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Bachner, J. P. (2004). The Lighting Equation. In: Gourmet Retailer, 25(8), 190–192.
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Baddiley, C. (2018). Light pollution modelling, and measurements at Malvern Hills AONB, of county conversion to blue rich LEDs. Journal of Quantitative Spectroscopy and Radiative Transfer, 219, 142–173.
Abstract: The introduction of blue rich colour, Correlated-Colour-Temperature (CCT) 6000K road lighting could increase skyglow significantly compared with CCT 3000K types, if the blue content reaches the sky.
Highways England have a policy for lighting specification on motorways advised by the author's work. This is a categorised environmental impact point system of summed brightness as a function of angle from vertically down to the cut off angle; but with no CCT limitation.
Modelling was done for Malvern-Hills Area-of-Outstanding-Natural-Beauty (MHAONB), for the nighttime environmental impact of the LED replacement of Low-Pressure-Sodium throughout Herefordshire. The study was extended to include High-Pressure-Sodium and to LEDs at several CCTs, for the same Photopic ground illuminance.
Dark-Sky-Survey geographic location results for the MHAONB (2012) are described. Near-Zenith sky brightness photometry became continuous from 2016 at 2 minute intervals in all weathers, not just clear nights, with a networked calibrated Unihedron Lensed Sky Quality Meter (LSQM). Samples were also taken of all-sky camera images, corrected for vignetting and near-Zenith calibrated with the LSQM, to study weather effects, Milky Way contribution, and Herefordshire lighting conversion to blue-rich LEDs (2013-15), compared with the less converted Severn valley direction.
Time-plots and histogram analysis showed a small reduction in brightness (2012-2018), 0.1 mag.arcsec−2. Most variation is from increased sampling of distant cloud cover effects. Mist or low cloud on the horizon obscures light sources beyond reducing local skyglow, while high cloud reflects, increasing clear sky brightness. The Milky Way is critically 20% above background. Darkest periods near Zenith reach 21.1 mag.arcsec−2, to 21.2 after rain or surrounding low-cloud or poor-visibility. Clear-sky brightness decreases into early hours (∼0.03 mag.arcsec−2/hr); dimming effects were not seen.
The Zenith brightness is still set by distant cities, while towards the horizon, commercial and private uncontrolled non-directional LED lighting is increasing, negating the improvements in road lighting.
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Bagan, H., Borjigin, H., & Yamagata, Y. (2018). Assessing nighttime lights for mapping the urban areas of 50 cities across the globe. Environment and Planning B: Urban Analytics and City Science, , 2399808317752926.
Abstract: Nighttime data from the Defense Meteorological Satellite Program Operational Linescan System have been widely used to map urban/built-up areas (hereafter referred to as “built-up area”), but to date there has not been a geographically comprehensive evaluation of the effectiveness of using nighttime lights data to map urban areas. We created accurate, convenient, and scalable grid cells based on Defense Meteorological Satellite Program/Operational Linescan System nighttime light pixels. We then calculated the density of Landsat-derived built-up areas within each grid cell. We explored the relationship between Defense Meteorological Satellite Program/Operational Linescan System nighttime lights data and the density of built-up areas to assess the utility of nighttime lights for mapping urban areas in 50 cities across the globe. We found that the brightness of nighttime lights was only in moderate agreement with the density of built-up areas; moreover, correlations between nighttime lights and Landsat-derived built-up areas were weak. Even in relatively sparsely populated urban regions (where the density of the built-up area is less than 20%), the highest correlation coefficient (R2) was only 0.4. Furthermore, nighttime lights showed lighted areas that extended beyond the area of large cities, and nighttime lights reduced the area of small cities. The results suggest that it is difficult to use the regression model to calibrate the Defense Meteorological Satellite Program/Operational Linescan System nighttime lights to fit urban built up areas.
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Bagan, H., & Yamagata, Y. (2015). Analysis of urban growth and estimating population density using satellite images of nighttime lights and land-use and population data. GIScience & Remote Sensing, 52(6), 765–780.
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Bailey, F., Sparks, C. P., Seabrook, A. H., Vignoles, W. A., Trotter, A. P., Gaster, L., et al. (1911). Discussion on: “Street lighting by modern electric lamps”. Journal of the Institution of Electrical Engineers, 46(205), 46–91.
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