城市尺度温室气体排放反演研究进展及发展趋势

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摘要城市是二氧化碳(CO₂)和甲烷(CH₄)排放的热点区域,占全球人为源总排放量的40%~50%,因此对其排放量的准确量化,直接影响着各国气候变化政策的制定与实施.虽然排放清单方法是当前核算排放量的最主要手段,但大量对比研究表明,温室气体排放量在城市尺度上估算的不确定性依旧大于50%,因此亟需开展城市尺度上温室气体排放量的准确核算,进而降低排放清单的不确定性.而基于浓度观测和大气传输模型的反演方法,从2010年起逐渐被用于城市尺度温室气体排放量的反演研究,但对浓度观测站点选取和排放量反演方法依旧存在认识的不统一与偏差.在我国制定“双碳”政策的战略背景下,本文系统梳理和综述了当前城市尺度温室气体排放量反演的研究进展,重点分析该方法面临的困难和挑战,并指出相较于国外已开展的研究,我国当前城市尺度的温室气体浓度观测网络依旧欠缺,限制了温室气体排放量时空格局和组分的有效拆分.而结合中精度浓度观测网络,并同时发展1km及以下空间分辨率的排放清单和气象驱动场,是未来准确解析城市尺度温室气体排放总量及其时空格局的重要发展方向.通过系统总结当前城市尺度温室气体排放量的研究现状、进展及问题,以及未来潜在发展趋势,预计可为我国“双碳”战略的实施提供方法参考和理论借鉴.

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关键词 ：温室气体, 排放量反演, 高塔, 大气传输模型

Abstract：Urban areas are hotspots for carbon dioxide (CO₂) and methane (CH₄), accounting for 40% to 50% of the global anthropogenic emissions. Therefore, the accurate quantification of their emissions directly impacts the formulation and implementation of climate change policies. The emission inventories are currently the main approach to account for emissions. However, numerous studies have shown that the uncertainty of greenhouse gas inventories at the urban scale is still larger than 50%. Hence, there is an urgent need to reduce the uncertainty in emission inventories. Inverse estimation methods, which is based on concentration observations and atmospheric transport model, have gradually been used for the estimation of urban-scale greenhouse gas emissions since 2010. However, there still exists many knowledge gaps for this approach. To meet the goal of "dual carbon" as announced in 2021, this paper reviews the research progress of urban-scale greenhouse gas emissions and discussed recent difficulties and challenges. It points out that the greenhouse gas concentration observation network is still lacking in China. And the use of medium-precision concentration observation networks and simultaneously developing of emission inventories and meteorological fields with spatial resolutions of 1km are important directions to accurately resolve the total emissions and spatiotemporal patterns of greenhouse gases at the urban scale in the future. By systematically summarizing the current research status, progress, and issues related to urban-scale greenhouse gas emissions in this paper, it is expected to provide methodological references and theoretical insights for the implementation of China's "dual carbon" strategy.

Key words： greenhouse gas emission inversion tower-based site atmospheric transport model

收稿日期: 2024-05-16

PACS:

X51

基金资助:国家自然科学基金资助项目(42475125,42105117,42021004,42005143);国家重点研发计划项目(2020YFA0607501);江苏省碳达峰碳中和科技创新专项资金(前沿基础)项目(BK20220020),江苏省杰出青年基金项目(BK20220055);江苏省“333人才”领军型人才团队(BRA2022023),中国气象局生态系统碳源汇重点开放实验室资助项目

通讯作者: 胡诚,副教授,chenghu@njfu.edu.cn E-mail: chenghu@njfu.edu.cn

作者简介: 胡诚(1989-),男,四川广安人,副教授,博士,主要从城市尺度温室气体排放量的反演研究.发表论文60余篇.chenghu@njfu.edu.cn.

引用本文:

胡诚, 张俊清, 刘慧丽, 史雪静, 孙帆, 肖薇. 城市尺度温室气体排放反演研究进展及发展趋势[J]. 中国环境科学, 2024, 44(12): 7036-7045. HU Cheng, ZHANG Jun-qing, LIU Hui-li, SHI Xue-jing, SUN Fan, XIAO Wei. Review of the greenhouse gas emission inversion approaches at city scale and future development trends. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(12): 7036-7045.

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http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2024/V44/I12/7036

[1] Szopa S, Naik V, Adhikary B, et al. Climate change 2021: The physical science basis. In contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change [J]. Eds. Cambridge University Press: Cambridge, UK, 2021,2(1):2391.
[2] Zhang Z, Poulter B, Knox S, et al. Anthropogenic emission is the main contributor to the rise of atmospheric methane during 1993~2017[J]. National Science Review, 2022,9(5):154-166.
[3] Spokas K A, Bogner J, Corcoran M. Modeling landfill CH₄emissions: CALMIM international field validation, using CALMIM to simulate management strategies, current and future climate scenarios [J]. Elem Sci Anth, 2021,9(1):00050.
[4] Li T, Canadell J G, Yang X Q, et al. Methane emissions from wetlands in China and their climate feedbacks in the 21 st century [J]. Environmental Science & Technology, 2022,56(17):12024-12035.
[5] Le Quéré C, Jackson R B, Jones M W, et al. Temporary reduction in daily global CO₂ emissions during the COVID-19forced confinement [J]. Nature climate change, 2020,10(7):647-653.
[6] Lin X, Zhang W, Crippa M, et al. A comparative study of anthropogenic CH₄ emissions over China based on the ensembles of bottom-up inventories [J]. Earth System Science Data, 2021,13(3): 1073-1088.
[7] Han P, Lin X, Zeng N, et al. Province-level fossil fuel CO₂ emission estimates for China based on seven inventories [J]. Journal of Cleaner Production, 2020,277:123377.
[8] 朴世龙,何悦,王旭辉,等.中国陆地生态系统碳汇估算:方法,进展,展望[J]. 中国科学:地球科学, 2022,52(6):1010-1020. Piao S L, He Y, Wu X H, et al. Estimation of China's terrestrial ecosystem carbon sink: Methods, progress and prospects [J]. Scientia Sinica(Terrae), 2022,52(6):1010-1020.
[9] Wang Y, Tian X, Duan M, et al. Optimal design of surface CO₂ observation network to constrain China’s land carbon sink [J]. Science Bulletin, 2023,68(15):1678-1686.
[10] Gao Y, Wang S, Chen L, et al. Achieving accurate regional carbon- sink accounting and its significance for “missing” carbon sinks [J]. The Innovation, 2024,5(1):100552-.
[11] Lin J C, Gerbig C, Wofsy S C, et al. A near‐field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time‐Inverted Lagrangian Transport (STILT) model [J]. Journal of Geophysical Research: Atmospheres, 2003,108(d16):4493.
[12] Hu C, Griffis T J, Xia L, et al. Anthropogenic CO₂ emission reduction during the COVID-19 pandemic in Nanchang City, China [J]. Environmental Pollution, 2022,309:119767.
[13] Hu C, Zhang J, Qi B, et al. Global warming will largely increase waste treatment CH₄ emissions in Chinese megacities: insight from the first city-scale CH₄ concentration observation network in Hangzhou, China [J]. Atmospheric Chemistry and Physics, 2023,23(7):4501-4520.
[14] Hopkins F M, Ehleringer J R, Bush S E, et al. Mitigation of methane emissions in cities: How new measurements and partnerships can contribute to emissions reduction strategies [J]. Earth's Future, 2016, 4(9):408-425.
[15] Floerchinger C, Shepson P B, Hajny K, et al. Relative flux measurements of biogenic and natural gas-derived methane for seven US cities [J]. Elem. Sci. Anth., 2021,9(1):000119.
[16] Cheng Z Z, Thomas P, Sally N, et al. Decadal decrease in Los Angeles methane emissions is much smaller than bottom-up estimates [J]. Nature Communications, 2023,14(1):5353-5353.
[17] 于贵瑞,郝天象,朱剑兴.中国碳达峰、碳中和行动方略之探讨[J]. 中国科学院院刊, 2022,37(4):423-434. Yu G R, Hao T X, Zhu J X. Discussion on Action Strategies of China's Carbon Peak and Carbon Neutrality [J]. Bulletin of Chinese Academy of Sciences, 2022,37(4):423-434.
[18] 胡诚,张弥,肖薇,等.基于WRF-STILT模型对高塔CO₂浓度的模拟研究[J]. 中国环境科学, 2017,37(7):2424-2437. Hu C, Zhang M, Xiao W, et al. Tall tower CO₂ concentration simulation using the WRF-STILT model [J]. China Environmental Science, 2017,37(7):2424-2437.
[19] Hu C, Xu J, Liu C, et al. Anthropogenic and natural controls on atmospheric δ13C-CO₂ variations in the Yangtze River delta: insights from a carbon isotope modeling framework [J]. Atmospheric Chemistry and Physics, 2021,21(13):10015-10037.
[20] Hu C, Xiao W, Timothy J Griffis, et al. Estimation of anthropogenic CH₄ and CO₂ emissions in Taiyuan-Jinzhong Region: One of the world's largest emission hotspots [J]. Atmospheres, 2023,128(8): e2022JD037915.
[21] Staufer J, Broquet G, Bréon F, 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.
[22] Davis J K, Deng A, Lauvaux T, et al. The Indianapolis Flux Experiment (INFLUX): A test-bed for developing urban greenhouse gas emission measurements [J]. Elementa: Science of the Anthropocene, 2017,5:21.
[23] Israel L, Xinrong R, E O S, et al. Wintertime CO₂, CH₄ and CO emissions estimation for the Washington DC / Baltimore metropolitan area using an inverse modeling technique [J]. Environmental science technology, 2020,54(5):2606-2614.
[24] Yadav V, Duren R, Mueller K, et al. Spatio-temporally resolved methane fluxes from the Los Angeles Megacity [J]. Journal of Geophysical Research: Atmospheres, 2019,124(9):5131-5148.
[25] Yadav V, Ghosh S, Mueller K, et al. The impact of COVID-19 on CO₂ emissions in the Los Angeles and Washington DC/Baltimore Metropolitan areas [J]. Geophysical Research Letters, 2021,48(11): e2021GL092744.
[26] Vineet Y, Kristal V, Riley D, et al. A declining trend of methane emissions in the Los Angeles basin from 2015 to 2020[J]. Environmental Research Letters, 2023,18(3):034004.
[27] Lian J, Lauvaux T, Utard H, et al. Can we use atmospheric CO₂ measurements to verify emission trends reported by cities? Lessons from a 6-year atmospheric inversion over Paris [J]. Atmospheric Chemistry and Physics, 2023,23:8823-8835.
[28] Vardag S N, Maiwald R. Optimising urban measurement networks for CO₂ flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL [J]. Geoscientific Model Development, 2024,17:1885-1902.
[29] Oda T, Oda T, Maksyutov S, et al. 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.
[30] Scarpelli R T, Jacob J D, Maasakkers D J, et al. A global gridded (0.1°×0.1°) inventory of methane emissions from oil, gas, and coal exploitation based on national reports to the United Nations Framework Convention on Climate Change [J]. Earth System Science Data, 2020,12(1):563-575.
[31] Liu Z, Guan D, Wei W, et al. Reduced carbon emission estimates from fossil fuel combustion and cement production in China [J]. Nature, 2015,524(7565):335-338.
[32] Li M, Liu H, Geng G, et al. Anthropogenic emission inventories in China: A review [J]. National Science Review, 2017,4(6):834-866.
[33] Thompson R L, Stohl A, Zhou L X, et al. Methane emissions in East Asia for 2000-2011estimated using an atmospheric Bayesian inversion [J]. Journal of Geophysical Research: Atmospheres, 2015,120:4352- 4369.
[34] Zhang Y, Fang S, Chen J, et al. Observed changes in China’s methane emissions linked to policy drivers [J]. Proceedings of the National Academy of Sciences of the United States of America, 2022,119(41): e2202742119-e2202742119.
[35] Derek V M, Logan E M, Lewis K, et al. Constraining urban CO₂ emissions using mobile observations from a light rail public transit platform [J]. Environmental science technology, 2020,54(24):15613- 15621.
[36] Pitt J R, Lopez-Coto I, Hajny K D, et al. New York City greenhouse gas emissions estimated with inverse modeling of aircraft measurements [J]. Elementa: Science of the Anthropocene, 2022, 10(1):00082.
[37] Pang X, Shang Q, Chen L, et al. Study of spatiotemporal variation and annual emission of CH₄ in Shaoxing Yangtze River Delta, China, Using a portable CH₄detector on the UAV [J]. Journal of Environmental Sciences, 2024,151:140-149.
[38] Wu D, Lin C J, Fasoli B, et al. A Lagrangian approach towards extracting signals of urban CO₂emissions from satellite observations of atmospheric column CO₂ (XCO₂): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”) [J]. Geoscientific Model Development, 2018,11(12):4843-4871.
[39] Ye X, Lauvaux T, Kort E A, et al. Constraining fossil fuel CO₂ emissions from urban area using OCO-2 observations of total column CO₂ [J]. Journal of Geophysical Research. Atmospheres, 2020,125(8): e2019JD030528.
[40] 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):e2019JD031922.
[41] 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.
[42] Mallia D V, Mitchell L E, Gonzalez Vidal, et al. Can we detect urban-scale CO₂ emission changes within medium-sized cities? [J]. Journal of Geophysical Research: Atmospheres, 2023,128: e2023JD038686.
[43] Feng S, Lauvaux T, Newman S, et al. Los Angeles megacity: a high- resolution land-atmosphere modelling system for urban CO₂ emissions [J]. Atmospheric Chemistry and Physics, 2016,16(14):9019- 9045.
[44] Hu C, Griffis J T, Liu S, et al. Anthropogenic methane emission and Its partitioning for the Yangtze River Delta Region of China [J]. Journal of Geophysical Research: Biogeosciences, 2019,124(5):1148- 1170.
[45] Peng S, Piao S, Bousquet P, et al. Inventory of anthropogenic methane emissions in China's mainland from 1980 to 2010[J]. Atmospheric Chemistry and Physics, 2016,16(22):14545-14562.
[46] Cai B, Lou Z, Wang J, et al. CH₄ mitigation potentials from China landfills and related environmental co-benefits [J]. Science Advances, 2018,4(7):eaar8400.
[47] Miller S M, Michalak A M, Detmers R G, et al. China's coal mine methane regulations have not curbed growing emissions [J]. Nature communications, 2019,10(1):303.
[48] Kirschke S, Bousquet P, Ciais P, et al. Three decades of global methane sources and sinks [J]. Nature Geoscience, 2013,6(10): 813-823.
[49] Saunois M, Jackson B R, Bousquet P, et al. The growing role of methane in anthropogenic climate change [J]. Environmental Research Letters, 2016,11(12):120207.
[50] Zhu T, Bian W, Zhang S, et al. An improved approach to estimate methane emissions from coal mining in China [J]. Environmental science & technology, 2017,51(21):12072-12080.
[51] Maier F, Gerbig C, Levin I, et al. Effects of point source emission heights in WRF-STILT: a step towards exploiting nocturnal observations in models [J]. Geoscientific Model Development, 2022, 15(13):5391-5406.
[52] Sargent M, Barrera Y, Nehrkorn T, et al. Anthropogenic and biogenic CO₂ fluxes in the Boston urban region [J]. Proceedings of the National Academy of Sciences of the United States of America, 2018,115(29): 7491-7496.
[53] Wu K, Davis K J, Miles N L, et al. Source decomposition of eddy-covariance CO₂ flux measurements for evaluating a high- resolution urban CO₂ emissions inventory [J]. Environmental Research Letters, 2022,17(7):074035.
[54] 马倩,王咏薇,苗世光,等.中国大城市具有建筑物分类的土地利用分类数据集构建研究[J]. 大气科学学报, 2022,45(1):135-147. Ma Q, Wang Y W, Miao S G, et al. Study on construction of land use classification datasets with building type in large cities of China [J]. Transactions of Atmospheric Sciences, 2022,45(1):135-147.
[55] Ma X, Zheng G, Chi X, et al. Mapping fine-scale building heights in urban agglomeration with spaceborne lidar [J]. Remote Sensing of Environment, 2023,285.
[56] Turner A J, Jacob D J. Balancing aggregation and smoothing errors in inverse models [J]. Atmospheric Chemistry and Physics, 2015,15(1): 1001-1026.
[57] Hu C, Griffis T J, Millet D B, et al. Top-down constraints on anthropogenic CO₂ emissions within an agricultural-urban landscape [J]. Journal of Geophysical Research: Atmospheres, 2018, 123:4674-4694.
[58] Peng Y Y, Hu C, Ai X Y, et al. Improvements of simulating urban atmospheric CO₂ concentration by coupling with emission height and dynamic boundary layer variations in WRF-STILT Model [J]. Atmosphere, 2023,14(2):223-223.