Near real-time inversion of high-resolution anthropogenic carbon emissions in the Pearl River Delta region based on the four-dimensional local ensemble transform Kalman filter

Abstract. For climate mitigation, it is necessary to address the dynamic updating and assessment of CO₂ emissions at regional scales. This study developed a kilometer-scale carbon assimilation system (the Guangzhou Regional Atmospheric Composition and Environment Forecasting System–Greenhouse Gas–Data Assimilation, GRACES-GHG-DA) by coupling the weather research and forecasting–greenhouse gas (WRF-GHG) model with the four-dimensional local ensemble transform Kalman filter (4D-LETKF). GRACES-GHG-DA constructs a near-real-time 4-km anthropogenic emission inventory, constrained by simulated CO₂ observation data from seven high-precision greenhouse gas monitoring stations in the Pearl River Delta (PRD) region, to analyze spatiotemporal emission distributions and their relationship with ambient CO₂ concentrations. The results indicate that: (1) GRACES-GHG-DA accurately downscales CO₂ concentrations from a resolution of 36 to 4 km, with the finer resolution better capturing meso- and micro-scale variations (hourly and monthly mean biases of −0.77 and −0.51 ppm, respectively). (2) In 2022, the inverted annual anthropogenic CO₂ flux in core PRD areas exceeded 7500 g C m⁻² a⁻¹, contrasting with values below 1000 g C m⁻² a⁻¹ in peripheral regions. Compared to the inversion estimates, statistical inventories (EDGAR, ODIAC, GCP, and MEIC) underestimated total emissions by 14.71% on average. (3) Seasonal anthropogenic emissions were 24.03, 29.86, 30.61, and 27.26 Tg C for spring, summer, autumn, and winter, respectively, showing a unimodal diurnal pattern largely influenced by fossil-fuel electricity generation.(4) Anthropogenic emissions are not the dominant factor governing atmospheric CO₂ concentrations in the PRD; vegetation carbon uptake/release, boundary layer evolution, and regional transport also play critical roles.