Synergistic observation of FY-4A&4B to estimate CO concentration in China: combining interpretable machine learning to reveal the influencing mechanisms of CO variations

Accurately estimating the concentration of carbon monoxide (CO) with high spatiotemporal resolution is crucial for assessing its meteorological-environmental-health impacts. Although machine learning models have high predictive ability in environmental research, there are relatively few explanations for model outputs. Utilizing the top-of-atmosphere radiation data of China's new generation geostationary satellites (FY-4A and FY-4B) and interpretable machine learning models, the 24-hour near-surface CO concentrations in China was conducted (resolution: 1 hour, 0.04 degrees). The model improved by 6.6% when using the all-sky dataset (cloud-contained model, R2 = 0.759) compared to the clear-sky dataset (cloud-removed model). The interpretability analysis of the CO estimation model used two methods, namely ante-hoc (model feature importance) and post-hoc (SHapley Additive exPlanations). The importance of daytime meteorological factors increased by 51% compared to nighttime. Combining partial dependency plots, the impact of key meteorological factors on CO was elucidated to gain a deeper understanding of the spatiotemporal variations of CO.