A Partition Modeling for Anthropogenic Heat Flux Mapping in China

Anthropogenic heat (AH) generated by human activities has a major impact on urban and regional climate. Accurately estimating anthropogenic heat is of great significance for studies on urban thermal environment and climate change. In this study, a gridded anthropogenic heat flux (AHF) estimation scheme was constructed based on socio-economic data, energy-consumption data, and multi-source remote sensing data using a partition modeling method, which takes into account the regional characteristics of AH emission caused by the differences in regional development levels. The refined AHF mapping in China was realized with a high resolution of 500 m. The results show that the spatial distribution of AHF has obvious regional characteristics in China. Compared with the AHF in provinces, the AHF in Shanghai is the highest which reaches 12.56 W·m−2, followed by Tianjin, Beijing, and Jiangsu. The AHF values are 5.92 W·m−2, 3.35 W·m−2, and 3.10 W·m−2, respectively. As can be seen from the mapping results of refined AHF, the high-value AHF aggregation areas are mainly distributed in north China, east China, and south China. The high-value AHF in urban areas is concentrated in 50–200 W·m−2, and maximum AHF in Shenzhen urban center reaches 267 W·m−2. Further, compared with other high resolution AHF products, it can be found that the AHF results in this study have higher spatial heterogeneity, which can better characterize the emission characteristics of AHF in the region. The spatial pattern of the AHF estimation results correspond to the distribution of building density, population, and industry zone. The high-value AHF areas are mainly distributed in airports, railway stations, industry areas, and commercial centers. It can thus be seen that the AHF estimation models constructed by the partition modeling method can well realize the estimation of large-scale AHF and the results can effectively express the detailed spatial distribution of AHF in local areas. These results can provide technical ideas and data support for studies on surface energy balance and urban climate change.

[1]  M. Flanner,et al.  Integrating anthropogenic heat flux with global climate models , 2009 .

[2]  Sean Beevers,et al.  High‐resolution (space, time) anthropogenic heat emissions: London 1970–2025 , 2012 .

[3]  Xi Li,et al.  Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China , 2013, Remote. Sens..

[4]  C. Grimmond,et al.  Investigation of the impact of anthropogenic heat flux within an urban land surface model and PILPS-urban , 2016, Theoretical and Applied Climatology.

[5]  D. Lu,et al.  Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies , 2004 .

[6]  Shanshan Chen,et al.  Parameterizing Anthropogenic Heat Flux with an Energy-Consumption Inventory and Multi-Source Remote Sensing Data , 2017, Remote. Sens..

[7]  Fredrik Lindberg,et al.  Impact of city changes and weather on anthropogenic heat flux in Europe 1995–2015 , 2013 .

[8]  M. Roth,et al.  Diurnal and weekly variation of anthropogenic heat emissions in a tropical city, Singapore , 2012 .

[9]  Ning Zhang,et al.  Numerical simulation of the anthropogenic heat effect on urban boundary layer structure , 2009 .

[10]  C. Rosenzweig.  Climate change and cities : first assessment report of the urban climate change research network , 2011 .

[11]  Wenze Yue,et al.  Estimation of Gross Domestic Product Using Multi-Sensor Remote Sensing Data: A Case Study in Zhejiang Province, East China , 2014, Remote. Sens..

[12]  David J. Sailor,et al.  Modeling the impacts of anthropogenic heating on the urban climate of Philadelphia: a comparison of implementations in two PBL schemes , 2005 .

[13]  Shem Heiple,et al.  Using building energy simulation and geospatial modeling techniques to determine high resolution building sector energy consumption profiles , 2008 .

[14]  Huazhong Ren,et al.  Characterizing spatiotemporal dynamics of anthropogenic heat fluxes: A 20-year case study in Beijing-Tianjin-Hebei region in China. , 2019, Environmental pollution.

[15]  Simone Bastianoni,et al.  A Thermodynamic Geography: Night-Time Satellite Imagery as a Proxy Measure of Emergy , 2014, AMBIO.

[16]  Jong‐Jin Baik,et al.  Effects of anthropogenic heat on ozone air quality in a megacity , 2013 .

[17]  Jong-Jin Baik,et al.  Estimation of anthropogenic heat emission in the Gyeong-In region of Korea , 2009 .

[18]  M. Hart,et al.  Development of a national anthropogenic heating database with an extrapolation for international cities , 2015 .

[19]  T. Pei,et al.  Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China's cities , 2012 .

[20]  D. Sailor.  A review of methods for estimating anthropogenic heat and moisture emissions in the urban environment , 2011 .

[21]  Keisuke Hanaki,et al.  Impact of anthropogenic heat on urban climate in Tokyo , 1999 .

[22]  Geoff Levermore,et al.  Estimating spatial and temporal patterns of urban anthropogenic heat fluxes for UK cities: the case of Manchester , 2009 .

[23]  Chenghu Zhou,et al.  Nighttime Light Derived Assessment of Regional Inequality of Socioeconomic Development in China , 2015, Remote. Sens..

[24]  D. Sailor,et al.  Comparative estimates of anthropogenic heat emission in relation to surface energy balance of a subtropical urban neighborhood , 2016 .

[25]  Wangming Yang,et al.  High-Resolution Mapping of Anthropogenic Heat in China from 1992 to 2010 , 2014, International journal of environmental research and public health.

[26]  Lei Yan,et al.  Comparison between the Suomi-NPP Day-Night Band and DMSP-OLS for Correlating Socio-Economic Variables at the Provincial Level in China , 2015, Remote. Sens..

[27]  K. Seto,et al.  The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity , 2013 .

[28]  D. Roberts,et al.  Census from Heaven: An estimate of the global human population using night-time satellite imagery , 2001 .

[29]  Amauri Pereira de Oliveira,et al.  Anthropogenic heat in the city of São Paulo, Brazil , 2011 .

[30]  Dengsheng Lu,et al.  Regional mapping of human settlements in southeastern China with multisensor remotely sensed data , 2008 .

[31]  Fredrik Lindberg,et al.  Global to city scale urban anthropogenic heat flux: model and variability , 2011 .

[32]  Manabu Kanda,et al.  Global anthropogenic heat flux database with high spatial resolution , 2017 .

[33]  Yang Su.  Climate Forcing due to Anthropogenic Heat Release over China , 2011 .

[34]  David J. Sailor,et al.  Correcting aggregate energy consumption data to account for variability in local weather , 2006, Environ. Model. Softw..

[35]  C. Grimmond.  The suburban energy balance: Methodological considerations and results for a mid-latitude west coast city under winter and spring conditions , 1992 .

[36]  M. Haeffelin,et al.  Spatio-temporal variability of the atmospheric boundary layer depth over the Paris agglomeration: An assessment of the impact of the urban heat island intensity , 2012 .

[37]  J. Muller,et al.  Mapping regional economic activity from night-time light satellite imagery , 2006 .

[38]  C. Elvidge,et al.  Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption , 1997 .

[39]  Ranhao Sun,et al.  A distributed model for quantifying temporal-spatial patterns of anthropogenic heat based on energy consumption , 2018 .