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A Critical Investigation of Common Lighting DesignMetrics for Predicting Human Visual Comfort in Officeswith DaylightKevin Van Den Wymelenberga & Mehlika Inaniciba University of Idaho Integrated Design Lab, Boise, Idaho, USAb University of Washington, Architecture, Seattle, Washington, USAPublished online: 20 Feb 2014.

To cite this article: Kevin Van Den Wymelenberg & Mehlika Inanici (2014) A Critical Investigation of Common Lighting DesignMetrics for Predicting Human Visual Comfort in Offices with Daylight, LEUKOS: The Journal of the Illuminating EngineeringSociety of North America, 10:3, 145-164

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LEUKOS, 10:145–164, 2014Copyright © Illuminating Engineering SocietyISSN: 1550-2724 print / 1550-2716 onlineDOI: 10.1080/15502724.2014.881720

A Critical Investigation of Common LightingDesign Metrics for Predicting Human Visual

Comfort in Offices with DaylightKevin Van DenWymelenberg1andMehlika Inanici21University of Idaho IntegratedDesign Lab, Boise, Idaho, USA2University of Washington,Architecture, Seattle,Washington, USA

ABSTRACT Existing visual comfort metrics are reviewed and critiqued basedupon their ability to explain the variability in human subjective responses in a daylitprivate office laboratory environment. Participants (n = 48) evaluated visual comfortand preference factors, totaling 1488 discreet appraisals, and luminance-based met-rics were captured with high dynamic range images and illuminance-based metricswere recorded. Vertical illuminance outperformed all commonly referenced visualcomfort metrics including horizontal illuminance, IES luminance ratios, daylightglare probability (DGP), and daylight glare index (DGI). The bounded border-line between comfort and discomfort is introduced, and preliminary visual comfortdesign criteria are proposed for several existing metrics. Fundamental limitations ofglare indices are documented, and the implications of inconsistent application ofluminance ratio calculation methods are quantified. Future research is detailed.

KEYWORDS daylight glare, daylight metrics, luminance ratio, vertical illuminance, visualcomfort

Received 23 September 2013, revised6 January 2014, accepted 7 January2014.

Address correspondence to Kevin VanDen Wymelenberg, University ofIdaho Integrated Design Lab,Architecture, 306 S. 6th Street, Boise,ID 83702, USA. E-mail:[email protected]

Color versions of one or more ofthe figures in this article can befound online at www.tandfonline.com/ulks.

1. INTRODUCTIONIt is generally accepted that daylight and views help to create healthy, comfortable,and productive work environments for users and therefore should be included incontemporary office spaces. Equally understood is the need to minimize discomfortglare, disability glare, and veiling glare for occupants in spaces with daylight. In cur-rent practice, design guidance to support visual comfort in daylit spaces has revolvedprimarily around horizontal illumination, simple luminance ratios, and, in advancedapplications, absolute luminance thresholds or glare indices. The application ofluminance-based techniques remains primarily within the research community andthey have gained little traction among design practitioners. Both illuminance- andluminance-based methods suffer from an established lack of confidence or con-sensus by the research and design communities regarding what metrics should beimplemented and what criteria are recommended. This is primarily because thereis presently inadequate human visual comfort research to support consensus-baseddesign recommendations.

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mailto:[email protected]

This article presents original research from a 6-monthrepeated-measures experimental design in which 48 par-ticipants assessed their visual comfort in a private daylitoffice. It begins with a brief review of common visualcomfort metrics (both illuminance and luminance based)and recommended criteria. It identifies the strengths andlimitations of these metrics and recommends revised mea-surement techniques and design criteria as relevant. Finally,it establishes the need for future research to develop a newsuite of luminance-based analysis metrics.

1.1. Illuminance-Based Metrics

Due to its ease of use and low cost to measure, horizon-tal illumination is the most widely applied architecturallighting design metric. However, even under electric lightsources only, illuminance preference varies greatly, from100 to 800 lux [Boyce and others 2006; Newsham andVeitch 2001; Veitch and Newsham 2000], and has a meanvalue between 400 and 500 lux. It has been reported thatthe choice of any fixed horizontal illumination value willonly be preferred by at most 55% of the users [Boyce et al.2006]. Few studies have reported user preference for illu-mination under daylight conditions alone. One found that300 lux of daylight was preferred (n = 20) [Laurentinand others 2000] and another study, conducted primarilyduring sunny winter days, found a wide range of pre-ferred desktop daylight illuminance and a preferred meanof 3623 lux (n = 18) [Van Den Wymelenberg and others2010].

1.2. Luminance-Based Metrics

As noted previously, human acceptance and preferencevary widely under primarily electrically illuminated spaces.Due to the complexities related to daylight in buildings(for example, variability with time of day, time of year,sky condition, view quantity, view quality, extremes ofbrightness values, discomfort glare, et cetera) the boundsof human preference are wider in spaces with daylight.Several attributes provide moderating effects to subjectivepreference assessments in daylit spaces. For example, recentresearch [Tuaycharoen 2011; Tuaycharoen and Tregenza2005, 2007] has identified and begun to quantify the mod-erating effects that the quality of a view has on humanassessment of glare from daylight (brightness accompa-nied by a better view is rated as less glaring). Though itis unlikely that any single measurement type (illuminance,luminance, view quality) will adequately describe the

bounds of human acceptance and preference in spaceswith daylight, it stands to reason that luminance-basedmetrics will more closely correlate with subjective accep-tance and preference measures than illuminance becauseluminance more closely relates to human perception ofbrightness (see discussion in Cuttle [2004]). This sectionreviews luminance ratios, absolute luminance values, andglare indices that have been used to characterize visualpreference and acceptance of the luminous environment.

1.2.1. Luminance ratios

Current recommendations by the IES [DiLaura and others2011, p. 12.20] list the maximum luminance ratios indaylight settings as “20:1 between daylight-media anddaylight-media-adjacent-surfaces.” No specific referencesare offered for the IES’s 20:1 recommendation, and otherratios cite the previous handbook [Rea 2000], which alsolacks substantial reference to original research. Few previ-ous studies describing preferred luminance ratios in set-tings with daylight are available. Halonen and Lehtovaara[1995] reported that under a wide range of daylight con-ditions, participants (n = 20) preferred average luminanceratios of a white paper-based task and a light-colored backwall opposite the window ranging from approximately1:2.25–10 with an average of approximately 1:5. Note thatratios using window luminance values were not reported.Sutter and others [2006] found that a space with daylightwas comfortable for users with luminance ratios of 1:6:20(task: adjacent: remote), twice as extreme as those tradi-tionally recommend by the IES (1:3:10) but in line withthe new recommendation for daylight media (1:20). Theauthors also found that users tolerated up to 1:50 as longas it was restricted to relatively small areas, comprising lessthan 5% of the field of view.

1.2.2. Glare indices

Glare indices have been used to evaluate visual com-fort in the luminous environment. Two recent literaturereviews provide historical overviews of the multitude ofglare indices [Eble-Hankins and Waters 2004; Osterhaus2005]. However, there are only two glare indices intendedfor use in daylit environments. The first, the daylight glareindex (DGI) was developed by Hopkinson and his col-leagues [Hopkinson 1972; Hopkinson and Collins 1963]using large-area electric light glare sources and updated byChauvel and others [1982] in a setting with daylight butwithout sunlight or reflected sunlight. The second, day-light glare probability (DGP), was developed by Wienold

146 K. Van Den Wymelenberg and M. Inanici

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and Christoffersen [2006] as an attempt to improve uponthe DGI.

The DGP tries to define “the probability that a personis disturbed instead of the glare magnitude” [Wienold andChristoffersen 2006, p. 753]. Wienold and Christoffersen[2006] found that the DGP outperformed the DGI, butthis must be qualified in several ways. First, the DGPequation was created to provide the best fit to the givendata, whereas the DGI was developed to fit a different dataset. Second, the DGP is a binary measure (comfortable oruncomfortable) rather than a four-point linear scale (suchas is the case with the DGI). Third, the DGP was devel-oped using only clear stable skies. Fourth, this correlationalfit was conducted on mean data from several bins along therange of the DGP rather than from continuous data. Andfifth, other researchers [Painter and others 2009; Van DenWymelenberg and others 2010] have demonstrated limita-tions to the DGP model when tested on data sets generatedwith other similar spaces. Still, the DGP has been shownto outperform the DGI [Van Den Wymelenberg and oth-ers 2010] in other tests. The basic equation for the DGPincludes vertical illuminance at the eye (Ev) as a primaryinput in addition to the common glare equation vari-ables (luminance of the glare sources and the backgroundand the size, location, and angular displacement of glaresources).

2. METHODOLOGYLaboratory research included daylong, longitudinal (oneday in summer and one day in fall), repeated-measureexperiments with 48 participants (45 repeated) in a mockprivate office space in Boise, Idaho. Each participant spentone working day, during two different times of the year,in the mock office environment. They assessed a range ofvisual conditions from very bright to very dim, under highsun angles to low sun angles (time of day and year), undernaturally occurring sky conditions, and experienced mul-tiple prescribed and user-defined light modifying elements(blind height, blind tilt, ambient electric lighting levels).This laboratory research built upon the methods employedin two pilot studies [Newsham and others 2008; Van DenWymelenberg and others 2010]. Extensive illuminanceand luminance data were collected as shown in Fig. 1.Nearly 1500 high dynamic range (HDR) data sets wereanalyzed encompassing 16 experimental conditions across93 participant-days.

2.1. Research Setting

Human factors tests were conducted using two identicalrooms (Figs. 1 and 2) in Boise, Idaho (43◦ N and 116◦ W).Each room measured 4.4 m × 3.7 m, 16.3 m2 (14 ft4 in. × 12 ft 3 in., 176 ft2) and had a southwest-facing

Fig. 1 Research setting, equipment room (left); participant room (right); with data collection equipment annotated.

Common Lighting Design Metrics 147

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Fig. 2 Research setting, equipment room, participant perspective (left); daylight guidance blinds (right).

window (35.5◦ west of south). One room was for theresearch participant (participant room) and the other roomhosted the lighting data collection instrumentation (equip-ment room) to ensure data accuracy and to provide anatural working environment for participants.

Each room had a double-pane window (0.64 visiblelight transmission) centered on the southwest wall withaluminum frames that extended from the floor to 2.7 mhigh and measured 2.3 m wide. A daylight guidancesemiperforated motorized louver blind was mounted insidethe window frame (Fig. 2, right). The automated blindcontrols were disabled and the participants controlled themotorized blinds manually from the computer interface orwith a remote control. A single T5HO dimmable recesseddirect electric light source (30–800 lux at desktop) waslocated approximately in the center of the room and wascontrolled with a remote control. Reflectances were as fol-lows: white walls (73.7%), ceiling (80.8%), floor (10.8%)desk (39.3%), and back of blinds when closed (20.3%).A 0.56-m (diagonal screen dimension) LCD computermonitor (max screen luminance of 130 cd/m2 measured ata distance of 1 m) and paper document holder were locatedon the desk.

2.3. Participants

A total of 48 people (24 female, 24 male) participatedin the first round of the study and 45 (22 female,23 male) repeated participation in the second roundof the study. Participants were recruited to create threeage-balanced (18–29 years, 30–49 years, 50–70 years)and gender-balanced groups. Participants were identifiedusing a recruitment e-mail sent to individuals within

the University of Idaho Boise general community andalumni by program administrators at the University ofIdaho.Participants chose to be compensated in one of twoways. The participant either elected to be entered into araffle with a chance to win a prize worth $500 or electedto receive a $75 gift card each day.

2.4. Procedures

The experiment was conducted between June 29 andDecember 19, 2011, from 8:30 AM to 4:00 PM for atotal of 93 participant-days. As expected, sky conditionsvaried throughout this period, but sunny days were mostprevalent representing 94% of hours in the first round(June 29–September 20) and 71% of the hours in thesecond round (September 21–December 19). A typicalparticipant’s day is outlined in Table 1. Upon arrival, par-ticipants reviewed and signed a consent form and weregiven basic training about how to operate the blinds andelectric lights and how to complete the questionnaire andobjective performance tasks. To avoid sequence bias, theorder that each group of conditions was presented waschanged monthly. A total of 16 conditions are outlined inTable 1 (annotated as C1–C16).

2.5. Questionnaire Items

Each of the 16 lighting conditions included a questionnairemodule. First, the participant was asked to confirm thathe or she had created the lighting condition according tothe given description. Then the following questions wereasked:


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TABLE 1 Typical participant-day

Condition order was changed monthly to avoid bias

Time (min) Activity Description

Put blinds down and rotated closed and electric lights on at full power to begin each participant-day9:50 (50) Conditions 1–3 by participant C1—Participant directed to create MP daylight environment

C2—Participant directed to improve environment by addingelectric light

C3—Participant directed to worsen environment by adjustingelectric light

10:40 (10) Morning break Put blinds all the way up and turn the electric lights off10:50 (50) Conditions 4–6 by participant C4—Participant directed to create JU glare daylight environment

C5—Can participant improve environment adding electric light?C6—Participant directed to just correct the glare problem by

adjusting blinds11:40 (20) Condition 7 by participant C7—Participant directed to create MP integrated lighting

environment12:00 (60) Lunch break Put blinds all the way up and turn the electric lights off13:00 (50) Conditions 8–10 by researcher with

participant confirmationC8—Participant directed to create MP daylighting environmentC9—Researcher sets an intentionally dark scene (blinds all the way

down and no electric lights)C10—Participant directed to create JU glare scene from daylight

alone13:50 (20) Afternoon break Put blinds all the way up and turn the electric lights off14:10 (50) Conditions 11–13 by researcher

with participant confirmationC11—Participant directed to create and maintain their MP

integrated lighting environmentC12—Leaving electric light as previous, researcher closes blinds all

the wayC13—Leaving electric light as previous, participant directed to

open blinds just enough to create a JU glare scenePut blinds all the way up and turn the electric lights off

15:00 (50) Conditions 14–16 by researcherwith participant confirmation

C14—Participant directed to create and maintain their MPintegrated lighting environment

C15—Leaving blinds as pervious, participant directed to dimelectric light until just too dim (or until off)

C16—Leaving blinds as previous, participant directed to increaseelectric lights until just too bright (or until on full)

15:50 (10) Debrief/dismiss

Rate the following statements using the scale provided(a 7-point Likert-type scale, 7 = very strongly agree, 6 =strongly agree, 5 = agree, 4 = neither agree nor disagree, 3 =disagree, 2 = strongly disagree, 1 = very strongly disagree):

1. This is a visually comfortable environment for officework. (QU1)

2. I am pleased with the visual appearance of the office.(QU2)

3. I like the vertical surface brightness. (QU3)4. I am satisfied with the amount of light for computer

work. (QU4)5. I am satisfied with the amount of light for paper-based

reading work. (QU5)

6. The computer screen is legible and does not havereflections. (QU6)

7. The lighting is distributed well. (QU7)

Rate the following using the semantic differential scaleprovided (from too bright to too dim):

1. When I look up from my desk the scene I see in frontof me seems: (front_scene)

2. When I look to my left the scene that I see seems:(left_scene)

3. When I look to my right the scene that I see seems:(right_scene)

4. I find the ceiling to be: (ceiling)


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2.6. Analysis Methods

All data were collected in discrete files, and scripts wereused to conduct data cleaning and organization. The val-idated HDR capture procedure has been shown to resultin less than 10% error in scene luminance values [Inanici2006]. In addition to basic descriptive statistics, inferen-tial statistical methods including one-way and two-way,paired and unpaired t tests (95% confidence interval), andPearson correlations were employed.

3. RESULTSFor the sake of brevity, several abbreviations are usedthroughout the rest of the article. MP refers to “mostpreferred” scenes, and JU refers to “just uncomfortable”scenes. “E” refers to illuminance measures and “L” refers

to luminance measures. “C8” refers to condition eight (thatis, participant is directed to create an MP daylight environ-ment during the afternoon) and “C10” refers to condition10 (that is, participant is directed to create a JU daylightenvironment in the afternoon). Therefore, it follows that“C8C10” refers to analysis of data from conditions 8 and10 together (that is, MP compared to JU daylight envi-ronments captured during the afternoon). “QU1” refers toquestion 1.

Several regions of interest, or masks, were studied indetail. “Mask 01” represents the full 180◦field of view(Fig. 3, right). “Mask 03” references a circular task zoneencompassing the keyboard and monitor (Fig. 3, left), anda multiplier of the mean luminance value of this mask wasused to identify the glare sources in order to calculate theDGP metric within the whole scene. “Mask 08” referencesthe masked region of the window (Fig. 4, left). “COV”

Fig. 3 Mask 03 encompasses a circular task about the computer monitor and keyboard (left); Mask 01 encompasses the entire 180◦ ×180◦ scene (right).

Fig. 4 Mask 08 encompasses the entire window “daylight source” (left), Mask 03 encompasses a circular task about the computermonitor and keyboard (right).


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refers to coefficient of variation or the standard deviationdivided by the mean.

3.1. Variability

Tables 2 and 3 illustrate the variability of selected met-rics across participants for comfortable daylit scenes anduncomfortable daylit scenes, respectively. Not surprisingly,the participant variability within and between MP and JUconditions is high and there is substantial overlap betweenconditions (Figs. 5–7). That said, comparisons between

Table 2 and Table 3 indicate the relative ability of metricsto discern between MP and JU conditions.

3.2. Correlation Matrix

Table 4 presents squared correlation coefficients, usingthe Pearson pairwise method, between selected lightingmetrics and questionnaire items for conditions C1, C2,C4, C6, C7, C8, C10, C11, C13, and C14 (hereafterthe composite data set). Note that some conditions (C3,C5, C9, C12, C15, C16) were ignored in these analyses

TABLE 2 MP daylit scenes (C1C8) with score of 5 or higher on QU1 (that is, participant directed to create MP daylighting environmentwhile he or she rated score of 5 and higher on “This is a visually comfortable environment for office work.”)

MP daylit scenes (C1C8) with score of 5 or higher on QU1

Min. 1st Quartile Median Mean 3rd Quartile Max. σ

IlluminanceE horizontal at desktop (lux) 52 419 950 1119 1387 4910 994E at ceiling (lux) 33 345 759 1026 1526 3013 787E vertical at camera (lux) 55 352 665 708 976 2602 436

LuminanceMean L scene (cd/m2) 35 142 241 268 358 805 164Standard deviation of scene L (cd/m2) 57 370 598 696 953 4588 530COV of scene luminance 1.23 2.13 2.57 2.58 2.89 5.83 0.7698th percentile of scene L (cd/m2) 195 1050 2056 2249 3080 8992 1534% Scene exceeding 2000 cd/m2 0.0% 0.7% 2.1% 2.1% 3.1% 6.5% 1.6%Mean L window: Mean L task 1.60 10.38 15.50 15.88 20.45 56.84 8.40DGI (findglare default) 1.10 7.43 11.12 10.19 12.60 16.38 3.40DGP (5∗ Mean L task) 16.5% 19.7% 21.4% 21.4% 23.0% 34.6% 2.7%Mean L window (cd/m2) 74 577 1001 1068 1473 3618 659

TABLE 3 All JU scenes (C4C5C10C13) with score of 3 or lower on QU1 (that is, participant directed to create JU glare daylightenvironment while he or she scored 3 or lower on “This is a visually comfortable environment for office work.”)

All JU scenes (C4C5C10C13) with score of 3 or lower on QU1


IlluminanceE horizontal at desktop (lux) 418 1435 2004 4288 2649 40,920 7142E at ceiling (lux) 115 1295 1688 1904 2385 3799 815E vertical at camera (lux) 51 1061 1402 1467 1755 4816 626

LuminanceMean L scene (cd/m2) 35 385 515 535 640 1568 233Standard deviation of scene L (cd/m2) 93 976 1451 1587 2040 5278 867COV of scene luminance 1.39 2.21 2.85 2.93 3.42 8.37 0.9098th Percentile of scene L (cd/m2) 463 3392 4272 4426 5540 14,530 1893% Scene exceeding 2000 cd/m2 0.0% 3.7% 4.5% 4.6% 5.2% 15.0% 2.1%Mean L window: Mean L task 3.62 18.26 23.59 22.87 27.32 51.82 7.40DGI (findglare default) 0.00 10.92 12.75 12.18 14.20 18.39 2.97DGP (5∗ mean L task) 16.8% 23.4% 25.4% 25.5% 27.2% 38.7% 3.4%Mean L window (cd/m2) 162 1702 2171 2123 2511 6128 803


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Desktop Illuminance Variability per Participant

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Window:Task Ratio Variability per Participant

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Fig. 6 Variability in the mean luminance ratio of window (Mask 08 Mean L): circular task (Mask 03 Mean L) for MP (red/left) and JU(blue/right) conditions by participant.

DGP Variability per Participant

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Fig. 7 Variability in DGP (based upon 5∗ Mean L of task) in MP (red/left) and JU (blue/right) conditions by participant.

because these conditions had unique characteristics meantfor other purposes. The designation of “filtered” wasappended to the condition string (for example, compos-ite_data_set_filtered) in cases where uncomfortable datawere filtered out of the MP data set and comfortabledata were filtered out of the JU data set based upon the

responses to QU4. Results from seven Likert items, foursemantic differential (too dim–too bright) items, andthe overall scene preference semantic differential (leastpreferred–most preferred; coded “light_in_scene”) itemare summarized for selected metrics. These results are


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153

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presented in ranked order by the item QU4. The metrics’

ranks with regard to QU4 is in the leftmost column of eachtable, and the abbreviated metric names are in the next col-umn to the right. Additionally, bolded text indicates thatthe metric’s r2 value was the highest ranked for a specificquestion. Pink fill indicates that the metric’s r2 value wasgreater than or equal to 0.20 and yellow fill indicates themetric’s r2 value was greater than or equal to 0.10 but lessthan 0.20.

The following sections present results for several com-monly cited metrics. Note that none of the graphs usedthe “filtered” data; instead, they used the entire data setfor the specified condition groups, C8 with C10 (hereafter,C8C10), and the composite data set.

First, metrics were investigated for their ability to con-sistently differentiate between MP and JU scenes withinsubjects using results from C8C10. This pair of conditionswas selected for two reasons. Firstly, C8 and C10 occurredwithin 30 min of one another, thus making these condi-tions ideal for analyses between MP and JU daylight sceneswithin subjects because they excluded most temporal con-founding factors (for example, variable sky conditions,sun position). Secondly, C8 and C10 always occurred inthe afternoon, thus increasing the potential of creatingJU scenes for C10 given the southwest-facing aperture.The participants were instructed to create their MP scenefor C8 and a JU scene for C10 using blind controls toadjust daylight levels and distribution and were instructedto leave the electric light off. Next, each metric was plot-ted using C8C10 data, ordered by the metric result, withdata points color-coded by the subject response to QU1.These plots are useful in discerning the most preferred andleast preferred ranges of the metric as well as the typicalchangeover range, described hereafter as the “bounded-borderline between comfort and discomfort” (bounded-BCD). These plots are therefore the most useful for indi-cating recommended performance criteria; however, thesemust be considered preliminary in nature. Due to spaceconstraints, selected metrics are reported. Complete resultsare available elsewhere [Van Den Wymelenberg 2012].

3.3. EV at the Top of the Monitor in ViewingDirection

Ev measured in the participants’ viewing direction fromthe top of the monitor (Ev-monitor, shown in Fig. 8)represents the existing metric with the highest squared cor-relation coefficient for many of the questionnaire items.Figure 9 shows the results for C8C10 with participant-daysresults ordered by C10 results. The metric correctly

Fig. 8 Ev measured in the participants’ viewing direction at thetop of monitor.

differentiates C10 (MP) from C8 (JU) scenes for mostcases, especially where C10 Ev >1600 lux. There are sev-eral cases where C10 scenes had lower Ev values thanother participant-day C8 cases. The single regression statis-tics can be seen in Table 5. Finally, Fig. 10 takes theC8C10 data, organizes it by the metric result, and colorcodes it by the response to QU1. This graphic revealsthree preliminary thresholds for criteria development asdescribed in Table 6.

3.4. Luminance Ratio of Window DaylightSource (Mask 08) to Circular Task (Mask 03)

The ratio of the mean luminance values between the day-light source (Mask 08 Mean L) and the circular task (Mask03 Mean L) did not perform as well as most other exist-ing metrics. This metric (Mean L Window: Mean L Task)can be interpreted to resemble the IES-recommendedluminance ratio criteria of 1:10 (now 1:20) between thetask and remote light surfaces. It did not have squared cor-relation coefficients higher than 0.1 for any questionnaireitems except for right_scene (r2 = 0.145). This metric


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EvTop of Monitor (C8 & C10)

Participants Ranked by C10 Results

(lux)

0

250

500

750

1000

1250

1500

1750

2000

2250

2500

2750

3000C8C10

Fig. 9 Vertical Illuminance (Ev) at top of monitor (in participants’ viewing direction) for C8 (MP) and C10 (JU), participant-days rankedby C10 results.

TABLE 5 Ev-monitor single regression results

C8C10: Ev-monitor (lux)

DV adjr2 F-statistic DF P-value

C8C10QU1 0.1418 28.93 168 2.48E-07right_scene 0.1436 29.33 168 2.09E-07

Composite_data_setQU1 0.1546 152.60 828 2.20E-16right_scene 0.2073 217.70 828 2.20E-16

C8C10_filteredQU1 0.3043 56.55 126 9.00E-12right_scene 0.2741 283.00 126 1.37E-10

Composite_data_set_filteredQU1 0.2378 209.10 666 2.20E-16right_scene 0.2971 283.00 666 2.20E-16

does not consistently differentiate C10 (MP) from C8 (JU)scenes. Nearly all C10 scenes fall within the range of otherparticipant-day C8 scenes as shown in Fig. 11. Figure 12takes the C8C10 data, organizes it by the metric result,and color codes it by the response to QU1. This graphicreveals one weak threshold of the bounded BCD that canbe identified and this is described in Table 7.

3.5. Daylight Glare Probability Using 5∗

Mean L of the Circular Task (Using Mask 03,Mask 01)

The DGP based upon 5∗ Mean L of the circular task(Mask 03; shown in Fig. 3, left) and calculated upon

the entire scene (Mask 01; Fig. 3, right) ranked bet-ter than DGI but not as high as several existing simplerilluminance- or luminance-based metrics. Figure 13 showsthe results for C8C10 with participant-day results orderedby C10 results. The metric correctly differentiates C10(MP) from C8 (JU) scenes for cases where C10 wasgreater than DGP of 24%. There are several cases whereC10 scenes had lower DGP values than other participant-day C8 cases. Figure 14 takes the C8C10 data, organizes itby the metric result, and color codes it by the response toQU1. This graphic reveals three preliminary thresholds forcriteria development as described in Table 8.

4. DISCUSSION4.1. Edesktop

Edesktop is the most commonly referenced metric indaylighting design and research. Using the composite dataset, the squared correlation coefficient for this metric withQU1 was adjr2 = 0.09, for right_scene it was also adjr2 =0.09, and for QU5 (paper-based reading) was adjr2 =0.11, not as strong as Ev metrics detailed in Section 3.2.In this study, the range of Edesktop in all MP conditions was50–5000 lux with a mean of 1100 lux and a standard devi-ation of 850 lux. For MP conditions under daylight only,the Edesktop spanned the same range (50–5000 lux) with amean of 1250 lux and a standard deviation of 1000 lux.For JU conditions only, Edesktop spanned a much widerrange (400–41,000 lux) with a higher mean and standarddeviation (= 4300, σ = 7000 lux). Few previous studies(outlined in Section 1.1) reported preferred Edesktop levelsunder daylight alone (300 lux) or in integrated lightingCommon Lighting Design Metrics 155

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Ev Top of Monitor (C8 & C10)

Ranked Results, Color−coded by QU1

(lux)

0

250

500

750

1000

1250

1500

1750

2000

2250

2500

2750

3000Likert Scale

1 2 3 4 5 6 7

3

3 6 5 6 7 5 55 3

7 5 6 66

75 5 4

4 5 3 6 6 5 6 7 3 5 7 3 5 5 2 3 3 6 4 5 5 2 5 63 3 7 5 5 5 6 3 3 5 1 5 2 3 2 1 2

2 3 3 2 2 2 2 5 1 3 33

33 3 4 1

32

3

46

7

6

Fig. 10 Vertical Illuminance (Ev) at top of monitor (in participants’ viewing direction) for C8 and C10, results ordered by metric andcolor-coded by response to QU1.

TABLE 6 Ev (at top of monitor in participants’ viewing direction)range and preliminary criteria

C8C10: Ev-monitor (lux) range


23 434 726 824 1145 5757 540

Preliminary criteria:x < 875 Likely to be comfortable875 > x < 1250 Bounded-BCDx > 1250 Likely to be uncomfortable

environments (typically 400–800 lux) and the findingsfrom this study were generally higher than levels previ-ously published. This is likely due to the abundant daylightresource available in this study.

This study provides some guidance for determiningan upper horizontal illumination comfort threshold. Onecould reference the mean Edesktop of JU scenes (4300 lux)or the upper threshold of the bounded-BCD approach (forthis metric, approximately 2000 lux). These data couldbe referenced by metrics that require an upper horizontal

Window:Task Ratio (C8 & C10)


Win

dow

:Tas

k R

atio

0

5

10

15

20

25

30

35

40

45

50

55C8C10

Fig. 11 Ratio of mean luminance between the window (Mask 08) and the circular task (Mask 03) for C8 (MP) and C10 (JU), participant-days ranked by C10 results.


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Window:Task Ratio (C8 & C10)


Win

dow

:Tas

k R

atio

0

5

10

15

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35

40

45

50

55 Likert Scale

2 3 4 5 61 7

6 7 47 5 5

3 7 4 5 6 75

5 3 5 7 7 6 5 6 3 6 3 3 5 4 2 5 3 5 3 3 1 3 6 6 5 2 1 5 6 5 5 7 2 5 3 4 6 6 6 7 5 3 4 3 2 3 2 2 6 5 3 5 2 3 3 2 5 5 2 5 5 3 3 6 2 3 3 2 62 2

1

3

2

Fig. 12 Ratio of mean luminance between the circular task (Mask 03) and window (Mask 08) for C8 and C10, results ordered by metricand color-coded by response to QU1.

TABLE 7 Mean L window: Mean L task range and preliminarycriteria

C8C10: Mean L window: Mean L task range


0.6 9.5 15.6 16.0 22.2 56.8 8.4

Preliminary criteria:x < 22 Likely to be comfortable— Bounded-BCDx > 22 Likely to be uncomfortable

DGP (C8 & C10)


DG

P

0.15

0.2

0.25

0.3

0.35C8C10

Fig. 13 DGP based upon 5∗ Mean L of the circular task (Mask 03) using the entire scene (Mask 01) for C8 (MP) and C10 (JU), participant-days ranked by C10 results.

illuminance threshold such as daylight saturation percent-age [Collaborative for High Performance Schools 2009],which, interestingly, suggests 400 fc (10 times the ambi-ent criteria of 40 fc; roughly 4300 lux) as the upper limit,or useful daylight illuminance [Mardaljevic and others2009, 2012; Nabil and Mardalijevic 2005], which ref-erences a lower level closer to the upper bounded-BCD(2000–3000 lux). However, as Fig. 5 demonstrates, anyupper horizontal illuminance threshold must be appliedwith knowledge that some individuals may accept, or even


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DGP (C8 & C10)


DG

P

0.15

0.2

0.25

0.3

0.35Likert Scale

1 2 3 4 5 6 7

6

33 5 6

7 5 7 4 76 6 5 2 5 6 5 7 5 5 5

5 35 6 6 2 7 2 5 5 4 5 3 6

6 3 5 2 3 5 3 6 3 3 3 5 5 5 5 1 5 5 5 3 4 2 6 3 3 2 3 2 5 2 2 3 2 21

5 2 3 36 3 3 1

3 23 3

61

3

2

5

Fig. 14 DGP based upon 5∗ Mean L of the circular task (Mask 03) using the entire scene (Mask 01) for C8 and C10, results ordered bymetric and color-coded by response to QU1.

TABLE 8 DGP (5∗ mean L task) range and preliminary criteria

C8C10: DGP (5∗ mean L task) (%) range

Min.1st

Quartile Median Mean3rd

Quartile Max. σ

16.5% 21.1% 23.7% 23.8% 25.9% 35.8% 3.8%

Preliminary criteria:x < 23% Likely to be comfortable23% > x < 25% Bounded-BCDx > 25% Likely to be uncomfortable

prefer, horizontal illuminance values as high as 5000 lux,and only the most extreme cases can be confidently identi-fied as uncomfortable.

4.2. EV

The highest overall squared correlation coefficient forexisting metrics reported (using the composite data set)was for right_scene and Ev at the top of the monitor1

measured in the participants’ viewing direction, produc-ing r2 = 0.298. The next highest squared correlationcoefficient for an illuminance-based metric was Ev atthe participants’ viewpoint direction (r2 = 0.267). Thebounded-BCD for Ev measured at the top of the monitorwas 875–1250 lux as shown in Fig. 10 (= 798 lux,σ = 500 lux). As expected, similar criteria were identi-fied for Ev measured from seated participants’ eye position(1000–1500 lux). The values from the top of the monitor

could be useful criteria for luminous environmental con-trol systems as well as in simulation-based design analysis,whereas the values from the seated users’ perspective arelimited to simulation-based design analysis because it isnot a feasible physical control point. It is interesting tonote that Ev, outperformed horizontal illuminance mea-sures. The only horizontally measured illuminance metricsthat ranked highest for any subjective item was Edesktop

for QU5 (r2 = 0.1282). It is not surprising that thehorizontal illuminance measure ranked highly for the ques-tion addressing paper-based tasks (QU5) because paper-based tasks are more often completed on a horizontalsurface.

4.3. Luminance Ratios

The most common luminance-based metric referencedby design guides and reported by daylighting researchare basic luminance ratios, typically between task: back-ground and bright light source: task. The results fromMean L Window: Mean L Task can be argued to repre-sent daylight source: task luminance ratio as outlined bythe current IES Lighting Handbook [DiLaura and others2011]. The squared correlation coefficient for this met-ric with QU1 was adjr2 = 0.10 and for right_scene it was

adjr2 = 0.16 across the composite data set. According to thehandbook, the result of this metric should not exceed 10:1(20:1 is for daylight source to adjacent background). Forthis metric, the MP scenes range from 0.5:1 to 57:1 with


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Example Comfortable Scene 373cd/m2 90th % Scene L 1400cd/m2 Mean L Window

1425cd/m2 5* Mean L Scene 1880cd/m2 Mean L Brightest 10% 1894cd/m2 Mean L Sources (5*mLTask)

3824cd/m2 98th % Scene L 4417cd/m2 Mean L Soures (5*mLScene) 8096cd/m2 Maximum Scene L

Fig. 15 Example comfortable scene with multiple source: task luminance ratios represented.

a mean of 14:1, and the JU scenes ranged from 3.6:1 to52:1 with a mean of 22:1. The mean values for the MPluminance ratios (14:1) are within the range suggested byEgan [1983] and the 10th Lighting Handbook [DiLauraand others 2011]; however, approximately half of the par-ticipants had one or more MP scenes with a luminanceratio in excess of 1:20 (Fig. 6).

Existing literature does not explicitly state how theseluminance ratios should be calculated in spaces with day-light and the method dramatically impacts the result(Fig. 15). The regression analysis reported herein usedonly the Mean L Window: Mean L Task luminance ratio;however, other luminance ratios are defensible within thecurrent loose definition. Figure 15 illustrates several logicalinterpretations of the luminance ratio metric as currentlydefined for a single comfortable daylight-only scene. Thisfigure interprets the task definition consistently as Mask 03(however, other masks could also be argued) and changes

only the bright light source definition and produces a rangeof luminance ratios from 5:1 to 102:1 for the same scene.This wide range of results suggests that further calculationdefinition is required for this metric to be useful. Thoughthis metric, as interpreted in this article, does not consis-tently differentiate between MP and JU scenes (Fig. 12)or establish a clear bounded-BCD (Fig. 3), it is possiblethat other variations on this metric could prove stronger.The simplicity of this metric is its greatest strength, butliterature is not available to defend the current recommen-dations [Boyce 1987a, b; Veitch 2001]. Future research iswarranted to establish a consistently applicable calculationmethod and defensible recommended criteria.

A few other promising luminance ratio metrics fromprevious research are not reviewed in detail becausetheir r2 results did not rank among the highest met-rics investigated in this study. These include the COVof the entire scene’s luminance (Mask 01) and the


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ratio of the 75th:25th luminance value in the entirescene (X01_25th_to_75th_percentile). The new LightingHandbook [DiLaura and others 2011] describes the coef-ficient of variation, or standard deviation divided by mean(herein COV), as a useful metric for describing the “aver-age difference from the average” or the “dispersion of thedata,” and Howlett and others [2007] suggested it as apromising metric in a scoping study. Though it is not asimple luminance ratio of different regions within a scene,it does address both adaptation and variance extremes,similar to simple luminance ratios. Nonetheless, this met-ric produced squared correlation coefficients among thelowest of existing metrics analyzed (Table 4). The samecan be said for the ratio between the 25th and 75th per-centile luminance values in Mask 01, one of the strongestmetrics found previously [Newsham and others 2008].This underscores potential challenges to generalizability.

4.4. Daylight Glare Probability

DGP values were relatively low for the entire study witha maximum value of approximately 45%. The range ofDGP values for the entire data set is shown in Table 9. It issurprising that some of the seemingly excessively glaringscenes did not have higher DGP values. As shown in Fig.14, the DGP consistently differentiates between MP andJU conditions above a value of 25%; however, below thisthreshold it did not do so reliably. This is to be expectedgiven that the DGP algorithm was founded upon a data setthat included very few data producing a DGP of 25% orlower [Wienold and Christoffersen 2006]. Wienold [2009]and Reinhart and Wienold [2011] stated that DGP val-ues below 35% are “imperceptible glare,” 36%–40% are“perceptible glare,” 41%–45% are “disturbing glare,” andabove 45% are “intolerable glare.” In this study, over 75%

of the scenes produced DGP results less than 25% acrossall conditions. Considering only JU scenes that were ratedwith a Likert score of 3 or lower on QU1 (that is, partic-ipants disagreed that the space was visually comfortable),75% of the scene produced DGP values below 27%. Onlythree of 201 (1.5%) JU scenes rated as uncomfortable onQU1 were above DGP 35% and none were above 40%.Finally, there was very little difference between the meansfor DGP in MP conditions (21.6%) versus JU conditions(24.2%). Together, these findings (in a space of similarcharacter and orientation to the original DGP testbed) sug-gest that the metric, as it is currently defined, may notbe sensitive enough for use as a daylighting design guideor as part of an automated blind control algorithm as asingular metric. It is possible that the view direction inthis study (perpendicular to the window) as opposed tothe view direction in the original research [Wienold andChristoffersen 2006] used to create the DGP (45◦towardthe window) partly explains the difference in DGP valuesfound. It is plausible that participants considered multipleview directions in their subjective assessment of the scene.Therefore, it may be that improved correlation wouldresult from using worst-case view direction for the calcu-lation of DGP, in a sense a reverse interpretation of the“adaptive zone” concept [Jakubiec and Reinhart 2012].

Together, Table 10 and Fig. 16 document another lim-itation of task-based glare indices, and in this case DGP.Glare indices attempt to account for adaptation by incor-porating a glare source identification step that is typicallybased upon a multiplier of the mean luminance of thetask or the entire scene. When direct sunlight enters thespace and is perceived as glare, it can also be incorrectlyincluded in the calculation of the adaptation component,essentially reducing the intensity or solid angle of the glaresources identified. This limitation can be exacerbated when

TABLE 9 Summary DGP results for all conditions (top) JU conditions rated below 3 on QU1 only (bottom)

All conditions DGP results

DGP metric Min. 1st Quartile Median Mean 3rd Quartile Max.

DGP (5∗ mean L task) 16.49% 19.36% 21.64% 22.06% 23.97% 44.03%DGP (5∗ mean L scene) 16.49% 19.20% 21.53% 22.00% 24.08% 44.56%DGP (>2000 cd/m2) 16.49% 19.08% 21.54% 21.93% 24.07% 44.41%DGP (>5000 cd/m2) 16.49% 18.75% 21.00% 21.65% 24.11% 44.55%

JU scenes (C4C5C10C13_QU1_Likert_below_3) DGP resultsDGP (5∗ mean L task) 0.1678 0.2342 0.2539 0.2551 0.2716 0.3868DGP (5∗ mean L scene) 0.1679 0.2305 0.2531 0.2534 0.2732 0.3662DGP (>2000 cd/m2) 0.1649 0.2307 0.2536 0.254 0.2744 0.3802DGP (>5000 cd/m2) 0.1649 0.2256 0.2542 0.2516 0.2725 0.3682


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TABLE 10 DGP results from a MP and a JU scene for a singleparticipant, with other selected metrics

Participant 36, round 2, 2011-10-17

C13 (JU) C14 (MP)

Mean L scene (cd/m2) 1092 1682Mean L task (cd/m2) 455 1305∗ Mean L scene (cd/m2) 5460 84105∗ Mean L task (cd/m2) 2275 650DGP (5∗ mean L scene) 32% 45%DGP (5∗ mean L task) 32% 44%Standard deviation of scene L

(cd/m2)3615 7300

calculating DGP by defining a task region as recommendby Evalglare [Wienold 2008]. In scenes where sunlightenters the task region (Fig. 16, left, a), it can make the glaresource identification threshold artificially high and causeglare sources to be missed. Ultimately, the resultant DGPcan be lower than might otherwise be expected.

Table 10 illustrates the difference between two scenescaptured on a sunny afternoon. The JU scene has a lowerDGP value (32%) than the MP scene (45%). Figure 16illustrates the same scenes graphically (JU at left, MP atright) where the first row shows the tone-mapped scenes(a), the second row shows the luminance false color (b),and the third (c) and fourth (d) rows illustrate the glaresource identification results. It is interesting to note that inthis example the glare source identification method (eitherfive times the mean luminance of the circular task or theentire scene) does not produce meaningful differences inthe DGP value. The comparison between the two scenes(MP and JU) clearly reveals the challenge presented to glaremetrics (DGP in this case) as sunlight slips through blindsand increases the adaptation level.

5. CONCLUSIONThis article provides the results of a 6-month-long humanfactors research project replete with extensive lighting datacollection that aims to study the limitations of existingilluminance- and luminance-based lighting quality guide-lines, particularly in relation to visual comfort, in single-occupancy office environments. A sample of 48 humansubjects was examined in a repeated-measures design ina mock office space under naturally occurring and sys-tematically categorized daylight conditions. The followingconclusions are reported to determine which lighting met-rics (illuminance and luminance based) are more strongly

associated with subjective measures of human visual pref-erence and acceptance (using Likert-type and semanticdifferential questionnaire items) in an office space withdaylight only and with both daylight and electric light(integrated lighting). The results are useful to provideresearch-based recommendations for improved integratedlighting design strategies, computational analysis methods,and lighting and blind control technologies, and to guidefuture research:

Vertical illuminance and simple luminance metrics(mean and standard deviation of scene luminance) outper-formed more complex metrics (such as DGP and DGI,or luminance ratios) for QU4, “I am satisfied with theamount of light for computer work.” Therefore, estab-lishing reliable vertical illuminance- and luminance-basedmetrics and design criteria that can be referenced indesign stages, through additional research, should lead toimproved occupant satisfaction in spaces adhering to thesecriteria.

Ev measured at a seated occupant’s eye position or at thetop of the monitor pointed in the same viewing directionas the occupant were both more capable than horizontalilluminance measures of fitting the range of human sub-jective responses to visual preference questionnaire items.Therefore, establishing reliable design criteria for Ev thatcan be referenced in design stages should lead to improvedoccupant satisfaction in spaces adhering to these criteria.Preliminary bounded-BCD criteria for Ev measured nearthe occupants’ point of view from this study range from1000 to 1500 lux.

Desktop illuminance was among the weakest of theexisting lighting design metrics. However, due to its ease ofuse and prevalence in practice, a range of upper horizontalilluminance comfort-based threshold (as used by daylightsaturation percentage and useful daylight illuminance) isproposed. Upper horizontal illuminance thresholds shouldbe set between 2000 and 4300 lux but must be appliedwith an understanding that some individuals may prefervalues as high as 5000 lux (during some parts of the year),thus confidently identifying only the most extreme cases asuncomfortable.

The luminance ratio between the mean luminance ofthe daylight source (Mask 08 Mean L) and the meanluminance of the circular task (Mask 03 Mean L) didnot yield squared correlation coefficients as high as otherexisting metrics with regard to the subjective visual com-fort ratings. The bounded-BCD suggested by this study(22:1) is higher than existing recommendations, andthe current IES recommendations are not supported by


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C13, 3:31pmJU Daylight Glare, Electric same as C11

C14, 2:16pmMP Integrated Daylight & Electric

Glare sources by 5*mL X01

2000

940

440

210

97

46

21

10

cd/m2

S036_2011-10-17-153101_c1 S036-2011-10-17-141648_c1

JU MP




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Fig. 16 Limitation of the circular task (Mask 03, X03) multiplier for glare source identification when using DGP.


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existing literature. Given these challenges, it would bereasonable to dismiss this metric entirely. However, thesimplicity of this metric is its greatest strength and is amajor reason it has persisted. There is sufficient evidenceto suggest additional research is warranted, but it is criticalthat future research explicitly state the method used whencalculating this metric.

The DGP performed better than the DGI. However,only 1.5% of the 201 JU scenes that were rated as uncom-fortable had DGP values that corresponded to “perceptibleglare” or “disturbing glare.” The other 98.5% of the scenesrated as uncomfortable in this study had DGP values inthe “imperceptible” portion of the glare scale. This couldbe in part due to the difference in the viewing direction inthis study compared to the formative study [Wienold andChristoffersen 2006]. Additionally, it was found that DGPvalues can be reduced if sun is in the defined circular taskarea, because it dramatically increases the glare identifica-tion threshold. Therefore, as DGP is currently defined, itdoes not appear to be sensitive or robust enough for use asa stand-alone daylighting visual comfort design guide andin some cases may underpredict glare sensation.

The capability of a particular metric to discern betweenMP and JU scenes was found to be an important consid-eration when evaluating their effectiveness. Furthermore,the range over which the metric could consistently dif-ferentiate between MP and JU scenes began to indicatethe thresholds for the bounded-BCD. The bounded-BCD serves as preliminary recommended design criteriaand can support both design analysis and environmentalcontrol.

Given that luminance measures closely relate to humanperception of brightness, it is probable that luminance-based metrics will more closely correlate with subjectiveacceptance and preference ratings than illuminance-basedmeasures. However, as evidenced by this study, com-monly reported luminance-based metrics do not appear tohave greater predictive ability than common illuminance-based metrics. Therefore, there is a need for futureresearch to develop a new suite of luminance-based analysismetrics.

NOTE

1. The vertical illuminance at the top of the monitor produced a slightlybetter r2 (0.2982) for right_scene than did the vertical illuminance atthe seated eye position (0.2662). In fact, it outperformed the seatedeye position on most subjective items. As expected, these two valueswere highly correlated (r2 = 0.94), with a mean difference of 134 lux(17.3%).

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