Spatial Distribution of Urban Anthropogenic Carbon Emissions Revealed from the OCO-3 Snapshot XCO2 Observations: A Case Study of Shanghai

The accurate quantification of anthropogenic carbon dioxide (CO2) emissions in urban areas is hindered by high uncertainties in emission inventories. We assessed the spatial distributions of three anthropogenic CO2 emission inventories in Shanghai, China—MEIC (0.25° × 0.25°), ODIAC (1 km × 1 km), and a local inventory (LOCAL) (4 km × 4 km)—and compared simulated CO2 column concentrations (XCO2) from WRF-CMAQ against OCO-3 satellite Snapshot Mode XCO2 observations. Emissions differ by up to a factor of 2.6 among the inventories. ODIAC shows the highest emissions, particularly in densely populated areas, reaching 4.6 and 8.5 times for MEIC and LOCAL in the central area, respectively. Emission hotspots of ODIAC and MEIC are the city center, while those of LOCAL are point sources. Overall, by comparing the simulated XCO2 values driven by three emission inventories and the WRF-CMAQ model with OCO-3 satellite XCO2 observations, LOCAL demonstrates the highest accuracy with slight underestimation, whereas ODIAC overestimates the most. Regionally, ODIAC performs better in densely populated areas but overestimates by around 0.22 kt/d/km2 in relatively sparsely populated districts. LOCAL underestimates by 0.39 kt/d/km2 in the center area but is relatively accurate near point sources. Moreover, MEIC’s coarse resolution causes substantial regional errors. These findings provide critical insights into spatial variability and precision errors in emission inventories, which are essential for improving urban carbon inversion.