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We present long-term (4-10 years) trends of light pollution observed at 26locations, covering rural, intermediate and urban sites, including the threemajor European metropolitan areas of Stockholm, Berlin and Vienna. Our analysisis based on i) night sky brightness (NSB) measurements obtained with SkyQuality Meters (SQMs) and ii) a rich set of atmospheric data products providedby the European Centre for Medium-Range Weather Forecasts. We describe the SQMdata reduction routine in which we filter for moon- and clear-sky data andcorrect for the SQM "aging" effect using an updated version of the twilightmethod of Puschnig et al. (2021). Our clear-sky, aging-corrected data revealsshort- and long-term (seasonal) variations due to atmospheric changes. Toassess long-term anthropogenic NSB trends, we establish an empiricalatmospheric model via multi-variate penalized linear regression. Our modelingapproach allows to quantitatively investigate the importance of differentatmospheric parameters, revealing that surface albedo and vegetation have byfar the largest impact on zenithal NSB. Additionally, the NSB is sensitive toblack 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 ofozone leaves impact on some rural places. The average increase in lightpollution at our 11 rural sites is 1.7 percent per year. At our nine urbansites we measure an increase of 1.8 percent per year and for the remaining sixintermediate sites we find an average increase of 3.7 percent per year. Thesenumbers correspond to doubling times of 41, 39 and 19 years. We estimate thatour method is capable of detecting trend slopes shallower/steeper than 1.5percent per year.

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