Estimation of anthropogenic heat emissions in China using Cubist with points-of-interest and multisource remote sensing data

Qian Chen; Xuchao Yang; Zutao Ouyang; Naizhuo Zhao; Qutu Jiang; Tingting Ye; Jun Qi; Wenze Yue

doi:10.1016/j.envpol.2020.115183

Estimation of anthropogenic heat emissions in China using Cubist with points-of-interest and multisource remote sensing data

Environ Pollut. 2020 Nov;266(Pt 1):115183. doi: 10.1016/j.envpol.2020.115183. Epub 2020 Jul 7.

Authors

Qian Chen¹, Xuchao Yang², Zutao Ouyang³, Naizhuo Zhao⁴, Qutu Jiang¹, Tingting Ye¹, Jun Qi⁵, Wenze Yue⁶

Affiliations

¹ Ocean College, Zhejiang University, Zhoushan, China.
² Ocean College, Zhejiang University, Zhoushan, China. Electronic address: yangxuchao@zju.edu.cn.
³ Department of Earth System Science, Stanford University, Stanford, CA, USA.
⁴ Institute of Land Resource Management, School of Humanities and Law, Northeastern University, Shenyang, China; Division of Clinical Epidemiology, McGill University Health Centre, Montreal, QC, Canada.
⁵ School of Environment, South China Normal University, Guangzhou, China; Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou, China.
⁶ Department of Land Management, Zhejiang University, Hangzhou, China.

PMID: 32673933
DOI: 10.1016/j.envpol.2020.115183

Abstract

Rapid urbanization and industrialization in China stimulated the great increase of energy consumption, which leads to drastic rise in the emission of anthropogenic waste heat. Anthropogenic heat emission (AHE) is a crucial component of urban energy budget and has direct implications for investigating urban climate and environment. However, reliable and accurate representation of AHE across China is still lacking. This study presented a new machine learning-based top-down approach to generate a gridded anthropogenic heat flux (AHF) benchmark dataset at 1 km spatial resolution for China in 2010. Cubist models were constructed by fusing points-of-interest (POI) data of varying categories and multisource remote sensing data to explore the nonlinear relationships between various geographic predictors and AHE from different heat sources. The strategy of developing specific models for different components and exploiting the complementary features of POIs and remote sensing data generated a more reasonable distribution of AHF. Results showed that the AHF values in urban centers of metropolises over China range from 60 to 190 W m^-2. The highest AHF values were observed in some heavy industrial zones with value up to 415 W m^-2. Compared with previous studies, the spatial distribution of AHF from different heating components was effectively distinguished, which highlights the potential of POI data in improving the precision of AHF mapping. The gridded AHF dataset can serve as input of urban numerical models and can help decision makers in targeting extreme heat sources and polluters in cities and making differentiated and tailored strategies for emission mitigation.

Keywords: Anthropogenic heat; China; Cubist; Points-of-interest; Remote sensing.

MeSH terms

China
Cities
Environmental Monitoring
Hot Temperature*
Remote Sensing Technology*
Urbanization