Inversion of CO Emissions in Greater Bay Area over Southern China Using a WRF-STILT-Bayesian Framework

As one of the major ambient pollutants in the atmosphere, carbon monoxide (CO) can cause adverse health effects on humans. Additionally, it can indirectly prolong the lifetime of methane and contribute to global warming. Therefore, controlling this pollutant is of great importance, and understanding the spatial distribution of its emissions is a crucial step in designing relevant control strategies. In this work, a hybrid Weather Research Forecast - Stochastic Time-Inverted Lagrangian Transport (STILT) – Bayesian Inversion Framework was constructed to correct CO emissions over the Greater Bay Area (GBA). Results show that after adjusting CO emissions, the average root mean squared error, normalized mean error, and correlation coefficient for the CO concentration simulations in February 2019 and 2020 changed from 0.31 ppm to 0.12 ppm, 0.35 to 0.13, and 0.47 to 0.87, respectively. This indicates that our proposed method is effective in correcting CO emissions. Based on the updated emission data, CO emissions during the Spring Festival and the COVID-19 lockdown period were lower than during normal periods, with reductions of 8.3% and 19.6% over the GBA, respectively. The source areas contributing to CO concentrations in population centers of major GBA cities have been analyzed; the average contributions from local emissions and emissions from other GBA cities reached 45.5% and 38.8%, respectively. The method developed in this work can be further used for CO adjustment in other regions and contribute to a deeper understanding of the characteristics of this important pollutant.