Abstract: In recent decades, there has been an increase in artificial lighting in the world due to urbanization and the revolution of LED lighting. Artificial lighting is an indicator of human activity, but can adversely affect natural ecosystems and people due to negative impacts of light pollution. Space-borne and airborne imagery as well as ground-based measurements enable to measure the intensity and spectra of artificial lights. One of the challenges in remote sensing of night-time lights is how to ground truth night-time imagery acquired by satellites, and how much do space-borne measurements represent the brightness as perceived by organisms. Most of the studies on night-time lights to-date were done using panchromatic sensors at large spatial extents, which did not allow to examine intra-urban variation in night light intensity and spectra. The aim of this study was to test the capability of the new Chinese satellite Jilin-1, which is the first commercial satellite to offer multispectral night-light imagery at a spatial resolution below 1 m, to characterize the night-time properties of urban areas. We examined the correspondence between light intensities as measured from different sensors at different spatial resolutions: two Jilin-1 images of the Jerusalem metropolitan area (0.89 m), VIIRS/DNB (500 m), Loujia-1 (130 m), unmanned aerial vehicle (UAV) color image (0.05 m) and hemispherical color photographs taken by a calibrated ground DSLR (digital single-lens reflex camera). In all the comparisons between different remote sensing tools, as the spatial resolution coarsened, the Pearson correlation coefficient increased, reaching > 0.5 (after resampling to 100 m). Stronger correlations were found for the red band, and weaker correlations were found for the blue band, probably due to atmospheric scattering. By identifying specific objects such as buildings and lightings, we found good correspondence () between Jilin-1 and the ground-based measurements of night-time brightness. We further examined the variability of night lights within different land use types and within different ethnic/religion composition of statistical areas. We found that residential areas of Orthodox Jews were characterized with the highest brightness at night compared with residential areas of Arabs in the West Bank that had the lowest brightness. At the statistical zone level (n = 299), more than 50% of the variability in night-time brightness, was explained by land cover properties (NDVI), infrastructure (roads and built volume) and the ethnic/religious composition. In addition, we found that the spectral ratio index which was based on the red and green bands, enabled to better distinguish between land use classes, than the spectral ratio index which was based on the green and blue bands. The availability of night-time multi-spectral imagery at fine spatial resolution now enables to study urban land-use and spatial inequality, and to better understand the factors explaining night-time brightness.

Abstract: Clouds are a kind of atmospheric factor that most effectively scatters the artificial light coming from the ground. Therefore, they have the most significant impact on the brightness of the night sky. The paper analyses the influence of both the level of cloudiness, as well as the genera of clouds and altitude of its base, on amplifying of the light pollution. The impact of cloudiness on the brightness of the night sky in places with different levels of light pollution was researched. Measurements of meteorological elements were used together with clouds genera assessments. The introduction of an innovative method of identifying some genera of clouds on the base of the all-night continuous measurements of the sky's brightness allowed for a similar analysis in the absence of observational data specifying the genera of clouds.
A linear correlation between the cloudiness and the brightness of the night sky was found. The determined linear correlation parameters allow for specifying the three types of light-polluted areas, possibly related to the density of population. It was found that among the nine genera of the identified night clouds, the Altocumulus, Cirrocumulus, and Cumulonimbus ones are responsible for this correlation. No dependence of the brightness of the night sky on the clouds’ albedo was found. In case of overcast sky, there was a clear relationship between the average altitude of the individual genus of clouds and the brightness of the night sky. Most of the night sky brightness comes from the light scattered on the lowest altitude clouds genera, while the least contribution comes from the light scattered on the high-level clouds. It was also found that at the freezing temperatures, the layer of aerosols forms below the level of the genera Nimbostratus or Stratus. This layer, thickening with the decreasing temperature, additionally scatters the artificial light.

Abstract: Long-term care residents often experience sleep disturbances as they are vulnerable to a variety of physical, psychosocial, and environmental factors that contribute to sleep disturbances. However, few studies have examined the combined impact of multiple factors on sleep among long-term care residents. This study aimed to identify the factors that influence sleep efficiency and sleep quality based on a modified senescent sleep model. A total of 125 residents were recruited from seven long-term care facilities in South Korea. Sleep patterns and sleep quality were collected using 3-day sleep logs and the Minimal Insomnia Screening Scale for Korean adults (KMISS), respectively. The mean sleep efficiency was 84.6% and the mean score on sleep quality was 15.25. A multiple linear regression analysis showed that greater dependence in activities of daily living (ADL), higher pain, and light at night were related to lower sleep efficiency. Higher pain and fatigue, less activity time, noise and light at night, and lower nighttime staffing levels were related to poorer sleep quality. This study highlights that psychosocial and environmental factors as well as physical factors could influence sleep for long-term care residents. Our findings could be foundational evidence for multi-faceted sleep intervention program development in long-term care settings.