Global health burden from acute exposure to fine particles emitted by fires

Global health burden from acute exposure to fine particles emitted by fires | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Global health burden from acute exposure to fine particles emitted by fires Sourangsu Chowdhury, Risto Hanninen, Mikhail Sofiev, Kristin Aunan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6344182/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Acute exposure to emissions from fires presents a significant and immediate threat to human health. Inhalation of wildfire smoke and other pollutants can lead to various health issues, including respiratory and cardiovascular problems. Our study uses the SILAM chemical transport model, integrated with the IS4FIRES fire information system, to assess population exposure to fire-related PM2.5, along with the health burden from all-cause, respiratory, and cardiovascular deaths. Our results show that while population-weighted all-source PM2.5 exposure has declined in Europe and high-income North America, fire-PM2.5 exposure has increased significantly in Eastern and Central Europe, high-income North America, Tropical Latin America, and sub-Saharan Africa. Extreme fire-PM2.5 events have tripled globally since the 1990s, with more than half of the global population experiencing minimum perpetual fire occurrence (least 1% of fire-PM 2.5 in PM2.5 for 50 instances of 3 consecutive days in a calendar year) in 2010–2018. Acute exposure to fire-PM2.5 contributed to 99,000 (95% CI − 55,000–149,000) all-cause deaths annually in 2010-18, with significant cardiovascular and respiratory disease burdens, particularly in Eastern Europe and sub-Saharan Africa. Our findings highlight the escalating health risks of fire emissions, emphasizing the urgent need for mitigation strategies as fire-PM2.5 becomes a growing contributor to global air pollution-related mortality. Earth and environmental sciences/Climate sciences/Climate change Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Earth and environmental sciences/Environmental social sciences/Climate change impacts Earth and environmental sciences/Environmental social sciences/Environmental impact Figures Figure 1 Figure 2 Figure 3 Introduction Landscape fires (mentioned as ‘fires’ hereafter), including both controlled or prescribed burns and wildfires, occur in natural vegetated areas like forests, grasslands, and agricultural lands ( 1 , 2 ). Although some wildfires are of natural origin, most are ignited by human beings, and they are becoming more frequent due to climate change driven by higher temperatures and drier conditions in all regions globally( 3 – 5 ). Fueled by conducive weather conditions, larger wildfires are burning for longer periods across all the global regions ( 6 , 7 ). On the other hand, prescribed fires help reduce wildfire risk and biomass load in specific areas( 8 ). In some regions, fires are deliberately ignited for land clearance strategies aiding agriculture or removing crop residues after harvest. However, like wildfires, other forms of fires release air pollutants, including toxic gases, fine particulate matter (PM 2.5 ), and volatile organic compounds, which can travel long distances and impact human health( 9 , 10 ). The severe effects of long-range transport of fire emissions are best exemplified by the periodic regional ‘haze’ episodes in North India, caused by emissions from burning crop residues upwind( 11 ). There are also evidences of large transport of emissions from African fires into South America outside of the Amazonian fire season( 12 ). Global area burnt was 384Mha in 2023, which is higher than any of the preceding three years but is 12% lower than the 2000–2010 average ( 13 ). While these trends in areas burnt have implications for global carbon emissions, ecosystems and society, the spatial extent of burning is not often closely linked to the impacts of fire on ecosystems and human health. The rapid growth of populations living at the human-nature interface has led to increased exposure to wildfire emissions and a rise in human-induced ignitions( 14 – 16 ). A recent study found that nearly half of all buildings and people worldwide are potentially impacted by the human-environmental hazards concentrated in the wildland-urban interface, and this is expected to increase in the future( 15 ). Acute exposure to emissions from fires poses a significant and immediate threat to human health ( 17 ). The effect of inhaling wildfire smoke and other pollutants can lead to a range of health issues, from respiratory and cardiovascular problems to mental health challenges ( 17 ). A recent study has found emissions from fires to be significantly more toxic compared to emissions from other sources of air pollution( 18 ). Numerous recent studies have linked exposure to landscape fire smoke with all-cause and cardiovascular mortality, though the association with respiratory mortality varies among studies( 17 ). A recent study, which analyzed data from 749 cities in 43 countries between 2000 and 2016, found positive association of wildfire smoke with all-cause, cardiovascular and respiratory mortality ( 19 ). Many more studies have associated fire emissions to respiratory morbidity, asthma, chronic obstructive pulmonary diseases, mental outcomes and birth outcomes, however the evidence for cardiovascular morbidity remains mixed( 17 ). Studies have employed various methods to estimate contribution of fires to ambient PM 2.5 , such as chemical transport models, atmospheric chemistry models, machine learning algorithms, in situ monitoring data, satellite data, or a combination of these tools( 10 , 12 , 20 ). Multiple regional studies, especially in North America and Australia have estimated the health and economic impact of chronic and acute exposure to emissions from fires ( 21 – 23 ). However, studies assessing the long-term global trends in health impacts of acute exposure to fire PM 2.5 are missing. Johnston and colleagues ( 24 ) used combination of chemical transport model and satellite observations to estimate 339,000 all-cause deaths annually from acute exposure to fires globally between 1997 and 2006, with sub-Saharan Africa and Southeast Asia being the most affected. Another recent study combined published dose-response functions with landscape fire PM2.5 data, estimating that exposure to landscape fire smoke results in approximately 677,745 premature all-cause deaths annually between 2016 and 2019 ( 25 ). Xu and colleagues ( 14 )found that 2.18 billion people were exposed to at least one day of substantial air pollution from fires annually from 2000 to 2019, without assessing the health impacts. In a previous study, we estimated the health effects of chronic exposure to fire-related PM 2.5 over thirty years in Europe ( 26 ). Our findings indicated that although overall PM 2.5 exposure has decreased across Europe, fire-related PM 2.5 has increased during this period, particularly in Eastern Europe. However, we note that acute exposure to emissions from wildfires holds immediate significance. These episodic events can cause significant short-term increases in air pollutant concentrations, which can have acute impacts on human health. We note that the aforementioned studies are limited in terms of geographical scope or temporal duration of assessment. Additionally, prior research often employs a uniform global exposure-response function linking fire exposure to health outcomes, and most studies use exposure response functions for all-source PM 2.5 . In our study, we tackle these limitations by using a global chemical transport model called SILAM (System for Integrated modelLling of Atmospheric composition, https://silam.fmi.fi/ ) ( 27 – 30 ) integrated with the IS4FIRES fire information system( 31 – 33 ) that is capable of forecasting fires to assess population exposure from 1990 to 2018. We also utilize data from a recent study ( 19 )to generate globally variable risk estimates from fire-PM 2.5 exposure to estimate health impacts of acute exposure to fire PM2.5. To the best of our knowledge, our study offers the first estimates of the health burden due to acute exposure to fire emissions, identifying long-term (1990–2018) trends across 190 countries and 21 global regions. The results are anticipated to inform policy decisions and help prioritize fire management efforts worldwide. Results We use the SILAM chemical transport model( 27 ), driven by anthropogenic emissions from the Community Emission Dataset (CEDS) and a unique fire forecasting model, at 2°x2° horizontal resolution with 29 vertical layers to assess the long-term trend in daily population exposure to fire PM 2.5 and their acute impacts on human health. The SILAM model has been extensively evaluated in dozens of international retrospective and operational forecasting projects ( 34 – 39 ). The current simulations have been additionally compared with AERONET optical thickness and in-situ observations. Please see the Supplementary Information Text for more details on the validation. Population exposure Figures S1 and S2 illustrate the spatial distribution of average population-weighted rolling 3-day mean monthly all-source PM 2.5 and fire-PM 2.5 , along with their annual rate of change from 1990 to 2018. Consistent with previous studies, we find that population-weighted all-source PM 2.5 exposure has decreased in Europe and high-income North America, while continuing to rise in developing regions of Western Africa and South Asia. Conversely, population-weighted fire-PM 2.5 has increased rapidly in Eastern and Central Europe and high-income North America at rates exceeding 0.02 µg/m³ per year during the boreal summer months of July and August, with a slower rate of increase in other months. Fire-PM 2.5 has also significantly increased in Tropical and Andean Latin America and sub-Saharan Africa during the boreal summer. During the boreal winter months of December, January, and February, we observe a significant increase in fire-PM 2.5 (exceeding 0.02 µg/m³ per year) in Central sub-Saharan African countries. Figure 1 depicts the global daily population weighted exposure to all-source PM 2.5 and fire-PM 2.5 from 1990 to 2018. We find that the median, 95th, and 99th percentile exposures (the 95th percentile exposure is identified as ‘extreme’ exposures) to fire-PM 2.5 have increased by 0.007, 0.02, and 0.03 µg/m³/year, respectively. Similarly, the median, 95th, and 99th percentile exposures to all-source PM 2.5 have risen by 0.75, 0.92, and 0.85 µg/m³/year, respectively. The global increases in extremes of population weighted all-source PM 2.5 are driven by large significant increases in their extremes in South Asia and sub-Saharan Africa (Table S1 ), while significant decrease is noticeable in Europe and High-income North America (SI Data 1, Figure S1 , Table S1 ). Unlike the trends for all-source PM 2.5 , we find that extreme levels of fire-PM 2.5 are increasing in most global regions (Table S2 ). Significant increases are observed in sub-Saharan Africa—specifically, 0.15, 0.08, 0.05, and 0.05 µg/m³ per year in Central, Southern, Western, and Eastern sub-Saharan Africa, respectively—as well as in Tropical Latin America (0.08 µg/m³ per year) and Eastern Europe (0.06 µg/m³ per year). We find that, on average, the total number of days with global population-weighted fire-PM 2.5 exposure above the 'extreme' levels (calculated globally) for 1990-94 ( EXdays ) was 16 days (range: 2–27 days) in the decade 1990-99. This number increased to 45.8 days (range: 20–72 days) in 2000–2009 and 66.1 days (range: 55–81 days) between 2010–2018 (Fig. 1 , Figure S3,4). EXdays increased in most global regions (Fig S4, calculated based on ‘extreme’ levels by region), with notable rises in Central sub-Saharan Africa (from 28.3 days in 1990–1999 to 50.5 days in 2010–2018), Eastern sub-Saharan Africa (from 21.4 to 45.3 days), Tropical Latin America (from 28.9 to 64.7 days), and Eastern Europe (from 18.4 to 40 days). Among countries, the largest increases in EXdays between 1990–1999 and 2010–2018 were observed in Mali (by 39 days), Brazil (37 days), the Democratic Republic of Congo (32.5 days), and Bolivia (30 days). In the East European countries of Russia and Georgia, EXdays increased by 25.4 days and 20.3 days, respectively (Figure S5, SI Data1). Our analysis shows that the contribution of fires to all-source PM2.5 has been increasing over time in most of Europe, North America, and South America, while decreasing in sub-Saharan Africa (Figure S6). This decrease in sub-Saharan Africa can be attributed to the rise of other emission sources rather than a reduction in fire-PM 2.5 . We found that on average, 51.8% of the global population (approximately 2.8 billion people) were exposed to at least 1% of fire-PM 2.5 in PM 2.5 for 50 instances of 3 consecutive days in a calendar year (1p50I or minimum perpetual fire occurrence) during the period 1990-99. This increased to an average of 53% of the global population (around 3.7 billion people) in the period 2010-18 (see SI Data2 for details on regional population exposure to 1p50I). More than 2.4% of the global population (104 million) in 1990-99 were exposed to the more extreme index 30p50I (30% of fire-PM 2.5 in PM 2.5 for 50 instances of 3 consecutive days in a calendar year, or substantial perpetual fire occurrence), while 3.2% (228 million) were exposed to 30p50I in 2010-18. The vast majority of the population exposed to 30p50I is from sub-Saharan Africa, accounting for 83% in 1990-99 and 80% in 2010-18. The second largest group exposed to 30p50I is in Latin America (10% in 1990-99 and 14% in 2010-18). In high-income North America, over 3.3 million people on average (1.5%) were exposed to 30p50I on average during the period 2010-18, compared to 0.27 million (0.1%) in 1990-99 (Table S3). The overall rankings of the GBD regions by average population exposed to 30p50I in 1990-99, 2000-09 and 2010-18 are depicted in Figure S7. Health impacts Exposure to fire-PM 2.5 is associated with deaths and disability-adjusted life years from multiple health endpoints including all-cause, cardiovascular and respiratory diseases. We used information from a recent study ( 19 ) using the Multi-City Multi-Country (MCC) health datasets covering 750 cities from 43 countries and regions to derive spatially heterogeneous risk functions for exposure to fire-PM 2.5 (see Figure S8). We applied these risk estimates to calculate the annual number of excess deaths resulting from the acute exposure to fire-PM 2.5 (i.e. 3-day moving average) from 1990 to 2018, by country and region. We found that the largest burden of all-cause deaths from fire exposure occurred in 2010, with 120 thousand (65–170 thousand) deaths. This was primarily due to extreme fire events in Eastern Europe (see Figures in SIData 2), which accounted for 40% of the global fire-PM 2.5 deaths in that year. Overall, we estimate an average of 69 (39–101) thousand all-cause deaths from acute exposure to fires in 1990-99, increasing to 89 (51–131) thousand in 2000-09, and 99 (55–149) thousand in 2010-18. In the same time period excess deaths from cardiovascular and respiratory diseases, associated with fire PM2.5 increased from an average of 17( 4 – 35 ) and 7( 2 – 19 ) thousand respectively in 1990-99 to 30(8–62) and 9( 3 – 24 ) thousand in 2010-18 respectively. Figure 2 shows the global timeline per mortality outcome. Table 1 shows excess deaths by cause (all-cause, cardiovascular, and respiratory diseases) and by region for the periods 1990-99, 2000-09, and 2010-18. We find that on average, a large proportion of the global all-cause deaths from exposure to fires occur in Eastern Europe (22% and 24% in 1990-99 and 2010-18, respectively) and sub-Saharan Africa, most prominently in Central, Western and Eastern sub-Saharan Africa (32% in 1990-99 and 28% in 2010-18). In South and East Asia, despite lower population exposure to fire extremes compared to other global regions (Figure S7), a larger population exposed to lower levels of fire PM2.5 and high baseline disease rates account for 8% and 10% of all-cause excess deaths in 2010-18. Table 1 Average excess deaths from acute exposure to fire PM 2.5 by cause (All cause deaths in 1000s, cardiovascular and respiratory in 100s) by region for 1990-99, 2000-09 and 2010-18. regions All-cause (in ‘1000) Cardiovascular (in ‘100) Respiratory (in ‘100) 1990-99 2000-09 2010-18 1990-99 2000-09 2010-18 1990-99 2000-09 2010-18 Central Asia 0.6 (0.5–0.7) 0.7 (0.6–0.8) 0.8 (0.6–1.0) 2.4 (1.6–3.1) 3.3 (2.2–4.2) 3.8 (2.5–4.9) 0.7 (0.4–1.1) 0.6 (0.3–0.9) 0.6 (0.3–1.0) Central Europe 3.8 (2.3–5.4) 4.3 (2.6–6.0) 5.4 (3.2–7.7) 11.2 (0.0–29.1) 12.4 (0.0–32.2) 15.2 (0.0–40.3) 0.8 (0.0–5.4) 0.8 (0.0–5.2) 1.0 (0.0–6.7) Eastern Europe 14.9 (9.3–20.7) 19.3 (12.0–26.7) 23.7 (14.6–33.0) 44.8 (0.0–113.9) 60.3 (0.0–152.6) 74.1 (0.0–188.8) 2.7 (0.0–16.8) 2.9 (0.0–18.3) 3.2 (0.0–21.2) Australasia 0.0 (0.0–0.0) 0.0 (0.0–0.1) 0.0 (0.0–0.1) 0.2 (0.1–0.3) 0.3 (0.2–0.4) 0.2 (0.1–0.3) 0.0 (0.0–0.1) 0.1 (0.0–0.1) 0.1 (0.0–0.1) High-income Asia Pacific 0.3 (0.3–0.4) 0.6 (0.5–0.7) 0.7 (0.6–0.9) 1.0 (0.5–1.5) 1.5 (0.7–2.2) 1.7 (0.8–2.7) 0.5 (0.3–0.7) 0.8 (0.5–1.2) 1.1 (0.6–1.6) High-income North America 0.7 (0.0–1.7) 0.8 (0.0–2.1) 1.0 (0.0–2.5) 4.3 (0.0–12.2) 4.8 (0.0–13.7) 5.1 (0.0–14.8) 3.2 (0.1–6.8) 4.4 (0.1–9.0) 5.4 (0.2–11.2) Southern Latin America 0.2 (0.0–0.4) 0.3 (0.0–0.6) 0.3 (0.0–0.7) 1.7 (0.3–3.3) 2.1 (0.3–4.2) 2.5 (0.4–5.0) 1.0 (0.3–1.8) 1.7 (0.5–3.2) 2.3 (0.7–4.2) Western Europe 3.4 (2.3–4.5) 4.1 (2.8–5.5) 4.6 (3.1–6.2) 14.5 (6.2–23.5) 15.7 (6.5–25.8) 16.4 (6.6–27.4) 0.2 (0.0–2.8) 0.2 (0.0–3.6) 0.3 (0.0–4.4) Andean Latin America 0.3 (0.1–0.4) 0.3 (0.1–0.6) 0.4 (0.2–0.8) 1.1 (0.3–2.1) 1.5 (0.4–2.8) 1.8 (0.4–3.7) 0.8 (0.2–1.8) 1.0 (0.3–2.1) 1.3 (0.4–3.0) Caribbean 0.1 (0.0–0.2) 0.1 (0.0–0.3) 0.1 (0.0–0.3) 0.6 (0.1–1.3) 0.9 (0.1–1.9) 1.1 (0.1–2.3) 0.4 (0.1–0.8) 0.6 (0.2–1.3) 0.7 (0.2–1.5) Central Latin America 0.6 (0.0–1.3) 0.7 (0.0–1.6) 0.9 (0.0–2.1) 3.1 (0.5–6.1) 4.1 (0.6–8.1) 5.7 (0.8–11.7) 3.4 (1.1–6.2) 3.6 (1.2–6.7) 4.4 (1.4–8.2) Tropical Latin America 0.5 (0.0–1.0) 0.7 (0.0–1.5) 1.0 (0.0–2.2) 3.5 (0.5–6.7) 5.0 (0.7–9.8) 7.4 (1.1–14.6) 2.6 (0.9–4.7) 3.7 (1.2–6.5) 6.0 (2.0–10.7) North Africa and Middle East 5.3 (0.4–11.2) 6.4 (0.5–13.7) 8.6 (0.6–18.5) 35.2 (10.0–67.3) 49.5 (14.1–94.5) 70.2 (19.6–135.6) 8.5 (0.0–43.3) 8.7 (0.0–43.5) 10.7 (0.0–52.2) South Asia 6.5 (5.1–8.1) 9.4 (7.4–11.7) 8.4 (6.5–10.7) 9.9 (6.2–13.8) 17.9 (11.3–24.5) 20.0 (12.4–27.7) 10.7 (6.0–16.5) 15.2 (8.6–23.1) 13.8 (7.6–21.4) East Asia 6.0 (4.4–7.8) 9.5 (7.1–12.1) 10.4 (7.6–13.7) 15.9 (8.3–25.6) 30.0 (15.8–46.5) 37.4 (19.1–58.7) 17.4 (9.7–25.2) 20.7 (13.1–29.5) 17.7 (11.3–27.1) Oceania 0.0 (0.0–0.1) 0.0 (0.0–0.2) 0.0 (0.0–0.2) 0.4 (0.2–0.6) 0.5 (0.3–0.7) 0.6 (0.4–0.9) 0.3 (0.2–0.6) 0.3 (0.2–0.5) 0.3 (0.2–0.5) Southeast Asia 3.9 (2.7–5.0) 4.4 (3.1–5.7) 4.6 (3.1–6.1) 6.1 (2.3–10.9) 8.4 (3.3–14.7) 10.2 (3.9–18.0) 5.1 (1.9–9.6) 5.1 (1.9–9.2) 5.0 (1.8–8.9) Central Sub-Saharan Africa 5.4 (2.9–8.0) 7.1 (3.8–10.6) 6.9 (3.6–10.8) 4.5 (1.1–9.4) 6.6 (1.6–13.5) 8.6 (2.1–18.0) 3.0 (0.0–11.5) 3.6 (0.0–14.2) 3.5 (0.0–13.7) Eastern Sub-Saharan Africa 6.5 (3.5–9.8) 6.9 (3.7–10.5) 6.8 (3.6–10.6) 4.3 (1.1–9.0) 5.2 (1.3–10.6) 7.4 (1.8–15.4) 3.4 (0.0–12.1) 3.2 (0.0–11.3) 3.3 (0.0–11.9) Southern Sub-Saharan Africa 1.2 (0.6–1.9) 2.2 (1.1–3.4) 2.2 (1.2–3.3) 1.4 (0.4–2.7) 2.1 (0.6–4.1) 2.9 (0.8–5.6) 0.6 (0.0–2.0) 0.8 (0.0–2.7) 1.0 (0.0–3.3) Western Sub-Saharan Africa 8.9 (4.9–13.2) 11.4 (6.2–17.0) 11.5 (6.0–17.7) 6.9 (1.7–15.1) 9.2 (2.2–19.3) 11.2 (2.7–23.5) 5.5 (0.0–18.9) 6.5 (0.0–22.0) 6.5 (0.0–22.3) Global 68.9(39–102) 89(51–131) 99(55–149) 173(41–357) 24(62–487) 303(75–620) 70(21–189) 85(28–214) 88(27–235) Europe, particularly Eastern Europe, was estimated to have the largest burden of deaths from cardiovascular diseases due to fire PM2.5, accounting for 41% in 1990-99 and 35% in 2010-18 (Table 1 ). This was followed by North Africa and the Middle East, which accounted for 20% in 1990-99 and 23% in 2010-18. This higher share is likely due to the increased relative risk (RR) of dying from cardiovascular diseases upon exposure to fire emissions in this region compared to others (Figure S8). Supporting this, when a uniform RR estimate is applied globally, the share of total cardiovascular disease burden in North Africa and the Middle East drops to 4.5% and 5% for 1990-99 and 2010-18, respectively. The largest respiratory disease burden occurred in East Asia (24% in 1990-99 to 25% in 2010-18), sub-Saharan Africa (18% in 1990-99 to 20% in 2010-18), and South Asia (15% in 1990-99 to 20% in 2010-18), see Table 1 for excess death estimates by regions. Globally, 27% of all-cause and 35% of respiratory deaths from fire PM2.5 in 1990-99 occurred among children under 5 years old, decreasing to 15% in 2010-18 (Figure S9). This large contribution globally is majorly attributed to the finding that over 40% of all-cause and respiratory fire PM2.5 deaths in sub-Saharan Africa occur among children under 5, across the years. Deaths from cardiovascular diseases mostly occur among the population older than 60 years (Figure S9). We estimate that the crude death rates (CDR) and age standardized death rates (ADR) per million population for all-cause deaths associated with exposure to fire PM2.5. We note a notable increase in CDR for all-cause deaths from 1990-99 to 2000-09, with the average CDR increasing from 12.5 (7.1–18) to 14.1 (8.1–20.8). This is followed by a slight decrease to 14 (7.8–21.1) in 2010-18. For cardiovascular deaths from fire PM 2.5 , the CDR rose steadily over the three decades, from 3.1 (0.8–6.6) in 1990-99 to 3.8 (1-7.7) in 2000-09, and further to 4.3 (1-8.8) in 2010-18. Like for all-cause deaths, for respiratory deaths, globally, the CDR increased from 1.3 (0.4–3.3) in 1990-99 to 1.34 (0.44–3.4) in 2000-09 before decreasing to 1.25 (0.37–3.3) in 2010-18 likely by a significant reduction in background deaths globally, and especially in sub-Saharan Africa supported by the decreasing trend in ADR for all cause, cardiovascular and respiratory diseases by 0.02, 0.0006 and 0.008 deaths/million population per year (Figure S11). Regionally, the CDR from all-cause fire PM2.5 deaths increased significantly in Eastern Europe (by 2/year from 1990 to 2018), Central Europe (0.7/year) and in all of Latin America (0.1/year) (see Fig. 3 , S12). While it decreased significantly in Central (-1.2/year), Eastern (-0.6/year) and Western (-0.4 year) sub-Saharan Africa. For cardiovascular diseases, the CDR increased significantly in Eastern (0.7/year) and Central (0.18/year) Europe and decreasing slightly in western and eastern sub-Saharan Africa and high-income North America (Fig. 3 , S13). CDR for respiratory deaths increased in Southern (0.07/year) and Tropical (0.06/year) Latin America and high-income Northern America (0.02) while decreasing in most of sub-Saharan Africa (Fig. 3 , S14). All the top 20 countries with largest CDR for all-cause deaths from fire PM 2.5 are in sub-Saharan Africa, Eastern and Central Europe (Figure S14), with Ukraine (128(78–180)), Bulgaria (122(73–176)) and Russia (117(72–161)) having the largest CDR in 2010-18, with largest rates of increase in Ukraine and Russia while decreasing in countries of sub-Saharan Africa. Countries in Eastern Europe including Ukraine, Russia and Bulgaria also had the largest ADR for cardiovascular diseases from exposure to fire PM 2.5 in 2010-18 and the largest rate of increases. While Bolivia, Sierra Leone and Guinea had the largest CDR from respiratory diseases in 2010-18 (Figure S15, SI Data 3). Discussions We employ a chemical transport model, which utilizes fire emissions data from an innovative Fire Forecasting model developed with machine learning techniques to estimate exposure to fire PM 2.5 . We combine the exposure with population distribution, baseline disease rates and spatially heterogeneous RR estimates to generate the first estimates of long-term trends of exposure to fires and assess the health burden due to acute exposure to fire emissions across 190 countries and 21 global regions. The data are made available publicly. Our analysis provides comprehensive and crucial insights into the evolving nature of fire-PM 2.5 and its implications for public health worldwide. Our findings indicate that, while population-weighted all-source PM 2.5 exposure has decreased globally from 1990 to 2018, especially in developed countries, fire-PM 2.5 has increased across all vegetated continents. The most significant increases have been observed in Eastern and Central Europe, high-income North America, Tropical Latin America, and sub-Saharan Africa. As anthropogenic sources of PM 2.5 decrease in developed countries, the contribution from fires has become more prominent in these regions. Fire-PM 2.5 extremes are significantly increasing across most vegetated continents (Table S2 ), with the average number of days exceeding the extreme levels of 1990-94 tripling in 2010-18 compared to 1990-99 and more than half of the global population are exposed to minimum perpetual fire occurrence in 2010-18, which is 40% more population exposed to this level in 1990-99 on average. Over the past decades, the annual number of fires, mean fire size, and total area burned have decreased globally ( 13 , 40 ). However, our study and others ( 14 , 16 , 26 ) show that global population exposure to fire-PM 2.5 is rising, significantly impacting human health. The reduction in burned areas is largely due to declines in human-driven fires in tropical savannas, with these decreases generally concentrated in regions with lower tree cover. Conversely, an increasing trend has been observed in closed-canopy forests( 41 ). While some forest fires are brought under control, approximately 10% develop into extreme fires that cannot be suppressed (WB/PROFOR 2020). Emissions from forest fires, especially extreme ones, are substantially larger than those from grass and shrub fires( 42 ). Which justifies our finding that the episodes of extreme exposures to fire-PM 2.5 are increasing and moreover highlighting the importance of our work in addressing the short-term, acute effects of exposure to fire PM 2.5 . We use spatially heterogeneous RR estimates from a recent study ( 19 ) to project the increase in excess deaths from all-cause, cardiovascular, and respiratory diseases over a given time period. We note that the CDR and ADR for all-cause and respiratory deaths due to fire-PM 2.5 are lower in 2010–2018 compared to 1990–1999. This reduction is attributed to a significant decrease in baseline mortality rates, which can be explained by examining the impact of changes in population size, population age, baseline disease rates, and fire-PM 2.5 on excess deaths (Figure S16). The strong decline in baseline disease rates, driven by improvements in medical conditions and health management, counteracts the effects of increasing fire-PM 2.5 levels and an aging, growing population on excess deaths from fire-PM 2.5 . We find that using a spatially homogeneous RR estimate as in earlier studies ( 24 , 25 ) results in at least 35% underestimation in excess deaths from fire PM 2.5 globally. Using Global Burden of Diseases methods, we find that chronic exposure to fire PM 2.5 results in 34( 24 – 49 ) thousand deaths in 2010-18, which is smaller than the all-cause deaths resulting from acute exposure to fires in the same time period, though comparing these estimates are difficult due to differences in methods and biases on covariate controls and double counting of deaths. Our results are aligned with previous studies but are more conservative, mainly due to differences in study design and the fire emissions model used. We found that there were 7.7 billion person-days in 2000–2009 when all-source PM 2.5 levels exceeded the WHO daily guideline of 15µg/m³, with fires contributing to at least 50% of this limit. This number increased to 12 billion person-days in 2010–2018. Although the trends are consistent, the absolute estimates are approximately eight times lower than those reported by Xu and colleagues ( 14 ). This difference can be attributed to our use of the fire forecasting model within the IS4FIRES fire information system, that uses MODIS fire radiative power (FRP) and ECMWF Reanalysis v5(ERA5) to estimate daily emissions from fires and use statistical methods to backcast fire emissions before the MODIS era. In comparison to Global Fire Emissions Database (GFED) v4.1 that is used by Xu and colleagues ( 14 ), the IS4Fires are based on actual fire counts, while GFED is based on burnt area observations, which are more homogeneous and a cumulative quantity. We note that the average population-weighted fire PM2.5 exposure in the analysis by Xu et al ( 14 ) is 2.5 µg/m³ (or 6.1% of all PM2.5) globally, which is ~ 5 times larger compared to our results and other works ( 26 , 43 ). We found it difficult to explain so high levels of Xu et al and consider our estimate (about 0.5 µg/m³) as more realistic, also better agreeing with independent works. The IS4Fires fire-induced emission is already on the upper side of emission range reported in the literature – owing to the top-down calibration of the IS4FIRES-SILAM models ( 43 ). Consequently, our estimates of excess deaths are smaller compared to earlier studies that utilize other sources of fire emissions. For instance, Roberts and Wooster( 25 ) estimate that 677,000 premature deaths annually result from exposure to landscape fire smoke between 2016 and 2019, which is approximately 12 times higher than our estimates. A more recent study( 44 ) estimates 1.5 million excess deaths associated with landscape fires, which roughly is 20% of all deaths associated with all PM2.5 and appears to be on the higher side compared to previous source attribution studies( 20 , 45 ). This discrepancy between studies arises from differences in the exposure-response models used, the methods of calculating deaths attributable to fires, and the fire emissions data employed. It is important to note that while all the above studies estimate total excess deaths from both acute and chronic exposure to fire-related PM2.5, our analysis focuses solely on the burden from acute exposure. A recent study ( 46 ) estimated approximately 1 million (95% CI: 690,000–1.3 million) all-cause excess deaths per year from 2000 to 2019 due to total PM2.5 exposure. Our estimates suggest that excess deaths from acute exposure to fire-related PM2.5 account for roughly 10% of these deaths. Consistent with earlier studies, we observe an increasing trend in health burdens from fires and for the first time we present estimates of exposure and health impacts of fires over a 29-year period, from 1990 up until 2018. We note that mortality, calculated and presented here, is just the tip of the iceberg, with hospitalizations, emergency room visits, asthma exacerbations, and reduced physical activity also posing significant health concerns. We acknowledge that the health burden from fires presented here and in other studies cannot be fully validated. While the estimates provided are conservative, we recommend focusing on the relative changes observed over 28 years, which are being presented here for the first time. Increasing fire contributions to ambient PM2.5 and related health burdens counteract clean air policies globally, especially in the global west where extremes of fires are increasing over the past decade. Studies show that extreme fire weather has intensified globally and is expected to increase in the future( 5 , 47 ). Acknowledging this threat, many country government including the European Union has emphasized improved cooperation countries to manage wildfires, enacting initiatives focused on prevention, preparedness, and response ( 26 , 48 – 50 ) though such efforts are largely missing in sub-Saharan Africa. These efforts have slowed the area burnt globally ( 42 ). Despite this, large wildfires continue to occur, and the extremes of fire-PM 2.5 have intensified over time. If fire emissions are found to be more toxic than other PM 2.5 sources, pending further assessment, implementing more sustainable forest management will be crucial to mitigate the population exposure and health burden. Materials and Methods We used the SILAM (System for Integrated modeLling of Atmospheric composition) offline 3D chemical transport model for atmospheric composition and air quality simulations ( 27 ) to simulate. For this study, SILAM was run at 2x2° horizontal resolution with 29 vertical layers up to 10.5 Pa globally, covering 1990–2018, we ignore later years due to onset of the COVID19 pandemic in 2019 and the availability of baseline death rates data until 2019. Meteorological variables from the European reanalysis ERA5 governed the simulations. We used the CEDS anthropogenic emission inventory ( 51 ) for primary emitted species. Fire emissions posed a particular challenge for the study: homogeneous datasets based on active-fire or burnt-area observations and covering several decades do not exist, owing to changes of satellites, their capabilities, resolution, and sensitivity. Therefore, we used the dataset generated by the fire prediction model of Integrated System for wild-land fires, IS4FIRES ( 10 , 52 ). We distinguish 7 types of fuel associated with land-use: grass, agriculture waste, tropical forest, temperate forest, boreal forest, shrub, tundra. IS4FIRES data were previously used in a short-term evaluation of the fire-related mortality in Europe ( 53 ). The FFM (Fire Forecasting Model) of IS4FIRES is a time-agnostic machine-learning algorithm trained and evaluated against the MODIS Fire Radiative Power product, 2003–2022 (training period was 2003–2014, evaluation was performed over 2015–2020) (Sofiev et al., in prep.). Fire-originated PM2.5 emissions are tracked using distinct variables for PM2.5 from fires, explicitly included in the simulations. For additional calibration of the FFM model, the fire originated PM2.5 concentration was scaled by a factor of 1.9, in order to provide the same global average fire-PM2.5 concentration as the model run when using the direct satellite retrieved fire-PM2.5 during the MODIS period. These fire-originated PM emissions have a defined size distribution but no specific chemical composition, encompassing both primary PM from fires and secondary PM from fire-related precursors. The rapid transformation of these particles is assumed to be instantaneous on a regional or continental scale. This method bypasses the uncertainties of source apportionment and enables direct identification of emission factors by fitting SILAM predictions to satellite data, Aeronet sun-photometer network, and in-situ PM measurements. SILAM simulations has undergone extensive evaluation in multiple past studies ( 26 , 54 ) and real-time operational applications ( http://atmosphere.copernicus.eu , https://dust.aemet.es , https://ews.tropmet.res.in , http://www.asdf-bj.net/gafis/index.html , https://hpfx.collab.science.gc.ca/~svfs000/na-aq-mm-fe/dist/ , https://www.nrlmry.navy.mil/aerosol/ ). For population exposure and health impact assessment, we estimated the 3-day moving average (lag 0–2 days) of all-source and fire PM2.5 and used a hybrid gridded demographic data for the world, at a 0.5 degree grid resolution. This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data, we break up the population data at 5-year population bands, using population age information from the GBD. We estimate the health burden from fire-related PM2.5 exposure across 190 countries and 21 global sub-regions. For this assessment, we use RR estimates from a study that analyzed daily death counts for all causes, cardiovascular causes, and respiratory causes in 749 cities across 43 countries from 2000 to 2016( 19 ). City-specific effect estimates were pooled using a random-effects meta-analysis to derive overall effect estimates at global and WHO regional levels. The pooled PM2.5-mortality association is expressed as the RR of death associated with a 10 µg/m³ increase in wildfire-related PM2.5. We mapped WHO regions onto the GBD regions to produce region-specific RR estimates (see Fig. S8). We used this information to build an exposure response function as in our previous studies( 55 , 56 ) for impacts of fire PM 2.5 on excess deaths from all-cause, cardiovascular and respiratory diseases, using the following relationship: $$\:RR={exp}^{\beta\:\varDelta\:x},\:\varDelta\:x=\text{m}\text{a}\text{x}[0,fire\:{PM}_{2.5}]$$ β was first estimated by taking ln-RR of for a 10 ppb increase in fire PM 2.5 . The β obtained was then used to obtain the RR for exposure to fire PM 2.5 . For sensitivity, we kept the RR uniform globally, by using a global pooled estimate of RR of 1·019 (95% CI 1·016–1·022) for all-cause mortality, 1·017 (1·012–1·021) for cardiovascular mortality, and 1·019 (1·013–1·025) for respiratory mortality. The burden of exposure to fire PM 2.5 estimated using equation [2]. A similar relation was applied in our earlier studies( 55 , 56 ) to estimate excess deaths $$\:Excess\:deaths=\:\frac{RR-1}{RR}\:\times\:p\:\times\:{y}_{0}$$ where p is the exposed population by age. Age-specific baseline mortality rates (y 0 ) for the age classes for all-cause, respiratory and cardiovascular deaths each country from the GBD ( https://vizhub.healthdata.org/gbd-compare/ ). we estimated crude and age-standardized death rates using established methods. We also calculate the variation in excess deaths caused by fire-PM 2.5 exposure for each year in comparison to 1990. We then attribute the changes observed in each country to changes in key factors, namely, baseline mortality rates, population size, population age structure and fire-PM 2.5 exposure, as detailed in an earlier study ( 56 ). The uncertainties and limitations related to our study are discussed in SI Text 2. Declarations Competing Interest Statement: Authors declare no competing interests. Author Contribution SC, KA and MS planned the study. SC, RH and MS performed the analysis and prepared the figures. SC wrote the manuscript with support from all the authors Acknowledgments The authors acknowledge the Belmont Forum Climate, Environment and Health-I project, HEATCOST (Academy of Finland grant no. 334798 and Research Council Norway grant no. 310672), the European Union's Horizon 2020 research and innovation program under Grant Agreement 820655 (EXHAUSTION) and the European Union's Horizon 2020 cooperation and support action program under Grant Agreement 101003966 (ENBEL). Data Availability SILAM is an open-source chemical transport model available from https://silam.fmi.fi/ . All other data generated and codes developed for this work will be made available upon request. References M. Gross, Learning to live with landscape fires. Curr. Biol. 25 , R693–R696 (2015). A. C. Scott, I. J. 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Supplementary Files SIv2.docx SIData.zip Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 May, 2025 Reviews received at journal 27 Apr, 2025 Reviews received at journal 23 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers invited by journal 02 Apr, 2025 Editor assigned by journal 01 Apr, 2025 Submission checks completed at journal 01 Apr, 2025 First submitted to journal 31 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6344182","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":446939159,"identity":"5b7e2f95-f55e-498c-a941-86d839f1b882","order_by":0,"name":"Sourangsu Chowdhury","email":"data:image/png;base64,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","orcid":"","institution":"CICERO Center for International Climate Research Oslo","correspondingAuthor":true,"prefix":"","firstName":"Sourangsu","middleName":"","lastName":"Chowdhury","suffix":""},{"id":446939160,"identity":"ddd8ad3d-85c9-4c3b-b9c2-40fafc546f10","order_by":1,"name":"Risto Hanninen","email":"","orcid":"","institution":"Finnish Meteorological Institute","correspondingAuthor":false,"prefix":"","firstName":"Risto","middleName":"","lastName":"Hanninen","suffix":""},{"id":446939161,"identity":"f7cbf754-7414-4b56-935e-8e9799b7cf2a","order_by":2,"name":"Mikhail Sofiev","email":"","orcid":"","institution":"Finnish Meteorological Institute","correspondingAuthor":false,"prefix":"","firstName":"Mikhail","middleName":"","lastName":"Sofiev","suffix":""},{"id":446939164,"identity":"2b18a53e-1870-4e6e-a6f8-3aceda826976","order_by":3,"name":"Kristin Aunan","email":"","orcid":"","institution":"CICERO Center for International Climate Research","correspondingAuthor":false,"prefix":"","firstName":"Kristin","middleName":"","lastName":"Aunan","suffix":""}],"badges":[],"createdAt":"2025-03-31 11:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6344182/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6344182/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81304148,"identity":"4b1847aa-2568-4833-97a0-8325cf8c5066","added_by":"auto","created_at":"2025-04-24 14:28:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":184633,"visible":true,"origin":"","legend":"\u003cp\u003eTimeseries of global population weighted PM\u003csub\u003e2.5 \u003c/sub\u003efrom all sources (in blue – left y-axis) and fire PM\u003csub\u003e2.5\u003c/sub\u003e (in red – right y-axis). The rate of change in 95\u003csup\u003eth\u003c/sup\u003e percentile values from 1990 to 2018 is mentioned in top left (blue for PM\u003csub\u003e2.5\u003c/sub\u003e and in red for fire PM\u003csub\u003e2.5\u003c/sub\u003e). The number of days in a year when the global population-weighted fire PM\u003csub\u003e2.5\u003c/sub\u003e exceeds the 95th percentile for 1990-1994 (EXdays) is shown on the upper x-axis, as indicated by the color bar. The figures for 21 individual Global Burden of Disease (GBD) regions are in SI Data1.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6344182/v1/c13d30b7f9eba9668349adde.png"},{"id":81302912,"identity":"e5bcca0d-8ffc-4cb3-9e32-30f72642899a","added_by":"auto","created_at":"2025-04-24 14:20:08","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":667351,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal timeline of excess deaths from acute exposure to fire PM\u003csub\u003e2.5\u003c/sub\u003e, categorized by all-cause (black), cardiovascular (orange), and respiratory (sky blue) diseases.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6344182/v1/53de2246704c0eb912708bd4.jpeg"},{"id":81302920,"identity":"1a8675de-2b72-4d35-b667-8421fc46f8a3","added_by":"auto","created_at":"2025-04-24 14:20:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":175965,"visible":true,"origin":"","legend":"\u003cp\u003eCrude death rates from acute exposure to fire PM\u003csub\u003e2.5\u003c/sub\u003e (per 1,000,000 population) by region and year.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6344182/v1/b37dce8c3c1a2f56e0e15f78.png"},{"id":81306375,"identity":"6921bbdc-71e6-448c-858f-62b1f5a1d89b","added_by":"auto","created_at":"2025-04-24 14:44:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1813346,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6344182/v1/a1c01627-d136-4804-a1fc-0974ede7f99a.pdf"},{"id":81302924,"identity":"12534f0d-9b75-495a-9603-fa498234787c","added_by":"auto","created_at":"2025-04-24 14:20:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6595773,"visible":true,"origin":"","legend":"","description":"","filename":"SIv2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6344182/v1/eab5cfbdfd05950032bfb9bd.docx"},{"id":81302927,"identity":"22ace713-aa8f-4e87-a890-591a516cb382","added_by":"auto","created_at":"2025-04-24 14:20:13","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":67773752,"visible":true,"origin":"","legend":"","description":"","filename":"SIData.zip","url":"https://assets-eu.researchsquare.com/files/rs-6344182/v1/0077759fa6e17a01ea1e65af.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global health burden from acute exposure to fine particles emitted by fires","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLandscape fires (mentioned as \u0026lsquo;fires\u0026rsquo; hereafter), including both controlled or prescribed burns and wildfires, occur in natural vegetated areas like forests, grasslands, and agricultural lands (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Although some wildfires are of natural origin, most are ignited by human beings, and they are becoming more frequent due to climate change driven by higher temperatures and drier conditions in all regions globally(\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Fueled by conducive weather conditions, larger wildfires are burning for longer periods across all the global regions (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). On the other hand, prescribed fires help reduce wildfire risk and biomass load in specific areas(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In some regions, fires are deliberately ignited for land clearance strategies aiding agriculture or removing crop residues after harvest. However, like wildfires, other forms of fires release air pollutants, including toxic gases, fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e), and volatile organic compounds, which can travel long distances and impact human health(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The severe effects of long-range transport of fire emissions are best exemplified by the periodic regional \u0026lsquo;haze\u0026rsquo; episodes in North India, caused by emissions from burning crop residues upwind(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). There are also evidences of large transport of emissions from African fires into South America outside of the Amazonian fire season(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobal area burnt was 384Mha in 2023, which is higher than any of the preceding three years but is 12% lower than the 2000\u0026ndash;2010 average (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). While these trends in areas burnt have implications for global carbon emissions, ecosystems and society, the spatial extent of burning is not often closely linked to the impacts of fire on ecosystems and human health. The rapid growth of populations living at the human-nature interface has led to increased exposure to wildfire emissions and a rise in human-induced ignitions(\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). A recent study found that nearly half of all buildings and people worldwide are potentially impacted by the human-environmental hazards concentrated in the wildland-urban interface, and this is expected to increase in the future(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAcute exposure to emissions from fires poses a significant and immediate threat to human health (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The effect of inhaling wildfire smoke and other pollutants can lead to a range of health issues, from respiratory and cardiovascular problems to mental health challenges (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). A recent study has found emissions from fires to be significantly more toxic compared to emissions from other sources of air pollution(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Numerous recent studies have linked exposure to landscape fire smoke with all-cause and cardiovascular mortality, though the association with respiratory mortality varies among studies(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). A recent study, which analyzed data from 749 cities in 43 countries between 2000 and 2016, found positive association of wildfire smoke with all-cause, cardiovascular and respiratory mortality (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Many more studies have associated fire emissions to respiratory morbidity, asthma, chronic obstructive pulmonary diseases, mental outcomes and birth outcomes, however the evidence for cardiovascular morbidity remains mixed(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies have employed various methods to estimate contribution of fires to ambient PM\u003csub\u003e2.5\u003c/sub\u003e, such as chemical transport models, atmospheric chemistry models, machine learning algorithms, in situ monitoring data, satellite data, or a combination of these tools(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Multiple regional studies, especially in North America and Australia have estimated the health and economic impact of chronic and acute exposure to emissions from fires (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). However, studies assessing the long-term global trends in health impacts of acute exposure to fire PM\u003csub\u003e2.5\u003c/sub\u003e are missing. Johnston and colleagues (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) used combination of chemical transport model and satellite observations to estimate 339,000 all-cause deaths annually from acute exposure to fires globally between 1997 and 2006, with sub-Saharan Africa and Southeast Asia being the most affected. Another recent study combined published dose-response functions with landscape fire PM2.5 data, estimating that exposure to landscape fire smoke results in approximately 677,745 premature all-cause deaths annually between 2016 and 2019 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Xu and colleagues (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)found that 2.18\u0026nbsp;billion people were exposed to at least one day of substantial air pollution from fires annually from 2000 to 2019, without assessing the health impacts. In a previous study, we estimated the health effects of chronic exposure to fire-related PM\u003csub\u003e2.5\u003c/sub\u003e over thirty years in Europe (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Our findings indicated that although overall PM\u003csub\u003e2.5\u003c/sub\u003e exposure has decreased across Europe, fire-related PM\u003csub\u003e2.5\u003c/sub\u003e has increased during this period, particularly in Eastern Europe. However, we note that acute exposure to emissions from wildfires holds immediate significance. These episodic events can cause significant short-term increases in air pollutant concentrations, which can have acute impacts on human health.\u003c/p\u003e \u003cp\u003eWe note that the aforementioned studies are limited in terms of geographical scope or temporal duration of assessment. Additionally, prior research often employs a uniform global exposure-response function linking fire exposure to health outcomes, and most studies use exposure response functions for all-source PM\u003csub\u003e2.5\u003c/sub\u003e. In our study, we tackle these limitations by using a global chemical transport model called SILAM (System for Integrated modelLling of Atmospheric composition, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://silam.fmi.fi/\u003c/span\u003e\u003cspan address=\"https://silam.fmi.fi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) integrated with the IS4FIRES fire information system(\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) that is capable of forecasting fires to assess population exposure from 1990 to 2018. We also utilize data from a recent study (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)to generate globally variable risk estimates from fire-PM\u003csub\u003e2.5\u003c/sub\u003e exposure to estimate health impacts of acute exposure to fire PM2.5. To the best of our knowledge, our study offers the first estimates of the health burden due to acute exposure to fire emissions, identifying long-term (1990\u0026ndash;2018) trends across 190 countries and 21 global regions. The results are anticipated to inform policy decisions and help prioritize fire management efforts worldwide.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe use the SILAM chemical transport model(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), driven by anthropogenic emissions from the Community Emission Dataset (CEDS) and a unique fire forecasting model, at 2\u0026deg;x2\u0026deg; horizontal resolution with 29 vertical layers to assess the long-term trend in daily population exposure to fire PM\u003csub\u003e2.5\u003c/sub\u003e and their acute impacts on human health. The SILAM model has been extensively evaluated in dozens of international retrospective and operational forecasting projects (\u003cspan additionalcitationids=\"CR35 CR36 CR37 CR38\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). The current simulations have been additionally compared with AERONET optical thickness and in-situ observations. Please see the Supplementary Information Text for more details on the validation.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePopulation exposure\u003c/h2\u003e \u003cp\u003eFigures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2 illustrate the spatial distribution of average population-weighted rolling 3-day mean monthly all-source PM\u003csub\u003e2.5\u003c/sub\u003e and fire-PM\u003csub\u003e2.5\u003c/sub\u003e, along with their annual rate of change from 1990 to 2018. Consistent with previous studies, we find that population-weighted all-source PM\u003csub\u003e2.5\u003c/sub\u003e exposure has decreased in Europe and high-income North America, while continuing to rise in developing regions of Western Africa and South Asia. Conversely, population-weighted fire-PM\u003csub\u003e2.5\u003c/sub\u003e has increased rapidly in Eastern and Central Europe and high-income North America at rates exceeding 0.02 \u0026micro;g/m\u0026sup3; per year during the boreal summer months of July and August, with a slower rate of increase in other months. Fire-PM\u003csub\u003e2.5\u003c/sub\u003e has also significantly increased in Tropical and Andean Latin America and sub-Saharan Africa during the boreal summer. During the boreal winter months of December, January, and February, we observe a significant increase in fire-PM\u003csub\u003e2.5\u003c/sub\u003e (exceeding 0.02 \u0026micro;g/m\u0026sup3; per year) in Central sub-Saharan African countries.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the global daily population weighted exposure to all-source PM\u003csub\u003e2.5\u003c/sub\u003e and fire-PM\u003csub\u003e2.5\u003c/sub\u003e from 1990 to 2018. We find that the median, 95th, and 99th percentile exposures (the 95th percentile exposure is identified as \u0026lsquo;extreme\u0026rsquo; exposures) to fire-PM\u003csub\u003e2.5\u003c/sub\u003e have increased by 0.007, 0.02, and 0.03 \u0026micro;g/m\u0026sup3;/year, respectively. Similarly, the median, 95th, and 99th percentile exposures to all-source PM\u003csub\u003e2.5\u003c/sub\u003e have risen by 0.75, 0.92, and 0.85 \u0026micro;g/m\u0026sup3;/year, respectively. The global increases in extremes of population weighted all-source PM\u003csub\u003e2.5\u003c/sub\u003e are driven by large significant increases in their extremes in South Asia and sub-Saharan Africa (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), while significant decrease is noticeable in Europe and High-income North America (SI Data 1, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Unlike the trends for all-source PM\u003csub\u003e2.5\u003c/sub\u003e, we find that extreme levels of fire-PM\u003csub\u003e2.5\u003c/sub\u003e are increasing in most global regions (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Significant increases are observed in sub-Saharan Africa\u0026mdash;specifically, 0.15, 0.08, 0.05, and 0.05 \u0026micro;g/m\u0026sup3; per year in Central, Southern, Western, and Eastern sub-Saharan Africa, respectively\u0026mdash;as well as in Tropical Latin America (0.08 \u0026micro;g/m\u0026sup3; per year) and Eastern Europe (0.06 \u0026micro;g/m\u0026sup3; per year).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe find that, on average, the total number of days with global population-weighted fire-PM\u003csub\u003e2.5\u003c/sub\u003e exposure above the 'extreme' levels (calculated globally) for 1990-94 (\u003cem\u003eEXdays\u003c/em\u003e) was 16 days (range: 2\u0026ndash;27 days) in the decade 1990-99. This number increased to 45.8 days (range: 20\u0026ndash;72 days) in 2000\u0026ndash;2009 and 66.1 days (range: 55\u0026ndash;81 days) between 2010\u0026ndash;2018 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Figure S3,4). \u003cem\u003eEXdays\u003c/em\u003e increased in most global regions (Fig S4, calculated based on \u0026lsquo;extreme\u0026rsquo; levels by region), with notable rises in Central sub-Saharan Africa (from 28.3 days in 1990\u0026ndash;1999 to 50.5 days in 2010\u0026ndash;2018), Eastern sub-Saharan Africa (from 21.4 to 45.3 days), Tropical Latin America (from 28.9 to 64.7 days), and Eastern Europe (from 18.4 to 40 days). Among countries, the largest increases in \u003cem\u003eEXdays\u003c/em\u003e between 1990\u0026ndash;1999 and 2010\u0026ndash;2018 were observed in Mali (by 39 days), Brazil (37 days), the Democratic Republic of Congo (32.5 days), and Bolivia (30 days). In the East European countries of Russia and Georgia, \u003cem\u003eEXdays\u003c/em\u003e increased by 25.4 days and 20.3 days, respectively (Figure S5, SI Data1).\u003c/p\u003e \u003cp\u003eOur analysis shows that the contribution of fires to all-source PM2.5 has been increasing over time in most of Europe, North America, and South America, while decreasing in sub-Saharan Africa (Figure S6). This decrease in sub-Saharan Africa can be attributed to the rise of other emission sources rather than a reduction in fire-PM\u003csub\u003e2.5\u003c/sub\u003e. We found that on average, 51.8% of the global population (approximately 2.8\u0026nbsp;billion people) were exposed to at least 1% of fire-PM\u003csub\u003e2.5\u003c/sub\u003e in PM\u003csub\u003e2.5\u003c/sub\u003e for 50 instances of 3 consecutive days in a calendar year (1p50I or \u003cem\u003eminimum perpetual fire\u003c/em\u003e occurrence) during the period 1990-99. This increased to an average of 53% of the global population (around 3.7\u0026nbsp;billion people) in the period 2010-18 (see SI Data2 for details on regional population exposure to 1p50I). More than 2.4% of the global population (104\u0026nbsp;million) in 1990-99 were exposed to the more extreme index 30p50I (30% of fire-PM\u003csub\u003e2.5\u003c/sub\u003e in PM\u003csub\u003e2.5\u003c/sub\u003e for 50 instances of 3 consecutive days in a calendar year, or \u003cem\u003esubstantial perpetual fire\u003c/em\u003e occurrence), while 3.2% (228\u0026nbsp;million) were exposed to 30p50I in 2010-18. The vast majority of the population exposed to 30p50I is from sub-Saharan Africa, accounting for 83% in 1990-99 and 80% in 2010-18. The second largest group exposed to 30p50I is in Latin America (10% in 1990-99 and 14% in 2010-18). In high-income North America, over 3.3\u0026nbsp;million people on average (1.5%) were exposed to 30p50I on average during the period 2010-18, compared to 0.27\u0026nbsp;million (0.1%) in 1990-99 (Table S3). The overall rankings of the GBD regions by average population exposed to 30p50I in 1990-99, 2000-09 and 2010-18 are depicted in Figure S7.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHealth impacts\u003c/h3\u003e\n\u003cp\u003eExposure to fire-PM\u003csub\u003e2.5\u003c/sub\u003e is associated with deaths and disability-adjusted life years from multiple health endpoints including all-cause, cardiovascular and respiratory diseases. We used information from a recent study (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) using the Multi-City Multi-Country (MCC) health datasets covering 750 cities from 43 countries and regions to derive spatially heterogeneous risk functions for exposure to fire-PM\u003csub\u003e2.5\u003c/sub\u003e (see Figure S8). We applied these risk estimates to calculate the annual number of excess deaths resulting from the acute exposure to fire-PM\u003csub\u003e2.5\u003c/sub\u003e (i.e. 3-day moving average) from 1990 to 2018, by country and region. We found that the largest burden of all-cause deaths from fire exposure occurred in 2010, with 120 thousand (65\u0026ndash;170 thousand) deaths. This was primarily due to extreme fire events in Eastern Europe (see Figures in SIData 2), which accounted for 40% of the global fire-PM\u003csub\u003e2.5\u003c/sub\u003e deaths in that year.\u003c/p\u003e \u003cp\u003eOverall, we estimate an average of 69 (39\u0026ndash;101) thousand all-cause deaths from acute exposure to fires in 1990-99, increasing to 89 (51\u0026ndash;131) thousand in 2000-09, and 99 (55\u0026ndash;149) thousand in 2010-18. In the same time period excess deaths from cardiovascular and respiratory diseases, associated with fire PM2.5 increased from an average of 17(\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) and 7(\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) thousand respectively in 1990-99 to 30(8\u0026ndash;62) and 9(\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) thousand in 2010-18 respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the global timeline per mortality outcome. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows excess deaths by cause (all-cause, cardiovascular, and respiratory diseases) and by region for the periods 1990-99, 2000-09, and 2010-18. We find that on average, a large proportion of the global all-cause deaths from exposure to fires occur in Eastern Europe (22% and 24% in 1990-99 and 2010-18, respectively) and sub-Saharan Africa, most prominently in Central, Western and Eastern sub-Saharan Africa (32% in 1990-99 and 28% in 2010-18). In South and East Asia, despite lower population exposure to fire extremes compared to other global regions (Figure S7), a larger population exposed to lower levels of fire PM2.5 and high baseline disease rates account for 8% and 10% of all-cause excess deaths in 2010-18.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage excess deaths from acute exposure to fire PM\u003csub\u003e2.5\u003c/sub\u003e by cause (All cause deaths in 1000s, cardiovascular and respiratory in 100s) by region for 1990-99, 2000-09 and 2010-18.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eregions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAll-cause (in \u0026lsquo;1000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eCardiovascular (in \u0026lsquo;100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eRespiratory (in \u0026lsquo;100)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990-99\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000-09\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2010-18\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1990-99\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2000-09\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2010-18\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1990-99\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2000-09\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2010-18\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6 (0.5\u0026ndash;0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7 (0.6\u0026ndash;0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8 (0.6\u0026ndash;1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4 (1.6\u0026ndash;3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.3 (2.2\u0026ndash;4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.8 (2.5\u0026ndash;4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.7 (0.4\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6 (0.3\u0026ndash;0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6 (0.3\u0026ndash;1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.8 (2.3\u0026ndash;5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3 (2.6\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.4 (3.2\u0026ndash;7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.2 (0.0\u0026ndash;29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.4 (0.0\u0026ndash;32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.2 (0.0\u0026ndash;40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.8 (0.0\u0026ndash;5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8 (0.0\u0026ndash;5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.0 (0.0\u0026ndash;6.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.9 (9.3\u0026ndash;20.7)\u003c/p\u003e \u003c/td\u003e 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\u003cp\u003eAustralasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0 (0.0\u0026ndash;0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0 (0.0\u0026ndash;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0 (0.0\u0026ndash;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2 (0.1\u0026ndash;0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3 (0.2\u0026ndash;0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2 (0.1\u0026ndash;0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0 (0.0\u0026ndash;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1 (0.0\u0026ndash;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1 (0.0\u0026ndash;0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3 (0.3\u0026ndash;0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6 (0.5\u0026ndash;0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7 (0.6\u0026ndash;0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0 (0.5\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5 (0.7\u0026ndash;2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.7 (0.8\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5 (0.3\u0026ndash;0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8 (0.5\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.1 (0.6\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income North America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.0\u0026ndash;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8 (0.0\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (0.0\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.3 (0.0\u0026ndash;12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.8 (0.0\u0026ndash;13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.1 (0.0\u0026ndash;14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.2 (0.1\u0026ndash;6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.4 (0.1\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.4 (0.2\u0026ndash;11.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2 (0.0\u0026ndash;0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3 (0.0\u0026ndash;0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 (0.0\u0026ndash;0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.7 (0.3\u0026ndash;3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1 (0.3\u0026ndash;4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5 (0.4\u0026ndash;5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0 (0.3\u0026ndash;1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.7 (0.5\u0026ndash;3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.3 (0.7\u0026ndash;4.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.4 (2.3\u0026ndash;4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1 (2.8\u0026ndash;5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6 (3.1\u0026ndash;6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.5 (6.2\u0026ndash;23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.7 (6.5\u0026ndash;25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.4 (6.6\u0026ndash;27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2 (0.0\u0026ndash;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2 (0.0\u0026ndash;3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.3 (0.0\u0026ndash;4.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndean Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3 (0.1\u0026ndash;0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3 (0.1\u0026ndash;0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4 (0.2\u0026ndash;0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1 (0.3\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5 (0.4\u0026ndash;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.8 (0.4\u0026ndash;3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.8 (0.2\u0026ndash;1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0 (0.3\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.3 (0.4\u0026ndash;3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1 (0.0\u0026ndash;0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1 (0.0\u0026ndash;0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 (0.0\u0026ndash;0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6 (0.1\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9 (0.1\u0026ndash;1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.1 (0.1\u0026ndash;2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4 (0.1\u0026ndash;0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6 (0.2\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7 (0.2\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6 (0.0\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7 (0.0\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9 (0.0\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.1 (0.5\u0026ndash;6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.1 (0.6\u0026ndash;8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.7 (0.8\u0026ndash;11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.4 (1.1\u0026ndash;6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.6 (1.2\u0026ndash;6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.4 (1.4\u0026ndash;8.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTropical Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5 (0.0\u0026ndash;1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7 (0.0\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (0.0\u0026ndash;2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5 (0.5\u0026ndash;6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.0 (0.7\u0026ndash;9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.4 (1.1\u0026ndash;14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.6 (0.9\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.7 (1.2\u0026ndash;6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.0 (2.0\u0026ndash;10.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.3 (0.4\u0026ndash;11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4 (0.5\u0026ndash;13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.6 (0.6\u0026ndash;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.2 (10.0\u0026ndash;67.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.5 (14.1\u0026ndash;94.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.2 (19.6\u0026ndash;135.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.5 (0.0\u0026ndash;43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.7 (0.0\u0026ndash;43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.7 (0.0\u0026ndash;52.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.5 (5.1\u0026ndash;8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.4 (7.4\u0026ndash;11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.4 (6.5\u0026ndash;10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.9 (6.2\u0026ndash;13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.9 (11.3\u0026ndash;24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.0 (12.4\u0026ndash;27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.7 (6.0\u0026ndash;16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.2 (8.6\u0026ndash;23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.8 (7.6\u0026ndash;21.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.0 (4.4\u0026ndash;7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.5 (7.1\u0026ndash;12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.4 (7.6\u0026ndash;13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.9 (8.3\u0026ndash;25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.0 (15.8\u0026ndash;46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37.4 (19.1\u0026ndash;58.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.4 (9.7\u0026ndash;25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.7 (13.1\u0026ndash;29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.7 (11.3\u0026ndash;27.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOceania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0 (0.0\u0026ndash;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0 (0.0\u0026ndash;0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0 (0.0\u0026ndash;0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4 (0.2\u0026ndash;0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5 (0.3\u0026ndash;0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6 (0.4\u0026ndash;0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3 (0.2\u0026ndash;0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3 (0.2\u0026ndash;0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.3 (0.2\u0026ndash;0.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoutheast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.9 (2.7\u0026ndash;5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4 (3.1\u0026ndash;5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6 (3.1\u0026ndash;6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.1 (2.3\u0026ndash;10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.4 (3.3\u0026ndash;14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.2 (3.9\u0026ndash;18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.1 (1.9\u0026ndash;9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.1 (1.9\u0026ndash;9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.0 (1.8\u0026ndash;8.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.4 (2.9\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.1 (3.8\u0026ndash;10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.9 (3.6\u0026ndash;10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.5 (1.1\u0026ndash;9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.6 (1.6\u0026ndash;13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.6 (2.1\u0026ndash;18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.0 (0.0\u0026ndash;11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.6 (0.0\u0026ndash;14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.5 (0.0\u0026ndash;13.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.5 (3.5\u0026ndash;9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9 (3.7\u0026ndash;10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.8 (3.6\u0026ndash;10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.3 (1.1\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.2 (1.3\u0026ndash;10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.4 (1.8\u0026ndash;15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.4 (0.0\u0026ndash;12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.2 (0.0\u0026ndash;11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.3 (0.0\u0026ndash;11.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.2 (0.6\u0026ndash;1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2 (1.1\u0026ndash;3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2 (1.2\u0026ndash;3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4 (0.4\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1 (0.6\u0026ndash;4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.9 (0.8\u0026ndash;5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6 (0.0\u0026ndash;2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8 (0.0\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.0 (0.0\u0026ndash;3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.9 (4.9\u0026ndash;13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.4 (6.2\u0026ndash;17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.5 (6.0\u0026ndash;17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.9 (1.7\u0026ndash;15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.2 (2.2\u0026ndash;19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.2 (2.7\u0026ndash;23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.5 (0.0\u0026ndash;18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.5 (0.0\u0026ndash;22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.5 (0.0\u0026ndash;22.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.9(39\u0026ndash;102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89(51\u0026ndash;131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99(55\u0026ndash;149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e173(41\u0026ndash;357)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24(62\u0026ndash;487)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e303(75\u0026ndash;620)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e70(21\u0026ndash;189)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e85(28\u0026ndash;214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e88(27\u0026ndash;235)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEurope, particularly Eastern Europe, was estimated to have the largest burden of deaths from cardiovascular diseases due to fire PM2.5, accounting for 41% in 1990-99 and 35% in 2010-18 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This was followed by North Africa and the Middle East, which accounted for 20% in 1990-99 and 23% in 2010-18. This higher share is likely due to the increased relative risk (RR) of dying from cardiovascular diseases upon exposure to fire emissions in this region compared to others (Figure S8). Supporting this, when a uniform RR estimate is applied globally, the share of total cardiovascular disease burden in North Africa and the Middle East drops to 4.5% and 5% for 1990-99 and 2010-18, respectively. The largest respiratory disease burden occurred in East Asia (24% in 1990-99 to 25% in 2010-18), sub-Saharan Africa (18% in 1990-99 to 20% in 2010-18), and South Asia (15% in 1990-99 to 20% in 2010-18), see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for excess death estimates by regions.\u003c/p\u003e \u003cp\u003eGlobally, 27% of all-cause and 35% of respiratory deaths from fire PM2.5 in 1990-99 occurred among children under 5 years old, decreasing to 15% in 2010-18 (Figure S9). This large contribution globally is majorly attributed to the finding that over 40% of all-cause and respiratory fire PM2.5 deaths in sub-Saharan Africa occur among children under 5, across the years. Deaths from cardiovascular diseases mostly occur among the population older than 60 years (Figure S9). We estimate that the crude death rates (CDR) and age standardized death rates (ADR) per million population for all-cause deaths associated with exposure to fire PM2.5. We note a notable increase in CDR for all-cause deaths from 1990-99 to 2000-09, with the average CDR increasing from 12.5 (7.1\u0026ndash;18) to 14.1 (8.1\u0026ndash;20.8). This is followed by a slight decrease to 14 (7.8\u0026ndash;21.1) in 2010-18. For cardiovascular deaths from fire PM\u003csub\u003e2.5\u003c/sub\u003e, the CDR rose steadily over the three decades, from 3.1 (0.8\u0026ndash;6.6) in 1990-99 to 3.8 (1-7.7) in 2000-09, and further to 4.3 (1-8.8) in 2010-18. Like for all-cause deaths, for respiratory deaths, globally, the CDR increased from 1.3 (0.4\u0026ndash;3.3) in 1990-99 to 1.34 (0.44\u0026ndash;3.4) in 2000-09 before decreasing to 1.25 (0.37\u0026ndash;3.3) in 2010-18 likely by a significant reduction in background deaths globally, and especially in sub-Saharan Africa supported by the decreasing trend in ADR for all cause, cardiovascular and respiratory diseases by 0.02, 0.0006 and 0.008 deaths/million population per year (Figure S11).\u003c/p\u003e \u003cp\u003eRegionally, the CDR from all-cause fire PM2.5 deaths increased significantly in Eastern Europe (by 2/year from 1990 to 2018), Central Europe (0.7/year) and in all of Latin America (0.1/year) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, S12). While it decreased significantly in Central (-1.2/year), Eastern (-0.6/year) and Western (-0.4 year) sub-Saharan Africa. For cardiovascular diseases, the CDR increased significantly in Eastern (0.7/year) and Central (0.18/year) Europe and decreasing slightly in western and eastern sub-Saharan Africa and high-income North America (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, S13). CDR for respiratory deaths increased in Southern (0.07/year) and Tropical (0.06/year) Latin America and high-income Northern America (0.02) while decreasing in most of sub-Saharan Africa (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, S14). All the top 20 countries with largest CDR for all-cause deaths from fire PM\u003csub\u003e2.5\u003c/sub\u003e are in sub-Saharan Africa, Eastern and Central Europe (Figure S14), with Ukraine (128(78\u0026ndash;180)), Bulgaria (122(73\u0026ndash;176)) and Russia (117(72\u0026ndash;161)) having the largest CDR in 2010-18, with largest rates of increase in Ukraine and Russia while decreasing in countries of sub-Saharan Africa. Countries in Eastern Europe including Ukraine, Russia and Bulgaria also had the largest ADR for cardiovascular diseases from exposure to fire PM\u003csub\u003e2.5\u003c/sub\u003e in 2010-18 and the largest rate of increases. While Bolivia, Sierra Leone and Guinea had the largest CDR from respiratory diseases in 2010-18 (Figure S15, SI Data 3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003eWe employ a chemical transport model, which utilizes fire emissions data from an innovative Fire Forecasting model developed with machine learning techniques to estimate exposure to fire PM\u003csub\u003e2.5\u003c/sub\u003e. We combine the exposure with population distribution, baseline disease rates and spatially heterogeneous RR estimates to generate the first estimates of long-term trends of exposure to fires and assess the health burden due to acute exposure to fire emissions across 190 countries and 21 global regions. The data are made available publicly. Our analysis provides comprehensive and crucial insights into the evolving nature of fire-PM\u003csub\u003e2.5\u003c/sub\u003e and its implications for public health worldwide.\u003c/p\u003e \u003cp\u003eOur findings indicate that, while population-weighted all-source PM\u003csub\u003e2.5\u003c/sub\u003e exposure has decreased globally from 1990 to 2018, especially in developed countries, fire-PM\u003csub\u003e2.5\u003c/sub\u003e has increased across all vegetated continents. The most significant increases have been observed in Eastern and Central Europe, high-income North America, Tropical Latin America, and sub-Saharan Africa. As anthropogenic sources of PM\u003csub\u003e2.5\u003c/sub\u003e decrease in developed countries, the contribution from fires has become more prominent in these regions. Fire-PM\u003csub\u003e2.5\u003c/sub\u003e extremes are significantly increasing across most vegetated continents (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), with the average number of days exceeding the extreme levels of 1990-94 tripling in 2010-18 compared to 1990-99 and more than half of the global population are exposed to \u003cem\u003eminimum perpetual fire\u003c/em\u003e occurrence in 2010-18, which is 40% more population exposed to this level in 1990-99 on average.\u003c/p\u003e \u003cp\u003eOver the past decades, the annual number of fires, mean fire size, and total area burned have decreased globally (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). However, our study and others (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) show that global population exposure to fire-PM\u003csub\u003e2.5\u003c/sub\u003e is rising, significantly impacting human health. The reduction in burned areas is largely due to declines in human-driven fires in tropical savannas, with these decreases generally concentrated in regions with lower tree cover. Conversely, an increasing trend has been observed in closed-canopy forests(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). While some forest fires are brought under control, approximately 10% develop into extreme fires that cannot be suppressed (WB/PROFOR 2020). Emissions from forest fires, especially extreme ones, are substantially larger than those from grass and shrub fires(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Which justifies our finding that the episodes of extreme exposures to fire-PM\u003csub\u003e2.5\u003c/sub\u003e are increasing and moreover highlighting the importance of our work in addressing the short-term, acute effects of exposure to fire PM\u003csub\u003e2.5\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eWe use spatially heterogeneous RR estimates from a recent study (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) to project the increase in excess deaths from all-cause, cardiovascular, and respiratory diseases over a given time period. We note that the CDR and ADR for all-cause and respiratory deaths due to fire-PM\u003csub\u003e2.5\u003c/sub\u003e are lower in 2010\u0026ndash;2018 compared to 1990\u0026ndash;1999. This reduction is attributed to a significant decrease in baseline mortality rates, which can be explained by examining the impact of changes in population size, population age, baseline disease rates, and fire-PM\u003csub\u003e2.5\u003c/sub\u003e on excess deaths (Figure S16). The strong decline in baseline disease rates, driven by improvements in medical conditions and health management, counteracts the effects of increasing fire-PM\u003csub\u003e2.5\u003c/sub\u003e levels and an aging, growing population on excess deaths from fire-PM\u003csub\u003e2.5\u003c/sub\u003e. We find that using a spatially homogeneous RR estimate as in earlier studies (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) results in at least 35% underestimation in excess deaths from fire PM\u003csub\u003e2.5\u003c/sub\u003e globally. Using Global Burden of Diseases methods, we find that chronic exposure to fire PM\u003csub\u003e2.5\u003c/sub\u003e results in 34(\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43 CR44 CR45 CR46 CR47 CR48\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) thousand deaths in 2010-18, which is smaller than the all-cause deaths resulting from acute exposure to fires in the same time period, though comparing these estimates are difficult due to differences in methods and biases on covariate controls and double counting of deaths.\u003c/p\u003e \u003cp\u003eOur results are aligned with previous studies but are more conservative, mainly due to differences in study design and the fire emissions model used. We found that there were 7.7\u0026nbsp;billion person-days in 2000\u0026ndash;2009 when all-source PM\u003csub\u003e2.5\u003c/sub\u003e levels exceeded the WHO daily guideline of 15\u0026micro;g/m\u0026sup3;, with fires contributing to at least 50% of this limit. This number increased to 12\u0026nbsp;billion person-days in 2010\u0026ndash;2018. Although the trends are consistent, the absolute estimates are approximately eight times lower than those reported by Xu and colleagues (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This difference can be attributed to our use of the fire forecasting model within the IS4FIRES fire information system, that uses MODIS fire radiative power (FRP) and ECMWF Reanalysis v5(ERA5) to estimate daily emissions from fires and use statistical methods to backcast fire emissions before the MODIS era. In comparison to Global Fire Emissions Database (GFED) v4.1 that is used by Xu and colleagues (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), the IS4Fires are based on actual fire counts, while GFED is based on burnt area observations, which are more homogeneous and a cumulative quantity. We note that the average population-weighted fire PM2.5 exposure in the analysis by Xu et al (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) is 2.5 \u0026micro;g/m\u0026sup3; (or 6.1% of all PM2.5) globally, which is ~\u0026thinsp;5 times larger compared to our results and other works (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). We found it difficult to explain so high levels of Xu et al and consider our estimate (about 0.5 \u0026micro;g/m\u0026sup3;) as more realistic, also better agreeing with independent works. The IS4Fires fire-induced emission is already on the upper side of emission range reported in the literature \u0026ndash; owing to the top-down calibration of the IS4FIRES-SILAM models (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsequently, our estimates of excess deaths are smaller compared to earlier studies that utilize other sources of fire emissions. For instance, Roberts and Wooster(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) estimate that 677,000 premature deaths annually result from exposure to landscape fire smoke between 2016 and 2019, which is approximately 12 times higher than our estimates. A more recent study(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) estimates 1.5\u0026nbsp;million excess deaths associated with landscape fires, which roughly is 20% of all deaths associated with all PM2.5 and appears to be on the higher side compared to previous source attribution studies(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). This discrepancy between studies arises from differences in the exposure-response models used, the methods of calculating deaths attributable to fires, and the fire emissions data employed. It is important to note that while all the above studies estimate total excess deaths from both acute and chronic exposure to fire-related PM2.5, our analysis focuses solely on the burden from acute exposure. A recent study (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) estimated approximately 1\u0026nbsp;million (95% CI: 690,000\u0026ndash;1.3\u0026nbsp;million) all-cause excess deaths per year from 2000 to 2019 due to total PM2.5 exposure. Our estimates suggest that excess deaths from acute exposure to fire-related PM2.5 account for roughly 10% of these deaths.\u003c/p\u003e \u003cp\u003eConsistent with earlier studies, we observe an increasing trend in health burdens from fires and for the first time we present estimates of exposure and health impacts of fires over a 29-year period, from 1990 up until 2018. We note that mortality, calculated and presented here, is just the tip of the iceberg, with hospitalizations, emergency room visits, asthma exacerbations, and reduced physical activity also posing significant health concerns. We acknowledge that the health burden from fires presented here and in other studies cannot be fully validated. While the estimates provided are conservative, we recommend focusing on the relative changes observed over 28 years, which are being presented here for the first time.\u003c/p\u003e \u003cp\u003eIncreasing fire contributions to ambient PM2.5 and related health burdens counteract clean air policies globally, especially in the global west where extremes of fires are increasing over the past decade. Studies show that extreme fire weather has intensified globally and is expected to increase in the future(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Acknowledging this threat, many country government including the European Union has emphasized improved cooperation countries to manage wildfires, enacting initiatives focused on prevention, preparedness, and response (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) though such efforts are largely missing in sub-Saharan Africa. These efforts have slowed the area burnt globally (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Despite this, large wildfires continue to occur, and the extremes of fire-PM\u003csub\u003e2.5\u003c/sub\u003e have intensified over time. If fire emissions are found to be more toxic than other PM\u003csub\u003e2.5\u003c/sub\u003e sources, pending further assessment, implementing more sustainable forest management will be crucial to mitigate the population exposure and health burden.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eWe used the SILAM (System for Integrated modeLling of Atmospheric composition) offline 3D chemical transport model for atmospheric composition and air quality simulations (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) to simulate. For this study, SILAM was run at 2x2\u0026deg; horizontal resolution with 29 vertical layers up to 10.5 Pa globally, covering 1990\u0026ndash;2018, we ignore later years due to onset of the COVID19 pandemic in 2019 and the availability of baseline death rates data until 2019. Meteorological variables from the European reanalysis ERA5 governed the simulations. We used the CEDS anthropogenic emission inventory (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) for primary emitted species.\u003c/p\u003e \u003cp\u003eFire emissions posed a particular challenge for the study: homogeneous datasets based on active-fire or burnt-area observations and covering several decades do not exist, owing to changes of satellites, their capabilities, resolution, and sensitivity. Therefore, we used the dataset generated by the fire prediction model of Integrated System for wild-land fires, IS4FIRES (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). We distinguish 7 types of fuel associated with land-use: grass, agriculture waste, tropical forest, temperate forest, boreal forest, shrub, tundra. IS4FIRES data were previously used in a short-term evaluation of the fire-related mortality in Europe (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). The FFM (Fire Forecasting Model) of IS4FIRES is a time-agnostic machine-learning algorithm trained and evaluated against the MODIS Fire Radiative Power product, 2003\u0026ndash;2022 (training period was 2003\u0026ndash;2014, evaluation was performed over 2015\u0026ndash;2020) (Sofiev et al., in prep.).\u003c/p\u003e \u003cp\u003eFire-originated PM2.5 emissions are tracked using distinct variables for PM2.5 from fires, explicitly included in the simulations. For additional calibration of the FFM model, the fire originated PM2.5 concentration was scaled by a factor of 1.9, in order to provide the same global average fire-PM2.5 concentration as the model run when using the direct satellite retrieved fire-PM2.5 during the MODIS period. These fire-originated PM emissions have a defined size distribution but no specific chemical composition, encompassing both primary PM from fires and secondary PM from fire-related precursors. The rapid transformation of these particles is assumed to be instantaneous on a regional or continental scale. This method bypasses the uncertainties of source apportionment and enables direct identification of emission factors by fitting SILAM predictions to satellite data, Aeronet sun-photometer network, and in-situ PM measurements. SILAM simulations has undergone extensive evaluation in multiple past studies (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) and real-time operational applications (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://atmosphere.copernicus.eu\u003c/span\u003e\u003cspan address=\"http://atmosphere.copernicus.eu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dust.aemet.es\u003c/span\u003e\u003cspan address=\"https://dust.aemet.es\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ews.tropmet.res.in\u003c/span\u003e\u003cspan address=\"https://ews.tropmet.res.in\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.asdf-bj.net/gafis/index.html\u003c/span\u003e\u003cspan address=\"http://www.asdf-bj.net/gafis/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hpfx.collab.science.gc.ca/~svfs000/na-aq-mm-fe/dist/\u003c/span\u003e\u003cspan address=\"https://hpfx.collab.science.gc.ca/~svfs000/na-aq-mm-fe/dist/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nrlmry.navy.mil/aerosol/\u003c/span\u003e\u003cspan address=\"https://www.nrlmry.navy.mil/aerosol/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor population exposure and health impact assessment, we estimated the 3-day moving average (lag 0\u0026ndash;2 days) of all-source and fire PM2.5 and used a hybrid gridded demographic data for the world, at a 0.5 degree grid resolution. This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data, we break up the population data at 5-year population bands, using population age information from the GBD.\u003c/p\u003e \u003cp\u003eWe estimate the health burden from fire-related PM2.5 exposure across 190 countries and 21 global sub-regions. For this assessment, we use RR estimates from a study that analyzed daily death counts for all causes, cardiovascular causes, and respiratory causes in 749 cities across 43 countries from 2000 to 2016(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). City-specific effect estimates were pooled using a random-effects meta-analysis to derive overall effect estimates at global and WHO regional levels. The pooled PM2.5-mortality association is expressed as the RR of death associated with a 10 \u0026micro;g/m\u0026sup3; increase in wildfire-related PM2.5. We mapped WHO regions onto the GBD regions to produce region-specific RR estimates (see Fig. S8). We used this information to build an exposure response function as in our previous studies(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e) for impacts of fire PM\u003csub\u003e2.5\u003c/sub\u003e on excess deaths from all-cause, cardiovascular and respiratory diseases, using the following relationship:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:RR={exp}^{\\beta\\:\\varDelta\\:x},\\:\\varDelta\\:x=\\text{m}\\text{a}\\text{x}[0,fire\\:{PM}_{2.5}]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eβ was first estimated by taking ln-RR of for a 10 ppb increase in fire PM\u003csub\u003e2.5\u003c/sub\u003e. The β obtained was then used to obtain the RR for exposure to fire PM\u003csub\u003e2.5\u003c/sub\u003e. For sensitivity, we kept the RR uniform globally, by using a global pooled estimate of RR of 1\u0026middot;019 (95% CI 1\u0026middot;016\u0026ndash;1\u0026middot;022) for all-cause mortality, 1\u0026middot;017 (1\u0026middot;012\u0026ndash;1\u0026middot;021) for cardiovascular mortality, and 1\u0026middot;019 (1\u0026middot;013\u0026ndash;1\u0026middot;025) for respiratory mortality.\u003c/p\u003e \u003cp\u003eThe burden of exposure to fire PM\u003csub\u003e2.5\u003c/sub\u003e estimated using equation [2]. A similar relation was applied in our earlier studies(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e) to estimate excess deaths\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Excess\\:deaths=\\:\\frac{RR-1}{RR}\\:\\times\\:p\\:\\times\\:{y}_{0}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere p is the exposed population by age. Age-specific baseline mortality rates (y\u003csub\u003e0\u003c/sub\u003e) for the age classes for all-cause, respiratory and cardiovascular deaths each country from the GBD (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-compare/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-compare/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). we estimated crude and age-standardized death rates using established methods. We also calculate the variation in excess deaths caused by fire-PM\u003csub\u003e2.5\u003c/sub\u003e exposure for each year in comparison to 1990. We then attribute the changes observed in each country to changes in key factors, namely, baseline mortality rates, population size, population age structure and fire-PM\u003csub\u003e2.5\u003c/sub\u003e exposure, as detailed in an earlier study (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). The uncertainties and limitations related to our study are discussed in SI Text 2.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interest Statement:\u003c/h2\u003e \u003cp\u003eAuthors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSC, KA and MS planned the study. SC, RH and MS performed the analysis and prepared the figures. SC wrote the manuscript with support from all the authors\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors acknowledge the Belmont Forum Climate, Environment and Health-I project, HEATCOST (Academy of Finland grant no. 334798 and Research Council Norway grant no. 310672), the European Union's Horizon 2020 research and innovation program under Grant Agreement 820655 (EXHAUSTION) and the European Union's Horizon 2020 cooperation and support action program under Grant Agreement 101003966 (ENBEL).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eSILAM is an open-source chemical transport model available from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://silam.fmi.fi/\u003c/span\u003e\u003cspan address=\"https://silam.fmi.fi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. All other data generated and codes developed for this work will be made available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eM. Gross, Learning to live with landscape fires. \u003cem\u003eCurr. Biol.\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, R693\u0026ndash;R696 (2015).\u003c/li\u003e\n\u003cli\u003eA. C. Scott, I. J. Glasspool, The diversification of Paleozoic fire systems and fluctuations in atmospheric oxygen concentration. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e \u003cstrong\u003e103\u003c/strong\u003e, 10861\u0026ndash;10865 (2006).\u003c/li\u003e\n\u003cli\u003eA. Barik, S. Baidya Roy, Climate change strongly affects future fire weather danger in Indian forests. \u003cem\u003eCommun. Earth Environ.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 1\u0026ndash;14 (2023).\u003c/li\u003e\n\u003cli\u003eJ. 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Inhalation of wildfire smoke and other pollutants can lead to various health issues, including respiratory and cardiovascular problems. Our study uses the SILAM chemical transport model, integrated with the IS4FIRES fire information system, to assess population exposure to fire-related PM2.5, along with the health burden from all-cause, respiratory, and cardiovascular deaths. Our results show that while population-weighted all-source PM2.5 exposure has declined in Europe and high-income North America, fire-PM2.5 exposure has increased significantly in Eastern and Central Europe, high-income North America, Tropical Latin America, and sub-Saharan Africa. Extreme fire-PM2.5 events have tripled globally since the 1990s, with more than half of the global population experiencing minimum perpetual fire occurrence (least 1% of fire-PM\u003csub\u003e2.5\u003c/sub\u003e in PM2.5 for 50 instances of 3 consecutive days in a calendar year) in 2010–2018. Acute exposure to fire-PM2.5 contributed to 99,000 (95% CI − 55,000–149,000) all-cause deaths annually in 2010-18, with significant cardiovascular and respiratory disease burdens, particularly in Eastern Europe and sub-Saharan Africa. Our findings highlight the escalating health risks of fire emissions, emphasizing the urgent need for mitigation strategies as fire-PM2.5 becomes a growing contributor to global air pollution-related mortality.\u003c/p\u003e","manuscriptTitle":"Global health burden from acute exposure to fine particles emitted by fires","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 14:20:04","doi":"10.21203/rs.3.rs-6344182/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-06T05:07:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-27T22:43:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-23T13:05:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302520760340715238391051260109508218155","date":"2025-04-23T11:30:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161311821260571130170863957721292050564","date":"2025-04-17T11:56:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-03T02:37:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-02T03:10:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-01T18:19:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Clean Air","date":"2025-03-31T11:00:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-clean-air","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Clean Air](https://www.nature.com/npjcleanair/)","snPcode":"44407","submissionUrl":"https://submission.springernature.com/new-submission/44407/3","title":"npj Clean Air","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b19de6ac-5547-4494-b5dc-246ebf99c95b","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":47571832,"name":"Earth and environmental sciences/Climate sciences/Climate change"},{"id":47571833,"name":"Earth and environmental sciences/Environmental sciences"},{"id":47571834,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":47571835,"name":"Earth and environmental sciences/Environmental social sciences/Climate change impacts"},{"id":47571836,"name":"Earth and environmental sciences/Environmental social sciences/Environmental impact"}],"tags":[],"updatedAt":"2025-07-27T06:23:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-24 14:20:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6344182","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6344182","identity":"rs-6344182","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}