Enhancing Space-Based Tracking of Fossil Fuel CO₂ Emissions via Synergistic Integration of OCO-2, OCO-3, and TROPOMI...

Top-down estimates of fossil fuel CO₂ (FFCO₂) emissions are crucial for tracking emissions and evaluating mitigation strategies. However, their practical application is hindered by limited data coverage and overreliance on NOx-to-CO₂ emission ratios from emission inventories. We developed the Machine Learning-Driven Mapping Satellite-based XCO_2en (ML-MSXE) model using the column-averaged dry-air mole fraction of CO₂ enhancement (XCO_2en) derived from OCO-2 and OCO-3 measurements to reconstruct the XCO_2en distribution for monitoring FFCO₂ emissions. Compared to the previous Machine Learning-Driven Deriving XCO_2en from Mapped XCO₂ (ML-DXEMX) model, ML-MSXE enhances the utilization of TROPOMI NO₂ measurements, increasing their relative contribution from 4.3 to 21.7%, thereby improving XCO_2en reconstruction accuracy and enhancing the ability to track emissions. Despite the COVID-19 lockdown, XCO_2en levels in China rose from 1.33 ± 1.06 in 2019 to 1.39 ± 1.01 ppm in 2021. In February 2020, while the national average rate of XCO_2en decline (16.3%) aligned with the reduction in FFCO₂ emissions estimated by inventories, XCO_2en further revealed varying rates of decline between cities. Furthermore, the spatial distribution of XCO_2en identified hotspots where FFCO₂ emissions might be underestimated by inventories. This study presents a space-based approach for monitoring FFCO₂ emissions, offering valuable insights for assessing carbon neutrality progress and informing policy.