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Using Bayesian optimization method and FLEXPART tracer model to evaluate CO emission in East China in springtime.

X L Pan1, Y Kanaya, Z F Wang

  • 1Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokosuka, Japan, xlpanelf@jamstec.go.jp.

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Summary
This summary is machine-generated.

This study evaluated carbon monoxide (CO) emissions in East China using Bayesian inversion. Results suggest a 37% underestimation by prior data, proposing a correction factor for spring CO emissions.

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Area of Science:

  • Atmospheric Chemistry
  • Environmental Science
  • Geophysics

Background:

  • Carbon monoxide (CO) is a key pollutant from incomplete combustion, impacting air quality.
  • Accurate CO emission data is crucial for understanding and mitigating air pollution in East China.

Purpose of the Study:

  • To evaluate CO emission fluxes in East China during springtime.
  • To assess the accuracy of existing CO emission inventories using an analytical Bayesian inverse method.

Main Methods:

  • Utilized the analytical Bayesian inverse method combined with atmospheric observations at Mount Hua.
  • Employed the Lagrangian Particle Dispersion Model (FLEXPART) to simulate source-receptor relationships (SRR).
  • Assessed inversion solution stability through repeated random sampling simulations.

Main Results:

  • Identified significant discrepancies in CO emission fluxes for the Beijing-Tianjin-Hebei and Yangtze River Delta regions.
  • Indicated that prior emission estimates (INTEX-B) may underestimate CO flux by 37%.
  • Suggested a correction factor of 1.26 for spring CO emissions in China, aligning with REAS2.0 inventory.

Conclusions:

  • The Bayesian inversion method provides a valuable tool for refining CO emission estimates in East China.
  • A correction factor of 1.26 is recommended for spring CO emissions, highlighting potential underestimation in current inventories.
  • Further research is needed to address uncertainties in both inversion and bottom-up approaches for CO emission assessment.