Reference solar spectra are used in many solar applications. Utilizing a reference solar spectrum in a unified manner is necessary for reliable evaluation for each application. Since reference solar spectra have been updated only recently, the most recent spectra have not yet been uniformly adopted for some applications. To improve this situation, understanding the evolution and details of reference solar spectra is important. However, there have been few reports on reference solar spectra and their generation models. This paper reviews the evolution of reference solar spectra and their generation models, particularly ASTM G173-03 and CIE 241:2020 reference solar spectra and their Simple Model for the Atmospheric Radiative Transfer of Sunshine (SMARTS) generation model. In addition, the key points in the SMARTS model that affect the calculation accuracy of the spectral irradiance are explained. Moreover, the issue of further comparing the SMARTS model with a rigorous model is revealed. The information presented will help users to understand the newly updated reference solar spectra and correctly use the SMARTS model in practice.

Night bright light is known to suppress melatonin secretion. However, novel photoreceptors, named intrinsically photosensitive retinal ganglion cells, are mainly responsible for projecting dark/bright information to the superchiasmatic nucleus and thus regulating the circadian system. It has been shown that a 100-Hz flickering light suppresses melatonin secretion more than the nonflickering one even if the dose of the light conditions were the same. In this study, we evaluated light-induced melatonin suppression under the 1000-Hz flickering and nonflickering light conditions. Twelve male participants between the ages of 20 and 24 years (mean±standard deviation=22.6±1.7 years) were exposed to three light conditions (dim, 1000-Hz flickering, and nonflickering blue light) from 1:00 A.M. to 2:30 A.M., and saliva samples were obtained just before 1:00 A.M. and at 2:30 A.M. A repeated measures t-test with the Bonferroni correction showed that the melatonin concentrations after the light exposure was significantly higher than before the light exposure under dim light conditions, whereas there was no significant difference between the concentrations under 1000-Hz flickering and nonflickering light conditions. These observations suggest that the 1000-Hz flickering light do not have acute impacts on human circadian system as induced by the 100-Hz flickering one.

The use of LEDs as a light source in plant factories with artificial lighting is expected to reduce costs, but it has been reported that some crops do not grow as expected due to differences in the wavelength of the light source. Therefore, it is necessary to design LEDs with the appropriate wavelength for each crop to be grown. On the other hand, there is research toward the realization of smart plant factories that utilize artificial intelligence, and artificial intelligence may contribute to the design of the light environment in plant factories. In this study, we selected mini-tomatoes as a model crop, and prepared fluorescent lamps and LEDs as the light environment during seedling growth, respectively, and searched for suitable light source wavelengths while investigating the relationship with growth conditions using statistical analysis methods, one of the artificial intelligence techniques. We investigated the relationship between multiple light environments, PPFD, R/B ratio, and spectrum of wavelengths respectively, using LEDs, fluorescent lamps, and dimming filters, and growth indices stem diameter in order to clarify the light environments that contribute to growth. The correlation between the measured light environment and crop growth results was objectively shown by PLS regression analysis, and the contributing wavelengths were explored by calculating the selectivity ratio and regression coefficient. As a result, it was suggested that stem diameter was promoted at around 550 nm and 630 nm, and suppressed at around 460 nm.