The Mexico City Metropolitan Area (MCMA) stands as one of the most densely populated urban regions globally. To quantify the urban CO 2 emissions in the MCMA, we independently assimilated observations from a dense column-integrated Fourier transform infrared (FTIR) network and OCO-3 Snapshot Area Map observations between October 2020 and May 2021. Applying a computationally efficient analytical Bayesian inversion technique, we inverted for surface fluxes at high spatio-temporal resolutions (1-km and 1hr). The fossil fuel (FF) emission estimates of 5.08 and 6.77 GgCO 2 /hr reported by the global and local emission inventories were optimized to 4.85 and 5.51 GgCO 2 /hr based on FTIR observations over this 7 month period, highlighting a convergence of posterior estimates. The modeled biogenic flux estimate of 0.14 GgCO 2 /hr was improved to 0.33 to 0.27 GgCO 2 /hr, respectively. It is worth noting that utilizing observations from three primary sites significantly enhanced the accuracy of estimates (13.6 ∼29.2%) around the other four. Using FTIR posterior estimates can improve simulation with the OCO-3 data set. OCO-3 shows a similar decreasing trend in FF emissions (from 6.37 GgCO 2 /hr to 6.36 and 5.04 GgCO 2 /hr) as FTIR, but its correction trends for biogenic sources differ, changing from 0.37 to 0.48 GgCO 2 /hr. The primary reason is OCO-3's lower temporal sampling density. Aligning the FTIR inversion timing with that of OCO-3 yielded comparable corrections for FF emissions, yet discrepancies in biogenic emissions persisted, which can be attributed to their different sampling locations in the rural region and discrepancy in XCO 2 observations. Our findings mark a significant step toward validating OCO-3 and FTIR inversion results in metropolitan region.

Plain Language Summary Urban areas are significant hotspots for CO 2 emissions due to their high energy consumption, prompting a strong push toward ambitious greenhouse gas reduction initiatives. Our study harnessed data from the OCO-3 satellite and an extensive ground-based sensor network to map CO 2 concentrations on an intra-city scale, aiming to update outdated emission inventories. We delved into the effects of observed CO 2 gradient differences on the optimization results, leveraging these two distinct data sources. Although both OCO-3 satellite and ground-based observations offer detailed insights into Mexico City's urban region, they reveal discrepancies in the sampling of rural area data. The local FF inventory, when constrained by ground-based observations, indicates an 18.73% overestimation, whereas the OCO-3 data set points to only a 6.44% overestimation. Our findings highlight that utilizing ground-based observations exclusively during OCO-3 overpass times aligns the correction from ground-based data (6.41% overestimation) with that derived from the OCO-3 data. However, biogenic emissions optimization differs significantly, primarily due to OCO-3's limited rural observations, the rural sampling locations, and the discrepancies in observed XCO 2 values in these data sets.