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TROPOMI NO2を用いた化石燃料CO2日排出量の間接推定

  • role： First author第一作者
  • Affiliation：

    School of Environment Science and Spatial Informatics,China University of Mining and Technology, Xuzhou 221116, China

  • Email：lulingxiao@cumt.edu.cn
  • Introduction：陆凌霄，研究方向为大气环境遥感。E-mail：lulingxiao@cumt.edu.cn
LU Lingxiao1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Environment Science and Spatial Informatics,China University of Mining and Technology, Xuzhou 221116, China

  • Email：qinkai@cumt.edu.cn
  • Introduction：秦凯，研究方向为大气环境遥感。E-mail：qinkai@cumt.edu.cn
QIN Kai1*,  
  • Affiliation：

    School of Environment Science and Spatial Informatics,China University of Mining and Technology, Xuzhou 221116, China

COHEN Jason Blake1,  
  • Affiliation：

    School of Environment Science and Spatial Informatics,China University of Mining and Technology, Xuzhou 221116, China

LI Xiaolu1,  
  • Affiliation：

    Satellite Application Center for Ecology and Environment, Beijing 100094, China

ZHOU Chunyan2

サマリー

「二酸化炭素排出量『ダブルカーボン』目標の達成」のために、衛星リモートセンシング技術を活用して化石燃料消費に伴う二酸化炭素（CO2）排出量を推定することが非常に重要である。しかし、CO2は大気中での寿命が長く、現有のCO2衛星センサーの空間カバレッジが限られているため、衛星観測から逆算したCO2柱濃度データを用いて直接的に排出量を推定することは困難である。化石燃料消費はCO2と窒素酸化物（NOx）を同時に排出し、NOxは寿命が短く、衛星リモートセンシングによる排出量推定の実現可能性が高い。本研究では、東部の28都市及び西部のエネルギーゴールデントライアングル地域1箇所を対象に、TROPOMI NO2柱濃度を基に化石燃料由来CO2の日排出量を間接推定する研究を行った。まず、2019年のTROPOMI NO2対流圏柱濃度データ製品と質量保存モデルを用いてNOxの日排出量及び不確実性を推定した。次に、多尺度排出インベントリモデル（MEIC）に基づく排出インベントリデータベースにおけるCO2とNOxの排出量関係を分析した。最後に化石燃料消費に伴うCO2の日排出量を推定した。結果は、推定値がMEICインベントリにおけるCO2排出の空間分布と一致したが、より高い空間分解能と時間頻度により統計資料に欠ける新興および小規模な排出源の把握が可能であることを示した。東部都市では北京を例に、都市中心部周辺の郊外地域でのリモートセンシング推定値はMEICインベントリより約104％高く、東部都市の急速な拡大とともに多くの新興排出源が出現していることを示している。エネルギーゴールデントライアングルでは榆林を例に、同地域の発電所、製鉄所、炭鉱地域の一部排出源はMEICインベントリでは全排出量の10％に過ぎないが、推定値では37％に達し、排出インベントリに含まれていない小規模な発電所や工業源が衛星リモートセンシングで捉えられることを示している。本研究結果は中国の化石燃料由来の炭素排出量算定に技術的な支援を提供できる。

キーワード

リモートセンシング;化石燃料;窒素酸化物;二酸化炭素;TROPOMI;排出インベントリ;間接推定;質量保存;新興排出源

References

  1. 1.
    Akimoto H, Ohara T, Kurokawa J I and Horii N. 2006. Verification of energy consumption in China during 1996-2003 by using satellite observational data. Atmospheric Environment, 40(40): 7663-7667
  2. 2.
    Berezin E V, Konovalov I B, Ciais P, Richter A, Tao S, Janssens-Maenhout G, Beekmann M and Schulze E D. 2013. Multiannual changes of CO2 emissions in China: indirect estimates derived from satellite measurements of tropospheric NO2 columns. Atmospheric Chemistry and Physics, 13(18): 9415-9438
  3. 3.
    Brasseur G P and Jacob D J. 2017. Modeling of Atmospheric Chemistry. Cambridge: Cambridge University Press
  4. 4.
    Canadell J G, Le Quéré C, Raupach M R, Field C B, Buitenhuis E T, Ciais P, Conway T J, Gillett N P, Houghton R A and Marland G. 2007. Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks. Proceedings of the National Academy of Sciences of the United States of America, 104(47): 18866-18870
  5. 5.
    Ciais P, Dolman A J, Bombelli A, Duren R, Peregon A, Rayner P J, Miller C, Gobron N, Kinderman G, Marland G, Gruber N, Chevallier F, Andres R J, Balsamo G, Bopp L, Bréon F M, Broquet G, Dargaville R, Battin T J, Borges A, Bovensmann H, Buchwitz M, Butler J, Canadell J G, Cook R B, Defries R, Engelen R, Gurney K R, Heinze C, Heimann M, Held A, Henry M, Law B, Luyssaert S, Miller J, Moriyama T, Moulin C, Myneni R B, Nussli C, Obersteiner M, Ojima D, Pan Y, Paris J D, Piao S L, Poulter B, Plummer S, Quegan S, Raymond P, Reichstein M, Rivier L, Sabine C, Schimel D, Tarasova O, Valentini R, Wang R, Van Der werf G, Wickland D, Williams M and Zehner C. 2014. Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system. Biogeosciences, 11(13): 3547-3602
  6. 6.
    Eldering A, Wennberg P O, Crisp D, Schimel D S, Gunson M R, Chatterjee A, Liu J, Schwandner F M, Sun Y, O’dell C W, Frankenberg C, Taylor T, Fisher B, Osterman G B, Wunch D, Hakkarainen J, Tamminen J and Weir B. 2017. The orbiting carbon observatory-2 early science investigations of regional carbon dioxide fluxes. Science, 358(6360): eaam5745
  7. 7.
    Goldberg D L, Lu Z F, Oda T, Lamsal L N, Liu F, Griffin D, McLinden Chris A, Krotkov N A, Duncan B N and Streets D G. 2019. Exploiting OMI NO2 satellite observations to infer fossil-fuel CO2 emissions from U.S. megacities. Science of the Total Environment, 695: 133805
  8. 8.
    Guan D B, Liu Z, Geng Y, Lindner S and Hubacek K. 2012. The gigatonne gap in China’s carbon dioxide inventories. Nature Climate Change, 2(9): 672-675
  9. 9.
    Hakkarainen J, Ialongo I, Oda T, Szeląg M E, O’Dell C W, Eldering A and Crisp D. 2023. Building a bridge: characterizing major anthropogenic point sources in the South African Highveld region using OCO-3 carbon dioxide snapshot area maps and Sentinel-5P/TROPOMI nitrogen dioxide columns. Environmental Research Letters, 18(3): 035003
  10. 10.
    Hakkarainen J, Ialongo I and Tamminen J. 2016. Direct space‐based observations of anthropogenic CO2 emission areas from OCO‐2. Geophysical Research Letters, 43(21): 11400-11406
  11. 11.
    Hammerling D M, Michalak A M, O’Dell C and Kawa S R. 2012. Global CO2 distributions over land from the Greenhouse Gases Observing Satellite (GOSAT). Geophysical Research Letters, 39(8): L08804
  12. 12.
    IPCC. 2014. Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. IPCC
  13. 13.
    Janssens-Maenhout G, Crippa M, Guizzardi D, Muntean M, Schaaf E, Dentener F, Bergamaschi P, Pagliari V, Olivier J G J, Peters J A H W, Van Aardenne J A, Monni S, Doering U, Petrescu A M R, Solazzo E and Oreggioni G D. 2019. EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970-2012. Earth System Science Data, 11(3): 959-1002
  14. 14.
    Konovalov I B, Berezin E V, Ciais P, Broquet G, Beekmann M, Hadji-Lazaro J, Clerbaux C, Andreae M O, Kaiser J W and Schulze E D. 2014. Constraining CO2 emissions from open biomass burning by satellite observations of co-emitted species: a method and its application to wildfires in Siberia. Atmospheric Chemistry and Physics, 14(19): 10383-10410
  15. 15.
    Konovalov I B, Berezin E V, Ciais P, Broquet G, Zhuravlev R V and Janssens-Maenhout G. 2016. Estimation of fossil-fuel CO2 emissions using satellite measurements of “proxy” species. Atmospheric Chemistry and Physics, 16(21): 13509-13540
  16. 16.
    Korsbakken J I, Peters G P and Andrew R M. 2016. Uncertainties around reductions in China’s coal use and CO2 emissions. Nature Climate Change, 6(7): 687-690
  17. 17.
    Kort E A, Frankenberg C, Miller C E and Oda T. 2012. Space‐based observations of megacity carbon dioxide. Geophysical Research Letters, 39(17): L17806
  18. 18.
    Li M, Liu H, Geng G N, Hong C P, Liu F, Song Y, Tong D, Zheng B, Cui H Y, Man H Y, Zhang Q and He K B. 2017a. Anthropogenic emission inventories in China: a review. National Science Review, 4(6): 834-866
  19. 19.
    Li M, Zhang Q, Kurokawa J I, Woo J H, He K B, Lu Z F, Ohara T, Song Y, Streets D G, Carmichael G R, Cheng Y F, Hong C P, Huo H, Jiang X J, Kang S C, Liu F, Su H and Zheng B. 2017b. MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP. Atmospheric Chemistry and Physics, 17(2): 935-963
  20. 20.
    Li X L, Cohen J B, Qin K, Geng H, Wu X H, Wu L L, Yang C L, Zhang R and Zhang L Q. 2023. Remotely sensed and surface measurement-derived mass-conserving inversion of daily NOx emissions and inferred combustion technologies in energy-rich northern China. Atmospheric Chemistry and Physics, 23(14): 8001-19
  21. 21.
    Liu F, Duncan B N, Krotkov N A. , Lamsal L N, Beirle S, Griffin D, McLinden C A, Goldberg D L and Lu Z F. 2020. A methodology to constrain carbon dioxide emissions from coal-fired power plants using satellite observations of co-emitted nitrogen dioxide. Atmospheric Chemistry and Physics, 20(1): 99-116
  22. 22.
    Nassar R, Hill T G, McLinden C A, Wunch D, Jones D B A and Crisp D. 2017. Quantifying CO2 emissions from individual power plants from space. Geophysical Research Letters, 44(19): 10045-10053
  23. 23.
    Oda T, Bun R, Kinakh V, Topylko P, Halushchak M, Marland G, Lauvaux T, Jonas M, Maksyutov S, Nahorski Z, Lesiv M, Danylo O and Horabik-Pyzel J. 2019. Errors and uncertainties in a gridded carbon dioxide emissions inventory. Mitigation and Adaptation Strategies for Global Change, 24(6): 1007-1050
  24. 24.
    Qin K, Lu L X, Liu J, He Q, Shi J C, Deng W Z, Wang S and Cohen J B. 2023. Model-free daily inversion of NOx emissions using TROPOMI (MCMFE-NOx) and its uncertainty: declining regulated emissions and growth of new sources. Remote Sensing of Environment, 295: 113720
  25. 25.
    Reuter M, Buchwitz M, Hilboll A, Richter A, Schneising O, Hilker M, Heymann J, Bovensmann H and Burrows J P. 2014. Decreasing emissions of NOx relative to CO2 in East Asia inferred from satellite observations. Nature Geoscience, 7(11): 792-795
  26. 26.
    Schwandner F M, Gunson M R, Miller C E, Carn S A, Eldering A, Krings T, Verhulst K R, Schimel D S, Nguyen H M, Crisp D, O’dell C W, Osterman G B, Iraci L T and Podolske J R. 2017. Spaceborne detection of localized carbon dioxide sources. Science, 358(6360): eaam5782
  27. 27.
    Solazzo E, Crippa M, Guizzardi D, Muntean M, Choulga M and Janssens-Maenhout G. 2021. Uncertainties in the Emissions Database for Global Atmospheric Research (EDGAR) emission inventory of greenhouse gases. Atmospheric Chemistry and Physics, 21(7): 5655-5683
  28. 28.
    Van Geffen J, Boersma K F, Eskes H, Sneep M, Ter Linden M, Zara M and Veefkind J P. 2020. S5P TROPOMI NO2 slant column retrieval: method, stability, uncertainties and comparisons with OMI. Atmospheric Measurement Techniques, 13(3): 1315-1335
  29. 29.
    Veefkind J P, Aben I, McMullan K, Förster H, De Vries J, Otter G, Claas J, Eskes H J, De Haan J F, Kleipool Q, Van Weele M, Hasekamp O, Hoogeveen R, Landgraf J, Snel R, Tol P, Ingmann P, Voors R, Kruizinga B, Vink R, Visser H and Levelt P F. 2012. TROPOMI on the ESA Sentinel-5 Precursor: a GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sensing of Environment, 120: 70-83
  30. 30.
    Wang Y L, Broquet G, Ciais P, Chevallier F, Vogel F, Kadygrov N, Wu L, Yin Y, Wang R and Tao S. 2017. Estimation of observation errors for large-scale atmospheric inversion of CO2 emissions from fossil fuel combustion. Tellus B: Chemical and Physical Meteorology, 69(1): 1325723
  31. 31.
    Wheeler D and Ummel K. 2008. Calculating carma: global estimation of CO2 emissions from the power sector. SSRN Electronic Journal
  32. 32.
    Wu D E, Lin J C, Fasoli B, Oda T, Ye X X, Lauvaux T, Yang E G and Kort E A. 2018. A Lagrangian approach towards extracting signals of urban CO2 emissions from satellite observations of atmospheric column CO2 (XCO2): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”). Geoscientific Model Development, 11(12): 4843-4871
  33. 33.
    Yang E G, Kort E A, Ott L E, Oda T and Lin J C. 2023. Using space‐based CO2 and NO2 observations to estimate urban CO2 emissions. Journal of Geophysical Research: Atmospheres, 128(6): e2022JD037736
  34. 34.
    Zheng B, Geng G N, Ciais P, Davis S J, Martin R V, Meng J, Wu N N, Chevallier F, Broquet G, Boersma F, Van Der A R, Lin J T, Guan D B, Lei Y, He K B and Zhang Q. 2020. Satellite-based estimates of decline and rebound in China’s CO2 emissions during COVID-19 pandemic. Science Advances, 6(49): eabd4998
  35. 35.
    Zheng B, Tong D, Li M, Liu F, Hong C P, Geng G N, Li H Y, Li X, Peng L Q, Qi J, Yan L, Zhang Y X, Zhao H Y, Zheng Y X, He K B and Zhang Q. 2018. Trends in China’s anthropogenic emissions since 2010 as the consequence of clean air actions. Atmospheric Chemistry and Physics, 18(19): 14095-14111

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