# Changes in night sky brightness after a countywide LED retrofit

Li-Wei Hung<EMAIL_ADDRESS>Sharolyn J Anderson  Ashley Pipkin  Kurt
Fristrup  Natural Sounds and Night Skies Division, National Park Service, 1201
Oakridge Dr., Suite 100, Fort Collins, CO 80525, USA

###### Abstract

The US National Park Service (NPS) Night Skies Program measured changes in sky
brightness resulting from a countywide lighting retrofit project. The retrofit
took place in Chelan County, a gateway community to North Cascades National
Park and Lake Chelan National Recreation Area in Washington State. The county
retrofitted all 3,693 county-owned high pressure sodium (HPS) street lamps to
full cutoff LEDs. This number is about 60% of the County’s total outdoor
street and area lights. About 80% of the newly installed lights were 3000K in
color temperature and 20% were 4000K. The 4000K LEDs were used to meet
Washington State Department of Transportation guidelines. To measure sky
brightness, we used the NPS night sky camera system before the retrofit
started in 2018 and after its completion in 2019. These images were
photometrically calibrated and mosaicked together to provide hemispherical
images in $V$ band. For comparison with our ground-based measurement, we
obtained the satellite imagery taken by Visible Infrared Imaging Radiometer
Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership satellite.
Our measurements show that the post-retrofit skyglow became brighter and
extended higher in the sky, but upward radiance, as measured by the day-night
band radiometer, decreased. These divergent results are likely explained by a
substantial increase in light emitted at wavelengths shorter than 500 nm, and
a relative decrease in zenith light emission due to better shielded
luminaires. These results also demonstrate that earlier models relating VIIRS
day-night band data to skyglow will – at a minimum – require substantial
revision to account for the different characteristics of solid state
luminaires.

###### keywords:

night sky brightness skyglow light pollution LED lighting retrofit VIIRS

##  1 Introduction

As of 2017, 19% of outdoor lighting in the United States (US) had been
converted to light-emitting diodes (LEDs), resulting in energy savings of 1.1
quadrillion British thermal units — just over 1% of US total energy
consumption (US Department of Energy Office of Energy efficiency & Renewable
Energy(2019), USDOE). In addition to reducing energy consumption and extending
service life, compact LED lamps can provide more precise control over the
spectrum, light level, and projection of light than legacy lamps, such as
High-Pressure Sodium (HPS) lamps, can. This transition in lighting will
dramatically alter skyglow across the planet, but it is unclear if the change
will improve or degrade the quality of the night sky.

There is no consensus on whether current practices for LED lighting retrofits
will increase or decrease skyglow. USDOE claims the increased directivity
associated with LED upgrades will result in reduced skyglow (Kinzey et al.,
2017). While directivity and shielding are important, they are not the only
factors at play. An LED retrofit often results in an increase of shorter
wavelength light. When lumens are equal the bluer spectral tone of most LEDs
results in increased atmospheric scattering (Luginbuhl et al., 2014). To
accurately model skyglow, we must also identify how the shift to LEDs changes
spectral power distribution (SPD) and luminous flux (Duriscoe et al., 2014).

Because of the reduced financial cost of lumens provided by LED technology,
increases in the amount of artificial light is expected to grow across the
planet (Kyba et al., 2017; Saunders et al., 2012; Borenstein, 2015). Luginbuhl
et al. (2014) predicts the retrofit to LEDs from HPS could result in a night
sky three times brighter, depending on the change in SPD. The USDOE, relying
heavily on assumed implementation of responsible lighting practices such as
mandatory shielding and dimming, states that residents near the city should
not expect to see increased skyglow and that conditions may improve (Kinzey et
al., 2017). With so many conflicting statements managers making decisions
about retrofits do not have consistent guidance to inform decisions.

Measurements of nighttime lighting have typically used the Day-Night Band
(DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the
Suomi National Polar-orbiting Partnership satellite (Lee et al., 2014). VIIRS
data are the dominant source of studying large scale outdoor lighting (Falchi
et al., 2016; Kyba et al., 2017; Barentine et al., 2018). There are, however,
two major concerns with using satellite data to characterize skyglow after an
LED retrofit. First, satellite estimates of skyglow are uniformly derived from
upward radiance, which can mischaracterize artificial light along the horizon.
Improved shielding and increased atmospheric scattering from LEDs may
exacerbate these inaccurate estimates (Luginbuhl et al., 2014). Second, there
are concerns about “blue blindness.” This refers to the inability of VIIRS to
detect short wavelength light ($<$500nm), which has been cited in previous
studies measuring light pollution during the shift to LEDs (Kyba et al., 2017;
Barentine et al., 2018; Falchi et al., 2016). The measured radiance values can
go down not because of decreases in light, but because VIIRS does not sense a
significant portion of the radiation of these bluer lights. Without accurate
measurements, we may mischaracterize this rapid global change.

Given these concerns, direct measurements of skyglow are crucial to overcoming
the remote sensing limitations of the satellite data. There are currently no
studies comparing VIIRS data with calibrated camera data that accurately
measure skyglow from the horizon to the zenith.

In 2018, Chelan County, Washington State, US (Figure 1) began an LED retrofit
that was completed in 2019. This presented a perfect opportunity to measure
skyglow after a direct one-for-one LED lighting retrofit. An important county
objective for this retrofit was to conserve energy, but there was also
interest in reducing the skyglow visible to many protected areas located
within the county, including North Cascades National Park and Lake Chelan
National Recreation Area. Community awareness about the negative impacts of
light pollution and increased short wavelength light on ecology (Longcore and
Rich, 2004; Hölker et al., 2010; Manfrin et al., 2017) and human health
(Chepesiuk, 2009; Cho et al., 2015; Falchi et al., 2011), as well as on
recreation (Collison and Poe, 2013) and local economies (Mitchell and
Gallaway, 2019), encouraged the county to request that information be
collected before and after the retrofit to determine if a reduction in skyglow
was achieved. The county retrofitted 3,693 HPS drop lens luminaires to full
cutoff LED luminaires.

Color temperature measurements of typical HPS lamps range from 2100K-2500K.
Most of the retrofitted LEDs were 3000K; 717 lights were retrofitted with
4000K LEDs to meet Washington State Department of Transportation guidelines
(Washington State Department of Transportation, 2019). Crude estimates of
around 18,400 lumens per light were calculated from wattage information of old
HPS lighting. Estimates for new LED lighting, based on specifications provided
by Chelan County, were approximately 9,500 lumens for each light.

This paper uses two methods to analyze the skyglow brightness before and after
an LED retrofit in Chelan County Washington. We measured the sky brightness
directly using the National Park Service (NPS) night sky calibrated CCD camera
system (Duriscoe et al., 2007). This camera system provides high-resolution,
panoramic measurements in the $V$ spectral band, closely matching the Johnson-
Cousins $V$, from the horizon to the zenith. We then compared the NPS CCD
camera skyglow measurements with VIIRS measurements of upward radiance.

Due to the limited spectral sensitivity and confined observing angle, we
believed that VIIRS data would underestimate skyglow after the one-for-one LED
retrofit in Chelan County. Difference in the spectral characteristics of the
relevant sources, filters, and detectors adds the complexity for this study.
Cao and Bai (2014) showed a nice comparison between the spectral curves of the
VIIRS DNB, LED, and HPS. Duriscoe et al. (2007) provided the details of the
$V$ band filter and how it compares to the spectral response of the human eye.
Using both satellite imagery and calibrated camera observations, we highlight
the challenges of quantifying this change using only VIIRS satellite data.
This case study provides insight on how LED lighting retrofits alter skyglow.

Figure 1: Location map for Chelan County Study Area with county outlined, five
cities within the county and a overview map of United States with Washington
State highlighted and Chelan County near the center of state.

##  2 Methods

###  2.1 Satellite Measurements of Upward Radiance

####  2.1.1 Nighttime Imagery

Satellite imagery is an efficient and consistent way to get a spatial
perspective of a particular location, in this case before and after snapshots
of Chelan County streetlights. The VIIRS instrument collects the highest
spatial and temporal resolution nighttime imagery over the area of interest.
This instrument has a DNB (day-night band) designed to collect high-quality
radiometric data at night. The VIIRS DNB data are collected daily, with a
local overpass at approximately 01:30 AM. VIIRS DNB has a spectral range from
500-900 nm, a 12-bit radiometric resolution, and spatial resolution of 742 m
at nadir (resampled to 500 m for composite images).

We downloaded six of the North American monthly average composites (Tile
1_75N180W) that are freely available from Payne Institute at the Colorado
School of Mines (April, May, and June of 2018 and 2019) (Elvidge et al., 2017)
and selected the datasets that were stray light corrected by the Payne
Institute (Mills et al., 2013). We clipped the satellite images to a
rectangular area encompassing Chelan County (Figure 1).

The monthly composite files with extensions "avg_rade9" contain floating point
radiance values with units in nWcm-2sr-1 with the original DNB radiance values
multiplied by one billion (1E9). The files with extension “cvg” are integer
counts of the number of coverages or total observations available for that
specific pixel that month; these were used to calculate the weighted three
month averages.

All spatial data were geo-referenced to an equal area projection, NAD 83
Albers with metric units. The VIIRS images were clipped to Chelan County
boundaries and potential noise was removed by setting all values less than or
equal to 0.5 to 0.0 (Duriscoe et al., 2018), or in other words, we changed
radiance values of 0.000000005 to 0.00.

We extracted two temporal datasets: one set contained the monthly composite
clipped images for May 2018 and for May 2019, and the other set was a three-
month average of April, May, and June for 2018 and for 2019. The vegetation
coverage and the ground albedo were very similar during the study timespan
because data were compared during the same months in both years. In May, our
field trips confirmed that the city streets are completely free of snow. By
using the average over the whole month of May and over the three-month period,
we reduced the selection bias caused by the daily atmosphere variation.

####  2.1.2 Satellite Data Analysis

Our satellite data analysis consisted of pre-processing the VIIRS imagery for
use in the study, extracting (using zonal statistics) the radiance values
within city and county boundaries (Stage 1), extracting streetlight data for
comparison statistics (Stage 2), and image subtraction to visualize the
differences (Stage 3).

####  2.1.3 Data Processing

Stage 1. Zonal Statistics were used to get the sum of lights (SOL) within
county and city boundaries. Zonal statistics are calculated for the pixels
whose centers are located within the zone boundary. This tool runs descriptive
statistics for pixel values (i.e., VIIRS radiance) within polygon boundaries,
bridging the vector and raster data formats used in spatial analysis. Since
the ground-based measurements were taken in May, we extracted the SOL from May
2018 and May 2019 VIIRS composites within the city boundaries, which provided
a direct month-to-month comparison with ground measurements. We also extracted
SOL for the three-month averages in 2018 and 2019 to reduce the monthly
variance in VIIRS measurements (Coesfeld et al., 2018).

Stage 2. We identified all of the pixels that had any streetlights present. To
extract the radiance per pixel, a fishnet dataset was created that aligned to
the VIIRS imagery. The following six attributes were created: 1) the VIIRS
values for the month of May, 2) the VIIRS monthly average, 3) the number of
streetlights in each pixel polygon, 4) binary-true-false attribute if all
lights in pixel polygon were retrofitted (Complete), 5) binary-true-false
attribute for a mix of retrofitted and not retrofitted lights (Partial), and
6) binary-true-false attribute for pixels with unmodified street lighting
(None). A paired Student t-test was run with these variables.

Stage 3. We subtracted the two temporal datasets: May 2019 minus May 2018, and
Average 2019 minus Average 2018. Overlaying this information with the
retrofitted and not retrofitted streetlights within each city, we examined the
spatial information. Image differencing calculated the changes between pixels
in two images on a pixel-by-pixel basis to give a quantitative and visual
assessment of temporal changes collected by the satellite data.

###  2.2 Camera Measurements of Sky Brightness

####  2.2.1 NPS Camera System and Data Collection

We measured the brightness of the night sky by taking images with the NPS
camera system. The system included a Nikon 50mm f/1.2 lens, a V-band filter,
and a research-grade charge-coupled device (CCD) (Duriscoe et al., 2007). The
system had $24^{\circ}$ x $24^{\circ}$ field of view with image resolution of
1.4 arcminute/pixel. A complete image acquisition sequence takes approximately
40 minutes and yields a data set of 45 images forming a hemispherical view.
The broadband $V$ filter approximates the astronomical Johnson-Cousins V
filter and lets only visible light pass through, allowing the detected signal
to closely represent what human eyes can see with photopic vision. There often
exists some differences between sensor sensitivity, human vision, and light
sources (eg. Sánchez de Miguel et al. 2017). At an urban transition site like
this, the peak sensitivity of human’s nighttime vision is shifted slightly
towards the blue.

The data were collected on Burch Mountain ($None$N, $None$W), located
approximately 13 km NNW of Wenatchee. This site was selected based on its
relatively high elevation (1,250 m), which offered a clear view of the horizon
above the largest population center in Chelan County. No other large
population centers are along the SSE line-of-sight within 300 km, so the
contamination from other distant lights was minimal.

Two nights of data were collected: one in 2018, before the retrofit started;
and one in 2019, after its completion. Details of each data collection event
are listed in Table 1. The extinction coefficients were measured from the
collected images to characterize the attenuation of light due to absorption
and scattering of the atmosphere. All the data were collected when the sun and
the moon were more than 18∘ below the horizon.

Table 1: Data sets collected on Burch Mountain

Date | Local Time (h) | Extinction Coefficient (mag/airmass) | Sky Condition
---|---|---|---
2018-05-07 | 22.8 | $0.296\pm 0.006$ | partly cloudy and humid
2018-05-07 | 23.4 | $0.284\pm 0.004$ | partly cloudy and humid
2018-05-08* | 0.1 | $0.256\pm 0.002$ | clear
2019-05-31* | 23.6 | $0.266\pm 0.002$ | clear but hazy during the day

*Best data set collected of the night.

To complement the hemispherical image, visual assessments of night sky
condition were recorded. Bortle Class was assigned based on visible celestial
objects and appearance of the night sky on an integer scale from 1-9 (Bortle,
2001). Limiting magnitude was determined by identifying the brightness of the
faintest star visible with naked eye. Zenith brightness also was measured with
the hand-held Unihedron SQM. The SQM has the half width at half maximum
angular sensitivity $42^{\circ}$.

####  2.2.2 Night Sky Image Processing

The observational data from Burch Mountain were processed using the NPS
pipeline (Duriscoe et al., 2007). The pipeline performs basic data reduction
including bias and dark subtraction, flat fielding, and detector linear
response correction. Absolute brightness and position calibration were done by
using the standard stars captured in the images. The calibrated images in a
set were then stitched together to form panoramic images with the resolution
of 0.05 degrees per pixel in V band.

The extinction coefficient for each data set was measured to characterize the
atmospheric conditions. If the atmospheric aerosol content is high, the
extinction coefficient will be high because it diminishes the star light via
increased absorption and scattering. Thus, measuring extinction coefficients
is a way to probe the optical property of the atmosphere. The coefficient was
measured using all the standard stars detected in the images. On average,
about 125 stars were used. They spread across the sky in all azimuthal
directions and from zenith to about 12 degrees above the horizon. We measured
the brightness of those stars and compared them to their absolute brightness.
By plotting the airmass derived based on their altitudes and the difference of
their absolute and apparent magnitudes, the points formed a linear trend. We
measured the extinction coefficient by finding the slope through the least
square fit. Table 1 listed the extinction coefficient measured in each data
set along with its uncertainty. The method we used here is the same as the one
published in the original research (Duriscoe et al., 2007). The measured
extinction coefficients were very close to each other, which means that the
atmospheric conditions, presumably the aerosol content, were very similar.

To obtain some sky brightness metrics with anthropogenic light only, we
subtracted out the natural sky brightness. A natural sky model (Duriscoe,
2013) was built based on the time and the location of the observation.
Components of natural sky model included zodiacal light, Milky Way, airglow,
and atmospheric diffuse light (ADL). Airglow and ADL were assumed to be
azimuthally symmetric. The model parameters were set to the same standard
values across the data sets except actual measured atmospheric extinction
coefficients were used for the zodiacal light, Milky Way, and airglow
components. The ADL accounts for the general brightening of the sky near
horizon because of multiple scattering, and the extinction profile for the ADL
was adopted directly from Duriscoe (2013).

##  3 Results

###  3.1 Changes in Upward Radiance

In Stage 1, once we extracted the mean radiance values and the sum of the
lights (SOL) within the county and city boundaries of Chelan County. The
difference in SOL for Chelan County from both temporal data sets was negative.
This indicates that the entire county was darker in 2019 than in 2018 as
detected by the VIIRS sensor. Then we did the same for the five cities. The
mean radiance values for the cities from May 2018 to May 2019 extracted from
the May VIIRS composites overall went down. The differences between SOL values
for the month of May in 2019 and 2018 showed all five cities had a lower SOL
value in 2019 (Table 2). The 2019 US Census of the city populations were
provided in the table as a measure of the city size. For the three-month
average data set, all five cities had a negative mean difference (Table 2).
This indicates less detectable light in 2019. From the three-month average
images, again all five cities had negative SOL values, lower in 2019 than in
2018. Recall a negative difference indicates that there was less light
detected in 2019 than in 2018 in Chelan County.

Table 2: Difference between Mean Radiance and Sum of Lights (SOL) for Cities in Chelan  City | Population | Pixel | May Difference | Three month Average Diff
---|---|---|---|---
Name | 2019 | CNT | MEAN | SOL | Mean | SOL
Cashmere | 3,172 | 15 | -3.601 | -54.020 | -3.367 | -50.508
Chelan | 4,237 | 81 | -0.351 | -28.420 | -0.020 | -1.636
Entiat | 1,280 | 26 | -0.381 | -9.920 | -0.576 | -14.984
Leavenworth | 2,029 | 17 | -0.021 | -0.349 | -0.057 | -0.968
Wenatchee | 34,360 | 123 | -1.957 | -240.670 | -2.891 | -355.549

Stage 2 of our analysis created a "fishnet" mesh of polygon boundaries aligned
with the VIIRS imagery pixels to create 731 pixel polygons. We then counted
the streetlights within each pixel polygon and separated the polygons into
three groups: Complete: all lights in the pixel polygon were retrofitted
(127); Partial: the pixel polygons included a mix of retrofitted and not
retrofitted lights (258); and None: no changes or retrofits in street lighting
(346). Running a paired Student t-test revealed that the VIIRS values were
significantly lower in 2019 for both the Complete and Partial groups, and not
significantly different for None group (Table 3).

Table 3: t-test of the VIIRS radiance values from 2018 and 2019 for pixels with streetlights  Streetlights | Number of | May | Three month Average
---|---|---|---
Retrofits | Pixel Polygons | t-stat | p-value | t-stat | p-value
Complete | 127 | 2.592 | 0.010 | 2.862 | 0.005
Partial | 258 | 5.826 | 0.000 | 8.701 | 0.000
None | 346 | -1.600 | 0.110 | -1.592 | 0.113

To understand if there was a significant decrease with retrofitted lights, we
ran a simple linear regression of the retrofitted lights and the difference
between the VIIRS radiance values. This regression represents only those
pixels that had lights replaced. The independent variable (Count Replaced) is
the number of lights that were replaced within that pixel (each point
represents a pixel polygon). The dependent variable (change between 2019 &
2018 or ‘delta’) represents the average radiance in that pixel in 2019 minus
the average radiance in that pixel in 2018. The linear regression showed that
when the number of replaced bulbs increases, delta decreases, demonstrating
that the VIIRS satellite data reports less (or darker) average radiation as
more bulbs are replaced with LEDs (Figure 2).

Figure 2: Regression plot showing a decrease in VIIRS radiance values as the
number of lights are retrofitted increases. The shaded area show the 68%
confidence interval.

Stage 3 was a simple image subtraction of the VIIRS data to visualize the
differences between the before and after lighting retrofit: the temporal 2019
datasets minus the 2018 datasets (Figure 3). The areas of blue are darker in
2019; white is the same both years; and gold is brighter in 2019. Combining
the information with the retrofitted and not retrofitted streetlights within
each city yields a spatial display of the information. The visual assessment
of these data supports the statistical analysis. The majority of Chelan County
remained the same, since most of the county is undeveloped and without
lighting. The overall reduction in radiance identified by VIIRS data mainly
occurred in the cities where streetlights had been retrofitted.

Figure 3: Difference between VIIRS 2019 and 2018 for the month of May and for
the 3 Month Average of April, May, and June.

Of the 3693 retrofitted streetlights in Chelan County, 3250 are completely
within the boundaries of the five cities. A closer look at the impact of the
retrofit on the cities shows an overall decrease in radiance received by the
VIIRS sensor for all five of them (Appendix A: Five cities in Chelan County,
Washington contains large scale maps for each of the cities within Chelan
County).

###  3.2 Changes in Sky Brightness

The observations on Burch Mountain show that the sky became brighter after the
lighting retrofit. Figure 4 shows the calibrated V-band images taken in 2018
(top) and 2019 (bottom), corresponding to before and after the lighting
retrofit. South is centered in the images. The Milky Way appears at the
similar sky location in both years, reaching about $45^{\circ}$ above the
eastern horizon. The big light dome near the image center is from Wenatchee,
the largest population center in Chelan County. The colorbar shows the level
of sky brightness measured in magnitude per square arcsecond (mag/arcsec2).

A sky background brightness of 22 mag/arcsec2 is considered pristine
(Garstang, 1989; Patat, 2008), and a sky background brightness $<20$
mag/arcsec2 would be considered substantially altered from the natural
condition. Purple and dark blue indicate unpolluted sky, and the Milky Way
under the natural condition appears green in this color scheme. In 2019, most
of the night sky has greatly deviated from the natural condition. Comparing
the 2019 image to the 2018 image, the Wenatchee light dome appears larger and
extends higher into the sky. The Milky Way is visible in both years but it
fades into the city light dome at a higher elevation after the lighting
retrofit. The zenith also shows skyglow.

Figure 4: Panoramic V-band images taken before (top) and after (bottom) the
lighting retrofit on Burch Mountain. These observed images capture light from
both natural and artificial sources. Sky brightness is displayed with the
false colors in mag/arcsec2. The observations show that the light dome became
bigger and extended higher into the sky. Overall, the sky became brighter
after the LED lighting retrofit.

We quantify this change with the sky brightness metrics derived from the
images. Five indicators (Duriscoe, 2016) based on the observed panoramic
images focus on different aspects of sky brightness: horizontal illuminance,
maximum vertical illuminance, zenith brightness, percentage of lost stars, and
all-sky light pollution ratio (ALR). Illuminance, the amount of visible light
incident on a unit surface area, quantifies how brightly the landscape is lit
from the night sky. The zenith brightness is the sky brightness overhead. The
lost star metric estimates the percentage of stars that become invisible under
the influence of skyglow. ALR is an index of total skyglow calculated by
dividing the total brightness of the skyglow by the brightness of the natural
dark sky. These quantitative measurements are summarized in Table 4.

Table 4: Sky Brightness Metrics

| 2018-05-08 | 2019-05-31 | Natural Reference
---|---|---|---
Horizontal Illuminance (mlx) | 1.55 | 2.31 | 0.80
Max. Vertical Illuminance (mlx) | 3.85 | 5.23 | 0.40
Zenith Brightness (mag/arcsec2) | 21.60 | 21.10 | 22.00
Lost Stars† (%) | 39 | 51 | 0
All-sky Light Pollution Ratio† | 2.30 | 3.69 | 0
Bortle Class | 6 | 5 | 1
Limiting Magnitude | 5.6 | 5.8 | 7.0
SQM (mag/arcsec2) | 21.20 | 21.03 | 22.00

†metrics derived with the natural sky brightness modeling

Two visual assessment metrics are also reported here: Bortle Class and
limiting magnitude. Bortle Class 6 corresponds to "bright suburban sky"
whereas 5 corresponds to "suburban sky." Limiting magnitude of 5.6 and 5.8
indicate the brightened sky background limits the faintest star visible with
naked eye. The final metric is a measure of zenith brightness determined with
the hand-held Unihedron SQM. The natural sky reference value of each metric is
listed in the last column of Table 4.

Overall, the night sky on Burch Mountain resembles suburban sky that is
getting brighter. The decrease of 0.5 mag/arcsec2 for zenith brightness
indicates the sky overhead on Burch Mountain was 60% brighter in 2019 than in
2018. The visual impact of the skyglow includes losing an estimated 51% of the
stars visible to the naked eye. When considering the entire sky, the ALR
values indicate the total artificial light was 2.30 times the natural light
level in 2018 and increased to 3.69 in 2019. The Bortle class and limiting
magnitude are assessed visually, in this case by the same two researchers on-
site. Visual assessment is more variable than the other methods of data
collection, and the changes measured here were smaller than the human eye can
confidently perceive, which means that the reported values for these metrics
are not as reliable as those of the other metrics. In summary, based on our
calibrated and objective measurements, the sky was brighter in 2019, after the
LED retrofit.

##  4 Discussion & Conclusion

The VIIRS satellite did not detect the increase of skyglow due to the LED
lighting retrofit. Our results show that the brightness of the retrofitted
area decreased from the satellite’s view, even though the measured skyglow
increased. These decreases in VIIRS radiance measurements were probably due to
improved directivity, shielding, and shift in spectrum of the LED
streetlights. Our findings support previous claims (Kyba et al., 2017) that
VIIRS DNB, due to its “blue blindness,” may falsely suggest a reduction in
light pollution in many cities after an LED retrofit.

Using satellite imagery has become an increasingly popular and accessible way
to estimate skyglow around the globe. For example, Light pollution map (Stare,
2019) and NASA’s Black Marble (Román et al., 2019) offer simple web interfaces
for public use. However, prior models relating VIIRS DNB data to skyglow will
– at a minimum – require substantial revision to account for the different
characteristics of solid state luminaires. This research is critical for the
local communities, lighting industry and scientific community in understanding
how lighting retrofits affect satellite measurements and modeling of light
pollution.

We feel confident that our ground observations reflect the actual sky
brightness change due to the lighting retrofit because those observations were
taken under similar conditions. Our finding that the sky get brighter is based
on the two nights of measurements. With a small sample size, selection bias
needs to be considered. Natural sources of brightness can vary from night to
night and sometimes in shorter time scale like in minutes or hours. Although
the natural variation could affect the measurements, the designed observation
time allow us to eliminate some seasonal and hourly variations from sources
such as the Milky Way and the zodiacal light. Furthermore, the measured
extinction coefficients show that the atmosphere exhibits similar scattering
and absorption property on those two nights. Because of the similarity of the
observing time and condition, we report our findings directly from the
observed images.

Comparing the images before and after the retrofit, the light dome over
Wenatchee became brighter and extended higher into the sky. The zenith sky
brightness on Burch Mountain increased by 60%. The actual brightening based on
human night vision is likely to be more than we measured. LEDs emit a
signature blue peak at about 450 nm, which does not pass through the V-band
filter attached to our camera. If the light from the blue peak were captured,
we would expect the results to show the sky had brightened even more.

Though expert visual assessments in the field suggested the sky background
became darker, our objective measurements showed otherwise. This reminds us
that visual sky assessments, even by professionals, are not reliable for
assessing the effects of lighting upgrades. Sky brightness is better measured
by radiometric methods. Calibrated camera images are the most accurate method
to document changes in the radiance and shape of light domes.

This case study shows that typical one-for-one HPS to 3000K LED replacements
are likely to increase light pollution. Chelan County retrofitted all of their
3,600+ county-owned street lights in a period of one year. The county staff
intentionally chose mostly 3000K LEDs, a color temperature generally regarded
as night sky friendly. Furthermore, the estimated average lumen output per
light decreased by about 50%. The new LEDs are more energy efficient, have
lower brightness, provide better directed light beams, and are fully shielded.
Yet, these advantages did not result in reduced skyglow.

The main motivations for streetlight upgrades are reduced costs due to lower
energy use and longer lamp lifetimes. Unarguably, LEDs are more energy
efficient than the old HPS streetlights. According to the data provided by
Chelan County, the total energy savings achieved by the end of 2019 from this
streetlight retrofit project topped 2.6 million kilowatt-hours — enough energy
to power 120 all-electric homes in the State of Washington for a year. Going
forward, it is easy to see how the cost savings can add up rapidly over time,
especially with the long expected lifetime of LED lights. Because of these
benefits, many cities, including Chicago and Denver, are in the middle of
large-scale LED retrofits, and many more cities are planning them.
Understanding societal and ecological impacts from lighting retrofits is an
increasingly important global concern.

How do we convert to LED technology in a way that reduces energy consumption,
reduces skyglow, preserves historical ambiance, and still meets the safety and
security needs of our communities?

We believe with proper design and usage, energy savings and light pollution
reductions can stand on the same side, both benefiting from LED retrofits.
This study implies reductions in skyglow will require lowering overall light
level (> 50%), using warmer lights (< 3000 K), or both. Perceived conflicts
between streetlighting objectives and light pollution can be further mitigated
through controls that dim lights during periods of low traffic. More case
studies are needed to pinpoint the exact criteria for a lighting retrofit to
reduce skyglow. Nonetheless, a strong proactive lighting design is the key to
converting to LED lights without increasing light pollution.

## Acknowledgements

The authors thank Jim White from Chelan County for providing lighting retrofit
data, including luminaire specification and energy savings; Bob Meadows for
processing data and selecting observation sites; and Kathlyn Nuessly for
assisting with graphic design.

## Appendix A: Five cities in Chelan County, Washington

This appendix provides a detailed description of the changes within the five
main cities in Chelan County. Table 5 summarizes the retrofitted lights in the
cities. The number of retrofitted lights within the five cities in Chelan
County varied from 107 to 3390. Table 6 shows that all of the cities had a
decrease in the mean value and the SOL for both the May-to-May and 3-month
average data sets. This indicates less light was sensed by the satellite. The
mean radiance values for the cities from May 2018 to May 2019 extracted from
the May VIIRS composites overall went down. The differences between SOL values
for the month of May in 2018 and 2019 showed all five cities had a lower SOL
value in 2019 (Table 6). The population of the cities were provided in the
table as a proxy for the size of the cities. The percentage of retrofitted
lights in each city ranges from 60% to 82%. This table is followed by the
differences in the three month average data set, again showing that all five
cities had a negative difference (Table 7).

Table 5: Streetlights Replaced within Cities in Chelan County  CityName | Streetlights | Replaced | Percent Retrofitted
---|---|---|---
Cashmere | 707 | 578 | 82
Chelan | 321 | 157 | 48
Entiat | 107 | 81 | 76
Leavenworth | 301 | 240 | 80
Wenatchee | 3390 | 2018 | 60
Table 6: Monthly May Mean Radiance and Sum of Lights (SOL) in nWcm-2sr-1 for Cities in Chelan County | Percent Lights | Population | Mean | Mean | Mean Diff | SOL | SOL | SOL Diff
---|---|---|---|---|---|---|---|---
City | Retrofitted | 2019 | 2018 | 2019 | 2019-2018 | 2018 | 2019 | 2019-2018
Cashmere | 82 | 3,172 | 17.63 | 14.03 | -3.60 | 264.46 | 210.44 | -54.02
Chelan | 48 | 4,237 | 5.57 | 5.22 | -0.35 | 450.84 | 422.42 | -28.42
Entiat | 76 | 1,280 | 3.56 | 3.18 | -0.38 | 92.59 | 82.67 | -9.92
Leavenworth | 80 | 2,029 | 10.77 | 10.75 | -0.02 | 183.12 | 182.77 | -0.35
Wenatchee | 60 | 34,360 | 29.79 | 27.83 | -1.96 | 3663.98 | 3423.31 | -240.67
Table 7: Changes in Three Month Average dataset
of Mean Radiance and Sum of Lights (SOL)
in nWcm-2sr-1 for Cities in Chelan County  | Mean Diff | SOL Diff
---|---|---
City | 2019-2018 | 2019-2018
Cashmere | -3.36 | -50.51
Chelan | -0.20 | -1.64
Entiat | -0.58 | -14.98
Leavenworth | -0.06 | -0.97
Wenatchee | -2.89 | -355.55

Each of the five cities within Chelan county has four maps that provide
geographical context about the retrofitted lighting within city limits:
Cashmere (Figure 5), Chelan (Figure 6), Entiat (Figure 7), Leavenworth (Figure
8), and Wenatchee (Figure 9).

Map A (top left) is a context map for cities. The base map has topography and
water features with some landscape features labeled. All streetlights are
shown with retrofitted (teal) and unchanged (khaki) lights. The scale bar
applies to all four maps.

Map B (top right) uses graduated symbology to represent the number of lights
retrofitted within the 500 m by 500 m pixel polygons.

Map C (bottom left) shows the change in upward radiance detected by VIIRS
between May 2019 and May 2018. The negative values that had less light in 2019
are blue; if no change was detected, those cells are white; and the brighter
values with more light after the retrofit are in yellow. This provides an
overview of the May differences between the before and after retrofit
measurements.

Map D (bottom right) shows the change in upward radiance detected by the
three-month average (April, May and June) of VIIRS monthly composites between
2018 and 2019. The negative values that had less light in 2019 are blue; if no
change was detected, those cells are white; and the brighter values with more
light after the retrofit are in yellow. Three month averages help mitigate the
variance between VIIRS monthly composites.

Figure 5: Example of Cashmere radiance changes between VIIRS 2019 and 2018 for
the month of May and for the 3 Month Average of April, May, and June. Figure
6: Example of Chelan radiance changes between VIIRS 2019 and 2018 for the
month of May and for the 3 Month Average of April, May, and June. Figure 7:
Example of Entiat radiance changes between VIIRS 2019 and 2018 for the month
of May and for the 3 Month Average of April, May, and June. Figure 8: Example
of Leavenworth radiance changes between VIIRS 2019 and 2018 for the month of
May and for the 3 Month Average of April, May, and June. Figure 9: Example of
Wenatchee radiance changes between VIIRS 2019 and 2018 for the month of May
and for the 3 Month Average of April, May, and June.

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