Mobile Light Pollution

Driving the Night Away

Ian Ashdown, P. Eng., FIES

Chief Scientist, Lighting Analysts Inc.

[ Please send all comments to ]

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.


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.


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


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.

Kruithof Revisited

A Human-Centric Perspective

Ian Ashdown, P. Eng., FIES

Chief Scientist, Lighting Analysts Inc.

[ Please send comments to ]

Related Posts

The Kruithof Curve

A previous blog article – The Kruithof Curve: A Pleasing Solution – examined the history of the Kruithof curve (Kruithof 1941]) shown in FIG. 1:

Kruithof Revisited - FIG 1

FIG. 1 – Kruithof curve, modern version (source: Wikipedia).

To summarize the article, “The Kruithof curve itself was thoroughly debunked a quarter-century ago with three exhaustive studies involving up to 400 participants (as opposed to two people in Kruithof’s study, including himself).” These studies were Bodman (1967), Boyce and Cuttle (1990), and Davis et al. (1990).

In other words, the weight of scientific evidence is firmly against use of the Kruithof curve as a guide to modern lighting design practices. Case closed, yes?


The problem with the above studies – and indeed any studies to date that have addressed our preferences regarding correlated color temperature (CCT) versus illuminance – is that they were based on constant CCT light sources.

This is completely understandable in that while we have had dynamic color-changing luminaires for over a decade (e.g., Ashdown 2006), color-changing luminaires for general illumination purposes have only become available in the past year or so. However, it begs the question: is there a relationship between CCT preferences and circadian rhythms?

Circadian Rhythms

Another blog article – Entraining Circadian Rhythms: Intensity versus Color – examined the question of whether the change in yellow-blue color ratio during twilight is more efficient in entraining circadian rhythms than changes in daylight intensity.

As noted in this article, the research (Walmsley et al. 2015) considered the behavior of wild mice rather than humans. The problem, of course, is that humans and mice see the world very differently in terms of perceived color. Whereas our retinal cones are responsive to the visible spectrum ranging from approximately 400 nm (violet) to 700 nm (deep red), those of wild mice are responsive to a spectrum ranging from below 330 nm (ultraviolet UVB) to 625 nm (medium red). Being dichromatic, mice are also likely to have poor color-distinguishing capabilities compared to humans.

Kruithof Revisited - FIG. 2

Figure 2 – Wild mouse spectral responsivity. Source: Walmsley et al. (2015).

Regardless, Walmsley et al. make a compelling argument that the ratio of yellow-to-blue light remains reasonably constant throughout the day, whereas the intensity of daylight may vary randomly and markedly due to cloud cover. From an evolutionary perspective, it therefore makes sense that human circadian rhythms are primarily entrained by changes in daylight color at dawn and dusk rather than changes in illuminance.

We must also remember, however, that illuminance and color are not the only factors influencing our circadian rhythms. Yetish et al. (2015) studied the sleep patterns of three pre-industrial societies: the Hadza in Tanzania, the Kalahari San in Namibia, and the Tsimane in Bolivia. What they found was that all three groups exhibited similar sleep patterns. With no cross-cultural influences, it is reasonable to assume that these represent core human sleep patterns for pre-industrial Homo sapiens.

Kruithof Revisited - FIG. 4

FIG. 3 – Light versus activity plots (Yetish et al., 2015)

The patterns are interesting. The hunter-gatherer/horticulturalists sleep on average 6.4 hours a day, with one more hour in winter than in summer. Most surprising, they fall asleep about 3.3 hours after sunset, during the nightly period of falling temperature. Further, they usually awaken before sunrise, when the daily temperature is lowest.

From a lighting perspective, it is equally interesting that light exposure was maximal in the morning and greatly decreased at noon, with all three groups seeking shade (FIG. 2). This is likely attributable to all three groups living in the tropics, but it has the effect of providing maximal light activation of the suprachiasmatic nucleus (SCN) and its influence on circadian rhythms in the morning.

From this, the authors surmise that the daily cycle of temperature change, “largely removed from modern sleep environments,” may be more important than daily changes in illumination in regulating sleep patterns. With near-constant temperatures in our offices and residences (and presumably sleep laboratories), temperature changes become yet another variable in establishing a baseline for circadian rhythm disruption studies.

Illuminance, color, ambient temperature … could it get any more complicated? Yes, of course! A paper published just today in Cell Metabolism (Breton et al. 2015) has shown that our appetites for food are controlled not only by our brains, but by intestinal bacteria telling us via proteins they produce that they are sated, and that we should stop eating on their behalf. Designing experiments to elucidate circadian rhythm behavior therefore involves much more than simply dimming the lights. When and what the test subjects eat or drink is a critical factor in designing repeatable and relevant circadian rhythm experiments.

Kruithof Revisited - FIG. 4

FIG. 4 – Intestinal bacteria signaling pathway (Source: Breton et al., 2015)

So what does this say about the Kruithof curve?

Circadian Kruithof

The problem with ascertaining preferences of any kind is that experiments need to be designed to control all the factors that may be involved. In doing so, the resultant laboratory conditions are often far removed from the real world.

This is precisely the problem with the Kruithof curve studies such as Bodman (1967), Boyce and Cuttle (1990), Davis et al. (1990), and others – they all (necessarily at the time) relied on constant CCT light sources, and ignored the test subjects’ circadian rhythm states.

Surprisingly, a review of the academic literature on this topic reveals … almost nothing. The closest relevant discussion is Poldma (2009), in which the author merely proposes “integrating [static] color and light theories with new contexts of dynamic, integrated human experiences of color and light in interior spaces.” Ellis et al. (2013a, 2013b) considers luminaire color temperature, but only in the context of matching daylight CCT for elderly patients with dementia.

The evidence of Walmsley et al. (2015) suggests that our preferences for CCT versus illuminance may be intimately associated with the state of our circadian rhythms. The popularity of products such as the Philips Wake-Up Lights with their simulated sunrise colors would certainly indicate that this is the case.

Kruithof Revisited - FIG. 5

FIG. 5 – Philips HF3510/60 Wake-up Light

There are of course many other examples, including “romantic mood” lighting in restaurants and CCT preferences for outdoor street and area lighting in residential areas. Our CCT preferences may vary throughout the day based on the state of our circadian rhythms and our activities.

At the end of the day, the modern interpretation of the Kruithof curve as shown in FIG. 1 remains “thoroughly debunked.” (The original curve presented by Kruithof in 1941 addressed a different issue.) However, it provides a framework for further research focused on human-centric lighting and circadian rhythms.

The problem, of course, lies in how to design an experiment that quantifies these preferences. It may be that the problem is intractable, at least in a laboratory setting. Perhaps the best answers will come from crowdsourced experiments – give millions of people the ability to change the luminaire CCTs in their environments and see what they prefer.

Today, this is mostly a theoretical approach. However, with the expected deployment of pervasive Internet-of-Things device in every luminaire over the coming few years, the necessary data will become available. Such an experiment may then be as simple as writing an appropriate search string for Google Analytics to process.


Ashdown, I. 2006. “Changing White Light,” LD+A 36(12):45-48 (December).

Bodman, H. W. 1967. “Quality of Interior Lighting Based on Luminance,” Transactions of the Illuminating Engineering Society of Great Britain 32(1):22.

Boyce, P. R., and C. Cuttle. 1990. “Effect of Correlated Colour Temperature on the Perception of Interiors and Colour Discrimination,” Lighting Research and Technology 22(1):19-36. (DOI:

Breton, J., et al. 2015. “Gut Commensal E. coli Proteins Activate Host Satiety Pathways following Nutrient-Induced Bacterial Growth,” Cell Metabolism (in press). (DOI:

Davis, R. G., and D. N. Ginthner. 1990. “Correlated Color Temperature, Illuminance Level, and the Kruithof Curve,” Journal of the Illuminating Engineering Society 19(1):27-38. (DOI:

Ellis, E. V., et al. 2013a. “Auto-tuning Daylight with LEDs: Sustainable Lighting for Health and Well-being,” Proc. ARCC 2013, pp. 465-473.

Ellis, E. V., et al. 2013b. “Chronobioengineering Indoor Lighting to Enhance Facilities for Ageing and Alzheimer’s Disorder,” Intelligent Buildings International Vol. 5 Supplement 1. (DOI:

Kruithof, A. A. 1941. “Tubular Luminescence Lamps for General Illumination,” Philips Technical Review Vol. VI, No. 3, pp. 65-73.

Poldma, T. 2009. “Learning the Dynamic Processes of Color and Light in Interior Design,” Journal of Interior Design 34(2):19-33. (DOI:

Walmsley, L., L. Hanna, J. Mouland, F. Martial, A. West, A. R. Smedley, D. A. Bechtold, A. R. Webb, R. L. Lucas, and T. M. Brown. 2015. “Colour as a Signal for Entraining the Mammalian Circadian Clock,” PLOS Biology, April 17. (DOI:

Yetish, G., et al. 2015. “Natural Sleep and Its Seasonal Variations in Three Pre-industrial Societies,” Current Biology 25:1-7. (DOI:

Seeing Ultraviolet

Keeping Time with Neuropsin

Ian Ashdown, P. Eng., FIES

Chief Scientist, Lighting Analysts Inc.

[ Please send comments to ]

UPDATE 15/11/08 – The following text briefly notes that some people can see near-ultraviolet radiation following cataract surgery due to the UV transmittance of their artificial intraocular lens. An example of this is reported in considerable detail here.

What does it mean to “see?” The word is ubiquitous in the English language, with dozens of different meanings. However, according to the Oxford English Dictionary, the most common definition is to “perceive with the eyes.” It is so common, in fact, that it ranks as one of the thousand most frequently used words in English.

“I see,” said the blind man, “what you mean.”

What we see are photons with varying wavelengths. Our eyes are most sensitive to photons with a wavelength of 555 nm, which we perceive as yellow-green. This sensitivity decreases towards ends of the visible spectrum, as shown by the CIE 1931 luminous efficiency function (FIG. 1). For all practical purposes, we cannot see photons with wavelengths shorter than 400 nm (deep violet) or longer than 700 nm (deep red). We are in particular blind to ultraviolet radiation (photons with wavelengths ranging from 100 nm to 400 nm) … or are we?

Seeing Ultraviolet - FIG. 1

FIG. 1 – CIE 1931 luminous efficiency function V(λ).

Ultraviolet Radiation

The CIE Lighting Vocabulary classifies ultraviolet radiation as follows:

Name Wavelength Range
UV-A 315 nm to 400 nm
UV-B 280 nm to 315 nm
UV-C 100 nm to 280 nm

Ultraviolet radiation offers both benefits and dangers to human health. UV-B radiation, for example, induces the production of vitamin D in exposed skin. This essential vitamin helps regulate bone health, and debatably provides other health benefits. Both UV-B and UV-A radiation promote the formation of melanin in the skin, which in addition to causing the skin to visibly tan, protects the skin cells from UV-B radiation damage.

The dangers of ultraviolet radiation include skin damage (sunburn and possible skin cancer through both direct and indirect DNA damage), and eye damage. Short-term exposure to UV-C (present in welders’ electric arcs) and UV-B (present in direct sunlight) can cause photokeratitis (“snow blindness” – basically sunburnt cornea), while long-term cumulative exposure can lead to the formation of cataracts in the lens of the eye and other eye diseases.

Given these dangers, it should come as no surprise that we cannot see ultraviolet radiation. The lens of the human eye is opaque to UV-A radiation, while the cornea blocks UV-B and UV-C radiation (FIG. 2). (Cataract removal operations involve the replacement of the lens with an artificial intraocular lens. These lenses were originally made from molded PMMA plastic, which were transparent to UV-A radiation. As a result, some patients could subsequently perceive ultraviolet radiation.)

Seeing Ultraviolet - FIG. 2FIG. 2 – Human eye. Source: Wikipedia.

We may not be able to see ultraviolet radiation, but other animals certainly can. Wild mice, for example, can see both UV-A and UV-B radiation (Kojima 2011). Other animals include birds, reptiles, fish, insects, and crustaceans, which use their ultraviolet vision for identifying food, sex recognition, and celestial navigation. Many nocturnal insects, for example, navigate by the ultraviolet emissions of celestial objects, and so are disoriented by and attracted to ultraviolet “bug zapper” traps.

Humans, on the other hand, are diurnal animals. Being active in the daytime under the tropical sun, it makes sense that our eyes evolved to protect the retina from ultraviolet radiation damage, notwithstanding the potential advantages of being able to see ultraviolet identification patterns on food sources. Ergo, we cannot see ultraviolet radiation … or can we?


We know that our eyes perceive more than just visual images projected onto the rods and cones of the retina. In addition to an estimated 4.5 million cone cells and 90 million rod cells (Curcio et al. 1990), there are also some 3,000 intrinsically photosensitive retinal ganglion cells (ipRGCs) in the retina (Dacey et al. 2005). The high density of cones and rods is necessary to form visual images; the ipRGCs need only detect light.

Lighting designers familiar with circadian-based lighting (e.g., Roos 2015) will recognize ipRGCs. These cells contain melanopsin, which is most sensitive to cyan light with a peak at 490 nm. Upon activation, the cells send electrical signals to the suprachiasmatic nucleus (SCN), a tiny region of some 20,000 cells located in the hypothalamus of the brain. The SCN is the master clock which controls our circadian rhythms.

Melanopsin (OPN4) is but one of a thousand or so known opsins, a group of light-sensitive proteins that occur in prokaryotes (single-celled organisms), some algae, and all animals (Terakita 2005, Shichida et al. 2009). Of particular interest are the “vertebrate visual opsins” that occur in human retinas:

Name Peak sensitivity Photo-receptor
Rh1 (rhodopsin) 510 nm Rod
OPN1SW (“blue opsin”) 440 nm Cone
OPN1MW (“green opsin”) 545 nm Cone
OPN1LW (“red opsin”) 570 nm Cone
OPN4 (melanopsin) 490 nm ipRGC

Rhodopsin provides us with scotopic vision, while the three “cone opsins” provide us with photopic color vision.

There are also other opsins in the human body, including OPN3 (encephalopsin), which is found mostly in the brain (Blackshaw et al. 1999), and OPN5 (neuropsin), which is found in the neural tissues of both humans and mice (Tarttelin et al. 2003, Kojima et al. 2011). Being photosensitive, their functions have been hypothesized to be related to the entrainment of our circadian and/or seasonal clocks in some manner, but the mechanisms are unknown.

Seeing Ultraviolet - FIG. 3FIG. 3 – Photosensitive opsins in the human retina. Source: Kojima et al. 2011.

Neuropsin is mostly sensitive to UV-A radiation, with peak sensitivity at 380 nm. When it absorbs ultraviolet photons, it converts into a blue-absorbing photoproduct with maximum absorption at 470 nm, which is stable in the dark. Orange illumination then converts it back into its ultraviolet-absorbing state (Kojima et al. 2011). It is present (“expressed”) in the retinal neurons of mice, which makes sense – they can see ultraviolet radiation (FIG. 4). However, it has also been found to be present in the cornea of the mouse eye, and presumably is also present in the cornea of the human eye (Buhr et al. 2015). What is it doing there?

Seeing Ultraviolet - FIG. 4FIG. 4 – Photosensitive opsins in the mouse retina. Source: Kojima et al. 2011.

(It should be noted that mouse and human opsins are not chemically identical, but rather are orthologs that evolved from common ancestors (Terakita 2005). Human blue opsin and mouse UV opsin, for example, evolved from a common ancestor but have different spectral responses. For neuropsin, however, it is assumed that they are biologically equivalent.)

Cellular Clocks

When we refer to the “circadian clock” in our bodies, we must remember that it is not a single mechanism located somewhere in our brain, but a holistic component of our entire body (Albrecht 2012). We have literally trillions of cells in our bodies, each of which (with a few rare exceptions) has a cellular clock to determine when to use energy, when to rest, when to repair or replicate DNA, and so on. This all happens on the molecular level of proteins and gene expression, with the SCN serving as the master timekeeper for the body, in part by instructing the pineal gland to secrete the hormone melatonin while we are sleeping.

The retina of the human eye has its own local circadian clock that is not synchronized with signals from the SCN (Storch et al. 2007). One of the more curious functions of this clock is to control the electrical coupling between the rods and cones (Ribelayga et al. 2008). Our rods are sensitive to dim light, while our cones are sensitive to bright light, giving us scotopic and photopic vision respectively. During the day, the electrical coupling between adjacent rods and cones is weak, which means that they operate independently in forming visual images. At night, however, the electrical coupling becomes remarkably robust. As a result, the cone circuitry is able to receive signals from the rods under low light-level conditions; this presumably facilitates the detection of large dim objects at night.

How the retinal circadian clock was entrained by the day-night cycle remained unknown until recently, when it was shown that entrainment was due to the presence of neuropsin in the mammalian retina and cornea (Buhr et al. 2015). Surprisingly, none of the other retinal opsins appear to be involved.

Kojima et al. (2011) noted that even if neuropsin is present in the human retina, there does not appear to be sufficient retinal irradiance to activate it. Our lenses are basically opaque to ultraviolet radiation, and even our sensitivity to violet light drops significantly as we age and our lenses turn progressively yellow (Fig. 5).

Seeing Ultraviolet - FIG. 5Figure 5 – Human eye lens spectral transmittance. Source: Turner et al. 2008.

(As noted by Turner et al. (2008), ipRGCs play a vital role in human physiology and health. As the lens transmittance in the blue and violet region of the visible spectrum decreases with age, we become increasingly susceptible to insomnia, depression, cognitive decline, and numerous systemic disorders due to the lack of circadian rhythm entrainment.)

These results notwithstanding, Buhr et al. (2015) reported that a breed of laboratory mice lacking rods, cones, and ipRGCs were still able to synchronize their retinal circadian rhythms to light/dark cycles, presumably by means of neuropsin in their retinas. Conducting their experiments ex vivo with fresh and cultured tissues, they conclusively demonstrated that OPN1SW (blue opsin) and OPN3 (encephalopsin) were not involved.

What they did not discuss is that Kojima et al. (2011) identified the epidermal and muscle cells of the outer ears as major sites of neuropsin expression in mice. Given that mouse ears typically have few hairs on their surfaces, it was hypothesized that the outer ears may perceive UV-A radiation (but it noted that further studies are required). Thus, while Buhr et al. (2015) demonstrated the role of ocular neuropsin in retinal circadian entrainment, it is not yet clear whether it is solely responsible for entrainment in vivo.

Contradicting Kojima et al. (2011), Buhr et al. (2015) surmised that even in humans with essentially UV-opaque lenses, there may be enough retinal irradiance in blue light to activate the retinal neuropsin when the eyes are exposed to full sunlight. This is possible but unlikely – it implies a presumably important biological function that relies on marginal signaling conditions. (Turner et al. 2008 note that the threshold for circadian rhythm entrainment via melanopsin appears to require daylight illuminance levels, especially for the elderly. The human lens and cornea, however, are mostly transparent to cyan light.)

The presence of neuropsin in the cornea is equally puzzling. Buhr et al. (2015) again surmised that it may involve an ocular (i.e., not just retinal) photoentrainment that is separate from SCN entrainment. However, the corneal cells which host this opsin are not as yet known, nor are the biochemical details of how it functions in vivo.

So what is neuropsin doing in the cornea? It is photosensitive, but then it is also found in the brain, where it has an unknown role. On the other hand, by being present in the human cornea, it is fully exposed to UV-A radiation. This suggests that it is involved in ocular photoentrainment.

Regardless of these unknowns, it is evident that the retinal circadian clock is dependent on neuropsin, and that it involves UV-A radiation rather than visible light. Ergo, we most likely perceive ultraviolet radiation.

Ultraviolet Radiation Requirements

From a human-centric lighting perspective then, this raises an interesting question: do we need exposure to ultraviolet radiation in order to maintain the health and nighttime performance of our eyes? Fluorescent lamps emit a small but significant amount of ultraviolet radiation, with eight hours of exposure at interior illumination levels roughly equivalent to one minute of direct sunlight exposure (NEMA 1999). However, LED lamps and modules emit no ultraviolet whatsoever. Whether this will affect, for example, long-term care patients who do not have daily access to sunlight or other near-ultraviolet radiation sources is an open question.

Yet another hypothesis – more research is required.


  1. Albrecht, U. 2012. “Timing to Perfection: The Biology of Central and Peripheral Circadian Clocks,” Neuron Review 74(2):246-260. (
  2. Blackshaw, S., et al. 1999. “Encephalopsin: A Novel Mammalian Extraretinal Opsin Discretely Localized in the Brain,” Journal of Neuroscience 19()10:3681-3690.
  3. Buhr, E. D., et al. 2015. “Neuropsin (OPN5)-mediated Photoentrainment of Local Circadian Oscillators in Mammalian Retina and Cornea,” Proceedings of the National Academy of Sciences (PNAS) Early Edition September 21. (
  4. Curcio, C. A., et al. 1990. “Human Photoreceptor Topography,” Journal of Comparative Neurology 292(4):497-523. (
  5. Dacey, D.M., et al. 2005. “Melanopsin-expressing Ganglion Cells in Primate Retina Signal Colour and Irradiance and Project to the LGN,” Nature 433:749–54. (
  6. Kojima, D., et al. 2011. “UV-Sensitive Photoreceptor Protein OPN5 in Humans and Mice,” PLoS ONE 6(10):e26388. (
  7. 1999. Ultraviolet Radiation from Fluorescent Lamps, LSD 7-1999. Rosslyn, VA: National Electrical Manufacturers Association.
  8. Ribelayga, C., et al. 2008. “The Circadian Clock in the Retina Controls Rod-Cone Coupling,” Neuron 59:790-801. (
  9. Roos, S. 2015. “The Case for Circadian Correct Lighting,” LD+A 45(1):32-36.
  10. Shichida, Y., et al. 2009. “Evolution of Opsins and Phototransduction,” Philosophical Transactions of the Royal Society B 364:2881-2895. (
  11. Storch, K.-F., et al. “Intrinsic Circadian Clock of the Mammalian Retina: Importance for Retinal Processing of Visual Information,” Cell 130:730-741. (
  12. Tarttelin, E. E., et al. 2003. “Neuropsin (Opn5): A Novel Opsin Identified in Mammalian Neural Tissue,” FEBS Letters 554:410-416. (
  13. Terakita, A. 2005. “The Opsins,” Genome Biology 6:213. (
  14. Turner, P. L., and M. A. Mainster. 2008. “Circadian Photoreception: Ageing and the Eye’s Important Role in Systemic Health,” British Journal of Ophthalmology 92:1439-1444. (


Light Pollution and Uplight Ratings

Swatting BUGs

Ian Ashdown, P. Eng., FIES

Chief Scientist, Lighting Analysts Inc.

[ Please send comments to ]

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)..


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.


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.


Thanks to Dawn DeGrazio for editorial assistance and historical clarifications.


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 ]

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.)


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.)


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.

Entraining Circadian Rhythms

Intensity versus Color

Ian Ashdown, FIES

Chief Scientist, Lighting Analysts Inc.

January 21, 2015

[ Please send comments to ]

There is a fascinating research paper called “Colour as a Signal for Entraining the Mammalian Circadian Clock” that has just been published in the open access journal PLOS Biology (Walmsley et al. 2015). While it is an exceedingly technical paper, the basic premise is this: the change in yellow-blue color ratio during twilight may be more effective in entraining circadian rhythms (at least in mice) than changes in daylight intensity.

Why is this important to professional lighting designers? Well, the answer involves the current interest in circadian-based (or biologically-effective) lighting. Quoting a recent LD+A article called “The Case for Circadian Correct Lighting” (Roos 2015), lighting designers are advised to:

Expose normal populations to high-levels of blue-rich light near 460 nm in the morning through early afternoon, and eliminate these shorter wavelengths and reduce light levels in the late-afternoon. After 10 p.m., total darkness is ideal – or if this is not practical – very low levels of warmer red-rich light. Even an incandescent lamp can disrupt the circadian cycle if it is too bright.

The goal, of course, is to provide electric lighting that mimics the temporal changes in natural daylight that we as a mammalian species experienced on a daily and seasonal basis prior to the introduction of electric lighting. Roos’s advice – which is reasonably good – is based on numerous medical studies over the past decade or so concerning our circadian rhythms and their relation to light exposure.

The latest paper, however, demonstrates in no uncertain terms that our knowledge of circadian rhythms is incomplete. While it does not necessarily negate the above advice, it does bring to mind new questions and possible opportunities for lighting design.

Circadian Rhythms

To better understand this topic, we begin with circadian rhythms, which Wikipedia defines as “any biological process that displays an endogenous [i.e., self-sustained], entrainable oscillation of about 24 hours.” In humans, these daily rhythms include those shown in Figure 1.

Entraining Circadian Rhythms - Fig. 1

Figure 1 – Human circadian rhythms. Source: Wikipedia.

These rhythms are entrained (i.e., synchronized) by external cues called zeitgebers (“time-givers”), the most important being – as you might expect – changes in daylight. We become acutely aware of these rhythms when we suffer from jet lag. Specifically, the change in daylight schedule disrupts our circadian clock, leaving us physically exhausted and having difficulties sleeping.

From a clinical perspective, there are several ways of measuring circadian rhythms in human subjects. One way is to measure the core body temperature, but this involves placing a temperature sensor somewhat uncomfortably “where the sun don’t shine” in the subject’s body. Another more reliable method is to measure the concentration of the hormone melatonin in the subject’s blood or saliva (e.g., Benloucif et al. 2005). The disadvantage, of course, is that the subject is sleeping when melatonin is present in measurable quantities. In short, reliably measuring circadian rhythms in humans is not an easy task.

There are numerous studies that correlate the secretion of melatonin in the body with circadian rhythms (e.g., Revell et al. 2005). What is more interesting to lighting designers, however, is the relation between melatonin secretion and the human eye.

Retinal Ganglion Cells

In addition to the cones and rods that provide us with color and night-time vision respectively, the mammalian retina has intrinsically photosensitive retinal ganglion cells (ipRGCs) that play a major role in entraining our circadian rhythms (e.g., Zaidi et al. 2007). The relationship between ipRGCs and melatonin secretion has been extensively studied (e.g., Lucas et al. 2013). These cells are connected to a region deep in the brain called the suprachiasmatic nucleus (SCN). When the ipRGCs do not receive light for an hour or more, the SCN triggers the pineal gland within the brain to begin secreting melatonin, which in turn promotes sleep in humans.

Of particular interest to lighting designers is the spectral responsivity of the ipRGCs. Whereas our cones and rods have spectral responsivities defined by the V(l) and V’(l) functions for photopic and scotopic vision, ipRGCs have a different spectral responsivity, referred to as melanopic (FIG. 2).

Entraining Circadian Rhythms - Fig. 2

Figure 2 – Human spectral responsivity.

This has become the basis for today’s evolving recommended practices in circadian-based (aka “biologically-effective” in Europe) lighting. For example, DIN SPEC 67600, Biologically Effective Illumination – Design Guidelines (DIN 2013) bases its recommendations solely on melanopic illumination. Similarly, luminaire manufacturers are currently looking at ways of optimizing the spectral power distributions of their products to produce biologically-effective white light; that is, white light with an abundance of blue light centered on the melanopic peak wavelength (e.g., Roos 2015).

… but science is all about endlessly attempting to prove that everything we think we know is either wrong or incomplete.

Experimental Design and Bias

Designing biological experiments is intrinsically difficult. You begin by hypothesizing that some action x will result in some event y. You then design an experiment in an effort to determine the correlation between the action and the event. For example, you may want to administer a vaccine and see whether it protects human subjects from contracting some viral disease.

The difficulty comes in designing the experiment. You do not, for example, want to administer an Ebola vaccine to a group of subjects living in North America – the likelihood of their being exposed to the Ebola virus is essentially zero. The experimental design in this case would be clearly biased towards an extremely positive (but essentially meaningless) correlation.

Taking circadian rhythms as another example, one problem is that the variability of retinal illuminance due to daylight exposure is typically both high and unpredictable due to cloud cover. The solution to this problem is appealingly simple: ensure that the subjects are exposed to electric lighting whose intensity and spectral power distribution (or at least its correlated color temperature) can be tightly controlled. By eliminating uncontrolled variables such as daylight, the experiment becomes more predictable and, most important, repeatable.

This solution however introduces significant experimental bias. In particular, the researcher typically assumes that:

  1. ipRGCs are sensitive primarily to blue light;
  2. ipRGCs are solely responsible for melatonin suppression; and
  3. Melatonin secretion is an indicator of the circadian rhythm associated with sleep.

These assumptions are of course based on many previous experimental results. They are still however assumptions – what if they prove to be wrong or incomplete?

Intensity versus Color

In their paper “Colour as a Signal for Entraining the Mammalian Circadian Clock,” Walmsley et al. (2015) began with a markedly different hypothesis. Noting that the mammalian circadian clock must have evolved over hundreds of millions of years, they reasoned that it makes sense to begin with natural daylight as the zeitgeber. They therefore began by measuring the spectral power distribution of daylight from 280 to 700 nm over a period of 41 days (September through October) from a location in Manchester, UK. The data were then carefully averaged to obtain a typical day in terms of absolute spectral irradiance.

The results were surprising. As shown in Figure 3, the variation in twilight color (horizontal axis) over 41 days is much less than the variation in irradiance (vertical axis), a result the authors attribute to ozone absorption in the upper atmosphere when the sun is below the horizon (Hulbert 1953).

Entraining Circadian Rhythms - Fig. 3

Figure 3 – Daylight color versus irradiance variability. Source: Walmsley et al. (2015).

The key here was not to assume, for example, that ipRGCs influence melatonin production and so eliminate as many experimental variables as possible, but to recreate as natural a luminous environment as possible for the test subjects (which in their experiments were laboratory mice).

(As an aside, it should be noted that there is evidence that circadian rhythms are influenced by input from not only ipRGCs, but also the retinal rods and cones. The authors cite half a dozen papers that address this topic.)

Using this information, the authors designed a LED-based lighting system for their laboratory mice. Unlike humans with their red-, green-, and blue-sensitive cones (technically long-, medium-, and short-wavelength sensitivity, designated SWS, MWS, and LMS respectively), the retinal cones of wild mice are primarily sensitive to ultraviolet (UVS) and green (MWS) wavelengths (FIG. 4), as determined by the various photosensitive opsins found in these specialized cells.

Entraining Circadian Rhythms - Fig. 4

Figure 4 – Wild mouse spectral responsivity. Source: Walmsley et al. (2015).

Unfortunately, the green-sensitive (MWS opsin) cones overlap in sensitivity with the rod (scotopic) and ipRGC (melanopic) sensitivities, which complicate the issue of measuring circadian clock entrainment. The authors therefore used a transgenic breed of mice called OpnlmwR, in which the MWS opsin cones are replaced by human long-wavelength sensitive (LMS) cones. These mice basically see ultraviolet (365 nm peak) and amber-red light (564 nm peak) only, regions of the spectrum to which the rods and ipRGCs have little to no sensitivity (Walmsley 2015, Lucas 2014).

The authors therefore used 400 nm ultraviolet and 590 nm amber high-flux LEDs for one of their lighting systems. The advantage of course is that these wavelengths minimally excite the rods and ipRGCs that presumably contribute to circadian rhythm entrainment. They further used Cnga3-/- mice, which lack cones but have retinal rods and ipRGCs, to confirm that rods and ipRGCs were not being significantly influenced by the bicolor illumination. (They also used a separate system with 365 nm, 460 nm, and 600 nm LEDs for experiments with normal “wild” mice.)

Rather than measuring melatonin levels, the authors surgically implanted tiny temperature loggers in the mice to measure core body temperature as a biological marker for circadian rhythms. The mice were then exposed to temporal lighting conditions that recreated a summer’s day in Stockholm, Sweden, including twilight, over a period of two weeks. The northern latitude was chosen specifically to achieve a protracted period of twilight and so maximize its influence on circadian rhythm entrainment.

The rest of the experiment involved some rather gruesome details involving decapitation and sliced brains in order to measure responses of the SCN to amber and ultraviolet light. What the authors found was that the ratio of amber-to-ultraviolet light (which correspond to the yellow-blue light ratio in humans[1]) had a considerably greater effect on circadian rhythm entrainment than did the variation in absolute intensity of amber-plus-ultraviolet light with constant color ratio.

What is significant about this is that the ratio of yellow-to-blue light remains reasonably constant throughout the day, but changes drastically and consistently before sunrise and after sunset (FIG. 5) over a period of about 30 minutes (that is, twilight). Conversely, the intensity of daylight may vary randomly and markedly throughout the day due to cloud cover. From an evolutionary perspective, it therefore makes more sense for the mammalian circadian clock to have evolved to respond to color rather than intensity changes in daylight as its zeitgeber.

Entraining Circadian Rhythms - Fig. 5

Figure 5 – Yellow-blue daylight ratio. Source: Walmsley et al. (2015).

From an electrical engineering perspective, it also makes sense that the color change (which happens rapidly and predictably) would become an important zeitgeber. In terms of phase-locked loop design, a sudden but consistent periodic pulse is better for entrainment than a variable and noisy signal such as daylight intensity.

This is not to say of course that ipRGCs and rods do not also play a role in circadian rhythm entrainment – they do (e.g., Güler et al. 2008). Further studies will (as always) be required to tease out the relationship between cones, rods, and ipRGCs in this process.

Lighting Design Perspective

From a lighting designer’s perspective … well, the world of circadian-based lighting has become considerably more interesting. Until now, it has been assumed that circadian rhythms are driven by input from the ipRGCs alone, and that these in turn are excited by blue-green light centered on 490 nm. (Melanopsin has a peak sensitivity at 480 nm [e.g., Lucas et al. 2014], but the ipRGC response is skewed towards 490 nm by the spectral transmittance of the adult human cornea, which preferentially absorbs blue light.)

This paper shows that the science may be much more complicated. Its value as an academic study is that it demonstrates that under “natural” conditions involving daylight, blue-yellow color discrimination complements, and may even dominate intensity changes via ipRGCs in circadian rhythm entrainment.

At the same time, however, these natural conditions do not reflect the decidedly artificial conditions we subject ourselves to with electric lighting, particularly at night or at the end of a night shift. It has, for example, been established that several hours of exposure to high-CCT illumination from tablet computers can significantly disrupt our circadian rhythms (van der Lely et al. 2015).

It is also important to recognize that it is the change in yellow-blue color ratio that influences circadian rhythm entrainment in natural lighting conditions, not the color ratio itself. Studies using lighting with constant color temperature (e.g., Bellia et al. 2014), which found that different color temperatures have little effect on circadian stimulus, may be eliminating precisely those experimental variables that are of the most interest.

Without predicting the results of future studies, suppose that the dominant geitzeber for circadian rhythm entrainment is the change in yellow-blue color ratio of daylight rather than constant or even changing melanopic illuminance. If this proves to be true, then the current recommendations for circadian-based / biologically effective lighting may be in serious need of revision.

If this does prove to be true, however, it may simplify the lighting design requirements. It would, for example, be much easier to use color-tunable luminaires with programmable color temperatures that change to signal the beginning and end of the day, than it would be to ensure absolute levels of melanopic illuminance.

There are conflicting opinions about whether we know enough about the circadian system in humans to design biologically-effective lighting systems. The current paper will do nothing to resolve this debate, as it shows how little we really do know.

On the other hand, I prefer to take a positive approach. It is difficult to imagine a situation where any reasonable lighting system design can physically harm people. Given this, I have no problem with taking the “best available science” and designing lighting systems accordingly. Whatever recommendations we have will most likely involve programming of color-tunable luminaires with varying spectral power distributions and intensity on a daily cycle. The beauty of this is that as the science improves, the lighting systems will only require a software or driver firmware update.

In the meantime, more research is (as always) required.


Bellia, L., A. Pedace, and G. Narbato. 2014. “Indoor Artificial Lighting: Prediction of the Circadian Effects of Different Spectral Power Distributions,” Lighting Research and Technology 46(4):650-660.

Benloucif, S., M. J. Guico, K. J. Reid, L. F. Wolfe, M. L’hermite-Balériaux, and P. C. Zee. 2005. “Stability of Melatonin and Temperature as Circadian Phase Markers and their Relation to Sleep Times in Humans,” Journal of Biological Rhythms 20(2):178-188.

DIN. 2013. Biologically Effective Illumination – Design Guidelines, DIN SPEC 67600 (2013-04). Deutsches Berlin, Germany: Institut für Normung e.V.

Güler, A. D., J. L. Ecker, G. S. Lall, S. Haq, C. M. Altimus, H. W. Liao, A. R. Barnard, H. Cahill, T. C. Badea, H. Zhao, M. W. Hankins, D. M. Berson, R. J. Lucas, K. W. Yau, and S. Hattar. 2008. “Melanopsin Cells are the Principal Conduits for Rod-cone Input to Non-image-forming Vision,” Nature 453(7191):102-105.

Hulbert, E. O. 1953. “Explanation of the Brightness and Color of the Sky, Particularly the Twilight Sky,” Journal of the Optical Society of America 43(2):113–118.

Lucas, R. J., S. N. Peirson, D. N. Berson, T. M. Brown, H. M. Cooper, C. A. Czeisler, M. G. Figueior, P. D. Gamlin, S. W. Lockley, J. B. O’Hagan, L. L. A. Price, I. Provencio, D. J. Skene, and G. C. Brainard. 2013. “Measuring and Using Light in the Melanopsin Age,” Trends in Neuroscience 37(1):1-9.

Revell, V. L. H. J. Burgess, C. J. Gazda, M. R. Smith, L. F. Fogg, and C. I. Eastman. 2005. “Advancing Human Circadian Rhythms with Afternoon Melatonin and Morning Intermittent Bright Light,” Journal of Clinical Endocrinology & Metabolism 91(1):54-59.

Roos, S. 2015. “The Case for Circadian Correct Lighting,” LD+A 45(1):32-36.

van der Lely, S., S. Frey, C. Garbazza. A. Wirz-Justice, O. G. Genni, R. Steiner, S. Wolf, C. Cajochen, V. Bromundt, and C. Schmidt. 2015. “Blue Blocker Glasses as a Countermeasure for Alerting Effects of Evening Light-Emitting Diode Screen Exposure in Male Teenagers,” Journal of Adolescent Health 56(1):113-119.

Walmsley, L., L. Hanna, J. Mouland, F. Martial, A. West, A. R. Smedley, D. A. Bechtold, A. R. Webb, R. L. Lucas, and T. M. Brown. 2015. “Colour as a Signal for Entraining the Mammalian Circadian Clock,” PLOS Biology, April 17.

Zaidi, F. H., J. T. Hull, S. N. Peirson, K. Wulff, D. Aeschbach, J. J. Gooley, G. C. Brainard, K. Gregory-Evans, J. F. Rizzo, C. A. Czeisler, R. G. Foster, M. J. Moseley, and S. W. Lockley. 2007. “Short-wavelength Light Sensitivity of Circadian, Pupillary, and Visual Awareness in Humans Lacking an Outer Retina,” Current Biology 17(24): 2122-2128.

[1] In humans, our neural circuitry combines the signals received from the short-wavelength sensitive (SMS) cones with the combined signals from the medium-wavelength sensitive (MWS) and long-wavelength sensitive (LWS) cones to generate blue-yellow discrimination. This opponent color theory posits that our color vision consists of blue-versus-yellow, red-versus-green, and black-versus-white channels that our brains further process. Given evolution’s penchant for recycling good ideas, it may be that the blue-versus-yellow signal also contributes to circadian rhythm entrainment. The authors speculate that this may even have been the original evolutionary purpose of color vision.

Solar Illumination

The Inconstancy of Constants

Ian Ashdown, FIES

Chief Scientist, Lighting Analysts Inc.

January 21, 2015

[ Please send comments to ]

Lighting design is based in part on the reasonable assumption that photometric units have precise definitions. The candela, for example, has a precise mathematical definition:

The candela is the luminous intensity, in a given direction, of a source that emits monochromatic radiation of frequency 540×1012 hertz and that has a radiant intensity in that direction of 1/683 watt per steradian.

and from this all other photometric units are derived, including luminous flux, luminous intensity, luminance, illuminance, and so forth.

You might expect then that the same applies for daylighting design … but you would be wrong.

Solar Constant

We begin with the solar constant, which is defined as the solar irradiance (measured in radiant watts per square meter) incident on a plane perpendicular to the Sun at a distance of one astronomical unit (AU), which is roughly the mean distance from the Sun to the Earth.

Defining the solar constant is easy; measuring it is not. Although it was first roughly measured in 1838, accurate measurements must be performed using a satellite above the Earth’s atmosphere. You then have to correct for the distance of the Earth from the Sun, which varies due to the ellipticity of the Earth’s orbit. (The actual direct solar irradiance at the top of the atmosphere varies by some 6.9% over the course of a year.)

The technology of precision spectroradiometers has of course improved over the years, and with it increasingly precise measurements of the solar spectrum and the solar constant. By 1993, satellite measurements had a precision of 0.01 percent (Kittler and Darula 1996).

Today, the extraterrestrial solar spectrum is formally defined by ASTM International Standard E490-00a (2014), “Standard Solar Constant and Zero Air Mass Solar Spectral Irradiance Tables.” This standard defines the solar spectrum (FIG. 1) over a range of wavelengths from 200 nanometers (far ultraviolet) to one millimeter (far infrared).

Solar Illumination - FIG 1FIG. 1 – Solar spectrum (Source: ASTM 2014)

Integrated over the entire solar spectrum, the solar constant is 1366.1 ± 0.58 watts per square meter. This is of course something of a fiction, as the total radiation output of the Sun varies by approximately 0.1 percent due to the 11-year sunspot cycle and other longer-term effects. In general however, the output has varied by less than 0.2 percent over the past two millennia.

Solar Illumination Constant

The solar constant is useful for things like satellite design and climate studies. For lighting design purposes, however, only visible light in the range of 380 nm to 780 nm is important. By multiplying each wavelength by the CIE 1931 luminous efficiency function V(l), according to:

Solar Illumination - EQN 1

the solar illumination constant can be calculated.

Solar Illumination - FIG 2FIG. 2 – CIE 1931 luminous efficiency function V(l)

Based on ASTM (2014), the calculated solar illumination constant Esc is 133.1 kilolux (IES 2010).

There is nothing controversial here … right?

Daylighting Design

The lighting design and simulation program Radiance has long been a standard tool within the daylighting research community. The name “program” is a bit of a misnomer, as Radiance is a set of hundreds of Unix-based utility programs that are executed via command-line scripts. One of these programs is gendaylit. This is an essential tool for daylighting research, as it calculates the absolute sky luminance distribution in accordance with the Perez sky model (Perez et al. 1993).

The issue is that gendaylit was written in 1994. At that time, the standard solar spectrum was Wehrli (1985), which resulted in a solar constant of 1367 watts per square meter and a solar illumination constant of 127.5 kilolux. These values were hard-coded as constants in gendaylit.

When the IES Lighting Handbook, Tenth Edition, was being prepared, the solar illumination constant was recalculated based on ASTM E-490-00a(2006). This document was first published in 2000, and has since been reapproved and published as ASTM E490-00a(2014).

So herein lies the problem: which solar illumination constant should be used? To be compatible with the IES Lighting Handbook, Tenth Edition, it should be 133.1 kilolux. However, to be compatible with the “gold standard” Radiance used by the daylighting research community, it should be 127.5 kilolux.

This is not exactly an academic question, as the difference is 4.4 percent. This difference permeates all absolute daylighting values calculated using the Perez sky model. For annual daylight metric calculations such as spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE), this difference can be significant (IES 2012).

It is an open question.


ASTM. 2014. Standard Solar Constant and Zero Air Mass Solar Spectral Irradiance Tables, ASTM E490-00a(2014). West Conshohocken, PA : ASTM International.

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

IES. 2013. Approved Method: IES Spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE). New York, NY: Illuminating Engineering Society.

Kittler, R., and S. Darula. Standardization of Daylight Phenomena and Algorithms: Luminous Solar Constant and its Variations. Bratislava, Slovakia: Institute of Construction and Architecture, Slovak Academy of Sciences.

Perez, R., R. Seals, and J. Michalsky, 1993. “All-Weather Model for Sky Luminance Distribution – Preliminary Configuration and Validation,” Solar Energy 50(3):235-245

Wehrli, C. 1985. Extraterrestrial Solar Spectrum, Publication No. 615. Davos, Switzerland: World Radiation Center.


The Kruithof Curve

A Pleasing Solution

Ian Ashdown, FIES

Chief Scientist, Lighting Analysts Inc.

[ Please send comments to ]

Related Posts

Kruithof Reconsidered

UPDATE 16/04/09 – This metastudy:

Fotois, S. 2106. “A Revised Kruithof Graph Based on Empirical Data,” Leukos. (Published online 08 April 2016, DOI 10.1080/15502724.2016.1159137.)

critically examined 29 studies in which the Kruithof curve was investigated. The author concluded that “… these [studies] do not support Kruithof. For pleasant conditions, these data suggest only avoiding low illuminances and do not favor any CCT.”

After 75 years of misconception and misuse, may this finally mark the end of the Kruithof Curve. If a lighting designer has a personal preference for warm or cool colors, fine – but please, do not try to justify it with scientific mumbo-jumbo.

[UPDATE 15/01/20 – Added Bartleson (1960) reference.]

Lighting designers today will surely recognize the Kruithof curve, in which the color temperature of the light source is related to a range of illuminances that we find “pleasing.” In its modern form, the Kruithof curve has become supposedly irrefutable evidence that the correlated color temperature (CCT) of LED-based lighting should not exceed 4000K for indoor applications.Kruithof - FIG 1FIG. 1 – Kruithof curve, modern version (source: Wikipedia).

In the process, luminaire manufacturers are being lambasted for promoting products with CCTs of 5000K and higher. Worse, some government agencies and non-profit organizations are adopting CCT limits that are presumably based on the Kruithof curve. The DesignLights Consortium, for example, stipulates that luminaires on its Qualified Products List must have CCTs of 5000K or less for most indoor applications.

Unfortunately, the modern version of the Kruithof curve is different from what A. A. Kruithof published 75 years ago. The upper and lower curves are approximately the same, but their interpretation is different from what Kruithof intended. In fact, the Kruithof curve appears to have been basically misinterpreted for the past three-quarters of a century.

The Kruithof curve itself was thoroughly debunked a quarter-century ago with three exhaustive studies involving up to 400 participants (as opposed to two people in Kruithof’s study, including himself). The Kruithof curve was somewhat belatedly removed from the IES Lighting Handbook five years ago (IES 2010).

This article is however not so much about the validity of the Kruithof curve as it is about a careful re-examination of his 1941 paper. While Kruithof has been rightly criticized over the years for not providing experimental details, he wrote enough for us to infer how he arrived at his findings. When you realize that he was working with early prototypes of the first fluorescent lamps, it is in itself an interesting story.

In the Beginning

The year was 1941. Captain America made his first appearance in a comic book, Europe was being torn asunder by World War II … and Philips Research was quietly developing its own fluorescent lamp technology in Eindhoven, the Netherlands. As part of this effort, Philips Technical Review published a paper by A. A. Kruithof titled, “Tubular Luminescence Lamps for General Illumination” (Kruithof 1941).

Kruithof’s paper was primarily about fluorescent lamp technology, which had been commercially released by General Electric in 1938. His experimental meter-long T12 “luminescence lamp” (FIG .2) was designed have a luminous flux output of 1000 lumens.

Kruithof - FIG 2FIG. 2 – Philips “luminescence” lamp (from Kruithof 1941).

Halophosphate-based phosphors were not invented until the following year (McKeag and Ranby 1942), and so these “white” fluorescent lamps used a combination of cadmium borate, willemite (zinc orthosilicate), and magnesium tungstate, which respectively fluoresce red, green, and blue when excited by the ultraviolet radiation emitted by the mercury-argon gas fill.

By themselves, the phosphors resulted in maximum luminous efficacies of approximately 70 lumens per watt for willemite and 35 lumens per watt for cadmium borate and magnesium tungstate. By combining the phosphors in various proportions, it was possible to generate white light with CCTs ranging from 2650K to 10000K.

Kruithof fabricated fluorescent lamps with CCTs of approximately 4200K and 5800K, plus a third lamp type that was so far off the blackbody curve as to be considered colored rather than white. Comparing these to the extant incandescent lamp technology with its typical 15 lumens per watt luminous efficacy, Kruithof was well justified in writing, “These properties give reason to expect that luminescent lamps will be widely used in the future.”

Kruithof also performed an extensive analysis of the color rendering capabilities of his lamps by observing the color shifts of 313 color cards (as opposed to the eight colors used for the CIE General Colour Rendering Index Ra). Using the phosphor and visible mercury line spectra published in his paper, it is possible to estimate CRI values of his lamps as:

Lamp CCT Ra R9
4200K 36 -110
5800K 54 -60

As for the original form of the Kruithof curve (FIG. 3), the paper includes a description of what the author referred to decades later as a “pilot study” of lamp CCT versus illuminance level. To fully understand this study, it is necessary to quote Kruithof (1941) extensively, beginning with:

“In the first place at a given level of illumination it is found that the colour temperature must lie within certain limits if the effect of the illumination is to be pleasing. Roughly, it may be said that a low or a high colour temperature corresponds to a low or a high level of illumination, respectively. We have investigated this relation experimentally somewhat more closely by introducing in a room a variable number of electric lamps whose current (i.e., the temperature of the filaments) could be varied.”

Kruithof - FIG. 3FIG. 3 – Kruithof curve, original version (from Kruithof 1941).

With vague phrases like “pleasing” illumination, “a room,” and a “variable number of electric lights,” we can at best only infer the experimental conditions that were used to develop this curve.

Kruithof continues:

“Below the lowest curve the illumination is ‘dim’ (at low colour temperature) or ‘cold’ (at high colour temperature). Above the highest curve the unnatural colour reproduction was unpleasant.”

There are two items of immediate interest here:

  1. Kruithof used the adjectives “dim” and “cold” rather than the modern interpretation of “appears bluish.”
  2. Kruithof used the adjectives “unnatural” and “unpleasant” rather than “appears reddish.”

It could be argued that the modern interpretation is intuitively valid, but that is not the point. Kruithof measured a specific psychometric parameter that he termed “pleasing” illumination. Recasting the results to support a different hypothesis effectively invalidates the experiment.

In terms of the illumination referenced in FIG. 1 appearing reddish or blush, it must be noted here that no academic studies published to date support this hypothesis. The modern form of the Kruithof curve appears to be an interpretation with no scientific evidence to support it.


“These obviously vague limits within which the illumination is considered ‘pleasing’ could in our experiments be determined at least with an accuracy of 20 or 30 percent.”

This is a blazing red flag that something may be seriously wrong with this study – how can you measure something as subjective as “pleasing” with an accuracy of 20 to 30 percent in terms of illuminance? What Kruithof said – without providing any evidence – is that we can apparently tell the difference between say 100 lux and 125 lux of illumination. This is not a side-by-side comparison of two illuminated surfaces, but by simply walking into a room and deciding whether or not the colors are “unnatural.”

Much has been written in the following years that Kruithof did not provide any significant details of his experimental apparatus or protocols, and so it is difficult to accept the Kruithof curve as being valid. However, the rest of his paper is reasonably detailed and informative, indicating that Kruithof was a careful researcher. He must therefore have had some reason for claiming an accuracy of 20 to 30 percent.

Continuing with Kruithof, the extensive caption he wrote for FIG. 3 reads in part:

“The left-hand part of the limiting curves, up to a colour temperature of 2850 ºK, is recorded by allowing electric lamps with variable (decreased) current to burn in a room, and varying the number of lamps. The illumination intensity on a table 80 cm high was here measured. In the right-hand part the lowest level which does not give the impression of coldness was determined by experiments with daylight itself and with the daylight luminescence lamps to be described below.”

This is all frustratingly vague, but there is a key point: “… varying the number of lamps.”


“The shape of the upper curve has been extrapolated in this region with the help of the fact that in direct sunlight (colour temperature 5000 ºK) even with the highest illumination intensities occurring (104 or 105 lux) the colour rendering is never found ‘unnatural’.”

This is a crucial quote in that Kruithof decided that direct sunlight was not – and by definition could not – be “unnatural.” This brings us back to the question of the upper curve for color temperatures below 2850K (FIG. 3), where dimmed incandescent lamps were used.

Looking deeper into the question of “unnatural color reproduction,” Kruithof stated that he used a selection of color cards from the Ostwald Color Atlas, a contemporary color classification scheme of the Munsell color system we use today (FIG. 4). With respect to this, he wrote:

“While the colour rendering can be judged by comparison when luminescence is used in combination with other light and the designation of the colour impression obtained and the saturation of the colour obtained must agree, when only luminescence lamps are used no comparison is possible. In judging the colour rendering therefore in this case one must have recourse to ‘colour memory’ which is chiefly confined only to the designation of colours.”

Kruithof - FIG. 4FIG. 4 – Ostwald color atlas (source: Wikipedia).

This is even more puzzling in that our ability to recall colors is mediocre at best. In general, we tend to remember colors as being more saturated than they really were (Bartleson 1960). It therefore makes even less sense that an accuracy (or more properly repeatability) of 20 to 30 percent could be perceived.

It does make sense, however, if Kruithof switched lamps on and off to vary the illuminance while maintaining constant color temperature. This would be a form of flicker photometry. We are mostly insensitive to absolute illuminances, but we are highly sensitive to changes in illuminance. Switching illumination levels would reveal even subtle changes in the perceived chromaticities of the color cards.

… which brings us to the first of two color appearance effects, the Bezold-Brücke hue shift effect. This effect was first reported in the 1870s, but it was not studied extensively until the 1930s (Purdy 1931). Even then, the study was published in the American Journal of Psychology. A lamp research engineer like Kruithof could be excused for not being aware of the paper and its implications.

The Bezold-Brücke effect results in perceived color hues changing with changes in luminance. As shown in FIG. 5, the wavelength shifts required to maintain constant perceived hue for monochromatic colors can be huge, particularly for red and cyan, with a ten-fold increase in luminance. The effect on Kruithof’s color cards would have been generally less noticeable, but may have been still evident with, for example, saturated red colors.

Kruithof - FIG. 5FIG. 5 – Bezold-Brücke effect for monochromatic light (from Wyszecki & Stiles 1982, Fig. 2(5.9))

The second color appearance effect would not have been known to Kruithof because it was not reported until the 1950s. The Hunt effect (Hunt 1950, 1952, 1953) results in the perceived chroma (i.e., colorfulness) of illuminated objects increasing with increased illuminance. This effect is important enough to have been built into the CIECAM02 color appearance model that is widely used for color management for displays, printers, and other imaging devices.

For a given CCT, Kruithof presumably began with a low illumination level and then increased the illuminance until color appearance of the color cards more or less matched his color memory of them. The changes in perceived color would presumably be due to both the Bezold-Brücke and Hunt effects.

As however he continued increasing the illuminance, the continuation of these effects would result in the colors no longer matching his color memory. This would result in, to use Kruithof’s own words, “unnatural color reproduction.”

If Kruithof had reduced the daylight illuminance using, for example, neutral density filters, he would likely have observed the same behavior. However, even if he had done so, the time taken to move the filters into position would likely have masked the color differences. He also would have had the conundrum of having to call natural daylight “unnatural” and “unpleasant.”

In Kruithof’s defense, he may have been one of the first researchers to observe the Bezold-Brücke and Hunt effects using white light illumination with constant CCT. Certainly color shifts with changes in CCT were well-known at the time and modeled by the von Kries chromatic adaptation model. Color shifts with constant CCT were, however, a different matter.

Kruithof was certainly aware of the color shifts that his fluorescent lamps produced, as shown by FIG. 6 and FIG. 7, where the circled areas labelled ‘2’ represent “clearly appreciable” color shifts.

Kruithof - FIG. 6FIG.6 – Color shifts between daylight and 5800K fluorescent lamp (from Kruithof 1941).

Kruithof - FIG. 7.FIG. 7– Color shifts between electric light and 4200K fluorescent lamp (from Kruithof 1941).

Interestingly, Kruithof wrote:

“Most colours are somewhat less saturated in luminescence light than in daylight.”

which is exactly what you would expect from the Hunt effect. Nevertheless, Kruithof had little choice but to describe the 5800K lamp as generating “pleasing” illumination above some threshold.

It must be emphasized, however, that this is only a hypothesis – it would need a carefully designed large-scale experiment to determine whether in fact the Bezold-Brücke and/or Hunt effects can satisfactorily explain Kruithof’s results. Even then, it will be impossible to know with any certainty because Kruithof described his experiments so frugally. On the other hand, they at least offer a plausible explanation of his claim to 20 to 30 percent repeatability.

Time Marches On

If Kruithof considered his work to be a mere “pilot study,” the follow-up studies have been anything but. Bodman (1967) for example performed studies wherein he varied the illuminance in a conference room illuminated by fluorescent lamps. Remarkably, more than 400 subjects took part in these studies. Like Kruithof, he found that people had a preferred illuminance range over which 90 percent of the subjects found the lighting to be “good.” However, As can be seen from FIG. 8, the preferences appear to be influenced more by the lamp spectral power distributions than by their CCTs. At the time that these studies were conducted, “deluxe” fluorescent lamps had CRI values of 90 or so, but warm white fluorescent lamps with their halophosphate phosphors had CRI values as low as 50.

Kruithof - FIG. 8FIG. 8 – Preferred illuminance range versus fluorescent lamp type (from Bodman 1961)

What are interesting are the terms that Bodman’s subjects used to describe the lighting (FIG. 9). The warm white fluorescent lighting (CCT < 3000K) was described as “excessive” and “artificial” (terms which may have influenced by the low CRI values) at high illuminances, but for white (CCT » 4000K) and daylight (CCT > 6000K), the terms used were “pleasant” and “lively.” This is consistent with what you might expect from an increase in colorfulness as provided by the Hunt effect.

Average Illuminance (lux) Color of light
Warm white White Daylight
< 700 Not unpleasant Dim Cool
700 – 3000 Pleasant Pleasant Neutral
> 3000 Excessive, artificial Pleasant, lively Pleasant

FIG. 9 – Subjective impressions of illumination levels (Bodman 1961).

Boyce and Cuttle (1990) performed similar experiments using fluorescent lamps with CCTs ranging from 2700K to 6300K, and with average illuminances ranging from 30 to 600 lux, to illuminate a small office space. All of the fluorescent lamps had CRI values ranging from 82 to 85.

Doing a detailed statistical analysis of 410 questionnaires completed by 15 subjects who spent 20 minutes becoming visually adapted to the room, the authors found that the lamp CCT had no statistically significant influence on the subjective assessments. Instead, the major factor in both color discrimination tests and subjective assessments was illuminance.

Interestingly, none of the subjects who were unfamiliar with lighting design used the terms “warm” or “cool” to describe the lighting.

Davis and Ginthner (1990) also performed similar experiments with 40 test subjects. Using 2750K and 5000K fluorescents lamps with CRI values of 90 and illuminance levels of 270, 600, and 1350 lux, they confirmed the findings of Boyce and Cuttle (1990) that the subjective ratings of preference were influenced by illuminance only. They also found that low light levels were rated as less colorful than high light levels for the same CCT, which again suggests the Hunt effect.

Finally, Viénot et al. (2008) performed experiments designed to investigate the validity of the Kruithof curve using LED modules rather than fluorescent lamps. To ensure high CRI with variable color temperature, they used LED clusters with independently-controlled blue, cyan, green, amber, orange, red, cool white, and warm white LEDs. These provided CRI values from 91 to 96 over an illuminance range of 150, 300, and 600 lux and a CCT range of 2700K, 4000K, and 6500K.

Unlike the previous studies, however,the  Viénot et al. (2008) experimental setup was not a room but a light booth measuring some 41 x 35 x 38 cm (16 x 14 x 15 inches) that had an 80-degree field of view with a dark surround. It is debatable whether the results of the 20 subjects can be applied to offices spaces, but the authors concluded that:

“In one sense, we have validated Kruithof’s statement that high CCT at low illuminance is unpleasant. Nevertheless, we cannot conclude that low CCT should be confined to low illuminance to arouse pleasant sensations.”


“When the colour rendering index is very high and the light spectrum is under control, there is no indication that high colour temperature is judged more pleasant than low colour temperature at higher illuminance levels.”


There have been many more studies related to the Kruithof curve, including Cockram et al. (1970), Denk et al. (2014), Dikel et al. (2014), Fotois et al. (2013), Hu et al. (2006), Ishi and Kakitsuba (2003), Juslén (2006), Küller et al. (2006), Logadóttir and Christoffersen (2008), Mills et al. (2007), Naoyuki and Tomimatsu (2005), Navvab (2001), Park et al. (2010), Pinto et al. 2008), Weintraub (2000), and Zhai (2014).

None of these, however, are as focused or comprehensive as those of Bodman (1967), Boyce and Cuttle (1990), and Davis and Ginthner (1990). The common conclusion of these authors is that while dim lighting at any CCT is seen as unpleasant, there is no observational evidence in support of Kruithof’s upper curve (FIG. 3).

What this discussion has shown, however, is that Kruithof may not have meant “unpleasant” in the sense of poor lighting quality, but rather in the sense of optimal color reproduction.

It must remembered that Kruithof was working with incandescent lamps with CCTs varying from 1800K to 2850K, 4000K and 5800K fluorescent lamps that probably had CRI values of less than 60, and “natural” daylight with unknown CCT. He likely would have never seen chromaticity shifts with changes in illuminance at constant CCT prior to his experiments, and probably (and quite reasonably) saw them as unnatural and hence unpleasant. This despite the fact that he observed exactly this when comparing his 5800K lamp with daylight, and explicitly commented on the fact.


The conclusion is straightforward, and indeed was established more than a quarter-century ago with three major studies: the Kruithof curve is essentially meaningless. There is no upper boundary to “pleasant” illumination at any CCT, and the best that can be said about the lower boundary is the obvious: dim lighting can be unpleasant, regardless of the CCT.

At the same time, however, Kruithof deserves credit for having been the first to investigate the topic. His failure to describe his experiments in more detail is regrettable, but perhaps understandable. Done as a pilot study, the brief discussion in his paper is basically a progress report with preliminary findings.

It has basically been through our continued misunderstanding of his term “pleasing” that the Kruithof curve continues to persist in lighting design practice. If Kruithof were able to comment on this today, he would likely have only two words to say (in Dutch):

“Stop daarmee!” (English translation: “Stop that!”)


Bartleson, C. J. 1960. “Memory Colors of Familiar Objects,” Journal of the Optical Society of America 50(1):73-77.

Bodman, H. W. 1967. “Quality of Interior Lighting Based on Luminance,” Transactions of the Illuminating Engineering Society of Great Britain 32(1):22.

Boyce, P. R., and C. Cuttle. 1990. “Effect of Correlated Colour Temperature on the Perception of Interiors and Colour Discrimination,” Lighting Research and Technology 22(1):19-36.

Cockram, A. H., J. B. Collins, and F. J. Langdon. 1970. “A Study of User Preferences for Fluorescent Lamp Colours for Daytime and Night-Time Lighting,” Lighting Research & Technology 2(4):249-256.

Davis, R. G., and D. N. Ginthner. 1990. “Correlated Color Temperature, Illuminance Level, and the Kruithof Curve,” Journal of the Illuminating Engineering Society 19(1):27-38.

Denk, E., P. Jimenez, and B. Schulz. 2014. “The Impact of Light Source Technology and Colour Temperature on the Well-being, Mental State and Concentration of Shop Assistants,” Lighting Research & Technology (in press).

Dikel, E. E., G. J. Burns, J. A. Veitch, S. Mancini, and G. R. Newsham. 2014. “Preferred Chromaticity of Color-Tunable LED Lighting,” Leukos 10(2):101­-115.

Fotois, S., S. Atli, C. Cheal, K. Houser, and A. Logadóttir. 2013. “Lamp Spectrum and Spatial Brightness at Photopic Levels: A Basis for Developing a Metric,” Lighting Research and Technology 0:1-23.

Hu, X., K. W. Houser, and D. K. Tiller. 2006. “Higher Colour Temperature Lamps May Not Appear Brighter,” Leukos 3(1):69-81.

Hunt, R. W. G. 1950. “The Effects of Daylight and Tungsten Light-Adaptation on Color Perception,” Journal of the Optical Society of America 40(6):362-371.

Hunt, R. W. G. 1952. “Light and Dark Adaptation and the Perception of Color,” Journal of the Optical Society of America 42(3):190-199.

Hunt, R. W. G. 1953. “The Perception of Color in 1º Fields for Different States of Adaptation,” Journal of the Optical Society of America 43(6):479-484.

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

Ishi, M., and N. Kakitsuba. 2003. “Preferred Color Temperatures at 200 lx during Exposure to Cool or Warm Environments for Middle-Aged Female Subjects,” Journal of the Human-Environmental System 6(2):93-100.

Juslén, H. 2006. Influence of the Colour Temperature of Preferred Lighting Level in an Industrial Work Area Devoid of Daylight,” Ingineria Illuminatului 18(8):25-36.

Kruithof, A. A. 1941. “Tubular Luminescence Lamps for General Illumination,” Philips Technical Review Vol. VI, No. 3, pp. 65-73.

Küller, R., S. Ballal, T. Laike, B. Mikellidesa, and G. Tonello. 2006. “The Impact of Light and Colour on Psychological Mood: A Cross-Cultural Study of Indoor Work Environments,” Ergonomics 49(14):1496-1507.

Logadóttir, A., and J. Christoffersen. 2008. “Individual Dynamic Lighting Control in a Daylit Space,” Proc. Eleventh International Conference on Indoor Air Quality and Climate (Indoor Air 2008). Technical University of Denmark.

McKeag, A. H., and P. W. Ranby. 1942. Great Britain Patent 578,192. Improvements in Luminescent Materials.

Mills, P. R., S. C. Tomkins, and L. J. M. Schlangen. 2007. “The Effect of High Correlated Colour Temperature Office Lighting on Employee Wellbeing and Work Performance,” Journal of Circadian Rhythms 5(2):1-9.

Naoyuki, O., and N. Tomimatsu. 2005. “Preference on Illuminance and Colour Temperature of Interior for Various Behavior Settings,” Proc. 2005 Annual Conference of the Illuminating Engineering Institute of Japan (in Japanese).

Navvab, M. 2001. “A Comparison of Visual Performance under High and Low Colour Temperature Fluorescent Lamps,” Journal of the Illuminating Engineering Society 30(2):170-175.

Park, B.-C., J.H. Chang, Y.-S. Kim, J.-W. Jeong, and A.-S. Choi. 2010. “A Study of the Subjective Response for Corrected Colour Temperature Conditions of a Specific Space,” Indoor and Built Environment 19:623-637.

Pinto, D. P., J. M. M. Linhares, and S. M. C. Nascimento. 2008. “Correlated Color Temperature Preferred by Observers for Illumination of Artistic Paintings,” Journal of the Optical Society of America 25(3):623-630.

Purdy, D. M. 1931. “Spectral Hue as a Function of Intensity,” American Journal of Psychology 43:541-559.

Wyszecki, G., and W. Stiles. 1982. Color Science: Concepts and Methods, Quantitative Data and Formulae, Second Edition. New York, NY: John Wiley & Sons.

Viénot, F., M.-L. Durand, and E. Mahler. 2009. “Kruithof’s Rule Revisited Using LED Illumination,” Journal of Modern Optics 56(13):1433-1446.

Weintraub, S. 2000. “The Color of White: Is there a ‘Preferred’ Color Temperature for the Exhibition of Works of Art?”, Western Association for Art Conservation Newsletter 21(3).

Zhai, Q.-Y., M.-R. Luo, and X.-Y. Liu. 2014. “The Impact of Illuminance and Colour Temperature on Viewing Fine Art Paintings under LED Lighting,” Lighting Research & Technology 2014 (in press).


Photometry and Photosynthesis

From Illuminance to PPFD

Ian Ashdown, FIES

Chief Scientist, Lighting Analysts, Inc.

[ Please send comments to ]

Related Posts

Botanical Light Pollution

UPDATE 15/04/13 – This article was first published on December 10, 2014. A revised version was published as “LED Lighting for Horticulture” in the Mar/Apr 2015 issue of LED Professional Review ( This update includes information from the published article.

UPDATE 15/11/05 – due to several Excel spreadsheet errors, the lux-to-PPFD conversion factors presented in Table 2 were miscalculated. These errors have been corrected.

UPDATE 16/01/12 – the description of the Emerson effect has been corrected.

UPDATE 16/02/10 – Added discussion of calculating lumen-to-PPFD conversion factors for overcast skies, as well as expanded notes and references on green and ultraviolet LEDs.

Horticultural lighting these days is big business. As agricultural land becomes scarce, the weather becomes more unreliable, and the migration of people to megacities continues, it increasingly makes economic sense to cultivate plants indoors with electric lighting.

The problem is that lighting designers and horticulturalists generally do not speak the same language. Whereas we speak in terms of lumens and illuminance, horticulturalists speak in terms of photosynthetically active radiation (PAR) and photosynthetic photon flux density (PPFD). Ask for an explanation of these terms and you will hear talk of micromoles, and possibly microeinsteins, of photons. Instead of luminous flux, there is quantum flux. It can be very confusing, not to say frustrating.

We need however to understand each other. As the horticultural industry transitions from high-intensity discharge arc lamps such as high-pressure sodium (HPS) and metal halide (MH) to solid-state lighting (e.g., Massa et al. 2008, Mitchell et al. 2012, Nelson and Bugbee 2014), it becomes possible to tailor the spectral power distribution of multicolor-LED luminaires for individual crops and plant species. For lighting designers working with horticulturalists, the need to understand PAR and calculate PPFD values using lighting design software becomes critical.

Photosynthetically Active Radiation

Photosynthesis is the process used by plants to convert electromagnetic radiation – light – into chemical energy that is used for growth and development. All that is needed for this process is carbon dioxide (CO2), nutrients, and water. The process itself is not particularly efficient; only 4 to 6 percent of the absorbed radiation is converted into chemical energy (Zhu et al. 2010, Table 2). Still, it is the engine that drives most life on this planet.

Photosynthetically active radiation (PAR) is defined as electromagnetic radiation over the spectral range of 400 nm to 700 nm that photosynthetic organisms are able to use in the process of photosynthesis to fix the carbon in CO­2 into carbohydrates. Horticulturalists measure PAR for both plant research and greenhouse lighting design (e.g., Barnes et al. 1993) using specialized photometers (e.g., Biggs et al. 1971).

A common unit of measurement for PAR is photosynthetic photon flux density (PPFD), measured in units of moles per square meter per second[1]. In this case, every absorbed photon, regardless of its wavelength (and hence energy), is assumed to contribute equally to the photosynthetic process. This is in accordance with the Stark-Einstein law, which states that every photon (or quantum) that is absorbed will excite one electron, regardless of the photon’s energy, between 400 nm and 700 nm. For this reason, photosynthetic photon flux is also referred to as quantum flux.

Whether a photon with a given wavelength is absorbed by a plant leaf is dependent on the spectral absorptance of the leaf, which in turn is determined largely by the leaf optical properties, including the concentration of plant pigments such as chlorophyll A and B, various cartrenoids (carotenes and xanthophylls), and anthocyanins. The chlorophylls are responsible for the characteristic green color of leaves; the other pigments contribute to the yellow, orange, and red colors respectively of autumn leaves after the chlorophylls decompose.

Typical absorptance spectra for chlorophyll A, chlorophyll B, beta-carotene, and two isoforms of phytochrome are shown in Figure 1. It must be noted, however, that these spectra are approximate. They are measured in vitro by dissolving the pigments as extracts in a solvent, which affects their absorptance spectra. By themselves, they suggest that blue and red LEDs alone are sufficient for horticultural applications. In reality, however, the situation is much more complicated.

Photosynthesis - FIG 1 (Update)

FIG. 1 – Photopigment spectral absorptances

McCree (1972a) measured the spectral absorptance (FIG. 2) and quantum yield of CO2 assimilation for the leaves of 22 species of crop plants[2]. Taking the average measurements at 25 nm intervals for all plant species (Table 1), he produced the photon-weighted relative quantum yield (Table 1) that is representative of most crop plants.

Photosynthesis - FIG 1

FIG. 2 – Average crop plant spectral absorptance

Wavelength (nm) Relative Spectral Quantum Yield
400 0.42
425 0.68
450 0.70
475 0.63
500 0.65
525 0.72
550 0.82
575 0.91
600 0.97
625 1.00
650 0.90
675 0.90
700 0.48

Table 1 – Relative Quantum Yield (average of 22 field species)

Yield Photon Flux

It is also possible to measure PAR in terms of energy rather than photons. The energy of a photon with wavelength λ is given by the Planck-Einstein relation:

E = hc / λ

where E is the energy in joules, h is Planck’s constant (6.626 x 10-34 joule-seconds), c is the speed of light (2.998 x 108 meters per second), and l is measured in meters. For example, one micromole of photons with a wavelength of 450 nm has 0.266 joules of energy. Scaling the photon-weighted relative quantum yield values by the wavelength and normalizing produces the energy-weighted relative quantum yield, also known as the action spectrum (FIG. 3). (An action spectrum is simply a plot of biological effectiveness as a function of wavelength of incident light.)

The energy-weighted photosynthetic photon flux is measured in watts (joules per second), and is referred to as the yield photon flux (YPF). (In terms of lighting design, it is synonymous with irradiance.) As shown by McCree (1972b), photon-weighted PPFD is a better predictor of photosynthesis when light sources with different spectral power distributions are considered. Given this, PPFD is the metric most commonly used by horticulturalists. However, energy-weighted YPFD is useful for energy-balance calculations involving photosynthetic organisms.

Photosynthesis - FIG 2

FIG. 3 – Relative quantum yield for crop plant photosynthesis

As noted by McCree (1972a), neither PPFD (quantum flux density) nor YPFD (irradiance) are perfect measures of photosynthetically active radiation in that both systematically overestimate the effectiveness of blue light relative to red. As can be seen from FIG. 3, the error is greater for YPFD, which explains why PPFD measurements are preferred by horticulturalists. Nevertheless, they are useful in that they are independent of any particular plant species, and they can be measured both in the laboratory and in the field using a radiometer with a spectrally-calibrated quantum sensor such as the LI-190SA with LI-COR (

From Lumens to Photosynthetic Photon Flux

As lighting designers, we need some method of converting lumens to quantum flux and illuminance to quantum flux density (PPFD). We can do so however only if we know or can estimate the spectral power distribution (SPD) of the light source.

Suppose then that we have a light source with a known relative spectral power distribution (SPD), such as for example a 5000K “cool white” LED (FIG. 4).

Photosynthesis - FIG 3

FIG. 4 – 5000K LED relative spectral power distribution

One watt of radiant power at 555 nm is by definition equal to 683 lumens. Given the CIE 1931 luminous efficiency function (FIG. 5), we can calculate the spectral radiant flux Φ(λ) in watts per nanometer for each lumen as:

Photosynthesis - EQN 1

where Wrel(λ) is the relative spectral power distribution, V(λ) is the luminous efficiency function at wavelength λ, and Δλ is the wavelength interval (typically 5 nm). For the above example, the spectral radiant flux per nanometer for each lumen at 440 nm is 22.5 microwatts, while the total radiant flux per lumen is 3.18 milliwatts.

Photosynthesis - FIG 4

FIG. 5 – CIE 1931 luminous efficiency function V(λ)

With this, we can calculate the photosynthetic photon flux (PPF) per nanometer in micromoles per second per nanometer:

Photosynthesis - EQN 2

(where Na is Avogardo’s constant), while summing over the range of 400 nm to 700 nm yields the photosynthetic photon flux (PPF) per lumen for the given light source:

Photosynthesis - EQN 3

Given an illuminance value (lumens per square meter) and knowing the light source SPD, we can similarly calculate the photosynthetic photon flux density (PPFD) in micromoles per second per square meter (μmol/sec-m2) for the given light source. Again for the above example, one kilolux is equal to 14.62 μmol/sec-m2.

Conversion Factors

It is easy enough to find graphical representations of light source spectral power distributions, but it is considerably more difficult to find this information in tabular form suitable for the above calculations. Fortunately, this information is published in CIE 15:4, Colorimetry (CIE 2004). It does not include white light LEDs, but this information can be obtained by digitizing manufacturers’ product catalog data (e.g., Philips 2014a).

Given such information, it possible to calculate kilolux-to-PPFD conversion factors for common light sources:

Light Source Conversion Factor
CIE A (incandescent, 2856K) 20.3
CIE 5000K daylight (D50) 18.1
CIE 5500K daylight (D55) 18.1
CIE 6500K daylight (D65) 18.3
CIE 7500K daylight (D75) 18.6
CIE HP1 (standard high-pressure sodium, 1959K) 11.7
CIE HP2 (color-enhanced high-pressure sodium, 2506K) 19.3
CIE HP3 (high-pressure metal halide, 3144K) 14.4
CIE HP4 (high-pressure metal halide, 4002K) 15.0
CIE HP5 (high-pressure metal halide, 4039K) 16.3
2700K white light LED (Philips Luxeon Rebel LXW9-PW27) 18.1
3000K white light LED (Philips Luxeon Rebel LXW9-PW30) 17.1
3500K white light LED (Philips Luxeon Rebel LXW7-PW35) 14.6
4000K white light LED (Philips Luxeon Rebel LXW8-PW40) 14.3
5000K white light LED (Philips Luxeon Rebel LXW8-PW50) 14.6

Table 2 – Illuminance (kilolux) to PPFD (μmol/sec-m2) conversion factors

Table 2 does not include commercial products such as the Sylvania SHP-TS Grolux (with a CCT of 2050K) because Sylvania and most other lamp manufacturers do not publish their lamp SPDs in tabular form. It is possible to digitize the graphical representations of white light LEDs because the bandwidth of the blue “pump” LEDs is at least 15 nm. With high-pressure sodium and metal halide lamps, however, it is impossible to digitize their published SPDs because the wavelength resolution is unknown. A subnanometer-wide line emission, for example, could vary in height by five times, depending on whether the wavelength binning is 1 nm or 5 nm.

Overcast Skies

Table 2 presents conversion factors for the CIE Clear Sky with CCTs ranging from 5000K to 7500K. Their spectral power distributions (shown in the Appendix) were calculated in accordance with the equations published in CIE 15:4, Colorimetry (CIE 2004), which were in turn derived from the spectral distributions of 622 samples of daylight (skylight, and sunlight plus skylight), as discussed in Judd et al. (1964). Considering the variability of daylight, these SPDs are sufficient for most purposes.

What however about overcast skies? To answer this question, we reference “Colors of the Daytime Overcast Sky” by Lee and Hernández-Andréz (2006), who defined “overcast” as meeting two criteria: 1) no clear sky can be visible anywhere; and 2) cloud cover must be sufficiently optically thick that any cast shadows are indistinct.

The authors made over 9,100 spectral irradiance measurements in Granada, Spain, and Annapolis, Maryland on 40 overcast days, including days with drizzle, light rain and snow, with the Sun at least five degrees above the horizon. While the paper offers many interesting details, two items are of particular interest.

First, the correlated color temperature of overcast skies that the authors measured ranged from 5800K to 9300K, with their typical overcast skies having CCTs ranging from 6000K to 6600K. Second, the authors provided SPDs of their two most extreme skies, with CCTs of 5800K and 9300K. These SPDs were used to calculate illuminance (kilolux) to PPFD (μmol/sec-m2) conversion factors of 17.8 and 20.5 respectively. From these values, the CCTs of typical overcast skies can be linearly interpolated to be 18.2 ± 0.2. In other words, no different from typical clear skies.

LED Lighting for Horticulture

At this time, high-pressure sodium (HPS) lamps are the most common light source for greenhouse lighting, where it is commonly used to supplement daylight during the winter months. However, with the growing interest in urban horticulture that relies exclusively on electric lighting, light-emitting diodes offer many advantages. This is particularly true for multilayer cultivation, where the close spacing of plants in vertical rack-mounted trays make HPS lighting impractical.

McCree (1972a) noted that the relative quantum yield for crop plant photosynthesis has two peaks at 440 nm and 620 nm. He also noted however, the Emerson effect, which states that photosynthesis in the presence of two or more wavelengths can be more efficient than the sum of that due to the individual wavelengths. In particular, adding white or red light (less than 680 nm) to deep red light (greater than 680 nm) can beneficially increase the rate of photosynthesis.

Green light is also used in photosynthesis, as can be seen from the leaf action spectrum (FIG. 2). It has been established that green light drives photosynthesis more effectively than red or blue light deep within the leaf (Terashima et al. 2009). Further, the insects used in greenhouses as pollinators and biological control agents see best in the green and ultraviolet regions of the spectrum. More interestingly, changes of even 10 nm in the peak wavelength of green light can have dramatic effects on the growth of plants such as lettuce (Johkan et al. 2012).

Plants also exhibit photomorphological responses (i.e., growth and development) to ultraviolet radiation (Zuk-Golaszewska et al. 2003). In the past, this has been mostly of theoretical interest to botanists. Now however with the rapid commercialization of ultraviolet LEDs (Shih 2015) with wavelengths covering the plant biologically-active spectrum of 280 nm to 400 nm, UV-A and UV-B LEDs will likely also find application in horticultural lighting.

It is likely for this reason that many horticultural LED modules feature efficient 450 nm indium-gallium-nitride (InGaN) deep blue LEDs and 660 nm aluminum-indium-gallium phosphide (AlInGaP) deep red LEDs. Typical examples of these LEDs are the Philips Luxeon Royal Blue (LXML-PRO1-0425) and Deep Red (LXM3-PD01) products (Philips 2014b). Both of these products are quite efficacious, converting some 45% of their electrical input power into visible light. Green LEDs, while beneficial, are rarely used because of their much lower radiant efficacies. (This may soon change, however, as OSRAM Opto recently announced the development of 530 nm InGaN green LEDs with 25% external quantum efficiency.)

Herein however lies a problem: 450 nm and 660 nm are close to the limits of our color vision (see FIG. 4). Consequently, Philips and other manufacturers typically express the optical performance of these products in radiometric rather than photometric terms – milliwatts instead of lumens.

So, the lighting design process becomes a bit more complicated. We first need to digitize the published LED spectral power distributions to determine the conversion factors between milliwatts and lumens – these will be needed for the lighting design simulations. These are given by:

Photosynthesis - EQN 4

where ΦL is the luminous flux, ΦR(λ) is the relative spectral radiant flux and V(λ) is the luminous efficiency function at wavelength λ.

Using the Philips Luxeon Royal Blue and Deep Red products as an example, the respective conversion factors are approximately 0.07 and 0.03 lumens per milliwatt (lm/mW). However, these figures must be approached with some caution, as they apply to 450 nm and 660 LEDs only. If for example the peak wavelength of deep blue LED was 440 nm rather than 450 nm, the conversion factor would be 0.05 lm/mW. Similarly, if the peak wavelength of the deep red LED was 650 nm rather than 660 nm, the conversion factor would be 0.06 lm/mW. The Philips LED binning ranges are 440 to 460 nm and 650 to 670 nm respectively, which equates to (from FIG. 4) conversion factor uncertainties of +75%, -50% for blue and +60%, -30% for red. The above conversion factors are therefore decidedly approximate.

(Some horticultural LED module manufacturers bin their LEDs more tightly, as peak maxima shifts as small as 10 nm have been shown to have dramatic effects on plant growth. Unless however the binning policy is stated in the manufacturer’s product literature, this cannot be assumed.)

A further word of caution: even the best illuminance meters can be wildly inaccurate when measuring deep blue and deep red light levels. Commercially available photometers are usually classified according to their f1 number (with f1  < 3% being preferred), which is basically a measure of how closely the spectral response of the meter matches that of the photopic visual efficiency function (FIG. 4). As noted in CIE 127:2007, Measurement of LEDs (CIE 2007), this is useful for white light measurements only. To quote, “In the case of single-color LEDs, the spectral mismatch errors can be very large even if f1 is reasonably small, due to the fact that some LED spectra are peaking in the wings of the V(λ) function where the deviation makes little effects on f1 but can cause large errors.”

With these conversion factors in hand, we can now calculate the illuminance-to-PPFD conversion factors for horticultural LEDs:

Light Source Conversion Factor
450 nm deep blue LED 119.4
525 nm green LED 8.4
660 nm deep red LED 130.5

Table 3 – Illuminance (kilolux) to PPFD (μmol/sec-m2) conversion factors

How horticulturalists choose to balance the ratio of red to blue light will likely depend on the specific plant species being cultivated and their stage of growth. Some plants like shade, while others prefer direct sunlight, with different SPD requirements. In addition, far-red 735 nm LEDs may be employed to induce flowering. Regardless, the above conversion factors will still be useful.

In addition to using chlorophylls and carotenoids for photosynthesis, plants use these and other photopigments for a wide variety of functions. The phytochromes Pr and Pfr, for example, respond to 660 nm red and 735 nm infrared radiation respectively, and in doing so induce seed germination and flowering, regulate leaf expansion and stem elongation, and trigger photoperiod and shade avoidance responses (see Appendix A).

Other photopigments regulate phototropism (leaf and stem orientation) and circadian rhythms (for which blue light is the most effective), photomorphogenesis (plant shape), root growth, stomatal opening, chloroplast movement … the list goes on, as horticultural researchers continue to explore the role between lamp SPDs and optimal plant health and growth. (See for an informative summary of plant photobiology.)


As a reminder, photosynthetically active radiation (PAR) does not consider the spectral response of plants (FIG. 3); it simply represents the number of photons (quanta) per unit area per second within the range of 400 to 700 nm. With the availability of color-tunable LED modules for greenhouse lighting, horticulturalists will likely want to experiment with different SPDs for specific crops and flowering plants, as well as both the directionality and daily timing (photoperiods) of the luminaires. Regardless, being able to convert predicted and measured illuminance values to PPFD values for common light sources will certainly ease the communication problem between lighting designers and horticulturalists.


Thanks to Tessa Pocock of the Smart Lighting Engineering Research Center, Rensselaer Polytechnic Institute, for her review and comments on this article.

Appendix A – Photosynthesis and Visible Light

For illumination engineers, it might seem suspicious that the photosynthetically active radiation is defined over the spectral range 400 nm to 700 nm – exactly the range we commonly assume for human vision. What about longer and shorter wavelengths?

When McCree [4] measured his 22 crop species both in the field and in laboratory growth chambers, he obtained the following action spectra:

Photosynthesis - FIG A1

Figure A1 – Growth Chamber Action Spectra

Photosynthesis - FIG A2

Figure A2 – Field Action Spectra

which clearly explain the logic of the 400 – 700 nm spectral range.

Below 400 nm, there is the risk of photooxidation that generates toxic radicals, which can destroy the cell’s chlorophyll and other cellular components. Under intense UV radiation, violaxanthin (which is involved in photosynthesis) is converted via the xanthophyll cycle into zeaxanthin. In doing so, it receives excess energy from chlolorphyll and releases it as heat. This process thereby offers the plant photoprotection.

At the same time, other plant photopigments, including cryptochromes and phototropins, do have sensitivities (as measured in vitro) that extend into the ultraviolet, and likely respond under dim light conditions. However, these are likely suppressed under high light conditions by the xanthophyll process.

Above 700 nm, the photon energy is too low to activate the photosynthetic process via the chlorophylls and various cartenoids. However, the phytochrome photopigment, which is responsible for stem elongation, leaf expansion, shade avoidance, neighbor perception, seed germination, and flower induction, has two isoforms called Pr and Pfr. In its ground state Pr, phytochrome has a spectral absorbance peak of 660 nm. When it absorbs a red photon, it converts to its Pfr state, which has a spectral absorbance peak of 730 nm. When the phytochrome molecule absorbs a far-red photon, it converts back to its Pr state, and in doing so triggers a physiological change in the plant.

Photosynthesis - FIG A3

Figure A3 – Phytochrome Action Spectra

Chlorophyllous leaves are transparent to infrared radiation, are so the phytochrome signaling mechanism is ideal for sensing the lighting environment on forest floors and in the presence of neighboring plants competing for available direct sunlight.

Appendix B – Light Source SPDs

Photosynthesis - CIE A SPDPhotosynthesis - CIE D50 SPDPhotosynthesis - CIE D55 SPDPhotosynthesis - CIE D65 SPDPhotosynthesis - CIE D75 SPD Photosynthesis - 2700K LED Photosynthesis - 3000K LED Photosynthesis - 3500K LED Photosynthesis - 4000K LEDPhotosynthesis - 5000K LEDPhotosynthesis - CIE HP1 SPD Photosynthesis - CIE HP2 SPD Photosynthesis - CIE HP3 SPD Photosynthesis - CIE HP4 SPDPhotosynthesis - CIE HP5 SPDPhotosynthesis - Horticultural LEDs


Barnes, C., T. Tibbitts, J. Sager, G. Deitzer, D. Bubenheim, G. Koerner, and B. Bugbee. 1993. “Accuracy of Quantum Sensors Measuring Yield Photon Flux and Photosynthetic Photon Flux,” Horticultural Science 28(122):1197-1200.

Biggs, W. W., A. R. Edison, Jerry D. Eastin, K. W. Brown, J. W. Maranville, and M. D. Clegg 1971. “Photosynthesis Light Sensor and Meter,” Ecology 52:125–131.

CIE. 2004. Colorimetry, Third Edition. CIE Technical Report 15:2004. Vienna, Austria: Commission Internationale de l’Eclairage.

CIE. 2007. Measurement of LEDs, Second Edition. CIE Technical Report 127:2007. Vienna, Austria: Commission Internationale de l’Eclairage.

Johkan, M, K. Shoji, F. Goto, S. Hahida, and T. Yoshihara. 2012. “Effect of Green Light Wavelength and Intensity on Photomorphogenesis and Photosynthesis in Lactuca sativa,” Environmental and Experimental Botany 75:128-133.

Judd., D. B., D. L. MacAdam, and G. Wyszecki. 1964. “Spectral Distribution of Typical Daylight as a Function of Correlated Color Temperature,” Journal of the Optical Society of America 54(8):1031-1039.

Lee, R. L., and J. Hernández-Andrés. 2006. “Colors of the Daytime Overcast Sky,” Applied Optics 44(27):5712-5722

Massa, G. D., H.-H. Kim, R. M. Wheeler, and C. A. Mitchell. 2008. “Plant Productivity in Response to LED Lighting,” Horticultural Science 43(7):1951-1956.

McCree, K. J. 1972a. “The Action Spectrum, Absorptance and Quantum Yield of Photosynthesis in Crop Plants,” Agricultural and Forest Meteorology 9:191-216.

McCree, K. J. 1972b. “Tests of Current Definitions of Photosynthetically Active Radiation Against Leaf Photosynthesis Data,” Agricultural and Forest Meteorology 10:443-453.

Mitchell, C. A., A.-J. Both, C. M. Bourget, J. F. Burr, C. Kubota, R. G. Lopez, R. C. Morrow, and E. S. Runkle. 2012. “LEDs: The Future of Greenhouse Lighting!”, Chronica Horticulturae 52(1):6-11.

Nelson, J. A., and B. Bugbee. 2014. “Economic Analysis of Greenhouse Lighting: Light Emitting Diodes vs. High Intensity Discharge Fixtures,” PLOS One 9(6).

Philips Lumileds. 2014a. Luxeon Rebel ES Portfolio Datasheet DS61. Philips Lumileds Lighting Company.

Philips Lumileds. 2014b. Luxeon Rebel Color Portfolio Datasheet DS68. Philips Lumileds Lighting Company.

Shih, M. 2015. “Emerging Applications for UV LEDs Drive Broad Interest,” LEDs Magazine 58(11/12):19-22 (Nov/Dec).

Terashima, I., T. Fujita, T. Inoue, W. S. Chow, and R. Oguchi. 1009. “Green Light Drives Leaf Photosynthesis More Efficiently than Red Light in Strong White Light: Revisiting the Enigmatic Question of Why Leaves are Green,” Plant & Cell Physiology 50(4):684-697.

Thimijan, R. W., and R. D. Heins. 1983. “Photometric, Radiometric, and Quantum Light Units of Measure: A Review of Procedures for Interconversion,” Horticultural Science 18(6):818-822.

Zhu, X.-G., S. P. Long, and D. R. Ort. 2010. “Improving Photosynthetic Efficiency for Greater Yield,” Annual Review of Plant Biology 61:235-261.

Zuk-Golaszewska, K., M. K. Upadhyaya, and J. Golaszewski. 2003. “The Effect of UV-B radiation on Plant Growth and Development,” Plant Soil Environ 49:135-140.

[1] A mole is a unit of measurement used in chemistry to express the number of elementary entities in a substance that is equal to the number of atoms in  12 grams of the isotope carbon-12. It corresponds to the Avogadro constant, whose value Na is 6.022 x 1023 particles (in this case photons) per mole. A micromole is one millionth of a mole. (A micromole [μmol] of photons was sometimes referred to by plant scientists as a microeinstein. However, this unit of measurement is not part of the International System of Units (SI), and so its use has been deprecated.)

[2] The quantum yield in photosynthesis is defined as the micromoles of carbon dioxide fixed per micromole of photons absorbed.

In Search of Luminance

Understanding What We See

Ian Ashdown, P. Eng., FIES

Chief Scientist, Lighting Analysts Inc.

[ Please send comments to ]

The IES Lighting Handbook, Tenth Edition (IES 2010), describes luminance as “perhaps the most important quantity in lighting design and illuminating engineering.” This is an accurate but curious description, as the editors neglected to include an entry for Section 5.7.3, Luminance, in the handbook’s index.

The section itself is a mere five paragraphs long, informing the curious reader that luminance is the “local surface density of light emitting power in a particular direction,” defined mathematically as:

In Search of Luminance - EQN 1

which for most readers will be completely and absolutely … opaque.

This is unfortunate, as luminance is undeniably the most important quantity, and indeed the most fundamental concept, in lighting design and illuminating engineering. More than a mathematical definition, professional lighting designers need to understand what it is that we see.

Luminance Understood

To understand luminance, we begin with a parallel beam of light. Ignore any thoughts of surfaces or light sources; just imagine a beam of light traveling through empty space in a given direction. Imagine also that this beam has a finite width; say, a rectangular beam one meter on a side.

If we take a cross-section of this beam at any point along its length, we can measure so many lumens of light (i.e., photons per second) per unit area. In photometric terms, this is the luminous flux Φ per unit area, or luminous flux density, of the beam. Being parallel, the beam does not diverge or converge, and so the luminous flux density remains constant along the length of the beam.

Now, what happens if the beam illuminates a real or imaginary surface at an angle? We have this:

In Search of Luminance - FIG 1

FIG. 1 – Illuminance of a surface A

The luminous flux per unit area received by the surface A is determined by the cosine of the angle of incidence θ from the surface normal n. Conceptually, as the angle of incidence becomes greater (i.e., more oblique), the illuminance E (lumens per unit area) of the surface decreases. The expression A cos θ represents the projected area of the illuminated surface, and is equal to the cross-sectional area of the beam.

This is nothing more than Lambert’s Cosine Law (Lambert 1760):

In Search of Luminance - EQN 2

If we imagine the area A as being infinitesimally small, we can designate it as dA (for “differential area”). Similarly, the amount of luminous flux Φ within the infinitesimally narrow beam approaches zero, and so we designate it as dΦ. This gives us:

In Search of Luminance - EQN 3

This is basic high school algebra! Ignore the symbols and concentrate on the underlying physical concept.

We can further imagine the beam not as a parallel beam that is infinitesimally narrow, but as an elemental cone whose infinitesimal solid angle we designate as . (See the previous article Solid Angles for an explanation of this concept.)

In Search of Luminance - FIG 2

FIG. 2 – Luminance of a differential surface dA

With this, we have the conceptual framework to understand the formal definition of luminance:

In Search of Luminance - EQN 4

where the factor d2Φ does not mean that the symbol d is being squared. Rather, it simply means that the luminous flux dΦ is being divided by the solid angle of the elemental cone dω and the area dA. Further, the parameter ψ indicates that the luminance may also vary when the beam is rotated horizontally by angle ψ around the surface normal n.

What this equation is saying is that the luminance L of the surface dA is equal to the amount of luminous flux Φ (lumens) leaving dA in the direction θ and contained within the elemental cone (i.e., parallel beam) dω. This is equivalent to the IES Lighting Handbook description of “local surface density of light emitting power in a particular direction.”

There is an important but underappreciated corollary to this definition of luminance. Recalling that the surface can be real or imaginary, we can imagine placing an imaginary surface that is perpendicular to the beam direction (i.e., θ is equal to zero) anywhere along its length. What this means is that the luminance of a parallel beam of light is constant along its length. In other words, luminance is not an intrinsic property of the surface, but of the beam itself. (As an example, the sky has a measurable luminance when viewed from the ground, but it has no real surface.)

Dispensing with the mathematics, we can therefore say:

Luminance is the amount of luminous flux per unit area as measured in a parallel beam of light in a given direction.

Photometry is traditionally taught using the concept that luminance is a property of real or imaginary surfaces. The problem with this approach is that you cannot easily explain why participating media such as the atmosphere, smoke, fog, colloidal suspensions in water, and so forth have measurable luminance. Thinking of luminance as a property of a beam of light rather than of surfaces eliminates this difficulty.

Luminance Perceived

How do we perceive luminance? Imagine that you are looking at a blank sheet of matte white paper. Being an approximately ideal diffuser (except at very oblique angles), this paper will scatter incident light equally in all directions.

Now, imagine that each point of the paper’s surface is a point source of light. In accordance with the inverse square law, the luminous flux density of this light will decrease with the square of the distance from the point source. That is:

In Search of Luminance - EQN 5

where I is the intensity of the point source, d is the distance from the source, and E is the illuminance of a surface (such as the cornea of your eye) at that distance … so why do we see and measure the luminance of the paper as being constant with distance?

To answer this, we need to look at the eye itself, which basically consists of a lens that focuses images onto the cones and rods of the retina. Each cone and rod has a finite width, and so it receives light from a finite area of the surface of the paper.

In Search of Luminance - FIG 3

FIG. 3 – Eye focusing a parallel beam onto the retina

But wait! This area of the paper is dependent on the distance of the paper from the eye. Moreover, it is proportional to the square of the distance … which exactly cancels out the inverse square law for a single point source. Therefore, we perceive the luminance of a finite area surface as being constant regardless of its distance from the eye.

There is a counterexample that emphasizes this point: the night sky. Even though the actual diameter of a star may be a million miles or so, it is so far away that we perceive its light as a parallel beam that is focused onto a single rod or cone of our retina. The luminance of this beam is constant, and so we see the star as having a specific perceived brightness (or visual magnitude). The inverse square law still applies to the star’s emitted light, however – it is after all a point source – and so its magnitude depends on its distance from the Earth. All other things being equal, more distant stars are inherently fainter.

How the eye sees a parallel beam of light, however, is the key point: wherever we look, we see luminance. We do not see luminous intensity or illuminance; we see the luminance of beams of light. Luminance really is the fundamental concept of lighting design.


A famous 20th-century physicist (whose name I regrettably cannot recall, even with Google’s assistance) once observed that until you can visualize a problem, you cannot truly understand the mathematics that describe it. He was likely referring to quantum mechanics, which nobody yet fully understands, but the observation still applies. In particular, knowing the mathematical definition of luminance is not enough; we must understand the concept of luminance. With this understanding, we can better understand its importance to lighting design and illumination engineering.


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

Lambert, J. H. 1760. Photometria (in Latin). English translation by D. L. DiLaura, 2001. New York, NY: Illuminating Engineering Society of North America.

Smith, W. 2008. Modern Optical Engineering, Fourth Edition. New York, NY: McGraw-Hill.