基于POI和多源遥感数据估算中国人为热排放

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摘要基于能源、交通、人口和GDP等统计资料,考虑工业、交通、建筑、人体新陈代谢4种来源,估算了2019年中国大陆地级市的人为热排放总量.采用自上而下的能源消费清单法,通过Cubist机器学习算法融合不同类别的兴趣点(POI)数据和多源遥感数据,分别对不同来源的人为热排放通量构建空间化模型,获得2019年中国大陆1km空间分辨率的人为热通量网格数据,并分析不同地理、环境变量与来自不同热源的人为热排放通量之间的关系.结果表明:人为热通量的高值区通常分布于一线城市和工业比较发达的城市,城市中心的人为热通量值在80~200W/m²之间,一些重工业区的人为热通量值甚至高于市区,极值高达519.2W/m²;Cubist机器学习算法能更好地刻画人为热排放通量与影响因子之间的非线性关系,且能对影响因子的变量重要性进行度量;POI是人为热通量空间分布的最重要指示性指标,通过融合POI数据可以更加有效地反映出不同来源人为热排放通量的空间分布特征.新的2019年人为热排放通量数据可以为中国城市天气气候、城市大气环境的数值模拟研究提供有力支持.

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	钱静
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	李飞翔
	吴心彤
	陈柏儒

关键词 ：人为热排放, POI数据, Cubist模型, 能源消费清单法, 中国

Abstract：Statistics on energy consumption, transportation, population and GDP had been used to estimate anthropogenic heat emissions from industries, transportation, buildings, and human metabolism at the prefecture level for mainland China in 2019. Using the top-down energy inventory method and Cubist machine learning algorithm, anthropogenic heat flux (AHF) models were built by integrating points-of-interest (POI) datafrom a variety of categories and multisource remote sensing data. A gridded anthropogenic heat ﬂux (AHF) benchmark dataset at 1km spatial resolution had generated for China in 2019. The relationships between various geographic predictors and AHF from different heat sources were further analyzed. Results showed that high-value areas of AHF were generally distributed to metropolitan areas and cities with relatively developed industries. The values of AHF in urban centers in mainland China ranged between 80 and 200W/m². The highest values were reported for some heavy industrial areas up to 519.2W/m². Cubist models can explore the nonlinear relationships between individual predictors and AHF from different heat sources and give the relative importance of the predictors. POI-related variables are the most important predictors and can further reﬁne the estimation of AHF. Compared with previous studies, the spatial distribution of AHF from different heating components was effectively distinguished, highlighting the potential of POI data in improving the precision of AHF mapping. The new gridded AHF dataset in 2019 for mainland China can serve as important input for urban climate and urban environment numerical models.

Key words： anthropogenic heat emission points-of-interest Cubist energy-consumption inventory approach China

收稿日期: 2022-09-17

PACS:

X16

基金资助:国家自然科学基金资助项目(41971019)

通讯作者: 杨续超,教授,yangxuchao@zju.edu.cn E-mail: yangxuchao@zju.edu.cn

作者简介: 钱静(2000-),女,浙江湖州人,浙江大学硕士研究生,研究方向为海岸带生态环境.发表论文1篇.22134128@zju.edu.cn

引用本文:

钱静, 毛利伟, 杨续超, 陈倩, 李飞翔, 吴心彤, 陈柏儒. 基于POI和多源遥感数据估算中国人为热排放[J]. 中国环境科学, 2023, 43(6): 3183-3193. QIAN Jing, MAO Li-wei, YANG Xu-chao, CHEN Qian, LI Fei-xiang, WU Xin-tong, CHEN Bo-ru. Estimation of anthropogenic heat in China using points-of-interest and multisource remote sensing data. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(6): 3183-3193.

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http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2023/V43/I6/3183

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