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Daylight and health: exploring the relationship between established daylighting metrics for green building compliance and new metrics for human health

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Daylight and health: exploring the relationship between established daylighting metrics for green building compliance and new metrics for human health

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Content 1

Daylight and Health

Exploring the Relationship Between Established Daylighting Metrics for Green
Building Compliance and New Metrics for Human Health
By
Shuyi Wu

Presented to the
FACUL TY OF THE
SCHOOL OF ARCHITECTURE
UNIVERSITY OF SOUTHERN CALIFORNIA
In partial fulfillment of the
Requirements of degree
MASTER OF BUILDING SCIENCE

2019.08

2

ACKNOWLEDGEMENTS
Firstly, I would like to express my deepest gratitude to my thesis chair, Professor Kyle Konis, and my
committee members, Professor Marc E. Schiler and Professor Karen Kensek. This research would not
finish without their detailed instructions, supportive ideas, and helpful comments. Without them, I cannot
imagine the thesis could be finished in time and be well-organized.

I would like to thank my family that always support me and never ask anything back. Without them, I
cannot get the chance to have this experience of studying board and learn so many things in the Master of
Building Science Program.

Lastly, I would like to thank all the friends and faculty in the Master of Building Science program. It is so
honorable for me to be a part of the School of Architecture in the University of Southern California.
3

COMMITTEE MEMBERS

Kyle Konis, Ph.D, AIA
Assistant Professor
USC School of Architecture
kkonis@usc.edu

Marc Schiler, FASES
Professor
USC School of Architecture
marcs@usc.edu

Karen Kensek, LEED BD+C
Professor
USC School of Architecture
kensek@usc.edu

4

ABSTRACT
Climate-based daylight modeling (CBDM) workflows are currently being used by professionals to generate
dynamic daylighting metrics such as spatial daylight autonomy (sDA) for the purpose of examining
lighting performance. However, existing daylighting metrics evaluate daylight illuminance on a horizontal
workplane, which does not reflect the vertical human eye exposure to light. The WELL Building Standard
for Lighting was established to minimize human circadian system disruption, promote productivity, benefit
sleep quality and ensure visual task needs. It uses a non-visual metric, Equivalent Melanopic Lux (EML),
to measure the circadian influences on humans and does evaluate light exposure vertically at eye level. To
calculate EML, a software program ALFA was used because it can evaluate the spectral quality of light and
is capable of evaluating light exposure at eye level.

A statistical data analysis method was developed involving correlation analysis and discrepancy
comparison to examine the relationship between the design guidance provided by non-visual metrics and
traditional visual metrics for denoting the “daylit area” of a building. The methodology developed to
compare EML with both point-in-time illuminance and sDA with the spatial circadian autonomy (sCA) to
calculate the point-in-time and annual level of discrepancies. The sCA was calculated by processing the
simplified “annual” (21
st
of each month) point-in-time EML data during the occupied hours.

ALFA is the latest software that can assess the EML result based on the 81-spectral Radiance engine and set
the calculation vectors at the gaze direction. By using publicly available software tools to implement the
visualization and calculations, such as Rhinoceros, Ladybug, Honeybee, and ALFA, the existing CBDM
workflow could not adequately evaluate the daylight performances efficiently. It was found that the
effectively daylit zone defined by sDA and sCA showed large discrepancies that cannot be ignored. Areas
that had qualified daylight autonomy (DA) performance can still have poor circadian autonomy (CA) result
that cause limited occupant access to the circadian stimulus. No consistent strong correlated connections
were found between the point-in-time photopic illuminance and EML. Compared to sCA, the sDA metric
frequently underestimated the effectively circadian daylit zone.

Sufficient daylight provided to the occupants are essential to ensure circadian health. Evidence-based tools
are significant to be used to simulate the circadian performance during the early design stage. Software
tools for sCA calculation is necessary to be developed and it is necessary to combine sCA and sDA for
evaluating both visual and circadian performances during the daylighting design.

Keywords ： Daylighting, circadian lighting, climate-based daylight modeling (CBDM), daylighting metrics,
circadian health, ALFA

Hypothesis: The effectively daylit zone predicted by existing Climate-based daylight modeling workflows
may differ substantially from the effectively daylit zone predicted by circadian lighting metrics.

5

Research Objectives
 Compare the outcomes of the existing CBDM approaches with a new approach focused on
assessing light exposure for human health.
 Prove or disprove that a space that has good performance for sDA can show poor performances for
annual EML or vice versa.
 Use statistical analysis method to compare photopic Illuminance and EML.
 Compare sCA and sDA result
 Learn data analysis tool and use Matlab for data processing

6

Table of Contents
ACKNOWLEDGEMENTS ................................................................................................................... 2
COMMITTEE MEMBERS .................................................................................................................. 3
ABSTRACT ...................................................................................................................................... 4
Table of Contents ........................................................................................................................... 6
Chapter 1. Introduction ............................................................................................................. 9
1.1. Daylight ..................................................................................................................................... 9
1.1.1. Health and productivity ........................................................................................................ 9
1.1.2. Daylight spectrum and color temperature ......................................................................... 10
1.1.3. Visual lighting metric .......................................................................................................... 10
1.2. Circadian lighting .................................................................................................................... 13
1.2.1. Intrinsically Photosensitive Retinal Ganglion Cells (ipRGCs) ............................................... 13
1.2.2. Circadian rhythms ............................................................................................................... 13
1.2.3. Circadian visual sensitivity .................................................................................................. 14
1.2.4. Circadian metrics ................................................................................................................ 15
1.3. Standards and codes for daylight design ................................................................................ 16
1.4. Early‐stage design analysis tools ............................................................................................. 17
1.5. Conceptual scenarios comparison .......................................................................................... 18
1.6. Summary ................................................................................................................................. 20
Chapter 2. Background and Literature Review .......................................................................... 21
2.1. Daylight design objectives ...................................................................................................... 21
2.1.1. Limitations of CBDM ........................................................................................................... 22
2.1.2. Calculation grid ................................................................................................................... 23
2.2. Circadian lighting .................................................................................................................... 23
2.2.1. Ganglion cells ...................................................................................................................... 23
2.2.2. Spectral sensitivity .............................................................................................................. 24
2.2.3. Circadian effect thresholds ................................................................................................. 25
2.3. Standards and codes ............................................................................................................... 26
2.3.1. California Title 24 ................................................................................................................ 26
2.3.2. LEED .................................................................................................................................... 28
2.3.3. The WELL building standard ............................................................................................... 28
2.4. Novel circadian metric ............................................................................................................ 29
2.5. Evaluation tools ...................................................................................................................... 30
2.5.1. Rhinoceros & Grasshopper ................................................................................................. 31
2.5.2. Ladybug & Honeybee .......................................................................................................... 31
2.5.3. DAYSIM and Radiance engine ............................................................................................. 32
2.5.4. ALFA .................................................................................................................................... 32
2.6. Summary ................................................................................................................................. 33
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Chapter 3. Methodology .......................................................................................................... 35
3.1. Daylight Simulation Modeling & Input ................................................................................... 36
3.1.1. Geometry ............................................................................................................................ 36
3.1.2. Settings ............................................................................................................................... 36
3.2. Experiment of design .............................................................................................................. 39
3.2.1. Simulation experiment 1 .................................................................................................... 39
3.2.2. Simulation experiment 2 (Annual metric) .......................................................................... 40
3.3. Data Analysis and data processing .......................................................................................... 42
3.3.1. Data analysis ....................................................................................................................... 42
3.3.2. Data processing for exercise 2 (annual analysis) ................................................................ 42
3.4. Summary ................................................................................................................................. 43
Chapter 4. Data and Results ..................................................................................................... 45
4.1. Simulation results for simulation experiment 1 (point‐in‐time data) ..................................... 46
4.1.1. Simulation results based on dates ...................................................................................... 46
4.1.2. Simulation results based on analysis grid point locations .................................................. 52
4.2. Simulation results for simulation experiment 2 (annual data) ............................................... 54
4.2.1. Circadian Autonomy

(CA) .................................................................................................... 55
4.2.2. sCA
50%
.................................................................................................................................. 55
4.2.3. sCA
75%
.................................................................................................................................. 57
4.2.4. sCA
90%
.................................................................................................................................. 58
4.3. sDA and sCA comparison ........................................................................................................ 59
4.3.1. Discrepancy ......................................................................................................................... 60
4.3.2. Similarity (Dual‐health) ....................................................................................................... 62
4.4. Summary ................................................................................................................................. 64
Chapter 5. Discussion ............................................................................................................... 65
5.1. Discussion of point‐in‐time analysis in simulation experiment 1 ........................................... 65
5.1.1. Luminance and illuminance ................................................................................................ 66
5.1.2. View direction effects ......................................................................................................... 66
5.1.3. Discussion of R‐squared findings ........................................................................................ 67
5.2. The significance of sCA ........................................................................................................... 70
5.2.1. Average sCA and Standard Deviation.................................................................................. 70
5.2.2. View direction effects in simulation experiment 2 ............................................................. 71
5.3. Comparison of sDA and sCA metrics ...................................................................................... 72
5.4. Suggestions for daylighting design for circadian health ......................................................... 73
5.5. Summary ................................................................................................................................. 74
Chapter 6. Limitations and Future Work ................................................................................... 75
6.1. Conclusions ............................................................................................................................. 75
6.2. Limitations .............................................................................................................................. 77
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6.3. Future work ............................................................................................................................ 78
6.3.1. Digital program for sCA and CBDM engine ......................................................................... 78
6.3.2. Validation and background research .................................................................................. 78
6.3.3. Adjust design guideline and standard ................................................................................. 78
6.3.4. Glare issues ......................................................................................................................... 79
6.4. Summary ................................................................................................................................. 79
References .................................................................................................................................... 80
Appendix A ................................................................................................................................... 84
Appendix B .................................................................................................................................... 85
Appendix C .................................................................................................................................... 86
Appendix D ................................................................................................................................... 90

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Chapter 1. Introduction
Architectural designers and lighting simulation specialists are moving towards evaluating and optimizing
their daylight designs based on the Climate-Based Daylight Modeling (CBDM) results. However,
conventional daylight design workflows ignore the human eye position and only set the measurement grid
as a horizontal workplane with sensor orientation upward. CBDM predicts the irradiance and illuminance
performances in a space based on the historical climate conditions and sun locations from the online
datasets (Ashdown, 2016) (Mardaljevic, Heschong, & Lee, 2009). It is proposed that the existing procedure
CBDM cannot adequately evaluate the daylight performances. The discovery of intrinsically photosensitive
retinal ganglion cells (ipRGCs), the non-image receptor different from the rod and cone photoreceptors in
the human eyes, raised the need for designers to address a new set of “non-visual” lighting needs for
building occupants. Light influences human circadian rhythms by adjusting hormone secretion and
sleep/wake cycle. Through ipRGCs, intense and high-frequency lights can stimulate the human body to be
alert, which will interrupt normal circadian rhythms (Schubert, 2006). Thus, daylighting design is
significant to ensure better circadian lighting performance, such as providing the proper dose of spectral
lighting to minimize circadian rhythm disruption but ensure the task lighting request at the same time. The
lighting quantity Equivalent Melanopic Lux (EML) is used by the WELL Building standard to measure
non-visual light influences on circadian rhythms, and the new software Adaptive Lighting for Alertness
(ALFA) can calculate EML result based on vertical sensor planes. Lighting metrics are developed to
qualitatively evaluate building with respect to proper daylight dose provided in space. Since EML is a
novel metric compared with those visual lighting metrics (such as sDA and photopic illuminance), it is
necessary to study the connections between them and explore the influences this non-visual metric can
cause. This chapter introduces the daylight and its non-visual effects, architectural daylighting design
objectives, new and existing daylighting performance metrics, and terminology related to circadian
performance.
1.1. Daylight
Besides providing light to see only, daylighting is known to be a profound component of our daily life.
Nowadays, people spend 80%-90% of their time indoors (staying at home or working), but the human body
has been adapted to the natural sunlight pattern for a million years (Hraska, 2015). It is well-established
that the people prefer to stay in daylit areas over artificial light such as a space designed with large glazing
areas, skylights, and good outdoor view (Hraska, 2015). Thus, integration in lighting and architecture
designs is required in mitigating lack of natural light problems.
1.1.1. Health and productivity
Even though it is late compared with thermal comfort or indoor air quality, lighting design has been
considered for a healthy indoor environment in recent years. While architects and engineers are designing a
building, accessing daylight can not only help save the energy used for electrical lighting but is also
significant to ensure occupants’ health.
Lighting design can affect human productivity and health. Instead of focusing on providing adequate
lighting and avoiding discomfort only, achieving a high-quality and healthy lighting environment for
10

employees can ensure their productivity (Melton et al., 2018). It is estimated that the inappropriate lighting
environment has the potential effects of glare problems and visual stress can cause an annual $2700 loss per
person in an office workplace (Choi & Zhu, 2015). Based on the sun position, angle and color temperature,
good daylighting design can optimize people's workspace lighting condition for high productivity and
maximized functionality.
1.1.2. Daylight spectrum and color temperature
Natural lighting contains the entire color spectrum but would keep shifting from moment to moment due to
the dynamic sky conditions (Ander, 2003). The sky color temperature varies in the morning, later afternoon
and dawn, ranging mostly from 6000 (overcast sky) to 10,000 (light blue sky) Kelvin (Hraska, 2015)
(Figure 1-1). In the mid-morning, it is full of the blue light spectrum, and the orange and red region will
rise for the late afternoon and dawn period. These spectrum changes will influence the production and
hormone sections of the human body (Hraska, 2015).

Figure 1-1 Natural daylight changes (“Circadian sun progression RGB,” 2019).
1.1.3. Visual lighting metric
Measuring the daylight performance is significant during the early architecture and daylighting design stage
to follow the required standards and codes, for example the LEED standard.
Lighting professionals and architects can change the daylighting strategies and adjust the building forms for
better daylighting performance. Due to the increased computing power, computer-based simulation codes
appeared to do the dynamic daylighting metric calculations based on the annual time series of illuminances
in a space (Reinhart, Mardaljevic, & Rogers, 2006) It is acknowledged that no single metric can address all
issues involved in the design process. Compared to the standard single point assessment at one
point-in-time result, the annual simulation outputs accounting for the climate variations are critical to
overcoming the inadequate analysis of old metrics (IESNA, 2012).
1.1.3.1. Climate-Based Daylight Modeling (CBDM)
CBDM was devised the first time in Mardaljevic’s paper title given to the 2006 CIBSE National
Conference (Mardaljevic, 2006). CBDM can predict absolute quantities depending on location (sun and sky
condition data), fenestration orientation, precise geometry (e.g., varying degrees of shadings), and material
11

properties (Mardaljevic et al., 2009). The Radiance (Section 2.5.3) engine is the first software to perform
CBDM simulations developed by Lawrence Berkeley National Laboratory (Ashdown, 2016). With CBDM,
daylight metrics such as spatial Daylight Availability (sDA) and Annual Sunlight Exposure (ASE) can be
calculated (see section 1.1.3.4. and 1.1.3.5) (IESNA, 2012). CBDM workflow is significant to be used both
in the advanced daylight design strategy verifications and daylighting evaluation to get credits for green
building compliance such as LEED v4 daylighting credit points (USGBC, 2013). CBDM extends the
service lighting consultants can offer through providing metrics that can help design and verify advanced
daylighting harvesting system or even cooperate with the architects during the conceptual design stage
(Ashdown, 2016).
1.1.3.2. Photopic illuminance (Illuminance)
Illuminance is used for the quantitative description of the brightness and measured in lux in SI derived units
(foot-candle in SAE units). Illuminance is defined as the total luminous flux received on a surface for per
unit area. It is wavelength-weighted to human eye perception. Lux is frequently used to measure the
artificial lighting levels for a given task performance on the horizontal work plane (IESNA, 2000) (Figure
1-2).

Figure 1-2 photopic illuminance calculation plane and human eye exposure direction
1.1.3.3. Daylight Factor (DF)
Daylight Factor was developed before the availability of computer to measure the ratio between the
daylight illuminance level at a given point indoor and the unshaded outdoor ground illuminance under a
standard CIE overcast sky conditions (Littlefair, 1998), The daylight factor calculation always excludes
sunlight that its value is independent of the sky luminance distribution (Tregenza & Wilson, 2011). DF is
simple to use as the ratio is set to avoid dealing with the frequent fluctuations of the daylight, which also
makes it as a common metric in countries having daylighting recommendations involved in building
standards or regulations (Boubekri, 2014). Due to its simplicity, it is easy to calculate in both the physical
models and computer programs. The limitation of this metric is that it addresses only overcast sky and that
the qualified DF daylighting design may get excess daylight under clear sky conditions (Mardaljevic et al.,
2009) (Boubekri, 2014). DF is recommended as the same value for all facade orientations and building
locations because it ignores the seasonal changes, sky condition variability, building orientation, and
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location. The DF method is uncertain to be suitable to do daylight design assessment depending on the
building’s geological location and prevailing sky conditions (Boubekri, 2014).
1.1.3.4. Daylight Autonomy (DA) & sDA
The IES Approved Method for sDA and ASE (LM-83) attempts to define a standardized calculation and
simulation-based modeling methodology to predict daylighting performance (Konis & Selkowitz, 2017).
Daylight autonomy (DA) is an innovative dynamic metric that was firstly proposed by the Association
Suisse des Eletriciens in 1989 and then improved by Christoph Reinhart (Boubekri, 2014). Different from
the former metrics, DA takes annual climate data information based on each specific geographic location
instead of just individual sky conditions (“Daylight Autonomy,” n.d.). DA is defined to evaluate in a space
a given point can get what percentage of annual occupied hours not less than an illuminance reference
level(Reinhart, Mardaljevic, & Rogers, 2006) (Figure 1-3).

Figure 1-3 Use DIVA to calculate the sDA and ASE results (Mccormick, 2017).
SDA is defined by IESNA and used as a metric to measure if a space can get sufficient daylight in its
annual occupied hours from 8:00 am to 6:00 pm each day, including weekends, 3650 h per year. An sDA
value is calculated as the percentage of an area that the DA for each calculation point in that space is above
selected illuminance level not less than certain percentage of the occupied hours, and reported as a
percentage number ranging from 0% to 100%. The sDA outcome has no upper limit illuminance settings
and only requires the minimum illuminance threshold to define the qualified daylight stimulus. Thus, it is
necessary to use another metric, Annual Sunlight Exposure (ASE), to identify the potential visual
discomfort in the working environment (IESNA, 2012).
1.1.3.5. Annual Sunlight Exposure
Annual Sun Exposure (ASE) defines the percentage of a space accepting overly direct sunlight that can
cause glare issue and indoor thermal heat gain. It is specified as the percentage of area that the illuminance
level above 1000 lux for more than 250 hours annually (Sterner, 2014).
13

ASE and sDA work together to define the lowest level of illuminance and keep the balance of the overall
map in check. Combine these two can support better-optimized daylight design. For instance, a space gets
its sDA
300,50%
as 48.2% and ASE
1000,250
as 42.7% by using the DIVA software program (Figure 1-3). The
SDA
300,50%
48.2% describes that 48.2% of the total area is equal or above 300 lux for at least 50% of the
annual occupant time. As defined before, ASE
1000,250
< 42.7% means that floor area gets more than direct
daylight above 1000 lux for 250 hours annually is no more than 42.7%.) DIV A is a Rhinoceros (Section
2.5.1) Plug-in that can generate daylighting and energy modeling evaluations, such as Radiance mapping,
Climate-based metrics, and Thermal zone Energy calculations (“DIV A for Rhino,” n.d.).
1.2. Circadian lighting
Lighting can have both visual and non-visual effects on humans. Lighting design industries normally use
visual-related metrics only to evaluate the lighting performance during the design stage, such as Photopic
Illuminance and Spatial Daylight Autonomy (sDA). However, things have changed after the discovery of a
new type receptor in human eyes which is Intrinsically photosensitive retinal ganglion cells
(ipRGCs)(Figure1-4).

Figure 1-4 Non-visual effects on human circadian rhythm and ipRGCs. (“Special Photoreceptors,” n.d.)
The image comes from the ALFA software description by SOLEMMA.
1.2.1. Intrinsically Photosensitive Retinal Ganglion Cells (ipRGCs)
Light influences human circadian rhythms by adjusting hormone secretion and sleep/wake cycle. Studies
have proven the non-visual effects of lighting on human circadian rhythm and discovered the third
photoreceptor in the human retina (Hattar, 2002). IpRGCs function as the non-image receptor different
from the rods and cones. Through ipRGCs, intense and high-frequency lights will stimulate the human
body to be alert, which will easily interrupt normal circadian rhythm at night (WELL, 2016). Section 1.2.3
discusses human eye sensitivity to various spectrum.
1.2.2. Circadian rhythms
The name of “circadian rhythm” is derived from the Latin words circa and dies which mean approximately
and day (Vitaterna et al., 2001).The circadian rhythm functions as the time-keeping system to allow the
14

organism to synchronize with the physical environment changes that connected with the cycle of day and
night (Vitaterna et al., 2001).

Figure Error! No text of specified style in document.-5 Biological Clock (“Circadian Rhythm Clock,” n.d.)
They are significantly related to human health by adjusting sleep-wake cycles, hormone secretion, digestion
habits or even body temperature, which is the reason people feel energized and sleepy at a similar time
every day. Natural factors inside human bodies produce circadian rhythms. However, stimulus from the
outside can also affect circadian rhythms, and daylight is the main reason. People can suffer from poor
sleep quality, less alertness, and health risks including diabetes, obesity, cardiovascular disease, and cancer.
Shift workers or populations engaged in a rotating schedule have higher risks for cancer, diabetes by being
required to unfollow their local 24-h light and dark cycle (Zelinski, Deibel, & Mcdonald, 2014).
1.2.3. Circadian visual sensitivity
Human eyes have significant sensitivity differences between red and blue lights to synchronize with
circadian rhythm (Figure 1-6). Therefore, the higher circadian efficacy of short-wavelength (blue) light
should be avoided in the late night for a healthy lighting environment (Schubert, 2006). Lighting design is
critical for balancing the task lighting requirements and circadian health at night (Oh, Yoo, Park, & Do,
2015).
15

Figure 1-6 Circadian efficacy curve to show differences between circadian and visual sensitivity (Schubert, 2006)
The lighting characteristics (spectrum, quality, distribution, and exposure history) play significant roles in
circadian rhythms. It has been proven that proper usage of lighting characteristics could provide lighting
therapy for patients to consolidate their sleep/wake patterns and promote health and well beings (Figueiro,
Bullough, & Rea, 2004).
1.2.4. Circadian metrics
Specific visual lighting requirements were the primary consideration in the past to guarantee sufficient
lighting for different visual tasks. As a result, the criterion indexes documented in standards and codes were
visual lighting metrics, such as illuminance level. The WELL Building Standard (WELL) is set for
measuring and certifying health and well-being performances of buildings. WELL provides guidelines that
minimize disruption to the body’s circadian system, enhance productivity, support good sleep quality and
provide appropriate visual acuity (WELL, 2016). In its “circadian Lighting Design” section, providing
sufficient melanopic light intensity for workspace is one of the certification preconditions (WELL, 2016).
Equivalent Melanopic Lux (EML) is used to measure the biological effects of light, and the term
“melanopic” is used to weigh the light intensity by the sensitivity of the melanopsin-containing ipRGCs.
EML can weigh the signals ipRGCs accept that influence the circadian system (WELL, 2016). The
proposed metric EML sets the calculation plane vertically at the seated eye-level height, which is an
alternative to the traditional lux (Photopic illuminance) based on a flat plane (Figure 1-7) (Figure 1-8).
16

Figure 1-7 EML and normal eye exposure direction
Based on the calculation plane, the result for EML and photopic illuminance can be presented as horizontal
vectors (EML) and vertical vectors (photopic illuminance) (Figure 1-8).

Figure 1-8 Calculation plane directions of photopic illuminance and EML
1.3. Standards and codes for daylight design
Standards are sets of technical definitions and instruments for designers and manufacturers and are usually
defined by public organizations and a state or federal government body. When the government bodies adopt
standards, the whole industry needs to follow it, and the standards turn to codes. Use the piping product as
an example, the code provides sets of rules of the minimum requirements for designers or manufacturers or
can also provide additional specific details not listed in standards or specifications (What is the difference,
n.d.) (Figure 1-9).
17

Figure 1-9 Differences between Code, Standard, and Specification in piping (What is the difference, n.d.)
Leadership in Energy and Environmental Design (LEED) is the most widely used voluntary certification
program developed by the U.S. Green Building Council (USGBC) to provide third-party verification for
buildings with sustainable design (What is LEED, n.d.).
Title 24 of the California Code of Regulations (CCR), known as the California Building Standards Code or
just "Title 24," contains the regulations that govern the construction of buildings in California. Cities and
counties in California are enforced to follow the CCR Title 24 listed in the Health and Safety Code
Sections 17958, 17960, 18938(b), & 1894. Title 24 applies for all building occupancies and is
published by the California Buildings standards commission.
Independent voluntary verification systems such as LEED, WELL, and Living Buildings are not codes.
Title 24 is a California code. Title 24 (Section 2.3.1), LEED (Section 2.3.2) and WELL (Section 2.3.3) all
have requirements for daylighting design.
1.4. Early-stage design analysis tools
Daylight can support the interior lighting healthy circadian stimulus and provide visual access to the
outdoor environment at the same time. Architecture firms can use early analysis tools (such as
DIV A-for-Rhino, Ladybug Tools) to get more enlightened design adjustments for interior space (Figure
1-10).
18

Figure 1-10 Architecture firms use early analysis tools (such as DIVA-for-Rhino, Ladybug Tools) to get more
enlightened design adjustments. (Sterner, 2014)
By using these early analysis software tools, parameters can be input to compare
 Building orientation
 Building form and footprint
 Placement of interior and exterior shading
 Window or skylight size
 Glazing and interior material (Surface Reflectance)
 Occupancy schedule
Those architectural elements can largely affect the building performances and can be enhanced through
software simulations during the early design stage.
The daylight design workflow combines modeling software and simulation engines by using a series of
software programs corresponding with each other. Rhinoceros, Grasshopper, Ladybug & Honeybee and
ALFA are used for investigating the relationships between visual and non-visual metrics. Simple
introductions (Section 2.5) and detailed descriptions of their function in the metrics comparison process are
provided in Chapter 2 and 3.
ALFA (Section 2.5.3) is a new software, released on June 2018, used for circadian performances evaluation
that is advanced at its broader spectral rendering improvement (“ALFA – SOLEMMA” n.d.). LARK was
developed by the University of Washington and ZGF Architects LLP. LARK, a Grasshopper plug-in
(Section 2.5.1), can get circadian performances analysis by inputting spectral data of specific sky color and
materials (“Lark Spectral Lighting,” n.d.).
1.5. Conceptual scenarios comparison
Visual lighting metrics have been researched and studied for a long time, and sophisticated software
programs and comparatively comprehensive metrics can be used to assess the daylighting design factors.
However, the circadian lighting design is relatively new, and there is still no yearly dynamic metrics to
measure the healthy circadian daylight zones. Based on the definition of sDA, the sDA performance is
calculated by accumulating each point-in-time illumine data during the annual occupied hours. Thus, an
annualized circadian daylight boundary can be drawn for each view direction after reviewing a large
amount of the point-in-time EML results.
19

The annualized circadian daylight boundary is based on a previous test model, and its sDA is equal to 39%,
which means spatial mapping of the annual performance show that about 40% of the area exceeds 300lux
for at least 50% of the annual occupied hours (Figure 1-11). According to the LEED standard (Section
2.4.2.) requirement, it should be not less than 50% to be a healthy daylight zone. Thus, this interior space is
rated as an inadequately daylit area based on the existing daylighting rating system.

Figure 1-11 sDA result
However, when it comes to circadian performances, the south oriented view might receive more circadian
stimulus than other directions. It is estimated that circadian performances based on an annual period for
each direction that the circadian daylit area result for four different view directions show discrepancy and
has deeper spatial mapping compared with sDA.; the arrow in this figure represents the calculation planes’
orientation as well as the eye exposure direction (Figure 1-12). The annualized circadian metric defines the
“circadian daylight” areas that shows the discrepancy with the sDA metric.

Figure 1-12 Estimated circadian daylit area result for four different view orientations
20

1.6. Summary
Daylighting is a significant component to ensure the occupants’ well-being. It is necessary for lighting
consultants to consider the biological effects of daylighting and use a more comprehensive metric to extend
the possibilities to ensure circadian health during the design stage. The newly discovered non-visual
receptor, ipRGCs, raised the research of circadian health. The WELL Building standard defined a new way
to more accurately measure the lighting effects on people compared to the existing CBDM workflow.
The existing procedure for CBDM requires sensors to be oriented horizontally, that poorly represents
human eye exposures. It is admitted that no single metric can address the daylight design factors adequately.
The metrics from the existing CBDM workflow, sCA and ASE, can get the annual climate variations
involved which are better than the single climate condition for the historical method such as daylight factor
(Heschong et al., 2013).
The lighting quantity EML is used by the WELL Building standard to measure non-visual lighting
influences on circadian rhythms on the vertical calculation plane to better represent human eye exposure
directions. By using the new software ALFA, EML can be assessed at the horizontal vector direction, in the
gaze direction of building occupants. There is a difference between how performance outcomes from new
tools and new metrics compare to existing methods of evaluating daylighting performance. If current
CBDM workflows show discrepancies to the novel circadian lighting metrics, the daylight designs can be
rated as “fake” sufficient daylit area by CBDM workflows that cannot ensure occupants’ circadian health.
However, the daylight metrics such as sDA are widely used for the daylight health assessment and listed in
the green building compliance such as LEED standard.
This study investigates the relationship between performance outcomes from new tools and new metrics
and existing methods of evaluating daylighting performance. The vertical calculation plane is necessary to
be used for calculating the circadian performance and better representing the normal occupants’ view
direction. The proposed annualized circadian metric is good for making up for the limitation that the EML
metric cannot take the exposed circadian dose and duration.
21

Chapter 2. Background and Literature Review
In the past few years, the indoor environment has been designed for providing a more comfortable and
energy-saving space. The typical working schedule in the United States is from 8:00 to 17:00 (Qiu & Konis,
2018). If properly integrating with daylight during the architectural design process, not only can the energy
consumption for electrical lighting be lower, but also occupant health. The most common lighting strategies
related to human health are lighting fixtures having a tunable color temperature (Culver, n.d.). However, a
good architectural design can enhance the indoor circadian lighting performances for the occupants’ health
as well as using daylighting.
Daylighting design used to focus on sustainable designs to balance visual task lighting with solar heat gain
(Qiu & Konis, 2018). However, after the concept of circadian lighting was developed, it was necessary for
the architectural firms to make use of daylighting to provide the proper spectral components for better
circadian performance. Studies show that the circadian lighting design is essential for human health, but it
is challenging to make the scientific research result to actionable standards or a specific threshold that
lighting designers should follow (Konis, 2017). Even the WELL building standard sets the EML threshold for
circadian performance ignores the significant lighting characteristics such as time and duration into
consideration, and only a few simulation tools have been developed. ALFA (section 2.5.3) is a new software
tool used to calculate the EML performances vertically.
This chapter explores how other researchers evaluate the existing lighting metrics and CBDM workflows
and investigates novel circadian metrics proposed. The limitations and benefits of the dynamic metrics
compared with other static visual metrics can be identified. Additionally, it is necessary to study if the new
circadian metric EML shares similarities with the visual daylighting metrics, and to investigate any
discrepancies on the analysis results. Chapter 2 discussed daylight design objectives, circadian lighting,
standard and codes, novel circadian metrics and evaluation tools.
2.1. Daylight design objectives
Visual lighting requirements were the primary consideration in the past and determined the criterion
indexes documented in standards and codes. The visual metrics lighting designers have been using are
Daylight Factor (DF), sDA, and point-in-time illuminance (PI) (e.g., illuminance on March 21st at 9:00 am)
that are all shown on flat calculation planes.
Conventional metrics are used to analyze static daylight performance and focus on the sky conditions
individually, such as DF (Reinhart et al., 2006). DF is limited to take information like climate, location,
building orientation into consideration is known for being used under overcast weather conditions
(Boubekri, 2014). It does not take direct sunlight into account. Thus, DF can highly possible to make
inaccurate predictions. Point-in-time illuminance can only get the worst or best scenarios, such as to show
glare issues and inadequate task lighting but cannot draw a comprehensive map of overall performances
(Sterner, 2014). In recent years, the profession realized these limitations and has moved to dynamic metrics,
such as Spatial Daylight Autonomy (sDA) and Annual Sun Exposure (ASE), which were used in LEED v4
by U.S. Green Building Council for balancing the requirement of sufficient daylight provided and the risk
22

of visual discomfort (USGBC, 2015). Dynamic daylight metrics are calculated based on time series of
illuminance or luminance data of the interior space of a building, which involves the consideration of daily
variation of the daylight quantity and character of the annual period (Reinhart et al., 2006). However, most
dynamic metrics are cumulative, which loses detailed information Equivalent Melanopic Lux (EML) is
used to measure the biological effects of light. IpRGCs is the non-visual receptor in human eyes and can
regulate circadian changes. EML can weigh the signals ipRGCs accept that influence the circadian system
(WELL, 2016). Notably, the proposed metric EML sets the calculation plane vertically at the eye-level
which can better represent the vertical eye exposure direction, which is an alternative to the traditional lux
(Photopic illuminance) based on a flat plane.
2.1.1. Limitations of CBDM
Exiting metrics from CBDM are advanced at predicting daylighting results by assessing lighting conditions
over a specific period (Heschong et al., 2013). The dynamic metrics, sDA and ASE (LM-83), are advanced
at linking the annual daylight performances with the occupants’ shading preferences assumptions (Konis &
Selkowitz, 2017).
Additionally, there is an issue in the assessment of glare. Glare happens when the occupants view the bright
sky directly inside the building which can cause visual discomfort (Hopkinson, 1972) (Figure 2-1). This
glare can be reduced by decrease the fenestration within a building to reduce the brightness of the interior
patch or just simply raise the interior space brightness (Hopkinson, 1972). The vertically oriented occupants’
eyes would be exposed to visual discomfort and glare because of the tremendous luminance contrast that
cannot be measured based on horizontal illuminance results (Choi & Zhu, 2015). The horizontal calculation
method for illuminance and sDA is a legacy of studies and experiments on horizontal illuminance level for
supporting specific visual tasks that do not follow the human normal visual orientated direction. No upper
limit for the EML might cause “circadian glare” issue.

23

Figure 2-1 Discomfort glare caused by the contrast between the bright windows and its surroundings in the Union
Station, Los Angeles (Own photo).
2.1.2. Calculation grid
The simulation result is largely determined by the relationship between geometry, sun position, calculation
grid height, and surface material. However, simulation result carried out on the illuminance result on the
work plane (desk height) cannot fully describe lighting issues, such as glare problems caused by massive
daylight and sufficient circadian stimulus, even it is the most widely-used metric.
Ceiling is one of the visible surfaces for occupants. By setting the calculation grid at the ceiling height, the
daylight distribution patterns and illuminance uniformity in the interior space can be studied (Mardaljevic
et al., 2009). Simulation result carried out across the grid at eye-level height can represent the visual
environment better than the horizontal work plane and can indicate the occurrence of direct sunlight for
glare issue.
2.2. Circadian lighting
It is significant for designers to know how lighting would affect the circadian system and related biological
reasons behind it. This section talks about the third receptor cells in human eyes, the various spectral
sensitivity of the circadian system and the latest threshold of quantifying circadian stimulus.
2.2.1. Ganglion cells
The light accepted by human eye exposures activates specific ganglion cells and is converted as signals to
the Suprachiasmatic Nucleus (SCN) in the brain. The SCN functions as the clock synchronizer in the
human body that regulates the individual hormone secretion (Bailey & Silver, 2014). SCN can set the daily
circadian pace by secreting the pineal hormone of darkness, melatonin, which is a signal sent to the human
body for sleep at night time. SCN can function intrinsically and keep its period even being isolated from the
outside stimulus for weeks (Figueiro, Bullough, & Rea, 2004). A good example is that an air traveler's
circadian rhythms keep synchronized with the original time zone (Konis, 2017). However, the internal
human circadian period is not perfectly equal to 24 hours. It ranges from 23.5 to 24.7 h among healthy
adults and gets reset every morning by light stimulus received at the retina to synchronize with the solar
day (Czeisler et al., 1999).
Rods and cones are the widely known ganglion cells working as photoreceptors in human’s visual systems.
However, another type of ganglion cells, intrinsically photoreceptive retinal ganglion cells (ipRGCs), were
discovered as the non-visual receptor that can ignore the light exposure patterns and the stimulus can be
accepted for even blind objectives (O’Hagan et al., 2013). Light at nighttime can interrupt the normal
sleep-wake cycle because it would suppress the secretion of hormones (Khademagha, Aries, Rosemann, &
van Loenen, 2016)(Thapan, Arendt, & Skene, 2001). The spectral sensitivity of circadian responses
compared to the visual reactions results in the various stimulations of the circadian system. It was found
that the ipRGCs have multiple sensitivities to the spectrum and take only a small portion in the ganglion
cells (Ecker et al., 2010) . Researchers have detected a stronger melatonin suppression shift by exposing to
short wavelengths spectrum (Khademagha et al., 2016).
24

2.2.2. Spectral sensitivity
Multiple photoreceptors in the human eyes can respond differently based on their various spectral
sensitivities (Figure 2-2). Rods show its maximum sensitivity at around 500nm.

Figure 2-2 Melanopic and visual responses to various wavelengths based on Table L2 in Appendix C of the WELL
building standard. The light wavelength increases at 5nm each time to measure the relative power effects (WELL,
2017).
Humans have three types of cones, S cones (peak sensitivity ~420nm), M cones (maximum sensitivity
~535nm), and L cones (peak sensitivity~565nm) (Figure 2-3) (O’Hagan et al., 2013). M represents the
non-visual receptor in ipRGCs and has a peak sensitivity at about 480nm, which indicates that the
melanopic is more active to blue range of light. However, because of the adult eye’s lens, the melanopic
response gets its peak at 490nm (WELL, 2017).
25

Figure 2-3 Simplified form to show photoreceptors sensitivity (R for rods, MC, LC, and SC separately represent three
different types of cone opsins. M stands for Melanopsin.)(O’Hagan et al., 2013)

2.2.3. Circadian effect thresholds
After discovering the non-image receptors in human eyes and the biological responses of daylighting
exposure, there was an urgent need to define a universal language, such as technology terms or metrics, for
lighting industries and academic studies to follow in measuring the circadian performances (Amundadottir,
Lockley, & Andersen, 2017). The new health-based standard WELL defines the EML as a point-in-time
metric to measure the circadian effects caused by light stimulus and set the threshold for the workspace as
250 EML (WELL, 2016). The melatonin suppression caused by the amount of light measured by EML was
created as a model that follows a non-linear curve (Konis, 2017) (Amundadottir et al., 2017) (Figure 2-3). It
is observed that the 250EML threshold defined by WELL can lead to an almost saturated melatonin
suppression (98.5%).
26

Figure 2-3 Relationship between light exposure and melatonin suppression level (Konis, 2017)
2.3. Standards and codes
Metrics sDA and ASE defined in LM-83 are accepted in the LEED daylighting credit section for
compliance evaluation (USGBC, 2015). LM-83 is the document “Approved Method: IES Spatial Daylight
Autonomy (sDA) and Annual Sunlight Exposure (ASE)” that describes the method allow evaluating the
sufficient daylight performance based on two different metrics that can measure the annual illuminance
sufficiency and the risk of the extreme amount of daylight.
Additionally, LM-83 is adopted in ASHRAE 100-2015 (Energy Efficiency in Existing Building), and
California’s energy efficiency standard (Title-24) set its evaluation methods as obligatory. Prescriptive
photocontrol requirements are referenced on LM-83 (Konis & Selkowitz, 2017).
2.3.1. California Title 24
The established standards and codes in green building compliance such as California Title 24 and
Leadership in Energy and Environmental Design (LEED) standard list the visual requirements. Part 6 in
California Title 24 listed the illuminance requirement in various functional areas and the lighting power
density. The General illuminance level requirements are set for different functional areas, and the
calculation is placed horizontally. Table 2-1, named “As Tailored Method Lighting Power Allowances”
showed a standard for lighting designers to ensure the electricity cost and standard visual brightness
requirements for various functional areas.
Table 2-1 Tailored Method Lighting Power Allowances (Calidornia Lighting Tachonolgy Center, 2016, p. 107 )
27

Primary Function Area
General
Illumination
Level (Lux)
Wall
Display
Power
(W/ft)
Allowed Combined
Floor Display
Power and Task
Lighting Power
(W / FT2)
Allowed
Ornamental/
Special Effect
Lighting
Auditorium Area 300 2.25 0.3 0.5
Civic Meeting Place 300 3.15 0.2 0.5
Convention, Conference
Multipurpose, and Meeting
Center Areas
300 2.50 0.4 0.5
Dining Areas 200 1.50 0.6 0.5
Exhibit, Museum Areas 150 15.00 1.2 0.5
Financial Transaction Area 300 3.15 0.2 0.5
Grocery Store Area 500 8.00 0.9 0.5
Hotel Function Area 400 2.25 0.2 0.5
Lobby Area
Hotel Lobby 200 3.15 0.2 0.5
Main Entry Lobby 200 0.00 0.2 0
Lounge Area 200 7.00 0 0.5
Malls and Atria 300 3.50 0.5 0.5
Religious Worship Area 300 1.50 0.5 0.5
Retail Merchandise Sales,and
Showroom Area
400 14.00 1 0.5
Theater Area
Motion picture 200 3.00 0 0.5
Performance 200 6.00 0 0.5
Transportation Function Area 300 3.15 0.3 0.5
Waiting Area 300 3.15 0.2 0.5

28

2.3.2. LEED
The LEED standard is provided by the U.S. Green Building Council, and it recognizes the significance of
circadian rhythm (“Circadian lighting in the built environment,” n.d.). “The consensus is that this rhythm is
much more important for human health than may have been previously thought. In response to the
numerous scientific findings that back this up, there are new lighting system products that can help us
reinforce circadian rhythms using electric lighting (“Circadian lighting in the built environment,” n.d.).”
The daylight points listed in the Environment Quality (EQ) credit focus on saving energy by adding more
daylighting access to the interior space instead of using artificial lighting. Three approaches listed below
for getting the credit points (USGBC, 2016), and the first two are based on CBDM simulation.
 Spatial Daylight Autonomy & Annual Sunlight Exposure
 Illuminance calculation
 Measurement

Explanation of those three approaches is all from the LEED V4 standard (USGBC, 2013). Spatial daylight
autonomy 300/50% and annual sunlight exposure1000,250 need to be calculated by annual software
simulations. There are three sDA thresholds set as 55%, 75% and 90% for different building types and the
ASE should not be higher than 10%. Points can be achieved based on the percentage of illuminance
calculation result the interior space can get between 300 lux and 3,000 lux. The illuminance measurement
sets the same requirement as the illuminance lux calculated by the software program.
2.3.3. The WELL building standard
The WELL building standard uses EML, “a proposed alternate metric that is weighted to the ipRGCs
instead of to the cones, which is the case with traditional lux. During performance verification, EML is
measured on the vertical plane at the eye level of the occupant” (WELL, 2017) (Table 2-2). However, it is
noted that the required EML stimulus has not been specified the proper time during a day. For example,
space is certified for the compliance criteria (250 EML for daily 4 h) from 12:00-16:00 is highly possible to
provide insufficient circadian stimulus in the morning can fail to reset the circadian rhythm.
Table 1-2 Melanopic Light Intensity Requirements in the WELL Building Standard
Function
Areas
Requirements Verification
Work Areas
Measured on vertical plane 1.2m (4ft) above the finished floor, at
least 75% of the workstations get no less than 200 EML between
9:00 and 13:00 every day
Spot Check
Living
Environments
No less than 200 EML are measured facing the wall, 1.2m (4ft)
above the finished floor, at central room area during the daytime
Spot Check
Breakrooms
Measured on the vertical plane facing forward surfaces 1.2m (4ft)
above the finished floor, an average of no less than 250 EML are
achieved
Spot Check
Learning Education for students under 25 years old: No less than 125 EML Spot Check
29

Areas is measured on 75% or above of desks, and the light should be
kept for at least 4 hours every day annually.
2.4. Novel circadian metric
The circadian stimulus was found to be impacted by the measured view direction while using the HDR
pictures to calculate the EML values, and it is essential to highlight the significance to assume the
occupants’ view orientation during the design phase (Konis, 2018). It is necessary to define a quantitative
measurement for spatial circadian effectiveness boundaries as well as “biological darkness,” where only
sufficient visual lighting is provided instead of the adequate circadian stimulus (Konis, 2018).
A new circadian metric, Circadian Frequency (CF), was proposed in 2016 to measure the effective
biological stimulus of daylight on a daily basis and get the frequency of an annual qualified effective
performance (Konis, 2017). CF is defined to measure the percentage of days in an annual time that a given
view vector can get a specific light stimulus threshold (measured in EML) during the specific daily analysis
interval and the CF metric sets the analysis time from 9:00 AM-1: 00 PM to measure if sufficient circadian
stimulus can be received (Konis, 2017) (Figure 2-4).

Figure 2-4 Circadian Frequency result for skylight evaluation (Konis, 2017)
Humans’ vision can respond to light in less than 1 second, which is much quicker than suppressing
melatonin(Ingling, Martinez, & Lewis, 1983). The limitation of EML is that it cannot take the stimulus
exposed time into consideration. Because the circadian lighting stimulus cannot get instant effects on the
occupants, this annual circadian dynamic metric can evaluate if the long-term occupancy would cause
disturbances of circadian rhythm without any extra circadian support from electrical lighting. The
daylighting exposure history will influence the circadian system sensitivity(Chang, Scheer, & Czeisler,
30

2011). Higher light exposure at daytime decreases the circadian system sensitivity. Conversely, the lower
level would increase.

The building architectural design can largely affect the indoor daylighting circadian health. The CF result
of three common building forms having same areas (Figure 2-5) shows that adding a courtyard in the
middle (Case B) can only get slightly circadian health improvement compared to the block shape (Case A),
and the worm-shaped building (Case C) has the best CF performance (Konis, 2018) ).

Figure 2-5 Three building forms for a commercial office building (96,875 ft2 (9000m2)) commercial office building
located in Los Angeles, CA. (Konis, 2018)
“Biological Darkness” is visualized by various color and size of the sphere to show area with poor
circadian performances (Konis, 2018) (Figure 2-6). The darker and larger the sphere is, the less circadian
dose it can receive.

Figure 2-6 Visualization of "biological darkness" ranging from 0% to 100% for three buildings forms
2.5. Evaluation tools
This section discusses all software tools used in two types of workflow for evaluating the circadian and
visual daylighting. The first is based on Adaptive Lighting for Alertness (ALFA), and the second is based
on, plug-in tools in Rhinoceros and Grasshopper, Ladybug and Honeybee (LB/HB). LB/HB were used in
31

Grasshopper for simulation analysis and daylighting performance calculations. Daylight simulations were
processed by Daysim and Radiance engine. Modeling and result visualizations were presented by
Rhinoceros (“Rhinoceros” n.d.).
2.5.1. Rhinoceros & Grasshopper
Rhinoceros (Rhino) is a graphic application software developed by Robert McNeel & Associates for 3D
drawing and computer-aided design (“Rhinoceros” n.d.). Grasshopper is a visual programming plug-in used
in Rhino and also be a platform for other simulation tools like Radiance and EnergyPlus (“Grasshopper -
Algorithmic Modeling for Rhino” n.d.). Grasshopper can build parametric geometries which can make the
architectural model adjustable during the whole design process (Figure 2-7).

Figure 2-7 Model the geometry in the Rhino space by using the Grasshopper program
2.5.2. Ladybug & Honeybee
Ladybug and Honeybee are both simulation tools designed for Grasshopper. Ladybug can get the daylight
analysis results based on the imported EnergyPlus Weather files (.EPW) in Grasshopper. Honeybee creates
daylighting and thermodynamic model for the later design stage (Figure 2-8). It uses Radiance Engine to
generate daylight simulation and EnergyPlus Engine to do thermal performance analysis. Because of the
wide usages, Honeybee is a plug-in widely used for environmental design.
32

Figure 2-8 Ladybug and Honeybee component used to run the illuminance simulation
2.5.3. DAYSIM and Radiance engine
DAYSIM is a RADIANCE-based daylighting analysis software that does the annualized daylight
calculation both for the building surroundings and interior space (“DAYSIM”, n.d.). Radiance has a
collection of software programs for visualization and calculation for the lighting design. After setting the
specified geometries, materials, time, and sky conditions, Radiance can do the illuminance predictions and
help evaluate the proposed lighting technologies (“Radiance”, n.d.).
2.5.4. ALFA
ALFA is a software program that was written as a collaborating by Solemma and Alertness CRC that lets
lighting designers and architects simulate, control and improve circadian lighting performances for better
health and higher productivity. Moreover, because the non-visual activities cannot be calculated through the
traditional Red/Green/Blue channels, ALFA extends the Radiance lighting engine to 81-color spectra to
render the result in high resolution (“ALFA – SOLEMMA” n.d.) (Figure 2-9).

Figure 2-9 Extended Radiance lighting engine for rendering in 81-color spectra (“ALFA – SOLEMMA” n.d.)
33

It can also make predictions for the non-visual light accepted by the observers, which is measured as EML.
After setting the calculation grid and view direction number in the Rhino space, ALFA can calculate and
visualize the point-in-time EML values for each view direction (Figure 2-10).

Figure 2-10 Point-in-time EML result visualization in ALFA. The total view direction for each calculation point is set
as four.
2.6. Summary
The spectral varieties in the timing of light cannot be ignored while using EML to evaluate the circadian
performances. Stronger alertness morning light can generate better circadian stimulus compared to the late
afternoon daylight. Thus, a space that got excellent sDA result might still be rated as circadian stimulus
deficiency.
Based on the literature review, there are limitations of the current CBDM workflows:
 The existing CBDM workflow has limitations to assess the daylight performances, and the
frequent human eye exposures cannot be represented by the traditional horizontal settings.
However, ALFA can set the calculation planes vertically to mimic the more frequent eye
exposures.
 The dynamic visual metric, sDA, in the current CBDM workflow is advanced at linking the annual
daylight performances with the occupants’ shading preferences assumptions to draw a healthy
daylit boundary. The assessment of the daylight boundary would affect the implementation of
artificial lighting fixtures and the function areas settings for the indoor environment.
 The biological lighting effects can be measured by EML, and the WELL standard has already set a
specific threshold for it. However, due to the non-visual metrics’ infancy, the EML has a limitation
in considering the daylighting exposure duration. It is essential to develop a more advanced
dynamic circadian metric based on the annual period and explore the similarity and discrepancy
with the existing visual dynamic metric.
 The visual programming software “Grasshopper” can be used to run visual metrics plug-ins such
34

as ALFA to calculate the circadian lighting performances.

35

Chapter 3. Methodology
Chapter 3 describes the workflow of investigating if the existing CBDM workflows can efficiently evaluate
the daylight performances and explore the discrepancies and similarities between circadian and visual
metrics. The existing CBDM workflows use the Rhino Plug-ins Ladybug, Honeybee, and Grasshopper to
get the visual-related metrics including sDA, Daylight Autonomy (DA), and photopic illuminance.
Geometry and basic settings of the modeling process in the Rhino modeling space are described first
(Figure 3-1). The following sections describe the two analysis exercises that were performed. The first
exercise focused on point-in-time metrics. After building a generic model, two groups of simulations were
generated separately based on dates and points locations. The statistical data analysis was performed to
examine if there is a strong correlation between EML at vertical orientation and illuminance at horizontal
for each group. Based on a robust or weak correlated relationship detected between these two metrics in
this simplified simulation with limited data, it would get a general idea of the connections between those
two point-in-time metrics.

Figure 3-1 Methodology diagram
The second analysis implemented an annual evaluation for comparing the yearly EML result with the
daylight autonomy (DA) based on the same model used in the point-in-time analysis. The DA map can be
evaluated to show the discrepancies and similarities with the circadian healthy-daylit zone boundaries and
the zone considered effectively daylit based on the DA criteria.
Due to the new findings of ipRGCs and newly-defined metric EML, it is necessary to test if the circadian
metrics could have any connection with the existing metrics used by industries. The statistical analysis
method was used to explore the correlated connections between EML and photopic illuminance. Moreover,
a new approach was used to get the dynamic circadian metric, Spatial Circadian Autonomy (Section 3.3.3),
to calculate the circadian “effectively daylit zone” to compare both differences and similarities with
effectively daylit zone defined by existing metric from CBDM workflows, Spatial Daylight
36

Autonomy(sDA). Window orientation was set as the only variable for the sCA and sDA comparison to
explore possible architectural lighting design methods for offices.
3.1. Daylight Simulation Modeling & Input
A cubic model was built for both the circadian and visual lighting calculations. Software Rhino,
Ladybug/Honeybee, and Grasshopper were used to get the point-in-time illuminance and sDA results.
ALFA was used for the point-in-time EML metric calculation based on the same model built in the Rhino
space.
3.1.1. Geometry
A 30 ft wide and 45 ft length side-lit space which had a 10 ft ceiling height was set as the first case study
(Table 3-1). Materials in ALFA have both the melanopic and photopic reflectance input, but the Radiance
reflectance components in Honeybee can only input photopic reflectance result. The materials in two
software (Table 3-2 & 3-3) had the same reflectance performances, and the reflectance values in Ladybug
& Honeybee were matched with ALFA.
Property Value
Window orientation South
Window Wall Ratio 30%
Floor plate length(E-W) 30’
Floor plate depth(N-S) 45’
Ceiling height 10’
Surface reflectance (Interior floor) 41.8%
Surface reflectance (Interior wall) 81.2%
Surface reflectance (Interior ceiling) 41.8%
Glazing visible light transmittance(VLT) 63.3%
Climate Los Angeles
Analysis grid spacing 10’
Number of view vectors per grid point 4
Table 3-1 Cubic Model Properties
Only one window was set at the cardinal orientations in each model for calculating the EML and
illuminance performances. Orientation is the only parameter to investigate the effects on the correlation
between EML and photopic illuminance in the simulation experiment.
3.1.2. Settings
The settings in ALFA and Rhino/Grasshopper environment should be the same to minimize the differences.
Location and climate weather file was set as Los Angeles.
3.1.2.1. Grid settings
37

Because the office areas in commercial buildings share similarities in the operation time and space layout, a
generic cubic site-lit space was used for daylight simulation in Ladybug & Honeybee space and ALFA. The
grid was set at 4ft high which follows the melanopic light intensity requirements in the WELL building
standard to represent seated human eyes’ height (WELL, 2016). Twelve points spaced 10 ft apart could
simplify the data processing process and shorten data analysis time (Figure 3-2). While using the new
Rhino plug-in ALFA, it sets vertical calculation planes which mimic regular occupants’ eye exposures.
Additionally, ALFA can build from minimum one direction vector to a maximum 16 direction vectors on a
grid plane for each point.

Figure 3-2 Analysis grid and points visualization
3.1.2.2. Window-wall-ratio (WWR) and materials
WWR ranges from 0 to 1.0 WWR ratio is the main component to decide the daylighting performances and
will also affect glare issue and solar heat gain. According to Title 24, commercial buildings cannot have
WWR larger than 40%. By using the Honeybee component “Honeybee_Glazing Parameter List”(Figure
3-3), the WWR for the cubic space was 30% in the center of the south wall. “Honeybee_Radiance Glass
Material” component sets the visual transmittance value as 0.633.
38

Figure 3-3 Honeybee component for creating glazing areas
The model space is set as 3 m (10 ft) height. The higher the ceiling height is, the larger the glazing areas
can be and this could lower the electricity cost of artificial lighting. Glazing provides the interior space with
access to daylight. Glazing material has properties including solar heat gain coefficient (SHGC), U-value
and visible transmittance (Tvis). However, the differences between photopic and melanopic reflectance are
the essential properties that impact the experiment study result. ALFA lists both the photopic and melanopic
reflectance values for each glazing material (Table 3-2 & Table 3-3). Simply synchronize the transmittance
and reflectance values in two software to get materials having identical photopic and melanopic
performances.
Table 3-2 Surface Reflectance
Surface
Photopic
Reflectance
Melanopic
Reflectance
M/P
Floor 41.8% 37.6% 0.9
Wall 81.2% 76.8% 0.95
Ceiling 41.8% 37.6% 0.9

Table 3-3 Glazing Material Transmittance
Surface
Photopic
Transmittance
Melanopic
Transmittance
M/P
Glazing Material 63.3% 61.7% 0.98

3.1.2.3. Weather file
The LAX airport in Los Angeles was chosen as the EPW weather file input location (Figure 3-4). EPW
stands for EnergyPlus Weather Data files provided by the United States Department of Energy. EPW files
are used to input the annual solar radiance changes and provide geometric information, such as longitude,
latitude, and elevation.
39

3.1.2.4. Radiance parameters input
sDA can always draw a boundary based on whether the DA percentage values for each calculation grid
plane is above 50%. To increase the simulation accuracy, the quality input of the “RADParameters”
component is changed to 1 which is the median quality and will take comparatively more extended time to
get a more accurate annual result (Figure 3-4). Table 3-4 shows the Radiance input parameters.
Table 3-4 Honeybee Radiance parameters settings
RAD_Parameters
Ambient
Bounces
(ab)
Ambient
Divisions
(ad)
Ambient
Super-samples(a
s)
Ambient
Resolution(ar)
Ambient
Accuracy
(aa)
Value 3 2048 2048 64 0.2

Figure 3-4 Weather file input
3.2. Experiment of design
Two types of simulation experiments, simulation experiment 1 and simulation experiment 2, were
implemented for comparing both the point-in-time metric (EML and photopic illuminance) and dynamic
metrics (sCA and sDA). Simulation experiment 1 focused on the point-in-time metrics and implemented the
simulation based separately on dates and analysis grid point locations. Simulation experiment 2 was
designed for comparing the similarity and discrepancy between sCA and sDA.
3.2.1. Simulation experiment 1
Simulation experiment 1 got all the point-in-time EML and photopic illuminance data for correlation
analysis based on simulation dates and the grid point locations.
40

3.2.1.1. Simulation based on dates
Mar 21st, Jun 21st, Sep 22nd, and Dec 21st were used to get their point-in-time illuminance and EML
values at 9:00, 12:00 and 15:00 for standard weather, which should have 12 sets data. Photopic illuminance
results were plotted as X-axis, and EML values for each direction were set as Y-axis. This experiment used
all the 12 points result for a single time spot to study the correlation between EML and photopic
illuminance. By using the function “CORREL” in Excel, it is easy to get the R-squared values varied by
time. The “CORREL (array1, array2)” was used to calculate the correlation coefficient between two data
set.
3.2.1.2. Simulation based on analysis grid point locations
The second experiment was designed to get the occupied hours results from 8:00 to 17:00 for every single
calculation point on Mar 21st, Jun 21st, Sep 22nd, and Dec 21st. The simulation was separated into 40
groups, and each contained the data on different dates and calculation locations. For instance, point 1 can
get both EML values for four directions and photopic illuminance from 8:00 to 17:00 on the selected four
dates. Different from the first simulation, the statistical analysis was conducted based on each calculation
point’s location and used all 10 time spot values on each date. Thus, it can get four R-squared values for
four directions on each point.
After getting the R-squared value for the south oriented window, then did the exact same calculation and
analysis for north, east and west oriented windows to detect if the window orientations would affect the
correlation between photopic illuminance and EML.
3.2.2. Simulation experiment 2 (Annual metric)
Point-in-time calculations were done first. However, sDA is a dynamic metric based on the illuminance
performance of annual occupied hours. There is currently no circadian metric based on annual EML
performances to measure the percentage of space can receive sufficient (for example not less than 250 EML)
circadian daylighting in a whole year period. As mentioned in Section 2.4, the limitation of EML is that it
ignores time and duration into while evaluating the circadian stimulus; the new metric Spatial Circadian
Autonomy (sCA) is proposed to fix that limitation. It is time-consuming to generate all the point-in-time
data for daily occupied hours and process them to get the CA and sCA results. Although this should be
eventually done, currently for each month, the 21st is selected to get 120 (12 days ×10 time-spots =120 sets)
set of point-in-time EML data from 8:00-17:00 for simplified sCA calculation.
After getting the total data from ALFA, MATLAB is used to combine the comma-separated value (CSV)
formatted files together in one Excel sheet for data processing. MATLAB was developed by MathWorks,
and provides a numerical computing platform and uses programming language to directly express array and
matrix that allowing manipulating matrix, plotting functions and data, implementing algorithms and
creating user interfaces (“What is MATLAB”, n.d.). Use the “INDEX” formula to separate data of all
vectors into 4 groups representing each view direction, and apply “COUNTIF” and basic mathematics
calculations formulas to count the percentage of EML results are equal and above 250 to get the CA values
for all 12 calculation points.
41

However, comparing with the sDA, the sCA data always contains four directions representing different
view directions. A new metric, Average sCA, that can combine all the sCA result of different view
directions might be used to get one percentage value for more direct comparison. The Average sCA is the
average value of the sCA percentages of four directions for a selected point, which also represents the
percentage of any given view of a specified point that can get qualified circadian stimulus of the annual
occupancy hours (Equation 1). Standard Deviation (SD) was used to measure the variation of sCA values
for four view directions. By combining Average sCA and SD results, it is easier to compare sCA with sDA
through one percentage number and also assess the sDA variations of different view directions (Table 3-5).
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑠𝐶𝐴 ∑
(1)
Window
Orientation
N W S E
Avg sCA
Standard Deviation
sDA
Table 3-5 Average sCA for each window facing direction and the standard deviation to measure the variation of sCA
values for four view directions
At the same time, use the Ladybug and Honeybee component “readAnnualResultsI” to get the DA and sDA
results (Figure 3-5). The geometries changed the window-oriented direction for South, East, North, and
West to calculate both sDA and sCA results, which is necessary to analyze the impacts of window
orientations on the relationship between dynamic photopic and circadian metrics.

42

Figure 3-5 sDA calculation component "readAnnualResultsI"on Honeybee
3.3. Data Analysis and data processing
Point-in-time data were analyzed and processed in simulation Exercise 1 to explore if there were consistent
strong correlated relationships between EML and photopic illuminance. For Exercise 2, the annual EML
result was processed to get the sCA. It is necessary to investigate the connections between sCA and sDA
that if the area getting sufficient circadian stimulus can show qualifying sDA result at the same time. Thus,
the similarities and discrepancies were implemented to calculate how many percentages of the annual
occupied hours that the sCA map would fall into the correct location of the sDA.
3.3.1. Data analysis
After getting the point-in-time results of photopic illuminance and EML, R
2
(R-squared), the coefficient of
determination was used to do the regression analysis. R-squared is well established for doing regression
analysis and defined to evaluate the variance of the regression model to successfully predict the dependent
variable from the independent variables (Nagelkerke, 2007). The R-squared value usually ranges from 0 to
1, and R-squared equals to 1 indicating the regression predictions 100% fit the data.
A preliminary study of R-squared result shows the correlated relationship between photopic illuminance
and EML. The higher the R-squared value is, the better the linear regression equation can predict the EML
result for a certain photopic illuminance (Figure 3-6). The right chart having higher R-squared value fits the
model better than the left one.

Figure 3-6 Example of R-squared to evaluate the correlated relationship between EML and Photopic illuminance
3.3.2. Data processing for exercise 2 (annual analysis)
After getting the monthly EML result the on 21
st
from 7:00 to 17:00 for each direction, it is easy to get each
grid percentage of the EML values not less than 250 lux for four directions.
The 250-lux threshold is based on the WELL Building standard. Then based on the Circadian Autonomy
(CA) result, the percentage for how many of the CA values are not less than 50% is defined as Spatial
Circadian Autonomy (sCA). Since a boundary can be drawn based on qualifying or not qualifying CA of
each grid to show the sCA annual mapping, it is significant to compare the similarities and discrepancies
with the sDA result and to get the percentage value of the CA map will fell the correct location of sDA.
43

Since circadian health is essential to ensure the occupants’ health and a higher CA percentage as the
threshold is significant. The 50% was set as the threshold to visualize and calculate the discrepancy and
Dual-health of sCA and sDA in section 3.4.2.1 and 3.4.2.2. 50% is selected to follow the threshold used in
the calculation procedure for sDA. It is necessary to set a higher percentage, such as 75% and 90%, to
ensure better circadian health for occupants.
3.3.2.1. Discrepancy
The discrepancy between the effectively daylit area defined by DA and the effectively daylit area
determined by the annual EML result is obtained by marking the CA values equal to greater than 50% grid
areas as 1, and the same for the DA not less than 50%. Next use DA result to subtract CA, there should be
only three types of outcome for each grid which is 1,0, and -1. Only the 0 number grids show the healthy
CA and DA overlap area. Then square all the subtracted data and add them all, that number is how many of
the total grid having different annual performance result. Finally divided by the total grid number, the
percentage value is the discrepancy between sDA and sCA metrics (Figure 3-7).

Figure 3-7 Discrepancy and similarity analysis
3.3.2.2. Similarity
Same as the first step of discrepancy process, mark the preferred DA and CA values as 1. Next, add those
two values together and put the new values in the same grid, which could only be 0,1, and 2. Then use the
“IF” formula in Excel software to input 1 when the added result is 2, otherwise input 0. When the final
values in the grid are 1, the two metrics both agree that point area is healthy. Later add all the 1 number
together and divide them by total grid number, the percentage is defined as “Dual-health.” Dual-health is
used to evaluate the agreement on both sDA and sCA metrics.
3.4. Summary
The occupant’s view direction needs to be considered in the future design. The connections between
circadian and visual metrics can be analyzed by investigating both the point-in-time and annual dynamic
metrics. The methodology for doing this included modeling, the experiment, and data analysis (Figure 3-8).
44

Figure 3-8 Methodology diagram
To compare the metrics from the existing CBDM (photopic illuminance and sDA) workflow and circadian
metrics (EML and sCA), two simulation experiments were set to explore both the point-in-time metrics
(simulation experiment 1) and annual metrics (simulation study 2). The same geometry was built in Rhino
for both the ALFA and LB/HB simulation. Materials, orientations, WWR, and the calculation grid were set
the same.
Correlation analysis was implemented to detect if there are consistent strong mathematic connections
between those two metrics. Next, sDA is the most widely used metric to define a healthy-daylit zone. Thus
a parallel annual dynamic EML metric should be used to measure the discrepancy. It is necessary to study
the connections between the dynamic metrics, sCA and sDA. The sCA metric was calculated based on a
large amount of point-in-time EML result by using Matlab and Excel. Similarity and discrepancy were
compared between sDA and sCA to get the percentage value of the CA map would fell the correct location
of sDA.

45

Chapter 4. Data and Results
Chapter 3 reported on the methodology used to gather the data for comparing the discrepancy between both
circadian metrics (EML and sCA) and the metrics from the existing CBDM workflow (photopic
illuminance and sDA) (Figure 4-1).

Figure 4-1 Methodology diagram
Chapter four reports data analysis results and data visualization to compare both point-in-time metrics
(Simulation experiment 1, photopic illuminance and EML) and annual dynamic metrics (Simulation
experiment 2, sCA and sDA). Section 4.1 shows the point-in-time photopic illuminance and EML outcomes
and presents the R-squared analysis results separately based on selected simulation time and grid points
location. One group of point-in-time data from 12 sets of date-based simulation results and another group
from 48 sets of points location-based simulation results are selected separately in section 4.1.1. and 4.1.2.
to show the original simulation data. All the point-in-time data in each section were arranged the same in
tables. Choosing one set of data for each can show the differences between the date-based and
location-based results and also present the EML and photopic illuminance performances.
Two correlation comparison simulation studies were completed, both based on a cubic model with 30%
WWR on the south side. The annual spatial mapping of circadian autonomy (CA) is presented to evaluate
the discrepancies and similarities between circadian autonomy (CA) and daylight autonomy (DA). Rhino
and Grasshopper were used for visualization. Circadian Autonomy is a new metric proposed to evaluate the
annual circadian daylight performance of a given view location, and it represents the circadian
effectiveness percentage during the annual occupied hours. For example, if the south view in a specific
point can get 75% of the EML values during the annual occupancy hours above 250 EML, 75% of the
annual occupancy time for that given view location can get effective circadian daylighting.
The analysis involved processing a total of 480 sets of EML data from ALFA to calculate CA, which was
composed of 10 point-in-time evaluations per day (hourly from 8:00-17:00), one day per month (21st), and
46

12 months for 4 different window-orientation models. For each point in time, 12 calculation points were set,
and each point contained four vectors oriented to south, north, east and west (Figure 4-2).

Figure 4-2 Example of R-squared results based on points location to show vectors
4.1. Simulation results for simulation experiment 1 (point-in-time data)
Section 4.1 shows the point-in-time photopic illuminance and EML outcomes and presents the R-squared
analysis results separately based on selected simulation time and grid points location. The R-squared values
varied from 0 to 1. Values above 0.7 are considered as strong correlations. R-squared numbers from 0.5 to
0.7 are a middle level of correlation connection, and below 0.5 are weak. One group of point-in-time data
from 12 sets of date-based simulation results and another group from 48 sets of points location-based
simulation results are selected separately in section 4.1.1. and 4.1.2. to show the original simulation data.
Others will be put in Appendix C and Appendix D. All the point-in-time data in each section is arranged the
same way in tables. Choosing one set of data for each can show the differences between the date-based and
location-based results and also present the EML and photopic illuminance performances.
4.1.1. Simulation results based on dates
To explore the effects of time on the correlation between EML and photopic illuminance, 4 days in a year
(Mar 21
st
, Jun 21
st
, Sep 22
nd
, and Dec 21
st
) for 3 times each day (9:00, 12:00, and 15:00) were chosen to
calculate the point-in-time performance for 12 points as one set of data. There were 12 sets of data in total
to get the R-squared values. The correlation formula in Excel was used to get the coefficient of
determination (R-squared) for photopic illuminance and EML of each view direction.
Twelve sets of numbers represent the result of each calculation point to show the photopic illuminance and
EML performance at 9:00 am on Jun 21
st
(Table 4-1).
47

Table 4-1 Simulation data on 6/21/9:00 for the horizontal surface and the four views
6/21/9:00 Photopic
(Lux)
EML(Lux)
Points E N W S
1 630 1429 479 615 902
2 1357 1578 850 1895 3675
3 447 528 628 1090 698
4 112 506 311 546 796
5 122 533 267 526 1433
6 100 481 279 693 957
7 25 305 137 250 505
8 24 237 276 260 683
9 34 185 86 291 427
10 17 217 286 290 495
11 14 212 299 250 469
12 13 160 163 203 386

Both the photopic illuminance and EML used the same color legend where lower numbers are blue and
higher values are red. All the south oriented views resulted in EML values above 250, and 75% of the total
48 views were above the specified 250 EML threshold. Even the point at the north-west corner (Point 10)
can get 75% of the views above 250 EML (Figure 4-3).
48

Figure 4-3 Illuminance results on 6/21/9:00. Numbers from 1 to 12 represent the calculation points placed in the
model.
Numbers represent calculation points in ALFA at 9:00 am on Jun 21
st
are the same as the illuminance result
(Figure 4-4).
49

Figure 4-4 EML results in ALFA on 6/21/9:00. Numbers represent calculation points same as Figure 4-3.
The colored melanopic and photopic illuminance maps followed the assumption from chapter 1, which was
that the photopic illuminance less than the required threshold (e.g., 300 lux) measured by horizontal view
could be qualified as sufficient EML stimulus (250 EML) on vertical view plane. Even the south side got
poor illuminance performances below 35 lux; the EML values were still above 380 on the south oriented
view. However, the illuminance performance showed that the south part could not get sufficient daylight.
For better visualization, Rhino and Grasshopper were used to input all the R-squared values and
represented the differences with colored vectors. The colored vector represents the view direction and the
R-squared values (Figure 4-5 & 4-6).
50

Figure 4-5 R-squared results based on dates for East and West view directions.

Figure 4-6 R-squared results based on dates for South and North view directions.
In December there was always a weak correlation between photopic illuminance and EML due to the direct
sunlight on the south area (Table 4-2). Numbers above 10000 lux were areas that received direct sunlight.
The R-squared values higher than 0.7 are shades in green which represent a strong correlation. R-squared
ranged from 0.5 to 0.7 means middle correlation and is shaded in yellow, and values below 0.5 (weak
correlation) are shaded in red.
Table 4-2 R-squared results based on simulation dates.
51

Time
View direction
E ast North West South
3/21/9:00 0.66 0.62 0.89 0.80
3/21/12:00 0.83 0.89 0.78 0.80
3/21/15:00 0.90 0.75 0.63 0.77
6/21/9:00 0.88 0.71 0.88 0.83
6/21/12:00 0.88 0.81 0.89 0.91
6/21/15:00 0.92 0.87 0.88 0.92
9/22/9:00 0.94 0.67 0.94 0.84
9/22/12:00 0.77 0.96 0.89 0.88
9/22/15:00 0.96 0.54 0.53 0.85
12/21/9:00 0.11 0.34 0.38 0.18
12/21/12:00 0.59 0.83 0.47 0.40
12/21/15:00 0.16 0.57 0.23 0.12

The photopic illuminance values above 100000 lux were highlighted, and the direct sunlight were located
around the south side (calculation point 1,2, and 3) (Table 4-3).
Table 4-3 Direct sunlight on the south area caused high illuminance. Highlighted numbers are illuminance values
above 10000 lux
Honeybee 12/21/9:00 12/21/12:00 12/21/15:00
Points Photopic (Lux) Photopic (Lux) Photopic (Lux)
1 746 574 171
2 11196 26476 13792
3 10934 26535 14176
4 166 603 13066
5 593 407 131
6 667 1060 402
7 394 974 774
8 111 431 735
9 193 185 67
10 207 354 175
11 123 322 293
12 55 213 299

For other times, the south still had the highest and R-squared values, and the north was relatively lower
than others. The R-squared values varied from 0 to 1, and no stable correlated connections was found to
exist between EML and photopic illuminance based on calculation time.
52

4.1.2. Simulation results based on analysis grid point locations
To investigate if correlation connections would be impacted by various point locations through the year,
simulations were performed for 4 days in a year (Mar 21st, Jun 21st, Sep 22nd, and Dec 21st), 10 times
each day (8:00-17:00). Based on the resulting data set, R-squared values were calculated for four directions
of all 12 points.
One group of data based on the analysis point 1 on the south-west corner on Jun 21
st
was used as an
example (Table 4-4). There were 12 points spacing 10ft in total, and 10 point-in-time data were selected
hourly from 8:00 to 17:00 for both the EML and photopic illuminance calculations.
Table 4-4 Simulation results based on points location for Point 1 on Jun 21st
Point1
EML
Photopic(lux)
E N W S
8:00 1032 1032 547 813 330
9:00 1215 617 576 924 645
10:00 1645 695 704 1124 818
11:00 2034 944 923 1348 708
12:00 2250 839 900 1377 713
13:00 2096 855 977 1489 720
14:00 1537 575 676 1005 604
15:00 1450 485 526 974 576
16:00 1063 369 416 712 468
17:00 1041 376 421 699 361

Similar to the data analysis method in section 4.1.1, the Excel formula was used to get the R-squared values
to explore the effects of point location on the relationship between EML and photopic illuminance. Vectors
show the R-squared results between EML and photopic illuminance for each eye exposure direction of 12
points (Figure 4-7). For each date, the R-squared values for each direction of one point were close. The
selected date can affect the correlation performance that March 21st got poor R-squared results which were
all lower than 0.5 (Figure 4-7).
53

Figure 4-7 R-squared results based on points location
No no consistent strong connections can be discovered between point-in-time EML and photopic
illuminance because a large percent of the R-squared values are below 0.5 (Table 4-5 & Table 4-6). The
R-squared values higher than 0.7 are shades in red which represent a strong correlation. R-squared ranged
from 0.5 to 0.7 means middle correlation and is shaded in yellow, and values below 0.5 (weak correlation)
are shaded in red. Thus, no more simulations based on point locations were conducted by changing the
window orientations to north, west and east.
Table 4-5 R-squared results based on points location on March 21st and Jun 21st
3/21 View Direction 6/21 View Direction
Point
Number
E N W S
Point
Number
E N W S
1 0.10 0.27 0.18 0.07 1 0.62 0.07 0.55 0.63
2 0.20 0.03 0.02 0.11 2 0.85 0.56 0.56 0.82
3 0.22 0.19 0.11 0.34 3 0.27 0.35 0.27 0.36
4 0.22 0.30 0.20 0.13 4 0.34 0.19 0.64 0.51
5 0.48 0.19 0.01 0.07 5 0.64 0.58 0.33 0.56
6 0.31 0.26 0.27 0.31 6 0.57 0.48 0.51 0.56
7 0.06 0.13 0.38 0.09 7 0.39 0.42 0.60 0.54
8 0.46 0.35 0.09 0.50 8 0.29 0.29 0.28 0.02
9 0.10 0.27 0.28 0.21 9 0.24 0.38 0.80 0.55
10 0.22 0.12 0.15 0.21 10 0.19 0.41 0.46 0.43
11 0.29 0.10 0.33 0.26 11 0.51 0.49 0.22 0.12
12 0.01 0.00 0.00 0.02 12 0.30 0.45 0.45 0.20

Table 4-6 R-squared results based on points location on Sep 21st and Dec 21st
9/22 View Direction 12/21 View Direction
Point
Number
E N W S
Point
Number
E N W S
1 0.66 0.87 0.94 0.88 1 0.13 0.52 0.40 0.42
54

2 0.54 0.67 0.62 0.76 2 0.86 0.89 0.68 0.92
3 0.63 0.63 0.47 0.55 3 0.38 0.54 0.17 0.33
4 0.83 0.81 0.85 0.88 4 0.73 0.78 0.82 0.84
5 0.86 0.89 0.46 0.86 5 0.57 0.79 0.42 0.89
6 0.80 0.66 0.62 0.82 6 0.66 0.57 0.44 0.69
7 0.80 0.46 0.59 0.68 7 0.54 0.70 0.74 0.76
8 0.79 0.48 0.29 0.55 8 0.65 0.35 0.23 0.63
9 0.70 0.65 0.74 0.81 9 0.63 0.48 0.36 0.68
10 0.59 0.30 0.44 0.44 10 0.33 0.67 0.52 0.57
11 0.33 0.55 0.34 0.60 11 0.68 0.61 0.38 0.54
12 0.55 0.55 0.55 0.68 12 0.50 0.26 0.26 0.48

4.2. Simulation results for simulation experiment 2 (annual data)
After getting the 480 sets of point-in-time EML values, MATLAB was used to combine all the EML results
in one Excel sheet “Raw EML data” for data processing (Figure 4-8). Next, all the EML data were sorted
by the window orientation direction into “NORTH/WEST/SOUTH/EAST oriented window” sheets. The
simplified “annual” (12 days selected monthly) EML values not less than 250 were counted as healthy, and
the percentage of good circadian lighting in the total simplified numbers for each point vector was obtained.
Finally, all the percentage values for sCA calculation were gathered and put in the “sCA sheet.”
Comparatively, the sDA results were put in the “sDA” sheet for later discrepancy analysis in section 4.3.

Circadian health is essential to ensure the occupants’ health and a higher CA percentage as the threshold is
significant. 50% is selected to follow the threshold used in the calculation procedure for sDA. It is
necessary to set a higher percentage to ensure better circadian health for occupants. The sCA threshold was
set as 50%, 75% and 90%, and area with the qualifying percentage of annual EML stimulus is defined as
“sufficient” circadian daylighting.
55

Figure 4-8 Excel screenshot for data processing and the CA data for the model with window oriented to East
4.2.1. Circadian Autonomy

(CA)
The CA numbers vary from 0% -100%. The higher the percentage, the better the view is for circadian
performance. For instance, a CA value equals to 93% means that 93% of the annual occupancy hours can
get healthy circadian daylighting for a specific view direction of a given point.
Colored vectors were used to show the CA results for each vector its percentage of qualifying EML
stimulus (Figure 4-9). Window oriented to the south direction (a) got the best performance outcomes.
Notably, it was discovered that the south-oriented vectors were not always the best annual circadian
stimulus, because points on the north-east corner could get higher CA value for the north view while the
window exposure was on the south (Figure 4-9 (a)).
Figure 4-9 Simplified Spatial Circadian Autonomy (sDA) visualization for four window orientations. Transparent blue
blocks show the window locations on the plan view.
4.2.2. sCA
50%
56

For each point and view direction, CA values less than 50% were transformed to 0, otherwise transformed
to 1 for this chapter section (Figure 4-10). The data transform was used for the healthy circadian lighting
visualization and preparation for discrepancy and similarity analysis. Essentially 1 or 0 is a pass or fail on
whether or not the percentage CA is adequate. As discussed in section 1.1.1, DA values greater than 50%
were assessed as sufficient daylight zone because the sDA
50%,300
was used in the existing green building
compliance such as the LEED building standard. Similar to DA, CA values lower than 50% were marked as
black arrows which were assumed to get insufficient circadian stimulus for health over the year, although it
should be noted that this 50% threshold is arbitrary and is, in this case, based on the 50% threshold used in
the calculation procedure for sDA.

Figure 4-10 CA values less than 50% turn to 0, otherwise change to 1. The data transform was used for the healthy
circadian lighting visualization and preparation for discrepancy analysis. This table is from the Excel “Visualization
Processing” sheet in Figure 4-8.
White arrows showed specific view directions with sufficient circadian daylighting based on the threshold
value (Figure 4-11). Matching with Figure 4-9, the South (a) and West (c) oriented window space got all
CA values higher than 50%. Comparing to (a) and (c), (b) and (d) set the opposite window orientations, and
both got poor sCA performances.
57

Figure 4-11 CA performance evaluation. Transparent blue blocks show the window locations on the plan view.
However, even for a building form like (b) with north-facing windows, the north-facing view could still get
100% sufficient circadian daylighting for the annual occupancy hours; even the sDA was 25% (Table 4-7).
The sCA data were highlighted when the window orientations and view directions having the opposite
direction to show the effects of window orientation on the vectors facing against the window that assumed
to get the least daylight. The calculation vectors facing away from the window can still get equitable
amount of qualifying circadian lighting because of the characters of the interior space and time.
Only the north-facing view with a south-facing window got the lowest sCA results; others still got good
performances greater than 80%. Moreover, the sDA always got the lowest percentage compared with sCA
of different facing directions.
Table 4-7 sCA50% and sDA50% result for each view direction. Highlighted numbers are the data when the window
orientations and view directions have the opposite direction.
sCA50% View direction
Window
orientation
East View North View West View South View sDA50% (%)
West 100% 100% 100% 100% 17%
South 100% 100% 100% 100% 50%
East 92% 58% 92% 100% 33%
North 58% 100% 58% 25% 25%

4.2.3. sCA
75%

The circadian threshold was previously set to 50%. To ensure better circadian health for occupants, 75%
was also evaluated. After changing the sufficient CA threshold from 50% to 75%, the sCA evaluation result
showed that the spaces with window oriented to north and east were affected the most (Figure 4-12). For (a)
and (b), the results didn’t change compared to the 50% threshold (Figure 4-8) because of their excellent CA
performance, all above 75%. However, a large number of vectors in (b) and (d) turned to black as
58

ineffectively daylit view directions. Notably, the spatial mapping of (b) changed all the south-facing vectors
into insufficient CA performances after setting 75% as qualifying CA threshold.

Figure 4-12 CA performance evaluation by setting the sufficient CA threshold as 75%. Transparent blue blocks show
the window locations on the plan view.
The sCA data were highlighted when the calculation vectors facing away from the window (Table 4-8).
Table 4-8 sCA50% and sDA50% results for each view direction. Highlighted numbers are the data when
the window orientations and view directions have the opposite direction.
sCA75% View direction
Window
orientation
East
View
North
View
West
View
South
View
sDA50%(%)
West 100% 100% 100% 100% 17%
South 100% 100% 100% 100% 50%
East 75% 25% 67% 92% 33%
North 50% 92% 42% 0% 25%

4.2.4. sCA
90%
The circadian threshold was previously set to 50% and then 75%. To ensure better circadian health for
occupants, 90% was also evaluated. 90% is demanding even more stringent performance. After changing
the sufficient CA threshold from 75% to 90%, the sCA evaluation result showed that the spaces with
window oriented to north and east were affected the most (Figure 4-13). Models with south-facing window
(a) and west-facing window (c) stared to show insufficient CA performances after changing the threshold
from 75% to 90%. For each calculation point, it was not necessary that the vector facing against the
window got insufficient CA.
59

Figure 4-13 CA performance evaluation by setting the sufficient CA threshold as 90%. Transparent blue blocks show
the window locations on the plan view.
The sCA data were highlighted when the window orientations and view directions having the opposite
direction (Table 4-9).
Table 4-9 sCA90% and sDA50% results for each view direction. Highlighted numbers are the data when the window
orientations and view directions have the opposite direction.
sCA90% View direction
Window
orientation
East View North View West View South View sDA50% (%)
West 58% 100% 67% 17% 17%
South 67% 75% 100% 67% 50%
East 50% 25% 42% 92% 33%
North 25% 75% 25% 0% 25%

4.3. sDA and sCA comparison
sDA is a widely used metric used by lighting professions, and it sets a clear boundary of no less than half of
the annual occupied hours the illuminance level is above 300 lux in the LEED standard as a pre-request
(USGBC, 2016). However, as discussed in Chapter 2, there are no dynamic circadian metrics that can
evaluate the annual performance. Based on the definition of sDA, sCA is a measurement of circadian
illuminance for a given area, reporting a percentage of a selected view direction of the whole floor area that
exceeds a specified circadian illuminance (e.g., 250 EML) for a specific percentage of the analysis period.
Thus, it is necessary to compare the connections between sCA and sDA, such as discrepancy and
similarities, since they base on two different thresholds as healthy daylighting and set various measurement
planes. 50%, 75%, and 90% were chosen as the threshold for sCA. To explore the relationship between
60

sCA and sDA, 50% was chosen for both of these values. It is easy to see the differences between photopic
and circadian sensitivities to be qualifying daylighting stimulus.
The sCA values were always greater than sDA while setting 50% as the healthy daylit threshold except for
the north-facing view in the model with a south-facing window (Figure 4-14). Notably, models with
west-facing and south-facing windows always got sCA50% as 100% for each view direction. For the model
with the east-facing window, the sCA for all directions were above 50%, even the sDA was lower than 20%.
However, even the model with a north-facing window had greater sDA than the model with a south-facing
window, the north-facing one still got its sCA of south view lower than 50%.

Figure 4-14 sCA50% and sDA50% for various window orientations.
The visualization of CA performance evaluation is available in Appendix A for showing the comparison of
the definition of effectively daylit defined by CA and DA with the threshold of 50%.
4.3.1. Discrepancy
This section presents results that compare the discrepancy between sCA and sDA. The objective of this
analysis is to quantify and document the spatial discrepancy between the daylit zone defined by sDA and
the daylit zones defined by sCA for each of view orientation. The threshold to determine if there is
sufficient DA stimulus is 50% of the annual occupied hour that the photopic illuminance lux is above 300
based on the sDA requirement in the LEED building standard (LEED, 2013). Calculation grid areas with
DA lower than 50% were painted into the black for models with different window orientations (Figure
4-15).
0% 20% 40% 60% 80% 100%
West
South
East
North
Window Orientation
sCA
50%
VS sDA
50%
sDA50%(%) South View West View North View East View
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Figure 4-15 DA performance evaluation. Transparent blue blocks show the window locations on the plan view.
A model with a north-facing window is used as an example to show the discrepancy between the spatial
performances of sCA and sDA, as it resulted in the most variation for the CA results of each view direction.
Black color area means that the calculation area had CA and DA values less than 50%, and the white color
was used for greater than or equal to 50% (Figure 4-16).

Figure 4-16 sCA and sDA results visualization for the North-facing Window model. The white grid areas represent
healthy daylight zone with CA and DA higher than 50%.
The red color was used to fill the grid areas with different CA and DA performances, and the discrepancy
percentage of the whole area can be calculated for each window facing direction (Figure 4-17). The full
discrepancy visualization is in Appendix B.

Figure 4-17 Discrepancy visualization for the North-facing window.
Following the method described in section 3.3.2.1, the qualifying CA and DA values in each grid square
were transformed into 1 (CA, DA≥50%), and the others into 0 (CA, DA<50%). The data processing
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method made the qualifying CA visualization in Rhino easy to show only the qualifying (1 turns to white
grid square) or unqualifying (0 turns to black grid square) daylight performance mapping and also used for
calculating the discrepancy percentages (Figure 4-18). After using the transformed numbers of DA in each
grid area to subtract the values of CA and then squaring the result, areas had the number of 1 showed the
spatial discrepancies between the CA and DA evaluations (Section 3.3.2). By adding them together and
dividing the sum by total grid square number, the discrepancy percentage can be calculated (Table 4-10).
Discrepancy
Window Facing Direction
N W S E
E view
50% 67% 50% 75%
N view
75% 67% 50% 42%
W view
33% 67% 50% 75%
S view
33% 67% 50% 83%
Table 4-10 Discrepancy between sCA and sDA

Figure 4-18 Data processing
For north and south facing windows, the view direction directly facing the window always got the most
significant discrepancy percentage. Areas measured by sCA had a deeper depth of sufficient daylight zone
than sDA. For east-facing and west-facing windows, the discrepancy areas were concentrated around the
corners.
4.3.2. Similarity (Dual-health)
When the DA and CA both get the qualifying 50% (have values greater than 50% ) for the same calculation
point, that area can be rated as “dual-health.” That means getting both sufficient circadian and visual
daylight. Dual-health partially represents the similarity of two dynamic metrics as well. The higher
percentage value of the dual-health is, the sufficiently existing CBDM workflow can evaluate daylight
63

health. The dual-health percentage was always lower than the sDA value, and the sDA frequently got the
lowest percentage among all the annual results (Table 4-11).
Table 4-11 sCA and sDA similarity (Dual-health) analysis results
Dual Health
Window Facing Direction
N W S E
E view
17% 33% 50% 17%
N view
25% 33% 50% 17%
W view
25% 33% 50% 17%
S view
8% 33% 50% 17%
sDA
25% 33% 50% 17%
The dual-health area existed around the window location (Figure 4-19). By comparing Figure 4-18 and the
visualization of sCA and sDA in Appendix A, the dual-health areas followed the sDA boundaries.

Figure 4-19 Dual Health visualization for the North-facing window
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The sum of percentages of dual-health, discrepancy, and DA&CA both less than 50% (unhealthy daylit area)
equal to 100%. Higher dual health and lower discrepancy are preferred to prove that the existing CBDM
workflow can follow this circadian metric. Because it means that the sufficiently daylit zone defined by the
sDA metric can largely follow the sufficient daylit zone defined by the sCA metric. The model with a
window facing west always got the best dual-health performance.
4.4. Summary
Chapter four presented the results from two simulation experiments. The point-in-time experiment uses
linear regression data analysis method to test the relationship between EML and photopic illuminance. The
annual study presented the sCA results and made comparisons between a novel circadian metric (sCA) and
sDA. The overall evaluation provided designers and researchers the idea of the limitations of existing
CBDM metrics and the importance of view direction.
The findings are listed below from those two exercises:
 No strong correlated connections were discovered between point-in-time photopic illuminance and
point-in-time EML
 The daylit zone defined by sCA frequently got a larger area than the daylit zone defined by sDA
 Vectors directly facing the window cannot always get the best sCA performances.
 The vectors facing the opposite direction of the window can get good sCA performances.
 A higher sCA threshold affected the area with comparatively poor sDA the most.
 A model with poor sDA can have excellent sCA performance for selected oriented vectors.

65

Chapter 5. Discussion
Key findings from the two simulation studies by following the methodology described in Chapter 3 are
discussed to gain greater insight into the relationship between two pairs of metrics, point-in-time EML versus
point-in-time photopic illuminance, and sCA versus sDA (Figure 5-1). The question of how and to what
extent the daylit zone defined by sDA differs spatially from sCA can be answered through discussing the
results of two simulation studies. To help lighting consultants better ensure effective circadian lighting
designs, an understanding of the underlying assumptions and discrepancies between sDA and new metrics
focused on circadian lighting is important.

Figure 5-1 Methodology diagram
Findings from Chapter 4 are discussed in this chapter are listed below:
 The new metric EML cannot be correctly estimated based on the photopic illuminance result
 View directions’ effects on circadian health cannot be ignored and using sDA only would
underestimate the daylight performance of a space.
 There are no immutable rules of vectors facing to the window can always get best EML and sCA
performances.
 Space has poor performance for sDA can show great performances for sCA and vice versa.
 The spatial discrepancies between the daylit zone defined by sDA and the daylit zones defined by
sCA for each of the 4 view orientations vary depending on the architectural design.
Chapter 5 reported the discussion of point-in-time analysis in simulation experiment 1, significance of sCA,
comparison of sDA and sCA metrics and suggestions for daylighting design for circadian health
5.1. Discussion of point-in-time analysis in simulation experiment 1
After acquiring all the point-in-time EML and photopic illuminance data based separately on calculation
dates and analysis grid points, correlation analysis was conducted to explore the connection between
photopic illuminance and EML.
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5.1.1. Luminance and illuminance
Illuminance is used to describe the amount of light falling on and spreading over a specific surface.
Luminance is a term used to describe the luminous power that can be perceived by eyes. It measures the
amount of lighting emitting, passing through or reflected from a particular surface from a solid angle and it is
always used to quantify the brightness of displays.
The photopic illuminance metric in the existing CBDM workflow is frequently used to measure the artificial
lighting levels for a given task performance on the horizontal work plane by setting the calculation plane
horizontally and based on the sky luminance and location data (IESNA, 2000) (Li, Lau, & Lam, 2005).
Photopic illuminance is wavelength weighted to human eye perception.
5.1.2. View direction effects
For visual lighting metrics, the measurement surface is set at the workstation surface, and the values will not
be affected by the view directions. However, circadian lighting quality varies in each view direction. It was
proposed in Section 1.5 that the view direction facing to the window can get better circadian performance via
global illuminance measured vertically at eye level than other directions.
Based on the result of point-in-time metrics in chapter 4, point 1 was set at the north-west corner, and its east
view always got the best EML performance because of being close to the south-facing window and (Table
5-1 and Table 5-2). Using the photopic illuminance only would ignore the potential area that can get
excellent circadian daylight performances. Because the effects of view direction can be shown in simulation
experiment 2 as well and the annual sCA result is the accumulated point-in-time circadian effects, further
discussion of view direction will be in section 5.2.2.
67

Table 5-1 Simulation data on 6/21/9:00 for the horizontal surface and the four views
6/21/9:00 Photopic
(Lux)
EML(Lux)
Points E N W S
1 630 1429 479 615 902
2 1357 1578 850 1895 3675
3 447 528 628 1090 698
4 112 506 311 546 796
5 122 533 267 526 1433
6 100 481 279 693 957
7 25 305 137 250 505
8 24 237 276 260 683
9 34 185 86 291 427
10 17 217 286 290 495
11 14 212 299 250 469
12 13 160 163 203 386

Table 5-2 Simulation results based on points location for Point 1 on Jun 21st
Point1
EML
Photopic(lux)
E N W S
8:00 1032 1032 547 813 330
9:00 1215 617 576 924 645
10:00 1645 695 704 1124 818
11:00 2034 944 923 1348 708
12:00 2250 839 900 1377 713
13:00 2096 855 977 1489 720
14:00 1537 575 676 1005 604
15:00 1450 485 526 974 576
16:00 1063 369 416 712 468
17:00 1041 376 421 699 361

5.1.3. Discussion of R-squared findings
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Two types of linear regression data analysis were conducted to find connections between point-in-time EML
and point-in-time photopic illuminance, and their R-squared data were separately presented in Table 5-3
(based on dates) and Table 5-4 & 5-5 (based on points location). The R-squared values higher than 0.7 are
shades in red which represent a strong correlation. R-squared ranged from 0.5 to 0.7 means middle
correlation and is shaded in yellow, and values below 0.5 (weak correlation) are shaded in red. The R-squared
values were high under certain conditions such as a south view with no direct sunlight or on a specific period
in a year. However, there were no consistent strong correlated connections detected between EML and
photopic illuminance. If a comparatively strong correlation exists between EML and photopic illuminance,
the EML performance can be estimated and calculated based on photopic illuminance from the existing
CBDM workflow. However, without this connection, new digital tools such as ALFA are needed for
circadian daylight calculations, because there is no clue about how the circadian performance on vertical
workplane would be if only based on the photopic illuminance result. If there were a direct and consistent
correlation, then vertical illuminance could be used as a proxy for luminance in that field of view.
Table 5-3 R-squared results based on simulation dates.
Time
View direction
E ast North West South
3/21/9:00 0.66 0.62 0.89 0.80
3/21/12:00 0.83 0.89 0.78 0.80
3/21/15:00 0.90 0.75 0.63 0.77
6/21/9:00 0.88 0.71 0.88 0.83
6/21/12:00 0.88 0.81 0.89 0.91
6/21/15:00 0.92 0.87 0.88 0.92
9/22/9:00 0.94 0.67 0.94 0.84
9/22/12:00 0.77 0.96 0.89 0.88
9/22/15:00 0.96 0.54 0.53 0.85
12/21/9:00 0.11 0.34 0.38 0.18
12/21/12:00 0.59 0.83 0.47 0.40
12/21/15:00 0.16 0.57 0.23 0.12

Table 5-4 R-squared results based on points location on March 21st and Jun 21st
3/21 View Direction 6/21 View Direction
Point
Number
E N W S
Point
Number
E N W S
1 0.10 0.27 0.18 0.07 1 0.62 0.07 0.55 0.63
2 0.20 0.03 0.02 0.11 2 0.85 0.56 0.56 0.82
3 0.22 0.19 0.11 0.34 3 0.27 0.35 0.27 0.36
4 0.22 0.30 0.20 0.13 4 0.34 0.19 0.64 0.51
5 0.48 0.19 0.01 0.07 5 0.64 0.58 0.33 0.56
6 0.31 0.26 0.27 0.31 6 0.57 0.48 0.51 0.56
69

7 0.06 0.13 0.38 0.09 7 0.39 0.42 0.60 0.54
8 0.46 0.35 0.09 0.50 8 0.29 0.29 0.28 0.02
9 0.10 0.27 0.28 0.21 9 0.24 0.38 0.80 0.55
10 0.22 0.12 0.15 0.21 10 0.19 0.41 0.46 0.43
11 0.29 0.10 0.33 0.26 11 0.51 0.49 0.22 0.12
12 0.01 0.00 0.00 0.02 12 0.30 0.45 0.45 0.20

Table 5-5 R-squared results based on points location on Sep 21st and Dec 21st
9/22 View Direction 12/21 View Direction
Point
Number
E N W S
Point
Number
E N W S
1 0.66 0.87 0.94 0.88 1 0.13 0.52 0.40 0.42
2 0.54 0.67 0.62 0.76 2 0.86 0.89 0.68 0.92
3 0.63 0.63 0.47 0.55 3 0.38 0.54 0.17 0.33
4 0.83 0.81 0.85 0.88 4 0.73 0.78 0.82 0.84
5 0.86 0.89 0.46 0.86 5 0.57 0.79 0.42 0.89
6 0.80 0.66 0.62 0.82 6 0.66 0.57 0.44 0.69
7 0.80 0.46 0.59 0.68 7 0.54 0.70 0.74 0.76
8 0.79 0.48 0.29 0.55 8 0.65 0.35 0.23 0.63
9 0.70 0.65 0.74 0.81 9 0.63 0.48 0.36 0.68
10 0.59 0.30 0.44 0.44 10 0.33 0.67 0.52 0.57
11 0.33 0.55 0.34 0.60 11 0.68 0.61 0.38 0.54
12 0.55 0.55 0.55 0.68 12 0.50 0.26 0.26 0.48

Moreover, the sDA and sCA metrics are calculated similarly based on annual point-in-time photopic
illuminance and EML, which would also affect the effectively daylit zone separately defined by sDA and
sCA. Thus, researchers and lighting consultants cannot use the photopic metrics alone to measure the
daylight performances or even to estimate the EML performances on the vertical calculation plane that
represneting normal human eye exposure direction based on formulas such as multiplying the visual lux (L)
with a melanopic Ratio (R) to get the EML result (Figure 5-2).
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Figure 5-2 Calculation plane directions of EML and photopic illuminance
The equation “EML L R” is used to calculate the EML values listed in Table L1 in the WELL building
standard (WELL, 2016). For example, the R of daylight is 1.1. If daylight can provide photopic illuminance
of 200 lux, it will also offer 220 EML (220 EML 1.1 220 lux ).
5.2. The significance of sCA
sCA is a circadian dynamic metric that can fix the limitation that the EML metric ignores time and duration
by calculating the annual percentage of space can receiver sufficient (250 EML) circadian daylighting in the
annual occupied hours. It is significant to compare sCA and sDA and assess the discrepancy and similarity.
5.2.1. Average sCA and Standard Deviation
It was observed that the sDA values were frequently much lower than sCA values. sCA is a metric used for a
specific view direction that one given point may contain more than one set of data, and always needs to
specify its view direction to compare with the sDA metric (Figure 4-14). The Average sCA data in Table 5-6
are the average value of the sCA percentages of four directions, which also represent the percentage of any
given view of a specified point that can get qualified circadian stimulus of the annual occupancy hours.
Standard Deviation (SD) was used to measure the variation of sCA values for four view directions. The
Average sCA is easier to compare with sDA, but it sacrifices the benefits of evaluating results of each view
direction and lose accuracy. Thus, section 5.3 will assess all the CA in each square by comparing the
discrepancy and similarity with the DA.
By combining Average sCA and SD results, it is easier to compare sCA with sDA through one percentage
number and also assess the sDA variations of different view directions. The model with the south facing
window had the highest Average sCA value and best sDA performance. The west window orientation got the
average sCA as 90% and also had SD values as low as 0.05, but the sDA is much lower as 33%. For circadian
lighting design, the preferred window facing direction might have a second option as west beside the choice
of the south, but the evaluation of if the area is effectively daylit could get two opposite result when using
sDA and sCA. The daylight performance of a space can frequently be underestimated when using sDA only.
71

Table 5-6 Average sCA for each window facing direction and the standard deviation to measure the variation of sCA
values for four view directions
Window
Orientation
N W S E
Avg sCA 56% 90% 94% 79%
Standard Deviation 28% 5% 3% 13%
sDA 25% 33% 50% 17%

5.2.2. View direction effects in simulation experiment 2
According to the qualifying sCA and sDA visualization in Figure 4-11, 4-12 and 4-13, human eye exposures
oriented to the window would have significant benefits of frequently achieving more circadian stimulus. The
model with a south-facing window facing had poor DA result on the north side, and in a newly designed
building with building forms like this model, the north part might be designed as restrooms or storage areas if
only DA was used a metric. However, 100% of the area got CA above 50% in the same model.
Based on the result in Figure 4-16, the south view in the space with a north-facing window barely received a
qualifying CA above 50%, but the north side the entire area still received a qualifying CA. However, the
considerable variations between each view need designers’ attention, because the area measured by sDA as
sufficient-daylight area might put occupants in a risk of cannot access sufficient circadian daylight if they
were not set at appropriate view direction.
From section 4.2.2 to 4.2.4, the qualifying sCA result based on threshold of 50%, 75% and 90% were
compared. It was noticed that a higher threshold affected the overall sCA the most on the models with
north-facing and east-facing windows. A higher threshold can enlarge the sCA percentage variations in
different view directions. For sCA75% and sCA50%, the space with windows facing south and west can get
100% qualifying sCA on all view directions. After changing the qualifying threshold from 75% to 90%, space
with window facing south and west got the sCA result varied in each direction (Table 5-7, 5-8 & 5-9).
Table 5-7 sCA50% and sDA50% results for each view direction.
sCA50% View direction
Window
orientation
East View North View West View South View sDA50% (%)
West 100% 100% 100% 100% 17%
South 100% 100% 100% 100% 50%
East 92% 58% 92% 100% 33%
North 58% 100% 58% 25% 25%

72

Table 5-8 sCA75% and sDA50% results for each view direction.
sCA75% View direction
Window
orientation
East View North View West View South View sDA50%(%)
West 100% 100% 100% 100% 17%
South 100% 100% 100% 100% 50%
East 75% 25% 67% 92% 33%
North 50% 92% 42% 0% 25%

Table 5-9 sCA90% and sDA50% results for each view direction.
sCA90% View direction
Window
orientation
East View North View West View South View sDA50%(%)
West 58% 100% 67% 17% 17%
South 67% 75% 100% 67% 50%
East 50% 25% 42% 92% 33%
North 25% 75% 25% 0% 25%

5.3. Comparison of sDA and sCA metrics
Notably, the sCA outcome values were higher than sDA except for the south-facing view in the model with a
north-facing window (Figure 4-15). A space which has poor performance for sDA can show good sCA
performance for all directions. For example, the model with 30% WWR on the east wall was measured by
sDA as lower than 20% which should be rated as an insufficient daylit zone in the LEED standard. But the
sCA data were all higher than 60% for all view directions, indicating that using sDA only would
underestimate the daylit zone area of a space and the vertical view directions of normal human eye exposure
cannot be ignored.
Four cases are listed in Table 5-10 to show various situations between sCA and sDA. A space evaluated as
insufficient daylight area by sDA can still have good sCA performances, which is the most common case that
the existing metrics ignore the normal human eye exposure direction by setting the calculation plane
horizontal only (Case 3). Case 4 happens when the interior space lacks daylight stimulus that both sDA and
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sCA get poor performances. Based on the result in chapter four, when the sDA is not less than 50%, it is
highly possible to be rated as sufficient daylight zone as well (Case 1). Since it was noticed that the sCA could
frequently be much higher than sDA and the situation in case 2 never happened in the simulation data, the
sDA higher than 50% was proposed to get qualifying sCA result greater than 50% as well. More research is
needed to verify this proposal.
Table 5-10 Four cases are listed to show various situations between sCA and sDA
sCA & sDA Comparison sCA 50% sCA<50%
sDA 50% Case 1 Case 2
sDA<50% Case 3 Case 4
The discrepancy is significant for detecting the variations of sufficient daylit zones defined by sDA and sCA.
Based on visualization results in Appendix B, the discrepancy areas were typically located at the opposite
side of the window orientation, which revealed that the sCA detected by vertical vectors could provide deeper
effectively daylit zone than sDA. Thus, lighting design consultants can make better use of the interior space
by setting the appropriate work station orientation to ensure occupants’ health by using the sCA metric. The
discrepancy outcomes in Table 4-10 were all higher than 33% which means that for any hour in the year the
spatial effectively daylit map of sCA will fall in less than 67% of the correct location of sDA. The 33%
discrepancy value is too high to be ignored, and the sCA is a metric necessary for evaluating the daylight
health.
As mentioned in chapter four, the “dual-health” areas both rated as good daylit zone were concentrated
around the window area. The workspace around the window areas got both circadian and visual daylighting
stimulus simultaneously.
In conclusion, reliance on a daylit area defined by sDA is problematic for informing design decisions based
on the following observations from this thesis:
 sDA cannot evaluate the daylit performance of various directions in the vertical plane and provide
suggestions of the workspace orientation and the seats’ location to maximize the healthy circadian
stimulus received by the eyes on the vertical plane.
 Some locations within the effectively daylit zone defined by sDA were found to be insufficient for
healthy circadian stimulus. (Figure 4-16)
 Underestimated effectively daylit zone measured by sDA would need artificial lighting for support,
and this leads to unnecessary consumption of electricity.
5.4. Suggestions for daylighting design for circadian health
The sCA metric is necessary for evaluation during the daylight design process to ensure areas can set better
designed workspace orientation and the location of seats to ensure the occupants’ prevailing view direction
can get sufficient circadian stimulus. Without considering the effects of vertical eye exposure, the areas
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cannot be fully used for providing good daylight circadian areas for occupants. Furthermore, because the
sufficient circadian stimulus is essential for circadian health, a higher threshold for CA could be updated in
future standards, such as CA values greater than 75% or even 95% can be rated as good daylight area.
The metric comparisons in both demonstrated that the view directions made differences in the circadian
performance, which provides evidence that the workspace orientation can affect the occupants’ exposure to
sufficient daylight for healthy circadian stimulus. The model with south-facing window had the best sCA and
sDA performance, but the west-facing window could be a second option to be combined with south facing
window design to upgrade the circadian daylighting performances (Figure 5-3).

Figure 5-3 Compare sCA50% with sDA 50%table
5.5. Summary
The methodology described in chapter 3 can process and evaluate both circadian metrics (EML and sCA) and
existing metrics from CBDM workflow (photopic illuminance and sDA). A new data processing method was
adopted for generating an “Average sCA” metric for easier to compare the dynamic circadian performance
with sDA. By setting a higher threshold of sCA, such as 75% and 90%, better circadian health can be ensured
for occupants.
The discrepancies between sCA and sDA are significant to show that using sDA only would underestimate
the effectively daylit areas of space and the vertical view directions of normal human eye exposure cannot be
ignored. The discrepancy analysis between sCA and sDA reveals that the spatial map of sCA cannot ensure
falling into the correct location of sDA’s. Thus, the circadian metrics researched, both EML and sCA, are
irreplaceable and can define the potential effectively daylit areas ignored by photopic illuminance and sDA.
The dual-health areas are frequently concentrated around the window and following the sDA boundary.
0% 20% 40% 60% 80% 100%
West
South
East
North
Window Orientation
sCA
50%
VS sDA
50%
sDA50%(%) South View West View North View East View
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Chapter 6. Limitations and Future Work
Daylight health cannot be ignored during the architectural design as well as the lighting design process. To
ensure daylight health, proper spectral components and brightness of daylight should be accessible to the
building occupants to lower the risk of interrupting their circadian rhythm. Metrics from the existing CBDM
workflow are all based on point-in-time photopic illuminance, even the advanced dynamic metric, spatial
daylight autonomy, is cumulative and is calculated based on the annual illuminance during the occupied
hours which ignores the circadian effects. Effective use of daylighting in buildings is significant to ensure
both visual task lighting through providing illuminance and circadian health such as providing an appropriate
dose of spectral lighting to minimize circadian rhythm disruption.
Thus, it is significant to provide well-designed daylighting exposure to ensure occupants’ well-being.
Evidence-based tools for measuring the circadian performance are fundamental for lighting consultants to
enhance their lighting design and maximize the circadian benefits. Climate-Based Daylight Modeling
(CBDM) has been used for daylighting design evaluation by lighting consultants. Metrics such as Spatial
Daylight Autonomy (sDA) are advanced at predicting the annual performances based on the annual climate
files. The proposed metric EML in the WELL building standard sets the calculation plan vertically at the
seated eye-level height, which is an alternative to the traditional lux (Photopic illuminance) based on a flat
plan (WELL, 2016). ALFA is a software program used to calculate the circadian lighting reported in the unit
of EML and based on extended Radiance engine (Render in high-resolution, 81-color spectra). Moreover, to
provide enough areas of effectively circadian daylit zone to the occupants, a parallel annual dynamic EML
metric, Spatial Circadian Autonomy (sCA), was developed as a novel metric to measure annualized circadian
daylight performance based on vertical plan and was proposed to make up for the deficiency that EML
ignores the exposure time and duration.
The research question was to explore if the effectively daylit zone predicted by existing CBDM workflows
differ substantially from the effectively daylit zone predicted by circadian lighting metrics. Chapter 6 reports
the conclusions, limitations, and future work of the initial study.
6.1. Conclusions
By following the methodology diagram in chapter 3, Ladybug/Honeybee and ALFA were used to build
same geometry and settings for calculating both existing metrics (photopic illuminance and sDA) and new
metrics (EML and sCA). Correlation analysis was performed to compare the relationship between
point-in-time illuminance and EML. Then the correlated connections between the point-in-time EML and
point-in-time photopic illuminance were compared to ascertain the discrepancy and similarities between
sCA and sDA. To get the sCA result, simplified point-in-time EML on 21
st
each month were used for data
processing.
76

Figure 6-2 Methodology diagram
After comparing the metrics, discrepancies were discovered to be significant. Setting the horizontal plane of
analysis grid points oriented vertically and using the metric sDA of the existing CBDM workflow only
cannot adequately assess the daylight health. By using two different software programs, Ladybug/Honeybee
and ALFA, and conducting a simplified annual experiment study, there were two important outcomes, ALFA
is a necessary digital tool for EML calculation, and sDA shows a large discrepancy with the sCA metrics:
 Weak correlation between point-in-time metrics
 The large discrepancy between dynamic metrics
The first outcome is from the comparison between the point-in-time EML and point-in-time photopic
illuminance. After conducting the statistical analysis between those two metrics based separately on the
simulation dates and grid point locations, no strong correlated connections were discovered due to the large
variance of the R-squared values. As a result, the EML metric with the calculation plane set vertically cannot
be estimated and calculated accurately based on the photopic illuminance result from the CBDM workflow. It
is necessary to use a digital tool such as ALFA to provide more accurate circadian calculations.
The second outcome is from the comparison between sCA and sDA to evaluate the similarities and
discrepancies. It was observed that the ineffectively daylit zone evaluated by sDA can still get good sCA
result for all directions. This is also the main gap between sCA and sDA in the proposal leading to the
ignoring potential circadian effectively daylit area. By setting the correct workstation orientation, the
occupants can still get a healthy dose of circadian stimulus throughout the year. Thus, using sDA only would
underestimate the sufficient daylit zone of a space, and the vertical view directions of normal human eye
exposure cannot be ignored. On the other hand, areas rated as effectively daylit by sDA can have directions
got poor sCA result, which would expose the occupants under the circadian dangers without notice.
Additionally, the discrepancy analysis revealed that the result of sCA might only correctly fall into the sDA
map less than 67% at any time during the yearly occupied hours. The dual health areas were frequently
77

concentrated around the window and following the sDA boundary due to the deeper effectively daylit zone
defined by sCA compared to sDA.
There is no universal rule for a given point such as the view facing to the window can always get the best sCA
performances after comparing with the sDA result. Windows oriented to the west and south can both get
excellent sCA performances, and the west-facing window can be a second option to enhance the circadian
health in the daylighting design. The average sCA metric can get only one percentage value and is convenient
to compare with the sDA by also taking the standard deviation of all directions into consideration to show the
variation level. However, by adding the sCA for all directions will hurt the most advanced property of the
sCA, which is to measure the performance of one specified view direction.
The circadian metrics EML and sCA can be used to define the potential effectively circadian daylit areas
ignored by photopic illuminance and sDA. Updating building standards, developing a new benchmark for
circadian health lighting design, and creating digital simulation tools could be future works for other parties.
Suggestions for future works and the limitations of this research are listed below.
6.2. Limitations
By investigating the relationship between existing metrics from CBDM workflow and circadian metrics, the
limitations of existing metrics can be detected. However, the sCA metric cannot be calculated based on the
full annual point-in-time data due to the limited research time. Because the ALFA software cannot generate
the annual result directly and continuously for the total occupancy hours, it was time-consuming to reset the
simulation time and restart the point-in-time EML calculation. As a result, 12 days in a year (21
st
in each
month) were selected to get the hourly EML results (8:00-17:00) for calculating simplified sCA performance,
which cannot be as accurate as of the real annual result.
Moreover, each calculation point set only four vectors for more straightforward data processing which would
cause problems in the real project. The occupants should not be limited at a specific narrow facing direction
range and lose the flexibility to set the workstation facing directions. Additionally, a generic model with only
30% WWR glazing areas was chosen for shortening simulation time due to the needs of resetting time input
in ALFA. The only variable was the window orientation that the 30% of glazing areas were put on south, west,
north, and east.
Lastly, the sDA higher than 50% was proposed to get qualifying sCA result greater than 50% as well. More
research is needed to verify this proposal. While comparing the discrepancy and similarity between sDA and
sDA, the 50% threshold was selected for both, but other number such as 75% and 90% can be used for both
sDA and sCA to compare those two metrics as well. The discrepancy and similarity study are discussed in
future work in section 6.3.3 for adjusting the existing design standard and guidelines.
Digital program for calculating sCA based on annual occupied hours is necessary to be developed and
provided for circadian lighting design. The digital program is a part of future work discussed in section
6.3.1. It is necessary that the digital tool can evaluate the performance based on more view directions for
each calculation point for instance from 1 to 16. With such a tool that can assess specific view direction
78

range based on the 3D model in modeling environment such as Rhino, it is easy to control and input more
variables to study the circadian result under different settings.
6.3. Future work
Findings of the primary study can reveal a basic relationship between circadian metrics and metrics from the
existing CBDM workflows due to the simplified model and limited research duration. More powerful digital
tools, research result validation, existing standard and guideline adjustment, and circadian glare issue can be
explored more in future research.
6.3.1. Digital program for sCA and CBDM engine
The ALFA software cannot continuously generate an annual result and needs to reset its time input each time.
A program plug-in based on the existing CBDM workflow or ALFA should be developed to calculate the sCA
for the circadian-health zone evaluation. The dynamic metric spatial daylight autonomy is advanced at
predicting the annual sufficient daylit boundary and can support the daylighting design by taking lighting
characteristics like time, duration into consideration. However, no current software program is used by the
lighting industry to get the annual circadian dynamic metrics for the set vectors representing human eye
exposures.
The existing CBDM workflow can also simulate the circadian performance but cannot be as accurate as
ALFA. The Radiance engine used by ALFA contains 81 spectral channels and is more sensitive to the
circadian lighting performance. Powerful digital programs, such as an advanced spectral sensitive engine and
weather files for more precise circadian stimulus evaluations in existing CBDM workflow, should be
developed to provide tools for the daylight design consultants.
6.3.2. Validation and background research
Lack of circadian measurement devices makes the circadian study mostly in the laboratory environment
(Jung & Inanici, 2018). The results of the digital model simulation can be validated by measuring a physical
model, such as using high dynamic range photography (Jung & Inanici, 2018). Additionally, doing a survey
to investigate the occupants’ preferences of various daylighting designs in their workspace and interview the
architects by selecting the best building forms from a list of geometries that can get best daylighting
performance based on their experiences can enhance the background of circadian metrics research and
provide significant data for updating standards and guidelines.
6.3.3. Adjust design guideline and standard
More research needs to be conducted for profoundly understanding how to translate the circadian lighting
research to a language such as standards and guidelines to make the lighting design follow the occupants’
well-being. After getting more accurate values for sDA and sCA based on the new digital tool, numbers
ranging from 50% to 99% can be used as a qualifying threshold to compare the connections between sDA
and sCA. It is significant to understand the effects of selected threshold (such as 250 EML in WELL, and
sDA
300lux, 50%
in LEED) and how the standards (LEED and WELL) should be modified. For instance, the
sDA
300lux, 50%
in LEED only requires a building to get qualifying 300 lux of half of the annual occupied
hours. But a building has a continuous half of the annual time getting poor photopic lux below 300 lux can
still be assessed as qualifying sufficient daylight space (Konis, 2017). Thus, the required 250 EML in the
79

WELL standard and the 50% threshold of sDA could be adjusted if research shows that those values are
better.
6.3.4. Glare issues
Glare not considered while defining sufficient circadian daylighting. In the visual lighting measurement,
there are metrics such as ASE or Daylight Glare Probability (DGP) to measure potential glare problems and
help designers with better lighting designs. However, no threshold, such as no upper limit boundary, has been
set to define glare with respect to circadian lighting.
6.4. Summary
Unlike visual lighting that can directly affect people’s visual sensation, circadian lighting changes cannot be
easily noticed by occupants. The metrics from the existing CBDM workflow cannot fully represent the
normal eye exposure direction if always based on the flat calculation plan. It was found that the effectively
daylit zone defined by sDA and sCA showed large discrepancies that cannot be ignored, and the point-in-time
photopic illuminance and EML had no stable correlated connections. Compared to sCA, the sDA metric
frequently underestimated the effectively circadian daylit zone. Moreover, areas that had qualified DA
performance can still have poor CA result which caused limitation for the occupants to get access to the
circadian stimulus.
The new sCA metric is more accurate at measuring the annualized circadian result of a certain view direction.
The existing CBDM workflows need to be improved to follow the modern environmental changes and
occupants’ well-being. Daylight is an essential element for health, and it is significant for lighting
professionals to use evidence-based tools to simulate circadian performance during the design process.
However, the circadian lighting metrics are still at an early stage and need more comprehensive development.
The metric Spatial Circadian Autonomy is necessary to be used as a circadian dynamic annualized metric to
ensure circadian health. By combining sCA and sDA, both sufficient visual and circadian performances can
be guaranteed for daylighting design.
80

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84

Appendix A
Visualization for qualifying CA and DA values. The left side shows the CA results, and the right column
represents the DA.

85

Appendix B
The discrepancy between sCA and sDA visualization.

86

Appendix C
Put all the raw point-in-time data for R-squared analysis based on dates in Appendix C.
Excel chart to show the original data for calculating R-squared based on dates. The E, N, W, and S
represent the ALFA calculation plane facing directions. There are 12 sets of data listed at 9:00, 12:00 and
15:00 on March 21st, Jun 21st, Sep 22nd, and Dec 21st.
ALFA data on March 21
st
and Jun 21
st
.
ALFA 3/21/9:00 3/21/12:00 3/21/15:00 6/21/9:00 6/21/12:00 6/21/15:00
Points EML (Lux) EML (Lux) EML (Lux) EML (Lux) EML (Lux) EML (Lux)
1
2049 2447 1278 832 1537 824
1473 1198 600 447 683 487
1537 1251 515 310 685 406
1384 1259 606 441 795 383
2
2695 5394 2678 2048 3929 2368
1643 2526 1132 727 1382 827
2795 3742 1740 1384 2489 1711
4161 6764 3496 3773 6013 4376
3
1772 3769 2363 1604 3032 1987
1147 2089 1405 741 1224 861
2969 5141 2726 1694 2891 2194
3753 6602 3909 3745 5888 4427
4
619 1233 1337 384 681 361
666 1272 1158 404 498 416
1192 3269 2326 775 1259 734
570 1673 1426 389 781 387
5
1436 1783 991 786 1388 738
605 895 427 454 605 457
841 932 568 532 626 421
1721 1866 1060 906 1412 657
6
1219 2215 1213 823 1536 804
681 925 658 484 683 517
1129 1323 630 858 727 486
2449 4130 1880 1816 2710 1535
7
577 1365 949 555 942 572
508 941 692 492 660 429
1450 1786 1186 1082 1233 779
2166 3951 2669 1949 2649 1620
87

8
526 1034 1097 442 677 604
407 640 805 313 541 554
1275 1805 1290 675 1193 957
1049 1925 1796 715 1240 995
9
822 1247 784 619 955 476
596 1032 502 532 681 309
432 891 334 360 403 323
890 1350 731 828 1207 565
10
523 986 618 748 909 516
661 1003 583 489 910 482
701 831 456 415 555 368
1150 1374 779 1164 1337 733
11
539 699 655 276 738 422
828 1134 691 564 669 440
785 1219 725 720 633 534
1026 1256 1062 933 1132 975
12
472 749 649 461 553 339
435 769 580 427 514 410
563 1203 848 603 642 687
672 1229 1429 730 861 627

Ladybug/Honeybee data on March 21
st
and Jun 21
st
.
Honeybee 3/21/9:00 3/21/12:00 3/21/15:00 6/21/9:00 6/21/12:00 6/21/15:00
Points Photopic
(Lux)
Photopic
(Lux)
Photopic
(Lux)
Photopic
(Lux)
Photopic
(Lux)
Photopic
(Lux)
1 550 186 237 357 328 275
2 2258 1441 2738 1779 2294 2074
3 2091 1465 2980 1680 2368 2131
4 178 201 785 195 333 449
5 140 86 133 105 141 160
6 271 162 307 215 395 343
7 193 130 412 242 319 329
8 74 103 240 81 130 181
9 58 49 57 52 70 74
10 76 59 95 71 102 118
11 60 45 92 65 117 105
12 39 37 102 54 67 76

ALFA data on Sep 22
nd
and Dec 21
st
.
88

ALFA 9/22/9:00 9/22/12:00 9/22/15:00 12/21/9:00 12/21/12:00 12/21/15:00
Points EML (Lux) EML (Lux) EML (Lux) EML (Lux) EML (Lux) EML (Lux)
1 2170 2494 968 1787 2232 984
1411 1224 564 2274 1751 531
1364 1169 507 1407 1306 440
1347 1239 556 1275 1227 432
2 2964 4978 2481 1782 4010 1689
1682 2390 1057 2258 3067 1075
2985 3383 1449 2170 2746 790
4251 5907 3392 2256 4415 1624
3 1883 4060 2439 1018 3088 1780
1301 2574 1363 1379 3260 1684
3106 4859 2764 2075 3857 1370
4132 5984 3956 1765 4290 1923
4 556 1104 1290 563 1454 1105
659 1311 1245 547 1789 1583
1086 2867 2196 911 2623 1371
660 1264 1317 553 1436 1093
5 1414 1676 904 2020 2496 834
669 913 389 1148 1035 454
767 914 501 1901 1138 502
1532 2021 801 2334 1993 640
6 1104 2009 1347 1504 2922 1335
620 946 604 1004 1312 515
996 1210 522 2029 1805 439
2583 3255 1708 2569 4324 1198
7 631 1255 917 525 1931 2022
646 1009 358 1015 1641 1060
1468 2052 955 1919 2887 1050
2169 3460 2285 1448 4371 2077
8 582 1188 851 506 1320 1842
691 965 865 488 947 1292
1313 1726 1158 794 2336 1576
1086 2040 1498 663 2356 1922
9 711 943 668 1012 1212 596
714 779 430 1037 821 376
605 960 426 869 790 446
1129 1380 671 1328 1287 563
89

10 603 1029 700 828 1341 919
784 944 550 1018 1476 487
772 826 538 1148 1003 353
962 1381 1076 1547 1926 890
11 558 1106 667 454 1220 775
796 967 618 668 1479 834
744 1095 511 1136 1258 694
913 1427 1057 1019 1936 1132
12 556 656 675 496 904 991
545 920 535 439 869 863
721 1363 699 721 992 1099
855 1230 1027 665 1386 1636

Ladybug/Honeybee data on Sep 22
nd
and Dec 21
st
.
Honeybee
9/22/9:00 9/22/12:00 9/22/15:00 12/21/9:00 12/21/12:00 12/21/15:00
Points Photopic
(Lux)
Photopic
(Lux)
Photopic
(Lux)
Photopic
(Lux)
Photopic
(Lux)
Photopic
(Lux)
1 328 379 294 746 574 171
2 1758 1744 1683 11196 26476 13792
3 1655 1780 1746 10934 26535 14176
4 220 368 391 166 603 13066
5 83 213 156 593 407 131
6 260 464 359 667 1060 402
7 223 411 335 394 974 774
8 106 220 206 111 431 735
9 44 107 80 193 185 67
10 93 154 132 207 354 175
11 66 158 124 123 322 293
12 49 120 90 55 213 299

90

Appendix D
Put all the raw point-in-time data for R-squared analysis based on analysis grid point location in Appendix
D.
Excel chart to show the original data for calculating R-squared based on grid point location. The E, N, W,
and S represent the ALFA calculation plane facing directions. The E, N, W, and S represents the ALFA
calculation plane facing directions. There are four sets of data listed for March 21
st
, Jun 21
st
, Sep 22
nd
, and
Dec 21
st
.
Data on March 21st
Point1 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 1376 2203 3146 3402 3109 2977 2209 1698 1036 1125
N 610 1429 1419 1511 1233 1382 720 783 429 485
W 1151 1792 1845 1584 1630 1654 871 772 551 631
S 1605 2020 2442 2479 1961 1879 1516 1040 655 716
Photopic(lux) 98 945 349 321 445 700 563 552 285 191

Point2 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 1057 1631 2570 3256 3468 3370 3396 2682 1618 1811
N 799 854 1297 1967 1570 1793 1345 890 567 644
W 1793 2600 3300 3777 4087 3380 2687 1747 1043 1091
S 2125 3668 4842 5669 6181 5630 4886 3604 2152 2204
Photopic(lux) 262 1764 957 875 1130 2090 1674 2342 1120 682

Point3 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 551 716 1063 1500 1749 1658 1659 1501 1133 1431
N 470 802 1084 1020 1328 1488 1324 1105 847 1026
W 1012 1648 1987 2797 3005 3344 3109 2402 1284 1475
S 676 952 1059 1733 1991 2317 2208 1963 1364 1659
Photopic(lux) 103 380 333 301 427 801 679 1380 763 426

Point4 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 622 872 1138 1574 1449 1506 1067 836 670 744
N 265 451 554 513 710 504 470 458 229 257
W 406 730 1068 1179 966 885 760 670 395 458
S 962 1707 2073 2328 1904 1836 1269 967 830 891
Photopic(lux) 20 102 88 63 82 136 88 110 69 34

Point5 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
91

E 393 575 819 994 1031 1260 1358 1018 653 762
N 228 325 331 583 416 533 460 374 238 265
W 682 1009 1111 1216 1141 1133 951 577 428 469
S 1042 1720 2423 2959 2677 2675 2157 1675 1088 1200
Photopic(lux) 30 126 82 78 114 177 175 234 88 65

Point6 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 346 581 801 931 995 1056 739 791 478 568
N 253 302 474 623 548 742 319 470 410 466
W 523 737 1020 1462 1116 1575 981 944 621 700
S 679 1056 1370 1841 1686 2060 1706 1459 1039 1243
Photopic(lux) 18 95 74 67 76 193 89 188 84 44

Point7 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 279 371 459 612 650 652 504 376 314 362
N 178 203 320 226 429 418 203 251 152 183
W 192 278 404 344 437 555 291 393 201 232
S 362 760 940 911 1152 1028 690 598 413 453
Photopic(lux) 5 33 26 14 19 48 32 47 24 14

Point8 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 189 323 388 464 426 457 518 412 359 412
N 152 239 161 333 400 359 246 164 184 218
W 396 381 336 337 542 361 356 233 249 284
S 569 867 1042 1086 1452 1013 1187 712 620 675
Photopic(lux) 5 28 24 20 41 42 38 34 19 13

Point9 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 141 232 376 429 536 470 291 283 172 205
N 196 198 206 310 317 399 307 234 111 130
W 333 356 400 520 661 595 582 568 259 287
S 370 660 807 788 1162 912 820 810 455 514
Photopic(lux) 6 26 22 14 20 47 38 38 26 10

Point10 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 156 164 371 311 286 326 265 222 196 217
N 286 167 413 398 273 513 307 168 138 156
W 200 206 266 390 167 393 277 141 108 129
S 146 302 571 517 534 399 476 319 303 336
Photopic(lux) 4 15 13 12 15 19 16 20 7 5

92

Point11 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 132 241 296 318 337 280 321 234 110 134
N 211 351 204 462 419 274 310 246 124 147
W 129 329 296 337 419 266 273 198 93 112
S 195 385 593 369 784 584 614 325 221 244
Photopic(lux) 3 25 17 9 15 17 17 18 8 10

Point12 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 157 205 223 343 221 385 214 225 160 198
N 228 180 350 408 343 396 260 232 103 124
W 169 323 418 515 452 317 362 272 146 164
S 253 456 533 734 764 476 509 363 368 415
Photopic(lux) 4 15 10 8 10 25 24 32 11 9

Data on Jun 21st
Point1 8:00 9:00
10:0
0
11:0
0
12:0
0
13:0
0
14:0
0
15:0
0
16:0
0
17:0
0
E 1032 1215 1645 2034 2250 2096 1537 1450 1063 1041
N 1032 617 695 944 839 855 575 485 369 376
W 547 576 703 923 900 977 676 526 416 421
S 813 924 1124 1348 1377 1489 1005 974 712 699
Photopic(lux
)
330 645 818 708 713 720 604 576 468 361

Point2 8:00 9:00
10:0
0
11:0
0
12:0
0
13:0
0
14:0
0
15:0
0
16:0
0
17:0
0
E 1150 1548 2261 2319 2119 2686 1906 2038 1307 1291
N 592 731 912 956 778 914 768 624 374 378
W 1489 1803 2205 3260 2440 2321 2165 1710 1046 1026
S 2917 3997 5116 5558 5453 5630 4576 4003 2728 2650
Photopic(lux
)
842 1416 1804 1812 1934 1960 1725 1714 1344 1014

Point3 8:00 9:00
10:0
0
11:0
0
12:0
0
13:0
0
14:0
0
15:0
0
16:0
0
17:0
0
E 597 558 796 904 697 970 696 637 492 512
N 482 414 761 864 628 743 709 603 475 483
W 980 1061 1661 2199 1726 1692 1546 1344 740 728
S 602 715 1006 1337 1124 1206 950 842 558 567
93

Photopic(lux
)
296 426 613 626 726 845 775 790 630 495

Point4 8:00 9:00
10:0
0
11:0
0
12:0
0
13:0
0
14:0
0
15:0
0
16:0
0
17:0
0
E 570 656 869 930 836 949 607 628 372 368
N 166 397 349 357 319 387 251 303 194 200
W 333 479 623 408 490 550 577 478 348 362
S 817 888 1463 1237 1100 1202 1047 1024 646 628
Photopic(lux
)
74 84 157 134 138 173 174 137 109 93

Point5 8:00 9:00
10:0
0
11:0
0
12:0
0
13:0
0
14:0
0
15:0
0
16:0
0
17:0
0
E 475 634 692 604 686 830 558 649 378 390
N 209 287 274 332 298 365 313 198 179 180
W 346 623 653 749 660 569 613 391 448 430
S 1007 1717 1716 1910 1775 1776 1572 1393 1013 992
Photopic(lux
)
101 127 230 225 226 295 236 208 133 120

Point6 8:00 9:00
10:0
0
11:0
0
12:0
0
13:0
0
14:0
0
15:0
0
16:0
0
17:0
0
E 400 482 515 655 634 792 597 485 288 291
N 196 268 267 379 322 413 301 231 188 192
W 385 603 860 1092 868 820 703 664 500 489
S 580 921 1067 1495 1421 1336 1060 1107 763 741
Photopic(lux
)
55 95 176 153 137 201 172 177 102 76

Point7 8:00 9:00
10:0
0
11:0
0
12:0
0
13:0
0
14:0
0
15:0
0
16:0
0
17:0
0
E 275 350 511 402 390 395 405 305 235 230
N 147 70 226 312 351 296 202 122 81 88
W 188 121 193 311 257 399 237 226 155 160
S 441 625 753 654 602 832 643 534 374 365
Photopic(lux
)
19 28 40 54 37 59 57 35 35 22

Point8 8:00 9:00
10:0
0
11:0
0
12:0
0
13:0
0
14:0
0
15:0
0
16:0
0
17:0
0
94

E 211 153 302 313 267 444 254 275 215 224
N 125 121 177 133 139 258 161 246 105 113
W 179 176 328 337 249 400 263 168 209 210
S 577 815 592 992 916 1020 728 586 612 595
Photopic(lux
)
19 26 59 37 46 52 62 47 44 23

Point9 8:00 9:00
10:0
0
11:0
0
12:0
0
13:0
0
14:0
0
15:0
0
16:0
0
17:0
0
E 177 308 289 344 423 399 185 222 179 190
N 152 218 284 293 259 219 228 248 162 165
W 192 300 316 455 350 498 401 275 313 305
S 326 602 491 743 701 839 575 514 502 498
Photopic(lux
)
21 23 44 55 42 61 53 40 34 29

Point10 8:00 9:00
10:0
0
11:0
0
12:0
0
13:0
0
14:0
0
15:0
0
16:0
0
17:0
0
E 251 199 282 412 236 353 168 228 125 132
N 154 182 211 198 345 365 203 164 144 146
W 108 125 207 183 251 206 254 71 147 153
S 238 309 486 658 320 481 410 351 224 223
Photopic(lux
)
10 13 26 23 24 28 24 19 21 12

Point11 8:00 9:00
10:0
0
11:0
0
12:0
0
13:0
0
14:0
0
15:0
0
16:0
0
17:0
0
E 100 257 192 249 241 303 238 160 147 148
N 180 188 284 235 230 306 253 177 225 221
W 154 174 186 246 192 173 265 233 220 218
S 257 651 234 515 566 463 505 346 273 268
Photopic(lux
)
9 14 23 29 23 28 26 21 17 13

Point12 8:00 9:00
10:0
0
11:0
0
12:0
0
13:0
0
14:0
0
15:0
0
16:0
0
17:0
0
E 146 219 184 226 237 245 242 142 84 91
N 160 233 239 94 337 168 306 177 115 121
W 148 212 219 240 294 272 220 225 118 121
S 200 322 258 537 465 429 300 339 272 270
95

Photopic(lux
)
10 13 22 22 24 24 33 21 15 12

Data on Sep 22
nd

Point1 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 1569 2333 3323 3275 3528 3115 2063 1408 795 857
N 1030 1297 1542 1265 1391 1269 870 665 390 430
W 1379 1722 1813 1767 1417 1321 1157 744 450 507
S 1239 2218 2272 2320 2231 2001 1282 1013 471 506
Photopic(lux) 768 1142 1066 1024 907 756 589 431 376 174

Point2 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 1199 1819 2542 3694 3902 3576 3293 2204 1610 1883
N 751 1031 1472 1855 2096 1564 1386 877 598 676
W 1867 2507 4080 3726 3662 3123 2342 1483 1077 1139
S 2506 3600 4874 5652 5956 5390 4443 3077 1833 1893
Photopic(lux) 1179 2014 1869 2047 2212 2143 1930 1460 1407 644

Point3 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 640 793 1187 1312 1710 1800 1846 1321 1055 1324
N 565 560 1002 1000 1574 1752 1475 1230 834 979
W 867 1651 2445 2634 3382 3391 3066 1927 989 1147
S 520 1043 1838 1390 2109 2065 2201 1558 1102 1369
Photopic(lux) 282 479 616 819 969 1121 1220 1001 961 388

Point4 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 698 709 1393 1303 1316 1332 1244 786 496 552
N 395 421 541 520 618 436 447 324 220 254
W 556 756 822 1058 1153 728 889 548 394 448
S 1188 1325 1859 2055 2107 1931 1472 864 582 629
Photopic(lux) 83 175 276 312 318 241 215 121 100 23

Point5 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 536 694 1061 1069 1321 1152 1042 1086 418 496
N 238 383 503 569 537 508 416 377 187 223
W 761 904 1203 1147 784 1054 765 633 387 416
S 1410 2046 2720 2689 2797 2388 2466 1485 948 1022
Photopic(lux) 106 193 339 417 463 396 368 231 127 69

96

Point6 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 408 507 884 849 1006 823 1028 877 376 448
N 269 311 529 519 555 445 457 455 275 331
W 462 823 1341 1380 1270 1508 936 719 527 590
S 631 1016 1680 2052 1852 2046 1800 1487 1098 1270
Photopic(lux) 69 109 187 293 293 320 316 217 115 59

Point7 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 269 426 417 573 711 528 452 394 255 293
N 254 240 292 249 386 312 219 217 191 219
W 385 267 402 462 385 477 430 334 235 271
S 581 958 794 997 1052 1061 1031 434 392 444
Photopic(lux) 18 38 69 86 82 76 56 36 23 10

Point8 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 230 298 468 460 652 456 432 399 261 316
N 148 197 333 308 258 245 215 160 203 249
W 510 364 537 420 384 536 369 278 193 226
S 902 994 1212 1008 1076 1046 1116 656 492 543
Photopic(lux) 17 33 90 99 103 86 66 44 24 10

Point9 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 138 304 257 420 373 443 390 414 214 256
N 155 274 263 339 367 303 218 230 130 159
W 322 313 515 649 564 708 364 382 284 322
S 497 546 622 1005 851 1305 994 827 339 375
Photopic(lux) 16 26 60 91 79 86 73 55 24 14

Point10 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 251 330 217 347 388 474 352 236 144 162
N 298 314 274 273 263 431 261 154 136 160
W 250 257 268 330 281 223 176 187 95 115
S 440 697 456 590 605 523 577 303 208 216
Photopic(lux) 12 17 30 34 36 34 30 21 11 5

Point11 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 231 216 277 283 425 412 288 194 229 270
N 237 254 376 346 380 280 240 198 190 208
W 313 188 327 338 383 234 221 240 148 168
S 521 385 582 529 685 593 620 357 310 351
97

Photopic(lux) 11 24 35 41 44 33 41 23 15 6

Point12 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
E 132 138 281 288 273 263 292 169 78 96
N 215 122 288 229 259 300 270 228 97 120
W 215 250 322 319 434 383 222 271 175 207
S 223 352 625 580 567 587 486 414 334 397
Photopic(lux) 9 18 26 36 34 45 35 21 16 9

Dec 21
st

Point1 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00
16:0
0
17:00
E 845 1907 2706 3234 2785 2141 1899 1276 397 547
N 1583 2426 2313 1979 1819 1454 1110 619 275 414
W 1411 1612 1862 1645 1556 1364 833 644 267 388
S 665 1393 2020 1713 1431 1334 922 611 255 335
Photopic(lux
)
2070
1034
3
1377
4
1481 1153 800 564 337 197 0

Point2 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00
16:0
0
17:00
E 584 1232 2042 2644 2877 3129 3323 2069 745 1099
N 680 1344 2351 2513 2905 2699 2199 1130 468 684
W 1235 2093 2981 3195 3354 2631 1720 940 394 490
S 1060 2153 2933 3523 3675 3434 2883 1751 563 662
Photopic(lux
)
2108
1072
8
1499
3
2314
5
2592
0
2190
8
2063
3
1377
1
3911 0

Point3 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00
16:0
0
17:00
E 493 691 1007 1448 1539 1853 1663 1166 787 1275
N 511 729 1097 1553 2019 2528 2112 1853 900 1471
W 870 1124 2057 2671 3254 3478 2263 1566 590 876
S 377 730 1155 1583 1720 1849 1758 1164 449 708
Photopic(lux
)
127 317 536 870 1232
1983
9
1977
3
1353
9
4106 0

Point4 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00
16:0
0
17:00
E 582 1231 1957 1934 1922 1525 1286 891 371 557
98

N 517 756 873 855 622 564 493 290 104 174
W 954 1468 1516 1418 1142 1028 873 556 189 287
S 1406 2232 2625 2566 2310 1770 1333 806 362 518
Photopic(lux
)
157 416 328 390 375 334 240 158 97 0

Point5 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00
16:0
0
17:00
E 383 713 812 1243 1898 2036 1950 1734 589 925
N 376 572 717 714 900 625 709 600 215 334
W 1051 1640 2019 1691 1635 1337 1126 682 192 290
S 759 1670 2485 3165 3570 3329 2611 1451 466 683
Photopic(lux
)
104 335 380 569 788 579 550 336 184 0

Point6 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00
16:0
0
17:00
E 307 636 683 1050 1322 1273 1646 1486 573 928
N 276 388 497 731 664 928 742 751 207 339
W 831 1346 1415 1746 2009 1743 1634 1432 372 567
S 577 1065 1348 1948 2476 2249 2543 2280 946 1492
Photopic(lux
)
47 134 180 377 528 418 515 519 316 0

Point7 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00
16:0
0
17:00
E 246 418 737 684 725 731 792 651 183 274
N 147 303 375 392 456 482 263 182 63 105
W 295 639 947 633 627 503 354 236 114 183
S 452 1325 1483 1135 1109 849 825 546 253 370
Photopic(lux
)
31 94 126 124 122 86 72 65 48 0

Point8 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00
16:0
0
17:00
E 177 396 609 510 699 659 653 417 201 311
N 123 270 367 470 359 593 257 196 107 152
W 451 722 619 703 627 610 509 286 204 301
S 551 1007 1361 1338 1630 1313 1385 628 268 401
Photopic(lux
)
23 86 91 131 171 121 149 115 72 0
99

Point9 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00
16:0
0
17:00
E 246 364 400 550 591 631 554 413 109 177
N 184 310 403 535 503 444 437 273 117 190
W 296 480 583 663 863 626 539 347 100 157
S 316 611 812 1138 1245 1073 1149 782 220 334
Photopic(lux
)
17 44 54 128 113 122 134 123 68 0

Point10 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00
16:0
0
17:00
E 183 310 257 481 405 567 678 414 109 168
N 182 291 401 470 396 271 386 292 90 147
W 223 229 410 404 424 331 247 420 114 179
S 345 467 592 608 583 584 553 615 245 356
Photopic(lux
)
11 39 34 51 49 35 29 31 19 0

Point11 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00
16:0
0
17:00
E 158 245 285 446 565 572 429 233 112 172
N 195 277 464 672 588 702 395 164 127 180
W 184 525 509 716 537 326 334 234 180 254
S 250 517 693 685 797 507 629 550 264 385
Photopic(lux
)
13 29 29 56 53 47 40 37 26 0

Point12 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00
16:0
0
17:00
E 112 199 277 335 361 346 391 330 46 76
N 137 279 251 451 531 344 270 304 52 85
W 228 398 350 378 675 360 327 226 98 139
S 271 411 657 569 635 533 880 356 121 173
Photopic(lux
)
10 21 28 37 44 62 51 47 17 0

Abstract (if available)

Abstract Climate-based daylight modeling (CBDM) workflows are currently being used by professionals to generate dynamic daylighting metrics such as spatial daylight autonomy (sDA) for the purpose of examining lighting performance. However, existing daylighting metrics evaluate daylight illuminance on a horizontal workplane, which does not reflect the vertical human eye exposure to light. The WELL Building Standard for Lighting was established to minimize human circadian system disruption, promote productivity, benefit sleep quality and ensure visual task needs. It uses a non-visual metric, Equivalent Melanopic Lux (EML), to measure the circadian influences on humans and does evaluate light exposure vertically at eye level. To calculate EML, a software program ALFA was used because it can evaluate the spectral quality of light and is capable of evaluating light exposure at eye level. ❧ A statistical data analysis method was developed involving correlation analysis and discrepancy comparison to examine the relationship between the design guidance provided by non-visual metrics and traditional visual metrics for denoting the “daylit area” of a building. The methodology developed to compare EML with both point-in-time illuminance and sDA with the spatial circadian autonomy (sCA) to calculate the point-in-time and annual level of discrepancies. The sCA was calculated by processing the simplified “annual” (21st of each month) point-in-time EML data during the occupied hours. ❧ ALFA is the latest software that can assess the EML result based on the 81-spectral Radiance engine and set the calculation vectors at the gaze direction. By using publicly available software tools to implement the visualization and calculations, such as Rhinoceros, Ladybug, Honeybee, and ALFA, the existing CBDM workflow could not adequately evaluate the daylight performances efficiently. It was found that the effectively daylit zone defined by sDA and sCA showed large discrepancies that cannot be ignored. Areas that had qualified daylight autonomy (DA) performance can still have poor circadian autonomy (CA) result that cause limited occupant access to the circadian stimulus. No consistent strong correlated connections were found between the point-in-time photopic illuminance and EML. Compared to sCA, the sDA metric frequently underestimated the effectively circadian daylit zone. ❧ Sufficient daylight provided to the occupants are essential to ensure circadian health. Evidence-based tools are significant to be used to simulate the circadian performance during the early design stage. Software tools for sCA calculation is necessary to be developed and it is necessary to combine sCA and sDA for evaluating both visual and circadian performances during the daylighting design.

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Wu, Shuyi (author)

Core Title Daylight and health: exploring the relationship between established daylighting metrics for green building compliance and new metrics for human health

School School of Architecture

Degree Master of Building Science

Degree Program Building Science

Publication Date 07/03/2019

Defense Date 05/06/2019

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag ALFA,circadian daylight zone,circadian health,climate-based daylight modeling (CBDM),daylighting,daylighting metrics,OAI-PMH Harvest

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Konis, Kyle (committee chair), Kensek, Karen (committee member), Schiler, Marc (committee member)

Creator Email shuyiwu@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-179728

Unique identifier UC11662615

Identifier etd-WuShuyi-7525.pdf (filename),usctheses-c89-179728 (legacy record id)

Legacy Identifier etd-WuShuyi-7525.pdf

Dmrecord 179728

Document Type Thesis

Format application/pdf (imt)

Rights Wu, Shuyi

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA