Constraining anthropogenic CO2 emissions using dense observations from OCO-3: Disentangling observed imprints over Xiamen-Zhangzhou-Quanzhou metropolitan area and challenges

Abstract

Quantification and monitoring of urban fossil fuel CO₂ (FFCO₂) emissions with sufficient accuracy and spatial granularity are critical to emission control and climate change mitigation. We use a top-down Bayesian inversion method to constrain FFCO₂ emissions from the Xiamen-Zhangzhou-Quanzhou metropolitan area, China, based on high-resolution areal snapshots of total column CO₂ (XCO₂) from OCO-3 snapshot area maps (SAMs) from September 2019 to July 2023. Based on five available overpasses, the observed XCO₂ enhancements range from 0.70 ± 0.53 ppm to 2.29 ± 1.16 ppm. Inversions are conducted to disentangle mixed imprints and jointly constrain emissions from area sources in Xiamen, local power plants in Xiamen, and other adjacent urban sources. An overall improvement in the fossil fuel XCO₂ enhancement is shown with the RMSE reduced by 24 % and the correlation coefficient improved by 65 %. While, sectoral and overpass scale variations of performance are revealed, with the observational representativity, amount of XCO₂ samples, and spatial displacements between modeled and observed XCO₂ plumes being primary limitation factors. Based on inversion results with sufficient robustness (on 26 December 2019 and 12 august 2022), the mean constrained emission of Xiamen is 1.68 × 10⁴ tCd⁻¹, close to bottom-up emission accounting based on local statistics and facility-level fuel consumptions, with the relative differences <18 %. Given that only two OCO-3 SAMs overpasses produce robust inversions over nearly four years, the results demonstrate more challenges than potential of using XCO₂ mapping observations to constrain sources with spatial granularity. Possible pathways for further improvements are discussed.