# 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
### Lagrange Particle Diffusion Model  
The Lagrangian atmospheric transport model employs a particle tracking approach to estimate the concentration distribution of pollutants at varying locations and times. This methodology entails monitoring the movement of individual or collective pollutant particles in both space and time. The model can provide information regarding the dispersion of pollutants from their source to the area of influence, as well as the temporal evolution of these pollutants. It is a commonly utilized methodology in gas dispersion simulation studies within a specified area [28].  
In a recent study, [PERSON] et al. employed an enhanced Lagrangian particle dispersion model (X-STILT) to extract amplified XCO\({}_{2}\) signals from XCO\({}_{2}\) data obtained from the OCO-2 satellite [29]. The X-STILT workflow is illustrated in Figure 3. [PERSON] et al. employed the X-STILT model to invert the XCO\({}_{2}\) data from the OCO-2 satellite, thereby retrieving CO\({}_{2}\) emissions from fires in Indonesia [8]. These emissions were approximately 30% lower than those obtained using GFAS1.2 and GFED4. In a comparative study conducted by [PERSON] et al., the XCO\({}_{2}\) enhancements observed by OCO-3 were contrasted with the CO\({}_{2}\) simulated by X-STILT [9]. The findings indicated that the model demonstrated superior capability in capturing the XCO\({}_{2}\) gradients within urban areas. By employing X-STILT's distinctive time-reversal methodology to attribute the observed XCO\({}_{2}\) enhancements to disparate emission sources at the surface, the researchers discovered that the discrepancy between the satellite-observed XCO\({}_{2}\) enhancement values and those derived from the X-STILT model is typically less than 1 ppm. [PERSON] et al. employed OCO-3 satellite data to monitor one source identified by OCO-3 and then extrapolate other sources to minimize the computational burden associated with X-STILT calculations [30]. They applied the algorithm to OCO-3 data for two cities, namely Los Angeles and Salt Lake City. The time required to identify CO\({}_{2}\) sources was reduced by 62% and 78%, respectively. In a recent study, [PERSON] et al. employed the Lagrangian atmospheric transport model to assess the efficacy of the MicroCarb satellite in estimating carbon emissions [31]. Additionally, they evaluated the impact of varying meteorological conditions, including cloud cover and changes in biological flux, on carbon emissions in two major urban centers: Paris and London.  
The X-STILT model represents an extension of the traditional surface-based receptor method to an air-column-based receptor scheme. This is based on satellite observations, meteorological data, and an a priori emission inventory. The model quantifies regional carbon emissions by simulating regional plume diffusion using meteorological data and combining this with enhanced values extracted from satellite observations of XCO\({}_{2}\) concentration data. Nevertheless, the inversion of carbon emissions from biomass burning remains uncertain due to the influence of factors such as the spatial resolution of meteorological data, the quality of satellite data, and emission inventories. Furthermore, due to the inherently unstable meteorological conditions at the regional scale, the X-STILT model is not suitable for simulations at this scale.