A distributed model for quantifying temporal-spatial patterns of anthropogenic heat based on energy consumption

Although energy-induced anthropogenic heat emissions form localized hotspots, they can influence the variability of urban climate. A lack of anthropogenic heat quantification in different locations and times has created a barrier in current research on urban climate. This study presented a distributed model of anthropogenic heat (DMA) that can be used for quantifying the temporal-spatial patterns of anthropogenic heat. The intensity of anthropogenic heat was estimated by separately considering the major sources of waste heat generated in urban environments from vehicular traffic, buildings, industry, and human metabolism individually. The contribution of anthropogenic heat to urban environments was assessed by the ratio of anthropogenic heat intensity to solar radiation (δ). The DMA was implemented inside the 5th ring-road of Beijing based on 6941 urban functional zone polygons which were attributed to seven zone types including agricultural, campus, commercial, industrial, preservation, public, and residential. Results showed that: (1) the total anthropogenic heat in Beijing reached 1.11 × 1018 J per year. Buildings occupied 45% of the total anthropogenic heat fluxes, followed by traffic (30%), industrial (20%), and human metabolism sources (5%); (2) the mean intensity of anthropogenic heat peaked at 135 Wm−2 in winter, 84 Wm−2 in autumn, 82 Wm−2 in summer, and 77 Wm−2 in spring; (3) the contribution of anthropogenic heat to urban climate decreased in the order of commercial (δ = 0.5), industrial (0.47), campus (0.33), residential (0.32), public (0.32), preservation (0.09), and agricultural zones (0.07). This study indicates that a greater focus on energy reduction would be most effective in mitigating the effects of anthropogenic heat in commercial and industrial zones and in winter. The DMA is a feasible tool that can be used to quantify the temporal and spatial variations of anthropogenic heat based on energy consumption data in other urban regions.