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Quantifying Resilience of Building Facades: Moisture and Mold Risks under Future Climate Stresses

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  • Harvard University Graduate School of Design

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Simulating Mold Risks under Future Climate Conditions
Pamela Cabrera1, Holly Samuelson1, Margaret Kurth2
1Harvard Graduate School of Design, Cambridge, MA, USA
2USACE Engineering Research and Development Center, Concord, MA, USA
Abstract
This research tests the potential susceptibility of a
prevalent, residential, wall assembly to the risk of
sustained mold-growth in predicted future climate
conditions. Here, the authors demonstrate a methodology
that combines hygrothermal and mold-growth simulation
tools with future weather files. This paper also explores
the sensitivity of the results to predicted weather
conditions, using nine weather context simulations for
one city and separating the effect of rainfall from other
climatic factors. The results illustrate how future climates
may provide adequate conditions for sustained mold-
growth in a wall assembly where no such problem exists
today. This study demonstrates the potentially widespread
need for this type of analysis. Further, the method lays the
groundwork for exploring building designs and material
selection from the perspective of climate resilience.
Introduction
Climate change is broadly changing temperature and
moisture regimes (Easterling et al., 2018), which will
inevitably translate into implications for physical
structures and indoor environments (deWilde, 2012).
Given that building standards and codes in the U.S. and
elsewhere have not yet evolved to require forward-
looking climate information (GAO, 2016), the incidence
of climate-sensitive building envelopes among the
existing building stock may be significant. Simulating
emerging risks can help guide design and lifecycle
decisions toward the goal of achieving buildings that are
more resilient (Kurth et al., 2018), in that they are better
able to plan and prepare for, absorb, recover from and
adapt to stressors and disruptive events (American
Institute of Architects (AIA), 2014; US National Research
Council (NRC), 2012).
A significant concern of envelope design and building
operation is moisture control (Odom and DuBose, 1996)
and consequently risk of mold, which can thrive on
building materials under specific temperature and
moisture conditions. Mold can have health and financial
consequences and is a sign of vulnerability of a timber
structure (Mjörnell et al., 2012; Heseltine and Rosen,
2009).
Whereas typical hygrothermal simulation studies focus on
vapor condensation within an envelope assembly,
understanding how best to enhance the resilience of
envelopes to future climates requires a more
comprehensive analysis of materials’ humidity and
temperature conditions over time. This is necessary in
order to identify potential future susceptibility to mold
growth and appropriate and cost-effective strategies to
mitigate risk, recover from failure, and adapt current
designs. Fortunately, there is a growing body of literature
on mold-growth risk, as well as several mathematical
models created to calculate mold-growth based on
moisture and temperature. (Gradeci et al., 2017; Hukka
and Viitanen, 1999; Isaksson et al., 2010; Togerö, 2011;
Sedlbauer, 2001; Johansson et al., 2010; Gobakken et al.,
2010, Moon, 2005; Geving, 1997; Clarke et al., 1999).
Therefore, simulation can play an essential role in
understanding the susceptibility of existing building
envelopes –which were originally designed to perform
under potentially outdated climatic norms– to mold
occurrence under new climatic regimes. Simulation
represents an important tool since the dynamics of
biological activity and environmental conditions yield
non-intuitive outcomes (Gradeci et al., 2018).
Numeric modeling, such as WUFI Pro 6 (Zirkelbach,
2007), applies exterior weather conditions (e.g.,
temperature and relative humidity) and operational
controls (e.g., HVAC and interior setpoints) to a defined
envelope assembly to simulate hygrothermal transfer
across each material. Several researchers have validated
the simulation capacity of WUFI PRO by comparing it to
field measurements of moisture levels in building
materials (Alev and Kalamees, 2016; Hägerstedt and
Arfvidsson, 2010; Mundt-Petersen and Harderup, 2013).
A secondary mathematical model such as the empirically-
based VTT model (Ojanen et al., 2010), described later,
can calculate the micro-environments where mold might
occur in and on each assembly material. At each time step,
based on governing factors, conditions could be found to
be favorable for mold, resulting in growth, or unfavorable,
resulting in delay or decline (Gradeci et al., 2017).
While the hygrothermal performance and, to a lesser
extent, mold-inhibiting performance of building
envelopes is relatively well-studied (Pihelo et al., 2016;
Langmans et al., 2012; Hall et al., 2013; Gullbrekken et
al., 2015), the question of how resilient current façade
designs will be to future climate-related shocks and
stresses remains underdeveloped. Specifically, the
existing mold-modeling literature has not included
simulation under future weather conditions and the
repercussions of a changing climate to envelope design
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https://doi.org/10.26868/25222708.2019.211130
(Viitanen et al., 2015; Watt et al., 2015; Clarke et al.,
1999).
The hypothesis, for which a modified methodology is
required, is that envelope designs warrant rethinking for
new climatic conditions. For example, in mixed (hot and
cold) climates, traditional placement of a vapor barrier
inboard of the insulation may prove to be suboptimal
under future climate scenarios, trapping internal
condensation and creating conditions for mold growth.
Therefore, in this paper, we demonstrate a framework for
identifying an elevated risk of mold-growth under future
climate conditions. This research takes a conventional
residential building envelope assembly in the northeast of
the United States and simulates its hygrothermal and
mold-growth performance under morphed future weather
files. The results demonstrate the potentially widespread
need for this type of analysis.
Methodology
Overview
The methodology consists of three main steps. First,
morphing weather files from current weather data to a
future year using existing EnergyPlus weather files
(EPW) or a Typical Meteorological Year data file (TMY).
Second, performing a hygrothermal simulation of a
defined building envelope in WUFI Pro. And third
analyzing the mold occurrence potential from simulated
temperature and moisture data of the interior of the wall,
with use of the VTT model (Viitanen 1996; Hukka and
Viitanen 1999; Viitanen et al. 2000; Viitanen and Ojanen
2007). The result is a mold-growth index time series that
spans both a three year and a 10-year simulation. Here,
this study tests the impact of both water vapor and
(separately) rainfall on mold-growth.
Future Weather Files
In this research, we select New York City for a case study,
because it lies within the American Society of Heating,
Refrigerating, and Air-conditioning Engineers
(ASHRAE) climate zone 4A, a mixed and humid climate
zone that will generally see an increase in heat and
specific humidity in the future, with little change in
relative humidity (Brown and DeGaetano, 2012; Dai,
2006; Trenberth et al., 2007; Willett et al., 2008).
The first step is to morph a typical weather file to a future
climate-change scenario. The tool used in this research is
CCWorldWeatherGen weather file generator V1.9, which
transforms a typical year weather file to a future
projection based on data from the Intergovernmental
Panel on Climate Change (IPCC) Third Assessment
Report, HadCM3 A2 experiment. “Morphing”
superimposes climate change simulation results on
existing hourly weather data series (Jentsch et al., 2008),
which allows for simulation under future weather
predicted conditions. Jentsch et al. (2013) state that the
morphed results are likely to underestimate the effects of
climate change, even though these future weather files are
morphed under the IPCC A2 emissions scenario.
This morphing methodology was initially developed to
transform weather data from the United Kingdom by the
Sustainable Energy Research Group. Today it has been
expanded to other weather file formats including EPW
and TMY2/3 by Belcher, Harcker and Powell (2005).
CCWorldWeatherGen is a free tool that works within
Microsoft Excel to run the morphing calculations. One
shortcoming of the tool is its inability to morph data
related to precipitation and radiation, including visibility,
ceiling height (m), aerosol optical depth (0.001), snow
depth (cm), albedo (0.1), liquid precipitation depth (mm),
and liquid precipitation quantity (hr). This shortcoming is
because the ‘present day’ CIBSE TRY/DSY data that is
the basis of the software does not contain information on
rainfall, and thus the tool does not include rain data in the
morphed weather file. (Jentsch, 2010)
Because the future precipitation patterns are particularly
uncertain, we chose to test the impact of rain data in our
hygrothermal simulations using two methods. First, we
ran the simulations with no precipitation. Second, we
manually added rain data, as described below, into the
morphed files via Excel.
The U.S. Department of Energy has three weather files for
New York City in their EnergyPlus database. Each
weather file has specific conditions of temperature,
relative humidity, and rain. We took two very different
cases: the weather data from New York J. F. Kennedy
(JFK) International Airport 744860 (TMY3), which
contains rain data (normal to a horizontal surface), and
New York Central Park 744860 (TMY2), which does not
include rain data. We morphed these two weather files
with CCWorldWeatherGen to the year 2080 and manually
added the missing rain data columns from the original
JFK TMY3 file.
Rainfall data can be essential for hygrothermal
simulations, and therefore WUFI Pro has a database of
weather files under the format of WUFI Binary Climate
(WBC) that contain rain load (as well as radiation load
incident on vertical surfaces). We elected to use rainfall
data that shows less precipitation overall (compared to the
WUFI database files of New York City) to be
conservative about the influence of rain. Figure 1 shows a
comparison with WUFI’s New York City weather files
provided: “cold year” and “warm year” which are the
10%-coldest and the 10%-warmest year selected from 30
years, and “ASHRAE Year 1”, which is the most severe
typical weather file “concerning moisture damage within
the building envelopes according to ASHRAE RP 1325”
(Zirkelbach et al., 2007; Wufiwiki Dialog).
Several authors have demonstrated that the validity of the
underlying ‘present day’ data is critical to the outcome of
future weather morphed files (Jentsch et al., 2013;
Belcher et al., 2005). Figure 2 shows the dry-bulb
temperature and relative humidity conditions in the
original and morphed (future) weather files. One can see
that even though the relative humidity levels drop slightly
(in average 4.16%) in the future files, the temperatures
increase in average 4.7°C. This means that the absolute
water content of the air increases in general by
approximately 4 g of water per kg of dry air. Mold growth
is sensitive to both high moisture and high temperature,
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Figure 1: A comparison of Rain data from different weather files of New York City. Top to bottom: WUFI Cold Year,
WUFI Warm Year, WUFI ASHRAE 1 (worst case scenario), and TMY3 John F. Kennedy Airport
Figure 2: Comparison of Relative Humidity and Dry Bulb
Temperature between two typical year weather files in
New York City and their 2080 future weather files.
̶ TMY3 JFK Airport
̶
2080 TMY3 JFK Airport
̶ TMY2 Central Park
̶
2080 TMY2 Central Park
Hours(1year)
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so this sets a more inducive environment for growth.
Figure 2 also shows the difference between the two EPW
files of New York City (JFK and Central Park). The
temperatures are similar between both EPW weather files,
but the difference in humidity will impact the mold-
growth results.
Hygrothermal Model
In this research, we use WUFI Pro 6 (Zirkelbach, 2007),
which is a dynamic hygrothermal simulation software that
simulates vapor diffusion, as well as moisture and heat
transport through building materials and exceeds the
steady-state calculation method used by most architects.
The program divides the parameters into three sections:
the component (comprised of a building envelope
assembly of materials, orientation, surface transfer
coefficient, and initial conditions); the control (the
calculation period and related numerical parameters); and
finally, the outdoor and indoor climates. WUFI Pro allows
the user to collect hygrothermal data from any depth on
the wall section, and therefore analyze how the materials
are reacting to temperature and moisture changes.
Wall Assembly
The component modeled is the exterior wall of a typical
lightweight timber building construction, often found in
existing residential buildings located in the mixed climate
of the northeast United States. This wall configuration is
part of the typical wall sections listed in the Exterior Wall
Assemblies section 11.293 of Architecture Standards
(Hedges, 2017).
Figure 3: Wall Section. Materials left (exterior) to the
right (interior): Fiber Cement Sheathing Board (8mm,
0.3 inches), Vapor Retarder 1 perm (1mm, .04 inches),
Oriented Strand Board (13mm, 0.5inches), Cellulose
Fiber Insulation (89mm, 3.5 inches), Air and Vapor
Barrier 3M 3015 (1mm, .04 inches) and Gypsum Board
(13mm, 0.5 inches)
As shown in Figure 3, the wall section includes cellulose
insulation between Douglas fir stud-framing. The total
thickness of the wall is 12.4 cm and has a U-Value of
0.345 W/(m2 K) [R-value of 15.39 (ft2 °F h)/BTU]. The
wall is oriented north to avoid most direct solar radiation
interference, and the initial condition of the assembly is
80% relative humidity and a temperature of 20°C (68°F).
WUFI Parameters
The research compares two simulation periods to
understand the growth and decay of mold. These are three
years for an initial comparison and then ten years to
confirm that mold will be sustained over time. The indoor
climate follows ASHRAE 160 standards (TenWolde,
2008), keeping indoor conditions between 21.1°C (70°F)
and 23.9°C (75°F) via mechanical cooling and heating.
We also specified the interior moisture loads (i.e., gains
due to people and indoor activities) as 0.00014 kg/s, and
the maximum indoor relative humidity is capped at 50%,
via mechanical dehumidification.
Mold Model
The VTT model, developed by VTT Technical Research
Centre of Finland (Hukka and Viitanen, 1999), was
selected for this research. The model’s overlaying
equations are a result of experimental studies in a
laboratory with pine sapwood (Viitanen and Ritschkoff,
1991), which later was updated to other building materials
by Ojanen et al. (2010). Several research papers validate
this model and detail the differential equations and
regression models used to arrive at a mold index (MGI)
(Viitanen et al. 2015; Ojanen et al., 2010; Gradeci et al.,
2018). The mold simulation model accounts for growth
and decline based on three parameters: dry-bulb
temperature, relative humidity (water content), and the
material sensitivity to mold-growth. The growth
computation is based on the original Equation 1:


∙.....𝑘𝑘 (1)
Where: T is temperature, RH is relative humidity and k1
and k2 are intensity coefficients. W refers to the timber
species and SQ the surface quality (that only applies to
types of wood, otherwise it is calculated as 0). The
calculation is computed hourly, and it simulates both
mold-growth and decay. The mold-growth index ranges
from zero, which represents no growth, to six, which
represents 100% surface coverage of "tight and heavy"
mold-growth.
Table 1: The VTT Mold Growth Index (MGI)
(Ojanen et al., 2010)
Index Growth Rate
0 No growth
1 Small amounts of mold detected only with a
microscope, initial stages of local growth
2 Several local mold-growth colonies on the surface
detected with microscopy
3 Visual findings of mold on surface <10% coverage,
or <50% coverage of mold (microscope)
4 Visual findings of mold on surface, 10%–50%
coverage, or >50% coverage of mold (microscope)
5 Plenty of growth detected visually, >50% visual
coverage
6 Very heavy and tight growth detected
visuall
y
(
covera
g
e 100%
)
Most building materials have enough nutrients for mold-
growth (Kalamees and Vinha, 2004), and lightweight
timber building envelopes with cellulose insulation are
especially prone to mold growth. Susceptible materials,
such as pine wood or paper products, start developing
mold-growth with a relative humidity of 80%.
This research uses the WUFIs Mould Index VTT 2.0
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program. This software uses simulation results from
WUFI Pro to test mold gro wth in each material separately.
The tool requires a user to export humidity and
temperature hourly data from the material WUFI
simulation as the “climate” input for the mold simulation.
In addition to this, a user needs to specify the sensitivity
of the material to mold growth, which can be custom or
chosen from a list of standard materials provided by VTT.
The relative humidity and temperature data from within
the wall assembly are extracted from the WUFI Pro
simulation sensors as a .csv or .asc file. We collected data
from several virtual interior sensors to understand the
moisture accumulation in each material of the wall. We
found that relative humidity was higher within the
insulation and timber cavity of the wall, therefore we
extracted the moisture and temperature data from each
side of the insulation layer. The interior face of the cavity
showed higher relative humidity. We then used this data
to simulate the mold-growth in the Douglas Fir timber
framing. The VTT classification of “pine sapwood”
includes Douglas Fir, a common wood species used in
lightweight timber residential walls. Pine sapwood is one
of the most sensitive material in the VTT library and
probably the most well-studied one since it was the base
substrate to produce the original mathematical VTT
model. We used the default sensitivity and specifications
of pine sapwood for our mold index simulations.
Figure 4: (a) Mold Index simulation of pine sapwood interior structure of wall assembly from five current typical
weather files. (b)Mold index comparison of pine sapwood under TMY3 JFK weather file and its morphed version to the
year 2080. (c) Mold index simulation of the interior pine sapwood from two future weather files of New York City, with
and without rain data. The dashed line in red shows the threshold of Mold Index of 3, where mold becomes visible to
the naked eye.
̶ TMY3 JFK with rain
̶ TMY2 Central Park without rain
̶
WUFI NYC Cold Year with rain
̶ WUFI NYC Warm Year with rain
̶ WUFI NYC ASHRAE 1 with rain
(extreme conditions)
̶ TMY3 JFK with rain
̶ 2080 Morphed TMY3 JFK with rain
̶ 2080 Morphed TMY3 JFK with rain
̶ 2080 Morphed TMY2 Central Park with rain
̶ 2080 Morphed TMY3 JFK without rain
̶ 2080 Morphed TMY2 Central Park without rain
(a) (b)
(c)
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Results
“Current Climate” Results
The interior of the wall assembly performs as expected
using current EPW weather files; it shows minimal mold-
growth that does not pose problems to the building
integrity or the indoor air quality, see Figure 4a. More
specifically, the simulation results using the five original
New York weather files (WUFI warm year, WUFI cold
year, WUFI ASHRAE 1, TMY3 JFK and TM2 Central
Park) do not surpass a mold index of 1.25. This low index
indicates minimal local mold that would only be
detectable under a microscope and is categorized as
acceptable by the VTT model (Gradeci et al., 2018).
“Future Climate” Results
However, the simulations from the 2080 future weather
files show a substantial increase in mold-growth
compared to the ones from current weather data. Under
future weather conditions, moisture builds up within the
wall due to the higher moisture content of the outside air
and the higher temperature difference between a warmer
exterior climate and a constant interior set-point
temperature of 23.9°C (75°F).
Figure 4b shows the simulation results from the TMY3
JFK file (red) and its morphed 2080 derivative (black).
The future weather file simulation yields five times the
mold-growth predicted under the current typical year file.
This result indicates that future conditions may have a
severe impact on existing walls, such as this case.
The Impact of Weather Files
The ‘present day’ climate results in Figure 4a show that
these five simulations yield very different results even
though all of them refer to the same wall assembly under
New York City weather conditions. One might expect
different outcomes from the WUFI “warm year,” “cold
year” and the “ASHRAE 1” extreme weather scenario but
may find it surprising that the EnergyPlus typical year
weather files also produced such different results. The
simulation from the TMY3 JFK station, shown in red, is
four times higher than the mold index from the TMY2
Central Park station simulation, shown in orange. One of
the main differences between these two files is that the
TMY2 Central Park file is missing rain data. However,
they also differ in other respects, such as relative
humidity, since these weather files are an amalgamation
of weather from typical days from a different pool of years
(Wilcox and Marion 2008). Therefore, we performed
several simulations to test the sensitivity of the results to
different weather files, testing both the impact of rain and
the other weather conditions.
Figure 4c shows a simulation of 10 years using the four
morphed EPW files from two sources, the TMY3 JFK and
TMY2 Central Park data, each with and without rain data.
All conditions result in mold-growth within the first six
months, partially due to the initial conditions of the
model, where all materials started with 80% relative
humidity and a temperature of 20°C (68°F). However, the
simulation from the 2080 TMY2 Central Park file without
rain (pink), does not reach a mold index of 3 and declines
to an index of 0 within five years, then remaining under a
threshold of significant growth.
In stark contrast, the simulation from the 2080 TMY3 JFK
weather file with rain (black), surpasses the index of 3
within the first winter and remains in seasonal fluctuation
just below the index of 4 for the entirety of the simulation
period. An index of 4 refers to visual findings of 10% to
50% mold surface coverage apparent to the naked eye, or
more than 50% coverage of mold under a microscope
(Ojanen et al., 2010). This high mold-growth is a
consequence of higher temperatures and higher moisture
in the exterior climate. Results from Figure 4c show the
potential importance of rain data, even though these are a
result of adding the record with the least precipitation of
the four weather files. Even in the same wall model, rain
data can either be the defining factor of mold-growth, or
it can be almost negligible. It seems that in scenarios
where the moisture and temperature conditions are at the
threshold of decay, like we see in the TMY2 Central Park
plot (pink), rain data (green) adds the extra moisture
needed for constant mold-growth and maintains a
permanent mold colony for ten years.
Discussion
Future climates may present an elevated risk of mold-
growth in building envelopes and subsequently, could
pose a risk to indoor environmental quality (Dales et al.,
1991), as well as building stability and useful life span via
damaged wall structures.
As described in the introduction, in traditionally mixed
(hot and cold) climates, such as New York City, architects
frequently placed a vapor barrier inboard of the insulation
layer to protect against vapor flow under cold conditions.
This decision is suboptimal on summer days when hot,
moist air migrates into the cool wall (kept so by insulation
and air-conditioning), raising its relative humidity,
sometimes even to the point of condensation, which
cannot dry toward the interior due to the vapor barrier. As
expected, the results of our simulations confirm that,
under the current climate conditions, this situation of
suboptimal performance is infrequent enough that
prolonged high-moisture and mold-growth are not a
serious risk in the wall assembly modeled. However, the
results also indicate that a warmer and moister (at least in
terms of absolute humidity) climate would indeed pose a
risk, since the situation that creates conditions for mold-
growth would occur more frequently and for a more
prolonged period.
Our study demonstrates one potentially problematic
scenario, thereby illustrating a widespread need for this
type of investigation since the problems identified here
are likely to be present in other combinations of wall
assemblies and future climate conditions. Furthermore,
climate change could pose an increased mold-growth risk
in other ways as well. For example, mold risk may be low
under normal operating conditions but amplified by
increasingly probable extreme weather events followed
by power outages, since a properly functioning HVAC is
often crucial to managing excess moisture inside
buildings. Wetting scenarios, e.g., wind-driven rain and
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flooding, may also pose increased risks and, therefore,
should be included in simulations to represent sources of
envelope failure. Ironically, well-intentioned climate
mitigation practices, such as adding more insulation to a
traditional design or switching to organic-based materials
to reduce embodied energy, might inadvertently
exacerbate the risk of mold growth in a warmer climate.
Future Work
Our study demonstrates a framework for this type of
analysis, i.e., one that includes hygrothermal and mold-
growth simulation considering future climates. It also
identifies some of the uncertainty of existing methods
considering the sensitivity of the results to the quality and
accuracy of the weather data input. Other authors have
indicated this relationship (e.g., Alev and Kalamees,
2016; Gradeci et al., 2018) and the necessity to use
accurate climate context data. Predicting climate data is
an extensive and quickly-growing field of research. Also,
more empirical studies are needed to underpin further the
mold modeling calculations, which is also an active field
of research.
In ongoing research, the authors are continuing to assess
envelope assemblies under projected climate conditions.
Future research will investigate how building envelope
design decisions, such as insulation levels and choice of
materials, can impact the risk of mold-growth. The
methodology presented here and similar ones for
understanding the performance of other building
components, in combination with financial and decision
analyses, are advancing the concept of resilience for the
building industry. For example, this approach can be
applied with a life-cycle cost analysis to translate risk and
resilience outputs into economic decisions for existing
constructions as well as to new and prospective building
designs.
Conclusions
This research develops a framework that combines
existing hygrothermal and mold-growth simulation tools
with future weather files, including a proxy for future
rainfall data. This method has the potential for
determining a metric for building resilience. Through this
study, we conclude that the wood stud assembly analyzed
is maladapted to future climate conditions and faces a risk
of prolonged mold-growth and moisture accumulation
inside of the wall. The wall assembly analyzed represents
a sizable number of buildings. Our finding of potential
future moisture/mold problems is critical because it
reveals the overarching issue of climate change adaptation
and the need to consider resilience in building standards.
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