CO 

and NO2. This would also increase H2O and particle concentrations in practice but this 

effect was not modeled. In order to assess the impact of ceasing this practice, separate 

simulations were performed for each city using a stove to heat the house for 4 h every 

evening in the winter. The oven emission rates are equivalent to those for breakfast 

cooking in Table 6. Results were then compared with the baseline case where the gas 

stove was only operated for cooking. 

- Tighten the exterior envelope 

A common suggestion to reduce residential energy consumption is to tighten a house’s 

exterior envelope. Tightening a building results in lower infiltration rates, which in turn 

reduces the number of outdoor contaminants entering the building, but also increases the 

indoor concentration of contaminants generated inside the building. As a result, this 

recommendation may actually have a negative impact on indoor air quality. To model 

this intervention, all exterior envelope leakage area elements were reduced by 40 % from

the baseline case. 

17
- Installation and operation of a mechanical ventilation system that meets the 

requirements of ASHRAE Standard 62.2-2004. 

ASHRAE Standard 62.2 requires the installation of a mechanical exhaust system and/or 

supply system to provide outdoor air to a dwelling (ASHRAE 2004). The amount of 

outdoor ventilation air is based on the house’s floor area and number of bedrooms. The 

continuous outdoor air requirement may be adjusted to an intermittent value based on the 

ventilation effectiveness and fractional time on. There are also some special stipulations 

for extreme climates. For all seasons in Boston and Seattle, an exhaust fan continuously 

operating at 24 L/s was installed in the master bathroom to meet the mechanical 

ventilation requirement. Due to Miami’s hot and humid climate, a mechanical supply 

system was used instead of an exhaust system. Since the outdoor air was provided on the 

same schedule as the air-conditioner operation (i.e., 10 min of every hour), the outdoor 

air was supplied at a rate of 142 L/s to provide the equivalent of 24 L/s continuous 

ventilation. 

Factorial Intervention Simulations 

In addition to investigating the impact of each intervention individually, a methodology was 

developed to rank combinations of interventions. The methodology involves a full factorial 

simulation design (Box et al., 1978), which tests the significance of individual interventions as 

well as ranks combinations of interventions. A full factorial design also has the advantage of 

being able to detect when variables do not act additively on a specific response. To demonstrate 

the value of the ranking system, a factorial simulation design was developed for the following 

four interventions: tightening, exhaust fans, mechanical ventilation, and enhanced filtration (see 

Table 15). The fall season in Boston was chosen as the model season and city, respectively. The 

impact of each combination of interventions was assessed based on the sum of individual 

exposures of the five occupants living in the house. 

Table 15. Factorial design for four intervention combinations. 

Simulation 

Number 

Tightened Exhaust Fan Mechanical

Ventilation 

Upgraded 

Filter 

1 no no no no

2 yes no no no

3 no yes no no

4 yes yes no no

5 no no yes no

6 yes no yes no

7 no yes yes no

8 yes yes yes no

9 no no no yes

10 yes no no yes

11 no yes no yes

12 yes yes no yes

13 no no yes yes

14 yes no yes yes

15 no yes yes yes

16 yes yes yes yes

18
SIMULATION RESULTS 

The results have been divided into three sections. The first section contains a discussion of the 

baseline results in terms of infiltration rates and contaminant concentrations for each city and 

season combination. Next, the individual intervention results are presented with impacts on air 

change rates, contaminant concentrations for specific zones, and occupant exposures for each 

city and season combination. Finally, results are presented for the factorial simulation 

methodology. 

Baseline Results 

A total of 12 baseline city/season combinations were simulated. For each city and season a two 

week simulation was conducted with the second week being used to determine the resultant 

impacts. Based on the second week of data, minimum, average, and maximum air change rates 

and contaminant concentrations were reported for each case. Airflow rates and concentrations 

are available for all 19 zones of the house, however, for reporting purposes a representative set of 

zones was chosen. 

Infiltration Rates 

A fan pressurization test was simulated for the baseline model and resulted in an air change rate 

at 50 Pa of 12.5 h-1 and an effective leakage area at 4 Pa of 640 cm2

. The normalized leakage 

area for this house is approximately 0.92 which is tighter than the national average of 1.72 but 

within its standard deviation of 0.84 (Sherman and Dickerhoff, 1998). Compared to the national 

average this house appears tight, however, many individual states were found to have average 

leakage areas below 1.0. For example, Sherman and Dickerhoff (1998) reported the average 

values for Massachusetts and Washington to be 0.53 and 0.44, respectively (an average value 

was not reported for Florida). 

Seasonal average whole house air change rates were determined for each baseline case and are 

reported in Table 16. The house’s air change rate directly impacts the indoor concentrations of 

each contaminant as well as contributing contaminants from outdoors. Since the same house was 

used for each city, the difference in air change rates was due solely to each city’s weather. 

Realistically, the house’s characteristics would likely vary based on geography, thereby also 

affecting air change rates. However, such building differences are not critical to this study due to 

the analysis being based on results relative to baseline values. As shown in Table 16, Boston had 

the highest air change rates followed by Seattle and then Miami. For each city, the highest air 

change rates occurred during the winter season. Boston and Seattle had the lowest air change 

rates in the summer, whereas Miami’s lowest values were in the fall. 

A metric to put these air change rates into context has been provided by ASHRAE Standard 62.2. 

Currently, the standard requires a house to have mechanical ventilation, which is not included in 

any of the baseline cases. In fact, most homes in America do not meet this requirement of 

ASHRAE Standard 62.2. Previously, ASHRAE Standard 62 (2001b) included a recommendation 

of a minimum air change rate of 0.35 h-1 in low-rise residential buildings, but did not specifically 

require mechanical ventilation. For the purposes of comparison, a benchmark air change rate 

value of 0.35 h-1 was used in this study. As shown in Table 16, air change rates in Boston and 

Seattle were generally greater than 0.35 h-1, with the exception of summer in both cities and 

spring in Seattle. In contrast, the weather in Miami resulted in air change rates below 0.35 h-1

19
more than 60 % of the time in the fall and summer and at least 20 % to 30 % of time in the 

winter and spring, respectively. 

The results shown in Table 16 show that a moderately leaky house may have low infiltration 

during mild weather, which can compromise IAQ yet, have high infiltration during the winter, 

which can have a steep energy penalty. As a result, some recommend for tighter building 

envelopes with mechanical ventilation (Persily 1998, ASHRAE 2001a). 

Table 16. Air change rates for baseline cases. 

City Season Minimum

(h-1) 

Average 

(h-1) 

Maximum 

(h-1) 

% < 0.35 h-1

Fall 0.32 0.76 1.1 2

Winter 0.72 0.98 1.5 0

Spring 0.36 0.66 0.96 0

Boston 

Summer 0.24 0.47 0.73 13

Fall 0.14 0.31 0.52 67

Winter 0.16 0.45 0.75 23

Spring 0.15 0.42 0.82 32

Miami 

Summer 0.13 0.33 0.66 61

Fall 0.53 0.75 1.0 0

Winter 0.65 0.80 1.1 0

Spring 0.17 0.57 0.96 12

Seattle 

Summer 0.13 0.49 0.70 20

Contaminant Concentrations 

Although efforts were made to achieve realistic contaminant concentrations in the house, the 

baseline concentrations should be considered reference values with which to evaluate the impact 

of the different interventions. In the statistical analysis that follows, the key parameter utilized in 

evaluating the interventions is the concentration relative to the baseline rather than comparison 

of absolute concentrations to guideline values. A summary of the total living area (utility room, 

living room, kitchen, bathrooms, and bedrooms) contaminant concentrations is provided for each 

city and season in Tables 17 – 19. The minimum, average and maximum values in these tables 

are based on one week of each season. 

In general, the contaminants from primarily indoor sources (CO, CO2, NO2, Rn, VOC1 (constant 

source), VOC2 (burst)) were highest during the seasons with lower air change rates. Likewise, 

contaminants with significant outdoor sources (particles, H2O) were highest during the seasons 

with higher air change rates. However, typical households may have additional sources of these 

contaminants that were not included in the model, which would impact this relationship. In that 

same vein, the average concentrations of CO, CO2, Rn, and VOCs were highest in Miami, the 

city with the lowest air change rates. Miami also has the highest outdoor relative humidity, 

resulting in the highest indoor concentrations of H2O. NO2 and particles were highest in Seattle 

and Boston due to the higher air change rates and the presence of a space heater in winter. 

The baseline concentrations were also used to characterize where contaminants are distributed in 

the house. For example, Figures 2 – 5 show a representative 24 h profile of CO concentrations in 

20
Boston for each season. In general, the CO concentrations tend to be higher than typical values, 

but again, it is the relative concentrations between cases that are the important metrics. As shown 

in these figures, the CO concentration has the highest peaks in the zones with a CO source – the 

gas stove in the kitchen and the space heater in the living room during the winter. The CO 

concentrations in the remaining zones are dictated by the model’s airflow pattern, which is 

seasonally dependent. For example, in the summer, CO emissions in the kitchen result in high 

peaks in the living room and basement stairway. Whereas, in the spring, the living room

concentrations of CO are relatively low with the higher peaks predicted in the second level 

stairway and third level hall and bathroom. The bedroom CO concentrations, where the 

occupants spend the most time, are typically in the middle for all seasons. 

The CO concentrations were distributed in a similar fashion in the townhouse located in Seattle 

with the peak concentrations slightly higher due to lower air change rates. The Miami townhouse 

CO concentrations, however, were distributed quite differently during the various seasons and 

reached higher peaks due to the lower air change rates. For example, in the fall, the highest peak 

CO concentrations in Boston and Seattle reached 10 mg/m3

 to 30 mg/m3 and were highest in the 

kitchen, living room and upstairs zones. In Miami, the highest peak of about 35 mg/m3

 was also 

in the kitchen, but the CO tended to move down through the house to the basement stairs and 

utility room. Miami’s mild weather resulted in smaller air change rates and a reverse stack flow 

of air in the townhouse. These differences in CO distribution show the importance of air change 

rates and source location on occupant exposure and reveal important information regarding the 

effectiveness of different interventions. 

Although radon originates outdoors, it behaves like an indoor pollutant source due to its entry 

location in the basement. However, unlike CO from indoor sources, radon is emitted on a 

continuous basis. Figure 6 shows the radon concentration for each zone in the house in Seattle in 

the fall. The outdoor temperatures during this season resulted in the air handling system

operating for heating purposes resulting in an influx of radon directly from the basement every 

hour to all zones with supply registers. The utility room and basement stairs had the highest 

concentrations followed by the kitchen on the next level up. Interestingly, concentrations in the 

third level rooms reached higher concentrations than in the living room and middle level 

bathroom. The stairway appears to serve as a conduit that bypasses these zones for certain 

weather conditions. 

Radon concentration levels are a function of the pressure difference across the basement floor 

and the air change rate in the house. So the greater pressure difference caused by increased 

indoor/outdoor temperature differences in cold seasons is counterbalanced by the associated 

increase in air change rate. For this house, the radon concentrations were highest in the summer 

when air change rates were the lowest (see Figure 7). This contaminant illustrates the impact of

the HAC system on contaminant distribution in the house. When a contaminant source is located 

in a less occupied zone of the house (e.g., basement), the HAC system serves to distribute the 

contaminant, often increasing the contaminant concentrations in more frequently occupied areas 

of the house (e.g., bedrooms). Conversely, when a contaminant source is generated in more 

frequently occupied areas of the house, the HAC system can serve to dilute the indoor 

contaminant concentrations. This potential benefit can be further enhanced when an effective air 

cleaner of the contaminant is used in the HAC system. 

21
Table 17. Boston Baseline Results – Living area averages 

Winte

r

S

p

rin

g

S

u

m

m

e

r

C

o

nta

min

a

nt Min Average

M

a

x

Min Average

M

a

x

Min Average 

M

a

x

M

CO (mg/m3

) 1.1 2.9 

9.0 1.1 2.4 10 1.1 2.9 11 1

CO2 (mg/m3

) 6

3

0 700 

7

9

5

6

3

0 729 

8

5

5

6

3

0 765 

9

7

2

6

H2O (%RH) 6.1 18 

5

0

2

4 39 

5

2

3

3 51 

6

6 1

NO2 (mg/m3

) 0.0

21 0.16 

0.6

4

0.016 0.073 

0.7

5

0.015 0.081 

0.8

6

0.

P1 (#/cm3

) 4

4 51 

8

4

4

2 51 

9

6

3

6 48 

9

8

3

P2 (#/cm3

) 2.2 3.2 11 2.2 3.5 14 1.8 3.5 16 1

P3 (#/cm3

) 0.17 0.24 1.6

0.16 0.25 

2.0

0.13 0.24 

2.1 0

P4 (#/cm3

) 0.0

3

4 0.052 1.7

0.0

2

6 0.054 

2.2

0.0

2

4 0.051 

2.4

0.

P5 (#/cm3

) 0.0

0

3

9 0.012 1.7

0.0

0

2

7 0.016 

2.2

0.0

0

3

0 0.017 

2.4

0.0

Rn (pCi/L) 0.99 1.5 1.9 1.9 2.5 

3.8

2.0 3.7 

5.4 1

VOC1 (mg/m3

) 0.14 0.17 

0.18

0.18 0.20 

0.2

5

0.2

5 0.21 

0.31 0

VOC2 (mg/m3

) 0.10 0.14 

0.9

0

0.10 0.16 1.2

0.10 0.20 1.4

0

Note: VOC1 is a continuous VOC source and VOC2 is a burst VOC source Table 18. Miami Baseline Results – Living area averages 

Winte

r

S

p

rin

g

S

u

m

m

e

r

C

o

nta

min

a

nt Min Average

M

a

x

Min Average

M

a

x

Min Average 

M

a

x

M

CO (mg/m3

) 1.1 3.0 12 1.1 3.0 11 1.4 3.2 11 1

CO2 (mg/m3

) 6

3

0 774 10

2

9

6

3

0 866 1316

6

3

5 925 12

5

3

6

H2O (%RH) 29 54 

7

8

3

9 55 

7

5

5

0 64 

7

9

5

NO2 (mg/m3

) 0.012 0.083 

0.9

0

0.0

0

9 0.071 

0.8

5

0.0

0

7 0.077 

0.8

3

0.

P1 (#/cm3

) 3

3 47 

9

3

2

4 41 

8

7

2

4 38 

8

3

2

P2 (#/cm3

) 1.7 3.5 16 1.1 2.9 12 1.0 2.9 12

0

P3 (#/cm3

) 0.10 0.23 

2.0

0.0

6

2 0.18 

2.1 0.0

6

5 0.17 

2.1 0.

P4 (#/cm3

) 0.016 0.049 

2.4

0.011 0.043 

2.5

0.010 0.036 

2.5

0.0

P5 (#/cm3

) 0.0

016 0.017 

2.3

0.0

011 0.016 

2.4

0.0

012 0.014 2.4 0.0

Rn (pCi/L) 2.1 4.3 

6.7 1.2 3.0 

6.0 1.4 3.1 

6.8 1

VOC1 (mg/m3

) 0.19 0.26 

0.3

7

0.18 0.28 

0.41 0.21 0.31 

0.41 0

VOC2 (mg/m3

) 0.10 0.19 1.1 0.10 0.21 1.2

0.10 0.23 1.2

0

Note: VOC1 is a continuous VOC source and VOC2 is a burst VOC source

22
Table 19. Seattle Baseline Results – Living area averages 

Winte

r

S

p

rin

g

S

u

m

m

e

r

C

o

nta

min

a

nt Min Average

M

a

x

Min Average

M

a

x

Min Average 

M

a

x

M

CO (mg/m3

) 1.1 3.1 

9.6 1.1 2.6 11 1.1 3.0 13 1

CO2 (mg/m3

) 6

3

0 707 

813

6

3

0 738 

9

9

7

6

3

0 752 

9

9

3

6

H2O (%RH) 23 30 

41 3

3 42 

5

8

41 49 

6

3 1

NO2 (mg/m3

) 0.019 0.19 

0.7

2

0.016 0.077 

0.8

5

0.016 0.087 

0.9

2

0.

P1 (#/cm3

) 4

4 51 

9

0

3

6 50 

9

8

3

4 49 

9

8

4

P2 (#/cm3

) 2.2 3.3 13 1.9 3.6 15 1.8 3.7 16

2

P3 (#/cm3

) 0.17 0.24 1.9

0.12 0.25 

2.0

0.11 0.24 

2.0

0

P4 (#/cm3

) 0.0

3

5 0.055 

2.1 0.016 0.051 

2.2

0.019 0.050 

2.3

0.

P5 (#/cm3

) 0.0

0

41 0.016 

2.1 0.0

017 0.015 

2.2

0.0

0

2

4 0.016 

2.3

0.0

Rn (pCi/L) 1.3 1.8 

2.2 1.5 2.7 

5.6

2.2 3.4 

6.4 1

VOC1 (mg/m3

) 0.16 0.18 

0.2

0

0.17 0.22 

0.3

4

0.19 0.24 

0.3

8

0

VOC2 (mg/m3

) 0.10 0.15 1.0

0.10 0.18 1.2

0.10 0.19 1.2

0

Note: VOC1 is a continuous VOC source and VOC2 is a burst VOC source 

23
0

5

10

15

20

25

0 5 10 15 20 25

Time (h)

CO Concentration (mg/

m3

)

basestr

util

lrdr

str2

kit

bath1

mbr

hall

bath2

br3

attc

Figure 2. 24 h CO concentrations in Boston in fall.

0

5

10

15

20

25

0 5 10 15 20 25

Time (h)

CO Concentration (mg/m3

)

basestr

util

lrdr

str2

kit

bath1

mbr

hall

bath2

br3

attc

Figure 3. 24 h CO concentrations in Boston in winter.

24
0

5

10

15

20

25

30

35

40

0 5 10 15 20 25

Time (h)

C

O Concentration (mg/m3

)

basestr

util

lrdr

str2

kit

bath1

mbr

hall

bath2

br3

attc

Figure 4. 24 h CO concentration in Boston in spring.

0

5

10

15

20

25

30

35

40

45

0 5 10 15 20 25

Time (h)

CO Concentration (mg/m3

)

basestr

util

lrdr

str2

kit

bath1

mbr

hall

bath2

br3

attc

Figure 5. 24 h CO concentration in Boston in summer.

25
0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 5 10 15 20 25

Time(h)

R

adon C

oncentr

atio

n (p

Ci/L)

basestr

util

lrdr

str2

kit

bath1

mbr

hall

bath2

br3

Figure 6. 24 h Radon concentration in Seattle in fall.

0

0 5 10 15 20

1

25

Time(h)

2

3

4

5

Radon Concentrat

6

7

8

9

ion (pCi/L)

basestr

util

lrdr

str2

kit

bath1

mbr

hall

bath2

br3

Figure 7. 24 h Radon concentration in Seattle in summer.

26
A pollutant primarily brought indoors from outside was P4 (2.5 μm to 5 μm particles). Figures 8 

– 10 show the concentration profile of P4 in each zone of the townhouse in Boston, Miami, and 

Seattle, respectively. To show the distribution of P4 in the townhouse for each city, a season was 

chosen that did not require the use of the air handling system. As shown in Figures 8 – 10, the 

zone with the highest concentration of P4 for all cities was the middle level bathroom. The zones 

with the next highest P4 concentrations, however, varied from city to city. This result illustrates 

the impact of weather on the penetration and distribution of outdoor contaminants. In general, 

particle concentrations were reduced during seasons with the air handling system operating due 

to removal by the furnace filter. 

Water vapor is another contaminant with both indoor and outdoor contribution. Figure 12 shows 

the concentration of H2O in the townhouse zones in Miami in winter. Peaks are associated with 

the shower and cooking events. In general, H2O concentrations in all zones of the house are of 

similar magnitude and follow the same pattern throughout the day. Contaminants with just an 

indoor source decay back to near zero, whereas the outdoor contribution of H2O results in a 

relatively high background level. 

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.1

0.08 ] 0.09

0 5 10 15 20 25

Time(h)

Particle 4 Concentration [#/cm^3

basestr

util

lrdr

str2

kit

bath1

mbr

mbth

hall

bath2

br2

br3

Figure 8. 24 h Particle 4 concentration in Boston in spring 

27
0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0 5 10 15 20 25

Time(h)

Particle 4 Concentration [#/cm^3]

basestr

util

lrdr

str2

kit

bath1

mbr

mbth

hall

bath2

br2

br3

Figure 9. 24 h Particle 4 concentration in Miami in winter

0

0.02

0.04

0.06

0.08

0.1

0.12

0 5 10 15 20 25

Time(h)

Particle 4 Concentration [#/cm^3]

basestr

util

lrdr

str2

kit

bath1

mbr

mbth

hall

bath2

br2

br3

Figure 10. 24 h Particle 4 concentration in Seattle in spring

28
0.1

0.12

m^3)

basestr

util

0.02

0.04

0.06

0.08

Particle 4 Concentration (#/c

lrdr

str2

kit

bath1

mbr

hall

bath2

br3

0

0 5 10 15 20 25

Time(h)

Figure 11. 24 h Particle 4 concentration in Seattle in winter. 

15

20

25

basestr

util

lrdr

str2

29

Figure 12. 24 h H2O concentration in Miami in winter. 

0

5

10

0 5 10 15 20

Time(h)

H20 Concentration [g/kg]

kit

bath1

mbr

mbth

hall

bath2

br2

br3
I

30

n

A total of eight interventions were implemented in the different cities, with the intervention 

sim pact of each intervention is 

pr rates and the concentrations of each of the 12 

contam

Infiltr n Rates 

The i included: intermittent exhaust fans, mechanical 

ve ition peration. A summary of the minimum, 

average, and maximum air change rates for these v ns in each city and season is 

provided in Table 20. The intervention with greatest positive impact on air change rates was the 

addition of m

of nge i ere e of 

0.35 (intervention in 

Miam as more reasin air ch e rate than the method using exhaust fans 

(in

The intermittent exhaust fans and air conditioner 

change rates. The exhaust fan pulled m tdoo ough the building’s envelope and the 

air o cau ore outdoor air to enter. Tightening the 

ho wev ramatically reduced the air change rate and 

resulted in values less than 0.35 h ates and cities. 

Cont

Th inant 

co tions with baseline simulation results for each city and season. Results are presented in 

Tables C1 – C36 of Appendix C by contam pact 

on every contam ost important 

in ccup o

ta

rv ied. 

onoxide (CO) concentrations in the hou primarily associated with the gas stove 

s, th wing zones were chosen for 

p rage he three bedrooms. Summaries of the 

endix C. For all three cities, the source removal intervention of iring th lty stov d 

gest p c ns. s of a n th

ittent exhaust f g 

g ione m

ht reduction in CO concentration, due to its sli impact on air change rates in the house (see 

ter

ulations run in the sam

ese

ven

nte

in

atio

tion Results 

e m

fec

anner as the baseline cases. The im

d in terms of its ef t on air change 

ants. 

nter

atio

Wit

h

ve

n,

ntio

 en

ns

vel

 tha

ope

t a

 tig

ffe

hte

cte

ni

d a

ng,

ir c

 an

han

d a

ge

ir c

 rate

ntil ond

 2. 

er o

inter

rates

ang

had

r ai

sing

er, d

an

se w

e f

 of t

In t

r in

ght 

entio

n all

echanical ventilation, increasing the average air change rate by as mu

low

ch as a factor 

h mechanic t

 ef

al v

fec

en

tive

tila

 at

tio

 inc

n, the air

g 

 cha

the 

 cities w never be he valu

-1 (see Table 21). Mechanical ventilation using a central HAC system

i) w

ter

 co

use

ven

ndi

 wi

tions in Boston and Seattle). 

 a sm

r thr

 m

all but positive impact on the air 

or

ces

e ou

tioner led to pressure differen als

thout any mechanical ventilation, ho

-1 nearly 100 % of the time in all clim

paring weeklong average contam

ami

mp

ntra

ms 

minant, the relevant interventions were ranked based on the total difference between the 

nan

act

t C

 of 

on

eac

cen

h i

tra

nte

tio

rve

ns 

e i nti

nce

ter

on was determined by com

inant and by city. Not all interventions had an im

inant. For each contaminant, three zones were identified as the m

of contaminant source location and o t exp sure time. For each city and 

con

intervention average concentrations and the baseline average concentrations for all seasons. 

Inte

Ca

in the kitchen and the space heater in the living room

and the tim

com

weekly m

App

the

interm

coo

slig

earlier discussion). For all three cities, tightening the building envelope increased the CO 

concentrations. 

en

n m

aris

tions resulting in a negative impact were also identif

rbo ere

 during the winter. Based on these sources 

e occupants spent in their bedroom ollo

on

in

: th

im

e k

um

itc

, av

hen

era

, li

ge

vin

, an

g r

d m

oom

ax

, 

im

and

um

 ave

 concentrations are provided in Tables C1 – C3 in 

 repa

latio

 p

sto

e fau

gy, 

ent

attl

e ha

urin

a 

 big

kin

 ove

ents

ral

. E

l im

ven

act 

per

on 

atin

CO

g th

onc

st ef

 air

ent

fective due to its reduction of

 co

ratio

ndit

erm

 sum

 ve

 in 

nti

Bo

 str

 c

nd 

ate

onc

Se

e 

ion

aus

an was the m

 o

o

e

eak

n a

rat

e c

s d

ev er ed 
31

abl

City

i

a ti

M

e 20. Interventio

Season

M

n air change rates (h-1). 

Baseline 

n Ave Max

Exh

Min

ust Fan

Ave Max

Mech. Ventila

Min Ave

on

ax

Tightening 

Min Ave Max 

Fall 0.32 0.76 1.1 0.32 0.80 1.5 0.53 0.94 1.3 0.13 0.26 0.36 

Winter 0.72 0.98 1.5 0.72 1.0 1.6 0.90 1.2 1.7 0.24 0.33 0.51 

Spring 0.36 0.66 0.96 0.42 0.70 1.2 0.54 0.84 1.1 0.12 0.22 0.31 

Bost

.

on

Summer 0 24 0.47 0.73 0.24 0.51 1.0 0.45 0.66 0.91 0.079 0.15 0.24 

Fall 0.14 0.31 0.52 0.16 0.36 0.83 0.49 0.65 0.86 0.056 0.12 0.18 

Winter 0.16 0.45 0.75 0.16 0.49 1.0 0.50 0.81 1.1 0.051 0.15 0.25 

Spring 0.15 0.42 0.82 0.15 0.46 0.97 0.49 0.76 1.2 0.056 0.15 0.28 

Mia

.13

mi

Summer 0 0.33 0.66 0.15 0.37 0.86 0.47 0.67 1.0 0.051 0.12 0.23 

Fall 0.53 0.75 1.0 0.53 0.79 1.2 0.72 0.94 1.2 0.18 0.25 0.35 

Winter 0.65 0.80 1.1 0.65 0.84 1.3 0.85 0.99 1.2 0.21 0.27 0.37 

Spring 0.17 0.57 0.96 0.21 0.61 1.2 0.42 0.76 1.2 0.057 0.19 0.32 

Seat

.17

tle

Summer 0 0.49 0.71 0.18 0.54 0.95 0.37 0.69 0.88 0.055 0.16 0.23 

Tabl of ho erio h

Fan i

e 21. Percentage

City Season 

urs during one week p

Baseline Exhaust

d of each season with air c

Mechanical 

Ventilation 

Tighten

ange rates < 0.35 h-1. 

ng Air

Conditioning

Fall 2 2 0 98 N/AWinter 0 0 0 69 N/ASpring 0 0 0 100 N/A

Boston

Summer 13 11 0 100 11

Fall 67 61 0 100 N/AWinter 23 20 0 100 N/ASpring 32 29 0 100 N/A

Miami

Summer 61 55 0 100 N/A

Fall 0 0 0 99 N/AWinter 0 0 0 99 N/ASpring 11 7.7 0 100 N/A

Seat

20 15 0 100

tle

Summer 

T

20
Figure 13 shows the impact of air change rate on CO concentrations in the kitchen and 

bedrooms. There appears to be a nonlinear relationship between air change rate and CO

oncentration. CO concentrations dramatically decrease as the air change rate increases until 

approximately 0.4 h-1, at which point the effect levels off. Thus, an air change rate of 1.2 h-1 does 

e 13

lso shows that using an exhaust fan to reduce source peaks is most effective at reducing CO 

y

as air cleaning. Comparing ventilation strategies, it is not 

rprising that the results are similar to those reported for air change rates (see Table 20). As 

C7

on

e

i house benefited more from tightening the building 

nvelope rather than adding more outdoor air. 

g

lear leader in effectiveness for both Boston and

eattle followed by mechanical ventilation, and summer air-conditioning. Envelope tightening 

7 to

H or

t

c

not have a significant impact on CO concentration over an air change rate of 0.4 h-1. Figur

a

exposure. 

Carbon dioxide (CO2) concentrations are reported for zones of high occupancy (living room, 

master bedroom, and children’s bedrooms) in Tables C4 – C6 of Appendix C. Since the onl

source of CO2 considered for this project was occupant respiration, source control was not an 

intervention option, and neither w

su

expected, mechanical ventilation, which provided the highest air change rates, also resulted in 

the lowest concentrations of CO2, followed by the exhaust fan and air conditioner in the summer 

of Boston and Seattle. Tightening the building envelope reduced the supply of outdoor air to the 

house and therefore increased the CO2 concentrations. 

The non-occupant sources of water vapor were located in the kitchen, master bathroom, and 

children’s bathroom. The resulting concentrations in these three zones are presented in Tables

– C9 of Appendix C. No source control options were modeled for this contaminant. The additi

of air conditioning to Boston and Seattle in the summer was the most effective intervention at 

reducing average concentrations, even when compared to the impact of exhaust fans and 

mechanical ventilation for the entire year. While the use of exhaust fans and mechanical 

ventilation was a positive intervention for Boston and Seattle, they were found to have a negativ

impact in Miami. Due to the higher outdoor humidity and removal of indoor humidity by the 

baseline air-conditioning system, the Miam

e

Unlike other contaminants, reducing average relative humidity may not be a good measure of 

effectiveness since problems such as mold may be associated with high local relative humidity, 

and the CONTAM model does not account for such local gradients. Therefore, the interventions 

were also ranked based on the reductions in maximum zone water vapor concentrations. Usin

this ranking method, exhaust fans become the c

S

still results in a negative impact for these climates. In Miami, the order of effectiveness does not 

change, however, exhaust fans have a net positive effectiveness (i.e., exhaust fans lower peak

relative humidity levels). 

Additionally, the effectiveness of the kitchen exhaust fans at reducing peak relative humidity 

could be greatly improved by extending the fan operation time. The original intervention 

simulations included kitchen fan operation only when during cooking. As seen in Tables C

C9, this operation schedule resulted in reductions of peak kitchen relative humidity of 5 % R

less. However, a simulation was performed for the Seattle spring case with the kitchen exhaus

fan operation time extended to include the period of dishwashing time, which is another 

32
significant source of water vapor in the kitchen. Extended exhaust fan operation improved the 

effectiveness, reducing the peak kitchen humidity by 21 % RH. 

0

12

14

6

8

ncentration (mg/m^3)

2

4

CO Co

10

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Air Change Rate (1/h)

Baseline - Kitchen Baseline - Bedrooms

Mechanical Ventilation - Kitchen Mechanical Ventilation - Bedrooms

Exhaust Fan - Kitchen Exhaust Fan - Bedrooms

Tightening - Kitchen Tightening - Bedrooms

Figure 13. Impact of air change rate on CO concentration in kitchen and bedrooms. 

The zones where occupants spent a majority of their time (i.e., kitchen, living room, and 

bedrooms) were chosen for analysis of the NO2 results (see Tables C10 – C12 of Appendix C). 

Source control through repair of the faulty stove and removal of the space heater were the most 

effective interventions for NO2. Due to low NO2 outdoor concentrations, exhaust fans and 

mechanical ventilation also reduced the concentrations of NO2, whereas tightening the building’s 

envelope had a negative impact. 

 

There were two primary sources of Particle 1 (P1: 0.3 μm to 0.5 μm) – outdoor air and indoor 

cooking events. As a result, the interventions tended to either reduce concentrations due to one 

source or the other, with the exception of the mechanical air filter, which applied to both. Results 

are presented in Tables C13 – C15 of Appendix C for the highest occupancy zones. In terms of 

average concentrations, tightening the house’s envelope was the most effective overall. 

However, tightening did result in higher peak concentrations in the kitchen source zone. For 

seasons during which the HAC system was operated, the improved filter was the second most 

effective intervention followed by the exhaust fan. The exhaust fan was effective at reducing 

peak concentrations, however, this removal was offset by its introduction of outdoor particles 

article 1 concentrations in Boston and Seattle summer due to the furnace filter in the system, 

through increased negative pressure. The air conditioner also had a positive impact on the

P

33
which is not active when the system is off (baseline Boston and Seattle summers). The outdo

air introduced by mechanical ventilation did not successfully dilute the indoor concentrations of 

Particle 1, resulting in a negative impact. 

In contrast to Particle 1, the indoor sources of Particle 2 (P2: 0.5 μm to 1.0 μm) are more 

significant than the outdoor contribution changing the relative impact of the different 

interventions (see Tables C16 – C18 of Appendix C). For this contaminant, the exhaust fan was

the most effective intervention followed by the mechanical air filter and mechanical ventilation, 

or

spectively. Given the indoor sources, the reduced air change rate caused by tightening the

,

ied from city to city. 

or example, in Boston, tightening the house’s envelope was the most effective followed by the 

g.

h

air change rates in Boston promoted 

nvelope tightening, whereas lower air change rates in Miami supported local indoor source 

see Tables

tilation also had a negative

pact for this contaminant. 

d

an

ither exhaust fans, mechanical 

entilation, nor tightening reduced indoor concentrations (see Tables C25 – C27 of Appendix C). 

re

house’s envelope results in a negative impact. 

The intervention impacts on Particle 3 (P3: 1.0 μm to 2.5 μm) are shown in Tables C19 – C21 of 

Appendix C. The model sources of P3 are significant both outdoors as well as indoors (e.g.

cooking and changing kitty litter). As a result, the intervention rankings var

F

exhaust fan and filter. In Miami, the exhaust fan and filter were more effective than tightenin

And for Seattle, the order was exhaust fan, tightening and filter. In all three cities, however, the 

mechanical ventilation resulted in a negative impact. Since the indoor and outdoor source 

strengths are the same for all three cities, the varying air change rates are impacting whic

intervention is the most effective at reducing P3. Higher

e

control. 

As discussed earlier, Particle 4 (P4: 2.5 μm to 5.0 μm) is primarily an outdoor pollutant that 

penetrates indoors, with a small fraction of indoor sources. As such, the most effective 

interventions for reducing indoor concentrations of P4 are tightening and air cleaning (

C22 – C24 of Appendix C). However, once again, tightening also has a negative impact by 

increasing peak concentrations. Peak concentrations are particularly high in the living room

where the kitty litter is changed. Exhaust fans and mechanical ven

im

Figure 14 further demonstrates the impact of ventilation on P4 concentrations in the kitchen an

bedrooms. In Figure 14, an air change rate greater than 0.4 h-1 results in doubling the P4 

concentration after tightening for bedroom concentrations. In the living room, where there is

indoor source, increasing the air change rate has little impact on P4 concentrations. 

Particle 5 (P5: 5.0 μm to 10 μm) is the one contaminant where ne

v

The kitty litter source of P5 overwhelmed the average concentration values such that only air 

cleaning had a positive impact. Although not included as an intervention for this project, source 

control would clearly be a more effective intervention. 

34
0

0.02

0.04

0.1

0.12

3)

0.06

0.08

ation (

#/cm

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Air Change Rate (1/h)

P

4 Concentr ^

Baseline - Bedrooms Mechanical Ventilation - Bedrooms Exhaust Fan - Bedrooms

Tightening - Bedrooms Baseline - Living Room Mechanical Ventilation - Living Room

Exhaust Fan - Living Room Tightening - Living Room

Figure 14. Impact of air change rate on P4 concentration in kitchen and bedrooms. 

Concentrations of radon were compared in the utility room, living room, and average of the 

bedrooms (see Tables C28 – C30 of Appendix C). Radon is a complicated contaminant to control 

due to the counteracting effects of negative pressure and air change. For all three cities, 

mechanical ventilation was the most effective intervention. Although it distributes the radon 

from the basement more efficiently to other rooms in the house, it also dilutes the concentration 

with outdoor air. The exhaust fans increase the negative pressure differential, drawing in more 

radon, but also result in more outdoor air ventilation, which ultimately resulted in lower indoor 

concentrations. Tightening had a negative impact on both the average and peak concentrations. 

The continuous VOC (VOC1) is another contaminant of indoor origin where ventilation 

strategies were the only options considered. Again, mechanical ventilation, exhaust fans and air 

conditioning reduced concentrations and tightening the building envelope had a negative impact 

(see Tables C31 – C33 of Appendix C). Other potential intervention options that were not 

evaluated include using lower emitting products and gaseous air cleaning. 

Tables C34 – C36 of Appendix C show the resulting concentrations for VOC2 in the higher 

occupancy zones. The burst source of VOC2 resulted in higher peak concentrations than VOC1, 

but the average concentrations were of similar magnitude. Mechanical ventilation, exhaust fans 

and air conditioning reduced both the peak concentrations and average values, whereas 

tightening increased both the peak and average values. 

35
Occupant Exposure 

A

36

n r way to analyze intervention impacts is in terms of occupant exposure. Exposure to a 

contam xposure time. Thus, the occupants who 

we he ho the ngest had the h es posure values. For this project, the mother and 4-

yea ld child were in the house 22 h per day and thus had the greatest exposure to all 

contam n the child’s due to her proximity to 

ind urces o in ts (e.g., c in lea

the house 14 h per weekday, resulting in a lower total exposure. 

The i ct of the interventions on occupant exposure did not deviate significantly from the 

res r t ome differences between occupants. 

Ta

of the father, mother, and 4-year old child for each contaminant. Impact was divided into 5 

ca greater than 

25 % c a an 1D for legend). H2O was not included 

in the lysis irect osure to H O is n IAQ concern. 

Operating the kitchen exhaust fan during a sour event had the most significant impact for all of 

the e at reducing the exposure of the mother 

an ig po rce events. Several combinations of 

city and season resulted in negative impact on zone concentrations, but only Miami/fall was 

 the 4-year old child. This result 

l t

crease in exposure to CO, CO2, 

2, P2, Rn, VOC1, and VOC2. Tightening did reduce osure to P4 and P5, more so for the 

ntion with negative impacts on 

w tio ase these particle exposures for all 

rventions with no negative impact on occupant exposure included source control (e.g., 

ng using a more efficient air filter. 

in ber of contaminants reduced. 

othe

re in

inant is a function of room

 t

 air con

igh

centra

t ex

tion and e

use lo

r o

oo

in

r so

ants. Th

 of c

e moth

ntam

er’s t

an

otal exposur

ook

e was

g, c

 highe

ning, etc.). In contrast, the father was only in 

r tha

mpa

ult

ble

s fo

s D1

he

– D

 zone

21 

 c

of 

onc

Ap

en

pen

trat

dix

io

 D

ns, 

 sh

alth

ow

ou

 th

gh

e im

 the

pact of each intervention on the average exposure 

re were s

tegorie

 redu

s of p

tion

erc

 to

ent

 gre

 reduc

ter th

tion o

 2

r in

5 %

cr

 in

eas

crea

e o

s

f co

e (se

nt

e 

am

Fig

inant exposure, ranging from

ure

 ana as d exp 2 ot an 

ce 

 oc

d h

cup

ighl

ants.

hts

 Th

 the

is in

 im

terve

rta

nti

nce

on

 of

 wa

 loc

s p

al 

art

ven

icu

til

larl

atio

y e

n d

ffe

ur

ctiv

ing sou

 for

 in

 exp

rve

cre

and

um

negative for the m

high

Tig

NO

fat

exp

res

Inte

rep

Th

ot

or

her

tan

 an

ce o

d n

f c

o c

on

ond

sid

iti

erin

ons

g 

 w

peo

ere

ple

 ne

’s exposure, not just zone concentrations. 

gative

ights he imp

htening the building envelope led to greater than 25 %

her

os

 th

ure 

an the

as 

 mo

mec

th

ha

er o

nica

r 4

l 

-ye

vent

ar 

ila

old ch

n, 

ild

whic

. T

h

he 

 ten

oth

de

er i

d to

nte

 in

idents.

airi

ese 

 th

terv

e f

en

ault

tio

y s

ns,

tov

 ho

e, 

we

rem

ver

ov

, w

al o

ere

f s

 m

pa

ore

ce 

 lim

hea

ite

ter

d i

, et

n t

c.) 

he n
37

ange in Family Exposure for Fac

Int Tight Exh. 

Fan Vent. 

O

Table

erv

2

en

# 

2.

ti

Ch

on

tor

CO

(%

ial Si

) 

C

(%

mulation Design

2 

) 

Mech. Filter NO2

(%) 

P1 

(%) 

P2 

(%) 

P3 

(%) 

P4 

(%) 

P5 

(%) 

Rn 

(%) 

1 no no no no 0 0 0 0 0 0 0 0 0 2 yes no no no 83 38 53 -20 5.5 -18 -25 12 180 3 no yes no no -19 -1.5 -33 -4.5 -16 -8.2 2.3 1.4 -8.7 4 yes yes no no 4.4 30 -27 -32 -34 -36 -21 13 130 5 no no yes no -10 -7.4 -12 4.5 0 6.6 12 3.6 -27 6 yes no yes no 24 5.8 24 -6.4 3.4 -4.2 -2.8 18 34 7 no yes yes no -22 -8.3 -36 1.0 -12 0.2 14 4.5 -32 8 yes yes yes no -12 3.0 -29 -14 -22 -17 0.4 19 15 9 no no no yes 0 0 0 -17 -18 -16 -12 -5.9 0 

10 yes no no yes 83 38 53 -42 -21 -35 -35 5.2 180 11 no yes no yes -19 -1.5 -33 -21 -31 -23 -9.7 -4.8 -8.7 12 yes yes no yes 4.4 30 -27 -50 -50 -49 -31 7.1 130 13 no no yes yes -10 -7.4 -12 -11 -16 -9.1 -0.7 -2.6 -27 14 yes no yes yes 24 5.8 24 -26 -18 -21.4 -15 10 34 15 no yes yes yes -22 -8.3 -36 -14 -26 -14 1.2 -1.6 -32 16 yes yes yes yes -12 3.0 -29 -31 -37 -31 -11 12 15 

Shaded cells indicate largest reduction in family exposure to that contaminant. 
Factorial Simulation Design 

If there is a single specific contaminant of concern, it is rather straightforward to determine the 

most effective intervention. However, typically there are several indoor air contaminants of 

concern that cannot all be addressed with a single intervention. As a result, combinations of 

interventions may be needed to address overall indoor air quality. To examine this issue, four 

interventions (mechanical ventilation, exhaust fans, improved filter, and envelope tightening) 

were identified for use in a factorial simulation design to determine the most effective 

combination of interventions for each contaminant based on occupant exposure. The impact on

family exposure (sum of all 5 occupant’s individual exposures) for each contaminant is sh

Table 22. In general, intervention combinations that include the use of exhaust fans and 

mechanical ventilation result in the greatest reduction in contaminant concentrations. Whereas 

intervention combinations that include tightening tend to increase contaminant concentrati

Factorial results were also compared across all contaminants by calculating the average p

change in concentration. Based on this analysis, intervention combinations were ranked as s

in Table 23. The combination with the largest decrease across all contaminants was operating 

exhaust fans, installing mechanical ventilation, and adding a more efficient air filter, witho

tightening the house. An ANOVA analys

own in

ons.

ercent

hown

ut

is on these results showed tightening the house to have

e most significant impact on contaminant concentrations (p < 0.001) followed by using 

(p < 0.01) and exhaust fans (p < 0.01). Adding a more efficient air filter 

to

e

n

able 23. Rank of Interventions with Positive Overall Impact on Average Percent Change 

th

mechanical ventilation

did not have a significant impact on the results and would be considered a much lower priority 

for these particular sources and building features. Although tightening the house was found

have the most significant impact, it is not a desirable intervention because the impact is in th

direction of increasing contaminant concentrations. Thus the most effective individual 

interventions at reducing all contaminant concentrations are the use of mechanical ventilatio

and exhaust fans.

T

in Family Exposure. 

Rank Intervention 

Combination 

Average Reduction Over 

All Contaminants (%) 

Negative Impact on Exposure to

Contaminants Below:

 exfan, filter, mv 15 P4 

1 

2 exfan, filter 13 

3 filter, mv 9.8 

4 exfan, mv 9.7 P1, P3, P4, P5 

5 exfan 7.7 P1, P3, P4, P5 

6 exfan, filter, 7.4 CO2, P5, Rn, VOC1, VOC2 

mv, tight 

7 filter 5.8 

8 mv 4.3 P1, P3, P4, P5 

9 exfan, mv, tight 2.0 CO2, P4, P5, Rn, VOC1, VOC2 

exfan: exhaust fan 

filter: improved particle filter 

mv: mechanical ventilation

tight: envelope tightening 

38
SUMMARY AND DISCUSSION 

One of HUD’s objectives for its Healthy Homes Initiative was to improve the indoor air quality 

of residential environments, particularly those of lower income housing. Health studies have

own the negative impact of elevated contaminant levels in such homes and indicate a need for 

er when developing an IAQ intervention strategy 

,

ect

tion. A

ations from HUD, ASHRAE 62.2-

004, common residential IAQ control strategies, and ease of implementation. Due to the 

t into practice, each intervention was

ethod 

ol

ol

er NO2 and CO concentrations year round. In fact, it was the single most effective 

tervention at reducing these contaminants in all climates. 

nts.

gency

sh

improvement. There are many factors to consid

for lower income housing including: cost, skill level of those performing interventions, climate

ratio of indoor/outdoor pollutant source contributions, and building characteristics. This proj

focused on a single lower income house with given envelope and HAC system characteristics

and subjected it to a range of climate conditions, outdoor pollutants, and indoor pollutant 

sources. This project also focused its analysis on IAQ impacts rather than energy consump

select list of interventions were chosen based on recommend

2

possibility that only a single intervention could be pu

analyzed individually for its impact on the concentrations of twelve common indoor air 

contaminants and associated occupant exposures. A small subset of interventions based on 

ASHRAE 62.2 requirements, were analyzed in different combinations to demonstrate a m

to develop a total strategy for a given house. Based on these analyses, a summary of each 

intervention strategy is provided below: 

Upgrading gas stove: 

Upgrading the gas stove by either tuning it or replacing it with a newer model is a source contr

intervention. For specific contaminants, source control interventions were always the most 

effective and had no negative impacts on concentration levels of other contaminants. Many 

indoor sources of contaminants, however, cannot be removed or reduced and there is no source 

control over contaminants originating outdoors. In the case of the gas stove, source contr

results in low

in

In general, older combustion appliances have lower efficiencies and tend to emit more polluta

Although a gas stove was used to illustrate this point in this project, the intervention would be

effective for reducing contaminants for any combustion appliance (e.g., furnace, gas dryer, etc.).

Educating occupants of dangers of using gas oven for heat: 

Educating occupants of the potential dangers of using a gas oven for heat is the least expensive

intervention strategy examined for this project. Operating a gas oven to heat a house is a 

dangerous practice that can elevate concentrations of CO and NO2 to unhealthful, even fatal, 

levels (see U.S. Consumer Product Safety Commission, U.S. Environmental Protection A

and the American Lung Association warning at http://www.epa.gov/iaq/pubs/combust.html). 

For this project, using the gas oven to heat the house resulted in the second highest average

tration of CO, assuming a properly operating oven was used. If a faulty oven had 

eded

d the

winter concen

been used, the occupants’ exposure to CO would have been much higher and may have exce

fatal levels. In a field study involving 60 apartments, Tsongas and Hader (1994) found that most 

gas ovens resulted in steady-state CO concentrations of 11 mg/m3

 (10 ppm(v)) or less but a few

resulted in steady-state CO concentrations exceeding 230 mg/m3

 (200 ppm(v)). Unfortunately, 

this practice does persist in lower income housing when the residents do not fully understan

39
risks involved. Stopping this practice is a source control option that would prevent excessive

perated for cold

easons in Boston and Seattle and hot seasons in Miami. In the other seasons, the HAC system

oving any particles. As a result, this intervention was only 

mperate 

perating an air conditioner in hot and humid seasons:

min 

gain,

indoor air

roblems. Air conditioning primarily reduces the concentration of water, but central air 

e

ts had

exposure to CO and NO2 in cold climates at little to no cost to the resident (unless faulty heating 

equipment needs to be replaced, which would entail significant cost). 

Properly venting space heater to the outside: 

Providing proper exhaust venting for space heaters would have a similar impact on indoor air

concentrations of CO and NO2 that were realized for ceasing the use of a gas oven for heat. 

Venting the combustion products from space heaters to the outside would essentially remove 

these pollutants from the living space, thereby significantly reducing occupants’ exposure to CO

and NO2 in cold climates. This intervention strategy would require both education for the 

occupants and the installation of only vented space heaters. 

Installing a more efficient air filter in HAC system: 

Air cleaning is a positive intervention that reduces contaminants originating indoors and 

outdoors with no negative impact on other contaminant concentrations. Most homeowners, 

however, only have access to air cleaners that remove particles, limiting the scope of the 

intervention. While an effective intervention for removing particles, an air filter only works 

when the HAC system is operating. For this project, the HAC system was o

s

was off and the air filter was not rem

available in cold or hot climates. This intervention would also be effective in more te

seasons, however, it is important to determine the balance between costs of operating the HAC 

system and removal of particles. ASHRAE 62.2-2004 recommends using an air filter with a

MERV rating of 6 with supply mechanical ventilation. As discussed below, this recommendation 

helps to mitigate the impact of outdoor contaminants that are introduced by mechanical 

ventilation. 

O

An air conditioner is essentially working like an air filter that removes moisture from the air 

stream. Many lower income homes do not have air conditioning resulting in elevated relative 

humidity levels in the summer. For this project, the air conditioner was added to operate 10

of every hour. Even this relatively short operation time resulted in significant decreases in 

humidity levels in the house. In fact, the benefit of operating an air conditioner for one season in

Boston and Seattle outweighed the benefits of other interventions operated year-round. A

there is a need to consider electricity costs of operation along with the benefits obtained by 

limiting relative humidity to levels low enough to prevent mold, allergens, and other

p

conditioning also has the side benefit of pulling the air through a filter, thereby reducing particl

concentrations. 

Exhaust fan: 

The exhaust fan was the most effective intervention strategy to reduce peak concentrations 

associated with cooking and showering. This reduction in concentration during source even

a significant impact on the occupants’ exposure to CO, NO2, P2, P3 and H2O. It was the single 

most effective intervention for year-round exposure to P2 and P3 in most climates, and the most 

effective ventilation intervention strategy for CO, H2O, NO2, P2 and P3 in most climates. 

40
Exhaust fans had a broader impact on contaminant concentrations than just for NO2, CO, P2, P3 

and H2O. During operation, the exhaust fan increased the house’s negative pressure causin

more outdoor air to enter through leaka

g

ge paths. As a result, the concentrations of contaminants 

om continuous sources in other parts of the house (e.g., VOC1 and radon) were also diluted by 

r,

n installed or if the current fan does not vent to the outside, there will be 

n installation cost associated with this intervention. However, in some cases, it is a matter of 

As

ng an

inants outdoors. 

tion:

dding mechanical ventilation using 

n exhaust fan is the least expensive option, whereas adding outdoor air supply is likely to be

ore expensive. There is also an incremental cost associated with cooling or heating the added 

utdoor air.

Mechanical ventilation was effective at reducing contaminants primarily originating indoors via 

a continuous source (e.g., CO2, Rn, and VOC1). Mechanical ventilation also effectively diluted 

concentrations from contaminants primarily from indoor burst sources (e.g., CO, NO2, and 

VOC2). Contaminants originating primarily outdoors were negatively impacted (e.g., H2O in 

Miami and particles). The negative impacts of mechanical ventilation tended to be greater than 

those of the exhaust fan intervention, since the outdoor air was continuously added with 

mechanical ventilation and only added during source events with the exhaust fan intervention. 

Effective filtration of the incoming air or recirculation air could reduce this impact. 

Mechanical ventilation is most beneficial during mild weather when conditions result in lower 

air change rates due to infiltration. 

Tightening the envelope: 

Tightening the building envelope has long been recommended for improving energy efficiency, 

but the resulting reduction in air change rate has dramatic effects on pollutants originating 

indoors. In fact, it was the single worst intervention in terms of increasing the concentrations of 

CO, CO2, NO2, P2, Rn, VOC1, and VOC2. Although it was most effective at reducing H2O in 

Miami, P1, and P4, tightening should not be implemented without considering the need for 

fr

the increased air change rate. The downside of this intervention strategy was the increase in 

concentrations of contaminants originating outdoors. This negative impact was significant for 

P4, P5, and H2O in Miami. The benefits of using an exhaust fan during source events, howeve

far outweighed the negative impacts. In fact, project results showed that using the exhaust fan 

during more source events (e.g., cleaning in kitchen or bathroom, or dishwasher operation) 

would have reduced concentrations and exposures even more. 

If there is no exhaust fa

a

educating the occupants to turn on the fan during source events. There is also an electricity cost 

associated with operating the exhaust fan on a regular basis that should also be considered.

shown by the project results, there is a potential negative impact from continuously operati

exhaust fan in areas with higher concentrations of contam

Continuous mechanical ventila

Continuous mechanical ventilation is another intervention that affects all indoor air 

contaminants, but not always positively. There are different ways to implement this intervention

based on climate. For this project, mechanical ventilation was achieved using a continuous 

exhaust fan in Boston and Seattle, and supply air in Miami. A

a

m

o

41
supplementary outdoor air. For example, ASHRAE (2001a) has recommended tightening the 

nvelope and adding properly designed mechanical ventilation. For this project, the combination 

tilation.

uring

minants). This 

enefit would have been extended even further if it were operated during more of the source 

nt air

f the

g the possible large impact on a single contaminant that is possible 

rough source control, any intervention effort should include a review of sources, particularly 

r

uilding type, incorporating an economic analysis of the options, and perhaps assigning a 

CKNOWLEDGEMENTS

f

o. I-OPC-21525. The

uthors express their appreciation to Andrew Persily, Dennis Leber, Brian Polidoro, Jon Kruft 

e

of tightening and mechanical ventilation via exhaust fans showed that tightening can still 

overwhelm the additional ven

Of all the intervention strategies analyzed for this project, the operation of an exhaust fan d

source events had the broadest effect (i.e., a beneficial impact on multiple conta

b

events. If only two interventions can be implemented, then the exhaust fan and more efficie

filter should be used, with the assumption that the HAC system is operating at least 15 % o

time. However, considerin

th

large or unusual ones. 

These recommendations are based on results for a specific house and do not account for cost o

contaminant toxicity. Future recommended research includes broadening the scope of the

b

ranking of contaminants based on human health effects. 

A

This work was sponsored by the U.S. Department of Housing and Urban Development, Office o

Healthy Homes and Lead Hazard Control under Interagency Agreement N

a

and Brian Shyu at NIST for their contribution to this work.

42
REFERENCES

SHRAE (2004). Ventilation and Acceptable Indoor Air Quality in Low-Rise Residential 

onditioning Engineers.

8, Ed. H.R. Trechsel. 

sis,”

.

York.

door

CA, pp. 428 – 431.

(3), 185 – 196. 

ystems 

AE Transactions, 101(1), 1100 – 1117. 

r Environment, 3(2): 232 – 240. 

ASHRAE (2001a). Fundamentals Handbook, American Society of Heating, Refrigerating, and 

Air-Conditioning Engineers. 

ASHRAE (2001b). Ventilation and Acceptable Indoor Air Quality, ASHRAE Standard 62, 

American Society of Heating, Refrigerating, and Air-Conditioning Engineers. 

A

Buildings, ASHRAE Standard 62.2, American Society of Heating, Refrigerating, and AirC

ASTM (1994). Moisture Control in Buildings, ASTM Manual 1

Axley, J.W. (1990). “Adsorption modeling for macroscopic contaminant dispersal analy

NIST-GCR-90-573, National Institute of Standards and Technology, Gaithersburg, MD. 

Berglund, B., Brunekreef, B., Knoppel, H., Lindvall, T., Maroni, M., Molhave, L., Skov, P

(1992). “Effects of indoor air pollution on human health,” Indoor Air, 2(1): 2 – 25. 

Box, G.E., Hunter, W.G., Hunter, J.S. (1978). Statistics for Experimenters: An Introduction to 

Design, Data Analysis and Model Building. J. Wiley & Sons, New

Brugge, D., Vallarino, J., Ascolillo, L., Osgood, N-D, Steinbach, S., Spengler, J. (2002). 

“Environmental Factors for Asthmatic Children in Public Housing,” in Proceedings of In

Air 2002, Monterey,

Emmerich, S.J., Howard-Reed, C., Nabinger, S.J. (2002). “Validation of multizone IAQ model

predictions for tracer gas in a townhouse,” Building Serv. Eng. Res. Technol., 25

Emmerich, S.J., Nabinger, S.J. (2001). “Measurement and simulation of the IAQ impact of 

particle air cleaners in a single zone building,” Journal of Heating, Ventilation, Air-Conditioning

and Refrigeration Research, 7, 223 – 244. 

Emmerich, S.J., Persily, A.K. (1996). “Indoor air quality impacts of residential HVAC s

phase II.A report: Baseline and preliminary simulations,” NISTIR 5559, National Institute of

Standards and Technology, Gaithersburg, MD. 

Fang, J.B., Persily, A.K. (1995). “Airflow and radon transport modeling in four large buildings,”

ASHR

Feustel, H.E., Dieris, J. (1992). “Survey of airflow models for multizone structures,” Energy and

Buildings, 18(2), 79 – 100. 

Haghighat, F., Bellis, L.D. (1993). “Control and regulation of indoor air quality in Canada,” 

Indoo

43
Howard-Reed, C., Nabinger, S.J., Emmerich, S.J. (2004). “Predicting the performance

gaseous air cleaners: measurements

of

and model simulations from a residential-scale pilot study,” 

ISTIR 7114, National Institute of Standards and Technology, Gaithersburg, MD. 

and air

oor sources in an occupied townhouse,”

tmospheric Environment, 37, 5295 – 5306. 

4.

ruger, U., Kraenzmer, M. (1996). “Thermal comfort and air quality in three mechanically 

aquatra, J., Maxwell, L.E., Pierce, M. (2002). “Indoor air pollutants, limited resource 

as Research Institute.

D.

r air,”

Rotko, T., Koistenen, K., Hanninen, O., Jantunen, M. (2000). “Sociodemographic descriptors of 

personal exposure to fine particles (PM2.5) in EXPOLIS Helsinki,” Journal of Exposure Analysis 

and Environmental Epidemiology, 10: 385 – 393. 

Rotko, T., Kousa, A., Alm, S., Jantunen, M. (2001). “Exposures to nitrogen dioxide in 

EXPOLIS-Helsinki: microenvironment, behavioral and sociodemographic factors,” Journal of 

Exposure Analysis and Environmental Epidemiology, 11: 216 - 223. 

Samet, J.M. (1993). “Indoor air pollution: A public health perspective,” Indoor Air, 3(4): 219 – 

226. 

Shaw, C.Y., Salares, V., Magee, R.J., Kanabus-Kaminska, M. (1999). “Improvement of indoor 

air quality in four problem homes,” Building and Environment, 34, 57 – 69. 

Sherman, M.H. (1999). “Indoor air quality for residential buildings,” ASHRAE Journal, 41(5): 26 

– 30. 

N

Howard-Reed, C., Wallace, L.A., Emmerich, S.J. (2003). “Effect of ventilation systems

filters on decay rates of particles produced by ind

A

Jones, A.P. (1999). “Indoor air quality and health,” Atmospheric Environment, 33: 4535 – 456

K

ventilated residential buildings,” Indoor Air, 6(3): 181 – 187. 

L

households and childcare facilities,” In Proceedings of Indoor Air 2002, Monterey, CA. 

Leslie, N.P., Ghassan, P.G., Krug, E.K. (1988). “Baseline characterization of combustion 

products at the GRI conventional research house,” GRI-89/0210, G

Marion, B., Urban, K. (1995). “New typical meteorological year data sets,” Proceedings from

1995 American Solar Energy Society Annual Conference, p. 220. 

Persily, A.K. (1998). “A modeling study of ventilation, IAQ and energy impacts of residential

mechanical ventilation,” NISTIR 6162, National Institute of Standards and Technology, 

Gaithersburg, M

Roberts and Dickey (1995). “Exposure of children to pollutants in house dust and indoo

Reviews of Environmental Contamination and Toxicology, Vol. 143, 59 – 79. 

44
Sherman, M.H., Dickerhoff, D.J. (1998 . “Airtightness of U.S. dwellings,” ASHRAE 

ransactions, 104

Krie ., t, N mental 

ntions to re ce asthma triggers in homes of low-income children in Seattle,” in 

nd 002, Monterey, CA. 

g l. (199 Reside r Air Qu Guide door Air

, Ventilatio nergy Cons ation, 5 onal Ja artier rence,

nd W r, 1994. “ ld Monit Elevate roductio rom

O edings ASHRA 4.

A. (1999) of Indoor A Emission Environm rotectio Agency.

1987) “E of Volatil rganic C nds from

er Products,” Atmospheric Environment, 21(2): 385-393. 

Ho d, C., (200 “Continu onitoring of particles in an occupied 

ar nal of th Air and nageme ciation, : 828 – 844. 

, L.A., Em S.J., Howa Reed, C Source ths of ne and

Due ith a s Stove mental & Tec ogy, 38(8), 

“CON M 2.1 tal user e and p

TIR 7049, National Institute of Standards and Technology, Gaithersburg, 

)

T (2), 1359 – 1367. 

Takaro, T.K., ger, J., Song, L Beaude ., Roberts, J. (2002). “Efficacy of environ

interve du

Proceedings of I oor Air 2

Tobin, R.S., Bour eau, M., et a 2). ntial Indoo ality lines. In

Quality n and E erv

th Internati cques C Confe

Montreal. 

Tsongas, G. a .D. Hage Fie oring of d CO P n f

Residential Gas vens”, Proce of E IAQ ’9

U.S. EP Sources ir s. U.S. ental P n

Wallace ( missions e O ompou Building Materials and 

Consum

Wallace, L.A., ward-Ree 2) ous m

home for the ye 2000,” Jour e Waste Ma nt Asso 52

Wallace merich, rd- . (2004). “ Streng Ultrafi

Fine Particles to Cooking w Ga ,” Environ Science hnol

2304 – 2311.

Walton, G., Dols, W.S. (2003). TA Supplemen guid rogram

documentation,” NIS

MD. 

45
Appendix A: Flo nts

eakage lements

e

No. of elements 

in

L Relati tion Mul

w Eleme

Exterior L E

Flow Element Element Nam

zone 

eakage data ve Eleva tiplier 

Utility 

wall-wall junction walltowall 10 1.5 cm2/m 1.22 m 2.74 m

sliding door slidingdoor 1 60 cm2 1 m 

exterior door frame tdoorframe 1.7 c 2/m2 1 m 2.92 m2 ex 1 m

window window 1 1.1 cm2/m 1.24 m 5.94 m

window frame windowframe 1.19 m2 1 1.7 cm2/m2 1.24 m

floor-wall interface floorwall 4 4 cm2/m 0 m 

electrical outlet elecoutlet 1 2.5 cm2 0.43 m 

ceiling-wall joint celngwalljnt 4 1.5 cm2/m 2.74 m 

fuse box fusebox 1 10 cm2 2 m 

furnace furnace 50 2 1 cm 1.22 m 

hot water heater hotwtrheater 1 20 cm2 1.37 m 

dryer vent dryervent 15 cm2 1 1.37 m 

Kitchen 

wall-wall junction walltowall 4 1.5 cm /m 1.22 m 2.74 m 2

Kitchen exhaust kitexhaust 1 40 cm2 2.4 m 

ceiling-wall joint celngwalljnt 2 1.5 cm2/m 2.74 m 

floor-wall interface floorwall 2 4 cm /m2 0 m 

window window 1 1.1 cm 7.05 m 2/m 1.23 m

window frame windowframe 2 2 3 1.7 cm2/m2 1.23 m m

single window singlewindow 2 0.87 cm /m 2.4 m 2 1 m

front door frontdoor 1 21 cm 1 m 2 

exterior door frame extdoorframe 1 1.7 cm2/m2 1 m 1.86 m2

Living/Dining Room 

floor-wall interface floorwall 3 4 cm /m2 0 m 

ceiling-wall joint celngwalljnt 3 1.5 cm2/m 2.74 m 

wall-wall junction walltowall 4 1.5 cm /m2 1.22 m 2.74 m

sliding door slidingdoor 1 60 cm2 1 m 

exterior door frame e 2.96 m2 extdoorfram 1 1.7 cm /m2 2 1 m

window window 2 1.1 cm /m 5.94 m 2 1.24 m

window frame rame 1 2 1.24 m 1.19 m2 windowf 2 .7 cm /m2

electrical outlet elecoutlet 3 2.5 cm 0.43 m 2 

Bathroom 1 

ceiling-wall joint celngwalljnt 2 1.5 cm2/m 2.74 m 

piping penetration pipingpenet 1 6 cm2 1 m 3 

electrical outlet elecoutlet 1 2.5 cm2 1.12 m 

floor-wall interface floorwall 2 4 cm2/m 0 m 

wall-wall junction walltowall 2 1.5 cm2/m 1.22 m 2.74 m

bath exhaust bathexhaust 1 20 cm2 2.4 m 

Stair2 

ceiling-wall joint celngwalljnt 2 1.5 cm2/m 2.74 m 

46
floor-wall interface floorwall 2 4 cm2

/m 0 m 

Bedroom 2 

floor-wall interface floorwall 2 4 cm2/m 0 m 

ceiling-wall joint celngwalljnt 2 1.5 cm2/m 2.74 m 

wall-wall junction walltowall 2 1.5 cm 2.74 2/m 1.22 m m

electrical outlet elecoutlet 1 2.5 cm 2 0.43 m

window window 1 1.1 cm /m 7.2 2 1.25 m m

window frame windowframe 1 1.7 cm 1 2.1 m 2/m2 .25 m 2

Bedroom 3 

wall-wall junction walltowall 2 1.5 cm 1 2.74 2/m .22 m m

ceiling-wall joint celngwalljnt 2 1.5 cm 2 2/m .74 m

floor-wall interface floorwall 2 4 cm /m 2 0 m

window window 1 1.1 cm /m 6.6 2 1.24 m m

wi e ndowframe 1.6 m2 ndow fram wi 1 1 2 2 .7 cm /m 1.24 m

electrical outlet elecoutlet 1 2.5 cm 2 0.3 m

Bathroom 2 

bath exhaust bathexhaust 1 20 cm 2.4 2 m

Master Bath 

bath exhaust bathexhaust 1 20 cm 2.4 2 m

Master Bedroom 

wall-wall junction walltowall 4 1.5 cm m 1.2 2.74 2/ 2 m m

window window 3 1.1 cm m 1.2 7.2 2/ 4 m m

wi e ndowframe 1.7 2 ndow fram wi 3 cm 1.2 2.1 2/m 4 m m2

electrical outlet elecoutlet 3 2.5 cm 0.3 2 3 m

ceiling-wall joint celngwalljnt 2 1.5 cm /m 2.7 2 4 m

floor-wall interface floorwall 2 4 cm 0 2/m m

Hall 

ceiling-wall joint celngwalljnt 1 1.5 cm m 1.2 2.74 2/ 2 m m

Stair2 

ceiling-wall joint celngwalljnt 1 1.5 cm /m 2.74 2 1.22 m m

Attic 

wall-wall junction walltowall 8 1.5 cm 2.74 2/m 1.22 m m

attic vent atticvent2 6 (orif ata) 5 

0.002 m2

ice area d 0 m

piping penetration pipingpenet 3 6 cm 2 3.8 m

attic fan atticfan 1 0.152 m 2 3 m

47
Appendix B: Occupancy Schedule Tables 

W y O che – F

C ) C ) C )

eekda ccupancy S dules (Monday riday)

Time Father Mother hild (13 yr old hild (10 yr old hild (4 yr old

0:00 Master Bedroom Master Bedroom Bedroom #3 Bedroom #3 Bedroom #2 

6:20 Master Bedroom Master Bedroom Bedroom #3 Bedroom #3 Bedroom #2 

6:25 Master Bedroom Master Bedroom Bedroom #3 Bedroom #3 Bedroom #2 

6:30 Kitchen Master Bathroom Bedroom #3 Kitchen Bedroom #2 

6:45 Kitchen Master Bathroom Bedroom #3 Kitchen Bedroom #2 

6:50 Kitchen Bedroom 2 Bedroom #3 Kitchen Bedroom #2 

7:00 Out Bathroom 2 Bedroom #3 Kitchen Bathroom #2 

7:05 Out Bathroom 2 Bedroom #3 Kitchen Bathroom #2 

7:10 Out Bedroom 2 Bathroom #2 Kitchen Bedroom #2 

7:15 Out Bathroom 2 Bathroom #2 Kitchen Bedroom #2 

7:20 Out Kitchen Bedroom #3 Bathroom #2 Kitchen 

7:30 Out Kitchen Kitchen Bedroom #3 Kitchen

7:40 Out Kitchen Kitchen Bedroom #3 Kitchen

7:55 Out Kitchen Kitchen Bedroom #3 Kitchen

8:00 Out Living Room Out Out Living Room

8:05 Out Living Room Out Out Living Room

8:20 Out Living Room Out Out Living Room

8:30 Out Living Room Out Out Living Room

9:00 Out Living Room Out Out Bathroom #1 

9:10 Out Living Room Out Out Living Room

9:30 Out Bathroom 1 Out Out Living Room

9:40 Out Living Room Out Out Living Room

10:00 Out Out Out Out Out

12:00 Out Kitchen Out Out Kitchen

12:50 Out Kitchen Out Out Bathroom #1

13:00 Out Bedroom 2 Out Out Bedroom #2

13:10 Out Living Room Out Out Bedroom #2

14:00 Out Bathroom 1 Out Out Bedroom #2

14:10 Out Living Room Out Out Bedroom #2

15:00 Out Living Room Living Room Living Room Living Room

15:05 Out Master Bathroom Living Room Living Room Living Room

15:10 Out Master Bathroom Living Room Living Room Living Room

15:20 Out Kitchen Living Room Bathroom #1 Living Room

15:25 Out Kitchen Living Room Bathroom #1 Living Room

15:30 Out Kitchen Kitchen Kitchen Kitchen

15:50 Out Living Room Bathroom #2 Living Room Living Room

16:00 Out Living Room Bedroom #3 Living Room Bathroom #1 

16:30 Out Bathroom 1 Bedroom #3 Living Room Bathroom #1 

16:40 Out Living Room Bedroom #3 Living Room Bathroom #1 

17:00 Kitchen Kitchen Bedroom #3 Living Room Bathroom #1 

17:50 Kitchen Kitchen Bedroom #3 Living Room Bathroom #1 

18:00 Kitchen Kitchen Kitchen Kitchen Kitchen

18:50 Kitchen Kitchen Kitchen Bathroom #1 Kitchen

18:55 Kitchen Kitchen Kitchen Bathroom #1 Kitchen

19:00 Living Room Kitchen Living Room Living Room Living Room

19:15 Living Room Kitchen Living Room Living Room Living Room

19:30 Living Room Kitchen Bathroom #2 Living Room Living Room

19:40 Living Room Bathroom 1 Bedroom #3 Living Room Living Room

19:50 Living Room Living Room Bedroom #3 Living Room Bathroom #2 

48
20:00 Bathroom 1 Bedroom 2 Bedroom #3 Living Room Bedroom #2 

20:10 Living Room Bedroom 2 Bedroom #3 Living Room Bedroom #2 

20:30 Living Room Living Room Bedroom #3 Living Room Bedroom #2 

21:00 Kitchen Master Bathroom Living Room Living Room Bedroom #2 

21:10 Living Room Living Room Bedroom #2 Living Room Bedroom #2 

21:20 Living Room Living Room Bedroom #2 Bathroom #2 Bedroom #2 

21:30 Living Room Bedroom 3 Bedroom #2 Bedroom #3 Bedroom #2 

21:40 Living Room Living Room Bedroom #2 Bedroom #3 Bedroom #2 

22:40 Living Room Master Bathroom Bedroom #3 Bedroom #3 Bedroom #2 

22:50 Master Bathroom Master Bedroom Bedroom #3 Bedroom #3 Bedroom #2 

Week-end Occupancy Schedules (Saturday and Sunday) 

Time Father Mother Child (13 yr old) Child (10 yr old) Child (4 yr old)

0:00 Master Bedroom Master Bedroom Bedroom #3 Bedroom #3 Bedroom #4 

9:10 Master Bedroom Master Bathroom Bedroom #3 Bathroom #2 Bedroom #4

9:20 Master Bedroom Master Bathroom Bedroom #3 Bathroom #3 Bedroom #4

9:30 Kitchen Bedroom 2 Bedroom #3 Kitchen Bedroom #4

9:40 Kitchen Bedroom 2 Bedroom #3 Kitchen Bathroom #2 

10:00 Out Kitchen Bathroom #2 Out Kitchen 

10:10 Out Kitchen Bedroom #3 Out Kitchen

10:15 Master Bedroom Kitchen Bedroom #3 Out Kitchen

10:20 Master Bedroom Kitchen Kitchen Out Kitchen

10:40 Master Bedroom Living Room Kitchen Out Living Room

10:50 Master Bedroom Living Room Bedroom #3 Out Living Room

11:00 Master Bedroom Bathroom #1 Bedroom #3 Out Living Room

11:10 Master Bedroom Living Room Bedroom #3 Out Living Room

11:30 Master Bedroom Master Bedroom Bedroom #3 Out Living Room

12:00 Kitchen Kitchen Kitchen Kitchen Kitchen 

12:40 Kitchen Kitchen Living Room Kitchen Bathroom #1 

12:50 Living Room Kitchen Living Room Bathroom #1 Living Room

13:00 Living Room Bathroom #2 Out Out Bathroom #2 

13:10 Living Room Out Out Out Bathroom #2 

14:00 Bath 1 Out Out Out Bathroom #2 

14:10 Living Room Out Out Out Bathroom #2 

15:00 Out Living Room Living Room Bathroom #1 Out

15:10 Out Living Room Living Room Living Room Out

15:20 Out Living Room Bathroom #1 Bathroom Out

15:30 Out Living Room Living Room Living Room Out

15:40 Out Hall Living Room Living Room Out

15:50 Out Bathroom #2 Living Room Living Room Out

16:00 Living Room Bathroom #3 Living Room Living Room Bathroom #1 

16:10 Living Room Living Room Living Room Living Room Living Room

16:40 Living Room Bathroom #2 Living Room Living Room Living Room

17:00 Kitchen Kitchen Bedroom #3 Living Room Living Room

17:50 Kitchen Kitchen Kitchen Bathroom #1 Living Room

18:00 Kitchen Kitchen Kitchen Kitchen Kitchen 

18:30 Kitchen Kitchen Kitchen Living Room Kitchen 

18:50 Kitchen Kitchen Kitchen Bathroom #1 Kitchen 

19:00 Living Room Kitchen Living Room Living Room Living Room

19:20 Living Room Kitchen Living Room Living Room Bathroom #1 

19:30 Living Room Kitchen Bathroom #1 Living Room Living Room

19:40 Living Room Bathroom #1 Living Room Living Room Living Room

19:50 Living Room Living Room Living Room Living Room Living Room

49
20:00 Bath 1 Living Room Living Room Living Room Living Room

20:10 Living Room Living Room Living Room Living Room Living Room

20:30 Living Room Living Room Living Room Bathroom #1 Living Room

20:40 Living Room Living Room Living Room Living Room Living Room

20:50 Living Room Living Room Living Room Living Room Bathroom #2 

21:00 Kitchen Bedroom #2 Living Room Living Room Bedroom #2

21:10 Living Room Bedroom #2 Living Room Living Room Bedroom #2 

21:20 Living Room Bedroom #2 Bathroom #1 Living Room Bedroom #2

22:40 Living Room Living Room Living Room Bathroom #2 Bedroom #2

22:50 Bath 1 Master Bathroom Bathroom #2 Bedroom #3 Bedroom #2

23:00 Living Room Master Bedroom Bedroom #3 Bedroom #3 Bedroom #2

23:40 Living Room Master Bath Bedroom #3 Bedroom #3 Bedroom #2

23:50 Master Bathroom Master Bedroom Bedroom #3 Bedroom #3 Bedroom #2 

 

50
Appendix C: Intervention Results – Contaminant Concentrations

Rank Intervention Season (mg/m ) 

M

n g/m

Min Ave Max 

) 

Table C1. Boston Carbon Monoxide 

Kitchen Conc. 

3

Min Ave ax

Living Room 

Co c. (m 3

) 

Bedrooms Conc. 

3 (mg/m

Min Ave Max

Fall 1.1 3.1 40 1.1 2.3 13 1.1 2.6 12 

Winter 1.1 3.0 29 1.1 3.9 12 1.1 3.0 11 

Spring 1.1 3.7 44 1.1 1.9 7.9 1.1 2.9 12 

0 Baseline 

Summer 1.1 4.5 45 1.1 2.8 17 1.1 2.7 12 

Fall 1.1 1.8 3.9 1.1 1.7 3.5 1.1 1.8 3.2

Winter 1.1 2.0 3.9 1.1 3.4 11 1.1 2.4 5.1

Spring 1.1 1.8 3.9 1.1 1.7 3.3 1.1 1.8 3.1

1 Repair 

Faulty Stove 

Summer 1.1 1.8 4.3 1.1 1.8 3.3 1.1 1.8 3.0

Fall 1.1 2.6 19 1.1 1.9 7.3 1.1 2.0 5.8

Winter 1.1 2.6 19 1.1 3.6 11 1.1 2.6 5.8

Spring 1.1 2.8 19 1.1 1.8 4.4 1.1 2.0 5.4

2 Exhaust Fan 

Summer 1.1 3.1 19 1.1 2.0 6.2 1.1 2.1 5.1

Fall 1.1 2.8 32 1.1 2.1 12 1.1 2.3 12 

Winter 1.1 2.8 25 1.1 3.6 11 1.1 2.7 12 

Spring 1.1 3.2 34 1.1 1.9 6.6 1.1 2.5 14 

3 Mechanical 

Summer 1.1 

Ventilation 

3.9 43 1.1 2.6 15 1.1 2.4 14 

4 Removal of 

Space Heater 

Winter 1.1 2.8 29 1.1 2.2 12 1.1 2.3 11 

5 Gas Oven

for Heat 

Winter 1.1 3.9 29 1.1 4.3 15 1.1 3.5 11 

6 Air 

Conditioner 

Summer 1.1 4.0 44 1.1 2.8 15 1.1 2.9 11 

Fall 1.2 5.4 59 1.2 3.7 14 1.3 4.2 14 

Winter 1.5 5.9 50 1.5 7.8 26 1.5 5.8 13 

Spring 1.2 7.5 63 1.1 2.5 9.0 1.3 5.2 14 

Neg. 

Impact 

Tightening 

Summer 1.2 10 64 1.2 4.9 20 1.7 4.8 14 

51
Table C2. Miami Carbon Monoxide 

Rank Intervention Season

Kitchen Conc. 

(mg/m3

) 

Min Ave Max

Living Room 

Conc. (mg/m3

) 

Min Ave Max 

Bedrooms Conc. 

(mg/m3

) 

Min Ave Max 

Fall 1.2 4.7 51 1.2 2.5 8.0 1.2 2.7 9.7

Winter 1.1 5.5 56 1.1 2.2 11 1.1 3.3 13

Spring 1.1 4.1 49 1.1 2.3 13 1.1 2.6 9.0

0 Baseline 

Summer 1.3 4.5 51 1.2 2.4 9.2 1.3 2.9 10

Fall 1.2 1.8 3.8 1.1 1.8 3.0 1.2 1.8 2.8

Winter 1.1 1.8 4.3 1.1 1.7 3.3 1.1 1.8 2.9

Spring 1.1 1.8 4.1 1.1 1.8 3.3 1.1 1.8 3.0

1 Repair 

Faulty Stove 

Summer 1.2 1.8 4.1 1.2 1.8 3.2 1.2 1.8 2.9

Fall 1.2 3.1 20 1.1 2.1 4.5 1.2 2.2 5.0

Winter 1.1 3.3 19 1.1 1.9 3.9 1.1 2.2 5.2

Spring 1.1 2.8 19 1.1 2.0 5.0 1.1 2.1 4.8

2 Exhaust Fan 

Summer 1.2 3.0 19 1.2 2.0 4.7 1.2 2.3 5.1

Fall 1.1 3.9 50 1.1 2.0 5.4 1.1 2.0 5.6

Winter 1.1 3.9 54 1.1 2.1 9.3 1.1 2.4 8.6

Spring 1.1 3.6 49 1.1 2.0 11 1.1 2.0 5.6

3 Mechanical 

Ventilation 

Summer 1.1 3.8 51 1.1 2.0 5.9 1.1 2.1 7.0

4 Gas Oven

for Heat 

Winter 1.1 8.0 56 1.1 2.7 11 1.1 4.6 16

Fall 3.2 9.0 67 2.6 5.3 12 3.1 5.7 14

Winter 1.2 12 71 1.3 3.1 11 1.6 6.1 15 

Spring 1.9 7.6 65 1.5 4.3 14 1.9 5.0 13

Neg. 

Im t

Tightening 

Summer 2.9

pac

8.5 66 2.4 5.0 13 2.7 5.8 15 

52
Table C3. Seattle Carbon Monoxide

.

) 

Min Ave Max 

Living Room 

x

Bedrooms Conc. 

Min Ave Max

Rank Intervention Season

Kitchen Conc

(mg/m3 Conc. (mg/m3

) 

Min Ave Ma

(mg/m3

) 

Fall 1.1 3.1 35 1.1 2.3 13 1.1 2.6 11

Winter 1.1 3.1 27 1.1 4.2 12 1.1 3.3 11

Spring 1.1 4.0 50 1.1 2.9 15 1.1 2.3 12

Baseline 

r 1

0

Summe 1. 4.8 56 1.1 3.2 15 1.1 2.8 12

Fall 1.1 1.8 3.9 1.1 1.7 3.3 1.1 1.8 3.2

Winter 1.1 2.0 3.8 1.1 3.6 11 1.1 2.5 5.5

Spring 1.1 1.8 4.3 1.1 1.7 3.4 1.1 1.7 2.9

Repair 

r 1

1

Faulty Stove

Summe 1. 1.8 4.5 1.1 1.7 3.3 1.1 1.7 2.9

Fall 1.1 2.6 20 1.1 1.9 5.7 1.1 2.0 5.7

Winter 1.1 2.7 19 1.1 3.8 11 1.1 2.8 5.8

Spring 1.1 2.9 19 1.1 2.0 7.4 1.1 1.9 4.6

Exhaust Fan 

r 1

2

Summe 1. 3.1 19 1.1 2.1 5.6 1.1 2.1 4.8

Fall 1.1 2.8 28 1.1 2.1 11 1.1 2.3 12

Winter 1.1 2.9 24 1.1 3.8 11 1.1 2.9 12

Spring 1.1 3.6 53 1.1 2.6 14 1.1 2.1 14 

Mechanical 

S er 1

3 

Ventilation 

umm 1. 3.8 46 1.1 2.5 12 1.1 2.3 14

4 Removal of

Space Heater 

Winter 1.1 2.9 27 1.1 2.3 12 1.1 2.5 11

5 Gas Oven

for Heat 

Winter 1.1 4.1 27 1.1 4.6 15 1.1 3.9 11

6 Air Summer

Conditioner 

1.1 4.2 54 1.1 3.1 13 1.1 3.1 12 

Fall 1.4 5.4 55 1.3 3.6 14 1.4 4.2 13 

Winter 1.7 6.6 49 1.7 8.6 26 2.0 6.8 14 

Spring 1.1 8.1 69 1.1 4.7 18 1.3 3.7 14 

Neg. 

Impact 

Tightening 

Summer 1.2 10 73 1.2 5.1 17 1.6 4.8 15

53
Table C4. Boston Carbon Dioxide 

Rank Intervention Season 

L

(

M

C

M

C

B

(

M

iving Room

Conc.

mg/m3) 

Min Ave Max

aster Bedroom

onc. (mg/m3

) 

in Ave Max

hildren

edrooms Conc.

mg/m3

) 

in Ave Max

Fall 630 740 1100 630 850 1600 630 720 960

Winter 630 720 1000 630 800 1200 630 680 850

Spring 630 730 1300 630 930 1600 630 710 940

0 Baseline 

Summer 630 810 1700 630 1000 2300 630 700 950 

Fall 630 720 1000 630 770 1000 630 690 860

Winter 630 700 980 630 750 960 630 670 800

Spring 630 720 1200 630 780 1000 630 680 850

1 Mechanical 

Ventilation 

Summer 630 780 1600 630 810 1100 630 670 850

Fall 630 730 1100 630 840 1600 630 700 880

Winter 630 710 1000 630 790 1200 630 680 810

Spring 630 730 1300 630 910 1600 630 690 850

6

2 Exhaust Fan 

Summer 30 800 1600 630 980 2100 630 680 920

3 Air

Conditioner

Summer 630 810 1500 630 920 1600 630 750 970 

Fall 640 980 1800 650 1200 2200 650 940 1400

Winter 640 890 1600 640 1000 1800 640 840 1100

Spring 630 950 2300 650 1500 3000 650 850 1300

Neg. 

Impact 

Tightening 

Summer 630 1200 3100 700 1800 4400 650 830 1300

Table C5. Miami Carbon Dioxide 

Rank Intervention Season Living Room 

Conc. (mg/m3

) 

Min Ave Max 

Master Bedroom 

Conc. (mg/m3

) 

Min Ave Max 

Children 

Bedrooms Conc. 

(mg/m3

) 

Min Ave Max 

Fall 630 930 1800 640 1100 2200 640 850 1200 

Winter 630 790 1700 630 1000 1900 630 730 1100 

Spring 630 850 1900 630 1000 2100 630 830 1200 

0 Baseline 

Summer 630 900 1600 640 1100 2400 640 870 1200 

Fall 630 820 1700 630 910 2000 630 690 810 

Winter 630 760 1500 630 840 1600 630 700 840 

Spring 630 780 1600 630 890 1800 630 700 830 

1 Mechanical 

Ventilation 

Summer 630 800 1500 630 910 2100 630 710 830 

Fall 630 900 1800 630 1000 2100 640 810 1200 

Winter 630 780 1700 630 1000 1900 630 710 990 

Spring 630 840 1900 630 980 2000 630 800 1100 

2 Exhaust Fan 

Summer 630 880 1600 640 1000 2000 640 840 1100 

Fall 760 1400 2700 810 1600 2800 850 1300 1800 

Winter 630 1100 2700 730 1800 4100 690 930 1500 

Spring 650 1300 2900 670 1500 3000 700 1200 1900 

Neg. 

Impact 

Tightening 

Summer 710 1400 2400 770 1600 3000 820 1300 1700 

54
Table C6. Seattle Carbon Dioxide

Rank Master Bedroom 

M A M

Be

M Av Ma

Intervention Season 

Living Room 

Conc. 

(mg/m3) 

Min Ave Max 

Conc. (mg/m3

) 

in ve ax

Children 

drooms Conc.

(mg/m3

) 

in e x

Fall 630 730 1100 630 840 1300 630 710 890

Winter 630 730 1000 630 820 1200 630 700 870

Spring 630 780 1700 630 930 1900 630 670 930

0 Baseline 

Summer 630 810 1700 630 950 1600 630 690 1000

Fall 630 710 1000 630 760 990 630 680 810 

Winter 630 710 1000 630 760 990 630 680 800

Spring 630 770 1800 630 780 1100 630 650 820

1 Mechanical 

Ventilation 

Summer 630 760 1400 630 800 1100 630 650 850

Fall 630 720 1000 630 830 1400 630 700 840

Winter 630 720 1000 630 810 1300 630 690 820

Spring 630 760 1600 630 900 1700 630 660 880

2 Exhaust Fan 

Summer 630 790 1600 630 930 1700 630 680 990

3 Air

Conditioner 

Summer 630 800 1500 630 890 1500 630 740 1000

Fall 650 950 1600 650 1100 2000 660 930 1200

Winter 640 930 1600 660 1100 1900 660 900 1200

Spring 630 1100 3000 650 1500 3500 640 770 1200

Neg. 

Impact 

Tightening 

Summer 630 1100 2800 660 1600 3100 660 820 1400

55
Table C7. Boston H2O

S

K

(

M

Rank Intervention eason

itchen Conc.

% RH) 

in Ave Max

Master Bathroom 

Conc. (% RH) 

Min Ave Max

Child Bathroom 

Conc. (% RH) 

Min Ave Max 

Fall 13 34 91 14 35 95 14 35 98 

Winter 6 20 67 5 19 83 5 19 92 

Spring 24 42 77 25 43 94 25 42 99 

0 Baseline 

Summer 32 54 90 28 48 95 27 47 97 

1 Air

Conditioner 

S

2

ummer

4 45 71 18 32 64 18 34 69 

Fall 13 33 89 14 34 87 14 33 85 

Winter 6 19 67 5 18 64 5 17 67 

Spring 24 40 75 23 41 79 22 40 81 

2 Exhaust Fan 

Summer 32 53 87 27 47 78 27 45 80 

Fall 13 33 85 14 34 87 13 34 99 

Winter 6 19 65 5 17 65 5 18 93 

Spring 24 41 69 24 40 77 22 41 98 

3 Mechanical 

Ventilation 

Summer 32 53 82 28 47 76 28 47 98 

Fall 14 39 100 17 40 95 15 39 99 

Winter 8 25 81 8 23 87 8 23 97 

Spring 26 48 100 28 50 97 28 47 100 

Neg. 

Impact 

Tightening 

Summer 37 61 100 34 53 96 31 50 99 

Table C8. Miami H2O 

Rank Intervention Season 

Kitchen Conc. 

(% RH) 

Min Ave Max 

Master Bathroom 

Conc. (% RH) 

Min Ave Max 

Child Bathroom 

Conc. (% RH) 

Min Ave Max 

Fall 50 66 100 45 65 91 48 69 93 

Winter 29 57 100 26 50 95 25 49 99 

Spring 40 59 95 34 54 86 35 57 88 

0 Baseline 

Summer 51 66 93 39 55 73 41 57 77 

Fall 34 48 88 32 47 84 33 50 88 

Winter 31 64 100 30 54 97 27 51 100 

Spring 30 46 86 24 41 84 25 43 88 

1 Tightening 

Summer 34 48 80 27 39 67 28 42 70 

Fall 51 67 100 45 67 94 48 70 97 

Winter 28 55 100 24 48 84 24 47 84 

Spring 40 59 97 34 54 85 35 57 85 

Neg. 

Impact 

Exhaust Fan 

Summer 52 67 94 39 56 75 41 58 77 

Fall 64 83 100 64 82 97 64 83 98 

Winter 28 56 99 24 48 92 24 48 99 

Spring 49 68 100 46 65 88 46 66 88 

Neg. 

Impact 

Mechanical 

Ventilation 

Summer 73 81 100 61 70 79 61 70 81 

56
Table C9. Seattle H2O 

Rank Intervention Season

M

Conc )

M

Ch

Conc.

i

Kitchen Conc.

(% RH)

Min Ave Max 

aster Bathroom

. (% RH

in Ave Max M

ild Bathroom

(% RH)

n Ave Max

Fall 15 39 72 16 39 91 15 39 97

Winter 22 32 59 21 29 87 20 29 94

Spring 32 45 91 34 45 97 32 44 99

0 Baseline 

Summer 41 52 93 38 46 92 36 45 96

1 Air

Conditioner 

Summer

29 43 69 21 31 64 21 33 68

Fall 15 38 72 16 38 80 15 38 82

Winter 22 31 56 21 28 71 20 28 71

Spring 32 44 87 32 43 81 31 42 83

2 Exhaust Fan 

Summer 41 51 88 37 44 74 36 43 76

Fall 15 38 70 16 38 78 15 39 98

Winter 22 31 56 20 28 71 20 29 95

Spring 32 44 87 32 43 80 31 43 99

3 Mechanical 

Summer 41

Ventilation 

51 82 37 45 74 34 45 97 

Fall 18 44 89 21 44 94 19 44 99

Winter 26 37 75 24 34 90 23 34 98 

Spring 35 51 100 36 51 98 33 48 99

Neg. 

Im

Tightening 

r

pact 

Summe 42 58 100 38 50 95 36 48 98

57
Table C10. Boston Nitrogen Dioxide 

Rank Intervention Season 

Kitchen Conc.

(mg/m3

)

Min Ave Max

Living Room Conc.

(mg/m3

) 

B

(m

Min Ave Max M

edrooms Conc.

g/m3

) 

in Ave Max

Fall 0.014 0.16 4.0 0.012 0.06 0.98 0.0066 0.071 0.76

Winter 0.021 0.17 3.0 0.022 0.39 1.7 0.015 0.15 0.76

Spring 0.015 0.22 4.3 0.017 0.04 0.45 0.0073 0.082 0.79

0 Baseline 

Summer 0.015 0.26 4.4 0.013 0.07 1.1 0.0072 0.064 0.81

Fall 0.014 0.05 0.63 0.012 0.03 0.19 0.0065 0.028 0.14

Winter 0.020 0.07 0.49 0.021 0.36 1.7 0.014 0.110 0.54

Spring 0.015 0.05 0.67 0.016 0.03 0.11 0.0072 0.027 0.15

1 

Repair 

Faulty Stove 

Summer 0.014 0.06 0.70 0.013 0.03 0.19 0.0072 0.025 0.14

Fall 0.014 0.12 2.0 0.012 0.04 0.47 0.0065 0.039 0.30

Winter 0.020 0.14 1.9 0.022 0.36 1.7 0.015 0.123 0.54

Spring 0.015 0.14 2.0 0.017 0.03 0.16 0.0073 0.033 0.25

2 Exhaust Fan 

Summer 0.014 0.15 2.0 0.013 0.03 0.27 0.0072 0.034 0.24

3 R

Space Heater 

emoval of Winter 0.021 0.14 3.0 0.021 0.07 0.93 0.014 0.068 0.76

4 Gas Oven W

for Heat 

inter 0.021 0.26 3.0 0.022 0.43 2.0 0.015 0.19 0.90

Fall 0.018 0.14 3.2 0.015 0.06 0.90 0.0098 0.067 0.87

Winter 0.023 0.15 2.5 0.023 0.36 1.6 0.016 0.133 0.84

Spring 0.018 0.18 3.4 0.019 0.04 0.35 0.0086 0.077 0.93

5 Mechanical 

Ventilation 

Summer 0.018 0.23 4.2 0.015 0.07 0.98 0.0078 0.067 0.89

6 Air

Conditioner 

Summer 0.015 0.21 4.3 0.013 0.07 1.1 0.0088 0.070 0.77

Fall 0.007 0.25 5.4 0.006 0.08 0.93 0.0032 0.080 0.75

Winter 0.015 0.31 4.8 0.013 0.68 3.0 0.0073 0.22 0.91

Spring 0.008 0.39 5.7 0.008 0.03 0.38 0.0018 0.086 0.57

Neg. 

Impact 

Tightening 

Summer 0.007 0.45 5.8 0.006 0.06 0.80 0.0015 0.069 0.54

58
59

Table C11. Miami Nitrogen Dioxide 

Rank Intervention Season 

Kitchen Conc. 

(mg/m3

) 

Min Ave Max

Living Room Conc.

(mg/m3

) 

Min Ave Max

Bedrooms Conc. 

(mg/m3

)

Min Ave Max 

Fall 0.006 0.23 4.8 0.007 0.04 0.41 0.0041 0.048 0.54 

Winter 0.010 0.31 5.3 0.014 0.04 0.52 0.0050 0.080 0.81 

Spring 0.009 0.20 4.7 0.008 0.04 0.82 0.0044 0.048 0.53 

0 Baseline 

Summer 0.008 0.22 4.9 0.008 0.04 0.49 0.0060 0.060 0.66 

Fall 0.006 0.04 0.75 0.007 0.02 0.08 0.0041 0.019 0.10 

Winter 0.010 0.06 0.83 0.012 0.03 0.09 0.0048 0.024 0.14 

Spring 0.007 0.05 0.74 0.007 0.03 0.14 0.0043 0.020 0.10 

1 Repair 

Faulty Stove 

Summer 0.008 0.04 0.77 0.008 0.02 0.10 0.0044 0.021 0.12 

Fall 0.006 0.12 2.0 0.007 0.03 0.19 0.0041 0.031 0.23 

Winter 0.010 0.16 1.9 0.014 0.03 0.10 0.0049 0.035 0.24 

Spring 0.008 0.12 2.0 0.007 0.03 0.22 0.0043 0.032 0.23 

2 Exhaust Fan 

Summer 0.008 0.12 1.9 0.008 0.03 0.21 0.0051 0.036 0.26 

3 Gas Oven

for Heat Winter 0.010 0.53 5.3 0.014 0.07 0.52 0.0057 0.14 0.82 

Fall 0.011 0.22 4.8 0.011 0.03 0.26 0.0082 0.036 0.26 

Winter 0.014 0.22 5.2 0.016 0.05 0.54 0.0101 0.060 0.54 

Spring 0.013 0.20 4.7 0.012 0.04 0.71 0.0082 0.036 0.26 

4 Mechanical 

Ventilation 

Summer 0.012 0.21 4.9 0.012 0.03 0.30 0.0089 0.046 0.43 

Fall 0.003 0.31 5.9 0.004 0.05 0.54 0.0025 0.068 0.65 

Winter 0.005 0.49 6.3 0.007 0.03 0.23 0.0014 0.075 0.48 

Spring 0.005 0.30 5.8 0.004 0.05 0.75 0.0025 0.067 0.64 

Neg. 

Im ct

Tightening 

Summer 0.004 

pa

0.31 5.9 0.004 0.05 0.56 0.0033 0.075 0.67 
Table C12. Seattle Nitrogen Dioxide

S ) 

 Min Ave Max 

Con

) 

M

Conc.

mg/m3

) 

Max

Rank Intervention eason

Kitchen Conc.

(mg/m3

Living Room

(mg/m

c. Bedrooms

( 3

Min Ave ax Min Ave

Fall 0.018 0.16 3.5 0.019 0.07 0.93 0.010 0.074 0.75 

Winter 0.022 0.18 2.8 0.022 0.42 1.7 0.012 0.17 0.74 

Spring 0.011 0.23 4.8 0.013 0.09 1.2 0.0072 0.047 0.61 

0 Baseline 

Summer 0.016 0.28 5.3 0.014 0.09 1.0 0.0081 0.064 0.55 

Fall 0.017 0.05 0.56 0.019 0.03 0.17 0.0097 0.028 0.14 

Winter 0.022 0.07 0.46 0.021 0.39 1.7 0.012 0.12 0.58 

Spring 0.009 0.06 0.75 0.013 0.03 0.21 0.0071 0.023 0.12 

1 Repair 

Faulty Stove 

Summer 0.016 0.06 0.83 0.009 0.03 0.18 0.0074 0.024 0.10 

Fall 0.017 0.12 2.0 0.019 0.04 0.29 0.0099 0.040 0.29 

Winter 0.022 0.14 1.9 0.022 0.39 1.7 0.012 0.14 0.59 

Spring 0.010 0.14 2.0 0.013 0.04 0.47 0.0074 0.026 0.18 

2 Exhaust Fan 

Summer 0.016 0.15 2.0 0.012 0.04 0.26 0.0076 0.034 0.19 

3 Rem

Space Heater 

oval of Winter 0.020 0.15 2.8 0.021 0.07 0.88 0.012 0.074 0.74 

Fall 0.021 0.14 2.8 0.021 0.06 0.82 0.012 0.069 0.87 

Winter 0.024 0.16 2.5 0.023 0.39 1.6 0.014 0.15 0.85 

Spring 0.020 0.20 5.0 0.017 0.08 1.1 0.0087 0.046 0.95 

4 

Ventilation 

S 0

Mechanical 

ummer .019 0.22 4.5 0.015 0.07 0.90 0.0094 0.064 0.89 

5 Gas Oven

for Heat 

Winter 0.022 0.28 2.8 0.022 0.46 2.0 0.012 0.21 0.97 

6 Air 

Conditioner 

Summer 0.017 0.22 5.1 0.014 0.09 0.98 0.0095 0.076 0.74 

Fall 0.010 0.26 5.2 0.010 0.08 0.90 0.0045 0.082 0.77 

Winter 0.014 0.32 4.6 0.013 0.71 3.0 0.0065 0.24 0.94 

Spring 0.009 0.40 6.1 0.007 0.09 1.1 0.0016 0.038 0.41 

Neg. 

Impact 

Tightening 

Summer 0.009 0.46 6.3 0.009 0.07 0.81 0.0022 0.061 0.38 

60
Table C13. Boston Particle 1 

Rank Intervention Season 

Kitchen Conc. 

(#/cm3

) 

Min Ave Max

Living Room 

Conc. (#/cm3

) 

Min Ave Max 

Bedrooms Conc. 

(#/cm3

)

Min Ave Max 

Fall 34 57 290 35 53 98 24 47 83 

Winter 46 59 250 47 55 110 38 50 86 

Spring 40 64 380 45 55 78 30 49 94 

0 Baseline 

Summer 41 65 390 37 52 120 30 47 91 

Fall 20 49 310 21 40 84 13 34 69 

Winter 31 52 310 31 44 85 23 38 72 

Spring 27 70 420 29 43 62 13 36 79 

1 Tightening 

Summer 25 76 430 21 40 93 12 35 80 

Fall 34 54 210 35 51 84 24 44 71 

Winter 46 57 210 47 54 80 38 49 74 

Spring 40 57 270 45 53 60 29 43 60 

2 Exhaust Fan 

Summer 38 54 270 36 48 59 29 43 65 

3 Filter Fall 26 51 280 27 47 95 15 35 75 

Winter 35 54 250 37 50 100 26 40 77 

4 Air

Conditioner 

Summer 35 54 290 34 48 94 30 43 82 

Fall 41 58 280 42 55 99 30 50 91 

Winter 48 60 240 49 57 100 40 52 92 

Spring 45 63 360 48 56 75 29 51 110

Neg. 

Impact Ventilation 

Summer 44

Mechanical 

65 390 41 54 110 30 51 110 

61
Table C14. Miami Particle 1 

Rank Intervention Season

Kitchen Conc. 

(#/cm3

) 

Min Ave Max

Living Room 

Conc. (#/cm3

)

Min Ave Max

Bedrooms Conc. 

(#/cm3

)

Min Ave Max 

Fall 23 47 320 25 41 77 18 36 75

Winter 35 67 400 38 51 85 25 44 92

Spring 25 53 320 26 48 78 20 39 81

0 Baseline 

Summer 26 49 320 27 44 69 20 37 78

Fall 13 41 330 15 29 63 11 25 64

Winter 19 79 440 27 38 67 10 32 76

Spring 15 44 330 14 35 67 12 28 69

1 Tightening 

Summer 15 42 330 16 31 63 11 27 66

Fall 13 36 310 16 32 55 9 24 51

2 Filter Spring 15 43 310 17 41 63 11 25 49

Summer 16 38 320 21 36 54 11 24 50

Fall 23 39 200 25 39 61 17 35 65

Winter 35 52 270 35 49 58 24 39 64

Spring 24 46 210 26 46 66 19 37 70

Exhaust Fan 

2

3 

Summer 6 41 200 27 43 63 19 35 67

Fall 35 57 330 36 48 72 30 46 68 

Winter 39 58 310 41 53 86 34 49 77

Spring 37 59 330 36 52 78 31 48 70 

Neg. 

I

Mechanical 

S

mpact Ventilation 

ummer 38 58 340 37 49 67 31 47 74 

62
Table C15. Seattle Particle 1 

Rank Intervention Season ) 

Kitchen Conc.

(#/cm3

Min Ave Max

Living Room

Conc. (#/cm3

) 

Min Ave Max

Bedrooms Conc. 

(#/cm3

) 

Min Ave Max 

Fall 42 57 270 45 54 98 33 47 83

Winter 46 58 270 46 54 96 36 48 85

Spring 33 66 390 38 55 130 26 46 83

0 Baseline

Summer 43 68 430 33 54 110 28 47 83

Fall 28 49 310 29 42 81 19 34 69

Winter 31 50 310 31 42 80 20 35 70

Spring 25 74 430 25 44 110 11 33 72

1 Tightening 

Summer 28 78 450 24 43 90 14 34 76

Fall 42 54 220 45 52 79 32 44 72

Winter 46 56 220 46 52 77 36 46 73

Spring 33 57 280 35 49 74 27 44 64

2 Exhaust Fan 

Summer 39 56 280 30 48 57 27 42 65 

3 Filter Fall 31 51 260 36 48 92 22 35 72

Winter 35 52 260 37 48 86 25 36 72 

4

Conditioner 

Air Summer 33 55 290 28 50 93 27 43 77

Fall 47 58 250 48 55 97 35 50 92

Winter 49 59 250 49 56 96 39 51 92

Spring 45 66 420 42 56 120 29 51 110

Neg. 

Impact Ventilation 

Mechanical

Summer 46 66 410 37 55 110 31 51 110

63
Table C16. Boston Particle 2 

Rank Intervention Season

Kitchen Conc.

x

(#/cm3

) 

Min Ave Max

Living Room

Conc. (#/cm3

) 

Min Ave Ma

Bedrooms Conc. 

(#/cm3

)

Min Ave Max

Fall 1.6 4.5 64 1.7 3.4 14 1.0 3.1 11 

Winter 2.3 4.4 51 2.3 3.5 16 1.8 3.2 12 

Spring 2.0 6.2 86 2.3 3.3 13 1.4 3.7 13 

0 Baseline 

Summer 2.1 6.9 87 1.8 3.7 21 1.4 3.3 13 

Fall 1.6 3.8 43 1.7 2.9 11 1.0 2.5 8 

Winter 2.3 3.9 40 2.3 3.1 11 1.8 2.8 9 

Spring 2.0 4.4 58 2.3 3.0 12 1.3 2.3 5 

1 Exhaust Fan 

Summer 2.1 4.3 58 1.8 2.6 12 1.4 2.4 5 

2 Filter Fall 1.2 4.0 64 1.3 2.9 14 0.6 2.1 10 

Winter 1.6 4.0 51 1.8 3.1 15 1.1 2.4 11 

3 Air

Conditioner 

Summer 1.8 4.9 64 1.6 3.3 15 1.4 3.0 11 

Fall 2.0 4.4 60 2.0 3.4 14 1.3 3.2 13 

Winter 2.5 4.3 48 2.5 3.5 15 1.9 3.3 13 

Spring 2.3 5.6 79 2.5 3.3 12 1.5 3.6 17 

4 

Ventilation 

S r

Mechanical 

umme 2.4 6.3 86 2.1 3.7 18 1.5 3.5 17 

Fall 0.9 5.3 72 0.9 3.1 15 0.5 2.7 10 

Winter 1.5 5.2 67 1.5 3.3 15 1.0 2.8 10 

Spring 1.4 10 98 1.4 2.8 14 0.5 3.6 12 

Neg. 

Impact Tightening 

Summer 1.2 12 99 0.9 3.4 17 0.5 3.1 12 

64
Table C17. Miami Particle 2 

Rank Intervention S

K

(

M

L

C

M M

eason

itchen Conc.

#/cm3

) 

in Ave Max

iving Room

onc. (#/cm3

) 

in Ave Max

Bedrooms Conc. 

(#/cm3

)

in Ave Max

Fall 0.94 5.3 73 1.1 2.6 14 0.68 2.4 10

Winter 1.7 7.7 91 2.0 3.2 13 1.1 3.5 14

Spring 1.1 5.1 71 1.1 2.9 13 0.78 2.5 10

0 Baseline 

Summer 1.1 5.2 74 1.2 2.7 12 0.79 2.6 11

Fall 0.94 3.1 43 1.1 2.2 12 0.69 1.9 7

Winter 1.7 4.2 57 1.8 2.7 13 1.0 2.2 5

Spring 1.0 3.4 43 1.1 2.6 13 0.78 2.1 8

1 Exhaust Fan 

Summer 1.1 3.2 43 1.2 2.4 12 0.77 2.0 8

Fall 0.60 4.4 73 0.80 2.0 13 0.34 1.4 5 

2 Filter Spring 0.62 4.4 71 0.79 2.5 13 0.40 1.4 5 

Summer 0.73 4.3 74 0.96 2.1 12 0.43 1.5 7 

Fall 1.7 5.6 74 1.7 2.8 13 1.4 2.7 7 

Winter 1.9 5.1 68 2.1 3.3 13 1.6 3.1 10

Spring 1.8 5.3 71 1.7 3.1 13 1.4 2.7 7 

3 Mechanical 

S 1

Ventilation 

ummer .8 5.4 75 1.8 2.9 12 1.4 2.8 8 

Fall 0.50 6.1 77 0.62 2.5 15 0.39 2.2 10

Winter 0.85 13 100 1.3 2.6 14 0.40 3.2 12

Spring 0.66 6.0 76 0.59 2.6 15 0.43 2.3 10

Tightening 

S 0.

Neg. 

Impact 

ummer 60 6.0 77 0.67 2.5 14 0.42 2.4 10

65
Table C18. Seattle Particle 2 

Rank

x

Be

(#

M

Intervention Season

Kitchen Conc.

(#/cm3

) 

Min Ave Ma

Living Room 

Conc. (#/cm3

)

Min Ave Max

drooms Conc.

/cm3

) 

in Ave Max

Fall 2.1 4.5 56 2.3 3.5 14 1.5 3.1 11

Winter 2.3 4.4 56 2.4 3.5 14 1.7 3.2 12

Spring 1.6 6.5 90 2.0 4.2 21 1.1 3.0 11

0 Baseline 

Summer 2.2 7.2 98 1.8 4.1 18 1.4 3.4 11

Fall 2.1 3.8 43 2.3 3.0 12 1.4 2.5 9

Winter 2.3 3.9 44 2.3 3.0 12 1.7 2.6 9

Spring 1.6 4.4 58 1.8 2.8 12 1.2 2.3 4

1 Exhaust Fan

Summer 2.2 4.4 59 1.5 2.7 12 1.3 2.4 5

2 Filter Fall 1.5 4.0 56 1.7 3.0 14 0.9 2.2 10

Winter 1.8 4.0 56 1.8 3.0 12 1.1 2.2 10

3 Air

Conditioner 

Summer 1.7 4.9 62 1.3 3.5 14 1.3 3.1 11

Fall 2.4 4.4 53 2.4 3.5 14 1.6 3.2 13

Winter 2.5 4.3 52 2.5 3.5 13 1.8 3.2 13

Spring 2.4 6.1 95 2.2 4.0 20 1.3 3.1 17

4 Mechanical 

Ventilation 

Summer 2.5 6.2 91 1.9 3.7 17 1.5 3.5 17 

Fall 1.3 5.3 69 1.3 3.1 16 0.8 2.7 10

Winter 1.4 5.2 70 1.5 3.2 16 0.8 2.8 10

Spring 1.4 10 100 1.1 4.1 20 0.4 2.6 10

Neg. 

Impact Tightening 

Summer 1.3 12 110 1.6 3.8 19 0.6 3.2 9 

66
Table C19. Boston Particle 3 

Rank 

Intervention Season ) ) 

Kitchen Conc.

(#/cm3

) 

Min Ave Max

Living Room

Conc. (#/cm3

Min Ave Max

Bedrooms Conc.

(#/cm3

Min Ave Max 

Fall 0.12 0.31 3.1 0.12 0.28 7.2 0.05 0.20 0.88 

Winter 0.18 0.32 2.6 0.19 0.30 7.1 0.12 0.23 0.89

Spring 0.14 0.38 4.2 0.17 0.31 7.8 0.07 0.22 1.0 

0 Baseline 

Summer 0.15 0.40 4.2 0.12 0.30 8.2 0.08 0.21 0.83 

Fall 0.06 0.27 3.5 0.05 0.22 8.7 0.02 0.13 0.85 

Winter 0.09 0.28 3.3 0.09 0.24 8.7 0.05 0.15 0.87

Spring 0.06 0.44 4.6 0.08 0.26 8.9 0.02 0.15 0.75 

1 Tightening 

Summer 0.07 0.49 4.7 0.05 0.25 9.2 0.02 0.15 0.54 

Fall 0.12 0.28 2.2 0.12 0.27 7.2 0.05 0.18 0.86 

Winter 0.18 0.30 2.1 0.19 0.29 7.1 0.12 0.21 0.88 

Spring 0.14 0.30 2.9 0.17 0.30 7.7 0.07 0.18 0.92 

2 Exhaust Fan 

Summer 0.15 0.30 2.9 0.11 0.27 8.2 0.08 0.18 0.63 

3 Filter Fall 0.09 0.28 3.1 0.10 0.26 7.2 0.03 0.14 0.56 

Winter 0.13 0.29 2.6 0.15 0.27 7.1 0.07 0.17 0.62 

4 Air

Conditioner 

Summer 0.12 0.30 3.1 0.10 0.27 8.2 0.07 0.18 0.89 

Fall 0.16 0.32 3.0 0.15 0.30 6.9 0.08 0.22 0.99 

Winter 0.20 0.33 2.5 0.20 0.31 6.9 0.13 0.25 1.0 

Spring 0.18 0.37 3.9 0.20 0.32 7.5 0.08 0.25 1.2 

Neg. 

Impact Ventilation 

Summer 0.19 

Mechanical 

0.40 4.2 0.14 0.32 8.0 0.08 0.25 1.2 

67
Table C20. Miami Particle 3 

Rank Intervention Season ) 

Living Room 

Conc. (#/cm3

)

Kitchen Conc. 

(#/cm3

) 

Min Ave Max Min Ave Max 

Bedrooms Conc.

(#/cm3

Min Ave Max

Fall 0.05 0.26 3.5 0.07 0.22 8.6 0.03 0.13 0.67

Winter 0.11 0.42 4.4 0.12 0.29 8.6 0.05 0.20 1.2

Spring 0.06 0.30 3.4 0.07 0.27 8.8 0.04 0.15 0.77

0 Baseline 

Summer 0.07 0.28 3.5 0.08 0.23 8.1 0.03 0.14 0.60

Fall 0.05 0.19 2.2 0.07 0.21 8.5 0.03 0.13 0.65

Winter 0.11 0.29 2.9 0.11 0.28 8.6 0.05 0.16 1.1

Spring 0.06 0.23 2.4 0.07 0.26 8.8 0.04 0.14 0.75

1 Exhaust Fan 

Summer 0.07 0.20 2.2 0.08 0.22 8.1 0.03 0.13 0.55

Fall 0.03 0.22 3.5 0.04 0.19 8.5 0.01 0.08 0.29

2 Filter Spring 0.04 0.25 3.4 0.05 0.24 8.8 0.02 0.09 0.31

Summer 0.04 0.23 3.5 0.06 0.20 8.1 0.02 0.09 0.35

Fall 0.02 0.26 3.7 0.04 0.18 9.3 0.01 0.10 0.77

Winter 0.05 0.51 4.7 0.06 0.24 9.3 0.01 0.13 0.76

Spring 0.02 0.27 3.6 0.03 0.21 9.4 0.02 0.10 0.79

3 Tightening 

Summer 0.03 0.26 3.7 0.04 0.19 9.1 0.01 0.10 0.70

Fall 0.11 0.32 3.6 0.11 0.26 8.6 0.08 0.20 0.49 

Winter 0.14 0.33 3.3 0.14 0.30 8.6 0.10 0.23 0.88

Spring 0.12 0.34 3.5 0.12 0.30 8.9 0.08 0.21 0.54

Neg. 

I

Mechanical 

S

mpact Ventilation 

ummer 0.12 0.33 3.6 0.12 0.26 8.1 0.08 0.21 0.51

68
Table C21. Seattle Particle 3 

69

Rank 

Intervention Season 

Kitchen Conc. 

(#/cm3

) 

Min Ave Max 

Living Room 

Conc. (#/cm3

) 

Min Ave Max 

Bedrooms Conc. 

(#/cm3

) 

Min Ave Max 

Fall 0.16 0.30 2.8 0.18 0.30 7.6 0.09 0.20 0.88 

Winter 0.19 0.31 2.8 0.19 0.30 7.4 0.11 0.21 0.90 

Spring 0.10 0.39 4.3 0.13 0.32 8.1 0.06 0.20 0.58 

0 Baseline 

Summer 0.17 0.41 4.7 0.09 0.31 8.2 0.07 0.21 0.76 

Fall 0.16 0.28 2.2 0.18 0.28 7.5 0.09 0.18 0.84 

Winter 0.19 0.29 2.2 0.19 0.28 7.4 0.11 0.19 0.88 

Spring 0.09 0.31 2.9 0.11 0.27 8.1 0.07 0.18 0.58 

1 Exhaust Fan 

Summer 0.16 0.30 3.0 0.08 0.27 8.2 0.07 0.18 0.74 

Fall 0.08 0.27 3.3 0.09 0.23 8.9 0.03 0.12 0.85 

Winter 0.10 0.28 3.4 0.10 0.24 8.9 0.04 0.13 0.87 

Spring 0.04 0.46 4.7 0.06 0.27 9.3 0.02 0.13 0.39 

2 

Summer 0.09 

Tightening

0.48 4.9 0.04 0.26 9.3 0.02 0.15 0.56 

3 Filter Fall 0.11 0.27 2.8 0.14 0.27 7.6 0.05 0.14 0.54 

Winter 0.14 0.28 2.8 0.15 0.27 7.4 0.07 0.15 0.85 

4 Air Summer 0.11 

Conditioner 

0.30 3.0 0.07 0.27 8.2 0.07 0.18 0.81 

Fall 0.19 0.31 2.6 0.20 0.31 7.4 0.10 0.22 1.0 

Winter 0.21 0.32 2.6 0.21 0.31 7.2 0.13 0.23 1.2 

Spring 0.17 0.39 4.6 0.16 0.32 8.0 0.08 0.24 1.1 

Neg. 

Impact Ventilation 

Mechanical 

Summer 0.20 0.39 4.4 0.12 0.32 7.9 0.09 0.25 1.2 
Table C22. Boston Particle 4 

Rank Intervention Season 

Kitchen Conc.

(#/cm3)

Min Ave Max

Living Room Conc. 

(#/cm3)

Min Ave Max

Bedroom

(#/cm

 Min Ave 

Fall 0.019 0.051 0.55 0.021 0.088 8.9 0.0067 0.03

Winter 0.036 0.059 0.52 0.040 0.089 8.8 0.0196 0.04

Spring 0.023 0.047 0.42 0.027 0.11 9.5 0.0083 0.03

0 Baseline 

Summer 0.025 0.054 1.9 0.016 0.11 10 0.0089 0.03

Fall 0.007 0.028 0.63 0.008 0.087 11 0.0017 0.02

Winter 0.015 0.033 0.62 0.016 0.086 11 0.0053 0.02

Spring 0.007 0.023 0.25 0.010 0.11 11 0.0011 0.01

1 Tightening 

Summer 0.008 0.029 1.0 0.004 0.11 11 0.0012 0.02

2 Filter Fall 0.013 0.046 0.23 0.019 0.084 8.9 0.0046 0.02

Winter 0.026 0.054 0.22 0.033 0.085 8.8 0.0133 0.03

3 Air Conditioner Summer 0.023 0.046 1.4 0.014 0.093 10 0.0093 0.03

Fall 0.022 0.052 0.55 0.021 0.089 8.9 0.0070 0.03

Winter 0.037 0.060 0.52 0.040 0.090 8.8 0.0196 0.04

Spring 0.024 0.049 0.42 0.030 0.11 9.5 0.0084 0.04

Neg. 

Impact 

Exhaust Fan 

Summer 0.027 0.055 1.9 0.016 0.11 10 0.0090 0.03

Fall 0.031 0.057 0.55 0.029 0.092 8.6 0.0087 0.04

Winter 0.044 0.064 0.52 0.045 0.094 8.5 0.0233 0.05

Spring 0.035 0.054 0.26 0.034 0.11 9.2 0.0120 0.04

Neg. 

Impact 

Mechanical 

Ventilation 

Summer 0.035 0.063 2.0 0.023 0.11 9.9 0.0104 0.05

70
Table C23. Miami Particle 4 

Rank Intervention Season 

Kitchen Conc.

(#/cm ) 3

Min Ave Max

Living Room Conc.

(#/cm3) 

Min Ave Max

Bedroom

(#/cm

Min Ave 

Fall 0.007 0.029 2.0 0.010 0.086 11 0.0025 0.02

Winter 0.015 0.049 2.1 0.016 0.11 11 0.0042 0.03

Spring 0.009 0.046 2.3 0.011 0.10 11 0.0030 0.02

0 Baseline 

Summer 0.010 0.037 2.1 0.012 0.084 10 0.0038 0.02

Fall 0.002 0.018 1.2 0.004 0.090 11 0.0010 0.01

Winter 0.005 0.028 1.3 0.005 0.11 11 0.0009 0.01

Spring 0.003 0.025 1.4 0.004 0.097 12 0.0010 0.01

1 Tightening 

Summer 0.003 0.022 1.3 0.005 0.088 11 0.0012 0.01

Fall 0.004 0.023 2.0 0.008 0.082 11 0.0014 0.01

2 Filter Spring 0.006 0.039 2.3 0.009 0.097 11 0.0017 0.01

Summer 0.006 0.031 2.1 0.009 0.081 10 0.0024 0.01

Fall 0.007 0.032 2.0 0.010 0.088 11 0.0026 0.02

Winter 0.018 0.050 2.1 0.016 0.11 11 0.0063 0.03

Spring 0.009 0.048 2.3 0.011 0.10 11 0.0039 0.02

Neg. 

Impact 

Exhaust Fan 

Summer 0.012 0.039 2.1 0.012 0.086 10 0.0038 0.02

Fall 0.017 0.044 2.0 0.018 0.096 11 0.011 0.04

Winter 0.023 0.053 1.8 0.022 0.11 11 0.013 0.04

Spring 0.019 0.058 2.3 0.018 0.11 11 0.011 0.04

Neg. 

Impact 

Mechanical 

Ventilation 

Summer 0.020 0.051 2.1 0.019 0.094 10 0.011 0.04

71
Table C24. Seattle Particle 4 

Rank Intervention Season 

Kitchen Conc.

(#/cm3)

Min Ave Max

Living Room Conc. 

(#/cm3) 

 Min Ave Max 

Bedroom

(#/cm

 Min Ave 

Fall 0.032 0.049 0.57 0.037 0.096 9.3 0.013 0.03

Winter 0.038 0.054 0.55 0.038 0.095 9.2 0.017 0.04

Spring 0.013 0.048 0.07 0.010 0.097 9.9 0.0093 0.03

0 Baseline

Summer 0.028 0.044 0.06 0.010 0.103 10 0.0079 0.03

Fall 0.013 0.026 0.64 0.016 0.093 11 0.0029 0.01

Winter 0.018 0.029 0.64 0.016 0.093 11 0.0047 0.02

Spring 0.003 0.024 0.04 0.003 0.11 11 0.0015 0.02

1 Tightening 

Summer 0.012 0.020 0.03 0.002 0.11 11 0.0015 0.02

2 Filter Fall 0.023 0.044 0.23 0.029 0.091 9.3 0.0086 0.02

Winter 0.030 0.049 0.22 0.031 0.090 9.2 0.012 0.03

3 Air Conditioner Summer 0.021 0.042 0.54 0.009 0.092 10 0.0085 0.03

Fall 0.032 0.050 0.57 0.037 0.097 9.3 0.013 0.03

Winter 0.039 0.055 0.55 0.039 0.096 9.2 0.017 0.04

Spring 0.013 0.050 0.11 0.014 0.099 9.9 0.0093 0.04

Neg.

Impact 

Exhaust Fan

Summer 0.028 0.046 0.13 0.010 0.11 10 0.0089 0.04

Fall 0.039 0.056 0.57 0.043 0.099 9.2 0.016 0.04

Winter 0.045 0.060 0.55 0.044 0.097 8.9 0.022 0.04

Spring 0.032 0.056 0.07 0.020 0.10 9.8 0.014 0.05

Neg.

Impact 

Mechanical

Ventilation 

Summer 0.039 0.052 0.06 0.019 0.11 9.8 0.015 0.05

72
Table C25. Boston Particle 5 

Rank Intervention Season 

Kitchen Conc.

(#/cm3)

Min Ave Max Max

Living Room Conc. 

(#/cm3)

Min Ave

Bedroom

(#/c

 Min Ave 

Fall 0.0020 0.0075 0.43 0.0019 0.041 9.1 0.0003 0.00

Winter 0.0041 0.0088 0.41 0.0046 0.038 9.0 0.0015 0.00

Spring 0.0021 0.0067 0.29 0.0026 0.051 9.6 0.0004 0.00

0 Baseline

Summer 0.0024 0.012 1.4 0.0012 0.055 10 0.0005 0.00

1 Filter Fall 0.0014 0.0065 0.17 0.0018 0.040 9.1 0.0003 0.00

Winter 0.0031 0.0078 0.16 0.0040 0.037 9.0 0.0011 0.00

2 Air Conditioner Summer 0.0023 0.010 1.2 0.0012 0.050 10 0.0005 0.00

Fall 0.0021 0.0077 0.43 0.0019 0.041 9.1 0.0004 0.00

Winter 0.0041 0.0089 0.41 0.0046 0.038 9.0 0.0015 0.00

Spring 0.0022 0.0071 0.29 0.0029 0.051 9.6 0.0004 0.00

Neg.

Impact

Exhaust Fan

Summer 0.0024 0.012 1.4 0.0012 0.055 10 0.0005 0.00

Fall 0.0036 0.0085 0.43 0.0029 0.041 8.8 0.0005 0.00

Winter 0.0052 0.0096 0.40 0.0054 0.039 8.7 0.0021 0.00

Spring 0.0036 0.0073 0.18 0.0034 0.049 9.4 0.0008 0.01

Neg.

Impact

Mechanical

Ventilation

Summer 0.0036 0.014 1.5 0.0019 0.054 10 0.0007 0.00

Fall 0.0006 0.0046 0.50 0.0006 0.052 11 0.0001 0.00

Winter 0.0014 0.0053 0.49 0.0015 0.050 11 0.0003 0.00

Spring 0.0006 0.0030 0.14 0.0009 0.064 11 0.0000 0.00

Neg.

Impact

Tightening

Summer 0.0007 0.0060 0.64 0.0002 0.067 11 0.0000 0.00

73
Table C26. Miami Particle 5 

Rank Intervention Season 

Kitchen Conc.

(#/cm3) 

 Min Ave Max 

Living Room Conc. 

(#/cm3) 

Min Ave Max

Bedroom

(#/c

 Min Av

Fall 0.0005 0.0098 1.6 0.0009 0.050 11 0.0001 0.00

Winter 0.0013 0.012 1.7 0.0013 0.055 11 0.0003 0.00

Spring 0.0007 0.014 1.7 0.0009 0.053 11 0.0001 0.00

0 Baseline 

Summer 0.0008 0.012 1.7 0.0010 0.046 10 0.0002 0.00

Fall 0.0004 0.0084 1.6 0.0009 0.049 11 0.0001 0.00

1 Filter Spring 0.0006 0.013 1.7 0.0008 0.052 11 0.0001 0.00

Summer 0.0006 0.011 1.7 0.0010 0.045 10 0.0002 0.00

Fall 0.0005 0.010 1.6 0.0009 0.051 11 0.0001 0.00

Winter 0.0016 0.012 1.7 0.0014 0.055 11 0.0004 0.00

Spring 0.0007 0.014 1.7 0.0009 0.053 11 0.0002 0.00

Neg. 

Impact 

Exhaust Fan 

Summer 0.0009 0.012 1.7 0.0010 0.046 10 0.0002 0.00

Fall 0.0012 0.011 1.6 0.0014 0.051 11 0.0006 0.00

Winter 0.0020 0.011 1.6 0.0019 0.051 11 0.0008 0.00

Spring 0.0014 0.015 1.8 0.0014 0.053 11 0.0007 0.00

Neg. 

Impact 

Mechanical 

Ventilation 

Summer 0.0014 0.013 1.7 0.0015 0.047 10 0.0007 0.00

Fall 0.0001 0.0065 0.76 0.0003 0.058 11 0.0000 0.00

Winter 0.0004 0.0069 0.89 0.0004 0.066 11 0.0000 0.00

Spring 0.0002 0.0081 0.83 0.0003 0.059 11 0.0000 0.00

Neg. 

Impact 

Tightening 

Summer 0.0002 0.0077 0.82 0.0004 0.056 11 0.0001 0.00

74
75

Rank Intervention Season 

chen nc

(#/c

 Min Ave Max 

oom

(#/cm3) 

 Min Ave Max 

Bedroom

(#/c

 Min A

Kit Co

m3) 

. Living R Conc.

Fall 0.0035 0.0071 0.44 0.0042 0.045 9.5 0.0008 0.00

Winter 0.0047 0.0080 0.43 0.0045 0.044 9.4 0.0013 0.00

Spring 0.0009 0.0059 0.009 0.0007 0.050 10 0.0005 0.00

0 Baselin

S 0.0030 

e

ummer 0.0052 0.007 0.0007 0.054 10 0.0006 0.00

1 Filter Fall 0.0027 0.0060 0.17 0.0035 0.044 9.5 0.0007 0.00

Winter 0.0038 0.0068 0.17 0.0038 0.043 9.4 0.0011 0.00

2 Air Conditioner Summer 0.0024 0.0063 0.42 0.0007 0.049 10 0.0006 0.00

Fall 0.0035 0.0074 0.44 0.0042 0.045 9.5 0.0009 0.00

Winter 0.0048 0.0082 0.43 0.0046 0.044 9.4 0.0013 0.00

Spring 0.0009 0.0062 0.025 0.0009 0.050 10 0.0005 0.00

Neg. 

Imp

Summer 0.0030 

act

Exhaust Fan 

0.0055 0.030 0.0007 0.054 10 0.0006 0.00

Fall 0.0048 0.0083 0.45 0.0050 0.045 9.4 0.0012 0.00

Winter 0.0059 0.0089 0.43 0.0053 0.043 9.1 0.0020 0.01

Spring 0.0033 0.0072 0.010 0.0018 0.050 9.9 0.0012 0.00

Neg. 

Impact

Mechanical 

Ventilation

Summer 0.0045 0.0066 0.009 0.0017 0.052 9.9 0.0013 0.00

Fall 0.0012 0.0044 0.51 0.0014 0.055 11 0.0001 0.00

Winter 0.0018 0.0049 0.50 0.0017 0.055 11 0.0003 0.00

Spring 0.0002 0.0025 0.004 0.0002 0.064 11 0.0001 0.00

Ne

Im

0.0001 0.067 11 0.0001 0.00

g.

pact 

Tightening

Summer 0.0011 0.0021 0.003

Table C27. Seattle Particle 5 
Table C28. Boston Radon 

Rank Intervention Season 

Utility Conc. Living Room Conc. 

(pCi/L)

Min Ave Max 

(pCi/L) 

Min Ave Max

Bedrooms Conc. 

(pCi/L) 

Min Ave Max

Fall 2.6 4.4 9.7 0.13 0.92 2.3 0.316 1.3 2.8

Winter 2.5 3.5 6.4 0.028 0.69 1.3 0.036 0.9 1.5

Spring 3.6 5.4 13 0.000 0.60 2.1 0.217 1.7 2.9

0 Baseline 

Summer 5.5 9.2 17 0.000 1.2 3.4 0.014 1.5 3.4 

Fall 2.4 3.8 7.8 0.079 0.68 1.5 0.212 0.9 2.0

Winter 2.3 3.2 6.3 0.020 0.55 1.0 0.040 0.7 1.1

Spring 3.3 4.6 8.6 0.000 0.42 1.4 0.246 1.1 1.9 

1 Mechanical 

Ventilation 

Summer 4.8 7.8 16 0.000 0.86 2.6 0.006 1.0 2.5

2 Air 

Conditioner 

Summer 4.6 7.1 12 0.070 1.3 2.6 0.108 1.7 2.8

Fall 2.4 4.2 9.1 0.077 0.84 2.3 0.220 1.2 2.8

Winter 2.5 3.4 6.4 0.026 0.63 1.3 0.039 0.8 1.5

Spring 3.4 5.2 12 0.000 0.54 2.0 0.181 1.5 2.3

3 Exhaust Fan 

Summer 5.1 9.0 17 0.000 1.1 3.3 0.011 1.4 3.4 

Fall 7.4 11 18 1.3 2.9 4.2 1.890 3.7 5.7

Winter 7.3 9.0 13 0.59 2.2 3.2 0.544 2.7 3.7

Spring 12 16 26 0 1.9 5.7 1.130 5.0 7.5

Neg. 

Impact 

Tightening 

Summer 18 28 46 0.001 3.8 9.9 0.378 4.9 9.5

Table C29. Miami Radon 

Rank Intervention Season 

Utility Room 

M

L

M M

Bedrooms Conc. 

 Min Ave Max 

Conc. (pCi/L) 

Min Ave ax

iving Room

Conc. (pCi/L) 

in Ave ax

(pCi/L) 

Fall 5.3 10 18 0.042 0.60 2.0 0.073 0.8 2.3 

Winter 4.3 11 20 0.000 0.75 2.1 0.027 2.2 4.7 

Spring 4.4 9.6 17 0.005 0.57 3.0 0.035 0.8 3.4 

0 Baseline 

Summer 4.9 9.6 18 0.023 0.55 3.0 0.076 0.8 3.5 

Fall 5.2 9.4 16 0.012 0.20 0.8 0.022 0.2 0.7 

Winter 3.4 7.3 12 0.017 0.58 1.2 0.129 0.9 1.8 

Spring 4.4 9.1 15 0.002 0.23 1.3 0.012 0.2 1.5 

1 

Ventilation 

S er

Mechanical

umm 4.7 9.0 16 0.005 0.19 1.2 0.022 0.3 1.2 

Fall 5.3 10 17 0.034 0.54 1.9 0.048 0.7 2.3 

Winter 3.9 10 19 0.000 0.65 2.0 0.021 1.8 4.3 

Spring 4.5 9.5 16 0.005 0.52 2.6 0.035 0.7 3.4 

2 Exhaust Fan 

Summer 4.9 9.6 17 0.021 0.50 2.7 0.044 0.7 3.3 

Fall 14 18 25 1.5 3.2 5.6 1.840 3.6 6.4 

Winter 15 30 48 0 2.4 5.9 0.740 6.6 11 

Spring 11 17 23 0.37 2.4 6.1 0.921 3.1 6.5 

Neg. 

I

Tightening 

r

mpact

Summe 13 18 25 0.96 2.8 6.4 1.560 3.5 7.1 

76
Table C30. Seattle Radon 

Rank Intervention Season 

Utility Conc. 

(pCi/L) 

Min Ave Max 

L

C

M

B

M

iving Room

onc. (pCi/L)

in Ave Max

edrooms Conc.

(pCi/L)

in Ave Max

Fall 2.5 3.7 5.2 0.14 0.81 1.4 0.37 1.3 1.9 

Winter 2.2 3.5 4.5 0.39 0.83 1.4 0.41 1.2 1.6 

Spring 3.1 6.1 17 0 1.3 3.5 0.003 1.3 4.0 

0 Baseline 

Summer 4.2 7.9 20 0.004 1.3 2.8 0.23 1.7 4.1 

Fall 2.3 3.4 4.6 0.088 0.62 1.1 0.23 1.0 1.4 

Winter 2.1 3.2 4.0 0.28 0.62 1.1 0.27 0.9 1.2 

Spring 2.8 5.5 17 0.001 0.90 2.5 0 0.7 2.8 

1 Mechanical 

Ventilation 

Summer 3.8 6.7 19 0.002 0.78 1.5 0.11 1.0 3.1 

Fall 2.5 3.7 5.2 0.11 0.74 1.4 0.29 1.2 1.8 

Winter 2.2 3.4 4.3 0.086 0.75 1.2 0.35 1.1 1.5 

Spring 3.0 5.9 16 0 1.1 3.0 0.002 1.1 3.0 

2 Exhaust Fan 

Summer 3.9 7.6 18 0.004 1.1 2.4 0.15 1.5 3.7 

3 Air S r

Conditioner

umme 3.6 6.3 14 0.20 1.4 2.8 0.75 1.8 3.3 

Fall 7.3 9.8 12 1.2 2.7 3.7 2.3 3.7 4.5 

Winter 6.9 9.3 11 1.6 2.7 3.8 2.2 3.5 4.3 

Spring 9.9 17.8 37 0.065 3.7 6.7 0.32 4.2 8.5 

Neg. 

pact 

Tightening 

Summer 14

Im

22 42 0.43 3.6 5.7 1.9 5.5 9.1 

Table C31. Boston VOC 1 

Rank Intervention Season 

Kitchen Conc. 

M A M

Bedrooms Conc. 

(mg/m3) 

in ve ax

Living Room

Conc. (mg/m3

) 

Min Ave Max

(mg/m3

) 

Min Ave Max

Fall 0.14 0.17 0.28 0.14 0.17 0.26 0.17 0.22 0.35

Winter 0.12 0.15 0.18 0.12 0.15 0.17 0.13 0.19 0.23

Spring 0.15 0.18 0.25 0.12 0.15 0.22 0.18 0.25 0.34

0 Baseline 

Summer 0.16 0.21 0.29 0.13 0.20 0.29 0.16 0.28 0.40

Fall 0.13 0.16 0.22 0.13 0.15 0.21 0.15 0.20 0.31

Winter 0.12 0.14 0.16 0.12 0.14 0.16 0.13 0.18 0.22

Spring 0.14 0.16 0.20 0.12 0.14 0.20 0.16 0.24 0.36

1 

Ventilation 

Su r 0

Mechanical 

mme .14 0.18 0.25 0.12 0.17 0.24 0.15 0.25 0.38

Fall 0.13 0.17 0.28 0.13 0.16 0.26 0.16 0.22 0.35

Winter 0.12 0.15 0.17 0.12 0.15 0.17 0.13 0.19 0.23

Spring 0.14 0.17 0.24 0.12 0.15 0.21 0.17 0.24 0.34

2 Exhaust Fan 

Summer 0.16 0.20 0.29 0.13 0.19 0.28 0.16 0.27 0.37

3 Air 

Conditioner 

Summer 0.16 0.22 0.29 0.13 0.21 0.30 0.16 0.26 0.35 

Fall 0.23 0.33 0.53 0.23 0.31 0.46 0.30 0.41 0.61

Winter 0.18 0.27 0.33 0.18 0.26 0.32 0.21 0.34 0.42

Spring 0.27 0.34 0.51 0.17 0.26 0.41 0.35 0.53 0.75

Neg. 

Impact 

Tightening 

Summer 0.33 0.42 0.60 0.19 0.40 0.62 0.37 0.64 0.91 

77
Table C32. Miami VOC 1 

Rank Intervention Season 

Kitchen Conc.

) 

.

(mg/m ) 3

)

Min Ave Max

Living Room

Conc. (mg/m3

Min Ave Max

Bedrooms Conc

(mg/m3

Min Ave Max

Fall 0.20 0.30 0.44 0.16 0.26 0.41 0.19 0.31 0.50

Winter 0.15 0.23 0.34 0.13 0.19 0.30 0.20 0.31 0.45

Spring 0.15 0.24 0.37 0.13 0.20 0.37 0.16 0.28 0.44

0 Baseline 

Summer 0.18 0.28 0.41 0.14 0.24 0.38 0.19 0.31 0.46

Fall 0.16 0.20 0.28 0.14 0.19 0.25 0.14 0.21 0.30

Winter 0.14 0.19 0.25 0.13 0.17 0.23 0.14 0.21 0.28

Spring 0.14 0.18 0.25 0.12 0.16 0.25 0.14 0.21 0.29

1 Mechanical 

Ventilation 

Summer 0.15 0.20 0.26 0.13 0.18 0.25 0.14 0.22 0.30

Fall 0.17 0.28 0.41 0.16 0.24 0.37 0.16 0.29 0.45

Winter 0.14 0.22 0.31 0.13 0.18 0.29 0.19 0.31 0.43

Spring 0.15 0.23 0.36 0.13 0.20 0.37 0.16 0.27 0.41

2 Exhaust Fan 

Summer 0.17 0.26 0.38 0.14 0.22 0.35 0.17 0.29 0.44

Fall 0.50 0.63 0.86 0.38 0.55 0.78 0.44 0.62 0.93

Winter 0.28 0.47 0.67 0.19 0.35 0.56 0.44 0.72 0.94 

Spring 0.30 0.50 0.72 0.22 0.42 0.69 0.32 0.54 0.80

Neg. 

Im

Tightening 

pact 

Summer 0.44 0.59 0.78 0.32 0.51 0.70 0.42 0.61 0.85

able C33. Seattle VOC 1

Rank Intervention Season 

Kitchen Conc. 

Min Ave Max 

Living Room 

Min Ave Max 

Bedrooms Conc. 

Min Ave Max 

T

(mg/m3

) Conc. (mg/m3) (mg/m3) 

Fall 0.14 0.17 0.20 0.13 0.16 0.19 0.17 0.21 0.27 

Winter 0.14 0.16 0.18 0.14 0.16 0.18 0.17 0.21 0.25 

Spring 0.14 0.18 0.31 0.13 0.19 0.32 0.18 0.27 0.39 

0 Baseline 

Summer 0.16 0.20 0.30 0.14 0.20 0.36 0.20 0.29 0.43 

Fall 0.14 0.15 0.18 0.13 0.15 0.17 0.15 0.20 0.25 

Winter 0.13 0.15 0.17 0.13 0.15 0.17 0.15 0.19 0.23 

Spring 0.14 0.16 0.23 0.13 0.17 0.27 0.16 0.24 0.38 

1 Mechanical 

Ventilation 

Summer 0.15 0.17 0.24 0.14 0.17 0.28 0.17 0.25 0.34 

Fall 0.14 0.17 0.20 0.13 0.16 0.19 0.16 0.21 0.27 

Winter 0.14 0.16 0.18 0.13 0.16 0.18 0.17 0.21 0.25 

Spring 0.14 0.18 0.27 0.13 0.18 0.29 0.17 0.26 0.38 

2 Exhaust Fan 

Summer 0.15 0.19 0.29 0.14 0.19 0.34 0.19 0.28 0.39 

3 Air

Conditioner 

Summer 0.17 0.21 0.32 0.14 0.21 0.36 0.19 0.26 0.39 

Fall 0.26 0.32 0.40 0.23 0.30 0.35 0.31 0.40 0.49 

Winter 0.24 0.30 0.34 0.25 0.29 0.35 0.31 0.39 0.46 

Spring 0.24 0.34 0.68 0.20 0.36 0.69 0.36 0.60 0.85 

Neg. 

Impact 

Tightening 

Summer 0.29 0.38 0.57 0.24 0.38 0.68 0.43 0.65 0.88 

78
Table C34. Boston VOC 2 

Rank Intervention Season

Kitchen Conc.

(mg/m3

) 

Min Ave Max

Living Room

Conc. (mg/m3

) 

Min Ave Max

Bedrooms Conc. 

(mg/m3

)

Min Ave Max 

Fall 0.10 0.19 2.3 0.10 0.14 1.5 0.10 0.18 4.4

Winter 0.10 0.17 1.8 0.10 0.14 1.5 0.10 0.16 4.4

Spring 0.10 0.22 2.2 0.10 0.13 1.4 0.10 0.20 4.6

0 Baseline 

Summer 0.10 0.29 2.4 0.10 0.19 1.5 0.10 0.22 4.7

Fall 0.10 0.17 2.1 0.10 0.13 1.4 0.10 0.16 4.3

Winter 0.10 0.16 1.7 0.10 0.13 1.4 0.10 0.15 4.4

Spring 0.10 0.20 2.1 0.10 0.12 1.4 0.10 0.17 4.4

1 Mechanical 

Ventilation 

Summer 0.10 0.23 2.2 0.10 0.15 1.5 0.10 0.18 4.3

Fall 0.10 0.19 2.3 0.10 0.14 1.5 0.10 0.18 4.4

Winter 0.10 0.17 1.8 0.10 0.14 1.5 0.10 0.16 4.4 

Spring 0.10 0.22 2.2 0.10 0.13 1.4 0.10 0.20 4.6

2 

S 0

Exhaust Fan 

ummer .10 0.29 2.4 0.10 0.19 1.5 0.10 0.21 4.7

3 Air

Conditioner 

Summer 0.10 0.27 2.6 0.10 0.20 1.6 0.10 0.22 4.8

Fall 0.10 0.36 2.8 0.10 0.26 2.0 0.10 0.33 5.4 

Winter 0.10 0.30 2.7 0.10 0.23 2.0 0.10 0.28 5.2

Spring 0.10 0.47 2.7 0.10 0.18 1.7 0.10 0.39 5.4

Neg. 

Impact 

Tightening 

Summer 0.10 0.63 3.0 0.10 0.35 1.8 0.12 0.44 5.1

Table C35. Miami VOC 2

Intervention Season 

Kitchen Conc. 

(mg/m3

) 

Min Ave Max 

Living Room 

Conc. (mg/m3

) 

Min Ave Max 

Bedrooms Conc. 

(mg/m3

) 

Min Ave Max 

Rank

Fall 0.10 0.33 2.8 0.10 0.19 1.8 0.10 0.21 4.3 

Winter 0.10 0.30 2.5 0.10 0.14 1.3 0.10 0.24 4.6 

Spring 0.10 0.29 2.9 0.10 0.17 1.7 0.10 0.20 4.9 

0 Baseline 

Summer 0.10 0.30 2.6 0.10 0.16 1.6 0.10 0.22 5.0 

Fall 0.10 0.26 2.6 0.10 0.14 1.7 0.10 0.14 4.2 

Winter 0.10 0.23 2.5 0.10 0.13 1.4 0.10 0.16 4.6 

Spring 0.10 0.24 2.6 0.10 0.13 1.6 0.10 0.14 4.7 

1 Mechanical 

Ventilation 

Summer 0.10 0.24 2.6 0.10 0.13 1.5 0.10 0.15 4.8 

Fall 0.10 0.32 2.7 0.10 0.18 1.8 0.10 0.20 4.3 

Winter 0.10 0.31 2.5 0.10 0.14 1.3 0.10 0.24 4.6 

Spring 0.10 0.28 2.7 0.10 0.16 1.7 0.10 0.19 4.8 

2 Exhaust Fan 

Summer 0.10 0.29 2.6 0.10 0.16 1.6 0.10 0.21 4.9 

Fall 0.20 0.67 4.1 0.18 0.44 2.1 0.19 0.47 5.1 

Winter 0.10 0.71 3.1 0.10 0.22 1.6 0.11 0.50 5.2 

Spring 0.13 0.56 4.0 0.11 0.35 2.0 0.12 0.43 5.5 

Neg. 

Impact 

Tightening 

Summer 0.17 0.61 3.8 0.14 0.37 2.0 0.16 0.47 5.7 

79
Table C36. Seattle VOC 2

Intervention Season (mg/m ) 

oom

Conc. (mg/m3

) 

Bedrooms Conc. 

(mg/m3

) 

x

Kitchen Conc. 

3

Living R

Rank

Min Ave Max Min Ave Max Min Ave Ma

Fall 0.10 0.19 2.0 0.10 0.14 1.6 0.10 0.18 4.6 

Winter 0.10 0.18 2.0 0.10 0.14 1.6 0.10 0.17 4.5

Spring 0.10 0.24 2.5 0.10 0.17 2.1 0.10 0.21 4.6 

0 Baseline 

Summer 0.10 0.26 2.4 0.10 0.19 2.0 0.10 0.22 4.6

Fall 0.10 0.17 1.9 0.10 0.13 1.5 0.10 0.16 4.4 

Winter 0.10 0.17 1.8 0.10 0.13 1.5 0.10 0.16 4.4 

Spring 0.10 0.21 2.2 0.10 0.15 1.9 0.10 0.18 4.4 

1 Mechanical 

Ventilation 

Summer 0.10 0.21 2.1 0.10 0.15 1.8 0.10 0.17 4.4 

2 Air 

Conditioner 

Summer 0.10 0.25 2.4 0.10 0.19 1.9 0.10 0.22 4.6 

Fall 0.10 0.19 2.0 0.10 0.14 1.6 0.10 0.18 4.6 

Winter 0.10 0.18 2.0 0.10 0.14 1.6 0.10 0.17 4.5 

Spring 0.10 0.24 2.5 0.10 0.18 2.1 0.10 0.21 4.6 

aust Fan

Summer 0.10 

3 Exh

0.26 2.4 0.10 0.19 2.0 0.10 0.22 4.6 

Fall 0.10 0.36 2.8 0.10 0.25 2.1 0.11 0.33 5.3 

Winter 0.10 0.34 2.8 0.10 0.25 2.0 0.10 0.31 5.3 

Spring 0.10 0.50 2.8 0.10 0.32 2.3 0.10 0.40 4.8 

Neg. 

Impact 

Tightening 

Summer 0.10 0.60 3.0 0.10 0.34 2.3 0.10 0.45 5.1 

80
APPENDIX D: Impact of Interventions on Occupant Exposure 

This Appendix contains tables showing the impact of each intervention on the exposure of the 

model’s father, mother, and 4-year old child to each contaminant. The impact of each 

interven is calculated based on its reduction or in se in contaminant exposure relative to

the baseline exposures. The results have been divided into 5 color classifications which are 

shown in Figure 1D. The color coding provides a relatively quick way to evaluate the impact of 

each intervention on occupant exposure. 

Figure 1D. Legend for Tables D

81

tion

= > 25 % reduction of contaminant exposure 

inant exposure 

= less than 5 % impact on contaminant e sure

= 5 % to 25 % increase of contaminant exposure 

= > 25 % increase of contaminant exposure 

= 5 % to 25 % reduction of contam

xpo

crea
OCCUPANT #1: Father

CO CO2 2 P1 P2 P3 P4 P5 Rn 

Table D1. Exhaust Fa terve on

on

n In nti

City Seas NO VOC

Fall Winter Spring 

Boston 

Summer

Fall Winter Spring 

Miami 

Summer

Fall Winter Spring 

Seattle 

Summer 

Table D2. Filter Inter tion

on

ven

City Seas CO CO2 NO2 P1 P2 P3 P4 P5 Rn VOC

Fall Winter Spring 

Boston 

Summer

Fall Winter Spring 

Miami 

Summer

Fall Winter Spring 

Seattle 

Summer 

82
Table D3. Tightening erven

on

Int tion

City Seas CO CO2 NO2 P1 P2 P3 P4 P5 Rn VOC

Fall Winter Spring 

Boston 

Summer

Fall Winter Spring 

Miami 

Summer 

Fall Winter Spring 

Seattle 

Summer 

Table D4. Mechanical ntilat Inter tion

on

Ve ion ven

City Seas CO CO2 NO2 P1 P2 P3 P4 P5 Rn VOC

Fall Winter Spring 

Boston 

Summer

Fall Winter Spring 

Miami 

Summer

Fall Winter Spring 

Seattle 

Summer 

83
Table D5. Repair Faulty Stove Intervention

City Season CO CO2 NO2 P1 P2 P3 P4 P5 Rn VOC

Fall Winter Spring

Boston 

Summer

Fall Winter Spring

Miami 

Summer

Fall Winter Spring 

Seattle 

Summer 

Table D6. Restricting Use of Gas Oven for Heat in Winter InterventionCity Season CO CO2 NO2 P1 P2 P3 P4 P5 Rn VOC

Fall Winter Spring 

Boston 

Summer 

Fall Winter Spring 

Miami 

Summer 

Fall Winter Spring 

Seattle 

Summer 

84
Table D7. Removal of Winter Space Heater Intervention

City Season CO CO2 NO2 P1 P2 P3 P4 P5 Rn VOC

Fall Winter Spring 

Boston 

Summer 

Fall Winter Spring 

Seattle 

Summer 

85
OCCUPANT #2: Mother 

Table DCity

8 t F ter n

CO CO2 NO2 P1 P2 P3 P4 P5 Rn OC

. Exhaus an In ventio

Season V

Fall Winter Spring

Boston 

Summer

Fall Winter Spring

Miami 

Summer

Fall Winter Spring 

Seattle 

Summer 

Table DCity

9 nt ion

CO CO2 NO2 P1 P2 P3 P4 P5 Rn OC

. Filter I ervent

Season V

Fall Winter Spring

Boston 

Summer

Fall Winter Spring

Miami 

Summer

Fall Winter Spring 

Seattle 

Summer 

86
Table D10. Tightening Intervention 

City Season CO CO2 NO2 P1 P2 P3 P4 P5 Rn VOC

Fall Winter Spring 

Boston 

Summer 

Fall Winter Spring 

Miami 

Summer 

Fall Winter Spring 

Seattle 

Summer 

Table D11. Mechanical Ventilation Intervention City Season CO CO2 NO2 P1 P2 P3 P4 P5 Rn VOC

Fall Winter Spring 

Boston 

Summer 

Fall Winter Spring 

Miami 

Summer 

Fall Winter Spring 

Seattle 

Summer 

87
Table D12. Repair Faulty Stove Intervention 

City Season CO CO2 NO2 P1 P2 P3 P4 P5 Rn VOC

Fall Winter Spring 

Boston 

Summer 

Fall Winter Spring 

Miami 

Summer 

Fall Winter Spring 

Seattle

Summer 

Table D13. Restricting Use of Gas Oven for Heat in Winter Intervention City Season CO CO2 NO2 P1 P2 P3 P4 P5 Rn VOC

Fall Winter Spring 

Boston 

Summer 

Fall Winter Spring 

Miami 

Summer 

Fall Winter Spring 

Seattle 

Summer 

88
Table D14. Removal of Winter Space Heater Intervention

City Season CO CO2 NO2 P1 P2 P3 P4 P5 Rn VOC

Fall Winter 

Boston

Spring 

Summer

Fall Winter Spring 

Seattle 

Summer

89
OCCUPANT #5: 4-year old child 

Table DCity

1 ust nte on

CO CO2 NO2 P1 P2 P3 P4 P5 Rn OC

5. Exha Fan I rventi

Season V

Fall Winter Spring

Boston 

Summer

Fall Winter Spring

Miami 

Summer

Fall Winter Spring 

Seattle 

Summer 

Table DCity

1 Inte tio

on CO CO2 NO2 P1 P2 P3 P4 P5 Rn OC

6. Fi

Seas

lter rven n

V

Fall Winter Spring

Boston 

Summer

Fall Winter Spring

Miami 

Summer

Fall Winter Spring 

Seattle 

Summer 

90
Table D17. Tightening Intervention 

City Season CO CO2 NO2 P1 P2 P3 P4 P5 Rn VOC

Fall Winter Spring 

Boston 

Summer 

Fall Winter Spring 

Miami 

Summer 

Fall Winter Spring 

Seattle 

Summer 

Table D18. Mechanical Ventilation InterventionCity Season CO CO2 NO2 P1 P2 P3 P4 P5 Rn VOC

Fall Winter Spring

Boston 

Summer

Fall Winter Spring

Miami 

Summer

Fall Winter Spring

Seattle 

Summer

91
Table D19. Repair Faulty Stove Intervention

City Season CO CO2 NO2 P1 P2 P3 P4 P5 Rn VOC

Fall Winter

Boston

Spring 

Summer

Fall WinterSpring 

Miami 

Summer

Fall Winter Spring 

Seattle 

Summer 

Table D20. Restricting Use of Gas Oven for Heat in Winter InterventionCity Season CO CO2 NO2 P1 P2 P3 P4 P5 Rn VOC

Fall Winter Spring 

Boston

Summer 

Fall Winter Spring 

Miami

Summer 

Fall Winter Spring 

Seattle

Summer 

92
93

Table D21. Removal of Winter Space Heater Intervention City Season CO COB2B NOB2B P1 P2 P3 P4 P5 Rn VOC

Fall Winter Spring 

Boston 

Summer Fall Winter Spring 

Seattle 

Summer