In this work, we investigated 39 façade lighting displays, all of which consisted of tri-chromatic light sources, namely blue-, green-, and red- light units, in Shenzhen, China. We extracted the spectral characteristics of the mean peak wavelength/full-width at half-maximum, and proposed universal spectral models. We further established the ‘chromaticity-performance’ relation to quantitatively assess the impact of light pollution on typical species based on corresponding action spectra. The findings provide a low-cost, fast and precise approach to assess light pollution of complicated light environment, and may help reduce energy waste and adverse environmental consequences associated with light pollution.
© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Light pollution, which causes a series of issues such as energy waste, affecting human circadian rhythm , causing fatal diseases , photosynthesis disruption , turtle hatching [4,5], confounding of migrating birds , insect praying, disruption of the physiology of amphibians and reptiles , and pollination problems , has become a global issue as the artificial light at night has expanded considerably in the past several decades. Light pollution has increased about 6% per year , and is harmful to the environment even if the intensity is lower than the brightness of the moon [10,11]. Moreover, the influenced areas may extend to hundreds of kilometers away from the light polluting sources .
The impacts of light pollution depend on both the intensities and spectra of light sources. The actions of species may vary with different spectral components, and researchers apply action spectra to describe the effect . Many action spectra that are related to human beings, plants, insects, and amphibians have been studied and presented, such as the photopic/scotopic spectral sensitivity curve , action spectra that affect melatonin production , and photosynthesis curves [16,17].
Nightscape lighting, especially façade lighting display, has been very hot in China and may thus aggravate light pollutions. Because of China’s subsidy policy and a series of events-driven landscape lighting, many large- and medium-sized cities conduct nightscape lighting projects. In the past several years, many cities in China have invested dozens of millions to billions of Chinese yuan in landscape lighting. Large-scale cities may spend several billion Chinese yuan on landscape lighting and small- and medium-sized cities may spend one tenth in comparison. The development of LED technology in terms of high luminous efficiency and eco-friendliness widely promotes applications in urban and landscape lighting. According to CSA’s data, the application scale of the LED lighting industry reached 428.6 billion Chinese yuan, of which landscape lighting took 13.5%, namely 57.9 billion Chinese yuan . It is estimated that the growth rate may remain at 10%, and the landscape lighting may reach about 95.4 billion Chinese yuan by 2020. In downtown areas of some large cities, such as Qingdao, Wuhan, Shanghai, and Shenzhen, many façades of buildings are decorated with LED lighting. Therefore, the impact of light pollution on the environment is getting increasingly critical in China, especially for living organisms that inhabit the eastern and coastal areas . The JGJ/T 163-2008  requires that the luminance for urban nightscape lighting levels should be maintained in the range from 5 to 25 cd/m2. Some surveys showed that the intensity of light is 30 to 60 times higher in Guangzhou , dozens of times higher than that in Shanghai . Large-scale applications of façade lighting displays, which broadcast dynamic images, have appeared in China in the past several years. At present, there are no effective approaches to precisely quantify and assess the degrees of such light pollution type, and most researchers still adopt an only luminance parameter in the assessment.
Different light sources have their own spectral characteristics and we might therefore be able to characterize the chromaticity and pollution performance of some of these sources. Hale’s work  showed that some traditional light sources, low pressure sodium, high pressure sodium, metal halide and mercury vapor lamps, could be classified from aerial night photograph using color information with a good accuracy. Light source colors may correspond to specific degree of light pollution for some specific species, and estimation of light source colors may bring benefit for assessing light pollution . Façade lighting displays mainly consist of red, green, and blue LED light sources, which present colorful images. LEDs, which also have strong spectral characteristics, may also be categorized from chromaticity information and applied to assess light pollution. At present, there is little research on the precise assessment of the light pollution of RGB light sources.
In this work, we tried to quantify light pollution level of small-scale façade lighting display, which was seldom studied. We obtained corresponding data based on an investigation of façade lighting display situations in Shenzhen by choosing typical districts with good nightscape economies such as the Futian, Nanshan, and Bao’an districts. The spectral characteristics of façade lighting are carefully studied, and a precise assessment approach light pollution impact is proposed. The work could precisely assess the impact of light pollution caused by façade lighting displays with intensity and chromaticity information, and provide guidance on reducing energy waste and adverse environmental consequences associated with light pollution.
2. Action efficiency of radiation
To measure the light pollution levels on different species, we introduce the action efficiency of radiation (AER) concept . The AER, similar to the luminous eﬃcacy/eﬃciency of radiation definition, is a measure of the fraction of electromagnetic power for the corresponding action spectra. The definition is shown in Eq. (1) , where P(λ) is the spectral power distribution (SPD) of the light source, A(λ) is the action spectra, and Ka is the correction coefficient differing for different action spectra.
We adopted action spectra of seven typica fauna and flora species from literatures existing in our investigated city environment as shown in Fig. 1, and they are ‘melanopic’, CIE scotopic 1951 (scotopic), CIE photopic 1924 (photopic) , ‘photosynthesis’ , Cleve Moth Attraction (moth) , Bee attraction (bee) , and van Grunsven insect phototaxis (insect) . The action spectra of ‘moth’ and ‘bee’ describe how light influence their behaviors. The ‘insect’ action spectrum is a composite metric for all insecta based on field study data of 14 orders of insects. In Eq. (1), the Ka is set to 1.0 by default corresponding to the relative AER. For several human-related action spectra, the mechanisms are well studied, e.g. Ka = 683 lm/W for photopic vision, Ka = 1699 lm/W for scotopic vision, and Ka = 832 blm/W for melanopic efficiency.
3.1 Measurement of luminance and SPD in situ
We used a spectral image radiance colorimeter EVERFINE SIRC-2000 to measure the luminance and SPDs of façade lighting displays. A total of 39 façade lighting displays distributed in the Nanshan, Futian, and Bao’an districts of Shenzhen city are measured. Because almost all the façade lighting displays broadcast dynamic contents, the colorimeter is set at an integrating time of 30 s, and then luminance and SPD information are recorded.
3.2 Establishment of universal spectral models and a universal ‘chromaticity-AER’ model for facade lighting assessment
We measured SPDs of a series of façade lighting displays and extracted the spectral characteristics, namely peak wavelength (PW) and full-width at half-maximum (FWHM) information, of these SPDs, based on which we establish universal spectral models. We adopted a Gaussian model, which may reach an acceptable precision, to simulate the B, G, and R spectra with the extracted PW and FWHM information. Based on the universal spectral models, we further established a universal ‘chromaticity-AER’ model for facade lighting assessment.
3.3 Effectiveness verification of the universal ‘chromaticity-AER’ model using a real façade lighting display with a static graph
To examine the effectiveness of the ‘chromaticity-AER’ model, we set-up a static graph with 28 color patches displayed on a façade display of 6 m × 3 m in size in the physical lab of Shenzhen University. The setup is shown in Fig. 2. These color patches are selected to be relatively distributed in the CIE 1931 chromaticity diagram and very saturated, because the greatest error would appear at pure color patches. Less saturated colors are synthesized with pure red, green and blue light, and errors are within minimum and maximum errors caused by corresponding pure colors.
4.1 Luminance levels of façade lighting displays
We investigated a total of 39 façade lighting displays in typical areas of Shenzhen shown in Fig. 3(a). Because the façade lighting displays are dynamic, bright and dark areas appear in a recorded luminance map, therefore we record the maximum luminance of each measured scene, e.g. 230 cd/m2 shown in Fig. 3(b). The luminances range from 50 to 1054 cd/m2. There are 30 scenes over 100 cd/m2, of which 9 scenes are over 400 cd/m2. Only 9 of the 39 scenes are less than 100 cd/m2, and above 50 cd/m2. The mean of the maximum luminance levels of the investigated façade lighting displays are 268 ± 220 cd/m2. These luminance levels are in photopic vision.
4.2 SPDs of typical façade lighting displays and establishment of universal spectral models
In our investigations, we found that all the façade lighting displays adopt B, G, and R monochromatic LED light sources, and their SPDs are shown in Fig. 4(a). The mean ± std. dev. of PWs and FWHMs for blue, green, and red light are 467/22, 520/31, and 631/17 nm, respectively in Fig. 4(b). The PWs of the blue, green, and red light are very concentrated, and the differences are within 10 nm. The differences in FWHM at the blue, green, and red light vary from 10 to 20 nm. We established universal spectral models of B, G, and R light sources using Gaussian distributions, as shown in Figs. 4(c) and 4(d).
4.3 Establishment of a universal ‘chromaticity-AER’ model
We calculated AERs as functions of chromaticities x, y in the CIE 1931 chromaticity diagram  using the universal spectral models, as shown in Fig. 5. The AERs are calculated using Eq. (1), and chromaticities are calculated with CIE 1931 color matching functions . We established a universal ‘chromaticity-AER’ model by performing a bivariate polynomial fit of these AERs using Eq. (2), where a–f are constants, and x, y are the chromaticity coordinates. We obtained Table 1, corresponding to AERs from seven action spectra. The fittings all reached a high goodness of fit adj. R2 over 0.999. These fitting functions describe chromaticity-AER relations, and in practical applications, we may assess AERs with chromaticity rather than SPD information.
4.4 Examinations of the real façade lighting scene with a static graph and error analysis
We examined the static graph of 28 color patches displayed on the real façade lighting display. Both luminances and SPDs of the 28 color patches were measured. Because the pixels of the façade lighting display are not well corrected, for each color patch, the luminance distributions have some differences. The luminances range from 103 to 552 cd/m2, and mean ± std. dev. luminance is 287 ± 122 cd/m2, in the photopic vision range. Figure 6(a) shows the chromaticity information of the 28 patches. The chromaticities are relatively uniformly distributed around the inset of the spectrum locus. Figure 6(b) shows relative errors of AERs calculated using measured spectra and using Eq. (2). Nearly all the errors are within 10%, except for several values calculated from patch 1, namely the pure red channel. The several AERs that cause great errors at the pure red channel correspond to action spectra of ‘melanopic’, ‘scotopic’, and ‘bee’, which are not sensitive to long wavelength part over 600 nm. Corresponding AERs of ‘melanopic’, ‘scotopic’, and ‘bee’ excited by red light are so small to cause any impact that could be neglected. The mean errors of the AERs are all within 5%, reaching a high degree of precision. It should be noted that the ‘scotopic’ AERs are theoretically calculated results, assuming the luminance levels in scotopic vision.
5. Discussion and conclusion
Based on a series of investigations and measurements of façade lighting displays in typical areas in Shenzhen, we assess the light pollutions of façade lighting displays. Unlike most other large-scale light pollution studies, we focus on small-scale lighting displays, which have been popular in some large cities in China. Moreover, we use both intensity and chromaticity to assess light pollution rather than intensity or luminance in most other studies.
LED light sources have their own PW and FWHM characteristics, based on which, universal spectral models are proposed to reconstruct B, G and R light units of façade lighting display. For a commercial façade lighting display, we found that applied LED products have similar characteristics. These LEDs have their PWs concentrated around 467, 520, and 631 nm, corresponding to blue, green, and red light, respectively. In addition, their FWHMs are concentrated around 22, 31, and 17 nm for blue, green, and red light, respectively. This spectral model reached an acceptable precision and could be applied for nearly all the façade lighting display in Shenzhen as per our investigations. Most façade lighting display might adopt similar LED chips due to similar light source suppliers. The spectral models might be further extended to applications for other cities in China after further verifications.
By taking the action spectra as the key factor in light pollution, we apply AER to assess the impact of light on specific species. We proposed chromaticity-AER relations for façade lighting displays based on the universal spectral models and established function models between chromaticities and AERs. The AER contours shown in Fig. 5 intuitively display the impact of red, green, and blue light on specific AER performance. Considering our seven listed action spectra, ‘melanopic’ and ‘photosynthesis’ are more sensitive to blue light, and ‘scotopic’, ‘photopic’, and ‘bee’ are more sensitive to green light. For most species, smaller AERs might lead to smaller light pollutions, except humans’ scotopic and photopic vision. This quantitative work may help designers apply suitable dynamic images on the façade lighting display to be more environmentally friendly. These chromaticity-AER relations allow us to intuitively assess AER performances for specific action spectra with chromaticity information.
Different AERs relating to action spectra representing different impacts of light on species. A large ‘melanopic’ AER value can inhibit melatonin secretion, which is not conducive to human relaxation and sleep. ‘Photopic’ AER is the same as luminous efficacy/efficiency of radiation of a light source, and the higher AER the higher efficiency. ‘Scotopic’ AER represents luminous efficacy/efficiency of radiation in scotopic vision. Although people cannot see colors in scotopic vision, the scotopic-, together with photopic-, function model could be used for AER function model in mesopic vision. High ‘photosynthesis’ AER at night may stimulate photosynthetic process, and change the circadian rhythm of plants. This may be harmful for some plants. Higher AERs for ‘moth’, ‘bee’ and ‘insect’ mean more attraction and greater influence of behaviors on insects, and may thus cause death of many insects.
Our assessment approach reaches high precision. The fitting functions reached an extremely high goodness of fit of over 0.999. The errors are within 5% by the examination of a façade lighting display with 28 color patches. The fitting functions provide an extremely simple, precise, and fast way to transfer color information to AER performances. In practical applications, we can record high- or moderate- precision chromaticity information of façade lighting displays with a luminance meter or a corrected camera, respectively. Therefore, our work could precisely assess the impact of the dynamic effect of façade lighting displays on the environment.
The recorded max luminances of 39 façade lighting displays range from 50 to 1200 cd/m2, and the mean of the max luminances is about 200 cd/m2. This luminance level is about a dozen times higher than standards. There should be a stricter restriction on luminance levels in Shenzhen. Although the higher intensity of light, the greater light pollution, it is difficult to directly relate luminance to light pollution for specific species. Because luminance, which is related to photopic sensitivity curve, is the light that human perceives, might be used to assess light impact on human beings rather than other species. Therefore, absolute AER including Ka information, together with intensity information, are better indicators to assess light pollution. However, the action spectra studies are still quite limited, and there might not be exact Ka values for some known action spectra. Therefore, in the assessment, we use relative AERs, and luminance information are still separated. In the future, we may precisely assess light pollution using the intensity and chromaticity information.
This work provides a novel approach to study the light pollution in vertical dimension, which is more in line with the perspective of human observation. Chromaticity information for specific façade lighting is for the first time introduced to assess the light pollution level. We apply chromaticities rather than large spectra data to assess light pollution, belonging to a data reduction operation. Therefore, complicated lighting environments, such as illuminated central business districts, tourist attractions, and landmarks, may be fast and precisely assessed. Applications of these findings may help us to reconstruct a vertical dimension light pollution image of an entire region.
There are still some limitations for the present work. First, most façade lighting displays broadcast dynamic patterns of images, and there is a lack of proper instruments to precisely record chromaticity information of these dynamic ones at present. Secondly, because of some ambiguous understanding of action spectra mechanisms, we use relative AERs in this work, while absolute AERs are more precise. Thirdly, new approaches are required to obtain large data to expand lighting situations in the vertical dimension. This work is a small-scale study by assessing façade lighting displays in typical areas. In the future, by virtue of moiling car cameras or camera-carrying unmanned aerial vehicles, it is possible to assess lighting situations in the vertical dimension of a city.
National Natural Science Foundation of China (51778549, 61605125).
The authors declare no conflicts of interest.
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