Parameterizing Anthropogenic Heat Flux with an Energy-Consumption Inventory and Multi-Source Remote Sensing Data

Anthropogenic heat (AH) generated by human activities is an important factor affecting the urban climate. Thus, refined AH parameterization of a large area can provide data support for regional meteorological research. In this study, we developed a refined anthropogenic heat flux (RAHF) parameterization scheme to estimate the gridded anthropogenic heat flux (AHF). Firstly, the annual total AH emissions and annual mean AHF of Beijing municipality in the year 2015 were estimated using a top-down, energy-consumption inventory method, which was derived based on socioeconomic statistics and energy consumption data. The heat released from industry, transportation, buildings (including both commercial and residential buildings), and human metabolism were taken into account. Then, the county-scale AHF estimation model was constructed based on multi-source remote sensing data, such as Suomi national polar-orbiting partnership (Suomi-NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light (NTL) data and moderate resolution imaging spectroradiometer (MODIS) data. This model was applied to estimate the annual mean AHF of the counties in the Beijing–Tianjin–Hebei region. Finally, the gridded AHF data with 500-m resolution was obtained using a RAHF parameterization scheme. The results indicate that the annual total AH emissions of Beijing municipality in the year 2015 was approximately 1.704 × 1018 J. Of this, the buildings contribute about 34.5%, followed by transportation and industry with about 30.5% and 30.1%, respectively, and human metabolism with only about 4.9%. The annual mean AHF value of the Beijing–Tianjin–Hebei region is about 6.07 W·m−2, and the AHF in urban areas is about in the range of 20 W·m−2 and 130 W·m−2. The maximum AHF value is approximately 130.84 W·m−2, mostly in airports, railway stations, central business districts, and other densely-populated areas. The error analysis of the county-scale AHF results showed that the residual between the model estimation and energy consumption statistics is less than 1%. In addition, the spatial distribution of RAHF results is generally centered on urban area and gradually decreases towards suburbs. The spatial pattern of the RAHF results within urban areas corresponds well to the distribution of population density, building density, and the industrial district. The spatial heterogeneity of AHF within urban areas is well-reflected through the RAHF results. The RAHF results can be used in meteorological and environmental modeling for the Beijing–Tianjin–Hebei region. The results of this study also highlight the superiority of Suomi-NPP VIIRS NTL data for AHF estimation.