1. Introduction
For several decades, the increase of artificial light at night (ALAN) has greatly altered the nocturnal integrity. ALAN contributes to the disruption of daily, lunar and seasonal cycles of natural light [
1] and has significant consequences on fauna [
1,
2], flora [
3], the starry sky [
4] and human health [
5].
It is well known that day/night light cycles regulate circadian rhythms and synchronise the circadian clock in humans [
6] and other animals. Light has also been shown to impact several physiological functions such as melatonin suppression [
7]. Indeed, melatonin is a hormone produced by the pineal gland in relation to the light detected by a non-visual retina photoreceptor, the melanopsin. This photoreceptor has a maximum spectral sensitivity at blue wavelengths [
8,
9]. There is strong evidence that many biological effects of artificial light at night (ALAN) are directly related to its spectral content. For example, some studies have shown that exposure to blue-enriched light may increase the risk of hormone-dependent cancers [
10,
11,
12]. One accurate way to detect the impact of ALAN on circadian systems is to monitor the level of melatonin in biofluids [
13], but such a procedure can be complex to perform on large samples and/or large areas.
In 2013, Aubé et al. [
14] proposed the Melatonin Suppression Index (MSI) as a new metric to evaluate the potential effect of the spectral distribution of the light on the melatonin suppression mechanism. The MSI only requires the knowledge of the ambient spectral content of ALAN to be determined. This index was designed to disentangle the effect associated with the spectral content from the one related to the absolute visual light level. The MSI typically ranges on a scale from 0 to ≈1, 0 being a spectrum with no impact on melatonin suppression and 1 being the impact of the sunlight spectrum. The sun-like CIE standard illuminant D65 [
15] was taken as a reference in the calculation of this index because the sun is the main natural source of light under which the human body adapted over time. It contains the whole visible part of the light spectrum. The MSI uses a fit of the Melatonin Suppression Action Spectrum (MSAS) data acquired by Thapan et al. [
16] and Brainard et al. [
9] as a weighting function. The MSI is mostly affected by blue-enriched light due to the high sensitivity of the melanopsin to this spectral range [
17]. Lamps can have a MSI higher than 1 even if it is not common in typical lighting technologies. Garcia-Saenz et al. [
10,
11] used the MSI as a proxy for the blue light content effect on breast, prostate and colorectal cancers in Madrid and Barcelona. They found correlations between the MSI from street lights nearby homes of participants to the MCC-Spain epidemiological study for the three cancers evaluated.
In a similar way to the MSI, the Star Light Index (SLI; [
14]) can be used to monitor the impact of the spectrum of ALAN on stellar observations. The threat to astronomical observation capacities is caused by the scattering of ALAN by molecules and aerosols in the atmosphere. The light travelling upward can then be redirected to an observer and hence compete with the low light levels coming from astronomical objects. The SLI also typically spans from 0 to ≈1, 0 being a colour that does not compete with stars visibility and 1 being a colour that affects star visibility like a D65 spectrum would do. The SLI uses the scotopic sensitivity as a weighting function. Blue-enriched light has also revealed to give higher SLI values, because that the SLI is more sensitive in the blue region.
Finally, the Induced Photosynthesis Index (IPI; [
14]) can be used to monitor the impacts of the spectrum of artificial lights on plants. As for the MSI and the SLI, the IPI typically spans from 0 to ≈1, 0 being a colour that does not stimulate the photosynthesis process and 1 being a colour that affects photosynthesis like a D65 spectrum would do. The IPI uses the Photosynthesis Action Spectrum (PAS; DIN5031–10 [
18]) as a weighting function.
The calculation of the MSI, SLI and IPI for a given light radiant flux depends on the shape of its spectral power distribution. This calculation requires the use of a spectrometer to sample the lamp spectrum. However, spectral measurements are not the easiest to obtain, especially if many of them are required. It has been shown that three-band Red, Green, Blue (RGB) photometric data can be used to determine spectral indices or photoreceptor stimuli from values recorded by a Digital single-lens reflex camera (DSLR) [
19,
20]. This method is much faster and easier to perform.
In this paper, we developed a similar method of calculating the MSI, SLI and IPI out of four-band RGB and Clear (RGBC) photometric data detected by the newly developed LANcube system. This method relies on a cross-calibration between RGBC values of the LANcube obtained using synthetic photometry method applied on a large spectral database along with the MSI, SLI and IPI determination by the integration of the same spectra.
Very few methods are available to map the measured ALAN-related parameters over large territories. A number of instruments may help to perform such remote sensing tasks, with each instrument having its specific capacities and limitations. Hänel et al. [
21] provided a good review of the most common instruments available. Basically they can be separated in two classes: imaging and non-imaging devices. For each class, we can distinguish between spectrum sensitive devices and panchromatic devices. For the sake of the present work, where we want to determine spectral indices, panchromatic devices cannot be of any help. It is clear that colour imaging systems installed on airborne or spaceborne platforms are well suited for such mapping. This can be performed using stratospheric balloons experiments [
22,
23], astronauts photography taken from the International Space Station [
20] of from unmanned aerial observing platforms [
24,
25]. However, there are very few stratospheric balloon flights being performed for that purpose and astronauts are more likely to shot at large emblematic cities than any other site. Unmanned aerial platforms are still sparsely available but they offer increasing potential as they become more and more readily common. In this paper, we show how the LANcube system installed on top of a moving vehicle can be used as an alternative to spaceborne and aerial imaging techniques.
3. Results
The method described above was applied to a large variety of spectral power distributions and indices. The results for MSI is presented in
Figure 4. In that figure, the light blue circles are the 314 spectra. The light blue transparent surface is the result of a fit of a 3D 2nd order polynomial surface to the whole dataset. The fitted parameters of Equation (
4) for each index are given in
Table 2.
For the three indices, the most important colour ratio is
. This reflects in the higher values of
and
. Nevertheless,
cannot be neglected at all. In the search for the optimal equation, we tried lower order polynomial functions but the results were not satisfying. In order to evaluate the intrinsic errors associated with the use of the fitted equations, we used them to calculate the three indices and compared the resultant indices to the accurate indices calculations obtained with the spectra. This is shown in
Figure 5,
Figure 6 and
Figure 7. The figures show a good correlation between the two ways of determining the indices. In these figures, the solid line is the 1:1 relation and the right panel of each figure shows the residuals. For the MSI, the standard deviation of the residuals is 0.024 while it is 0.056 for the SLI. The correlation is slightly lower for the IPI with a standard deviation of 0.107. The standard deviations translate in the margin of error (95 percent confidence interval) of
for MSI,
for SLI, and
for IPI. The complete dataset used in the fit is available on the project github repository [
30].
Maps of the MSI and SLI for Sherbrooke, Canada
As a direct result of the method that we developed, and to demonstrate the new possibility it delivers, we scanned the region of Sherbrooke City in Québec, Canada using the LAN3 device installed on top of a car. Sherbrooke is a city of ∼160,000 inhabitants spread over an area of ∼350 km. Sherbrooke is the sixth largest city in the province of Québec and the thirtieth largest in Canada. It is located in the southern part of the Québec province, next to the US border. Sherbrooke is part of the first International DarkSky Reserve. Its commitment to protecting the night sky translates in a lighting regulation that restricts the blue light content of any new lighting installation. As a result, a significant part of the street lights are phosphor-converted amber LEDs.
During the experiment, the LAN
3 was aligned parallel to the moving direction and only the lateral sensors (toward houses) were used. Before the scan, we verified that the car headlights were not impacting the lateral sensors measurements by turning on and off the headlights. Using the lateral sensors allows us to form a better idea on the light pollution that can enter bedrooms and potentially impact human health. We have circulated in the city streets for 12 non-consecutive evenings. The
r,
g and
b are calculated with data
R,
G,
B and
C data collected with the LAN
3 using Equations (
5)–(
7),
with
where
A and
are, respectively, the sensor gain and the integration time(s) recorded by the LAN
3. Note that in Equation (
7),
is expressed in seconds while the LAN
3 is recording it in units of milliseconds. The recorded values have to be divided by 1000 prior to using Equation (
7). In general, it is better to make the correction because the spectral response curves of
Figure 1a were determined for
and
s. However, in the present study, it is not mandatory because, for a given sensor, the gain and integration time are the same for all bands and then taking the bands’ ratios cancel their effect. In the LAN
3, the gain varies from 1 to 60 and the integration time from 2.4 to 614 ms depending on the ambient light level.
The
and
values were converted into MSI, SLI and IPI values using Equation (
4). We excluded extrapolated data that are outside the span of values of data used for the fit. This was done by excluding indices lower than zero and higher than two. During the calculation of an index, all recorded
R,
G,
B and
C values are subject to a threshold value set at 20. Such a threshold value ensure that the Signal to Noise Ratio is higher than 20 (i.e., noise represents less than 5% of the signal). The two criteria excluded 410 measurements out of 14,894 raw data. It represents less than 3% of the database. In order to generate the maps from the localised measurements, we used the nearest neighbour interpolation with a maximum interpolation distance of 30 m. The resulting maps are shown in
Figure 8,
Figure 9 and
Figure 10. The relation between the lighting technologies and the map index classes are given in
Table 3,
Table 4 and
Table 5. The complete dataset used to produce the maps is available on the project github repository [
30].
4. Discussion and Conclusions
We estimate that the equations found to convert the
r,
g and
b values of the LAN
3 into the three spectral indices—MSI, SLI and IPI—are suited for the mapping of large territories. The margin of error (95 percent confidence interval) is
for the MSI,
for the SLI and
for the IPI. The margin of error for MSI appears to be sufficiently small for many health and ecosystems studies. For the SLI, the margin of error is larger but it is still useful as a tool for night sky protection.
Table 3 and
Table 4 show that the margins of errors associated with the MSI and the SLI are small enough to separate the type of light technologies in regard to their spectral content. Therefore, we should expect the maps produced to be precise enough for most applications. The margin of error is much larger for the IPI and therefore we think this index requires further analysis in order to find better suited colour ratios to establish a better index calculation formula.
The index maps show that the averaged value all over the city for the MSI, SLI and IPI are 0.34, 0.54 and 0.96, respectively. Such numbers can be used to estimate a city performance to protect its territory against ALAN related issues. As an example, for the MSI, the average value is relatively small (0.34). As an element of comparison, this value is typical of 3000 K lamps. However, as the sensor detects all light emissions, this value is the result of a combination of both public and private lights. The sensitivity to private lights is probably higher given that we are using the lateral sensors and in Sherbrooke private lights are generally installed at a lower height. We used the lateral sensors because that it correspond to the orientation of propagation of the light that can enter the houses. In the city of Sherbrooke, the street lights are mainly composed of High-Pressure Sodium (HPS) (MSI
; [
14]) and PC Amber LEDs (MSI
; [
14]).
The indices maps allow us to identify critical zones in terms of their expected potential impact. As an example,
Figure 8 shows only few values above MSI = 0.6 in the city of Sherbrooke (blue points in
Figure 8a). Such data correspond to critical locations with respect to their potential impact on the melatonin suppression. In Sherbrooke, many of them are located in the main commercial zones. The residential Mi-Vallon sector is an exception (square A of
Figure 8a,b for a zoomed-in view). The peculiarity of that residential area is clear when comparing zone B and zone C in
Figure 8a. Zone C presents lower values of MSI but both zones are residential. In the Mi-Vallon sector, we can even distinguish two regimes between the northern (zone D in
Figure 8b) and southern zones (zone B in
Figure 8b). Zone B shows a much higher occurrence of high MSI values, but both zones are residential. They actually differ mainly by their date of construction, with zone D being the most recent. Lower MSI values in zone D actually reflect that the city administration is now gradually installing/converting street lights to PC amber, but another reason for this difference is that more houses are equipped with front door white bulbs in zone B than in zone D. Such private lamps pose a potential threat to citizens’ health, but as they are under the control of the citizens themselves, it highlights the need for public outreach measures. Fortunately, we can find many places with very low MSI (<0.1) in the city. This situation happens because of the new city policy to replace or install street lights by using PC amber LEDs and sometimes 2200K LEDs along with the absence of white private lamps.
A similar analysis can be made for the SLI and IPI index with
Figure 9 and
Figure 10 respectively. One should keep in mind that the margin of error for SLI is twice that of MSI and that IPI margin of error is a 4-fold of the MSI. In the case of SLI, the blue pixel corresponds to SLI higher than 0.8. These places are the one to consider first when it is time to restore the starry sky. Such information is highly strategic in the case of Sherbrooke because of the current project under way to create an Urban Dark Sky Oasis. This project aims to give access to the Milky Way to the citizens in the Mont Bellevue city park. The limits of the park are identified by a green polygon in
Figure 9a.
The mapping method presented here can rapidly identify the potentially harmful installations and then prepare targeted interventions to reduce or eliminate the problem. In a future work, we plan to find empirical relationships between the RGB and illuminance (lux) and the Correlated Colour Temperature (CCT). One other improvement could be to search for better set of colour ratios, including the clear channel, that could increase the correlation between the fitted equation and the MSI, the SLI and the IPI established with spectral data. This later task would be particularly useful for the IPI index given its large associated margin of error.