# Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion
## 3 Analysis of the Current Situation and Limitations of Top-Down Monitoring Methods
### GEOS-Chem Model Inversion  
Atmospheric chemistry transport models have increasingly become a standard tool for investigating global and regional changes in atmospheric CO\({}_{2}\) concentrations. GEOS-Chem is a three-dimensional global atmospheric chemistry transport model developed by Harvard University in the United States. The model is capable of simulating the concentration distribution and evolution process of various atmospheric components based on the physical and chemical effects of atmospheric composition and its transport process. It has been employed extensively in the domain of atmospheric chemistry transport research [32]. The CEOS-Chem nested technology route is illustrated in Figure 4.  
[PERSON] et al. constructed an inversion model using GEOS-Chem coupled with the ensemble square root Kalman filter (EnSRF) to estimate both the global and China's terrestrial carbon fluxes for 2019. Their findings showed that the global terrestrial and oceanic carbon sinks accounted for 2.12 and 2.53 Pg C per year, respectively, which corresponds to 21.1% and 25.1% of global fossil fuel CO\({}_{2}\) emissions. [33]. In a study published in 2021, [PERSON] et al. employed the GEOS-Chem model to investigate the responsiveness of CO\({}_{2}\) concentrations in East Asia to interannual fluctuations in sources and sinks [19]. Their findings indicated that alterations in biomass burning emissions may account for up to 7% of the observed variability, corresponding to a range of 0.8-1.2 ppm. [PERSON] et al. discovered through GEOS-Chem simulations that the biomass burning emission inventory in northern Eurasia was overestimated due to the utilization of emission factors based on US vegetation types [34]. Figure 3: X-STILT model CO\({}_{2}\) emission inversion flowchart (XCO\({}_{2}\)_obs: satellite-observed value; XCO\({}_{2}\)_bg: model-calculated background value; _f:_ footprint value). This further underscores the necessity of verifying and enhancing biomass burning emission inventories. [PERSON] et al. demonstrated through a comparison of GEOS-Chem model simulations using GFASv1.2 as a priori parameters with infrared measurements that the GEOS-Chem model underestimated the transport of wildfire emissions to the Arctic [35]. [PERSON] et al. employed a combination of the GEOS-Chem model and inversion algorithms to infer posteriori fluxes from NOAA and GOSAT data, utilizing prior emission inventories [36]. The researchers discovered that biomass burning in the tropical regions has been the primary driver of changes in carbon concentrations since the 2014/2016 El Nino event. [PERSON] et al. quantified the impact of biomass burning carbon emissions on global atmospheric CO\({}_{2}\) concentration changes on the grid-scale based on the GEOS-Chem model [3]. Their findings indicate that biomass burning can lead to an increase of 2.4 ppm in the global average atmospheric CO\({}_{2}\) concentration each year. [PERSON] et al. estimated the carbon emissions from biomass burning in winter agricultural fires in Heilongjiang Province, China, using the GEOS-Chem model [32]. They then combined these data with heavy haze events in Northeast China to conduct a quantitative traceability analysis. The GEOS-Chem model is capable of simulating the impact of biomass burning emissions on the dynamic changes in atmospheric CO\({}_{2}\) concentrations on a range of spatial scales (from regional to global). The model can be used as a forward model to assimilate multi-source observational data based on Bayesian principles to invert and obtain high-precision posterior biomass burning emission inventory data, thus enabling the quantification of the global carbon flux from biomass burning in real time [37]. However, the accuracy of the inversion is largely dependent on the precision of the a priori emission inventory, the timeliness of the observational data, and the accuracy of the inversion algorithm [38].