适用于城市碳排放反演的CO<sub>2</sub>格点化排放清单编制——以杭州市为例

王锐; 张羽中; 赵爽; 王馨陆

引用本文:	王锐,张羽中,赵爽,王馨陆.适用于城市碳排放反演的CO₂格点化排放清单编制——以杭州市为例[J].环境监控与预警,2023,15(5):75-83
	WANG Rui,ZHANG Yuzhong,ZHAO Shuang,WANG Xinlu.A Gridded CO₂ Emission Inventory Applicable to Urban Carbon Monitoring： A Case Study of Hangzhou[J].Environmental Monitoring and Forewarning,2023,15(5):75-83

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适用于城市碳排放反演的CO₂格点化排放清单编制——以杭州市为例
王锐^1,2，张羽中^1,2*，赵爽^1,2，王馨陆^1,2
1.西湖大学，工学院，浙江省海岸带环境与资源研究重点实验室，浙江杭州 310030；2.西湖大学，浙江西湖高等研究院，浙江杭州 310024

摘要:

基于碳监测网络测算城市人为碳排放通量，需要二氧化碳（CO₂）格点化排放清单作为反演计算的先验信息。现有格点化清单大多针对全球或全国尺度编制，排放源的空间位置不确定性高，不足以支撑城市碳监测工作。以杭州市为例，构建了高空间分辨率（1 km）、分部门（工业能源、工业过程、交通等6类排放部门）的城市CO₂格点化排放清单，并对其不确定性进行了表征。该格点化清单基于中国城市温室气体工作组编制的《中国城市二氧化碳排放数据集（2020）》，依据848个点源的精确位置信息和一系列空间代理数据，对各部门的城市CO₂排放量进行格点化分配，得到杭州市高分辨率排放清单模型。与现有清单，如欧洲开发的全球大气研究排放数据库（EDGAR）、清华大学开发的中国多尺度排放清单模型（MEIC）等相比，本研究编制的格点化清单能合理地反映杭州市CO₂排放的空间格局，包括人口、路网密集的市中心，萧山区和钱塘区的工业园区，钱塘江中上游沿岸的水泥企业等高排放热点，可以作为杭州市CO₂反演的人为源先验清单。

关键词: 碳监测二氧化碳反演排放清单杭州不确定性

DOI：10.3969/j.issn.1674-6732.2023.05.011

分类号:X87

基金项目:国家重点研发计划重点专项（2022YFE0209100）；国家自然科学基金资助项目（42007198）

A Gridded CO₂ Emission Inventory Applicable to Urban Carbon Monitoring： A Case Study of Hangzhou

WANG Rui^1,2， ZHANG Yuzhong^1,2*， ZHAO Shuang^1,2， WANG Xinlu^1,2

1.Key Laboratory of Coastal Environment and Resources of Zhejiang Province， School of Engineering， Westlake University， Hangzhou， Zhejiang 310030， China；2.Westlake Institute for Advanced Study， Westlake University， Hangzhou， Zhejiang 310024， China

Abstract:

The retrieval of urban anthropogenic carbon emissions based on carbon monitoring requires a gridded CO₂ emission inventory as prior dataset. Existing gridded inventories are mostly established at global or national scales， with high uncertainty in spatial information of emission sources， which is insufficient to support the urban carbon monitoring. In this study， taking Hangzhou in 2020 as an example，we established a gridded CO₂emission inventory with high spatial resolution（1 km） and multiple sub-sectors（including 6 emission sectors such as industrial energy， energy processing， transportation， etc.）， and characterized the uncertainty. We spatially disaggregate sectoral CO₂ emissions from the “Urban CO₂Emissions Dataset， 2020” compiled by the China City Greenhouse Gas Working Group， based on the precise location information of 848 point sources and a series of spatial proxy data. Compared with existing inventories（such as EDGAR and MEIC）， the gridded inventory developed in this study provides a better representation of the spatial pattern of CO₂ emission in Hangzhou. It captured high emission hotspots such as densely inhabit areas， the central business district with dense road networks， industrial regions in Xiaoshan District and Qiantang District， as well as cement enterprises along the middle upstream coast of the Qiantang River. This gridded inventory can serve as priori inventory for anthropogenic CO₂ emission retrieval in Hangzhou.

Key words: Carbon monitoring Carbon dioxide Inversion Emission inventory City level Uncertainty