Filtered LEDs and Light Pollution

An Astronomical Problem

Ian Ashdown, P. Eng., FIES

Chief Scientist, Lighting Analysts Inc.

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

UPDATE 2016/03/03 – Revised Figure 6.

The problem is astronomical – the blue light emitted by LED roadway luminaires has been shown to contribute to light pollution, especially when cool white LEDs are used. Blue light is preferentially scattered by air molecules, and so the higher the correlated color temperature (CCT), the greater the light pollution problem becomes. It is for this reason that the International Dark Sky Association requires a maximum CCT of 3000K for its Fixture Seal of Approval outdoor lighting certification program.

Sometimes, however, even warm white LED street lighting is not enough. For cities that are in close proximity to astronomical observatories, such as Flagstaff, AZ and the nearby US Naval Observatory Flagstaff Station, any amount of blue light is bad news.

Until recently, low-pressure sodium (LPS) street lighting has been the preferred choice. LPS luminaires are ideal light sources in that their monochromatic radiation (590 nm) is easily filtered out for astronomical observations. However, the large physical size of the lamps makes it difficult to control the luminous intensity distributions. For this and other reasons, municipalities are looking at “filtered LED” (FLED) street lighting as an option.

The reasoning is simple: combine a white light LED with a yellow filter and you can eliminate the blue peak that plagues astronomical observations. Figure 1, for example, shows the spectral power distributions (SPDs) of 2700K and 5000K white light LEDs with their characteristic blue peaks, while Figure 2 shows the SPDs of the same LEDs combined with yellow filters. The blue peaks have not been alleviated; they have been completely eliminated.

FIG. 1 – White light LED spectral power distributions.

FIG. 1 – White light LED spectral power distributions.

FIG. 2 – Filtered white light LED spectral power distributions.

FIG. 2 – Filtered white light LED spectral power distributions.

So, FLEDs are good for astronomical purposes, but what about lighting design?

Luminous Efficacy

At first glance, you might assume that filtering out the blue light will significantly reduce luminous efficacy. Perhaps surprisingly, this is not the case. Based on the SPDs shown in Figure 1 and Figure 2, the loss of luminous efficacy is less than ten percent for both warm white and cool white LEDs,

As a practical example, the SPDs shown in the above figures were taken from the photometric laboratory test reports of two commercial products from CW Energy Solutions. The salient data for these products are:

  WW-CW8-450 CW-CW7-350
Luminaire efficacy (lumens / watt) 106 122
CIE 1931 chromaticity x = 0.5223 y = 0.4072 x = 0.4719 y = 0.5176
CRI Ra 55.1 38.8
CRI R9 -56.5 -81.9

Table 1 – CW Energy Solution filtered LED roadway luminaire product specifications

To be clear, this is not an endorsement of these commercial products. This information is being provided for educational purposes only.

Chromaticity

We can plot the chromaticity xy coordinates shown in Table 1 on a CIE 1931 chromaticity diagram (FIG. 3), but what do the actual colors look like? Unfortunately, most such diagrams reproduce the actual colors of the CIE 1931 color space very poorly. (Worse, it is impossible to display most saturated colors using the RGB color gamut of video displays.)

FIG. 3 – CIE 1931 xy chromaticity diagram. (Source: Wikipedia)

FIG. 3 – CIE 1931 xy chromaticity diagram. (Source: Wikipedia)

To answer this question, we can convert the xy chromaticity coordinates into CIE XYZ tristimulus values, and from there, assuming a video display with a 6500K white point, into RGB values for display. The chromaticity coordinates listed in Table 1 then appears much like these colors on a calibrated video display:

FIG. 4A - WW-CW8-450 light source color   

FIG. 4A – WW-CW8-450 light source color

FIG. 4B - CW-CW7-350 light source color

FIG. 4B – CW-CW7-350 light source color

These are clearly not the sort of “white light” luminaires we would normally use for retail or residential lighting … but wait, there is more to this than meets the eye.

Color Rendering Capabilities

Looking again at Table 1, we see that the CIE General Colour Rendering Index Ra values for these products are frankly abysmal – 55 for the filtered 2700k (warm white) LEDs and 38 for the filtered 5000K (cool white) LEDs. The CIE Special Colour Rendering Index R9 values are even worse, with values of -56.5 and -81.9 respectively.

(As a reminder, a CRI value of 100 means that there is no perceptible color shifts with the eight CRI test color samples viewed under the test and reference lamps. It is quite possible, however, to have negative CRI values for the Special CRI values. Low-pressure sodium lamps, for example, have a CRI Ra values of -17.)

It is also interesting, and indeed useful, for lighting designers to understand why these perceived color shifts occur. Johann von Kries, a physiological psychologist who investigated chromatic adaptation in human color vision, noted in 1905 that we tend to see white objects as “white” regardless of the color temperature of the dominant light source. He postulated that our visual system adjusts the “gain” of the signals received from the red-. green- and blue-sensitive cones[1] in our retinae that are responsible for our color vision (von Kries 1905).

von Kries’ theory was formalized by the polymath Herbert Ives in 1912 as the von Kries transform, a mathematical operation that forms the basis of the calculation method for the CIE Colour Rendering Indices. While this psychophysiological “gain adjustment” works well (but not perfectly) in enabling us to perceive white surfaces under light sources with different CCTs (e.g., from 2800K incandescent lighting to 8000K overcast daylight), it tends to distort our perception of colored surfaces. (By way of analogy, think of adjusting the bass and treble controls on an audio system – particular settings may work for some music, but be unsuitable for other music.)

The beauty of the von Kries transform, however, is that it enables us to mathematically predict the color shifts due to a given test illuminant. Given a set of test colors – the Gretag-Macbeth ColorChecker™ is an obvious choice – we can predict and display what these colors will look like (e.g., Figure 5).

FIG. 5 – Filtered LED color shifts from 6500K daylight.

FIG. 5 – Filtered LED color shifts from 6500K daylight.

True – these color shifts are starkly evident, and would be completely unacceptable for retail and residential lighting. However, we need to remember that the topic of discussion is roadway lighting, specifically where municipalities are considering replacing high-pressure sodium (HPS) lamps with LED modules. With this, we need to look at the SPD of a typical HPS lamp (Figure 6).

FIG. 6 – 2100K high-pressure sodium lamp spectral power distribution.

FIG. 6 – 2100K high-pressure sodium lamp spectral power distribution.

There are three points of interest here. First, the correlated color temperature (CCT) rating of 2100K is nominal – the CIE 1931 xy chromaticity coordinates of this lamp are not particularly close to the blackbody locus, and so by definition the CCT rating is technically meaningless (CIE 2004).

Second, HPS lamps have a CRI Ra value of 24 – worse than filtered LEDs.

Third – and this is the key point – most municipalities have been using HPS street lighting ever since it replaced the mostly unlamented mercury vapor street lighting in the 1980s. After thirty years of use, most residents have known nothing but their orange-yellow glow.

Putting aside the roadway luminaire manufacturers’ arguments that most people prefer “white” light, it is instructive to visualize the color rendering capabilities of filtered LEDs versus HPS lamps (FIG. 7).

FIG. 7 – Filtered LED color shifts from 2100K daylight.

FIG. 7 – Filtered LED color shifts from 2100K daylight.

What is there to say, other than “oh …”? The point is that color rendering under filtered LED illumination is no worse, and arguably somewhat better, than under today’s prevalent HPS roadway illumination. It is not the color of the roadway luminaires that is important; it is the perceived colors of the objects that they illuminate.

The deciding factor for most municipalities will likely be whether residents like, dislike, or are simply neutral regarding the color rendering capabilities of filtered LED roadway lighting. In many cases, a test installation will likely be needed. Before then, however, it is important not to dismiss filtered LEDs simply because they are not “white light.” Furthermore, it is equally important not to compare them with white light LEDs solely on the basis of their CCT, CRI, or chromaticity values.

Conclusions

The purpose of this article is not to promote filtered LEDs as an alternative to low-pressure sodium lamps, or even as a preferred solution to light pollution problems. Rather, it is an attempt to take the various metrics describing the color rendering qualities of filtered LEDs and visualize them.

How lighting designers, roadway luminaire manufacturers, municipal engineers, and community activists choose to use this information is beyond the scope of this article. All that needs to be said is, “a picture is worth a thousand words.”

Acknowledgements

Thanks to Bob Adams of CW Energy Solutions and Tim Robinson of Esterline Corporation for providing the product technical information used in this article.

Thanks to George Livadaras for reporting an error in Figure 6.

References

CIE. 1995. CIE 13.3-1995, Method of Measuring and Specifying Colour Rendering Properties of Light Sources. Vienna, Austria: CIE Central Bureau.

CIE. 2004. CIE 15:2004, Colorimetry, Thirds Edition. Vienna, Austria: CIE Central Bureau.

von Kries, J. 1905. Die Gesichtsempfindungen. Handbuch der Physiologie der Menschen.

[1] These are technically referred to as long-, medium-, and short-wavelength, or LMS, retinal cones.

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

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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 ]

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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.