Sports Lighting Regulations

Play Ball and Play Fair!

Ian Ashdown, P. Eng., FIES

Senior Scientist, Lighting Analysts Inc.

[ Please send all comments to allthingslighting@gmail.com ]

This blog article has a somewhat frustrating history. About a year ago, I was asked to volunteer my time to write a primer of light and color as it relates to sports lighting regulations. I was told the name of the organization I was volunteering my time for, but I did not pay much attention – it seemed like a good cause.

I should have perhaps paid more attention before agreeing to volunteer – the Green Sports Alliance is not the poorest of socially responsible organizations.

Upon completing the primer, I was told that it was far too technical for its intended audience. Hopefully, you as my readers will disagree.

Sports Lighting Requirements

Sports lighting has specific requirements that may not be familiar to many lighting designers. The Illuminating Engineering Society publishes detailed recommendations related to sports lighting (IES 2009, 2010a, 2015), while various professional sports organizations have their own specific requirements (for example, FIFA 2007, FIH 2011, NCAA 2010a and 2010b, and Lewis and Brill 2013).

Illuminance

In sports lighting, there are two forms of illuminance measurements that are of interest: horizontal illuminance and vertical illuminance.

Horizontal illuminance is typically measured on a horizontally oriented imaginary surface one meter (~3 feet) above the field surface. Multiple measurements are usually measured (or calculated during the lighting design phase) on a grid. The National Football League, for example (Lewis and Brill 2013), specifies a grid spacing of 5 meters (~16 feet).

Vertical illuminance is measured on a vertically oriented imaginary surface. Unlike horizontal illuminance, both the position and orientation of the vertical surface must be specified. To understand why, consider a vertical surface illuminated by a single light source (FIG. 1).

FIG. 5 – Illuminance of surface depends on angle of illumination

FIG. 1 – Illuminance of surface depends on angle of illumination

As the angle of illumination decreases, the lumens per square meter decrease as well, until at grazing angles the surface is barely illuminated at. This can clearly be seen with a sphere illuminated by a single light source (FIG. 2).

FIG. 6 – Sphere illuminated by a single distant light source

FIG. 2 – Sphere illuminated by a single distant light source

In practice, there will be multiple luminaires illuminating the field, each of which will contribute to the illumination of a vertical surface – such as a player’s face. It is therefore important to ensure that the vertical illuminance is within minimum and maximum limits so that the players’ faces and team numbers can always be seen.

With this in mind, the “falloff” in illuminance with distance from a single luminaire must also be kept in mind. As shown in FIG. 3, a light source S illuminates two imaginary surfaces, the first one at distance d from the light source, and the second at twice the distance. Both surfaces receive the same amount of light (lumens) from S, but the area of the second surface is four times that of the first. Consequently, its illuminance (lumens per square meter) is only one-quarter that of the first surface.

FIG. 7 – Inverse Square Law

FIG. 3 – Inverse Square Law

Generalizing this to any distance, it is easy to see that the illuminance from a single luminaire will decrease, or “fall off,” according to the square of the distance. This is the basis of the inverse square law used by lighting designers.

Finally, “TV illuminance” is occasionally used for television broadcasting purposes (IES 2015). It is the illuminance measured at a position on the playing field when the illuminance meter is aimed directly at a specified camera position.In practice, of course, multiple luminaires are used to (more or less) evenly illuminate a playing field.

Uniformity

Uniformity of illumination is important for sports. It enables both the players and the spectators to easily follow the action, and it provides consistent lighting for the television cameras and photographers. Sports field lighting for internationally televised events must meet exacting standards, while more leeway is generally allowed for other events.

There are three measures (or more properly metrics) used to specify the desired uniformity of horizontal and vertical illuminance on the playing field. The simplest metric is the maximum-to-minimum ratio, commonly referred to as the uniformity ratio. Using NFL requirements as an example, horizontal illuminance is designated Eh, and so the uniformity ratio is expressed as Ehmax/Ehmin. Using a measurement grid for the playing field with 5-meter spacing, this ratio for all measurement values must be 1.4:1 or less.

Again using the NFL requirements, vertical illuminance is designated Ev, and the uniformity ratio Evmax/Evmin must also be 1.4:1 or less.

The NFL requirements go further in specifying that: 1) the ratio of the average horizontal illuminance Ehavg to average vertical illuminance Evavg as seen from camera #1 (that is, with each vertical surface facing the camera) must be between 1.0 and 2.0, with a target value of 1.5; 2) the ratio of vertical illuminances at any point on the field between the four imaginary vertical surfaces facing the four sides of the field shall be between 0.6 and 0.9; and 3) the average vertical illuminance Evavg facing towards camera #1 shall not be less that Evavg for the other three orthogonal (that is, right-angle) orientations. In other words, it can get complicated.

The second uniformity metric is the coefficient of variation, designated CV. Without delving into the mathematics of this statistical value, it can be likened to the point spread in sports betting. (If you must know the details, the equation is:

Sports Lighting Primer - EQN. 1

with details left to the interested reader – see [IES 2009, 2015].) It is basically a measure of how “smooth” the lighting distribution is across the playing field.

The third metric is the uniformity gradient, designated UG. It is defined as the ratio between illuminance values between adjacent measuring points on a square grid. Whereas CV describes the average non-uniformity for the entire field, UG describes the maximum nom-uniformity. It is particularly important in sports with fast-moving balls and the like, as changes in illuminance can make it more difficult to judge their speed.

Visual Glare

Visual glare occurs when the luminance of the luminaires within the observer’s field of view (either a player or spectator) is sufficiently greater than the average luminance to which the observer’s eye have adapted. It may cause visual discomfort (in response to which we tend to squint), or it may impair the vision of objects and details (such as past-moving balls and the like).

As a psychophysiological phenomenon, glare is both literally and figuratively “in the eye of the beholder.” All lighting researchers can do is present subjects in a laboratory with a lighting setup and ask them to rate the glare on a subjective scale. While it cannot be directly measured in the field, a glare rating metric, designated GR, can be calculated (typically at the design phase) in accordance with CIE 112-1994, Glare Evaluation System for Use with Outdoor Sports and Area Lighting (CIE 1994).

Central to these calculations are five parameters:

  1. The luminances of the luminaires as seen by the observer;
  2. The angular extent of the luminaires in the observer’s field of view;
  3. The position of the luminaires in the observer’s field of view relative to the line of sight;
  4. The number of luminaires in the observer’s field of view; and
  5. The average luminance of the observer’s entire field of view.

It is important to note that the GR metric depends on where the observer is positioned relative to the luminaires, and the line of sight direction. Consequently, any GR requirements must specify these parameters. The NFL requirements, for example, require that GR be less than 40 for all main cameras (Lewis and Brill 2013).

Color

Many sports organizations specify the allowable correlated color temperature, designated CCT, for sports field lighting. For example:

Organization CCT
FIFA ≥ 4000K
FIH > 4000K
NCAA > 3600K
NFL 5600K (alternatively 5000K to 7000K)

where the symbol ‘K’ represents kelvins (where one kelvin is equal to one degree Celsius).

To put these numbers into context, quartz halogen and warm white LED lamps typically have CCTs of approximately 3000K, metal halide lamps typically have CCTs of 4000K, and daylight LED lamps typically have CCTs of 5000K.

FIG. 8 – Light source correlated color temperatures

FIG. 4 – Light source correlated color temperatures

Our eyes adapt quite well to light sources with different CCTs, ranging from 2700K for 100-watt incandescent lamps to 10000K for the blue sky. Even though the light itself may look colored (FIG. 8), objects seen under these light sources appear to have approximately the same colors, with whites looking white.

The same is not true with television and digital cameras, however, which must be adjusted (color-balanced) to display the colors we expect to see. This is why it is important that all the luminaires in a sports lighting installation have approximately the same CCT. If they do not, the television cameras will display annoying color shifts as they pan across the field.

Many sports organizations also specify the minimum allowable color rendering index, designated CRI, for sports lighting. For example:

Organization CRI Ra
FIFA ≥ 65
FIH > 65
NCAA > 65
NFL ≥ 90

where the CRI Ra metric is a measure of the average color shift of various colors viewed under the light source when compared to viewing the colors under an incandescent or daylight source with the same CCT. A detailed explanation of color rendering is beyond the scope of this introductory chapter, but the topic is fully explained in CIE 13.3-1995, Method of Measuring and Specifying Colour Rendering Properties of Light Sources (CIE 1995).

In general, a minimum CRI of 65 is merely adequate, and is representative of what could be achieved with high-wattage metal halide lamps. With today’s solid-state lighting, a minimum CRI of 80 or greater is common, and CRIs of 90 and above are preferred.

It must also be emphasized that Ra metric represents the average color shift. Solid-state lighting products may also specify a CRI R9 metric, which represents the color shift specifically for red colors. A high R9 value is desirable, especially where team outfits feature saturated red colors.

In terms of television broadcast cameras, a more appropriate color rendering metric is the Television Lighting Consistency Index TLCI-2012 (EBU 2014). Like the CRI Ra metric, this is a measure of the average color shift of various colors viewed under the light source; the difference is that the observer is a color television camera rather than a human.

Spectrally Enhanced Lighting

There is some interest in the topic of spectrally enhanced lighting for sports field applications. For some visually demanding tasks, the recommended illuminance values can be reduced through the use of light sources with high blue content. A full discussion is presented in IES TM-24-13, An Optional Method for Adjusting the Recommended Illuminance for Visually Demanding Tasks Within IES Illuminance Categories P through Y Based on Light Source Spectrum (IES 2013).

It could be argued TM-24-13 can be applied to sports lighting, as it defines (p. 3) “visually demanding tasks” as “… tasks that are based on the ability to discern visual detail to ensure speed and/or accuracy.” In this situation, “visual detail” could be interpreted as a fast-moving ball or hockey puck.

Furthering the argument, TM-24-13 applies to illuminance categories P through Y, which the IES Lighting Handbook, 10th Edition (IES 2010a) defines in Table 4.1, Recommended Illuminance Targets, as interior and exterior lighting installations where the illuminance targets are in excess of 300 lux. Categories P (average 300 lux) through W (average 3000 lux) specifically include “some sports situations” (without defining them).

There are several problems, however. The first is that most sports organizations specify minimum horizontal and vertical illuminances without taking spectrally enhanced lighting into account. Any sports lighting that reduced these values based on TM-24-13 would not be in compliance with these specifications.

The second problem is that the recommended illuminance targets for sports lighting involving television broadcasting are based on the minimum illuminance requirements of the television cameras. These are of course independent of the human visual system, and so the reduced illuminance values calculated in accordance with TM-24-13 do not apply.

The third problem is the most crucial: the Illuminating Engineering Society issued a lengthy position statement (included in TM-24-13) that unequivocally states (in boldface type), “TM-24 should not be used for the development of energy policy or energy efficiency programs purposes for any lighting applications, as this goes against current IES recommendations.”

Light Pollution

Outdoor lighting illuminates not only objects on the ground, but the overhead sky as well. The International Dark-Sky Association reminds us that this unintentional light pollution threatens professional and amateur astronomy, disrupts nocturnal ecosystems, affects circadian rhythms of both humans and animals, and wastes over two billion dollars of electrical energy per year in the United States alone.

It might seem obvious that sports field lighting is a major contributor to light pollution, but this is true only in a local sense. According to a US Department of Energy study (DOE 2010), stadium lighting contributes a maximum of 6 percent (compared to 48 percent for roadway lighting and 34 percent for parking lot lighting) on a national scale. (This further assumes that the stadium lighting is always on at night.)

Outdoor Lighting Percent Lumens
Roadway 48
Parking 34
Building exteriors 10
Stadiums 6
Billboards 1
Traffic signals 1

On a local scale, however, light pollution from stadiums and sports fields can be a concern, particularly for surrounding residential neighborhoods. This includes not only light that is reflected from the ground and illuminates the sky overhead, but also light trespass and glare from improperly shielded luminaires.

IES TM-15-11, Luminaire Classification System for Outdoor Luminaires (IES 2011a) and the Joint IDA-IES Model Lighting Ordinance (MLO) with User’s Guide (IES 2011b) provide detailed information on designing outdoor lighting systems that minimize unintended light pollution.

References

CIE. 1994. CIE 112-1994, Glare Evaluation System for Use within Outdoor Sports and Area Lighting. Vienna, Austria: Commission International de l’Eclairage.

CIE. 1995. CIE 13.3-1995, Method of Measuring and Specifying Colour Rendering Properties of Light Sources. Vienna, Austria: Commission International de l’Eclairage.

DOE. 2010. 2010 U.S. Lighting Market Characterization, U.S. Department of Energy Building Technologies Program.

EBU. 2014. Tech 3355, Method for the Assessment of the Colorimetric Properties of Luminaires: The Television Lighting Consistency Index (TLCI-2012) and the Television Luminaire Matching Factor (TLMF-2013. Geneva, Switzerland: European Broadcast Union.

FIFA. 2007. Football Stadiums: Technical Recommendations and Requirements, 4th Edition. Zurich, Switzerland: Fédération Internationale de Football Association.

FIH. 2011. Guide to the Artificial Lighting of Hockey Pitches, 6th Edition. Lausanne, Switzerland: International Hockey Federation.

IES. 2009. IES RP-6-09, Recommended Practice for Sports and Recreational Area Lighting. New York, NY: Illuminating Engineering Society.

IES. 2010a. IES Lighting Handbook, 10th Edition. New York, NY: Illuminating Engineering Society.

IES. 2011a. IES TM-15-11, Luminaire Classification System for Outdoor Luminaires. New York, NY: Illuminating Engineering Society.

IES. 2011b. Joint IDA-IES Model Lighting Ordinance (MLO) with User’s Guide. New York, NY: Illuminating Engineering Society.

IES. 2013. IES TM-24-13, An Optional Method for Adjusting the Recommended Illuminance for Visually Demanding Tasks Within IES Illuminance Categories P through Y Based on Light Source Spectrum. New York, NY: Illuminating Engineering Society.

IES. 2015. IES RP-6-15, Sports and Recreational Area Lighting. New York, NY: Illuminating Engineering Society.

Lewis, D., and S. Brill. 2013. Broadcast Lighting: NFL Stadium Lighting. The Design Lighting Group Inc.

NCAA. 2010a. NCAA Basketball Championships Best Lighting Practices. National Collegiate Athletic Association.

NCAA. 2010b. NCAA Best Lighting Practices. National Collegiate Athletic Association.

Appendix A

A.1.         What is Light?

A primer on sports lighting must answer the obvious question: what is light? The Oxford English Dictionary, the pre-eminent dictionary of the English language, describes light rather loosely as, “the natural agent that stimulates the sense of sight.” More technically, light is electromagnetic radiation.

What we see as visible light is only a tiny fraction of the electromagnetic spectrum, extending from very low-frequency radio waves through microwaves, infrared, visible light, and ultraviolet to x-rays and ultra-energetic gamma rays. Our eyes respond to visible light; detecting the rest of the electromagnetic spectrum requires an arsenal of scientific instruments ranging from radio receivers to scintillation counters.

Our interest however is solely in visible light – it is what we see when we look at the world.

A.2.         Quantifying Light

We can think of light as massless subatomic particles called photons. They are emitted by light sources such as metal halide lamps and light-emitting diodes (LEDs), and travel through space until they encounter physical objects. They may then be reflected, refracted, scattered, or absorbed. Some of those photons will intersect our eyes, enabling us to see (FIG. A1).

FIG. 1 - Photons emitted by light source S

FIG. A1 – Photons emitted by light source S

The number of photons emitted by a typical light source per second is unimaginably large (think of the number ten followed by 30 to 40 zeroes), and so we express this quantity in lumens, where one lumen is approximately the number of photons emitted per second by a wax candle[1]. A typical light source will emit tens of thousands of lumens.

A.3.         Measuring Light

Photons emitted by light sources travel outwards in random directions. When these photons encounter a surface, they illuminate the surface (FIG. A2). From the perspective of the surface, it does not matter where the light comes from; it can be a single light source, multiple sources, or even the entire sky.

FIG. 2 - Light illuminating a surface A

FIG. A2 – Light illuminating a surface A

We can use a device called a photometer to measure the number of photons arriving at (incident upon) the surface per second. Of course, this number will depend on the surface area of the photometer’s sensor, and so we express the illuminance of the surface in terms of lumens per square meter, or lux. (Lumens per square foot are referred to as a foot-candle – please do not ask why.)

Note that the illuminated surface can be real or imaginary. We can, for example, imagine a “surface” positioned one meter above a physical surface, such as a playing field. The light will of course pass through this imaginary surface, but we can still measure its illuminance with a photometer (which is also called an “illuminance meter” by lighting designers or an “incident light meter” by photographers).

Illuminance is one of the two fundamental units of measurement for lighting designers. While we can measure illuminance with a photometer, we cannot see illuminance. For this, we need another fundamental unit of measurement.

Imagine looking at a computer display. The display consists of an array of a million or so pixels. We see each pixel because some of the photons it is emitting intersect our eye. We can therefore think of these photons as a ray of light, where all of the photons are traveling in the same direction. The more photons per second there are in the ray, the brighter the pixel appears to our eye. This is the luminance of the ray, sometimes referred to as “photometric brightness.”

FIG. 3 – Light ray from a computer display pixel as seen by observer

FIG. A3 – Light ray from a computer display pixel as seen by observer

Textbooks on lighting design typically define luminance as the property of a real or imaginary surface, which leads to the very confusing unit of measurement, “lumens per square meter per steradian,” or lm/m2-sr. It is much easier, however (and just as accurate), to think of luminance as a property of the light ray itself. (The light we see coming from the blue sky, for example, has luminance, but it does not have a real or imaginary surface.)

We can easily measure the luminance of a ray by using a telescope to focus a narrow beam of light onto a photometer sensor (FIG. A4). This is a luminance meter; it measures what we see.

FIG. 4 – Luminance meter

FIG. A4 – Luminance meter

[1] A century ago, national standards for measuring light relied on precisely specified wax candles made from spermaceti (whale oil).

Botanical Light Pollution

Red is the New Blue

Ian Ashdown, P. Eng., FIES

Chief Scientist, Lighting Analysts Inc.

[ Please send all comments to allthingslighting@gmail.com ]

Related Posts

Color Temperature and Outdoor Lighting

Light Pollution and Uplight Ratings

Mobile Light Pollution

Blue-rich light from LED streetlights, we are told, is the nemesis of professional and amateur astronomers. Blue light is preferentially scattered by the atmosphere, resulting in potentially unacceptable levels of light pollution for astronomical observations. Unfortunately, LED streetlights emit more blue light on a per-lumen basis than the high-pressure sodium streetlights they are rapidly replacing.

Botanists and horticulturalists, however, may choose to differ. For them, it is red light from streetlights that is the problem. Depending on the species and various environmental factors, even low levels of light trespass from roadway and outdoor area luminaires can have harmful effects on both wild and domesticated plants. LED streetlights likewise emit more red light on a per-lumen basis than high-pressure sodium streetlights.

This is not a newly discovered problem. Botanists were aware of the deleterious effects of incandescent street lighting on trees eighty years ago (Matske 1936), while horticulturalists became aware of the problem with respect to ornamental plants some forty years ago (Cathey and Campbell 1975).

The lighting community can perhaps be excused for not following the latest research in publications such as American Journal of Botany and Journal of Arboriculture, but we were in fact made aware of the issue through publication of an article in Lighting Design and Application (Cathey and Campbell 1974). However, given that the proposed solution then was to avoid using high-pressure sodium (HPS) lamps and instead use less-efficient mercury-vapor lamps with their ghoulish color rendering capabilities … well, we understandably ignored the advice.

Soybeans and Trees

This is not to say that farmers are not aware of the problem. If you are growing soybeans, you quickly learn not to plant them in a field adjacent to HPS roadway lighting (FIG.1). The nighttime illumination – even as little as two to eight lux – can reduce crop yield by 20 to 40 percent due to delayed flowering and ripening (Chen et al. 2009).

Botanical Light Pollution - FIG. 1FIG. 1 – Effect of light pollution on soybean crop. (Source: Chen et al. 2009)

Landscape designers and arborists are also aware of the problem. A publication from Purdue University, for example, lists 65 trees and shrubs that are vulnerable to artificial light (Chaney 2002). Exposure to nighttime illumination, particularly from HPS street lighting, may result in disruption of the plant’s shoot growth, flowering, leaf expansion and abscission, and bud dormancy. In temperate climates, this may make the plants more susceptible to frost, fungal infections, and insect infestations. Again, however, the advice was to avoid using HPS lighting and use mercury vapor lighting instead. For lighting designers, this is pointless advice – mercury vapor lamps were long ago replaced by high-pressure sodium lamps, and these in turn are being replaced by solid state lighting.

… and herein lies today’s issue. LED-based outdoor lighting may – and the emphasis is on the word may – exacerbate the problem from the perspective of wild and domesticated plants. High-pressure sodium lamps emit much more red light than mercury vapor lamps on a per-lumen basis, and white light LEDs may (depending on their correlated color temperature) emit even more. What was once a minor problem for landscape designers and urban arborists may become something that lighting designers will need to consider.

To better understand this issue, we first need to understand the role of photopigments in plant growth and development.

Phytochrome

Plants perform their magic of photosynthesis using a photopigment called chlorophyll, but this is only one of many different photopigments plants use to harvest and detect light. Equally important is phytochrome, which regulates a long list of plant functions, including:

  • Seed germination and development
  • Stem elongation
  • Leaf expansion and abscission
  • Photosynthesis development
  • Flowering
  • Ripening
  • Dormancy

Taken together, these functions basically outline the life cycle from seed to adult plant.

The sum of these light-induced changes is called photomorphogenesis. There are other photopigments involved, including blue light-sensitive cryptochromes (Lin 2002) and ultraviolet-sensitive UVR8 (Goto et al. 2006, Kami et al. 2010). However, it is phytochrome that dominates plant growth and development.

Phytochrome itself is an interesting pigment in that it has two states (or isoforms) called Pr and Pfr (e.g., Smith 2000). The Pr isoform strongly absorbs red light, with a spectral peak at about 660 nm (FIG. 2), making it look turquoise-blue when dissolved in solution. This is its biologically inactive state.

When a phytochrome molecule absorbs a red photon, it switches to its Pfr isoform, making it look slightly more greenish. This is its biologically active state, which signals to the plant that red light has been sensed. While in this state, phytochrome has a different spectral absorption distribution (FIG. 2), with a spectral peak at about 730 nm. (Horticulturalists and plant biologists refer to the spectral range of 700 nm to 800 nm as “far-red,” which explains the “fr” subscript.)

When the Pfr isoform absorbs a far-red photon, it reverts to its Pr isoform. Thus, phytochrome performs the function of a resettable biological switch to initiate or terminate photomorphological processes.

Botanical Light Pollution - FIG. 2

FIG. 2 – Phytochrome absorption spectra. (Source: Plants in Action, First Edition[1])

This biological switch behavior has some interesting consequences. While even low levels of red light can initiate many physiological responses, applying far-red light soon thereafter may reset the switch and terminate the response. Light pulses as short as one minute at night – think car headlights on a country road – are enough to induce or prevent the flowering of some plants (Borthwick et al. 1952). Worse, some plants have flower induction thresholds of less than four lux (Botto et al. 1996, Whitman et al. 1998).

Photoperiodism

Phytochrome may act as a biological switch, but how plants respond to its signaling varies by species and even cultivar. What all plants have in common, however, is photoperiodism, their physiological reaction to the length of the day. Like humans and all other animals, plants have circadian rhythms.

In terms of flowering, plants can generally be divided into three categories: 1) short day; 2) long day; and 3) day-neutral. For short day plants, flowering is initiated, advanced, or promoted when the dark nighttime period is sufficiently long to allow enough phytochrome Pfr to revert to Pr. For long day plants, the opposite is true: flowering is initiated, advanced, or promoted when the dark nighttime period is sufficiently short to increase nighttime levels of phytochrome Pr. As for day-neutral plants, their time of flowering is determined by other environmental cues, such as temperature and moisture.

From the perspective of wild and domesticated plants growing outdoors, artificial light can be a problem. For horticulturalists, however, it can be a boon. Florists have long used incandescent lamps with their copious red and infrared emissions to modify the growth and development of flowering plants in greenhouses. This promotes flowering in long day plants such as asters, azaleas, and fuchsias, while delaying flowering in short day plants such as chrysanthemums, begonias, and poinsettias.

The recent availability of high-power red and far-red LEDs has provided new opportunities for both florists and horticulturalists. Independently switching or dimming these LEDs enables greenhouse operators to precisely control phytochrome as a biological switch. This, combined with the secondary effects of activating cryptochromes using blue light, provides remarkable control of plant growth and development (e.g., Gautam et al. 2015, Islam et al. 2014, Kitazaki et al. 2015, and Lee et al. 2015).

Light Pollution

Outside of the greenhouse environment, however, adding red and far-red radiation to the environment is not a good thing. We can call it what it is: botanical light pollution. For soybean farmers and urban arborists, it may be a nuisance. However, there can also be more insidious and detrimental effects for wild plants and the pollinating insects that depend on them (e.g., Bennie et al. 2016).

The question is, how do we quantify this pollution? It is reasonably easy to quantify astronomical light pollution because we have comprehensive mathematical models of atmospheric physics and optics. However, the best that botanists can do for us is to identify plants as short day, long day, or day neutral.

Pragmatically speaking, we do not need to quantify botanical light pollution in an absolute sense of so many micromoles of radiation per square meter per second or whatever. From a lighting design perspective, the goal is to illuminate an area with so many lumens per square meter while doing our best to prevent wasted spill light. The question then becomes, what is the best light source for plants?

Comparing Light Sources

The phytochrome absorptance spectra (FIG. 2) were obtained by extracting phytochrome from plants and dissolving it in solution for analysis in vitro with a spectrophotometer. When in the plant itself, however, phytochrome is surrounded by other photopigments, especially chlorophyll. Both chlorophyll A and chlorophyll B have absorptance spectra that overlap with those of the phytochrome isoforms (FIG. 3), so it is reasonable to ask whether this influences (or “screens”) the phytochrome absorptance spectra in vivo.

Botanical Light Pollution - FIG. 3

FIG. 3 – Photopigment spectral absorptances.

Fortunately, a variety of studies of the effect of monochromatic radiation on plant growth and development have shown that the absorptance spectra of phytochrome in vitro reasonably predict the plant physiological response. For example, Withrow et al. (1957) studied the “induction and reversion of hypocotyl hook opening” in bean seedlings. A plot of their results as induction and reversion “action spectra” shows a remarkable correlation with the in vitro absorptance spectra of phytochrome (FIG. 4).

Botanical Light Pollution - FIG. 4

FIG. 4 – Typical phytochrome action spectra. (Source: Smith 1977)

Given this, we can use the phytochrome absorptance spectra as a species-independent measure of the effect of red and far-red radiation on plant growth and development (Sager et al. 1988). For a given light source, the probability of a phytochrome molecule absorbing a photon with a given wavelength is determined by the absorptance spectra of the isoform and the relative number of photons with that wavelength.

For a light source, we typically have its relative spectral power distribution (SPD), which is measured in watts per nanometer. However, from the Planck-Einstein relation, we know that a photon’s energy is inversely proportional to its wavelength. Therefore, to determine the relative spectral photon flux distribution, we need only multiply the lamp SPD by the wavelength for each wavelength and normalize the resultant graph. (An example is shown in FIG. 5.)

Botanical Light Pollution - FIG. 5

FIG. 5 – Radiant versus photon flux for a 3000K warm white LED.

With this, we now have the means to compare light sources with different spectral power distributions. Given a reference lamp (say, HPS) and a test lamp (say, a 3000K warm white LED), the calculations consist of:

  1. Multiply the SPD values of each lamp by the CIE 1931 luminous efficiency function V(l) shown in FIG. 6 from 400 nm to 700 nm.
  2. Sum the results of Step 1 to obtain the relative lumens Fref and Ftest generated by the two lamps.
  3. Multiply the SPD values of the test lamp by Fref / Ftest.

The two SPDs now represent the same number of photopic lumens (i.e., luminous flux) emitted by the lamps. With this:

  1. Multiply the SPD values of each lamp by the wavelength to obtain the lamp spectral photon flux distributions from 500 nm to 800 nm.
  2. Multiply the results of Step 4 by the phytochrome Pr spectral absorptance spectrum.
  3. Sum the results of Step 5 to obtain the Pr action values pAref,r and pAtest,r.
  4. Multiply the results of Step 4 by the phytochrome Pfr spectral absorptance spectrum.
  5. Sum the results of Step 7 to obtain the Pfr action values pAref,fr and pAtest,fr.

and finally:

  1. Add the Pr and Pfr action values for each lamp to obtain the lamp phytochrome action values pAref and pAtest.
  2. Divide pAtest by pAref to obtain the relative action value for the test lamp compared to the reference lamp.

Botanical Light Pollution - FIG. 6

FIG. 6 – CIE 1931 luminous efficiency function V(l).

A few explanatory notes:

  1. As shown in FIG. 2, the phytochrome absorptance spectra have secondary peaks in the near-ultraviolet. These are ignored because: a) it is difficult to disentangle the effects of phytochrome from the effects of the blue-sensitive cryptochrome photopigments; and 2) the photomorphological effects of blue light are less pronounced than those resulting from red and far-red radiation. The lower limit of 500 nm was chosen based on the phytochrome absorptance spectra minima.
  2. The spectral peak of Pfr is only 60 percent that of Pr, but the area under each spectral curve between 500 nm and 800 nm is almost the same. Also, phytochrome action spectra for various plant species have shown that equal red and far-red radiant fluences at the spectral peaks of 660 nm and 730 nm have approximately equal effect on the physiological responses. This justifies the final step of adding the two action values.

It must be emphasized that these “action values” are approximate at best, and should not be considered as formally quantifiable metrics. They are introduced here only to explore the potential effects of botanical light pollution.

With this caveat then, the following light sources were selected for comparison:

Light Source Manufacturer Product Code
High-pressure sodium (test) Damar 1782 LU100M
2700K white light LED Lumileds LUXEON Rebel ES LXW9-PW27
3000K white light LED Lumileds LUXEON Rebel ES LXW9-PW30
3500K white light LED Lumileds LUXEON Rebel ES LXW8-PW35
4000K white light LED Lumileds LUXEON Rebel ES LXH7-PW40
5000K white light LED Lumileds LUXEON Rebel ES LXW8-PW40

Table 1 – Comparison light sources.

The HPS lamp SPD was measured in the laboratory with 0.1 nm resolution and averaged to 5 nm bins, while the Lumileds SPDs were digitized from the published datasheet (Lumileds 2014). The equal-lumen SPDs for these light sources are shown in FIG. 7.

Botanical Light Pollution - FIG. 7

FIG. 7 – Equal-lumen spectral power distributions.

Following the above calculation procedure with the HPS lamp as the test source, the relative phytochrome action values are:

Light Source Relative Phytochrome Action
Pr Pfr Pr + Pfr
High-pressure sodium 1.0 1.0 1.0
2700K white light LED 1.7 2.3 1.9
3000K white light LED 1.5 2.0 1.7
3500K white light LED 1.0 1.2 1.1
4000K white light LED 1.0 1.0 1.0
5000K white light LED 0.9 1.0 0.9

Table 2 – Relative phytochrome action values.

From this, it can be seen that while 2700K and 3000K white light LEDs produce the least astronomical light pollution (see related article Color Temperature and Outdoor Lighting), they also unfortunately produce the most botanical light pollution.

It should be noted however that these results apply to Lumileds LUXEON products only. Looking at FIG. 7, it is evident that the 2700K and 3000K products use a different phosphor formulation than the 3500K, 4000K, and 5000K products. Different major LED manufacturers will have their own proprietary phosphor formulations, and so the above results should not be applied to LEDs based solely on their nominal CCTs.

Add More Red

It seems counterintuitive, but one solution to the problem of excess red light generated by low-CCT LEDs is to add more red light.

Some of the early LED modules combined phosphor-coated white and red LED dice in order to compensate for the low-efficiency red phosphors then available. This produced a warm white light with good CIE Ra values, but relatively poor R9 values due to the quasimonochromatic red emissions.

One roadway luminaire manufacturer has recently taken this approach with a new product line that was reportedly designed to comply with the International Dark Sky Association’s Fixture Seal of Approval program requirements for a maximum CCT of 3000K. While the approach works (with a measured CCT of 3145K), the massive spike in red light peaking at 625 nm (see Fig. 8) would seem to be a botanist’s nightmare spectrum.

Botanical Light Pollution - FIG. 8

FIG. 9 – 3000K LED versus 3145K white+red LED equal-lumen spectral power distribution.

Surprisingly, the situation may not be as bad as it appears. First, there is relatively little far-red radiation being emitted. Second, the 625 nm peak occurs where the phytochrome Pr absorptance spectrum is only 50 percent of maximum. This results in a calculated phytochrome action value (relative to the HPS reference lamp) of 0.9 – half that of the 3000K LED.

Light Source Relative Phytochrome Action
Pr Pfr Pr + Pfr
White+red LED 0.9 0.9 0.9

Table 3 – White+red relative phytochrome action values.

Color Filters

Another solution to the problem of excess red light is simply to add a color filter with a sharp cutoff at 625 nm. Red light beyond the cutoff wavelength contributes only ten percent to the luminous flux of a 3000K white light LED, so it is may be a reasonable tradeoff. (The resultant color will, however, be slightly cyan in hue.)

Whether it is possible to develop a suitable dye or coating for the LED optics that is both inexpensive and resistant to fading is, of course, an open question.

Chlorophyll Screening

The preceding analysis necessarily assumes that the phytochrome is not screened by the other plant photopigments, and that the isoform absorptance spectra represent the phytochrome action spectra for any given plant. In practice, this is not necessarily true. Phytochrome is present in very low concentrations in plant tissues. As a result, the much higher concentrations of chlorophyll tend to screen phytochrome by absorbing much of the incident red radiation. (See Fig. 3 for spectral overlapping between phytochrome Pr and chlorophyll A.)

A study by Beggs et al. (1980) demonstrated that if mustard seedlings are treated with the herbicide Norflurazon, the chlorophyll in the plant tissue becomes photobleached, resulting in white rather than green seedlings. With white seedlings, the phytochrome action spectrum had a peak at 660 nm, following the phytochrome Pr absorptance spectrum. With untreated green seedlings, however, the action spectrum was shifted to approximately 630 nm – which is well within the range of the 625 nm LED emission of the white+red LEDs (FIG. 9).

Botanical Light Pollution - FIG. 9

FIG. 9 – Chlorophyll screening of phytochrome Pr action spectrum. (Source: Beggs et al. 1980)

Summary

First and foremost, the phytochrome action metric presented in this article is not intended as a formal light source metric in any sense; it was introduced solely as a means of evaluating the potential impact of red and far-red light on both wild and domestic plants.

Second, the effects of applying red and/or far-red radiation will depend on the physiological state of the plant, the physiological response being mediated, and the time of application. Any excess (i.e., artificial) red radiation will convert the Pr isoform in the exposed plant to Pfr , while any excess far-red radiation will convert the Pfr isoform to Pr. Either action will upset the plant’s phytochrome photostationary state (Sager et al. 1988). What effect this will have on a given plant species at any given time of the night and season is unknown.

While phytochrome may function as a biological switch for plants, how individual plants species respond to its signaling will vary. Given that phytochrome mediates so many plant functions, the botanist’s characterization of short day, long day, and day neutral flowering plants is probably about all they will have in common.

If the above analysis has shown anything, it is that by changing roadway and outdoor area lighting from high-pressure sodium to white light LEDs, we may – and again, the emphasis is on may – be upsetting the ecological balance in unexpected ways. By examining what we do know and applying it on a theoretical basis, we can at least be better prepared to respond in the future if we need to.

References

Beggs, C. J., M. G. Holmes, M. Jabben, and E. Schäfer. 1980. “Action Spectra for the Inhibition of Hypocotyl Growth by Continuous Irradiation in Light and Dark-grown Sinapis alba L. Seedlings,” Plant Physiology 66:615-618.

Bennie, J. T. W. Davies, D. Cruse, and K. J. Gaston. 2016. “Ecological Effects of Artificial Light on Wild Plants,” Journal of Ecology (in press).

Borthwick, H.A., S. B. Hendricks, M. W. Parker, E. H. Toole, and V. K. Toole. 1952. “A Reversible Photoreaction Controlling Seed Germination,” Proceedings of the National Academy of Science 38:662–666.

Botto, J. F., R. A. Sánchez, G. C. Whitelam, and J. J. Casal. 1996. “Phytochrome A Mediates the Promotion of Seed Germination by Very Low Fluences of Light and Canopy Shade Light in Arasbidopsis,” Plant Physiology 110:439-444.

Cathey, H. M., and L. E. Campbell. 1974. “Lamps and Lighting – A Horticultural View,” Lighting Design and Application 4(11):41-52.

Cathey, H. M., and L. E. Campbell. 1975. “Security Lighting and its Impact on the Landscape,” Journal of Arboriculture 1(10):181-187.

Chaney, W. R. 2002. Does Night Lighting Harm Trees? Circular FNR-FAQ-17, Department of Forestry and Natural Resources, Purdue University.

Chen, C.-L., Y.-H. Su, C.-J. Liu, and Y.-C. Lee. 2009. “Effect of Night Illumination on Growth and Yield of Soybean,” Journal of Taiwan Agricultural Research 58(2):146-154.

Gautam, P., M. T. Terfa, J. E. Olsen, and S. Torre. 2015. “Red and Blue Light Effects on Morphology and Flowering of Petunia x hybrid,” Scientia Horticulturae 184:171-178.

Goto, N., T. Kumagai, and M. Koornneef. 2006. “Flowering Responses to Light-breaks in Photomorphogenic Mutants of Arabidopsis thaliana, a Long-day Plant,” Physiologia Plantarum 83(2):209-215.

Islam, M. A., D. Tarkowská, J. L. Clarke, D.-R. Blystad, H. R. Gislerød, S. Torre, and J. E. Olsen. 2014. “Impact of End-of-day Red and Far-red Light on Plant Morphology and Hormone Physiology of Poinsetta,” Scientia Horticulturae 174:77-86.

Kami, C., S. Lorrain, P. Hornitschek, and C. Fankhauser. 2010. “Light-regulated Plant Growth and Development,” Current Topics in Developmental Biology 91:29-66.

Kitazaki, K., S. Watanabe, A. Okamoto, M. Matsuo, S. Furuya, and K. Sameshima. 2015. “Far-red Light Enhances Removal of Pericarps in Tartary Buckwheat (Fagopyrum tataricum Gaertn.) Sprout Production under Artificial Lighting,” Scientia Horticulturae 185:167-174.

Lee, M.-J., S.-Y. Park, and M.-M. Oh. 2015. “Growth and Cell Division of Lettuce Plants under Various Ratios of Red to Far-red Light-emitting Diodes,” Horticulture, Environment, and Biotechnology 56(2):186-194.

Lin, C. 2002. “Blue Light Receptors and Signal Transduction,” The Plant Cell 14:S205-S225.

Lumileds. 2014. LUXEON Rebel ES Datasheet DS61 20140630.

Matzke, E. B. 1936. “The Effect of Street Lights in Delaying Leaf-fall in Certain Trees,” American Journal of Botany 23(6):446-452.

Sager, J. C., W. O. Smith, J. L. Edwards, and K. L. Cyr. 1988. “Photosynthetic Efficiency and Phytochrome Equilibria Determination Using Spectral Data,” Trans. ASAE 31(5):1882-1889.

Smith, H., Ed. 1977. The Molecular Biology of Plant Cells. Berkely, CA: University of California Press.

Smith, H. 2000. “Phytochromes and Light Signal Perception by Plants – An Emerging Synthesis,” Nature 407:585-591.

Whitman, C.M., R. D. Heins, A. C. Cameron, and W. H. Carlson. 1998. “Lamp Type and Irradiance Level for Daylength Extensions Influence Flowering of Campanula carpatica ‘Blue Clips’, Coreopsis grandiflora ‘Early Sunrise’, and Coreopsis verticillata ‘Moonbeam’,” Journal of the American Society of Horticultural Science 123:802–807.

Withrow, R. B., W. H. Klein, and V. Elstad. 1957. “Action Spectra of Photomorphogenic Induction and its Photoinactivation,” Plant Physiology 32:453-462

 

[1] Plants in Action is a plant physiology textbook published by the Australian Society of Plant Scientists, New Zealand Society of Plant Biologists, and the New Zealand Institute of Agricultural and Horticultural Science. It is freely available online at http://plantsinaction.science.uq.edu.au.

Mobile Light Pollution

Driving the Night Away

Ian Ashdown, P. Eng., FIES

Chief Scientist, Lighting Analysts Inc.

[ Please send all comments to allthingslighting@gmail.com ]

Related Posts

Botanical Light Pollution

Color Temperature and Outdoor Lighting

Light Pollution and Uplight Ratings

UPDATE 16/01/22 – Minor corrections to Figure 3 due to Table 4.27 transcription errors.

At first glance, this appears to be an innocuous question:

How much light pollution is attributable to automotive headlights?

It is also a good question in that if we are to address light pollution, we need to know what causes it. For this, we first need to look at the U.S. Department of Energy publication, 2010 U.S. Lighting Market Characterization (DOE 2010).

Section 4.2.4, Outdoor Results, tabulates the estimated number of outdoor lamps and their wattages nationwide by application in Tables 4.27 and 4.28 (Fig. 1), while Table C.2, System Efficacy Assumptions, tabulates the lamp efficacies (Table 1).

Mobile Light Pollution - FIG. 2A - DOE Table 4.27

Mobile Light Pollution - FIG. 2B - DOE Table 4.28Fig. 1 – Estimated inventory of outdoor lamps. (Source: DOE 2010)

Lamp Type Luminous Efficacy (lm/W)
Incandescent 12.2
Halogen 16.5
CFL 54.6
Linear Fluorescent 73.7
Mercury Vapor 30.5
Metal Halide 60.0
High Pressure Sodium 83.6
Low Pressure Sodium 89.2
LED 45.3
Other 75.8

Table 1 – System efficacy assumptions. (Source: DOE 2010)

With this information, we can estimate the relative lamp lumens per application (Fig. 3):

Mobile Light Pollution - FIG. 3 - Relative Lamp Lumens

Fig. 3 – Relative lamp lumens by application. (Source: DOE 2010)

The interesting thing about this chart is that while the mix of lamp types has changed markedly since 2010 – LEDs are rapidly replacing high-pressure sodium (HPS) and metal halide (MH) lamps in roadway applications, for example – the relative lamp lumens by application should remain relatively constant[1].

With this, we can see that roadway lighting, outdoor parking lots, building exterior lighting, and stadium lighting contribute the most to light pollution on a per-lumen basis. Billboards (0.8 percent) and airfields (0.1 percent) may significantly impact surrounding residential neighborhoods, but they are mostly insignificant once you get outside of urban centers to dark-sky observing sites.

Automobiles

What the 2010 U.S. Lighting Market Characterization report does not address, of course, is automotive lighting, specifically headlamps. For this information, we have to go in search of data in order to synthesize an answer.

According to Wikipedia, the most common types of headlamps in North America are HB1/9004 dual-filament tungsten-halogen lamps, which generate 700 lumens, and high-intensity discharge (HID) lamps, which generate 2,800 to 3,500 lumens, both on low-beam. To this, we can add a growing number of LED and, in the future, laser headlamps.

Going forward, however, it is reasonable to assume that the average lumen output of an automotive headlamp will be approximately 3,000 lumens, giving 6,000 lumens per vehicle.

In calculating light pollution using various mathematical models such as those by Garstang (1986) and Aubé (2015), it is commonly assumed that the average person in urban environments requires between 1,000 and 1,500 lumens of outdoor lighting (i.e., per capita). At 6,000 lumens per vehicle, it is then reasonable to ask whether motor vehicles, including automobiles, contribute to light pollution.

The next part of the question is to ask how many vehicles there are per capita. Again, Wikipedia provides an answer: in the United States, there are 0.809 vehicles per capita. (As a curious aside, the miniscule microstate of the Most Serene Republic of San Marino has 1.263 vehicles per capita – over 41,000 of them in a country of 24 square miles that is surrounded by a relatively impoverished Italy.)

Now, however, comes the difficult part of the question: how many of these vehicles are on the road at any given time? This is not an easy question to answer. A common metric for transportation planners and engineers is the Annual Average Daily Traffic (AADT). This is determined in principle by counting all the vehicles on a highway or road for a year and then dividing by 365 days.

In practice, it is costly to install and maintain permanent automated traffic counters, and so portable automatic counters (those pneumatic tubes you sometimes see on the road) or traffic observers count vehicles for a few days in the year. There is then a fair amount of black magic applied (e.g., Ivan et al. 2002) to arrive at the estimated AADT.

What we are interested in, however, is the Average Daily Traffic (ADT) on an hourly basis. The transportation departments of most major cities collect this data for their major feeder routes, and quite often post the data online. The Province of British Columbia, for example, offers data from a hundred or so traffic counters throughout the province via their Traffic Data Program Web site.

Agreed, this is too much information, but it does offer a fascinating insight into urban traffic patterns: they are almost identical on an hourly basis throughout the week, regardless of the city under consideration. A good example is the hourly traffic distribution for all roads in Great Britain in 2014, as reported by the United Kingdom Department for Transport (Fig. 4):

Mobile Light Pollution - FIG. 4 - Normalized Hourly Traffic Count

Fig. 4 – Normalized hourly traffic count. (Source: Table TRA0307, UK Department for Transport)

Equally surprising is that these numbers do not change significantly throughout the year (Fig. 5):

Mobile Light Pollution - FIG. 5 - Normalized Monthly Traffic Count

Fig. 5 – Normalized monthly traffic count. (Source: Table TRA0305, UK Department for Transport)

The problem, however, is that these traffic counts apply to single traffic counters. At best, all transportation engineers can do is to monitor the major roads and highways separating different sectors of a major urban center (collectively called “screenlines”) and estimate the traffic flow across them. What happens within these sectors with their hundreds to thousands of possible routes along municipal roads is anyone’s guess.

So, it is at this point that we have to make some ballpark estimates. We assume (admittedly with no supporting evidence) that the average urban commute time (round trip) per day is two hours, and that 50 percent of the 0.809 vehicles per capita in the United States are driven on any given day. Thus, at any given time, there are on average 0.809 * 0.5 * 2 hours / 24 hours = 0.033 vehicles per capita on the road at any given time.

With the normalized hourly traffic counts from Fig. 4 and these data and assumptions, we therefore have:

Hour Vehicles per Capita Lumens per Capita
00:00 – 01:00 0.006 36
01:00 – 02:00 0.004 25
02:00 – 03:00 0.003 20
03:00 – 04:00 0.004 23
04:00 – 05:00 0.006 35
05:00 – 06:00 0.013 77
17:00 – 18:00 0.061 367
18:00 – 19:00 0.049 296
19:00 – 20:00 0.035 212
20:00 – 21:00 0.025 152
21:00 – 22:00 0.019 112
22:00 – 23:00 0.014 83
23:00 – 24:00 0.009 56

Table 2 – Automotive lumens per capita and hour

How this table should be interpreted is a matter for debate – it depends on when astronomical twilight begins and ends (i.e., when the sun is 18 degrees below the horizon), and thus on both the time of the year and the observer’s latitude. Apart from the winter months, it seems safe to say that automotive headlights contribute less than ten percent to light pollution after astronomical evening twilight.

What happens in San Marino is another question entirely.

Acknowledgements

Thanks to Dawn DeGrazio (Lighting Analysts, Inc.) for reviewing an earlier draft of this article.

References

Aubé, M. 2015. “Physical Behaviour of Anthropogenic Light Propagation into the Nocturnal Environment,” Philosophical Transactions of the Royal Society B 370(1667):20140117.

DOE. 2012. 2010 U.S. Lighting Market Characterization. U.S. Department of Energy Building Technologies Program.

Garstang, R. H. 1986. “Model for Artificial Night-Sky Illumination,” Publications of the Astronomical Society of the Pacific 98:364-375.

Ivan, J. N., W. E. ElDessouki, M. Zhao, and F. Guo. 2002. Estimating Link Traffic Volumes by Month, Day of Week, and Time of Day. Technical Report JHR 02-287, Connecticut Transportation Institute, University of Connecticut.

 

[1] LED luminaire efficiencies are typically better than their fluorescent and HID counterparts, but these will in general be similar across all applications.

Light Pollution and Uplight Ratings

Swatting BUGs

Ian Ashdown, P. Eng., FIES

Chief Scientist, Lighting Analysts Inc.

[ Please send comments to allthingslighting@gmail.com ]

Related Posts

Botanical Light Pollution

Color Temperature and Outdoor Lighting

Mobile Light Pollution

“Oh, East is East, and West is West, and never the twain shall meet.”

When Rudyard Kipling wrote this line in his poem The Ballad of East and West (Kipling 1892), he was referring to cultural misunderstandings between the British and their colonial subjects in India (where “twain” means two). As a proverb, however, it has worked equally well for the lighting industry and the astronomical research community for the past four decades.

The meeting concerns light pollution, wherein roadway and area lighting contribute to the diffuse sky glow that limits our ability to observe the stars at night. The International Dark-Sky Association (IDA) has campaigned since 1988 to limit the use of outdoor lighting, and to employ luminaires that are designed to limit undesirable spill light. Unfortunately, the equivalent of cultural misunderstandings have until recently worked against this effort.

Those in the lighting industry will be familiar with IES TM-15-11, Luminaire Classification System for Outdoor Luminaires with its BUG (Backlight-Uplight-Glare) rating system (IES 2011); those in the astronomical research community will be familiar with Garstang’s light pollution model (Garstang 1986) and its derivatives. These documents have led to the development of the IDA/IES Model Lighting Ordinance (IDA/IES 2011) by the lighting industry and the lesser-known Pattern Outdoor Lighting Code (Luginbuhl 2010) by the astronomical research community.

Of particular interest to professional lighting designers is the BUG rating system of IES TM-15-11. While the IDA/IES Model Lighting Ordinance (MLO) has seen at best sporadic adoption by individual municipalities and states, BUG ratings are integral to the LEED v4 Light Pollution Reduction credit. While it is only one credit, it may make the difference between, for example, LEED Silver and Gold certification.

Related to this is the IDA’s Fixture Seal of Approval program, which “provides objective, third-party certification for luminaires that minimize glare, reduce light trespass, and don’t pollute the night sky.” While it is not directly related to IES TM-15-11 or LEED v4, outdoor luminaires with this “dark-sky friendly” certification are useful in promoting environmental responsibility in building design.

Curiously, recent changes to this program have removed all references to the BUG rating system, replacing it with the much simpler requirement that the luminaires be full-cutoff, or to quote the IDA FSA Web site, “fixtures must emit no light above 90 degrees.” In other words, after campaigning for lighting pollution control and working with the lighting industry through the Illuminating Engineering Society for the past decade or more, the International Dark-Sky Association apparently no longer recognizes the IES BUG rating system.

What happened here … and why is your humble scribe looking guilty?

History – Astronomical Research

Going back to 1973, the astronomer P. S. Treanor wrote a paper called, “A Simple Propagation Law for Artificial Night-Sky Illumination” (Treanor 1973). In it, he developed an empirical equation for the overhead sky brightness at night due to light pollution from a distant city. As befits the astronomical research community, his equation involved Mie scattering from aerosol particles (dust and smoke), atmospheric density, and extinction coefficients – topics not in the lexicon of most lighting designers. The light source was modeled as a single point source with constant intensity.

In 1986, the astronomer R. H. Garstang wrote a paper called, “Model for Artificial Night-Sky Illumination” (Garstang 1986). Again as befits the astronomical research community, his equations involved Rayleigh scattering from air molecules, Mie scattering from aerosol particles, reflections from the ground, and more. Most important, he empirically modeled the luminous intensity distribution of roadway cobrahead luminaires that were prevalent at the time.

Swatting Bugs - FIG. 1

FIG. 1 – Garstang’s luminous intensity function (green line). (Source: Luginbuhl et al. 2009)

In his own words, however, “The choice of the function … is purely arbitrary … these properties seem to be true for most street lights and for at least some other forms of outdoor lighting.”

Swatting Bugs - FIG. 2

FIG. 2 – Typical cobrahead roadway luminaire

… and never the twain shall meet. The lighting industry has relied on measured luminous intensity distributions to characterize luminaires for nearly a century. It would be unthinkable for a lighting researcher to model such distributions with a “purely arbitrary” function that might “seem to be true.”

In Garstang’s defense, however, a metropolis illuminated with randomly oriented cobrahead luminaires circa 1986 probably did have a composite luminous intensity distribution (i.e., for the entire city) that was reasonably approximated by his empirical function. As evidence of this, recent studies by Duriscoe et al. 2013 and others have mostly validated the sky glow predictions made by Garstang’s model.

That, however, was three decades ago. Things have changed.

History – Lighting Industry

The IDA/IES Model Lighting Ordinance has a long and somewhat contentious history. It was first developed by the IDA without significant input from lighting industry. One of the early drafts defined outdoor luminaires in terms of their wattage, with no reference whatsoever to their luminous flux output. East is East and West is West …

An IES meeting of outdoor lighting industry representatives first saw this proposed ordinance as an existential threat, as recorded in the meeting minutes. Eventually however, it was decided that it was better to work with the astronomical research community rather than to fight it. In 2005 therefore, the Joint IDA/IES Task Force was formed to collaboratively develop the MLO.

This led in turn to the development of the Luminaiure Classification System (LCS), published in IES TM-15-07, with the BUG rating system added in 2009 and subsequently revised in IES TM-15-11. The first public review of the MLO occurred in 2009, the second public review in 2010, and the final Joint IDA-IES Model Lighting Ordinance (MLO) with User’s Guide document was published in June 2011 (IDA/IES 2011). The BUG rating system of IES TM-15-11 is incorporated in the MLO as Table C, Maximum Allowable Backlight, Uplight and Glare (BUG) Ratings.

Referring to IES TM-15-11, it defines six uplight ratings for luminous flux (maximum zonal lumens) emitted above 90 degrees by the luminaire (Table 1). There are two uplight zones, designated UL for vertical angles 90 to 100 degrees and UH for angles 100 to 180 degrees (FIG. 3).

  U0 U1 U2 U3 U4 U5
UH 0 10 50 500 1000 >1000
UL 0 10 50 500 1000 >1000

Table 1 – IES TM-15-11 Uplight Ratings (maximum zonal lumens)

Swatting Bugs - FIG. 3

FIG. 3 – IES TM-15-11 BUG uplight zones (Source: Chinnis et al. 2011)

The question that must be asked, however, is where did these lumen values come from? The only publicly-available documentation appears to be a Leukos paper titled “IES TM-15 BUG Value-Setting and Adjustment Methodology” (Chinnis et al. 2011). One quote from this paper is of particular significance:

“The BUG values were established by the [IDA/IES MLO Task Force] based on professional experience and analysis efforts with a very wide variety of outdoor lighting applications, including variations in ambient brightness, site geometry and function.”

Referring to the astronomical research comment above, it would be unthinkable for an astronomer to specify values in a standard “based on professional experience and analysis efforts” without providing the data needed for impartial and independent verification.

East is East and West is West … as easy as it may be to poke fun at both sides in this matter, it is not constructive. As long as the BUG rating system is being used as a basis for the LEED v4 Light Pollution Reduction credit, there is a need to understand whether the maximum zonal lumens shown in Table 1 are appropriate.

Sadly, they are not.

Measuring Uplight

A year after IES TM-15-11 was published, another Leukos paper titled “Photometric Imprecision Can Limit BUG Rating Utility” investigated the practical issues of measuring luminaires in the laboratory for BUG uplight ratings (Ashdown 2012). The abstract, while extensive, usefully summarizes the results:

“There are, however, limits to what can be measured in the laboratory. IES TM-15–11 requires that a luminaire with an uplight rating of U0 emits zero lumens into the upper hemisphere, while a U1 uplight rating or a G0 glare rating for high viewing angles requires fewer than 10 lumens. Given that the luminaire is emitting thousands of lumens and that the laboratory room surfaces have a diffuse reflectance of at least two percent, it is physically impossible to measure zero lumens, and extremely difficult to measure fewer than 10 lumens. Consequently, a U0 glare rating can only be obtained by physical examination of the luminaire and post-processing of the measured photometric data. Similarly, a U1 uplight rating or a G0 glare rating for high viewing angles is likely the result of data manipulation.”

The paper explained that “post-processing of the measured photometric data” is indeed a common practice in photometric laboratories. If the laboratory technician can clearly see that the luminaire emits no light at or above 90 degrees, it is entirely reasonable to zero out the data for vertical angles greater than zero degrees, as these only record the diffuse interreflections from the laboratory room surfaces.

Of course, it is also possible that the laboratory technician saw that there was some stray light being emitted into the UL zone, but decided that it was probably less than 10 lumens and so reason enough to zero out the data. (Estimating total emitted lumens simply by looking at a luminaire presumably requires professional experience.)

From an engineering perspective, this is an untenable position. The problem is that if you cannot measure something, then it is pointless to divide it into different categories (in this case U0 through U2 uplight ratings).

Calculating Uplight

Kipling’s pessimism aside, it is possible to reconcile the interests of the lighting industry on one hand and the astronomical research community on the other. The approach is simple: given that Garstang’s light pollution model has been validated, it is entirely straightforward to substitute measured luminous intensity distributions for Garstang’s generic and arbitrary distribution (FIG. 1). It did not make sense to do this in 1986, but it certainly does today with the emphasis on BUG ratings. The question to be answered is, what influence do various UL and UH ratings have on light pollution if you assume that the same luminaires are used throughout an entire metropolis?

To be fair, the astronomical research community has addressed this question in several papers, including Aubé et al. (2005), Aubé (2015), Baddiley (2007), and Cizano and Castro (2000). In particular, an open source software program for sky glow modeling called Illumina imports IES LM-63 photometric data files. The problem, however, is twofold: 1) the papers were written for and published within the astronomical research community; and 2) software programs such as Illumina are sophisticated research tools that are designed to answer more pressing questions than whether the BUG uplight rating lumen values are appropriate.

This need not dissuade us, however. Garstang’s light pollution model is not particularly complicated, and it was clearly described in the original paper. It is also not particularly difficult to implement in software – it was after all originally developed to run on a 1980s-era Apple II computer (Garstang 1986). The only difference is that calculations that likely took days to weeks to run in 1986 now execute in a few seconds.

The result is SkyGlowCalc, a program written expressly to answer the above question for the IDA Task Force (FIG. 4). The software was developed on a volunteer basis in the author’s capacity as a member of the Task Force, mostly because the question itself was inherently interesting.

Swatting Bugs - FIG. 4

FIG. 4 – SkyGlowCalc (Source: Lighting Analysts Inc.)

This program is, of course, more than what Garstang envisioned some three decades ago. In addition to importing IES LM-63 photometric data files, it also allows the user to specify common lamp types with their different spectral power distributions (SPDs). The program then calculates the resultant SPDs of the emitted light contributing to sky glow at a remote observing site. As shown in FIG. 4, the wavelength-dependent effects of Rayleigh scattering greatly increase the blue content at the observing site (dashed line). A more detailed discussion of this is presented in the AGi32 blog article, “Color Temperature and Outdoor Lighting.”

For astronomical purposes, the most important output of this program is the sky brightness or its equivalent limiting visual magnitude. The goal was to take the photometric data files of 63 commercial luminaires from the IDA Fixture Seal of Approval program and see what differences in sky brightness there would be, assuming that all the luminaires emitted the same amount of luminous flux and all other parameters were equal (as shown in FIG. 4). The results of this analysis are shown in Table 2.

UL Uplight Rating LPS CIE HP1 3000K LED 5000K LED
U0 24.4 21.4 19.0 18.7
U1 24.4 21.4 19.0 18.7
U2 24.3 21.3 19.3 18.6

Table 2 – Uplight Rating versus Limiting Magnitude

The visual magnitude of the calculated sky glow determines the faintest stars you can see directly overhead at midnight on a moonless night. With the unaided eye, we can see stars as faint as magnitude 6; telescopes gather more light and so allow us to see fainter stars. The scale is logarithmic, with a difference of 0.1 magnitude representing a difference of approximately 10 percent in photometric intensity. These differences are near the limit of our ability to distinguish differences in intensity.

Simply put, not only can we not measure the differences between U0, U1 and U2 ratings in the laboratory with luminaires, we cannot distinguish the resultant differences in sky glow in the night sky.

Shortly after these results were presented to the IDA Task Force, the decision was made to remove BUG rating requirements from the IDA Fixture Seal of Approval program.

Model Lighting Ordinance

As noted above, the BUG rating system is incorporated in the IDA/IES Model Lighting Ordinance (IDA/IES 2011) as Table C. However, there is a twist that is often overlooked (FIG. 5).

Swatting Bugs - FIG. 5

FIG. 5 – Model Lighting Ordinance Table C-2

Put another way, Table C-2 says that different uplight ratings are permitted for different lighting zones, but only for luminaires that are not used for street lighting or area lighting. In other words, only luminaires with U0 ratings are permitted for street and area lighting (which basically includes all significant outdoor lighting)..

Conclusions

Prior to 2007, roadway luminaires were classified as having cutoff, semicutoff, noncutoff, and full cutoff luminous intensity distributions, with “full cutoff” meaning luminaires with no luminous flux emitted at or above 90 degrees vertical, as well as limited intensity at or above 80 degrees (IES 2011). The BUG rating system was developed by the Joint IDA/IES MLO Task Force to address light pollution issues, thereby replacing these mostly empirical definitions.

It seems, however, that we mostly have come full circle – the International Dark Sky Association no longer makes use of the BUG rating system in its Fixture Seal of Approval program. All that is required is that the luminaires do not emit luminous flux above 90 degrees vertical.

This is not an example of backtracking. Rather, it is how science (and hopefully standards development) works. The IDA executive presumably reviewed the above analysis and concluded that the BUG rating system offers no value to light pollution abatement policies. The FSA program requirements were simply updated in accordance with the best available information. (LEED v4 authors, please take note.)

As for Kipling, the problem has always been that the lighting industry and the astronomical research community speak different languages. The International Dark-Sky Association has been accused in the past of “selling out” to the lighting industry in partnering with the Illuminating Engineering Society, but this is unfair. Having reviewed the original MLO drafts in the 1990s, it was painfully clear that neither side understood the other, from technology to terminology. The Joint IDA/IES MLO Task Force did a credible job of bridging this cultural gap over four years, but “professional experience and analysis” can only go so far. SkyGlowCalc was developed solely to assist both sides in finally bridging the communications gap. (The author is himself an amateur astronomer.)

As for the BUG rating system, it must be remembered that its backlight and glare components (except G0)  are still presumed valid, and so it is still useful in environmentally responsible lighting design. It rightfully retains its position in the IDA/IES Model Lighting Ordinance.

Postscript

The reason why uplight from U0- and U1-rated outdoor luminaires has so little effect on sky glow is simple. Taking the full-cutoff 250-watt metal halide luminaire from IES TM-15-11 as an example, it emits 13,553 lumens downwards. Assuming that the ground has a reflectance (albedo) of 15 percent (Gillet and Rombauts 2001), the amount of light diffusely reflected into the upper hemisphere is 2,033 lumens. The portion of light reflected into the UL zone is 406 lumens, with the remaining 1,627 lumens being reflected into the UH zone. In other words, the luminaire in its natural surroundings has a UL rating of U2 (nearly U3) and a UH rating of U4. Adding a few more lumens of directly-emitted luminous flux will not make any difference.

Together, roadway and outdoor parking luminaires account for over 80 percent of all outdoor lighting on a per-lumen basis (Navigant 2012). If we are to tame light pollution, it must be through a combination of limiting roadway and parking lot illuminance requirements, and perhaps more important employing smart networked lighting technologies to dim or turn off the luminaires when they are not needed.

In the meantime, the twain have hopefully and finally met.

Acknowledgements

Thanks to Dawn DeGrazio for editorial assistance and historical clarifications.

References

Ashdown, I. 2012. “Photometric Imprecision Can Limit BUG Rating Utility,” Leukos 9(2):79-88.

Aubé, M., L. Franchomme-Fossé, P. Robert-Staehler, and V. Houle. 2005. “Light Pollution Modelling and Detection in a Heterogeneous Environment: Toward a Night Time Aerosol Optical Depth Retrieval Method,” Proc. SPIE Volume 5890.

Aubé, M. 2015. “Physical Behaviour of Anthropogenic Light Propagation into the Nocturnal Environment,” Philosophical Transactions of the Royal Society B 370(1667):20140117.

Baddiley, C. 2007. “A Model to Show the Differences in Skyglow from Types of Luminaires Designs,” Starlight 2007. La Palma, Canary Islands.

Chinnis, D., M. Mutmansky, and N. Clanton. 2011. “IES TM-15 BUG Value-Setting and Adjustment Methodology,” Leukos 8(1):25-39.

Cinzano, P., and F. J. D. Castro. 2000. “The Artificial Sky Luminance and the Emission Angles of the Upward Light Flux,” Journal of the Italian Astronomical Society 71(1):251.

Navigant Consulting, Inc. 2012. 2010 U.S. Lighting Market Characterization. Washington, DC: U.S. Department of Energy.

Duriscoe, D. M., C. B. Luginbuhl, and C. D. Elvidge. 2013. “The Relation of Outdoor Lighting Characteristics to Sky Glow from Distant Cities,” Lighting Research and Technology 46(1):35-49.

Garstang, R. H. 1986. “Model for Artificial Night-Sky Illumination,” Publications of the Astronomical Society of the Pacific 98:364-375.

Gillet, M., and P. Rombauts. 2001. “Precise Evaluation of Upward Flux from Outdoor Lighting Installations (Applied in the Case of Roadway Lighting),” Proc. International Conference on Light Pollution. Serena, Chile.

IDA/IES. 2011. Joint IDA-IES Model Lighting Ordinance (MLO) with User’s Guide. New York, NY: Illuminating Engineering Society.

IES. 2011. IES TM-15-11, Luminaire Classification System for Outdoor Luminaires. New York, NY: Illuminating Engineering Society.

Kipling, R. 1892. The Ballad of East and West, in Barrack-room Ballads. London, UK: Methuen Publishing.

Luginbuhl, C. B., V. E. Walker, and R. J. Wainscoat. 2009. “Lighting and Astronomy,” Physics Today, December, pp. 32-37.

Luginbuhl, C. B. 2010. Pattern Outdoor Lighting Code (USA). Flagstaff, AZ: U.S. Naval Observatory.

Treanor, P. J. 1973. “A Simple Propagation Law for Artificial Night-Sky Illumination,” Observatory 93:117.

 

Color Temperature and Outdoor Lighting

Examining the Limits of CCT

Ian Ashdown, P. Eng., FIES

Chief Scientist, Lighting Analysts Inc.

[ Please send comments to allthingslighting@gmail.com ]

Related Posts

Botanical Light Pollution

Mobile Light Pollution

Light Pollution and Uplight Ratings

UPDATE: Sports field lighting analysis added 15/10/12.

[An edited version of this article was published as “STREET LIGHTS: Light pollution depends on the light source CCT” in the October 2015 issue of LEDs Magazine.]

Most of you will be familiar with the International Dark-Sky Association (IDA), which was founded in 1988 to call attention to the problems of light pollution. It reminds us that light pollution threatens professional and amateur astronomy, disrupts nocturnal ecosystems, affects circadian rhythms of both humans and animals, and wastes over two billion dollars of electrical energy per year in the United States alone.

The IDA’s Fixture Seal of Approval program “provides objective, third-party certification for luminaires that minimize glare, reduce light trespass, and don’t pollute the night sky.” Recent changes to this program have reduced the maximum allowable correlated color temperature (CCT) from 4100K (neutral white) to 3000K (warm white). Previously-approved luminaires with CCTs greater than 3000K will have one year to comply with the new standard.

There are several reasons for this revised CCT limit. One reason is that many people prefer low-CCT outdoor lighting, especially in residential areas. As noted by Jim Benya in his LD+A article “Nights in Davis” (Benya 2015), the City of Davis was obliged to replace newly-installed 4800K street lighting with 2700K luminaires at a cost of $350,000 following residents’ complaints. As was noted in the article, “2700K LEDs are now only 10 percent less efficacious than 4000K,” so there was presumably minimal impact on the projected energy savings.

But another, arguably more important, reason is that high-CCT luminaires contribute more to light pollution on a per-lumen basis than do low-CCT luminaires. This is perhaps best demonstrated by the widely-disseminated graph presented in Luginbuhl et al. (2014) and shown in FIG. 1:

Color Temperature -FIG. 1 FIG. 1 – Overhead scotopic sky brightness ratio (Source: Luginbuhl et al. 2014)

Luginbuhl et al. calculated this graph using a modified version of Garstang’s sky brightness model (Garstang 1986). What it shows is that the light pollution due to 5100K cool-white LED street lighting is approximately twice that of equivalent 2400K warm-white LED street lighting. According to the model, this relationship holds true regardless of the distance from the city to the remote astronomical observing site.

From the perspective of both professional and amateur astronomers as publicly represented by the International Dark-Sky Association, this graph is reason enough to require a maximum CCT of 3000K for the IDA’s Fixture Seal of Approval program.

There is however more to this story. While the graph shown in FIG. 1 may present clear evidence of the relationship between CCT and light pollution, we must remember that its data were calculated rather than measured. The question is whether it is reasonable to trust Garstang’s sky brightness model and its modification by Luginbuhl et al.

Garstang’s Model

Garstang’s sky brightness model is conceptually simple. Referring to FIG. 2, imagine a city C and a distant observer O. The sky glow as seen by the observer is due to light emitted by the city streetlights that is scattered by the air molecules and aerosols in the atmosphere along the path of the observer’s view direction. At any point along this path, the light will be scattered from the volume dV. The sky glow as seen by observer O is simply the sum of the scattered light for all such volumes along the path due to all of the luminaires within the city C.

Color Temperature -FIG. 2

FIG. 2 – Garstang’s sky brightness model

Understanding the mathematics of Garstang’s model requires a reasonably good understanding of atmospheric optics (e.g., Liou 2002). This topic will not be discussed here beyond presenting (without explanation) Garstang’s equation for sky brightness b:

Color Temperature - EQN. 1

What is important for this discussion is that Garstang’s model assumes that the street lighting is monochromatic. He assumed a wavelength of 550 nm as being representative for visual astronomy.

We can have confidence that Garstang’s sky brightness model is reasonably accurate, based on recently-published validation studies by, for example, Duriscoe et al. 2013. With cities ranging from Flagstaff, AZ to Las Vegas, NV however, it is simply not possible to measure the influence of correlated color temperature on light pollution.

Wavelength Dependencies

Light pollution is due to both Rayleigh scattering from air molecules and Mie scattering from aerosols such as dust, smoke, and haze. Rayleigh scattering is strongly wavelength-dependent, with a probability proportional to λ-4, where λ is the wavelength. The sky is blue because of Rayleigh scattering. Mie scattering is wavelength-independent, which is why the clear sky appears pale blue or white in heavily-polluted urban areas. (As an aside, the average distance a photon of blue light will travel through the atmosphere at sea level before undergoing Rayleigh scattering – its mean free path – is about 50 km. By comparison, the mean free path for a photon of red light is about 200 km.)

Luginbuhl et al. (2014) used these relationships to extend Garstang’s model for visible wavelengths between 400 nm and 700 nm in order to calculate FIG. 1:

Color Temperature - EQN. 1

While justifiable, this modification to Garstang’s model is somewhat ad hoc. In particular, the original model is a gross simplification of an exceedingly complex physical situation. While it has been validated in terms of sky brightness, this says nothing about whether Luginbuhl’s modifications result in similarly accurate solutions.

Radiative Flux Transfer

There have been more advanced light pollution models developed over the intervening thirty years, including Garstang 1991, Cinzano et al. 2000, Gillet et al. 2001, Aubé et al. 2005, Baddiley 2007, Kocifaj 2007, Luginbuhl et al. 2009, Kocifaj 2010, Kocifaj et al. 2010, Cizano and Falchi 2012, Kocifaj et al. 2014, Luginbuhl et al. 2014, and Aubé 2015.

Perhaps the most comprehensive light pollution model developed to date is Illumina, an open source program that was described in Aubé et al. 2005, and which is still under development. Unlike Garstang’s model (which was designed to execute on a 1980s-era Apple II computer), Illumina is a voxel-based radiative flux transfer program that can require weeks of computer time on a supercomputer with several thousand CPUs and terabytes of RAM (Aubé 2015).

The situation is similar to weather prediction models, where a simple model will give you a rough idea of what is going to happen, but it requires a supercomputer to perform massive amounts of data processing in order to have full confidence in the predictions. Simply put, Illumina models light pollution in a manner that would have been inconceivable thirty years ago.

Color Temperature -FIG. 3

FIG. 3 – Illumina calculation model (Source: Illumina Web site)

Unlike Garstang’s model, Illumina explicitly considers diffuse reflections from the ground and in-scattering of scattered light from volumes m into the volumes n visible to the observer. Garstang’s model includes an entirely ad hoc term for double scattering, but it is impossible to determine whether it correctly models the atmospheric optics.

The details of the program, however, are not as important for the purposes of this article as are the results recently reported by its author (Aubé 2015).

Modeling Sky Glow

What Aubé found with Illumina is that the combination of Rayleigh and Mie scattering results in a wavelength dependency described by λ, where α varies from 3.6 to 2.7 as the distance from the city center increases (FIG. 4).

Color Temperature -FIG. 4

FIG. 4 – Wavelength-dependent scattering exponent (Source: Aubé 2015, Fig. 20.)

What FIG. 4 shows is that near the city center, Rayleigh scattering dominates. This is to be expected, as Rayleigh scattering is not directional – the light is scattered equally in all directions, including back down towards the observer.

FIG. 4 also shows that away from the city center, Mie scattering begins to dominate. This is also to be expected, as Mie scattering is directional – the light is preferentially scattered in the forward direction. It is therefore more likely to be scattered to a remote observer as it travels horizontally through the atmosphere.

Sky Glow versus CCT

To apply Aubé’s results to the question of the influence of CCT on sky glow, we first need some representative white light LED spectral power distributions. The following normalized SPDs were digitized from Philips Lumileds’ Luxeon Rebel product catalog (FIG. 5):

Color Temperature -FIG. 5

FIG. 5 – Typical LED spectral power distributions (normalized)

To provide a fair comparison, these SPDs need to be scaled such that the LEDs generate the same luminous intensity. To do this, we multiply the SPDs by the photopic luminous efficiency function at 5 nm intervals (FIG. 6):

Color Temperature -FIG. 6

FIG. 6 – Photopic luminous efficiency function V(l)

and then sum the results to obtain the relative photopic intensities:

CCT Relative Luminous Intensity
2700K 0.88
3000K 1.00
3500K 1.12
4000K 1.17
5000K 0.94

Table 1

Dividing the normalized SPDs by these values gives:

Color Temperature -FIG. 7

FIG. 7 – Typical LED spectral power distributions (constant luminous flux)

What FIG. 7 shows are the different spectral power distributions of the street lighting at city center for each CCT, assuming the same luminous flux output.

Now, using Aubé’s results and assuming an observing site 80 km (50 miles) from the city center, we multiply each 5 nm interval by (λ/550 nm)-2.7 to represent the wavelength dependency (FIG. 8):

Color Temperature -FIG. 8

FIG. 8 – Overhead sky glow spectral power distribution at 80 km

This is precisely what we might expect – blue light is preferentially scattered, bolstering our assumption that high-CCT lighting results in increased sky glow. (These SPDs represent the relative spectral radiance distribution at zenith from the observing site, which is perhaps the most useful definition of sky glow.)

If we assume scotopic (i.e., dark-adapted) visual observing conditions, we need to multiply these SPDs by the scotopic luminous efficiency function at 5 nm intervals (FIG. 9):

Color Temperature -FIG. 9

FIG. 9 – Scotopic luminous efficiency function V’(l)

and sum the results to obtain the relative scotopic zenith luminance. The results are shown in Table 2:

CCT Relative Scotopic Luminance
2700K 0.96
3000K 1.00
3500K 1.04
4000K 1.12
5000K 1.42

Table 2 – Relative sky glow luminance at 80 km

This however is for a remote astronomical observing site, such as a dark-sky preserve. To understand what happens within the city center, we repeat the above procedure with α = 3.6 as per FIG. 4. Rayleigh scattering predominates, as shown by FIG. 10 with its greatly exaggerated blue peaks.

Color Temperature -FIG. 10

FIG. 10 – Overhead sky glow spectral power distribution at city center

When we calculate the relative scotopic luminance of sky glow, however, we find almost identical results (Table 3).

CCT Relative Scotopic Luminance
2700K 0.96
3000K 1.00
3500K 1.05
4000K 1.14
5000K 1.45

Table 3 – Relative sky glow luminance at city center

This assumes, however, that the observer is completely dark-adapted. In an urban setting, the surrounding street lighting will most likely result in only partial dark adaptation, and so mesopic vision will apply. This means a blending of the photopic and scotopic luminous efficiency functions (FIG. 6 and FIG. 9). With the photopic function being much less sensitive to 450 nm blue light, the differences in relative sky glow luminance at city center will be (depending on the visual adaptation field of the observer) somewhere between that of Table 3 and Table 4, which assumes photopic adaptation:

CCT Relative Photopic Luminance
2700K 0.99
3000K 1.00
3500K 1.01
4000K 1.02
5000K 1.06

Table 4 – Relative sky glow luminance at city center (photopic adaptation)

Of course, with full photopic adaptation, the observer will not be able to see anything but the brightest stars and planets in the night sky, so it is best to rely on Table 3 for comparison purposes.

Given the above, the answer to our question is yes, it is reasonable to trust Garstang’s sky brightness model and its modification by Luginbuhl et al. Aubé’s results, based on the much more comprehensive radiative flux transfer model used by Illumina, basically confirms the relationship between CCT and sky brightness as calculated by Luginbuhl et al. (2014).

Astronomical Considerations

According to Table 2, the increase in scotopic sky brightness for 4000K LEDs compared to 3000K LEDs is only 12 percent. Our perception of brightness, following Steven’s Power Law for extended light sources, means that we would see an increase in perceived sky brightness of only four percent! Surely this is not a reasonable justification for the IDA reducing the maximum allowable CCT from 4100K to 3000K for its Fixture Seal of Approval program?

Professional and amateur astrophotographers would vehemently disagree. Richard Wainscoat, Principal Investigator of the NASA-funded Pan-STARRS search for Near Earth Objects at the University of Hawaii, aptly called spectral power distributions of high-CCT LEDs such as that shown in FIG. 8 the “nightmare spectrum” (Betz 2015). Unfortunately, the peak 450 nm emission is right in the spectral region where natural airglow is low and there are important astronomical hydrogen and oxygen emission lines. Unlike the basically monochromatic emissions of low-pressure sodium lamps, it is impossible to filter out the blue LED emissions with band rejection filters. Limiting the CCT to 3000K reduces the contribution to light pollution in the blue region of the spectrum by a factor of two to three.

Allowing 4100K LEDs may be acceptable for casual stargazing, but not for astronomical research or astrophotography.

Ecological Considerations

According to the Fixture Seal of Approval requirements on the IDA Web site:

The case against blue light is well founded with regard to discomfort glare, circadian rhythm disruption, light scattering, sky glow, and biological system disruption in wildlife.

Outdoor lighting with high blue light content is more likely to contribute to light pollution because it has a significantly larger geographic reach than lighting with less blue light. In natural settings, blue light at night has been shown to adversely affect wildlife behavior and reproduction. This is true even in cities, which are often stopover points for migratory species.

The comment about cities is particularly germane in view of FIG. 10, where the light pollution in the blue region of the spectrum from 5000K LEDs is nearly three times that from 3000K LEDs. (To be fair however, this applies to clear skies only. For cloudy skies, Mie scattering from the water droplets dominates, and so the spectral power distribution of the reflected street lighting is essentially that of the lighting itself. On the other hand, much more light is reflected back towards the ground, greatly increasing light pollution.)

Summary

The purpose of this article was to examine the International Dark-Sky Association’s requirement of LEDs with CCTs of 3000K or less for their Fixture Seal of Approval program. Using recent research results based on a comprehensive light pollution model (Aubé 2015), it was found that the concerns over high-CCT LEDS are well-founded. While 4000K LEDs may be acceptable for casual star-gazing, they are anathema for astronomers and wildlife.

In short, requiring LED street lighting with CCTs of 3000K or less is completely justifiable.

UPDATE 2015/10/12

The analysis presented above assumes a gray world with spectrally neutral reflectance. This is a reasonable assumption in that most roadway surfaces – concrete and asphalt – are not strongly colored. In other words, the light reflected from the ground will have approximately the same spectral power distribution as the incident light.

Suppose, however, that we have an outdoor sports arena with a grass field. The spectral reflectance distribution for Kentucky bluegrass (Poa pratensis) is shown in FIG. 11. The pronounced green peak is expected, given the grass-green color. What is more interesting, however, is the relatively low reflectance in the blue region of the visible spectrum.

Color Temperature -FIG. 11

FIG. 11 – Kentucky bluegrass (Poa pratensis) spectral reflectance distribution

If we multiply the typical LED spectral power distributions shown in FIG. 7 with the grass spectral reflectance distribution on a per-wavelength basis, the overhead sky glow spectral power distribution at 50 km from the city center becomes that shown in FIG. 12. The blue peaks are still present, but they have been reduced by a factor of four relative to the remainder of the spectral power distribution.

Color Temperature -FIG. 12

FIG. 12 – Overhead sky glow spectral power distribution at 50 km due to grass field

The number of outdoor sports arenas may be relatively small, but they generate a surprising amount of light when they are illuminated at night. Using data from the U.S. Department of Energy’s 2010 U.S. Lighting Market Characterization report (DOE 2012), it can be estimated (with reasonable assumptions for typical lamp lumens) that the distribution of outdoor lighting in the United States is:

Outdoor Lighting Percent Lumens
Roadway 48.2
Parking 34.0
Building Exterior 10.2
Stadium 6.0
Billboard 0.8
Traffic Signals 0.7
Airfield 0.1
Railway 0.0

Table 5 – Light pollution sources (approximate)

This is, of course, a global view – light pollution next to a large outdoor sports arena can be a significant concern for residential neighborhoods. The best that can be done is shield the luminaires appropriately, and to turn on the sports field lighting only when it is needed.

In terms of correlated color temperature, the Fédération Internationale de Football Association (FIFA) specifies a minimum CCT of 4000K for football stadiums (FIFA 2007), while the National Football League (NFL) requires a CCT of 5600K (Lewis and Brill 2013). These are arguably acceptable in that green grass fields greatly alleviate the “nightmare spectrum” problem.

(Thanks to Brad Schlesselman of Musco Lighting for providing the grass spectral reflectance distribution and thereby inspiring this analysis.)

References

Aubé, M., L. Franchomme-Fossé, P. Robert-Staehler, and V. Houle. 2005. “Light Pollution Modelling and Detection in a Heterogeneous Environment: Toward a Night Time Aerosol Optical Depth Retrieval Method,” Proc. SPIE Volume 5890.

Aubé, M. 2015. “Physical Behaviour of Anthropogenic Light Propagation into the Nocturnal Environment,” Philosophical Transactions of the Royal Society B 370(1667):20140117.

Baddiley, C. 2007. “A Model to Show the Differences in Skyglow from Types of Luminaires Designs,” Starlight 2007. La Palma, Canary Islands.

Benya, J. R. 2015. “Nights in Davis,” LD+A 45(6):32-34.

Betz, E. 2015. “A New Fight for the Night,” Astronomy 43(6):46-51.

Cinzano, P., and F. J. D. Castro. 2000. “The Artificial Sky Luminance and the Emission Angles of the Upward Light Flux,” Journal of the Italian Astronomical Society 71(1):251.

Cinzano, P., and F. Falchi. 2012. “The Propagation of Light Pollution in the Atmosphere,” Monthly Notices of the Royal Astronomical Society 427(4):3337-3357.

DOE. 2012. 2010 U.S. Lighting Market Characterization. U.S. Department of Energy Building Technologies Program.

Duriscoe, D. M., C. B. Luginbuhl, and C. D. Elvidge. 2013. “The Relation of Outdoor Lighting Characteristics to Sky Glow from Distant Cities,” Lighting Research and Technology 46(1):35-49.

FIFA. 2007. Football Stadiums: Technical Recommendations and Requirements, 4th Edition. Zurich, Switzerland: Fédération Internationale de Football Association.

Garstang, R. H. 1986. “Model for Artificial Night-Sky Illumination,” Publications of the Astronomical Society of the Pacific 98:364-375.

Garstang, R. H. 1991. “Dust and Light Pollution”, Publications of the Astronomical Society of the Pacific 103:1109-1116.

Gillet, M., and P. Rombauts. 2001. “Precise Evaluation of Upward Flux from Outdoor Lighting Installations (Applied in the Case of Roadway Lighting),” Proc. International Conference on Light Pollution. Serena, Chile.

Kocifaj, M. 2007. “Light-Pollution Model for Cloudy and Cloudless Night Skies with Ground-Based Light Sources,” Applied Optics 46(15):3013-3022.

Kocifaj, M. 2010. “Modelling the Spectral Behaviour of Night Skylight Close to Artificial Light Sources,” Monthly Notices of the Royal Astronomical Society 403:2105-2110.

Kocifaj, M., M. Aubé, and I. Kohút. 2010. “The Effect of Spatial and Spectral Heterogeneity of Ground-Based Light Sources on Night-Sky Radiances,” Monthly Notices of the Royal Astronomical Society 409:1203-1212.

Kocifaj, M., and S. Lamphar. 2014. “Skyglow: A Retrieval of the Approximate Radiant Intensity Function of Ground-Based Light Sources,” Monthly Notices of the Royal Astronomical Society 443:3405-3413.

Lewis, D., and S. Brill. 2013. Broadcast Lighting: NFL Stadium Lighting. The Design Lighting Group Inc.

Liou, K. N. 2002. An Introduction to Atmospheric Radiation, Second Edition. New York, NY: Academic Press.

Luginguhl, C. B., D. M. Duriscoe, C. W. Moore, A. Richman, G. W. Lockwood, and D. R. Davis. 2009. “From the Ground Up II: Sky Glow and Near-Ground Artificial Light Propagation in Flagstaff, Arizona.” Publications of the Astronomical Society of the Pacific 121 (876):204-212.

Luginbuhl, C. B., P. A. Boley, and D. R. Davis. 2014. “The Impact of Light Source Spectral Power Distribution on Sky Glow,” Journal of Quantitative Spectroscopy & Radiative Transfer 139:21-26.

Giving Light

A New Philosophy for Lighting Design

Ian Ashdown, P. Eng., FIES

Chief Scientist, Lighting Analysts Inc.

[ Please send comments to allthingslighting@gmail.com. ]

Giving light … this phrase symbolizes a new philosophy of lighting design, a philosophy in the sense of how we think about the lighting design process. Much like the modernist movement in architectural design a century ago, it offers a reconciliation of lighting design practices with today’s rapid technological advancements and societal changes.

The innovations we are seeing in lighting hardware today are fascinating, but we are as always in danger of seeing these innovations in terms of existing technology. It is much like the first automobiles, which looked just like what they were called – horseless carriages. In some cases, these early and primitive vehicles came complete with buggy whip holders. As useless as they were, these accessories symbolized the inability of designers to fully adopt the new technology of internal combustion (and yes, electric) engines. The horse may have been absent, but it was still basically a 19th-century carriage.

Fig. 1 - ElectrobatFig. 1 – Electrobat – first successful electric car (1894)

We may laugh at the silliness of such thinking, but in reality we are no different. Look at today’s solid-state lighting: we insist on emulating century-old incandescent lamp form factors and worse, attempting to control them with AC phase-cut dimmers. We may mutter about market acceptance and existing installations, but the truth is that we are not all that comfortable imagining what is possible with solid state lighting technology.

The innovations we are seeing in lighting hardware are not only fascinating, but part of a much larger movement now called the Internet of Things. Just as the first mobile phones have brought us today’s smartphones, today’s seemingly unrelated innovations in solid-state lighting are about to lead us into a brave new world of lighting design that we are only beginning to understand.

The question is, do we as lighting designers want to quietly accept whatever products the large corporations may develop and market, or do we want to direct the development of this brave new world?

We begin with a look at our current philosophy …

The Philosophy of Lighting

For the past eight hundred millennia or more [1], we have had a clear and persistent understanding of light and lighting. Simply put, we view light as an intrinsic property of the light source. It is a world view that has both informed and limited how we approach the art and science of lighting design.

Our ancestors were intimately familiar with, and likely revered, fire as a source of light and lighting. Certainly fire occupied a central role in the religious beliefs of Zoroastrianism and Hinduism. Agni, the Vedic god of fire and sacrifice, took the form of fire, lightning, and the Sun. In Abrahamic theologies, the universe began with fiat lux – “let there be light.”

We are no different today. We have a much better understanding of the physics of fire and its derivatives (including the cosmological “Big Bang”, first introduced as a theory some eight centuries ago [2]), but we arguably still perceive and understand light and lighting as our distant ancestors did. It does not matter whether it is a burning torch, an incandescent lamp, or an organic light-emitting diode (OLED) – we think of emitted light as an intrinsic property of the light source.

We extend this thinking – this philosophy – to our lighting systems in terms of controlling the emitted light. From trimming the wick of a smoking tallow candle to sending digital commands to wireless lighting networks from our smartphones, we similarly view lighting control as an intrinsic property of the lighting system.

There is a German word – Weltanschauung – that translates as “world view.” It is a framework of ideas and beliefs that form a global description through which we interpret our world and interact with it. In this sense, our understanding of light and lighting is very much a world view. We intuitively think of light as something which illuminates the space around us and of lighting systems as something that we interact with. Light and lighting systems are an integral part of this experiential world.

We have of course undergone numerous paradigm shifts (aka “revolutions”) in lighting over the past two centuries or so, including gas lighting, incandescent lamps, fluorescent and high-intensity discharge lamps, electronic ballasts, fiber optics, solid-state lighting, and more. However, we have done so without changing how we think about light and lighting. To us, a light source is just that – a source of light.

A Lighting Abstraction

“Let me give light, but let me not be light.”

Portia, The Merchant of Venice

Fig. 2 - Portia

FIG. 2 – Ellen Terry as Portia by Albert Joseph Moore (c. 1885)

Shakespeare most likely meant “give light” in the sense of Portia having loose morals within her pending marriage. The phrase however is too evocative to ignore. The thought of us “giving light” is clearly an abstraction, but it is an exceedingly useful one from our perspective. It shifts the focus from designing for the illuminated environment to designing for people. The distinction is subtle but important.

But why an abstraction? The answer is that something as broad as a philosophy requires us to look at lighting design without being encumbered by any particular technology or hardware issues.

We all have our desires and preferences in terms of lighting, including intensity and dynamics, color temperature and color, and directionality and modeling. Wherever possible, we interact with lighting systems to satisfy our preferences. We turn the lights on and off when we enter or leave our offices, we dim the lights during a presentation in the conference room, and we open and close the blinds in response to daylight and weather conditions. We currently think of this in terms of controlling the light sources, of light being an intrinsic property of the light source.

Thinking …

What however if we turn this thinking – this philosophy – on its head? What if we consider light and light as intrinsic properties of ourselves? In this sense, we may abstractly “give light” to the environments we happen to be in.

Fig. 3 - The Thinker

Fig. 3 – The Thinker by Auguste Rodin (1879 – 1889)

We may give light to our personal environments, including private offices and our residences. However, we also implicitly follow social norms. We rarely for example consider adjusting the lighting in common areas when other people are present, and we do not even think about controlling the lighting in public spaces such as restaurants, theatres, and hotel lobbies. Outdoor lighting in particular we simply accept for what it is, although we may occasionally complain about poor lighting design.

There are however socially-accepted exceptions to the rule. The introduction of solid-state lighting a decade ago brought with it a wealth of interactive public art displays wherein viewer interaction was not only encouraged, but often considered an integral component of the display. The artist in effect provided the public with a mostly blank canvas on which to express their lighting preferences.

Fig. 4 - Philips Lighting Lumiblade OLEDs

Fig. 4 – Philips Lighting Lumiblade OLEDs

After ten years, the novelty of such displays has mostly gone. These were however early examples of people giving light to their illuminated environments.

Personal Lighting Control

There are other examples of giving light to public spaces where different people may have different lighting preferences. Networked lighting systems for offices have been around for the past twenty years or so, with the first commercial system arguably being the Ergolight system from what is now Philips Ledalite. Its original product features today form the backbone of most networked lighting systems.

Fig. 5 - Philips Ledalite Ergolight

Fig. 5 – Philips Ledalite Ergolight (1996)

What is interesting about these lighting control systems is that they provide each worker with a considerable degree of control over the lighting of their workspaces. They can dim and switch the downlight from the overhead luminaires, while integral occupancy sensors and timers can dim or turn off the lighting when the worker is not present. Integral photosensors can also be used to implement daylight harvesting where appropriate.

What is surprising is that since its introduction, the concept of personal lighting control has never been seriously challenged. Numerous academic studies have shown that office workers in general approve of such lighting control systems [3]. Even better, their use contributes significantly to energy savings.

As lighting designers, we have therefore been enabling people to give light to their workplace environments for the past two decades. It has been a fundamental change in how we think about light and lighting design – a change so subtle that we barely noticed that it had occurred. More than an abstraction, giving light has long been an accepted lighting design practice.

But now it is time to take this design philosophy to a new and more exciting level …

Our Networked Society

In 1959, the futurist Arthur C. Clarke wrote, “… the time will come when we will be able to call a person anywhere on Earth merely by dialing a number” [4]. A little over half a century later, there are reportedly some 4.5 billion mobile phone users in almost constant communication with each other. We are, in the words of the phone manufacturer Ericsson, a globally “networked society” [5].

Fig. 6 - Arthur C. Clarke

Fig. 6 – Arthur C. Clarke – Profiles of the Future

This is another change in our world view – who could have imagined a decade ago that we would so dependent today on cellular phones and smartphones for our daily activities? Even this however is only the beginning of the revolution – the Internet of Things (IoT) will connect us to almost every device and service imaginable in our daily lives. Analysts at Gartner, Inc. have predicted that by 2020, the installed base of IoT devices will be 26 billion units [6].

Lighting systems will of course be an important part of all this. Going beyond interactive public art displays and personal lighting control in open offices, we will soon have the technology to control lighting systems to a much greater extent than we do now. As lighting designers, we need to understand this technology and imagine the ways in which we can design lighting systems that benefit the user.

If we are to avoid thinking in terms of “horseless carriages,” we need to look beyond the technologies to the lighting design process itself. The philosophy of giving light provides the necessary mental framework. With such a framework in mind, we can consider the implementation details.

Identification and Geolocation

To control lighting systems, we first need to communicate with them. While such topics as wireless communications and networks may seem outside the realm of lighting design, they are anything but. It is not necessary to understand the technical details, but it is necessary to understand what is possible with today’s mobile communication devices.

Most of us are aware that law enforcements agencies can track mobile phones through cell towers and global positioning system (GPS) satellites and determine their position (“geolocation”) to within some 500 feet or so. This is however but one example of “real-time location services” [7]. Using a combination of GPS, cell tower communications, WiFi hot spots, and Bluetooth Low Energy (BLE) devices, it is possible to geolocate a mobile phone in three dimensions with an accuracy of approximately two feet with 95 percent accuracy whenever the device is turned on [8].

We may not always carry our smartphones with us, but the trend today is towards smartwatches, wearable computers that are as unobtrusive as old-fashioned wrist watches. Featuring a long and growing list of capabilities, these will likely become indispensable accessories for life in our networked society. With GPS and BLE capabilities, they will also – with our permission – tell the world who and where we are.

FIG. 7 - Samsung Galaxy Gear 2 Smartwatch

Fig. 7 – Samsung Galaxy Gear 2 Smartwatch

Public Profile

“You have zero privacy anyway – get over it.”

Scott McNealy, Sun Microsystems CEO (1999)

The operative word here is permission. We object to our loss of privacy mostly because it is being constantly invaded by corporations and governments without our knowledge, let alone our permission. Corporations harvest our personal information for the purposes of targeted advertising and business intelligence, while governments track us for various political reasons (and increasingly simply because they can). Commercial services such as for example Apple’s iBeacon have been developed expressly for commercial interests to track our movements and present us with targeted advertising … without our permission.

Suppose however that we consciously choose to publicly broadcast this information. Rather than having commercial and political interests trying to surreptitiously determine our preferences, we could maintain public profiles of ourselves. More than simple lists, these profiles would be even more richly detailed than those maintained by the retailers and credit card companies – but fully under our individual control. More important, these profiles would be electronically bound to our physical presence (albeit stored “somewhere in the cloud”). They would in a very real sense be an intrinsic property of ourselves.

With this capability, we can choose to tell the world who and where we are. In terms of lighting systems, all we need to do is to wirelessly broadcast a unique identifier; the system can then access our public profile via the Internet to determine our desires and preferences related specifically to lighting (if we so choose).

Visible Light Communications

From a lighting designer’s perspective, this is where it becomes interesting. The first lighting networks introduced some twenty years ago relied on wired RS-485 communications. These were superseded by faster Ethernet communications, and more recently by wireless mesh networks such as Zigbee Light Link [9].

An unfortunate disadvantage of wireless networks is that there can be numerous devices operating at the same frequency. As with shared Internet access and mobile phone usage, too many devices attempting to communicate at the same time may result in unacceptably poor system performance. This situation will only get worse as the Internet of Things gains traction.

Visible light communications, often referred to as “LiFi,” provides a solution. Beginning in the 1970s, inventors began developing modulation techniques for fluorescent lamps that enabled the broadcasting of audio signals using general illumination [10]. These saw some commercial success [11], but it was the development of light-emitting diodes and solid-state lighting that has renewed particular interest in the technology [12].

Fig. 8 - US Patent 3900404

Fig. 8 – US Patent 3,900,404 – Optical Communication System

The LiFi Advantage

LiFi offers several advantages over wireless communications. It is for example primarily line-of-sight, which results in potentially more secure communications. Solid-state lighting can also be modulated at high frequencies, providing up to four times the bandwidth of 3G mobile phone systems. Further, there are no restrictions on the carrier frequency or spectrum licensing requirements, so multiple systems can easily co-exist.

The one disadvantage is that LiFi is basically a broadcast system. Luminaires with LiFi capabilities can broadcast information, but receiving devices generally require infrared or wireless transmitters to respond. A local WiFi router or Bluetooth transceiver can for example receive the responses and communicate with the luminaires using Ethernet or a wireless network.

The true advantage of LiFi however is that it is no longer necessary for the lighting system to geolocate occupants with accuracies of a foot or less. All that is needed is for the luminaires to continually broadcast their unique identifiers, and for the occupant’s smartphone or smartwatch to detect these identifiers with its camera or a photosensor. The device can then wirelessly respond, “I see you” with the occupant’s public profile identifier. This is an exceedingly brief transaction that minimizes the device’s battery power requirements.

What is exciting about this is that this is not some futurist’s wish list for advanced technology. The technology already exists, and it is already being commercialized.

Commercial Products

Royal Philips recently introduced an “intelligent in-store LED lighting system” that communicates information to shoppers via their smartphones and LED-based luminaires [14]. All the shoppers have to do is to point their smartphone cameras at the nearest overhead luminaire.

Fig. 9 - Philips Connected Retail Lighting System

Fig. 9 – Philips Connected Retail Lighting System

Philips has even more recently introduced a “smartphone-controlled connected office lighting system” [14]. This system enables office workers to control both the lighting and room temperature using their smartphones in communication with the overhead luminaires and Power over Ethernet (PoE). The feature set is no different from what was offered two decades ago with networked lighting systems, but now the communication relies on LiFi rather than wired network cables.

There are undoubtedly many more such products to come. However, they are still based on light as an intrinsic property of the light source. Something more is needed to implement the abstraction of “giving light.”

Intelligent Lighting Control

Lighting researchers have been looking at the possibility of intelligent lighting control in buildings for over a decade [15]. Often referred to as “ambient intelligence” and “auto-adaptive” lighting, the basic approach has been to use artificial intelligence (AI) techniques such as neural networks and fuzzy logic to learn a user’s lighting preferences by observing their behavior [16]. Most of the research has assumed a single or typical user, although some work has been done on reconciling different users’ preferences [17].

An advantage of intelligent lighting control is that by learning the user’s behavior, it can anticipate what sort of lighting is desired without the user having to interact with manual controls. This may work well for offices and residences where the system has the opportunity to learn the user’s behavior, but it does not work well otherwise. At best, the system must default to an anonymous “typical user” whose behavior is the average of many users. (Regardless, intelligent lighting controls typically result in energy savings.)

This is where the “networked society” concept has so much to offer. If an intelligent lighting control system can identify the user and access their public profile, it can determine the user’s desires and preferences and respond accordingly [18]. Even better, it can observe the user’s behavior and update their public profile if desired. Learning goes from a single isolated system to wherever the user encounters intelligent lighting control systems, often without the user even being aware of their presence.

Fig. 10 - US Patent App 20120184299

Fig. 10 – US Patent Application 2012/0184299

Public Places

The research to date has mostly focused on offices and residences, but it becomes even more interesting when public spaces are considered. Examples include retail stores and shopping malls, restaurants and hotel lobbies, bars and nightclubs, and even outdoor plazas and public parks at night. Normally, we never consider interacting with the lighting of such spaces. With public profiles however, we can easily give light to these environments in a socially acceptable manner.

As a prosaic example, consider walking through a park at night. Municipalities are already equipping pole-mounted walkway lighting with WiFi transceivers and occupancy sensors, which is all the technology that is needed for someone to turn on the lights using a smartphone [19]. It is a small step from here for the lighting system to recognize the person through their public profile and set the lights for a particular path.

Fig. 11 - Lighting in Public Places

Fig. 11 – Lighting in public places

More interesting examples arise when we consider light itself as a social medium. Color in particular can be used to announce the arrival of VIPs at a nightclub or to announce goals during a game at a sports bar. Light levels in restaurants can adapt to the preferences of patrons and their activities. The list goes on with possibilities that are limited only by the creativity of the lighting designers who develop the systems and the users who interact with them.

Language of Light

If anything, we may need to invent a new “language of light,” a non-verbal means of expressing not only our desires and preferences for lighting, but also of expressing our moods and social standing. More than likely, this will evolve by itself in the manner of cultural norms. We may however be surprised, if the prior introduction of personal lighting control is any indication. We may embrace the concept of giving light with the same aplomb as we have exhibited in adopting smartphones. It will become interwoven into the fabric of our lives, with our children wondering what light switches were for.

Just as it is difficult to explain a philosophy in five hundred words or less, it is difficult to explain the nuances of light as an intrinsic property of ourselves and the concept of “giving light” in a single discussion. It is all too easy to think of new technologies in term of what they replace, much as today’s LED lamps closely resemble A19 incandescent lamps. It is even more difficult here in that there are no new technologies involved; we already have the tools that we need.

All that is needed is for lighting designers to adopt a new philosophy and consider the possibilities.

References

  1. Zhong, M., et al. 2014 “On the Possible Use of Fire by Homo Erectus at Zhoukoudian, China.” Chinese Science Bulletin 59(3):335-343.
  2. Bower, R. G., et al. 2014. “A Medieval Universe: Mathematical Modeling of the 13th Century Universe of Robert Grossteste.” Available from http://arxiv.org/abs/1403.0769.
  3. Galasiu, A. D., G. R. Newsham, C. Suvagau, and D. M. Sander. 2007. “Energy Saving Lighting Control Systems for Open-Plan Offices: A Field Study,” Leukos 4(1):7-29.
  4. Clarke, A. C. 1962. Profiles of the Future: An Inquiry into the Limits of the Possible. New York: Harper and Row.
  5. Ericsson. 2013. Ericsson Mobility Report – On the Pulse of the Networked Society. Stockholm, Sweden: Ericsson.
  6. Anon. 2013. “Gartner Says the Internet of Things Installed Base Will Grow to 26 Billion Units by 2020.” Gartner, Inc. http://www.gartner.com/newsroom/id/2636073.
  7. ISO/IEC FDIS 24730 – Real-Time Locating Systems (RTLS).
  8. Woodman, O., and R. Harle. 2008. “Pedestrian Localization for Indoor Environments,” Proc. Tenth International Conference on Ubiquitous Computing, pp. 114-123.
  9. See http://zigbee.org/Standards/ZigBeeLightLink/Overview.aspx.
  10. Dachs, M. R. 1975. US Patent 3,900,404. Optical Communication System.
  11. For example, Talking Lights LLC (www.talking-lights.com).
  12. Daukantus, P. 2014. “Optical Wireless Communications: The New Hot Spots?,” Optics & Photonics News 25(3):34-41.
  13. EuroShop Retail Trade Fair, February 2014 (Dusseldorf, Germany).
  14. Light + Building, March 2014. (Frankfurt, Germany).
  15. Guillemin, A., and N. Morel. 2001. “An Innovative Lighting Controller Integrated in a Self-Adaptive Building Control System,” Energy and Buildings 33:477-487.
  16. Vainio, A.-M., M. Valtonen, and J. Vanhala. 2008. “Proactive Fuzzy Control and Adaptation Methods for Smart Homes,” IEEE Intelligent Systems 23(2):42-49.
  17. Granderson, J., and A. Agogino. et al. 2006. “Intelligent Office Lighting: Demand-Responsive Conditioning and Increased User Satisfaction,” Leukos 2(3):185-198.
  18. Loveland, D., A. Vermeullen, and I. Ashdown. 2012. US Patent Application 2012/0184299, Systems and Methods for Managing Interaction with Controllable Lighting Networks.
  19. Badger, E. 2013. “The Streetlight of the Future Will Do So Much More Than Light Your Streets.” The Atlantic Cities, March 13, 2013.

Mesopic Photometry and Statistics

Ian Ashdown, P. Eng., FIES

Chief Scientist, Lighting Analysts Inc.

[ Please send comments to allthingslighting@gmail.com. ]

Related Posts

Understanding Mesopic Photometry

One of the joys of statistics is that you can never be proven wrong …

In a previous All Things Lighting article titled “Understanding Mesopic Photometry” (October 8th, 2013), I wrote:

Some publications on mesopic lighting have indicated that the S/P ratio of a lamp can be estimated from its correlated color temperature (CCT), but this is incorrect …

I continued on with an example that compared the spectral power distributions and scotopic-to-photopic (S/P) ratios of a phosphor-coated white light LED:

FIG 5A

Fig. 1– Phosphor-coated LED module

and a red-green-blue LED:

FIG 5B

Fig. 2 – Red-green-blue LED module

Both lamp modules had the same correlated color temperature (CCT) of 3500K, but their S/P ratios were 1.41 and 2.02 respectively. I concluded that:

Simply put, the only way to accurately determine the S/P ratio of a light source is through calculation using its spectral power distribution.

While this statement is technically correct, it is not particularly useful when you need to know the S/P ratio of a lamp or lamp module for mesopic roadway or area lighting calculations.

Measurements and Equations

One of the publications I chose not to reference was the “City of San Jose Public Streetlight Design Guide” [Anon. 2011]. This report presented a list of eight light sources with their reported S/P ratios, which were derived from [CIE 2010] and [Berman 1992]:

Source S/P Ratio CCT
Low pressure sodium 0.25 1700
High pressure sodium 0.65 2100
Warm white metal halide 1.35 3500
Daylight metal halide 2.45 5500
Warm white fluorescent 1.00 3000
Cool white fluorescent 1.46 3700
Triphosphor fluorescent 1.54 4100
Daylight fluorescent 2.22 7500

Table 1 – S/P Ratio versus CCT [Anon. 2011]

This list is somewhat selective, as Berman reported the S/P ratio versus CCT of sixteen light sources:

FIG. 3

Fig. 3 – S/P Ratio versus CCT [Berman 1992]

The report noted:

Although the S/P ratio is derived from the spectral power distribution of the light source, it approximately corresponds to the correlated color temperature of that source.

However, this was immediately followed by:

To determine the S/P ratio for any given CCT, the following equation can be used:

S/P ratio = -7 * 10-8 (CCT)2 + 0.001 * CCT – 1.3152

While I otherwise agree with the report, I must disagree with this statement. Of the tens of thousands of lamp types that are commercially available, you cannot fit a quadratic curve through a mere eight data points and generalize it to any light source. This is especially true when the light sources include the near-monochromatic spectral power distribution (SPD) of low-pressure sodium (LPS) lamps.

Worse, there is no indication of the expected error with this equation. You may calculate an S/P ratio for a given CCT, but you have no idea whether it is accurate. Is it for example 1.65, 1.6, or somewhere between 1.0 and 2.0?

Statistics

Based on the work of [Berman 1992], it is evident that the S/P ratio of a white light source “approximately corresponds” to its CCT. However, the evidence in support of this conclusion is statistically weak, and further does not consider today’s phosphor-coated white light LEDs.

What is needed is a random sampling of many commercial white light sources. Ideally, the work would be done by an independent photometric testing laboratory so as not to inadvertently skew the results towards the products of a single lamp manufacturer.

Having the results for many different light sources serves two purposes. First, it provides enough data points to have confidence that an equation fitted to the data fairly represents most commercial lamps and LED lamp modules.

Second, it provides the all-important confidence interval for any given S/P ratio. That is, given a calculated S/P ratio for a specific CCT, you can have (say) 95% confidence that the value is accurate to within a given range of values.

This is important because photometric measurements and calculations always include implicit confidence intervals. For example, electric lighting calculations are typically accurate to within ±10 percent when compared to careful in situ measurements of the completed project. It makes no sense therefore to perform (for example) mesopic roadway lighting calculations if your assumed S/P ratio varies by ±0.5.

Recommendations

The good news is that we now have the necessary information. LightLab International Inc. (www.lightlabint.com) recently collated the results of some 90 tests of LED-based roadway and area lighting luminaires that they performed for their customers. In accordance with the requirements of LM-79 testing procedures [IESNA 2008], the test reports included spectral power distribution measurements, and with them (although not required by LM-79) calculated S/P ratios.

As you might expect, the lamp CCTs clustered around the industry-standard nominal values:

FIG. 4

Fig. 4 – Lamp CCTs

Perhaps less expected is that they exhibited a reasonably linear relationship between S/P ratio and CCT:

FIG. 5

Fig. 5 – S/P ratio versus CCT

I will not repeat the curve-fitting equation here, as it has a meaningless precision of 15 decimal points. What is important is this table of recommended values (where the 2700K values were extrapolated from the measured data):

CCT S/P Ratio Range
2700K 1.1 – 1.4
3000K 1.2 – 1.5
3500K 1.3 – 1.6
4000K 1.4 – 1.8
5000K 1.6 – 2.0
6000K 1.9 – 2.2

Table 2 – S/P Ratio versus CCT for LED lamps

Note carefully that this table applies to LED-based white light sources only; it does not apply to fluorescent (including magnetic induction) or HID lamps, and certainly not to LPS lamps. (Metal halide lamps in particular as reported in Table 1 are outside of the range of this table.)

Looking at Figure 1, it is perhaps not surprising that LED lamps exhibit a strong correlation between S/P ratio and CCT. Virtually all of today’s high-flux LEDs for roadway lighting applications rely on a blue pump LED (which accounts for the 450 nm peak in Figure 1) and broadband emission phosphors between 500 and 700 nm. With minor differences due to different phosphor combinations, most white light LED SPDs will look something like Figure 1, with the CCT mostly determined by the ratio of the blue peak to the phosphor emissions.

The counterexample of course is the red-green-blue LED SPD shown in Figure 2, with its anomalous S/P ratio of 2.02 for a CCT of 3500K. It is not coincidental that the SPD somewhat resembles that of a triphosphor fluorescent or metal halide lamp.

Ideally, we would have S/P ratio versus CCT data for thousands of white light sources. It is unlikely that the recommended S/P values above would change by more than 0.1 units, but it would improve our statistical confidence in the results.

On the other hand, these results show that the S/P ratio varies by ±0.2 for any given CCT, or about ±10 percent of the median value. This is commensurate with the expected accuracy of most electric lighting applications.

Granted, it would be preferable to have S/P ratios available for every lighting product. (S/P ratios are also integral to IES TM-24-13, Incorporating Spectral Power Distribution into the IES Illuminance Determination System for Visual Task Categories P through Y [IESNA 2013].) As was explained in “Understanding Mesopic Photometry” however, there are practical reasons why this is unlikely to occur.

In retrospect, this likely does not matter.. Lighting designers can rarely assume the use of particular product when performing photometric calculations. With competitive bidding for commercial and government projects, it is best to simply specify luminaires with a given CCT. Given that most new roadway and area lighting installations will involve LED-based luminaires, Table 2 provides lighting designers with the confidence that they can assume a usefully narrow range of S/P ratios for design and specification purposes.

References

Anonymous. 2011. City of San Jose Public Streetlight Design Guide. Available as www.sanjoseca.gov/DocumentCenter/Home/View/242.

Berman, S. W. 1992. “Energy Efficiency Consequences of Scotopic Sensitivity,” Journal of the IES 21(1):3-14.

CIE. 2010. Recommended System for Mesopic Photometry Based on Visual Performance. CIE Technical Report 191:2010. Vienna, Austria: Commission International de l’Eclairage.

IESNA. 2008. IES LM-79-08. Electrical and Photometric Measurements of Solid-State Lighting Products. New York, NY: Illuminating Engineering Society of North America.

IESNA. 2013. IES TM-24-13. Incorporating Spectral Power Distribution into the IES Illuminance Determination System for Visual Task Categories P through Y. New York, NY: Illuminating Engineering Society of North America.

Acknowledgements

Thanks to Eric Southgate of LightLab International Inc. (www.lightlabint.com) for sharing the S/P versus CCT data on which this article was based.

Thanks also to Dawn DeGrazio of Lighting Analysts Inc. for invaluable editorial assistance.

Understanding Mesopic Photometry

Ian Ashdown, P. Eng., FIES

Chief Scientist, Lighting Analysts Inc.

[ Please send comments to allthingslighting@gmail.com. ]

Related Posts

Mesopic Photometry and Statistics

If you are involved with outdoor area or roadway lighting design, you will undoubtedly encounter such terms as mesopic multipliers, scotopic lumens, and S/P ratios, and you will sooner or later need to consider mesopic photometry in your design efforts. For example, you may encounter a government specification that states, “Luminaires must have a minimum S/P ratio of x.” What does this mean and, equally important, where can you find this information?

It begins with the human eye:

FIG 1

Fig. 1 – Human eye anatomy (source: Wikipedia)

where light entering the eye through the cornea is focused on the retina. The retina consists of approximately six million cones located mostly at the center of the retina, surrounded by some 120 million rods. The cones, which are responsible for our color vision, function best in bright light, while the color-blind rods are responsible for our night vision.

For scenes with an average luminance above approximately 5.0 cd/m2, photopic vision dominates. The cones have an average spectral response that is described by the photopic luminous efficiency function V(λ) with peak responsivity at 555 nm (Fig. 2). Below approximately 0.005 cd/m2, scotopic vision dominates, with the rods having a spectral response that is described by the scotopic luminous efficiency function V'(λ) with peak responsivity at 507 nm (Fig. 2).

FIG 2

Fig. 2 – Luminous efficiency functions

Mesopic vision occurs when the average scene luminance is between approximately 0.005 and 5.0 cd/m2, as both the rods and cones contribute to what we perceive.

We can directly perceive this blending of photopic and scotopic vision due to the Purkinje effect. Cones are more sensitive to red light than are rods. As the light levels dim, red colors appear to darken more quickly than other colors:

FIG 3

Fig. 3 – Purkinje effect (source: Wikipedia)

This is due to the gradual shift from the photopic to the scotopic luminous efficiency function as the rods begin to predominate.

A light source will have a characteristic spectral power distribution (SPD), such as this one for a typical cool white fluorescent lamp:

 FIG 4

Fig. 4 – Cool white fluorescent lamp spectral power distribution

Calculating the photopic lumens generated by a light source is easy: multiply the SPD by the photopic luminous efficiency function on a per-wavelength basis (typically at 5 nm intervals) sum the results, and scale as required. Calculating the scotopic lumens is the same, only using the scotopic luminous efficiency function.

Herein lies the crucial point: because the photopic and scotopic luminous efficiency functions are different, they will yield different values for the scotopic and photopic lumens. The ratio of these two values is the scotopic-to-photopic (S/P) ratio. In general, light sources with more blue light will have higher S/P ratios. For example:

Lamp Type Typical S/P Ratio
Low-pressure sodium 0.2
High-pressure sodium 0.4 to 0.6
Halogen headlamp 1.4
Linear fluorescent 1.3 to 2.3
Metal halide 1.2 to 2.1
Warm white LED 1.2
Cool white LED 2.0

Table 1 – Typical S/P ratios

More detailed tables are available (e.g., IES TM-12-12), but the question has to be asked: will the S/P ratio change when the luminaires are relamped?

Some publications on mesopic lighting have indicated that the S/P ratio of a lamp can be estimated from its correlated color temperature (CCT), but this is incorrect except for incandescent lamps (which have little practical application to mesopic lighting). Here for example are two LED modules with the same CCT of 3500 K but very different spectral power distributions and different S/P ratios.

FIG 5A

Fig. 5A – Phosphor-coated 3500 K LED (SP = 1.41)

FIG 5B

Fig. 5B – Red-green-blue 3500 K LED module (S/P = 2.02)

Simply put, the only way to accurately determine the S/P ratio of a light source is through calculation using its spectral power distribution.

Scotopic lumens are important because they better represent how bright objects appear under low light level conditions than do photopic lumens. This leads to the concept of effective luminance factors (ELF). These factors are defined simply as:

ELF = mesopic luminance / photopic luminance

Comprehensive ELF tables are presented in IES TM-12-12 and CIE 191:2010, but they can be summarized as:

S/P

0.01

0.03

0.10

0.30

1.00

3.00

0.25

0.25

0.48

0.70

0.82

0.91

0.97

0.50

0.50

0.69

0.81

0.88

0.94

0.98

0.75

0.78

0.85

0.91

0.94

0.97

0.99

1.00

1.00

1.00

1.00

1.00

1.00

1.00

1.25

1.18

1.12

1.08

1.05

1.02

1.00

1.50

1.35

1.17

1.16

1.10

1.05

1.01

1.75

1.50

1.35

1.23

1.15

1.07

1.02

2.00

1.65

1.46

1.30

1.19

1.10

1.02

2.25

1.79

1.56

1.37

1.24

1.12

1.03

2.50

1.93

1.66

1.44

1.28

1.14

1.04

Table 2 – Effective Luminance Factors (cd/m2)

Table 4.2 of the IES Lighting Handbook somewhat confusingly presents mesopic multipliers, which are intended “to adjust recommended photopic illuminance target values for mesopic adaptation.” They are the inverse of effective luminance factors.

Some caution is needed here in that a government or industry specification may have already taken mesopic lighting into account, in which case the specified minimum luminance has already had a mesopic multiplier applied. This will undoubtedly be the case if the specification also includes a minimum S/P ratio for the light sources. When in doubt, ask.

But now the fun begins – a specification is much more likely to specify a minimum photopic illuminance value, expressed in lumens per square meter (or foot). Now what?

To begin with, mesopic photometry (for lighting applications) assumes that all surfaces being illuminated are: a) grey or pastel-colored; and b) diffuse reflectors. In this case, the luminance L of the illuminated object is given by:

Lρ * E / π

where ρ is the reflectance of the surface and E is the illuminance. (As a reminder,  π is approximately 3.14.)

If the surface is strongly colored, then the spectral power distribution of the reflected light will be very different from the SPD of the light source, and so the S/P ratio will not apply in terms of the surface luminance.

This assumes of course that the surface reflectance is known or can be estimated. For example, the diffuse reflectance of roadway surfaces ranges from approximately 8 percent for bituminous asphalt to 17 percent for concrete (Gillet 2001). Ideally, the target reflectance will be included in the specification.

We must now ask, “What is the meaning of average scene luminance?” The point here is that mesopic photometry depends on the visual adaptation of the viewer to the average scene luminance. Unlike other photometric quantities such as lumens, luminance, luminous intensity, luminous exitance, and illuminance, mesopic quantities are meaningless without a viewer whose state of visual adaptation is known.

As it turns out, the majority of the cones in the retina occupy only the central 2 degrees or so of our visual field of view (called the fovea). It may seem nonsensical, but we perceive color only within a region about the diameter of two thumbnails held at arm’s length. We may think we perceive color within our entire field of view, but this is only because our brains are filling in the details as we visually scan a scene.

What this means is that our mesopic adaptation is mostly determined by the luminance of the surface we happen to be looking at (Moon 1943). The background surround has very little influence on this adaptation (less than 10 percent). As we shift our gaze towards different objects, our visual adaptation state changes accordingly.

Visual adaptation is not, of course, instantaneous. It is a complex mechanism involving mechanical changes in pupil size, photochemical changes (pigment bleaching in the retina), and neural changes (synaptic interactions). This is likely why the IES Lighting Handbook (Section 4.12.3, “Spectral Effects”) recommends that mesopic photometry not be applied to roadway lighting where the speed limit is greater than 40 kph (25 mph). (This limit may be increased in the future, subject to ongoing roadway vision research.)

In view of the above, IES TM-12-12 defines the mesopic adaptation field as the point on a surface that the viewer is looking at, and then recommends the following calculation procedure:

  1. Obtain the lamp S/P ratio.
  2. Determine the target design illuminance Edesign.
  3. Determine the target reflectance ρ.
  4. Determine the required design luminance Ldesign = Edesignρ / π
  5. Calculate the photopic illuminance Ephotopic of the target.
  6. Determine the target photopic luminance Lphotopic = Ephotopicρ / π
  7. Determine the effective luminance multiplier ELF based on S/P and Lphotopic.
  8. Convert the photopic luminance to the effective (mesopic) luminance Leffective = Lphotopic * ELF
  9. Reiterate steps 5 to 8 while modifying the design until Leffective equals or exceeds Ldesign.
  10. If necessary, calculate the effective target illuminance Eeffective = Leffectiveπ / ρ.

Given this, you would expect that roadway and outdoor area luminaire manufacturers would provide S/P ratios for their products, but to date this has not been the case. Information on lamp S/P ratios can be exceedingly difficult to find. Possible reasons include:

  1. The S/P ratio is a function of the lamp or lamp module rather than the luminaire. For fluorescent and high-pressure discharge (HID) lamps, the luminaire manufacturer typically has no control over what lamps the contractor or owner may install in the luminaires.
  2. Most lamp manufacturers do not provide SPDs or S/P ratios for their products. This is difficult to explain, as they can be easily measured by a spectroradiometer by an independent photometric laboratory.
  3. The SPDs of fluorescent and HID lamps is a function of their proprietary gas fill mixtures and phosphors. If lamp manufacturers were to provide S/P ratios for their products, they might have to change their product numbers if these proprietary formulations were to change.
  4. The SPDs of white light LEDs are dependent on the dominant wavelength of the blue pump LEDs and the proprietary phosphors and phosphor mixtures used to down-convert the blue light to longer wavelengths. Again, if LED manufacturers were to provide S/P ratios for their products, they might have to change their product numbers if these proprietary designs were to change. (For whatever reason, phosphor manufacturers are equally protective of the detailed SPDs for their products.)
  5. Many LED lamp module manufacturers purchase their LEDs from third parties, and so have no control over changes to the LEDs apart from specifying minimum luminous flux output and CCT binning.
  6. Many luminaire manufacturers purchase their LED modules from third parties, which makes it even more difficult for them to guarantee the S/P ratios of their products.

In an ideal world, lighting designers would have unfettered access to the S/P ratios of the luminaires they specify in order to perform mesopic lighting calculations. Unfortunately, this will require changes to the entire supply chain of phosphors, fluorescent and HID lamps, LEDs and LED modules, and luminaires. These changes are possible, but it may take some time for the lighting industry to adapt to the brave new world of mesopic photometry.

References

CIE. 2010. Recommended System for Mesopic Photometry Based on Visual Performance. CIE Technical Report 191:2010. Vienna, Austria: Commission International de l’Eclairage.

Gillet, M., and P. Rombauts. 2001. “Precise Evaluation of Upward Flux from Outdoor Lighting Installations (Applied in the Case of Roadway Lighting),” Proc. International Conference on Light Pollution. Serena, Chile.

IES. 2010. IES Lighting Handbook, Tenth Edition. New York, NY: Illuminating Engineering Society of North America.

IES 2012. Spectral Effects of Lighting on Visual Performance at Mesopic Lighting Levels. IES TM-12-12. New York, NY: Illuminating Engineering Society of North America.

Moon, P., and D. E. Spencer. 1943. “The Specification of Foveal Adaptation,” J. Optical Society of America 33(8):444-456.