Long-term trends of light pollution assessed from SQM measurements and an empirical atmospheric model

We present long-term (4-10 yr) trends of light pollution observed at 26 locations, covering rural, intermediate, and urban sites, including the three major European metropolitan areas of Stockholm, Berlin, and Vienna. Our analysis is based on (i) night sky brightness (NSB) measurements obtained with sky quality metres (SQMs) and (ii) a rich set of atmospheric data products. We describe the SQM data reduction routine in which we filter for moon- and clear-sky data and correct for the SQM 'aging' effect using an updated version of the twilight method of Puschnig et al. (2021). Our clear-sky, aging-corrected data reveal short- and long-term (seasonal) variations due to atmospheric changes. To assess long-term anthropogenic NSB trends, we establish an empirical atmospheric model via multivariate penalized linear regression. Our modelling approach allows to quantitatively investigate the importance of different atmospheric parameters, revealing that surface albedo and vegetation have by far the largest impact on zenithal NSB. Additionally, the NSB is sensitive to black carbon and organic matter aerosols at urban and rural sites, respectively. Snow depth was found to be important for some sites, while the total column of ozone leaves impact on some rural places. The average increase in light pollution at our 11 rural sites is 1.7 per cent per year. At our nine urban sites, we measure an increase of 1.8 per cent per year and for the remaining six intermediate sites we find an average increase of 3.7 per cent per year. These numbers correspond to doubling times of 41, 39, and 19 yr. We estimate that our method is capable of detecting trend slopes shallower/steeper than ±1.5 per cent per year.