Journal of Imaging

Article Measurements of Brightness in the Veneto Region of Italy: Sky Quality Meter Network Results and Differential Photometry by Digital Single Lens Reflex

Andrea Bertolo 1,* , Renata Binotto 1, Sergio Ortolani 2,3 and Simone Sapienza 1 1 Regional Environmental Protection Agency of Veneto, Via Ospedale Civile 24, 35121 Padova, Italy; [email protected] (R.B.); [email protected] (S.S.) 2 Department of Physics and Astronomy, University of Padova, vicolo dell’Osservatorio 2, 35122 Padova, Italy; [email protected] 3 INAF, Osservatorio Astronomico di Padova, Vicolo dell’Osservatorio 5, 35122 Padova, Italy * Correspondence: [email protected]

 Received: 3 April 2019; Accepted: 21 May 2019; Published: 24 May 2019  Abstract: In this paper, we present the implementation of a monitoring network for artificial light at night (ALAN), based on Sky Quality Meter devices (SQM) installed in seven locations of the Veneto region. The system is coordinated by the Regional Environmental Protection Agency (ARPA-Veneto) and the Department of Physics and Astronomy of the University of Padova, in collaboration with a local dark-sky association, Venetostellato. A new centralized database containing zenith brightness (NSB) data was implemented to collect data from all SQM stations of the regional territory, not only in real time (since 2017), but in some stations since 2011. We now have a dataset to determine how is affecting astronomical observatories. A WEB portal was created to offer different downloads from these NSB data. We present the results of some elaborations for the 2018 dataset (statistics, histograms, annual and cumulative plots) for seven monitoring sites. For Ekar and Pennar sites, we also present the NSB monthly trend from 2014 until the time of the study. We purchased a reflex camera with a fish eye lens, appropriately calibrated with the software (SW) Sky Quality Camera, which allowed us to study ALAN using differential photometry. Here, we present our first results obtained by studying the night evolution of light pollution in the urban location of Padova.

Keywords: Night Sky Brightness; SMQ Network; Light Pollution; Differential Photometry

1. Introduction The night sky is never completely dark, even in the most isolated places there is a background glow resulting both from the natural component of terrestrial (such as auroral light) or extraterrestrial (such as ) origin, and from the artificial component originating from human activities. Nowadays, most of the population in industrialized nations live under where the artificial light at night (ALAN) exceeds natural light by thousands of times, with this being due to the luminous flux produced by artificial lighting, directly or indirectly sent towards the sky. The main effect of ALAN is the loss of perception of the Universe around us, as the increase in the brightness of the night sky due to artificial light prevents the vision of the starry sky, and also causes a strong environmental impact with negative effects on the biosphere, flora and fauna, and on human life, as well as influencing cultural aspects. The Veneto region in the north-east of Italy (between 44 and 46 degrees North in latitude), is one of the most polluted regions in the world, especially with regard to light pollution [1], due to the presence of the contiguous industrial and commercial areas, and the continuous increase of lighting systems.

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Its geographical conformation, which includes the Alps, the northern part of the Adriatic Sea, and a large plain, allows us to study the brightness of the night sky in relation to geographical location and altitude using instruments located in areas with significantly different environmental conditions and high population density. Therefore, the aim of this study is to establish an effective and reliable network that allows monitoring over time of ALAN in different geographical areas, integrating the measurements carried out at astronomical professional observatories with those of amateur astronomy, thus realizing an excellent example of citizen science. The monitoring of light pollution is expressly required by the law against the light pollution of the Veneto Region [2] that assigns this task to a committee coordinated by ARPA-Veneto. In this work, we present the statistical analysis of the data collected by the monitoring network in 2018, and also study the temporal trend of brightness in two professional astronomical observatories in Asiago [3]. We also present our first results found with differential all-sky photometry with a commercial Digital Single Lens Reflex (DSLR) camera at a location of the monitoring network in urban area (Padova) [4].

2. Materials and Methods Night sky brightness (NSB) is measured with a simple instrument named the Sky Quality Meter (SQM) [5] that consists of a specially calibrated sensor able to record light within a given field of view. The instrument is located in a fixed position and oriented towards the zenith. Due to their reliability, versatility, and cheapness, SQM devices have a great diffusion and are well suited to implement monitoring networks [6–11]. The NSB has a high degree of variability (not only from one night to another, but also within the same night) and includes contributions from natural sources, artificial diffused light (due to traffic, signs and streets lamps, public and private illumination, monument lights, advertising signs, etc.), and from astronomical sources (integrated light of stars and cosmic sources, diffused galactic light, zodiacal light, , and auroral light). The SQM is a low cost instrument, composed of a silicon photodiode (TAOS TSL237S) that, oriented towards the zenith and with an opening angle of about 20◦, allows the recording of integrated brightness of the sky expressed in magnitude per second of square arc (mag arcsec 2), with an · − uncertainty of the order of 10% [12]. The continuous monitoring of the NSB allows the recording of specific light pollution at each location, taking into account the variation of weather conditions, climate, and different moon phases, as well as the ability to study long-term trends. In order to be able to analyse the data set recorded by SQM, we must ensuring its significance, which until now had been based exclusively on the instrumental calibration carried out by the manufacturer, and, therefore, validation of the data collected by each SQM cannot be ignored [12]. To ensure the traceability of the data to a reference standard, we carried out an inter-comparison test, based on a manual SQM “transfer” instrument, previously checked with a series of comparisons with reference instrumentation, in different light pollution conditions from the city to the darkest skies. In these tests, the instrument confirmed the stability of the measurements over time, as well as the variation of the microclimatic conditions. Given the complexity of the observations and the reduction of data, the observations at telescopes have been limited to a few distributed over the years, but are an important reference for the absolute calibration of the measurements of the SQMs that, although proven to be very stable and reliable, integrate a signal in a very wide spectral range that does not correspond exactly to the international astronomical standards of the blue (B) or visible (V) bands, in which the astronomical observations were historically made instead. J. Imaging 2019, 5, 56 3 of 13

Transformations can be based on specific telescope observations or even using indications in the literature [5]. There is no simple transformation from the SQM system to the standard V system (visual) as there is dependence on the spectral distribution of the emission of the sky, which is dependent on the sources and the intensity of light pollution. For this reason, the SQM has been tested at different sites, at different levels of light pollution, and used in combination with the measurements of the sky in standard systems with some telescopes (La Silla, Chile; La Palma, Spain; Asiago, Italy). Brightness measurements with the telescope are reduced for accounting that light pollution brightness measurements are made "below the atmosphere". The differences between data by SQM and those by the standard telescope system are of the order of 0.30–0.55 magnitudes, in accordance with an internal report by Cinzano [13]: additional differences are explained by the presence or absence of the inside the field of view or by some bright sources that in the SQM are integrated with the sky. Taking into account these sources, repeated measurements proved stable within a few percent, both between SQM and telescopes, and between different SQMs. Recently, we have used the differential all-sky photometry with a commercial DSLR camera—Canon EOS 70D with a fish eye lens (Sigma with focal length 4.5 mm and aperture f 2.8). This technique allows the recording of information on the spatial distribution of light pollution in three spectral bands RGB [14–17]. The use of this technique in a stable and confined location allows for the recording images at regular intervals throughout the night. Here, we present our first results about the NSB evolution. For the elaboration of the images we used the SW “Sky Quality Camera” (version 1.9.2, Euromix, Ljubljana, Slovenia), with a calibration carried out by the manufacturer of the SW [14].

3. Results and Discussion The characteristics of the Veneto NSB monitoring network are presented in Table1.

Table 1. Veneto’s Sky Quality Meter (SQM) Network.

Name Latitude (N) Longitude (E) Altitude (m) Type Owner

Padova 45◦24009.7” 11◦53005.5” 12 Urban ARPA-Veneto Nove 45◦42032.92” 11◦40041.95” 77 Suburban VenetoStellato Montebello 45◦2807.40” 11◦2101.60” 212 Suburban VenetoStellato Pennar 45◦51057.52” 11◦31036.88” 1050 Rural University of Padova Monte Baldo 45◦41052.00” 10◦51032.00” 1208 Mountain VenetoStellato Cima Ekar 45◦50055.39” 11◦3408.34” 1366 Mountain University of Padova Passo Valles 46◦20019.79” 11◦4808.96” 2032 Mountain ARPA-Veneto

3.1. Veneto’s SQM Network and SQMWEB Portal The NSB data were measured by the SQMs every 5 min and were recorded on a text file (ASCII) created by the same instrument. These files were then transferred (passing through some File Transfer Protocol servers), checked, processed, and sent to the database where they were stored. From the early hours of the morning, the NSB data were available on the "Live Data" page of ARPA-Veneto [18], where they were visible together with the data of the two previous nights. Measurements of all SQMs were stored in a unique Oracle10g database owned by ARPA-Veneto. We now have more than 1,680,000 entries in the main table containing brightness data (for a few SQM we have data since 2011) collected in more than 13,000 nights. The data are available for users through a Drupal 7 portal named SQMWEB. This web portal includes some pages with different functionality: a simple reading page with nightly data from a single location/single date with calculation of the lunar phase, mean and median values of NSB in the astronomic night, slopes of brightness curves in different time intervals, and hours, and types of night (cloudy or not). Furthermore, there are some statistic tools to calculate histograms of absolute frequencies, “hourglass” diagrams, “jellyfish” plots, diagrams of NSB monthly and annual trends, among other items. A page of download is also enabled with a filtering for location, time period, moon phase, or sky conditions and it gives a jellyfish J. Imaging 2019, 5, x FOR PEER REVIEW 4 of 13

“hourglass” diagrams, “jellyfish” plots, diagrams of NSB monthly and annual trends, among other items. A page of download is also enabled with a filtering for location, time period, moon phase, or J.sky Imaging conditions2019, 5, 56 and it gives a jellyfish plot and a histogram of brightness. This portal is still evolving,4 of 13 and we have used it experimentally to download the data for this research. plot and a histogram of brightness. This portal is still evolving, and we have used it experimentally to download3.2. Statistical the Analysis data for this research.

Table 2 shows the data used (interval 12–22 magSQM·arcsec−2) and the results of the statistical 3.2. Statistical Analysis analysis, in which the full width at half maximum (FWHM) is referred to the peak of the histogram, only Tableof the2 values shows of the the data new used moon (interval nights without 12–22 mag cloud cover.arcsec 2) and the results of the statistical SQM· − analysis, in which the full width at half maximum (FWHM) is referred to the peak of the histogram, only of the valuesTable of the 2. Data new information moon nights and without statistics cloud night cover.sky brightness (NSB) values. Full Width at Half Table 2. Data information and statistics night skyModal brightness Value (NSB) values. Station No. of Data No. of Nights Maximum (FWHM) 1 (magSQM·arcsec−2) Modal Value Full Width at Half Maximum −2 Station No. of Data No. of Nights (magSQM·arcsec ) (mag arcsec 2) (FWHM) 1 (mag arcsec 2) Padova 44,120 338 SQM· 18.0− SQM· 0.6 − Padova 44,120 338 18.0 0.6 NoveNove 43,82443,824 355355 19.6 19.6 0.7 0.7 MontebelloMontebello 39,19939,199 318 318 19.7 19.7 0.6 0.6 Pennar 41,644 323 20.8 0.3 Pennar ObservatoryObservatory 41,644 323 20.8 0.3 MonteMonte Baldo Baldo 37,825 37,825 345 345 20.7 20.7 0.5 0.5 Ekar Observatory 44,480 342 20.9 0.2 Ekar ObservatoryPasso Valles 42,292 44,480 331 342 21.3 20.9 0.2 0.2 Passo Valles 42,292 1 Moonless and 331 cloudless nights only. 21.3 0.2 1 Moonless and cloudless nights only. NSB histogramhistogram plotsplots forfor 2018 2018 are are presented presented for for all al networkl network monitoring monitoring stations stations (Figure (Figure1) in 1) the in 2 interval brightness 12–22 magSQM arcsec− .− In2 addition, we also present the cumulative frequency of the interval brightness 12–22 magSQM· ·arcsec . In addition, we also present the cumulative frequency NSBof NSB values values (blue (blue curve). curve). These These figures figures were were obtained obtained from from the entirethe entire dataset, dataset, including including nights nights with cloudswith clouds and . and moonlight.

Passo Valles Ekar 2400 100 2400 100

2200 90 2200 90 2000 2000 80 80 1800 1800 70 70 1600 1600 1400 60 1400 60 1200 50 1200 50

1000 40 1000 40 Frequency (n) 800 Frequency (n) 800 30 30 600 600 20 Cumulative frequency (%) 20 Cumulative frequency (%) 400 400 200 10 200 10 0 0 0 0 12 13 14 15 16 17 18 19 20 21 22 12 13 14 15 16 17 18 19 20 21 22

-2 -2 NSB (magSQM arcsec ) NSB (magSQM arcsec )

(a) (b)

Monte Baldo Pennar

2000 100 1800 100 1800 90 1600 90 1600 80 1400 80 1400 70 70 1200 1200 60 60 1000 1000 50 50 800 800 40 40 Frequency (n) Frequency (n) 600 600 30 30 Cumulative frequency (%) 400 20 400 20 Cumulative frequency (%)

200 10 200 10

0 0 0 0 12 13 14 15 16 17 18 19 20 21 22 12 13 14 15 16 17 18 19 20 21 22

-2 -2 NSB (magSQM arcsec ) NSB (magSQM arcsec )

(c) (d)

Figure 1. Cont.

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Montebello Nove 1800 100 2200 100 2000 1600 90 90 1800 80 1400 80 1600 70 70 1200 1400 60 60 1000 1200 50 50 800 1000 40 40 Frequency (n) Frequency (n) 800 600 30 30 600 Cumulative frequency (%) 400 20 Cumulative frequency (%) 400 20 200 10 200 10 0 0 0 0 12 13 14 15 16 17 18 19 20 21 22 12 13 14 15 16 17 18 19 20 21 22 -2 NSB (magSQM arcsec ) -2 NSB (magSQM arcsec ) (e) (f)

Padova 2000 100

1800 90

1600 80

1400 70

1200 60

1000 50

800 40 Frequency (n) 600 30 Cumulative frequency (%) 400 20

200 10

0 0 12 13 14 15 16 17 18 19 20 21 22 -2 NSB (magSQM arcsec ) (g)

Figure 1. Histograms of absolute frequencies of NSB recordedrecorded duringduring 20182018 inin thethe followingfollowing locations:locations: ((a)) PassoPasso Valles,Valles, ((bb)) CimaCima Ekar,Ekar, ((cc)) MonteBaldo,MonteBaldo, ( (dd)) Pennar, Pennar, ( e(e)) Montebello, Montebello, ( f()f) Nove, Nove, ( g(g)) Padova. Padova.

It is noticeable that that the the distribution distribution of of absolute absolute frequencies frequencies of of brightness brightness (Figure (Figure 1)1 )has has a acharacteristic characteristic shape shape depending depending on on the the type type of of station. station. In In particular, particular, histograms histograms of of urban and suburban stationsstations are are clearly clearly bimodal, bimodal, with awith maximum a maximum at darker at brightness darker brightness values that correspondsvalues that tocorresponds moonless and to moonless cloudless and nights, cloudless and the nights, brightest and peak the brightest that includes peak both that nights includes with both the nights presence with of the moonpresence and of those the moon with aand cloud those cover. with a cloud cover. The widthwidth ofof bothboth the the brightness brightness peaks peaks changed changed according according to theto the effects effects of the of cloudiness,the cloudiness, but thatbut atthat high at high magnitudes magnitudes depended depended on the on light the light pollution pollution also presentalso present in the in area. the area. The positions of the peaks of the NSBNSB distributiondistribution represent the typical brightnessbrightness of the location, while the distancedistance between them depends mainly on the amplificationamplification caused by the reflectionreflection of thethe cloud cover, although in darker mountain sites it mainly includes includes the the contribution contribution of of the the moonlight moonlight [19]. [19]. As altitude increased and SQM devices were located far away from the urban site, the ALAN decreased. In In rural rural and mountain locations, the the distribution lost lost its its bimodality, which was due to two mainmain factors:factors: First,First, ALANALAN was was lower lower and, and, consequently, consequently, even even in in the the case case of cloudof cloud cover, cover, the skythe remainedsky remained darker darker (especially (especially in the in case the of case more of remotemore remote stations, stations, for example, for example, Passo Valles, Passo as Valles, the stop as ofthe natural stop of light); natural Second, light); some Second, extremely some darkextremel valuesy dark were values likely were caused likely by peculiar caused phenomena,by peculiar suchphenomena, the presence such ofthe low presence clouds, of and low nights clouds, when and snownights covered when snow the sensor covered [9, 19the,20 sensor]. [9,19,20]. The widthwidth of of the the distribution distribution around around the darkestthe darkest peak canpeak be can studied be studied by selecting by selecting only moonless only andmoonless cloudless and cloudless nights ("flat" nights night ("flat" curves night wherecurves thewhere diff erencethe difference between between NSB values NSB values are less are than less 2 0.05than mag0.05 magarcsecSQM·arcsecfor−2 at for least at 6 least h). The 6 calculatedhours). The FMHW calculated was diFMHWfferent betweenwas different locations between and it SQM· − waslocations consistent and withit was what consistent had already with been what discussed. had already The value been in darkerdiscussed. mountain The locationsvalue in wasdarker less thanmountain half that locations of plain was locations, less than most half likely that due of to plain the di locations,fferent atmospheric most likely variability, due to includingthe different the presenceatmospheric of particulate variability, matter. including The peculiaritythe presence of theof particulate station of Montematter. Baldo The (apeculiarity mountain of site the but station with aof typical Monte FWHM Baldo of(a plainmountain location site with but thewith absence a typi ofcal bimodality) FWHM of canplain be location attributed with in the the first absence analysis of tobimodality) the meteorological can be attributed characteristics in the of first the site, analysis located to notthe too meteorological far from a large characteristics lake basin (Garda of the Lake). site, located not too far from a large lake basin (Garda Lake).

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J. ImagingIn Figure 2019, 5, x2 FORwe PEERshow REVIEW the cumulative frequency of NSB for each monitoring site—in urban6 andof 13 partiallyIn Figure in suburban2 we show stations the cumulative there was a frequency saturation of at NSB smaller for each NSB monitoring values, and site—in the distribution urban and of partiallyNSB Inwas Figure in due suburban to2 we cloud show stations reflection, the there cumulative wasas already a saturation frequency discussed at of smaller NSB above, for NSB eachconfirming values, monitoring and a thepreviously distributionsite—in urbanpublished of NSBand wasanalysispartially due tobyin cloudBaràsuburban [19]. reflection, stations as alreadythere was discussed a saturation above, at confirming smaller NSB a previously values, and published the distribution analysis byof BarNSBà [was19]. due to cloud reflection, as already discussed above, confirming a previously published analysis by Barà [19]. 100 Padova 90 Nove 100 MontebelloPadova 80 Pennar 90 Nove 70 MonteMontebello Baldo 80 EkarPennar 60 Valles 70 Monte Baldo 50 Ekar 60 Valles 40 50 30

40 Cumulative frequency (%) 20 30

10 Cumulative frequency (%) 20 0 12 13 14 15 16 17 18 19 20 21 22 10

-2 NSB (magSQM arcsec ) 0 12 13 14 15 16 17 18 19 20 21 22 -2 NSB (magSQM arcsec ) Figure 2. Cumulative frequencies of NSB in seven monitoring stations: red lines correspond to an urban station, blue to a suburban station, green to a rural station, and black to a mountain station. Figure 2.2. Cumulative frequencies of NSB in sevenseven monitoringmonitoring stations: red lines correspond to an urban station, blue to a suburban station, green to a rural station, and black to a mountain station. 3.3. Seasonalurban station, Variations blue toof aNSB suburban station, green to a rural station, and black to a mountain station. 3.3. Seasonal Variations of NSB 3.3. SeasonalIn order Variations to understand of NSB the seasonal variations of NSB in the different sites, we present the monthlyIn order modal to understandvalues (i.e., thethe seasonalmost frequent variations value) of NSBof NSB in the in Table different 3 and sites, Figure we present 3. the monthly In order to understand the seasonal variations of NSB in the different sites, we present the modal values (i.e., the most frequent value) of NSB in Table3 and Figure3. monthly modal values (i.e.,Table the most3. Monthly frequent modal value) NSB valueof NSB (mag in SQMTable arcsec 3 and−2). Figure 3. 2 Table 3. Monthly modal NSB value (magSQM arcsec− ). Station Jan FebTable Mar 3. Monthly Apr modalMay NSB valueJun (magJulSQM arcsecAug −2). Set Oct Nov Dec PassoStation Valles Jan20.8 Feb21.1 Mar21.4 Apr21.6 May 21.5 Jun 21.4 Jul21.4 Aug21.4 Set21.3 Oct21.3 Nov21.1 Dec21.0 Station Jan Feb Mar Apr May Jun Jul Aug Set Oct Nov Dec CimaPasso Ekar Valles 20.8 20.7 21.1 20.7 21.4 20.7 21.620.9 21.5 20.8 21.4 21.0 21.421.0 21.420.9 21.320.8 21.320.9 21.120.9 21.020.8 MontePassoCima VallesBaldo Ekar 20.7 20.8 20.720.721.1 20.720.921.4 20.920.921.6 20.8 20.721.5 21.0 20.721.4 21.020.621.4 20.920.621.4 20.820.821.3 20.920.721.3 20.920.721.1 20.820.621.0 Monte Baldo 20.8 20.7 20.9 20.9 20.7 20.7 20.6 20.6 20.8 20.7 20.7 20.6 CimaPennar Ekar 20.820.7 20.3 20.7 20.5 20.7 20.820.9 20.8 20.9 21.0 20.821.0 20.720.9 20.620.8 20.820.9 20.9 20.720.8 MontePennar Baldo 20.8 20.8 20.320.7 20.520.9 20.820.9 20.8 20.7 20.9 20.7 20.820.6 20.720.6 20.620.8 20.820.7 20.920.7 20.720.6 MontebelloMontebello 20.1 20.1 19.919.9 19.719.7 19.819.8 19.8 19.8 19.6 19.6 19.719.7 19.719.7 19.719.7 19.619.6 19.919.9 19.919.9 PennarNoveNove 19.6 19.620.8 19.419.420.3 19.419.420.5 19.719.720.8 19.5 19.520.8 19.6 19.620.9 19.619.620.8 19.419.420.7 19.219.220.6 19.619.620.8 19.719.720.9 19.819.820.7 Padova 18.4 18.1 17.6 18.3 18.1 17.8 18.1 18.3 18.1 18.0 18.0 17.8 MontebelloPadova 18.420.1 18.119.9 17.619.7 18.319.8 18.119.8 17.819.6 18.119.7 18.319.7 18.119.7 18.019.6 18.019.9 17.819.9 Nove 19.6 19.4 19.4 19.7 19.5 19.6 19.6 19.4 19.2 19.6 19.7 19.8 Padova 18.4 18.1 17.6 18.3 18.1 17.8 18.1 18.3 18.1 18.0 18.0 17.8 22,0

) 21,5 -2 21,022,0 Passo Valles

) 20,521,5

-2 Cima Ekar arcsec 20,021,0 Monte Baldo SQM Passo Valles 19,520,5 PennarCima Ekar arcsec

(mag 19,020,0 MontebelloMonte Baldo SQM 18,519,5 NovePennar monthly

(mag Padova 18,019,0 Montebello

NSB 17,518,5 Nove monthly Padova 17,018,0

NSB 17,5 JAN FEB MAR APR MAY JUN JUL AUG SET OCT NOV DEC 17,0 Month JAN FEB MAR APR MAY JUN JUL AUG SET OCT NOV DEC Figure 3. Monthly modalMonth value of NSB during 2018. Figure 3. Monthly modal value of NSB during 2018.

Figure 3. Monthly modal value of NSB during 2018.

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The NSB monthly evolutions were rather similar, but with some interesting specific The NSB monthly evolutions were rather similar, but with some interesting specific characteristics. characteristics. In the winter months, there was an NSB minimum (more clear in Passo Valles), likely due to the In the winter months, there was an NSB minimum (more clear in Passo Valles), likely due to Milky Way and snow cover of the ground [9]. The Milky Way disappears in spring when Monte Baldo the Milky Way and snow cover of the ground [9]. The Milky Way disappears in spring when Monte and Passo Valles have both a maximum value of NSB. Baldo and Passo Valles have both a maximum value of NSB. In particular, the less polluted sites, such as Passo Valles, but also partly Ekar and Pennar, In particular, the less polluted sites, such as Passo Valles, but also partly Ekar and Pennar, suffer more from local lighting, and for this reason we see a deterioration of the sky from November suffer more from local lighting, and for this reason we see a deterioration of the sky from to February coinciding with the tourist period for skiing and holidays, whereas spring is the tourist November to February coinciding with the tourist period for skiing and holidays, whereas spring is off-season and, therefore, it recorded the darkest sky. the tourist off-season and, therefore, it recorded the darkest sky. It is noticeable that the highest seasonal variability is in the urban and suburban sites, probably due It is noticeable that the highest seasonal variability is in the urban and suburban sites, probably to great but similar seasonal variability of cloud coverage, meteorological (humidity, rain, and fog), due to great but similar seasonal variability of cloud coverage, meteorological (humidity, rain, and and environmental conditions (particularly air pollution) [7,21]. In the Padova site, a minimum value fog), and environmental conditions (particularly air pollution) [7,21]. In the Padova site, a minimum in December could be due to Christmas illuminations also, and a maximum value in August due to value in December could be due to Christmas illuminations also, and a maximum value in August traffic decreasing for the summer holydays [22]. due to traffic decreasing for the summer holydays [22]. 3.4. Nocturnal Development of NSB 3.4. Nocturnal Development of NSB Figure4 shows the NSB recorded in all locations in a clear and moonless night on 14 February 2018 and 15Figure February 4 shows 2018. the The NSB NSB recorded hourly increasein all locations rate was in calculated a clear and in themoonless period night of 21:00–01:00, on 14 February and is shown2018 and in 15 Table February4. 2018. The NSB hourly increase rate was calculated in the period of 21:00–01:00, and is shown in Table 4.

22,0

21,5

21,0

20,5 ) -2 20,0 arcsec

SQM 19,5

19,0 NSB (mag 18,5

18,0 EKAR PENNAR PADOVA NOVE 17,5 MONTEBALDO MONTEBELLO PASSOVALLES 17,0 18:00 19:00 20:00 21:00 22:00 23:00 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 hour

Figure 4. NSB data from 14 February 2018 to 15 February 2018. Figure 4. NSB data from 14 February 2018 to 15 February 2018. Table 4. Hourly increase rate of NSB between 21:00 and 01:00. Table 4. Hourly increase rate of NSB between 21:00 and 01:00. Station Hourly Increase Rate (mag/hour) Station Hourly Increase Rate (mag/hour) Passo Valles 0.148 CimaPasso Ekar Valles 0.148 0.070 MonteCima Baldo Ekar 0.070 0.073 MontePennar Baldo 0.073 0.083 MontebelloPennar 0.083 0.075 MontebelloNove 0.075 0.080 Padova 0.053 Nove 0.080 Padova 0.053 The decrease in NSB during the night was confirmed—the variation in traffic, reduction in luminousThe decrease flux of streetin NSB lighting, during and the thenight switching was confirmed—the off of part of advertisingvariation in signstraffic, and reduction of private in illuminationluminous flux caused of street the lighting, NSB to come and downthe switching [21,23]. Inoffthe of urbanpart of site advertising (Padova), signs there and was of a smallerprivate illumination caused the NSB to come down [21,23]. In the urban site (Padova), there was a smaller decrease (the SQM device is located downtown), while it was similar in the rural and subrural

J. Imaging 2019, 5, 56 8 of 13 J. Imaging 2019, 5, x FOR PEER REVIEW 8 of 13 decreaselocations, (the with SQM a maximum device is located value in downtown), Passo Valles, while probably it was similar due to in the the influence rural and of subrural local lights locations, in a withsituation a maximum of a very value dark insky. Passo Valles, probably due to the influence of local lights in a situation of a very dark sky. 3.5. Long-Term Trends in the NSB 3.5. Long-Term Trends in the NSB In Figure 5 we represent the long-term trend of the monthly moonless modal value from 2014 to 2019In Figurefor the5 weEkar represent and Pennar the long-term sites (Asiago trend plateau), of the monthly with its moonless trend line modal (linear value interpolation). from 2014 to 2019These for sites the Ekar are and of Pennar particular sites (Asiagointerest plateau), as the with headquarters its trend line (linearof professional interpolation). astronomical These sites areobservatories—given of particular interest its location, as the headquarters the NSB recorded of professional in these two astronomical places was observatories—given influenced not only by its location,local lights, the NSBbut recordedalso by the in these lighting two placessystems was in influenced the Padan not Plain, only bywhose local edges lights, butlie just also bya few the lightingkilometres systems away. in the Padan Plain, whose edges lie just a few kilometres away.

Ekar Observatory 21,4 ) -2 21,2

arcsec 21,0

SQM SQM 20,8 20,6 (mag (mag 20,4 monthly 20,2 NSB 20,0 2014 2015 2016 2017 2018 Year

(a)

Pennar Observatory 21,4 )

-2 21,2

21,0 arcsec

SQM SQM 20,8

20,6 (mag (mag 20,4 monthly 20,2 NSB 20,0 2014 2015 2016 2017 2018 Year

(b)

Figure 5. Ekar Observatory ( a) and Pennar Observatory ( b) monthly NSB trend.

In both sites sites (only (only 5 5 km km away, away, but but in in different different geographical geographical and and altitudes altitudes situations) situations) there there is isa amodal modal NSB NSB value value that that progressively progressively tends tends to to rise, rise, with with a a completely completely overlapping overlapping trend within the uncertainties (slope value of the linear regression equalequal toto 0.00290.0029 forfor EkarEkar andand 0.00320.0032 forfor Pennar).Pennar). This resultresult seemsseems to to be be in contrastin contrast with with the increasethe increase in the in number the number of public of andpublic private and lightingprivate installationslighting installations in our region, in our both region, around both the around observatories the observatories and especially and in especially the adjacent in Padanthe adjacent Plain, roughlyPadan Plain, estimated roughly for estimated those years for at those 10% (unpublishedyears at 10% (unpublished ARPA-Veneto ARPA-Veneto data). data). It will bebe necessarynecessary to accuratelyaccurately study this crucia cruciall aspect aspect in in order to understand if if it it is a realreal decrease in light pollution,pollution, or whetherwhether related toto changeschanges inin environmentalenvironmental parameters,parameters, such as humidity and particulate matter, and/or and/or possible ch changesanges over the years of the spectral components, caused by the progressive replacement of discharge lamps with Light Emitting Diode LED solid-state lamps, whichwhich couldcould causecause an an underestimation underestimation in in the the ALAN ALAN due due to theto the bandwidth bandwidth of the of SQMthe SQM used used [24], or[24], by or a degradationby a degradation of SQM of SQM instruments instruments caused caused by a by decrease a decrease of the of transparencythe transparency of the of window.the window. We also did not correct the data for the decrease of solar activity. This could be an explanation for the observed decrease in the sky brightness but, since it is a small amount, further work is still needed to confirm this.

J. Imaging 2019, 5, 56 9 of 13

We also did not correct the data for the decrease of solar activity. This could be an explanation for the observed decrease in the sky brightness but, since it is a small amount, further work is still needed J. Imagingto confirm 2019, 5 this., x FOR PEER REVIEW 9 of 13

3.6.3.6. Differential Differential Photomet Photometryry with with a Digital a Digital Camera Camera DifferentialDifferential photometry photometry using using aa digitaldigital camera camera with with afisheye a fisheye lens lens was used,was used, not only not to only represent to representlight pollution light pollution in all-sky, in but all-sky, also to investigatebut also to the investigate temporal changethe temporal of night change sky brightness. of night sky brightness.For this purpose, a time series of images were taken between 4 February 2019 at 19:00 to 5 FebruaryFor this 2019purpose,at 05:30 a time local series time fromof images the ARPA-Veneto were taken between headquarters 4 February in the city2019 centre at 19:00 of Padova, to 5 Februaryin a typical 2019 urbanat 05:30 environment, local time from at regular the ARPA-V intervalseneto of 15headquarters min (ISO sensitivity in the city 1600; centre f 2.8;of Padova, exposure in a typical urban environment, at regular intervals of 15 minutes (ISO sensitivity 1600; f 2.8; time 150). exposureIn Figuretime 15").6 we present a clear night, the all-sky images, and the luminance maps obtained through In Figure 6 we present a clear night, the all-sky images, and the luminance maps obtained the SW Sky Quality Camera that show the difference of ALAN during the night; the differential through the SW Sky Quality Camera that show the difference of ALAN during the night; the luminance map was obtained by subtracting the image with lower luminance (03:30) from the image differential luminance map was obtained by subtracting the image with lower luminance (03:30) with higher luminance (20:00). The subtracted map shows that the decrease in luminance occurs from the image with higher luminance (20:00). The subtracted map shows that the decrease in throughout whole sky, but is more pronounced in the city area visible on the horizon. Note the luminance occurs throughout whole sky, but is more pronounced in the city area visible on the switch-off during the night of the lighting of the Sant’Antonio’s church, identified by the orange arrow horizon. Note the switch-off during the night of the lighting of the Sant'Antonio’s church, identified in the figure. by the orange arrow in the figure.

(All-sky 20:00) (All-sky 03:30)

(Luminance map 20:00) (Luminance map 03:30)

Figure 6. Cont.

J. Imaging 2019, 5, 56 10 of 13 J. Imaging 2019, 5, x FOR PEER REVIEW 10 of 13

J. Imaging 2019, 5, x FOR PEER REVIEW 10 of 13

(Luminance difference)

Figure 6. The upper row shows the all-sky RGB images at Padova, Italy, for a clear sky on 4 February Figure2019 at6. 20:00The localupper time row and shows 5 February the all-sky 2019 at RGB 03:30 images local time. at Padova, The middle Italy, row for shows a clear luminance sky on maps 4 Februaryfor the same2019 at time. 20:00 The local lower time row and shows 5 February the difference 2019 at between 03:30 local luminance time. The obtained middle from row subtracting shows images. The orange arrow indicates Sant’Antonio ’s church. luminance maps for the same time. The lower row shows the difference between luminance obtained from subtracting images. The oran(Luminancege arrow indicatesdifference) Sant’Antonio ’s church. The computing of the average luminance in the sector shown in red in Figure6, which includes the

partThe of computing the city visible of the up average to the horizon luminance not covered in the sect by buildings,or shown arein red reported in Figure in Table 6, which5 in comparison includes thewith partFigure the of luminancethe 6. city The visible upper measured uprow to shows with the thehorizonthe SQM, all-sky not and RGB covered confirming images by atbuildings, thePadova, results. Italy,are reported for a clear in skyTable on 54 in comparisonFebruary with 2019 the atluminance 20:00 local measured time and 5with February the SQM, 2019 andat 03:30 confirming local time. the The results. middle row shows Table 5. Average luminance in red sector of Figure6, compared with the zenith NSB from Figure7 on Forluminance the purpose maps of forexhaustive the same information, time. The lower the SQ rowM valuesshows recordedthe difference by the between monitoring luminance unit at the nights of 4 February 2019 and 5 February 2019. the sameobtained times arefrom also subtracting shown, images.as well Theas the oran SQgeM arrow trend indicates recorded Sant’Antonio for the same ’s church. night in Figure 7. Data Source 2019/04/02 20:00 2019/05/02 03:30 ∆ (mcd/m2) Ratio The computing of the average luminancePadova in the sector shown in red in Figure 6, which includes Luminance (mcd/m2) 22.78 19.29 3.49 1.18 the part of the city visible2 up to the horizon not covered by buildings, are1 reported in Table 5 in SQM (magSQM arsec18,8− ) 18.26 18.43 0.78 1.19 comparison with the luminance measured1 Calculated with the byEquation SQM, and (1). confirming the results. For the purpose of18,6 exhaustive information, the SQM values recorded by the monitoring unit at )

the same times are also-2 18,4 shown, as well as the SQM trend recorded for the same night in Figure 7.

18,2 arcsec Padova SQM 18,018,8

17,8 NSB (mag 18,6

17,6) -2 18,4

17,418,2 arcsec 17:00 18:00 19:00 20:00 21:00 22:00 23:00 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 SQM 18,0 hour

17,8 Figure 7.NSB (mag NSB at Padova by SQM for the night 2019/04/02–2019/05/02. 17,6

In order to make a comparison17,4 with the luminance value obtained from the luminance map, the NSB measured at zenith by17:00 SQM 18:00 19:00 was 20:00 converted 21:00 22:00 23:00 into 0:00 1:00luminance 2:00 3:00 4:00 at zenith 5:00 6:00 using7:00 the approximate hour formula [15]: 2 4 −0.4 × L Figure 7. NSBNSBL at at (cd/m Padova Padova) ≅by by 10.8 SQM × 10for the × 10 night 2019/04/02–2019/05/02.SQM 2019. /04/02–2019/05/02. (1)

TableForIn order 5. the Average purpose to make luminance of aexhaustive comparison in red sector information, with of theFigure lumi the 6, nancecompared SQM valuesvalue with obtained recorded the zenith from by NSB the thefrom monitoring luminance Figure 7 unit map, at theon sameNSB the nightsmeasured times of are 4 February alsoat zenith shown, 2019 by asSQMand well 5 Februarywas as theconverted SQM 2019. trend into recordedluminance for at the zenith same using night the in approximate Figure7. formula [15]: Data Source 2019/04/02 20:00 2019/05/02 03:30 Δ (mcd/m2) Ratio Luminance (mcd/m2) L (cd/m22.78 2) ≅ 10.8 × 104 × 19.2910−0.4 × LSQM. 3.49 1.18 (1)

Table 5. Average luminance in red sector of Figure 6, compared with the zenith NSB from Figure 7 on the nights of 4 February 2019 and 5 February 2019.

Data Source 2019/04/02 20:00 2019/05/02 03:30 Δ (mcd/m2) Ratio Luminance (mcd/m2) 22.78 19.29 3.49 1.18

J. Imaging 2019, 5, 56 11 of 13

In order to make a comparison with the luminance value obtained from the luminance map, J. theImaging NSB 2019 measured, 5, x FOR PEER at zenith REVIEW by SQM was converted into luminance at zenith using the approximate11 of 13 formula [15]: −2 2 4 0.4 L 1 SQM (magSQM arsec ) L (cd/m18.26)  10.8 10 10 18.43− × . 0.78 1.19 (1) × × SQM 1 Calculated by Equation (1). The comparison between the luminance difference data shows that the decrease during the night wasThe in absolutecomparison much between smaller the at thelumina zenithnce in difference comparison data with shows that that of horizon, the decrease but fully during comparable the night in wasrelative in absolute terms, much confirming smaller that at the light zenith pollution in compar is dominatedison with by that artificial of horizon, light but dispersed fully comparable from the city. in relativeWe terms, also confirming presented that for lig completenessht pollution is (Figure dominated8) Colour by artifi Correlatedcial light disperse Temperatured from (CCT)the city. maps obtainedWe also through presented the SWfor Skycompleteness Quality Camera (Figure by 8)the Colour same Correlated images, and Temperature the differential (CCT) CCT maps map obtainedobtained through by subtracting the SW theSky image Quality at 03:30 Camera from by one the at same 20:00 images, [25]. and the differential CCT map obtained by subtracting the image at 03:30 from one at 20:00 [25].

(All-sky 20:00) (All-sky 03:30)

(CCT difference)

Figure 8. The upper row shows all-sky CCT images at Padova-Italy for a clear sky on 4 February 2019 Figureat 20:00 8. localThe timeupper and row 5 Februaryshows all-sky 2019 at CCT 03:30 images local time. at Pa Thedova-Italy lower row for showsa clear the sky di ffonerence 4 February between 2019CCT at obtained 20:00 local from time subtracting and 5 February images. 2019 The at orange 03:30 local arrow time. indicates The lower Sant’Antonio row shows s’ church. the difference between CCT obtained from subtracting images. The orange arrow indicates Sant’Antonio s’ church. The study by differential photometry of the night-time evolution of light pollution and correlation withThe the study main ALANby differential sources will photometry be the focus of ofthe our night-time future analyses. evolution of light pollution and correlation with the main ALAN sources will be the focus of our future analyses. 4. Conclusions 4. ConclusionsWe present our first complete analysis of 2018 night sky brightness data recorded night after night by aWe Veneto present regional our first SQM complete network. analysis This dataset of 2018 allows night us sky to establishbrightness how data the recorded light pollution night aafterffects nightsome by observatories. a Veneto regional SQM network. This dataset allows us to establish how the light pollution affects some observatories. The continuous monitoring of the NSB allows for the recording of the specific light pollution of each location, taking into account the variation of weather conditions, climate, and different moon phases, as well as the ability to study long-term trends.

J. Imaging 2019, 5, 56 12 of 13

The continuous monitoring of the NSB allows for the recording of the specific light pollution of each location, taking into account the variation of weather conditions, climate, and different moon phases, as well as the ability to study long-term trends. 2 The annual night sky brightness modal value varied from 18.0 to 21.3 magSQM arcsec− , confirming great variability of light pollution depending on the site position. The NSB absolute frequency distribution of the data generally appeared bimodal, but with significant differences in the widths of peaks and with relative distances. In particular, the altitude of the site, its distance from main pollution sources, but also the different climatic variations, seem to play an important role. The study of seasonal variations of NSB shows the greatest variability in urban and semi-urban locations, but also highlights the peculiar situations in the most remote stations. The pooled study of the ALAN evolution in a single night in all the locations suggest several influencing factors, both natural (weather) and anthropogenic (air pollution, but also traffic and tourism). In the Asiago plateau, the analysis of time trends over the last four years shows a decrease in light pollution, in contrast with the increase in regional lighting systems, which will need to be specifically investigated (in particular, the influence of spectral components and/or the degradation of SQM windows). Finally, the use of all-sky images has been implemented: our first results indicate the great potential of this technique, used here to study the evolution of light pollution during the night. It shows that light pollution decreases at night time, but even at the zenith is due to the lights generated by the city. In future papers, we will study the night sky brightness in correlation with meteorological and environmental factors (in particular the atmospheric particulate) and its night evolution in the network locations, also with the support of all-sky images.

Author Contributions: Conceptualization, A.B., R.B., and S.O.; data curation, R.B. and S.S.; methodology, A.B., R.B., and S.O.; software, R.B. and S.S.; supervision, A.B. and S.O.; validation, S.O.; writing—original draft, A.B. and R.B.; writing—review and editing, A.B., R.B., and S.O. Funding: This research received no external funding. Acknowledgments: The authors wish to thank Filippo Turetta (ARPA-Veneto) for uploading SQM data into databases. Conflicts of Interest: The authors declare no conflict of interest.

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