ARTIFICIAL LIGHT AT NIGHT (ALAN)
Primary Principal Investigator: Dr. Qian Xiao
This work is funded by NASA’s Health and Air Quality Applied Science Team Program. The aims of the project is to apply satellite data to improve the mapping of ALAN in the US for public health surveillance.
Background:
“Over the past century, global nightscapes have been drastically changed by the rapid growth of electric lighting. Although electric lighting has tremendous benefits, including promoting commercial activities, social actions, and public safety, these benefits are also accompanied by serious economic, ecological, and public health consequences. Higher levels of Artificial Light at Night (ALAN) cause higher energy costs, greater greenhouse gas emissions, and detrimental effects to the natural environment and ecosystems.”
Project Aims:
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- Characterize average ALAN levels and temporal trends in ALAN between 2012 and 2019 in all U.S. continental counties to identify areas with the highest and lowest ALAN exposures, and with the largest increase and decrease in ALAN during this period.
- Examine the relationship between ALAN and county-level population and gross domestic product (GDP) as potential contributing factors to ALAN patterns in the U.S. Specifically, we investigated to what degree changes in GDP and population in a county explained changes in ALAN during the study period.
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Data & Methods/What We Did:
“We used the VNP46A4 data product in NASA’s Black Marble suite to derive annual measures of ALAN levels throughout the U.S. at both county and tract-level for the period of 2012-2020. Because the observation of ALAN by VIIRS DNB is influenced by view angles and snow coverage on the ground, we used the yearly composite measure generated for multiple view-angle categories and upon snow free surfaces throughout the year. The VNP46A4 data product are available on NASA’s website [insert link to NASA data]. All data processing and mapping were performed using R.
We used a mixed-effect multiple linear regression model to determine changes in ALAN for each county between 2012 and 2019. Because the distribution of county-level ALAN was highly skewed to the right, log transformation was applied to improve normality for both outcome variables. The model included year and state as fixed effects, as well as county-level random effects for both the intercept and the slope for the year variable. To determine the relationship between average ALAN and GDP and population, we calculated Spearman correlation coefficients between each pair of these three measures. To determine the relationship between temporal trends of ALAN and GDP and population, we applied the aforementioned mixed-effect multiple linear regression models to calculate annual changes in GDP and population separately.”
Project Outcome Products:
Dashboard: This R Shiny dashboard presents geospatial patterns and temporal trends of ALAN in the U.S. from 2012 to 2020. ALAN can be interactively visualized at the state-level, raster, and over time. We provide download links for the county- and tract-level shapefiles generated from our work.
https://spatiotemporal-data-science.shinyapps.io/ALAN/
Publications:
[1] Qian Xiao, Yue Lyu, Meng Zhou, Jiachen Lu, Kehe Zhang, Jun Wang & Cici Bauer. (2023). Artificial light at night and social vulnerability: An environmental justice analysis in the U.S. 2012-2019, Environmental International, 108: 108096. DOI: https://doi.org/10.1016/j.envint.2023.108096
[2] Xiao Q, Gierach GL, Bauer C, Blot WJ, James P, & Jones RR. (2021). The Association between Outdoor Artificial Light at Night and Breast Cancer Risk in Black and White Women in the Southern Community Cohort Study, Environ Health Perspect., 129 (8): 87701. DOI: 10.1289/EHP9381
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