A Review of City-Scale Methane Flux Inversion Based on Top-Down Methods

As urbanization intensifies, the quantification of methane (CH4) emissions at city scales
faces unprecedented challenges due to spatial heterogeneities from industrial and trans-
portation activities and land use changes. This paper provides a review of the current
state of top-down atmospheric CH4 emission inversion at the city scale, with a focus on
CH4 emission inventories, CH4 observations, atmospheric transport models, and data
assimilation methods. The Bayesian method excels in capturing spatial variability and
managing posterior uncertainty at the kilometer-scale resolution, while the hybrid method
of variational and ensemble Kalman approaches has the potential to balance computational
efficiency in complex urban environments. This review highlights the significant discrep-
ancy between top-down inversion results and bottom-up inventory estimates at the city
scale, with inversion uncertainties ranging from 11% to 28%. This indicates the need for
further efforts in CH4 inversion at the city level. A framework is proposed to fundamentally
shape city-scale CH4 emission inversion by four synergistic advancements: developing 
high-resolution prior emission inventories at the city scale, acquiring observational data
through coordinated satellite–ground systems, enhancing computational efficiency using
artificial intelligence techniques, and applying isotopic analysis to distinguish CH4 sources.