oEPA
EPA/600/R-18/121F
July 2018
Update for Chapter 19 of the Exposure Factors Handbook
Building Characteristics
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC 20460
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
DISCLAIMER
This document has been reviewed in accordance with U.S. Environmental Protection
Agency policy and approved for publication. Mention of trade names or commercial products
does not constitute endorsement or recommendation for use.
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Chapter 19—Building Characteristics
TABLE OF CONTENTS
LIST OF TABLES 19-iv
LIST OF FIGURES 19-v
19. BUILDING CHARACTERISTICS 19-1
19.1. INTRODUCTION 19-1
19.2. RECOMMENDATIONS 19-2
19.3. RESIDENTIAL BUILDING CHARACTERISTICS STUDIES 19-10
19.3.1. Key Study of Volumes of Residences 19-10
19.3.1.1.U.S. DOE (2017, 2013, 2008a)—Residential Energy Consumption
Survey (RECS) 19-10
19.3.2. Relevant Studies of Volumes of Residences 19-10
19.3.2.1. Versar (1990)—Database on Perfluorocarbon Tracer (PFT) Ventilation
Measurements 19-10
19.3.2.2.Murray (1997)—Analysis of RECS and PFT Databases 19-11
19.3.2.3.U.S. Census Bureau (2017)—American Housing Survey for the United
States: 2015 19-11
19.3.3. Other Factors 19-11
19.3.3.1. Surface Area and Room Volumes 19-11
19.3.3.2.Products and Materials 19-12
19.3.3.3.Mechanical System Configurations 19-12
19.3.3.4.Type of Foundation 19-13
19.4. NONRESIDENTIAL BUILDING CHARACTERISTICS STUDIES 19-14
19.4.1. U.S. DOE (2008b, 2016)—Nonresidential Building Characteristics—
Commercial Buildings Energy Consumption Survey (CBECS) 19-14
19.5. TRANSPORT RATE STUDIES 19-15
19.5.1. Air Exchange Rates 19-15
19.5.1.1. Key Study of Residential Air Exchange Rates 19-16
19.5.1.2. Relevant Studies of Residential Air Exchange Rates 19-16
19.5.1.3.Key Study of Nonresidential Air Exchange Rates 19-18
19.5.2. Indoor Air Models 19-19
19.5.3. Air Infiltration Models 19-20
19.5.4. Vapor Intrusion 19-21
19.5.5. Deposition and Filtration 19-22
19.5.5.1.Depositio n 19-22
19.5.5.2.Filtratio n 19-23
19.5.6. Interzonal Airflows 19-23
19.5.7. House Dust and Soil Loadings 19-24
19.5.7.1.Roberts etal. (1991)—Development and Field Testing of a
High-Volume Sampler for Pesticides and Toxics in Dust 19-24
19.5.7.2. Thatcher and Layton (1995)—Deposition, Resuspension, and
Penetration of Particles within a Residence 19-24
19.6. CHARACTERIZING INDOOR SOURCES 19-24
19.6.1. Source Descriptions for Airborne Contaminants 19-25
19.6.2. Source Descriptions for Waterborne Contaminants 19-26
19.6.3. Soil and House Dust Sources 19-27
19.7. ADVANCED CONCEPTS 19-27
19.7.1. Uniform Mixing Assumption 19-27
19.7.2. Reversible Sinks 19-28
19.8. REFERENCES FOR CHAPTER 19 19-28
APPENDIX A A-l
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LIST OF TABLES
Table 19-1. Summary of Recommended Values for Residential Building Parameters 19-4
Table 19-2. Confidence in Residential Volume Recommendations 19-5
Table 19-3. Summary of Recommended Values for Nonresidential Building Parameters 19-6
Table 19-4. Confidence in Nonresidential Volume Recommendations 19-7
Table 19-5. Confidence in Air Exchange Rate Recommendations for Residential and Nonresidential
Buildings 19-8
Table 19-6. Average Estimated Volumes of U.S. Residences, by Housing Type, Census Region, and
Urbanicity 19-37
Table 19-7. Average Volume of Single Family, Multifamily and Mobile Homes by Type 19-38
Table 19-8. Residential Volumes in Relation to Year of Construction 19-38
Table 19-9. Summary of Residential Volume Distributions Based on U.S. DOE (2008a) 19-39
Table 19-10. Summary of Residential Volume Distributions Based on Versar (1989) 19-39
Table 19-11. Number of Residential Single Detached and Mobile Homes by Volume3 (m3) and Median
Volumes by Housing Type 19-40
Table 19-12. Dimensional Quantities for Residential Rooms 19-41
Table 19-13. Examples of Products and Materials Associated with Floor and Wall Surfaces in Residences 19-41
Table 19-14. Residential Heating Characteristics by U.S. Census 19-42
Table 19-15. Residential Heating Characteristics by Climate Region 19-44
Table 19-16. Residential Air Conditioning Characteristics by U.S. Census Region 19-46
Table 19-17. Percentage of Residences with Basement, by Census Region and EPA Region 19-48
Table 19-18. Percentage of Residences with Basement, by Census Region 19-48
Table 19-19. States Associated with EPA Regions and Census Regions 19-49
Table 19-20. Percentage of Residences with Certain Foundation Types by Census Region 19-50
Table 19-21. Average Estimated Volumes of U.S. Commercial Buildings, by Primary Activity 19-51
Table 19-22. Nonresidential Buildings: Hours per Week Open and Number of Employees 19-52
Table 19-23. Nonresidential Heating Energy Sources for Commercial Buildings 19-53
Table 19-24. Air Conditioning Energy Sources for Nonresidential 19-57
Table 19-25. Summary Statistics for Residential Air Exchange Rates (in ACH), by Region 19-61
Table 19-26. Distribution of Air Exchange Rates in (ACH) by House Category 19-61
Table 19-27. Summary of Major Projects Providing Air Exchange Measurements in the PFT Database 19-62
Table 19-28. Distributions of Residential Air Exchange Rates (in ACH) by Climate Region and Season 19-63
Table 19-29. Distribution of Measured 24-hour Average Air Exchange Rates in 31 Detached Homes in
North Carolina 19-64
Table 19-30. Air Exchange Rates in Commercial Buildings by Building Type 19-64
Table 19-31. Summary Statistics of Ventilation Rates 19-65
Table 19-32. Statistics of Estimated Normalized Leakage Distribution Weighted for all Dwellings in the
United States 19-66
Table 19-33. Particle Deposition During Normal Activities 19-66
Table 19-34. Deposition Rates for Indoor Particles 19-66
Table 19-35. Measured Deposition Loss Rate Coefficients 19-67
Table 19-36. Total Dust Loading for Carpeted Areas 19-67
Table 19-37. Particle Deposition and Resuspension During Normal Activities 19-68
Table 19-38. Dust Mass Loading after 1 Week without Vacuum Cleaning 19-68
Table 19-39. Simplified Source Descriptions for Airborne Contaminants 19-69
Table A-l. Terms Used in Literature Searches A-l
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LIST OF FIGURES
Figure 19-1. Elements of residential exposure 19-70
Figure 19-2. Configuration for residential forced-air systems 19-70
Figure 19-3. Idealized patterns of particle deposition indoors 19-71
Figure 19-4. Air flows for multiple-zone systems 19-72
Figure 19-5. Average percentage per capita indoor water use across all uses 19-73
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19. BUILDING CHARACTERISTICS
19.1. INTRODUCTION
This document is an update to Chapter 19
(Building Characteristics) of the Exposure Factors
Handbook; 2011 Edition. New information that has
become available since 2011 has been added, and the
recommended values have been revised, as needed to
reflect the additional information. The chapter
includes a comprehensive review of the scientific
literature through 2017. The new literature was
identified via formal literature searches conducted by
EPA library services as well as targeted internet
searches conducted by the authors of this chapter.
Appendix A provides a list of the key terms that were
used in the literature searches. Revisions to this
chapter have been made in accordance with the
approved quality assurance plan for the Exposure
Factors Handbook.
As described in Chapter 1 of the Exposure
Factors Handbook: 2011 Edition (U.S. EPA, 2011),
key studies represent the most up-to-date and
scientifically sound for deriving recommendations for
exposure factors, whereas other studies are designated
"relevant," meaning applicable or pertinent, but not
necessarily the most important. For example, studies
that provide supporting data or information related to
the factor of interest (e.g., building materials, building
foundation types), or have study designs or approaches
that make the data less applicable to the population of
interest (e.g., studies not conducted in the United
States) have been designated as relevant rather than
key. Key studies were selected based on the general
assessment factors described in Chapter 1 of the
Handbook.
Unlike previous chapters in this handbook, which
focus on human behavior or characteristics that affect
exposure, this chapter focuses on building
characteristics. Assessment of exposure in indoor
settings requires information on the availability of the
chemical(s) of concern at the point of exposure,
characteristics of the structure and microenvironment
that affect exposure, and human presence within the
building. The purpose of this chapter is to provide data
that are available on building characteristics that affect
exposure in an indoor environment. This chapter
addresses residential and nonresidential building
characteristics (volumes, surface areas, mechanical
systems, and types of foundations), transport
phenomena that affect chemical transport within a
building (airflow, chemical-specific deposition and
filtration, and soil tracking), information on indoor
water uses, and on various types of indoor
building-related sources associated with airborne
exposure and soil/house dust sources. Source-receptor
relationships in indoor exposure scenarios can be
complex due to interactions among sources, and
transport/transformation processes that result from
chemical-specific and building-specific factors.
There are many factors that affect indoor air
exposures. Indoor air models generally require data on
several parameters. This chapter provides
recommendations on two parameters, volume and air
exchange rates. Other factors that affect indoor air
quality are furnishings, siting, weather, ventilation and
infiltration, environmental control systems, material
durability, operation and maintenance, occupants and
their activities, and building structure. Available
relevant information on some of these other factors is
provided in this chapter, but specific recommendations
are not provided, as site-specific parameters are
preferred.
Figure 19-1 illustrates the complex factors that
must be considered when conducting exposure
assessments in an indoor setting. The primary cause of
indoor pollution is the release of gases or particles into
the air from indoor and outdoor sources. In addition to
sources within the building, chemicals of concern may
enter the indoor environment from outdoor air, soil,
gas, water supply, tracked-in soil, and industrial work
clothes worn by the residents. Indoor concentrations
are affected by loss mechanisms, also illustrated in
Figure 19-1, involving chemical reactions, deposition
to and re-emission from surfaces, and transport out of
the building. Particle-bound chemicals can enter
indoor air through resuspension. Indoor air
concentrations of gas-phase organic chemicals are
affected by the presence of reversible sinks formed by
a wide range of indoor materials. In addition, the
activity of human receptors greatly affects their
exposure as they move from room to room, entering
and leaving areas with different levels and types of
chemicals. Data on human activities, such as time
spent at various rooms in the house, can be found in
Chapter 16 of this handbook.
Inhalation of airborne chemicals in indoor settings
are typically modeled by considering the building as
an assemblage of one or more well-mixed zones. A
zone is defined as one room, a group of interconnected
rooms, or an entire building. At this macroscopic level,
well-mixed assumptions form the basis for
interpretation of measurement data as well as
simulation of hypothetical scenarios. Exposure
assessment models on a macroscopic level incorporate
important physical factors and processes. These
well-mixed, macroscopic models have been used to
perform indoor air quality simulations (Axley, 1989),
as well as indoor air exposure assessments (McKone,
1989; Ryan, 1991). Nazaroff and Cass (1986) and
Wilkes etal. (1992) have used computer programs
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featuring finite difference or finite element numerical
techniques to model mass balance. A simplified
approach using desktop spreadsheet programs has
been used by Jennings etal. (1987a).
U.S. Environmental Protection Agency (EPA) has
created two useful indoor air quality models: the
(I-BEAM) (https://www.epa.gov/indoor-air-qualitv-
iaq/indoor-air-qualitv-building-education-and-
assessment-model). which estimates indoor air quality
in commercial buildings and the Multi-Chamber
Concentration and Exposure Model (MCCEM)
(https://www.epa.gov/tsca-screening-tools/multi-
chamber-concentration-and-exposure-model-mccem-
version-12). which estimates average and peak indoor
air concentrations of chemicals released from
residences.
Major air transport pathways for airborne
substances in buildings include the following:
• Air exchange across the building
envelope—Air leakage through windows,
doorways, intakes and exhausts, and
"adventitious openings" (i.e., cracks and
seams) that combine to form the leakage
configuration of the building envelope plus
natural and mechanical ventilation;
• Interzonal airflows—Transport through
doorways, ductwork, and service chaseways
that interconnect rooms or zones within a
building; and
• Local circulation—Convective and advective
air circulation and mixing within a room or
within a zone.
The air exchange rate is generally expressed in
terms of air changes per hour (ACH), with units of
(hour1). It is defined as the ratio of the airflow
(m3 hour1) to the volume (m3). The distribution of
airflows across the building envelope that contributes
to air exchange and the interzonal airflows along
interior flowpaths is determined by the interior
pressure distribution. The forces causing the airflows
are temperature differences, the actions of wind, and
natural and mechanical ventilation systems. Basic
concepts on distributions and airflows have been
reviewed by the American Society of Heating
Refrigerating & Air Conditioning Engineers
(ASHRAE, 2013). Indoor-outdoor and room-to-room
temperature differences create density differences that
help determine basic patterns of air motion. During the
heating season, warmer indoor air tends to rise to exit
the building at upper levels by stack action. Exiting air
is replaced at lower levels by an influx of colder
outdoor air. During the cooling season, this pattern is
reversed: stack forces during the cooling season are
generally not as strong as in the heating season
because the indoor-outdoor temperature differences
are not as pronounced.
The position of the neutral pressure level (i.e., the
point where indoor-outdoor pressures are equal)
depends on the leakage configuration of the building
envelope. The stack effect arising from indoor-outdoor
temperature differences is also influenced by the
partitioning of the building interior. When there is free
communication between floors or stories, the building
behaves as a single volume affected by a generally
rising current during the heating season and a
generally falling current during the cooling season.
When vertical communication is restricted, each level
essentially becomes an independent zone. As the wind
flows past a building, regions of positive and negative
pressure (relative to indoors) are created within the
building; positive pressures induce an influx of air,
whereas negative pressures induce an outflow. Wind
effects and stack effects combine to determine a net
inflow or outflow.
The final element of indoor transport involves the
actions of natural and mechanical ventilation systems.
Natural ventilation uses pressure differences indoors
and outdoors that arise from natural forces through
openings such as windows, while mechanical systems
circulate indoor air through the use of fans. There are
generally three air distribution methods used for room
ventilation: mixed ventilation, displacement
ventilation, and stratum ventilation (Cheng and Lin,
2015). A mixed ventilation results in a uniform
environment since air is supplied by jets.
Displacement ventilation uses gravity to form a
stratified environment. In stratum ventilation, the air is
directly delivered to occupants' head level.
Mechanical ventilation systems may be connected
to heating/cooling systems that, depending on the type
of building, recirculate thermally treated indoor air or
a mixture of fresh air and recirculated air. Mechanical
systems also may be solely dedicated to exhausting air
from a designated area, as with some kitchen range
hoods and bath exhausts, or to recirculating air in
designated areas as with a room fan. Local air
circulation also is influenced by the movement of
people and the operation of local heat sources.
19.2. RECOMMENDATIONS
Table 19-1 presents the recommendations for
residential building volumes and air exchange rates.
Table 19-2 presents the confidence ratings for the
recommended residential building volumes. The 2009
Residential Energy Consumption Survey (RECS) data
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indicates a 446 m3 average living space
(approximately 2000 ft2 area, assuming an 8 ft ceiling
height) (U.S. DOE, 2013). However, these values vary
depending on the type of housing (see
Section 19.3.1.1). The recommended lower end of
housing volume is 154 m3 (approximately 675 ft2 area
assuming ceiling height of 8 ft). The 10th percentile is
based on EPA's analysis of the data from the 2005
RECS survey. Other percentiles are available in
Section 19.3.1.1.
Residential air exchange rates vary by region of
the country and seasonally. The recommended median
air exchange rate for all regions combined is 0.45
ACH. The arithmetic mean is not preferred because it
is influenced fairly heavily by extreme values at the
upper tail of the distribution. This value was derived
by Koontz and Rector (1995) using the
perflourocarbon tracer (PFT) database and is
supported by Persily etal. (2010). Although Persily
et al. (2010) provides more recent information on air
exchange rates, the data were based on modeling data
from two databases including the RECS database and
the U.S. Census Bureau American Housing Survey
(AHS) database. Koontz and Rector (1995) also has an
advantage over Persily et al. (2010) in that it provides
data for the various regions of the country.
Section 19.5.1.1.1 presents distributions for the
various regions of the country. For a conservative
value, the 10th percentile for the PFT database
(0.18 ACH) is recommended (see Section 19.5.1.1.1).
Table 19-3 presents the recommended values for
nonresidential building volumes and air exchange
rates. Volumes of nonresidential buildings vary with
type of building (e.g., office space, malls). They range
from 1,889 m3 for food services to 287,978 m3 for
enclosed malls. The mean for all buildings combined
is 5,575 m3. These data come from the Commercial
Buildings Energy Consumption Survey (CBECS)
(U.S. DOE, 2008b). The last CBECS for which data
are publicly available was conducted in 2012.
However, microdata from this survey year have not
been analyzed by EPA. Instead, analyses of the 2003
data were conducted by EPA to derive
recommendations for nonresidential building volume
and air exchange rates. Table 19-4 presents the
confidence ratings for the nonresidential building
volume recommendations. The mean air exchange rate
for all nonresidential buildings combined is 1.5 ACH.
The 10th percentile air exchange rate for all buildings
combined is 0.60 ACH. These data come from Turk
etal. (1987).
Table 19-5 presents the confidence ratings for the
air exchange rate recommendations for both
residential and nonresidential buildings. Air exchange
rate data presented in the studies are extremely limited.
Therefore, the recommended values have been
assigned a "low" overall confidence rating, and these
values should be used with caution.
Volume and air exchange rates can be used by
exposure assessors in modeling indoor-air
concentrations as one of the inputs to exposure
estimation. Other inputs to the modeling effort include
rates of indoor pollutant generation and losses to (and,
in some cases, re-emissions from) indoor sinks. Other
things being equal (i.e., holding constant the pollutant
generation rate and effect of indoor sinks), lower
values for either the indoor volume or the air exchange
rate will result in higher indoor-air concentrations.
Thus, values near the lower end of the distribution
(e.g., 10th percentile) for either parameter are
appropriate in developing conservative estimates of
exposure.
There are some uncertainties in, or limitations on,
the distribution for volumes and air exchange rates that
are presented in this chapter. In addition, there are no
systematic survey studies of air exchange rate. For
example, the RECS contains information on floor area
rather than total volume. The PFT database did not
base its measurements on a sample that was
statistically representative of the national housing
stock or balanced by time of the year. PFT has been
found to underpredict seasonal average air exchange
by 15 to 35% Sherman (1989). Using PFT to
determine air exchange can produce significant errors
when conditions during the measurements greatly
deviate from idealizations calling for constant,
well-mixed conditions. Principal concerns focus on
the effects of naturally varying air exchange and the
effects of temperature in the permeation source. Some
researchers have found that failing to use a
time-weighted average temperature can greatly affect
air exchange rate estimates (Leaderer et al., 1985). A
final difficulty in estimating air exchange rates for any
particular zone results from interconnectedness of
multizone models and the effect of neighboring zones
as demonstrated by Sinden (1978) and Sandberg
(1984).
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Table 19-1. Summary of Recommended Values for Residential Building Parameters
Mean 10th Percentile Source
Volume of residence3 446 m3 (central estimate)b 154 m3 (lower percentile)0 EPA analysis of U.S. DOE,
(2013,2008a)
Air exchange rate 0.45 ACH (central estimate)"1 0.18 ACH (lower percentile)6 Koontz and Rector (1995);
Persily et al. (2010)
a Volumes vary with type of housing. For specific housing type volumes, see Tables 19-6 and 19-7.
b Mean value presented in Table 19-6 recommended for use as a central estimate for all single family homes, including
mobile homes and multifamily units.
c 10th percentile value from Table 19-9 recommended to be used as a lower percentile estimate.
d Median value recommended to be used as a central estimate based across all U.S. census regions and various housing
types (see Tables 19-25 and 19-26).
e 10th percentile value across all U.S. census regions recommended to be used as a lower percentile value (see
Table 19-25).
ACH = Air changes per hour.
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Table 19-2. Confidence in Residential Volume Recommendations3
General Assessment Factors
Rationale
Rating
Soundness
Adequacy of Approach
The study was based on primary data. Volumes were
estimated assuming an 8-foot ceiling height. The effect of
this assumption has been tested by Murray (1997) and
found to be insignificant.
Medium
Minimal (or defined) bias
Selection of residences was random.
Applicability and utility
Exposure factor of interest
The focus of the studies was on estimating house volume
as well as other factors.
Medium
Representativeness
Residences in the United States were the focus of the
study. The sample size was fairly large and representative
of the entire United States. Samples were selected at
random.
Currency
The most recent RECS surveys for which volume data are
available were conducted in 2005 and 2009.
Data collection period
Data were collected in 2005 and 2009.
Clarity and completeness
Accessibility
The RECS database is publicly available.
High
Reproducibility
Direct measurements were made.
Quality assurance
Not applicable.
Variability and uncertainty
Variability in population
Distributions are presented by housing type and regions,
but some subcategory sample sizes were small.
Medium
Uncertainty
Although residence volumes were estimated using the
assumption of 8-foot ceiling height, Murray (1997) found
this assumption to have minimal impact.
Evaluation and review
Peer review
The RECS database is publicly available. Some data
analysis was conducted by EPA.
Medium
Number and agreement of studies
Only one study was used to derive recommendations.
Other relevant studies provide supporting evidence.
Overall Rating
Medium
a See Section 1.5.2 in Chapter 1 of the Exposure Factors Handbook: 2011 Edition (U.S. EPA, 2011) for a detailed
description of the evaluation criteria used in this table.
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Table 19-3. Summary of Recommended Values for Nonresidential Building
Parameters
Meana
10thPercentileb
Source
Volume of building (m3)c
Vacant
4,789
408
Office
5,036
510
Laboratory
24,681
2,039
Nonrefrigerated warehouse
9,298
1,019
Food sales
1,889
476
Public order and safety
5,253
816
Outpatient healthcare
3,537
680
Refrigerated warehouse
19,716
1,133
Religious worship
3,443
612
Public assembly
4,839
595
EPA analysis of
Education
8,694
527
U.S. DOE (2008b)
Food service
1,889
442
Inpatient healthcare
82,034
17,330
Nursing
15,522
1,546
Lodging
11,559
527
Strip shopping mall
7,891
1,359
Enclosed mall
287,978
35,679
Retail other than mall
3,310
510
Service
2,213
459
Other
5,236
425
All buildings'1
5,575
527
.. „ , „ . e Mean (SD)1.5 (0.87) ACH
Air Exchange Rate* Range 0.3-4.1 ACH
0.60 ACH
Turk et al. (1987)
a Mean values are recommended as central estimates for nonresidential buildings (see Table 19-21).
b 10thpercentile values are recommended as lower estimates for nonresidential buildings (see Table 19-21).
c Volumes were calculated assuming a ceiling height of 20 feet for warehouses and enclosed malls and
12 feet for other structures (see Table 19-21).
d Weighted average assuming a ceiling height of 20 feet for warehouses and enclosed malls and 12 feet for
other structures (see Table 19-21).
e Air exchange rates for commercial buildings (see Table 19-30).
SD = Standard deviation.
ACH = Air changes per hour.
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Table 19-4. Confidence in Nonresidential Volume Recommendations3
General Assessment Factors
Rationale
Rating
Soundness
Adequacy of approach
All nonresidential data were based on one study: CBECS
(U.S. DOE, 2008b). Volumes were estimated assuming a
20-foot ceiling height assumption for warehouses and a
12-foot height assumption for all other nonresidential
buildings based on scant anecdotal information. Although
Murray (1997) found that the impact of an 8-foot ceiling
assumption was insignificant for residential structures, the
impact of these ceiling height assumptions for
nonresidential buildings is unknown.
Medium
Minimal (or defined) bias
Selection of residences was random for CBECS.
Applicability and utility
Exposure factor of interest
CBECS (U.S. DOE, 2008b) contained ample building size
data, which were used as the basis provided for volume
estimates.
High
Representativeness
CBECS (U.S. DOE, 2008b) was a nationwide study that
generated weighted nationwide data based upon a large
random sample.
Currency, data collection period
The data were collected in 2003.
Clarity and completeness
Accessibility
The data are available online in both summary tables and
raw data.
http://www.eia.doe.eov/emeu/cbecs/contents.html.
High
Reproducibility
Direct measurements were made.
Quality assurance
Not applicable.
Variability and uncertainty
Variability in population
Distributions are presented by building type, heating and
cooling system type, and employment, but a few
subcategory sample sizes were small.
Medium
Uncertainty
Volumes were calculated using speculative assumptions
for building height. The impact of such assumptions may
or may not be significant.
Evaluation and review
Peer review
There are no studies from the peer-reviewed literature.
Low
Number and agreement of studies
All data are based upon one study: CBECS (U.S. DOE,
2008b).
Overall Rating
Medium
a See Section 1.5.2 in Chapter 1 of the Exposure Factors Handbook: 2011 Edition (U.S. EPA, 2011) for a detailed
description of the evaluation criteria used in this table.
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Table 19-5. Confidence in Air Exchange Rate Recommendations for Residential and
Nonresidential Buildings3
General Assessment Factors
Rationale
Rating
Soundness
Adequacy of approach
The studies were based on primary data; however, most
approaches contained major limitations, such as assuming
uniform mixing, and residences were typically not selected
at random.
Low
Minimal (or defined) bias
Bias may result because the selection of residences and
buildings was not random or balanced by time of the year.
The commercial building study (Turk et al., 1987) was
conducted only on buildings in the northwest United
States.
Applicability and utility
Exposure factor of interest
The focus of the studies was on estimating air exchange
rates as well as other factors.
Low
Representativeness
Study residences were typically in the United States, but
only RECS (U.S. DOE, 2008a and 2013) and the AHS
selected residences randomly. PFT residences were not
representative of the United States. Distributions are
presented by housing type and regions; although some of
the sample sizes for the subcategories were small. The
commercial building study (Turk et al., 1987) was
conducted only on buildings in the northwest United
States.
Currency
Measurements in the PFT database were taken between
1982-1987. The Turk et al. (1987) study was conducted in
the mid-1980s.
Data Collection Period
Only short-term data were collected; some residences were
measured during different seasons; however, long-term air
exchange rates are not well characterized. Individual
commercial buildings were measured during one season.
Clarity and completeness
Accessibility
Papers are widely available from government reports and
peer-reviewed journals.
Medium
Reproducibility
Precision across repeat analyses has been documented to
be acceptable.
Quality assurance
Not applicable.
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Table 19-5. Confidence in Air Exchange Rate Recommendations for Residential and Nonresidential
Buildings3 (Continued)
General Assessment Factors
Rationale
Rating
Variability and uncertainty
Variability in population
For the residential estimates, distributions are presented by
U.S. regions, seasons, and climatic regions, but some of
the sample sizes for the subcategories were small. The
commercial estimate comes from buildings in the
northwest United States representing two climate zones,
and measurements were taken in three seasons (spring,
summer, and winter).
Medium
Uncertainty
Some measurement error may exist. Additionally, PFT has
been found to underpredict seasonal average air exchange
by 15-35% (Sherman, 1989). Turk et al. (1987) estimates
a 10-20% measurement error for the technique used to
measure ventilation in commercial buildings.
Evaluation and review
Peer review
The studies appear in peer-reviewed literature.
Low
Number and agreement of studies
Three residential studies are based on the same PFT
database. The database contains results of 20 projects of
varying scope. The commercial building rate is based on
one study.
Overall rating
Low
a See Section 1.5.2 in Chapter 1 of the Exposure Factors Handbook: 2011 Edition (U.S. EPA, 2011) for a detailed
description of the evaluation criteria used in this table.
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19.3. RESIDENTIAL BUILDING
CHARACTERISTICS STUDIES
19.3.1. Key Study of Volumes of Residences
19.3.1.1. U.S. DOE (2017, 2013,
2008a)—Residential Energy
Consumption Survey (RECS)
Measurement surveys have not been conducted to
directly characterize the range and distribution of
volumes for a random sample of U.S. residences.
Related data, however, are regularly collected through
the U.S. Department of Energy's (DOE) RECS. In
addition to collecting information on energy use, this
survey collects data on housing characteristics
including direct measurements of total and heated
floor space for buildings visited by survey specialists.
The last three surveys were conducted in 2005, 2009,
and 2015. Data from these survey years were made
available in 2008, 2013, and 2017, respectively. For
the most recent survey conducted in 2015, a multistage
probability sample of more than 5,600 residences was
surveyed, representing 118.2 million housing units
nationwide
(www.eia.gov/coiisumptioiti/residential/about.plip').
However, not all of the data from the 2015 survey were
available in time for the revisions to this chapter. For
example, the floor space area from the residences
surveyed in 2015 is not available yet. In 2009, the
survey consisted of a multistage probability sample of
12,083 residences, representing 113.6 million housing
units nationwide. The 2009 survey response rate was
79% (U.S. DOE, 2013). Housing volumes were
estimated using the RECS 2009 data since the data
from the 2015 were not available. These were
estimated by multiplying the heated floor space area
by an assumed ceiling height of 8 feet. The data and
data tables were released to the public in 2013 and are
available from
https://www.eia.gov/consumDtion/residential/data/20
09/index.plip?view=cliaracteristics.
Table 19-6 presents results for average residential
volume by type of residence, census region, and
urbanicity (i.e., urban vs. rural). The predominant
housing type—single-family detached homes—also
had the largest average volume. Multifamily units and
mobile homes had volumes averaging about half that
of single-family detached homes, with single-family
attached homes about halfway between these
extremes. The average house volume for all types of
units for all years was estimated to be 446 m3.
Table 19-7 presents the average residential volume for
single family homes, multifamily homes, and mobile
homes by housing unit type, census region, and
urbanicity. Data on the relationship of residential
volume to year of construction are provided in
Table 19-8 and indicate a slight decrease in residential
volumes between 1950 and 1979, followed by an
increasing trend. A ceiling height of 8 feet was
assumed in estimating the average volumes, whereas
there may have been some time-related trends in
ceiling height. It is important to note that the available
data used to derived volumes included all basements,
finished or conditioned (heated or cooled) areas of
attics, and conditioned garage space that is attached to
the home. Unconditioned and unfinished areas in attics
and attached garages are excluded.
In 2010, the EPA conducted an analysis of the
RECS 2005 survey microdata files. The RECS 2005
survey consisted of a sample of 4,382 residences
representing 111 million housing units nationwide.
The response rate in the 2005 RECS survey was 71%
(U.S. DOE 2008a). Table 19-9 presents distributions
of residential volumes for all house types and all units
estimated by the EPA using the 2005 microdata.
Similar analysis has not been conducted with the more
recent data sets from 2009 and 2015.
The advantages of this study were that the sample
size was large, and it was representative of houses in
the United States. Also, it included various housing
types. A limitation of this analysis is that volumes
were estimated assuming a ceiling height of 8 feet.
Volumes of individual rooms in the house cannot be
estimated. In addition, not all the data from the most
recent survey years have been released.
19.3.2. Relevant Studies of Volumes of
Residences
19.3.2.1. Versar (1990)—Database on
Perfluorocarbon Tracer (PFT)
Ventilation Measurements
Versar (1990) compiled a database of
time-averaged air exchange and interzonal airflow
measurements in more than 4,000 residences. These
data were collected between 1982 and 1987. The
residences that appear in this database are not a
random sample of U.S. homes. However, they
represent a compilation of homes visited in about
100 different field studies, some of which involved
random sampling. In each study, the house volumes
were directly measured or estimated. The collective
homes visited in these field projects are not
geographically balanced. A large fraction of these
homes are located in southern California. Statistical
weighting techniques were applied in developing
estimates of nationwide distributions to compensate
for the geographic imbalance. The Versar (1990) PFT
database found a mean value of 369 m3 (see
Table 19-10).
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The advantage of this study is that it provides a
distribution of house volumes. However, more
up-to-date data are available from RECS 2009
(U.S. DOE, 2013).
19.3.2.2. Murray (1997)—Analysis of RECS and
PFT Databases
Using a database from the 1993 RECS and an
assumed ceiling height of 8 feet, Murray (1997)
estimated a mean residential volume of 382 m3 using
RECS estimates of heated floor space. This estimate is
slightly different from the mean of 369 m3 given in
Table 19-10. Murray's (1997) sensitivity analysis
indicated that when a fixed ceiling height of 8 feet was
replaced with a randomly varying height with a mean
of 8 feet, there was little effect on the standard
deviation of the estimated distribution. From a
separate analysis of the PFT database, based on
1,751 individual household measurements, Murray
(1997) estimated an average volume of 369 m3, the
same as previously given in Table 19-10. In
performing this analysis, the author carefully reviewed
the PFT database in an effort to use each residence
only once, for those residences thought to have
multiple PFT measurements.
Murray (1997) analyzed the distribution of
selected residential zones (i.e., a series of connected
rooms) using the PFT database. The author analyzed
the "kitchen zone" and the "bedroom zone" for houses
in the Los Angeles area that were labeled in this
manner by field researchers, and "basement," "first
floor," and "second floor" zones for houses outside of
Los Angeles for which the researchers labeled
individual floors as zones. The kitchen zone contained
the kitchen in addition to any of the following
associated spaces: utility room, dining room, living
room, and family room. The bedroom zone contained
all the bedrooms plus any bathrooms and hallways
associated with the bedrooms. The following summary
statistics (mean ± standard deviation) were reported
by Murray (1997) for the volumes of the zones
described above: 199 ± 115 m3 for the kitchen zone,
128 ± 67 m3 for the bedroom zone, 205 ± 64 m3 for the
basement, 233 ± 72 m3 for the first floor, and
233 ± 111 m3 for the second floor.
The advantage of this study is that the data are
representative of homes in the United States.
However, more up-to-date data are available from the
RECS 2009 (U.S. DOE, 2013).
19.3.2.3. U.S. Census Bureau (2017)—American
Housing Survey for the United States:
2015
The American Housing Survey (AHS) is
conducted by the Census Bureau for the Department
of Housing and Urban Development. It collects data
on the Nation's housing, including apartments,
single-family homes, mobile homes, vacant housing
units, household characteristics, housing quality,
foundation type, drinking water source, equipment and
fuels, and housing unit size. National data are
collected biennially between May and September in
odd-numbered years. The 2015 survey was comprised
of a national sample of 5,686 housing units
representing 118.2 million occupied primary
households in the United States. The U.S. Census
Bureau (2017) lists the number of residential single
detached and manufactured/mobile homes in the
United States within the owner or renter categories,
based on the AHS (see Table 19-11). Assuming an
8-foot ceiling, these units have a median size of
340 m3; however, these values do not include
multifamily units, but include single detached and
manufactured/mobile homes. It should be mentioned
that 8 feet is the most common assumed ceiling height,
and Murray (1997) has shown that the effect of the
8-foot ceiling height assumption is not significant.
The advantage of this study is that it was a large
national sample and, therefore, representative of the
United States. The limitations of these data are that
distributions were not provided by the authors, and the
analysis did not include multifamily units.
19.3.3. Other Factors
19.3.3.1. Surface Area and Room Volumes
The surface areas of floors are commonly
considered in relation to the room or house volume,
and their relative loadings are expressed as a surface
area-to-volume, or loading ratio. Table 19-12 provides
the basis for calculating loading ratios for typical-sized
rooms. Constant features in the examples are a room
width of 12 feet and a ceiling height of 8 feet (typical
for residential buildings), or a ceiling height of 12 feet
(typical for some types of commercial buildings).
Volumes of individual rooms are dependent on
the building size and configuration, but summary data
are not readily available. The exposure assessor is
advised to define specific rooms, or assemblies of
rooms, that best fit the scenario of interest. Most
models for predicting indoor air concentrations
specify airflows in m3 per hour and, correspondingly,
express volumes in m3. A measurement in ft3 can be
converted to m3 by multiplying the value in ft3 by
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0.0283 in3/ft3. For example, a bedroom that is 9 feet
wide by 12 feet long by 8 feet high has a volume of
864 ft3 or 24.5 m3. Similarly, a living room with
dimensions of 12 feet wide by 20 feet long by 8 feet
high has a volume of 1,920 ft3 or 54.3 m3, and a
bathroom with dimensions of 5 feet by 12 feet by
8 feet has a volume of 480 ft3 or 13.6 m3.
19.3.3.2. Products and Materials
Table 19-13 presents examples of assumed
amounts of selected products and materials used in
constructing or finishing residential surfaces (Tucker,
1991). Products used for floor surfaces include
adhesive, varnish, and wood stain; and materials used
for walls include paneling, painted gypsum board, and
wallpaper. Particleboard and chipboard are commonly
used for interior furnishings such as shelves or
cabinets but could also be used for decking or
underlayment. It should be noted that numbers
presented in the table for surface area are based on
typical values for residences, and they are presented as
examples. In contrast to the concept of loading ratios
presented above (as a surface area), the numbers in the
table also are not scaled to any particular residential
volume. In some cases, it may be preferable for the
exposure assessor to use professional judgment in
combination with the loading ratios given above. For
example, if the exposure scenario involves residential
wall to wall carpeting in a room of 3 x 4 m with a
ceiling height of 2.5 m (approximately 8 feet), it will
have a loading ratio of 0.4 in2in 3 (Tichenor, 2006).
This can be multiplied by an assumed residential
volume and assumed fractional coverage of carpeting
to derive an estimate of the surface area. More
specifically, a residence with a volume of 300 m3, a
loading ratio of 0.4 in2m 3. and coverage of 80%,
would have 96 m2 of carpeting. The estimates
discussed here relate to macroscopic surfaces; the true
surface area for carpeting, for example, would be
considerably larger because of the nature of its fibrous
material.
19.3.3.3. Mechanical System Configurations
Mechanical systems for air movement in
residences can affect the migration and mixing of
pollutants released indoors and the rate of pollutant
removal. Three types of mechanical systems are
(1) systems associated with heating, ventilating, and
air conditioning (HVAC); (2) systems whose primary
function is providing localized exhaust; and
(3) systems intended to increase the overall air
exchange rate of the residence.
Portable space heaters intended to serve a single
room, or a series of adjacent rooms, may or may not
be equipped with blowers that promote air movement
and mixing. Without a blower, these heaters still have
the ability to induce mixing through convective heat
transfer. If the heater is a source of combustion
pollutants, as with unvented gas or kerosene space
heaters, then the combination of convective heat
transfer and thermal buoyancy of combustion products
will result in fairly rapid dispersal of such pollutants.
The pollutants will disperse throughout the floor
where the heater is located and to floors above the
heater, but may not disperse to floors below.
Central forced-air HVAC systems are common in
many residences. Such systems, through a network of
supply/return ducts and registers, can achieve fairly
complete mixing within 20 to 30 minutes (Koontz
etal., 1988). The air handler for such systems is
commonly equipped with a filter (see Figure 19-2) that
can remove particle-phase contaminants. Further
removal of particles, via deposition on various room
surfaces (see Section 19.5.5), is accomplished through
increased air movement when the air handler is
operating.
Figure 19-2 also distinguishes forced-air HVAC
systems by the return layout in relation to supply
registers. The return layout shown in the upper portion
of the figure is the type most commonly found in
residential settings. On any floor of the residence, it is
typical to find one or more supply registers to
individual rooms, with one or two centralized return
registers. With this layout, supply/return imbalances
can often occur in individual rooms, particularly if the
interior doors to rooms are closed. In comparison, the
supply/return layout shown in the lower portion of the
figure by design tends to achieve a balance in
individual rooms or zones. Airflow imbalances can
also be caused by inadvertent duct leakage to
unconditioned spaces such as attics, basements, and
crawl spaces. Such imbalances usually depressurize
the house, thereby increasing the likelihood of
contaminant entry via soil-gas transport or through
spillage of combustion products from vented
fossil-fuel appliances such as fireplaces and gas/oil
furnaces.
Mechanical devices such as kitchen fans,
bathroom fans, and clothes dryers are intended
primarily to provide localized removal of unwanted
heat, moisture, or odors. Operation of these devices
tends to increase the air exchange rate between the
indoors and outdoors. Because local exhaust devices
are designed to be near certain indoor sources, their
effective removal rate for locally generated pollutants
is greater than would be expected from the dilution
effect of increased air exchange. Operation of these
devices also tends to depressurize the house, because
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replacement air usually is not provided to balance the
exhausted air.
An alternative approach to pollutant removal is
one which relies on an increase in air exchange to
dilute pollutants generated indoors. This approach can
be accomplished using heat recovery ventilators
(HRVs) or energy recovery ventilators (ERVs). Both
types of ventilators are designed to provide balanced
supply and exhaust airflows and are intended to
recover most of the energy that normally is lost when
additional outdoor air is introduced. Although
ventilators can provide for more rapid dilution of
internally generated pollutants, they also increase the
rate at which outdoor pollutants are brought into the
house. A distinguishing feature of the two types is that
ERVs provide for recovery of latent heat (moisture) in
addition to sensible heat. Moreover, ERVs typically
recover latent heat using a moisture-transfer device
such as a desiccant wheel. It has been observed in
some studies that the transfer of moisture between
outbound and inbound air streams can result in some
re-entrainment of indoor pollutants that otherwise
would have been exhausted from the house
(Andersson etal., 1993). Inadvertent air
communication between the supply and exhaust air
streams can have a similar effect.
Studies quantifying the effect of mechanical
devices on air exchange using tracer-gas
measurements are uncommon and typically provide
only anecdotal data. The common approach is for the
expected increment in the air exchange rate to be
estimated from the rated airflow capacity of the
device(s). For example, if a device with a rated
capacity of 100 ft3 per minute, or 170 m3 per hour, is
operated continuously in a house with a volume of
400 m3, then the expected increment in the air
exchange rate of the house would be
170 m3 hour V400 m3, or approximately 0.4 ACH.
U.S. DOE RECS contains data on residential
heating characteristics. The data show that most
homes in the United States have some kind of heating
and air conditioning system (U.S. DOE, 2017). The
types of system vary regionally within the United
States. Table 19-14 shows the type of primary and
secondary heating systems found in U.S. residences.
The predominant primary heating system in the
Midwest is natural gas (used by 67.0% of homes there)
while most homes in the South (60.1%) primarily heat
with electricity. Nationwide, 36.6% of residences have
a secondary heating source, typically an electric
source.
Table 19-15 shows the type of heating systems
found in the United States by climate region. It is
noteworthy that 51.4% of residences in very cold/cold
climate use central heating compared to 19.7% in hot
humid climate.
Table 19-16 shows that 87.2% of U.S. residences
have some type of cooling system: 65.2% have central
air while 26.7% use individual air conditioning units.
Like heating systems, cooling system type varies
regionally as well. In the South, 95.3% of residences
have either central or room air conditioning units
whereas only 54.9% of residences in the Western
United States have air conditioning.
19.3.3.4. Type of Foundation
The type of foundation of a residence is of interest
in residential exposure assessment. It provides some
indication of the number of stories and house
configuration, as well as an indication of the relative
potential for soil-gas transport. For example, such
transport can occur readily in homes with enclosed
crawl spaces. Homes with basements provide some
resistance, but still have numerous pathways for
soil-gas entry. By comparison, homes with crawl
spaces open to the outside have significant
opportunities for dilution of soil gases prior to
transport into the house. Using data from the 2015
AHS, of total housing units in the United States, 31%
have a basement under the entire building, 11% have
a basement under part of the building, 22% have a
crawl space, and 36% are on a concrete slab
(U.S. Census Bureau, 2017).
19.3.3.4.1. Lucas et al. (1992)—National
Residential Radon Survey
The estimated percentage of homes with a full or
partial basement according to the National Residential
Radon Survey of 5,700 households nationwide was
44% (see Table 19-17) (Lucas etal., 1992). The
National Residential Radon Survey provides data for
more refined geographical areas, with a breakdown by
the 10 EPA Regions. The New England region
(i.e., EPA Region 1), which includes Connecticut,
Maine, Massachusetts, New Hampshire, Rhode Island,
and Vermont, had the highest prevalence of basements
(93%). The lowest prevalence (4%) was for the South
Central region (i.e., EPA Region 6), which includes
Arkansas, Louisiana, New Mexico, Oklahoma, and
Texas. Section 19.3.3.4.2 presents the states
associated with each census region and EPA region.
19.3.3.4.2. U.S. DOE (2008a, 2013,
2017)—Residential Energy Consumption Survey
(RECS)
The three most recent RECS (described in
Section 19.3.1.1) were administered in 2005, 2009,
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and 2015 (U.S. DOE, 2008a, 2013, 2017). The type of
information requested by the survey questionnaire
included the type of foundation for the residence
(i.e., basement, enclosed crawl space, crawl space
open to outside, or concrete slab). This information
was not obtained for multifamily structures with five
or more dwelling units or for mobile homes. EPA
analyzed the RECS 2015 data (U.S. DOE, 2017) to
estimate the percentage of residences with basements
by census region. Table 19-18 indicates that 43.5% of
residences have basements nationwide. Table 19-19
shows the states associated with each EPA region and
census region. Table 19-20 presents the percentage of
residences with each foundation type, by census
region, and for the entire United States. The
foundation type data (other than basements) were not
included in the RECS 2015 survey. Therefore, the
values presented in Table 19-20 are based on data
from the RECS 2009 survey (U.S. DOE, 2013). The
percentages can add up to more than 100% because
some residences have more than one type of
foundation; for example, many split-level structures
have a partial basement combined with some
crawlspace that typically is enclosed. The data in
Table 19-20 indicate that 39.9% of residences
nationwide have a basement. It also shows that a large
fraction of homes have concrete slabs (46.5%). There
are also variations by census region. For example,
around 74.7 and 72.5% of the residences in the
Northeast and Midwest regions, respectively, have
basements. In the South and West regions, the
predominant foundation type is concrete slab.
The advantage of this study is that it had a large
sample size, and it was representative of houses in the
United States. Also, it included various housing types.
A limitation of this analysis is that homes have
multiple foundation types, and the analysis does not
provide estimates of square footage for each type of
foundation. Also, the information collected varied
slightly across survey years and the data from the most
recent survey were not available to be analyzed.
19.4. NONRESIDENTIAL BUILDING
CHARACTERISTICS STUDIES
19.4.1. U.S. DOE (2008b, 2016)—Nonresidential
Building Characteristics—Commercial
Buildings Energy Consumption Survey
(CBECS)
The U.S. Department of Energy conducts the
CBECS to collect data on the characteristics and
energy use of commercial buildings. CBECS is a
national survey of U.S. buildings that DOE first
conducted in 1979. The survey is conducted every
4 years. In 2010, EPA conducted an analysis of the
U.S. DOE CBECS 2003 data, released in 2008.
CBECS defines "Commercial" buildings as all
buildings in which at least half of the floorspace is
used for a purpose that is not residential, industrial, or
agricultural, so they include building types that might
not traditionally be considered commercial, such as
schools, correctional institutions, and buildings used
for religious worship.
The 2003 CBECS provided nationwide estimates
for the United States based upon a weighted statistical
sample of 5,215 buildings. DOE releases a data set
about the sample buildings for public use. The 2003
CBECS Public Use Microdata set includes data for
4,820 nonmall commercial buildings (U.S. DOE,
2008b). A second data set is available that includes
information on malls, lacks building characteristics
data. Building characteristics data provided by
CBECS includes floor area, number of floors, census
division, heating and cooling design, principal
building activity, number of employees, and weighting
factors. Although DOE released the Microdata from
the 2012 survey in 2016, EPA did not analyze these
data to estimate volumes of commercial buildings, the
number of hours per week they are open, and the
number of employees during the main shift because of
the amount of effort involved and the likelihood that
values have not changed considerably.
Table 19-21 shows that nonresidential buildings
vary greatly in volumes. The table shows average
volume for a numbers of structures including offices
(5,036 m3), restaurants (food services) (1,889 m3),
schools (education) (8,694 m3), hotels (lodging)
(11,559 m3), and enclosed shopping malls
(287,978 m3). Each of these structures varies
considerably in size as well. The large shopping malls
are over 500,000 m3 (90th percentile). The most
numerous of the nonresidential buildings are office
buildings (17%), nonfood service buildings (13%),
and warehouses (12%).
Table 19-22 presents data on the number of hours
various types of nonresidential buildings are open for
business and the number of employees that work in
such buildings. In general, places of worship have the
most limited hours. The average place of worship is
open 32 hours per week. On the other extreme are
healthcare facilities, which are open 168 hours a week
(24 hours per day, 7 days per week). The average
restaurant is open 86 hours per week. Hours vary
considerably by building type. Some offices, labs,
warehouses, restaurants, police stations, and hotels are
also open 24 hours per day, 7 days per week, as
reflected by the 90th percentiles. Table 19-22 also
presents the number of employees typically employed
in such buildings during the main shift. Overall, the
average building houses 16 workers during its primary
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shift, but some facilities employ many more. The
average hospital employs 471 workers during its main
shift, although those in the 10th percentile employ only
175, and those in the 90th employ 2,250.
EPA used the 2012 CBECS, however, to update
the information on the heating and cooling sources
using the summary tables tabulated by the U.S. Energy
Information Administration of the U.S. DOE and
released to the public in 2016 (U.S. DOE, 2016).
Tables 19-23 and 19-24 present these data.
Table 19-23 indicates that electricity and natural gas
are the heating sources used by a majority of
nonresidential buildings. Of those buildings heated by
fuel oil, most are older buildings.
Table 19-24 describes nonresidential building
cooling characteristics. About 80%
(i.e., 4,461/5,557 x 100) of nonresidential buildings
have air conditioning, but this varies regionally from
14% in the Northeast to 40% in the South. Nationwide,
79% (i.e., 4,413/5,557 x 100) of nonresidential
buildings use electricity for air conditioning. The
remaining fraction use natural gas or chilled water.
It should be noted, however, that there are many
critical exposure assessment elements not addressed
by CBECS. These include a number of elements
discussed in more detail in the Residential Building
Characteristics Studies section (i.e., Section 19.3).
Data to characterize the room volume, products and
materials, and foundation type for nonresidential
buildings were not available in CBECS.
Another characteristic of nonresidential buildings
needed in ventilation and air exchange calculations is
ceiling height. Unseen spaces (e.g. above ceiling tiles)
complicate the volume and mixing assumptions by
creating rather large separate compartments. In the
residential section of this chapter, ceiling height was
assumed to be 8 feet, a figure often assumed for
residential buildings. For nonresidential buildings,
EPA has assumed a 20-foot ceiling height for
warehouses and enclosed shopping malls and a 12-foot
average ceiling height for other structures. These
assumptions are based on EPA's professional
judgment. Murray (1997) found that the impact of
assuming an 8-foot ceiling height for residences was
insignificant, but nonresidential ceiling height varies
more greatly and may or may not have a significant
impact on calculations.
19.5. TRANSPORT RATE STUDIES
19.5.1. Air Exchange Rates
Air exchange is the balanced flow into and out of
a building and is composed of three processes:
(1) infiltration—air leakage through random cracks,
interstices, and other unintentional openings in the
building envelope; (2) natural ventilation—airflows
through open windows, doors, and other designed
openings in the building envelope; and (3) forced or
mechanical ventilation—controlled air movement
driven by fans (Breen et al., 2014).
For nearly all indoor exposure scenarios, air
exchange is treated as the principal means of diluting
indoor concentrations. The air exchange rate is
generally expressed in terms of ACH (with units of
hours-1). It is defined as the ratio of the airflow
(m3 hours-1) to the volume (m3). Thus, ACH and
building size and volume are negatively correlated.
Air exchange rates can affect the dynamic and the
steady state behavior of indoor air pollutants (Breen
et al., 2014).
Air exchange rates are influenced by many factors
including building characteristics, type of ventilation
system affecting air flow patterns (includes natural and
mechanical), temperature differentials between rooms
and floors and between indoors and outdoors,
seasonality, occupant behavior (e.g., walking from
room to room, opening of windows) and measurement
techniques (Lee et al., 2016; Wu and Lin, 2015; Breen
etal., 2014). Higher air exchange rates have been
observed in the summer and during occupied daytime
periods (Beko etal., 2016; Lee etal., 2016; Wu and
Lin, 2015; Breen etal., 2014; Kearney etal 2014;
Zhao and Zeng, 2009).
The primary method for measuring air exchange
rates in a building consist of releasing a nonreactive
gas tracer into the building and allowing it to mix with
the indoor air. The tracer gas can be injected into the
building using an emitter device (e.g., SF6) or released
from the exhaled breath of building occupants in the
form of CO2. These tracer concentrations are
monitored to estimate the air exchange rates. The gas
tracer methods are based on a mass balance approach
assuming that the gas tracer is well mixed, the tracer
concentration outdoor is zero, and accounting for air
leakage (Breen et al., 2014).
No measurement surveys have been conducted to
directly evaluate the range and distribution of building
air exchange rates. In addition, there is almost no
information on the use of natural ventilation (e.g., how
much or often windows are kept open). Although a
significant number of air exchange measurements
have been carried out over the years, there has been a
diversity of protocols and study objectives. Since the
early 1980s, however, an inexpensive PFT technique
has been used to measure time-averaged air exchange
and interzonal airflows in thousands of occupied
residences using essentially similar protocols (Dietz
etal., 1986). The PFT technique utilizes miniature
permeation tubes as tracer emitters and passive
samplers to collect the tracers. Sampling periods
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(e.g., days, weeks, months) vary depending on the
study design. The passive samplers are returned to the
laboratory for analysis by gas chromatography. These
measurement results have been compiled to allow
various researchers to access the data (Versar, 1989).
19.5.1.1. Key Study of Residential Air Exchange
Rates
19.5.1.1.1. Koontz and Rector
(1995)—Estimation of distributions for residential
air exchange rates
In analyzing the composite data from various
projects (2,971 measurements), Koontz and Rector
(1995) assigned weights to the results from each state
to compensate for the geographic imbalance in
locations where PFT measurements were taken. The
results were weighted in such a way that the resultant
number of cases would represent each state in
proportion to its share of occupied housing units, as
determined from the 1990 U.S. Census of Population
and Housing.
Table 19-25 shows summary statistics from the
Koontz and Rector (1995) analysis, for the country as
a whole and by census regions. Based on the statistics
for all regions combined, the authors suggested that a
10th percentile value of 0.18 ACH would be
appropriate as a conservative estimator for air
exchange in residential settings, and that the
50th percentile value of 0.45 ACH would be
appropriate as a typical air exchange rate. In applying
conservative or typical values of air exchange rates, it
is important to realize the limitations of the underlying
database. Although the estimates are based on
thousands of measurements, the residences
represented in the database are not a random sample of
the U.S. housing stock. Also, the sample population is
not balanced in terms of geography or time of year,
although statistical techniques were applied to
compensate for some of these imbalances. In addition,
PFT measurements of air exchange rates assume
uniform mixing of the tracer within the building. This
is not always so easily achieved. Furthermore, the
degree of mixing can vary from day to day and house
to house because of the nature of the factors
controlling mixing (e.g., convective air monitoring
driven by weather, and type and operation of the
heating system). The relative placement of the PFT
source and the sampler can also cause variability and
uncertainty. It should be noted that sampling is
typically done in a single location in a house that may
not represent the average from that house. In addition,
very high and very low values of air exchange rates
based on PFT measurements have greater
uncertainties than those in the middle of the
distribution. Despite such limitations, the estimates in
Table 19-25 are believed to represent the best
available information on the distribution of air
exchange rates across U.S. residences throughout the
year.
19.5.1.1.2. Persily et al. (2010)—Modeled
infiltration rate distributions for U.S. housing
Persily etal. (2010) generated frequency
distributions of residential infiltration rates using
CONTAM, a multizone airflow model. A collection of
209 residences was selected to be representative of
80% of the U.S. housing stock. The residences were
taken from a database resulting from two residential
housing surveys: the U.S. Department of Energy
Residential Energy Consumptions Survey (RECS) and
the U.S. Census Bureau American Housing Survey
(AHS). Together, these data sets included over
60,000 U.S. residences. The RECS 1997 was
conducted between mid-April to the middle of June
1997 (U.S. DOE, 1997). The residences were grouped
into four categories: detached, attached, manufactured
homes, and apartments, and include key
characteristics such as age, floor area, number of
floors, foundation type, and garage. Representations of
these residences were created in the airflow model
CONTAM, and were used in this study to provide
distributions for infiltration rates. The simulations
were conducted for 19 cities representing U.S.
climates and accounted for the impacts of ventilation
system operation on infiltration rates.
Distributions of air change rates for various house
categories are presented in Table 19-26. The 10th and
50th percentiles national average air change rate for
single family homes were 0.16 and 0.44 ACH,
respectively. For all house categories, the 50th
percentile air change rate ranged from 0.09 to
0.58 ACH. In general, houses built after 1970 are
tighter and show lower air exchange rates than those
built before 1970.
The advantages of this study are that it is based on
a relatively large number of homes and that the
residences are representative of homes across the
United States. However, the results of the study are
based on modeling and the data used to generate the
simulations were collected in 1997.
19.5.1.2. Relevant Studies of Residential Air
Exchange Rates
19.5.1.2.1. Nazaroff et al. (1988)—Radon entry
via potable water
Nazaroff etal. (1988) aggregated the data from
two studies conducted earlier using tracer-gas decay.
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At the time these studies were conducted, they were
the largest U.S. studies to include air exchange
measurements. The first (Grot and Clark, 1981) was
conducted in 266 dwellings occupied by low-income
families in 14 different cities. The geometric
mean ± standard deviation for the air exchange
measurements in these homes, with a median house
age of 45 years, was 0.90 ±2.13 ACH. The second
study (Grimsrud etal., 1983) involved 312 newer
residences, with a median age of less than 10 years.
Most of the houses were located in Washington,
California, Colorado, New York and Ontario, Canada.
Based on measurements taken during the heating
season, the geometric mean ± standard deviation for
these homes was 0.53 ± 1.71 ACH. Based on an
aggregation of the two distributions with proportional
weighting by the respective number of houses studied,
Nazaroff etal. (1988) developed an overall
distribution with a geometric mean of 0.68 ACH and a
geometric standard deviation of 2.01.
The limitation of this study is that houses did not
represent all climatic regions of the United States and
the number of houses included in the studies was
small.
19.5.1.2.2. Versar (1989)—Database of PFT
ventilation measurements
The residences included in the PFT database do
not constitute a random sample across the United
States. They represent a compilation of homes visited
in the course of about 100 separate field-research
projects by various organizations, some of which
involved random sampling, and some of which
involved judgmental or fortuitous sampling.
Table 19-27 summarizes the larger projects in the PFT
database, in terms of the number of measurements
(samples), states where samples were taken, months
when samples were taken, and summary statistics for
their respective distributions of measured air exchange
rates. For selected projects (Lawrence Berkeley
Laboratory, Research Triangle Institute, Southern
California—SOCAL), multiple measurements were
taken for the same house, usually during different
seasons. A large majority of the measurements are
from the SOCAL project that was conducted in
Southern California. The means of the respective
studies generally range from 0.2 to 1.0 ACH, with the
exception of two California projects—RTI2 and
SOCAL2. Both projects involved measurements in
Southern California during a time of year (July) when
windows would likely be opened by many occupants.
The limitation of this study is that the PFT
database did not base its measurements on a sample
that was statistically representative of the national
housing stock. PFT has been found to underpredict
seasonal average air exchange by 15 to 35% (Sherman,
1989). Using PFT to determine air exchange can
produce significant errors when conditions in the
measurement scene greatly deviate from idealizations
calling for constant, well-mixed conditions.
19.5.1.2.3. Murray and Burmaster
(1995)—Residential air exchange rates in the
United States: empirical and estimated parametric
distributions by season and climatic region
Murray and Burmaster (1995) analyzed the PFT
database using 2,844 measurements (essentially the
same cases as analyzed by Koontz and Rector (1995),
but without the compensating weights). These authors
summarized distributions for subsets of the data
defined by climate region and season. The months of
December, January, and February were defined as
winter; March, April, and May were defined as spring;
and so on. Table 19-28 summarizes the results of
Murray and Burmaster (1995) Neglecting the summer
results in the colder regions, which have only a few
observations, the results indicate that the highest air
exchange rates occur in the warmest climate region
during the summer. As noted earlier, many of the
measurements in the warmer climate region were from
field studies conducted in Southern California during
a time of year (July) when windows would tend to be
open in that area. Data for warmer climate region in
particular should be used with caution because other
areas within this region tend to have very hot summers,
and residences use air conditioners, resulting in lower
air exchange rates. The lowest rates generally occur in
the colder regions during the fall.
19.5.1.2.4. Diamond et al. (1996)—Ventilation
and infiltration in high-rise apartment buildings
Diamond etal. (1996) studied air flow in a
13-story apartment building and concluded that "the
ventilation to the individual units varies considerably."
With the ventilation system disabled, units at the lower
level of the building had adequate ventilation only on
days with high temperature differences, while units on
higher floors had no ventilation at all. At times, units
facing the windward side were over-ventilated. With
the mechanical ventilation system operating, they
found wide variation in the air flows to individual
apartments. Diamond etal. (1996) also conducted a
literature review and concluded there were little
published data on air exchange in multifamily
buildings, and that there was a general problem
measuring, modeling, and designing ventilation
systems for high-rise multifamily buildings. Air flow
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was dependent upon building type, occupants'
behavior, unit location, and meteorological conditions.
19.5.1.2.5. Graham et al. (2004)—Contribution
of vehicle emissions from an attached garage to
residential indoor air pollution levels
There have been several studies of vehicle
emission seepage into homes from attached garages,
which examined a single home. Graham et al. (2004)
conducted a study of vehicle emission seepage of
16 homes with attached garages. On average, 11% of
total house leakage was attributed to the house/garage
interface (equivalent to an opening of 124 cm2), but
this varied from 0.6 to 29.6%. The amount of in-house
chemical concentrations attributed to vehicle
emissions from the garage varied widely between
homes from 9 to 85%. Greater leakage tended to occur
in houses where the garage attached to the house on
more than one side. The home's age was not an
important factor. Whether the engine was warm or
cold when it was started was important because
cold-start emissions are dominated by the by-products
of incomplete combustion. Cold-start tail pipe
emissions were 32 times greater for carbon monoxide
(CO), 10 times greater for nitrogen oxide (NOx), and
18 times greater for total hydrocarbon emissions than
hot-start tailpipe emissions.
19.5.1.2.6. Price et al. (2006)—Indoor-outdoor
air leakage of apartments and commercial
buildings
Price et al. (2006) compiled air exchange rate data
from 14 different studies on apartment buildings in the
United States and Canada. The authors found that
indoor-outdoor air exchange rates seem to be twice as
high for apartments as for single-family houses. The
observed apartment air exchange rates ranged from 0.5
to 2 ACH.
19.5.1.2.7. Breen et al. (2010)—Residential air
exchange rates from questionnaires and
meteorology: model evaluation in central North
Carolina
Breen et al. (2010) conducted a study comparing
air exchange rate predictions from two mechanistic
models with measurements from 31 detached homes
in central North Carolina. Air monitoring was
performed for 7 consecutive days in each of four
consecutive seasons from summer 2000 to spring
2001. The study included two cohorts. The Raleigh
cohort consisted of low to moderate socioeconomic
status neighborhoods and the Chapel Hill cohort
include moderate socioeconomic status
neighborhoods (Breen et al., 2010). Daily 24-hour air
exchange rates were measured using the PFT method.
Distributions of air exchange rate for each season and
number of days that windows were opened are
presented in Table 19-29. It is important to note that
information about amount of time that windows were
open during the day is lacking.
19.5.1.2.8. Yamamoto et al. (2010)—Residential
air exchange rates in three U.S. metropolitan
areas: results from the relationship among indoor,
outdoor, and personal air study 1999—2001
Between 1999 and 2001, Yamamoto et al. (2010)
conducted approximately 500 indoor-outdoor air
exchange rate calculations based on residences in
metropolitan Elizabeth, NJ; Houston, TX; and Los
Angeles, CA. The median air exchange rate across
these urban areas was 0.71 ACH; 0.87 in California,
0.88 in New Jersey, and 0.47 in Texas. In Texas, the
measured air exchange rates were lower in the summer
cooling season (median =0.37 ACH) than in the
winter heating season (median = 0.63 ACH), likely
because of the reported use of room air conditioners.
The measured air exchange rates in California were
higher in summer (median =1.13 ACH) than in winter
(median = 0.61 ACH) because summers in Los
Angeles County are less humid than New Jersey or
Texas, and residents are more likely to utilize natural
ventilation through open windows and screened doors.
In New Jersey, air exchange rates in the heating and
cooling seasons were similar.
19.5.1.3. Key Study of Nonresidential Air
Exchange Rates
19.5.1.3.1. Turk et al. (1987)—Commercial
building ventilation rates and particle
concentrations
Few air exchange rates for commercial buildings
are provided in the literature. Turk etal. (1987)
conducted indoor air quality measurements, including
air exchange rates, in 38 commercial buildings. The
buildings ranged in age from 0.5 to 90 years old.
One test was conducted in 36 buildings, and two tests
were conducted in 2 buildings. Each building was
monitored for 10 working days over a 2-week period
yielding a minimum sampling time of 75 hours per
building. Researchers found an average ventilation
measurement of 1.5 ACH, which ranged from 0.3 to
4.1 ACH with a standard deviation of 0.87.
Table 19-30 presents the results by building type.
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19.5.1.3.2. Bennett et al. (2012)—Ventilation,
temperature, and HVAC characteristics in small
and medium commercial buildings in California
HVAC system characteristics and ventilation
rates of commercial buildings in California were
evaluated by Bennett et al. (2012). A total of 37 small
and medium commercial buildings (SMCBs) were
selected for study and were classified into small
(24 buildings, 90-1,100 m2), medium (7 buildings,
1,100-2,300 m2), and medium/large (6 buildings,
2,300-4,600 m2). The majority of the SMCBs were
selected to be representative of retail establishments,
offices and restaurants, the most frequent building
types in California. Other building types, selected for
their potential for indoor pollutant sources, included
beauty salons, dental offices, gas stations and gyms.
For each building, the heating, ventilating, and air
conditioning (HVAC) systems were inspected and
measurements of air exchange and indoor
environmental quality parameters, such as CO2 levels,
temperature and relative humidity were taken. In
addition, whole building ventilation rates were
determined using a tracer decay method.
Ventilation measurements for the buildings are
presented in Table 19-31. The mean air exchange rate
was 1.6 ± 1.7 exchanges per hour, and was similar
between buildings with or without outdoor air
provided.
This study provides useful information on the
HVAC system characteristics and ventilation rates of
SMCBs. However, the sample size was relatively
small and all of the SMCBs were located in California
which may not be representative of SMCBs located in
other areas of the United States.
19.5.2. Indoor Air Models
Achieving adequate indoor air quality in a
nonresidential building can be challenging. There are
many factors that affect indoor air quality in buildings
(e.g., building materials, building configuration,
outdoor environment, ventilation systems, operation
and maintenance, occupants and their activities).
Indoor air models are typically used to study, identify,
and solve problems involving indoor air quality in
buildings, as well as to assess efficiency of energy use.
The emphasis of most models is on the physical
processes, but for some chemical reactions indoor
which may be an important, but variable sink. Models
generally assume a known and constant rate of
reaction.
Indoor air quality models generally are not
software products that can be purchased as "off-the-
shelf' items. Most existing software models are
research tools that have been developed for specific
purposes and are being continuously refined by
researchers. Leading examples of indoor air models
implemented as software products are as follows:
¦ CONTAM 3.2—CONTAM was developed at
the National Institute of Standards and
Technology (NIST) with support from EPA
and the U.S. DOE. (Dols and Polidoro, 2016;
Wang et al., 2010; Axley, 1988). CONTAM
has been used by others to study the effects of
model parameters (e.g., wind speed, presence
of natural and mechanical ventilation) and the
presence of an attached garage on the
infiltration of contaminants indoors (Nirvan
et al., 2012).
¦ IAQX—The Indoor Air Quality and Inhalation
Exposure model is a Windows-based
simulation software package developed by
EPA (Guo, 2000).
¦ CPIEM 2.0—The California Population Indoor
Exposure Model was developed for the
California Air Resources Board (Rosenbaum
et al., 2002).
¦ TEM—The Total Exposure Model was
developed with support from EPA and the U.S.
Air Force (Wilkes, 1998; Wilkes and Nuckols,
2000).
¦ RISK—RISK was developed by the Indoor
Environment Management Branch of the EPA
National Risk Management Research
Laboratory (Sparks, 1997).
¦ TRIM—The Total Risk Integrated
Methodology is an ongoing modeling project
of EPA's Office of Air Quality Planning and
Standards (Efroymson and Murphy, 2001;
Palmaetal., 1999).
¦ TOXLT/TOXST—The Toxic Modeling
System Long-Term was developed along with
the release of the new version of the EPA's
Industrial Source Complex Dispersion Models
(U.S. EPA, 1995).
¦ MIAQ—The Multi-Chamber Indoor Air
Quality Model was developed for the
California Institute of Technology and
Lawrence Berkeley National Laboratory.
Documentation last updated in 2002. (Nazaroff
and Cass, 1986; Nazzaroff and Cass, 1989a).
¦ MCCEM 1.2—the Multi-Chamber Consumer
Exposure Model was developed for EPA
Office of Pollution Prevention and Toxics
(EPA/OPPT) (GEOMET, 1989; Koontz and
Nagda, 1991).
¦ ART—Advanced Regulation, Evaluation,
Authorization and restriction of Chemicals
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(REACH) Tool was designed to model whei
inhalation exposures in the occupational setting
for a defined group of workers sharing specific
operational conditions (Tielemans et al., 2011,
2008; Cherrie etal., 2011)
Price (2001) evaluated the use of many of the
above products (TOXLT/TOXST, MCCEM, IAQX,
CONTAM, CPIEM, TEM, TRIM, and RISK) in a
tiered approach to assessing exposures and risks to
children. The information provided is also applicable
to adults.
19.5.3. Air Infiltration Models
A variety of mathematical models exist for
prediction of air infiltration rates in individual
buildings. A number of these models have been
reviewed, for example, by Breen etal., (2014),
Liddament and Allen (1983), and by Persily and
Linteris (1984). Basic principles are concisely
summarized in the ASHRAE Handbook of
Fundamentals (ASHRAE, 2013). These models have
a similar theoretical basis; all address indoor-outdoor
pressure differences that are maintained by the actions
of wind and stack (temperature difference) effects.
The models generally incorporate a network of
airflows where nodes representing regions of different
pressure are interconnected by leakage paths.
Individual models differ in details such as the number
of nodes they can treat or the specifics of leakage paths
(e.g., individual components such as cracks around
doors or windows versus a combination of
components such as an entire section of a building).
Such models are not easily applied by exposure
assessors, however, because the required inputs
(e.g., inferred leakage areas, crack lengths) for the
model are not easy to gather.
Another approach for estimating air infiltration
rates is developing empirical models. Such models
generally rely on the collection of infiltration
measurements in a specific building under a variety of
weather conditions. The relationship between the
infiltration rate and weather conditions can then be
estimated through regression analysis and is usually
stated in the following form:
A-° + ^,-T,\ + cV
4 = air exchange rate (hours-1),
Ti = indoor temperature (°C),
To = outdoor temperature (°C),
U = windspeed (m/second),
n is an exponent with a value typically
between 1 and 2, and
a, b and c are parameters to be estimated.
Relatively good predictive accuracy usually can
be obtained for individual buildings through this
approach. However, exposure assessors often do not
have the information resources required to develop
parameter estimates for making such predictions.
A reasonable compromise between the theoretical
and empirical approaches has been developed in the
model specified by Dietz etal. (1986). The model,
drawn from correlation analysis of environmental
measurements and air infiltration data, is formulated
as follows:
^ = zf0.006Ar—t/L5l
v C ) (Eqn. 19-2)
where:
A = average ACH or infiltration rate,
hours-1,
L = generalized house leakiness factor
(l
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Chapter 19—Building Characteristics
3 (0.006 x 20 +0.03/5 x 51.5), or 0.56 ACH. This
prediction applies under the condition that exterior
doors and windows are closed and does not include the
contributions, if any, from mechanical systems (see
Section 19.3.3.3). Occupant behavior, such as opening
windows, can, of course, overwhelm the idealized
effects of temperature and wind speed.
Chan etal. (2005) analyzed the U.S. Residential
Air Leakage database at Lawrence Berkley National
Laboratory (LBNL) containing approximately
70,000 air leakage measurements from 30 states
(predominantly Ohio, Alaska, and Wisconsin). They
present the following equation for estimating ACH:
ACH = 48
2.5 )°3 NL
H
HF
M
(Eqn. 19-3)
where:
ACH = air changes per hour,
H = building height (meters),
NL = normalized leakage (unitless),
F = scaling factor (unitless), and
h = hours.
Chan et al. (2005) found that "older and smaller
homes are more likely to have higher normalized
leakage areas than newer and larger ones."
Table 19-32 summarizes the normalized leakage
distributions in the United States.
It should be noted that newer homes were
generally built tighter until about 1997 when the
construction trend leveled off. Sherman and Matson
(2002) also examined LBNL's U.S. Residential Air
Leakage database and found that average normalized
leakage for 22,000 houses already in the database was
1.18 NL (total leakage cm2 normalized for dwelling
size m2), but leakage among the 8,300 newer homes
averaged 0.30 NL.
19.5.4. Vapor Intrusion
Vapor intrusion is the process by which
contaminants present in the subsurface (both soil and
groundwater) migrate through the soil via diffusion
and advection and can enter building structures
through the foundation cracks (U.S. EPA 2015, 2012;
Murphy and Chan, 2011; Yao etal., 2011). In 1998,
concerns about subsurface contamination of soil or
ground water impacting indoor air quality led the EPA
to develop a series of models for estimating health
risks from subsurface vapor intrusion into buildings
based on the analytical solutions of Johnson and
Ettinger (1991). Models describing the vapor entry
into buildings generally consist of two main parts. One
part describes the vapor transport in the soil and the
other its entry into the building (Yao and Suuberg,
2013). Models can vary from simple 1-dimentional
screening tools to more complex 3-dimentional
models requiring numerical solutions (Yao and
Suuberg, 2013). Since 1991, the models have been
revised, and new models have been added. The
3-phase soil contamination models theoretically
partition the contamination into three discrete phases:
(1) in solution with water, (2) sorbed to the soil
organic carbon, and (3) in vapor phase within the
air-filled pores of the soil. Two new models have been
added, allowing the user to estimate vapor intrusion
into buildings from measured soil gas data (U.S. EPA
2000a). When Non-Aqueous Phase Liquid (NAPL) is
present in soils, the contamination includes a fourth or
residual phase. In such cases, the new NAPL models
can be used to estimate the rate of vapor intrusion into
buildings and the associated health risks. The new
NAPL models use a numerical approach for
simultaneously solving the time-averaged soil and
building vapor concentration for each of up to 10 soil
contaminants (U.S. EPA 2000a). This involves a
series of iterative calculations for each contaminant. A
spreadsheet with these models is available online from
EPA at https://www.epa.gov/vaporintrusion/epa~
spreadsheet-modeling-subsurface-vapor-intrusion.
Technical information and resources pertaining to
vapor intrusion can be found in
https://www.epa.gov/vaporintrusioii/vapor-intrusion-
resources.
Although mathematical models such as the
Johnson and Ettinger (1991) have been widely used,
vapor intrusion modeling has been the focus of more
recent studies (Yao and Suuberg, 2013). Other
analytical approximations have been applied to
estimate contaminant subslab concentrations and
study the effects of foundation features and source
location on vapor intrusion (Yao etal., 2012, Yao
etal., 2011). Other researchers have developed a
systematic approach to model steady state advective
and diffusive fluxes between multimedia
compartments including ground water, soil, and air
with applications to vapor intrusion calculations
(Murphy and Chan, 2011). They determined that the
presence of abasement significantly reduces first floor
exposures. In addition, they concluded that the
resistance associated with diffusion in ground water
and water table fluctuations cannot be neglected
(Murphy and Chan, 2011.) In addition to foundation
characteristics, Yao and Suuberg (2013) observed that
biodegradation plays a significant role in subslab
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concentration attenuation. However, other processes,
like reaction mechanisms and kinetics, are not well
understood. The lack of formal vapor intrusion model
validation continues to be a challenge (Yao and
Suuberg, 2013).
19.5.5. Deposition and Filtration
Deposition refers to the removal of airborne
substances to available surfaces that occurs as a result
of gravitational settling and diffusion, as well as
electrophoresis and thermophoresis. Filtration is
driven by similar processes, but is confined to material
through which air passes. Filtration is usually a matter
of design, whereas deposition is a matter of fact.
Outdoor particles can penetrate (infiltrate)
building structures and become a source of indoor
particle exposure (Gao and Zhang etal., 2009).
Infiltration factors are affected by numerous elements
including: air exchange rates, forced air heating,
exhaust fan operation, air conditioning use, the use of
filtration devices, meteorological parameters such as
wind speed, indoor-outdoor temperature differentials,
particle size, and composition of particulate matter
(e.g.,volatile chemicals) (Kearney etal., 2014). Air
exchange rates can have a significant effect on particle
number concentrations indoor under stable outdoor
particle number concentrations. Generally, a higher
ACH results in lower particulate number
concentrations indoors (Guo et al., 2008). Models
have been developed that help predict indoor
concentrations of outdoor particles in residences (El
Orchetal., 2014).
Semivolatile organic compounds (SVOC) are also
present in indoor air environments. Sources of these
compounds include for example: indoor materials,
consumer products (e.g., personal care products,
household cleaning products), combustion products,
environmental tobacco smoke, and intrusion from
outdoor air (Singer etal., 2003; Weschler and
Nazaroff 2008). The formation of organic films on
indoor surfaces have been confirmed by both direct
and indirect measurements (Weschler and Nazaroff,
2017). Weschler and Nazaroff (2017) developed a
simple model of organic film growth to improve
estimates of human exposure to SVOCs.
Gases can also penetrate the building envelope
from attached garages. In addition to automobile
exhaust, people often store gasoline, oil, paints,
lacquers, and yard and garden supplies in garages.
Appliances such as furnaces, heaters, hot water
heaters, dryers, gasoline-powered appliances, and
wood stoves may also impact indoor air quality.
Garages can be a source of volatile organic
compounds (VOCs) such as benzene, toluene,
ethylbenzene, m,p-xylene, and o-xylene. Emmerich
et al. (2003) conducted a literature review on indoor
air quality and the transport of pollutants from
attached garages to residential living spaces. The
authors found the body of literature on the subject was
limited and contained little data with regard to
airtightness and geometry of the house-garage
interface, and the impact of heating and cooling
equipment. They concluded, however, that there is
substantial evidence that the transport of contaminants
from garages has the potential to negatively impact
residences.
19.5.5.1. Deposition
The deposition of particulate matter and reactive
gas-phase pollutants to indoor surfaces is often stated
in terms of a characteristic deposition velocity
(m hour 1) allied to the surface-to-volume ratio
(m2 in 3) of the building or room interior, forming a
first order loss rate (hour-1). Theoretical
considerations specific to indoor environments have
been summarized in comprehensive reviews by
Nazaroff and Cass (1989b) and Nazaroff et al. (1993).
For airborne particles, deposition rates depend on
aerosol properties (size, shape, density) as well as
room factors (thermal gradients, turbulence, surface
geometry). The motions of larger particles are
dominated by gravitational settling; the motions of
smaller particles are subject to convection and
diffusion. Consequently, larger particles tend to
accumulate more rapidly on floors and up-facing
surfaces while smaller particles may accumulate on
surfaces facing in any direction. Figure 19-3 illustrates
the general trend for particle deposition across the size
range of general concern for inhalation exposure
(<10 |im). Nano-particles have been demonstrated to
have higher deposition rates and lower penetration
efficiencies (Guo et al., 2008). Penetration refers to the
infiltration of particles in the air that passes through
the building shell (Chen and Zhao, 2011) (See also
Section 19.5.7). The current thought is that theoretical
calculations of deposition rates are likely to provide
unsatisfactory results due to knowledge gaps relating
to near-surface air motions and other sources of
inhomogeneity (Nazaroff et al., 1993).
19.5.5.1.1. Thatcher and Layton
(1995)—Deposition, resuspension, and penetration
of particles within a residence
Thatcher and Layton (1995) evaluated removal
rates for indoor particles in four size ranges (1-5,
5—10, 10-25, and >25 |im) in a study of one house
occupied by a family of four. Table 19-33 lists these
values. In a subsequent evaluation of data collected in
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100 Dutch residences, Layton and Thatcher (1995)
estimated settling velocities of 2.7 m hour 1 for
lead-bearing particles captured in total suspended
particulate matter samples.
19.5.5.1.2. Wallace (1996)—Indoor particles: a
review
In a major review of indoor particles, Wallace
(1996) cited overall particle deposition per hour
(hour1) for respirable (PM2.5), inhalable (PM10), and
coarse (difference between PM10 and PM25) size
fractions determined from EPA's Particle Total
Exposure Assessment Methodological Study
(PTEAM) study. These values, listed in Table 19-34,
were derived from measurements conducted in nearly
200 residences.
19.5.5.1.3. Thatcher et al. (2002)—Effects of
room furnishings and air speed on particle
deposition rates indoors
Thatcher et al. (2002) measured deposition loss
rate coefficients for particles of different median
diameters (0.55 to 8.66 mm) with fans off and on at
various airspeeds in three types of experimental
rooms: (1) bare (unfurnished with metal floor),
(2) carpeted and unfurnished, and (3) fully furnished.
Table 19-35 summarizes the results.
19.5.5.1.4. He et al. (2005)—Particle deposition
rates in residential houses
He et al. (2005) investigated particle deposition
rates for particles ranging in size from 0.015 to 6 |im.
The lowest deposition rates were found for particles
between 0.2 and 0.3 |im for both minimum (air
exchange rate: 0.61 ± 0.45 hour-1) and normal (air
exchange rate: 3.00 ± 1.23 hour1) conditions. Thus,
air exchange rate was an important factor affecting
deposition rates for particles between 0.08 and 1.0 |im.
but not for particles smaller than 0.08 |im or larger
than 1.0 1.1111.
19.5.5.2. Filtration
A variety of air cleaning techniques have been
applied to residential settings. EPA (2009)
summarizes available information on residential air
cleaners. Basic principles related to residential-scale
air cleaning technologies have also been summarized
in conjunction with reporting early test results
(Offerman etal., 1984). General engineering
principles are summarized in ASHRAE (2016). In
addition to fibrous filters integrated into central
heating and air conditioning systems, extended surface
filters and High Efficiency Particle Arrest filters, as
well as electrostatic systems, are available to increase
removal efficiency. Free-standing air cleaners
(portable and/or console) are also being used.
Shaughnessy and Sextro (2007) discuss the testing
process to evaluate the efficacy of portable air
cleaners. Product-by-product test results reported by
Hanley etal. (1994); Shaughnessy etal. (1994); and
Offerman et al. (1984) exhibit considerable variability
across systems, ranging from ineffectual
(<1% efficiency) to nearly complete removal.
19.5.6. Interzonal Airflows
Exposure assessments for indoor air pollutants
generally assume a well-mixed environment.
However, pollutant concentrations vary with distance
from the source, ventilation rate, and relative height of
the source (Acevedo-Bolton et al., 2012).
Residential structures consist of a number of
rooms that may be connected horizontally, vertically,
or both horizontally and vertically. Before considering
residential structures as a detailed network of rooms,
it is convenient to divide them into one or more zones.
At a minimum, each floor is typically defined as a
separate zone. For indoor air exposure assessments,
further divisions are sometimes made within a floor,
depending on (1) locations of specific contaminant
sources and (2) the presumed degree of air
communication among areas with and without
sources.
Defining the airflow balance for a multiple-zone
exposure scenario rapidly increases the information
requirements as rooms or zones are added. As shown
in Figure 19-4, a single-zone system (considering the
entire building as a single well-mixed volume)
requires only two airflows to define air exchange.
Further, because air exchange is balanced flow (air
does not "pile up" in the building, nor is a vacuum
formed), only one number (the air exchange rate) is
needed. With two zones, 6 airflows are needed to
accommodate interzonal airflows plus air exchange;
with three zones, 12 airflows are required. In some
cases, the complexity can be reduced using judicious
(if not convenient) assumptions. Interzonal airflows
connecting nonadjacent rooms can be set to zero, for
example, if flow pathways do not exist. Symmetry also
can be applied to the system by assuming that each
flow pair is balanced.
Axley (2007) discusses the history and theory of
multizonal airflow models. Examples of interzonal
airflow models include CONTAM (developed by
NIST) and COMIS (Haas etal., 2002; Feustel, 1999;
Feustel and Raynor-Hoosen, 1990).
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19.5.7. House Dust and Soil Loadings
House dust is a complex mixture of biologically
derived material (animal dander, fungal spores, etc.),
particulate matter deposited from the indoor aerosol,
and soil particles brought in by foot traffic. House dust
may contain VOCs (Wolkoff and Wilkins, 1994;
Hirvonen etal., 1995), pesticides from imported soil
particles as well as from direct applications indoors
(Roberts etal., 1991), and trace metals derived from
outdoor sources (Layton and Thatcher, 1995). The
indoor abundance of house dust depends on the
interplay of deposition from the airborne state,
resuspension due to various activities, direct
accumulation, and infiltration.
In the absence of indoor sources, indoor
concentrations of particulate matter are significantly
lower than outdoor levels. For some time, this
observation supported the idea that a significant
fraction of the outdoor aerosol is filtered out by the
building envelope. The ratios of indoor to outdoor
particle concentrations vary depending on factors such
as: the difference in size-dependent indoor particle
emission rates, the geometry of the cracks in building
envelopes, and the air exchange rates (Chen and Zhao,
2011).
It should be noted that carpet dust loadings may
be higher than previously believed. This is important
because embedded dust is a reservoir for organic
compounds. Fortune et al. (2000) compared the mass
of dust in carpets removed using conventional
vacuuming to that removed by vacuuming with a
beater-bar to remove deeply embedded dust. The
amount removed was 10 times that removed by
conventional vacuuming.
19.5.7.1. Roberts et aL (1991)—Development and
Field Testing of a High-Volume Sampler
for Pesticides and Toxics in Dust
Dust loadings, reported by Roberts et al. (1991),
were measured in conjunction with the
Nonoccupational Pesticide Exposure Study (NOPES).
In this study, house dust was sampled from a
representative grid using a specially constructed
high-volume surface sampler. The surface sampler
collection efficiency was verified in conformance with
ASTMF608 (ASTM, 1989). Table 19-36 summarizes
data collected from carpeted areas in volunteer
households in Florida encountered during the course
of NOPES. Seven of the nine sites were single-family
detached homes, and two were mobile homes. The
authors noted that the two houses exhibiting the
highest dust loadings were only those homes where a
vacuum cleaner was not used for housekeeping.
19.5.7.2. Thatcher and Layton
(1995)—Deposition, Resuspension, and
Penetration of Particles within a
Residence
Relatively few studies have been conducted at the
level of detail needed to clarify the dynamics of indoor
aerosols. One intensive study of a California residence
(Thatcher and Layton, 1995), however, provides
instructive results. Using a model-based analysis for
data collected under controlled circumstances, the
investigators verified penetration of the outdoor
aerosol and estimated rates for particle deposition and
resuspension (see Table 19-37). The investigators
stressed that normal household activities are a
significant source of airborne particles larger than
5 |im. During the study, they observed that just
walking into and out of a room could momentarily
double the concentration. The airborne abundance of
submicrometer particles, on the other hand, was
unaffected by either cleaning or walking. They also
concluded that large particles (over 25 |im) settle eight
times faster than small particles (1-5 |im).
Mass loading of floor surfaces (see Table 19-38)
was measured in the study of Thatcher and Layton
(1995) by thoroughly cleaning the house and sampling
accumulated dust, after 1 week of normal habitation
and no vacuuming. The methodology, validated under
ASTM F608 (ASTM, 1989), showed fine dust
recovery efficiencies of 50% with new carpet and 72%
for linoleum. Tracked areas showed consistently
higher accumulations than untracked areas,
confirming the importance of tracked-in material.
Differences between tracked areas upstairs and
downstairs show that tracked-in material is not readily
transported upstairs. The consistency of untracked
carpeted areas throughout the house, suggests that, in
the absence of tracking, particle transport processes
are similar on both floors.
19.6. CHARACTERIZING INDOOR
SOURCES
Product- and chemical-specific mechanisms for
indoor sources can be described using simple emission
factors to represent instantaneous releases, as well as
constant releases over defined time periods; more
complex formulations may be required for
time-varying sources. Guidance documents for
characterizing indoor sources within the context of the
exposure assessment process are limited (see, for
example, Jennings etal., 1987b; Wolkoff, 1995).
Fairly extensive guidance exists in the technical
literature, however, provided that the exposure
assessor has the means to define (or estimate) key
mechanisms and chemical-specific parameters. Basic
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concepts are summarized below for the broad source
categories that relate to airborne contaminants,
waterborne contaminants, and for soil/house dust
indoor sources.
19.6.1. Source Descriptions for Airborne
Contaminants
Table 19-39 summarizes simplified indoor source
descriptions for airborne chemicals for direct emission
sources (e.g., combustion, pressurized propellant
products), as well as emanation sources
(e.g., evaporation from "wet" films, diffusion from
porous media), and transport-related sources
(e.g., infiltration of outdoor air contaminants, soil gas
entry).
Direct-emission sources can be approximated
using simple formulas that relate pollutant mass
released to characteristic process rates. Combustion
sources, for example, may be stated in terms of an
emission factor, fuel content (or heating value), and
fuel consumption (or carrier delivery) rate. Emission
factors for combustion products of general concern
(e.g., CO, NOx) have been measured for a number of
combustion appliances using room-sized chambers
(see, for example, Relwani etal., 1986). Other
direct-emission sources would include volatiles
released from water use and from pressurized
consumer products. Resuspension of house dust (see
Section 19.5.5.1) would take on a similar form by
combining an activity-specific rate constant with an
applicable dust mass.
Diffusion-limited sources (e.g., carpet backing,
furniture, flooring, dried paint) represent probably the
greatest challenge in source characterization for
indoor air quality. Vapor-phase organics dominate this
group, offering great complexity because (1) there is a
fairly long list of chemicals that could be of concern,
(2) ubiquitous consumer products, building materials,
coatings, and furnishings contain varying amounts of
different chemicals, (3) source dynamics may include
nonlinear mechanisms, and (4) for many of the
chemicals, emitting as well as nonemitting materials
evident in realistic settings may promote reversible
and irreversible sink effects. Very detailed
descriptions for diffusion-limited sources can be
constructed to link specific properties of the chemical,
the source material, and the receiving environment to
calculate expected behavior (see, for example,
Schwope etal., 1992; Cussler, 1984). Validation to
actual circumstances, however, suffers practical
shortfalls because many parameters simply cannot be
measured directly.
The exponential formulation listed in Table 19-39
was derived based on a series of papers generated
during the development of chamber testing
methodology by EPA (Dunn, 1987; Dunn and
Tichenor, 1988; Dunn and Chen, 1993). This
framework represents an empirical alternative that
works best when the results of chamber tests are
available. Estimates for the initial emission rate (E0)
and decay factor (k ;) can be developed for hypothetical
sources from information on pollutant mass available
for release (M) and supporting assumptions.
Assuming that a critical time period (tc) coincides
with reduction of the emission rate to a critical level
(Ec) or with the release of a critical fraction of the total
mass (Me), the decay factor can be estimated by
solving either of these relationships:
Ec -kt
C £ Kslc
f
0 (Eqn. 19-4)
where:
Ec
= emission rate to a critical level (|ig
hour1),
E0
= initial emission rate (|ig hour1),
ks
= decay factor (|ig hour1), and
tc
= critical time period (hours),
or
M (Eqn. 19-5)
where:
Mc = critical mass (|ig). and
M = total mass (ng).
The critical time period can be derived from
product-specific considerations (e.g., equating drying
time for paint to 90% emissions reduction). Given
such an estimate for k:„ the initial emission rate can be
estimated by integrating the emission formula to
infinite time under the assumption that all chemical
mass is released:
M = JE0 e - kstdt = —-
0 ks (Eqn. 19-6)
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The basis for the exponential source algorithm has
also been extended to the description of more complex
diffusion-limited sources. With these sources,
diffusive or evaporative transport at the interface may
be much more rapid than diffusive transport from
within the source material, so that the abundance at the
source/air interface becomes depleted, limiting the
transfer rate to the air. Such effects can prevail with
skin formation in "wet" sources like stains and paints
(see, for example, Chang and Guo, 1992). Similar
emission profiles have been observed with the
emanation of formaldehyde from particleboard with
"rapid" decline as formaldehyde evaporates from
surface sites of the particleboard over the first few
weeks. It is then followed by a much slower decline
over ensuing years as formaldehyde diffuses from
within the matrix to reach the surface (see, for
example, Zinn et al., 1990).
Transport-based sources bring contaminated air
from other areas into the airspace of concern.
Examples include infiltration of outdoor
contaminants, and soil gas entry. Soil gas entry is a
particularly complex phenomenon and is frequently
treated as a separate modeling issue (Provoost et al.,
2010; Little et al., 1992; Sextro, 1994). Room-to-room
migration of indoor contaminants would also fall
under this category, but this concept is best considered
using multizone models.
19.6.2. Source Descriptions for Waterborne
Contaminants
Residential water supplies may be a route for
exposure to chemicals through ingestion, dermal
contact, or inhalation. These chemicals may appear in
the form of contaminants (e.g., trichloroethylene) as
well as naturally occurring by-products of water
system history (e.g., chloroform, radon). Among
indoor water uses, showering, bathing, and
hand-washing of dishes or clothes provide the primary
opportunities for dermal exposure. The escape of
volatile chemicals to the gas phase associates water
use with inhalation exposure. The exposure potential
for a given chemical will depend on the source of
water, the types and extents of water uses, and the
extent of volatilization of specific chemicals. Primary
types of residential water use include
showering/bathing, toilet use, clothes washing,
dishwashing, and faucet use (e.g., for drinking,
cooking, general cleaning, or washing hands).
Information about household water use has been
investigated by the Water Research Foundation and
published in the Residential End Use of Water (REU)
(DeOreo et al., 2016). The survey collected data from
2010 through 2013 from randomly selected
single-family houses in the United States and Canada.
The average per capita indoor water use was
58.6 gal/day. Figure 19-5 shows the relative
percentage of indoor per capita water use across all
uses. Toilet flushing was the largest indoor water use
in gallons per capita per day (14.2 gpcd, 24%). Other
relevant information on activity patterns (e.g., time
showering, time indoors, etc.) can be bound in
Chapter 16 of the Exposure Factors Handbook
(U.S. EPA 2011).
Upper-bounding estimates of chemical release
rates from water use can be formulated as simple
emission factors by combining the concentration in the
feed water (g m 3) with the flow rate for the water use
(m3 hour1), and assuming that the chemical escapes to
the gas phase. For some chemicals, however, not all of
the chemical escapes in realistic situations due to
diffusion-limited transport and solubility factors. For
inhalation exposure estimates, this may not pose a
problem because the bounding estimate would
overestimate emissions by no more than
approximately a factor of two. For multiple exposure
pathways, the chemical mass remaining in the water
may be of importance. Refined estimates of volatile
emissions are usually considered under two-resistance
theory to accommodate mass transport aspects of the
water-air system (see, for example, U.S. EPA, 2000b;
Howard-Reed etal., 1999; Moya etal., 1999; Little,
1992; Andelman, 1990; McKone, 1987). More
detailed descriptions of models used to estimate
emissions from indoor water sources including
showers, bathtubs, dishwashers, and washing
machines are included in EPA, (2000b). Release rates
(S) are formulated as
S = KmFw
r -
V-' SA!
£L
H
(Eqn. 19-7)
where:
S
= chemical release rate (g hour1),
K-m
= dimensionless mass-transfer
coefficient,
Fw
= water flow rate (m3 hour1),
Cw
= concentration in feed water (g m 3).
Ca
= concentration in air (g m 3). and
H
= dimensionless Henry's Law
constant.
Because the emission rate is dependent on the air
concentration, recursive techniques are required. The
mass-transfer coefficient is a function of water use
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Chapter 19—Building Characteristics
characteristics (e.g., water droplet size spectrum, fall
distance, water film) and chemical properties
(diffusion in gas and liquid phases). Estimates of
practical value are based on empirical tests to
incorporate system characteristics into a single
parameter (see, for example, Giardino etal., 1990).
Once characteristics of one chemical-water use system
are known (reference chemical, subscript r), the
mass-transfer coefficient for another chemical (index
chemical, subscript /') delivered by the same system
can be estimated using formulations identified in the
review by Little (1992):
/ \ 1'
fDT ^
K
\DLr j
KLr
1 1
KGr H
Dn
\ ^Gi J
/ \1'
fDT ^
\DLr j
(Eqn. 19-8)
where:
Dl = liquid diffusivity (m2 second-1),
DG = gas diffusivity (m2 second-1),
KL = liquid-phase mass-transfer
coefficient,
KG = gas-phase mass transfer coefficient,
and
H = dimensionless Henry's Law
constant.
19.6.3. Soil and House Dust Sources
The rate process descriptions compiled for soil
and house dust provide inputs for estimating indoor
emission rates:
(Eqn. 19-9)
where:
Sd
Md
Rd
Af
= dust emission (g hour-1),
= dust mass loading (g m-2),
= resuspension rates (hour-1), and
= floor area (m2).
Because house dust is a complex mixture, transfer
of particle-bound constituents to the gas phase may be
of concern for some exposure assessments. For
emission estimates, one would then need to consider
particle mass residing in each reservoir (dust deposit,
airborne).
19.7. ADVANCED CONCEPTS
19.7.1. Uniform Mixing Assumption
Many exposure measurements are predicated on
the assumption of uniform mixing within a room or
zone of a house. Mage and Ott (1994) offer an
extensive review of the history of use and misuse of
the concept. Experimental work by Baughman et al.
(1994) and Drescher et al. (1995) indicates that, for an
instantaneous release from a point source in a room,
fairly complete mixing is achieved within 10 minutes
when convective flow is induced by solar radiation.
Another study by Gadgil etal. (2003) showed that
mixing time depended on the room airflow the source
location. However, up to 100 minutes may be required
for complete mixing under quiescent (nearly
isothermal) conditions. While these experiments were
conducted at extremely low air exchange rates
(<0.1 ACH), based on the results, attention is focused
on mixing within a room.
The situation changes if a human invokes a point
source for a longer period and remains in the
immediate vicinity of that source. Personal exposure
in the near vicinity of a source can be much higher than
the well-mixed assumption would suggest. A series of
experiments conducted by GEOMET (1989) for the
EPA involved controlled point-source releases of
carbon monoxide tracer (CO), each for 30 minutes.
Breathing-zone measurements located within 0.4 m of
the release point were 10 times higher than for other
locations in the room during early stages of mixing and
transport.
Similar investigations by Acevedo-Bolton et al.
(2012) studied the proximity of source effects in two
naturally ventilated homes in Northern California.
They found high variability of CO concentrations
measured within 1 m from the source with 5 minute
averages varying more than 100 fold. Other research
conducted by Furtaw et al. (1996) involved a series of
experiments in a controlled-environment, room-sized
chamber. Furtaw etal. (1996) studied spatial
concentration gradients around a continuous point
source simulated by sulfur hexafluoride (SF6) tracer
with a human moving about the room. Average
breathing-zone concentrations when the subject was
near the source exceeded those several meters away by
a factor that varied inversely with the ventilation
intensity in the room. At typical room ventilation rates,
the ratio of source-proximate to slightly-removed
concentration was on the order of 2:1.
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19.7.2. Reversible Sinks
The sorption of SVOCs onto indoor surfaces are
referred to as the "sink effect." Different building
materials sorb different compounds based on polarity,
indoor humidity, and temperature (Won et al., 2001).
Surface roughness also plays a role in the absorption
of chemicals onto surfaces (Wu et al., 2017). The
subsequent re-emission of these compounds into
indoor air is referred to as a "reversible sink." The
reversible sink effect can significantly affect the fate
and transport of indoor SVOCs (Wu et al., 2017). For
some chemicals, the actions of reversible sinks are of
concern. For an initially "clean" condition in the sink
material, sorption effects can greatly deplete indoor
concentrations. However, once enough of the
chemical has been adsorbed, the diffusion gradient
will reverse, allowing the chemical to escape. For
persistent indoor sources, such effects can serve to
reduce indoor levels initially, but once the system
equilibrates, the net effect on the average
concentration of the reversible sink is negligible. Over
suitably short time frames, this can also affect
integrated exposure. For indoor sources whose
emission profile declines with time (or ends abruptly),
reversible sinks can serve to extend the emissions
period as the chemical desorbs long after direct
emissions are finished. Reversible sink effects have
been observed for a number of chemicals in the
presence of carpeting, wall coverings, and other
materials commonly found in residential
environments. As an example, in the case of
environmental tobacco smoke, clothing and human
skin have been found to serve as a reversible sink. The
lingering residues of tobacco products are referred to
as third-hand smoke (Sleiman et al., 2010).
Interactive sinks (and models of the processes) are
of special importance; while sink effects can greatly
reduce indoor air concentrations, re-emission at lower
rates over longer time periods could greatly extend the
exposure period of concern. For completely reversible
sinks, the extended time could bring the cumulative
exposure to levels approaching the sink-free case.
Publications (Axley and Lorenzetti, 1993; Tichenor
et al., 1991) show that first principles provide useful
guidance in postulating models and setting
assumptions for reversible-irreversible sink models.
Sorption/desorption can be described in terms of
Langmuir (monolayer) as well as
Brunauer-Emmet-Teller (BET, multilayer)
adsorption.
19.8. REFERENCES FOR CHAPTER 19
Acevedo-Bolton, V; Cheng, K-C; Jiang, R-T; Ott,
WR; Klepeis, NE; Hildcmann. LM. (2012)
Measurement of the proximity effect for
indoor air pollutant sources in two homes. J
Environ Monit 14(1):94-104.
Andelman, JB. (1990) Total exposure to volatile
organic compounds in potable water. In:
Ram, NM; Christman, RF; Cantor, KP; eds.
Significance and treatment of volatile organic
compounds in water supplies. Chelsea, MI:
Lewis Publishers; pp 485-504.
Andersson, B; Andersson, K; Sundell, J; Zingmark, P-
A. (1993) Mass transfer of contaminants in
rotary enthalpy heat exchangers. Indoor Air
3(2):143-148.
ASHRAE (American Society of Heating Refrigerating
& AC Engineers). (2016) ASHRAE
handbook: HVAC systems and equipment.
Atlanta. GA: ASHRAE.
ASHRAE. (American Society of Heating
Refrigerating & AC Engineers). (2013)
ASHRAE handbook: fundamentals. Atlanta,
GA: ASHRAE.
ASTM (American Society for Testing and Materials).
(1989) Standard laboratory test method for
evaluation of carpet-embedded dirt removal
effectiveness of household vacuum cleaners.
Standard F 608-89. Philadelphia, PA:
ASTM.
Axley, JW. (1988) Progress toward a general
analytical method for predicting indoor air
pollution in buildings: indoor air quality
modeling phase III report. NBSIR 88-3814.
National Bureau of Standards, Gaithersberg,
MD. Available online at
https://archive.org/details/progresstowardge
8838axle.
Axley, JW. (1989) Multi-zone dispersal analysis by
element assembly. Build Environ 24(2): 113-
130.
Axley, JW; Lorenzetti, D. (1993) Sorption transport
models for indoor air quality analysis. In:
Nagda, NL; ed. Modeling of indoor air
quality and exposure. ASTM STP 1205.
Philadelphia, PA: ASTM; pp. 105-127.
Axley, J. 2007. Multizone airflow modeling in
buildings: history and theory. HVAC&R Res
13( 6):907-928.
Baughman, AV; Gadgil, AJ; Nazaroff, WW. (1994)
Mixing of a point source pollutant by natural
convection flow within a room. Indoor Air
4(2): 114-122.
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ual-model-scenarios-vapor-iiitrusion-
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Page 19-35
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
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July 2018
Page 19-36
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-6. Average Estimated Volumes of U.S. Residences, by Housing Type, Census
Region, and Urbanicity
Volume (m3)a
% of Total
Housing Type
Single-family detached
562
63.3
Single-family attached
401
5.9
Apartments in 2-4 unit buildings
249
7.9
Apartments in 5 or more unit buildings
192
16.8
Mobile homes
246
6.1
Census Region
Northeast
480
18.3
Midwest
515
22.8
South
423
37.1
West
387
21.8
Urban and Ruralb
Urban
421
77.6
Rural
536
22.4
All housing types
446
NA
a Volumes calculated from floor areas assuming a ceiling height of 8 feet. Includes all basements, finished or
conditioned (heated or cooled) areas of attics, and conditioned garage space that is attached to the home.
Unconditioned and unfinished areas in attics and attached garages are excluded.
b Housing units are classified as urban or rural using definitions created by the U.S. census bureau.
Source: U.S. DOE (2013).
July 2018
Page 19-37
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-7. Average Volume of Single Family, Multifamily and Mobile Homes by Type3
Number of Stories
Single Family
Multifamily
Mobile Homes
or Levels in
Housing Unit
Volume (m3)
% of Total
Volume (m3)
% of Total
Volume (m3) % of Total
1 story
438
58.8
199
90.8
NA
NA
2 stories
705
37.7
321
8.5
NA
NA
3 or more stories
777
2.0
494
0.7
NA
NA
Split level
635
1.5
NA
NA
NA
NA
Census region
Northeast
644
16.2
224
27.0
233
7.2
Midwest
616
24.5
217
19.9
247
15.9
South
506
37.8
209
29.9
256
56.5
West
476
21.5
191
23.1
225
20.3
Urbanicityb
Urban
531
73.4
210
95.7
227
50
Rural
598
26.6
225
4.3
266
50
a Volumes calculated from floor areas assuming a ceiling height of 8 feet. Includes all basements, finished
conditioned (heated or cooled) areas of attics, and conditioned garage space that is attached to the home.
Unconditioned and unfinished areas in attics and attached garages are excluded.
b Housing units are classified as urban or rural using definitions created by the U.S. Census Bureau.
or
Source: U.S. DOE (2013).
Table 19-8. Residential Volumes in Relation to Year of Construction
Year of Construction
Volume3 (m3)
% of Total
Before 1940
483
12.7
1940-1949
421
4.6
1950-1959
419
11.9
1960-1969
397
11.7
1970-1979
382
16.1
1980-1989
401
15.0
1990-1999
498
14.4
2000-2009
558
13.7
All years
447
100
a Volumes calculated from floor areas assuming a ceiling height of 8 feet. Includes all basements, finished or
conditioned (heated or cooled) areas of attics, and conditioned garage space that is attached to the home.
Unconditioned and unfinished areas in attics and attached garages are excluded.
Source: U.S. DOE (2013).
July 2018
Page 19-38
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-9. Summary of Residential Volume
Distributions Based on U.S. DOE (2008a)a (m3)
Parameter
Volume
Arithmetic mean
492
Standard deviation
349
10th percentile
154
25th percentile
231
50th percentile
395
75th percentile
648
90th percentile
971
a All housing types, all units.
Source: EPA's Analysis of U.S. DOE (2008a).
Table 19-10. Summary of Residential Volume
Distributions Based on Versar (1989) (m3)
Parameter
Volume
Arithmetic mean
369
Standard deviation
209
10th percentile
167
25th percentile
225
50th percentile
321
75th percentile
473
90th percentile
575
Source: Versar (1989); based on PFT database.
July 2018
Page 19-39
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-11. Number of Residential Single Detached and Mobile Homes by Volume3 (m3)
and Median Volumes by Housing Type
Volume (m3)a
Total Housing Units
Occupied
Seasonal
Vacant
Less than 113.3
2,738
2,218
133
388
113.3-169.7
7,940
6,368
339
1,233
169.9-226.3
13,805
11,409
383
2,012
226.5-339.6
27,098
23,563
664
2,871
339.8-452.8
21,635
19,657
356
1,621
453.1-566.1
14,007
13,028
167
813
566.3-679.4
7,290
6,817
83
390
679.6-905.9
7,075
6,593
93
389
906 or more
3,313
3,024
66
223
Not reported/don't know
29,889
25,614
638
3,637
Median volume (m3)b
340
340
261
NA
Includes single detached and manufactured/mobile homes.
Converted from ft2. Assumes 8-foot ceiling.
Source: U.S. Census Bureau (2015).
July 2018
Page 19-40
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-12. Dimensional Quantities for Residential Rooms
Nominal Dimensions
Length
(meters)
Width
(meters)
Height
(meters)
Volume
(m3)
Wall Area
(m2)
Floor Area
(m2)
Total Area
(m2)
8-foot ceiling
12' x 15'
4.6
3.7
2.4
41
40
17
74
12' x 12'
3.7
3.7
2.4
33
36
13
62
10' x 12'
3.0
3.7
2.4
27
33
11
55
9' x 12'
2.7
3.7
2.4
24
31
10
51
6' x 12'
1.8
3.7
2.4
16
27
7
40
4' x 12'
1.2
3.7
2.4
11
24
4
32
12-foot ceiling
12' x 15'
4.6
3.7
3.7
61
60
17
94
12' x 12'
3.7
3.7
3.7
49
54
13
80
10' x 12'
3.0
3.7
3.7
41
49
11
71
9' x 12'
2.7
3.7
3.7
37
47
10
67
6' x 12'
1.8
3.7
3.7
24
40
7
54
4' x 12'
1.2
3.7
3.7
16
36
4
44
Table 19-13. Examples of Products and Materials Associated with Floor and Wall Surfaces in Residences
Material Sources
Assumed Amount of Surface Covered3 (m2)
Silicone caulk
0.2
Floor adhesive
10.0
Floor wax
50.0
Wood stain
10.0
Polyurethane wood finish
10.0
Floor varnish or lacquer
50.0
Plywood paneling
100.0
Chipboard
100.0
Gypsum board
100.0
Wallpaper
100.0
a Based on typical values for a residence.
Source: Adapted from Tucker (1991).
July 2018
Page 19-41
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-14. Residential Heating Characteristics by U.S. Census (%)
Space Heating Characteristics
Housing Units
U.S. Census Region
%a
Northeast
Midwest
South
West
Total homes
100.0
100.0
100.0
100.0
100.0
Space heating equipment
Use space heating equipment
96.0
100.0
100.0
95.9
89.4
Have space heating equipment but do not use it
2.8
Q
N
3.6
6.4
Do not have space heating equipment
1.2
N
N
0.7
4.2
Main heating fuel and equipment15
Natural gas
47.3
53.8
67.0
28.8
53.4
Central warm-air furnace
38.1
31.9
59.8
24.1
44.7
Steam or hot water system
5.5
19.0
5.7
1.1
1.9
Built-in room heater
1.8
1.9
Q
1.6
3.4
Other equipment
1.9
Q
0.8
2.0
3.4
Electricity
36.3
14.8
20.8
60.1
29.2
Central warm-air furnace
15.1
3.3
9.1
26.6
11.4
Heat pump
10.2
3.3
2.7
20.0
6.8
Built-in electric units
7.6
6.2
7.2
8.3
8.0
Portable electric heater
2.5
Q
Q
4.5
2.3
Other equipment
0.8
N
Q
0.7
0.8
Fuel oil/kerosene
5.0
22.4
Q
2.0
Q
Central warm-air furnace
3.1
13.3
Q
1.4
Q
Steam or hot water system
1.4
7.1
Q
Q
Q
Other equipment
0.6
1.9
Q
Q
Q
Propane
4.7
3.3
8.7
3.8
3.4
Central warm-air furnace
3.6
2.4
7.6
2.3
2.3
Other equipment
1.2
Q
1.1
1.4
0.8
Wood
1.9
2.9
2.3
1.1
2.7
Heating stove
1.5
1.9
1.5
0.9
1.9
Other equipment
0.4
0.5
Q
Q
0.8
Some other fuelc
Q
Q
Q
N
Q
Do not have or use heating equipment
4.0
Q
N
4.3
10.6
Main heating equipment (including all fuels)
Central warm-air furnace
60.1
51.4
77.3
54.5
59.1
Heat pump
11.6
3.8
3.4
22.1
8.3
Steam or hot water system
7.9
28.1
7.6
1.4
3.0
Built-in electric units
7.6
6.2
7.2
8.3
8.0
Built-in oil or gas room heater
2.6
3.3
1.1
2.5
3.8
July 2018
Page 19-42
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-14. Residential Heating Characteristics by U.S. Census (%) (Continued)
Housing Units
U.S. Census Region
Space Heating Characteristics
%a
Northeast
Midwest South
West
Portable electric heater
2.5
Q
Q
4.5
2.3
Heating stove burning wood
1.5
1.9
1.5
0.9
1.9
Built-in pipeless furnace
1.0
Q
Q
0.7
1.9
Fireplace
0.6
Q
Q
0.5
1.1
Some other equipment
0.8
Q
Q
0.7
Q
Do not use heating equipment
4.0
Q
N
4.3
10.6
Secondary heating fuel and equipment
Secondary heating equipment used
36.6
41.0
39.8
35.4
32.2
Natural gas
6.3
6.7
7.6
5.6
6.4
Fireplace
5.5
5.7
6.4
4.7
6.1
Some other equipment
0.8
Q
1.1
0.9
0.4
Electricity
19.4
21.9
22.0
18.0
16.7
Portable electric heaters
17.0
18.6
19.7
16.4
14.0
Some other equipment
2.4
3.3
2.3
1.6
2.7
Wood
7.9
7.6
7.6
8.1
7.6
Heating stove
3.1
4.8
3.0
2.5
3.0
Fireplace
4.7
2.9
4.2
5.6
4.5
Some other equipment
Q
N
Q
N
N
Some other fuel
3.0
4.3
2.3
3.6
1.5
Do not use secondary heating equipment
59.4
59.0
60.2
60.6
57.2
a Total United States includes all primary occupied housing units in the 50 states and the District of Columbia. Vacant
housing units, seasonal units, second homes, military housing, and group quarters are excluded. Housing
characteristics data were collected between August 2015 and April 2016.
b Use of heating equipment for another housing unit also includes the use of the heating equipment for a business or
farm building as well as another housing unit.
c Some other fuel includes coal and district steam.
Q = Data withheld either because the Relative Standard Error (RSE) was greater than 50% or fewer than 10 households
were sampled
N = No cases in reporting sample.
Notes: Because of rounding, data may not sum to totals.
Source: EPA Analysis of U.S. DOE (2015).
July 2018
Page 19-43
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-15. Residential Heating Characteristics by Climate Region (%)
Space Heating
Housing
Units %a
Climate Region
b
Very
Cold/
Cold
Mixed-
Humid
Mixed-
Dry/
Hot-Dry
Hot-
Humid
Marine
Total homes
100.0
100.0
100.0
100.0
100.0
100.0
Space heating equipment
Use space heating equipment
96.0
99.8
100.0
84.5
89.9
93.9
Have space heating equipment but do not use it
2.8
Q
Q
10.9
7.0
4.5
Do not have space heating equipment
1.2
Q
Q
4.7
3.1
Q
Main heating fuel and equipment0
Natural gas
47.3
61.6
42.9
54.3
22.8
48.5
Central warm-air furnace
38.1
51.4
31.0
44.2
19.7
40.9
Steam or hot water system
5.5
7.8
8.3
2.3
Q
Q
Built-in room heater
1.8
1.2
1.2
4.7
2.2
3.0
Other equipment
1.9
1.2
2.7
3.9
0.9
3.0
Electricity
36.3
19.3
41.7
27.9
64.5
36.4
Central warm-air furnace
15.1
7.1
16.1
13.2
31.6
9.1
Heat pump
10.2
3.1
15.2
7.0
18.4
10.6
Built-in electric units
7.6
7.3
7.1
5.4
8.3
13.6
Portable electric heater
2.5
0.9
3.0
2.3
5.3
3.0
Other equipment
0.8
1.2
Q
Q
Q
Q
Fuel oil
5.0
8.3
6.8
N
Q
Q
Central warm-air furnace
3.1
5.7
3.6
N
Q
Q
Steam or hot water system
1.4
2.1
2.1
N
N
N
Other equipment
0.6
0.7
1.2
N
N
N
Propane
4.7
6.4
6.3
1.6
1.8
3.0
Central warm-air furnace
3.6
5.2
4.5
Q
0.9
Q
Other equipment
1.2
1.2
1.5
Q
0.9
Q
Wood
1.9
2.8
1.8
Q
0.4
4.5
Heating stove
1.5
2.1
1.5
Q
Q
3.0
Other equipment
0.4
0.7
Q
Q
Q
Q
Some other fueld
Q
Q
Q
N
N
N
Do not have or use heating equipment
4.0
Q
Q
15.5
10.1
6.1
Main heating equipment (including all fuels)
Central warm-air furnace
60.1
69.6
55.1
58.1
52.6
51.5
Heat pump
11.6
3.3
17.9
8.5
18.9
10.6
Steam or hot water system
7.9
11.6
11.6
2.3
Q
Q
July 2018
Page 19-44
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-15. Residential Heating Characteristics by Climate Region (%) (Continued)
Climate Region
b
Space Heating
Housing
Units %a
Very
Cold/
Cold
Mixed-
Humid
Mixed-
Dry/
Hot-Dry
Hot-
Humid
Marine
Built-in electric units
7.6
7.3
7.1
5.4
8.3
13.6
Built-in oil or gas room heater
2.6
2.1
2.1
4.7
2.6
4.5
Portable electric heater
2.5
0.9
3.0
2.3
5.3
3.0
Heating stove burning wood
1.5
2.1
1.5
Q
Q
3.0
Built-in pipeless furnace
1.0
0.7
0.9
2.3
Q
Q
Fireplace
0.6
0.5
Q
Q
Q
Q
Some other equipment
0.8
1.7
Q
N
Q
Q
Do not have or use heating equipment
4.0
Q
Q
15.5
10.1
6.1
Secondary heating fuel and equipment
Secondary heating equipment used
36.6
41.5
41.1
23.3
25.4
45.5
Natural gas
6.3
7.8
6.8
6.2
3.5
4.5
Fireplace
5.5
6.6
6.0
6.2
2.6
4.5
Some other equipment
0.8
1.2
0.9
Q
Q
Q
Electricity
19.4
21.9
21.4
10.9
14.5
25.8
Portable electric heaters
17.0
18.6
19.6
10.9
13.2
19.7
Some other equipment
2.4
3.3
1.8
Q
1.3
6.1
Wood
7.9
8.0
8.6
5.4
6.6
12.1
Heating stove
3.1
4.5
3.9
Q
Q
6.1
Fireplace
4.7
3.5
4.8
4.7
6.1
6.1
Some other equipment
Q
Q
Q
N
N
N
Some other fuel
3.0
4.0
4.2
Q
1.3
3.0
Do not use secondary heating equipment
59.4
58.3
58.9
61.2
64.5
48.5
Do not use any heating equipment
4.0
Q
Q
15.5
10.1
6.1
a Total United States includes all primary occupied housing units in the 50 states and the District of Columbia. Vacant
housing units, seasonal units, second homes, military housing, and group quarters are excluded. Housing
characteristics data were collected between August 2015 and April 2016.
b These climate regions were created by the Building America program, sponsored by the U.S. Department of Energy's
Office of Energy and Efficiency and Renewable Energy (EERE).
c Use of heating equipment for another housing unit also includes the use of the heating equipment for a business or
farm building as well as another housing unit.
d Some other fuel includes coal and district steam.
Q = Data withheld either because the Relative Standard Error (RSE) was greater than 50% or fewer than 10 households
were sampled.
N = No cases in reporting sample.
Notes: Because of rounding, data may not sum to totals.
Source: EPA Analysis of U.S. DOE (2015).
July 2018
Page 19-45
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-16. Residential Air Conditioning Characteristics by U.S. Census Region (%)
Housing
Units %a
Northeast
Midwest
South
West
All homes
100.0
100.0
100.0
100.0
100.0
Air-conditioning equipment
Use air-conditioning equipment
87.2
85.7
92.0
95.3
70.1
Do not use air-conditioning equipment
12.8
14.3
7.6
5.0
29.9
Type of air-conditioning equipment used (more
than one may apply)
Use central air-conditioning equipment
65.2
36.2
70.8
81.5
54.9
Do not use central air-conditioning equipment
34.8
63.8
29.2
18.5
45.1
Use individual air-conditioning units
26.7
53.3
26.1
19.6
18.2
With 1 unit
13.3
21.9
15.2
9.0
11.7
With 2 units
8.0
17.6
8.0
5.4
4.5
With 3 or more units
5.5
13.8
2.7
5.2
1.9
Do not use individual air-conditioning units
73.3
46.7
73.9
80.6
81.8
Air-conditioned basement
Yes
11.9
10.0
30.3
6.1
4.9
No
15.0
34.3
24.2
6.1
4.9
Not asked (air-conditioned homes with no
basement)
33.8
8.6
14.4
54.7
38.3
Not asked (unair-conditioned homes, apartments,
and mobile homes)
39.3
47.1
30.7
33.3
51.9
Air-conditioned attic
Yes
1.4
2.9
1.9
0.9
0.8
No
33.8
29.0
36.4
41.4
22.3
Not asked (air-conditioned homes with no attic)
25.5
21.4
31.1
24.3
25.0
Not asked (unair-conditioned homes, apartments,
and mobile homes)
39.3
47.1
30.7
33.3
51.9
Air-conditioned, attached garage
Yes
0.8
Q
0.8
1.1
0.8
No
35.0
27.1
41.3
34.9
35.2
Not asked (air-conditioned homes with no attached
garage)
24.8
25.2
26.9
30.6
12.5
Not asked (unair-conditioned homes, apartments,
and mobile homes)
39.3
47.1
30.7
33.3
51.9
Dehumidifier usage
Use a dehumidifier
14.0
25.2
26.5
7.7
3.4
Less than 4 months
4.9
10.0
9.1
2.0
1.5
4 to 6 months
5.5
8.1
12.1
3.2
0.8
July 2018
Page 19-46
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-16. Residential Air Conditioning Characteristics by U.S. Census Region (%) (Continued)
Housing
Units %a
Northeast
Midwest South
West
7 to 9 months
1.7
3.3
2.7
1.1
Q
10 to 11 months
Q
Q
Q
Q
N
Turned on all 12 months
1.8
3.3
2.7
1.4
Q
Do not use a dehumidifier
86.0
74.8
73.5
92.3
96.6
Use an evaporative or swamp cooler (asked only
in arid areas)
Yes
2.4
N
N
1.1
8.7
No
46.4
N
N
71.8
86.7
Not asked
51.3
100.0
100.0
27.0
4.5
Fan types used (more than one may apply)
Ceiling fans
72.3
58.6
75.4
81.5
64.4
Floor, window, or table fans
45.9
51.9
52.7
38.7
46.6
Whole house fans
5.2
4.3
5.7
4.3
6.8
Attic fans
7.4
8.6
8.0
7.7
5.3
Number of ceiling fans used
0
27.7
41.4
24.6
18.7
35.6
1
17.9
18.1
20.5
13.5
23.1
2
16.0
14.8
17.4
17.1
13.6
3
12.8
11.4
13.6
14.6
9.5
4 or more
25.5
14.8
23.5
36.3
18.2
a Total United States includes all primary occupied housing units in the 50 states and the District of Columbia. Vacant
housing units, seasonal units, second homes, military housing, and group quarters are excluded. Housing
characteristics data were collected between August 2015 and April 2016.
Q = Data withheld either because the Relative Standard Error (RSE) was greater than 50% or fewer than 10 households
were sampled.
N = No cases in reporting sample.
Notes: Because of rounding, data may not sum to totals.
Source: EPA Analysis of U.S. DOE (2015).
July 2018
Page 19-47
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-17. Percentage of Residences with
Basement, by Census Region and EPA Region
Census Region EPA Regions % of Residences With Basements
Northeast
1
93.4
Northeast
2
55.9
Midwest
3
67.9
Midwest
4
19.3
South
5
73.5
South
6
4.1
South
7
75.3
West
8
68.5
West
9
10.3
West
10
11.5
All Regions
45.2
Source: Lucas et al. (1992).
Table 19-18. Percentage of Residences with Basement, by Census Region3
Census Regionb Census Divisions
% of Residences with Basements0
Northeast New England
82.9
Northeast Mid Atlantic
84.8
Midwest
East North Central
75.8
Midwest
West North Central
84.1
South
South Atlantic
26.5
South
East South Central
23.1
South
West South Central
Q
West
Mountain
31.7
West
Mountain North
65.5
West
Mountain South
Q
West
Pacific
All Divisions
14.5
43.5
a
b
c
Q
Housing characteristics data were collected between August 2015 and April 2016.
Housing units are classified using criteria created by the U.S. Census Bureau based on 2010 Census data. Urbanized
areas are densely settled groupings of blocks or tracts with 50,000 or more people, while urban clusters have at least
2,500 but less than 50,000 people. All other areas are rural.
Total United States includes all primary occupied housing units in the 50 states and the District of Columbia. Vacant
housing units, seasonal units, second homes, military houses, and group quarters are excluded. Includes single family
detached and attached homes.
= Data withheld either because the Relative Standard Error (RSE) was greater than 50% or fewer than 10 households
were sampled.
Source:
EPA Analysis of U.S. DOE (2017).
July 2018
Page 19-48
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-19. States Associated with EPA Regions and Census Regions
EPA Regions
Region 1
Region 4
Region 6
Region 8
Connecticut
Alabama
Arkansas
Colorado
Maine
Florida
Louisiana
Montana
Massachusetts
Georgia
New Mexico
North Dakota
New Hampshire
Kentucky
Oklahoma
South Dakota
Rhode Island
Mississippi
Texas
Utah
Vermont
North Carolina
Wyoming
South Carolina
Region 7
Region 2
Tennessee
Iowa
Region 9
New Jersey
Kansas
Arizona
New York
Region 5
Missouri
California
Illinois
Nebraska
Hawaii
Region 3
Indiana
Nevada
Delaware
Michigan
District of Columbia
Minnesota
Region 10
Maryland
Ohio
Alaska
Pennsylvania
Wisconsin
Idaho
Virginia
Oregon
West Virginia
Washington
U.S. Census Bureau Regions
Northeast region
Midwest region
South region
West region
Connecticut
Illinois
Alabama
Alaska
Maine
Indiana
Arkansas
Arizona
Massachusetts
Iowa
Delaware
California
New Hampshire
Kansas
District of Columbia
Colorado
New Jersey
Michigan
Florida
Hawaii
New York
Minnesota
Georgia
Idaho
Pennsylvania
Missouri
Kentucky
Montana
Rhode Island
Nebraska
Louisiana
Nevada
Vermont
North Dakota
Maryland
New Mexico
Ohio
Mississippi
Oregon
South Dakota
North Carolina
Utah
Wisconsin
Oklahoma
Washington
South Carolina
Wyoming
Tennessee
Texas
Virginia
West Virginia
Source: RECS Terminology available on line at: https://www.eia.gov/consumDtion/residential/termmologv.phi>#c
July 2018
Page 19-49
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-20. Percentage of Residences with Certain
Foundation Types by Census Region
% of Residences3-b
Census
Region
With
Basement
With
Crawlspace
With
Concrete Slab
Northeast
74.7
18.4
27.8
Midwest
72.5
26.1
28.9
South
14.7
32.6
59.6
West
16.7
39.2
60.2
All Regions
39.9
29.8
46.5
a Percentage may add to more than 100 because more than one foundation
type may apply to a given residence.
b Included single family attached and detached homes and apartments in
buildings of 2-4 units.
Source: EPA Analysis of U.S. DOE, 2013.
July 2018
Page 19-50
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-21. Average Estimated Volumes3 of U.S. Commercial Buildings,
by Primary Activity
Primary Percentiles
Building
Activity
N
Mean
SE of
Mean
10th
25th
50th
75th
90th
%of
Total
Vacant
134
4,789
581
408
612
1,257
3,823
11,213
3.7
Office
976
5,036
397
510
714
1,359
3,398
8,155
17.0
Laboratory
43
24,681
1,114
2,039
5,437
10,534
40,776
61,164
0.2
Nonrefrigerated
warehouse
473
9,298
992
1,019
1,812
2,945
7,504
16,990
12.0
Food sales
125
1,889
106
476
680
951
2,039
3,398
4.6
Public order and
safety
85
5,253
482
816
1,019
1,699
3,398
8,495
1.5
Outpatient
healthcare
144
3,537
251
680
1,019
2,039
3,398
6,966
2.5
Refrigerated
warehouse
20
19,716
3,377
1,133
1,699
3,398
8,212
38,511
0.3
Religious
worship
311
3,443
186
612
917
2,039
4,163
8,325
7.6
Public assembly
279
4,839
394
595
1,019
2,277
4,417
7,136
5.7
Education
649
8,694
513
527
867
2,379
10,194
23,786
7.9
Food service
242
1,889
112
442
680
1,189
2,039
3,568
6.1
Inpatient
healthcare
217
82,034
5,541
17,330
25,485
36,019
95,145
203,881
0.2
Nursing
73
15,522
559
1,546
5,097
10,534
17,330
38,737
0.4
Lodging
260
11,559
1,257
527
1,376
4,078
10,194
27,184
2.5
Strip shopping
mall
349
7,891
610
1,359
2,277
4,078
6,966
19,709
4.3
Enclosed mall
46
287,978
14,780
35,679
35,679
113,268
453,070
849,505
0.1
Retail other than
mall
355
3,310
218
510
680
1,631
3,398
6,116
9.1
Service
370
2,213
182
459
629
934
2,039
4,587
12.8
Other
64
5,236
984
425
544
1,427
3,398
9,175
1.4
All buildingsb
5,215
5,575
256
527
816
1,699
4,248
10,194
100
a Volumes calculated from floor areas assuming a ceiling height of 12 feet for other structures and
20 feet for warehouses.
b Weighted average calculated from floor areas assuming a ceiling height of 12 feet for all
buildings except warehouses and enclosed malls, which assumed 20-foot ceilings.
iV = Number of observations.
SE = Standard error.
Source: EPA Analysis of U.S. DOE (2008b).
July 2018
Page 19-51
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-22. Nonresidential Buildings: Hours per Week Open and Number of Employees
Number of Hours/Week Open Number of Employees During Main Shift
Primary Building
Activity
N
%
Mean
SE of
Mean
10th
Percentiles
25th 50th 75th
90th
Mean
SE of
Mean
Percentiles
10th
25th
50th
75th
90th
Vacant
134
2.8
6.7
1.2
0
0
0
0
40
0.35
0.08
0
0
0
0
0
Office
976
20.2
54.7
1.6
40
45
54
65
168
34.2
2.8
4
11
57
300
886
Laboratory
43
0.9
103.5
0.8
50
58
98
168
168
105.6
4.5
20
55
156
300
435
Nonrefrigerated warehouse
473
9.8
66.2
4.8
20
40
55
80
168
7.0
0.9
0
1
8
25
64
Food sales
125
2.6
107.3
2.5
60
80
109
127
168
6.3
0.5
1
2
4
15
50
Public order and safety
85
1.8
103.0
7.6
10
40
168
168
168
19.1
2.2
1
4
15
60
200
Outpatient healthcare
144
3.0
52.0
2.8
40
45
54
70
168
21.5
1.9
5
8
40
125
200
Refrigerated warehouse
20
0.4
61.3
0.7
44
53
102
126
168
18.2
2.4
4
8
38
61
165
Religious worship
311
6.5
32.0
2.4
5
13
40
60
79
4.6
0.5
1
1
3
10
19
Public assembly
279
5.8
50.3
3.8
12
40
63
96
125
8.7
1.5
0
2
5
22
80
Education
649
13.5
49.6
1.0
38
42
54
70
85
32.4
00
00
3
14
38
75
133
Food service
242
5.0
85.8
2.6
40
66
84
105
130
10.5
0.9
2
4
8
15
33
Inpatient healthcare
217
4.5
168.0
*
168
168
168
168
168
471.0
40.4
175
315
785
1,300
2,250
Nursing
73
1.5
168.0
*
168
168
168
168
168
44.8
2.5
15
25
50
80
170
Lodging
260
5.4
166.6
0.8
168
168
168
168
168
12.3
2.0
1
3
10
25
80
Retail other than mall
355
7.4
59.1
1.5
42
50
62
80
105
7.8
0.7
2
3
6
22
72
Service
370
7.7
55.0
2.1
40
40
50
68
105
5.9
0.6
1
2
4
10
35
Other
64
1.3
57.8
7.1
12
40
51
90
168
12.3
1.7
1
2
10
44
150
All Activities
4,820
100.0
61.2
1.2
30
45
60
98
168
15.7
1.2
1
3
14
66
300
* All sampled inpatient healthcare and nursing buildings reported being open 24 hours a day, 7 days a week.
iV = Number of observations.
SE = Standard error.
Source: EPA Analysis of U.S. DOE (2008b).
July 2018
Page 19-52
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-23. Nonresidential Heating Energy Sources for Commercial Buildings
Primary Space-Heating Energy Source
Useda
All Buildings with Natural District
Buildings Space Heating Electricity Gas Fuel Oil Heat
All buildings
5,557
4,722
1,819
2,322
205
47
Building floorspace (square feet)
1,001 to 5,000
50
48
51
44
58
Q
5,001 to 10,000
22
22
22
22
18
Q
10,001 to 25,000
16
17
15
19
16
Q
25,001 to 50,000
6
6
6
7
Q
13
50,001 to 100,000
4
4
4
4
3
21
100,001 to 200,000
2
2
1
2
1
19
200,001 to 500,000
1
1
0
1
Q
11
Over 500,000
0
0
0
0
Q
4
Principal building activity
Education
7
8
8
8
8
26
Food sales
3
3
5
2
Q
N
Food service
7
8
8
8
Q
Q
Health care
3
3
3
4
2
4
Inpatient
0
0
Q
0
Q
2
Outpatient
3
3
3
3
Q
Q
Lodging
3
3
5
2
Q
9
Mercantile
11
12
13
12
Q
Q
Retail (other than mall)
8
9
9
8
Q
Q
Enclosed and strip malls
3
3
4
4
Q
Q
Office
18
21
23
21
16
26
Public assembly
6
7
5
7
Q
15
Public order and safety
2
2
Q
2
Q
Q
Religious worship
7
9
7
9
Q
N
Service
11
11
7
12
23
Q
Warehouse and storage
14
9
10
9
Q
Q
Other
2
2
2
2
Q
Q
Vacant
5
2
2
2
Q
Q
July 2018
Page 19-53
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-23. Nonresidential Heating Energy Sources for Commercial Buildings (Continued)
All
Buildings
Buildings with
Space Heating
Primary Space-Heating Energy Source
Useda
Electricity
Natural
Gas
Fuel Oil
District
Heat
Year constructed
Before 1920
7
7
4
8
20
11
1920 to 1945
9
9
6
11
12
15
1946 to 1959
11
11
10
11
14
11
1960 to 1969
11
12
9
14
18
19
1970 to 1979
12
13
12
13
Q
21
1980 to 1989
16
16
20
14
Q
4
1990 to 1999
15
14
15
14
10
4
2000 to 2003
7
7
8
6
Q
9
2004 to 2007
6
6
9
5
Q
4
2008 to 2012
5
6
7
4
Q
Q
Census region and division
Northeast
14
15
8
16
69
32
New England
5
6
2
3
45
Q
Middle Atlantic
9
10
5
12
23
19
Midwest
22
23
11
33
Q
13
East North Central
13
14
5
23
Q
6
West North Central
9
9
6
10
Q
9
South
40
39
57
28
16
38
South Atlantic
20
18
31
10
10
17
East South Central
7
7
8
6
Q
Q
West South Central
14
13
18
12
Q
ll
West
23
22
24
24
Q
15
Mountain
6
6
4
8
Q
Q
Pacific
17
16
20
16
Q
ll
Climate regionb
Very cold/cold
37
38
19
47
76
36
Mixed-humid
31
33
36
31
25
43
Mixed-dry/hot-dry
15
14
18
14
N
9
Hot-humid
14
13
26
5
N
Q
Marine
3
2
Q
4
N
Q
Ownership and occupancy
Nongovernment owned
86
85
88
84
86
45
Owner occupied
44
47
46
44
53
28
July 2018
Page 19-54
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-23. Nonresidential Heating Energy Sources for Commercial Buildings (Continued)
Primary Space-Heating Energy Source
Useda
All
Buildings
Buildings with
Space Heating
Electricity
Natural
Gas
Fuel Oil
District
Heat
Leased to tenant(s)
31
31
34
32
25
Q
Owner occupied and leased
6
7
7
7
Q
4
Unoccupied
4
1
Q
1
Q
Q
Government owned
14
15
12
16
14
55
Federal
1
1
Q
1
Q
2
State
3
4
3
3
Q
38
Local
10
10
8
12
13
15
Energy sources
(more than one may apply)
Electricity
94
100
100
100
100
100
Natural gas
53
61
28
100
7
36
Fuel oil
8
10
5
5
100
21
District heat
1
1
Q
Q
Q
100
District chilled water
1
1
l
0
N
55
Propane
9
10
7
2
23
Q
Other
3
4
2
2
Q
2
Energy end uses
(more than one may apply)
Buildings with space heating
85
100
100
100
100
100
Buildings with cooling
80
90
95
92
66
91
Buildings with water heating
80
90
88
93
82
94
Buildings with cooking
29
32
31
33
28
28
Buildings with manufacturing
5
5
5
5
Q
Q
Buildings with electricity
generation
7
8
7
9
12
32
Percentage of floorspace heated
Not heated
15
N
N
N
N
N
1 to 50
13
15
20
11
15
Q
51 to 99
13
15
15
16
14
15
100
59
70
65
74
71
85
July 2018
Page 19-55
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-23. Nonresidential Heating Energy Sources for Commercial Buildings (Continued)
Primary Space-Heating Energy Source
Useda
All
Buildings
Buildings with
Space Heating
Electricity
Natural
Gas
Fuel Oil
District
Heat
Heating equipment
(more than one may apply)
Heat pumps
11
13
27
5
Q
4
Furnaces
14
16
11
21
Q
Q
Individual space heaters
22
26
22
27
40
17
District heat
1
1
Q
Q
Q
100
Boilers
10
12
5
15
35
Q
Packaged heating units
50
59
58
65
41
6
Other
1
1
1
1
Q
Q
a Additionally, 261,000 buildings used propane and 67,000 buildings used wood, coal, or some other energy source for
primary space heating.
b These climate regions were created by the Building America program, sponsored by the U.S. Department of Energy's
Office of Energy Efficiency and Renewable Energy (EERE).
Q = Data withheld either because the Relative Standard Error (RSE) was greater than 50% or fewer than 20 buildings
were sampled.
N = No cases in reporting sample.
Source: EPA Analysis of U.S. DOE (2016).
July 2018
Page 19-56
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-24. Air Conditioning Energy Sources for Nonresidential (%)
Cooling Energy Sources Used (More Than One May Apply)
Floor Space by Cooling Energy Sources Used (More Than One May
Apply) (million ft2)
All
Building
Buildings
with
s Cooling
Elect-
ricity
Natural
Gas
District
Chilled Water
All
Buildings
Buildings with
Cooling
Electricity
Natural
Gas
District
Chilled
Water
All buildings (N)
5,557
4,461
4,413
12
54
87,093
79,294
76,034
732
4,608
Building floorspace (ft2)
1,001 to 5,000
50
46
47
Q
Q
8,041
6,124
6,107
Q
Q
5,001 to 10,000
22
23
23
Q
Q
8,900
7,304
7,252
Q
Q
10,001 to 25,000
16
17
17
Q
17
14,105
12,357
12,211
Q
145
25,001 to 50,000
6
7
7
Q
Q
11,917
10,813
10,615
Q
Q
50,001 to 100,000
4
4
4
Q
19
13,918
13,069
12,618
Q
567
100,001 to 200,000
2
2
2
Q
17
12,415
12,152
11,034
Q
1,273
200,001 to 500,000
1
1
1
Q
7
10,724
10,518
9,887
Q
1,064
Over 500,000
0
0
0
(*)
2
7,074
6,958
6,310
167
1,306
Principal building activity
Education
7
8
8
Q
46
12,239
11,811
10,673
Q
1,292
Food sales
3
4
4
N
N
1,252
1,190
1,190
N
N
Food service
7
8
8
N
Q
1,819
1,712
1,668
N
Q
Health care
3
3
3
(*)
Q
4,155
4,148
3,966
200
523
Inpatient
0
0
0
(*)
2
2,374
2,374
2,227
176
477
Outpatient
3
3
3
Q
Q
1,781
1,774
1,739
Q
Q
Lodging
3
3
3
Q
Q
5,826
5,700
5,308
Q
Q
Mercantile
11
13
13
Q
N
11,330
11,121
11,121
Q
N
Retail (other than mall)
8
9
9
N
N
5,439
5,230
5,230
N
N
Enclosed and strip malls
3
4
4
Q
N
5,890
5,890
5,890
Q
N
July 2018
Page 19-57
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-24. Air Conditioning Energy Sources for Nonresidential (%) (Continued)
Cooling Energy Sources Used (More Than One May Apply)
Floor Space by Cooling Energy Sources Used (More Than One May
Apply) (million ft2)
All
Buildings
Buildings
with
Cooling
Elect-
ricity
Natural
Gas
District
Chilled Water
All
Buildings
Buildings with
Cooling
Electricity
Natural
Gas
District
Chilled
Water
Office
18
22
22
Q
19
15,952
15,882
15,179
Q
1,096
Public assembly
6
7
7
N
9
5,559
5,235
4,629
N
880
Public order and safety
2
2
2
Q
Q
1,440
1,384
1,358
Q
Q
Religious worship
7
8
8
N
Q
4,557
4,271
4,271
N
Q
Service
11
10
10
N
N
4,630
3,773
3,758
N
N
Warehouse and storage
14
9
9
Q
N
13,077
10,120
10,059
Q
N
Other
2
2
2
Q
Q
2,002
1,820
1,806
Q
Q
Vacant
5
1
1
N
Q
3,256
1,125
1,048
N
Q
Year constructed
Before 1920
7
6
6
N
Q
3,983
3,087
2,908
N
Q
1920 to 1945
9
8
8
Q
Q
6,025
5,215
5,081
Q
Q
1946 to 1959
11
11
11
Q
Q
7,381
6,679
6,569
Q
203
1960 to 1969
11
12
12
Q
20
10,362
9,634
8,962
Q
923
1970 to 1979
12
13
13
Q
17
10,846
10,031
9,440
Q
811
1980 to 1989
16
16
16
Q
6
15,230
14,011
13,830
Q
310
1990 to 1999
15
15
15
Q
19
13,803
12,402
11,924
Q
664
2000 to 2003
7
7
7
Q
9
7,215
6,939
6,463
Q
Q
2004 to 2007
6
7
7
Q
11
6,524
6,071
5,722
Q
418
2008 to 2012
5
5
5
Q
Q
5,723
5,225
5,135
Q
Q
Census region and division
Northeast
14
13
13
50
13
15,534
13,949
13,303
305
794
New England
5
4
4
Q
Q
4,302
3,482
3,317
Q
Q
July 2018
Page 19-58
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-24. Air Conditioning Energy Sources for Nonresidential (%) (Continued)
Cooling Energy Sources Used (More Than One May Apply)
Floor Space by Cooling Energy Sources Used (More Than One May
Apply) (million ft2)
All
Buildings
Buildings
with
Cooling
Elect-
ricity
Natural
Gas
District
Chilled Water
All
Buildings
Buildings with
Cooling
Electricity
Natural
Gas
District
Chilled
Water
Middle Atlantic
9
9
9
25
Q
11,232
10,467
9,986
216
656
Midwest
22
22
22
Q
4
18,919
17,144
16,826
Q
585
East North Central
13
13
14
Q
4
12,742
11,675
11,474
Q
420
West North Central
9
8
8
Q
Q
6,178
5,469
5,352
Q
Q
South
40
42
42
Q
65
34,279
31,734
29,950
Q
2,479
South Atlantic
20
21
21
Q
41
17,981
17,094
16,368
Q
1,202
East South Central
7
8
7
Q
Q
4,904
4,710
4,307
Q
Q
West South Central
14
14
14
Q
ll
11,394
9,931
9,275
Q
773
West
23
23
23
Q
17
18,360
16,467
15,955
Q
749
Mountain
6
6
6
Q
2
4,981
4,489
4,205
Q
Q
Pacific
17
17
17
Q
15
13,379
11,978
11,749
Q
329
Climate region3
Very cold/cold
37
34
34
67
13
31,898
28,228
27,377
403
1,227
Mixed-humid
31
33
33
25
33
27,873
26,365
24,968
272
2,027
Mixed-dry/hot-dry
15
15
15
Q
13
12,037
10,887
10,490
Q
Q
Hot-humid
14
16
15
Q
39
12,831
11,624
11,043
Q
752
Marine
3
2
2
Q
Q
2,454
2,190
2,157
Q
Q
Ownership and occupancy
Nongovernment owned
86
86
86
92
31
67,550
60,960
59,329
542
2,104
Owner occupied
44
46
46
Q
26
30,637
28,174
26,984
147
1,478
Leased to tenant(s)
31
32
32
Q
4
26,115
23,907
23,688
Q
297
Owner occupied and leased
6
7
7
Q
2
8,873
8,602
8,379
Q
329
July 2018
Page 19-59
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-24. Air Conditioning Energy Sources for Nonresidential (%) (Continued)
Cooling Energy Sources Used (More Than One May Apply)
Floor Space by Cooling Energy Sources Used (More Than One May
Apply) (million ft2)
All
Buildings
Buildings
with
Cooling
Elect-
ricity
Natural
Gas
District
Chilled Water
All
Buildings
Buildings with
Cooling
Electricity
Natural
Gas
District
Chilled
Water
Unoccupied
4
1
1
N
N
1,925
278
278
N
N
Government owned
14
14
14
Q
69
19,543
18,334
16,705
Q
2,504
Federal
1
1
1
Q
Q
1,573
1,573
1,403
Q
Q
State
3
4
3
Q
37
5,539
5,252
4,086
Q
1,448
Local
10
10
10
Q
30
12,431
11,508
11,217
Q
612
Q
N
(*)
Notes:
These climate regions were created by the Building America program, sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable
Energy (EERE).
= Data withheld either because the Relative Standard Error (RSE) was greater than 50% or fewer than 20 buildings were sampled.
= No cases in reporting sample.
= Value rounds to zero in the units displayed.
Because of rounding, data may not sum to totals.
Source: EPA Analysis of U.S. DOE (2016).
July 2018
Page 19-60
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-25. Summary Statistics for Residential Air Exchange Rates (in ACH),a by Region
West
Region
North Central
Region
Northeast
Region
South
Region
All
Regions
Arithmetic mean
0.66
0.57
0.71
0.61
0.63
Arithmetic standard deviation
0.87
0.63
0.60
0.51
0.65
Geometric mean
0.47
0.39
0.54
0.46
0.46
Geometric standard deviation
2.11
2.36
2.14
2.28
2.25
10th percentile
0.20
0.16
0.23
0.16
0.18
50th percentile
0.43
0.35
0.49
0.49
0.45
90th percentile
1.25
1.49
1.33
1.21
1.26
Maximum
23.32
4.52
5.49
3.44
23.32
a ACH = Air changes per hour
Source: Koontz and Rector (1995).
Table 19-26. Distribution of Air Exchange Rates in
(ACH)a by House Category
House Category
5%
10%
25%
50%
75%
90%
95%
Single family—national average
0.10
0.16
0.27
0.44
0.70
1.00
1.21
Single family—built before 1940
0.17
0.25
0.39
0.58
0.92
1.33
1.57
Single family—built 1941-1969
0.14
0.21
0.34
0.54
0.81
1.10
1.28
Single family—built 1970-1989
0.09
0.14
0.22
0.36
0.55
0.76
0.89
Single family—built 1990 or
newer
0.05
0.09
0.15
0.26
0.43
0.60
0.70
Detached—East North Central
0.11
0.17
0.28
0.42
0.75
1.10
1.31
Detached—East South Central
0.08
0.13
0.24
0.48
0.67
0.95
1.12
Detached—Middle Atlantic
0.14
0.20
0.30
0.41
0.76
1.09
1.29
Detached—Mountain
0.09
0.14
0.24
0.50
0.63
0.84
0.98
Detached—New England
0.15
0.22
0.32
0.44
0.82
1.18
1.39
Detached—Pacific
0.15
0.20
0.29
0.40
0.61
0.83
0.97
Detached—South Atlantic
0.07
0.12
0.22
0.48
0.63
0.88
1.04
Detached—West North Central
0.11
0.18
0.29
0.45
0.79
1.16
1.39
Detached—West South Central
0.09
0.15
0.28
0.42
0.67
0.90
1.06
Apartments built before 1940
0.11
0.16
0.21
0.31
0.46
0.61
0.72
Apartments built 1941-1969
0.09
0.13
0.18
0.29
0.42
0.56
0.65
Apartments built 1970-1989
0.06
0.10
0.15
0.23
0.39
0.49
0.55
Apartments built 1990 or newer
0.05
0.07
0.08
0.14
0.18
0.31
0.39
ACH = Air changes per hour.
Source: Persily et al. (2010).
July 2018
Page 19-61
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-27. Summary of Major Projects Providing Air Exchange Measurements in the
PFT Database
Mean Air Percentiles
Number of Exchange
Project Code
State
Month(s)a
Measurements
Rate (ACH)
SDb
10th
25th
50th
75th
90th
ADM
CA
5-7
29
0.70
0.52
0.29
0.36
0.48
0.81
1.75
BSG
CA
1,8-12
40
0.53
0.30
0.21
0.30
0.40
0.70
0.90
GSS
AZ
1-3, 8-9
25
0.39
0.21
0.16
0.23
0.33
0.49
0.77
FLEMING
NY
1-6, 8-12
56
0.24
0.28
0.05
0.12
0.22
0.29
0.37
GEOMET1
FL
1,6-8, 10-12
18
0.31
0.16
0.15
0.18
0.25
0.48
0.60
GEOMET2
MD
1-6
23
0.59
0.34
0.12
0.29
0.65
0.83
0.92
GEOMET3
TX
1-3
42
0.87
0.59
0.33
0.51
0.71
1.09
1.58
LAMBERT1
ID
2-3, 10-11
36
0.25
0.13
0.10
0.17
0.23
0.33
0.49
LAMBERT2
MT
1-3, 11
51
0.23
0.15
0.10
0.14
0.19
0.26
0.38
LAMBERT3
OR
1-3, 10-12
83
0.46
0.40
0.19
0.26
0.38
0.56
0.80
LAMBERT4
WA
1-3, 10-12
114
0.30
0.15
0.14
0.20
0.30
0.39
0.50
LBL1
OR
1-4, 10-12
126
0.56
0.37
0.28
0.35
0.45
0.60
1.02
LBL2
WA
1-4, 10-12
71
0.36
0.19
0.18
0.25
0.32
0.42
0.52
LBL3
ID
1-5, 11-12
23
1.03
0.47
0.37
0.73
0.99
1.34
1.76
LBL4
WA
1-4, 11-12
29
0.39
0.27
0.14
0.18
0.36
0.47
0.63
LBL5
WA
2-4
21
0.36
0.21
0.13
0.19
0.30
0.47
0.62
LBL6
ID
3-4
19
0.28
0.14
0.11
0.17
0.26
0.38
0.55
NAHB
MN
1-5,9-12
28
0.22
0.11
0.11
0.16
0.20
0.24
0.38
NYSDH
NY
1-2,4, 12
74
0.59
0.37
0.28
0.37
0.50
0.68
1.07
PEI
MD
3-4
140
0.59
0.45
0.15
0.26
0.49
0.83
1.20
PIERCE
CT
1-3
25
0.80
1.14
0.20
0.22
0.38
0.77
2.35
RTI1
CA
2
45
0.90
0.73
0.38
0.48
0.78
1.08
1.52
RTI2
CA
7
41
2.77
2.12
0.79
1.18
2.31
3.59
5.89
RTI3
NY
1^1
397
0.55
0.37
0.26
0.33
0.44
0.63
0.94
SOCAL1
CA
3
551
0.81
0.66
0.29
0.44
0.66
0.94
1.43
SOCAL2
CA
7
408
1.51
1.48
0.35
0.59
1.08
1.90
3.11
SOCAL3
CA
1
330
0.76
1.76
0.26
0.37
0.48
0.75
1.11
UMINN
MN
1^1
35
0.36
0.32
0.17
0.20
0.28
0.40
0.56
UWISC
WI
2-5
57
0.82
0.76
0.22
0.33
0.55
1.04
1.87
1 = January, 2 = February, etc.
SD = Standard deviation.
Source: Adapted from Versar (1990).
July 2018
Page 19-62
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-28. Distributions of Residential Air Exchange Rates (in ACH)a by Climate Region
and Season
Climate
Regionb Season
Sample Size
Arithmetic
Mean
Standard
Deviation
Percentiles
10th
25th
50th
75th
90th
Coldest Winter
161
0.36
0.28
0.11
0.18
0.27
0.48
0.71
Spring
254
0.44
0.31
0.18
0.24
0.36
0.53
0.80
Summer
5
0.82
0.69
0.27
0.41
0.57
1.08
2.01
Fall
47
0.25
0.12
0.10
0.15
0.22
0.34
0.42
Colder Winter
428
0.57
0.43
0.21
0.30
0.42
0.69
1.18
Spring
43
0.52
0.91
0.13
0.21
0.24
0.39
0.83
Summer
2
1.31
—
—
—
—
—
—
Fall
23
0.35
0.18
0.15
0.22
0.33
0.41
0.59
Warmer Winter
96
0.47
0.40
0.19
0.26
0.39
0.58
0.78
Spring
165
0.59
0.43
0.18
0.28
0.48
0.82
1.11
Summer
34
0.68
0.50
0.27
0.36
0.51
0.83
1.30
Fall
37
0.51
0.25
0.30
0.30
0.44
0.60
0.82
Warmest Winter
454
0.63
0.52
0.24
0.34
0.48
0.78
1.13
Spring
589
0.77
0.62
0.28
0.42
0.63
0.92
1.42
Summer
488
1.57
1.56
0.33
0.58
1.10
1.98
3.28
Fall
18
0.72
1.43
0.22
0.25
0.42
0.46
0.74
a ACH = air changes per hour.
b The coldest region was defined as having 7,000 or more heating degree days, the colder region as 5,500-6,999 degree
days, the warmer region as 2,500-5,499 degree days, and the warmest region as fewer than 2,500 degree days.
— Few observations for summer results in colder regions. Data not available.
Source: Murray and Burmaster (1995).
July 2018
Page 19-63
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-29. Distribution of Measured 24-hour Average Air Exchange Rates in 31 Detached
Homes in North Carolina
Season:
Year3 or
Cohort
Number
of
Detached
Homes
Number of
days
Windows
Openedb
Air Exchange Rates (h"1)
Sample
Size
Mean
SD
Min
P5
P10
P25
P50
P75
P90
P95
Max
Summer:
2000
29
90(44%)
203
0.50
0.58
0.05
0.16
0.21
0.26
0.36
0.50
0.70
1.53
4.83
Fall:
2000
27
63(38%)
167
0.60
0.37
0.09
0.21
0.24
0.35
0.51
0.77
1.03
1.29
2.24
Winter:
2000-01
23
29(22%)
129
1.11
0.88
0.23
0.34
0.40
0.56
0.81
1.25
2.53
3.34
4.87
Spring:
2001
23
71(50%)
143
0.64
0.48
0.15
0.20
0.22
0.34
0.53
0.72
1.16
1.76
3.17
Raleigh
cohortc
27
215(39%)
555
0.70
0.66
0.05
0.21
0.24
0.32
0.51
0.77
1.29
2.00
4.87
Chapell
Hill
cohortd
4
38(44%)
87
0.56
0.44
0.06
0.12
0.16
0.26
0.45
0.70
1.25
1.43
2.58
All
31
253(39%)
642
0.68
0.63
0.05
0.20
0.23
0.32
0.50
0.76
1.27
1.85
4.87
a
b
c
d
SD
Summer: June, July, and August; fall: September, October, and November; winter: December, January, and
February; spring: March, April, and May.
Percentage of days windows are opened in parenthesis relative to corresponding sample size.
Low to moderate socioeconomic status neighborhoods.
Moderate socioeconomic status neighborhoods.
= Standard deviation.
Source:
Breen et al. (2010).
Table 19-30. Air Exchange Rates in Commercial Buildings by Building Type
Building Type
N
Mean
(ACHa) SD
10th Percentile
Range
(ACH)
Educational
1
1.9
0.8 to 3.0
Office (<100,000 ft2)
8
1.5
0.3 to 4.1
Office (>100,000 ft2)
14
1.8
0.7 to 3.6
Libraries
3
0.6
0.3 to 1.0
Multiuse
5
1.4
0.6 to 1.9
Naturally ventilated
3
0.8
0.6 to 0.9
Total (all commercial)
40
1.5 0.87
0.60b
0.3 to 4.1
a ACH = air changes per hour.
b Calculated from data presented in Turk et al. (1987), Table IV.C.l.
iV = Number of observations.
SD = Standard deviation.
Source: Turk et al. (1987).
July 2018
Page 19-64
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-31. Summary Statistics of Ventilation Rates
Measurement
n
Mean SD Min 25th % Median 75th %
95th % Max
Whole building
ventilation rate
Ventilation rate per
area (L/s per m2)
40
1.4 1.4 0.1 0.6 1.0 1.5
3.9 7.7
Ventilation rate per
person (L/s per
person)
40
61 71 7 17 36 72
261 321
Air exchange rate (per
hour)
40
1.6 1.7 0.3 0.7 1.0 1.9
4.7 9.1
Air exchange rate,
doors open (per hour)
7
3.1 2.9 0.6 1.0 2.3 4.0
9.1 9.1
Air exchange rate,
doors shut (per hour)
33
1.3 1.1 0.3 0.7 1.0 1.5
4.3 5.1
HVAC ventilation3
Outdoor air delivery
rate by HVAC units
per
Unit floor area (L/s
per m2)
23
1.2 1.4 0.1 0.3 0.6 1.3
3.4 5.4
Outdoor air delivery
rate by HVAC units
per person (L/s per
person)
23
35 30 2 10 26 69
83 95
Percentage of total
ventilation supplied
through HVAC unitsb
(%)
14
39 25 8 14 35 63
78 78
Additional ventilation
rate (per hour)c
In buildings with
doors kept open
7
2.9 3.0 0.4 1.2 1.8 4.0
9.1 9.1
In buildings with
doors shut
29
0.5 0.6 0.0 0.0 0.4 0.7
1.9 1.9
a Fourteen buildings had HVAC units that did not provide outdoor air. Complete measurements could not be made on
three buildings.
b Fourteen buildings had 0% of outdoor air provided through the HVAC units, and nine buildings were estimated to
have 100% of outdoor air provided through HVAC units.
c One of the 14 buildings that did not provide HVAC ventilation had leakage into the system, and thus, is not included
in the calculation for additional ventilation.
Source: Bennett et al. (2012).
July 2018
Page 19-65
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-32. Statistics of Estimated Normalized Leakage Distribution Weighted for all
Dwellings in the United States
Estimated Normalized Leakage Percentiles Estimated
House Code
5th 10th 25th 50th 75th 90th 95th GM GSD
Low income 0.30 0.39 0.62 0.98 1.5 2.2 2.7 0.92 1.9
Conventional 0.17 0.21 0.31 0.48 0.75 1.1 1.4 0.49 1.9
Whole United 0.17 0.22 0.33 0.52 0.84 1.3 1.7 0.54 2.0
States
GM = Geometric mean.
GSD = Geometric standard deviation.
Source: Chan et al. (2005).
Table 19-33. Particle Deposition During Normal Activities
Particle Removal Rate
Particle Size Range
(hour-1)
1-5
0.5
5-10
1.4
10-25
2.4
>25
4.1
Source: Adapted from Thatcher and Layton (1995).
Table 19-34. Deposition Rates for Indoor Particles
Size Fraction
Deposition Rate (hour ')
PM2.5
0.39
PM10
0.65
Coarse
1.01
Source: Adapted from Wallace (1996).
July 2018
Page 19-66
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-35. Measured Deposition Loss Rate Coefficients (hour"
Fans Off
Room Core Airspeed
5.4 cm/second
Room Core Airspeed
14.2 cm/second
14.2 cm/s
Room Core Airspeed
19.1 cm/second
Median particle
diameter (|im)
Bare room
surfaces
Carpeted
room
Fully
furnished
Bare room
surfaces
Carpeted
room
Fully
furnished
Bare room
surfaces
Carpeted
room
Fully
furnished
Bare room
surfaces
Carpeted
room
Fully
furnished
0.55
1.10
0.12
0.20
0.10
0.13
0.23
0.09
0.18
0.23
0.14
0.16
0.27
0.65
0.10
0.12
0.20
0.10
0.13
0.23
0.10
0.19
0.24
0.14
0.17
0.28
0.81
0.10
0.11
0.19
0.10
0.15
0.24
0.11
0.19
0.27
0.15
0.19
0.30
1.00
0.13
0.12
0.21
0.12
0.20
0.28
0.15
0.23
0.33
0.20
0.25
0.38
1.24
0.20
0.18
0.29
0.18
0.28
0.38
0.25
0.34
0.47
0.33
0.38
0.53
1.54
0.32
0.28
0.42
0.27
0.39
0.54
0.39
0.51
0.67
0.51
0.59
0.77
1.91
0.49
0.44
0.61
0.42
0.58
0.75
0.61
0.78
0.93
0.80
0.89
1.11
2.37
0.78
0.70
0.93
0.64
0.84
1.07
0.92
1.17
1.32
1.27
1.45
1.60
2.94
1.24
1.02
1.30
0.92
1.17
1.46
1.45
1.78
1.93
2.12
2.27
2.89
3.65
1.81
1.37
1.93
1.28
1.58
1.93
2.54
2.64
3.39
3.28
3.13
3.88
4.53
2.83
2.13
2.64
1.95
2.41
2.95
3.79
4.11
4.71
4.55
4.60
5.46
5.62
4.41
2.92
3.43
3.01
3.17
3.51
4.88
5.19
5.73
6.65
5.79
6.59
6.98
5.33
3.97
4.12
4.29
4.06
4.47
6.48
6.73
7.78
10.6
8.33
8.89
8.66
6.79
4.92
5.45
6.72
5.55
5.77
8.84
8.83
10.5
12.6
11.6
11.6
Source: Thatcher et al. (2002).
Table 19-36. Total Dust Loading for Carpeted Areas
Total Dust Load
Household
(g/m2)
Fine Dust (<150 (im) Load (g/m2)
1
10.8
6.6
2
4.2
3.0
3
0.3
0.1
4
2.2; 0.8
1.2; 0.3
5
1.4; 4.3
1.0; 1.1
6
0.8
0.3
7
6.6
4.7
8
33.7
23.3
9
812.7
168.9
Source: Adapted from Roberts et al. (1991).
July 2018
Page 19-67
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-37. Particle Deposition and Resuspension During Normal Activities
Particle Size Range (|im)
Particle Deposition Rate (hour ')
Particle Resuspension Rate (hour ')
0.3-0.5
(Not measured)
9.9 x 10-7
0.6-1
(Not measured)
4.4 x 10-7
1-5
0.5
1.8 x 10-5
5-10
1.4
8.3 x 10-5
10-25
2.4
3.8 x 10~4
>25
4.1
3.4 x 10-5
Source: Adapted from Thatcher and Layton (1995).
Table 19-38. Dust Mass Loading after 1 Week without Vacuum Cleaning
Location in Test House
Dust Loading (g/m2)
Tracked area of downstairs carpet
2.20
Untracked area of downstairs carpet
0.58
Tracked area of linoleum
0.08
Untracked area of linoleum
0.06
Tracked area of upstairs carpet
1.08
Untracked area of upstairs carpet
0.60
Front doormat
43.4
Source: Adapted from Thatcher and Layton (1995).
July 2018
Page 19-68
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Table 19-39. Simplified Source Descriptions for Airborne Contaminants
Description
Components
Dimensions
Direct emission rate
Combustion emission rate
E/HfMf
g hour-1
Ef = emission factor
gJ-1
Hf = fuel content
J mol 1
Mf = fuel consumption rate
mol hour-1
Volume emission rate
Qp CP_e
g hour-1
QP = volume delivery rate
m3 hour-1
CP = concentration in carrier
gm-3
e = transfer efficiency
gg~'
Mass emission rate
Mp We e
g hour-1
Mp = mass delivery rate
g hour-1
We = weight fraction
gg~'
e = transfer efficiency
gg~'
Diffusion limited emission rate
(D/fT1 )(&-C,)4,
g hour-1
Df = diffusivity
m2 hour-1
S = boundary layer thickness
meters
Cs = vapor pressure of surface
gm-3
Ci = room concentration
gm-3
Ai = area
m2
Exponential emission rate
AiEo &kt
g hour-1
Ai = area
m2
E0 = initial unit emission rate
g hour-1 m-2
k = emission decay factor
hour-1
t = time
hours
Transport
Infiltration
Q/'Q
g hour-1
Interzonal
Qji = air flow from zone /
m3 hour-1
Soil gas
Cj = air concentration in zone j
gm-3
July 2018
Page 19-69
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
Air In
Water In
Soil In
Concentration, C
Source
Exposure, E for Occupant(s)
Resus pension
Decay
Removal
* *
Reversible
Sinks
Out
Figure 19-1. Elements of residential exposure.
COM*** RETURN LAYOUT
a»« i 2m I | n
01
F
-------
Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
10-1
T 10-2
w
E
& 10-3
o
o
0)
> 10"4
c
O
S
g 10*®
a.
a
10"6
10-'*
111 ii 1 n 11 0 1 1 10
Particle Diameter (fjnri)
Figure 19-3. Idealized patterns of particle deposition indoors.
Source: Adapted from Nazaroff and Cass (1989a).
July 2018
Page 19-71
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
SWOLE-ZOUE
SYSTEM
TWOJONE
SYSTEM
fHRK-KX*
SVSTEM
N-Zone System Defined by N-(N+1} Airflows
Figure 19-4. Air flows for multiple-zone systems.
Source: Koontz and Rector (1995).
July 2018
Page 19-72
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
I Shower, 11.1 gal/day
I Faucet, 11.1 gal/day
Clothes washer, 9.6
gal/day
I Toilet, 14.2 gal/day
Leak, 7.9 gal/day
Other, 2.5 gal/day
I Bath, 1.5 gal/day
I Dishwasher, 0.7 gal/day
Figure 19-5. Average percentage per capita indoor water use across all uses.
Source: DeOreo et al. (2016). Reprinted with permission. © Water Research Foundation.
July 2018
Page 19-73
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Update for Chapter 19 of the Exposure Factors Handbook
Chapter 19—Building Characteristics
APPENDIX A
Table A-l. Terms Used in Literature Searches
Indoor air and pollutant
Indoor air and mixing
Indoor air and exposure
Indoor air and quality
Indoor air and sinks
Indoor air and exchange
Infiltration rates
Vapor intrusion
House volume
Room volumes
Dunn JE
Axley JW
Koontz MD
Nazaroff WW
Targeted search terms
Uniform mixing
Vapor intrusion
Soil gas entry indoors
Residential air leakage models
Indoor particles
Interzonal airflow models
House dust and soil loadings
July 2018
Page A-l
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