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• W4306907908 abstract "We present long-term (4-10 years) 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 Meters (SQMs) and ii) a rich set of atmospheric data products provided by the European Centre for Medium-Range Weather Forecasts. 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 reveals short- and long-term (seasonal) variations due to atmospheric changes. To assess long-term anthropogenic NSB trends, we establish an empirical atmospheric model via multi-variate penalized linear regression. Our modeling 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 percent per year. At our nine urban sites we measure an increase of 1.8 percent per year and for the remaining six intermediate sites we find an average increase of 3.7 percent per year. These numbers correspond to doubling times of 41, 39 and 19 years. We estimate that our method is capable of detecting trend slopes shallower/steeper than 1.5 percent per year." @default.
• W4306907908 created "2022-10-21" @default.
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• W4306907908 date "2022-10-20" @default.
• W4306907908 modified "2023-10-16" @default.
• W4306907908 title "Long-term trends of light pollution assessed from SQM measurements and an empirical atmospheric model" @default.
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• W4306907908 doi "https://doi.org/10.1093/mnras/stac3003" @default.
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