simulating circadian light: multi-dimensional illuminance ... · simulating circadian light:...

Proceedings of the 15th IBPSA ConferenceSan Francisco, CA, USA, Aug. 7-9, 2017

2363https://doi.org/10.26868/25222708.2017.660

Simulating Circadian Light: Multi-Dimensional Illuminance Analysis

Phillip H. Ewing1, John Haymaker2, Eve A. Edelstein1,3,4

1Human Experience Lab, Perkins+Will2Research Labs, Perkins+Will

3Design + Health CoLab AIA Research Consortium4Center for Healthy Environments NSAD (NewSchool of Architecture & Design)

AbstractThe effects of building design on human circadian rhythmshave been linked to health, behavior, and performanceoutcomes. Limited options, however, are available forpredicting circadian stimuli during the design process,other than via quantification of a single variable knownas melanopic illuminance. Several additional circadianilluminance quantities (including rhodopic-, cyanopic-,chloropic-, and erythropic-lux) have not, to date, beenutilized in simulating circadian exposure. We demonstratehow daylight, spatial, and material choices may alter thecontribution of five currently-known photoreceptor chan-nels to regulating circadian rhythms. This novel 3D render-ing system will also support future circadian research andapplied solutions.

IntroductionVisual and Non-Visual Responses to Light

There is a mounting body of evidence for lighting’s in-fluence on “nearly every physiological, metabolic and be-havioral system” (Lucas et al. 2014). Human exposureto light and dark patterns is associated with changes inendocrine function, growth, digestion, core body tempera-ture, reaction times, fatigue, cognitive function and moodstates. Research has demonstrated statistically-significantchanges in heart rate variability (HRV), a reliable indica-tor of health risk as well as cognitive engagement, evenin the presence of short-term exposure to electrical light(Edelstein et al. 2008). Light therapy has been applied toameliorate conditions such as seasonal affective disorder(SAD), dementia, the effects of traumatic brain injury, andvarious sleep disorders; further, it has been used to coun-teract fatigue from jetlag, night shift work, and even spaceflight (Zatz [ed.] 2005).For most of evolutionary history, solar light was the pri-mary stimulus to indicate the time of day, and to entrainhuman ‘circadian rhythms’, the biological functions thatcycle around the time of day. The advent of electrical light-ing at the turn of the 20th century, however, has disruptedthis relationship. This presents numerous challenges, aswell as potential opportunities, for architectural designsthat more effectively serve human wellbeing and perfor-mance. Indeed, the American Medical Association (AMA)

adopted a policy statement in 2012 citing evidence thatlinks circadian rhythm disruption to impacts on humanhealth, including “cell cycle regulation, DNA damage re-sponse, and metabolism” (Stevens et al. 2013). Further,the AMA notes that there is accumulating “epidemiologicsupport for a link of circadian disruption from light at nightto breast cancer” (p. 343).Historically, the scientific community thought that the con-ventional visual photoreceptors of the human eye – therod cells and three types of cone cells in the retina – wereresponsible for entraining these circadian rhythms. Rela-tively recently, however, Brainard et al. (2001) and Thapanet al. (2001) discovered a new category of photoreceptors:intrinsically photosensitive retinal ganglion cells (ipRGCs).These cells are primarily responsible for ‘non-visual’ pro-cessing of light integrated over time. These cells – andassociated retinal bio-circuitry that include the rods, cones,and various connector cells – transmit neural signals toa part of the brain known as the suprachiasmatic nucleus(SCN), the body’s ‘master’ circadian clock. The SCN, inturn, innervates a complex network of neural and endocrinesystems that send hormones coursing through the bloodstream, and influence the brain, mind, body, and behavior.To date, however, most physiological models of human reti-nal function continue to quantify light in terms of the visualresponses of the rods and cones alone. In particular, thecommonly-used photopic spectral sensitivity function (alsoknown as the ‘luminosity’ function in color science andrelated disciplines) describes the contributions of mostlylong- and middle-wavelength cone cells to an aspect ofvisual function that hardly includes shorter wavelengthsof light. This photopic function (V (λ ), or y(λ )) is derivedfrom the CIE RGB model of color perception, and usedas the color matching function for the Y channel in thetristimulus CIE XYZ color space model, first publishedby the Commission Internationale de L’Eclairage (CIE)in 1932. Although updates have been made, the originalRGB and XYZ models are still widely used in colorimet-ric applications, and still remain the basis for the SystemeInternational (SI) photometric units.

Visual Lighting Simulation and Rendering Systems

As a result of this narrow focus, a majority of conven-tional lighting simulation software platforms compute the


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appearance of light and materials in the form of red, greenand blue (RGB) color channels. The behavior of lightin the real world however, is a complex interplay of vari-ous wavelengths of light being emitted from, transmittedthrough, and reflected off various physical objects in theenvironment. Although it is computationally efficient tocollapse the representation of light into three primary val-ues – as opposed to performing raytracing calculations forevery wavelength of light at every point in a given field ofview – tristimulus-based simulations pose known discrep-ancies in relation to the accurate simulation or perceptionof light. Since it is possible for two materials with differentspectral reflectances to correspond to the same RGB valueunder certain lighting conditions – a phenomena knownas metamerism (Wyszecki and Stiles 2000)– RGB-basedcalculations can, and occasionally do, yield incorrect coloror illuminance values for certain scenes. Further, the CIERGB color model and the associated photopic spectralsensitivity function are known to distort the complex con-tribution of blue light to color and illuminance perceptionfor the sake of developing a linear, additive model forphotometric units (Rea and Figuerio 2010).

Melanopic Lighting Simulation and RenderingSystemsFurther, the CIE RGB color space and associated modelsdo not correspond precisely to our current knowledge of thenon-visual, circadian impact of light. Over the past decade,however, much attention has been placed on analyzing therole of ipRGCs, which have the greatest photosensitivity tolight with wavelengths in the range of 447-484 nm (roughlythe ‘blue-cyan’ portion of the visible electromagnetic spec-trum for a monochromatic light source; included in Figure1) (Lucas et al. 2014). Since ipRGCs gain their photo-sensitivity from the presence of a photopigment knownas melanopsin, various curves that have been proposed inscientific literature for characterizing the spectral sensitiv-ity of ipRGCs have often been referred to as ‘melanopic’functions. In contrast, the CIE photopic spectral sensitivityfunction, which describes visual perception of brightness,has a peak at 555 nm (corresponding to ‘tennis ball yellow’in appearance), and groups together the response of all ofthe associated retinal bio-circuitry into a single metric ofphotopic illuminance.A number of researchers have developed methods for sim-ulating the melanopic component of circadian illuminancein the context of building design. Rea et al. (2012) incorpo-rated biological research in their Circadian Stimulus (CS)calculator, which includes the effects of sub-additivity andcolor opponency processes in its version of a melanopicspectral sensitivity function, and facilitates the calculationof circadian efficiency for a given light source. Inaniciet al. (2015) implemented a full-spectral rendering tech-nique developed by Ruppersberg and Bloj (2006, 2008)as the basis from which the authors calculated melanopicilluminance for given 3D scenes. This technique is fur-ther developed in their Lark Spectral Lighting Tool forlighting simulation and rendering (Inanici and ZGF Archi-tects 2015). Based upon this increasing body of research,

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Probability-Normalized α-Opic Sensitivity Curves

erythropic chloropic rhodopic

melanopic cyanopic photopic

Figure 1: The five α-opic spectral sensitivity curves rec-ommended by Lucas et al. (2014), along with the photopicfunction for reference. As further recommended by theauthors, each curve has been ‘probability-normalized’ orscaled for equal area underneath each curve. The colorof each curve (excluding the photopic function) roughlycorresponds to the apparent color of the peak wavelength.

melanopic illuminance analyses are now informing thebuilding design profession. For example, the InternationalWELL Building Standard (International WELL BuildingInstitute 2016), recently provided a computational methodto calculate equivalent melanopic lux (EML), and have rec-ommended minimum EML exposure durations and illumi-nance guidelines for projects seeking WELL certification.

Pentachromic Lighting SimulationBy focusing on melanopic illuminance alone, however,these systems do not consider the complex, nuanced con-tribution of other circadian functions. More recent findingsdemonstrate that multiple retinal and physiologic factors,including the rods, cones and ipRGCs, contribute to non-visual as well as visual mechanisms. Ho Mien et al. (2014)demonstrated that alternating red light – light with wave-lengths beyond currently-proposed peak spectral sensitivityranges for melanopsin – can mediate circadian phase re-setting of physiologic rhythms in some individuals. Theirresults also show that sensitivity thresholds differ acrossnon-visual light responses, suggesting that cones may con-tribute differentially to circadian phase resetting, melatoninsuppression, and the pupillary light reflex during exposureto continuous light. Gooley et al. (2012) further demon-strate that rods, cones, and ipRGCs play different rolesin mediating pupillary light responses during exposure tocontinuous light, and suggest that it might be possibleto enhance non-visual light responses to low-irradianceexposures by using intermittent light to activate cone pho-toreceptors repeatedly in humans. Lucas et al. (2012)reviewed electrophysiological and behavioral data to pro-vide a model in which each photoreceptor class plays adistinct role in encoding the light from the environment.As the intact retina is a composite of extrinsic (rod/cone)and intrinsic (ipRGC) mechanisms, the authors proposethat all three photoreceptor classes, including the ipRGCs,contribute light information to the brain’s circadian clock.


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A seminal paper published by leading experts in circadianresearch noted that currently, “the most appropriate use ofthat capacity [referencing the ability to record or simulatethe spectral power distribution of light sources] would beto calculate the effective irradiance experienced by eachof the rod, cone and melanopsin photoreceptors capableof driving non-visual responses” (Lucas et al. 2014, p.6). They devised a new light measurement strategy thattakes into account these complex non-visual mechanisms,and categorized illuminance stimuli into five individualphotoreceptor components: cyanopic (short-wavelengthcones), chloropic (medium-wavelength cones), erythropic(long-wavelength cones), rhodopic (rods), and melanopic(ipRGCs) illuminance quantities. Each of these photore-ceptor components (collectively referred to as ‘α-opic’components) have different spectral sensitivity curves (seeFigure 1). In addition, the authors provided a spreadsheetfor calculating these illuminance quantities for a givenlight source of a specified spectral power distribution andphotopic illuminance value.

A Novel Pentachromic Lighting Simulation and Ren-dering System

While the spreadsheet developed by Lucas et al. (2014)allows for calculation of multiple circadian illuminancesgiven a single light source of known spectral power distri-bution, the building professions must also begin to considerhow light reflection, absorption and transmission throughbuilding materials may alter lights’ spectral characteristicsand impact circadian exposure calculations. In addition,designers must consider each occupant’s field of view andexposure to light as it interacts with the geometry and spa-tial arrangement of materials. The calculation methodologyto be presented in this paper addresses these considerations;further, it is applicable to all building types, as the circa-dian impact on human function is relevant to any place thathumans occupy (Edelstein et al. 2008).In order to simulate circadian light in ways that incorpo-rate the varying photoreceptor functions mentioned above,we developed a raytracing-based computational method torender the spectral reflectance and transmission of daylightentering and interacting with a 3D model, from a user’ssingular point of view and location, at a particular dateand time. These analyses are carried out across an arbi-trary number of spectral channels or bins (nine, in the caseof this paper), as opposed to conventional three-channelraytracing and simulations.

MethodologySetting

Sprout Space,TM (Figure 2) a high-performance, modular,single-room classroom system, was used as a vehicle forexploring the impact of conceptual design and materialchoices on metrics describing circadian exposure. A dig-ital model was developed at geographic coordinates andEPW weather conditions for Los Angeles, CA (34.0522◦N,118.2437◦W). June 21, 9:00AM PDT was the chosen dateand time for all analyses performed. The sky dome color

Figure 2: Sprout Space,TM a modular, single-room class-room system, as-built. This design concept was used as astarting point for material design exploration.

correlated temperature (CCT) was set to 12,000K; this isconsistent with typical blue sky CCT measurements occur-ring in the range of 9,000-25,000K (Lechner 2014). Noartificial light sources were included in the model. The cho-sen viewpoint for the analyses is in the center of the class-room, standing height (chosen as 1.829 m above finishedfloor), looking straight north, with a view angle towardstwo windows facing northeast and northwest, respectively.A 0.6 m roof overhang extended around the entire building.Matte-finish Munsell color chip reflectance spectra, mea-sured in 1 nm increments and compiled by Spectral ColorResearch Group at the University of Eastern Finland (n.d.),are used as proxy data for simulating opaque materialchoices. Spectral properties for glazing choices, measuredin 5 nm increments, were retrieved from the InternationalGlazing Database 14-5 (Lawrence Berkeley National Lab-oratory 201l). Material data included in the analysis areprovided in Tables 1 and 2.

Calculation MethodologyWe extend the calculation methods proposed by Inanici etal. (2015) to include coefficients for circadian illuminancein rhodopic-, cyanopic-, chloropic-, and erythropic-lux fornine-channel spectral lighting simulations to be performedusing the Radiance Lighting Simulation and RenderingSystem (Ward 1994). In Radiance, three-channel (RGB)lighting calculations and how they relate to photopic lumi-nance or illuminance quantities can be described as shownin Equation 1:

L = 179(0.2651R+0.670G+0.065B) (1)

where the coefficients for Radiance are defined as R (586-780 nm), G (498-586 nm), and B (380-498 nm) to corre-spond to each channels’ relative contribution to photopicluminance. The luminous efficacy factor for equal-energywhite light in Radiance is 179 (lm/W). Some readers maynote that the peak spectral efficiency of the photopic lumi-nosity function is 683.002 lm/W at 555 nm, approximatelycorresponding to ‘tennis ball yellow’ in apparent color.Lighting in Radiance, however, is considered spectrally-neutral and not ‘tennis ball yellow’, and the factor 179corresponds to average luminous efficacy for all visiblewavelengths of light (380-780 nm).The nine spectral bin intervals proposed by Inanici et al.(2015) are also retained for the purposes of this analysis.


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Table 1: Munsell color chip reflectance spectra to be in-cluded in α-opic illuminance analyses.

Specification Description5R 9/2 Pale Red

5R 4/12 Medium Red5YR 6/12 Medium Red-Yellow5YR 2.5/1 Dark Red-Yellow5Y 7/10 Medium Yellow

5GY 8/10 Medium Green-Yellow5BG 5/8 Medium Blue-Green5PB 6/10 Medium Purple-Blue

‘Specification’ denotes the official Munsell name for thematerial, whereas ‘Description’ denotes the name to beused in this paper.

Table 2: Munsell color chip reflectance spectra to be in-cluded in α-opic illuminance analyses.

Specification DescriptionPilkington NorthAmerica Optiwhite

Clear Glazing,TVis = 0.91

Hankuk Glass IndustriesInc. HanGlas HanliteGreen 8mm

Green-Tinted Glazing,TVis = 0.68

Pilkington North Amer-ica Graphite Blue

Blue-Tinted Glazing,TVis = 0.61

‘Specification’ denotes the IGDB manufacturer and prod-uct name for the selection, whereas ‘Description’ denotesthe name to be used in this paper.

This allows for increased spectral resolution over typicalthree-band increments, in order to more accurately captureinflections in lighting and material spectral data, as well aseach of the various α-opic spectral irradiance or functions.The 24 illuminance analyses and raytracings of the examplemodel included in this paper can be completed within thecourse of an 8-hour working day. Finer spectral resolutionmay be achieved by designating more bins, at the cost ofperforming additional renderings and incurring additionalanalysis time.Spectral photosensitivity functions Nα (λ ) (also referredto as ‘filters’ for shorter reference) for the five humanphotopigments included by Lucas et al. (2014) in theIrradiance Toolbox spreadsheet, normalized to unity insurface (

∫ 780380 Nα(λ )dλ = 1), are integrated over each of

the nine spectral bins as follows (Equation 2):

cα,n =

λn,1∫λn,0

Nα(λ )dλ (2)

where cα,n is the spectral band coefficient to be calculated,λn,0 and λn,0 are the corresponding boundaries in nm forthe given spectral bin, and

∫ 780380 Nα(λ )dλ = 1. This yields

the following coefficients shown in Table 3. For melanopicilluminance, we use the Lucas et al. (2014) spectral sensi-

tivity function. As bin coefficients were summed from datainterpolated (Catmull spline) to 1 nm resolution from theoriginal 5 nm data in Lucas et al.’s (2014) supplementaryIrradiance Toolbox spreadsheet, bin intervals were alsostart-offset by 1 nm from the values defined by Inanici etal. to avoid overlapping values at bin boundaries.It should be noted that Inanici et al.’s (2015) approach usesphotopic and melanopic spectral sensitivity functions ofequal peak amplitude (683 lm/W), whereas Lucas et al.(2014) and the methodology presented here begin withspectral photosensitivity functions of differing amplitude,but with equal areas under each curve (refer to Figure 1).Both methods aim to offer mathematical convenience inthe sense that α-opic illuminance quantities have similarorders of magnitude, and can be more readily comparedto each other in a given set of analysis results. It shouldnot be construed, however, that the magnitude of eachquantity implies any ‘functional’ weighting with respectto photoreceptors’ contribution to circadian responses, asfurther scientific research is needed to accurately describethe relative contribution of each photoreceptor to circadianresponses, as well as under what scenarios.Next, raytracing analyses for each of nine spectral bins areperformed in Radiance. For materials, the reflectance ortransmittance quantities for each run are derived by takingthe average reflectance or transmittance over each spectralbin. These material spectral reflectance or transmittancequantities are then assigned, three at a time, to sub-analysesin order to perform illuminance calculations and renderingsin Radiance.To calculate α-opic illuminance (or per-pixel α-opic lu-minance for renderings) each spectral bin result pn in ra-diometric units (watts, W) is weighted by a correspondingcα,n, summed, and scaled by the 179 lm/W Radiance lumi-nous efficacy constant (Equation 3):

Lα = 179n

∑i=1

cα,n pα,n (3)

In the specific case of the analyses performed in this paper,this calculation may be simplified to a notation similar tothe equations given in Inanici et al. (2015), as shown inEquation 4:

Lα = 179(cα,1 p1 + cα,2 p2 + cα,3 p3

+cα,4 p4 + cα,5 p5 + cα,6 p6

+cα,7 p7 + cα,8 p8 + cα,9 p9)

(4)

An important note is that that here, the Radiance lumi-nous efficacy constant 179 lm/W is held constant for allof the α-opic illuminance calculations, and is not scaledas in Inanici et al. (2015). This is to conform with therecommendation by Lucas et al. (2014) in their Irradi-ance Toolbox spreadsheet documentation that the variousα-opic illuminance values are always equal to photopicilluminance (and each other) for a theoretical equal-energyradiator. Inanici et al. (2014) recommend a melanopicluminous efficacy constant of 148 lm/W for the Lucas et al.(2014) melanopsin photosensitivity curve and 130 lm/Wfor the Rea (2005) version. Melanopic illuminance results


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Table 3: Nine spectral analysis bins and corresponding weighting coefficients for calculating α-opic illuminances.

Coeff. Wavelength Erythropic Chloropic Rhodopic Melanopic Cyanopic PhotopicB1 380-422 0.005353 0.006532 0.012830 0.017681 0.141026 0.000421B2 423-460 0.027821 0.050377 0.124047 0.184631 0.618178 0.009797B3 461-498 0.083861 0.157113 0.290932 0.398948 0.230136 0.053295G1 499-524 0.119148 0.181933 0.246991 0.244785 0.009820 0.131072G2 525-550 0.169969 0.216596 0.199600 0.118204 0.000748 0.224349G3 551-586 0.279473 0.260651 0.111940 0.033460 0.000074 0.316548R1 587-650 0.292456 0.124616 0.013745 0.002134 3.91E-06 0.248866R2 651-714 0.021719 0.002299 0.000087 0.000013 4.22E-08 0.015700R3 715-780 0.000249 0.000022 1.02E-06 1.73E-07 1.02E-09 0.000201

calculated via Inanici et al.’s (2015) method may be scaledby 179/148 for the Lucas curve, or 179/130 for the Reacurve, to convert to the method delineated in this paper.We analyzed twenty-three (23) material design alternativesfor the Sprout Space, grouped into seven sets of runs, forcomparison to an asserted baseline design: Munsell colorsof yellow-green walls, a dark red ceiling, a pale red floor,and clear glazing (Figure 3a).A breakdown of these seven experiments are as follows:

• Wall material: 5 alternatives

• Ceiling material: 5 alternatives

• Floor material: 5 alternatives

• Wall specularity (‘shininess’): 2 alternatives

• Ceiling specularity (‘shininess’): 2 alternatives

• Floor specularity (‘shininess’): 2 alternatives

• Glazing material: 2 alternatives

The purpose of this limited exploration is to begin to ex-plore the relative impacts of design choices on circadianexposure, and to test the efficacy of the calculations. Fu-ture work will include more systematic exploration of thedesign space.

Results and EvaluationDesign Decision-Making

Although the precise impact of different and combineddosages of the various circadian illuminance levels are notyet well-defined in the scientific literature, it is possible tocompare the impact of design choices to each other basedon their impact on the various components of circadian illu-minance. This paper limits its scope of decision-making tovisualization and analysis of design choices on maximizingor minimizing each α-opic illuminance value.

Results

In Figure 3, the false-color scale (y-axis) shows the differ-ence from the photopic visible spectrum, with the greatestdifference in yellow (12,000 cd/m2) and no difference inpurple (0.00 cd/m2). In the baseline condition with theyellow-green wall (Figure 3a-3f), the greatest absolutedifference from photopic luminance is observed with thecyanopic filter (3d), and a moderate difference is seen withthe rhodopic (3b) and then melanopic (3c) filters. The leastdifference is observed with the erythropic (3f) and thenchloropic filters (3e). In the test condition with a purple-blue wall (Figure 3g-3l), the false-color scale also showsthe greatest difference from the photopic filter (3g) with the

(a) “visible spectrum” green wall

(b) |rhodopic [minus] photopic|

(c) |melanopic [minus] photopic|

(d) |cyanopic [minus] photopic|

(e) |chloropic [minus] photopic|

(f) |erythropic [minus] photopic|

(g) “visible spectrum” blue wall

(h) |rhodopic [minus] photopic|

(i) |melanopic [minus] photopic|

(j) |cyanopic [minus] photopic|

(k) |chloropic [minus] photopic|

(l) |erythropic [minus] photopic|

Absolute di�erence (α-opic [minus] photopic)

12000.0 cd/m2

0.00000 cd/m2

3204.10

887.167

245.643

22.6243

6.26433

Figure 3: Baseline α-opic luminance and illuminance analysis run for a Sprout Space design scenario.

Legend: The fisheye renderings show the absolute difference in luminance of each of the a-opic filters minus the photopicfilter. The false color scale shows the greatest difference ranging from yellow (12,000 cd/m2) to purple (0.00 cd/m2).


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900100011001200130014001500160017001800

V Nr Nz Nlc Nmc Nlc

(a) Series I: Wall Material

(Medium Green-Yellow) Medium Purple-Blue

Medium Blue-Green Medium Yellow

Medium Yellow-Red Medium Red

900100011001200130014001500160017001800

V Nr Nz Nsc Nmc Nlc

(b) Series II: Ceiling Material

(Medium Green-Yellow) Medium Purple-Blue



900100011001200130014001500160017001800

V Nr Nz Nsc Nmc Nlc

(c) Series III: Floor Material

(Pale Red) Medium Purple-Blue



1000

1100

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1300

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V Nr Nz Nsc Nmc Nlc

(d) Series IV: Wall Specularity

(Specularity = 0.0) Specularity = 0.1

Specularity = 0.2

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V Nr Nz Nsc Nmc Nlc

(e) Series V: Floor Specularity


Specularity = 0.2

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(f) Series VI: Ceiling Specularity


Specularity = 0.2

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V Nr Nz Nsc Nmc Nlc

(g) Series VII: Glazing Material

(Clear) Blue-Tint Green-Tint

Abbreviations

V Photopic-luxNr Rhodopic-luxNz Melanopic-luxNsc Cyanopic-luxNmc Chloropic-luxNlc Erythropic-lux

Figure 4: Results for the 24 illuminance analysis runs, grouped into seven series (I-VII, a-g).

Legend: Series I (a) compares the impact of wall material changes on various -opic circadian illuminances while holdingall other design choices constant with respect to the baseline analysis run; Series II (b) explores changes in ceiling material;Series III (c) explores floor material; Series IV (d) explores wall specularity; Series V (e) explores floor specularity; SeriesVI (f) explores ceiling specularity; Series VII (g) explores glazing material.

cyanopic filter (3j). However, the compared to the baselineroom condition in the row above, there is greater change inthe melanopic (3i) than in the the rhodopic (3h) filter. Theleast change is observed in the erythropic (3l) rather thanin the chloropic filter (3k).We observe almost no difference for α-opic quantities onthe ceiling in the baseline scenario. Aside from the darkcolor of the ceiling limiting reflected light in general, an-other observation would be that if any differences in α-opicluminances were to occur due to reflected light from theyellow-green walls, we might expect the yellow-green-sensitive chloropic filter to most strongly illustrate suchdifferences (3e). Since the photopic filter is already heav-ily weighted towards the chloropic filter, little differenceoccurs in this scenario. In contrast, when the purple-bluewall is introduced (Figure 3g-3l), we see some absolutedifference in the ceiling luminances in the rhodopic (3h),melanopic (3i) and cyanopic (3j) filters, which are all inthe blue range of the spectrum.The α-opic illuminance values in Figure 4 show changesfor each decision variable of wall, ceiling, floor or glazingmaterials. Across all of the trials, we notice that cyanopicilluminances are typically the highest among the variousα-opic illuminance quantities. This makes sense, given theshort-wavelength (‘blue’) light from the clear blue sky isthe dominant light source in the scene.It is also noticeable that the photopic illuminance quanti-ties for each run are often more similar to the chloropicand erythropic illuminance quantities than to other α-opicilluminance quantities. This is consistent with Rea andFiguerio’s (2010) discussion of how photopic spectral sen-

sitivity weightings (defined by the luminosity functionV (λ )) are primarily derived from the photosensitivitiesof medium-wavelength cones (chloropic-lux) and long-wavelength cones (erythropic-lux). Further, in our wallanalysis trials (Figure 4a), the ‘spike’ in photopic illumi-nance for a medium yellow wall material is mirrored in thechloropic and erythropic illuminance quantities, but not therhodopic, melanopic or cyanopic illuminance quantities.In the ceiling material trial (Figure 4b), α-opic illuminancevalues typically increased for all changes from the base-line analysis run. Given the dark material chosen for theceiling (a dark-red Munsell color sample), it makes sensethat lighter materials would generally reflect more lightand increase illuminance almost across all α-opic quanti-ties. The peak values for each α-opic illuminance quantity,however, correspond with the peak reflected wavelengthof each material. For chloropic illuminance, which re-flects the spectral photosensitivity of medium-wavelengthor ‘green’ cone cells, medium blue-green ceiling materialresults in the highest chloropic-lux value.Conversely, α-opic illuminance quantities typically de-creased for almost every alternative floor material choice(Figure 4c), compared to the baseline analysis run. Sincethe baseline floor material reflectivity was relatively highto start, it makes sense that the series of darker materialalternatives generally absorbed more light and decreasedα-opic illuminances.In the glazing material trial (Figure 4g), clear glass (TVis =0.91), unsurprisingly, yields the greatest α-opic illumi-nance values. The green-tinted glass (TVis = 0.68) yieldedgreater α-opic illuminance values than the blue-tinted glass


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Wall C

olor

Floor Color

Ceiling C

olor

Glazing

Wall Specularity

Ceiling Specularity

Floor Specularity

Photopic-lux

Rhodopic-lux

Melanopic-lux

Cyanopic-lux

Chloropic-lux

Erythropic-lux

Pale Red

Medium Red

Dark Red

Medium Yellow-Red

Medium Yellow

Medium Green-Yellow

Green

Medium Blue-Green

Blue

Medium Purple-Blue

Clear

0.0

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Parallel Coordinates Plot

Figure 5: Parallel coordinates plot of different material inputs and illuminance levels.

Legend: Each y axis denotes a particular design parameter or feature; input parameters for each run are denoted in the lefthalf, and results are denoted on the right half. The black line denotes the baseline configuration; the purple line denotesthe impact of changing the walls to a purple-blue material; the light orange line denotes the impact of increasing ceilingspecularity (S = 0.2) while holding all other variables constant; the dark orange line denotes the same, for an intermediatespecularity (S = 0.1); the green line denotes the impact of green-tinted glazing (TVis = 0.68); the blue line denotes theimpact of blue-tinted glazing (TVis = 0.61).

(TVis = 0.61), which yielded the lowest α-opic illuminancevalues among all of the 24 analysis runs. This result maydemonstrate the effect of reduced visual transmittance forthe green- and blue-tinted glazing selections on visual trans-mittance compared to the clear glazing. Continued explo-ration of the design decision space would be a way offurther evaluating this finding.The parallel coordinate plot in Figure 5 explores the in-teraction of multiple architectural variables on the α-opicilluminance values for the Sprout Space design alternatives.This method allows visualization of interactions that canbe a useful design tool to evaluate a series of decisions.The physical input variables are plotted on the left side ofthe graph, and the resultant α-opic illuminance values areplotted on the right. The black line represents the initialbaseline run using Munsell colors of yellow-green walls, adark red ceiling, a pale red floor, and clear glazing.Relative to this baseline, increasing the ‘shininess’ or spec-ularity S of the dark red ceiling surfaces (up to S = 0.2)was associated with the largest values across photopic,rhodopic, melanopic, chloropic and erythropic illuminancequantities (Figure 5, light orange line), as well as a rela-tively high value for cyanopic illuminance. The intermedi-ate setting for ceiling specularity (S= 0.1), however, showsa decrease in the various α-opic illuminances compared tothe ‘shiniest’ ceiling (Figure 5, dark orange line).However, a change to purple-blue walls (Figure 5, purpleline), even with the lowest specularity settings for ceiling,walls and floor (S= 0.0), yields a clear cyanopic peak (withceiling, floor, and glazing colors maintained at the baseline

design configuration).In comparison to clear glazing used in the baseline con-dition, the introduction of a green-tinted glazing with alower transmittance (TVis = 0.68), demonstrates a drop inall α-opic illuminance values (Figure 5, green line). Here,we again see that the blue glazing material (Figure 5, blueline) tested had the lowest transmittance (TVis = 0.61), andyielded the lowest α-opic illuminance values.A more systematic exploration of the design space andcontinued evaluation of such interactions are needed to val-idate these findings, and to understand more precisely howchanges to material properties, geometries and proximitiesmay impact α-opic illuminance quantities.

ConclusionCurrent physiologic research demonstrates that a pen-tachromic visual system influences human circadian re-sponses, and yet few simulation and rendering techniqueshave attempted to calculate more than a single melanopicfunction. The methods reported in this paper demonstratethe computation and rendering of light in terms of five reti-nal irradiance functions, which may then be applied to ar-chitectural design scenarios and design decision processes.In addition, the system described shows the disparate im-pact that material choices may have on the interactionsbetween the various α-opic spectral irradiance functions,simulated in both visual and false-color renderings.With this tool, we can use visual pattern and color recogni-tion to rapidly assess where maximal circadian exposurewould occur. With this tool, the differential impact of each


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filter can be computed, simulated and predicted in isolationand in combination with changes to material parameters.The literature to date shows us that these different opicfilter impact human outcomes. By using this tool in combi-nation with on-site, real-world and empirical studies, wecan advance our understanding of the relative impact ofeach retinal irradiance function in human terms.Although it is not yet possible to predict the relative impactof each retinal irradiance function, the value of continuedresearch is clear. Clinical studies confirm the deleteriouseffects of both over and under exposure to light on thebrain, mind, body and behavior. Yet, lighting trends andpreferences often result in exposure to unnatural wave-lengths and intensities, and the pervasive use of computermonitors, smart screens, and street lighting systems addfurther risk to human health.However, the development of programmable LED lightingsystems may provide the dynamic control necessary foreach individual to tune their lighting exposure to their vi-sual acuity, circadian status, and non-visual sensitives. Theoutput of such lighting systems would take into accountthe effects of circadian exposure that vary as a function oftime, duration, and wavelength of light.With the advancement of research that defines the specificinfluences of different wavelengths, rendered simulationsmay assist in guiding architectural programming, planningand design. The design of the material properties, spatialgeometries, and architectural fenestration will offer a morenuanced means to optimize light for human visual andcircadian health.

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