Evaluation of Indoor Mold Growth Relative to Indoor Humidity Using a Multi-zone Modeling

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Evaluation of Indoor Mold Growth Relative to Indoor Humidity
Using a Multi-zone Modeling
Daranee Jareemit#1, Shi Shu#, Mohammad Heidarinejad*
, Yang-Seon Kim*,
Jiying Liu#, Zuhaira Alhafi#, and Jelena Srebric#
#Department of Architectural Engineering, The Pennsylvania State University
104 Engineering Unit A, University Park PA 16802, USA
1duj134@psu.edu
* Department of Mechanical Engineering, The Pennsylvania State University, USA
Abstract
ASHRAE Standard 62.1 specifies ventilation rate requirements to provide
acceptable indoor air quality. This study numerically investigated indoor
environmental conditions for a building located in two different climates. The
predictions of indoor environmental conditions used multi-zone modeling to first
calculate ventilation rates for a cold as well as hot and humid climate. The
predicted ventilation rates are used to further estimate moisture content brought by
outside air from air handling units and through leakage paths. Finally, the hourly
predictions of indoor environmental conditions are used to calculate the mold
growth index, and to evaluate monthly variations of mold growth probability on
building surface. The results show that indoor mold growth probability increases
when a building is operated to supply a high outdoor air fraction. Indoor humidity
increases when a ventilation system carries warm moist air from the outside into
internal building zones. Consequently, high outdoor air fractions for buildings
located in a hot and humid climate result in a higher risk of mold growth than for
buildings in a cold climate. The present study provides designers with an
understanding of the occurrence of indoor mold growth influenced by ventilation
rates under various weather conditions and provides recommendations for future
studies.
Keywords - Ventilation; indoor humidity; mold growth probability; multi-zone
modeling; cold and hot and humid climates; mold growth index
1. Introduction
The purpose of ventilation rates required by the ASHRAE Standard 62.1
is to improve indoor air quality [1]; however, high ventilation rates from
mechanical ventilation and infiltration may lead to indoor humidity
problems, especially for buildings located in a hot and humid climate. High
indoor humidity levels can cause unsatisfactory thermal comfort as well as a
high risk of mold growth. Existing research studies have primarily focused
on the impact of ventilation rates on indoor humidity levels related to human

comfort [2, 3]. This study primarily investigates the impact of transient
ventilation rates on indoor humidity levels and the mold growth probability
for a building in two different climates. The simulation results show that
indoor humidity and associated mold growth risk could be decreased by
reducing outdoor air ventilation rates.
2. Methodology
The indoor humidity levels and associated mold growth probabilities are
assessed in the same building for an entire year with the following four
ventilation scenarios:
1. actual building ventilation rates in a cold climate
2. minimum ventilation rates in a cold climate
3. actual ventilation rates in a hot and humid climate
4. minimum ventilation rates in a hot and humid climate
A multi-zone modeling is used to evaluate indoor humidity levels for the
four ventilation scenarios. The simulation results provide an hourly indoor
relative humidity at a controlled temperature of 25°C (77°F). Finally, the
mold growth index, defined by Abe [4], is applied to predict the probability
of mold growth using the simulation results for the indoor relative humidity
levels.
3. Building Description
The case study building is a retail grocery store with a 24-hour operating
schedule. The store is in a one-story building with the total area of 19,890m2
(214,094ft2), and the building volume of 99,450m3 (3,512,040ft3). This entire
volume uses three zone, including the retail, service and entrance zones, in
the multi-zone modeling, which is the assumption based on measured data
and a walk-through investigation [5].
4. Model Setting
The CONTAM multi-zone model is used to assess hourly variation of
the indoor humidity ratio [6]. The model setting was previously calibrated
with measured data using indoor CO2 concentration as a calibration
parameter [5]. Figure 1 shows less than 6% errors between measured data
and calibrated simulation results. The details of the calibration process are
not provided in this paper due to the page limit. The calibrated model
provides transient ventilation rates including outdoor air fraction and
infiltration rates. In the actual building, the outdoor air fraction rates are
higher than the minimum required rate by the ASHARE Standard 62.1. The
night ventilation rates are lower. The actual ventilation schedule is used in
the model setting to obtain realistic indoor humidity levels for a whole year.

Fig. 1 The calibration model between measured data and simulation
The only indoor moisture source considered in the simulation model is
the occupants, although it is possible that moisture can be generated other
indoor activities. An average daily occupant moisture production by an adult
is 7.2 kg/day (8.33×10-5 kg/s) [7]. Outdoor moisture sources include the
ventilation system and infiltration through openings and leakage paths.
Specifically, the deployed multi-zone model does not provide a function for
the dehumidification process at a cooling coil. Instead, a function for coil
efficiency uses 0.17 (17%) based on an ideal coil capacity from Persily’s
study [8]. According to the design minimum ventilation rate, this building
has a minimum operation at 28% outdoor air fraction. The design humidity
ratio for the mixed air condition is 0.012kgwater/kgdryair, and the humidity ratio
for the air leaving the cooling coil is 0.009kgwater/kgdryair. Therefore, the
design cooling coil efficiency is 0.25 (25%).
5. Simulation Results of Indoor Humidity
In winter, the simulation results show that indoor humidity ratios are
greater than those of outdoor conditions because 1) dehumidification is not
used in the cooling system, and 2) indoor humidity increases due to the
occupant presence. Figure 2 shows the annual variations of indoor humidity
levels. The actual operation provides lower indoor humidity ratios than does
the minimum ventilation rates because high ventilation rates bring dry air
from outside in conditioned space. During summer when dehumidification is
used, indoor humidity ratios are lower than those of outside. Indoor humidity
ratios are sometimes above the thermal comfort levels (12gwater/kgdryair)
required by the ASHRAE Standard 55 [9].

Fig. 2 Indoor and outdoor humidity ratios in a cold climate (SCE: State College PA, USA) with
17% coil efficiency
When multi-zone simulations of this building are conducted for a hot
and humid climate, indoor humidity ratios are above the thermal comfort
zone requirements for all seasons as shown in Figure 3. Reducing outdoor air
fraction based on the minimum values required by the ASHARE Standard
62.1 can help to control humidity levels within the thermal comfort
conditions, except in summer. Further, Figure 4 shows a comparison of
cooling coil efficiencies to remove moisture content from the supply air.
Using 25% coil efficiency can decrease the humidity ratios to 16% compared
to the humidity ratios using 17% coil efficiency; however, the indoor air
conditions are still unsatisfactory according to the thermal comfort
requirements.
Fig. 3 Indoor and outdoor humidity ratios in a hot and humid climate (BKK: Bangkok,
Thailand) with 17% coil efficiency

Fig. 4 Comparison of indoor humidity ratios in a hot and humid climate (BKK: Bangkok
Thailand) with different coil efficiencies
6. Mold Growth Index
Figure 5 presents mold growth and thermal comfort conditions in the
psychrometric chart. The mold growth probabilities at various temperatures
and relative humidity are adapted from the mold growth index defined by
Abe’s study [4]. According to the mold growth index, mold appears at a
relative humidity of approximately 70%. The highest mold index, 179
response units/week (ru/week), was found at a relative humidity range of
90% to 95% and a temperature range between 20°C (68°F) and 30°C (86°F).
Fig.5 Mold growth index of Xerophilic fungus. Circle: germination within 24 hours; square:
germination 2 to 7 days; triangle: germination 8 to 30 days. The number on the figures are
fungal indices [ru/week]

7. Mold Growth Probabilities
The mold growth probability is defined as the fraction of hours that have
an indoor relative humidity above 69% divided by the total number of
occurrences. The probability calculation uses the following cumulative
distribution function:

















 2
2
1
2
1
)(1)'(


x
erfxFxF
  
where F(x) is the cumulative probability whose occurrences are less than
or equal to x. F(x') is the complementary probability whose occurrences are
more than x. An average number () can be calculated as following:

n
x
n
ii

1

  
The equation for general standard deviation () is:

1
)(
1
2



n
x
n
ii


  
According to Abe’s study, mold can grow when the indoor relative
humidity is more than 70% (X>70) and cannot grow when the indoor
relative humidity is less than or equal to 69% (X≤69) [4].
8. Results
Figure 6 shows a cumulative probability distribution for indoor mold
growth in each month for the building with the actual ventilation rates in the
studied cold climate. The dashed line is the critical value of 70% relative
humidity to prevent mold growth. The left hand side of the dashed line
shows building operation hours that have indoor relative humidity levels
lower than or equal to 69% (no mold growth). In contrast, the right hand side
shows building operation hours with indoor relative humidity higher levels
than 69%, at which mold can be found. There is no mold growth in a cold
climate when a building operates with the actual building ventilation rates or
minimum ventilation. Figures 7 and 8 show mold growth probabilities for
the building in the studied hot and humid climate when the building has the

actual ventilation rates and reduced outdoor fraction to the minimum
required rates by the ASHRAE Standard 62.1.
Fig. 6 Cumulative probability distribution function of mold growth for each month when the
building has actual operation in a cold climate (SCE: State College PA, USA)
Fig. 7 Cumulative probability distribution function of mold growth in each month when
building has actual operation in a hot and humid climate (BKK: Bangkok, Thailand)

Fig. 8 Cumulative probability distribution function of mold growth in each month when
building has minimum ventilation in a hot and humid climate (BKK: Bangkok, Thailand)
Table 1 Indoor mold growth probabilities in different climates and outdoor air fractions
Location
(Operation schedule)
Monthly Mold Growth Probability (%)
1
2
3
4
5
6
7
8
9
10
11
12
SCE
(Actual operation)
0
0
0
0
0
0
0
0
0
0
0
0
SCE
(Minimum ventilation)
0
0
0
0
0
0
0
0
0
0
0
0
BKK
(Actual operation)
8
0
7
11
11
28
7
16
7
11
12
0
BKK
(Minimum ventilation)
0
0
0
0
1
3
0
0
0
0
0
0
From Table 1, the calculated probabilities show that mold growth does
not occur in the studied cold climate when a building has the actual building
ventilation rates or minimum ventilation. In the studied hot and humid
climate, a potential mold growth is found almost during the entire year of
operation when the building has the actual ventilation rates. The highest
mold growth probability is 28% found in June. No mold growth is found in
February and December. Although this building reduces outdoor fraction to
the minimum requirement, the probability of mold growth is still found 3%
in June and 1% in May, even though these are negligible values. Table 2
shows that reducing outdoor air fraction significantly decreases the indoor
mold growth probabilities. Specifically, the mold growth probability could
be reduced by 100% for an entire year when the coil efficiency is increased
to 25%.

Table 2 Mold growth probabilities in a hot and humid climate with different coil efficiencies
Coil Efficiency
(operation)
Mold Growth Probability in a Hot and
Humid Climate (%)
% of mold growth
probability reduction
compared to the
actual operation of
an ideal coil in a
year
1
2
3
4
5
6
7
8
9
10
11
12
17%
(Actual operation)
8
0
7
11
11
28
7
16
7
11
12
0
-
25%
(Actual operation)
2
0
1
2
2
7
2
4
1
0
0
0
20%
17%
(Minimum ventilation)
0
0
0
0
1
3
0
0
0
0
0
0
80%
25%
(Minimum ventilation)
0
0
0
0
0
0
0
0
0
0
0
0
100%
9. Discussion
This study presented a method to predict probabilities of mold growth
related to indoor humidity levels by using the multi-zone modeling.
However, there are many assumptions used in this study. Therefore, the
simulation results may not accurately represent a real building operation. For
example, neither absorption nor desorption in building materials is accounted
for in this study. If these transport mechanisms are considered, the indoor
moisture levels as well as potential mold growth could be different from the
predicted simulation results. Further, in hot and humid climates, a range of
acceptable thermal comfort conditions can be extended. As a result, an
increase in room temperatures can lower relative humidity levels. Therefore,
adjusted room thermal condition can reduce indoor humidity levels and
associated probabilities of indoor mold growth.
10. Conclusions
Outdoor air fraction significantly affects mold growth probabilities in
hot and humid climates. High outdoor air fractions lead to high indoor
humidity levels and associated indoor mold growth probabilities, which can
be found almost through the entire year of building operation in the studied
hot and humid climate. Reducing outdoor air intake can significantly
decreases mold growth probability up to 80%, while increasing coil capacity
can decrease indoor mold growth probabilities by 20% only. However, it
should be noted that decreasing outdoor air fraction can lead to other
contaminant concentration problems. In addition, the outdoor air intake

should not be maintained lower than the minimum design requirements
provided by the ASHRAE Standard 62.1.
Acknowledgment
This study was funded by the ASHRAE RP-1596 project. We would
also like to acknowledge the Royal Thai Government for their financial
support, and the colleagues from building science group at the Penn State
University for the support in data collection and analyses.
References
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