Climate-Responsive, Evidence-Based Green-Roof Design Decision Support for U.S. Climates

ABSTRACT
A number of trends have recently
emerged in the areas of environmental
building design and high-performance systems.
However, in spite of many design and
technical efforts to improve the performance
using multiple building enclosure components—
especially green roofs—the critical
uncertainty of existing mechanisms, such
as predefined computational modeling and
design guidelines, has frequently resulted
in lower building performance efficiency
than intended. In reality, examination of
many actual green roof performance cases
revealed an even larger energy usage and/
or lower environmental performance of the
building, where implemented, than those of
the adopted base cases.
To address this challenge, we developed
a Climate-Responsive, Evidence-Based
Green-Roof Design Decision Support Tool
that uses finely tuned performance modeling
with calibration by actual measured
data from existing best practices. By utilizing
these composite best-practice cases
as a source for reference data, this project
can provide stakeholders (e.g., architects,
engineers, facility managers, owners, etc.)
with readily applicable and reliable green
roof design solutions for new/renovation
projects. A design solution algorithm that
was developed by this project adopted multiple
computational data-mining methods
and performance simulation modeling. This
project approach can lead to effective green
roof design decisions in an early stage of
an individualized project with various climate
and geometric conditions, based on
integrated principles of design and building
architectural configurations.
A grant by the RCI Foundation to the
author’s research group at the University of
Southern California supported development
of this tool.
INTRODUCTION
There is widespread recognition and a
growing literature of measured data that
suggest that green roofs can reduce building
energy consumption as well as provide environmental
benefits.[1–3] Vegetative or green
roofs can act as urban heat-island effect
mitigation tools, where water evaporation
from the vegetation, as well as the thermal
mass and thermal resistance of the
green roof, contribute to reduced indoor
and outdoor temperatures in buildings and
urban areas.[4–6] This, in turn, helps reduce
the cooling load for a building, resulting
in reduced cooling air requirements.
Therefore, energy consumption is reduced,
as well as the associated output of atmospheric
carbon[5,7] and the downsizing of the
HVAC system for the building.
However, the question arises as to
whether the roof assembly is performing
according to its design, and whether any
alteration made to the assembly could make
a difference in the building’s thermal performance.
In addition, the complexity of
design configurations in design parameters
(which should be considered in environmentally
responsive design principles)
demands considerable project effort and
financial expenditure. As a result, most
stakeholders routinely follow what they have
done in previous projects and/or adopt
a “rule-of-thumb” experience that includes
skipping the step that requires an in-depth
climate-responsive design optimization process.
Such an imperfect design process
would probably result in much higher energy
use and increased greenhouse-gas emission,
while sacrificing effective environmental
benefits in urban heat-island effects.[2,8] Therefore, the goal of this project is
to provide a design decision support tool
for green roofs that would be useful in
3 2 • I n t e r f a c e J a n u a r y 2 0 1 7
Figure 1 – Conceptual diagram of the design tool process of this project.
providing an environmentally responsive
parametric design with consideration of the
climate and seasonal characteristics of a
project site (Figure 1). Thus, the developed
tool will assist stakeholders in establishing
optimized design solutions without sacrificing
conventional design and construction
processes.
OBJECTIVES
1. Effectively model green roof assemblies
for a building in an energymodeling
program, and calibrate
those models based upon the use of
existing data collected from a selected
reference site.
2. Identify the role of the different
parameters of a green roof assembly,
and quantify their impact on a building’s
heating and cooling loads.
3. Determine if a green roof (as a roofing
option for different climate types)
is a better alternative for cooling a
roof, in terms of the thermal performance
of the building.
4. Estimate environmental performance
based on evaluated energy
performance and design configurations.
5. Develop research findings in the
form of a Web-based decision support
tool that is accessible to the
public.
PROJECT METHODS
ROOF MODELING AND CALIBRATION
Objective 1: Effectively model a green
roof assembly on a building in an energymodeling
program, and calibrate that model
to match existing data.
Task procedure:
• Select an existing
building with a
green roof installed.
• Collect roof performance
data pertinent
to the selected
building.
• Model the building
with energy-modeling
software according
to reference
data collected.
• Run a simulation
for the appropriate
climate zone, and
record the results.
• Compare simulation
results to
existing data, and
identify areas of
disagreement/mismatch.
• Calibrate and finetune
a model so that its performance
is closer to that of the selected real
green roof.
The research tasks associated with
Objective 1 are critical since the collected
data from existing facilities were used as
reference data for the purpose of model
calibration. Considering the variations in
climate zones in the U.S., the project selected
existing green roof building sites located
in three representative climate zones: Los
Angeles, California (Climate Zone #3); Rolla,
Missouri (Climate Zone #4); and Chicago,
Illinois (Climate Zone #5); as defined by the
2012 International Energy Conservation
Code (IECC).[9] These selected climate
zones have been validated as
ideal climatic conditions for vegetation
without concern about maintenance,
precipitation, and temperature.
The site chosen for Climate Zone #3 was
the Burbank Water and Power Building,
located in Burbank, CA (Figure 2). Burbank
has a Mediterranean climate.
The site chosen in Climate Zone #4
was Emerson Electric Company Hall at the
Missouri University of Science and Technology
in Rolla, Missouri (Figure 3). The climate is
humid subtropical, with 48.4 inches (1227
mm) average annual rainfall. As part of the
roof renovation, a GAF Gardenscapes green
roofing system with an area of 3245 sq. ft.
was installed in the year 2013. For Climate
Zone #5, we selected the Chicago City Hall
(Figure 4). The climate is heat-dominant, and
the sky condition is clear or cloudy overall,
with cloudy conditions in the winter season.
J a n u a r y 2 0 1 7 I n t e r f a c e • 3 3
Figure 2 – Green roof on Burbank Water and Power Building.
Figure 3 – Green roof on Emerson
Electric Company Hall.
Figure 4 – Green roof on the
Chicago City Hall building.
Figure 5 clearly shows different weather
patterns at each selected climate site.
Reference Data Collection
For reference data collection at the selected
sites, the project adopted LM-35 (thermocouple)
and HOBO sensory devices (manufactured
by Onset Computer Corporation) to
measure dry bulb temperature and relative
humidity (RH). All of the data were recoded
every ten minutes. In the vegetated area, a
sensor was placed under the soil at a depth
of 4 inches, and another sensor was placed
below the concrete surface from inside the
building, as shown in Figure 6.
ROOF PARAMETRIC DATA ANALYSIS
Objective 2: Identify the role of the different
parameters of a green roof assembly, and
quantify their impact on a building’s heating
and cooling loads.
Task procedure:
• Correctly and accurately model different
layers of green roof assembly.
• Select one parameter (layer) and
change its value for each simulation
run, and record the impact on the
building loads.
• Repeat the process for each parameter,
and record the results.
We considered those structural parameters
as input variables in the building simulation
software. The major physical parameters
included height, foliage area (leaf area),
leaf reflectivity, leaf emissivity, soil moisture,
soil depth, and insulation thickness (Figure
7). However, we selected leaf area index, soil
depth, and insulation as design parameters
to simulate the green roof performance of
each selected site climate.
Model Parameters
Selected
We selected four
major physical parameters
in order to narrow down the parameters
for parametric testing: leaf area index, soil
depth, insulation, and climate type. These
are currently adopted for modeling in the
Energy Plus – Design Builder interface,
based on the computation method designed
by Dr. D.J. Sailor.[10] The variables selected
in each parameter are as follows:
1) Leaf Area Index
• LAI = 1
• LAI = 3
• LAI = 5
2) Soil Depth
• 3-in.-thick soil (extensive)
• 6-in.-thick soil (semi-intensive)
• 12-in.-thick soil (intensive)
3) Insulation
• No insulation
• 4 in. insulation
• 6 in. insulation
• 8 in. insulation
THERMAL PERFORMANCE ANALYSIS
Objective 3: Determine if a green roof (as a
roofing option for different climate types) is a
better alternative for cooling a roof in terms
of the thermal performance of the building.
Objective 4: Estimate environmental performance
based on the evaluated energy performance
and design configurations.
Task procedure:
• Replace green roofs with cool roofs
(thermal emittance: 0.75) in an energy
model, and run simulation.
• Compare the simulation results on
the baseline model performance.
• Estimate the environmental performance
and water usage/quality man-
3 4 • I n t e r f a c e J a n u a r y 2 0 1 7
Figure 5 – Heating and cooling degree-days at each selected climate site (Y-axis unit: degree days).
Figure 6 – Section of the green roof showing the placement of
sensors.
HOBO 1: Above roof (ambient temperature)
HOBO 2: On top surface of the roof
HOBO 3: Inside the soil
HOBO 4: Beneath the concrete surface roof
HOBO 5: On top of the glass pebbles
HOBO 6: Below the glass pebbles
HOBO 7: Beneath the concrete surface
HOBO 8: At working level (inside building) Figure 7 – Test parameters and their subset variables.
agement as a function of the energy
performance and design conditions.
Based on the optimized parametric combinations
per climate zone (investigated in
the previous tasks), we built a prototype
building to evaluate the thermal performance
of an optimally designed green roof.
The building contains 53,600 sq. ft. (163.8 x
109.2 ft.), five zones on each floor, and three
stories (Figure 8). The code-compliance conditions
for ASHRAE 90-1 were applied per
climate condition.
DEVELOPMENT OF A WEB-BASED
DESIGN DECISION TOOL
Objective 5: Develop the research findings
in the form of a Web-based decision support
tool that is accessible to the public.
Task procedure:
• Complete data interpretation and
comparisons.
• Develop reliable computational models
to estimate energy and environmental
performance for each
combination of selected parameters’
configured variables of green roofs.
• Develop a Web-based design decision
tool that incorporates the estimation
models and thermal performance
data.
DATA COLLECTION AND ANALYSIS
Data Analysis and Interpretation
Once the baseline validation model was
established to simulate each building performance
with an acceptable accuracy, the
green roof was reconfigured with various
parametric combinations of the roof’s physical
components selected. The simulation
test was done for all of the 36 different
assembly types in three different climates,
with one variable of a parameter being
changed with each simulation run, with the
purpose of understanding how that variable
affected the different thermal performance
metrics that had been selected for this project.
Thirty-six parametric combinations can
be generated based on the three parameters
as follows (defined under “Model Parameters
Selected,” above):
• LAI = Leaf Area Index (unitless) = 3
types
• SD = Soil Depth (inches) = 3 types
• IN = Insulation (inches) = 4 types
The nomenclature followed here is the
same as described above and remains consistent
throughout the report. For example,
“B134” indicates an assembly with LAI = 1,
SD = 3 inches, and IN = 4 inches. The other
factors considered for simulation were the
choice of one hot day and one cold day at
each selected climate site.
Burbank, CA (Climate Zone #3)
After simulating a green roof based on
various parameter assemblies, the estimated
cooling and heating energy loads in each
design’s peak cooling and heating days,
respectively, are summarized in Figure 9.
The estimated energy loads vary depending
on the design assembly.
Per design peak cooling or heating day,
a best ten-design assembly was generated,
as in Figures 10 and 11. These figures illus-
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Figure 8 – Isometric view of the baseline
model adopted [11].
Figure 9 – Heating and cooling energy loads per sq. ft. (Burbank).
trate a design assembly that generates the
lowest heating energy load.
Rolla, MO (Climate Zone #4)
In Figure 12 (like Burbank’s results), the
estimated energy loads vary, depending on
the design assemblies. The design assembly
with no insulation showed a higher heating
energy load than the baseline (adopted
cool roof), and the cooling energy load with
no insulation also revealed higher values
than the baseline in most cases in Rolla.
However, LAI with IN seemed to contribute
to the cooling energy load reduction significantly.
Among the parameters, IN was
selected as the most significant attribute to
building performance. LAI was estimated as
a second significant parameter, while SD
was counted as an insignificant attribute.
Per design cooling or heating day, a
best ten-design assembly was generated in
Figures 13 and 14. As illustrated in these
two figures, a design assembly generating
the lowest heating energy load, for example,
does not guarantee its lowest cooling load,
or vice versa. This was also similar to the
finding in Burbank. Therefore, a duration
of each season—i.e., cooling and heating
seasons—should be considered to find an
optimal design assembly that can minimize
a total heating and cooling energy load
in a whole year. This feature is discussed
below, and the Web-based design support
tool incorporated a formula into the design
algorithm when the duration of heating and
cooling seasons could be considered.
Chicago, IL (Climate Zone #5)
Overall, the findings were very similar to
those of Rolla. In the heating energy load analysis,
LAI was not a significant component,
as compared to IN and SD. However, in the
cooling load analysis, all of the parameters
were found to be significant variables.
DESIGN DECISION SUPPORT TOOL
Support Model
Since this project considered three climate
site conditions, the simulated data
from the calibrated models totaled 216 data
sets (216 = 36 design assemblies x 3 cities x
2 conditioning seasons [i.e., cooling or heating]).
In the Data Collection and Analysis
section above, LAI, SD, and IN contributed
to the energy load/performance very differently,
depending on climate conditions. In
addition, a specific design assembly for one
season did not guarantee its application
to the other season as an optimal design
solution. Therefore, the length of cooling
or heating should be considered so that we
can find an “optimal” design assembly to
efficiently fit into the energy-effective performance
of a project for one whole year.
Since this project focused on finding an
optimal design assembly of the green roof
parameters based on the use of simulation
data generated by trustworthy simulation
models, we put much weight on “differences”
of the estimated energy performance by
design. A climate condition is one of the significant
variables that affects green roof performance
and helps determine total building
energy performance. Therefore, to establish
a design decision algorithm, the project considered
major climate condition attributes,
which included heating and cooling degree
days (HDD and CDD), 99% dry bulb temperature
(DB 99) for heating, 2% dry bulb
temperature (DB 2) for cooling, mean daily
temperature range (MDR) for cooling, and
3 6 • I n t e r f a c e J a n u a r y 2 0 1 7
Figure 10 – Heating energy load in Burbank.
Figure 13 – Heating energy load in Rolla.
Figure 11 – Cooling load in Burbank.
Figure 14 – Cooling energy load in Rolla.
Figure 12 – Heating and cooling energy loads per sq. ft. (Rolla).
2% wet bulb temperature (WB 2) for cooling.
This climate data information was taken
from the ASHRAE Handbook-Fundamentals
and ASHRAE Standard 90.1-2013 [12].
The formulas are as shown in Table 1. As
shown, the cooling and heating performance
models generated R-squared (R-sq) values of
97.59% and 97.74%, respectively. Thus, the
study revealed that the variations of cooling
and heating performance could be accounted
by the developed energy load prediction
formulas as a function of LAI, SD, IN, and
fundamental climate information by more
than 97%.
In Table 1, all of the selected variables
were statistically significant with p-values (a
statistical significance threshold) lower than
0.10 (error rate), except SD (p = 0.17) in
the heating energy-load performance estimation.
Based on the estimated energy load per
cooling and heating season, HDD and CDD
were adopted in this project to estimate the
length of each season and to calculate the
total energy load for one year. These conditioning
time lengths were multiplied to each
J a n u a r y 2 0 1 7 I n t e r f a c e • 3 7
Figure 16 – Total heating load in Chicago. Figure 17 – Total cooling load in Chicago.
Figure 15 – Heating and cooling energy loads per sq. ft. (Chicago).
SO YOU DON T TAKE YOUR LAST
estimated energy load per season, and the
calculation results were used to select an
optimal design assembly that could minimize
the energy load for the whole year.
Web-based design decision tool
The Web-based design tool is available at
http://www.hbilife.com/rcif/. This section
introduces each page of the tool and provides
some instruction on how to use it and how to
interpret the design decisions.
1. “Decision tool” page: A user can
select a state and city of a project
site by using a drop-down menu.
The embedded database contains
data on 300 major cities in the U.S.
Once a project site is selected, a
summary of weather data, including
dry bulb temperature (2% and 99%),
wet bulb temperature (2%), and
mean daily temperature range, as
well as heating (60) and cooling degree
(50) days are on the following
page.
2. Result page: Based on the formula
discussed in the previous section,
the climate data of a site (selected
by a user) and the green roof design
assembly are processed to estimate
the performance ranking of
the assembly options for a cooling/
heating energy load, and the estimated
energy use intensities (EUIs
or Kbtu/ft2) are displayed using the
estimation engine embedded in the
Web-based tool.
3. Design recommendation: Based on
the estimated total of EUIs for cooling
and heating, the web tool selects
a design assembly that provides the
lowest EUI estimation for recommending
a best design solution for a
whole year.
CONCLUSION AND
PROJECT LIMITATIONS
Conclusion
The data-driven Web-based decision
support tool for a green roof design developed
in this project provides a simple, quick,
and easy, but evidence-based design solution-
finding approach, using an advanced
data-mining logic. Building a simulation
model is a challenge to construction stakeholders,
such as architects, owners, and contractors,
mainly due to technical, time, and
financial barriers. This developed tool adopts
data-driven regression algorithms that are
based on best-practice collected data, calibrated
simulated models, and computational
data-mining strategies in three different
climate conditions. Since this design tool is
already available to the public, it can be utilized
for early design decision-making on any
type of green roof project.
Limitations
Limitations with respect to this research
involve a lack of field data for validation in
other climate zones. Although the United
States is divided into six main climate
zones, the scope of this research is limited
to three climate zones only. Even though
this project adopted 216 data sets generated
from calibrated simulation models,
the data size may not be large enough to
generalize the findings and estimations for
3 8 • I n t e r f a c e J a n u a r y 2 0 1 7
Table 1 – Design decision support regression models. As shown, the cooling and heating
performance models generated R-squared (R-sq) values of 97.59% and 97.74%, respectively.
Thus, the study revealed that the variations of cooling and heating performance could be
accounted by the developed energy load prediction formulas as a function of LAI, SD, IN,
and fundamental climate information by more than 97%.
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all U.S. site climate conditions. Therefore,
the study of green roof performance in other
climate zones would give us a much better
understanding of the thermal performance
of a green roof that pertains to a specific
climate zone. In addition, this project adopted
only four parameters—leaf area index,
soil depth, insulation, and climate condition.
Various other parameters, such as
soil moisture, reflectance, emissivity, and
absorption could be selected and tested to
identify robust findings and to incorporate
them into the estimation algorithm.
Furthermore, future work could involve
consideration of various other architectural
parameters, such as types of roofing systems
(sloped, flat, shaded, nonshaded, etc.),
and different types of buildings, such as
museums and hospitals, where an internal
heat gain is not critical. Any or all of these
could be investigated.
It would also be interesting to study the
parameters that affect on-site air temperature
and solar shading conditions that are
mainly affected by neighboring buildings,
especially in a high-rise district in an urban
area. The air temperature and bounded
solar radiation at a site could vary, depending
on the construction of neighboring
buildings. To study the impact of these on
the site would be an interesting research
topic that could help people in calibrating
to validate a model in a super-fine resolution.
In spite of the environmental benefits
of green roofs, one of the main reasons not
to choose a green roof may be the possible
(technical) difficulty in physical management
and the cost of maintenance. It would
also be necessary to develop and study life
cycle and cost-benefit analyses of green
roofs based on the design composition in
each climate zone of the U.S.
ACKNOWLEDGEMENT
This research was supported by a grant
from the RCI Foundation.
REFERENCES
1. H.F. Castleton, V. Stovin, S.B.M.
Beck, and J.B. Davison. “Green
Roofs: Building Energy Savings
and the Potential for Retrofit.”
Energy Build. 42 (2010) 1582–1591.
doi:10.1016/j.enbuild.2010.05.004.
2. C. Clark, P. Adriaens, and F.B. Talbot.
“Green Roof Valuation: A Probabilistic
Economic Analysis of Environmental
Benefits.” Environ-mental Science and
Technology. 42 (2008) 2155–2161.
doi:10.1021/es0706652.
3. H. Akbari, M. Pomerantz, and H.
Taha. “Cool Surfaces and Shade
Trees to Reduce Energy Use and
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November 13-14
Orlando, Florida
The Omni Resort
at ChampionsGate
RCI, Inc. | rci-online.org/building-envelope-edu | 800-828-1902
2017 Building Envelope Technology Symposium
RECOMMENDATION:
Based on the simulation results of the weather data of Los Angeles, California, for optimal
green roof performance, the Green Roof Design Decision Tool can recommend:
Leaf Area Index: 5
Soil Depth (inches): 12
Insulation Thickness (inches): 8
295–310. doi:10.1016/S0038-
092X(00)00089-X.
4. R. Kumar and S.C. Kaushik.
“Performance Evaluation of Green
Roof and Shading for Thermal
Protection of Buildings.” Building
and Environment. 40 (2005)
1505–1511. doi:10.1016/j.buildenv.
2004.11.015.
5. E. Obernodorfer, J. Lundholm, B.
Bass, R.R. Coffman, and H. Doshi.
“Green Roofs as Urban Ecosystems:
Ecological Structures, Functions,
and Services.” Bioscience. 57 (2007)
823. doi:10.1641/B571005.
6. M. Santamouris, C. Pavlou, P.
Doukas, G. Mihalakakou, A. Synnefa,
A. Hatzibiros, et al. “Investigating
and Analysing the Energy and
Environmental Performance of an
Experimental Green Roof System
Installed in a Nursery School
Building in Athens, Greece.” Energy.
32 (2007) 1781–1788. doi:10.1016/j.
energy.2006.11.011.
7. K.L. Getter, D.B. Rowe, G.P. Robertson,
B.M. Cregg, and J.A. Andresen.
“Carbon Sequestration Potential
of Extensive Green Roofs.” Environmental
Science and Technology.
43 (2009) 7564–7570. doi:10.1021/
es901539x.
8. P. La Roche. “Low Cost Green Roofs
for Cooling.” Csupomona.edu. (2009)
22–24. http://www.mendeley.com/
research/low-cost-green-roofs-cooling/
(accessed September 11, 2014).
9. IECC, 2012 International Energy
Conservation Code®, (2012).
http://shop.iccsafe.org/2012-int
e r n a t i o n a l – e n e r g y – c o n s e r v a –
tion-code-soft-cover.html (accessed
September 12, 2015).
10. D.J. Sailor, T.B. Elley, and M.
Gibson. “Exploring the Building
Energy Impacts of Green Roof Design
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doi:10.1177/1744259111420076.
11. Department of Energy, HVAC
Package for Small and Medium
Sized Commercial Buildings,
(2015). http://energy.gov/sites/
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12. ASHRAE, ANSI/ASHRAE/IES
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for Buildings Except Low-Rise
Residential Buildings, 2013. http://
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1865966? (accessed February
23, 2015).
Dr. Choi’s primary
research interests
are in the areas of
advanced controls
for human-building
integration, sustainable
building
design/perf o r –
mance, and indoor
e n v i r o n m e n t a l
quality. He has published
more than
40 research papers
in prestigious
international journals and peer-reviewed
conference proceedings. His academic
achievements have been recognized by major
research and conference organizations, and
he received a Best Paper Award from the
Architecture Institute of Korea and a New
Investigator Award from the U.S. Architectural
Research Centers Consortium.
Joon-Ho Choi, PhD,
LEED AP
4 0 • I n t e r f a c e J a n u a r y 2 0 1 7
The Occupational Safety and Health
Administration (OSHA) has fined Weathercraft
Incorporated, a commercial
roofing and waterproofing company,
$12,471 following the heatstroke death
of a 47-year-old roofer.
Darren Laird was installing roofing
materials at Helias High School in
Jefferson City, Missouri, on August
17, 2016, when he collapsed. The
heat index was approximately 90ºF
(32ºC). He died the next day after
being hospitalized with a core body
temperature above 107ºF (41.6ºC).
The employer, charged with the
maximum penalty for a “serious” violation, allegedly
“exposed employees to the recognized hazard of excessive
heat during roofing operations.” Weathercraft has received at
least five OSHA citations in the past for failing to provide fall
protection and fire protection.
“This tragedy occurred on this worker’s third day on the
job,” said Karena Lorek, OSHA’s acting director in Kansas City.
“His needless death underscores how critical it is for employers
to ensure that workers are acclimated to heat conditions. A
review of heat-related deaths across industries finds most
workers were new to the job and not physically used to the
constant heat and sun exposure. Workers should have frequent
access to water, rest, and shade to prevent heat illness and
injuries during the hot summer months and during hot indoor
conditions and be trained to recognize and respond to the signs
of heat-related illness.”
“If there is any indication of heat stroke, immediately call
911, move the employee to a cooler location, and provide water
to try and reduce the core body temperature,” Lorek said. “In
the summer of 2016, nationwide, OSHA conducted more than
200 heat-related inspections and…[investigated] 13 fatalities.”
In 2014, 2630 workers in all industries suffered from heat
illness, and 18 died from heatstroke and related causes while
on the job, according to OSHA.
OSHA has a “Heat Safety Tool” app that provides the heat
index, available in English and Spanish for Android and iPhone
devices. Find it at https://www.osha.gov/SLTC/heatillness/
heat_index/heat_app.html.
— ABC17News.com
Roofer’s Heat Death Results in OSHA Fine