城市碳源汇监测研究进展

王爽; 蔡兆男; 陶梦初; 刘毅; 李旭祥; 吴凯; 吴林; 杨东旭; 曹军骥

doi:10.13205/j.hjgc.202504001

城市碳源汇监测研究进展

doi: 10.13205/j.hjgc.202504001

王爽^1,2,
蔡兆男²,
陶梦初^2,,
刘毅^2,3,
李旭祥¹,
吴凯²,
吴林²,
杨东旭²,
曹军骥²

1. 西安交通大学人居环境与建筑工程学院,西安 710049;
2. 中国科学院大气物理研究所,北京 100029;
3. 中国科学院大学,北京 100049

基金项目:

国家重点研发计划（2022YFB3904802）

详细信息

作者简介:
王爽，女，博士，主要研究方向为基于多源数据的碳源汇估算。wangshuang68@mail.iap.ac.cn

通讯作者:
陶梦初，女，副研究员，主要研究方向为大气成分模拟和卫星观测、大气化学传输模式开发。mengchutao@mail.iap.ac.cn
陶梦初，女，副研究员，主要研究方向为大气成分模拟和卫星观测、大气化学传输模式开发。mengchutao@mail.iap.ac.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2023-10-07
- 录用日期: 2024-04-01
- 修回日期: 2023-12-13
- 刊出日期: 2025-04-01

Research progress and prospects of monitoring of carbon sources and sinks in urban areas

1. School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an 710049, China;
2. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;
3. University of Chinese Academy of Science, Beijing 100049, China

摘要

摘要: 尽管城市面积不足全球陆表的3%，但直接和间接产生的CO₂占全球能源使用所产生的CO₂排放量的70%以上，且城市生态系统具有直接增汇、间接减排作用。基于此，梳理了城市碳源汇监测方面的国内外研究进展：城市碳源汇的监测技术包括原位监测和遥感监测方式（地基遥感和卫星遥感），国际上多个城市已着手建立城市温室气体监测网；城市人为碳排放源和城市生态系统碳通量的估算方法主要包括“自下而上”（清单法、样地清查法、涡度相关法、模型模拟法等）和“自上而下”方法（主要包括碳同化反演法等）；对城市人为碳源和城市生态系统碳通量方面的研究进展进行了详细综述；最后，展望了新的监测手段和估算方法，以期能更好地服务“双碳”目标的最终实现。
- 城市碳源汇 /
- 城市人为碳排放源 /
- 城市生态系统碳通量 /
- 监测 /
- 估算 /
- 研究进展
Abstract: Although urban areas account for less than 3% of the global land surface area， their direct and indirect CO₂ emissions account for more than 70% of the global CO₂ emissions from energy use. Moreover， ecosystems in urban areas have direct sink increase and indirect emission reduction effects. This paper summarized the domestic and foreign research progress about monitoring of carbon sources and sinks in urban areas. The monitoring technology of carbon sources and sinks in urban areas includes in-situ monitoring and remote sensing monitoring methods （ground-based remote sensing and satellite remote sensing）. In addition， many cities worldwide have already begun to establish urban greenhouse gas monitoring networks. The estimation methods of anthropogenic carbon emission sources and urban ecosystem carbon flux in urban areas mainly include "bottom-up" （mainly including inventory method， sample plot inventory method， eddy correlation method， model simulation method， etc.） and "top-down" method （mainly including carbon assimilation inversion method， etc.）. The research results on anthropogenic carbon sources and ecosystem carbon flux in urban areas were summarized. Finally， new monitoring methods and estimation methods were prospected to better serve the "dual carbon" goals. It could also provide an important scientific basis for predicting atmospheric CO₂ content and the global warming trend. In the future， the "top-down" and "bottom-up" methods can be better combined， satellite remote sensing monitoring， ground observation， model assimilation and other methods can be coordinated， and multi-source data can be further integrated based on artificial intelligence， big data and other technologies to obtain more monitoring data of urban carbon emissions and carbon sinks with high spatial and temporal resolution， and the accuracy of assimilation inversion can be improved. In order to provide a more scientific basis for estimating anthropogenic carbon emission sources and ecosystem carbon flux in urban fine spatial scale.
- carbon sources and sinks in urban areas /
- anthropogenic carbon emission sources in urban areas /
- urban ecosystem carbon flux /
- monitoring /
- estimation /
- research progress

HTML全文

1 “自下而上”CO₂排放估算原理

1. The principle of "bottom-up" estimation of carbon dioxide emissions

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2 基于ODAIC清单的CO₂排放空间分布

2. Spatial distribution of CO₂ emissions based on ODAIC

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3 “自上而下”CO₂通量估算原理

3. The principle of CO₂ flux estimation based on the "top-down" method

下载: 全尺寸图片幻灯片

1 城市碳源汇监测方法

1. Monitoring methods of carbon sources and sinks in urban areas

监测方式		原理	优点	缺点	类型
原位监测		采取抽气方法对大气中的气体进行测量，即用采样瓶或抽气泵将空气导入测量腔或样品池中进行测量^[17]	可对大气中的CO₂浓度进行实时在线监测，且该监测方法测量时间短、精度高，能及时发现气团或污染等干扰因子^[17]	监测仅局限于一点，难以对大尺度范围内的CO₂气体浓度进行有效监测，且对CO₂气体垂直廓线也难以监测^[18]。此外，下垫面及垂直气团传输也会对监测结果造成影响^[18]	主要包括腔衰荡光谱技术、傅里叶变换红外光谱技术、可调谐红外激光吸收光谱技术和非色散红外光谱技术等^[17]
地基遥感监测		基于地基监测设备对直射太阳光进行实时采集，并对所采集的太阳光谱进行反演，以获取大气中CO₂的总柱浓度值^[19]	基于地基高分辨率傅里叶变换红外光谱技术（FTIR）的地基遥感因其髙准确性、高精度以及连续测量等特征可作为大气CO₂时空分布和变化特征长期监测的理想技术，也是探究CO₂潜在源的重要数据来源^[17]	地基遥感监测虽然精度高、可靠性强，但仅对单点进行监测，缺乏统一的探测方法，以及对区域和全球大范围的CO₂进行实时监测的能力^[20]	国际上主要的地基傅里叶变换红外光谱仪（FTS）网络包括全球碳柱浓度观测网（TCCON）和大气成分变化探测网络-红外工作组（NDACC-IRWG）^[17]
卫星遥感监测	碳卫星监测	基于探测器监测的CO₂吸收带附近的辐射光谱反演计算CO₂柱浓度或CO₂柱平均干空气混合比（XCO₂）^[21]	碳卫星不仅可对区域或全球CO₂柱浓度分布进行探究，还可基于特定算法反演出CO₂在大气中的垂直分布，是有效监测CO₂时空分布的重要方法^[22]	碳卫星监测结果对地表浓度变化不敏感，且地表下垫面以及地面反照率的差异会使得碳卫星所监测的底层大气CO₂数据具有较大误差，难以准确评估区域大气CO₂数据的变化^[17]。此外，受城市地区云和气溶胶的影响，卫星探测在城市地区的有效观测数据很少	目前在轨观测大气中CO₂总柱浓度的卫星有日本的GOSAT、GOSAT-2、美国NASA的OCO-2、OCO-3，中国的髙分五号卫星等^[17]
卫星遥感监测	夜间灯光遥感监测	基于夜间灯光遥感数据与所获取的部分碳排放数据建立关联模型^[23]，其可被用于估算未知区域的碳排放	夜间灯光遥感数据作为城市化、经济发展、人口密度、能源消耗等人类活动的直接体现，可用于估算人为碳排放的范围和强度^[23]	该监测方法不能很好地适用于人口稀少的城市地区^[24]。且DMSP-OLS数据存在数值饱和及晕变问题、缺少星上校准、空间分辨率也较低^[25]	包括DMSP-OLS（美国军事气象卫星（DMSP）上所搭载的OLS传感器）^[25]、NPP-VIIRS（S-NPP卫星和JPSS卫星VIIRS传感器的DNB日/夜波段的夜间灯光成像信息）^[26，27]

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2 人为碳排放源清单汇总

2. Summary of anthropogenic CO₂ emission sources

数据	ODIAC	EDGAR	PKU-CO₂	FFDAS	CHRED	MEIC	NJU
时间覆盖范围	2000—2021	1970—2022	1960—2014	1997—2015	2007, 2012	1990—2020	2000—2015
时间分辨率	月	年	月	小时/年	2年或3年	月	年
空间分辨率	1 km	0.1°	0.1°	0.1°/km	10 km	0.25°	0.25°
排放量估算	全球/国家	全球/国家	全球/国家	全球/国家	国家/省级	国家/省级	国家/省级
区域源	夜间灯光	人口密度、夜间灯光	植被和人口密度、夜间灯光	夜间灯光	人口密度、土地利用、人类活动	人口密度、土地利用、能源消费、民用源	人口密度、国内生产总值
版本名称	ODIAC2022	EDGAR v8.0	PKU-CO₂-v2	FFDAS v2.2	CHRED	MEIC v1.4	NJU-CO₂v2017
发布/更新年份	2023	2023	2016	2014	2017	2023	2017
文献	[51]	[52]	[53,54]	[53,55]	[53，56，57]	[58，59]	[53,60]

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参考文献(127)

[1]	ZHANG Z，REN Y M，DONG H J. Research on carbon emissions peaking and low-carbon development of cities:a case of Shanghai［J］. Environmental Engineering，2020，38（11）:12-18. 张哲，任怡萌，董会娟. 城市碳排放达峰和低碳发展研究:以上海市为例［J］. 环境工程，2020，38（11）:12-18.
[2]	CAO J J，ZENG N，LIU Y，et al. Preface to the special issue on carbon neutrality:important roles of renewable energies，carbon sinks，NETs，and non-CO₂ GHGs［J］. Advances in Atmospheric Sciences，2022，39（8）:1207-1208.
[3]	ZENG N，HAUSMANN H. Wood vault:remove atmospheric CO₂ with trees，store wood for carbon sequestration for now and as biomass，bioenergy and carbon reserve for the future［J］. Carbon Balance Manag，2022，17（1）:2.
[4]	XING Y Y. Dynamics of carbon sink pattern and urban carbon sink in Xi'an under urban spatial growth［D］. Xi'an:Shaanxi Normal University，2012. 邢燕燕. 城市空间增长下的西安市碳汇格局动态与城市碳增汇研究［D］. 西安:陕西师范大学，2012.
[5]	FRIEDLINGSTEIN P，O'SULLIVAN M，JONES M W，et al. Global carbon budget 2022［J］. Earth System Science Data Discussions，2022:1-159.
[6]	XIONG T，LIU Y，YANG C，et al. Research overview of urban carbon emission measurement and future prospect for GHG monitoring network［J］. Energy Reports，2023，9:231-242.
[7]	LIU P，ZHA T，ZHANG F，et al. Environmental controls on carbon fluxes in an urban forest in the Megalopolis of Beijing，2012-2020［J］. Agricultural and Forest Meteorology，2023，333:109412.
[8]	United Nations Department of Economic and Social Affairs. World urbanization prospects:the 2018 revision［M］. United Nations，2019. DOI: 10.18356/b9e995fe-en.
[9]	WANG S J，SHI C Y，FANG C L，et al. Examining the spatial variations of determinants of energy-related CO₂ emissions in China at the city level using Geographically Weighted Regression Model［J］. Applied Energy，2019，235:95-105.
[10]	ZANG H K，YANG W S，ZHANG J，et al. Research on carbon dioxide emissions peaking in Beijing-Tianjin-Hebei city agglomeration［J］. Environmental Engineering，2020，38（11）:19-24，77. 臧宏宽，杨威杉，张静，等. 京津冀城市群二氧化碳排放达峰研究［J］. 环境工程，2020，38（11）:19-24，77.
[11]	XU B X. Inversion of surface CO₂ flux assimilation based on GEOS-Chem model［D］. Xuzhou:China University of Mining and Technology，2016. 徐博轩. 基于GEOS-Chem模型的陆表CO₂通量同化反演研究［D］. 徐州:中国矿业大学，2016.
[12]	FANG J Y，GUO Z D. Search for lost terrestrial carbon sinks［J］. Nature Journal，2007（1）:1-6. 方精云，郭兆迪. 寻找失去的陆地碳汇［J］. 自然杂志，2007（1）:1-6.
[13]	HUTYRA L R，DUREN R，GURNEY K R，et al. Urbanization and the carbon cycle:current capabilities and research outlook from the natural sciences perspective［J］. Earth's Future，2014，2（10）:473-495.
[14]	WANG H Y. Research on urban carbon source/sink based on vorticity observation and remote sensing technology［D］. Xuzhou:China University of Mining and Technology，2016. 王宏莹. 基于涡度观测和遥感技术的城市碳源/汇研究［D］. 徐州:中国矿业大学，2016.
[15]	CHEN B，ZHANG X X，WANG H，et al. Ideas on the establishment of urban carbon flux monitoring system［J］. Contemporary Chemical Industry Research，2022（6）:51-53. 陈波，张晓旭，王好，等. 关于城市碳通量监测体系建立的思路［J］. 当代化工研究，2022（6）:51-53.
[16]	VESALA T，JÄRVI L，LAUNIAINEN S，et al. Surface-atmosphere interactions over complex urban terrain in Helsinki，Finland［J］. Tellus B:Chemical and Physical Meteorology，2008，60（2）:188-199.
[17]	Shan C G. Spatial-temporal distribution and variation characteristics of atmospheric CO₂ based on ground-based high-resolution Fourier transform infrared spectroscopy［D］. Hefei:University of Science and Technology of China，2019. 单昌功. 基于地基高分辨率傅里叶变换红外光谱技术研究大气CO₂时空分布和变化特征［D］. 合肥:中国科学技术大学，2019.
[18]	GURNEY K R，LAW R M，DENNING A S，et al. Towards robust regional estimates of CO₂ sources and sinks using atmospheric transport models［J］. Nature，2002，415（6872）:626-630.
[19]	WUNCH D，TOON G C，BLAVIER J F，et al. The total carbon column observing network［J］. Philosophical Transactions of the Royal Society A-Mathematical Physical and Engineering Sciences，2011，369（1943）:2087-2112.
[20]	LIU Y，LV D R，CHEN H B，et al. A review of the progress in techniques and methods of satellite remote sensing of atmospheric CO₂［J］. Remote Sensing Technology and Application，2011，26（2）:247-254. 刘毅，吕达仁，陈洪滨，等. 卫星遥感大气CO₂的技术与方法进展综述［J］. 遥感技术与应用，2011，26（2）:247-254.
[21]	CHEN X，LIU Y，CAI Z N. A review of shortwave infrared CO₂ retrieval radiative transfer models［J］. Remote Sensing Technology and Application，2015，30（5）:825-834. 陈曦，刘毅，蔡兆男. 短波红外CO₂反演辐射传输模式综述［J］. 遥感技术与应用，2015，30（5）:825-834.
[22]	KUAI L，WORDEN J，KULAWIK S，et al. Profiling tropospheric CO₂ using Aura TES and TCCON instruments［J］. Atmospheric Measurement Techniques，2013，6（1）:63-79.
[23]	ZHANG X W，WU J S，PENG J，et al. The uncertainty of nighttime light data in estimating carbon dioxide emissions in China:a comparison between DMSP-OLS and NPP-VIIRS［J］. Remote Sensing，2017，9（8）:20.
[24]	MENG L N，GRAUS W，WORRELL E，et al. Estimating CO₂（carbon dioxide）emissions at urban scales by DMSP/OLS（Defense Meteorological Satellite Program's Operational Linescan System）nighttime light imagery:nethodological challenges and a case study for China［J］. Energy，2014，71:468-478.
[25]	CAO X，HU Y，ZHU X L，et al. A simple self-adjusting model for correcting the blooming effects in DMSP-OLS nighttime light images［J］. Remote Sensing of Environment，2019，224:401-411.
[26]	XIONG X，BUTLER J，CAO C，et al. Optical Sensors—VIS/NIR/SWIR［M］. Science Direcrt，2017.
[27]	ELVIDGE C D，BAUGH K，ZHIZHIN M，et al. VIIRS night-time lights［J］. International Journal of Remote Sensing，2017，38（21）:5860-5879.
[28]	RUNNING S W，BALDOCCHI D D，TURNER D P，et al. A global terrestrial monitoring network integrating tower fluxes，flask sampling，ecosystem modeling and EOS satellite data［J］. Remote Sensing of Environment，1999，70:108-127.
[29]	ÁLVAREZ-SALGADO X A，MILLER AEJ. Simultaneous determination of dissolved organic carbon and total dissolved nitrogen in seawater by high temperature catalytic oxidation:conditions for precise shipboard measurements［J］. Marine Chemistry，1998，62:325-333.
[30]	MILES N L，MARTINS D K，RICHARDSON S J，et al. Calibration and field testing of cavity ring-down laser spectrometers measuring CH₄，CO₂，and delta（CH₄）-C-13 deployed on towers in the Marcellus Shale region［J］. Atmospheric Measurement Techniques，2018，11（3）:1273-1295.
[31]	ZHANG G L，XING L QI，ZHANG L，et al. Research progress of carbon sink measurement and monitoring methods for urban green space［J］. Journal of Landscape Architecture，2022，39（1）:4-9，49. 张桂莲，邢璐琪，张浪，等. 城市绿地碳汇计量监测方法研究进展［J］. 园林，2022，39（1）:4-9，49.
[32]	LIU X M，CHENG X L，HU F. Gradient characteristics of CO₂ concentration and flux in Beijing urban area part I:concentration and virtual temperature［J］. Chinese Journal of Geophysics-Chinese Edition，2015，58（5）:1502-1512.
[33]	BUSCHMANN M，DEUTSCHER N M，SHERLOCK V，et al. Retrieval of XCO₂ from ground-based mid-infrared（NDACC）solar absorption spectra and comparison to TCCON［J］. Atmos Meas Tech，2016，9（2）:577-585.
[34]	CHE K，LIU Y，CAI Z N，et al. Application of portable Fourier transform infrared spectrometer in atmospheric greenhouse gas observation［J］. Remote Sensing Technology and Application，2021，36（1）:44-54. 车轲，刘毅，蔡兆男，等. 便携式傅里叶变换红外光谱仪在大气温室气体观测中的应用进展［J］. 遥感技术与应用，2021，36（1）:44-54.
[35]	LIU Y，WANG J，CHE K，et al. Satellite remote sensing of greenhouse gases:progress and trend［J］. Journal of Remote Sensing，2021，25（1）:53-64. 刘毅，王婧，车轲，等. 温室气体的卫星遥感:进展与趋势［J］. 遥感学报，2021，25（1）:53-64.
[36]	YANG D X，HAKKARAINEN J，LIU Y，et al. Detection of anthropogenic CO₂ emission signatures with TanSat CO₂ and with Copernicus Sentinel-5 Precursor（S5P）NO₂ measurements:first results［J］. Advances in Atmospheric Sciences，2023，40（1）:1-5.
[37]	LIU Y，WANG J，YAO L，et al. The TanSat mission:preliminary global observations［J］. Sci Bull（Beijing），2018，63（18）:1200-1207.
[38]	China successfully launched Fengyun-3D weather satellite［J］. Science and Technology Review，2017，35（22）:6. 中国成功发射“风云三号D”气象卫星［J］. 科技导报，2017，35（22）:6.
[39]	LIU W Q." Development of Gaofen-5 satellite Payload" album［J］. Journal of Atmospheric and Environmental Optics，2019，14（1）:1. 刘文清.“高分五号卫星载荷研制”专辑［J］. 大气与环境光学学报，2019，14（1）:1.
[40]	China successfully launched atmospheric environment monitoring satellite［J］. Infrared，2022，43（4）:50. 我国成功发射大气环境监测卫星［J］. 红外，2022，43（4）:50.
[41]	TONG C M，BAO Y F，HUANG Q L，et al. Research progress of solar induced chlorophyll fluorescence satellite remote sensing technology［J］. Space Return and Remote Sensing，2022，43（2）:45-55. 仝迟鸣，鲍云飞，黄巧林，等. 太阳诱导叶绿素荧光卫星遥感技术研究进展［J］. 航天返回与遥感，2022，43（2）:45-55.
[42]	FENG H，ZHANG W. China's first terrestrial ecosystem carbon monitoring satellite was launched successfully［J］. Science，Technology and Industry for National Defense，2022（8）:40-41. 冯华，张未.“句芒”探碳看点多:我国首颗陆地生态系统碳监测卫星发射成功［J］. 国防科技工业，2022（8）:40-41.
[43]	LAUVAUX T，MILES N L，DENG A，et al. High-resolution atmospheric inversion of urban CO₂ emissions during the dormant season of the Indianapolis Flux Experiment（INFLUX）［J］. Journal of Geophysical Research:Atmospheres，2016，121（10）:5213-5236.
[44]	BRÉON F M，BROQUET G，PUYGRENIER V，et al. An attempt at estimating Paris area CO₂ emissions from atmospheric concentration measurements［J］. Atmos Chem Phys，2015，15（4）:1707-1724.
[45]	VERHULST K R，KARION A，KIM J，et al. Carbon dioxide and methane measurements from the Los Angeles Megacity Carbon Project-Part 1:calibration，urban enhancements，and uncertainty estimates［J］. Atmospheric Chemistry and Physics，2017，17（13）:8313-8341.
[46]	VELASCO E，ROTH M. Cities as net sources of CO₂:review of atmospheric CO₂ exchange in urban environments measured by eddy covariance technique［J］. Geography Compass，2010，4（9）:1238-1259.
[47]	WU D，LIU J，WENNBERG P O，et al. Towards sector-based attribution using intra-city variations in satellite-based emission ratios between CO₂ and CO［J］. Atmospheric Chemistry and Physics，2022，22（22）:14547-14570.
[48]	ZHENG B，GENG G，CIAIS P，et al. Satellite-based estimates of decline and rebound in China’s CO₂ emissions during COVID-19 pandemic［J］. Science Advances，2020，6（49）:eabd4998.
[49]	STECHEMESSER K，GUENTHER E. Carbon accounting:a systematic literature review［J］. Journal of Cleaner Production，2012，36:17-38.
[50]	HUANG C. Multi-dimensional assessment and scenario simulation of urban carbon emissions［D］. Shanghai:East China Normal University，2020. 黄诚. 城市碳排放的多维度评价与情景模拟［D］. 上海:华东师范大学，2020.
[51]	ODA T，MAKSYUTOV S，ANDRES RJ. The Open-Source Data Inventory for Anthropogenic CO₂，Version 2016（ODIAC2016）:a global monthly fossil fuel CO₂ gridded emissions data product for tracer transport simulations and surface flux inversions［J］. Earth System Science Data，2018，10（1）:87-107.
[52]	JANSSENS-Maenhout G，CRIPPA M，GUIZZARDI D，et al. EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970-2012［J］. Earth System Science Data，2019，11（3）:959-1002.
[53]	HAN P F，ZENG N，ODA T，et al. Evaluating China's fossil-fuel CO₂ emissions from a comprehensive dataset of nine inventories［J］. Atmospheric Chemistry and Physics，2020，20（19）:11371-11385.
[54]	WANG R，TAO S，CIAIS P，et al. High-resolution mapping of combustion processes and implications for CO₂ emissions［J］. Atmospheric Chemistry and Physics，2013，13（10）:5189-5203.
[55]	ASEFI-Najafabady S，RAYNER P J，GURNEY K R，et al. A multiyear，global gridded fossil fuel CO₂ emission data product:Evaluation and analysis of results［J］. Journal of Geophysical Research-Atmospheres，2014，119（17）:19.
[56]	CAI B F，LIANG S，ZHOU J，et al. China high resolution emission database（CHRED）with point emission sources，gridded emission data，and supplementary socioeconomic data［J］. Resources Conservation and Recycling，2018，129:232-239.
[57]	WANG J N，CAI B F，ZHANG L X，et al. High resolution carbon dioxide emission gridded data for China derived from point sources［J］. Environmental Science& Technology，2014，48（12）:7085-7093.
[58]	ZHENG B，TONG D，LI M，et al. Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions［J］. Atmospheric Chemistry and Physics，2018，18（19）:14095-14111.
[59]	LIU Z，GUAN D B，WEI W，et al. Reduced carbon emission estimates from fossil fuel combustion and cement production in China［J］. Nature，2015，524（7565）:335.
[60]	LIU M，WANG H，WANG H，et al. Refined estimate of China's CO₂ emissions in spatiotemporal distributions［J］. Atmospheric Chemistry and Physics，2013，13（21）:10873-10882.
[61]	WU D. Top-down constraints on CO emissions from wildfire inventories using a receptor-oriented Lagrangian particle dispersion model［D］. Salt Lake City:The University of Utah，2016.
[62]	GATELY C K，HUTYRA L R. Large uncertainties in urban-scale carbon emissions［J］. Journal of Geophysical Research:Atmospheres，2017，122（20）:11，242-211，260.
[63]	HAN P F，ZENG N，ODA T，et al. A city-level comparison of fossil-fuel and industry processes-induced CO₂ emissions over the Beijing-Tianjin-Hebei region from eight emission inventories［J］. Carbon Balance and Management，2020，15（1）:16.
[64]	CHE K，CAI Z N，LIU Y，et al. Lagrangian inversion of anthropogenic CO₂ emissions from Beijing using differential column measurements［J］. Environmental Research Letters，2022，17（7）:10.
[65]	HUO D，HUANG X，DOU X，et al. Carbon monitor cities near-real-time daily estimates of CO₂ emissions from 1500 cities worldwide［J］. Scientific Data，2022，9（1）:533.
[66]	SU Y，CHEN X，YE Y，et al. The characteristics and mechanisms of carbon emissions from energy consumption in China using DMSP/OLS night light imageries［J］. Acta Geogr Sin，2013，68:1513-1526.
[67]	WANG J S，WANG C X，REN W X，et al. Research on"double carbon" from the perspective of geography:themes，achievements and prospects［J］. Advances in Earth Sciences:1-12. 王建事，王成新，任婉侠，等. 地理学视角下“双碳”研究:主题、成效及展望［J］. 地球科学进展:1-12.
[68]	LIU K，ZHANG H，KONG L H，et al. Research progress of carbon sink assessment methods in terrestrial ecosystems［J］. Acta Ecologica Sinica，2023，43（10）:4294-4307. 刘坤，张慧，孔令辉，等. 陆地生态系统碳汇评估方法研究进展［J］. 生态学报，2023，43（10）:4294-4307.
[69]	ZHANG L，SONG Y H，ZHANG H L，et al. Comparison of urban forest carbon sink and estimation methods［J］. Modern Horticulture，2023，46（11）:73-78. 张岚，宋钰红，张慧琳，等. 城市森林碳汇及估算方法比较［J］. 现代园艺，2023，46（11）:73-78.
[70]	YU G R，ZHANG L M，SUN X M. Progress and prospects of the Chinese Terrestrial Ecosystem Flux Observation and Research Network（ChinaFLUX）［J］. Progress in Geography，2014，33（7）:903-917. 于贵瑞，张雷明，孙晓敏. 中国陆地生态系统通量观测研究网络（ChinaFLUX）的主要进展及发展展望［J］. 地理科学进展，2014，33（7）:903-917.
[71]	CAO X P，LIN Y Y，DU P Y，et al. Research on urban vegetation carbon sink based on hyperspectral remote sensing data［J］. Science and Technology Innovation，2016（16）:11-13. 曹晓裴，林殷怡，杜鹏宇，等. 高光谱遥感数据下城市植被碳汇的研究［J］. 科技与创新，2016（16）:11-13.
[72]	JI C，ZHANG J，YAO F. The yield estimation of rapeseed in Hubei province by BEPS process-based model and MODIS satellite data［C］// Springer，2015:643-652.
[73]	XU F，WANG X，LI L. NPP and vegetation carbon sink capacity estimation of urban green space using the optimized CASA model:a case study of five Chinese Cities［J］. Atmosphere，2023，14（7）:1161.
[74]	YUAN W，LIU S，ZHOU G，et al. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes［J］. Agricultural and Forest Meteorology，2007，143（3/4）:189-207.
[75]	ZHANG J P，LIU C L，HAO H G，et al. Spatial and temporal changes of carbon storage and carbon sink in grassland ecosystem in the Source region of Three Rivers based on MODIS GPP/NPP data［J］. Journal of Ecology and Environment，2015，24（1）:8. 张继平，刘春兰，郝海广，等. 基于 MODIS GPP/NPP 数据的三江源地区草地生态系统碳储量及碳汇量时空变化研究［J］. 生态环境学报，2015，24（1）:8.
[76]	YIN W D，SU J Y，XU Z Y，et al. Application of remote sensing technology to urban green space carbon storage estimation［J］. Journal of Landscape Architecture，2022，29（5）:24-30. 殷炜达，苏俊伊，许卓亚，等. 基于遥感技术的城市绿地碳储量估算应用［J］. 风景园林，2022，29（5）:24-30.
[77]	WU D，LIN J C，DUARTE H F，et al. A model for urban biogenic CO₂ fluxes:Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes（SMUrF v1）［J］. Geoscientific Model Development，2021，14（6）:3633-3661.
[78]	YANG X Y，WANG Z T，PAN G G，et al. Advances in atmospheric observation of greenhouse gases by satellite remote sensing［J］. Journal of Atmospheric and Environmental Optics，2022，17（6）:581-597. 杨晓钰，王中挺，潘光等. 卫星遥感温室气体的大气观测技术进展［J］. 大气与环境光学学报，2022，17（6）:581-597.
[79]	HU C. Simulation of atmospheric CO₂ concentration based on WRF-STILT model［D］. Nanjing:Nanjing University of Information Science and Technology，2017. 胡诚. 基于WRF-STILT模型的大气CO₂浓度模拟［D］. 南京:南京信息工程大学，2017.
[80]	HU H M，WANG C，ZHANG J T. International progress in inversion of urban regional carbon emission measurement［J］. Acta Metrologica Sinica，2017，38（1）:7-12. 胡鹤鸣，王池，张金涛. 城市区域碳排放测量反演研究国际进展［J］. 计量学报，2017，38（1）:7-12.
[81]	FRIEDLINGSTEIN P，O'SULLIVAN M，JONES M W，et al. Global carbon budget 2020［J］. Earth System Science Data，2020，12（4）:3269-3340.
[82]	JIANG F，WANG H，CHEN J M，et al. Regional CO₂ fluxes from 2010 to 2015 inferred from GOSAT XCO₂ retrievals using a new version of the Global Carbon Assimilation System［J］. Atmospheric Chemistry and Physics，2021，21（3）:1963-1985.
[83]	HE W，VAN DER VELDE I R，ANDREWS A E，et al. CTDAS-Lagrange v1. 0:a high-resolution data assimilation system for regional carbon dioxide observations［J］. Geoscientific Model Development，2018，11（8）:3515-3536.
[84]	STAUFER J，BROQUET G，BREON F M，et al. The first 1-year-long estimate of the Paris region fossil fuel CO₂ emissions based on atmospheric inversion［J］. Atmospheric Chemistry and Physics，2016，16（22）:14703-14726.
[85]	GURNEY K R，LIANG J，ROEST G，et al. Under-reporting of greenhouse gas emissions in U.S. cities［J］. Nature Communications，2021，12（1）:553.
[86]	Dang X C. Simulation of carbon dioxide source，sink and spatiotemporal distribution in East Asia［D］. Nanjing:Nanjing University of Information Science and Technology，2012. 党小晨. 东亚地区二氧化碳源、汇及时空分布模拟［D］. 南京:南京信息工程大学，2012.
[87]	WANG H，JIANG F，LIU Y，et al. Global terrestrial ecosystem carbon flux inferred from TanSat XCO₂ Retrievals［J］. Journal of Remote Sensing，2022，2022.
[88]	CHE K，LIU Y，CAI Z N，et al. Characterization of regional combustion efficiency using Delta XCO:delta XCO₂ observed by a portable fourier-transform spectrometer at an urban site in Beijing［J］. Advances in Atmospheric Sciences，2022，39（8）:1299-1315.
[89]	ZHENG Q M，SETO K C，ZHOU Y Y，et al. Nighttime light remote sensing for urban applications:progress，challenges，and prospects［J］. ISPRS Journal of Photogrammetry and Remote Sensing，2023，202:125-141.
[90]	ZUO C，GONG W，GAO ZY，et al. Correlation analysis of CO₂ concentration based on DMSP-OLS and NPP-VIIRS Integrated Data［J］. Remote Sensing，2022，14（17）:18.
[91]	RAUPACH M R，RAYNER P J，PAGET M. Regional variations in spatial structure of nightlights，population density and fossil-fuel CO₂ emissions［J］. Energy Policy，2010，38（9）:4756-4764.
[92]	LIU L Y，CHEN L F，LIU Y，et al. Methods，progress and challenges of satellite remote sensing monitoring for global carbon inventory［J］. Journal of Remote Sensing，2022，26（2）:243-267. 刘良云，陈良富，刘毅，等. 全球碳盘点卫星遥感监测方法、进展与挑战［J］. 遥感学报，2022，26（2）:243-267.
[93]	ELVIDGE C D，IMHOFF M L，BAUGH K E，et al. Night-time lights of the world:1994-1995［J］. ISPRS Journal of Photogrammetry and Remote Sensing，2001，56（2）:81-99.
[94]	SU Y X，CHEN X Z，YE Y Y，et al. Characteristics and mechanisms of carbon emissions from energy consumption in China based on night light data［J］. Acta Geographica Sinica，2013，68（11）:1513-1526. 苏泳娴，陈修治，叶玉瑶，等. 基于夜间灯光数据的中国能源消费碳排放特征及机理［J］. 地理学报，2013，68（11）:1513-1526.
[95]	WANG S J，LIU X P. China's city-level energy-related CO₂ emissions:spatiotemporal patterns and driving forces［J］. Applied Energy，2017，200:204-214.
[96]	WU J S，NIU Y，PENG J，et al. Energy consumption dynamics of prefecture-level cities in China from 1995 to 2009 based on DMSP/OLS night light data［J］. Geographical Research，2014，33（4）:625-634. 吴健生，牛妍，彭建，等. 基于DMSP/OLS夜间灯光数据的1995-2009年中国地级市能源消费动态［J］. 地理研究，2014，33（4）:625-634.
[97]	XIA S Y，SHAO H Y，WANG H，et al. Spatio-temporal dynamics and driving forces of multi-scale CO₂ emissions by integrating DMSP-OLS and NPP-VIIRS data:a case study in Beijing-Tianjin-Hebei，China［J］. Remote Sensing，2022，14（19）.
[98]	LI J J，SHI J A，DUAN K F，et al. Efficiency of China's urban development under carbon emission constraints:a city-level analysis［J］. Physics and Chemistry of the Earth，2022，127:14.
[99]	MA J J，GUO J Y，AHMAD S，et al. Constructing a new inter-calibration method for DMSP-OLS and NPP-VIIRS nighttime light［J］. Remote Sensing，2020，12（6）:15.
[100]	LABZOVSKII L D，JEONG S J，PARAZOO NC. Working towards confident spaceborne monitoring of carbon emissions from cities using Orbiting Carbon Observatory-2［J］. Remote Sensing of Environment，2019，233:12.
[101]	LEI R，FENG S，DANJOU A，et al. Fossil fuel CO₂ emissions over metropolitan areas from space:a multi-model analysis of OCO-2 data over Lahore，Pakistan［J］. Remote Sensing of Environment，2021，264:112625.
[102]	YANG D，BOESCH H，LIU Y，et al. Toward high precision XCO₂ retrievals from TanSat observations:retrieval improvement and validation against TCCON measurements［J］. Journal of Geophysical Research-Atmospheres，2020，125（22）:26.
[103]	KIEL M，ELDERING A，ROTEN D D，et al. Urban-focused satellite CO₂ observations from the Orbiting Carbon Observatory-3:a first look at the Los Angeles megacity［J］. Remote Sensing of Environment，2021，258:112314.
[104]	REUTER M，BUCHWITZ M，SCHNEISING O，et al. Towards monitoring localized CO₂ emissions from space:co-located regional CO₂ and NO₂ enhancements observed by the OCO-2 and S5P satellites［J］. Atmospheric Chemistry and Physics，2019，19（14）:9371-9383.
[105]	PARK C，JEONG S，PARK H，et al. Evaluation of the potential use of satellite-derived XCO₂ in detecting CO₂ enhancement in megacities with limited ground observations:a case study in Seoul using orbiting carbon observatory-2［J］. Asia-Pacific Journal of Atmospheric Sciences，2021，57（2）:289-299.
[106]	SILVA S J，ARELLANO AF. Characterizing regional-scale combustion using satellite retrievals of CO，NO₂ and CO₂［J］. Remote Sensing，2017，9（7）:15.
[107]	FINCH D P，PALMER P I，ZHANG T R. Automated detection of atmospheric NO₂ plumes from satellite data:a tool to help infer anthropogenic combustion emissions［J］. Atmospheric Measurement Techniques，2022，15（3）:721-733.
[108]	STEENBERG JWN，RISTOW M，DUINKER P N，et al. A national assessment of urban forest carbon storage and sequestration in Canada［J］. Carbon Balance and Management，2023，18（1）.
[109]	MUSLIH A M，NISA A，ARLITA T. The role of urban forests as carbon sink:a case study in the urban forest of Banda Aceh，Indonesia［J］. Jurnal Sylva Lestari，2022，10（3）:417-425.
[110]	NOWAK D J. Atmospheric carbon reduction by urban trees［J］. Journal of Environmental Management，1993，37（3）:207-217.
[111]	JO H K，MCPHERSON G E. Carbon storage and flux in urban residential greenspace［J］. Journal of Environmental Management，1995，45（2）:109-133.
[112]	HONG S O，KIM J，BYUN Y H，et al. Intra-urban variations of the CO₂ fluxes at the surface-atmosphere interface in the Seoul Metropolitan Area［J］. Asia-Pacific Journal of Atmospheric Sciences，2023:1-15.
[113]	MCPHERSON E G，SIMPSON J R. Potential energy savings in buildings by an urban tree planting programme in California［J］. Urban Forestry& Urban Greening，2003，2（2）:73-86.
[114]	VACCARI F P，GIOLI B，TOSCANO P，et al. Carbon dioxide balance assessment of the city of Florence（Italy），and implications for urban planning［J］. Landscape and Urban Planning，2013，120:138-146.
[115]	JO H K. Impacts of urban greenspace on offsetting carbon emissions for middle Korea［J］. Journal of Environmental Management，2002，64（2）:115-126.
[116]	NOWAK D J，CRANE D E，STEVENS J C，et al. A ground-based method of assessing urban forest structure and ecosystem services［J］. Arboriculture and Urban Forestry，2008，34（6）:347-358.
[117]	NOWAK D J，GREENFIELD E J，HOEHN R E，et al. Carbon storage and sequestration by trees in urban and community areas of the United States［J］. Environmental Pollution，2013，178:229-236.
[118]	PASHER J，MCGOVERN M，KHOURY M，et al. Assessing carbon storage and sequestration by Canada's urban forests using high resolution earth observation data［J］. Urban Forestry& Urban Greening，2014，13（3）:484-494.
[119]	HE X T. Research on estimation method of urban green space carbon sink for overall planning stage［D］. Xi'an:Xi'an University of Architecture and Technology，2021. 和晓彤. 面向总体规划阶段的城市绿地碳汇量估算方法研究［D］. 西安:西安建筑科技大学，2021.
[120]	WU D. Quantifying CO₂ emissions for cities around the globe from space-based measurements［M］. 2020.
[121]	LIU C，LI X. Carbon storage and sequestration by urban forests in Shenyang，China［J］. Urban Forestry& Urban Greening，2012，11（2）:121-128.
[122]	ZHANG Y，MENG W，YUN H，et al. Is urban green space a carbon sink or source？—a case study of China based on LCA method［J］. Environmental Impact Assessment Review，2022，94:106766.
[123]	GAO Q，WAN Y，LI Y，et al. Effects of topography and human activity on the net primary productivity（NPP）of alpine grassland in northern Tibet from 1981 to 2004［J］. International Journal of Remote Sensing，2013，34（6）:2057-2069.
[124]	YANG H，CHEN W. Spatio-temporal pattern of urban vegetation carbon sink and driving mechanisms of human activities in Huaibei，China［J］. Environmental Science and Pollution Research，2022:1-15.
[125]	ELDERING A，TAYLOR T E，O'DELL C W，et al. The OCO-3 mission:measurement objectives and expected performance based on 1 year of simulated data［J］. Atmospheric Measurement Techniques，2019，12（4）:2341-2370.
[126]	YANG E G，KORT E A，WU D，et al. Using space-based observations and Lagrangian modeling to evaluate urban carbon dioxide emissions in the Middle East［J］. Journal of Geophysical Research-Atmospheres，2020，125（7）:20.
[127]	WU K，PALMER P I，WU D，et al. Theoretical assessment of the ability of the MicroCarb satellite city-scan observing mode to estimate urban CO₂ emissions［J］. Atmospheric Measurement Techniques，2023，16（2）:581-602.