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

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.