Full-coverage mapping high-resolution atmospheric CO2 concentrations in China from 2015 to 2020: Spatiotemporal variations and coupled trends with particulate pollution

Understanding the spatiotemporal dynamics of atmospheric carbon dioxide (CO2) is crucial for achieving carbon neutrality and safeguarding planetary well-being. Satellite-based carbon monitoring is increasingly instrumental in studying atmospheric CO2 changes, offering a broad spatial view. However, current research lacks a comprehensive exploration of both day-to-day variations in atmospheric CO2 at a finer-resolution grid and its coupled relationship with particulate pollution, owing to a substantial number of missing values in satellite-retrieved CO2 data. To address these issues, this study presents an enhanced regression-based machine learning model, reconstructing full-coverage daily atmospheric CO2 concentrations in China from 2015 to 2020 at a 0.01 degrees spatial resolution. Utilizing spatiotemporal high-resolution column-averaged dry-air mole fraction of CO2 (XCO2) data from the Orbiting Carbon Observatory 2 (OCO-2) as the dependent variable and multi-source environmental factors as independent variables, we achieved overall, spatial, and temporal cross-validation R2 [RMSE] results of 0.98 [0.74 ppm], 0.95 [1.15 ppm], and 0.93 [1.44 ppm], respectively. The resulting full-coverage estimates revealed significant spatial heterogeneity over time, with the highest CO2 levels primarily in spring in the North China Plain, and the lowest in summer in northern China and autumn in the Qinghai-Tibet Plateau and the south. National weekend averages of mean symmetrized residuals in XCO2 surpassed weekday averages. Within megacities such as Shanghai and Wuhan, industry-intensive areas exhibited higher XCO2 values compared to other urban areas within the same cities. Despite spatial and seasonal fluctuations, the country -average XCO2 increased from 2015 to 2020 but has decelerated recently. A moderate correlation was observed between the spatial pattern of long-term CO2 concentrations and particulate aerosols, viewed from the total atmospheric column perspective, with some regional discrepancies. The present modeling method advances carbon monitoring techniques, and the predictive data foster an enriched understanding of the carbon cycle, climate change, and sustainable development.