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Simon Pallin,
1
Philip Boudreaux,
1
Soo Jeong Jo,
2
Meghan Perez,
3
and Amy Albaugh
4
Simulations of Indoor Moisture
Generation in U.S. Homes
Citation
Pallin, S., Boudreaux, P., Jo, S. J., Perez, M., and Albaugh, A., “Simulations of Indoor Moisture
Generation in U.S. Homes,” Advances in Hygrothermal Performance of Building Envelopes:
Materials, Systems and Simulations,ASTM STP1599, P. Mukhopadhyaya and D. Fisler, Eds.,
ASTM International, West Conshohocken, PA, 2017, pp. 261–290, http://dx.doi.org/10.1520/
STP159920160111
5
ABSTRACT
In residential buildings, there are many sources that contribute to the total
hourly moisture generation, including occupants and their activities as well as
some appliances. In cases of high indoor moisture generation, indoor air quality,
building envelope durability, and heating, ventilation, and air conditioning efficiency
can all be compromised. Oak Ridge National Laboratory designed a simulation
tool, the generation of indoor heat and moisture (GIHM) tool, to capture the
probabilistic nature of hourly indoor moisture and heat generation caused
by residential building type, occupant behavior, climate zone, incidences of
appliances, and other variables. In this paper, we focus on the moisture aspect of
Manuscript received August 19, 2016; accepted for publication January 4, 2017.
1
Oak Ridge National Laboratory, Building Technologies Research and Integration Center, 1 Bethel Valley Rd.,
Oak Ridge, TN 37830 S. P. http://orcid.org/0000-0001-7197-6746 P. B. http://orcid.org/0000-0002-
2956-4665
2
Virginia Tech, School of Architecture and Design, Blacksburg, VA 24061 http://orcid.org/0000-0003-
0802-6363
3
Johns Hopkins University, Dept. of Geography and Environmental Engineering, Baltimore, MD 21218
4
University of Tennessee, Dept. of Mechanical Engineering, Knoxville, TN 37996
5
ASTM Symposium on Advances in Hygrothermal Performance of Building Envelopes: Materials, Systems and
Simulations on October 26–27, 2016 in Orlando, FL.
6
This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the
U.S. Department of Energy. The United States Government retains a non-exclusive, paid-up, irrevocable,
world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so,
for United States Government purposes. The Department of Energy will provide public access to these
results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/
downloads/doe-public-access-plan).
Copyright V
C2017 by ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959.
ADVANCES IN HYGROTHERMAL PERFORMANCE OF BUILDING ENVELOPES 261
STP 1599, 2017 / available online at www.astm.org / doi: 10.1520/STP159920160111
this tool. Results from the GIHM tool, as sets of hourly profiles of indoor moisture
generation for specifically defined households, can be used as inputs for building
energy simulation software, such as EnergyPlus. If many of these profiles are
used as inputs, then the performance of an energy efficiency measure can be
evaluated for the range of expected operating conditions in different homes. The
GIHM tool can aid in designing heating, ventilation, and air conditioning systems
that control temperature and humidity well, accessing the moisture durability of
envelope components and understanding how different building designs and
materials affect occupant comfort.
Keywords
indoor humidity, moisture sources, relative humidity, simulations, mold, rot,
durability, energy efficiency, generation of indoor heat and moisture (GIHM)
Background
When architects and builders design buildings, it is important to consider moisture
because it can affect the structural performance, such as through deterioration, and
can impact indoor air quality and thus residents’ health [1]. The amount of mois-
ture generated inside homes is also relevant when designing and optimizing heat-
ing, ventilation, and air conditioning (HVAC) systems; accurate moisture load
information, in particular, enables efficient HVAC system development because of
the relationship between latent and sensible heat loads. Despite the importance of
moisture control in buildings, few researchers study moisture generation processes
in residential buildings because of the complex relationship among indoor moisture
generation, outdoor climate, the moisture-buffering capacity of building materials,
and HVAC operation and its impact on the humidity inside the home. Mainly, the
moisture content of the indoor air in residential buildings is a combination of infil-
trated outdoor humidity, the generation of indoor moisture inside the building, the
moisture-buffering capacity of materials inside the building, and the dehumidifying
effect of the HVAC system under cooling cycles. The indoor humidity, or actual
moisture content, is usually higher than the outdoor air moisture content because
of the moisture-generating activities that take place inside the building. However, in
hot, humid climates or during the cooling season, or both, an HVAC system may
remove more moisture than is generated inside the home and therefore lower the
moisture content of the indoor air compared with the outside. To estimate the rela-
tive humidity inside a home, each of these variables must be considered.
Despite the prevalent research of indoor relative humidity levels in residential
buildings in the United States, there is a lack of research that seeks to understand
and compile indoor moisture loads inside homes [2]. Although ASHRAE 160,
Criteria for Moisture Control Design Analysis in Buildings, provides a guideline for
indoor moisture generation rates [3], it does not specify various indoor conditions
other than the number of residents.
Naturally, there is a large variation in relative humidity levels inside residential
buildings. To understand these ranges, further study in identifying moisture loads
262 STP 1599 On Advances in Hygrothermal Performance of Building Envelopes
and understanding the occupants’ behavior inside the homes is needed. This research
seeks a more detailed understanding of indoor moisture generation by creating a sim-
ulation tool that accounts for stochastic variations in moisture-generating activities
and behavioral patterns. The result aims to help designers better predict the moisture
durability performance of building envelope components, optimize the HVAC sys-
tem, and if needed, to create an appropriate moisture control plan.
Introduction
Many sources affect moisture generation in a residential building, such as appliances,
indoor activities, and human bodies. The amount of water vapor in the home increases
because of these sources, and it influences the moisture content of the building materi-
al and the efficiency of the air conditioning system. Other factors can impact indoor
humidity as well, such as outdoor humidity levels, air leakages between the inside and
outside, characteristics of the HVAC system, and the moisture storage capacity of the
building materials. These factors make it complicated to predict and estimate indoor
humidity levels in residential buildings. Of those factors, indoor moisture generation
likely has the highest impact on indoor humidity levels, but at the same time, it is the
hardest to predict. Oak Ridge National Laboratory (ORNL) has developed a simula-
tion tool, called the generation of indoor heat and moisture (GIHM) tool, to estimate
the amount of heat and moisture produced from indoor sources per hour for an entire
year. This paper focuses on the moisture-generating activities and sources listed here:
•Bathing
•Showering
•Grooming
•Cleaning (e.g., mopping)
•Laundry
•Respiration from humans and animals
•Perspiration from humans and animals
•Humidifier operation
•Food preparation
•Hand dishwashing
•Dishwashing machine
•Faucets
•Flushing toilets
•Houseplants
•Aquariums
•Ironing
Because these sources are affected by occupant behaviors, we researched occu-
pant behavior patterns to understand when and how long an activity occurs that
generates indoor moisture. To find such information, appliance and time use sur-
veys of occupants were needed. Once such information is found, a tool can combine
the behavioral analytics with the amount of moisture generated from each activity.
Fig. 1 illustrates how essential information is combined to predict the amount of
moisture generated in residential buildings.
PALLIN ET AL., DOI 10.1520/STP159920160111 263
Method
The GIHM tool is designed to utilize probabilistic input data to produce hourly
indoor moisture generation rates. As Fig. 1 shows, the type of building, number of
residents, and their ages, along with the presence of moisture-generating appliances,
are elements that will impact the overall usage pattern in homes. Further, these user
patterns will define when, for how long, and how frequent activities and appliance
use take place. Once user patterns and the amount of moisture generated from each
activity and appliance have been established, indoor moisture-generation levels can
be estimated.
Each activity and appliance that generates moisture in residential buildings has
a natural variability. To allow for these variances to influence the estimated mois-
ture generation rates, a tool for handling probability distribution input is necessary.
In this work, @Risk by Palisade Corporation [4] has been used as the statistical
computational and analytical tool to estimate moisture generation in residential
buildings.
Procedure
The moisture sources in the following subsections contribute to the indoor mois-
ture generation within a home and directly affect indoor moisture levels. After cal-
culating the hourly moisture generation for each of the moisture sources, we can
add them to determine the total moisture generated in a specific building at any giv-
en hour of the day. The basic input data for each simulation include the type of
dwelling, number of occupants, demographic of the occupants, size of the residence,
and its location.
FIG. 1 Flow chart of information and activities needed to estimate indoor moisture
generation through computer simulations.
Time of the
Event
Duration of the
Event
Moisture
Generation Levels
Measurements of Moisture
Production Rates
Statistics /
Surveys
Indoor Moisture Generation
Type of
Dwelling
Family Members,
Ages and Gender
Incidences of
Appliances
User Behavior
264 STP 1599 On Advances in Hygrothermal Performance of Building Envelopes
To determine the type of dwelling, it is assumed that 74 % of homes are single
family and the rest are multifamily (manufactured homes are not considered) [5].
Once the dwelling type is probabilistically selected, the number of occupants is
determined based on Table 1. As the table shows, the most likely number of occu-
pants for a single family dwelling is two.
For this work, the input was set to always use single family dwellings. For single
family homes, 2009 Residential Energy Consumption Survey data were used to
determine the square footage of homes per occupant for one to six occupants [6]. A
triangular distribution for each case was created using the average and standard
deviation of ft
2
/occupant shown in Table 2. For each simulation run, the floor area
per occupant was chosen from the appropriate triangular distribution and then
multiplied by the number of occupants to yield the total floor area of the home.
Note the average floor area of homes with one to six occupants is around 2,400 ft
2
.
To determine the location of the household, a discrete probability distribution
was created based on the number of houses in each state using data from the U.S.
Census Bureau. Finally, the demographics of the household—single with no chil-
dren, married with children, roommates, or single parent with children—was deter-
mined based on probabilities derived from the U.S. Census data. Most of the
moisture generation rates from the sources described in the following sections are
based on one or more of the inputs described earlier.
SHOWER
To calculate the moisture generation from showering, the percentage of buildings
with a shower facility should be taken into account. In the United States, 99.7 % of
residential homes have a shower [7]. According to a California activity pattern
survey of 1,579 households [8], the probability that a person will shower varies
throughout the day. These probabilities are displayed in Fig. 2.
TABLE 1 Distribution of number of occupants for each housing type.
a
No. Occupants
123456
Single family 20.99 % 33.33 % 16.92 % 15.90 % 7.76 % 5.22 %
Multifamily 45.91 % 26.69 % 12.81 % 8.54 % 4.27 % 1.78 %
Note:
a
Data from the American Housing Survey, 2013.
TABLE 2 Square feet per person for single family households.
No. Occupants
123456
Average (ft
2
) 2,004 1,245 804 662 530 399
Upper and lower limit (ft
2
)61,306 6811 6524 6431 6345 6260
PALLIN ET AL., DOI 10.1520/STP159920160111 265
Climate influences shower frequency, which varies between hot summer
months and cooler winter months [8]. Therefore, the simulation tool accounts for
the location of the home and the annual variation in outdoor temperature.
Another important variable for calculating the total moisture generated from
showering is the duration. According to the National Renewable Energy Laborato-
ry, the average shower duration is 7.8 min with a lognormal distribution, and the
standard deviation is 3.5 min [9]. Once the duration of a given shower event is
determined, a probabilistic time distribution is defined based on the measured
moisture generation rates presented in Table 3.
BATHING
To determine the moisture generated from bathing, first the likelihood that a home
has a bathtub is determined. In the United States, 97.7 % of homes have at least one
bathtub [16]. To simulate the hourly probability of bathing, diurnal water usage
patterns have been used [17](
Fig. 3). This percent of water usage from bathing does
not provide information on the actual probability of bathing but rather the shape of
the probability curve. Therefore, the water usage pattern is combined with the daily
probability of bathing [18](
Table 4). This probability varied between children and
adults, as adults tend to shower more, whereas children tend to bathe more [18].
Table 4 and the water usage pattern provide enough information to create an hourly
probability curve that represents the likelihood of bathing.
FIG. 2 The probability of showering for any U.S. resident during any given day [8].
TABLE 3 Estimated total moisture generation from showering from various studies.
Angell [10] Christian [11] CIBSE [12] Hansen [13] Rousseau [14] Kalamees, T. [15]
0.55
(lb/5 min)
0.49
(lb/5 min)
0.44–0.84
(lb/5 min)
0.51
(lb/5 min)
0.77
(lb/5 min)
0.66
(lb/5 min)
266 STP 1599 On Advances in Hygrothermal Performance of Building Envelopes
Once the probability of taking a bath is defined, the amount of moisture gener-
ated must be estimated. To calculate the moisture generated from bathing, the dura-
tion and water temperature need to be taken into account. For bath duration, the
length of time appeared to vary slightly among studies—from 15 min [11]to18
min [19]. One study [20] specifies durations with seasonal variation (Table 5). This
study also estimated seasonal variations in water temperature (Table 6).
The indoor air temperature and absolute humidity will impact the amount of
moisture that evaporates from a water surface into the bathroom’s ambient air. To
estimate typical indoor air conditions, the standard CEN EN 15026, Hygrothermal
FIG. 3 Diurnal water usage from bathing in residential buildings.
TABLE 4 The time probability of bathing frequency for adults and children [18].
Bathing Time Probability Child (%) Adult (%)
More than once per day 5.3 4.9
Once per day 38.8 12.6
Less than once per day 55.6 82.2
TABLE 5 Seasonal bath duration distribution presented as mean, maximum, minimum, and stan-
dard deviation for each season [20].
Duration of Bathing (min:sec)
Summer Autumn Winter
Average 12:49 16:50 11:33
Standard deviation 4:48 5:53 4:50
Maximum 21:25 28:48 24:16
Minimum 4:17 5:22 6:02
PALLIN ET AL., DOI 10.1520/STP159920160111 267
Performance of Building Components and Building Elements. Assessment of Mois-
ture Transfer by Numerical Simulation, can be used [21]. The evaporation rate from
bathing is estimated using this standard along with ASHRAE 90.2, Energy Efficient
Design of Low-Rise Residential Buildings [22].
WHIRLPOOL BATHS
Whirlpool bathing is similar to normal bathing. Consequently, we assume the water
temperatures and durations for non-whirlpool bathing in the previous section are
applicable to whirlpool baths as well. However, the evaporation rate from whirlpool
bathing is typically twice that as for bathing in a standard bathtub because of the
more turbulent water surface [22]. For bathing in regular bathtubs, the incidence
factor is important to consider when determining the probability of a whirlpool
bath. According to a drinking water research study, an estimated 26.1 % of U.S. res-
idential homes have a whirlpool bathtub [7].
DRYING OFF
After a resident showers, bathes, or uses a whirlpool, the individual must dry off. For
the purpose of this research, residents are assumed to dry off using towels. A towel
takes about 15 h to dry once it is used [19]. This study enabled estimation of how
long it takes a towel to dry and how much moisture evaporates hourly (Tab l e 7).
TABLE 6 Seasonal bath water temperature distribution, giving the mean, maximum, minimum, and
standard deviation for each season [20].
Temperature (F)
Summer Spring/Autumn Winter
Average 106.0 105.8 106.2
Standard deviation 4.5 3.1 3.1
Maximum 110.7 110.8 109.4
Minimum 95.4 100.0 101.1
TABLE 7 Percentage of moisture generated and evaporated into the air from a drying cloth
material [19].
Time after Wash (h) 0 1 2 3 4 5 6 7
Moisture generation for each hour (%) … 19.2 15.0 11.7 9.9 7.8 6.9 5.4
Cumulative moisture generation for
each hour (%)
… 19.2 34.2 45.9 55.8 63.6 70.5 75.9
Time after Wash (h) 8 9 10 11 12 13 14 15
Moisture generation for each hour (%) 4.5 4.2 3.6 3.0 2.7 2.2 2.0 1.7
Cumulative moisture generation for
each hour (%)
80.4 84.6 88.2 91.2 93.9 96.2 98.3 100.0
268 STP 1599 On Advances in Hygrothermal Performance of Building Envelopes
The total amount of water that will be stored in a towel depends on the user’s
skin surface area and their hair length. The amount of water varies from 0.55 lb to
0.77 lb for men and 0.55 lb to 1.10 lb for women [23]. The moisture remaining in a
towel after children dry is assumed to be 30 % of the distribution for men.
Meal Preparation
Preparing meals is an important component in the moisture generation tool. To
estimate the hourly time probability that a resident will prepare breakfast, the hour-
ly probability that a resident will eat breakfast was applied [24]. Because this proba-
bility does not consider whether the breakfast is prepared and consumed inside the
household, other data must be used. Research from 2003 studied the likelihood of
eating out [25]. The likelihood a person will eat a commercially prepared meal a
given number of times in a week is introduced in Table 8.
Many studies on moisture generation from food preparation fail to give many
specifics on how much moisture will be produced for each meal. Rather, studies are
usually based on food preparation moisture generation for an entire day, lumping
all three meals together [12–14]. Based on two studies that provided individual
moisture generation amounts for each meal [10,19], the percent of each meal’s
moisture generation out of the daily total was calculated. These values are given
in Table 9.
Based on the percentages listed in Table 10, the sources are divided to yield a
total moisture generation for the day [12–14,26] and each meal’s moisture genera-
tion. These values were then coupled with the original two sources [10,19] to pro-
duce an average moisture generation for each meal as well as a standard deviation.
It is important to note some of the time probabilities of eating lunch overlap
with eating breakfast at one end and eating dinner at the other end [24]. This over-
lap can be seen in Fig. 4.
TABLE 8 Frequency probability distribution for a U.S. citizen to eat a meal prepared by a restaurant
each week [25].
Weekly Frequency
<11 2 34to5>6
Average (%) 23.7 20.5 14.4 11.8 15.2 14.5
Standard Deviation (%) 1.0 1.0 0.8 0.8 0.8 0.9
TABLE 9 Percent of moisture generation from food preparation for each meal [10,19,26].
Breakfast Lunch Dinner
Average moisture produced 15 % 34 % 51 %
PALLIN ET AL., DOI 10.1520/STP159920160111 269
Usually, moisture generation from meal preparation in homes is estimated
based on a family of four. Obviously, the actual moisture generation will depend on
the number of occupants and must be adjusted accordingly. In the simulation tool,
a reduction or increase of 25 % in moisture generation for each person is applied to
families that deviate from four members [27].
The moisture generation for each meal in Table 10 does not address if a gas
stove is used during the food preparation process. If a gas stove is used instead of
an electric stove, additional moisture is generated from the combustion of gas. The
incidence factor for having a gas stove in the United States is 31.07 % [6]. When a
gas stove is used in the food preparation process, the total moisture generated
increases by approximately 30 % [10].
DISHWASHING MACHINE
Not only does the presence of a dishwasher in a residence depend on the number of
people living in a home, but it also depends on the type of residence [26]. Generally,
single family houses are more likely to have dishwashers than multifamily dwell-
ings, and more residents usually correlates with a higher probability of owning a
dishwasher. This correlation can be seen in Table 11.
TABLE 10 The average estimated moisture generation for each meal based on various sources
and Table 9.
Breakfast Lunch Dinner
Mean (lb) 0.60 1.59 2.86
Standard deviation (lb) 0.11 0.37 0.84
FIG. 4 Hourly time distribution for preparing meals in the United States [24].
270 STP 1599 On Advances in Hygrothermal Performance of Building Envelopes
However, dishwasher use varies among homes. The frequency dishwashers are
used largely depends on the number of residents in the household [6]. As Table 12
shows, a higher frequency of dishwasher use tends to be correlated with having a
larger number of household members.
Once the frequency of usage is identified, the hourly time distribution for the
probability a dishwasher will be operated can then be applied. This hourly distribu-
tion is shown in Fig. 5.
The moisture generated from a dishwasher mainly depends on the efficiency of
the drying process. In general, water is left on the interior surfaces of the machine,
on the dishes, and in the air inside the dishwasher, which adds up to 0.44 lb to 0.88
lb of moisture [26]. According to ASHRAE standards, the typical moisture genera-
tion rate of a dishwasher is 0.40 lb/h [28], which is similar to the reference. For this
study, a uniform distribution of 0.44 lb to 0.88 lb per cycle is applied.
HAND DISHWASHING
The incidence factor of having a kitchen sink in a U.S. home is 99 % [5]. When a
kitchen sink is present, U.S. residents sometimes wash their dishes by hand, even if
there is a dishwasher in the residence. The incidence factor of this scenario is 13.4 %
[29]. The probability of hand dishwashing is correlated with the probability of
preparing and consuming food. For this study, the probability of hand washing dishes
is correlated to the probability of food preparation (Fig. 4).
TABLE 11 The probability a household owns a dishwashing machine based on the number of
residents and housing type [6].
No. Residents Single Family Residence Multifamily Units All Types of Homes
1 52.4 % 35.4 % 47.6 %
2 72.2 % 48.8 % 65.6 %
3 68.1 % 46.0 % 61.9 %
4 72.9 % 49.2 % 66.2 %
>5 64.1 % 43.3 % 58.3 %
TABLE 12 Probability per week for U.S. households to use a dishwasher based on the number of
household members in the residence [6].
No. Occupants
1 Member 2 Members 3 Members 4 Members >5 Members
>1/Day 5.4 % 12.4 % 18.9 % 29.8 % 43.2 %
4–6/Week 5.4 % 18.0 % 20.7 % 26.0 % 20.3 %
2–3/Week 28.2 % 40.6 % 37.8 % 25.0 % 16.2 %
1/Week 29.5 % 13.3 % 9.0 % 7.7 % 5.4 %
<1/Week 31.5 % 15.8 % 13.5 % 11.5 % 14.9 %
PALLIN ET AL., DOI 10.1520/STP159920160111 271
The amount of moisture generated by handwashing dishes depends on the
number of dishes cleaned. Because the number of residents influences the amount
of dishes, a reduction or increase in moisture generated is applied. For a family of
four, the amount of moisture generated from handwashing is presented in Table 13
[10–14]. A reduction or increase of 25 % is applied to the generated moisture for
numbers of family members different than four [27].
Laundry
The number of times that a household does laundry depends on the number of peo-
ple living in the home. More household members usually implies more loads of
laundry per week [6]. Table 14 shows this relationship.
The frequency of usage per week significantly affects the hourly probability
throughout a day of using a washing machine, as shown in Fig. 6. More household
members typically correlate to an increased likelihood of having a washing machine.
Also, the type of building impacts whether a household has a washing machine [6].
Using the information in Table 12,Table 15, and Fig. 6, the likelihood of washing
machine use can be estimated. However, the usage of a washing machine would not
FIG. 5 The hourly time probability of dishwasher usage during any day of the week [17].
TABLE 13 The amount of moisture generated from handwashing dishes after each meal, derived
from multiple studies [10–14].
Breakfast Lunch Dinner
Average (lb) 0.220 0.176 0.705
Standard deviation (lb) 0.00675 0.00675 0.0202
272 STP 1599 On Advances in Hygrothermal Performance of Building Envelopes
generate moisture that evaporates into the indoor environment [10] but using a
dryer would. The amount of moisture generated from a dryer will depend on the
level of drying efficiency, the load size, and the ventilation rate of the moist air from
TABLE 14 Probability per week for U.S. households to use a washing machine based on the num-
ber of household members in the residence [6].
No. Occupants
Usage per Week 1 2 3 4 >5
15 0.0 % 0.3 % 1.9 % 3.6 % 9.7 %
10–15 0.9 % 4.9 % 9.6 % 17.3 % 28.3 %
5–10 11.3 % 35.0 % 51.0 % 51.8 % 41.6 %
2–4 62.4 % 53.7 % 35.0 % 25.2 % 18.6 %
>1 25.4 % 6.5 % 2.5 % 1.4 % 1.8 %
FIG. 6 Probability distribution of using a washing machine [17].
TABLE 15 Probability of a household having a washing machine based on number of residents and
type of dwelling [6].
No. Residents Single Family Multifamily All Types
1 71.4 % 38.9 % 68.1 %
2 90.6 % 49.3 % 86.3 %
3 91.1 % 49.5 % 86.7 %
4 93.0 % 50.6 % 88.5 %
>5 93.4 % 50.8 % 89.0 %
PALLIN ET AL., DOI 10.1520/STP159920160111 273
the machine. For this study, it is assumed that a dryer is used directly after the usage
of a washing machine. Table 16 shows the probability of dryer ownership in different
types of residences.
According to a dryer manufacturing company, the efficiency of clothes dryers is
around 80 % to 100 %. Consequently, about 0 % to 20 % of the moisture might escape
from the drying process [30]. The total moisture generation from drying wet clothing
from various studies is given in Table 17. For this research, the moisture added to the
indoor space is estimated by applying 0 % to 20 % to the total moisture rates.
For homes with no dryer, the clothing load is assumed to be dried indoors. The
drying efficiency for natural drying clothes is applied in accordance with the ap-
proach presented in the “Drying Off” section and in CEN EN 15026 [19]. The sea-
sonal use of a clothesline for drying is not considered in this study.
Ironing
For ironing, moisture is produced through the steam, which is used to remove
wrinkles in clothing. In the United States, about 95 % of households own an iron
[31]. There are few studies conducted measuring the moisture generation rate from
ironing. An Asian study stated that the average moisture generation rate for a steam
iron is 1.290 lb per h [19]. Ironing clothes is less common in the United States and,
as a result, surveys on the patterns of iron use are rare and hard to find. Therefore,
hourly probability data provided by the Harmonized European Time Use Survey
on ironing trends are used [26,29]. Hourly probabilities for using an iron are pre-
sented in Fig. 7 for one, three, and five (or more) family member households.
In addition to the difficulty in finding a probability distribution for ironing,
there is also little to no information on the duration of ironing once the activity is
determined to occur. Thus, this research uses information from a study conducted
in Europe where the average amount of time spent for ironing per month is 38 min,
while the total time spent ironing each month ranges from 31 to 53 min [32].
TABLE 16 The probability that a particular residence will have and use a clothes dryer in their
home based on housing type [6].
Single Family Multifamily All Types
Clothes dryer 94.1 % 38.0 % 79.4 %
TABLE 17 Estimates for the amount of moisture released from drying a load of wet clothes when
all of the moisture is unvented and released into the room.
Angell [10] CIBSE [12] Yik [19] Rousseau [14] Hansen [13]
4.85–6.44 2.76–7.72 3.66 3.86 4.23
274 STP 1599 On Advances in Hygrothermal Performance of Building Envelopes
Floor Mopping
To estimate the amount of moisture generated from mopping floors, the hourly
time probability for interior cleaning is used, as shown in Fig. 8 [33].
The square footage of the building is also required to estimate the moisture
generation from floor mopping. The ranges in square footage of typical U.S. homes
are presented in Table 18 [34].
Various studies present the amount of moisture generated from floor mopping
as per square foot [10,12–14] and range from 0.0205 lb/ft
2
to 0.0307 lb/ft
2
.However,
FIG. 7 Probability distribution for ironing in Swedish homes are applied to predict
probability distribution for ironing in American homes [26,29].
FIG. 8 The compiled hourly time distribution for a resident cleaning the interior of a
home [33].
PALLIN ET AL., DOI 10.1520/STP159920160111 275
some of these studies are relatively old, and there is a great chance that the floor
mops of today leave less water on the floor surface after cleaning. In a more recent
study [19], the amount of moisture generated from floor mopping is measured at
0.0010 lb/ft
2
. For this study, the amount of moisture generated from floor mopping is
estimated probabilistically based on the presented generation rates and square footage
of each simulated household. In addition, the ratio of carpet versus other flooring is
accounted for, the ratio of carpeting to other flooring within a home is about 50610 %,
which subsequently is applied to this study [35].
Toilets
Toilets generate a small amount of moisture during flushing but also from evapora-
tion of the water left inside the toilet bowl. The latter occurs only if the lid is left up.
According to a study by an American manufacturing company, the incidence factor
of leaving a toilet lid down is 70 % [36]. The likelihood that a U.S. dwelling will
have a certain number of toilets is given in Table 19.
The amount of moisture evaporated from the water surface inside the toilet
bowl is estimated based on the approach presented in the “Bathing” section by us-
ing the standards CEN EN 15026 [21] and ASHRAE 90.2 [22]. One of the parame-
ters required to estimate the evaporation rate is the water surface area exposed to
the bathroom air. According to various Web sites, the surface area of the water in a
toilet bowl ranges from 0.14 ft
2
to 0.83 ft
2
, with an average of 0.50 ft
2
[37].
However, more evaporation occurs when flushing. Due to the lack of available
studies, this research assumes that a toilet generates ten times more vapor when
flushing than still water in the toilet bowl. The number of flushes depends on the
TABLE 18 Residential floor area per family members, for single and multifamily homes.
No. Members in the Household
123456
Single family 1935.86–
2072.14
1215.12–
1274.88
778.27–
829.73
638.17–
685.83
497.14–
562.86
377.45–
420.55
Multifamily 777.6–
842.4
476.16–
515.84
331.04–
362.96
252.7–
279.3
206.11–
249.89
140.77–
191.23
All types 1401.66–
1494.34
1023.97–
1070.03
661.71–
708.29
559.12–
600.88
437.11–
494.89
337.95–
378.05
TABLE 19 The probability for the number of toilets in U.S. homes [7].
No. Toilets 0 1 2 3 4 5–9
Probability (%) 0.20 % 11.30 % 43.80 % 32.10 % 9.70 % 3.00 %
276 STP 1599 On Advances in Hygrothermal Performance of Building Envelopes
number of family members. Fig. 9 shows the frequency trend for the number of toi-
let flushes depending on number of occupants.
Once the number of flushes per household per day is probabilistically deter-
mined, the hourly probabilities determine when the flushes take place. These proba-
bilities are shown in Fig. 10.
FIG. 9 The probabilities of flushing toilets in homes with one, three, and five family
members [38].
FIG. 10 Hourly time distribution of flushing toilets in homes [17].
PALLIN ET AL., DOI 10.1520/STP159920160111 277
Faucets
Using faucets adds water vapor to the air. The evaporation occurs from the running
water but also from the film of water at the sink surface, which eventually evapo-
rates. Due to the lack of available studies on the frequency of faucet usage, it is as-
sumed that a person uses the faucet before eating and after using the toilet.
Therefore, this activity is correlated to both preparing/eating meals and using the
toilet. For this study, the amount of moisture produced in association with using a
faucet is estimated at 0.033 lb.
Humans
Human respiration and perspiration are an important component of the moisture
produced within a home, and they depend on the type of human activities that are
taking place. The type of activities can vary according to the family size, day of the
week, gender, and the ages of the residents. Therefore, each household combination
is classified into one of the categories listed in Table 20 based on surveys conducted
by the U.S. Census and the U.S. Energy Information Administration [39].
Each of the individuals in every household composition presented in Table 20 is
grouped into categories of: single with children, single without children, married
with children, married without children and child. For example, in the household
combination of “four roommates” each individual is classified as “single without
TABLE 20 Probabilities for household compositions, categorized by number of residents and
dwelling type [39].
Single Family Multifamily All Types
1 Single with no children 21.0 % 46.3 % 27.6 %
Married couple without children 23.9 % 19.2 % 22.6 %
2 Two roommates or extended relatives 4.4 % 3.5 % 4.1 %
Single parent with one child 5.0 % 4.0 % 4.7 %
Married with one child 11.1 % 8.2 % 10.5 %
3 Single parent with two children 4.8 % 3.6 % 4.5 %
Three roommates 1.0 % 0.7 % 0.9 %
Married with two children 9.9 % 5.6 % 8.6 %
4 Single parent with three children 1.8 % 1.0 % 1.5 %
Four roommates 0.3 % 0.2 % 0.3 %
Three adults with one child 3.9 % 2.2 % 3.4 %
Married with three children 6.3 % 3.2 % 5.5 %
5 Single parent with four children 1.4 % 0.7 % 1.2 %
Five roommates 0.1 % 0.1 % 0.1 %
Married with four children 3.3 % 1.2 % 2.9 %
6 Single parent with five children 1.7 % 0.6 % 1.5 %
Six roommates 0.1 % 0.0 % 0.1 %
278 STP 1599 On Advances in Hygrothermal Performance of Building Envelopes
children.” Based on these classifications, the amount of time that each individual
spends on various activities is found, and time probability curves for each activity
can be derived [33]. Table 21 presents the moisture generation from respiration and
perspiration for different activities.
The moisture generation rate from each specified activity is based on ASHRAE
fundamentals [28]. Because there are variations in the moisture generation rate
from respiration and perspiration depending on the size, gender, and age of the
individual, the amount of moisture produced by females and children is assumed as
a fraction of the production by males. The fraction for women is estimated to be
85 %, while the fraction for children is estimated to be 75 % [28]. The estimated mois-
ture produced by a man within the home ranged anywhere from 0.06 lb/h to 0.41 lb/h,
depending on the activity [11,12,19,22]. For activities simulated to take place outside
the home, no contribution to the indoor moisture generation is assumed.
Aquarium
There are few studies conducted regarding the incidence of aquariums in U.S.
homes. From the Residential Energy Consumption Survey, an incidence factor of
3.9 % is used for heated aquariums with a capacity of 20 gal or more [6]. Another
approach to determine the incidence of aquariums is to use the number of house-
holds that own pet fish. According to a study by the American Veterinary Medical
Association, 6.73 % of U.S. households have pet fish [40]. For this study, the inci-
dence of having an aquarium is estimated to concur with the second study.
TABLE 21 List of daily activities (in no specific order) in which U.S. residents participate [33].
Activity Rate (lb/h)
Sleeping 0.066
Eating and drinking 0.146
Grooming and other personal care 0.179–0.220
Working and work-related activities 0.146–0.220
Homework and research 0.146
Food preparation 0.179
Kitchen and food cleanup 0.179–0.260
Housework 0.179–0.260
Laundry 0.220–0.454
Maintenance, decoration, and repair 0.220–0.600
Playing games 0.097–0.146
Social life 0.112–0.146
Relaxing and thinking 0.097–0.112
Computer and Internet use for leisure 0.097–0.112
Reading 0.097
Watching TV 0.097–0.112
PALLIN ET AL., DOI 10.1520/STP159920160111 279
The moisture generation from aquariums is similar to bathtubs in that the
evaporation rate of the water in the aquarium relies on the surface area and turbu-
lence of the water. The evaporation of water also depends on factors such as the hu-
midity and temperature of the indoor air and the temperature of the water [41].
Based on these factors, the moisture generation rate can be calculated. Another ap-
proach is to measure the amount of water needed to refill tanks while in use.
Depending on whether or not there is a hood covering the aquarium, the values
seem to vary from 6.6 lb to 22 lb per week [27]. The amount of water evaporated
from each aquarium also seemed dependent on aquarium size and the amount of
mechanical circulation, as stated earlier. For aquarium size, the exposed water surface
area varies between 2.69 ft
2
and 10.76 ft
2
[42]. To account for varying amounts of
water circulation within the aquarium, an average water evaporation rate of 0.042 lb/ft
2
is assumed [41]. Combining the areal evaporation rate together with the water surface
area resulted in a total evaporation rate ranging from 8.5 lb to 34 lb per week or
1.2–4.9 lb per day. Because these ranges are on the higher end of what have been found
in aquarium forums, we use the evaporation rate based on the frequency and quantity
that aquariums need to be filled (i.e., 6.6 lb to 22 lb per week) [27].
Pets
Like humans, pets also produce moisture from perspiration and respiration. We as-
sume that the moisture generation rate for pets is proportional to that of an adult
male, based on body mass [10], while sitting and performing light tasks. This activi-
ty is estimated to generate 0.146 lb of moisture per hour [28]. The moisture genera-
tion rate of a pet is estimated by multiplying the given moisture generation rate
with the body mass difference between an adult male and the pet in question; this is
shown in Table 22. A uniform distribution is assumed for the range of moisture gen-
eration so, for a given household, the moisture generation rate from a certain pet is
randomly chosen from the range.
TABLE 22 The probability that a U.S. household will own the given type of pet [40] and the range
of typical body mass for each of the pets.
Type of Pet Probability (%) Body Mass (lb) Moisture Generation (lb/day)
Dogs 37.69 6.6–143.3 0.132–2.834
Cats 31.41 8.8–24.3 0.175–0.480
Birds 3.19 0.1–2.2 0.002–0.043
Rabbits 1.22 0.9–6.6 0.018–0.132
Guinea pigs, hamsters, and gerbils 1.70 1.5–2.7 0.031–0.053
Mice, rats, and other rodents 0.34 0.1–1.4 0.001–0.029
Turtles 1.15 0.7–11.0 0.013–0.218
Reptiles 1.43 0.0–44.1 0.001–0.864
280 STP 1599 On Advances in Hygrothermal Performance of Building Envelopes
Table 22 also presents the probabilities of having pets commonly present in U.S.
households [40].
In the United States, both cats and dogs often have outdoor access. Some spend
most, if not all, of their lives outside. This has been accounted for when estimating
the moisture generation rates from pets. Based on information given by the
Humane Society of the United States [43] together with information from the
American Veterinary Medical Association [40], 46 % of the dogs in the United
States live outside. For cats, it is found that an estimated 40 % to 70 % of the popu-
lation have access to the outdoors [44]. According to another study, these domestic
cats with outdoor access typically spend anywhere from 4 h to 6 h a day outside
[45]. When these cats are outside, they are not producing moisture in the home.
This is taken into account in the simulation process.
Plants
Plants generate moisture through the processes of evaporation and transpiration.
Transpirationisdefinedastheprocessofplantsreleasingwatervaporfrompores
in their cells to the ambient air. When talking about these processes in conjunc-
tion with one another, evaporation and transpiration can be combined into the
term evapotranspiration. Actually, only 0.2 % of water used to water an indoor
plant goes toward its growth, and the rest goes through the process of evapotrans-
piration [11].
The amount of moisture generated depends on the size and type of plant as
well as the frequency with which the plant is watered [10]. A few other factors,
which are not taken into account for this particular study, are indoor temperature
and humidity as well as the plant’s exposure to solar radiation [26].
For this study, the frequency of watering plants is assumed to be affected by
climate zone through a created seasonal factor. This seasonal factor for watering
plants is assumed to vary between 50 % and 100 % to account for seasonal varia-
tions in watering needs. The seasonal variation is presumed dominant in climates
with high seasonal variation in solar radiation and outdoor temperature.
Various studies have given a relatively wide range of moisture generation rates
per day, but they all seem to depend on the previously stated parameters of plant
type, size, and watering frequency. Because of this, a distribution was created based
on these findings. The distribution had a variation between 0.09 lb and 0.33 lb per
day per plant to account for different plant types and sizes. These values were then
multiplied by the seasonal factor to determine the moisture generation rate per day
for each plant in the house. The moisture generation rates of plants from various
studies are listed in Table 23.
Because there is little data for how many plants a U.S. residence is likely to
have, we adopt data from the UK [46]. According to this study, 75 % of the popula-
tion has at least one plant, with an average of six plants per household. Twenty per-
cent of the households in the UK have 12 or more plants [46].
PALLIN ET AL., DOI 10.1520/STP159920160111 281
Humidifier
Because the winter months in the United States are generally colder and less humid,
a humidifier is sometimes needed to bring the relative humidity level of the home
up and to allow for better indoor comfort. ENERGY STAR counts that the inci-
dence factor for having a humidifier in U.S. homes is 15 % [47]. Even though a
household owns a humidifier, the frequency of use needs to be established as well
because the seasonal effect does not occur year-round and the house does not usual-
ly have constant dry air.
The number of months in which a home uses a humidifier, as shown in Table 24,
ranges from one month to the entire year. It must be noted that the proportions pre-
sented in Tab le 24 are out of the homes that use a humidifier.
In order to model the seasonal effect, where winter is the coldest and least humid
and summer is the opposite, we adjusted our code so that the center of usage for a
humidifier is in the winter months. For example, if homes were to only run their
humidifier for one month of the year, it would be in January. If they were to run it for
five months, it would be from November to March. According to a recent market and
industry scoping report, a humidifier is used an average of 70.3 h per month [47].
Additionally, for each day of the possible months that a humidifier is used, it is assumed
that the humidifier would run for an average of 10 h per day. Based on this assumption
and reported humidifier usage per month, it is estimated that a humidifier would be
used about seven times a month. According to general product specifications in com-
mercial markets, the moisture generation rate for a humidifier is 16.7 to 25.1 lb/day.
Discussion of Results
All of the aforementioned sources mentioned are combined to yield year-long hour-
ly moisture generation profiles. To combine these sources, the Monte Carlo method
TABLE 23 The range of moisture generation rates resulting from changing variables, such as type
of plant and the size of the plant [26].
Indoor Plant Moisture Generation (lb/day-plant)
Christian [11] Rousseau [14] Yik [19] Angell [10]
0.26–1.10 0.18 0.04 0.14
TABLE 24 The probability in which a U.S. home will use a humidifier for a certain number of
months given that they use a humidifier in the home [17].
Frequency of Use for Humidifiers
1–3 Months 4–6 Months 7–11 Months Turned on All Year
Proportion of U.S. households 53 % 33 % 6 % 6.98 %
282 STP 1599 On Advances in Hygrothermal Performance of Building Envelopes
was used to randomly choose values from the various probability curves, so that
values with higher probability were chosen more often. Distributions of average dai-
ly moisture generation rates inside homes using the GIHM tool are shown in Fig. 11
as a function of the number of occupants. The graphs demonstrate the correlation
between an increased overall moisture generation and the number of household
members. In addition, the result also reveals that the range of moisture generation
becomes broader when the number of residents increases. When comparing the
simulation result with ASHRAE 160 guidelines, the recommended moisture genera-
tion rates seems to fall somewhere between the fiftieth and seventy-fifth percentile
of the spreads.
In order to appreciate how much the indoor moisture generation may vary, an
analysis of the distribution is presented in Table 25. The variations are depicted
through percentiles in the amounts of 50, 75, 90, and 95 for each type of household.
Table 25 also presents the corresponding design value for each household type. As
shown in Fig. 11, the differences among the values are greater when the number of
residents increases. The underlined values represent extreme cases that exceed
ASHRAE standards.
In order to investigate the correlation between different activities that generate
moisture and the overall indoor moisture generation, a sensitivity analysis was com-
pleted for the different moisture sources. According to Fig. 12, there exist activities
with strong correlations. The correlation is ranked from 0 to 1, where “1” represents
FIG. 11 Daily-moisture generation rates for one to four household members.
PALLIN ET AL., DOI 10.1520/STP159920160111 283
full correlation. The analysis reveals that baths/showers have the strongest correla-
tion. Respiration and perspiration of human/pet and food preparation activities are
also highly correlate to moisture generation as well.
A sensitivity analysis was also completed for select input variables to see how
they affected the indoor moisture generation, and this is shown in Fig. 13. For these
simulations, only single family homes were considered (so the dwelling type is not
shown). Notice that the number of residents has the highest correlation to indoor
moisture generation and climate zone has very little correlation to moisture genera-
tion. This provides information that can help designers and hygrothermal modelers
focus on the most important design parameters when building moisture-durable
TABLE 25 Variations in moisture generation in residential buildings through percentiles for one to
four household members. Underlined values exceed those recommended by ASHRAE
160 design guidelines [3].
Moisture Generation (lb/day)
50th Percentile 75th Percentile 90th Percentile 95th Percentile ASHRAE 160
1 resident 11.4 13.4 15.9 17.9 –
2 residents 17.0 19.3 21.9 25.3 16.8
3 residents 21.9 25.2 28.0 31.1 26.4
4 residents 27.5 31.0 35.0 38.0 31.2
FIG. 12 Moisture generation sensitivity of select sources.
284 STP 1599 On Advances in Hygrothermal Performance of Building Envelopes
envelopes that are sensitive to indoor moisture—that it is important to understand
how many occupants will reside in the building.
The simulation results also allowed study of the daily variation in moisture gen-
eration on an hourly basis, as seen in Fig. 14. The result is based on 365,000 simulat-
ed hours of moisture generation in various types of households and climate zones.
FIG. 13 Moisture generation sensitivity to select input data.
FIG. 14 Hourly distribution of moisture generation in U.S. homes.
PALLIN ET AL., DOI 10.1520/STP159920160111 285
According to the result, there is a strong correlation between the peaks and daily
meals. The global moisture peak occurs around 5 to 6 p.m. and remains relatively
high until 9 p.m.
Year-long profiles of hourly generation rates from the GIHM tool can be pro-
vided with associated metadata (household occupant number and demographic,
square footage, and climate zone) to the building science and design community.
We hope this information will be useful for hygrothermal modeling of envelopes in
the range of indoor moisture conditions the envelope will see in the U.S. building
stock and for other useful applications.
Conclusion
This research identified sources of indoor moisture generation in residential build-
ings, the probabilistic variations in moisture generation rates, and occupant behav-
ior patterns. The GIHM tool was created to account for probabilistic variations in
residential moisture loads for different residential building types and numbers of
occupants. The GIHM tool yields hourly rates of indoor moisture generation that
can be used in numerous simulation tools such as EnergyPlus. We also examined
the correlations between yearly moisture generation and different moisture sources.
Results showed that baths and showers, and occupant respiration and perspiration,
are the main factors that affect the overall indoor moisture generation. For input
variables, number of occupants has the biggest effect on the yearly moisture.
Results showed similar hourly patterns of indoor moisture generation regard-
less of climate zone, which implies that the residents’ living patterns and house
activities (such as taking a shower in the morning or preparing dinner after work)
do not considerably vary among different climates. The hourly data can be utilized
as a reference for extreme-case moisture analysis. For example, a builder can devel-
op a wall system with a maximum moisture protection capacity at the peak value
from the hourly data.
Lastly, the outcome of this research can supplement and verify moisture gener-
ation ranges from existing guidelines. Even though the current ASHRAE standard
provides a guideline for indoor moisture generation, the one-member household
case is absent. The standard also assumes an average moisture generation rate per
occupant; our results include a range of moisture generation rates, which could be
implemented to create a more robust standard.
Future Research
The work presented can continue to be refined and improved as more moisture
sources are added, such as indoor firewood storage, and more studies are done to
measure the moisture generation rates and probability distributions of household
activities in the United States. The GIHM tool can also be applied to other buildings
types, such as commercial and industrial. A similar approach for identifying the
moisture sources and probabilistic uses and schedules would need to be completed
286 STP 1599 On Advances in Hygrothermal Performance of Building Envelopes
for these buildings. Then the model outputs can aid in determining the indoor
moisture in hospitals, schools, offices, sports facilities with indoor swimming pools,
or gymnasiums where people are concentrated and have high moisture outputs.
These types of facilities could expect extreme load cases where it would be beneficial
to model the interior moisture generation to aid in design.
Furthermore, this research can contribute to quicker and more affordable stud-
ies on building envelope systems that can be completed with modeling rather than
field studies. Because moisture durability is an important criterion for building
materials and assemblies, more robust building envelope models can be developed
with the use of this research. In professional practice, building envelope details are
generally developed based on conventional construction or on guidelines. However,
those conventions or guidelines mostly focus on the humidity from outside or for
an average U.S. home. The utilization of this research can improve the conventional
building envelope models by reflecting different living patterns of households,
which would result in accessing the moisture durability of envelopes at the extreme
cases of high indoor moisture generation as well as the average and below.
ACKNOWLEDGMENTS
This material is based upon work supported by the U.S. Department of Energy, Office
of Energy Efficiency and Renewable Energy, Building America program.
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290 STP 1599 On Advances in Hygrothermal Performance of Building Envelopes
