Losing rural darkness
Cox, D.T.C., Sánchez de Miguel, A., Bennie, J., Dzurjak, S.A., & Gaston, K.J. 2022. Majority of artificially lit Earth surface associated with non-urban, non-industrial population. Science of the Total Environment 841, 156782.
Key to understanding the negative impacts of artificial light at night (ALAN) on human health and the natural environment is its relationship with human density. ALAN has often primarily been considered an urban issue, however although over half of the population is urbanized, the 46 % that are not inhabit a dispersed array of smaller settlements. Here, we determine the global relationships between two dimensions of ALAN, namely direct emissions (radiance) and skyglow, and human density, and howthese relationships vary across continents.We correct the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) product for albedo, skyglow, airglow, the aurora and permanent snow and ice to represent upward radiance overland at 1.61 ∗ 2.12 km resolution from artificial sources only. For skyglow we use the World Atlas of Artificial Sky Brightness. Globally (between 59°N and 55°S), direct emissions were detected over 26.5 % and skyglow over 46.9 % of land area. Over half of all cumulative direct emissions (54.9 %) were emitted at low levels by the non-urban population, whilst these populations experienced the negative impacts of over two-thirds of all cumulative skyglow (69.8 %). This emphasises the extent of ALAN outside of urban areas, and its similarity in this regard to a number of other forms of pollution. Although powerful sources of rural direct emissions (e.g., industry, recreation) are important contributors of light pollution, cumulatively they only contributed 10% to total direct emissions. The relationship between each dimension of ALAN and population density varied across continents, driven by powerful rural emissions, non-urban populations and urban design. These relationships reflect the unique socio-economic and geographical make-up of each region and inform on where best to target light pollution mitigation strategies, not only in urban areas but also in rural ones.
- Sampling bias & environmental niches
- Lighting up food webs