Large air quality and human health impacts due to Amazon forest and vegetation fires

Vegetation fires across the tropics emit fine particulate matter (PM2.5) to the atmosphere, degrading regional air quality and impacting human health. Extensive vegetation fires occur regularly across the Amazon basin, but there have been no detailed assessments of the impacts on air quality or human health. We used updated exposure-response relationships and a regional climate-chemistry model, evaluated against a comprehensive set of observational data, to provide the first in-depth assessment of the potential public health benefits due to fire prevention across the Amazon Basin. We focused on 2012, a year with emissions similar to the 11-year average (2008 to 2018). Vegetation fires contributed >80% of simulated dry season mean surface PM2.5 in the western Amazon region particularly in Bolivia and Brazilian states of Rondônia, Acre, and Mato Grosso. We estimate that the prevention of vegetation fires would have averted 16 800 (95UI: 16 300–17 400) premature deaths and 641 000 (95UI: 551 900–741 300) disability adjusted life years (DALYs) across South America, with 26% of the avoided health burden located within the Amazon Basin. The health benefits of fire prevention in the Amazon are comparable to those found in Equatorial Asia.


Introduction
Vegetation and peat fires are an important source of particular matter (PM) and trace gases to the atmosphere, which can degrade regional air quality and adversely impact human health. Ambient PM 2.5 (PM with an aerodynamic median diameter less than 2.5 μm) is a leading risk factor contributing to mortality, morbidity and reduced life expectancy (Cohen et al 2017. Exposure to PM 2.5 from vegetation and peat fires is estimated to cause 179 000-339 000 premature deaths each year, equivalent to 5% of the present-day global burden of disease due to ambient PM 2.5 exposure (Johnston et al 2012, Lelieveld et al 2015. Fires in tropical and sub-tropical regions are responsible for 90% of global PM 2.5 fire emissions , Van Der Werf et al 2017, and fires are the dominant source of PM pollution across much of the tropics (Johnston et al 2012, Lelieveld et al 2015. Fires in the tropics are influenced by both climate and land-use change (Heald and Spracklen 2015). Drought increases the incidence of fire in the Amazon (Arãgao et al 2008, da Silva et al 2018, Aragão et al 2018. Fire is used across the tropics to clear forest and other vegetation and prepare land for agriculture. In the Amazon, fire emissions are greater in years with higher deforestation rates (Arãgao et al 2008. Deforestation and forest degradation  result in a fragmented forest landscape that is increasingly prone to fire (Cano-Crespo et al 2015, Alencar et al 2015). Deforestation also alters regional climate, increasing local temperatures (Baker and Spracklen 2019) and reducing regional rainfall (Spracklen et al 2012, Spracklen and Garcia-Carreras 2015, Zemp et al 2017. Smoke from fires further reduces rainfall through interactions with clouds and radiation (Kolusu et al 2015, Liu et al 2020. Positive feedbacks between deforestation, drought, fire and smoke exacerbate the potential for tipping points in the Amazon climate (Nepstad et al 2008, Lovejoy andNobre 2018).
Vegetation fires are the dominant source of PM over the Amazon (Martin et al 2010, Mishra et al 2015. The Amazon exhibits a strong seasonal cycle in vegetation fires and consequently PM concentrations (Martin et al 2010). During the wet season when there is little fire activity, PM 2.5 concentrations across central Amazonia can be as low as 1.5 μg m −3 (Artaxo et al 2013). In contrast, during the dry season (August-October) when there are a large number of vegetation fires, regional dry season mean PM 2.5 concentrations can exceed 30 μg m −3 (Artaxo et al 2013, Reddington et al 2016, Reddington et al 2019b with daily mean peak concentrations exceeding 100 μg m −3 . Global modelling studies confirm that fires are a dominant source of regional PM 2.5 concentrations across the Amazon during the dry season (Johnston et al 2012, Lelieveld et al 2015, Reddington et al 2016, Reddington et al 2019b. There is strong evidence of acute adverse health outcomes due to exposure to PM from Amazon fires. Positive associations between PM from vegetation fires and increased hospital admissions for respiratory health in children and the elderly have been demonstrated in the southern Amazon (Ignotti et al 2010, Do Carmo et al 2013, Machado-Silva et al 2020. PM from fires has also been found to exacerbate respiratory health in children and the elderly to a greater extent during drought years (Smith et al 2014, Machado-Silva et al 2020. The relationship between respiratory health and PM from fires has also been highlighted by positive associations between reduced peak expiratory flow in schoolchildren and increased PM during the dry season (Jacobson et al 2012, Jacobson et al 2014. Toxicology analysis has demonstrated PM from Amazon fires results in DNA damage in human lung cells (de Oliveira Alves et al 2017), shedding light on the mechanisms by which exposure to PM from vegetation fires adversely impact human health.
Despite studies demonstrating the impact of PM from fires on human health there have been no regional assessments quantifying the potential health burden using high resolution models. Previous studies of PM from vegetation fires have focused on the impacts on Amazonian weather and climate through aerosol-radiation and aerosol-cloud interactions (Zhang et al 2008, Zhang et al 2009, Wu et al 2011, Kolusu et al 2015, Thornhill et al 2018, Liu et al 2020. Health burden assessment of the degraded air quality caused by Amazon fires have been restricted to coarse resolution global models (>100 km horizontal resolution) (Johnston et al 2012, Lelieveld et al 2015, Nawaz and Henze 2020 with limited in-depth analysis at a regional scales at finer resolutions. In comparison, Equatorial Asia, where vegetation and peat fires also result in poor regional air quality, has been studied in detail (Huang et al 2013, Reddington et al 2014, Kiely et al 2019 and there are numerous assessments of the health burden. Reddington et al (2019a) found that the elimination of vegetation fires would avert 8 000 premature deaths annually across Southeast Asia (Myanmar, Thailand, Laos, Cambodia, and Vietnam). Marlier et al (2019) found the elimination of fires across Equatorial Asia (Indonesia, Malaysia, and Singapore) had the potential to avert 24 000 premature deaths per year. Similarly, Kiely et al (2020) estimated that the prevention of fires across Indonesia would avert an average of 15 000 premature deaths annually in 2004, 2006, and 2009. A number of studies focused on 2015, when drought conditions caused extensive fires and a major haze event, resulting in an estimated 44 000-100 300 premature mortalities across Equatorial Asia (Crippa et al 2016, Koplitz et al 2016, Kiely et al 2020. Here we quantify the impact of vegetation fires in South America on regional air quality and human burden of disease, with a focus on the Amazon. We used a high spatial resolution regional climatechemistry model, evaluated against a comprehensive set of observational data to improve understanding of the magnitude and spatial distribution of simulated air pollutants from vegetation fires. We then used exposureresponse relationships to provide an in-depth assessment of the burden of disease associated with exposure to PM 2.5 from vegetation fires. Our findings provide advancement on previous air quality and health assessments within this region by combining a state-of-the-art high resolution climate-chemistry model with newly established exposure-response relationships.

Model description
We used the Weather Research and Forecasting online-coupled Chemistry model (WRF-Chem) version 3.7.1 (Grell et al 2005). The model domain covers most of South America (figure 1) with a horizontal resolution of 30 km, extending vertically from the surface to 10 hPa. Details of model setup are shown in supplementary table 1 is available online at stacks.iop.org/ERC/2/095001/mmedia. Gas-phase chemistry is calculated using the extended Model for Ozone and Related Chemical Tracers, version 4 (MOZART-4) (Emmons et al 2010. Aerosol chemistry and microphysics is simulated using an updated Model for Simulating Aerosol Interaction and Chemistry (MOSAIC) with aqueous chemistry and four sectional discrete aerosol size bins: 0.039-0.156 μm, 0.156-0.625 μm, 0.625-2.5 μm, 2.5-10 μm (Zaveri et al 2008, Hodzic and). An updated volatility basis set mechanism was also included for secondary organic aerosol (SOA) formation .
Microphysics is simulated using the Morrison 2-moment scheme (Morrison et al 2009) and the Grell 3D parameterisation is used for simulating convection (Grell and Dévényi 2002). Initial and boundary chemistry and aerosol conditions were taken from 6-hourly simulation data from the MOZART-4/Goddard Earth Observing System Model version 5 (GEOS5) (NCAR 2019). Initial and boundary meteorological conditions were taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) global reanalysis (Dee et al 2011) During model simulations, we nudged the meteorological components, horizontal and vertical wind, potential temperature and water vapour mixing ratio, to ECMWF re-analysis in all model levels above the planetary boundary layer over 6 h.

Model simulations
WRF-Chem simulations were conducted for the year 2012 at horizontal resolution of 30 km. We focused on the dry season (defined here as August 1st to October 31st) when vegetation fires are most active. We performed simulations from April to December, discarding the first month as model spin-up. We performed two types of simulations: one simulation excluding fire emissions ('fire_off') and one simulation including vegetation fire emissions ('fire_on'). The contribution of fires to PM 2.5 was calculated as the difference in concentrations between these two simulations.   (Giglio et al 2003). Each fire count is assigned a burned area (0.75 km 2 for grassland and savannah and 1 km 2 for other land covers). To account for missing fire retrievals due to cloud cover, FINN averages fire emissions over two days, assuming that the detected burned area will be half the original size the following day , Pereira et al 2016. Trace gas and aerosol emissions are calculated using emission factors  in conjunction with MODIS Land Cover Type and Vegetation Continuous Fields. Fire emissions are emitted with a diurnal cycle that peaks in the early afternoon (local-time) based on Giglio (2007).
Buoyancy due to fire plumes can cause rapid injection of fire emissions above the ground surface (e.g.,

Measurements
We evaluate our WRF-Chem simulations against a comprehensive set of observational datasets from surface and aircraft collected as part of the South American Biomass Burning Analysis (SAMBBA) field campaign, which took place over the southern Amazon in September and October 2012 (Johnson et al 2016, Reddington et al 2019b. We compliment SAMBBA observations with additional surface and satellite measurements.

Statistical methods
In order to compare WRF-Chem to measurements, we linearly interpolated model output to the time and location of measured data. Comparison to aerosol mass measurements was conducted using simulated mass within the instrument detection ranges. Model evaluation was quantified using Pearson correlation coefficient (r), mean bias (MB) and normalised mean bias factor (NMBF) (Yu et al 2006): Table 1. Domain wide and Amazon Basin annual and dry season (August-October) total organic carbon (OC) and black carbon (BC) emissions from FINN (v1.5). Emissions in 2012 are compared against the 11-y (2008 to 2018) average in parentheses.

Emission species
Annual domain (Tg a −1 ) Dry season domain (Tg a −1 ) Amazon Basin annual Amazon Basin dry season OC 3.6 (3.1) 2.6 (2.0) 2.4 (2.1) where M and O are the model and observation value at location and timestep i. MB shows the deviation of the model to observation in the same units. NMBF is unitless and is interpreted as a factor NMBF+1 by which the model under or overestimates the observation.

Aircraft measurements
We used aerosol measurements taken during flights of the Facility for Airborne Atmospheric Measurements

Aerosol optical depth measurements
We used spectral columnar aerosol optical depth (AOD) data from the Aerosol Robotic Network (AERONET) Cimel sun photometers (Holben et al 1998). We used Version 3 Level 2 cloud-screened and quality-assured daytime average AOD data (Giles et al 2019), retrieved at 500 nm at five stations located across the Amazon Basin (figure 1). Measurements were taken at 12:00 UTC. Satellite-derived AOD was obtained from Moderate resolution Imaging Spectroradiometer (MODIS) on Aqua (MYD04_L2) and Terra (MOD04_L2) satellites. Collection 6.1, level 2, AOD was acquired at 550 nm for the dataset 'Dark Target Deep Blue Combined' (Levy et al 2013). Swaths of 10 km (at nadir) were resampled to 0.1°×0.1°resolution. Data were aggregated to daily means. Because daytime overpass times are different for both Terra (10:30 LT) and Aqua (13:30 LT), we used model simulated AOD averaged between both overpass times and evaluated only on days when satellite data were available.

Radiosonde measurements
We used radiosonde measurements of potential temperature, water mixing ratio, relative humidity, wind speed and direction taken from the University of Wyoming database of radiosonde measurements (http://weather. uwyo.edu/upperair/sounding.html). Atmospheric sounding data were obtained at 12:00 UTC at 3 stations within the Amazon Basin: Porto Velho UNIR, Alta Floresta and Cuiabá-Miranda. At all 3 locations, WRF-Chem performs reasonably at simulating key atmospheric variables potential temperature, water mixing ratio, relative humidity, wind speed and direction (NMBF=−0.24 to 0.14, r=0.7 to 0.99) during the dry season (supplementary figure 2).

Health burden calculation
We used simulated annual mean surface PM 2.5 concentrations to quantify the health impact due to fires through the disease burden attributable to air pollution exposure. To estimate annual mean PM 2.5 we assumed simulated concentrations in May and December are representative of January-April, when fire emissions are also low.
Using population attributable fractions of relative risk taken from associational epidemiology, interventiondriven variations in exposure (i.e., population exposure including and excluding vegetation fires) were used to predict associated variations in health burden outcomes. The population attributable fraction (PAF) was estimated as a function of population (P) and the relative risk (RR) of exposure: The RR was estimated through the Global Exposure Mortality Model (GEMM) (Burnett et al 2018). We used the GEMM for non-accidental mortality (non-communicable disease, NCD, plus lower respiratory infections, LRI), using parameters including the China cohort, with age-specific modifiers for adults over 25 years of age in 5-year intervals. The GEMM functions have mean, lower, and upper uncertainty intervals. The theoretical minimum-risk exposure level for the GEMM functions is 2.4 μg m -3 . The toxicity of PM 2.5 was treated as homogenous with no differences for source, shape, or chemical composition, due to a lack of associations among epidemiological studies.
The effect of air pollution is known to be significantly different for morbidity and mortality from cardiovascular outcomes (IHD and STR) (Cohen et al 2017), and the relative risks from equation (1) were adjusted by equation (2)  The health impacts of PM 2.5 depend non-linearly on exposure, with impacts starting to saturate at high PM 2.5 concentrations. We estimate the health benefits that would arise if fires were prevented, as the health burden from a scenario with fires (fire_on) minus the health burden from a scenario without fires (fire_off) (but including other emission sources). This is described as the 'subtraction' method

Results and discussion
3.1. Surface PM Figure 3 shows measured and simulated surface aerosol mass concentrations at Porto Velho, a location heavily influenced by vegetation fires. Before the dry season (May to July), measured PM 2.5 concentrations are typically less than 3 μg m −3 , peaking at 30-50 μg m −3 in August and September, followed by a decline in early October to less than 10 μg m −3 (figure 3(a)). The model captures this seasonal cycle relatively well (r=0.57), but overestimates concentrations (NMBF=0.72, MB=7.6 μg m −3 ) largely due to an over prediction from mid-August to September. Simulated PM 2.5 concentrations are underestimated in early August, possibly due to the missing fires at the start of the dry season in the FINN dataset (Reddington et al 2019b). Vegetation fires contributed ∼86% to simulated PM 2.5 concentrations in August and September. In the simulation without fires, PM 2.5 concentrations remain below 3 μg m −3 throughout May to December. At urban locations far from the fires, the model simulates annual mean surface PM 2.5 concentrations to within 25% (supplementary figure 3; NMBF=−0.26, MB=−3.47 μg m −3 ).

AOD
Comparison against AOD at 550 nm retrieved by MODIS (AOD550), confirms the model overestimates in the western Amazon and underestimates eastern regions (supplementary figure 4). AERONET AOD500 and MODIS AOD550 are found to compare well (supplementary figure 5) despite the different wavelengths. Evaluating against MODIS separately over evergreen broadleaf forest and savannah (cerrado) biomes and across the western and eastern regions shows an overall very small low bias in western forest bias regions with an equally small high bias in eastern cerrado regions (supplementary figure 6). The underestimation over savannah regions is considerably less than the underestimate against the one aircraft flight in the east, suggesting comparison against this one flight may not be representative. However, Reddington et al (2019b) also found PM and AOD were underestimated over regions with savannah and grassland fires, possibly suggesting FINN underestimates fire emissions in these regions.  Reddington et al (2019b) for a review) underestimate AOD and scale-up fire emissions to enable the model to match observed AOD. We find a consistent evaluation of regional boundary layer PM concentrations and AOD, with a slight overestimation over forested regions in the western Amazon and underestimation over savanna regions. Overall, simulated PM 2.5 was typically within 25% of measurements both close to fires in the western Amazon and in urban regions far from fires. We therefore chose not to alter fire emissions and we use PM 2.5 concentrations from the model runs with and without fire emissions to estimate impacts on human health. Figure 6 shows simulated surface dry season PM 2.5 concentrations. Greatest dry season concentrations (45 μg m −3 ) occur in the southern and western Amazon. Vegetation fires contribute up to 80%-95% of simulated dry season PM 2.5 concentrations, with contributions >60% over most of the Brazilian Amazon, Bolivia, and much of Peru and Paraguay. Figure 7 shows the regional distribution of dry season mean simulated PM 2.5 concentrations. Vegetation fires increased regional mean PM 2.5 concentrations by 260% over the Amazon Basin in 2012, exposing 20 million people to dry season mean concentrations above 10 μg m −3 and 3.3 million people to concentrations over 25 μg m −3 . Similarly large increases are also simulated at the national scale for Peru (394%) and Bolivia (509%) where 7% (1.36 million people) and 4% (0.42 million people) are exposed to PM 2.5 levels above 25 μg m −3 , respectively. Fires have a marked impact on annual concentrations and thus chronic exposure (see also supplementary figure 7), increasing annual mean population-weighed PM 2.5 concentrations by 35% and 137% in Peru and Bolivia, respectively (table 2). By comparison, vegetation fires increased the national  Histograms show mean PM 2.5 distribution for WRF-Chem simulations with (fire_on) and without (fire_off) fire emissions, with vertical dashed lines representing the distribution mean for each simulation. The number of people exposed (in millions M) to mean concentrations above 25 (WHO 24-h guideline) and 10 (WHO annual guideline) μg m −3 is also shown. regional dry season mean PM 2.5 by a smaller amount in Brazil (148%) in 2012, exposing 14.4 million people to levels above 25 μg m −3 in the dry season, increasing annual mean population-weighed PM 2.5 concentrations by 10%.

Fire impacts on simulated PM 2.5 and burden of disease
Due to the proximity of fires, western states of Brazil are impacted by fires disproportionately. Figure 8 shows regional distribution of dry season mean simulated PM 2.5 concentrations in four western states of Brazil (Acre, Amazonas, Mato Grosso, and Rondônia) and the state of São Paulo in south-eastern Brazil. Vegetation fires increase regional PM 2.5 concentrations considerably in western states (296%-791%), exposing the majority of state populations to dry season mean PM 2.5 concentrations above 25 μg m −3 in Rondônia (53%) and Acre (75%). Fires similarly increase annual mean population-weighted PM 2.5 concentrations considerably in these western states (59%-278%), highlighting the impact on chronic exposures. In contrast, exposure to unhealthy  PM 2.5 concentrations in outflow regions such as São Paulo is largely due to other anthropogenic emissions with fires playing a limited intermittent contribution. Nevertheless fires can contribute to very high concentrations in outflow regions, leading to a 39% increase in dry season regional mean concentrations and a 5% increase in annual mean population-weighted concentrations in São Paulo. Figure 9 shows the estimated reduction in the regional burden of disease that would occur if all vegetation fires were prevented. We estimate that the prevention of vegetation fire emissions had the potential to avoid 641 00 (95UI: 551 900-741 300) DALYs and 16 800 (95UI: 16 300-17 400) premature deaths across our South American domain in 2012. We found that approximately 26% of the avoided DALYs (167 900 (95UI: 143 800-194 900)) and deaths (4 300 (95UI: 4 100-4 500 )) due to fire prevention were located inside the Amazon Basin. At the national level, preventing vegetation fires could have prevented 9 770 (95UI: 9 690-9 870) premature mortalities in Brazil, 1 467 (95UI: 1 340-1 590) in Peru and 1 195 (95UI: 985-1 410) in Bolivia (table 3). Per capita health burdens remove population size dependence highlighting the impact of fires on public health. The per capita avoided heath burden is greatest in Bolivia (789 (95UI: 605-979) DALYs per 100 000 people) and Paraguay (644 (95UI: 531-772) DALYs per 100 000 people) followed by Brazil (597 (95UI: 524-680) DALYs per 100 000 people). Brazilian states of Rondônia, Acre, and Mato Grosso benefit the most from fire prevention with 1300-1800 DALYs per 100 000 people avoided ( figure 9 and supplementary table 4). High disease burden rates in these western Brazilian states and wider Amazon Basin, highlights the adverse impact of vegetation fires on regional public health.
Our estimated health impacts are consistent with previous estimates for South America from global models but provide advancement due to the use of a high resolution regional model and updated exposure-response relationships. Johnston et al (2012) estimated preventing fires would avoid 10 000 premature deaths annually  between 1997-2006. Reddington et al (2015) estimated prevention of vegetation fires would avert ∼7000 to 9700 premature deaths annually between 2002-2011. In contrast to the Amazon Basin and the wider South American region, numerous air quality health assessments due to vegetation fires have been conducted across Equatorial Asia. Marlier et al (2019) found the prevention of fires across Equatorial Asia (Indonesia, Malaysia, and Singapore) would avert 24 000 premature deaths annually in the present-day. Using a similar WRF-Chem setup as used in this study, Reddington et al (2019a) estimated the prevention of fires would avoid 8,000 premature deaths annually across Southeast Asia (Myanmar, Thailand, Laos, Cambodia, and Vietnam) in the present-day. Using a similar WRF-Chem setup as used in this study, Kiely et al (2020) found that the prevention of vegetation and peat fires would avert 15 000 premature deaths and 500 000 DALYs annually in 2004, 2006, and 2009  Both the Vodonos et al (2018) exposure-outcome association and the GEMM NCD+LRI rely on epidemiological studies from ambient PM 2.5 exposure only, including some from high-exposure locations, and include all non-accidental causes of death. The current integrated exposure-response (IER) association from Global Burden of Disease GBD2017 is based on studies of ambient and household air pollution, passive smoking, and active smoking exposures and is cause-specific for six causes of death (Burnett et al 2014). The risk responses of Vodonos et al (2018) and the GEMM NCD+LRI are similar for exposures up to 50 μg m −3 . At exposures greater than 50 μg m −3 these functions diverge, with the GEMM NCD+LRI risk flattening off at higher concentrations. The IER has lower risks than either Vodonos et al (2018) and the GEMM NCD+LRI, and flattens off at lower concentrations. For example, Nawaz and Henze (2020) estimated 4 407 premature deaths per year on average between 2016 and 2019 in Brazil from biomass burning derived ambient PM 2.5 , which is approximately half of our estimate for Brazil in 2012, primarily due to their use of the IER from GBD2016 which has approximately half the attributable risks of the GEMM NCD+LRI that we used in this study.
These large differences emphasise the need to reduce these uncertainties and for further epidemiological studies from highly polluted regions of the world Cohen 2020, Pope et al 2020). We used the GEMM NCD+LRI here to be consistent with the latest exposure-outcome associations for ambient PM 2.5 exposure only, to include causes beyond that considered by the current IER in the GBD2017, and to be conservative of risk estimates at higher exposures.
Fire emissions in the tropics depend on land-use change and climate conditions and can exhibit strong interannual variability. Reddington et al (2015) found greater health impacts in years with greater fires due to drought or deforestation. Fire emissions in the Amazon in 2012 were comparable to the 11-y average (figure 1). Years with more fires, due either to drought conditions or greater deforestation and land-use change, would have greater emissions, PM concentrations and likely greater associated public health impacts. Future work is needed to understand the year to year variability in health impacts due to PM from fires in the region.

Conclusion
We used a high-resolution regional air quality model to assess the impacts of vegetation fires on regional South American air quality and estimate the public health benefits resulting from prevention of fires. We studied 2012, a year with emissions similar to the long-term (2008 to 2018) average. PM 2.5 and AOD was evaluated against a comprehensive set of surface, aircraft and satellite measurements, with model typically matching measurements within 25%. Fires are the dominant pollution source, contributing more than 80% of surface PM 2.5 concentrations across the southern Amazon during the dry season. Fires in the Amazon account for ∼70% of fire emissions across our South American domain and 12% of global fire emissions.
We found that the prevention of vegetation fires in the region would avert 641 000 (95UI: 551 900-741 300) DALYs and 16 800 (95UI: 16 300-17 400) premature deaths due to the reduction in PM 2.5 exposure across South America. The greatest reduction in disease burden rates occurs in regions close to intense fire activity: Bolivia, Paraguay, and the western states of Brazil, including Rondônia, Acre, and Mato Grosso, with 26% of the avoided health burden located within the Amazon Basin. We find that exposure to PM from fires in the Amazon has a similar public health impacts to fires in Equatorial Asia, which have been more extensively studied. Our analysis highlights the substantial public health benefits that could be achieved through prevention of vegetation fires across the Amazon. Future work needs to quantify the air quality degradation specifically caused by fires associated with deforestation and forest degradation, providing further evidence for the health benefits that would result from reduced deforestation . The deforestation rate in the Amazon increased from 2014 to 2019 with more fires in 2019 compared to recent years (Barlow et al 2020). The future frequency of fire in the Amazon will depend on land-use and climate change, with projected increases in fire occurrence of 20%-100% over the coming decades (Fonseca et al 2019). Achieving reductions in fire in a hotter and potentially drier Amazon (Boisier et al 2015) will require strong environmental governance.