Unravelling Variability: Discrepancies in Amazonian Biomass Burning Emissions Under Different Emission Factor Scenarios 

Biomass burning (BB) plays a key role in the biosphere–atmosphere interaction. It is a major source of trace gases and aerosols that alters the atmosphere and the water cycle. Additionally, these emissions are often related to other detrimental impacts including biodiversity loss in fire-sensitive biomes, increase of respiratory diseases, and massive economic losses. BB emissions are used as inputs in models that estimate air quality and the effect of fires on Earth’s climate. Hence, an accurate estimation of BB emissions is paramount. While BB emissions spread over most of the global vegetated areas, the integration of orbital remote sensing and modelling is the most effective approach to estimate them from regional to global scales. BB emission estimation follows the relationship between burned biomass and the emission factor (EF - mass emitted of a given species, for example carbon dioxide, per mass of dry matter burned). The burned biomass can be estimated using two approaches: (i) based on the relationship among burned area, above-ground biomass, and combustion completeness; or (ii) based on fire radiative power (FRP), a quantitative measurement that is directly related to the rate of burned biomass and is estimated to each active fire detected by several orbital sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. EF values, which are Land Use and Land Cover (LULC) based, are required to estimate BB emissions independently on the approach adopted to estimate the burned biomass. Although novel approaches to improve the accuracy of BB emissions have been developed, the impact of EF values on the final estimated emissions remains uncertain. We have evaluated the impact of the EFs on the final estimate of fine particulate matter (PM2.5) emitted from BB in the Brazilian Amazon during a nineteen years’ time series (2002-2020) by running the PREP-CHEM-SRC emissions preprocessor tool under four EF scenarios: the tool original EF values based on the work of Andreae and Merlet (2001), the average EF values recently updated by Andreae (2019), and the minimum and maximum EF values also proposed by this author. The minimum (maximum) EF values were defined as the average EF value for each LULC class minus (plus) one standard deviation. The PM2.5 emissions were estimated at the spatial resolution of 0.1º using the FRP approach implemented on PREP-CHEM-SRC (3BEM_FRP model) having MODIS active fires as input, since this approach requires fewer inputs and the impact of the EFs over the emissions would be more evident. Our results showed that the annual average PM2.5 emission in the Amazon varied by 163% between the four EF scenarios (from1,426 Gg and 3,747 Gg), while the scenario based on the average values was the closest to the one based on PREP-CHEM-SRC original EF values (2,582 Gg and 2,213 Gg, respectively – an increase of 17%). These results contribute to the better understanding of how this single parameter impacts on the estimation of BB emissions.