African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data

Greenhouse gas emission from prescribed fires is influenced by vegetation types in West African Savannas

Greenhouse gas (GHG) emissions from prescribed fires are poorly investigated, resulting in a high uncertainty in GHG budgets. Using, a carbon mass balance approach and experimental prescribed fires in 80 plots, this study assessed carbon emissions and established emission factors (EFs) for carbon dioxides (CO2), carbon monoxide (CO), and methane (CH4) across climate zones and vegetation types. In grass and shrub savannas, fires could burn intensely due to the lower moisture content and continuous spatial distribution of biomass fuel, causing greater carbon emissions with 1.61 ± 0.13 t C ha-1 and 1.01 ± 0.13 t C ha-1, respectively. Despite their low carbon emissions, tree savannas (1658.17 ± 11.13 g kg-1) and woodlands (1629.94 ± 12.23 g kg-1) have the highest EFs, which can be attribute to the high carbon content of biomass fuel in these vegetation types. Vegetation types and their interaction with climate zones have a substantial impact on carbon emissions and carbon species EFs, and should therefore be considered in assessing GHG emissions from fires. The findings from this study provide a useful basis for improving the national measurement, reporting, and verification of GHG emissions and for improving the measurement of the global balance of GHG emissions from fires.

Substantial Underestimation of Fine-Mode Aerosol Loading from Wildfires and Its Radiative Effects in Current Satellite-Based Retrievals over the United States

Wildfires generate abundant smoke primarily composed of fine-mode aerosols. However, accurately measuring the fine-mode aerosol optical depth (fAOD) is highly uncertain in most existing satellite-based aerosol products. Deep learning offers promise for inferring fAOD, but little has been done using multiangle satellite data. We developed an innovative angle-dependent deep-learning model (ADLM) that accounts for angular diversity in dual-angle observations. The model captures aerosol properties observed from dual angles in the contiguous United States and explores the potential of Greenhouse gases Observing Satellite-2's (GOSAT-2) measurements to retrieve fAOD at a 460 m spatial resolution. The ADLM demonstrates a strong performance through rigorous validation against ground-based data, revealing small biases. By comparison, the official fAOD product from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Multiangle Imaging Spectroradiometer (MISR) during wildfire events is underestimated by more than 40% over western USA. This leads to significant differences in estimates of aerosol radiative forcing (ARF) from wildfires. The ADLM shows more than 20% stronger ARF than the MODIS, VIIRS, and MISR estimates, highlighting a greater impact of wildfire fAOD on Earth's energy balance.

Application of the wildland fire emissions inventory system to estimate fire emissions on forest lands of the United States

BACKGROUND

Forests are significant terrestrial biomes for carbon storage, and annual carbon accumulation of forest biomass contributes offsets affecting net greenhouse gases in the atmosphere. The immediate loss of stored carbon through fire on forest lands reduces the annual offsets provided by forests. As such, the United States reporting includes annual estimates of direct fire emissions in conjunction with the overall forest stock and change estimates as a part of national greenhouse gas inventories within the United Nations Framework Convention on Climate Change. Forest fire emissions reported for the United States, such as the 129 Tg CO2 reported for 2022, are based on the Wildland Fire Emissions Inventory System (WFEIS). Current WFEIS estimates are included in the Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2022 published in 2024 by the United States Environmental Protection Agency. Here, we describe WFEIS the fire emissions inventory system we used to address current information needs, and an analysis to confirm compatibility of carbon mass between estimated forest fire emissions and carbon in forest stocks.

RESULTS

The summaries of emissions from forests are consistent with previous reports that show rates and interannual variability in emissions and forest land area burned are generally greater in recent years relative to the 1990s. Both emissions and interannual variability are greater in the western United States. The years with the highest CO2 emissions from forest fires on the 48 conterminous states plus Alaska were 2004, 2005, and 2015. In some years, Alaska emissions exceed those of the 48 conterminous states, such as in 2022, for example. Comparison of forest fire emission to forest carbon stocks indicate there is unlikely any serious disconnect between inventory and fire emissions estimates.

CONCLUSIONS

The WFEIS system is a user-driven approach made available via a web browser. Model results are compatible with the scope and reporting needs of the annual national greenhouse gas inventories.

Wild herbivores and cattle have differing effects on postfire herbaceous vegetation recovery in an African savanna

Fire and herbivory have profound effects on vegetation in savanna ecosystems, but little is known about how different herbivore groups influence vegetation dynamics after fire. We assessed the separate and combined effects of herbivory by cattle and wild meso- and megaherbivores on postfire herbaceous vegetation cover, species richness, and species turnover in a savanna ecosystem in central Kenya. We measured these vegetation attributes for five sampling periods (from 2013 to 2017) in prescribed burns and unburned areas located within a series of replicated long-term herbivore exclosures that allow six different combinations of cattle and wild meso- and megaherbivores (elephants and giraffes). Vegetation cover (grasses, mainly) and species richness were initially reduced by burning but recovered by 15-27 months after fire, suggesting strong resilience to infrequent fire. However, the rates of recovery differed in plots accessible by different wild and domestic herbivore guilds. Wildlife (but not cattle) delayed postfire recovery of grasses, and the absence of wildlife (with or without cattle) delayed recovery of forbs. Herbivory by only cattle increased grass species richness in burned relative to unburned areas. Herbivory by cattle (with or without wildlife), however, reduced forb species richness in burned relative to unburned areas. Herbivory by wild ungulates (but not cattle) increased herbaceous species turnover in burned relative to unburned areas. Megaherbivores had negligible modifying effects on these results. This study demonstrates that savanna ecosystems are remarkably resilient to infrequent fires, but postfire grazing by cattle and wild mesoherbivores exerts different effects on recovery trajectories of herbaceous vegetation.

Relative Humidity Modulates the Physicochemical Processing of Secondary Brown Carbon Formation from Nighttime Oxidation of Furan and Pyrrole

Light-absorbing secondary organic aerosols (SOAs), also known as secondary brown carbon (BrC), are major components of wildfire smoke that can have a significant impact on the climate system; however, how environmental factors such as relative humidity (RH) influence their formation is not fully understood, especially for heterocyclic precursors. We conducted chamber experiments to investigate secondary BrC formation from the nighttime oxidation of furan and pyrrole, two primary heterocyclic precursors in wildfires, in the presence of pre-existing particles at RH < 20% and ∼ 50%. Our findings revealed that increasing RH significantly affected the size distribution dynamics of both SOAs, with pyrrole SOA showing a stronger potential to generate ultrafine particles via intensive nucleation processes. Higher RH led to increased mass fractions of oxygenated compounds in both SOAs, suggesting enhanced gas-phase and/or multiphase oxidation under humid conditions. Moreover, higher RH reduced the mass absorption coefficients of both BrC, contrasting with those from homocyclic precursors, due to the formation of non-absorbing high-molecular-weight oxygenated compounds and the decreasing mass fractions of molecular chromophores. Overall, our findings demonstrate the unique RH dependence of secondary BrC formation from heterocyclic precursors, which may critically modulate the radiative effects of wildfire smoke on climate change.

Coarse-resolution burned area datasets severely underestimate fire-related forest loss

Global coarse-resolution (≥250 m) burned area (BA) products have been used to estimate fire related forest loss, but we hypothesised that a significant part of fire impacts might be undetected because of the underestimation of small fires (<100 ha), especially in the tropics. In this paper, we analysed fire-related forest cover loss in sub-Saharan Africa (SSA) for 2016 and 2019 based on a BA product generated from Sentinel-2 data (20 m), which was observed to have significantly lower omission errors than the coarse-resolution BA products. Using these higher resolution BA datasets, we found that fires contribute to >46 % of total forest losses over SSA, more than twice the estimates from coarse-resolution BA products. In addition, burned forest areas showed more than twofold likelihood of subsequent loss compared to unburned ones. In moist tropical forests, the most fire-vulnerable biome, burning had even six times more chance to precede forest loss than unburned areas. We also found that fire-related characteristics, such as fire size and season, and forest fragmentation play a major role in the determination of tree cover fate. Our results reveal that medium-resolution BA detects more fires in late fire season, which tend to have higher impact on forests than early season ones. On the other hand, small fires represented the major driver of forest loss after fires and the vast majority of these losses occur in fragmented landscapes near forest edge (<260 m). Therefore medium-resolution BA products are required to obtain a more accurate evaluation of fire impacts in tropical ecosystems.

A monthly gridded burned area database of national wildland fire data

We assembled the first gridded burned area (BA) database of national wildfire data (ONFIRE), a comprehensive and integrated resource for researchers, non-government organisations, and government agencies analysing wildfires in various regions of the Earth. We extracted and harmonised records from different regions and sources using open and reproducible methods, providing data in a common framework for the whole period available (starting from 1950 in Australia, 1959 in Canada, 1985 in Chile, 1980 in Europe, and 1984 in the United States) up to 2021 on a common 1° × 1° grid. The data originate from national agencies (often, ground mapping), thus representing the best local expert knowledge. Key opportunities and limits in using this dataset are discussed as well as possible future expansions of this open-source approach that should be explored. This dataset complements existing gridded BA data based on remote sensing and offers a valuable opportunity to better understand and assess fire regime changes, and their drivers, in these regions. The ONFIRE database can be freely accessed at https://zenodo.org/record/8289245 .

In temperate Europe, fire is already here: The case of The Netherlands

Landscape fires are usually not associated with temperate Europe, yet not all temperate countries record statistics indicating that actual risks remain unknown. Here we introduce new wildfire statistics for The Netherlands, and summarize significant events and fatalities. The period 2017-2022 saw 611 wildfires and 405 ha burned per year, which Copernicus' European Forest Fire Information System satellite data vastly underestimate. Fires burned more heathland than forest, were small (mean fire size 1.5 ha), were caused by people, and often burned simultaneously, in Spring and in Summer drought. Suppression, restoration and traffic delays cost 3 M€ year-1. Dozens of significant events illustrate fire has never been away and has major societal impact amidst grave concerns for firefighter safety. Since 1833, 31 fatalities were reported. A legal framework is needed to ensure continuity of recordkeeping, as the core foundation of integrated fire management, to create a baseline for climate change, and to fulfill international reporting requirements.

Madagascar's burned area from Sentinel-2 imagery (2016-2022): Four times higher than from lower resolution sensors

Madagascar is one of the most burned regions in the world, to the point that it has been called the 'Isle of fire' or the 'Burning Island'. An accurate characterization of the burned area (BA) is crucial for understanding the true situation and impacts of fires on this island, where there is an active scientific debate on how fire affects multiple environmental and socioeconomic aspects, and how fire regimes should be in a complex context with differing interests. Despite this, recent advances have revealed that BA in Madagascar is poorly characterised by the currently available global BA products. In this work, we present, validate, and explore a BA database at 20 m spatial resolution for Madagascar covering the period 2016-2022. The database was built based on 75,010 Sentinel-2 images using a two-phase BA detection algorithm. The validation with independent long-term reference units showed Dice coefficients ≥79 %, omission errors ≤24 %, commission errors ≤18 %, and a relative bias ≥ - 8 %. An intercomparison with other available global BA products (GABAM, FireCCI51, C3SBA11, or MCD64) demonstrated that our product (i) exhibits temporal consistency, (ii) represents a significant accuracy improvement, as it reduces BA underestimations by about eightfold, (iii) yields BA estimates four times higher, and (iv) shows enhanced capability in detecting fires of all sizes. The observed BA spatial patterns were heterogeneous across the island, with 32 % of the grasslands burning annually, in contrast to other land cover types such as the dense tropical forest where <2 % burned every year. We conclude that the BA characterization in Madagascar must be addressed using imagery at spatial resolution higher than MODIS or Sentinel-3 (≥250 m), and temporal resolution higher than Landsat (16 days) to deal with cloudiness, the rapid attenuation of burn scars signals, and small fire patches.

Estimating annual GHG and particulate matter emissions from rural and forest fires based on an integrated modelling approach

Rural and forest fires represent one of the most significant sources of emissions in the atmosphere of trace gases and aerosol particles, which significantly impact carbon budget, air quality, and human health. This paper aims to illustrate an integrated modelling approach combining spatial and non-spatial inputs to provide and enhance the estimation of GHG and particulate matter emissions from surface fires using Italy as a case study over the period 2007-2017. Three main improvements characterize the approach proposed in this work: (i) the collection and development of comprehensive and accurate data inputs related to burned area; (ii) the use of the most recent data on fuel type and load; and (iii) the modelling application to estimate fuel moisture, burning efficiency, and fuel consumption considering meteorological factors and combustion phases. On average, Italy's GHG and particulate matter emissions were 2621 Gg yr-1, ranging from a minimum of 772 Gg yr-1 in 2013 to a maximum of 7020 Gg yr-1 in 2007. Emissions from fire disturbances in broadleaf forests, shrublands, and agricultural fuel types account for about 76 % of the total. Results were compared with global and national inventories and showed good agreement, especially considering CO2 and particulate matter. The approach of this study added confidence in emission estimates, and the results can be utilized in decision support systems to address air quality management and fire impact mitigation policies.

Associations between landscape fires and child morbidity in southern Mozambique: a time-series study

BACKGROUND

Epidemiological evidence linking exposure to landscape fires to child health remains scarce. We assessed the association between daily landscape fire smoke and child hospital visits and admissions in the Manhiça district, Mozambique, an area characterised by frequent forest and cropland fires.

METHODS

In this time-series analysis (2012-20), our primary metric for exposure to landscape fires was fire-originated PM2·5 from smoke dispersion hindcasts. We also assessed total and upwind fire exposure using daily satellite-derived fire density data. Daily numbers of hospital visits and admissions were extracted from an ongoing paediatric morbidity surveillance system (children aged ≤15 years). We applied quasi-Poisson regression models controlling for season, long-term trend, day of the week, temperature, and rainfall, and offsetting by annual population-time at risk to examine lag-specific association of fires on morbidity.

FINDINGS

A 10 μg/m3 increase in fire-originated PM2·5 was associated with a 6·12% (95% CI 0·37-12·21) increase in all-cause and a 12·43% (5·07-20·31) increase in respiratory-linked hospital visits on the following day. Positive associations were also observed for lag 0 and the cumulative lag of 0-1 days. Null associations were observed for hospital admissions. Landscape fires mostly occurred in forested areas; however, associations with child morbidity were stronger for cropland than for forest fires.

INTERPRETATION

Landscape fire smoke was associated with all-cause and respiratory-linked morbidity in children. Improved exposure assessment is needed to better quantify the contribution of landscape fire smoke to child health in regions with scarce air pollution monitoring.

FUNDING

H2020 project EXHAUSTION, Academy of Finland, Spanish Ministry of Science and Innovation, Generalitat de Catalunya, and Government of Mozambique and Spanish Agency for International Cooperation and Development.

A study on wildfire impacts on greenhouse gas emissions and regional air quality in South of Orléans, France

Wildfire events are increasing globally which may be partly associated with climate change, resulting in significant adverse impacts on local, regional air quality and global climate. In September 2020, a small wildfire (burned area: 36.3 ha) event occurred in Souesmes (Loir-et-Cher, Sologne, France), and its plume spread out over 200 km on the following day as observed by the MODIS satellite. Based on measurements at a suburban site (∼ 50 km northwest of the fire location) in Orléans and backward trajectory analysis, young wildfire plumes were characterized. Significant increases in gaseous pollutants (CO, CH4, N2O, VOCs, etc.) and particles (including black carbon) were found within the wildfire plumes, leading to a reduced air quality. Emission factors, defined as EF (X) = ∆X/∆CO (where, X represents the target species), of various trace gases and black carbon within the young wildfire plumes were determined accordingly and compared with previous studies. Changes in the ambient ions (such as ammonium, sulfate, nitrate, chloride, and nitrite in the particle- and gas- phase) and aerosol properties (e.g., aerosol water content, aerosol pH) were also quantified and discussed. Moreover, we estimated the total carbon and climate-related species (e.g., CO2, CH4, N2O, and BC) emissions and compared them with fire emission inventories. Current biomass burning emission inventories have uncertainties in estimating small fire burned areas and emissions. For instance, we found that the Global Fire Assimilation System (GFAS) may underestimate emissions (e.g., CO) of this small wildfire while other inventories (GFED and FINN) showed significant overestimation. Considering that it is the first time to record wildfire plumes in this region, related atmospheric implications are presented and discussed.

Threefold reduction of modeled uncertainty in direct radiative effects over biomass burning regions by constraining absorbing aerosols

Absorbing aerosols emitted from biomass burning (BB) greatly affect the radiation balance, cloudiness, and circulation over tropical regions. Assessments of these impacts rely heavily on the modeled aerosol absorption from poorly constrained global models and thus exhibit large uncertainties. By combining the AeroCom model ensemble with satellite and in situ observations, we provide constraints on the aerosol absorption optical depth (AAOD) over the Amazon and Africa. Our approach enables identification of error contributions from emission, lifetime, and MAC (mass absorption coefficient) per model, with MAC and emission dominating the AAOD errors over Amazon and Africa, respectively. In addition to primary emissions, our analysis suggests substantial formation of secondary organic aerosols over the Amazon but not over Africa. Furthermore, we find that differences in direct aerosol radiative effects between models decrease by threefold over the BB source and outflow regions after correcting the identified errors. This highlights the potential to greatly reduce the uncertainty in the most uncertain radiative forcing agent.

Global biomass fires and infant mortality

Global outdoor biomass burning is a major contributor to air pollution, especially in low- and middle-income countries. Recent years have witnessed substantial changes in the extent of biomass burning, including large declines in Africa. However, direct evidence of the contribution of biomass burning to global health outcomes remains limited. Here, we use georeferenced data on more than 2 million births matched to satellite-derived burned area exposure to estimate the burden of biomass fires on infant mortality. We find that each additional square kilometer of burning is associated with nearly 2% higher infant mortality in nearby downwind locations. The share of infant deaths attributable to biomass fires has increased over time due to the rapid decline in other important causes of infant death. Applying our model estimates across harmonized district-level data covering 98% of global infant deaths, we find that exposure to outdoor biomass burning was associated with nearly 130,000 additional infant deaths per year globally over our 2004 to 2018 study period. Despite the observed decline in biomass burning in Africa, nearly 75% of global infant deaths due to burning still occur in Africa. While fully eliminating biomass burning is unlikely, we estimate that even achievable reductions-equivalent to the lowest observed annual burning in each location during our study period-could have avoided more than 70,000 infant deaths per year globally since 2004.

Spatial and temporal heterogeneity of air pollution in East Africa

East Africa's air pollution levels are deteriorating due to anthropogenic and biomass burning emissions and unfavorable weather conditions. This study investigates the changes and influencing factors of air pollution in East Africa from 2001 to 2021. The study found that air pollution in the region is heterogeneous, with increasing trends observed in pollution hot spots (PHS) while it decreased in pollution cold spots (PCS). The analysis identified four major pollution periods: High Pollution period 1, Low Pollution period 1, High Pollution period 2, and Low Pollution period 2, which occur during Feb-Mar, Apr-May, Jun-Aug and Oct-Nov, respectively. The study also revealed that long range transport of pollutants to the study area is primarily influenced by distant sources from the eastern, western, southern, and northern part of the continent. The seasonal meteorological conditions, such as high sea level pressure in the upper latitudes, cold air masses from the northern hemisphere, dry vegetation, and a dry and less humid atmosphere from boreal winter, further impact the transport of pollutants. The concentrations of pollutants were found to be influenced by climate factors, such as temperature, precipitation, and wind patterns. The study identified different pollution patterns in different seasons, with some areas having minimal anthropogenic pollution due to high vegetation vigor and moderate precipitation. Using Ordinary Least Square (OLS) regression and Detrended Fluctuation Analysis (DFA), the study quantified the magnitude of spatial variation in air pollution. The OLS trends indicated that 66 % of pixels exhibited decreasing trends while 34 % showed increasing trends, and DFA results indicating that 36 %, 15 %, and 49 % of pixels exhibited anti-persistence, random, and persistence in air pollution, respectively. Areas in the region experiencing increasing or decreasing trends in air pollution, which can be used to prioritize interventions and resources for improving air quality, were also highlighted. It also identifies the driving forces behind air pollution trends, such as anthropogenic or biomass burning, which can inform policy decisions aimed at reducing air pollution emissions from these sources. The findings on the persistence, reversibility, and variability of air pollution can inform the development of long-term policies for improving air quality and protecting public health.

Record-high CO2 emissions from boreal fires in 2021

Extreme wildfires are becoming more common and increasingly affecting Earth's climate. Wildfires in boreal forests have attracted much less attention than those in tropical forests, although boreal forests are one of the most extensive biomes on Earth and are experiencing the fastest warming. We used a satellite-based atmospheric inversion system to monitor fire emissions in boreal forests. Wildfires are rapidly expanding into boreal forests with emerging warmer and drier fire seasons. Boreal fires, typically accounting for 10% of global fire carbon dioxide emissions, contributed 23% (0.48 billion metric tons of carbon) in 2021, by far the highest fraction since 2000. 2021 was an abnormal year because North American and Eurasian boreal forests synchronously experienced their greatest water deficit. Increasing numbers of extreme boreal fires and stronger climate-fire feedbacks challenge climate mitigation efforts.

Atmospheric phosphorus deposition amplifies carbon sinks in simulations of a tropical forest in Central Africa

Spatial redistribution of nutrients by atmospheric transport and deposition could theoretically act as a continental-scale mechanism which counteracts declines in soil fertility caused by nutrient lock-up in accumulating biomass in tropical forests in Central Africa. However, to what extent it affects carbon sinks in forests remains elusive. Here we use a terrestrial biosphere model to quantify the impact of changes in atmospheric nitrogen and phosphorus deposition on plant nutrition and biomass carbon sink at a typical lowland forest site in Central Africa. We find that the increase in nutrient deposition since the 1980s could have contributed to the carbon sink over the past four decades up to an extent which is similar to that from the combined effects of increasing atmospheric carbon dioxide and climate change. Furthermore, we find that the modelled carbon sink responds to changes in phosphorus deposition, but less so to nitrogen deposition. The pronounced response of ecosystem productivity to changes in nutrient deposition illustrates a potential mechanism that could control carbon sinks in Central Africa. Monitoring the quantity and quality of nutrient deposition is needed in this region, given the changes in nutrient deposition due to human land use.

The Effects of Trash, Residential Biofuel, and Open Biomass Burning Emissions on Local and Transported PM2.5 and Its Attributed Mortality in Africa

Long-term exposure to ambient fine particulate matter (PM2.5) is the second leading risk factor of premature death in Sub-Saharan Africa. We use GEOS-Chem to quantify the effects of (a) trash burning, (b) residential solid-fuel burning, and (c) open biomass burning (BB) (i.e., landscape fires) on ambient PM2.5 and PM2.5-attributable mortality in Africa. Using a series of sensitivity simulations, we excluded each of the three combustion sources in each of five African regions. We estimate that in 2017 emissions from these three combustion sources within Africa increased global ambient PM2.5 by 2%, leading to 203,000 (95% confidence interval: 133,000-259,000) premature mortalities yr-1 globally and 167,000 premature mortalities yr-1 in Africa. BB contributes more ambient PM2.5-related premature mortalities per year (63%) than residential solid-fuel burning (29%) and trash burning (8%). Open BB in Central Africa leads to the largest number of PM2.5-attributed mortalities inside the region, while trash burning in North Africa and residential solid-fuel burning in West Africa contribute the most regional mortalities for each source. Overall, Africa has a unique ambient air pollution profile because natural sources, such as windblown dust and BB, contribute strongly to ambient PM2.5 levels and PM2.5-related mortality. Air pollution policies may need to focus on taking preventative measures to avoid exposure to ambient PM2.5 from these less-controllable sources.

Abrupt loss and uncertain recovery from fires of Amazon forests under low climate mitigation scenarios

Tropical forests contribute a major sink for anthropogenic carbon emissions essential to slowing down the buildup of atmospheric CO2 and buffering climate change impacts. However, the response of tropical forests to more frequent weather extremes and long-recovery disturbances like fires remains uncertain. Analyses of field data and ecological theory raise concerns about the possibility of the Amazon crossing a tipping point leading to catastrophic tropical forest loss. In contrast, climate models consistently project an enhanced tropical sink. Here, we show a heterogeneous response of Amazonian carbon stocks in GFDL-ESM4.1, an Earth System Model (ESM) featuring dynamic disturbances and height-structured tree-grass competition. Enhanced productivity due to CO2 fertilization promotes increases in forest biomass that, under low emission scenarios, last until the end of the century. Under high emissions, positive trends reverse after 2060, when simulated fires prompt forest loss that results in a 40% decline in tropical forest biomass by 2100. Projected fires occur under dry conditions associated with El Niño Southern Oscillation and the Atlantic Multidecadal Oscillation, a response observed under current climate conditions, but exacerbated by an overall decline in precipitation. Following the initial disturbance, grassland dominance promotes recurrent fires and tree competitive exclusion, which prevents forest recovery. EC-Earth3-Veg, an ESM with a dynamic vegetation model of similar complexity, projected comparable wildfire forest loss under high emissions but faster postfire recovery rates. Our results reveal the importance of complex nonlinear responses to assessing climate change impacts and the urgent need to research postfire recovery and its representation in ESMs.

Madagascar's fire regimes challenge global assumptions about landscape degradation

Narratives of landscape degradation are often linked to unsustainable fire use by local communities. Madagascar is a case in point: the island is considered globally exceptional, with its remarkable endemic biodiversity viewed as threatened by unsustainable anthropogenic fire. Yet, fire regimes on Madagascar have not been empirically characterised or globally contextualised. Here, we contribute a comparative approach to determining relationships between regional fire regimes and global patterns and trends, applied to Madagascar using MODIS remote sensing data (2003-2019). Rather than a global exception, we show that Madagascar's fire regimes are similar to 88% of tropical burned area with shared climate and vegetation characteristics, and can be considered a microcosm of most tropical fire regimes. From 2003-2019, landscape-scale fire declined across tropical grassy biomes (17%-44% excluding Madagascar), and on Madagascar at a relatively fast rate (36%-46%). Thus, high tree loss anomalies on the island (1.25-4.77× the tropical average) were not explained by any general expansion of landscape-scale fire in grassy biomes. Rather, tree loss anomalies centred in forests, and could not be explained by landscape-scale fire escaping from savannas into forests. Unexpectedly, the highest tree loss anomalies on Madagascar (4.77×) occurred in environments without landscape-scale fire, where the role of small-scale fires (<21 h [0.21 km² ]) is unknown. While landscape-scale fire declined across tropical grassy biomes, trends in tropical forests reflected important differences among regions, indicating a need to better understand regional variation in the anthropogenic drivers of forest loss and fire risk. Our new understanding of Madagascar's fire regimes offers two lessons with global implications: first, landscape-scale fire is declining across tropical grassy biomes and does not explain high tree loss anomalies on Madagascar. Second, landscape-scale fire is not uniformly associated with tropical forest loss, indicating a need for socio-ecological context in framing new narratives of fire and ecosystem degradation.

Atmospheric Black Carbon Loadings and Sources over Eastern Sub-Saharan Africa Are Governed by the Regional Savanna Fires

Vast black carbon (BC) emissions from sub-Saharan Africa are perceived to warm the regional climate, impact rainfall patterns, and impair human respiratory health. However, the magnitudes of these perturbations are ill-constrained, largely due to limited ground-based observations and uncertainties in emissions from different sources. This paper reports multiyear concentrations of BC and other key PM2.5 aerosol constituents from the Rwanda Climate Observatory, serving as a regional receptor site. We find a strong seasonal cycle for all investigated chemical species, where the maxima coincide with large-scale upwind savanna fires. BC concentrations show notable interannual variability, with no clear long-term trend. The Δ14C and δ13C signatures of BC unambiguously show highly elevated biomass burning contributions, up to 93 ± 3%, with a clear and strong savanna burning imprint. We further observe a near-equal contribution from C3 and C4 plants, irrespective of air mass source region or season. In addition, the study provides improved relative emission factors of key aerosol components, organic carbon (OC), K+, and NO3-, in savanna-fires-influenced background atmosphere. Altogether, we report quantitative source constraints on Eastern Africa BC emissions, with implications for parameterization of satellite fire and bottom-up emission inventories as well as regional climate and chemical transport modeling.

Building a small fire database for Sub-Saharan Africa from Sentinel-2 high-resolution images

Coarse resolution sensors are not very sensitive at detecting small fire patches, making current estimations of global burned areas (BA) very conservative. Using medium or high-resolution sensors to generate BA products becomes then a priority, particularly in areas where fires tend to be small and frequent. Building on previous work that developed a small fire dataset (SFD) for Sub-Saharan Africa for 2016, this paper presents a new version of the dataset for 2019 using the two Sentinel-2 satellites (A and B) and VIIRS active fires. Total estimated BA was 4.8 Mkm². This value was much higher than estimations from two global, coarser-spatial resolution BA products based on MODIS data for the same area and period: 80 % greater than estimates from FireCCI51 (based on MODIS 250 m bands) and 120 % larger than MCD64A1 (based on MODIS 500 m bands). The main differences were observed in those months with higher fire occurrence (November to January for the Northern Hemisphere regions and June to September for the Southern Hemisphere ones). Accuracy assessment of the SFD product was based on a novel sampling strategy designed to obtain independent fire reference perimeters. Validation results showed remarkable high accuracy values comparing to existing global BA products. Overall omission errors (OE) were estimated as 8.5 %, commission errors (CE) as 15.0 %, with a Dice Coefficient of 87.7 %. All of these estimations implied significant improvements over the global, coarser spatial resolution BA products (OE > 50 % and CE > 20 % for the same area and period), as well as over the previous SFD product for 2016 of the same area, generated from a single Sentinel-2 satellite and MODIS active fires (OE = 26.5 % and CE = 19.3 %). Temporal accuracies greatly increased as well with the new product, with 92.5 % of fires detected within the first 10 days of occurrence.

Compositional Constraints are Vital for Atmospheric PM2.5 Source Attribution over India

India experiences some of the highest levels of ambient PM_2.5 aerosol pollution in the world. However, due to the historical dearth of in situ measurements, chemical transport models that are often used to estimate PM_2.5 exposure over the region are rarely evaluated. Here, we conduct a novel model comparison with speciated airborne measurements of fine aerosol, revealing large biases in the ammonium and nitrate simulations. To address this, we incorporate process-level changes to the model and use satellite observations from the Cross-track Infrared Sounder (CrIS) and the TROPOspheric Monitoring Instrument (TROPOMI) to constrain ammonia and nitrogen oxide emissions. The resulting simulation demonstrates significantly lower bias (NMB_Modified: 0.19; NMB_Base: 0.61) when validated against the airborne aerosol measurements, particularly for the nitrate (NMB_Modified: 0.08; NMB_Base: 1.64) and ammonium simulation (NMB_Modified: 0.49; NMB_Base: 0.90). We use this validated simulation to estimate a population-weighted annual PM_2.5 exposure of 61.4 μg m^-3, with the RCO (residential, commercial, and other) and energy sectors contributing 21% and 19%, respectively, resulting in an estimated 961,000 annual PM_2.5-attributable deaths. Regional exposure and sectoral source contributions differ meaningfully in the improved simulation (compared to the baseline simulation). Our work highlights the critical role of speciated observational constraints in developing accurate model-based PM_2.5 aerosol source attribution for health assessments and air quality management in India.

Wild, tamed, and domesticated: Three fire macroregimes for global pyrogeography in the Anthropocene

Climate and natural vegetation dynamics are key drivers of global vegetation fire, but anthropogenic burning now prevails over vast areas of the planet. Fire regime classification and mapping may contribute towards improved understanding of relationships between those fire drivers. We used 15 years of daily active fire data from the MODIS fire product (MCD14ML, collection 6) to create global maps of six fire descriptors (incidence, size inequality, season length, interannual variability, intensity, and fire season modality). Using multiple correspondence analysis (MCA) and hierarchical agglomerative clustering, we identified three fire macroregimes: Wild, Tamed, and Domesticated, each of which splitting into prototypical and transitional regimes. Interpretation of the six fire regimes in terms of their main drivers relied on the global maps of anthromes and Köppen climate types. The analysis yielded a two-dimensional space where the principal dimension of variability is primarily defined by interannual variability in fire activity and fire season length, and the secondary axis is based mainly on fire incidence. The Wild fire macroregime occurs mostly in cold wildlands, where burning is sporadic and fire seasons are short. Tamed fires predominate in seasonally dry tropical rangelands and croplands with high fire incidence. Domesticated fires are characteristic of humid, warm temperate and tropical croplands and villages with low fire incidence. The Tamed and Domesticated fire macroregimes, representing managed burning, account for 86% of all active fires in our dataset and for 70% of the global burnable area. Fourteen percent of active fires were found in the cold wildlands, and in the rangelands and forests of steppe and desert climates of the Wild macroregime. These results highlight the extent of human control over global pyrogeography in the Anthropocene.

Process-oriented analysis of dominant sources of uncertainty in the land carbon sink

The observed global net land carbon sink is captured by current land models. All models agree that atmospheric CO₂ and nitrogen deposition driven gains in carbon stocks are partially offset by climate and land-use and land-cover change (LULCC) losses. However, there is a lack of consensus in the partitioning of the sink between vegetation and soil, where models do not even agree on the direction of change in carbon stocks over the past 60 years. This uncertainty is driven by plant productivity, allocation, and turnover response to atmospheric CO₂ (and to a smaller extent to LULCC), and the response of soil to LULCC (and to a lesser extent climate). Overall, differences in turnover explain ~70% of model spread in both vegetation and soil carbon changes. Further analysis of internal plant and soil (individual pools) cycling is needed to reduce uncertainty in the controlling processes behind the global land carbon sink.

The global net land sink is relatively well constrained. However, the responsible drivers and above/below-ground partitioning are highly uncertain. Model issues regarding turnover of individual plant and soil components are responsible.

Development of a Novel Burned-Area Subpixel Mapping (BASM) Workflow for Fire Scar Detection at Subpixel Level

The accurate detection of burned forest area is essential for post-fire management and assessment, and for quantifying carbon budgets. Therefore, it is imperative to map burned areas accurately. Currently, there are few burned-area products around the world. Researchers have mapped burned areas directly at the pixel level that is usually a mixture of burned area and other land cover types. In order to improve the burned area mapping at subpixel level, we proposed a Burned Area Subpixel Mapping (BASM) workflow to map burned areas at the subpixel level. We then applied the workflow to Sentinel 2 data sets to obtain burned area mapping at subpixel level. In this study, the information of true fire scar was provided by the Department of Emergency Management of Hunan Province, China. To validate the accuracy of the BASM workflow for detecting burned areas at the subpixel level, we applied the workflow to the Sentinel 2 image data and then compared the detected burned area at subpixel level with in situ measurements at fifteen fire-scar reference sites located in Hunan Province, China. Results show the proposed method generated successfully burned area at the subpixel level. The methods, especially the BASM-Feature Extraction Rule Based (BASM-FERB) method, could minimize misclassification and effects due to noise more effectively compared with the BASM-Random Forest (BASM-RF), BASM-Backpropagation Neural Net (BASM-BPNN), BASM-Support Vector Machine (BASM-SVM), and BASM-notra methods. We conducted a comparison study among BASM-FERB, BASM-RF, BASM-BPNN, BASM-SVM, and BASM-notra using five accuracy evaluation indices, i.e., overall accuracy (OA), user’s accuracy (UA), producer’s accuracy (PA), intersection over union (IoU), and Kappa coefficient (Kappa). The detection accuracy of burned area at the subpixel level by BASM-FERB’s OA, UA, IoU, and Kappa is 98.11%, 81.72%, 74.32%, and 83.98%, respectively, better than BASM-RF’s, BASM-BPNN’s, BASM-SVM’s, and BASM-notra’s, even though BASM-RF’s and BASM-notra’s average PA is higher than BASM-FERB’s, with 89.97%, 91.36%, and 89.52%, respectively. We conclude that the newly proposed BASM workflow can map burned areas at the subpixel level, providing greater accuracy in regards to the burned area for post-forest fire management and assessment.

Reimagine fire science for the anthropocene

Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the "firehose" of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

Fire use practices, knowledge and perceptions in a West African savanna parkland

Understanding people’s practices, knowledge and perceptions of the use of fire and fire regimes can inform fire management plans that could contribute to savanna conservation and sustainable management. We investigated the frequency of fire use, control and perceptions of fire regimes for selected livelihood and socio-cultural activities in six districts in the Guinea savanna of Ghana. The six districts were selected to have a good representation of fire prone areas in the region based on fire frequency data obtained from the CSIR Meraka Institute, South Africa. A multiple regression analysis showed that people’s use of fire for the selected socio-cultural activities from district, occupation, gender, age and ethnic group significantly predicted fire use for the activities R² = 0.043, F (_5,498) = 5.43, p < 0.000. Age and occupation added significantly to the use of fire. The study revealed that the majority of respondents (83%) across the study districts used fire once a year for at least one of the following activities: land preparation, weed/pest control, burning postharvest stubble, bush clearing around homesteads, firebreaks, charcoal burning and hunting. The study also showed a higher frequency of fire use for land preparation for cropping than for the other activities. Less than a fifth of the respondents (17%) indicated that they do not use fire for any of the selected activities. The majority of respondents (65%) mentioned that they controlled their use of fire to prevent destruction to property or injuring humans. The study revealed a higher frequency of fire use in the dry season for land preparation for cropping. However, respondents rated season of burning as the most important attribute, with little attention to the other attributes of a fire regime, contrary to what is theoretically recognized. Understanding traditional fire use practices in terms of how to regulate the mix of frequency, intensity/severity, season, size and type of fire for these and other socio-cultural purposes could help to mitigate and/or manage bushfires in West African savannas and enhance savanna conservation and management. Hence, the need to better understand people’s knowledge and perceptions of fire regimes in fire assisted socio-cultural practices in West Africa.

Reduced global fire activity due to human demography slows global warming by enhanced land carbon uptake

SignificanceFire is an increasing climate-driven threat to humans. While human demography can strongly modulate fire ignition rates or fire suppression, changes in CO₂ released by fires feed back to climate. We show that human demography could reduce future fire activity, which would in turn attenuate global warming via an enhanced land carbon sink. This mitigation is strongest in a low-CO₂-emission world, corresponding to ∼5 to 10 y of global CO₂ emissions at today's levels by 2100. We highlight the strong role of human demography in global fire reduction and the potential for climate change mitigation by enhanced land carbon sequestration. We also note possible trade-offs, including loss of biodiversity in fire-dependent ecosystems and increases in severe fire events.

Lightning patterns in the Pantanal: Untangling natural and anthropogenic-induced wildfires

The identification of fire causes and characteristics is of fundamental importance to better understand fire regimes and drivers. Particularly for Brazil, there is a gap in the quantification of lightning-caused fires. Accordingly, this work is a novel probabilistic assessment of the spatial-temporal patterns of lightning-ignited wildfires in the Pantanal wetland. Here, remote sensing information such as VIIRS active fires, MODIS burned area (BA) and STARNET lightning observations from 2012 to 2017, were combined to estimate the location, number of scars and amount of BA associated with atmospheric discharges on a seasonal basis. The highest lightning activity occurs during summer (December-February), and the lowest during winter (June-August). Conversely, the highest fire activity occurred during spring (September-November) and the lowest during autumn (March-May). Our analysis revealed low evidence of an association between fires and lightning, suggesting that human-related activities are the main source of ignitions. Weak evidence of natural-caused fire occurrence is conveyed by the low spatial-temporal match of lightning and fire throughout the studied period. Natural-caused fires accounted for only 5% of the annual total scars and 83.8% of the BA was human-caused. Most of the fires with extension larger than 1000 ha were not related to lighting. Lightning-fires seem an important element of the summer fire regime given that around half of the total BA during this season may be originated by lightning. By contrast, in the rest of the year the lightning-fires represent a minor percentage of the fire activity in the region. The density of lightning-ignited fires varies considerably, being higher in the north part of the Pantanal. This work provides a basis for a better understanding of lightning-related fire outbreaks in tropical ecosystems, particularly wetlands, which is fundamental to improve region-based strategies for land management actions, ecological studies and modeling climatic and anthropogenic drivers of wildfires.

Characterizing Global Fire Regimes from Satellite-Derived Products

We identified four global fire regimes based on a k-means algorithm using five variables covering the spatial, temporal and magnitude dimensions of fires, derived from 19-year long satellite burned area and active fire products. Additionally, we assessed the relation of fire regimes to forest fuels distribution. The most extensive fire regime (35% of cells having fire activity) was characterized by a long fire season, medium size fire events, small burned area, high intensity and medium variability. The next most extensive fire regime (25.6%) presented a long fire season, large fire events and the highest mean burned area, yet it showed the lowest intensity and the least variability. The third group (22.07%) presented a short fire season, the lowest burned area, with medium-low intensity, the smallest fire patches and large variability. The fourth group (17.3%) showed the largest burned area with large fire patches of moderate intensity and low variability. Fire regimes and fuel types showed a statistically significant relation (CC = 0.58 and CC’ = 0.67, p < 0.001), with most fuel types sustaining all fire regimes, although a clear prevalence was observed in some fuel types. Further efforts should be directed towards the standardization of the variables in order to facilitate comparison, analysis and monitoring of fire regimes and evaluate whether fire regimes are effectively changing and the possible drivers.

Anthropogenic and Lightning Fire Incidence and Burned Area in Europe

Fires can have an anthropogenic or natural origin. The most frequent natural fire cause is lightning. Since anthropogenic and lightning fires have different climatic and socio-economic drivers, it is important to distinguish between these different fire causes. We developed random forest models that predict the fraction of anthropogenic and lightning fire incidences, and their burned area, at the level of the Nomenclature des Unités Territoriales Statistiques level 3 (NUTS3) for Europe. The models were calibrated using the centered log-ratio of fire incidence and burned area reference data from the European Forest Fire Information System. After a correlation analysis, the population density, fractional human land impact, elevation and burned area coefficient of variation—a measure of interannual variability in burned area—were selected as predictor variables in the models. After parameter tuning and running the models with several train-validate compositions, we found that the vast majority of fires and burned area in Europe has an anthropogenic cause, while lightning plays a significant role in the remote northern regions of Scandinavia. Combining our results with burned area data from the Moderate Resolution Imaging Spectroradiometer, we estimated that 96.5 ± 0.9% of the burned area in Europe has an anthropogenic cause. Our spatially explicit fire cause attribution model demonstrates the spatial variability between anthropogenic and lightning fires and their burned area over Europe and could be used to improve predictive fire models by accounting for fire cause.

How Much of a Pixel Needs to Burn to Be Detected by Satellites? A Spectral Modeling Experiment Based on Ecosystem Data from Yellowstone National Park, USA

We present a simple modeling technique based on linear spectral mixture analysis to assess satellite detectability of sub-pixel burned area. Pixel observations are modeled using a linear combination of pure land covers, called endmembers. We executed an experiment using spectral data from Yellowstone National Park, USA. Using endmember samples from spectral libraries, pixel samples were assessed on burn detectability using the widely used differenced Normalized Burn Ratio (dNBR). While individual samples yielded differing results for Landsat 8, Sentinel-2, and the Moderate Resolution Imaging Spectroradiometer (MODIS), the average park-wide detectability of burned area was consistent across satellites. For the commonly used dNBR threshold of 0.15, the results indicated that detectability is reached when around a quarter of a pixel’s area is burned. However, a significant percentage of the modeled burned pixels remained undetectable, especially those with low pre-fire vegetation cover. This has consequences for burned area estimates, as smaller fires in sparsely vegetated terrain may remain undetected in moderate resolution burned area products.

Machine learning-based observation-constrained projections reveal elevated global socioeconomic risks from wildfire

Reliable projections of wildfire and associated socioeconomic risks are crucial for the development of efficient and effective adaptation and mitigation strategies. The lack of or limited observational constraints for modeling outputs impairs the credibility of wildfire projections. Here, we present a machine learning framework to constrain the future fire carbon emissions simulated by 13 Earth system models from the Coupled Model Intercomparison Project phase 6 (CMIP6), using historical, observed joint states of fire-relevant variables. During the twenty-first century, the observation-constrained ensemble indicates a weaker increase in global fire carbon emissions but higher increase in global wildfire exposure in population, gross domestic production, and agricultural area, compared with the default ensemble. Such elevated socioeconomic risks are primarily caused by the compound regional enhancement of future wildfire activity and socioeconomic development in the western and central African countries, necessitating an emergent strategic preparedness to wildfires in these countries.

A new study develops a machine learning framework to observationally constrain CMIP6-simulated fire carbon emissions, finding a weaker increase in 21st-century global fires but higher increase in their socioeconomic risks than previously thought.

New land-use-change emissions indicate a declining CO2 airborne fraction

About half of the anthropogenic CO2 emissions remain in the atmosphere and half are taken up by the land and ocean1. If the carbon uptake by land and ocean sinks becomes less efficient, for example, owing to warming oceans2 or thawing permafrost3, a larger fraction of anthropogenic emissions will remain in the atmosphere, accelerating climate change. Changes in the efficiency of the carbon sinks can be estimated indirectly by analysing trends in the airborne fraction, that is, the ratio between the atmospheric growth rate and anthropogenic emissions of CO2 (refs. 4-10). However, current studies yield conflicting results about trends in the airborne fraction, with emissions related to land use and land cover change (LULCC) contributing the largest source of uncertainty7,11,12. Here we construct a LULCC emissions dataset using visibility data in key deforestation zones. These visibility observations are a proxy for fire emissions13,14, which are - in turn - related to LULCC15,16. Although indirect, this provides a long-term consistent dataset of LULCC emissions, showing that tropical deforestation emissions increased substantially (0.16 Pg C decade-1) since the start of CO2 concentration measurements in 1958. So far, these emissions were thought to be relatively stable, leading to an increasing airborne fraction4,5. Our results, however, indicate that the CO2 airborne fraction has decreased by 0.014 ± 0.010 decade-1 since 1959. This suggests that the combined land-ocean sink has been able to grow at least as fast as anthropogenic emissions.

Comparing the Ability of Burned Area Products to Detect Crop Residue Burning in China

Burning crop residues is a common way to remove them during the final stages of crop ripening in China. To conduct research effectively, it is critical to reliably and quantitatively estimate the extent and location of a burned area. Here, we investigated three publicly available burned area products—MCD64A1, FireCCI 5.1, and the Copernicus Burnt Area—and evaluated their relative performance at estimating total burned areas for cropland regions in China between 2015 and 2019. We compared these burned area products at a fine spatial and temporal scale using a grid system comprised of three-dimensional cells spanning both space and time. In general, the Copernicus Burnt Area product detected the largest annual average burned area (37,095.1 km2), followed by MCD64A1 (21,631.4 km2) and FireCCI 5.1 (12,547.99 km2). The Copernicus Burnt Area product showed a consistent pattern of monthly burned areas during the study period, whereas MCD64A1 and FireCCI 5.1 showed frequent changes in monthly burned area peaks. The greatest spatial differences between all three products occurred in Northeast and North China, where cultivated land is concentrated. The burned area detected by Copernicus in Xinjiang Province was larger than that detected by the other two products. In conclusion, we found that all three products underestimated the amount of crop residues present in a burned area. This limits the ability of end users to understand fire-related impacts and burned area characteristics, and hinders them in making an informed choice of which product is most appropriate for their application.

Potentially Toxic Substances and Associated Risks in Soils Affected by Wildfires: A Review

The presence of toxic substances is one of the major causes of degradation of soil quality. Wildfires, besides affecting various chemical, physical, and biological soil properties, produce a mixture of potentially toxic substances which can reach the soil and water bodies and cause harm to these media. This review intends to summarise the current knowledge on the generation by wildfires of potentially toxic substances, their effects on soil organisms, and other associated risks, addressing the effects of fire on metal mobilisation, the pyrolytic production of potentially toxic compounds, and the detoxifying effect of charcoal. Numerous studies ascertained inhibitory effects of ash on seed germination and seedling growth as well as its toxicity to soil and aquatic organisms. Abundant publications addressed the mobilisation of heavy metals and trace elements by fire, including analyses of total concentrations, speciation, availability, and risk of exportation to water bodies. Many publications studied the presence of polycyclic aromatic hydrocarbons (PAH) and other organic pollutants in soils after fire, their composition, decline over time, the risk of contamination of surface and ground waters, and their toxicity to plants, soil, and water organisms. Finally, the review addresses the possible detoxifying role of charcoal in soils affected by fire.

Effects of Wood Smoke Constituents on Mucin Gene Expression in Mice and Human Airway Epithelial Cells and on Nasal Epithelia of Subjects with a Susceptibility Gene Variant in Tp53

BACKGROUND

Exposure to wood smoke (WS) increases the risk for chronic bronchitis more than exposure to cigarette smoke (CS), but the underlying mechanisms are unclear.

OBJECTIVE

The effect of WS and CS on mucous cell hyperplasia in mice and in human primary airway epithelial cells (AECs) was compared with replicate the findings in human cohorts. Responsible WS constituents were identified to better delineate the pathway involved, and the role of a tumor protein p53 (Tp53) gene polymorphism was investigated.

METHODS

Mice and primary human AECs were exposed to WS or CS and the signaling receptor and pathway were identified using short hairpin structures, small molecule inhibitors, and Western analyses. Mass spectrometric analysis was used to identify active WS constituents. The role of a gene variant in Tp53 that modifies proline to arginine was examined using nasal brushings from study participants in the Lovelace Smokers Cohort, primary human AECs, and mice with a modified Tp53 gene.

RESULTS

WS at 25-fold lower concentration than CS increased mucin expression more efficiently in mice and in human AECs in a p53 pathway-dependent manner. Study participants who were homozygous for p53 arginine compared with the proline variant showed higher mucin 5AC (MUC5AC) mRNA levels in nasal brushings if they reported WS exposure. The WS constituent, oxalate, increased MUC5AC levels similar to the whole WS extract, especially in primary human AECs homozygous for p53 arginine, and in mice with a modified Tp53 gene. Further, the anion exchange protein, SLC26A9, when reduced, enhanced WS- and oxalate-induced mucin expression.

DISCUSSION

The potency of WS compared with CS in inducing mucin expression may explain the increased risk for chronic bronchitis in participants exposed to WS. Identification of the responsible compounds could help estimate the risk of pollutants in causing chronic bronchitis in susceptible individuals and provide strategies to improve management of lung diseases. https://doi.org/10.1289/EHP9446.

Large contribution of biomass burning emissions to ozone throughout the global remote troposphere

Ozone is the third most important anthropogenic greenhouse gas after carbon dioxide and methane but has a larger uncertainty in its radiative forcing, in part because of uncertainty in the source characteristics of ozone precursors, nitrogen oxides, and volatile organic carbon that directly affect ozone formation chemistry. Tropospheric ozone also negatively affects human and ecosystem health. Biomass burning (BB) and urban emissions are significant but uncertain sources of ozone precursors. Here, we report global-scale, in situ airborne measurements of ozone and precursor source tracers from the NASA Atmospheric Tomography mission. Measurements from the remote troposphere showed that tropospheric ozone is regularly enhanced above background in polluted air masses in all regions of the globe. Ozone enhancements in air with high BB and urban emission tracers (2.1 to 23.8 ppbv [parts per billion by volume]) were generally similar to those in BB-influenced air (2.2 to 21.0 ppbv) but larger than those in urban-influenced air (-7.7 to 6.9 ppbv). Ozone attributed to BB was 2 to 10 times higher than that from urban sources in the Southern Hemisphere and the tropical Atlantic and roughly equal to that from urban sources in the Northern Hemisphere and the tropical Pacific. Three independent global chemical transport models systematically underpredict the observed influence of BB on tropospheric ozone. Potential reasons include uncertainties in modeled BB injection heights and emission inventories, export efficiency of BB emissions to the free troposphere, and chemical mechanisms of ozone production in smoke. Accurately accounting for intermittent but large and widespread BB emissions is required to understand the global tropospheric ozone burden.

Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images

Sentinel-2 satellite images allow high separability for mapping burned and unburned areas. This problem has been extensively addressed using machine-learning algorithms. However, these need a suitable dataset and entail considerable training time. Recently, extreme learning machines (ELM) have presented high precision in classification and regression problems but with low computational cost. This paper proposes evaluating ELM to map burned areas and compare them with other machine-learning algorithms broadly used. Several indices, metrics and training times were used to assess the performance of the algorithms. Considering the average of datasets, the best performance was obtained by random forest (DICE = 0.93; omission and commission = 0.08) and ELM (DICE = 0.90; omission and commission = 0.07). The training time for the best model was from ELM (1.45 s) and logistic regression (1.85 s). According to results, ELM was the best burned-area classification algorithm, considering precision and training time, evidencing great potential to map burned areas at global scales with medium-high spatial resolution images. This information is essential to fire-risk systems and burned-area records used to design prevention and fire-combat strategies, and it provides valuable knowledge on the effect of fires on the landscape and atmosphere.

Long-term (64 years) annual burning lessened soil organic carbon and nitrogen content in a humid subtropical grassland

Burning has commonly been used to increase forage production and nutrients cycling in grasslands. However, its long-term effects on soil organic carbon (SOC) and nitrogen (N) pools within the aggregates and the relation between aggregates-associated SOC and soil CO₂ emissions need further appraisal. This study evaluated the effects of 64 years of annual burning on SOC and N dynamics compared to annual mowing and undisturbed treatments in a grassland experiment established in 1950. Soils were sampled from four depths representing the upper 30 cm layer and fractionated into macroaggregates, microaggregates and silt + clay fractions. The macroaggregates were further fractionated into three occluded fractions. The SOC in the bulk soil and aggregates were correlated to soil CO₂ effluxes measured under field conditions. Compared to the undisturbed treatment, annual burning decreased aggregates stability, SOC and N in the upper 30 cm layer by 8%, 5% and 12%, respectively. Grassland mowing induced greater aggregates stability than burning only in the upper 5 cm. Burning also decreased SOC in the large macroaggregates (e.g., 0-5 cm) compared to mowing and the undisturbed grasslands but proportionally increased the microaggregates and their associated SOC. Soil N associated with aggregates decreased largely following grassland burning, for example, by 8.8-fold in the microaggregates within the large macroaggregates at 20-30 cm compared to the undisturbed grassland. Burning also increased soil CO₂ emissions by 33 and 16% compared to undisturbed and mowing, respectively. The combustion of fresh C and soil organic matter by fire is likely responsible for the low soil aggregation, high SOC and N losses under burned grassland. These results suggested a direct link between grass burning and SOC losses, a key component for escalating climate change severity. Therefore, less frequent burning or a rotation of burning and mowing should be investigated for sustainable grasslands management.

A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine

A preliminary version of a global automatic burned-area (BA) algorithm at medium spatial resolution was developed in Google Earth Engine (GEE), based on Landsat or Sentinel-2 reflectance images. The algorithm involves two main steps: initial burned candidates are identified by analyzing spectral changes around MODIS hotspots, and those candidates are then used to estimate the burn probability for each scene. The burning dates are identified by analyzing the temporal evolution of burn probabilities. The algorithm was processed, and its quality assessed globally using reference data from 2019 derived from Sentinel-2 data at 10 m, which involved 369 pairs of consecutive images in total located in 50 20 × 20 km2 areas selected by stratified random sampling. Commissions were around 10% with both satellites, although omissions ranged between 27 (Sentinel-2) and 35% (Landsat), depending on the selected resolution and dataset, with highest omissions being in croplands and forests; for their part, BA from Sentinel-2 data at 20 m were the most accurate and fastest to process. In addition, three 5 × 5 degree regions were randomly selected from the biomes where most fires occur, and BA were detected from Sentinel-2 images at 20 m. Comparison with global products at coarse resolution FireCCI51 and MCD64A1 would seem to show to a reliable extent that the algorithm is procuring spatially and temporally coherent results, improving detection of smaller fires as a consequence of higher-spatial-resolution data. The proposed automatic algorithm has shown the potential to map BA globally using medium-spatial-resolution data (Sentinel-2 and Landsat) from 2000 onwards, when MODIS satellites were launched.

Implementation of the Burned Area Component of the Copernicus Climate Change Service: From MODIS to OLCI Data

This article presents the burned area (BA) product of the Copernicus Climate Change Service (C3S) of the European Commission. This product, named C3SBA10, is based on the adaptation to Sentinel-3 OLCI images of a BA algorithm developed within the Fire Climate Change Initiative (FireCCI) project, which used MODIS data. We first reviewed the adaptation process and then analysed the results of both products for common years (2017–2019). Comparisons were performed using four different grid sizes (0.05°, 0.10°, 0.25°, and 0.50°). Annual correlations between the two products ranged from 0.94 to 0.99. Global BA estimates were found to be more similar when the two Sentinel-3 satellites were active (2019), as the temporal resolution was closer to that of the MODIS sensor. Global validation was performed using reference data derived from Landsat-8 images, following a stratified random sampling design. The C3SBA10 showed commission errors between 16 and 21% and omission errors from 48 to 50%, similar to those found in the FireCCI product. The temporal reporting accuracy was also validated using 19 million active fires. In total, 87% of the detections were made within 10 days after the fire by both products. The high consistency between both products ensures global BA data provision from 2001 to the present. The datasets are freely available through the Copernicus Climate Data Store (CDS) repository.

Climate models generally underrepresent the warming by Central Africa biomass-burning aerosols over the Southeast Atlantic

The radiative budget, cloud properties, and precipitation over tropical Africa are influenced by solar absorption by biomass-burning aerosols (BBA) from Central Africa. Recent field campaigns, reinforced by new remote-sensing and aerosol climatology datasets, have highlighted the absorbing nature of the elevated BBA layers over the South-East Atlantic (SEA), indicating that the absorption could be stronger than previously thought. We show that most of the latest generation of general circulation models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project 6 (CMIP6) underestimates the absorption of BBA over the SEA. This underlines why many (~75%) CMIP6 models do not fully capture the intense positive (warming) direct radiative forcing at the top of the atmosphere observed over this region. In addition, underestimating the magnitude of the BBA-induced solar heating could lead to misrepresentations of the low-level cloud responses and fast precipitation feedbacks that are induced by BBA in tropical regions.

Multi-Sensor, Active Fire-Supervised, One-Class Burned Area Mapping in the Brazilian Savanna

Increasing efforts are being devoted to understanding fire patterns and changes highlighting the need for a consistent database about the location and extension of burned areas (BA). Satellite-derived BA mapping accuracy in the Brazilian savannas is limited by the underestimation of burn scars from small, fragmented fires and high cloudiness. Moreover, systematic mapping of BA is challenged by the need for human intervention in training sample acquisition, which precludes the development of automatic-generated products over large areas and long periods. Here, we developed a multi-sensor, active fire-supervised, one-class BA mapping algorithm to address several of these limitations. Our main objective is to generate a long-term, detailed BA atlas suitable to improve fire regime characterization and validation of coarse resolution products. We use composite images derived from the Landsat satellite to generate end-of-season maps of fire-affected areas for the entire Cerrado. Validation exercises and intercomparison with BA maps from a semi-automatic algorithm and visual photo interpretation were conducted for the year 2015. Our results improve the BA mapping by reducing omission errors, especially where there is high cloud frequency, few active fires are detected, and burned areas are small and fragmented. Finally, our approach represents at least a 45% increase in BA mapped in the Cerrado, in comparison to the annual extent detected by the current coarse global product from MODIS satellite (MCD64), and thus, it is capable of supporting improved regional emissions estimates.

Proximate Causes of Forest Degradation in the Democratic Republic of the Congo Vary in Space and Time

Forest degradation, generally defined as a reduction in the delivery of forest ecosystem services, can have long-term impacts on biodiversity, climate, and local livelihoods. The quantification of forest degradation, its dynamics and proximate causes can help prompt early action to mitigate carbon emissions and inform relevant land use policies. The Democratic Republic of the Congo is largely forested with a relatively low deforestation rate, but anthropogenic degradation has been increasing in recent years. We assess the impact of eight independent variables related to land cover, land use, infrastructure, armed conflicts, and accessibility on forest degradation, measured by the Forest Condition (FC) index, a measure of forest degradation based on biomass history and fragmentation that ranges from 0 (completely deforested) to 100 (intact). We employ spatial panel models with fixed effects using regular 25 × 25 km units over five 3-year intervals from 2002 to 2016. The regression results suggest that the presence of swamp ecosystems, low access (defined by high travel time), and forest concessions are associated with lower forest degradation, while built up area, fire frequency, armed conflicts result in greater forest degradation. The impact of neighboring units on FC shows that all variables within the 50 km spatial neighborhood have a greater effect on FC than the on-site spatial determinants, indicating the greater influence of drivers beyond the 25 km2 unit. In the case of protected areas, we unexpectedly find that protection in neighboring locations leads to higher forest degradation, suggesting a potential leakage effect, while protected areas in the local vicinity have a positive influence on FC. The Mann-Kendall trend statistic of occurrences of fires and conflicts over the time period and until 2020 show that significant increases in conflicts and fires are spatially divergent. Overall, our results highlight how assessing the proximate causes of forest degradation with spatiotemporal analysis can support targeted interventions and policies to reduce forest degradation but spillover effects of proximal drivers in neighboring areas need to be considered.

The role of fire in global forest loss dynamics

Fires, among other forms of natural and anthropogenic disturbance, play a central role in regulating the location, composition and biomass of forests. Understanding the role of fire in global forest loss is crucial in constraining land-use change emissions and the global carbon cycle. We analysed the relationship between forest loss and fire at 500 m resolution based on satellite-derived data for the 2003-2018 period. Satellite fire data included burned area and active fire detections, to best account for large and small fires, respectively. We found that, on average, 38 ± 9% (± range) of global forest loss was associated with fire, and this fraction remained relatively stable throughout the study period. However, the fraction of fire-related forest loss varied substantially on a regional basis, and showed statistically significant trends in key tropical forest areas. Decreases in the fraction of fire-related forest loss were found where deforestation peaked early in our study period, including the Amazon and Indonesia while increases were found for tropical forests in Africa. The inclusion of active fire detections accounted for 41%, on average, of the total fire-related forest loss, with larger contributions in small clearings in interior tropical forests and human-dominated landscapes. Comparison to higher-resolution fire data with resolutions of 375 and 20 m indicated that commission errors due to coarse resolution fire data largely balanced out omission errors due to missed small fire detections for regional to continental-scale estimates of fire-related forest loss. Besides an improved understanding of forest dynamics, these findings may help to refine and separate fire-related and non-fire-related land-use change emissions in forested ecosystems.