# Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion
## 1 Introduction  
Biomass burning represents a significant source of global carbon emissions, exerting considerable influence on the global and regional carbon cycles [1]. This combustion process exerts a significant influence on carbon stocks and alters the biomass composition and structure of terrestrial ecosystems, thereby affecting their material cycle and energy flows [2]. Concurrently, the greenhouse gases (GHGs), pollutant gases, trace gases, and aerosols released into the atmosphere during combustion exert a significant influence on the global and regional atmospheric GHG balance, the radiation balance, the atmospheric environment, and air quality. As indicated in the 2023 Global Carbon Budget report, global CO\({}_{2}\) emissions reached 40.9 Gt in 2023, with an atmospheric CO\({}_{2}\) growth rate of 5.1 Gt yr\({}^{-1}\), resulting in a global average atmospheric CO\({}_{2}\) concentration of 419.3 ppm (parts per million) [2]. In additionto fossil fuel combustion and land-use change, biomass burning represents a significant source of CO\({}_{2}\) emissions (Figure 1). As indicated by the Global Fire Emissions Database 4.1s [1], global CO\({}_{2}\) emissions from biomass burning fires between 1997 and 2023 amounted to 7.19 \(\mathrm{G}\)\(\mathrm{G}\)\(\mathrm{C}\)\(\mathrm{O}\)\(\mathrm{Z}\)\(\mathrm{Y}\)\(\mathrm{e}\), with 2023 CO\({}_{2}\) emissions reaching 8.43 \(\mathrm{G}\)[2; 3]. As stated in the Blue Book of Carbon Emissions Research from Forest Fires (2023), published by the Chinese Academy of Sciences, global forest fires emitted 33.9 \(\mathrm{G}\)\(\mathrm{G}\)\(\mathrm{G}\)\(\mathrm{G}\)\(\mathrm{G}\)\(\mathrm{O}\)\(\mathrm{Z}\) between 2001 and 2022. This resulted in an increase of 4.35 ppm in the atmospheric CO\({}_{2}\) concentration [4]. In the case of the extreme 2023 wildfire season in Canada, the direct CO\({}_{2}\) emissions from forest fires exceeded 1.5 \(\mathrm{G}\)\(\mathrm{G}\), which is higher than the total combined CO\({}_{2}\) emissions from forest fires in Canada over the past 22 years (1.374 \(\mathrm{G}\)) [1; 5]. The accurate quantification of carbon emissions from biomass burning is of critical importance for the comprehension of the carbon cycle in terrestrial ecosystems and is fundamental for the elucidation of global and regional carbon balances. Furthermore, carbon emissions from biomass combustion represent a crucial input parameter for atmospheric chemistry transport models [6]. The establishment of accurate and reliable biomass combustion carbon emission inventories has the potential to enhance the precision and reliability of model simulations. It is therefore of great significance to the carbon cycle of terrestrial ecosystems and GHG emission reduction that scientific and effective accounting of biomass burning carbon emissions be carried out. The phenomenon of biomass burning, which encompasses a range of activities, including forest fires, grassland fires, crop residue burning, household fuelwood burning, and domestic waste burning, is distinguished by several characteristics. These include periodicity, randomness, the presence of multiple point sources, and a wide range, which collectively pose significant challenges to effective monitoring. There are numerous parameters that must be considered when estimating carbon emissions from biomass combustion. The discrepancy in the estimation accuracy of each data source is evident, and this significantly impacts the precision of carbon emission estimation. Furthermore, the influence of emission source intensity and meteorological conditions introduces additional complexity and uncertainty to the factors affecting biomass combustion, which presents a significant challenge to accurately estimating biomass combustion carbon emissions [7; 8; 9; 10]. The accurate accounting and inventorying of carbon emissions represents a significant challenge within the broader field of atmospheric environmental science and global carbon cycle research.