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• W4281717999 abstract "Abstract We estimated CO 2 emissions from the Beijing region of China using differential ground-based observations of the column-averaged dry air mole fractions of CO 2 ( <?CDATA $Delta X{text{C}}{{text{O}}_{2,{text{ obs}}}}$?> <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML overflow=scroll> <mml:mi mathvariant=normal>Δ</mml:mi> <mml:mi>X</mml:mi> <mml:mrow> <mml:mtext>C</mml:mtext> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mtext>O</mml:mtext> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mrow> <mml:mtext> obs</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> ; urban minus upwind background column observations) in winter (1 November 2020–1 March 2021). Beijing is one of the most world’s populated cities and the CO 2 emissions from this region contain large uncertainties in different bottom-up anthropogenic inventories. Differential measurements are potentially able to capture urban signals over Beijing (3.46 ± 2.35 ppm). The simulated X CO 2 enhancements were calculated ( <?CDATA $Delta X{text{C}}{{text{O}}_{2,{text{ sim}}}}$?> <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML overflow=scroll> <mml:mi mathvariant=normal>Δ</mml:mi> <mml:mi>X</mml:mi> <mml:mrow> <mml:mtext>C</mml:mtext> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mtext>O</mml:mtext> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mrow> <mml:mtext> sim</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> ) based on three emission inventories (the Open-source Data Inventory for Anthropogenic CO 2 (ODIAC), Multiresolution Emission Inventory for China (MEIC) and Emissions Database for Global Atmospheric Research (EDGAR) datasets) for Beijing. The <?CDATA $Delta X{text{C}}{{text{O}}_{2,{text{ sim}}}}$?> <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML overflow=scroll> <mml:mi mathvariant=normal>Δ</mml:mi> <mml:mi>X</mml:mi> <mml:mrow> <mml:mtext>C</mml:mtext> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mtext>O</mml:mtext> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mrow> <mml:mtext> sim</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> values based on the ODIAC dataset were much higher than the observations, whereas the values from the EDGAR dataset were much lower and the MEIC dataset was more consistent. We performed a Lagrangian inversion framework based on Bayesian theory. The average and uncertainty of a priori estimates (12.18 ± 8.0, 7.09 ± 7.5 and 3.53 ± 11.4 μ mol (m 2 s) −1 ) were optimized to the posterior emissions (9.44 ± 5.7, 7.13 ± 4.9 and 7.15 ± 5.7 μ mol (m 2 s) −1 ), suggesting that the three posterior estimates tended to converge to become more consistent, transport errors (especially the horizontal transport errors) and the spatially uneven corrections in Beijing were the main reason for the differences between the posterior estimates. Sensitivity tests suggested that the prescribed spatial and temporal structures affected up to about 12.9%, 4.9% and 20.8%, respectively, of the three inventories." @default.
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• W4281717999 date "2022-06-16" @default.
• W4281717999 modified "2023-10-18" @default.
• W4281717999 title "Lagrangian inversion of anthropogenic CO₂ emissions from Beijing using differential column measurements" @default.
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• W4281717999 doi "https://doi.org/10.1088/1748-9326/ac7477" @default.
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