Evaluating human photoreceptoral inputs from night-time lights using RGB imaging photometry

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Night-time lights interact with human physiology through different pathways starting at the retinal layers of the eye; from the signals provided by the rods; the S-, L- and M-cones; and the intrinsically photosensitive retinal ganglion cells (ipRGC). These individual photic channels combine in complex ways to modulate important physiological processes, among them the daily entrainment of the neural master oscillator that regulates circadian rhythms. Evaluating the relative excitation of each type of photoreceptor generally requires full knowledge of the spectral power distribution of the incoming light, information that is not easily available in many practical applications. One such instance is wide area sensing of public outdoor lighting; present-day radiometers onboard Earth-orbiting platforms with sufficient nighttime sensitivity are generally panchromatic and lack the required spectral discrimination capacity. In this paper, we show that RGB imagery acquired with off-the-shelf digital single-lens reflex cameras (DSLR) can be a useful tool to evaluate, with reasonable accuracy and high angular resolution, the photoreceptoral inputs associated with a wide range of lamp technologies. The method is based on linear regressions of these inputs against optimum combinations of the associated R, G, and B signals, built for a large set of artificial light sources by means of synthetic photometry. Given the widespread use of RGB imaging devices, this approach is expected to facilitate the monitoring of the physiological effects of light pollution, from ground and space alike, using standard imaging technology.
© 2019 by the authors. We acknowledge the support of the Spanish Network for Light Pollution Studies (MINECO AYA2011-15808-E) and also from ACTION, a project funded by the European Union H2020-SwafS-2018-1-824603. This work has been partially funded by the Spanish MICINN, (AYA2016–75808–R), by the Madrid Regional Government through the TEC2SPACE-CM Project (P2018/NMT-4291), by Xunta de Galicia/FEDER (grant ED431B 2017/64), by the EMISSI@N project (NERC grant NE/P01156X/1) and the ORISON project (H2020-INFRASUPP-2015-2), the Cities at Night project, the European Union’s Horizon 2020 research and innovation program under grant agreement no. 689443 via project GEOEssential, FPU grant from the Ministerio de Ciencia y Tecnologia and F. Sánchez de Miguel.
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