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

Abstract

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°). 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.