Quantifying the influence of urban sources on night light emissions
Introduction
Night lighting is a clear indicator of human activity. Several previous studies have used nighttime light data from the United States National Oceanic and Atmospheric Administration (NOAA) to detect human footprints and economic growth (Doll et al., 2006, Elvidge et al., 1997, Elvidge et al., 1997, Liu et al., 2012, Yi et al., 2014). Recently, however, light pollution has become a significant environmental problem (Pauwels et al., 2019) since it causes sky glow, changing the night sky. The impact of light pollution on astronomical and ecological systems has been well documented (Davies et al., 2013, Davies et al., 2014, Hill, 1990, Longcore and Rich, 2004, Pauwels et al., 2019, Rodrigues et al., 2011). Because light pollution can also disturb human health, such as inducing insomnia, depression, and cancer, excessive artificial light is now widely recognized as a form of pollution (Claudio, 2009, Gallaway et al., 2010).
Researchers increasingly recognize light pollution in urban locations as a complex, serious problem (Ma, Zhou, Pei, Haynie, & Fan, 2014). Although artificial light is a resource for a city’s safety and aesthetics, its role in keeping inhabitants safe is not uniform, and brighter is not always better (Chalfin et al., 2019, Fotios and Price, 2017, Marchant, 2004, Marchant, 2010). Recent studies have also shown that economic growth, safety, and security can attend conservative light use (Kyba et al., 2015, Kyba et al., 2017, Steinbach et al., 2015). Thus, diagnosing the current intensity levels of artificial light radiance and identifying light pollution’s potential sources in urban environments are essential elements in properly managing light pollution in a city.
Artificial light intensity in cities has been quantified in various ways, including remote sensing satellite images, direct equipment measurement (e.g., sky-quality meters, luminance meters, and illuminometers), and image processing of camera photos, including those from the International Space Station (Kotarba and Aleksandrowicz, 2016, Kyba et al., 2015, Levin and Duke, 2012, Ma et al., 2014, Zamorano et al., 2011). In particular, processing satellite imagery is common (Bennie et al., 2014, Cinzano et al., 2001, Han et al., 2014, Kim and Choi, 2015). Specifically, the Defense Meteorological Satellite Program/Operational Linescan System (DMSP-OLS) sensor has been widely used by previous studies to monitor night light changes on the global level (Aubrecht et al., 2009, Aubrecht et al., 2010, Bennie et al., 2014, Cinzano et al., 2001, Doll, 2008, Li et al., 2013, Zhang et al., 2015). The National Aeronautics and Space Administration’s (NASA) Visible Infrared Imaging Radiometer Suite day-night band (VIIRS-DNB) data collected by the Suomi National Polar-Orbiting Partnership (NPP) satellite has been used in studies since 2011 (Cao and Bai, 2014, Cinzano and Falchi, 2014, Falchi et al., 2016, Kyba et al., 2015, Rybnikova and Portnov, 2017). Fig. 1 shows an example of VIIRS-DNB data in use in the South Korean context.
Previous studies have shown that light emissions in urban areas differ depending on urban–built environment characteristics and human activity, such as industrial and commercial activities and residential patterns in urban environments. These studies have mentioned population density, built-up areas, roads, large infrastructure, sports facilities, and industrial and commercial areas as potential sources of light pollution that should be monitored in-depth (Kyba et al., 2015). While most previous studies that have used remote sensing data have been conducted primarily at the global or national levels, intra-city research should be conducted more actively at the micro- and meso-spatial scales to improve our understanding and quantify the impact of urban built environments on night light emissions (“light pollution”).
The purpose of this study is therefore to examine the relationship between light pollution and urban built environments in a more systematic, quantitative way. Toward this end, we applied log-log regression models on VIIRS-DNB composite data and land-use data using grid cells at a spatial resolution of 15 arc seconds. Based on previous literature, including the work of Kuechly et al. (2012), we established a list of built-environment variables that might cause high degrees of light emissions in urban areas and investigated their impacts on night light emissions. Based on our results, we discuss various possibilities for the statistical analysis of intra-city light pollution using remote sensing data as well as the potential ecological and socioeconomic applications of light-pollution management and policies.
Section snippets
Monitoring night light patterns
Levin et al. (2020) recently offered a comprehensive review of the historical development of nighttime light remote sensing, applications of night light data, and challenges related to current optical sensor technologies. The first attempt to monitor and assess night light emissions using satellite data was done using the DMSP-OLS sensor in 1992. DMSP-OLS data were widely used for monitoring night light changes until the sensor’s retirement in 2013 (Aubrecht et al., 2009, Aubrecht et al., 2010,
Study area
This study focuses on South Korea’s capital, Seoul, which has a population of 10 million or 23.5 million for the metropolitan area, including neighboring cities. Based on the VIIRS-DNB composite data, we created a yearly composite for 2016 that reveals the amount of upward light at night in the Seoul metro area’s urban regions. As shown in Fig. 2, light emissions were over 40 nanowatts per steradian per square centimeter ()1
Correlation analysis
Based on exploratory correlation analyses (see Table 2), the office and commercial facilities primarily showed positive correlations with light emissions: office buildings (0.455); general commercial areas (zoning) (0.427); station influence areas (0.407); neighborhood living facilities (0.355); and cafés, bars, and restaurants (0.331). In addition, general residential area type 3 (zoning) showed positive correlations with light emissions. Exclusive residential areas (types 1, 2) (zoning),
Discussion and conclusion
Light-emission intensity in urban areas depends on urban–built environment characteristics and land-use activities. To minimize the impact of light pollution in urban environments, we contributed to the literature by quantitatively modeling the detailed relationships between urban physical environments and light-emission levels within a city. We found that industrial and commercial activities and residential patterns are closely associated with light pollution. Given the scant literature that
Author statement
SangHyun Cheon led the study, developed the research design and the statistical models, analyzed the data, interpret the results, and wrote the manuscript. Jung-A Kim conducted data analyses and wrote the manuscript.
Acknowledgements
The authors appreciate constructive comments by the associate editor and anonymous reviewers.
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2016R1A2B4013843).
This research was supported by a grant (#18CTAP-C129890-02) from the Land, Infrastructure and Transportation R&D Program (Science Technology Promotion Research Project) funded by the Ministry of Land, Infrastructure and Transport of the Korean government.
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