Using Bayesian optimization method and FLEXPART tracer model to evaluate CO emission in East China in springtime
2014
Pan, X. L. | Kanaya, Y. | Wang, Z. F. | Tang, X. | Takigawa, M. | Pakpong, P. | Taketani, F. | Akimoto, H.
Carbon monoxide (CO) is of great interest as a restriction factor for pollutants related to incomplete combustions. This study attempted to evaluate CO emission in East China using the analytical Bayesian inverse method and observations at Mount Hua in springtime. The mixing ratio of CO at the receptor was calculated using 5-day source-receptor relationship (SRR) simulated by a Lagrangian Particle Dispersion Model (FLEXPART) and CO emission flux. The stability of the inversion solution was evaluated on the basis of repeated random sampling simulations. The inversion results demonstrated that there were two city cluster regions (the Beijing–Tianjin–Hebei region and the low reaches of the Yangtze River Delta) where the difference between a priori (Intercontinental Chemical Transport Experiment-Phase B, INTEX-B) and a posteriori was statistically significant and the a priori might underestimate the CO emission flux by 37 %. A correction factor (a posteriori/a priori) of 1.26 was suggested for CO emission in China in spring. The spatial distribution and magnitude of the CO emission flux were comparable to the latest regional emission inventory in Asia (REAS2.0). Nevertheless, further evaluation is still necessary in view of the larger uncertainties for both the analytical inversion and the bottom-up statistical approaches.
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