SAMBBI: A New Open-access Biomass Burning Inventory for South America

Biomass burning plays a fundamental role in shaping landscapes and global ecosystem dynamics, with far-reaching impacts on the carbon balance, biodiversity, atmospheric composition, climate, air quality, and human health. In South America, which accounts for approximately 15% of global biomass burning emissions, accurate and accessible emission estimates are essential for long-term monitoring and for delineating policies to support neutral carbon development. We introduce the South American Biomass Burning Inventory (SAMBBI), the first open-access, continuous biomass burning emission inventory for South America, based on the regional model Brazilian Biomass Burning Emissions Model with Fire Radiative Power (BEM_FRP). SAMBBI represents a major advancement in understanding biomass burning emission dynamics and patterns by providing continuous, regularly updated emission estimates from 2003 onwards. This inventory will include emission estimates for the following species released during biomass burning: carbon monoxide (CO), carbon dioxide (CO₂), methane (CH₄), and fine and coarse particulate matter (PM₂.₅ and PM₁₀). SAMBBI aims to achieve five key goals: (1) automating routines and processes to ensure continuous and standardised emission estimates; (2) ensuring the continuity and consistency of emission estimates in the post-MODIS era; (3) facilitating access to emission estimates for researchers, policymakers, and society; (4) predicting biomass burning emissions using artificial intelligence; and (5) quantifying the extent to which fire suppression in the Amazon improves air quality in the largest cities of Brazil, including the São Paulo Metropolitan Area with a population of over 20 million inhabitants. To achieve these goals, SAMBBI will (1) develop a pioneering approach to integrate data from multiple sensors, ensuring continuity in emission time series; (2) create a web platform for dashboard visualisation and seamless access to emission estimates across multiple spatial and temporal resolutions by scientists and stakeholders; (3) develop and train artificial intelligence models using environmental, climatic, and land-use predictors to forecast biomass burning emissions; and (4) conduct air quality simulations with and without Amazonian fire emissions using SAMBBI-driven inputs to quantify the urban pollution burden attributable to Amazonian fires and the potential gains from fire suppression. With these advancements, SAMBBI will constitute an innovative and accessible inventory for enhanced regional and global air pollution assessments, serving as a reference for environmental research and evidence-based policymaking.