Estimating anthropogenic CO2 emissions from China's Yangtze River Delta using OCO-2 observations and WRF-Chem simulations

Abstract

Satellite-based measurements have emerged as an effective method for the top-down estimates of anthropogenic CO₂ emissions. Changes in the column-averaged dry-air mole fractions of CO₂ (XCO₂) in the atmosphere reflect contributions from both human activities and natural processes, posing challenges in accurately extracting anthropogenic XCO₂ signals and quantifying urban CO₂ emissions. Here, we introduce a novel method based on spatial autocorrelation to directly identify anthropogenic XCO₂ signals from satellite measurements of Orbiting Carbon Observatory-2 (OCO-2). These signals serve as constraints for atmospheric transport model simulations, enabling the verification of emission inventory over urban areas. Utilizing 35 OCO-2 overpasses over the Yangtze River Delta urban agglomeration, we demonstrate the effectiveness of local Moran's I statistics in detecting localized anthropogenic XCO₂ enhancements. The results show an average XCO₂ increase of 1.36–4.41 ppm in proximity to major cities and areas with intensive industrial activity. A case study near Nanjing, based on eight overpasses, reveals XCO₂ enhancements with peaks ranging from 2.26 to 4.72 ppm. To establish the relationship between these XCO₂ enhancements and CO₂ emissions, we conducted WRF-Chem simulations driven by emissions from the Emissions Database for Global Atmospheric Research (EDGAR). Discrepancies between observed and simulated XCO₂ enhancements were primarily attributed to uncertainties in the prior emissions, the calculation of urban XCO₂ enhancements from OCO-2 data, and complex atmospheric transport dynamics. From our estimates, the daily CO₂ emissions in Nanjing is 0.65 ± 0.15 MtCO₂/day, which is different from the EDGAR inventory by −10.5 % to 77.3 % (i.e., 0.17 ± 0.14 MtCO₂ /day). Error analysis suggests an uncertainty in CO₂ emission estimates associated with XCO₂ enhancement and wind speed ranging from 16 % to 32 % (i.e., 0.08–0.15 MtCO₂/day). This study proposes an objective approach to assess urban CO₂ emissions, leveraging satellite XCO₂ observations to improve accuracy and reliability in emission inventories.