Value Metrics for Better Lighting

SPIE PRESS

P.O. Box 10Bellingham, WA 98227-0010

ISBN: 9780819493224SPIE Vol. No.: PM228

Lighting Research Center

We often do not fully understand what lighting can do for us. We know that we need lighting, but often that is as far as the thinking goes. We do a really good job, however, of conceptualizing the costs of those lighting systems because we can readily measure those costs. Reducing costs will certainly increase the value ratio for lighting if the benefits of the lighting system are held constant. Without a clear purpose for the lighting system, and no clear idea of benefits, there is little else that can be used in the value engineering process. This book is dedicated to the notion that our society undervalues light because we do not properly measure the benefits of light, in terms of both the lighting system and how it is applied. Consequently, we unnecessarily waste our natural and capital resources. The problems associated with inadequate light measurement systems are not hard to grasp or even to fix, and are the subject of Value Metrics for Better Lighting. This book was written as a starting point for thoughtful consideration, discussion, and action by those vested in better and more sustainable lighting, including manufacturers, practitioners, regulators, advocates, educators, and, of course, users.

Bellingham, Washington USA

Library of Congress Cataloging-in-Publication Data

Rea, Mark Stanley, 1950-Value metrics for better lighting / Mark S. Rea.

pages cmIncludes bibliographical references and index.ISBN 978-0-8194-9322-41. Lighting. 2. Electric lighting–Energy consumption. 3. Lighting,

Architectural and decorative. I. Title.TH7703.R28 2013621.32–dc23

2012037160

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Printed in the United States of America.

First printing

Contents

Acknowledgments ..................................................................................... vii

Chapter 1 Introduction ........................................................................... 1

Chapter 2 Measurement Matters ............................................................. 5

2.1 What Is Light? .................................................................................................. 5

2.1.1 The photopic luminous efficiency function................................... 7

2.1.2 Where does V(λ) apply and where doesn’t it?........................... 11

2.2 What Is Color? ................................................................................................. 12

2.2.1 Color appearance.............................................................................. 13

2.2.2 Color matching................................................................................... 14

2.2.3 Colorimetry ......................................................................................... 15

2.2.4 Color rendering and tint of illumination......................................... 17

2.3 Color Rendering Index ................................................................................... 17

2.4 Correlated Color Temperature...................................................................... 18

Chapter 3 Added Value from New Metrics................................................ 19

3.1 Beyond lm/W and lm/m2 ................................................................................ 19

3.1.1 Mesopic vision ................................................................................... 21

3.1.2 Apparent brightness.......................................................................... 23

3.2 Beyond “Light”.................................................................................................. 28

3.2.1 Circadian light .................................................................................... 29

3.3 Beyond CRI and CCT .................................................................................... 36

3.3.1 Color rendering .................................................................................. 37

3.3.2 Tint of illumination ............................................................................. 40

3.4 Lighting Energy Efficiency............................................................................. 42

3.4.1 Application efficacy in the temporal domain................................ 43

3.4.2 Application efficacy in the spatial domain .................................... 45

3.4.3 Calculating lighting energy efficiency ........................................... 48

Chapter 4 An Invitation .......................................................................... 51

4.1 Unified Illuminance ......................................................................................... 54

v

vi Contents

4.2 Bright Illuminance ........................................................................................... 58

4.3 Circadian Illuminance ..................................................................................... 60

4.4 “Class A” Color................................................................................................. 62

4.5 Lighting Energy Efficiency............................................................................. 64

4.6 What’s Next? .................................................................................................... 66

Chapter 5 Conclusion ............................................................................ 67

5.1 We Believe What We Hear ........................................................................... 67

5.2 What to Talk About.......................................................................................... 69

Appendix 1 Acronyms, Abbreviations, and Notation .................................... 71

Appendix 2 Determinations of Chromaticity ................................................ 73

Appendix 3 Color Rendering Metric Calculations......................................... 79

Appendix 4 How to Optimize Illumination on a Residential Wall Display ........ 87

Appendix 5 Relative SPDs of the Light Sources in Table 4.1 ......................... 91

Appendix 6 Luminous Efficiency Functions for Different Benefit Metrics....... 99

Appendix 7 Resources for Maximizing the Value of Daylight Controls ......... 103

References .............................................................................................. 105

Index ...................................................................................................... 111

Acknowledgments

The Lighting Research Center (LRC) is a great and influential place towork. Founded in 1988, it remains a close community of researchers,educators, and technical staff dedicated to our mission to “advance theeffective use of light.” Professor Russell Leslie, LRC Associate Directorand cofounder of the LRC, has been as good a partner and collaboratorin building and fostering the mission of the LRC as anyone could be orcould hope to be. He was kind enough and more than capable enough toprovide a complete edit of an early draft of the book. Dennis Guyon, who(fortunately or unfortunately for him) sits across the hall from my office,prepared all of the graphics for the book and helped organize the contentin a way that I could efficiently write and rewrite its contents. I appreciatethe technical input from Jeremy Snyder and Leora Radetsky, and both InesMartinovic and Rebekah Mullaney were very helpful in organizing thebook and in providing me with important, additional edits.

My greatest joys in writing this book and, indeed, in everyday professionalinteractions, were and are the continued collaborations with my formerstudents who are still at the LRC. They continue to do amazing researchand teaching and make a huge difference to our collective success. JenniferBrons helped prepare one of the spatial application efficacy analyses.John Bullough did more of the analyses for spatial application efficacyand provided helpful input in preparing related sections of the book. JeanPaul Freyssinier helped with the color calculations. Andrew Bierman, whoalso (fortunately or unfortunately for him) sits across the hall from myoffice, deserves a great deal of credit for helping me prepare nearly all ofthe technical data presented here. Mariana Figueiro was my “value-addedsounding board” and critic in conceptualizing and organizing the book.Our ongoing discussions were essential for framing the entire enterprise.

To everyone, thank you very much.

Finally, I wish to thank the LRC Partner organizations, listed below, who,through their continued support have enabled all of us to do what we loveto do at the LRC—add value to lighting:

vii

viii Acknowledgments

• 3M• AES Latin America• GE Lighting• New York State Energy Research & Development Authority• OSRAM SYLVANIA, Inc.• Philips Lighting• Swedish Energy Agency• Xcel Energy

Mark S. ReaJanuary 2013

Mark Rea is Director of the LRC, which is celebrating its 25th year.Value Metrics for Better Lighting was written to help commemorate thismilestone.

Chapter 1Introduction

Light is ubiquitous. Therefore, we often take light for granted and givelittle thought to its value. This will change as the world population expandsexponentially and we strive for a more sustainable planet. Commoditieslike light, air, water, and nutrition will begin to take on increasedsignificance and will begin to be seen as more valuable.

But how does one measure the value of light? Value is calculated as aratio of the benefits provided by a desired product or service divided bythe costs to procure that product or service. To calculate the value of lightthen, we first have to decide on the purpose of the lighting. Will it be usedto thread a needle, complete a jigsaw puzzle, avoid objects in the roadway,detect deer coming onto the highway, illuminate a photograph on the wall,or even to sleep well? Once we decide on the purpose (i.e., define thedesired benefit), we then need to accurately measure that benefit as well asthe costs needed to deliver that benefit.

Unless we have expectations, it is very hard to meet them. Surprisinglyperhaps, we often do not fully understand what lighting can do for us. Weknow that we need lighting, but often that is as far as the thinking goes.We know that lighting is installed in and on every building, motor vehicle,airplane, and on most roadways and streets. We accept and we copy whathas been done before, even if we do not fully understand or measure thebenefits that lighting provides in those situations. We do a really good job,however, of conceptualizing the costs of those lighting systems becausewe can readily measure those costs. We precisely measure the price of thelight source, the fixture, its installation, the costs of maintenance, and theamount of electricity needed to energize the source. Whether we do simplepayback calculations or life-cycle cost analyses, we have a firm grasp onhow much lighting systems cost.

Since we can so precisely measure cost, it becomes the primarybasis for lighting decisions. In fact, the value proposition for lighting issynonymous today with reducing costs. Most construction firms use a“value engineering” process after the architect or designer has specifiedthe lighting system, and that process is almost always a matter of findingways to reduce the costs of lighting. Reducing costs will certainly increase

1

2 Chapter 1

the value ratio for lighting if the benefits of the lighting system are heldconstant. Without a clear purpose for the lighting system, and no clearidea of benefits, there is little else that can be used in the value engineeringprocess. Implicit in the commonly used specification phrase “or equal” isa failure to define a benefit for lighting. “Or equal” really means no oneunderstands or can measure the benefits provided by the lighting or, bydefault, that the prescribed illuminance level is the benefit.

Despite this cynicism, it must be acknowledged that the pressures toreduce costs have considerably increased the value of electric lighting formany years. Technological innovations have made lighting systems morereliable, easier to maintain, and more efficacious by reducing the wattageneeded to generate the same amount of light. Significantly too, pressuresfor lower cost and for lower impact on the environment have encouragedsanctioning bodies to lower recommended levels of illumination forarchitectural applications. All of these pressures for cost reduction haveunquestionably increased the value of modern lighting systems.

In contrast, very little has been done to increase the value oflighting systems by increasing the benefits of those lower-cost systems.Manufacturers, regulatory institutions, and specifiers implicitly, butexclusively, rely on photometry and colorimetry to characterize thebenefits of light. These orthodox measurement systems are nearly acentury old and, as a reflection of their antiquity, they can only crudelycharacterize the numerator in the value ratio for light. Lumens per watt(luminous efficacy), lumens per surface area (illuminance), color renderingindex (CRI) and correlated color temperature (CCT) are used almostexclusively in today’s lighting specifications, regulatory documents, anddescriptions of lighting products. These lighting metrics do not accuratelycharacterize how bright a space appears, how well the light source revealsobject colors, nor how the light affects our biological rhythms. We donot even correctly measure how much waste we create by lighting emptybuildings and lighting places that should be dark.

We do what we measure. If we measure the wrong things, we dothe wrong things. We waste our natural and capital resources by usingorthodox photometry and colorimetry as the sole measurement systems forquantifying the benefits provided by light. We also waste these resourcesby failing to measure when and where we provide light. Ironically, muchof the problem lies with current regulatory infrastructure purportedlydevoted to promoting energy efficient, sustainable lighting. Because theseregulations are based on the wrong measurement systems, they becomethe main barrier to actually increasing the penetration of energy efficient,sustainable lighting into the market.

This book is dedicated to the notion that our society undervalues lightbecause we do not properly measure the benefits of lighting, nor do

Introduction 3

we properly measure how much lighting is being wasted. The problemsassociated with inadequate lighting measurement systems are not hard tograsp or even to fix, as I hope to make clear and interesting in the followingchapters. It remains to be seen, however, whether industry and regulatoryinertia can be overcome to functionally improve the perceived as well asthe actual value of light.

Chapter 2Measurement Matters

2.1 What Is Light?

Light is associated with a very narrow region of the electromagnetic spec-trum between about 380 and 780 nanometers (nm), and is formally de-fined as optical radiation that can evoke a visual response in humans. Twoclasses of photoreceptors—rods and cones—found in the human retinatransduce electromagnetic radiation into neural signals that ultimatelyevoke visual responses. Interestingly, light is the only physical quantity de-fined in terms of the human condition. All other physical quantities, suchas length, mass, and time would continue to exist if the human specieswere to become extinct (Bureau International des Poids et Mesures, 1983;2005; 2006). Only the definition of light would have to undergo a majorrevision by the species that succeeds us.

Photometry is the measurement of light. Several orthodox photometricquantities are used to characterize light sources and light fixtures and tospecify or regulate the application of light. The base unit in orthodoxphotometry is the candela (cd), which is a measure of the luminousintensity of a light source in a particular direction. The luminous intensityof a source varies with angle, so light sources will produce differentluminous intensities in different directions. The luminous intensitydistribution of a frosted incandescent lamp is nearly the same in anydirection, whereas an automobile headlight will have a maximum intensityin the direction of travel with much lower luminous intensities orthogonalto the maximum.

Formally, one candela is defined as having a radiant intensity of 1/683watts (W) per unit solid angle at 555 nm. For polychromatic light sources(i.e., all practical sources of illumination) the photopic luminous efficiencyfunction [V(λ)] is almost always used to weight energy in the electromag-netic spectrum for the determination of luminous intensity (Fig. 2.1). Thespectral power distribution (SPD) of the radiation emitted by a sourceis integrated with V(λ) to determine the photopic luminous intensity (incandelas) of the source in the direction of measurement. This quantityis equal to the number of lumens (lm) per steradian (sr) in the direction

5

6 Chapter 2

Figure 2.1 Formally sanctioned spectral luminous efficiency functions by theCommission Internationale de l’Éclairage (CIE) (CIE, 1978; 1990; 2005). V(λ) isthe original photopic luminous efficiency function adopted by the CIE in 1924. Itis based on the spectral sensitivity of central (2-deg) vision and therefore largelyreflects the spectral sensitivity of the cone photoreceptors in the fovea. V ′(λ) isthe scotopic luminous efficiency function based on the spectral sensitivity of theperipheral retina under very dim lighting conditions where only rod photoreceptorsprovide input to visual sensation. VM(λ), the so-called Judd–Vos correction, is asecond photopic luminous efficiency function adopted by CIE to reflect slightlyenhanced sensitivity to short wavelengths in central (2-deg) vision. V10(λ) is athird photopic luminous efficiency function adopted by CIE to reflect the spectralsensitivity of central (10-deg) vision. V(λ) is the only luminous efficiency functionincorporated into commercially available photometric instruments and the only oneused internationally for lighting application standards.

of measurement. The significance of V(λ) for determining the benefit oflight will be discussed in much more detail, but this particular spectralweighting function underlies all units in orthodox photometry. (A completelist of acronyms, abbreviations, and notation is provided in Appendix 1.)

Often the total amount of light (or total luminous flux) emitted bythe source is of interest rather than the luminous intensity distribution.The total luminous flux emitted by a source is actually a special caseof luminous intensity and is equal to the number of lumens emitted intoa sphere of 4πsr surrounding the source. Thus, the total luminous fluxemitted by a source is the sum of the luminous intensities of the sourcein every direction.

Luminance is often referred to as photometric brightness because it isthe measure of light that is most closely associated with how bright sources

Measurement Matters 7

or objects appear. Qualitatively, luminance is the product of the lightfalling on a surface multiplied by the reflectance from (or transmissionthrough) the surface. More formally, luminance is a measure of theintensity of the light per unit area in the direction of view and is mostcommonly measured in units of nits (or cd/m2). Although luminancemeters are commercially available, they are rarely used because they arerelatively expensive and most lighting standards consider the light fallingon a surface rather than the light reflected from or transmitted through asurface.

We rely most commonly on two measures of light, luminous efficacyand illuminance. Luminous efficacy is defined as the ratio of the totallumens emitted by a source or a fixture divided by the wattage neededto emit those lumens (lm/W). Those sources or fixtures that emit morelumens per watt of electric power are often considered more “energyefficient,” but as will be discussed later, this measure of “energy efficiency”can easily obscure the efficacious application of light. Illuminance isperhaps the most common measure of light in architectural applications,both for specifying and regulating the amount of light to be used in aspace. Illuminance is defined as the number of lumens incident on asurface area, such as a desk or a roadway. Lux (lm/m2) is presentlythe most common unit of measurement for illuminance. Recommendedand regulated levels of illuminance vary depending on the type of taskbeing conducted in an architectural application. Tasks associated withhigher levels of illuminance are those deemed by sanctioning bodies andregulators as more difficult to see or tasks in which errors are moreimportant to avoid.

Table 2.1 is a summary of the most common photometric units usedto measure light. As already noted, all photometric units integrate theelectromagnetic spectrum emitted by a source or surface with the photopicluminous efficiency function [V(λ)].

2.1.1 The photopic luminous efficiency function

All practical measurements of light are based on V(λ) shown in Fig. 2.1.In photometry, V(λ) weights the effectiveness of the electromagnetic

Table 2.1 The most common photometric units used to measure light.

Unit Abbreviation Equivalence

Luminous intensity candela cd lm/srLuminous flux lumen lm lm/4πsrIlluminance lux lx lm/m2

Luminance nit — cd/m2

Luminous efficacy — — lm/W

8 Chapter 2

spectrum generated by every light source irrespective of its intendedapplication. V(λ) was developed from a particular set of human visualpsychophysical studies performed in the 1920s. In those early studieshuman subjects used their foveae to view a small, 2-deg field (about thesize of a beer bottle cap at arm’s length) of light in an otherwise darkvisual field. The fovea is a small region of the retina corresponding towhat is called central vision and has the highest density of photoreceptors,thus providing the highest spatial resolution (i.e., acuity). In fact, onlycone photoreceptors are found in the fovea, and among those, most arelong-wavelength sensitive (L) cones and middle-wavelength sensitive (M)cones. The third cone photoreceptor type, the short-wavelength sensitive(S) cones, are largely absent from the central fovea (Fig. 2.2). Twotechniques were used to assess the spectral sensitivity of human subjectsto different narrowband, nearly monochromatic sources of light. Bothof these techniques employed methods aimed at measuring “equality ofsensation” while viewing the different sources of light. The first technique,known as side-by-side heterochromatic brightness matching, was verystraightforward. The halves of the 2-deg field were each filled withdifferent monochromatic lights, and the radiant power (in watts) of onehalf-field was adjusted by the human subjects to appear equally bright asthe other half-field. The experimenter recorded the radiant power neededto make the brightness match so that the relative sensitivity to the twowavelengths could be computed. So, for example, it was found that it takesslightly more radiant power from a 550-nm light to match the brightnessof a 555-nm light. The relative sensitivity at any wavelength can bedetermined by the reciprocal of the watts needed to match the referencewavelength, which is the wavelength that takes the fewest watts for equalbrightness.

It became apparent to researchers at the time that this technique workedwell only when the wavelength differences between the lights in the half-fields were small. Matching a 630-nm light with a 430-nm light was, forexample, difficult for subjects, and the matches were highly variable, bothbetween and within subjects. Thus, a second technique was developed,known as flicker photometry. Again, subjects viewed a 2-deg field, butinstead of making a side-by-side brightness match, the two lights werematched temporally; that is, the two lights were matched in brightnesswhile they were very quickly oscillated. At very slow oscillations, the2-deg field would alternately appear as two distinct colors (e.g., redand yellow), but as the oscillation rate increased, the 2-deg field wouldappear as one color (e.g., orange), but it would appear to flicker inbrightness. The subject would carefully adjust the radiant power of onecolor (e.g., red) until the (orange) light appeared to just stop flickering.As with the heterochromatic brightness matching technique, the relative


Figure 2.2 The photopic luminous efficiency function V(λ) reflects the spectralsensitivities of just two (L and M cones) of the three cone types in the retina. Scones are functionally omitted from photometry but participate in many visual andnonvisual responses to optical radiation incident on the retina.

watts needed at each wavelength to just make the flicker disappear wasused to determine relative sensitivity to the different narrowband lights.This technique was much more reliable and precise than the side-by-side heterochromatic matching technique, and, consequently, the resultsof these flicker photometry studies were considered more important in thedeliberations surrounding the definition of light.

A committee of interested parties reviewed the data sets obtainedfrom these psychophysical studies, deliberated and debated, and finallyapproved what is now known as V(λ) in 1924 (CIE, 1924), and thisfunction has served as the basis for measuring light ever since. In fact, thisdefinition of light remains the foundation for all international commerce,building standards, and safety standards where light is being sold orregulated.

Reliance on V(λ) for all of these commercial and regulatory functionsis a problem. A significantly deeper understanding of the human visualsystem was gained over the next quarter century. During that time, itbecame clearer that there were two classes of photoreceptors in the humanretina: cones, which operate in bright light, and rods, which operate invery dim light. In 1951, a committee similar to the one convened in 1924approved the scotopic luminous efficiency function [V ′(λ)] for very dimconditions (CIE, 1951). Thus, since 1951, there have been technically,

10 Chapter 2

albeit confusingly, two definitions of light, one definition to be used underhigh light levels [V(λ)] and one to be used under dim light levels [V ′(λ)].As it turns out, the scotopic luminous efficiency function is only applicableunder lighting conditions comparable to starlight—moonlight is brightenough to involve cones—so little attention has ever been given to thisfunction outside of experimental laboratory conditions. Since the scotopicfunction could effectively be ignored by manufacturers, the photopicluminous efficiency function became the universally accepted spectralweighting function for light.

As psychophysics continued to enhance our understanding of humanvision, researchers felt compelled to have their own data reflected ininternational standards, so more and more luminous efficiency functionswere created. Figure 2.1 shows the spectral weighting functions currentlyofficially sanctioned for defining light (CIE, 1978; 1990). Parenthetically,none of these definitions are based on light for other species (animals andplants) or for other purposes (optical communication, drying, or curing)—again, the formal definitions of light in Fig. 2.1 are all associated withhumans.

These multiple definitions of light are both a liability and an assetfor characterizing the value of lighting. Having multiple definitions oflight would be a disaster for international commerce. Multiple definitionswould lead to misuse and confusion in the world marketplace aswould, for example, multiple definitions of electrical power. Unlessevery manufacturer agrees on one definition of light, each country ormanufacturer could define their own photometric quantities, makingstandardization impossible, thereby wreaking havoc for the global saleof lighting products. In terms of commerce, all of the spectral weightingfunctions except V(λ) in Fig. 2.1 are ignored, and rightly so.

Multiple definitions of light are also a potential asset because theirexistence provides prima facia evidence to the users of light that humanvisual perception responds to optical radiation in a variety of ways. Sincelight can produce different visual consequences, it becomes important tocapture and formalize these different definitions of light so that a desiredvisual effect can be reliably and efficiently achieved. Moreover, thesemultiple definitions of light demonstrate that reliance on a single spectralweighting function [i.e., V(λ)] inherently limits the value of lighting. Inother words, delivering light based only in terms of V(λ) will not producereliable visual effects for many applications, and the delivered light will bewasteful in terms of both capital costs and electric energy. This is a veryimportant point.

Visual perception is not a single, monolithic process that provides thebrain with a photopically weighted movie of the physical environment.Rather, multiple neural channels connecting the retina to the brain


perform different and distinct functions that, when combined in thebrain, provide an integrated representation of the physical environment.Each of these neural channels weights the spectrum differently usingdifferent combinations of the same photoreceptor types. Thus, theluminous efficiency functions in Fig. 2.1 reflect different channelresponse characteristics, not necessarily different photoreceptor types.Most importantly, if we define light only in terms of the photopic luminousefficiency function, we are inherently unable to quantify light as itaffects different neural channels and, thus, our perception of the physicalenvironment. Since we rely exclusively on V(λ) to characterize light, weare inherently unable to maximize the benefits of light.

2.1.2 Where does V(λ) apply and where doesn’t it?

As previously stated, V(λ) applies to commerce and should be theonly definition of light for that purpose. Multiple definitions of lightfor different countries or for different manufacturers would make itnearly impossible to compare products for purchase. For commerce, theunderlying physiology, as well as its applicability to the practice of lightingengineering and design, is largely irrelevant. We get into trouble when weapply V(λ) to lighting applications and to lighting standards as if it werethe only measure of the benefits of light for practical purposes.

The photopic luminous efficiency function is not wrong, nor is ituseless for some applications. In fact, it is the best spectral weightingfunction for some lighting applications. A very nice review by Lennie et al.(1993), simply called Luminance, provides a clear understanding of thephotoreceptors and the physiology underlying V(λ), as well as the types oftasks (e.g., acuity, foveal reaction times, speed, and accuracy of reading)where V(λ) can be used appropriately to quantify the visual stimulus. Aspreviously discussed, V(λ) is relevant only to cones, of which there arethree types, each with a peak sensitivity at long (L cones), middle (Mcones), and short (S cones) wavelengths. These three cone types provideour trichromatic color vision, which will be discussed later in this chapter.Figure 2.2 shows the spectral sensitivity of the three cone types (Smithand Pokorny, 1975). Interestingly, only the L and M cones contribute toV(λ); the S cone does not contribute to the photopic luminous efficiencyfunction at all. This is an artifact of the flicker-photometry method thatlargely underlies V(λ). The channel that provides visual information fromthe S cone to the brain is actually slower to respond to rapid changes inlight level than the neural channels carrying information from the L andM cones to the brain. Consequently, the S cone contributes nothing tovisual perception when the flicker criterion is reached. Since the channelthat handles the combined inputs from the L and M cones is faster thanthat which carries information to the brain from S cones, only two of

12 Chapter 2

three photoreceptor types contribute to flicker photometry and, therefore,to V(λ).

If it is not already obvious, lighting standards based on V(λ) (whichare, in fact, all of them) do not capture visual perception of nonflickeringlighted environments (which are, in fact, all of them). In effect, a lightmeter calibrated in terms of the photopic luminous efficiency functionis “blind” to short-wavelength light that quite readily stimulates humanvision under normal viewing conditions. Therefore, to ensure the mostenergy-efficient and cost-effective lighting, we must separate the need fora single measure of light for commerce from the application and regulationof light where we are trying to maximize visual benefits while minimizingcosts. The following chapters are largely devoted to this basic premise:The photopic luminous efficiency function should not be the sole basisfor quantifying light. Although V(λ) is a satisfactory and now orthodoxmeasure of light for commerce, it should not be used all of the time inthe numerator of the lighting value ratio. Rather, the value ratio shouldinclude a quantitative measurement of light relevant to the purpose ofapplying that light. Where fine discrimination by the fovea is required (e.g.,threading a needle), the numerator of the value ratio is appropriately basedon the photopic luminous efficiency function. However, where a subjectivesense of personal security is important (e.g., a parking lot at night), thenumerator in the value ratio should be based on an accurate measureof light as it provides a sense of brightness. Simply put, the photopicluminous efficiency function does not characterize the light for thesepurposes, and therefore, should not be used in the design or regulationof these applications.

2.2 What Is Color?

There are two basic approaches to the study and measurement of color,one based on color appearance and one based on color matching. Theformer approach is, as the name implies, related to measuring how lightsources and illuminated objects are subjectively seen and described. Thelatter approach is related to measuring how physically different lightspectra match, or appear indistinguishable, without regard to how they aresubjectively seen and described.

Color matching is the basis for colorimetry. Because it is so precise andmathematically simple, this system of measuring color is used exclusivelyby lighting manufacturers and, where deemed important, by sanctioningbodies and regulators to characterize the color of light sources andilluminated objects. This implies, as it is true, that the lighting industrydoes not have an orthodox system for measuring the color appearanceof light sources and illuminated objects. Rather, they impose informaldescriptive terms for color appearance on a system of measurement based


on color matching. This is asking for trouble because, unfortunately, theword “color” can be used in two very different ways. “Color” can beused to describe how a light source or an object appears psychologically,and “color” can be used as a mathematically precise description of theoptical radiation emitted by a light source or reflected from an object. Bothusages of the word “color” are correct, but they should never be consideredsynonymous.

2.2.1 Color appearance

In this measurement system, three psychological dimensions are used todescribe apparent color; various names are given to these three dimensions,but here they will be referred to as hue, lightness, and chroma. Hue refersto the qualitative appearance of the color: red, orange, yellow, chartreuse,green, cyan, blue, and violet. Lightness refers to the degree to which thecolor appears dark or bright, from black through shades of grey to white.Chroma refers to the degree to which the hue is seen to be desaturatedby lightness. Figure 2.3 is a much simplified representation of the three-dimensional psychological color-appearance space. It will be noted fromthis figure that the hues are arranged as opposites where, for example,green is opposite red on the hue circle. This reflects, more or less, thephysiology of color appearance. Spectrally opponent, red versus green(r–g) and blue versus yellow (b–y), neurons in the retina provide hueinformation to the brain. Hybrid colors, such as orange, are formed bya “red” signal from the r–g neurons and “yellow” from the b–y neurons.Unique hues such as “red” and “yellow” are formed by signals from justone of the two sets of spectrally opponent neurons. The third channelforming color appearance is the achromatic, luminance channel. This isthe neural channel largely, but not completely, responsible for signalinghow bright the object or the light appears.

Measuring and predicting color appearance is extremely difficult. Thecolor appearance of light sources and illuminated objects is alwayscontextual, meaning that the physical properties of the light source orilluminated object do not uniquely determine how it is subjectively seen.For example, the same patch of paper can appear brighter or darker, moreor less tinted, and more or less saturated depending on the background onwhich it is placed. The patch of paper will look darker and less saturated ifplaced on a bright background, or lighter and more saturated if placed on adark background. Similarly, the patch will appear tinted with the spectrallyopponent hue of its surround. If the patch of paper is placed on a dark-redbackground for example, the patch will appear to have a greener (less-red)tint. The same phenomenon will occur in the temporal domain. Staringat a red-colored patch of paper placed on a white background for severalseconds will induce a spectrally opponent, green after-image on the white

14 Chapter 2

Figure 2.3 The three subjective dimensions of color appearance: hue, lightness,and chroma. Hue and chroma form planes in the subjective color space definedby the spectrally opponent hues, red (RED) versus green (GRN), and blue (BLU)versus yellow (YEL), and the degree of chromatic saturation or vividness of thosehues. The lightness dimension forms the achromatic spine of the subjective colorspace running from black (BLK) to white (WHT).

background when the patch is removed. Thus, the physical characteristicsof the light reflected from the patch do not define its color appearance.Figure 2.4 illustrates how physically identical areas of the page appearquite different depending on the color of the surrounding area.

2.2.2 Color matching

Color matching is a technique whereby the color of a light or object can bemeasured precisely and simply from the spectral power distribution of thesource and the spectral reflectance of the object, with no considerationwhatsoever to viewing context. Like color appearance, however, colormatching is a three-dimensional system. Psychophysical studies conductedin the 19th century and later confirmed, both experimentally andanalytically, show that the appearance of any arbitrary light source canbe perfectly matched from a unique combination of three primary lights.These three primary lights are usually narrowband, nearly monochromaticlight sources that, seen alone, appear red, green, and blue. Thus, even


Figure 2.4 Color appearance depends on context. The two blue and the twoorange disks are each identical but appear quite different depending on the hueand chroma of their surround. The blue and orange discs will also appear quitedifferent depending on the level of illumination falling on the page.

though the arbitrary light source and the matching combination of the threeprimary lights are spectrally different, the human eye cannot see that theyare different in any way. Lights that are physically different but appearidentical are known as metamers. Since any arbitrary light source canbe metameric with a unique combination of three primaries, the relativecontribution of each primary needed to match the light defines its color.

2.2.3 Colorimetry

Colorimetry is the measurement of color. The system of colorimetry usedthroughout the lighting industry is based on color matching experiments,not on color appearance experiments. Since any arbitrary light can bematched exactly with variable proportions of three primary lights, it ispossible to mathematically transform the spectral characteristics of thethree primary lights into imaginary primary lights that, for example,formally tie colorimetry to photometry. The unique combination of theseimaginary primaries “mathematically matches” the arbitrary light source,and thereby also defines its color. Figure 2.5 shows the colors of differentlight sources using the CIE 1931 system of colorimetry (CIE, 1932). Thissystem of measuring color is based on a set of imaginary primaries known

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Figure 2.5 The CIE 1931 chromaticity diagram (CIE, 1932). The color of any reallight source can be plotted within the white area of the chromaticity diagram definedby the spectrum locus, from 380 to 700 nm, and the straight line connecting thespectral extremes. The chromaticities, or colors, of the CIE reference illuminants,A and D65, are plotted together with those associated with a red, a green, and ablue light-emitting diode (LED). Also shown is the line of blackbody radiation (solidblack line) defining the chromaticities of different color temperatures from 1000 to24000 K and the line of daylight chromaticities (dashed black line). Several lines ofconstant correlated color temperature (CCT) are illustrated with thin black lines.

as the color matching functions approved by international consensus.In this system, the proportions of each color matching function, x(λ),y(λ), z(λ), needed to mathematically match a light source defines itscolor. Since the CIE system is based on proportions that sum to unity,the color of a light source can be reduced to a single point in a two-dimensional chromaticity space like that illustrated in Fig. 2.5. Appendix 2works through some examples showing how the CIE 1931 color matchingfunctions are used to define the colors of light sources and illuminatedobjects.

It is important to stress again that colorimetry does not predict colorappearance. In colorimetry, two physically different lights that matchunder one condition will match under any other viewing condition eventhough both may change appearance considerably. For example, two


metameric lights that might appear orange at high radiances can appearbrown at lower levels. Since only the proportions of the imaginaryprimaries (i.e., the color matching functions) need to be the same for thetwo metameric lights, and since the spectral radiance distributions of thetwo lights did not change with level, their color did not change even thoughthey appeared orange at high radiances and brown at low.

2.2.4 Color rendering and tint of illumination

Only two aspects of color are considered important to measure by lightingmanufacturers, the color rendering properties of illumination and the tintof illumination, both of which are based on the system of colorimetrydeveloped in the 1930s. Color rendering is an imprecise concept, but,generally, a light source with good color rendering properties shouldprovide illumination that (1) shows a full palate of perceptible colors,(2) reveals subtle differences among hues, and (3) does not make objectsappear “unnatural.” Color rendering index (CRI) is used by the industry tocharacterize the color rendering properties of fabricated light sources usedfor illumination. Light sources used for illumination are also describedin terms of their perceived “tint.” Correlated color temperature (CCT) isused by the industry to characterize how “warm” (yellow tint) or “cool”(blue tint) the illumination appears. In fact, CRI and CCT are elegantlyintertwined (as is photometry), but these two metrics have led to confusionwith regard to predicting both the color rendering properties and the tintof illumination provided by fabricated light sources. This confusion arisesbecause qualitative interpretations of color appearance are imposed on thecolorimetric calculations that are, again, unrelated to color appearance.

2.3 Color Rendering Index

CRI is a measure of how eight (or sometimes fourteen) special chips ofdifferent spectral reflectance (Appendix 2) change color (chromaticity)when illuminated by a fabricated light source compared to a referencelight source. By definition, the reference source has a CRI of 100, themaximum value. Like all colors, those of the eight special chips used inthe CRI calculation are based on physical measurements of their spectralreflectance when illuminated by a source; the subsequent calculationsdetermine their positions in chromaticity space. CRI is a measure of theamount of shift in the chromaticities of the eight special chips when theyare illuminated by a fabricated light source compared to the referencelight source of the same color temperature. In general, the larger thechromaticity shifts, the lower the numerical value of CRI. Appendix 3describes the calculation process for CRI.

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2.4 Correlated Color Temperature

The reference spectra used in the CRI calculations were chosen asideal sources of illumination that were defined in terms of their colortemperature. As a practical material is heated to incandescence, it beginsto glow red, then, as the temperature increases, it will appear yellow, and atstill higher temperatures, it will appear blue (if it does not vaporize). Thus,the apparent colors of these incandescing materials are directly related totheir actual temperatures, in kelvin (K). The chromaticities of ideal sourcesalso systematically change color as their temperature increases. Figure 2.5shows the chromaticities of ideal, blackbody sources. Practical materialsthat incandesce are said to have CCTs that are directly related to the colortemperatures of blackbody sources. Figure 2.5 shows lines of constantCCT. These are created by drawing the shortest line from the chromaticityof the practical incandescing source to the ideal blackbody line. The CCTof the real source then can be determined graphically or by the calculationsprovided in Appendix 3.

Whether a light source incandesces or not, light emitted from that sourcewill have a chromaticity associated with a specific CCT. Daylight, too, willhave an associated CCT, as shown in Fig. 2.5. For the CRI calculation, thespectral power distribution of a blackbody radiator is used as the referencesource for fabricated sources of illumination with CCTs below 5000 K.For sources with CCTs at and above 5000 K, idealized daylight spectra areused as the reference sources.

Lighting standards that rely on colorimetry (which are, in fact, allof them) cannot be expected to accurately describe color appearance.Therefore, to have the most valuable lighting, we should not relyexclusively on CRI and CCT when trying to maximize color informationbenefits of light while minimizing costs. Sections of the ensuing chaptersare devoted to this basic premise: CRI and CCT should not be thesole bases for quantifying color. Although these metrics are satisfactorymeasures of color for commerce, they should not be used exclusivelyin the numerator of the lighting value ratio. Rather, the value ratioshould include quantitative measurements of color more relevant to theapplication. Where color rendering is important (e.g., retail applications),the numerator in the value ratio should be based on, for example, anaccurate measure of light as it provides illumination for revealing the“naturalness” of fruits and vegetables. Where the tint of illumination isimportant, the amount of hue perceived in the illumination should bedescribed. Simply put, CRI and CCT do not accurately characterize theapparent colors of objects and the tint of illumination, and therefore,should not be used exclusively in the design or regulations of lightingapplications where color appearance is important.

Chapter 3Added Value from NewMetrics

The benefits of all lighting systems are currently measured in termsof orthodox photometry and colorimetry. These systems underliequantification of candelas and lumens and of CRI and CCT. Althoughprecise, these two measurement systems limit our collective ability toprovide benefits and, therefore, value in many lighting applications.

3.1 Beyond lm/W and lm/m2

The lumen has very limited value in two types of applications, one wheredetection of hazards or threats is important and one where subjectiveimpressions of brightness are more important than high spatial resolution(i.e., high acuity). In both cases, the benefit of the light is to support off-axis vision, not foveal vision on which the lumen is based.

Off-axis detection is important for driving an automobile. The validityof this statement can be easily illustrated while driving by obscuringeverything in the visual scene except the fovea. It is quite frighteningto drive while looking through a small hollow tube with one eye. Inthis scenario, the fovea continues to provide visual information, but theperipheral retina has no access to the roadway environment. The reversesituation is not nearly so disturbing. Looking with one eye at ones thumbat arm’s length (or a beer bottle cap stuck on the windshield) will obscurefoveal vision of the roadway, but the peripheral retina will still be gatheringinformation about the roadway environment. The peripheral retina is thereto maintain a sense of direction and to search (without moving our eyes)for potential hazards, while the fovea is there to identify those potentialhazards as life threatening or unimportant.

The peripheral retina has a different spectral sensitivity to light thanthe fovea. This is true when only cones are providing input to the visualsystem, but this difference is even larger at lower light levels whenboth cones and rods participate in visual perception. Figure 2.1 showsV10(λ), the luminous efficiency function of the peripheral retina at light

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levels where only cones are operating. The subscript 10 denotes a setof experimental conditions where human subjects made heterochromaticbrightness matches (not flicker photometry) while viewing a circulardisk 10 deg in diameter (a 6-in dinner plate at arm’s length). Thisfield size covers the on-axis fovea as well as a part of the off-axisperipheral retina. V10(λ) has an enhanced sensitivity to short-wavelengthlight relative to V(λ) because all three cone types, L, M, and S, participatein heterochromatic brightness matches where the off-axis peripheral retinais involved. Recommended light levels for roadways with large trafficvolumes at night are usually high enough to exclude rod participationin off-axis vision. Since roadway lighting standards for these relativelyhigh light levels are based on V(λ), the benefits for off-axis detection fromlight sources that emit short-wavelength radiation can be underestimated.For cone-only light levels where off-axis detection is important, V10(λ) isprobably a better spectral weighting function for estimating the efficacy(visual benefit per watt) of light sources.

A more common and even more profound efficacy penalty is associatedwith roadway lighting for suburban and residential applications. Thelight levels prescribed for these applications are invariably lower thanthose associated with large traffic volumes. As will be discussed morethoroughly in the next subsection of this chapter, the calculation ofefficacy is seriously flawed when selecting light sources for suburban andresidential roadway applications if V(λ) is used to quantify the visualbenefit per watt rather than the spectral sensitivity of the peripheral retinaunder mesopic conditions, where both rods and cones participate in off-axis detection.

The second type of application where V(λ) does not apply includesthose associated with outdoor and indoor applications that do not requirefine spatial discrimination or off-axis detection. For these spaces, theapparent brightness of the room or outdoor space is the most importantdesign criterion. Many outdoor pedestrian malls or indoor hallways andfoyers are designed for relaxed interactions, not for removing a splinterfrom a finger. These gathering and transitional spaces need only enoughlight to feel safe, interact socially, read a menu (albeit more slowly), andto avoid trips and falls. Research has also shown that perceptions of safetyand security at night are well correlated with perceptions of brightness,not with levels of illuminance based on V(λ). For the greatest value, aspectral weighting function that characterizes apparent brightness, ratherthan V(λ), is needed to measure the benefit of the lighting system for theseapplications.

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3.1.1 Mesopic vision

The prefix meso means between. Mesopic vision relates to light levelsbetween those where only rods are active (scotopic conditions) and thosewhere only cones are active (photopic conditions). For many years itwas hoped that a unified system of photometry could be developed thatwould integrate the scotopic and photopic luminous efficiency functionsthrough the mesopic region, thereby making it possible to have a standardway to measure light at any level. Vision scientists tried to unravel themechanisms responsible for the transition from rods to cones (and viceversa) with the hope of developing a unified system of photometry. Thosescientific efforts were all based on studies of apparent brightness fromvery low, scotopic light levels through the mesopic region to photopiclevels. Precise and consistent means of predicting visual responses throughthe mesopic region frustrated many of these endeavors because of thevery complicated nature of apparent brightness. As will be discussed inthe next subsection of this chapter on apparent brightness, the results ofthese experiments showed distinctly nonlinear effects where, for example,adding two lights together actually made the mixture look less bright thaneither light seen separately. Commerce simply could not tolerate a systemof photometry where more light looked less bright!

Recently, a much simpler approach was taken to bridge the scotopicand photopic luminous efficiency functions into a unified system ofphotometry (Rea et al., 2004a). Rather than attempt to map out all ofthe complicated interactions between the neural channels responsible forapparent brightness at low and high light levels, the research problem wassimplified by using reaction times to briefly flashed targets. Unlike studiesusing apparent brightness, reaction times are not significantly influencedby the slower spectrally opponent channels responsible for hue. Thus,in principle, the neural mechanisms underlying reaction times would besimilar to those responsible for flicker photometry, the main method usedin the development of the photopic luminous efficiency function.

Reaction times were measured to 2-deg diameter luminous disksthat were briefly presented either to the fovea or to the peripheralretina. The luminous disks were presented on a large, uniform fieldof the same spectral composition; different spectral compositions (diskand background) were used to estimate the spectral sensitivity of thefovea and of the peripheral retina at different mesopic light levels. Therelationships between reaction times and the photopic luminances of thespectrally different disk/background combinations presented to the foveawere exactly the same, demonstrating (again) that the spectral sensitivityof the fovea is well characterized by V(λ). However, when those samedisk/background combinations were presented to the peripheral retina,reaction times were not simply related to their photopic luminances. Those

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disk/background combinations having greater spectral energy at shortwavelengths at the same photopic luminance resulted in faster reactiontimes and, moreover, the differences increased as the background lightlevels were reduced.

To develop the unified system of photometry, a compromise had tobe adopted. There are no rods in the fovea, so mesopic conditions onlyapply to the peripheral retina where there are both rods and cones. Thephotopic luminous efficiency function is based on the spectral sensitivityof L and M cones in the fovea, not peripheral cones. Therefore, thetransition through the mesopic region from rod-only vision to cone-onlyvision should be better represented by a transition from V ′(λ) to V10(λ),not to V(λ), which excludes S-cone contribution to luminous efficiency.Since photometry based on V(λ) is so deeply engrained into commerce andstandards, it was proposed that the differences between characterizing thelight sources with V10(λ) and with V(λ) should be ignored for a new unifiedsystem of photometry. Based on the experimental results and accepting thiscompromise, it was then possible to model the transition from the scotopicconditions through the mesopic region to photopic conditions, and viceversa, with the following simple equation:

Vmes = XV(λ) + (1 − X)V ′(λ) (3.1)

The parameter X, a luminous efficiency coefficient, describes therelative contribution of the photopic luminous efficiency function [V(λ)]to the photometric measurement. Figure 3.1 shows how X changes as afunction of photopic luminance and the ratio of the scotopic luminanceto the photopic luminance provided by a light source, the so-called S/Pratio. At high, cone-only light levels, X = 1, while at very low, rod-only levels, X = 0. The ratio S/P is a convenient and simple way tocharacterize the spectral radiance distributions provided by different lightsources because the scotopic and photopic luminous efficiency functionsrepresent the extreme spectral weighting functions that can be used inthe unified system of photometry. Therefore, the relative impact of anyspectral radiance distribution on the unified photometric quantities can becharacterized by its S/P value.

Figure 3.2 shows how the luminous efficiency functions change withlight level in the unified system of photometry as well as the resultingchanges in absolute luminous efficacies for the light-emitting diode (LED)and high-pressure sodium (HPS) sources. As light levels are reduced, theLED becomes more efficacious than the HPS source, and vice versa. Verysimilar approaches aimed at developing a system of mesopic photometrywere taken in Asia (Lin et al., 2006) and in Europe (Goodman et al.,2007), providing the consensus necessary for a recommended method formeasuring light under mesopic conditions (CIE, 2010).


Figure 3.1 The unified system of photometry showing the transition (X ) fromscotopic to photopic conditions plotted as a function of photopic luminance (cd/m2)and of light source spectral radiance characterized by the ratio of scotopicluminance to photopic luminance (S/P). [Figure adapted from Rea et al. (2004a).]Also shown are the S/P values for two light sources used to illuminate roadways inNorth America, a 6500 K LED and HPS. The photopic luminances of illuminatedroadways in North America usually range between 0.1 and 1.0 cd/m2.

Although very few lighting applications are at pure scotopiclevels, mesopic conditions apply to many outdoor, nighttime lightingapplications, as illustrated in Fig. 3.1. Therefore, from a practicalperspective, the unified system of photometry or the CIE system canbe used to select light sources that minimize electric energy useat any prescribed luminance level. By selecting a source tuned tovisual sensitivity for that application, the value of the lighting system(benefit/costs) can be maximized.

3.1.2 Apparent brightness

Self-luminous objects (e.g., the sun, exit signs, and traffic signals) as wellas illuminated objects that reflect light (e.g., the moon, room walls, androadways) have an apparent brightness. As more light is generated by atraffic signal or reflected from a roadway, each will appear brighter. Aluminance meter is used to measure photometric brightness, and if theinstrument optics is focused on the traffic signal or on the roadway, thereadings from the luminance meter will increase as more light is generated

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Figure 3.2 A selection of luminous efficiency functions used in the unified systemof photometry together with the relative spectral power distributions (SPDs) of twolight sources, a 6500 K LED and an HPS. Also shown are the luminous efficacies ofthe two light sources using the unified system of photometry. Following photometricconvention, the SPDs were normalized to 683 lm/W at 555 nm to determine theirabsolute luminous efficacies.


or reflected from these two objects. Luminance meters are all calibratedin terms of V(λ) which is, again, based only on the combined spectralsensitivities of the L and M cones, thereby ignoring rod and S conecontributions to visual perception. Therefore, those sources that providerelatively greater amounts of short-wavelength radiation will generallyprovide relatively higher object brightness at equal photopic luminancelevels. This difference is most obvious and very well documented inoutdoor, nighttime applications.

HPS has been the source of choice for illuminating streets, parking lots,and building facades since the 1970s when it was first produced and widelysold. Although HPS has many practical advantages, such as high lightoutput, low depreciation of light output, and long life, a major reason forits dominance in the marketplace was its high luminous efficacy (lm/W).At 100 to 120 lm/W, its luminous efficacies were 3 to 8 times higher thanthose of the sources used for outdoor applications prior to the 1970s. In the1990s and 2000s, however, other light sources, such as metal halide (MH)and “white” LEDs, became more common and less expensive. Comparedto HPS, these light sources provided “whiter” light, which is a liabilityin the competition for high luminous efficacy. Unlike HPS, which haslimited emission at short wavelengths, most “white” light sources emitradiation across the entire visible spectrum. Since short wavelengths havelittle influence on the photopic luminous efficiency function underlyingluminous efficacy (lm/W) and illuminance (lm/m2), white light sourcesare penalized by these measures relative to HPS; the same effect occursfor unified illuminance, as illustrated in Fig. 3.2.

Many studies have shown that roadways illuminated at night by “white”light sources will look brighter than those illuminated to the same photopiclight levels by HPS (Bullough et al., 2011; Ferguson and Stevens, 1956;Fotios and Cheal, 2007a; Fotios and Cheal, 2007b; Rea, 1996; Rea et al.,2009; Rea et al., 2011). For the light levels prescribed for roadways,the visual channels that carry information from both rods and S conesalso contribute to brightness perception. Thus, “white” light sources thatemit radiation at short wavelengths where rods and S cones are sensitivewill make illuminated surfaces appear brighter than those same surfacesilluminated by HPS at the same photopic luminance level.

At high light levels where rods do not participate in consciousperception, the visual channels that carry S-cone information willcontribute to apparent brightness even more than they did at the relativelylow levels prescribed for outdoor lighting. “Cool” sources generally emita higher proportion of short-wavelength radiation than “warm” sources, soin general, offices and schools lighted to a prescribed photopic illuminancewill appear brighter if they are lighted with sources having a highCCT (e.g., 5000 K fluorescent lamps) than by those with a low CCT

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(e.g., 3000 K fluorescent lamps). This is largely true, again, because thephotopic luminous efficiency function is based solely on the L- and M-cone input, minimizing the contribution of S cones to apparent brightness.Nevertheless, CCT can be a misleading metric for estimating the S-conecontribution to apparent brightness (Hu et al., 2006); the SPD of the sourcealways must be considered.

One common method of estimating apparent brightness cobblestogether photometric and colorimetric measurements of light (Alman,1977). Two lights of different chromaticities are compared visually wherethe designated test light is adjusted in radiance to match the brightness ofa reference light. After a visual match is made, the brightness–luminanceratio (B/L) is computed by dividing the luminance value of the test light,designated as B, by the luminance value of the reference light, designatedas L. Thus, the apparent brightness of any light specified in terms of itschromaticity can be quantified in terms of a unitless ratio, its B/L value, fora given reference light source. Figure 3.3 shows constant B/L contours inthe 1931 CIE chromaticity space [adapted from Guth et al. (1980)] where a

Figure 3.3 Contours of equal brightness/luminance ratio (B/L) values in theCIE 1931 chromaticity space. [Figure adapted from Guth et al. (1980).] Thechromaticities of three light sources, designated RED, GRN, and YEL in Table 3.1,are also plotted.


“white” light was used as the reference source (x = 0.37, y = 0.33). TheseB/L contours were determined for a high light level of about 2000 cd/m2.

Although the photopic luminous efficiency function does not properlyweight the S-cone contribution to apparent brightness, the photoreceptors,in fact, are not directly responsible for perception. Rather, apparentbrightness is based on input from three neural channels, red versus green(r–g), blue versus yellow (b–y), and luminance (L). Because of this three-channel input to apparent brightness perception, two lights of differenthue but of equal luminance (i.e., photometric brightness) will only rarelylook equally bright. This is most clearly demonstrated when comparingthe brightness of signal lights of different hue. In particular, lights havinga distinct hue will appear significantly brighter than a “white” light. At thesame luminance, highly saturated red or blue signal lights can appear twoto three times brighter than a “white” signal light.

Although the difference between luminance and apparent brightnesscan be profound, what is even more remarkable about apparent brightnessis that under some conditions, adding two colored lights together willactually make the combination of lights look less bright than either lightalone. In other words, more light appears less bright. This is the result ofthe spectrally opponent color channels contributing to apparent brightness.This highly nonlinear nature of apparent brightness is illustrated in Fig. 3.3and Table 3.1.

Consider the three lights described in Table 3.1 and labeled RED, GRN,and YEL in Fig. 3.3. Luminance is additive, so combining the RED andthe GRN lights together will, of course, produce a higher luminance forthe mixture (10 cd/m2 + 15 cd/m2 = 25 cd/m2). What is remarkable, andentertaining to demonstrate, is that the YEL light, which is the combinationof RED and GRN, actually looks less bright than the RED and GRN lookalone (26.8 for YEL versus 29.3 for RED and 32.3 for GRN).

If the additive systems of photometry and colorimetry were predictiveof apparent brightness, all lights of different chromaticity but of equalluminance would appear equally bright. If apparent brightness could bepredicted by a single, fixed factor, every chromaticity associated with agiven B/L ratio (e.g., 1.56 in Fig. 3.3) would be seen as equally bright atthe same luminance, and the relationship between lights of different B/L

Table 3.1 Characteristics of the three light sources plotted in Fig. 3.3 illustratingthe nonlinear characteristics of apparent brightness.

Apparenthue

Dominantλ

Chromaticity(x, y)

Luminance B/L Apparent brightness

RED red 630 nm 0.70, 0.30 10 cd/m2 2.93 10 cd/m2 × 2.93 = 29.3GRN green 520 nm 0.07, 0.83 15 cd/m2 2.15 15 cd/m2 × 2.15 = 32.3YEL yellow 578 nm 0.48, 0.49 25 cd/m2 1.07 25 cd/m2 × 1.07 = 26.8

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values would be constant at any light level. This is not the case. Becauseof the highly nonlinear nature of apparent brightness, these B/L contourschange shape and position in the chromaticity diagram at different radiancelevels and for different viewing conditions. Therefore, the B/L contours inFig. 3.3 only should be taken as approximate values for different viewingconditions and light levels.

To date, it is impossible to accurately predict apparent brightnessexcept for a fixed set of experimental viewing conditions. Science hassimply not progressed far enough to account for all of the nonlinearinteractions between the three neural channels distributed throughoutthe retina and brain. Progress has been made recently, however, towardreliably estimating the apparent brightness of narrowband signal lights ofdifferent hues, as might be seen by airline pilots approaching an airfield(Bullough et al., 2007). These findings have helped optimize detectionand recognition of signal light colors with energy and cost savings.It remains a challenge to provide quantitative predictions of apparentbrightness for polychromatic, “white” light sources used in architecturalapplications although a provisional model for use in these applicationshas been proposed (Bullough et al., 2011; Rea et al., 2011). Generally, asthe proportion of short-wavelength radiation increases in a “white” lightsource, the model predicts that the relative apparent brightness increasesfor the same photopic light level. Moreover, as light level increases,short-wavelength radiation contributes an increasing amount to apparentbrightness perception. Table 3.2 shows the relative apparent brightnessfor blackbody radiators of different color temperature at the same low(≈0.3 cd/m2) and at the same high (≈30 cd/m2) photopic light levels.Similar findings have been reported by Okawa et al. (2009).

3.2 Beyond “Light”

By definition, light is optical radiation reaching the retina that evokesa visual response. However, light incident on the retina also evokes

Table 3.2 Relative apparent brightness for blackbody radiator spectra of differentcolor temperatures at low and high photopic light levels. Values are based on theprovisional model by Rea et al., 2011, where apparent brightness B(λ) for a givenSPD is a function of the photopic luminous efficiency function V(λ) and the S-conefundamental S (λ) modified by a light-level-dependent parameter g. For the enteredvalues, B(λ) = V(λ) + g × S (λ); g = 2 for the low light level, and g = 3 for thehigh light level.

Blackbody Color Temperature2700 K 3000 K 3500 K 4000 K 5000 K

Modelg = 2 100% 107% 118% 129% 148%g = 3 100% 109% 124% 138% 164%


nonvisual responses. Unlike vision, we have no conscious access to theneural circuitry that controls these nonvisual effects. By observing otherpeople, we have known for centuries that the iris responds to light incidenton the retina (not on the iris itself), but we are not aware of the irisresponse to light of our own eyes. In addition to the iris response, thereis a surprisingly wide variety of nonvisual effects that are also initiated bylight incident on the retina. For example, alertness at night, and perhapsduring the day, can be enhanced by light exposure on the retina, much likealertness is enhanced by caffeine or other chemical stimulants.

Perhaps the most important nonvisual effect of light incident on theretina is its impact on regulating the timing of the biological clockin our brain. The biological clock is at the center of what is termedthe circadian (approximately one day) system, which orchestrates ourdaily cycles of behavior and physiology. These circadian cycles wouldoccur in complete darkness, but the exact timing of our behavioral andphysiological functions is governed by the 24-hour light–dark patternincident on the retina (Czeisler et al., 1986; 1999). Humans have been ableto populate nearly every corner of the globe because we can synchronizeour biological clock to the local times of sunrise and sunset. Interestingly,the characteristics of light important for synchronization are not the sameas those that govern visual perception. For example, the peak spectralsensitivity of the fovea is at 555 nm, whereas the peak spectral sensitivityof the circadian system is near 460 nm. Also, the visual system is quitesensitive at low light levels, while the circadian system has a very highthreshold for activation. With regard to timing, spectrum, and amount,daylight is the perfect light stimulus for the circadian system. Arguably,understanding how light affects the biological clock is the most importantscientific frontier for lighting applications because control of circadianlight (and dark) holds great potential for the development of an entirelynew value proposition for lighting.

3.2.1 Circadian light

The human biological clock, located in the suprachiasmatic nuclei (SCN)of the hypothalamus, sets the timing for every function in our bodies tooperate on a circadian cycle of approximately 24 hours. The SCN enablesour biology to anticipate the best time to perform essential functions, fromsleep to DNA repair in single cells. The 24-hour light–dark pattern onthe retina ensures that these functions will be performed at the right time(Refinetti, 2006).

For survival as a diurnal species, humans evolved such that all of ourbiological functions support activity during the day and rest at night. Thebiological functions of nocturnal species such as mice and owls are alsoregulated by the light–dark pattern but support an active night and a restful

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day. Evolutionarily, the light–dark pattern was determined by the naturalrising and setting of the sun every 24 hours, but in modern times electriclighting provides us, and perhaps other species, with the opportunity tochange the period, phase, or the consistency of the retinal light–darkpattern.

Research over the past 20 years has shown that disruption of a regular,24-hour pattern of light and dark can significantly affect our well-being.We are most aware of circadian disruption when it affects our sleep.Rapid travel across multiple time zones will affect when and how wellwe sleep. We are also often aware of its effects on digestion, alertness, andperformance. These “jet lag” symptoms are indicative of a deeper, moreprofound disorganization in our basic biology that had “expected” todayto be just like yesterday. Those experiencing prolonged disruption of thelight–dark cycle, such as commercial aviation flight attendants and pilots,as well as rotating-shift workers in hospitals and in law enforcement, aremore likely to develop breast cancer, diabetes, and cardiovascular diseasethan their 9-to-5 counterparts because the light–dark pattern on the retinais not consistently cycling every 24 hours (Lucas et al., 1999; Pan et al.,2011; Rafnsson et al., 2001; Schernhammer et al., 2001; 2003; 2006; Wanget al., 2011; Young and Bray, 2007).

Since disruption of the retinal light–dark pattern can have such profoundeffects on our sleep, performance, well-being, and health, it is particularlyimportant that a definition of circadian light be developed. Strictlyspeaking, however, light that regulates the circadian system is not light.By definition, light must evoke a visual sensation in humans. Interestingly,there are some “blind” individuals who have no conscious perception oflight, yet they are perfectly well synchronized to the light–dark cycle onthe retina. The neural apparatus that converts optical radiation on the retinato synchronizing signals for the biological clock is intact even thoughthey have no conscious perception of light. For this reason then, we mustmodify the noun light with an adjective and call optical radiation incidenton the retina that evokes a response in the SCN circadian light. In thepast decade, much has been learned about the spectral sensitivity of thecircadian system such that we can more precisely define circadian light.

A major breakthrough in our understanding came from electrophysi-ological recordings in a very small set of neurons in the retina. RussellFoster had shown in the 1990s that animals experimentally deprived of theknown photoreceptors, rods and cones, could, like some clinically “blind”individuals, become entrained to the light–dark pattern on the retina(Foster et al., 1991). David Berson began searching through the retina foranother type of neuron that might be directly responsive to light. His dis-covery of an intrinsically photosensitive retinal ganglion cell (ipRGC) sur-prised nearly everyone (Berson et al., 2002). Many studies replicating his


findings have now demonstrated that these ipRGCs are central to convert-ing optical radiation on the retina into neural signals that control the timingof the SCN. This class of neuron, in apparently all mammals, is maximallysensitive to radiation at 480 nm (Enezi et al., 2011).

A variety of electrophysiological studies in animals and psychophysicalstudies in humans have shown, however, that the ipRGCs do not act alonein converting light into neural signals for circadian regulation (Hattaret al., 2003). Empirically, the peak spectral sensitivity of the humancircadian system is near 460 nm (Gall and Bieske, 2004). This suggests,as has been validated experimentally, that S cones also play an importantrole in defining circadian light. A model of circadian phototransductionincorporating known neurophysiology of the retina postulates that the b–ycolor channel provides input to the ipRGC (Rea et al., 2005; 2012). Thesecombined photoreceptor mechanisms place the peak spectral sensitivityat 460 nm. Moreover, the b–y channel input introduces, as it doesfor brightness perception, a nonlinear, subadditive response to opticalradiation. The functional significance of this spectral-opponent input tothe ipRGC is unknown, but it implies that the circadian system has arudimentary form of color vision. This would mean that “blue” light carriesinformation about the environment to the circadian system without specificregard to the amount of short-wavelength radiation.

Although it is a fascinating possibility, the significance or even theexistence, of “circadian color” has not been established. What has beenfairly well established is the spectral sensitivity of the circadian systemto individual, narrowband lights (Brainard et al., 2001; Rea et al., 2005;Thapan et al., 2001). Figure 3.4 shows the spectral sensitivity of thehuman circadian system to narrowband lights incorporating responsesfrom the ipRGCs, as well as processed information from rods and conesaccording to a recent model of circadian phototransduction. This firstplot for narrowband spectra does not, however, accurately illustrate thecircadian system’s response to polychromatic lights (i.e., lights composedof multiple wavelengths). A second plot based on the model is also shownfor a polychromatic, “white” light source. This plot illustrates the modeledsubadditive contribution to the circadian system response by the negativelobe in sensitivity to wavelengths longer than 507 nm. Since 507 nm is theestimated crossover point for the spectrally opponent b–y system response,this wavelength is also seen as unique green for color-normal individuals.

Circadian darkness is just as important as circadian light. In fact, it is the24-hour pattern of light and dark that regulates our biological rhythms, notlight per se. It would actually be difficult for humans (or another speciesfrom Earth) to colonize a planet unless it had a 24-hour pattern of sunriseand sunset. A day on Mars, for example, is approximately 24.6 hours long.Since we are programmed to respond to a 24-hour Earth day, it would

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Figure 3.4 Spectral sensitivity of the human circadian system to narrowband(dashed line) and polychromatic (dotted line) spectra. Also shown for referenceis the photopic luminous efficiency function, V(λ) (solid line).

be difficult, if not impossible, to have a “normal” life on Mars where weare active during the Martian day and at rest during the Martian night. Toavoid circadian disruption, we would have to create supplementary electriclighting schemes to keep us entrained to a specific location on the surfaceof Mars. Closer to home, United States submariners are on 18-hour workschedules when they are underway. The officers and crew are expected towork two-thirds of the time and be asleep one-third of the time in their 18-hour day. In fact, submariners never really adjust to 18-hour days. They areconstantly out of rhythm with their work and sleep schedules as measuredby performance and sleep efficiency. The effects on the long-term health ofsubmariners are currently unknown, but anecdotal evidence suggests thatthese individuals have sleep problems well after discharge from militaryservice.

To understand the impact of modern life (e.g., jet airplane travel, spacetravel, undersea military operations, and playing hockey at night), it isimportant to measure the degree to which people are disrupted. Withouta measure of circadian disruption, it will be difficult to address theacute (performance, sleep, digestion) and the chronic (cancer, diabetes)consequences of modern life. Toward this end, methods have beendeveloped to measure circadian disruption, using a personal circadianlight meter, accelerometers, and phasor analysis, a technique borrowedfrom signal processing to examine the relationship between the periodic


patterns of light–dark and activity–rest. The Daysimeter was developedto measure circadian light exposures at the cornea together with headmotions over multiple days (Fig. 3.5). The device was designed asa field instrument to quantify the input–output relationship exhibitedby the circadian system while a human subject lives his/her normallife. The Daysimeter is calibrated in terms of circadian light using thespectral response characteristics illustrated in Fig. 3.4. Phasor analysisquantifies the resonance between the measured light–dark patterns andthe activity–rest patterns; the greater the degree of resonance measuredover multiple days, the lower the degree of circadian disruption. Thismeasurement-analytical system has been successfully used to measurecircadian disruption in a variety of populations where irregular light–darkpatterns have been linked to poor performance (e.g., submariners), poorsleep (e.g., institutionalized senior adults), and breast cancer (e.g., rotatingshift workers). Figure 3.6 illustrates the type of data obtained from theDaysimeter and the resulting phasors.

The synchrony between a light–dark pattern and an activity–rest pattern,like those shown in the left two panels of Fig. 3.6, is determined bycalculating the resonance between these two time series of data. Thetime-series data are treated as an infinitely repeating sequence of lightand activity values that can be incrementally shifted in time. After eachshift, a correlation (r, not r2) between the two data sets is determined.A circular correlation function results from continuously incrementingthe two time series with respect to one another. The circular correlationfunctions for the data from the day-shift nurse and those from the rotating-shift nurse are shown in the middle panels of Fig. 3.6. A resulting circularcorrelation function is then decomposed using Fourier analysis. The 24-hour frequency component is extracted from the Fourier power spectrum

Figure 3.5 The Daysimeter, a circadian light and activity measurement deviceused to assess circadian disruption in the field.

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and is used to characterize the circadian (24 hour) synchrony between thelight–dark and the activity–rest time series. The magnitude of the 24-hourcomponent is used as the measure of circadian disruption; the smallerthe phasor magnitude, the greater the level of circadian disruption. Thephasor angle of the 24-hour component reflects the temporal relationshipbetween the light–dark pattern and the activity–rest time series. Generally,for humans, phasors are in the first quadrant (I) of a phasor diagram, likethose shown in the right two panels, indicating that activity usually persistsin the evening under lighting conditions too dim to stimulate the circadiansystem, but adequate enough to provide visual perception.

As previously noted, women who work rotating shifts are morelikely to get breast cancer. Figure 3.7 shows the distribution of phasormagnitudes for female nurses working different numbers of nights. Ascan be appreciated from this figure, working rotating shifts shortens thephasor magnitudes, indicating greater circadian disruption. Perhaps ofsome interest, working three nights a week may have the greatest impacton circadian disruption.

Much remains to be learned about circadian disruption, but thisimportant topic depends on accurate, ecological measurements ofcircadian light. A great deal of concern has been expressed about theimpact of light at night on circadian disruption, but a surprisingly smallamount of data exists on quantifying light exposures (how much andhow long) as they might affect circadian disruption. Equally importantis measuring light during the day. With deep core buildings and littleaccess to natural daylight, it is conceivable that people are exposed to

Figure 3.7 Phasor magnitudes for day-shift (0 nights worked per week) androtating-shift nurses (1 to 5 nights worked per week). [Figure adapted from Milleret al. (2010).]

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too little light during the day. Key to progress in this area, therefore, isthe development and refinement of measurement tools like the Daysimeterand analytical methods like phasor analysis to determine how circadianlight influences our lives.

3.3 Beyond CRI and CCT

Color is important. It provides us with information about the visualenvironment:

• Are the bananas ripe?

• You look pale; are you not feeling well?

• Is that a blue or a black suit?

Fabricated light sources are usually expected to provide thatinformation, but they do so to various degrees depending on the SPD ofthe source. Unfortunately, since the metrics used by the lighting industryto describe color are based on color matching, not color appearance, it isnot always possible to provide satisfactory answers to color questions likethese.

It is impossible to know how the light source will render object colorsif the objects being illuminated are unknown. A light source that mightmake it easier to determine how ripe the bananas are might make a person’sskin look pale. Moreover, light level is important for color perception so adesignation of the lamp’s color rendering properties will not necessarily bepredictive of color appearance. In particular, at low levels of illumination itis impossible for the human visual system to see color. Therefore, even thebest possible lamp for differentiating between a blue and a black suit willnot be helpful unless the amount of light incident on the clothes is high.The visual system of the individual viewer is also important. Since colorperception is formed in the brain from neural signals from the r–g, b–y,and L channels, color-blind individuals may not be able to differentiateripe from unripe bananas or black suits from blue suits, no matter what thelamp produces.

Although perhaps less important than the color rendering propertiesof a light source, characterizing the tint of illumination is important forsome architectural lighting applications. Some designers or users mayprefer “warm” tints for the bedroom while preferring “cool” tones forthe kitchen, while others may want no tint at all in either application.Lighting manufacturers use CCT to communicate the tint of illumination,but, because this metric is based on the chromaticity of the light emittedby the source, it will not always accurately characterize how “warm” or“cool” the light emitted by a source will appear.


3.3.1 Color rendering

CRI was developed in the 1960s through a consensus process similar tothat which led to the internationally accepted definition of the photopicand scotopic luminous efficiency functions. Daylight was observedto be an excellent source of illumination for revealing colors, forcolor discrimination, and for making natural objects appear natural.However, a problem with using actual daylight as a reference sourcefor comparison to fabricated light sources was that it constantly variesdepending on atmospheric conditions, latitude, and time of day. Withouta standard reference, there was no consistent way in which electriclight sources could be compared in terms of their color renderingproperties. Without a consistent way to determine the color renderingproperties of a fabricated source, international commerce was limited. Formathematical convenience, a range of ideal reference spectra, based onmany measurements of daylight, was adopted by international consensusfor the determination of CRI. It was also important to ensure that the rangeof all possible colored objects would be considered in the calculation.A set of eight standard chips were selected through the same process ofinternational consensus. These chips were chosen to be both representativeof a full palate of spectral hues and easy to fabricate from readily availablepigments. From the ideal reference sources and the standard chips, itwas possible to compare fabricated sources by a standard and consistentmethodology. This system has been used exclusively since the early 1960sto describe the color rendering properties of light sources.

All fabricated lamps used to illuminate rooms and roadways have adesignated CRI value. As with the lumen, CRI enabled manufacturers tocompare their lamps with a metric used around the world. Like the lumen,however, CRI is not a completely satisfactory measure of the benefit ofa light source for color rendering. Several recent studies have shown thatCRI is a poor predictor of how well people like the appearance of fruits andvegetables, skin, and wood (Bodrogi and Schanda, 2009; Davis and Ohno,2009; Jost-Boissard et al., 2009; Narendran and Deng, 2002; Rea andFreyssinier, 2010; Rea and Freyssinier-Nova, 2008). Indeed, some studieshave shown a negative correlation between CRI and color preference. Theinability of CRI to predict color discrimination (i.e., seeing the subtledifference in hue) or color preference was acknowledged by the developersof CRI. Nevertheless the industry settled on CRI as the sole measure ofthe color rendering properties of a light source used for illumination. Fewother metrics of color rendering have even been considered until recently,following the wide distribution of solid state lighting for illumination(Smet et al., 2010; Žukauskas et al., 2011).

As noted earlier, color rendering is a multidimensional construct.Depending on context, good color rendering could mean that objects such

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as skin and fruit look natural, or that one can tell the difference betweena dark blue suit and a black suit, or it could mean that dresses and tieslook vivid and colorful. The fact is that color rendering can mean verydifferent things and, moreover, that no single measure of color renderingcan capture all of those meanings. Although this same point was madenearly 50 years ago (Judd, 1967), recent studies have shown that lightsources used to illuminate fruits and vegetables should score high on atleast two metrics, one related to the consistency with which a fabricatedlight source renders colors compared to a source very similar to actualdaylight, and one related to the vividness with which a fabricated lightsource renders the colors. Although both metrics are based on colorimetry,CRI does well on the first dimension, and gamut area index (GAI) doeswell on the second. Neither by itself does well on both dimensions.

GAI is a convenient way of characterizing in chromaticity space howsaturated the illumination makes objects appear. Figure 3.8 shows two ar-eas bounded by the chromaticities of the eight color chips used to calculateCRI, one for a clear mercury source of 5891 K and one for the reference(daylight) source of the same CCT. The eight chips form two irregularpolygons in chromaticity space; the areas enclosed by the chromaticitiesof the illuminated eight chips define the gamut areas of the two sources.GAI is a relative number whereby an imaginary equal-energy spectrum(i.e., the radiant power is equal at all wavelengths) is scored as 100. UnlikeCRI, which has a maximum value of 100, GAI can exceed 100, mean-ing that some sources saturate colors more than an equal-energy spectrumsaturates color. Appendix 3 describes the calculation procedure for GAI.

As can be seen in Fig. 3.8, the gamut area defined by the eight chipsis larger for the reference source emulating daylight than it is for theclear mercury source. Therefore, the figure illustrates two important colorrendering characteristics of a light source: (1) penalties are assigned to alight source if the positions of the eight color chips move in chromaticityspace relative to daylight, and (2) penalties are assigned if the area definedby those eight chips in chromaticity space is too small or too largerelative to an equal-energy spectrum. The clear mercury lamp causes theeight chips to move in chromaticity space relative to the reference sourceand results in a smaller gamut area than that for the reference source.Therefore, by this dual-metric system, the mercury lamp would not beexpected to render colors as well as daylight, and indeed this is true.

From a practical perspective then, if it is important to enhance vividnesswithout inducing color distortion, the two-metric system of color renderingis superior to CRI alone. It must be noted, however, that lamp designationswith CRI alone or those using the two-metric system do not necessarilyguarantee the best possible results. As previously noted, unless the objectsbeing illuminated, the level of illumination, and the person viewing the


Figure 3.8 CIE 1931 chromaticity diagram (CIE, 1932) showing the blackbodylocus (black line), the chromaticity of an equal-energy spectrum (filled triangle), thechromaticity of daylight at 5891 K (open triangle), and the chromaticities of the eightstandard chips used in the CRI calculation under a mercury vapor source of 5891K (gray circles) and under the reference (daylight) source of the same CCT (opencircles). The von Kries transformation was used to locate the chromaticities for theclear mercury source. GAI was calculated before the von Kries transformation.

objects are known, it is impossible to predict color appearance. The food-display lamp is a good example of how blind adherence to a number(or numbers), particularly a number based on the chromaticity, canmisrepresent the value of a light source, in this case the color benefit perdollar spent. Meat looks gray under daylight. To enhance (i.e., obscure)the color of the meat in the butcher case, the food-display lamp makes itappear a more palatable pink because this lamp has a very high GAI. This,as might be expected, makes the gray hamburger appear both unnatural andmore vivid, but it apparently sells hamburger. Again, it is also impossibleto predict color appearance without knowing the object being illuminated.When only one object is to be lighted, it is easier to select a light sourcefor the desired outcome (albeit, usually by trial and error). In effect then,the concept of color rendering is only useful for general illumination ofarchitectural spaces where the objects to be illuminated, the light level,and the viewer are not always known.

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3.3.2 Tint of illumination

The illumination from fabricated light sources is also usually “tinted” or“off-white” to some degree. CCT is used to describe the tint of illuminationfrom these sources even though they do not incandesce. Imposing a colorappearance designation (i.e., tint) on the chromaticities of light sources hascreated some confusion. This confusion arises for several reasons. Unlikethe CCT of materials heated to incandescence, the apparent “tint” of mostpractical light sources is entirely independent of the actual temperatureof the source. All light sources that provide illumination with a CCTbelow 3500 K are usually considered “warm” sources, with an implied“yellowish-white” tint consistent with what would occur if a materialwas heated to a temperature between 2700 and 3500 K. Those providingillumination with a CCT above 5000 K are usually considered “cool” or“bluish-white” in appearance, again, consistent with what would occur if amaterial was heated to those high temperatures. Obviously, the apparenttints of illumination from phosphor-based sources such as LEDs andfluorescent lamps are completely independent of their actual temperature.For example, a fluorescent light source that has a CCT of 6500 K andproduces “bluish-white” illumination is operated at exactly the same(room) temperature as a fluorescent lamp with a CCT of 2700 K thatproduces “yellowish-white” illumination.

The apparent tint of incandescent materials can be readily predictedfrom their actual temperature, but for other sources of illumination,their actual temperature is completely independent of apparent tint.Confusingly too, the description of color appearance of illumination usedby the industry, that is “warm” or “cool,” is actually opposite that ofthe relative temperatures of ideal sources. Physically hotter stars in thegalaxy, for example, have a higher CCT but appear “cooler.” Perhaps mostproblematic, illumination from light sources with the same CCT, but ofdifferent chromaticities, will not appear identical. Moreover, light sourcesof the same CCT may look neither “yellowish-white” nor “bluish-white.”Rather, they may appear to have a different tint altogether. Figure 3.9shows the chromaticities of three light sources, A, B, and C, all of whichhave a CCT of 4100 K. None appear to have the same tint and none look“yellow–white” or “blue–white.” One looks “greenish–white” (A), onelooks “purplish–white” (B), and one has no tint at all (C). CCT is welldefined in chromaticity space but is a poor and confusing description ofapparent tint.

Recent research has examined the apparent tint of illumination andfound, as one might well expect, that there is no relationship between CCTand how “white” the light sources appear. Rather than follow the line ofblackbody radiation, “white” sources of illumination take a very differ-ent path through the chromaticity diagram. Figure 3.9 shows this trace


Figure 3.9 Lines of constant tint. The heavy black line represents the blackbodylocus, and the thin black lines are lines of constant CCT. The intersectionsof the blackbody locus with the CCT lines are at or near the current targetchromaticities for light sources manufactured as sources of illumination forarchitectural applications. Also shown are the traces of constant apparent tint ofillumination, including “white” (0%). For an explanation of points A, B, C, 1, and 2,see text. [Figure adapted from Rea and Freyssinier (2011).]

(0% tint), illustrating the chromaticities of light used for illumination thatappears to have minimum tint. Although lights with these chromaticitieslook very similar, they are not metameric. Nearly all have subtle “off-white” appearances but, importantly, sources of illumination along the“white” line often appear more similar to one another than they do tosources on the line of blackbody radiation with exactly the same CCT.For example, in Fig. 3.9, illumination from light source 1 and 2 both havea CCT of 2700 K. However, illumination from light source 1 looks lesssimilar to illumination from source 2 than does illumination from lightsources 1 and C. Illumination from both 1 and C appear “white,” whilelight source 2, corresponding to that from a common incandescent A lamp,appears distinctly “yellow–white.”

Arguably, the results of these color appearance studies are obvious.Color appearance is dependent on the three color channels, and thereshould be regions in the chromaticity diagram where the signals from ther–g and b–y spectral opponent channels are minimized. Indeed, theoretical

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work demonstrates that chromaticities along the “white” line in Fig. 3.9are well described by this minimization.

Figure 3.9 also shows lines of constant tint. It is important to point out,however, that the apparent hue changes along a given line of constanttint. Illumination from a source on a line of constant tint designatedwith a minus sign (−) may appear slightly purple, pink, or blue. Thosealong a line of constant tint designated with a plus sign (+) can appeargreen, chartreuse, or yellow. From a spectral-opponent theory of colorappearance, the tint of each point in the chromaticity chart can, inprinciple, be predicted. It must be remembered, however, that colorappearance is contextual. Chromaticity based on the SPD simply does notpredict color appearance. Nevertheless, color appearance of illuminationcan be better predicted from the “white” line and from the lines of constanttint than they can from CCT.

3.4 Lighting Energy Efficiency

The previous discussion has centered on the neglected numerator of thelighting value ratio, the measured benefits provided by lighting. Obviously,the denominator is also important to the lighting value ratio, and much ofthe developments in lighting have been focused on reducing the cost oflighting by reducing the energy needed to provide light. Certainly energy isa central discussion among advocates, government regulators, academics,and manufacturers.

Regulators commonly attempt to limit lighting energy in buildings byrestricting lighting power density; that is, the lighting watts per buildingarea (W/m2). This approach to energy reduction is crude because it doesnot consider when or where the lighting is operated nor, as discussedabove, whether that operation benefits the people within the building. Theconcept of lighting energy efficiency (LEE) was developed to measure theutilization of the lighting system by people. LEE is based on the conceptthat the value of light (benefit/cost) is increased by minimizing wastedlight, properly characterized by the benefits provided, in both the temporaland the spatial domains.

The electric energy consumed by a building for lighting depends onthree factors: the lighting system wattage (W), the hours (h) of lightingoperation, and the size (z) of the building. If the magnitude of any of thosethree factors equals zero, the building is uninhabitable. Either the electriclights don’t exist (W = 0), the lighting system is never energized (h = 0),or there is no building (z = 0)!

The goal of producing energy-efficient lighting should not be tocreate uninhabitable architectural spaces within and outside buildings.Regulations should not be directed to limiting wattage, hours of use, or thesizes of architectural spaces but, rather, to ensure that these three factors


are being utilized by people. In other words, energy should not be wastedby operating the lighting when no one can benefit from it or where light isnot needed.

The concept of LEE was developed as an alternative to limiting lightingpower density to better characterize the effective utilization of light intime and in space. The concept of application efficacy in the temporal andspatial domains underlies LEE.

3.4.1 Application efficacy in the temporal domain

We can divide buildings into different architectural spaces and measurewhen the space is occupied and when the lights are being operated. For agiven architectural space (zi), we can measure how much energy (power× time) is being temporally wasted (wt) by subtracting the hours of lightoperation during occupancy (LO) from the total hours of light operation(LT). Thus, waste in the temporal domain can be defined as follows:

wt = LT − LO (3.2)

A building space where wt = 0 is a building space where the electric lightsare only operated when that space is occupied. Figure 3.10 illustrates thewasted lighting energy measured in several school classrooms. The figureshows two zones of wasted lighting energy, one of high application efficacy(light gray) and one of low application efficacy (dark gray). Classrooms inthe light gray zone are rooms where there is little wasted energy, and thosein the dark gray zone are rooms with much higher levels of wasted lightingenergy. The upper limit of the light gray zone is the 45-deg line in thisfigure and represents the perfect utilization of light: every time the lightsare on, the space is occupied. Thus, this is the line of minimum temporallywasted lighting energy (wt = 0). What is most important to note fromthis figure is that the classrooms with the highest hours of light operationwere not necessarily the classrooms with the greatest wasted energy. Thepoint labeled A shows a classroom with high light operation, but it also hashigh hours of occupancy. Therefore, there is little wasted lighting energy.Conversely, the point labeled B shows a classroom with nearly the samehours of light operation as A, but B is unoccupied much more often. Itis interesting to note that the classroom labeled C is “better than perfect.”In this classroom, the teacher showed movies and slides, so the classroomwas often occupied when the lights were not operated.

The lighting in all of these classrooms was manually controlled by theteachers. As can be readily deduced from this figure, some teachers werequite vigilant about coordinating light operation with occupancy (e.g.,classroom A), while some were not (e.g., classroom B). It is interestingand important to note that those classrooms assigned to only one teacher

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Figure 3.10 Application efficacy in the temporal domain showing therelationships between light operation (L) and occupancy (O) in different classroomsof one school. Application efficacy is greater for those classrooms in light gray (e.g.,classroom A) than for those in dark gray (e.g., classroom B). Perfect utilizationof light by occupants is along the slope of 1.0. Classroom C is “better thanperfect” because movies were often shown in the classroom, so the classroom wasoccupied without electric lighting. [Figure adapted from Rea and Jaekel (1983).]

almost always minimized wasted lighting energy (i.e., were in the lightgray zone). Those classrooms used by several teachers during the day werethe rooms where wasted lighting energy was more prevalent. “Ownership”of the classroom, and its lighting, tends to be associated with lower levelsof wasted lighting energy. When no one is responsible for the room’slighting, wasted lighting energy is more likely to occur.

Automatic controls, based on motion sensing, are often used for thepurpose of reducing wasted lighting energy. One advantage of motionsensors is that the building occupants are freed from having to rememberto turn the lights off when they leave a space. This is very important inspaces where there is no “owner.” However, automatic motion controlsare notorious for missing occupants of a room when the occupants do notmove. The most common solution to this problem is to introduce timedelays before turning the lights off with the expectation that a person willmove enough to activate the sensor before turning the lights off. Wastedlighting energy is increased, however, if the person actually leaves thespace while the lights remain energized. Another problem with motionsensors is false positive responses. People passing in the corridor outsidethe office can trigger the motion sensor, activating the electric lights.


This obviously leads to wasted lighting energy. Consequently, motionsensors often need commissioning by an expert to balance the missesagainst the false positives. At either extreme, wasted lighting energy canoccur both because of long delays to avoid misses or improper placementcausing false positives. Automatic motion sensors work best to reducewasted lighting energy in multiuse spaces (i.e., ones with no “owners”).In “owned” spaces, motion sensors often leave lights on after the occupanthas left the classroom or office, resulting in higher wasted light energy thanwould occur with manual switching. So, as suggested in Fig. 3.10, motionsensors are not necessarily a panacea for lighting energy efficiency.

The same principles can obviously be applied to outdoor architecturalspaces illuminated at night. When these spaces are unoccupied, electriclight is unnecessary, and energy is being wasted. The expectationsassociated with outdoor lighting and the technologies needed to limitwasted lighting energy at night are, however, different from thoseassociated with the interior of buildings. On roadways it is importantfor high-speed automobile drivers to be able to see hazards prior toarrival into the illuminated area. Technologies that can control lightoperation prior to actual occupancy are certainly conceivable but areprobably expensive and not presently available commercially. The samebasic problem exists for parking lots and streets where personal safety isan important consideration. Pedestrians want to see potential threats inparking lots and streets before venturing into these spaces (Boyce et al.,2000). Simple motion sensors with timer delays, as already described,may miss occupants if they do not move for a period of time. Again,technologies that could limit misses while still limiting false positivesare conceivable but well outside current expectations for cost-effectivecontrol technology. Consequently, regulatory curfews may be needed tocoordinate light operation with occupancy in these outdoor spaces (Bronset al., 2008).

3.4.2 Application efficacy in the spatial domain

Just as light can be wasted when an architectural space is lighted at thewrong time, light can be wasted by lighting areas and surfaces that donot need light. The time of operation may be correct, but the placementof light or the level of light is incorrect for meeting the benefit goal oflighting. For example, daylight can offset the use of electric lighting inmany spaces to enable occupants to read and move about. An office mightbe occupied, but there is no need to illuminate this space beyond whatis provided by natural light coming through the windows. Using electriclights to illuminate spaces that do not need additional illumination to meetthe expected benefits wastes lighting energy. Unfortunately, many moderncontrol technologies do not take advantage of daylight to provide visual

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and/or circadian benefits, so it is common to find energized electric lightingin spaces with already adequate illumination from windows and skylights(e.g., corridors, atria, single-story warehouses). It also is not uncommon tofind street lights energized during the day. Energy is wasted where light isprovided from two sources, but only one is needed.

Even the best intentions for saving energy can lead to wasted lightingenergy in the spatial domain. Motion sensors are installed to reduce wastein the temporal domain, but if they turn the lights on when a space isoccupied and the space has adequate daylight, wasted lighting energy isincreased in the spatial domain. To avoid this very problem, many motionsensors today are coupled with photosensor controls or are programmedas “automatic-off and manual-on” devices. A person entering a space mustactively turn the lights on, so if there is adequate daylight, the person willbe less likely to waste lighting energy.

Another way to avoid wasted lighting energy is through optical control,that is, by directing illumination only to where it is needed. Using opticalcontrol to properly illuminate a painting or picture on the wall is importantfor home owners and museum curators alike. Selecting the appropriateplacement and the right beam angle for the light source so that it minimizesreflected glare and maximizes spatial application efficacy is often a matterof trial and error. Appendix 4 is offered as a practical guideline for homeowners to help make light source placement and beam angle selectioneasier.

Table 3.3 and the accompanying images (Fig. 3.11) illustrate theconcept of application efficacy in the spatial domain using appropriateoptical control for illuminating a table top. The goal of the three lightingsystems is to illuminate the table top to a level preferred for reading a book.All light fixtures utilize incandescent lamps; one fixture can house the two

Table 3.3 Application efficacy in the spatial domain. The 130-V A19 lamp isoperated at 120 V, matching the wattage of the PAR30 lamp (i.e., 50 W), whereasthe neodymium A19 lamp operated at 60 W generates the same lumens as thePAR30 lamp (i.e., 630 lm). Application efficacy is defined here as the number oflumens projected into the solid angle defined by the table, 2 m below the ceilingplane (Ω). Thus, application efficacy in the spatial domain is defined as intensityper watt (cd/W) because cd = lm/sr, and Ω ≈ A/d2, where A = area of table(2.28 m2), and d = 2 m.

Lamp Type(incand.)

Wattage(W)

LampLumens

(lm)

FixtureLumens

(lm)

FixtureEfficacy(lm/W)

ApplicationEfficacy(cd/W)

Illuminance(lx)

A19 (130 V) 50 570 (@120 V) 476 9.5 3.2 90A19 neodymium 60 630 526 8.8 2.9 99PAR30 (35-deghalogen flood)

50 630 620 12.4 7.9 328


Figure 3.11 Illustration of application efficacy in the spatial domain together withthe fixtures used in the examples from Table 3.3. The fixture illustrated in (a)houses the A19 lamps in Table 3.3, and the fixture illustrated in (b) houses thePAR30 lamp in Table 3.3.

different A19 lamps (the “A” designation indicates that the lamp has a“pear” shape, and the diameter of the bulb is 19-eighths of an inch, or 23/8 inches) and the other fixture is designed to contain a PAR30 (the “PAR”designation stands for parabolic aluminized reflector, and the diameter ofthe lens is 30-eighths of an inch, or 3 3/4 inches.). For comparison, thewattage can be the same (50 W) for two of the light fixtures, or the lumensgenerated by the sources can be the same (630 lm) for two light fixtures.The light distributions from the two types of fixtures are illustrated in therenderings. Both types of fixtures are designed to be down-lights, but thelamp type makes a very large difference as to their application efficacy inthe spatial domain. As the tabulated values demonstrate, the applicationefficacy of the PAR30 is greatest because the spatial distribution of lightemitted from the fixture best matches the surface area of the table, thusmeeting the benefit goal for illuminating the table to the desired level forreading. In fact, it would take three or more of the A19 fixtures to meet thedesired illuminance level.

Application efficacy in the spatial domain is also very important foroutdoor locations and was the foundation for a computational systemknown as outdoor site-lighting performance (OSP) (Brons et al., 2008).

48 Chapter 3

OSP provides a comprehensive method for controlling light pollutionduring nighttime operation of outdoor lighting systems that illuminateparking lots, roadways, plazas, and sports fields. The OSP computationalmethod is based on the premise that light leaving a property can bequantified, measured, and thereby systematically controlled. A virtual boxis placed over the property to calculate the amount of light leaving the boxcontributing to sky glow. Light leaving the box in a given direction can beused to determine two other aspects of light pollution: light trespass ontoan adjacent property and discomfort glare from the light sources on theproperty. From those calculations, the amount of wasted light generated bythe lighting systems on the property can be both calculated and measured.

In general, wasted light can be quantified using the concept ofapplication efficacy in the spatial domain. The intensity of the light sourceis defined as the amount of light generated in a given direction (φ/Ωi).The solid angle of the illuminated surface (Ωt) is defined as the area ofthe table divided by the square of the distance to the light source (A/d2).The amount of light reaching the surface is the illuminance on that surfacearea (φ/A). If all of the lumens generated by the source reach the surface,no light is wasted. This is rarely possible, but to minimize waste, the ratioof the solid angle of the task (Ωt) should closely match the solid angle ofillumination on the surface that the source provides (Ωi). More explicitly,waste in the spatial domain (ws) that can be minimized with optical controlcan be defined as follows:

ws = (φ/Ωi − φ/A/d2)/φ/Ωi

ws = φ/Ωi/φ/Ωi − φ/A/d2/φ/Ωi (3.3)

ws = 1 − φ/A/d2/φ/Ωi

ws = 1 −Ωt/Ωi

It should be noted that ws values of less than 0 do not meet the benefitgoal and, therefore, should not be used.

3.4.3 Calculating lighting energy efficiency

Wasted lighting can be reduced by proper implementation of temporalcontrols (motion sensors and manual switches) and spatial controls(photosensors and optics). A lighting system that delivers light where andwhen it can benefit people is energy efficient. If the lighting system doesnot meet the benefit goal, it is not energy efficient by definition. Thus, theLEE of a building can be defined as the ratio of the utilized lighting energy(in watt hours) in a building (Whzutilized) divided by the total lightingenergy (also in watt hours) consumed in the building (Whztotal) assumingthe lighting system meets the benefit goal. As LEE approaches unity, the


building is more energy efficient:

Whzutilized/Whztotal = LEE (3.4)

The ability to measure Whzutilized depends on the ability to measure ofwasted lighting energy, in both the spatial and the temporal domains. Thus,

Whzutilized = Whztotal(1 − ws)(1 − wt) (3.5)

or

LEE = (1 − ws)(1 − wt) (3.6)

Chapter 4An Invitation

This chapter is an invitation to make a positive change for society. Byfocusing on the value of lighting, we can improve our quality of life andincrease the sustainability of our planet. To foster this positive change,first, we must be more specific than we have been in the past in definingthe expected benefits of lighting and, second, we must utilize practicalmetrics that characterize those benefits in applications.

Understanding the difference between precision and accuracy isessential for moving forward to increase the value of lighting. Figure 4.1is an illustration of this difference. The cluster of red arrows on the edge ofthe roundel is a tight, precise grouping relative to the cluster of blue arrows.However, the blue arrows are, as a group and individually, more accurateat hitting the center of the roundel. Indeed, the best of the red arrows isworse than the worst of the blue arrows in terms of hitting the center ofthe target. By analogy, we are quite good at precise measurements of light

51

52 Chapter 4

Figure 4.1 Illustration of the difference between precision and accuracy. The redarrows are more precisely distributed on the target than the blue arrows but areless accurate in hitting the target center.

through photometry and colorimetry, but like the red arrows, we are oftenprecisely off target with the metrics we use to characterize the benefits inlighting applications.

The development of simplified metrics representing complex, nonlinearphysiological processes is essential for more accurately characterizingthe benefits, and, therefore, the value of lighting. Completely accuratecharacterizations of human neurophysiological responses to light, bothvisual and nonvisual, will continue to be significant challenges for basicscience. However, our inability to completely understand these complexneural systems should not be a barrier to utilizing new benefit metricsaimed at increasing the value of lighting now.

Inertia, whether it’s physical or sociological, can be difficult toovercome. Simplifying a problem is often the first step toward positivechange. Toward this end, the benefit metrics previously described in thisbook are simplified in this chapter, and supporting examples are providedthat help illustrate the magnitude of change that could be realized fromthese metrics.

The premise is that simplified, more accurate metrics characterizing thebenefits of lighting will effect a greater positive change on society and theenvironment than will the precise metrics we are currently using.

An Invitation 53

The following simplified benefit metrics for lighting applications areoffered as additions to the traditional lighting metrics of luminousintensity, luminous efficacy, and CRI and CCT that are, rightly, used forcommerce:

• Unified illuminance• Bright illuminance Low levels High levels• Circadian illuminance• “Class A” color CRI + GAI “White” lighting• Lighting energy efficiency Temporal application efficacy Spatial application efficacy

As new scientific insights are gained, these metrics should be refinedand expanded to make them more accurate. These metrics are offered hereto help us get started on increasing the value of lighting. It should benoted in passing, however, that the proposed metrics discussed here donot compromise precision in favor of accuracy. Rather, the ones proposedare just as precise as the metrics we currently use based on orthodoxphotometry and colorimetry.

The supporting examples in each of the following sections are basedon actual light sources used in lighting applications today. Table 4.1 liststhe different light sources chosen for the supporting examples, includingrelevant details for each light. Appendix 5 shows the relative spectralpower distributions for the sources in Table 4.1.

Note that the light sources in Table 4.1 have been divided into threegroups. The light sources in the first group generate very high intensitiesand are often used in luminaires to illuminate outdoor applications. Thelight sources in the second group generate more-modest intensities and aretypically used in luminaires to illuminate indoor applications. The thirdgroup includes a blue LED, a fluorescent lamp used in aquaria, and afluorescent lamp used to illuminate meat cases in grocery stores. Listedwith each light source is their system (lamp and driver where required) andluminous efficacy in lumens per watt.

The table provides information to compare light sources in terms ofrelative efficacy using the proposed benefit metrics. The benefit metricsused in the efficacy calculations are based on the luminous efficiencyfunctions described in the subsequent sections. The absolute efficacyvalues are unimportant for comparing light sources, so within each groupa light source was chosen as a reference source to which all others in

54 Chapter 4

Table 4.1 Three groups of commerically available light sources. Sources in group1 generate high intensities and are typically used in outdoor applications; thosein group 2 generate more-modest intensities and are typically used for interiorapplications. Sources in group 3 are typically used for specialty applications.

# Light source CCT CRI GAI x y Systemlm/W

S/P

1.1 HPS, 400 W 2050 15 14 0.5208 0.4134 96 0.661.2 Pulse-start MH, 320 W 4159 58 51 0.3799 0.3984 72 1.611.3 Mercury vapor, 400 W clear 5891 15 25 0.3191 0.4317 42 1.331.4 LPS, 180 W 1785 −42 0 0.5681 0.4285 144 0.251.5 Xenon, 1000 W 5853 97 91 0.3245 0.3439 26 2.371.6 White LED, 6500 K 6528 72 84 0.3116 0.3340 80 2.062.1 A lamp, 60 W (frosted) 2764 100 49 0.4555 0.4109 14 1.362.2 A lamp, 60 W (neodymium doped) 2789 78 65 0.4460 0.3962 11 1.522.3 Halogen, 3277 K 3279 100 65 0.4184 0.3969 20 1.602.4 CFL, 15 W 2653 83 48 0.4652 0.4141 61 1.112.5 Fluorescent, F34T12 cool white 4022 62 58 0.3833 0.3905 63 1.482.6 Fluorescent, FO32T8RE835 3308 86 69 0.4157 0.3939 86 1.442.7 Fluorescent, F40T12 daylight 4861 90 84 0.3502 0.3645 50 1.972.8 Ceramic MH, 100 W 4075 93 80 0.3773 0.3749 66 1.792.9 White LED, 2700 K 2706 84 49 0.4575 0.4072 65 1.212.10 White LED, 6500 K 6528 72 84 0.3116 0.3340 80 2.063.1 Blue LED, 470-nm peak N/A N/A 4 0.1247 0.0929 10 12.793.2 Fluorescent, F20T12 aquarium

lamp13408 83 103 0.2676 0.2719 51 2.76

3.3 Fluorescent food-display lamp 3195 61 128 0.3869 0.3153 36 1.97

that group are compared in terms of their relative benefit per watt. HPSis the most common light source used today for outdoor applications andserves as the reference source for the first group; cool-white fluorescentwas, but no longer is, the most common light source used for commercialapplications. It simply serves as a convenient reference source for thesecond group. Obviously, since the values are relative, sources can beeasily compared to one another or to a different source that might serveas a reference.

It will be noted that a white LED source is common to the first twogroups. This source offers a means for comparing sources across outdoorand indoor applications because the intensities of the luminaires containingLED sources are largely dependent on the number of individual LEDsused in the luminaire, rather than the intensities of the individual LEDsthemselves.

4.1 Unified Illuminance

A system of unified photometry would support all lighting applicationswhere visual performance (speed and accuracy) is important, from verylow to very high light levels. However, any system of unified photometrymust be a greatly simplified characterization of the luminous stimuli for

An Invitation 55

very complex and distinct visual channels. V(λ) is based solely on thespectral sensitivity of the luminance channel with input from just twotypes of cones, L and M, that populate the central fovea. V ′(λ) is basedsolely on the spectral sensitivity of the rods that populate the peripheralretina. Both functions completely ignore the S-cone spectral sensitivity. Asystem of photometry that unifies V(λ) and V ′(λ) implicitly ignores S-conecontribution to vision as well as the basic architecture of the retina wherethe different types of photoreceptors are found at different locations.

Moreover, for accurate implementation, any system of unifiedphotometry must be more concerned with luminance than withilluminance. The visual system responds to brightness (or luminance)—light reflected from (or transmitted through) a surface to the eye. Totake into account the change from a cone-only to a rod-only responseby the visual system, both the directional reflectance of the surface andthe directional irradiance on that surface must be considered. Because thedirectional reflectance characteristics of the illuminated surface are oftenunknown and difficult to determine, nearly all lighting practice is based onspecification of illuminance, rather than luminance.

Application of a completely accurate unified system of photometrymust, therefore, take into account the complexities of the retinalarchitecture and the directional reflectance of the various surfaces ofobjects in the visual environment. This is practically impossible for mostapplications. For practical reasons then, with the aim of greater accuracyat the expense of high precision, four luminous efficiency functions areoffered here for implementation into a system of lighting practice basedon illuminance. These four luminous efficiency functions are defined andtabulated in Appendix 6 and are illustrated in Fig. 4.2. Each functionis designed to be used over a different, limited range of light levels.For ease of application, the luminous efficiency functions supportinga system of unified illuminance are referenced to current, orthodoxphotopic illuminance levels (in lux) that can be found in every documentrecommending light levels.

V(λ) and V ′(λ) are the orthodox photopic and scotopic luminousefficiency functions; Vmh(λ) and Vml(λ) are proposed as high and lowmesopic luminous efficiency functions, respectively. These latter twospectral weighting functions would be appropriate for light levels currentlyrecommended for roadways, parking lots, and parks by the IlluminatingEngineering Society (DiLaura et al., 2011). The proposed system of unifiedilluminance would be based, therefore, on the application of four level-specific, spectral weighting functions of irradiance. This system wouldgreatly streamline lighting specifications while better characterizing visualperformance than a system based on V(λ) alone, as is current practice.

The benefits of a system of unified photometry would be to firstmaintain current lighting design practice at high light levels where

56 Chapter 4

Figure 4.2 Luminous efficiency functions for unified illuminance.

foveal visual performance (speed and accuracy) is the important designconsideration. All current lighting recommendations are based on V(λ),so accepting a system of unified illuminance would imply no changes tocurrent illuminance recommendations for offices, schools, and factorieswhere visual performance is the important design criterion. The greatestimplications for practice with a system of unified illuminance are foroutdoor applications where low, mesopic light levels are currently applied.For those light levels where both rods and cones participate in peripheral

An Invitation 57

vision, the benefits of unified illuminance would be to support off-axisvisual detection. This benefit would be particularly important for roadwayand security applications.

It should be noted that automobile headlights and the fixed roadwaylighting system are often redundant because they are both trying toaccomplish the same safety benefit, namely to illuminate hazards onthe roadway directly in front of the moving automobile. To maximizesafety benefits, these two lighting systems should be better coordinated,each trying to accomplish slightly different, but reinforcing objectives(while also minimizing wasted lighting energy in the spatial domain). Thefixed roadway lighting system should illuminate the roadway margins toenhance off-axis visual detection, while the headlights should illuminatethe roadway itself to maximize on-axis visual detection and recognition.With regard to the unified illuminance, light source selection for the fixedroadway lighting system should be based on the appropriate mesopicluminous efficiency function in Fig. 4.2, whereas the headlights shouldbe chosen based on V(λ), also shown in Fig. 4.2.

Four light sources from Table 4.1 were chosen to illustrate howthe proposed unified illuminance system would be used. Quantitativecomparisons of electric power savings from several different lamp typesused in outdoor applications are shown in Table 4.2.

To make these comparisons, it was first necessary to characterize thelight sources in terms of their S/P ratio, the relative effectiveness of theSPD of the light source for the scotopic [V ′(λ)] and the photopic [V(λ)]luminous efficiency functions. (The S/P ratios for most fabricated lightsources are readily available from lamp manufacturers.) Depending onthe desired light level, which is usually based on recommended photopicilluminance levels, one of the four spectral weighting functions is appliedto the SPD. Normalizing, in this case to the most common light sourceused for outdoor applications, HPS, it is then possible to compare theenergy savings associated with the different light sources associated withthe different levels of unified illuminance. Again, it is assumed that the

Table 4.2 High-intensity sources selected from Table 4.1 to compare the relativepower needed to meet criterion levels of unified illuminance. Values highlighted inpink and in green are those where the relative power needed to meet the desiredlighting benefit is greater than and less than that for the reference source (HPS),respectively.

System with source Relative power# Description (S/P) lm/W V(λ) Vmh(λ) Vml(λ) V′(λ)1.1 HPS, 400 W (0.66) 96 100 100 100 1001.2 Pulse-start MH, 320 W (1.61) 72 1.33 0.98 0.68 0.551.4 LPS, 180 W (0.25) 144 0.67 0.79 1.12 1.791.6 (2.10) White LED, 6500 K (2.06) 80 1.20 0.78 0.50 0.38

58 Chapter 4

same visual benefit is associated with a given level of unified illuminanceand, taking into account all of the capital and operational costs, the valueof the lighting system can be calculated. It should perhaps be noted againthat the perceived benefit is visual performance, not apparent brightness,which is a different design criterion.

4.2 Bright Illuminance

Apparent brightness depends on the achromatic color channel and thetwo spectrally opponent color channels, r–g and b–y. Much more isknown about the achromatic channel response underlying the photopicluminous efficiency function [V(λ)] than is known about the nonlinearinteractions between these three channels in actual applications. In fact,the latest science can only approximate the combined brightness responsefrom these three channels. Broadly, and ignoring rod-only contributionto apparent brightness, the relative contributions of these three channelsvary considerably with light level. At low, mesopic levels, the b–y channelcontributes very little to brightness perception, whereas at very muchhigher levels, the b–y channel begins to dominate (Purdy, 1931; 1937).Over the range of lighting levels provided by electric light sources, outdooras well as indoor, all three channels contribute to brightness perceptionbut, again, to varying degrees. Proposed here are two additional spectralweighting functions, VB2(λ) for low, outdoor applications and VB3(λ) forhigh, indoor applications. These two functions are based on the provisionalmodel for apparent brightness proposed by Rea et al. (2011). The functionsare defined and tabulated in Appendix 6 and are illustrated in Fig. 4.3.Unlike the nonlinear brightness perceptual response functions discussedearlier in the book, these two spectral weighting functions are additive anddirectly analogous with traditional luminous efficiency functions such asthose used in conventional photometry and the mesopic spectral weightingfunctions discussed in the previous section.

Directly analogous to unified illuminance, a proposed system of brightilluminance is introduced that would obviate the complex geometricalrelationships between the light source, the object being illuminated,and the position of the observer. To encourage practical application ofthe perceived brightness concept, three level-specific, spectral weightingfunctions of irradiance would be used. The proposed system of brightilluminance would better characterize visual response to optical radiationin applications where visual performance was not the primary designcriterion. These would be applications where ambient lighting conditionsare not intended to support visual performance as the primary benefit.

The benefit of designing lighting applications based on brightilluminance would be to maintain a subjective sense of safe andsecure nighttime outdoor environments, and cheerful and open indoor

An Invitation 59

Figure 4.3 Luminous efficiency functions for bright illuminance.

architectural spaces. Several light sources from Table 4.1 were chosento illustrate how bright illuminance would be utilized to compare powerrequirements from the different types of light sources that might be usedin outdoor and indoor applications.

Depending on a chosen light level, a different spectral weightingfunction would be applied to the light source SPD. For outdoorapplications, the power reductions relative to HPS are compared to variouslight sources in Table 4.3. For indoor applications, the power reductions arecompared to the cool-white linear fluorescent source in Table 4.4. Again,

60 Chapter 4

Table 4.3 High-intensity sources selected from Table 4.1 to compare the relativepower needed to meet criterion levels of bright illuminance. Values highlighted inpink and in green are those for which the relative power needed to meet the desiredlighting benefit is greater than and less than that for the reference source (HPS),respectively.

System with source Relative power# Description lm/W VB2(λ) V′(λ)1.1 HPS, 400 W 96 100 1001.2 Pulse-start MH, 320 W 72 0.95 0.551.4 LPS, 180 W 144 0.78 1.791.6 (2.10) White LED, 6500 K 80 0.65 0.38

Table 4.4 Moderate intensity sources selected from Table 4.1 to compare therelative power needed to meet a criterion level of bright illuminance. Valueshighlighted in pink and in green are those for which the relative power needed tomeet the desired lighting benefit is greater than and less than that for the referencesource (cool-white fluorescent), respectively.

System with source Relative power# Description lm/W VB3(λ)2.5 Fluorescent, F34T12 cool white 63 1002.1 A lamp, 60 W (frosted) 14 5.792.4 CFL, 15 W 61 1.382.6 Fluorescent, FO32T8RE835 86 0.802.7 Fluorescent, F40T12 daylight 50 1.072.8 Ceramic MH, 100 W 66 0.892.9 White LED, 2700 K 65 1.251.6 (2.10) White LED, 6500 K 80 0.56

for the same level of bright illuminance, it is assumed that the same visualbenefit is achieved. For a given level of bright illuminance then, the valueof the lighting system can be determined after taking into account all ofthe capital and operational costs.

4.3 Circadian Illuminance

Before electric lighting, the natural 24-hour pattern of sunrises and sunsetssynchronized our biological rhythms with the local environment, no matterwhere we were on Earth. Electric light has the potential to disrupt thisnatural rhythm and, in doing so, negatively affect our health and well-being.

The term circadian light is used here to describe optical radiationthat stimulates the retinal mechanisms leading to the master clock andgoverns the timing of our biological rhythms. Much has been learnedrecently about the phototransduction processes that underlie stimulationof our master clock. It has become clear that mechanisms similar to thosegoverning apparent brightness participate in this process. In particular, the

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subadditive, b–y spectral opponent channel provides input to the ipRGCwhich, in combination with its own response to optical radiation, conveyscircadian light information to the master clock.

Although more complicated, a single luminous efficiency function forcircadian illuminance [Vc(λ)] is offered here for architectural lightingpractice. As with unified illuminance and bright illuminance, the geometricrelationships between source, object, and observer are not considered.Unlike the other luminous efficiency functions, the proposed function forcircadian illuminance has a negative region between 550 and 730 nmthat reflects the subadditive nature of circadian phototransduction. Thisluminous efficiency function to be used with polychromatic, whitelight used in architectural lighting practice is defined and tabulated inAppendix 6 and illustrated in Fig. 4.4. The human circadian system hasa very high threshold for response, particularly for broadband, white-lightsources used in architectural applications. For this reason, a minimumilluminance level of 100 lx has been set for its application. Below thislevel, white-light sources used for illumination are largely ineffective forstimulating the human circadian system.

The benefits associated with defining circadian illuminance arepotentially profound. Since all of our biological rhythms are dependenton the light–dark cycle incident on the retina, it is quite importantthat we begin to measure and apply light (and dark) with the purposeof minimizing circadian disruption. Personal light sensors, reflectinginsights gained by recent research, are being developed to measurecircadian disruption in the field. These sensors are calibrated in termsof the human circadian system response to light. Several light sourcesfrom Table 4.1 were chosen to illustrate how they compare in termsof efficaciously providing circadian illuminance during the daytime.

Figure 4.4 Luminous efficiency function for circadian illuminance.

62 Chapter 4

Table 4.5 Moderate intensity sources selected from Table 4.1 to compare therelative power needed to meet a criterion level of circadian illuminance. Valueshighlighted in pink and in green are those for which the relative power needed tomeet the desired lighting benefit is greater than and less than that for the referencesource (cool-white fluorescent), respectively.

System with source Relative power# Description lm/W VC(λ)2.5 Fluorescent, F34T12 cool white 63 1002.1 A lamp, 60 W (frosted) 14 6.712.4 CFL, 15 W 61 2.352.6 Fluorescent, FO32T8RE835 86 0.852.7 Fluorescent, F40T12 daylight 50 0.822.8 Ceramic MH, 100 W 66 0.742.9 White LED, 2700 K 65 1.711.6 (2.10) White LED, 6500 K 80 0.43

Quantitative comparisons of energy savings from the different lamp typesthat might be used in daytime applications are shown in Table 4.5.These comparisons do not, however, fully characterize the value of thelighting system. It must be remembered that providing circadian darknessis also important for maintaining entrainment to the local environment.Thus, sources with poor efficacy for delivering circadian illuminance yethigh efficacy for delivering photopic illuminance can be quite desirablefor use at night. Therefore, temporal controls based on individual lightexposures over a 24-hour period must become an essential part of lightingpractice. As such, it is more difficult to determine the value of circadianilluminance applications simply because the sensors and controls are notreadily available on the market. Until then, a simple rule of thumb isoffered—provide high levels of circadian illuminance during the daytimeand minimize or eliminate circadian illuminance during the evening andat night.

4.4 “Class A” Color

Color rendering is recognized as an important lighting applicationcriterion. Presently, CRI is used exclusively as the benefit metric for thispurpose. Recent studies have shown, however, that CRI does not alwayspredict people’s color preferences for fruit, vegetables, skin, and othernatural objects, but that light sources high in CRI and high (but not toohigh) in GAI were predictive of color preferences where natural objectswere illuminated. Neither metric by itself could be used to predict colorpreferences (Rea and Freyssinier-Nova, 2008; Rea and Freyssinier, 2010).

Light sources used for illumination do not always provide “white” light.Rather, the illumination provided by a source is often tinted to somedegree. CCT is used to characterize how “warm” (yellowish-white) and

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“cool” (bluish-white) light sources appear. Recent studies indicate thatpeople prefer light sources that provide minimum tint in the illuminationfor outdoor and for residential applications (Rea et al., 2009; Rea andFreyssinier, 2012). Surprisingly perhaps, it is possible to find a “white”point for any CCT in the chromaticity diagram between 6500 and 2700 K.Therefore, the tint of the illumination is not directly related to CCT.

Both color rendering and the color of illumination can be important tousers. The color characteristics of a light source are particularly importantin retail and health care applications. In fact, according to an online surveyof lighting specifiers, the benefits of a light source that provides whiteillumination with good color rendering properties are judged greater thanthe cost reductions from energy savings (Rea et al., 2004). Proposed hereis a designation for Class A color light sources used for illumination.These sources would have CRI values greater than or equal to 80 and GAIvalues greater than 80 but less than 100. They would also provide “white”illumination as defined by the line of minimum tint described by Rea andFreyssinier (2011) and shown in Fig. 4.5. The chromaticities of severallight sources in Table 4.1 are plotted in this figure; those that meet theClass A color designation are presented as filled diamonds. Sources withClass A color designations do not necessarily imply a reduction in relativeefficacy using other benefit metrics.

Figure 4.5 Chromaticities (CIE, 1932) for a selection of light sources fromTable 4.1 used for illumination plotted with the line of blackbody radiation and theline of minimum tint. The light sources labeled 1.5, 2.7 and 2.8 in Table 4.1 meetthe criteria for a Class A color designation.

64 Chapter 4

4.5 Lighting Energy Efficiency

The metrics previously discussed in this chapter are associated withweighting the SPDs of light sources that might be used in an applicationaccording to the benefits expected from those light sources. Light sourcescan be readily compared according to the metrics outlined in this chapterbefore the lighting is actually installed. Utilization of the lighting energyefficiency (LEE) concept becomes significantly more complicated becausedetermination of the amount of wasted lighting energy in both the spatialand temporal domains requires an understanding of the architectural spacebeing illuminated. Moreover, implementation of LEE to increase thevalue of lighting becomes significantly more complicated because controltechnologies and analytic tools are required.

As discussed in Chapter 3, to minimize wasted lighting energy in thetemporal domain, it is necessary to match times of occupancy with times oflight operation. Techniques aimed at minimizing lighting energy (kilowatthours) alone, such as centralized switching for a building, reduce the valueof lighting and should not be recommended nor used. Operation of manualswitches and motion sensors for local spaces within the building, bothof which depend on occupant presence, can be and usually are effectivecontrol technologies for reducing wasted lighting energy. Therefore, bothof these local control technologies significantly increase the value of thelighting system over centralized switching, which has no regard to localoccupancy. Nevertheless, both of these common local control technologieshave limitations for reducing wasted lighting energy because they areimperfect in matching the times of lighting operation with times ofoccupancy. Sometimes people forget to turn lights off manually as theyexit a space and, to avoid false off and false on commands, motion sensorsdo not accurately track occupancy. Local control technology platformsinterfaced with personal phones equipped with GPS technology may bebetter able to match time of occupancy with time of light operation in thenear future, adding greater value to the lighting system by tightening therelationship between the times of light operation and times of occupancy.Irrespective of the specific technology, however, value-added lightingcontrol technologies aimed at reducing wasted lighting energy in thetemporal domain must be able to relate the times of light operation to thetimes of occupancy.

In the spatial domain, there are two strategies for reducing wastedlighting energy. The first is to avoid illuminating the same space twice,and the second is to match the optical distribution of the light source to thespecified area that requires illumination.

With proper maintenance, daylight controls for streetlights usually workvery well and are inexpensive, thus avoiding lighting the street withelectric lights when daylight alone can provide the visual benefit. With this

An Invitation 65

simple control technology, even the times of operation are well coordinatedwith seasonal changes and weather conditions. Daylight controls forinterior applications where natural light from windows offsets electriclight operation in the ceiling continue to be problematic in practice, butsome good system technologies and design software have been developedto coordinate window daylight with ceiling electric light. However, it isimportant to be able to find these technologies and software. Appendix 7provides resources that may be helpful when attempting to maximize thevalue of daylight controls in buildings.

Analogous to the integration of daylight with electric light for exteriorand interior applications, there are also untapped opportunities to reducewasted lighting energy by coordinating streetlights with automobileheadlights. Both streetlights and headlights are designed to providevisibility for automobile drivers, but the optical designs of these twolight sources often conflict and actually reduce the visual benefit they areseparately designed to provide. New strategies for integrating streetlightsand headlights have not been developed but, again, because streetlightand headlight standards are developed independently of one another,existing standards are actually the primary barrier to increasing the valueof roadway lighting.

Optics are also important for minimizing wasted lighting energy.Illumination that strikes areas outside of the specified area of interest iswasted. Notwithstanding the lack of coordination among roadway lightingstandards, optical controls of automobile headlights and of streetlightsare excellent and continue to improve. Both types of lighting systemsprovide illumination only to those places prescribed for illumination, andboth utilize readily available optical design software to minimize wastedlighting energy.

Illuminating specific task areas for many health care applications(dentistry, neurosurgery, dermatology) are also excellent, and wastedlighting energy is significantly minimized by good optical control.Sophisticated, commercially available optical design programs arecommonly utilized in the design of fixtures, and the following Internetsearch terms can help locate these resources:

• “lighting simulation software”• “luminaire design software”• “optical design software”• “reflector design software”• “optical engineering software”

A different class of software is used for architectural lighting design.For designing architectural spaces, both indoor and outdoor, commercialsoftware can be used with the published photometric distributions of

66 Chapter 4

fabricated lighting systems to minimize wasted lighting energy. Thefollowing Internet search terms can be used to find architectural lightingdesign software:

“lighting software”“lighting design software”“lighting application software”“lighting analysis software”“lighting planning software”“lighting building software”“lighting building simulation software”“lighting simulation software”

4.6 What’s Next?

Simplifying ideas is certainly an important step toward positive change,but socializing those ideas may be even more important. The invitationoffered at the beginning of this chapter implicitly suggests that there willbe a gathering of interested people where the ideas expressed in this bookcan be discussed, debated, and improved. This is a critical, perhaps mostimportant, next step toward increasing the value of lighting. Without thissocialization process, it seems much less likely that the ideas expressed inthis book will effect a positive change for society and the environment.

Chapter 5Conclusion

5.1 We Believe What We Hear

In a classic study of human nature, Russo and Schoemaker (1989) showedthat we believe what we hear, whether or not what we hear is actuallytrue. They compared people’s beliefs about health risks with what waspublished in newspapers about health risks. They found that beliefs werelargely based on the frequency of newspaper reports on health risks, noton the facts. For example, stomach cancer is a greater killer than all motorvehicle accidents combined, but stomach cancer is not covered by thenewspapers, while motor vehicle accidents are (Table 5.1). Consequently,most people believe that motor vehicle accidents kill more people thanstomach cancer kills.

The lighting community is not immune to this phenomenon. Inspired byRusso and Schoemaker, we undertook a similar study aimed at examininghow trade press coverage of a technology influenced what professionallighting engineers and designers believed about a technology. Wemeasured the amount of press coverage devoted to different light sourcesused for outdoor lighting applications and then queried practitionersabout the light sources they chose for illuminating outdoor parkinglots and roadways. We found a nearly perfect correlation between how

Table 5.1 Comparison between the actual number of deaths in the United Statesby various causes, the number of newspaper reports of each cause of death and,by pair, the public’s perception of the number of deaths. [Adapted from Russo andSchoemaker (1989).]

Cause of Death Annual U.S. Total(× 1000)

(Typical) NewspaperReports per Year

Public’s RelativeChoice for Each Pair

Stomach cancer 95 1 14%Motor vehicle accidents 46 137 86%

Emphysema 22 1 45%Homicide 19 264 55%

Tuberculosis 4 0 23%Fire and flames 5 24 77%

67

68 Chapter 5

much coverage was given to a technology in the trade press and whichtechnology actually got specified (Fig. 5.1). Despite clear, objective,and irrefutable evidence that the least-specified technology (low-pressuresodium) was superior to all others based on existing lighting designmetrics, the (then) newest and least cost-effective light source (metalhalide) was preferred by practitioners (Rea and Bullough, 2004).

Our study was conducted before LEDs became the hot topic in lighting.If that earlier study was to be repeated today, it seems highly likely thatLED technologies would now be rated highest among specifiers as thepreferred source for outdoor applications because there are very, very fewarticles in the trade press today on any other technology. The fact thatLEDs are the most expensive source to specify using existing metricsseems to have little to do with what gets specified.

Imagine then a world where the value of lighting was actively discussed.Even if the new metrics proposed in the present book were completelyignored, I believe profit and sustainability would markedly and measurablyimprove because the topic of value for lighting would be vigorouslydiscussed. It is important then to modify a statement made in the previouschapter with a single word:

The premise is that discussing simplified, more accurate metricscharacterizing the benefits of lighting will effect a greater positive change

Figure 5.1 Percentage of times low-pressure sodium (LPS), high-pressuresodium (HPS), and metal halide (MH) light sources are mentioned in lighting trademagazine articles and the percentage of times each lamp type was identified asa better choice for outdoor lighting applications in a survey of lighting specifiers.[Figure adapted from Rea and Bullough (2004).]

Conclusion 69

on society and the environment than the precise metrics we are currentlyusing.

Simple, evidence-based results are not going to be effective on theirown. Positive change will only follow discussions that acknowledge thefact that the benefits of lighting go well beyond photopic illuminancelevels, luminous efficacy, CRI, and CCT. Talk is cheap, but if the talkcenters on the following points, it will be the most important next steptoward more valuable lighting.

5.2 What to Talk About

This book was written with the aim of making several strategic and logicalpoints to those individuals wanting to discuss and improve the value oflighting:

• Value is defined as the ratio of benefits to costs, so the value of light canbe increased both by reducing costs and by increasing benefits.• Less attention has been given to the benefits of light than to the costs

of light and, as such, value has been implicitly and functionally definedonly in terms of reducing the costs, not increasing benefits.• Orthodox photometry and colorimetry only sometimes define the

benefits of light for applications.• For commerce, it is perfectly reasonable to define and measure light in

terms of candelas and lumens and define and measure color in terms ofCRI and CCT.• The benefits of light for applications need to be, first, defined and,

second, measureable.• If we are to minimize wasted capital and natural resources, it is

necessary to think in terms of the value of lighting and, thus, it isimportant to measure the benefits as well as the costs of light foreach application. It is equally important to reduce wasted lightingby matching light operation with occupancy and by matching lightdistribution with the area to be illuminated.

So, this book was written as a starting point for thoughtfulconsideration, discussion, and action by those vested in better and moresustainable lighting, including manufacturers, practitioners, regulators,advocates, educators, and, of course, users. It is far from a final proposalon how to best implement more valuable lighting. Science will continueto improve our understanding of the impact of light on people, and thatinformation should inform and improve the benefit metrics offered here.Engineering will continue to improve temporal and spatial controls, andthose technologies and software will further reduce wasted lighting energy.However, the fact that we do not understand everything about light and itsimpact on the human condition and the fact that we do not have the perfect

70 Chapter 5

technologies and software to minimize waste are not legitimate excusesfor doing nothing. By defining and better measuring the benefits of lightingtogether with evaluating the utilization of lighting in different applications,we can add value to society now.

Appendix 1

Acronyms, Abbreviations,and Notation

B/L ratio of brightness to luminanceBLK blackBLU blueb–y blue versus yellowCCT correlated color temperaturecd candelaCFL compact fluorescent lightCIE Commission Internationale de l’ÉclairageCRI color rendering indexCS circadian light stimulusEES equal energy spectrumGAI gamut area indexGRN greenHPS high-pressure sodiumipRGC intrinsically photosensitive retinal ganglion cellK kelvinkm 683 lm/WL luminanceLED light-emitting diodeLEE lighting energy efficiencylm lumenLPS low-pressure sodiumlx luxMH metal halidenm nanometerOSP outdoor site-lighting performancePAR parabolic aluminized reflectorRED redr–g red versus greenSCN suprachiasmatic nucleiS/P ratio of scotopic luminance to photopic luminance

71

72 Appendix 1

SPD spectral power distributionsr steradianTCS test color sampleV(λ) photopic luminous efficiency functionV ′(λ) scotopic luminous efficiency functionVB2(λ), VB3(λ) spectral sensitivities for apparent brightnessVC(λ) spectral sensitivity of the human circadian systemVmh(λ), Vml(λ) spectral sensitivities under mesopic light levelsW wattWHT whiteYEL yellowφ luminous fluxΩ solid angle

Appendix 2

Determinations ofChromaticity

Presented here is basic information about the determinations of chro-maticity.

In Table A2.1 the relative SPDs [E(λ)] of two CIE reference lightsources, illuminants D65 and A (shown simply as “A”), are tabulatedfor 10-nm intervals [∆10(λ)]. The spectral reflectances [ρ(λ)] of the eighttest color samples (TCSs) for determining CRI are tabulated in the nextcolumn grouping. The spectral tristimulus values needed to determine theCIE 1931 chromaticity coordinates of an SPD are given in the remainingcolumns. These values represent the amount of the ideal primaries in theCIE (CIE, 1932) colorimetry system needed to match each wavelength.

Each corresponding value—spectral power, spectral reflectance factor,and tristimulus value—is plotted for the complete wavelength range(380–730 nm) in Figs. A2.1–A2.3.

Finally, a truncated example of the calculation procedure for chromatic-ity is given in Table A2.2 using CRI color sample #8 and CIE illuminant A.

Example

The chromaticity, or “color,” of sample #8 for CRI calculations will bedetermined when it is irradiated by illuminant A.

1. Multiply the spectral irradiances of illuminant A at every wavelengthinterval from 380 nm to 730 nm, in 10-nm intervals (second column)by the spectral reflectance of sample #8 (third column) at everycorresponding wavelength interval.

2. Keeping track of the wavelength intervals, multiply these products, inturn, by the values at each of the three color matching functions, x, y, z,for every wavelength interval (last three columns).

3. Sum these products for each of the three color matching functions todetermine the tristimulus values, X, Y , and Z.

4. Calculate the relative contribution from each tristimulus value to thechromaticity of the illuminated chip, x, y, z, where their sum adds

73

74 Appendix 2

to unity:x = X/(X + Y + Z)y = Y/(X + Y + Z)z = Z/(X + Y + Z)

5. Since the sum of x, y, and z add to unity, it is possible to describe the“color” of any light in the two-dimensional CIE 1931 diagram. By con-vention, x and y are used and are termed the chromaticity coordinates.

Figure A2.1 Tristimulus value plotted for the wavelength range (380–730 nm).

Figure A2.2 Spectral reflectance factor plotted for the wavelength range(380–730 nm).

Determinations of Chromaticity 75

Tab

leA

2.1

SP

Ds

ofill

umin

ants

D65

and

Ata

bula

ted

for

10-n

min

terv

als.

The

spec

tral

refle

ctan

ces

ofth

eei

ght

test

colo

rsa

mpl

es(T

CS

s)fo

rde

term

inin

gC

RI

are

inth

ene

xtgr

oup

ofco

lum

ns.

The

tris

timul

usva

lues

need

edto

dete

rmin

eth

eC

IE19

31ch

rom

atic

ityco

ordi

nate

sof

anS

PD

are

give

nin

the

rem

aini

ngco

lum

ns.

Wav

elen

gth

(nm

)L

ight

Sour

ces

(val

ues

for

1000

lm)

CR

IT

CS

Refl

ecta

nce

(ρ)

Tri

stim

ulus

Val

ues

D65

(W/n

m)

A(W

/nm

)1

23

45

67

8x

yz

380

000

692

000

133

021

90

070

006

50

074

029

50

151

037

80

104

000

140

0000

000

6539

00

0075

70

0016

40

252

008

90

070

009

30

310

026

50

524

017

00

0042

000

010

0201

400

001

146

000

200

025

60

111

007

30

116

031

30

410

055

10

319

001

430

0004

006

7941

00

0126

70

0024

00

252

011

80

074

012

40

319

049

20

559

046

20

0435

000

120

2074

420

001

294

000

285

024

40

121

007

40

128

032

60

517

056

10

490

013

440

0040

064

5643

00

0120

00

0033

50

237

012

20

073

013

50

334

053

10

556

048

20

2839

001

161

3856

440

001

452

000

390

023

00

123

007

30

144

034

60

544

054

40

462

034

830

0230

174

7145

00

0162

10

0044

90

225

012

70

074

016

10

360

055

60

522

043

90

3362

003

801

7721

460

001

632

000

513

022

00

131

007

70

186

038

10

554

048

80

413

029

080

0600

166

9247

00

0159

10

0058

20

216

013

80

085

022

90

403

054

10

448

038

20

1954

009

101

2876

480

001

606

000

655

021

40

150

010

90

281

041

50

519

040

80

352

009

560

1390

081

3049

00

0150

70

0073

20

216

017

40

148

033

20

419

048

80

363

032

50

0320

020

800

4652

500

001

515

000

813

022

30

207

019

80

370

041

30

450

032

40

299

000

490

3230

027

2051

00

0149

30

0089

70

226

024

20

241

039

00

403

041

40

301

028

30

0093

050

300

1582

520

001

452

000

984

022

50

260

027

80

395

038

90

377

028

30

270

006

330

7100

007

8253

00

0149

20

0107

40

227

026

70

339

038

50

372

034

10

265

025

60

1655

086

200

0422

540

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167

023

60

272

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20

367

035

30

309

025

70

250

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00

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20

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10

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90

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560

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386

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20

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312

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80

253

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264

059

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9950

000

3957

00

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50

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50

298

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20

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028

00

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40

256

027

20

7621

095

200

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580

001

327

001

553

034

10

335

031

50

247

026

00

225

025

40

278

091

630

8700

000

1759

00

0122

90

0165

20

390

034

10

285

021

40

232

022

10

270

029

51

0263

075

700

0011

(con

tinue

don

next

page

)

76 Appendix 2

Tab

leA

2.1

(con

tinue

d)

Wav

elen

gth

(nm

)L

ight

Sour

ces

(val

ues

for

1000

lm)

CR

IT

CS

Refl

ecta

nce

(ρ)

Tri

stim

ulus

Val

ues

D65

(W/n

m)

A(W

/nm

)1

23

45

67

8x

yz

600

001

247

001

752

042

40

342

026

40

185

021

00

220

030

20

348

106

220

6310

000

0861

00

0124

20

0185

10

442

034

20

252

016

90

194

022

00

344

043

41

0026

050

300

0003

620

001

215

001

949

045

00

341

024

10

160

018

50

223

037

70

528

085

440

3810

000

0263

00

0115

40

0204

70

451

033

90

229

015

40

180

023

30

400

060

40

6424

026

500

0000

640

001

160

002

144

045

10

338

022

00

151

017

60

244

042

00

648

044

790

1750

000

0065

00

0110

90

0224

00

450

033

60

216

014

80

175

025

80

438

067

60

2835

010

700

0000

660

001

112

002

334

045

10

334

021

90

148

017

50

268

045

20

693

016

490

0610

000

0067

00

0114

00

0242

60

453

033

20

230

015

10

180

027

80

462

070

50

0874

003

200

0000

680

001

085

002

516

045

50

331

025

10

158

018

60

283

046

80

712

004

680

0170

000

0069

00

0096

60

0260

50

458

032

90

288

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50

192

029

10

473

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70

0227

000

820

0000

700

000

993

002

690

046

20

328

034

00

170

019

90

302

048

30

721

001

140

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000

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00

0103

10

0277

40

464

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60

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017

00

199

032

50

496

071

90

0058

000

210

0000

720

000

854

002

855

046

60

324

043

10

166

019

60

351

051

10

725

000

290

0010

000

0073

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40

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40

195

037

60

525

072

90

0014

000

050

0000

Determinations of Chromaticity 77

Figure A2.3 Spectral power plotted for the wavelength range (380–730 nm).

Table A2.2 Truncated example of calculating the chromaticity or “color” of sample#8 irradiated by illuminant A.

Wavelength* (nm) EA (W/m2) ρ8 c × x c × y c × z

380 0.00013 0.104 0.0001 0.0000 0.0006390 0.00016 0.170 0.0008 0.0000 0.0037400 0.00020 0.319 0.0062 0.0002 0.0296410 0.00024 0.462 0.0329 0.0009 0.1571420 0.00029 0.490 0.1304 0.0039 0.6266430 0.00034 0.482 0.3178 0.0130 1.5509440 0.00039 0.462 0.4286 0.0283 2.1500450 0.00045 0.439 0.4536 0.0513 2.3910460 0.00051 0.413 0.4183 0.0863 2.4013470 0.00058 0.382 0.2957 0.1377 1.9485480 0.00066 0.352 0.1517 0.2206 1.2900| | | | | |

| | | | | |

690 0.00260 0.717 0.2890 0.1044 0.0000700 0.00269 0.721 0.1510 0.0543 0.0000710 0.00277 0.719 0.0789 0.0286 0.0000720 0.00285 0.725 0.0409 0.0141 0.0000730 0.00293 0.729 0.0204 0.0073 0.0000

X = 46.4240 Y = 33.8959 Z = 14.3384x = 0.4904 y = 0.3581 z = 0.1515

* Center of the 10-nm wavelength interval for integration.c(λ) = km × EA(λ) × ρ8(λ) × ∆10(λ)where: EA(λ) = spectral irradiance in W/m2

ρ8(λ) = spectral reflectance of sample #8∆10(λ) = 10-nm wavelength interval for summationkm = 683 lm/W

Appendix 3

Color Rendering MetricCalculations

The following step-by-step calculation methods for color renderingindex and gamut area index are adapted from ASSIST recommends,“Recommendations for Specifying Color Properties of Light Sources forRetail Merchandising” (ASSIST, 2010).

A.3.1 Calculating Color Rendering Index

The general color rendering index (CRI) of a light source is determinedfrom the spectral power distribution (SPD) and other standard conditions.The example below follows the method outlined in “CIE Technical ReportNo. 13.3-1995” (CIE, 1995), where the method is described in detail, andstandard colorimetric data needed for the calculations are available.

The steps below detail the CRI calculation process using a givenlight source’s SPD; in this example, an equal energy spectrum (EES) isnormalized to 1.0.

Step 1 Derive the correlated color temperature (CCT) of the light sourcefrom its chromaticity coordinates in the CIE 1960 (u, v) uniformcolor space.

Step 1(a) Determine the CIE 1931 XYZ tristimulus values of thelight source:

X = kSPD

780∑λ=380

SPD(λ)x(λ)∆λ

Y = kSPD

780∑λ=380

SPD(λ)y(λ)∆λ

Z = kSPD

780∑λ=380

SPD(λ)z(λ)∆λ

79

80 Appendix 3

where:

• kSPD = 100/[ΣSPD(λ)y(λ)∆λ] and is used to normalize Y to100 for a given SPD,• SPD(λ) is the relative SPD of the light source,• x(λ), y(λ), z(λ) are the color matching functions for the CIE

1931 2 Standard Observer, and• ∆λ is the wavelength increment of the SPD.

For this EES, kSPD = 0.9358.

Step 1(b) Determine the CIE 1960 (u, v) values of the light source:

u =4X

X + 15Y + 3Z

v =6X

X + 15Y + 3ZFor an EES, u = 0.2105, and v = 0.3158.

Step 1(c) Determine the CCT of the light source. For an EES, CCT= 5454 K.

Step 2 Determine the reference illuminant based on the CCT of the lightsource. The reference illuminant is mathematically defined and hasthe same CCT as the light source of interest. The reference illuminantis calculated in one of the following two ways:

Step 2(a) If the CCT of the light source is less than 5000 K, thereference illuminant is a Planckian radiator of the same CCTand can be calculated as

SPDRef (λ) = 2 × π × h × c2

×(1 × 10−9 × λ

)−5÷

(e[(h×c÷k)÷(TC×1×10−9×λ)] − 1

)where:

• c = 299792458• h = 6.6260693 × 10−34

• k = 1.3806505 × 10−23

• λ = each wavelength in the range of interest; for example,380 nm, 382 nm, 384 nm. . . 780 nm• TC = the CCT of interest in kelvin

Step (2b) If the CCT of the light source is greater than or equalto 5000 K, the reference illuminant is a mathematically definedphase of daylight of the same CCT and can be calculated as

SPDRef (λ) = S 0(λ) + [M1 × S 1(λ)] + [M2 × S 2(λ)]

where:

Color Rendering Metric Calculations 81

• S 0(λ), S 1(λ), and S 2(λ) are daylight distribution vectors,

• M1 =−1.3515 − 1.7703xD + 5.9114yD

0.0241 + 0.2562xD − 0.7341yD

• M2 =0.0300 − 31.4424xD + 30.0717yD

0.0241 + 0.2562xD − 0.7341yD

• xD =−4.6070 × 109

T 3C

+2.9678 × 106

T 2C

+0.09911 × 103

TC+

0.244063, if CCT ≤ 7000 K

• xD =−2.0064 × 109

T 3C

+1.9018 × 106

T 2C

+0.24748 × 103

TC+

0.237040, if 7000 K < CCT ≤ 25000 K

• yD = −3.000 (xD)2 + 2.870xD − 0.0275• Tc = 5454 K for this EES

For the reference illuminant of this EES (5454 K), kSPD =

0.9898, u = 0.2048, and v = 0.3209.

Step 3 Determine the CIE 1960 (u, v) values for each of the eight testcolor samples (TCSs) (CIE, 1995) for both the light source of interestand the reference illuminant.

Step 3(a) Derive the tristimulus values (X,Y , and Z) (for i = 1 to8) for each of the eight TCSs:

Xi = kSPD

780∑380

SPD(λ)x(λ)TCSi(λ)∆λ

Yi = kSPD

780∑380

SPD(λ)y(λ)TCSi(λ)∆λ

Zi = kSPD

780∑380

SPD(λ)z(λ)TCSi(λ)∆λ

where:

• kSPD is from Step 1• SPD(λ) is the relative SPD of the light source or the reference

illuminant• x(λ), y(λ), z(λ) are the color matching functions for the CIE

1931 2 Standard Observer• TCSi(λ) is the spectral reflectance of the TCS denoted by

number i [for tabulated values see (CIE, 1995)]• ∆λ is the wavelength increment of the SPD

Table A3.1 shows the results of the calculations for Step 3(a)

82 Appendix 3

Table A3.1 CIE 1931 tristimulus values of the eight TCSs when illuminated by anEES and the reference illuminant of the same CCT.

EES Reference illuminantX Y Z X Y Z

TCS1 35.50 30.46 22.61 33.93 30.18 20.52TCS2 29.48 29.28 13.65 28.41 29.18 12.61TCS3 25.32 30.40 9.06 24.63 30.56 8.45TCS4 21.16 28.90 19.22 20.57 29.19 18.12TCS5 25.63 30.23 36.81 24.60 30.46 33.93TCS6 28.90 29.25 52.92 27.40 29.38 48.35TCS7 35.01 29.50 49.09 33.08 29.38 44.18TCS8 40.34 31.95 41.91 38.19 31.66 37.76

Step 3(b) Derive the CIE 1960 (u, v) values for each TCS using theequations

u =4X

X + 15Y + 3Z

v =6X

X + 15Y + 3Z

Table A3.2 shows the results of the calculations for Step 3(b).

Table A3.2 CIE 1960 (u, v) chromaticities of the TCSs when illuminated by anEES and a reference illuminant of the same CCT.

EES Reference illuminantu v u v

TCS1 0.2534 0.3262 0.2476 0.3303TCS2 0.2314 0.3447 0.2255 0.3474TCS3 0.1991 0.3587 0.1938 0.3607TCS4 0.1652 0.3385 0.1604 0.3415TCS5 0.1739 0.3077 0.1687 0.3133TCS6 0.1846 0.2802 0.1788 0.2875TCS7 0.2242 0.2833 0.2183 0.2907TCS8 0.2500 0.2971 0.2439 0.3033

Step 4 Apply the von Kries adaptive color shift to account for thedifferences in chromatic adaptation states between the light sourceof interest and the reference illuminant (CIE, 1995).

Step 4(a) Derive constants c and d for both the light source (sub-index t) and the reference illuminant (sub-index ref ) using thefollowing equations:

c =1v

(4 − u − 10v)

d =1v

(1.708v + 0.404 − 1.481u)


For the EES, ct = 1.9985, dt = 2.0001; for the referenceilluminant, cref = 1.8235, and dref = 2.0217.

Step 4(b) Apply the adaptive color shift to each TCS under thelight source using the following equations (CIE, 1995):

ut,i =10.872 + 0.404 cref

ctct,i − 4 dref

dt,idt,i

16.518 + 1.481 cref

ctct,i −

dref

dt,idt,i

vt,i =5.520

16.518 + 1.481 cref

ctct,i −

dref

dt,idt,i


Table A3.3 CIE 1960 (u, v) chromaticities of the eight TCSs after applying theadaptive color shift.

EESut,i vt,i

TCS1 0.2489 0.3304TCS2 0.2262 0.3478TCS3 0.1934 0.3610TCS4 0.1584 0.3426TCS5 0.1670 0.3136TCS6 0.1777 0.2873TCS7 0.2188 0.2900TCS8 0.2456 0.3029

Step 5 Determine the CIE 1964 W∗U∗V∗ values for each TCS whenilluminated by the light source (sub-index t) and the referenceilluminant (sub-index ref ) using the following equations (CIE,1995):

W∗i = 25 (Yi)1/3 − 17

U∗i = 13(W∗

i)

(ui − u)V∗i = 13

(W∗

i)

(vi − v)

Table A3.4 shows the results of the calculations for Step 5.

Step 6 Determine the individual color rendering indices for each TCS.

Step 6(a) Determine the color shift ∆E for each TCS for i = 1 to 8using the following equation (CIE, 1995):

∆Ei =

√(U∗ref ,i − U∗t,i

)2+

(V∗ref ,i − V∗t,i

)2+

(W∗

ref ,i −W∗t,i

)2

84 Appendix 3

Table A3.4 CIE 1964 W∗, U∗, and V∗ values of each TCS when illuminated byan EES, and the reference illuminant of the same CCT.

EES Reference illuminantW∗ U∗ V∗ W∗ U∗ V∗

TCS1 61.1 35.0 7.4 60.8 33.8 7.4TCS2 60.1 16.7 20.9 60.0 16.1 20.6TCS3 61.0 −9.1 31.8 61.2 −8.8 31.6TCS4 59.7 −36.0 16.8 60.0 −34.6 16.0TCS5 60.9 −29.9 −5.8 61.1 −28.7 −6.1TCS6 60.0 −21.1 −26.2 60.1 −20.3 −26.2TCS7 60.2 11.0 −24.2 60.1 10.5 −23.7TCS8 62.3 33.0 −14.6 62.1 31.6 −14.3

Step 6(b) Determine the individual color rendering index for i = 1to 8 using the equation

Ri = 100 − 4.6∆E

Table A3.5 shows the results of Step 6(a).

Table A3.5 Color shift and individual CRI for each TCS when illuminated by anEES.

∆E Ri


Step 7 Determine the general CRI:

Step 7(a) Derive the arithmetic mean of the eight individual CRIs,R1 to R8:

Ra =18

8∑1

Ri

For an EES, Ra = 95.

A.3.2 Calculating Gamut Area Index

Gamut area of a light source is commonly calculated as the area of thepolygon defined by the chromaticities in CIE 1976 u′v′ color space of the


eight CIE TCSs specified in CIE Technical Report No. 13.3-1995 (CIE,1995) when illuminated by a test light source. For purposes here, the gamutarea of the EES is scaled to 100 and defined as gamut area index (GAI)(Rea and Freyssinier-Nova, 2008; Rea and Freyssinier, 2010). The gamutarea of any other light source is scaled accordingly. GAI is a convenientmetric to supplement CRI because, like CRI, it is derived from the SPDof a light source and the resulting chromaticities of the same eight CIEstandard color samples.

The following steps show how to derive the gamut area for an EES, butthe process is the same for a light source of any SPD and can be completedwhile calculating the CRI of the light source.

Step 1 Determine the CIE 1976 (u′, v′) values for each of the eight TCSs(CIE 1995) when illuminated by the light source.

Step 1(a) Derive the CIE 1931 tristimulus values (X,Y , and Z) foreach TCS, for i = 1 to 8:

Xi = kSPD

780∑380

SPD(λ)x(λ)TCSi(λ)∆λ

Yi = kSPD

780∑380

SPD(λ)y(λ)TCSi(λ)∆λ

Zi = kSPD

780∑380

SPD(λ)z(λ)TCSi(λ)∆λ

where:

• kSPD is a constant to normalize Y to 100,• SPD(λ) is the relative SPD of the light source or the reference

illuminant,• x(λ), y(λ), z(λ) are the color matching functions for the CIE

1931 2 Standard Observer,• TCSi(λ) is the spectral reflectance of the TCS denoted by

number i [for tabulated values see (CIE, 1995)], and• ∆λ is the wavelength increment of the SPD.

Table A3.6 shows the results of the calculations for Step 1(a).

Step 1(b) Derive the CIE 1976 (u′, v′) values for each TCS usingthe equations

u′ =4X

X + 15Y + 3Z

v′ =9X

X + 15Y + 3Z

86 Appendix 3

Table A3.6 CIE 1931 tristimulus values of the eight TCSs when illuminated by anEES.

EESX Y Z

TCS1 35.50 30.46 22.61TCS2 29.48 29.28 13.65TCS3 25.32 30.40 9.06TCS4 21.16 28.90 19.22TCS5 25.63 30.23 36.81TCS6 28.90 29.25 52.92TCS7 35.01 29.50 49.09TCS8 40.34 31.95 41.91

Table A3.7 CIE 1976 (u′v′) chromaticities of each TCS when illuminated by anEES.

EESu′ v′



Step 1(c) Derive the gamut area of the polygon created by the CIE1976 (u′, v′) values of each TCS.The gamut area of an EES can be calculated as 0.007354.

Step 1(d) Derive the gamut area index (GAI) of the light source.Because this gamut area is a very small number, GAI wasdeveloped to normalize to 100 the gamut area of an EES (Reaand Freyssinier-Nova, 2008; Rea and Freyssinier, 2010). Thegamut area of any light source is scaled accordingly but is notlimited in value to that of an EES. Thus, GAI is a number thatcould be as small as 0 or greater than 100. To derive the GAI ofany light source, simply divide its gamut area by 0.007354 andmultiply the result by 100:GAI = (Gamut area ÷ 0.007354) × 100For the EES example,GAIEES = (0.007354 ÷ 0.007354) × 100 = 100

Appendix 4

How to Optimize Illuminationon a Residential Wall Display

Figure A4.1 illustrates how to illuminate a picture on a wall from theceiling to minimize reflected glare and to maximize spatial applicationefficacy. The location of the light source should be between 30 and 45deg from the plane of the picture to minimize reflected glare. Dependingon ceiling height, the light source should be placed in or on the ceiling atthe distances from the wall illustrated in Fig. A4.1.

Generally, a light source with internal optics will be used in a recessedfixture or in a track light to illuminate the picture. The spatial distributionof these light sources will usually be radially symmetric around the centerof the beam. For rectangular pictures, circular beams will result in somewasted light. Nevertheless, waste can be minimized by matching thecircular beam angle of the light source to the longer dimension of therectangular picture (usually width). Table A4.1(a) presents the circularbeam angles needed to maximize spatial application efficacy for different

Table A4.1 (a) Light source beam angles needed in Fig. A4.1 to maximize spatialapplication efficacy while minimizing reflected glare. (b) Wattages needed fromtwo types of light sources (incandescent and LED) to meet the illuminance levelrequirement of 1000 lx.

(a) Beam angle needed for 45-deg locationPicture width (m)

Ceiling height (m) 0.3 0.6 0.9 1.2

2.4 13 deg 27 deg 39 deg 50 deg2.7 10 deg 20 deg 30 deg 39 deg3.0 8 deg 16 deg 24 deg 32 deg

(b) Wattage needed (independent of ceiling height)Picture width (m)

Light source/angle 0.3 0.6 0.9 1.2

Incandescent/45 deg 13 W 50 W 113 W 201 WIncandescent/30 deg 18 W 71 W 160 W 284 W

LED/45 deg 4 W 15 W 34 W 60 WLED/30 deg 5 W 21 W 48 W 85 W

87

88 Appendix 4

combinations of picture width and ceiling height. This table assumes thatthe center of the picture is 1.5 m above the floor, as shown in Fig. A4.1.The wattages necessary to meet the highest spatial application efficaciesfor two common light sources are given in Table A4.1(b).

Obviously, it is also important that sufficient illumination be provided tothe picture to highlight its presence on the wall. Table A4.1(b) presents thewattages necessary to illuminate the picture to 1000 lx from two types ofcommercially available light sources, incandescent parabolic aluminized

Figure A4.1 Placement of a light source to minimize reflected glare.

How to Optimize Illumination on a Residential Wall Display 89

reflector (PAR) lamps and white LEDs. This table shows that for the sameapplication efficacy, significant power reductions are possible with LEDtechnology compared to the more commonly used incandescent PAR lamp.

Figure A4.1 and Tables A4.1(a) and A4.1(b) should provide usefulguidance for most residential applications. Ceiling heights, picturedimensions, and technologies differ considerably, but, hopefully, theprinciples illustrated here can provide useful guidance for other lightingapplications where the visual benefits are provided with the highest spatialapplication efficacy.

Appendix 5

Relative SPDs of the LightSources in Table 4.1

Presented on the following pages are the relative SPDs of the light sourcesdescribed in Table 4.1 with associated technical information. Each figurenumber corresponds with the number given in the first column of the table.

91

92 Appendix 5

Relative SPDs of the Light Sources in Table 4.1 93

94 Appendix 5

96 Appendix 5

Appendix 6

Luminous EfficiencyFunctions for DifferentBenefit Metrics

Presented here are the tabulated luminous efficiency functions for thedifferent benefit metrics described in the text that might be used inlighting applications. Each function represents the spectral sensitivity of ahuman visual or circadian channel. The conditions recommended for theirutilization is described in the text.

V(λ) is the photopic luminous efficiency function underlying thedefinition of the candela and the lumen universally used by the lightingindustry. This function represents the spectral sensitivity of the foveato achromatic tasks. It also underlies orthodox photometry and allcommercial trade.

V ′(λ) is the scotopic luminous efficiency function representing thespectral sensitivity of the rods, absent from the fovea, under very low lightlevels equivalent to starlight on a moonless night.

Vmh(λ) and Vml(λ) represent two spectral sensitivities under mesopiclight levels where both rods and cones contribute to off-axis detection.The relative contribution of rods and cones varies with light level; forany light level, the mesopic spectral sensitivity [Vm(λ)] is defined asVm(λ) = X × V(λ) + (X − 1) × V ′(λ). Vmh(λ) reflects a higher proportion ofcones (X = 0.44) than does Vml(λ) (X = 0.12).

VC(λ) represents the spectral sensitivity of the human circadiansystem to polychromatic, white-light sources; the negative region reflectsthe subadditive nature of circadian phototransduction. Spectral powerdistributions that yield negative values when convolved with this functionshould be set equal to 0.

VB2(λ) and VB3(λ) represent two spectral sensitivities for apparentbrightness where the proportional contributions of the S cone [S (λ)] andV(λ) vary with light level. The contribution of S cones relative to V(λ)to the spectral sensitivity of apparent brightness is defined as VB(λ) =V(λ) + g × S (λ). VB2(λ) represents a lower proportion of S cones (g = 2)

99

100 Appendix 6

for low, outdoor light levels than does VB3(λ)(g = 3) for high, daytimelight levels.

Figure A6.1 plots the proposed luminous efficiency functions for therecommended benefit metrics corresponding to the tabulated values inTable A6.1.

Figure A6.1 Proposed luminous efficiency functions for lighting applicationscorresponding to the tabulated values in Table A6.1.

Luminous Efficiency Functions for Different Benefit Metrics 101

Table A6.1 Luminous efficiency functions.

λ (nm) V(λ) V′(λ) Vmh(λ) Vml(λ) VC(λ) VB2(λ) VB3(λ)

380 0.0000 0.0000 0.0000 0.0000 0.0011 0.0000 0.0000390 0.0001 0.0022 0.0015 0.0021 0.0065 0.0001 0.0000400 0.0004 0.0093 0.0064 0.0088 0.0691 0.0667 0.0670410 0.0012 0.0348 0.0238 0.0329 0.1765 0.1936 0.1946420 0.0040 0.0966 0.0664 0.0912 0.3811 0.5027 0.5052430 0.0116 0.1998 0.1390 0.1890 0.6395 0.8139 0.8171440 0.0230 0.3281 0.2303 0.3109 0.8313 0.9742 0.9765450 0.0380 0.4550 0.3226 0.4319 0.9378 1.0000 1.0000460 0.0600 0.5670 0.4086 0.5398 1.0000 0.9611 0.9573470 0.0910 0.6760 0.4973 0.6460 0.9738 0.7803 0.7703480 0.1391 0.7930 0.6002 0.7620 0.8792 0.5397 0.5203490 0.2081 0.9040 0.7101 0.8750 0.7427 0.3741 0.3422500 0.3231 0.9820 0.8221 0.9629 0.5941 0.2935 0.2423510 0.5031 0.9970 0.9262 1.0000 0.4453 0.3040 0.2233520 0.7101 0.9350 0.9932 0.9683 0.3001 0.3697 0.2554530 0.8622 0.8110 0.9901 0.8714 0.1729 0.4328 0.2940540 0.9542 0.6500 0.9311 0.7321 0.0693 0.4722 0.3185550 0.9952 0.4810 0.8401 0.5787 −0.0057 0.4895 0.3292560 0.9952 0.3288 0.7388 0.4359 −0.0536 0.4886 0.3282570 0.9522 0.2076 0.6357 0.3167 −0.0769 0.4671 0.3137580 0.8702 0.1212 0.5354 0.2251 −0.0834 0.4267 0.2865590 0.7572 0.0655 0.4393 0.1583 −0.0780 0.3712 0.2492600 0.6311 0.0332 0.3519 0.1119 −0.0678 0.3093 0.2077610 0.5031 0.0159 0.2735 0.0793 −0.0553 0.2466 0.1656620 0.3811 0.0074 0.2041 0.0557 −0.0424 0.1867 0.1253630 0.2651 0.0033 0.1407 0.0370 −0.0296 0.1299 0.0872640 0.1750 0.0015 0.0925 0.0238 −0.0197 0.0858 0.0576650 0.1070 0.0007 0.0564 0.0143 −0.0120 0.0524 0.0352660 0.0610 0.0003 0.0321 0.0081 −0.0069 0.0299 0.0201670 0.0320 0.0002 0.0168 0.0042 −0.0036 0.0157 0.0105680 0.0170 0.0001 0.0089 0.0022 −0.0019 0.0083 0.0056690 0.0082 0.0000 0.0043 0.0011 −0.0009 0.0040 0.0027700 0.0041 0.0000 0.0022 0.0005 −0.0005 0.0020 0.0014710 0.0021 0.0000 0.0011 0.0003 −0.0002 0.0010 0.0007720 0.0011 0.0000 0.0006 0.0001 −0.0001 0.0005 0.0003730 0.0005 0.0000 0.0003 0.0001 −0.0001 0.0003 0.0002

Appendix 7Resources for Maximizing theValue of Daylight Controls

The following resources can be used to help maximize the value ofdaylight controls in buildings:

Photosensors: http://www.lrc.rpi.edu/programs/NLPIP/PDF/VIEW/SR_Photosensors.pdf

Photosensor tutorial: http://www.lrc.rpi.edu/education/outreachEducation/photosensorTutorial.asp

Control algorithm: http://www.lrc.rpi.edu/programs/NLPIP/tutorials/photosensors/control.asp

Photosensor lighting control: http://www.lrc.rpi.edu/researchAreas/reducingBarriers/pdf/developDemoPhotosensor.pdf

Daylight Dividends program: http://www.lrc.rpi.edu/programs/daylighting/index.asp

103

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110 References

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Index

Aabsolute efficacy, 53achromatic, luminance channel,

13acuity, 8, 19apparent brightness, 21, 23, 27,

58application efficacy, 45, 46, 48

Bbiological clock, 29biological rhythms, 2blackbody, 18blackbody line, 18blackbody radiators, 28breast cancer, 30bright illuminance, 53, 58brightness, 55brightness–luminance ratio (B/L),

26

Ccandela (cd), 5, 69cardiovascular disease, 30chroma, 13chromaticities, 40chromaticity, 17, 63chromaticity space, 16, 17, 38CIE 1931 system of colorimetry,

15circadian disruption, 30, 32, 61circadian illuminance [Vc(λ)], 53,

60, 61circadian light, 29, 60

circadian phototransduction, 31circadian synchrony, 35circadian system, 29, 61circular correlation, 33class A color, 53, 62color appearance, 12, 13, 36color matching, 12, 14, 36color perception, 36color preference, 37color rendering, 17, 18, 36, 62color rendering index (CRI), 2, 17color temperature, 18color vision, 11, 31colorimetry, 2, 12, 15, 69cones, 5, 9, 20controls, 44, 64, 69correlated color temperature

(CCT), 2, 17, 36, 62, 69CRI, 17, 36, 37, 53, 62, 69

Ddaylight, 18Daysimeter, 33diabetes, 30discomfort glare, 48distribution, 69distribution of light, 47

Eefficacy, 20, 43, 62, 63electric energy, 10energy efficient, sustainable

lighting, 2energy-efficient lighting, 42

111

112 Index

Fflicker photometry, 8, 12fluorescent lamps, 40fovea, 8, 20, 21

Ggamut area index (GAI), 38, 53,

62

Hheterochromatic brightness

matches, 20high-pressure sodium (HPS), 22,

25hue, 13, 18, 21hypothalamus, 29

Iilluminance, 2, 7, 48, 55illuminance (lm/m2), 25illumination, 18, 62incandescence, 18incandescent lamps, 46intensity, 48intrinsically photosensitive retinal

ganglion cell (ipRGC), 30irradiance, 55, 58

LL cones, 11LED, 40, 54, 68light, 1, 5, 10light pollution, 48light source, 5light trespass, 48light-emitting diode (LED), 22lighting, 1lighting energy efficiency (LEE),

42, 64lightness, 13low-pressure sodium, 68lumen (lm), 5lumens per watt, 7luminance, 6, 7, 27, 55

luminance meter, 7, 23luminous efficacy, 2, 7, 53, 69luminous efficacy (lm/W), 25luminous efficiency, 53luminous efficiency coefficient,

22luminous flux, 6, 7luminous intensity, 5, 7lux (lm/m2), 7

MM cones, 11mesopic levels, 58mesopic light, 56mesopic luminous efficiency, 55mesopic photometry, 22mesopic vision, 20metal halide, 68metamers, 15motion sensors, 44, 45

Nneural channels, 27nit (cd/m2), 7

Ooccupancy, 43, 64, 69off-axis detection, 20optical radiation, 10outdoor site-lighting performance

(OSP), 47

PPAR, 47phasor, 35phasor analysis, 33phasors, 33photometric units, 7photometry, 2, 5, 7, 15, 69photopic (conditions), 21photopic (function), 10photopic illuminance, 25, 62photopic luminous efficiency, 37photopic luminous efficiency

function V(λ), 5, 7, 9

Index 113

photopic luminous efficiencyfunctions, 55

photoreceptors, 5, 8, 9, 11photosensor, 46phototransduction, 60physiology, 11polychromatic lights, 31

Rradiant power, 8reflectance, 55retina, 21roadway lighting, 20rods, 5, 9

SS cones, 11S/P ratio, 22, 57scotopic (conditions), 21scotopic (function), 10scotopic luminous efficiency, 37scotopic luminous efficiency

function V ′(λ), 9scotopic luminous efficiency

functions, 55sensors, 64short-wavelength radiation, 25side-by-side heterochromatic

brightness matching, 8signal lights, 27sky glow, 48spatial application efficacy, 53spatial controls, 48spatial resolution, 8

spectral power distribution (SPD),5, 57

spectral reflectance, 14spectral sensitivity, 21, 30, 31spectral weighting, 58steradian (sr), 5suprachiasmatic nuclei (SCN), 29

Ttemporal application efficacy, 53temporal controls, 48tint, 63tint of illumination, 17, 39tints, 36

Uunified illuminance, 53–55unified photometry, 54unified system of photometry, 21

Vvalue engineering, 1value of light, 1visual perception, 10, 25visual performance, 56vividness, 38V(λ), 11, 20, 21, 55V ′(λ), 55V10(λ), 19Vmh(λ), 55Vml(λ), 55

Wwhite light, 28

Mark S. Rea, Ph.D., is Director of the LightingResearch Center (LRC) and Professor of Architectureand Cognitive Sciences at Rensselaer PolytechnicInstitute. Rea has served as Director since the LRC wasestablished in 1988. He teaches courses in leadershipand in visual and circadian processes, and supervisesgraduate students at M.S. and Ph.D. levels. Rea iswell known for his research in circadian photobiology,mesopic vision, psychological responses to light,

lighting engineering, and visual performance. He is the author of more than250 scientific and technical articles related to vision, lighting engineering,and human factors and was the editor-in-chief of the 8th and 9th editions ofthe Illuminating Engineering Society of North America (IESNA) LightingHandbook. Rea has been elected Fellow of the Society of Light andLighting (UK) and of the IESNA. In addition, he is recipient of theIESNA Medal. Rea has also been honored with the William H. WileyDistinguished Faculty Award for those who have won the respect of thefaculty at Rensselaer through excellence in teaching, productive research,and interest in the totality of the educational process.

SPIE PRESS

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ISBN: 9780819493224SPIE Vol. No.: PM228

Lighting Research Center

We often do not fully understand what lighting can do for us. We know that we need lighting, but often that is as far as the thinking goes. We do a really good job, however, of conceptualizing the costs of those lighting systems because we can readily measure those costs. Reducing costs will certainly increase the value ratio for lighting if the benefits of the lighting system are held constant. Without a clear purpose for the lighting system, and no clear idea of benefits, there is little else that can be used in the value engineering process. This book is dedicated to the notion that our society undervalues light because we do not properly measure the benefits of light, in terms of both the lighting system and how it is applied. Consequently, we unnecessarily waste our natural and capital resources. The problems associated with inadequate light measurement systems are not hard to grasp or even to fix, and are the subject of Value Metrics for Better Lighting. This book was written as a starting point for thoughtful consideration, discussion, and action by those vested in better and more sustainable lighting, including manufacturers, practitioners, regulators, advocates, educators, and, of course, users.

Value Metrics for Better Lighting

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