Although the importance of solar radiation for vitamin D3 synthesis in the human body is well known, the solar exposure time required to prevent vitamin D deficiency has not been determined in Japan. This study attempted to identify the time of solar exposure required for vitamin D3 synthesis in the body by season, time of day, and geographic location (Sapporo, Tsukuba, and Naha) using both numerical simulations and observations. According to the numerical simulation for Tsukuba at noon in July under a cloudless sky, 3.5 min of solar exposure are required to produce 5.5 μg vitamin D3 per 600 cm2 skin corresponding to the area of a face and the back of a pair of hands without ingestion from foods. In contrast, it took 76.4 min to produce the same quantity of vitamin D3 at Sapporo in December, at noon under a cloudless sky. The necessary exposure time varied considerably with the time of the day. For Tsukuba at noon in December, 22.4 min were required, but 106.0 min were required at 09:00 and 271.3 min were required at 15:00 for the same meteorological conditions. Naha receives high levels of ultraviolet radiation allowing vitamin D3 synthesis almost throughout the year.

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Built environment stocks have attracted much attention in recent decades because of their role in material and energy flows and environmental impacts. Spatially refined estimation of built environment stocks benefits city management, for example, in urban mining and resource circularity strategy making. Nighttime light (NTL) data sets are widely used and are regarded as high-resolution products in large-scale building stock research. However, some of their limitations, especially blooming/saturation effects, have hampered performance in estimating building stocks. In this study, we experimentally proposed and trained a convolution neural network (CNN)-based building stock estimation (CBuiSE) model and applied it to major Japanese metropolitan areas to estimate building stocks using NTL data. The results show that the CBuiSE model is capable of estimating building stocks at a relatively high resolution (approximately 830 m) and reflecting spatial distribution patterns, although the accuracy needs to be further improved to enhance the model performance. In addition, the CBuiSE model can effectively mitigate the overestimation of building stocks arising from the blooming effect of NTL. This study highlights the potential of NTL to provide a new research direction and serve as a cornerstone for future anthropogenic stock studies in the fields of sustainability and industrial ecology.

Fill material flows created by land development earthworks are anthropogenic agents that generate massive energy use from their heavy loads. However, formal quantification of these flows has been neglected. We use Osaka Prefecture in Japan as a case study to quantify fill flows and associated CO2 emissions. We collected data on fill flows, including fill generation and acceptance. We mapped these publicly uncounted fill flows and calculated the CO2 emissions from the associated energy use. We also simulated a scenario in which optimized shortest-distance matching is achieved between fill generators and acceptors. We estimated the current fill flows based on distance and weight and broke down the total by type of site and activity. We compared our estimates of current fill flows with estimates from our matching simulation and found the simulation could achieve an 8448 km reduction in flow length and a 5724 t-CO2 reduction in emissions associated with transportation. We discussed the implications of flexible matching, especially in different construction sectors, and the importance of continuous, spatially geo-referenced monitoring of these fill flows toward further environmental impact mitigation. The approach presented here could apply to assessing environmental loads arising from landform changes in other cities and lead to development of a new regional- and global-scale fill material science in the Anthropocene.