Estimating multi-temporal anthropogenic heat flux based on the top-down method and temporal downscaling methods in Beijing, China

Anthropogenic heat variation during the COVID-19 pandemic control measures in four Chinese megacities

“…A significant ( p < 0.001) correlation between the AH of RS-SEB and global AH datasets was found ( Table 2 ), and the correlation of the adjusted results was markedly enhanced, indicating a stronger consistency between the adjusted AH and the common definition of AH at present. In addition, the adjusted AH had a more similar spatial distribution to the results of the model based on more refined data ( Liu et al, 2021b ; Sun et al, 2018 ), further demonstrating the validity of the high-resolution AH estimation method proposed in this study, which can be applied to AH monitoring during the COVID-19 pandemic.…”

“…The hourly AH was derived from the hourly profile factors and the monthly AH results obtained from the model, as shown in Eqs. (1) , (2) , (3) : where is the monthly multi-source AH derived from the model ( ) based on the energy inventory method and machine learning proposed in the previous study ( Qian et al, 2022 ) and can be divided into monthly industrial heat and building and transportation heat ; is the hourly mean anthropogenic heat for the time corresponding to the passage of Landsat 8; (%) is the hourly factor of industrial heat based on previous studies ( Liu et al, 2021b ; Zheng and Weng, 2018 ); (%) is the hourly factor estimated from the gridded hourly population heat value ( ), which depicts the distribution of people in the city in real-time based on the geographic location of cell phone users, which is one of the products of geographic big data and can effectively reflect the dynamic changes of the population ( Lin et al, 2020 ). This study applied the hourly relative population heat values for each grid to reflect the intra-day variation of human activity intensity (Eq.…”

“…(3) ), which provides a reasonable basis for the estimation of hourly profiles of building and transportation heat. is the hourly average value of the satellite crossing moment, which can express the instantaneous value of the AH at that moment given that AH does not vary much within one hour ( Dong et al, 2017 ; Liu et al, 2021b ). will be applied to the heat storage adjustment of the RS-SEB in Section 3.3.2.…”

“…In addition, the decrease in land surface temperature and albedo owing to building shadow could contribute to the partial AH underestimation of RS-SEB ( Yu et al, 2021 ). The AH estimated from the RS-SEB cannot directly reflect human activities and energy consumption in comparison with the inventory-based method ( Allen et al, 2011 ; Dong et al, 2017 ), which has advanced considerably in recent years owing to machine learning and big data techniques ( Liu et al, 2021b ; Ming et al, 2022 ; Qian et al, 2022 ; Xu et al, 2021 ). However, during COVID-19 control measures, the inventory-based method was limited by insufficient data and empirical reliance, while RS-SEB could reflect the surface energy composition more objectively based on remote sensing images and meteorological conditions.…”

Spatiotemporal patterns and drivers of summer heat island in Beijing-Tianjin-Hebei Urban Agglomeration, China

“…Urban areas have many high-temperature centers with low internal air pressure and density, which promote the heat island circulation from the rural areas to urban areas, resulting in various industrial waste gas and other harmful gases entering the urban areas [6]. Not only does it affect vegetation activities [7], reduce aboveground carbon storage [8] and accelerate the formation of haze and air pollution [9], but it has a direct negative impact on human health and global energy consumption [10]. In recent years, a lot of cities in China have experienced similar problems [11], [12].…”