Achieving Brazil's Deforestation Target Will Reduce Fire and Deliver Air Quality and Public Health Benefits. Issue 12 (5th December 2022) - Butt, Edward W.; Conibear, Luke; Smith, Callum; Baker, Jessica C. A.; Rigby, Richard; Knote, Christoph; Spracklen, Dominick V.
Abstract: Climate, deforestation, and forest fires are closely coupled in the Amazon, but models of fire that include these interactions are lacking. We trained machine learning models on temperature, rainfall, deforestation, land‐use, and fire data to show that spatial and temporal patterns of fire in the Amazon are strongly modified by deforestation. We find that fire count across the Brazilian Amazon increases by 0.44 percentage points for each percentage point increase in deforestation rate. We used the model to predict that the increased deforestation rate in the Brazilian Amazon from 2013 to 2020 caused a 42% increase in fire counts in 2020. We predict that if Brazil had achieved the deforestation target under the National Policy on Climate Change, there would have been 32% fewer fire counts across the Brazilian Amazon in 2020. Using a regional chemistry‐climate model and exposure‐response associations, we estimate that the improved air quality due to reduced smoke emission under this scenario would have resulted in 2, 300 fewer deaths due to reduced exposure to fine particulate matter. Our analysis demonstrates the air quality and public health benefits that would accrue from reducing deforestation in the Brazilian Amazon. Plain Language Summary: Fire in the Amazon is influenced by both climate and human activity such as deforestation, but the strength of these relationships remains uncertain. Understanding what influences fire severity is important due to impacts on climate, air quality and public health. Here, we trained machine learning models with multiple observational datasets including climate (temperature and rainfall) and land‐use characteristics (deforestation and land cover classifications) to predict fire in the Brazilian Amazon from 2003 to 2020. We found that a good fire prediction was only possible when information on deforestation was included in model training. We showed that recent increases in Brazilian deforestation between 2013 and 2020 resulted in a 42% increase in fire counts in 2020. Importantly, we found that had Brazil achieved deforestation targets under the National Policy on Climate Change, there would have been 32% fewer fire counts across the Amazon in 2020 resulting in 2, 300 fewer deaths due to reduced exposure to fire‐related fine particulate matter. Overall, our work demonstrates that achieving deforestation targets has positive benefits to air quality and public health. Key Points: Using a machine learning approach, we show the spatiotemporal pattern of fire counts in the Amazon is strongly modified by deforestation We show increases in deforestation in the Brazilian Amazon between 2013 and 2020 were responsible for a 42% increase in fire counts in 2020 We estimate 32% fewer fire counts and improved air quality health outcomes in 2020 if Brazil had achieved their Amazon deforestation target