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Atmospheric Chemistry and Physics (ACP)
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https://doi.org/10.5194/acp-20...
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Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations

انبعاثات الميثان من الصين: انعكاس عالي الدقة لملاحظات الأقمار الصناعية TROPOMI
Authors: Zichong Chen; Daniel J. Jacob; Hannah Nesser; Melissa P. Sulprizio; Alba Lorente; Daniel J. Varon; Xiao Lu; +4 Authors

Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations

Abstract

Abstract. We quantify methane emissions in China and the contributions from different sectors by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. The inversion uses as a prior estimate the latest 2014 national sector-resolved anthropogenic emission inventory reported by the Chinese government to the United Nations Framework Convention on Climate Change (UNFCCC) and thus serves as a direct evaluation of that inventory. Emissions are optimized with a Gaussian mixture model (GMM) at up to 0.25∘×0.3125∘ resolution. The optimization is done analytically assuming log-normally distributed errors on prior emissions. Errors and information content on the optimized estimates are obtained directly from the analytical solution and also through a 36-member inversion ensemble. Our best estimate for total anthropogenic emissions in China is 65.0 (57.7–68.4) Tg a−1, where parentheses indicate the uncertainty range determined by the inversion ensemble. Contributions from individual sectors include 16.6 (15.6–17.6) Tg a−1 for coal, 2.3 (1.8–2.5) for oil, 0.29 (0.23–0.32) for gas, 17.8 (15.1–21.0) for livestock, 9.3 (8.2–9.9) for waste, 11.9 (10.7–12.7) for rice paddies, and 6.7 (5.8–7.1) for other sources. Our estimate is 21% higher than the Chinese inventory reported to the UNFCCC (53.6 Tg a−1), reflecting upward corrections to emissions from oil (+147 %), gas (+61 %), livestock (+37 %), waste (+41 %), and rice paddies (+34 %), but downward correction for coal (−15 %). It is also higher than previous inverse studies (43–62 Tg a−1) that used the much sparser GOSAT satellite observations and were conducted at coarser resolution. We are in particular better able to separate coal and rice emissions. Our higher livestock emissions are attributed largely to northern China where GOSAT has little sensitivity. Our higher waste emissions reflect at least in part a rapid growth in wastewater treatment in China. Underestimate of oil emissions in the UNFCCC report appears to reflect unaccounted-for super-emitting facilities. Gas emissions in China are mostly from distribution, in part because of low emission factors from production and in part because 42 % of the gas is imported. Our estimate of emissions per unit of domestic gas production indicates a low life-cycle loss rate of 1.7 % (1.3 %–1.9 %), which would imply net climate benefits from the current “coal-to-gas” energy transition in China. However, this small loss rate is somewhat misleading considering China's high gas imports, including from Turkmenistan where emission per unit of gas production is very high.

Keywords

Atmospheric Science, Atmospheric sciences, China, Environmental Engineering, Emission inventory, QC1-999, Astronomy, Organic chemistry, Stratospheric Chemistry and Climate Change Impacts, Oceanography, Greenhouse gas, Emission Modeling, Inversion (geology), Environmental science, Low-Cost Air Quality Monitoring Systems, Meteorology, Atmospheric Aerosols and their Impacts, Paddy field, QD1-999, Climatology, Global and Planetary Change, Geography, Physics, FOS: Environmental engineering, Paleontology, Fossil fuel, Geology, FOS: Earth and related environmental sciences, Air quality index, Structural basin, Earth and Planetary Sciences, Chemistry, Coal, Archaeology, Emissions, Satellite, Global Methane Emissions and Impacts, Environmental Science, Physical Sciences, Methane

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
57
Top 10%
Top 10%
Top 1%
hybrid