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城市及区域尺度碳同化反演研究进展

寇星霞 彭珍 张美根 苗世光 陈敏 赵秀娟

寇星霞, 彭珍, 张美根, 苗世光, 陈敏, 赵秀娟. 城市及区域尺度碳同化反演研究进展[J]. 环境工程, 2024, 42(10): 209-217. doi: 10.13205/j.hjgc.202410024
引用本文: 寇星霞, 彭珍, 张美根, 苗世光, 陈敏, 赵秀娟. 城市及区域尺度碳同化反演研究进展[J]. 环境工程, 2024, 42(10): 209-217. doi: 10.13205/j.hjgc.202410024
KOU Xingxia, PENG Zhen, ZHANG Meigen, MIAO Shiguang, CHEN Min, ZHAO Xiujuan. RESEARCH PROGRESS IN URBAN AND REGIONAL-SCALE ATMOSPHERIC INVERSIONS OF CARBON SOURCES AND SINKS[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(10): 209-217. doi: 10.13205/j.hjgc.202410024
Citation: KOU Xingxia, PENG Zhen, ZHANG Meigen, MIAO Shiguang, CHEN Min, ZHAO Xiujuan. RESEARCH PROGRESS IN URBAN AND REGIONAL-SCALE ATMOSPHERIC INVERSIONS OF CARBON SOURCES AND SINKS[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(10): 209-217. doi: 10.13205/j.hjgc.202410024

城市及区域尺度碳同化反演研究进展

doi: 10.13205/j.hjgc.202410024
基金项目: 

中央级公益性科研院所基本科研业务费专项资金(IUMKY202440)

中国科学院大气物理研究所大气边界层物理和大气化学国家重点实验室开放课题(LAPC-KF-2023-04)

国家重点研发计划项目(2022YFB3904801)

国家自然科学基金项目(42275153)

详细信息
    作者简介:

    寇星霞(1988-),女,副研究员,主要研究方向为温室气体和污染物的模拟同化技术。xxkou@ium.cn

    通讯作者:

    寇星霞(1988-),女,副研究员,主要研究方向为温室气体和污染物的模拟同化技术。xxkou@ium.cn

RESEARCH PROGRESS IN URBAN AND REGIONAL-SCALE ATMOSPHERIC INVERSIONS OF CARBON SOURCES AND SINKS

  • 摘要: 双碳战略背景下,对碳源、汇的准确估算提出了迫切需求。尽管"自上而下"碳同化反演理论严谨,但从大气浓度变化反演碳源汇,长期以来是一个具有挑战性的科学问题。以往基于卫星和地面监测的大气反演,已在全球尺度上提升了陆地和海洋碳源汇的认识。然而,城市和区域尺度碳源汇估算仍有很大的不确定性。一方面,在区域尺度,我国陆地生态系统碳源汇反演大多采用全球大气传输模式,在月和周时间尺度上同化,有限的观测资料和模式分辨率导致反演的不确定性很大。基于中尺度大气传输模式的区域碳同化,通过提升碳源汇估算的时空分辨率,改进陆地碳源汇反演水平。另一方面,在城市尺度,城市是人为碳排放的主要来源,基于能源消耗统计数据的"自下而上"清单法不确定性大且更新慢。通过碳同化反演,可获得客观及时的碳排放数据,实现与"自下而上"清单的相互校验。总体上,近年来城市和区域尺度碳同化取得了很大进展,未来亟须进一步降低模式和观测不确定性的影响,开展自然源和人为源的精准反演,为双碳目标提供科学支撑。
  • [1] UNFCCC. The Paris Agreement on Climate Change[R]. 2015, available at https://www.nrdc.org/sites/default/files/paris-climate-agreement-IB.pdf.
    [2] PIAO S, HE Y, WANG X, et al. Estimation of China’s terrestrial ecosystem carbon sink: methods, progress and prospects[J]. Science China Earth Sciences, 2022, 65(4): 641-651.
    [3] PIAO S, YUE C, DING J, et al. Perspectives on the role of terrestrial ecosystems in the "carbon neutrality" strategy[J]. Science China Earth Sciences, 2022, 65(6): 1178-1186.
    [4] IPCC 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories[R]. Prepared by the National Greenhouse Gas Inventories Programme.
    [5] IPCC 2019. 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventory[R]. Buendia C E, Guendehou S, Limmeechokchai B, (eds).
    [6] ZHENG B, TONG D, LI M, et al. Trends in China’s anthropogenic emissions since 2010 as the consequence of clean air action[J]. Atmospheric Chemistry Physics, 2018, 18: 14095-14111.
    [7] PETERS W, JACOBSON A R, SWEENEY C, et al. An atmospheric perspective on North American carbon dioxide exchange: carbonTracker[J]. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104: 18925-18930.
    [8] GURNEY K R. Transcom 3 inversion intercomparison: model mean results for the estimation of seasonal carbon sources and sinks[J]. Global Biogeochemical Cycles, 2004, 18: GB1010.
    [9] GURNEY K R, MENDOZA D L, ZHOU Y Y, et al. High-resolution fossil fuel combustion CO2 emissions fluxes for the United States[J]. Environmental Science & Technology, 2009, 43: 5535-5541.
    [10] BAKER D F, DONEY S C, SCHIMEL D S. Variational data assimilation for atmospheric CO2[J]. Tellus B, 2006, 58: 359-365.
    [11] ENGELEN R J, SERRAR S, CHEVALLIER F, et al. Four-dimensional data assimilation of atmospheric CO2 using AIRS observations[J]. Journal of Geophysical Research: Atmospheres, 2009, 114: D03303.
    [12] FENG L, PALMER P I, BÖSCH H, et al. Estimating surface CO2 fluxes from space-borne CO2 dry air mole fraction observations using an ensemble Kalman Filter[J]. Atmospheric Chemistry Physics, 2009, 9: 2619-2633.
    [13] 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.
    [14] LIU Z, DENG Z, HE G, et al. Challenges and opportunities for carbon neutrality in China[J]. Nature Reviews Earth & Environment, 2022, 3: 141-155.
    [15] TARANTOLA A. Inverse problem theory and methods for model parameter estimation[M]. Philadelphia: Society for Industrial and Applied Mathematics, 2005: 342.
    [16] GURNEY K R, Law R M, Denning A S, et al. Toward robust regional estimates of CO2 sources and sinks using atmospheric transport models[J]. Nature, 2002, 415: 626-630.
    [17] PRATHER M, ZHU X, STRAHAN S E, et al. Quantifying errors in trace species transport modeling[J]. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105: 19617-19621.
    [18] SCHüRMANN G J, KAMINSKI T, KÖSTLER C, et al. Constraining a land-surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System V1.0[J]. Geoscientific Model Development, 2016, 9: 2999-3026.
    [19] SCHOLZE M, KAMINSKI T, KNORR W, et al. Mean European carbon sink over 2010—2015 estimated by simultaneous assimilation of atmospheric CO2, soil moisture and vegetation optical depth[J]. Geophysical Research Letters, 2019, 46: 13796-13803.
    [20] PETERS W, KROL M, VAN DER WERF G, et al. Seven years of recent European net terrestrial carbon dioxide exchange constrained by atmospheric observations[J]. Global Change Biology, 2010, 16: 1317-1337.
    [21] ZHANG H, CHEN B Z, van der LAAN-LUIJKX I T, et al. Net terrestrial CO2 exchange over China during 2001—2010 estimated with an ensemble data assimilation system for atmospheric CO2[J]. Journal of Geophysical Research: Atmospheres, 2014, 119(6): 3500-3515.
    [22] 麦博儒, 邓雪娇, 安兴琴,等. 基于碳源汇模式系统Carbon Tracker的广东省近地层典型CO2过程模拟研究[J]. 环境科学学报, 2014, 34(7): 1833-1844.
    [23] TIAN X, XIE Z, Liu Y, et al. A joint data assimilation system (Tan-Tracker) to simultaneously estimate surface CO2 fluxes and 3-D atmospheric CO2 concentrations from observations[J]. Atmospheric Chemistry Physics, 2014, 14: 13281-13293.
    [24] JIANG F, WANG H M, 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: 1963-1985.
    [25] JIANG F, JU W M, HE W, et al. A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO2 retrievals (GCAS2021)[J]. Earth System Science Data, 2022: 3013-3037.
    [26] TAKAGI H, SAEKI T, ODA T, et al. On the benefit of GOSAT observations to the estimation of regional CO2 fluxes[J]. Sola, 2011, 7: 161-164.
    [27] LIU Z, BAMBHA R P, PINTO J P, et al. Toward verifying fossil fuel CO2 emissions with the Community Multi-scale Air Quality (CMAQ) model: motivation, model description and initial simulation[J]. Journal of the Air & Waste Management Association, 2013, 64: 419-435.
    [28] AHAMDOV R, GERBIG C, KRETSCHMER R, et al. Comparing high resolution WRF-VPRM simulations and two global CO2 transport models with coastal tower measurements of CO2[J]. Biogeosciences, 2009, 6: 807-817.
    [29] PÉREZ-LANDA G, CIAIS P, GANGOITI G, et al. Mesoscale circulations over complex terrain in the Valencia coastal region, Spain-Part 2: modeling CO2 transport using idealized surface fluxes[J]. Atmospheric Chemistry Physics, 2007, 7: 1851-1868.
    [30] PILLAI D, GERBIG C, KRETSCHMER R, et al. Comparing Lagrangian and Eulerian models for CO2 transport: a step towards Bayesian inverse modeling using WRF/STILT-VPRM[J]. Atmospheric Chemistry Physics, 2012, 12: 8979-8991.
    [31] KOU X X, ZHANG M G, PENG Z. Numerical simulation of CO2 concentrations in east asia with RAMS-CMAQ[J]. Atmospheric and Oceanic Science Letters, 2013, 6(4): 179-184.
    [32] KOU X X, ZHANG M G, PENG Z, et al. Assessment of the biospheric contribution to surface atmospheric CO2 concentrations over East Asia with a regional chemical transport model[J]. Advances in Atmospheric Sciences, 2015, 32(3): 287-300.
    [33] CHEVILLARD A, KARSTENS U, CIAIS P, et al. Simulation of atmospheric CO2 over Europe and western Siberia using the regional scale model REMO[J]. Tellus B, 2002, 54: 872-894.
    [34] van DER MOLEN M K, DOLMAN A J. Regional carbon fluxes and the effect of topography on the variability of atmospheric CO2[J]. Journal of Geophysical Research: Atmospheres, 2007, 112: D1.
    [35] SARRAT C, NOILHAN J, LACARRERE P, et al. Atmospheric CO2 modeling at the regional scale: application to the CarboEurope Regional Experiment[J]. Journal of Geophysical Research: Atmospheres, 2007, 112: D12.
    [36] AHMADOV R, GERBIG C, KRETSCHMER R, et al. Mesoscale covariance of transport and CO2 fluxes: evidence from observations and simulations using the WRF-VPRM coupled atmosphere-biosphere model[J]. Journal of Geophysical Research: Atmospheres, 2007, 112: D22.
    [37] LI R, ZHANG M G, CHEN L F, et al. CMAQ simulation of atmospheric CO2 concentration in East Asia: comparison with GOSAT observations and ground measurements[J]. Atmospheric Environment, 2017, 160: 176-185.
    [38] 梁周彤, 唐文瀚, 曾宁, 等. 基于WRF模式的京津冀地区地表大气CO2浓度的模拟研究[J]. 大气科学学报, 2022,45 (3): 387-396.
    [39] HUANG Z K, PENG Z, LIU H N, et al. Development of CMAQ for East Asia CO2 data assimilation under an EnKF framework: a first result[J]. Chinese Science Bulletin, 2014, 59: 3200-3208.
    [40] ZHANG Q W, LI M Q, WEI C, et al. Assimilation of OCO-2 retrievals with WRF-Chem/DART: a case study for the Midwestern United States[J]. Atmospheric Environment, 2021, 246: 118106.
    [41] PENG Z, ZHANG M G, KOU X X, et al. A regional carbon flux data assimilation system and its preliminary evaluation in East Asia[J]. Atmospheric Chemistry and Physics, 2015, 15: 1087-1104.
    [42] KOU X X, TIAN X J, ZHANG M G, et al. Accounting for CO2 variability over East Asia with a regional joint inversion system and its preliminary evaluation[J]. Journal of Meteorological Research, 2017, 31(5): 834-851.
    [43] 寇星霞. 东亚地区大气CO2数值模拟和地表通量反演研究[D]. 北京: 中国科学院, 2015.
    [44] ZHENG T, FRENCH N H F, BAXTER M. Development of the WRF-CO2 4D-Var assimilation system v1.0[J]. Geoscientific Model Development, 2018, 11: 1725-1752.
    [45] PENG Z, KOU X X, ZHANG M G, et al. Towards high-resolution inversion of CO2 fluxes by developing a regional Joint Data Assimilation System based on CMAQ and EnSRF with surface observations[J]. Journal of Geophysical Research: Atmospheres, 128: e2022JD037154.
    [46] GUO L F, ZHANG X Y, ZHONG J T, et al. Construction and Application of a Regional Kilometer-Scale Carbon Source and Sink Assimilation Inversion System (CCMVS-R)[J]. Engineering, 2014, 33(2), doi: 10.1016/j.eng.2023.02.017.
    [47] ZHONG J T, ZHANG X Y, GUO L F, et al. Ongoing CO2 monitoring verify CO2 emissions and sinks in China during 2018—2021[J]. Chinese Science Bulletin, 2023, 68: 2467-2476.
    [48] KOU X X, PENG Z, ZHANG M G, et al. The carbon sink in China as seen from GOSAT with a regional inversion system based on the Community Multi-scale Air Quality (CMAQ) and ensemble Kalman smoother (EnKS)[J]. Atmospheric Chemistry and Physics, 2023, 23: 6719-6741.
    [49] CANADELL J G, LE QUERE C, RAUPACH M R, et al. Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104: 18866-18870.
    [50] MCKAIN K, WOFSY S C, NEHRKORN T, et al. Assessment of ground-based atmospheric observations for verification of greenhouse gas emissions from an urban region[J]. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109: 8423-8428.
    [51] 王少剑, 莫惠斌, 方创琳. 珠江三角洲城市群城市碳排放动态模拟与碳达峰[J]. 科学通报, 2022, 67(7): 670-684.
    [52] MARLAND G. Uncertainties in accounting for CO2 from fossil fuels[J]. Journal of Industrial Ecology, 2008, 12: 136-139.
    [53] STAUFER J, BROUQUET G, BRÉON 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: 14703-14726.
    [54] BRÉON F M, BROUQUET G, PUYGRENIER V, et al. An attempt at estimating Paris area CO2 emissions from atmospheric concentration measurements[J]. Atmospheric Chemistry and Physics, 2015, 15: 1707-1724.
    [55] GURNEY K R, LIANG J M, PATARASUK R, et al. Reconciling the differences between a bottom-up and inverse-estimated FFCO2 emissions estimate in a large US urban area[J]. Elementa Science of the Anthropocene, 2017, 5: 44.
    [56] WU L, GROQUET G, CIAIS P, et al. What would dense atmospheric observation networks bring to the quantification of city CO2 emissions?[J]. Atmospheric Chemistry and Physics, 2016, 16: 7743-7771.
    [57] 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: 5213-5236.
    [58] PILLAI D, BUCHWITZ M, GERBIG C, et al. Tracking city CO2 emissions from space using a high-resolution inverse modelling approach: a case study for Berlin, Germany[J]. Atmospheric Chemistry and Physics, 2016, 16: 9591-9610.
    [59] BRIOUDE J, ANGEVINE W M, AHMADOV R, et al. Top-down estimate of surface flux in the Los Angeles Basin using a mesoscale inverse modeling technique: assessing anthropogenic emissions of CO, NO<em>x and CO2 and their impacts[J]. Atmospheric Chemistry and Physics, 2013, 13: 3661-3677.
    [60] SUPER I, HUGO A C, VAN DER GON D, et al. Optimizing a dynamic fossil fuel CO2 emission model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an urban area using atmospheric observations of CO2, CO, NO<em>x, and SO2[J]. Geoscientific Model Development, 2020, 13: 2695-2721.
    [61] VAN DER VELDE I R, MILLER J B, VAN DER MOLEN M K, et al. The CarbonTracker Data Assimilation System for CO2 and δ13C (CTDAS-C13 v1.0): retrieving information on land-atmosphere exchange processes[J]. Geoscientific Model Development, 2018, 11: 283-304.
    [62] BASU S, MILLER J B, LEHMAN S. Separation of biospheric and fossil fuel fluxes of CO2 by atmospheric inversion of CO2 and 14CO2 measurements: Observation System Simulations[J]. Atmospheric Chemistry and Physics, 2016, 16: 5665-5683.
    [63] BASU S, LEHMAN S J, MILLER J B, et al. Estimating US fossil fuel CO2 emissions from measurements of 14C in atmospheric CO2[J]. Proceedings of the National Academy of Sciences of the United States of America, 2020, 117(24): 13300-13307.
    [64] ZHOU W J, NIU Z C, WU S G, et al. Fossil fuel CO2 traced by radiocarbon in fifteen Chinese cities[J]. Science of the Total Environment, 2020, 729: 138639.
    [65] 李新, 马瀚青, 冉有华, 等. 陆地碳循环模型-数据融合: 前沿与挑战. 中国科学: 地球科学, 2021, 51(10): 1650-1663.
    [66] 杨元合, 石岳, 孙文娟, 等.陆地生态系统碳源汇特征及其对实现碳中和目标的贡献[J]. 中国科学:生命科学, 2022, 52: 534-574.
    [67] 于贵瑞, 张雷明, 张杨建, 等. 大尺度陆地生态系统状态变化及其资源环境效应的立体化协同联网观测[J]. 应用生态学报, 2021, 32(6): 1903-1918.
    [68] FU Y, LIAO H, TIAN X J, et al. Impact of prior terrestrial carbon fluxes on simulations of atmospheric CO2 concentrations[J]. Journal of Geophysical Research: Atmospheres, 2021, 126, e2021JD034794.
    [69] PIAO S L, FANG J Y, CIAIS P, et al. The carbon balance of terrestrial ecosystems in China[J]. Nature, 2009, 458, 23: 1009-1013.
    [70] JIANG F, CHEN J M, ZHOU L X, et al. A comprehensive estimate of recent carbon sinks in China using both top-down and bottom-up approaches[J]. Scientific Reports, 2016, 6: 22130.
    [71] HE H L, WANG S Q, ZHANG L, et al. Altered trends in carbon uptake in China’s terrestrial ecosystems under the enhanced summer monsoon and warming hiatus[J]. National Science Review, 2019, 6: 505-514.
    [72] CHEN B Z, ZHANG H F, WANG T, et al. An atmospheric perspective on the carbon budgets of terrestrial ecosystems in China: progress and challenges[J]. Chinese Science Bulletin, 2021, 66: 1713-1718.
    [73] 陈报章, 张慧芳. 全球碳同化系统及其应用研究[M]. 北京:科学出版社, 2015.
    [74] WANG J, FENG L, PALMER P I, et al. Large Chinese land carbon sink estimated from atmospheric carbon dioxide data[J]. Nature, 2020, 586: 720-735.
    [75] WANG Y L, WANG X H, WANG K, et al. The size of the land carbon sink in China[J]. Nature, 2022, 603, E7-E12.
    [76] SCHUH A E, BYRNE B, JACOBSON A R, et al. On the role of atmospheric model transport uncertainty in estimating the Chinese land carbon sink[J]. Nature, 2022, 603, E13-E16.
    [77] PIAO S L, FANG J Y, CIAIS P, et al. The carbon balance of terrestrial ecosystems in China[J]. Nature, 2009, 458, 23: 1009-1013.
    [78] TIAN H, XU X, LU C, et al. Net exchanges of CO2, CH4, and N2O between China’s terrestrial ecosystems and the atmosphere and their contributions to global climate warming[J]. Journal of Geophysical Research: Atmospheres, 2011, 116: G02011.
    [79] HE H L, WANG S Q, ZHANG L, et al. Altered trends in carbon uptake in China’s terrestrial ecosystems under the enhanced summer monsoon and warming hiatus[J]. National Science Review, 2019, 6: 505-514.
    [80] van der LAAN-LUIJKX I T, van der VELDE I R, van der VEEN E, et al. The carbontracker data assimilation shell (CTDAS) v1.0: implementation and global carbon balance 2001—2015[J]. Geoscientific Model Development, 2017, 10: 2785-2800.
    [81] RÖDENBECK C, ZAEHLE S, KEELING R, et al. How does the terrestrial carbon exchange respond to inter-annual climatic variations? A quantification based on atmospheric CO2 data[J]. Biogeosciences, 2018, 15(8): 2481-2498.
    [82] HE W, JIANG F, WU M, et al. China’s terrestrial carbon sink over 2010—2015 constrained by satellite observations of atmospheric CO2 and land surface variables[J]. Journal of Geophysical Research: Biogeosciences, 2022, 127, e2021JG006644.
    [83] JACOBSON A R, SCHULDT K N, MILLER J B, et al. CarbonTracker CT2019B, http://dx.doi.org/10.25925/20201008 [EB/OL]. https://gml.noaa.gov/ccgg/carbontracker/CT2019B/.
    [84] LIU Y, WANG J, YAO L, et al. The TanSat mission: preliminary global observations[J]. Chinese Science Bulletin, 2018, 63(18): 1200-1207.
    [85] ELDERING A, WENNBERG P O, CRISP D, et al. The Orbiting Carbon Observatory-2 early science investigations of regional carbon dioxide fluxes[J]. Science, 2017: 358: 188.
    [86] 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. Atmospheric Measurement Techniques, 2019, 12: 2341-2370.
    [87] KUZE A, SUTO H, NAKAJIMA M, et al. Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring[J]. Applied Optics, 2009, 48: 6716-6733.
    [88] GLUMB R, DAVIS G, LIETZKE C. The TANSO-FTS-2 instrument for the GOSAT-2 greenhouse gas monitoring mission[C]//2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, 1238-1240. doi: 10.1109/IGARSS.2014.6946656.
    [89] 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.
    [90] 李新, 马瀚青, 冉有华, 等. 陆地碳循环模型-数据融合: 前沿与挑战[J].中国科学: 地球科学, 2021, 51 (10): 1650-1663.
    [91] 张勇, 颜鹏, 靳军莉, 等. 中国大气本底观测: 减污降碳背景下主要大气成分变化趋势[J]. 气象科技进展, 2022, 12 (1): 19-25.
    [92] FRIEDLINGSTEIN P, O’SULLIVAN M, JONES M W, et al. Global carbon budget 2023[J]. Earth System Science Data, 2023, 15: 5301-5369.
    [93] 罗文蓉, 车慧正, 苗世光, 等. 城市碳通量监测研究进展[J]. 环境工程, 2023, 41(10): 230-244.
    [94] 张立, 谢紫璇, 曹丽斌, 等. 中国城市碳达峰评估方法初探[J]. 环境工程, 2020, 38(11): 1-5.
    [95] 蔡博峰, 朱松丽, 于胜民, 等.《IPCC2006年国家温室气体清单指南2019修订版》解读[J]. 环境工程, 2019, 37(8): 1-11.
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  • 收稿日期:  2024-03-31
  • 网络出版日期:  2024-11-30

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