Forest fires release various chemical compounds that directly degrade air quality and endanger human health. This study examines the occurrence of forest fires in six Brazilian biomes over a 22-year period (1999–2021). The primary purpose is to identify significant meteorological predictors for the monthly number of hot spots using a standardized statistical framework. Fire hotspots were identified using satellite thermal sensors (AVHRR and MODIS), and we employed a standardized negative binomial regression modeling approach to analyze the relationship between meteorological variables and fire hotspots in all six Brazilian biomes simultaneously, providing a comprehensive comparative perspective often lacking in studies focused on isolated regions. The results show that the Amazon and Cerrado biomes have the highest absolute number of fires, which is consistent with their size and vegetation structure. To avoid bias associated with biome size, fire occurrence was additionally estimated using hotspot density normalized by biome area (hotspots per km2). Using these models, significant factors for fire occurrence were identified, namely the main meteorological variables—temperature, precipitation and wind speed. By comparing the performance of the models in different biomes, we aimed to better understand regional fire dynamics. The model’s ability to predict the expected number of fires based on these variables provides a key tool for preventive air quality monitoring. Such a predictive model serves as a basis for developing early warning systems, assessing potential health risks for the population, and adopting targeted fire management policies.
Kovač-Andrić et al. (2026) studied this question.