CO₂ Emissions Estimate From Mexico City Using Ground‐ and Space‐Based Remote Sensing

Abstract The Mexico City Metropolitan Area (MCMA) stands as one of the most densely populated urban regions globally. To quantify the urban 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 1‐hr). The fossil fuel (FF) emission estimates of 5.08 and 6.77 Gg/hr reported by the global and local emission inventories were optimized to 4.85 and 5.51 Gg/hr based on FTIR observations over this 7 month period, highlighting a convergence of posterior estimates. The modeled biogenic flux estimate of −0.14 Gg/hr was improved to −0.33 to −0.27 Gg/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 Gg/hr to 6.36 and 5.04 Gg/hr) as FTIR, but its correction trends for biogenic sources differ, changing from 0.37 to 0.48 Gg/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 X observations. Our findings mark a significant step toward validating OCO‐3 and FTIR inversion results in metropolitan region.