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.