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Home , Light pollution, Skyglow

Article Evidence That Reduced Air and Road Traffic Decreased Artificial -Time during COVID-19 Lockdown in , Germany

Andreas Jechow 1,2,3,* and Franz Hölker 2

1 Department of Experimental , Leibniz Institute of Freshwater and Inland Fisheries, Zur Alten Fischerhütte 2, 16775 Stechlin, Germany 2 Department of Ecohydrology, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany; [email protected] 3 Remote Sensing and Geoinformatics, GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany * Correspondence: [email protected]

 Received: 3 September 2020; Accepted: 15 October 2020; Published: 17 October 2020 

Abstract: Artificial skyglow, the brightening of the night by artificial at night that is scattered back to Earth within the atmosphere, is detrimental to astronomical observations and has an impact on as a form of light . In this work, we investigated the impact of the lockdown caused by the COVID-19 pandemic on the urban skyglow of Berlin, Germany. We compared brightness and correlated (CCT) measurements obtained with all-sky cameras during the COVID-19 lockdown in March 2020 with data from March 2017. Under normal conditions, we expected an increase in night (or skyglow, respectively) and CCT because of the transition to LED. This is supported by a measured CCT shift to slightly higher values and a time series analysis of night-time light data showing an increase in artificial light emission in Berlin. However, contrary to this observation, we measured a decrease in artificial skyglow at zenith by 20% at the city center and by more than 50% at 58 km distance from the center during the lockdown. We assume that the main cause for the reduction of artificial skyglow originates from improved air quality due to less air and road traffic, which is supported by statistical data and satellite image analysis. To our knowledge, this is the first reported impact of COVID-19 on artificial skyglow and we conclude that should shift more into the focus of research.

Keywords: light pollution; skyglow; artificial light at night; COVID-19; lockdown; coronavirus; SARS-COV2; night-time; night sky brightness; all-sky imaging; air pollution

1. Introduction The current pandemic caused by the novel severe acute respiratory syndrome corona virus (SARS-COV2) and the coronavirus disease that first occurred in Wuhan, China, in late 2019 (COVID-19) [1], has had a huge impact on modern societies [2–11] and changed human life dramatically in early 2020. Starting in Wuhan, China, in January 2020, several countries have imposed so-called “lockdowns” to parts or all of their territories. During these lockdowns, private and social life as well as industrial and commercial activities were reduced to a minimum to break the exponential increase in the infection chain of the pandemic and reduce the strain on health systems and reduce the fatality rate accordingly. Besides the effects on the growth rates of virus infections [2,3], these lockdowns had several unintended effects [4–11]. These include a negative impact on the economy [4], on and sleeping behavior [5] or the food intake of obese children [6], but also unintended positive effects on

Remote Sens. 2020, 12, 3412; doi:10.3390/rs12203412 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 3412 2 of 23 the environment, including improved air quality in heavily industrialized areas [7], improved surface [8,9] and the reduction of heavy metal pollution in subsurface waters [10]. Night-time or artificial light at night (ALAN) can be observed from space by satellite imagery [11–13] and can be used to track human activities, economic growth or decline, war crisis, natural disasters, etc. (see a recent review for night-time light and applications in [12]). The excessive use of ALAN is a form of environmental pollution, light pollution, that is detrimental to night-time observations by astronomers [14]. However, ALAN can also have diverse negative impacts on flora and fauna in diverse habitats [15–20] and potentially human health [20,21]. Artificial skyglow is the brightening of the night sky that originates from ALAN that is scattered back towards Earth within the atmosphere [22,23]. This anthropogenic skyglow is very dynamic as it depends on atmospheric constituents like particulate matter from air pollution [24], weather-phenomena like clouds [25,26], ground reflectance [27] or the dynamics of ALAN sources itself [28–31]. Furthermore, there are other components like vehicle and residential that contribute to skyglow but are difficult to disentangle from each other [31]. In a recent night-time satellite data analysis, an exponential growth rate in ALAN of more than 2% per year globally was determined [13]. A recent study of satellite night-time lights in China showed that the COVID-19 pandemic had a clear impact on night-time lights observed from space [11]. They found a decrease in night-time light emission in commercial areas, an increase in residential areas and a change in road illumination. However, to our knowledge, no study on the impact of COVID-19 on ALAN at ground level or on artificial skyglow exists to date. In this paper, ground-based all-sky night-time photometry [32–35] was used to measure the night sky brightness, correlated color temperature (CCT) and illuminance at different distances from the city center of Berlin, Germany, during the COVID-19 lockdown in March 2020. Results are compared to measurements obtained in the same season and phase in March 2017 that were part of an investigation of the impact of clouds on the night sky brightness [35]. Furthermore, night-time light data from and statistical data on air traffic were investigated to link it to the observations.

2. Materials and Methods

2.1. COVID-19 Lockdown in Germany, Spring 2020 Germany was the European country with the earliest detected COVID-19 patients with a small cluster with mild conditions [36]. The German government declared the Chinese province Wuhan as a risk region for travel on 26 January 2020, and in February 2020, several other countries were declared as risk regions as well [37]. A warning to avoid any travel to foreign countries was declared on 17 March 2020, and most borders were closed for travel. Social, cultural and commercial activities were slowly minimized in February starting with the ban of large-scale events. On 22 March 2020, the German government declared to reduce social life to an absolute minimum, to close bars restaurants, schools and alike and to keep distance. At this point, only essential businesses and industries were allowed to operate and most people were working from home, were on forced holiday or were on reduced work hours (“Kurzarbeit”), which was financially supported by the government. Air and road travel were reduced dramatically. This was the strongest part of the so called “lockdown” in Germany, which, however, was more relaxed than in other countries and did not include a strict curfew or similar measures.

2.2. Study Sites To determine the night-sky brightness at different distances from the city center of Berlin, a transect with twelve stops was designed during an earlier study that focused on the impact of clouds [35]. The stops ranged from the city center near Berlin, Alexanderplatz, to a rural location about 60 km south of the city center. A map of the stops is shown in Figure1 using simulated skyglow data from Falchi et al. [23]. Five stops were within the city limit of Berlin and seven outside of the city limit. Sites were selected to have a minimum of direct lights incident at the camera. Thus, mostly urban Remote Sens. 2020, 12, x FOR PEER REVIEW 3 of 23 limit. Sites were selected to have a minimum of direct lights incident at the camera. Thus, mostly urban parks were selected. The locations and measurement times are given in Table A1 in Appendix A and more details about site selection can be found in a recently published paper [35]. Measurements in 2017 were obtained during a clear night (0 octas) on 29 March during astronomical night between ca. 1:00 a.m. and 4:45 a.m. local time. The moon was at about 1% illumination, at a new moon and waxing crescent and between 27° and 35° below the horizon. Measurements in 2020 were performed on 26 March, which was a few days after the start of the strongest lockdown in Germany. Conditions were very similar to 2017 with a clear sky (0 octas). Measurements were obtained during astronomical night between 0:50 a.m. and 3:45 a.m. local time. The new moon was at about 0.5% illumination and between 29° and 40° below the horizon. These similarRemote conditions Sens. 2020, 12 should, 3412 guarantee the smallest deviation possibly caused by seasonal or3 weather of 23 effects. Furthermore, the same seasonal period (late March) gives some confidences that hourly changes in human lighting behavior or automated switch-offs are very similar for the two parks were selected. The locations and measurement times are given in Table A1 in AppendixA and studied. Furthermore, vegetation or ground albedo should be roughly similar (e.g., no snow on the more details about site selection can be found in a recently published paper [35]. ground).

Figure 1. Map of the transect stops around the city of Berlin, Germany using modeled skyglow data from Figure 1. Map of the transect stops around the city of Berlin, Germany using modeled skyglow data the world atlas of artificial night sky brightness [23] for the Berlin area (source: lightpollutionmap.info from the world atlas of artificial night sky brightness [23] for the Berlin area (source: by Jurij Stare). lightpollutionmap.info by Jurij Stare). Measurements in 2017 were obtained during a clear night (0 octas) on 29 March during astronomical 2.3. Calibratednight between Camera ca. System 1:00 a.m. and and Night 4:45 Sky a.m. Brightness local time. Processing The moon Software was at about 1% illumination, at a new moon and waxing crescent and between 27◦ and 35◦ below the horizon. Measurements in 2020Ground-based were performed all-sky on night-time 26 March, which imagery was was a few performed days after with the start a calibrated, of the strongest commercial lockdown digital camerain Germany. equipped Conditions with a fisheye were verylens similar[32–35]. to The 2017 full with procedure a clear sky is (0 described octas). Measurements in detail in were a recent methodobtained paper during [34]. astronomicalThis method night has betweenseveral 0:50advantages a.m. and compared 3:45 a.m. local to single time. The channel new moon photometers was like theat about sky quality 0.5% illumination meters (SQMs) and betweenthat are 29commonl◦ and 40y◦ belowused to the monitor horizon. temporal These similar changes conditions of the night sky brightnessshould guarantee but lack the smallestcolor and deviation spatial possiblyinformation caused (see by seasonaldiscussion or weather in [34]). e ffBriefly,ects. Furthermore, a Canon EOS 6D withthe same a Sigma seasonal EX periodDG circular (late March) fisheye gives lens some with confidences 180° field that of hourlyview (f changes = 8 mm, in human aperture lighting 3.5) was mountedbehavior on ora tripod. automated Auxiliary switch-o equipmentffs are very similarincluded for thea remote two nights control, studied. heating Furthermore, pads to vegetationavoid dew or ice formationor ground on albedo the lens, should and be a roughly bull’s similareye spirit (e.g., level no snowto align on thethe ground). camera with respect to the horizon. 2.3.To Calibratedacquire all-sky Camera Systemimages, and most Night relevant Sky Brightness for astronomical Processing Software light pollution, the camera was aligned with the imaging sensor in the horizontal plane, i.e., the lens pointing towards the zenith. Ground-based all-sky night-time imagery was performed with a calibrated, commercial digital For pristine , the EOS 6D was operated at ISO 3200 or 6400, and up to 120 s time. In camera equipped with a fisheye lens [32–35]. The full procedure is described in detail in a recent method paper [34]. This method has several advantages compared to single channel photometers like the sky quality meters (SQMs) that are commonly used to monitor temporal changes of the night sky

brightness but lack color and spatial information (see discussion in [34]). Briefly, a Canon EOS 6D with a Sigma EX DG circular fisheye lens with 180◦ field of view (f = 8 mm, aperture 3.5) was mounted on a tripod. Auxiliary equipment included a remote control, heating pads to avoid dew or ice formation on the lens, and a bull’s eye spirit level to align the camera with respect to the horizon. To acquire all-sky images, most relevant for astronomical light pollution, the camera was aligned with the imaging sensor in the horizontal plane, i.e., the lens pointing towards the zenith. For pristine Remote Sens. 2020, 12, 3412 4 of 23 skies, the EOS 6D was operated at ISO 3200 or 6400, and up to 120 s exposure time. In urban environments, ISO 1600 and exposure times as short as a 0.4 s were used. The aperture was always set to a maximum of 3.5 and images were stored in an unaltered raw format. The images were processed with a commercial software (“Sky Quality Camera” SQC version 1.8.1, Euromix, Ljubljana, Slovenia). SQC calculates luminance using the green channel of the camera and the photometric calibration is realized by brightness and measurements, while lens errors are corrected in the laboratory. The SQC software processes the luminance Lv,sky of the sky for each pixel of the camera. From the spatially resolved luminance maps, the software can derive the cosine corrected illuminance Ev,cos in the imaging plane (light that is falling in from zenith is weighted more than light incident at a shallow angle), and the scalar illuminance Ev,scal,hem for the imaging hemisphere without cosine correction (light from all directions is weighted equally). From the three spectral channels, the correlated color temperature (CCT) can be calculated. For the definition and explanation of illuminances and the CCT calculation, see our method paper [34].

2.4. Night-Time Light Analysis Monthly composites of night-time light data from the Suomi National Polar-Orbiting Partnership (NPP) instrument Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) were used for the analysis, which are freely available at the website of the Earth Observation Group at Payne Institute [38]. These data were composed of moonless and cloud-free data and were shown to be a robust source for night-time light analysis [11–13]. The monthly compsites of March 2017 and March 2020 of the Tile2_75N060W () were downloaded and processed in QGIS (Version 3.10) [39]. The region of interest was plotted and the monthly composite data from March 2017 were subtracted from the March 2020 data. The time series analysis was performed using the web tool radiance light trends [40] that allows the simple time-series analysis of small areas of VIIRS DNB data without any GIS skills. A polygon can be drawn or uploaded and then the time period and months of interest can be selected. The time frame between January 2017 and March 2020 was investigated for Berlin, the transect region investigated and several towns nearby and at the transect. The web app automatically selects the months between September and March for these latitudes to exclude effects. More information on the location and the time series data for average radiance is given in the appendix.

3. Results

3.1. Ground-Gased Imaging Data Figure2 shows the luminance maps of two of the twelve stops for both measurement campaigns. The upper row shows the luminance data measured at stop 1, (Berlin Museumsinsel) just at the city center (0.3 km distance to Alexanderplatz) and the lower row the luminance data at stop 12 (Petkus) that are the farthest away from the city center (58.5 km). The left column shows data obtained in 2017 (no lockdown) and the right column the data obtained in 2020 (during lockdown). In the imaging data shown in Figure2, a decrease in zenith brightness from the 2017 to the 2020 measurements can be assumed. There is a small shade in the center of Figure2b and the dark blue region in Figure2d is larger than in Figure2c. Figure3 shows the CCT maps of two of the twelve stops for both measurement campaigns in the same arrangement as in Figure2. Here, the changes are even more obvious for the stop in the city center. For example, the light of the Berlin TV tower (lower left in Figure3a,b) is switched on in 2020 (Figure3b) but o ff in 2017 (Figure3a). Remote Sens. 2020, 12, x FOR PEER REVIEW 5 of 23

Remote Sens. 2020, 12, 3412 5 of 23 Remote Sens. 2020, 12, x FOR PEER REVIEW 5 of 23

Figure 2. Luminance maps for two of the twelve stops near Berlin Germany. (a,b) stop 1 near city centerFigure and 2. Luminance (c,d) stop maps12 farthest for two ofaway thetwelve from stopsthe city near center. Berlin Germany.(a,c) 2017—no (a,b) stop lockdown 1 near city and center (b,d) Figure 2. Luminance maps for two of the twelve stops near Berlin Germany. (a,b) stop 1 near city 2020—lockdown.and (c,d) stop 12 farthestThe full away imaging from data the cityset center.is given (a ,inc) Appendix 2017—no lockdown A. and (b,d) 2020—lockdown. centerThe fulland imaging (c,d) stop dataset 12 isfarthest given in away Appendix fromA .the city center. (a,c) 2017—no lockdown and (b,d) 2020—lockdown. The full imaging dataset is given in Appendix A.

Figure 3. Correlated color temperature (CCT) maps for two of the twelve stops near Berlin, Germany. Figure 3 Correlated color temperature (CCT) maps for two of the twelve stops near Berlin, Germany. (a,b) stop 1 near city center, and (c,d) stop 12 farthest away from the city center. (a,c) 2017—no lockdown (a,b) stop 1 near city center, and (c,d) stop 12 farthest away from the city center. (a,c) 2017—no Figureand ( b3), Correlated and (d) 2020—lockdown. color temperature The (CCT) full imaging maps datasetfor two isof given the twelve in Appendix stops Anear. Berlin, Germany. lockdown and (b), and (d) 2020—lockdown. The full imaging dataset is given in Appendix A. (a,b) stop 1 near city center, and (c,d) stop 12 farthest away from the city center. (a,c) 2017—no lockdown and (b), and (d) 2020—lockdown. The full imaging dataset is given in Appendix A.

Remote Sens. 2020, 12, x FOR PEER REVIEW 6 of 23 Remote Sens. 2020, 12, 3412 6 of 23 3.2. Zenith Luminance and Artificial Skyglow From the imaging data, the luminance at zenith 𝐿 , was extracted as defined by a 10 circle at 3.2. Zenith Luminance and Artificial Skyglow , the zenith (center of image). Figure 4 shows the zenith luminance for different distances for both datasetsFrom on the a imagingdouble data,logarithmic the luminance plot. It atis zenithobviousLv, zenfrom, was the extracted data, that as defined the zenith by a brightness 10 circle at thehas zenithdecreased (center for of all image). twelve Figure measurement4 shows thestops zenith from luminance 2017 to 2020. for di Thefferent full distances data are forlisted both in datasets Table 1 onshowing a double zenith logarithmic luminance plot. and It isradiance obvious in from units the of data, the thatSQM. the The zenith strongest brightness decrease has decreased in zenith forbrightness all twelve was measurement observed at stops the city from center. 2017 toTh 2020.ere, the The luminance full data aredropped listed infrom Table 11.301 showing mcd/m2 zenithmeasured luminance in 2017 and to 8.90 radiance mcd/m in2 unitsmeasured of the in SQM. 2020. The Thus, strongest the value decrease obtained in zenith during brightness the lockdown was 2 observedwas 79% atof thethe cityvalue center. measured There, earlier. the luminance At the two dropped measurement from 11.30 points mcd farthest/m measured away from in 2017the city to 2 8.90center mcd (stop/m measured11 and 12), in the 2020. zenith Thus, luminance the value meas obtainedured during the lockdownlockdown waswas 79%81% ofof thethe valuevalue measuredmeasured earlier. before. AtAt the stop two 11, measurement a luminance pointsof 0.27 farthestmcd/m2away (21.5 frommagSQM the/arcs city2) center was measured (stop 11 and in 2017 12), theand zenith a value luminance of 0.22 mcd/m measured2 (21.7 during magSQM the/arcs lockdown2) was obtained was 81% in of 2020. the value At stop measured 12, a luminance before. At of stop 0.26 2 2 2 11,mcd/m a luminance2 (21.6 mag of 0.27SQM/arcs mcd2/)m was(21.5 obtained magSQM in /2017arcs and) was a measuredvalue of 0.21 in 2017mcd/m and2 (21.8 a value mag ofSQM 0.22/arcs mcd2) /wasm 2 2 2 (21.7measured magSQM in/ arcs2020.) It was is obtainedinteresting in 2020.to note At that stop the 12, luminance a luminance values of 0.26 obtained mcd/m (21.6in 2020 mag inSQM the/arcs rural) 2 2 waslocations obtained are among in 2017 the and lowest a value zenith of 0.21 luminance mcd/m valu(21.8es mag thatSQM we/ arcsever )measured was measured in the inBerlin 2020. region It is interestingfor clear sky to conditions. note that the luminance values obtained in 2020 in the rural locations are among the lowest zenith luminance values that we ever measured in the Berlin region for clear sky conditions.

10.0 2017

2020

1.0 (mcd/m²) zen L

0.1 0.1 1.0 10.0 100.0 Distance (km) Figure 4. Zenith luminance at different distances from the city center of Berlin, Germany, Figure 4. Zenith luminance at different distances from the city center of Berlin, Germany, measured measured during a clear night in 2017 (black solid line and circles) and a clear night during the during a clear night in 2017 (black solid line and circles) and a clear night during the COVID-19 COVID-19 lockdown in 2020 (grey dashed dotted line and boxes). lockdown in 2020 (grey dashed dotted line and boxes). Table 1. Zenith luminance (Lv,zen) data for the two measurement transects and ratios between the two

datasetsTable 1. obtained Zenith luminance by dividing (𝑳 the𝒗,𝒛𝒆𝒏 zenith) data brightness for the two values measurement from 2020 transects by that of and 2017. ratios between the two datasets obtained by dividing the zenith brightness values from 2020 by that of 2017. L L L L v,zen v,zen Ratio SQM,zen SQM,zen Stop Distance (mcd/m2) (mcd/m2) (mag /arcsec2) (mag /arcsec2) 𝑳𝒗,𝒛𝒆𝒏 𝑳𝒗,𝒛𝒆𝒏 2020/2017 𝑳SQM𝑺𝑸𝑴,𝒛𝒆𝒏 SQM𝑳𝑺𝑸𝑴,𝒛𝒆𝒏 2017 2020 Ratio 2017 2020 Stop Distance (mcd/m2) (mcd/m2) (magSQM/arcsec2) (magSQM/arcsec2) 2020/2017 1 0.32017 11.30 2020 8.90 0.792017 17.4 17.72020 2 1.5 7.50 6.80 0.91 17.9 18.0 1 0.3 11.30 8.90 0.79 17.4 17.7 3 3.3 6.00 5.50 0.92 18.1 18.2 2 41.5 6.9 7.50 4.30 6.80 3.80 0.91 0.88 17.918.5 18.6 18.0 3 53.3 10.5 6.00 3.60 5.50 3.10 0.92 0.86 18.118.7 18.8 18.2 4 66.9 15.0 4.30 1.90 3.80 1.70 0.88 0.89 18.519.4 19.5 18.6 7 28.0 0.67 0.63 0.94 20.5 20.6 5 810.5 34.5 3.60 0.42 3.10 0.38 0.86 0.90 18.721.0 21.1 18.8 6 915.0 38.3 1.90 0.35 1.70 0.32 0.89 0.91 19.421.2 21.3 19.5 7 1028.0 43.5 0.67 0.33 0.63 0.28 0.94 0.85 20.521.3 21.4 20.6 8 1134.5 51.5 0.42 0.27 0.38 0.22 0.90 0.81 21.021.5 21.7 21.1 12 58.5 0.26 0.21 0.81 21.6 21.8 9 38.3 0.35 0.32 0.91 21.2 21.3 10 43.5 0.33 0.28 0.85 21.3 21.4 11 51.5 0.27 0.22 0.81 21.5 21.7 12 58.5 0.26 0.21 0.81 21.6 21.8

Remote Sens. 2020, 12, 3412 7 of 23

The zenith luminance is the sum of the background sky luminance at zenith Lv,zen,back and the artificial skyglow component at zenith Lv,zen,arti f . Thus, the artificial skyglow component can be extracted (approximately) when subtracting the background from the actual measurement at the zenith:

L = L L (1) v,zen,arti f v,zen,measured − v,zen,back A commonly used value for the sky background is 0.17 mcd/m2 (22.0 mag/arcs2)[23]. The corrected results are shown in Table2. When considering the artificial zenith sky brightness only, the ratio between the values obtained in 2020 and 2017 decreased in the rural areas as expected. The biggest change occurred at the site farthest away from the center with more than a 50% decrease in artificial skyglow.

Table 2. Artificial component of zenith luminance (Lv,zen,arti f ) for the two measurement transects and between the two datasets obtained by dividing the artificial component of the zenith brightness values from 2020 by that of 2017.

L (mcd/m2) L (mcd/m2) Ratio Stop Distance v,zen,artif v,zen,artif 2017 2020 2020/2017 1 0.3 11.13 8.73 0.78 2 1.5 7.33 6.63 0.90 3 3.3 5.83 5.33 0.91 4 6.9 4.13 3.63 0.88 5 10.5 3.43 2.93 0.85 6 15.0 1.73 1.53 0.88 7 28.0 0.50 0.46 0.92 8 34.5 0.25 0.21 0.84 9 38.3 0.18 0.15 0.83 10 43.5 0.16 0.11 0.68 11 51.5 0.10 0.05 0.49 12 58.5 0.09 0.04 0.43

3.3. Illuminance

As outlined in the methods section, horizontal (Ev,hor) and (hemispheric) scalar (Ev,scal,hem) illuminance can be calculated from the spatially resolved luminance maps. Figure5 shows (a) the horizontal illuminance, and (b) the scalar illuminance for the different distances for both datasets on a double logarithmic plot. The data are listed in Table3. For all measurement spots, apart from stop 2, the horizontal illuminance Ev,hor is reduced for the measurements obtained in 2020 compared to 2017. The strongest reduction was again observed in the city center at stop 1, where Ev,hor decreased from 72.6 mlx to 51.9 mlx to 71%. For Ev,hor, the reduction was less strong in the rural places than in the city center and the outskirts of the city. It is interesting, that for stop 2, Ev,hor was about the same for both measurements, although the zenith brightness had decreased by about 10% from 2017 to 2020. This was presumably caused by the lighting from the windows of buildings in the vicinity of the measurement stop. For the (hemispheric) scalar illuminance, Ev,scal,hem, the results were very similar to those for the horizontal illuminance. Ev,scal,hem decreased most in the city center at stop 1 to 80% as well as just near the city border at stop 5 to 72% and just outside of the city at stop 6 to 75%. Just at stop 2 Ev,scal,hem increased from 2017 to 2020 by about 20%, which again can be attributed to window lighting. At the rural locations farthest away from the city, Ev,scal,hem decreased only by a few percent or remained about constant. Please be aware that within the city limit, direct lights could not be fully avoided at all stops, although much care was taken (see methods section). However, outside of the city limit, the effect of nearby lamps on the illuminance can be largely ruled out. Remote Sens. 2020, 12, x FOR PEER REVIEW 8 of 23 Remote Sens. 2020, 12, 3412 8 of 23

100

2017

2020

(mlx) 10 hor E

1 0.1 1.0 10.0 100.0 Distance (km) (a)

2017 100 2020 (mlx) scal

E 10

1 0.1 1.0 10.0 100.0 Distance (km) (b) FigureFigure 5. 5.(a ()a Horizontal) Horizontal and and (b) ( scalarb) scalar illuminance illuminance for di forfferent different distances distances from thefrom city the center city ofcenter Berlin, of Germany,Berlin, Germany, measured measured during a during clear night a clear in 2017night (black in 2017 solid (black line solid and lin circles)e and and circles) a clear and night a clear during night theduring COVID-19 the COVID-19 lockdown lockdown in 2020 (grey in 2020 dashed (grey dotted dashed line dotted and boxes).line and boxes).

Table 3. Horizontal illuminance (Ev,hor) and (hemispheric) illuminance (Ev,scal,hem) data for the two Table 3. Horizontal illuminance (𝐸,) and (hemispheric) illuminance (𝐸,,) data for the two measurement transects and ratios for 2017/2020. measurement transects and ratios for 2017/2020.

Distance Ev,hor (mlx) Ev,hor (mlx) Ev,scal (mlx) Ev,scal (mlx) Stop Distance 𝑬𝒗,𝒉𝒐𝒓 (mlx) 𝑬𝒗,𝒉𝒐𝒓 (mlx) Ratio 𝑬𝒗,𝒔𝒄𝒂𝒍 (mlx) 𝑬𝒗,𝒔𝒄𝒂𝒍 (mlx) Ratio Stop (km) 2017 2020 Ratio 2017 2020 Ratio (km) 2017 2020 2017 2020 11 0.30.3 72.6 72.6 51.9 51.9 0.71 263.0 263.0 210.0 210.0 0.80 0.80 2 1.5 34.8 35.5 1.02 88.6 106.3 1.20 32 3.31.5 32.4 34.8 27.5 35.5 0.851.02 88.6 83.3 106.3 65.6 1.20 0.79 43 6.93.3 26.2 32.4 20.7 27.5 0.790.85 83.3 64.2 65.6 50.0 0.79 0.78 54 10.56.9 19.0 26.2 14.2 20.7 0.750.79 64.2 41.6 50.0 29.8 0.78 0.72 65 15.010.5 14.0 19.0 11.0 14.2 0.790.75 41.6 45.8 29.8 34.4 0.72 0.75 76 28.015.0 4.614.0 11.04.0 0.870.79 45.8 13.3 34.4 11.6 0.75 0.87 8 34.5 2.6 2.3 0.88 7.2 6.9 0.96 97 38.328.0 2.0 4.6 1.9 4.0 0.950.87 13.3 5.4 11.6 5.5 0.87 1.02 108 43.534.5 1.8 2.6 1.7 2.3 0.940.88 7.2 4.7 6.9 4.4 0.96 0.94 119 51.538.3 1.4 2.0 1.3 1.9 0.930.95 5.4 3.4 5.5 3.3 1.02 0.97 1210 58.543.5 1.3 1.8 1.2 1.7 0.920.94 4.7 3.4 4.4 3.1 0.94 0.91 11 51.5 1.4 1.3 0.93 3.4 3.3 0.97 3.4. Correlated12 Color58.5 Temperature 1.3 1.2 0.92 3.4 3.1 0.91 The CCT values for the full image and at the zenith were shown in Figure6 for di fferent distances to the city center. The CCT data are listen in Table4. The CCT increased for 18 of the 24 data points, remained the same for five data points and decreased only at one data point. The highest increase was

Remote Sens. 2020, 12, x FOR PEER REVIEW 9 of 23

3.4. Correlated Color Temperature The CCT values for the full image and at the zenith were shown in Figure 6 for different

Remotedistances Sens. 2020to the, 12 ,city 3412 center. The CCT data are listen in Table 4. The CCT increased for 18 of the9 of 24 23 data points, remained the same for five data points and decreased only at one data point. The highest increase was 300–400K and occurred near the city edge or just outside of the city (stops 4–7), 300–400while in K the and city occurred center nearand theat large city edgedistances or just fr outsideom the ofcity the the city CCT (stops remained 4–7), while rather in the constant. city center We andnoted at that large the distances TV tower from lights the seem city theed to CCT have remained no measurable rather constant.impact on We the noted CCT thatat stop the 1 TV although tower lightsthis was seemed an obvious to have feature no measurable in the imaging impact data on the (see CCT Figure at stop 2a,b). 1 although this was an obvious feature in the imaging data (see Figure2a,b).

4200

3800

3400

3000 CCT (K)

2600 2017 zenith 2020 zenith 2200 2017 all-sky 2020 all-sky 1800 0.1 1.0 10.0 100.0 Distance (km) Figure 6. Correlated color temperature (CCT) for all-sky and at zenith at different distances from the Figure 6. Correlated color temperature (CCT) for all-sky and at zenith at different distances from the city center of Berlin, Germany, measured during a clear night in 2017 (black solid circles and triangles) city center of Berlin, Germany, measured during a clear night in 2017 (black solid circles and and a clear night during the COVID-19 lockdown in 2020 (grey diamonds and squares). triangles) and a clear night during the COVID-19 lockdown in 2020 (grey diamonds and squares). Table 4. List of all CCTs for the full image and zenith including the difference in CCTs. Table 4. List of all CCTs for the full image and zenith including the difference in CCTs. CCT (K) CCT (K) CCT (K) CCT (K) ∆ ∆ Stop Distance 2017 2020 2017 2020 CCT (K) CCT (K) (2020–2017)Δ CCT (K) CCT (K) (2020–2017)Δ Stop Distance 2017 (full) 2020(full) (2020– 2017(zenith) (zenith)2020 (2020– 1 0.3(full) 2700(full) 2800 2017) 100 (zenith) 4100 (zenith) 4100 02017) 1 20.3 1.5 2700 3000 2800 3100 100 100 4100 3700 4000 4100 300 0 3 3.3 3300 3500 200 3800 4100 300 2 41.5 6.93000 33003100 3700 100 400 3700 3700 41004000 400300 3 53.3 10.53300 34003500 3700 200 300 3800 3800 41004100 300300 4 66.9 15.03300 30003700 3400 400 400 3700 3600 40004100 400400 5 710.5 28.03400 28003700 3200 300 400 3800 3400 37004100 300300 6 815.0 34.53000 28003400 3000 400 200 3600 3500 37004000 200400 9 38.3 2900 3100 200 3700 3700 0 7 1028.0 43.52800 30003200 3100 400 100 3400 3700 37003700 0 300 8 1134.5 51.52800 30003000 3100 200 100 3500 3700 37003700 0 200 9 1238.3 58.5 2900 3000 3100 3000 200 0 3700 3800 3700 3700 100 0 − 10 43.5 3000 3100 100 3700 3700 0 3.5.11 Night-Time51.5 Light Analysis 3000 3100 100 3700 3700 0 12 58.5 3000 3000 0 3800 3700 −100 Figure7 shows the VIIRS DNB monthly composites for (a) March 2017 and (b) March 2020. The subtraction of the March 2017 data from the March 2020 data is shown in Figure7c and in a 3.5. Night-Time Light Analysis map in Figure7d. Overall, the brightness in the area increased between March 2017 and March 2020 (see FigureFigure7 c7 blueshows areas the and VIIRS also DNB Appendix monthlyB for composites the time series for (a) analysis) March particularly2017 and (b) in March the city 2020. center The ofsubtraction Berlin and of in the the March larger 2017 towns data along from the the transect. March 2020 A striking data is featureshown in Figure the data 7c is and that in the a map major in motorwaysFigure 7d. Overall, leading tothe the brightness south and in west the of area Berlin increased (A2 and between A9 marked March with 2017 red circlesand March in Figure 20207) (see are clearlyFigure visible7c blue inareas the 2017and also data Appendix but not in B the for 2020 the time data. series This isanalysis) also visible particularly in the subtracted in the city data center as redof Berlin lines. and The in time the series larger analysis towns along (shown the in transect Figure . A7A strikingin Appendix featureB) resultedin the data in almostis that the constant major night-timemotorways light leading emission to the for south the transect and west ( 0.1%of Berlin/y annual (A2 and change), A9 marked a small with increase red incircles Berlin in ( +Figure0.6%/y), 7) − a small decrease for the town of Luckenwalde ( 0.4%/y), a substantial increase in Ludwigsfelde, − the largest town on the route outside of Berlin (+5.8%/y), and a decrease in Baruth, a smaller town near the route ( 1.9%/y). − Remote Sens. 2020, 12, x FOR PEER REVIEW 10 of 23

are clearly visible in the 2017 data but not in the 2020 data. This is also visible in the subtracted data as red lines. The time series analysis (shown in Figure A7 in Appendix B) resulted in almost constant night-time light emission for the transect (−0.1%/y annual change), a small increase in Berlin (+0.6%/y), a small decrease for the town of Luckenwalde (−0.4%/y), a substantial increase in RemoteLudwigsfelde, Sens. 2020, 12 the, 3412 largest town on the route outside of Berlin (+5.8%/y), and a decrease in Baruth,10 of a 23 smaller town near the route (−1.9%/y).

(a) (b)

(c) (d)

FigureFigure 7.7. Visible Infrared Imaging Radiometer Radiometer Suit Suitee (VIIRS) (VIIRS) Day–Night Day–Night Band Band (DNB) (DNB) monthly monthly composites for (a) March 2017, (b) March 2020, (c) the subtraction of the March 2017 data from the composites for (a) March 2017, (b) March 2020, (c) the subtraction of the March 2017 data from the March 2020 data and (d) a map of the area. Major motorways to the south and west are marked with March 2020 data and (d) a map of the area. Major motorways to the south and west are marked with red circles. red circles.

4.4. Discussion Discussion TheThe skyglow skyglow surveysurvey duringduring the COVID-19 lockdo lockdownwn showed showed measurable measurable differences differences in in zenith zenith luminance,luminance, illuminance, illuminance, asas wellwell asas changeschanges in the the CCT CCT compared compared to to earlier earlier data. data. Since Since 2016, 2016, the the state state ofof Berlin Berlin has has replaced replaced conventional conventional lighting lighting , technology, mainly mainly high-pressure high-pressure sodium sodium lamps lamps with with LED luminairesLED luminaires and used and LEDsused LEDs for all for new all installations.new installations. Because Because high-pressure high-pressure sodium sodium lamps lamps have have a low CCTa low of CCT typically of typically 2100 K 2100 and theK and newly the installednewly installed LEDs typicallyLEDs typically have ahave higher a higher CCT betweenCCT between 3000 K and3000 4000 K and K, we4000 expected K, we expected that the that CCT the would CCT have would increased have increased in Berlin. in ThisBerlin. is supportedThis is supported by our databy asour 18 data of 24 as measurement 18 of 24 measurement locations locations showed ash higherowed CCTa higher in 2020 CCT compared in 2020 compared to 2017. to 2017. Furthermore, an increase in the overall brightness was expected because typically highly Furthermore, an increase in the overall brightness was expected because typically highly efficient efficient LED luminaires with higher luminous flux replaced older lamps. The number of LED LED luminaires with higher luminous flux replaced older lamps. The number of LED luminaires luminaires is continuously growing in Berlin (19.500 LEDs in 2017 vs. 34,500 LEDs of 225,000 is continuously growing in Berlin (19.500 LEDs in 2017 vs. 34,500 LEDs of 225,000 streetlights in streetlights in December 2019) [41]. In contrast to this hypothesis, we observed a decrease in artificial December 2019) [41]. In contrast to this hypothesis, we observed a decrease in artificial skyglow skyglow (background corrected luminance at zenith) at all measurement points. In the center, the (background corrected luminance at zenith) at all measurement points. In the center, the artificial skyglow component measured in 2020 decreased by more than 20% compared to that measured in 2017, although the TV tower was lit in 2020 and not in 2017. At the rural site farthest away from the city center, the artificial skyglow component decreased by more than 50% between 2017 and 2020. The illuminance decreased for almost all measurement stops apart from stop 2, which was the only one Remote Sens. 2020, 12, 3412 11 of 23 with buildings with windows nearby, that probably caused an increase of 20% for the scalar illuminance measurement. The highest decrease in illuminance was measured at the city center by almost 30%. There are several potential causes for such a reduction in skyglow:

Drastic natural atmospheric changes (desert dust, fires, ); • Seasonal changes (leaves, ground albedo, snow); • Reduction of ALAN emissions by automated and manual switch offs; • Change to modern (shielded) lighting technology [42]; • Anthropogenic atmospheric changes (air quality, air pollution); • Reduction of ALAN emissions by changed behavior. • Several of these causes can be ruled out (or are at least unlikely). There were no reports of desert dust, fires or volcanic ash or any heavy snow event near both measurement periods. The measurements were (fortunately) timed during the same season, both during a working weekday (Wednesday in 2017 vs. Thursday in 2020), for very similar weather conditions (clear sky) and about the same moon illuminance (near new moon and well below horizon) and for almost exactly the same time. Thus, the external seasonal influences like ground albedo (snow, vegetation), shading by leaves, etc. should be minimized as much as possible. Furthermore, we expect that automated switch-offs of lighting that can occur late in the night are comparable between the observation period (weekday, same season). Large-scale changes to modern and shielded lighting technology can be detected with all-sky imagery [42]. However, a change that explains the reduced artificial skyglow as strongly as we measured would have been detected as a large drop in night-time satellite data, particularly if this goes with a change to LED technology with a higher CCT. We indeed observed a slightly higher CCT with our ground-based measurements. Because the satellite sensor (VIIRS, DNB) lacks sensitivity for shortwave blue light that is stronger for modern LEDs, a change to LEDs at the same illuminance would appear as decrease for the satellite (see Figure 5 in [13]). However, our night-time light analysis shows no such drop in brightness but rather an increase in night-time light emission in Berlin and the largest town on the route. Thus, a strong increase in skyglow should be observed on the ground which is contrary to our observations. This leaves anthropogenic atmospheric changes and changed light use behavior as the main causes of the observed reductions of skyglow. Atmospheric changes are highly likely because the travel in general and air traffic in particular were reduced dramatically. According to the statistics of the airports in Berlin [43], the air traffic in February 2020 was about the same as in February 2019 ( 0.12%). However, in March 2020 the air traffic did decrease remarkably ( 46.5%) compared to March − − 2019 and in April 2020 almost no air traffic was present compared to April 2019 ( 93.2%). This was − further supported by the data from the European Organization for the Safety of Air Navigation, EUROCONTROL, that show that for the 28 and 29 March 2020, the air traffic in Germany was reduced already by about 80% compared to the same day in 2019 [44]. The exhaust emission of aircrafts can produce contrails, which can be a source of very thin cirrus clouds that are difficult to detect but are assumed to play a complex role in climate change because of radiative forcing [45]. Modeled data for April 2020 using the EUROCONTROL data show a drastic reduction in contrails due to reduced air traffic during the COVID-19 pandemic [46]. Daytime optical depth (AOD) measurements show a slight decrease in the last hours before the nights of measurement from 2017 to 2020 (see AppendixC). The absence of polluting will reduce skyglow as shown in a study in Cracow, Poland [24], but also means less extinction towards the satellite, which is exactly what we have observed. A side effect is that with reduced air and road traffic, also a part of the light that travels through the atmosphere horizontally (and that is most likely not detected by the satellites) will be reduced, which is one component of skyglow. How large the contribution of vehicle lighting to skyglow is remains an open research question. In a seminal work, Bará et al. used the temporal information of zenithal night sky brightness measured with an SQM in two towns in Galicia, Spain, to identify the Remote Sens. 2020, 12, 3412 12 of 23 contributions of different light sources [31]. By a modal expansion of the time representation they were able to acquire time signatures of the different sources of light. They concluded that the contribution of vehicles was rather small (few percent) in the larger town (A Coruña, 250,000 inhabitants), but larger (nearly 10%) in a smaller town (Arteixo, 30,000 inhabitants). No such data are available for Berlin and its surroundings. However, an aerial study in Berlin showed that lighting associated with streets has been found to be the dominant source of zenith directed light pollution (31.6%) [47]. We want to point out that this fraction largely depends on the amount of traffic and the number of lit roads, type of lamps and illumination levels. In a settlement with shielded lamps, few lit roads and low illumination levels but high traffic, this fraction could become substantial. The other component is changed lighting behavior during the pandemic like switching off public lighting such as road lights or private lights from large businesses etc. This is very difficult to disentangle as shown in a recent large-scale switch-off experiment [30]. Nevertheless, the night sky brightness decreased by about 20% in a switch-off for ornamental lights in a town in Catalunya, Spain [28], by more than 5% in the city center when switching off streetlights in Tucson, , USA [30], and by about 8% during the WWF 2018 in Berlin, Germany [29]. During the COVID-19 survey and later (personal observation, AJ) it seemed that no public lighting was reduced during the pandemic and that also most small business lights remained switched on. This could be explained by the fact that night-time lighting was not high up on the agenda of government decisions as there were so many urgent pandemic-related matters to be taken care of. Furthermore, night-time light seems to be also an inherent proxy of societal functionality as some newspapers were advertising “the lights will stay on during pandemic” [48].

5. Conclusions In conclusion, it was possible to detect a reduction of skyglow during the COVID-19 lockdown in Berlin, Germany. This was surprising, because the overall light emission trend in the region as observed by satellite data was increasing. Luminance and illuminance decreased most strongly in the city center but also far away from the city. The strongest influence of the lockdown on the night sky brightness is assumed to originate from reduced air pollution due to a smaller reduction in industrial activity and dramatically reduced traffic. The latter is supported by statistical data of airport traffic and a comparison of the night-time satellite data where the road traffic reduced evidently. Other possible causes are changes in private lighting and reduced horizontal light from less traffic. Air pollution has not attracted much interest by the ALAN and the light pollution community to date. However, our results call for further interdisciplinary collaboration between health scientists, climate scientists, astronomers, and ecologists. A reduction of air pollution should also become a focus of light pollution research and mitigation efforts. A well managed use of sustainable and energy-efficient lighting technology will reduce energy consumption as well as unwanted ALAN emissions. In regions with electricity produced by fossil fuel, a reduction in energy consumption could also reduce air pollution.

Author Contributions: Conceptualization, A.J.; methodology, A.J.; validation, A.J.; formal analysis, A.J.; investigation, A.J. and F.H..; resources, A.J. and F.H.; data curation, A.J.; writing—original draft preparation, A.J.; writing—review and editing, F.H..; visualization, A.J. All authors have read and agreed to the published version of the manuscript. Funding: This work received no external funding. Acknowledgments: The authors thank Christopher C.M. Kyba who was involved in the design of the cloud study performed in 2017 of which part of the data was used. The publication of this article was funded by the Open Access Fund of the Leibniz Association and by the Open Access Fund of the Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB). Conflicts of Interest: The authors declare no conflict of interest. Remote Sens. 2020, 12, 3412 13 of 23

Remote Sens. 2020, 12, x FOR PEER REVIEW 13 of 23 Appendix A Appendix A The full imaging dataset is shown here with Figures A1–A3 showing luminance maps and The full imaging dataset is shown here with Figures A1–A3 showing luminance maps and Figures A4–A6 showing CCT maps. The positions of the measurement locations and measurement Figures A4–A6 showing CCT maps. The positions of the measurement locations and measurement times are given in Table A1. times are given in Table A1.

Figure A1. Luminance maps for stops 1–4. (a,c,e,g) 2017 data, (b,d,f,h) 2020 data. Figure A1. Luminance maps for stops 1–4. (a,c,e,g) 2017 data, (b,d,f,h) 2020 data.

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Figure A2. Luminance maps for stops 5–8. (a,c,e,g) 2017 data, (b,d,f,h) 2020 data. Figure A2. Luminance maps for stops 5–8. (a,c,e,g) 2017 data, (b,d,f,h) 2020 data.

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Figure A3. Luminance maps for stops 9–12. (a,c,e,g) 2017 data, (b,d,f,h) 2020 data. Figure A3. Luminance maps for stops 9–12. (a,c,e,g) 2017 data, (b,d,f,h) 2020 data.

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Figure A4. CCT maps for stops 1–4. (a,c,e,g) 2017 data, (b,d,f,h) 2020 data. Figure A4. CCT maps for stops 1–4. (a,c,e,g) 2017 data, (b,d,f,h) 2020 data.

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Figure A5. CCT maps for stops 5–8. (a,c,e,g) 2017 data, (b,d,f,h) 2020 data. Figure A5. CCT maps for stops 5–8. (a,c,e,g) 2017 data, (b,d,f,h) 2020 data.

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Figure A6. CCT maps for stops 9–12. (a,c,e,g) 2017 data, (b,d,f,h) 2020 data. Figure A6. CCT maps for stops 9–12. (a,c,e,g) 2017 data, (b,d,f,h) 2020 data.

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Remote Sens. 2020, 12, 3412 19 of 23 Table A1. Information on the measurement locations, distance to city center and measurement times.

Stop Name Distance (km) Latitude Longitude 2017 Time 2020 Time Table A1. Information on the measurement locations, distance to city center and measurement times. 1 Museumsinsel 0.3 52.52004 13.39994 1:11 0:51 2 StopWaldeckpark Name Distance 1.5 (km) Latitude52.50654 Longitude13.40359 20171:38 Time 2020 Time1:02 3 1 Park am Museumsinsel Gleisdreieck 0.3 3.3 52.52004 52.49472 13.39994 13.3788 1:11 1:57 0:51 1:11 4 2 Hans Baluschek Waldeckpark Park 1.5 6.9 52.50654 52.46498 13.4035913.35863 1:38 2:23 1:02 1:27 3 Park am Gleisdreieck 3.3 52.49472 13.3788 1:57 1:11 5 4Gemeindepark Hans Baluschek Lankwitz Park 6.910.5 52.46498 52.43101 13.3586313.35208 2:23 2:44 1:27 1:45 6 5 GemeindeparkGut Osdorf Lankwitz 10.515 52.4310152.39304 13.3520813.33466 2:443:02 1:451:54 7 6Wietstock Gut Osdorf 15 28 52.3930452.2761 13.3346613.30828 3:023:31 1:542:11 7 Wietstock 28 52.2761 13.30828 3:31 2:11 8 8Christinendorf Christinendorf 34.5 34.5 52.2165752.21657 13.29815 3:563:56 2:302:30 9 9Alexanderdorf Alexanderdorf 38.3 38.3 52.1797852.17978 13.31725 4:054:05 2:402:40 10 10Sperenberg Sperenberg 43.5 43.5 52.1289952.12899 13.36726 4:194:19 2:552:55 11 Stülpe 51.5 52.06222 13.3261 4:31 3:09 11 12Stülpe Petkus 58.5 51.5 51.9944352.06222 13.3509913.3261 4:474:31 3:213:09 12 Petkus 58.5 51.99443 13.35099 4:47 3:21 Appendix B Appendix B The VIIRS DNB time series data extracted from the web app “radiance light trends” [39] as well as the shapesThe VIIRS of the DNB selected time polygons series data in theextracted web app from are the shown web in app Figures “radiance A7 and light A8 trends”for the transect[39] as well (a), Berlinas the (b),shapes the townof the of selected Baruth (c),polygons the town in the of Luckenwalde web app are (d)shown and thein Figures town of A7 Ludwigsfelde and A8 for (e).the Furthermore,transect (a), Berlin the light (b), trends the town from of the Baruth exponential (c), the fits town through of Luckenwalde the monthly data(d) and are providedthe town inof TableLudwigsfelde A2. (e). Furthermore, the light trends from the exponential fits through the monthly data are provided in Table A2.

FigureFigure A7. A7.VIIRS VIIRS DNBDNB timetime seriesseries obtainedobtained fromfrom radianceradiance lightlighttrend trendweb webtool tool[ 40[40]] for for the the study study area area (green(green diamonds), diamonds), BerlinBerlin (black(black solidsolid circles),circles), thethe towntown ofof Baruth Baruth (blue(blueopen opentriangles), triangles), thethe towntown ofof LuckenwaldeLuckenwalde (red (red solid solid squares) squares) and and the the town town of Ludwigsfeldeof Ludwigsfelde (grey (grey solid solid triangles). triangles). Exponential Exponential fits arefits shown are shown in the in same the same colors colors as the as areas the areas investigated. investigated.

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Table A2. Annual change in the average radiance for the areas investigated. Table A2. Annual change in the average radiance for the areas investigated. Name Annual Change TransectName area Annual−0.1%/y Change TransectBerlin area +0.6%/y0.1%/y − LudwigsfeldeBerlin +5.8%/y+0.6%/y LuckenwaldeLudwigsfelde −+0.4%/y5.8%/y Luckenwalde 0.4%/y Baruth −−1.9%/y Baruth 1.9%/y −

(a) (b)

(c) (d)

(e) FigureFigure A8.A8.VIIRS VIIRS DNB DNB time time series series obtained obtained from from radiance radiance light light trend trend web web tool [tool39]: [39]: (a) the (a) study the study area; (area;b) Berlin; (b) (Berlin;c) the town(c) the of Baruth;town of (d )Baruth; the town (d of) Luckenwalde;the town of andLuckenwalde; (e) the town and of Ludwigsfelde.(e) the town of Ludwigsfelde. Appendix C AppendixAerosol C optical depth was retrieved from the AERONET measurement station at Freie Universität BerlinAerosol (FUB). Onlyoptical data depth for the was days retrieved before the from measurements the AERONET were available measurement for both station years. Theat Freie data ofUniversität the last two Berlin hours (FUB). of the Only day weredata for averaged the days for before the values the measurements given for 675 nm, were 500 available nm and for 440 both nm. Theyears. result The is data listed of inthe Table last twoA3. hours of the day were averaged for the values given for 675 nm, 500 nm and 440 nm. The result is listed in Table A3. Table A3. Aerosol optical depth (AOD) for the three wavelengths in the visible range obtained from the AERONET measurement station at Freie Universität Berlin. Table A3. Aerosol optical depth (AOD) for the three wavelengths in the visible range obtained from the AERONET measurementAERONET station FUB at Freie 28 Universität March 2017 Berlin. 25 March 2020 AERONETAOD 440 nm FUB 28 March 0.19 2017 25 March 0.18 2020 AOD 500 nm 0.17 0.15 AOD 440 nm 0.19 0.18 AOD 675 nm 0.13 0.1 AOD 500 nm 0.17 0.15 AOD 675 nm 0.13 0.1

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