# 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
### Gaussian Plume Model  
The Gaussian plume model is a widely utilized tool for simulating the transport and diffusion processes of point source emissions. By combining real-time updated remote sensing data from carbon satellites with the Gaussian plume model, it is possible to identify the plumes of CO\({}_{2}\) emissions from point sources (forests, grasslands, etc.) of ground biomass burning. This allows for the conversion of these emissions into CO\({}_{2}\) emissions, which can then be used to assess the emission characteristics of different biomass burning sources and to update and optimize existing emission inventories. A case study illustrating this application is shown in Figure 2.  
Presently, atmospheric CO\({}_{2}\) concentration data obtained from GHG satellite observations, including the Greenhouse Gasses Observing Satellite (GOSAT), Orbiting Carbon Observatory-2 (OCO-2), and the Carbon Dioxide Observation Satellite (TanSat), are extensively utilized for remote sensing estimation and inversion of carbon emissions from substantial point sources such as biomass burning and coal-fired power plants [24]. [PERSON] et al. were the first to propose that remote sensing satellites can detect strong local  
Figure 2: Illustration of emission estimation using the Gaussian plume model for Zouxian on 19 January 2021. (**a**) Background value map showing plume (red), background (blue), and reference points (black). (**b**) Observed XCO\({}_{2}\) enhancement with the plume area identified. (**c**) Simulated X CO\({}_{2}\) enhancement from the Gaussian plume model. (**d**) Simulated X CO\({}_{2}\) enhancement overlaid on OCO-3 satellite data [10]. and regional point source CO\({}_{2}\) emissions and quantify them [7]. Subsequently, [PERSON] et al. employed the Gaussian plume model in conjunction with OCO-2 data to quantify CO\({}_{2}\) emissions at the scale of a single facility (power plant), and the findings demonstrated a notable correlation between remotely sensed carbon emissions and daily reported emissions [25]. [PERSON] et al. employed in situ airborne remote sensing CO\({}_{2}\) measurements and inverse Gaussian plume models to ascertain the emission source location and the CO\({}_{2}\) emissions from the plume overlap [26]. [PERSON] et al. employed OCO-2 satellite data in conjunction with a Gaussian plume model to ascertain CO\({}_{2}\) emissions from multiple point sources in China [27]. The resulting total emissions exhibited a high degree of concordance with those reported in other anthropogenic emission inventories, particularly with the MEIC, showing 27.1% higher estimates during the cold season and 5.2% lower estimates during the warm season. The results also aligned reasonably with global inventories such as the EDGAR inventory and the Open-Source Data Inventory for Anthropogenic CO\({}_{2}\) (ODIAC). [PERSON] et al. enhanced the quality of the OCO-2 satellite data and the input parameters required by the Gaussian plume model by incorporating higher-resolution ERA-5 wind data, implementing sub-footprint fitting for improved plume modeling, and adopting a new automated background selection method specifically for wind speed and background values [25]. They also inverted CO\({}_{2}\) emissions from large point sources in various regions, including the United States, Poland, Russia, and South Korea. [PERSON] et al. integrated CarbonSat observation data and optimized the background value determination submodule of the Gaussian plume model to achieve the remote sensing inversion of carbon emissions from very large, extremely large, and large point sources in China [10]. This approach provides insights and data support for the remote sensing inversion of carbon emissions from biomass burning and multi-source carbon emissions. The integration of Gaussian plume models and carbon satellite data can enhance the temporal and precision capabilities of bottom-up models.