SAMBBI: Towards a New Generation of Biomass Burning Emission Inventories

• Grant number: 25/07124-4
• Support Opportunities: Research Grants - Young Investigators Grants
• Start date: January 01, 2026
• End date: December 31, 2030
• Field of knowledge: Physical Sciences and Mathematics - Geosciences
• Principal Investigator: Guilherme Augusto Verola Mataveli
• Grantee: Guilherme Augusto Verola Mataveli
• Host Institution: Instituto Nacional de Pesquisas Espaciais (INPE). São José dos Campos , SP, Brazil
• City of the host institution: São José dos Campos
• Associated researchers: Alber Hamersson Sánchez Ipia ; Cibele Hummel Do Amaral ; Gabriel de Oliveira ; Gabriel Pereira ; Karla Maria Longo de Freitas ; Liana Oighenstein Anderson ; Lucas Andrigo Maure ; Luiz Eduardo Oliveira e Cruz de Aragão ; Matthew William Jones ; Paulo Eduardo Artaxo Netto ; Saulo Ribeiro de Freitas ; Stephen Alexander Sitch ; Yosio Edemir Shimabukuro

Abstract

Biomass burning emissions play a critical role in the global carbon balance, affecting climate, air quality, and human health. In South America, accurate and accessible emission estimates are essential for long-term monitoring. This project aims to develop the South American Biomass Burning Inventory (SAMBBI), an open-access and continuous biomass burning emission inventory specific to South America, using the regional 3BEM_FRP model. SAMBBI will be a significant advancement, providing continuous and updated estimates from 2003. The project addresses unprecedented challenges and aims to achieve five key goals: (1) automating routines and processes to ensure continuous and standardized estimates generation; (2) ensuring the continuity and consistency of emission estimates post-MODIS; (3) facilitating access to estimates, making them useful for researchers, policymakers, and society; (4) predicting emissions using artificial intelligence; and (5) quantifying whether fire suppression in the Amazon improves air quality in the São Paulo metropolitan area. The project's innovation is structured around four main areas: (i) developing a pioneering method to integrate data from multiple sensors, ensuring the continuity of emission time series; (ii) creating a web platform for visualization and download of estimates with different spatial and temporal resolutions; (iii) applying artificial intelligence to predict emissions; and (iv) simulating the influence of Amazonian fires on air pollution in São Paulo, scientifically quantifying the connection between these regions. With these advancements, SAMBBI will be an innovative and accessible inventory, becoming a reference for environmental studies and evidence-based policymaking. (AU)