TY - JOUR
T1 - Temporal and spatial heterogeneity research of urban anthropogenic heat emissions based on multi-source spatial big data fusion for Xi'an, China
AU - Xu, Duo
AU - Zhou, Dian
AU - Wang, Yupeng
AU - Meng, Xiangzhao
AU - Gu, Zhaolin
AU - Yang, Yujun
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/6/1
Y1 - 2021/6/1
N2 - Anthropogenic heat emission (AHE) influences the local energy balance and intensify the urban heat island (UHI) effect. An accurate calculation of the AHE can improve the precision of UHI predictions. However, reliable AHE calculations with high temporal and spatial resolution in domestic research is still lacking. Therefore, this study proposes an approach to estimate the dynamic AHE by integrating multi-source Internet big data and high-precision urban spatial data. First, we quantified the dynamic distribution of residents’ trajectories by tracking multi-stage Internet geographic location data, real-time traffic conditions of Xi'an city, supplemented by on-site drone monitoring. Then the parameters of cooling and the thermal load coefficient of building emissions, personnel cooling loads, and traffic densities were introduced. Finally, the temporal and spatial dynamic rules of the AHE were revealed. Results showed the AHE was subject to a large changing amplitude. The diurnal AHE values of 64% of the blocks ranged from 93 to 498 W/m2, especially in some core commercial areas, the value could reach above 1000 W/m2 during the peak stages. Compared with previous research, this study dynamically evaluates the temporal and spatial heterogeneity of the AHE under different emission scenarios with a short update cycle and high spatial resolution.
AB - Anthropogenic heat emission (AHE) influences the local energy balance and intensify the urban heat island (UHI) effect. An accurate calculation of the AHE can improve the precision of UHI predictions. However, reliable AHE calculations with high temporal and spatial resolution in domestic research is still lacking. Therefore, this study proposes an approach to estimate the dynamic AHE by integrating multi-source Internet big data and high-precision urban spatial data. First, we quantified the dynamic distribution of residents’ trajectories by tracking multi-stage Internet geographic location data, real-time traffic conditions of Xi'an city, supplemented by on-site drone monitoring. Then the parameters of cooling and the thermal load coefficient of building emissions, personnel cooling loads, and traffic densities were introduced. Finally, the temporal and spatial dynamic rules of the AHE were revealed. Results showed the AHE was subject to a large changing amplitude. The diurnal AHE values of 64% of the blocks ranged from 93 to 498 W/m2, especially in some core commercial areas, the value could reach above 1000 W/m2 during the peak stages. Compared with previous research, this study dynamically evaluates the temporal and spatial heterogeneity of the AHE under different emission scenarios with a short update cycle and high spatial resolution.
KW - Anthropogenic heat emission
KW - Dynamic trajectory
KW - Multi-source spatial data
KW - Spatiotemporal heterogeneity
KW - Traffic conditions
UR - https://www.scopus.com/pages/publications/85102977353
U2 - 10.1016/j.enbuild.2021.110884
DO - 10.1016/j.enbuild.2021.110884
M3 - 文章
AN - SCOPUS:85102977353
SN - 0378-7788
VL - 240
JO - Energy and Buildings
JF - Energy and Buildings
M1 - 110884
ER -