Forest Fire Emissions Research Articles

The Global Forest Fire Emissions Prediction System version 1.0

Abstract. The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in near-real time for global air quality forecasting. The model uses a bottom-up approach, based on remotely sensed hotspot locations, and global databases linking burned area per hotspot to ecosystem-type classification at a 1 km resolution. Unlike other global fire emissions models, GFFEPS provides dynamic estimates of fuel consumption, fire behaviour and fire growth based on the Canadian Forest Fire Danger Rating System, plant phenology as calculated from daily global weather and burned-area estimates using near-real-time Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-detected hotspots and historical burned-area statistics. Combining forecasts of daily fire weather and hourly meteorological conditions with a global land classification, GFFEPS produces fuel consumption and emission predictions in 3 h time steps (in contrast to non-dynamic models that use fixed consumption rates and require a collection of burned area to make post-burn estimates of emissions). GFFEPS has been designed for use in operational forecasting applications as well as historical simulations for which data are available. A study was conducted showing GFFEPS predictions through a 6-year period (2015–2020). Regional annual total smoke emissions, burned area and total fuel consumption per unit area as predicted by GFFEPS were generated to assess model performance over multiple years and regions. The model's fuel consumption per unit area results clearly distinguished regions dominated by grassland (Africa) from those dominated by forests (boreal regions) and showed high variability in regions affected by El Niño and deforestation. GFFEPS carbon emissions and burned area were then compared to other global wildfire emissions models, including the Global Fire Assimilation System (GFAS), the Global Fire Emissions Database (GFED4.1s) and the Fire INventory from NCAR (FINN 1.5 and 2.5). GFFEPS estimated values lower than GFAS and GFED (80 % and 74 %) and had values similar to FINN 1.5 (97 %). This was largely due to the impact of fuel moisture on consumption rates as captured by the dynamic weather modelling. Model evaluation efforts to date are described – an ongoing effort is underway to further validate the model, with further developments and improvements expected in the future.

Global rise in forest fire emissions linked to climate change in the extratropics.

Climate change increases fire-favorable weather in forests, but fire trends are also affected by multiple other controlling factors that are difficult to untangle. We use machine learning to systematically group forest ecoregions into 12 global forest pyromes, with each showing distinct sensitivities to climatic, human, and vegetation controls. This delineation revealed that rapidly increasing forest fire emissions in extratropical pyromes, linked to climate change, offset declining emissions in tropical pyromes during 2001 to 2023. Annual emissions tripled in one extratropical pyrome due to increases in fire-favorable weather, compounded by increased forest cover and productivity. This contributed to a 60% increase in forest fire carbon emissions from forest ecoregions globally. Our results highlight the increasing vulnerability of forests and their carbon stocks to fire disturbance under climate change.

Spatiotemporal variation of polycyclic aromatic hydrocarbons in Tibetan lake sediment cores reveals the influence of forest fires

Despite declining anthropogenic emissions of polycyclic aromatic hydrocarbons (PAHs) due to global control strategies, forest fire emissions have been increasing, significantly affecting PAH dynamics in global sinks. This study investigated the spatiotemporal variations of sedimentary PAHs in three Tibetan lakes—Yiong Tso, Yamdrok Yumtso, and Urru Tso—to determine the influence of forest fires on PAH levels and historical trends. Yiong Tso Lake, located in a fire-affected watershed, exhibited the highest PAH concentrations (average of 43.4 ± 25.7 ng/g) with significant fluctuations since the 1920s, peaking in the 1960s (46.3 ng/g) and 1980s (91.3 ng/g), corresponding to periods of intense forest fires. This pattern aligned with source contribution estimates using the modified Cohen's d (mcd), indicating the dominance of forest fires as a PAH source until the 1990s. PAH concentrations decreased with increasing distance from the southeastern Tibetan Forest, as observed in Yamdrok Yumtso (average of 36.1 ± 19.9 ng/g) and Urru Tso (average of 16.4 ± 6.9 ng/g). Temporal variations in PAH concentrations and mcd values from these lakes also reflected a response to forest fires during the 1960s, suggesting a widespread influence of forest-fire-derived PAHs across the plateau. The impact of forest fires on sedimentary PAHs was expected to persist for decades, with an estimated half-life of approximately 11–12 years. These findings highlight significant emissions of PAHs from forest fires in the Tibetan Plateau, potentially transforming regional PAH dynamics and influencing global cycling.

Strategic approach to accelerating regional bioenergy development: Bioelectricity for emission reduction and sustainability

The western Himalayas hold significant potential for generating bioelectricity from dry pine needle biomass. This approach provides multiple benefits, including forest fire prevention, electricity generation, and emission reduction. However, despite these advantages, the growth of this sector has fallen short of expectations. The projected electricity generation potential from pine needle biomass in this region could lead to a significant annual emission reduction of 1.7 MtCO2e through forest fire prevention and an additional 0.49 MtCO2e by displacing carbon-intensive grid electricity. Regional bioenergy development plays a crucial role in the global energy transition and aligns with SDG 13, "Climate Action," and SDG 7, "Affordable and Clean Energy." By focusing on bioenergy as a renewable and accessible energy source, local and regional communities can contribute to climate action while simultaneously ensuring affordable and clean energy for their communities. Therefore, this paper employs a hybrid SWOT-AHP analysis as a strategic planning tool to achieve emission reduction targets by stimulating regional bioenergy growth. Twenty-nine SWOT factors were identified under four variables, and then the AHP technique was employed to assign priority weights to both SWOT variables and factors. Combining SWOT with AHP analysis provides quantitatively determined priorities for the factors, enabling their objective comparison. Eventually, this study offers a comprehensive perspective that leads to policy recommendations and serves as a valuable resource for relevant stakeholders, policymakers, and researchers seeking to achieve emission reduction goals through bioelectricity generation.

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

BackgroundForests 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.ResultsThe 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.ConclusionsThe 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.

Emissions of Benzо(a)pyrene and Carbon Monoxide from Forest Fires as a Source of Atmospheric Pollution in Cities of the Irkutsk Region

An assessment is made of the release of benz(a)pyrene (B(a)P) and accompanying toxic products of incomplete combustion – carbon monoxide and soot – from forest fires as a source of atmospheric air pollution in cities in the Irkutsk region. Mass gross emissions, t/year, averaged for 2014–2020. of the studied substances in all 33 districts of the Irkutsk region were calculated. The dependences of gross emissions B(a)P are establishedDepends on the indicators of forest fires: the amount, area, mass of burnt plant materials, gross emissions of carbon monoxide and soot. The conclusion is made about the pollution of the atmospheric air of cities with carcinogenic B(a)P and toxic carbon monoxide from forest fire emissions.

Distribution of ozone, carbon monoxide and oxides of nitrogen over an urban location in the foothills of the North-Western Himalayas

Increasing anthropogenic activities and biomass burning events over the Northern Indian region severely affect the pristine environment of North-Western Himalayas. To investigate their influence in this data sparse region, continuous monitoring of O3, CO and NOx are made during January 2018 to December 2019 over Dehradun, an urban location in the foothills of North-western Himalayas. Ozone shows daytime broad peak and CO and NOx show two peaks during morning and evening hours. The maximum daytime ozone is observed during spring with 58.2 ± 2.0 ppbv (56.2 ± 2.7 ppbv) during 2018 (2019). CO and NOx show maximum value during winter and minimum during summer-monsoon. Their concentrations are found to be influenced from stubble burning and forest fire emissions during late spring. WRF-Chem simulations are made with and without biomass burning emissions to quantify the contribution of these emissions on the variability of these gases over Dehradun. Sensitivity simulations reveal that biomass burning over northern Indian region contributed 43 ppbv, 79 ppbv and 1.2 ppbv increase in O3, CO and NOx mixing ratios over Dehradun. In addition, dust storm event in June 2018 contributed for lower ozone levels in 2018 (16.1 ± 9.3 ppbv) and stratosphere troposphere exchange process enhanced surface ozone in June 2019 (53.6 ± 22.3 ppbv).

Nitrogen allocation in PM2.5 smoke-exposed plants: implications for ecosystem nitrogen cycling and stress response

BackgroundWith the increase in forest fire emissions, an increasing amount of nitrogen is released from combustibles and taken up by plant leaves in the form of PM2.5 smoke deposition. Concurrently, the stress from PM2.5 also disrupts the physiological processes of plants. This study aims to reveal the migration paths of N in combustibles in smoke and plants during forest fires and the stress response of plant leaves to smoke particle deposition. This study conducted a simulated smoke deposition treatment on Schima superba and Cunninghamia lanceolata, analyzing the changes in plant 15N content and stress-related products.ResultsThe main findings include the following: (1) Nitrogen in combustibles can be transported to plant leaves via PM2.5 smoke during combustion and can be allocated and assimilated in various parts of the plant after being absorbed by the leaves. (2) The stress response of Schima superba to PM2.5 is less pronounced than that of Cunninghamia lanceolata. (3) Under PM2.5 stress, the correlation between nitrogen accumulation in the leaves of Schima superba and Cunninghamia lanceolata and their respective stress responses differs.ConclusionsIn forest fires involving different tree species, there are variations in the migration pathways of nitrogen and the stress effects of PM2.5 on leaves, with a significant correlation observed between leaf nitrogen accumulation and stress response.Graphical

Methane Emissions in Boreal Forest Fire Regions: Assessment of Five Biomass-Burning Emission Inventories Based on Carbon Sensing Satellites

Greenhouse gases such as CH4 generated by forest fires have a significant impact on atmospheric methane concentrations and terrestrial vegetation methane budgets. Verification in conjunction with “top-down” satellite remote sensing observation has become a vital way to verify biomass-burning emission inventories and accurately assess greenhouse gases while looking into the limitations in reliability and quantification of existing “bottom-up” biomass-burning emission inventories. Therefore, we considered boreal forest fire regions as an example while combining five biomass-burning emission inventories and CH4 indicators of atmospheric concentration satellite observation data. By introducing numerical comparison, correlation analysis and trend consistency analysis methods, we explained the lag effect between emissions and atmospheric concentration changes and evaluated a more reliable emission inventory using time series similarity measurement methods. The results indicated that total methane emissions from five biomass-burning emission inventories differed by a factor of 2.9 in our study area, ranging from 2.02 to 5.84 Tg for methane. The time trends of the five inventories showed good consistency, with the Quick Fire Emissions Dataset version 2.5 (QFED2.5) having a higher correlation coefficient (above 0.8) with the other four datasets. By comparing the consistency between the inventories and satellite data, a lagging effect was found to be present between the changes in atmospheric concentration and gas emissions caused by forest fires on a seasonal scale. After eliminating lagging effects and combining time series similarity measures, the QFED2.5 (Euclidean distance = 0.14) was found to have the highest similarity to satellite data. In contrast, Global Fire Emissions Database version 4.1 with small fires (GFED4.1s) and Global Fire Assimilation System version 1.2 (GFAS1.2) had larger Euclidean distances of 0.52 and 0.4, respectively, which meant that they had lower similarity to satellite data. Therefore, QFED2.5 was found to be more reliable while having higher application accuracy compared to the other four datasets in our study area. This study further provided a better understanding of the key role of forest fire emissions in atmospheric CH4 concentrations and offered reference for selecting appropriate biomass burning emission inventory datasets for bottom-up inventory estimation studies.

Assessment of forest fire emissions in Uttarakhand State, India, using Open Geospatial data and Google Earth Engine.

In the recent past, forest fires have increased due to the changing climate pattern. It is necessary to analyse and quantify various gaseous emissions so as to mitigate their harmful effects on air pollution. Satellite remote sensing data provides an opportunity to study the greenhouse gases in the atmosphere. The multispectral sensor of the Tropospheric Monitoring Instrument (Sentinel-5) is capable of recording the reflectance of wavelengths vital for measuring the atmospheric concentrations of methane, formaldehyde, aerosol, carbon monoxide, etc., at a spatial resolution of 0.01°. The present study utilized the Google Earth Engine (GEE) platform to study the emissions caused by forest fires in four districts of Uttarakhand State of India, which witnessed unprecedented fires in April-May 2021. All the datasets were ingested in GEE, which has the capability to analyse large datasets without the need to download them. The pre-fire period chosen was September 2020; the fire period was February-May 2021, and the post-fire period was June 2021. The variables chosen were aerosol absorbing index (AAI), carbon monoxide (CO) and nitrogen dioxide (NO2). The climate parameter temperature (Moderate Resolution Imaging Spectroradiometer Land Surface Temperature) and precipitation (from Climate Hazards Group InfraRed Precipitation (CHIRPS) Pentad) were also studied for the period mentioned. The results indicate a different trend for emissions in each district. For AAI, maximum emissions were noted in district Nainital followed by Almora, Tehri Garhwal and Garhwal. For CO emissions, the most affected district was Almora followed by Nainital, Garhwal and Tehri Garhwal. For NO2 emissions, the most affected district was Garhwal, followed by Nainital, Tehri Garhwal and Almora. Delta Normalized Burn Ratio was computed from Sentinel data (difference of pre-fire and post-fire images) to assess the burnt area severity. The Delta Normalized Burn Ratio values observed that the district with the most burnt area is Garhwal, followed by Nainital, Almora and Tehri Garhwal. The elevated temperatures and scanty rainfall patterns regulated the intensity and duration of forest fire. Monitoring the gaseous emissions as a consequence of forest fire in the GEE platform is much easier and more convenient at a regional level. Such data is much needed for mitigation measures to be implemented in time.

Impact of forest fire on the mercury stable isotope composition in litter and soil in the Amazon

Mercury (Hg) emissions from forest fires, especially tropical forests such as the Amazonian forest, were shown to contribute significantly to the atmospheric mercury budget, but new methods are still necessary to improve the traceability and to reduce the great uncertainties related to this emission source. Recent studies have shown that the combustion process can result in Hg stable isotope fractionation that allows tracking coal combustion Hg emissions, as influenced by different factors such as combustion temperature. The main goal of the present study was, therefore, to investigate for the first time the potential of Hg stable isotopes to trace forest fire Hg emissions and pathways. More specifically, small-scale and a large scale prescribed forest fire experiments were conducted in the Brazilian Amazonian forest to study the impact of fire severity on Hg isotopic composition of litter, soil, and ash samples and associated Hg isotope fractionation pathways. In the small-scale experiment, no difference was found in the mercury isotopic composition of the samples collected before and after burning. In contrast, the larger-scale experiment resulted in significant mass dependent fractionation (MDF δ202Hg) in soils and ash suggesting that higher combustion temperature influence Hg isotopic fractionation with the emission of lighter Hg isotopes to the atmosphere and enrichment with heavier Hg in ashes. As for coal combustion, mass independent fractionation was not observed. To our knowledge, these results are the first to highlight the potential of forest fires to cause Hg isotopic fractionation, depending on the fire severity. The results also allowed to establish an isotopic fingerprint for tropical forest fire Hg emissions that corresponds to a mixture of litter and soil Hg isotopic composition (resulting atmospheric δ202Hg, Δ200Hg and Δ199Hg were −1.79 ± 0.24‰, −0.05 ± 0.04‰ and −0.45 ± 0.12‰, respectively).

Comparative Analysis and Assessment of Methodologies Applied in the Russian Federation for Calculating Greenhouse Gas Absorption by Forest Ecosystems

The assessment of the forest carbon balance is of great importance for the building of the climate policy of the Russian Federation at both national and international levels. At the same time, the results of such assessments conducted by different scientific groups vary depending on the approaches and methodologies used. This study considers the key systems for assessing the carbon balance of forest ecosystems in the Russian Federation: Integrated Land Information System, IZIS (International Institute for Applied Systems Analysis, Austria), The Carbon Budget Model of the Canadian Forest Sector, CBM-CFS (Canada), Regional Forest Carbon Budget Assessment, ROBUL (Russia), the methodology of the All-Russian Research Institute of Forestry and Mechanization of Forestry (Russia). The methodologies are compared with respect to their compliance with the IPCC requirements. The study identifies the individual characteristics of the methodologies and their application, and proposes recommendations for improving the accuracy of carbon balance estimates. The main key differences between the estimates of different scientific groups, include: compliance with the recommendations of IPCC; selection between the methods of “gain−loss” and “stock−difference”; approach to the identification of managed forests; calculation method of forest fire emissions; sources of initial data, and their reliability. The study notes the importance of scientific discussion and the necessity of compliance of the methodologies with international standards, emphasizes the problem of outdated initial data and underestimation of forest fire emissions, regardless of the chosen methodology. In general, the currently used methodology satisfactorily estimates forest carbon balance. It is recommended to improve the estimates based on remote sensing data and the second cycle of the State Forest Inventory (SFI). The implementation of the Strategy of socio-economic development of the Russian Federation with low greenhouse gas emissions until 2050 should be provided not only by changes in the method of calculating the carbon balance, but rather through real forest protection measures. Any significant adjustment to the methodology must be accompanied by an adjustment to national climate goals.

NPP and Carbon Emissions under Forest Fire Disturbance in Southwest and Northeast China from 2001 to 2020

With climate change, frequent forest fires and prolonged fire period occur all over the world. Moreover, carbon emission from forest fire affects the carbon cycle of the forest ecosystem. However, this effect varies by region with no uniform conclusions, and fewer comparative studies exist on such differences between regions. In this paper, net primary productivity (NPP) data MOD17A3 were used as an important parameter of forest carbon absorption, along with MODIS fire spot data MCD14DL and burned area data MCD64A1. Forest carbon lost under forest fire interference in the northeast and southwest natural forest areas of China was studied to explore the role of forest fire in the carbon cycle process and its differences in the unlike regions of China. Here, by means of kernel density analysis and M-K trend test, the characteristics of forest fires in China’s southwest and northeast forests were calculated. Forest carbon emission under forest fire disturbance was quantified by reference to the forest fire emission factor list. We show that (1) the total number of forest fire spots in the southwest region from 2001 to 2020 was 1.06 × 105, 1.28 times that of Northeast China. However, the total burned area in the southwest was only 67.84% of that in the northeast. (2) The total carbon emissions from forest fires in the southwest from 2001 to 2020 was 37,559.94 Gg, 10.77% larger than the northeast forest, CH4 and CO2 were 13.52% and 11.29% larger respectively. Moreover, the carbon emissions of forest fire in the northeast showed a downward trend, R2 = 0.16 (p < 0.1), while it remained basically unchanged in the southwest. The contribution of carbon emissions from forest fires changed with forest types, it was shown as: evergreen needleleaf forest (14.98%) > evergreen broadleaf forest (10.81%) > deciduous needleleaf forest (6.52%) > deciduous broadleaf forest (5.22%). (3) From 2001 to 2020, under the premise that the NPP both manifested upward trends, the NPP of the burned areas showed a significant downward trend in the southwest forest, with R2 = 0.42 (p < 0.05), while it increased in the northeast forest, with R2 = 0.37 (p < 0.05). It showed negative correlation between NPP of burned areas and forest fire carbon emissions, and forest fire disturbance had no significant effect on forest NPP in Northeast China, while net carbon loss occurred in Southwest China. In general, under different forest fire characteristics, NPP, which represents forest carbon uptake, and carbon emissions from forest fires show differences. The impact of forest fire disturbance on forest carbon process varies with regions. The study can provide some ideas on the effects of forest fire disturbance on climate change.

Editorial: Forest fire emissions and their impact on global climate change

Editorial: Forest fire emissions and their impact on global climate change

Ionic Compositions of Sequential Rainfall Samples as Source Signatures of Forest Fire Emissions

Ionic Compositions of Sequential Rainfall Samples as Source Signatures of Forest Fire Emissions

“Forest fire emissions: A contribution to global climate change”

Forest fires are emitting substantial amounts of greenhouse gases and particulate matter into the atmosphere than assumed in state climate targets. It can play an important role in combustible environments, such as shrublands, grasslands, and forests, and contribute to climate change. Thus, forest fire, and climate change is intertwined concepts. As vegetation burns, release the carbon stored within them. This is the main reason why large-scale forest fires release atmospheric carbon dioxide (CO2) and hence, are responsible for increasing the rate of climate change to a great extent. It is extremely significant to measure the contribution of global forest fire and emissions trends of greenhouse gases. In this context, continental-scale carbon emissions assessments were primarily attempted using ground-based datasets for forest ecosystem fires. Considerable research has been published employing remote sensing data from coast to coast. While ground-based data are valuable, they have some restrictions that can be overcome by remote sensing. Ground-based fire data are primarily limited to the total burned area, with their completeness changing yearly with the location. Remote sensing can provide additional spatio-temporal fire information to improve fire emission estimates. In this paper, the factors driving forest fire, with a brief discussion on the triangular relationship between fire, land degradation, and climate change, the role of Remote Sensing and Geographic Information Systems (GIS), machine learning (ML), and a critical overview of state-of-the-art global climate change are presented.

Pixel-Based Long-Term (2001–2020) Estimations of Forest Fire Emissions over the Himalaya

Forest/wildfires have been one of the most notable severe catastrophes in recent decades across the globe, and their intensity is expected to rise with global warming. Forest fire contributes significantly to particulate and gaseous pollution in the atmosphere. This study has estimated the pixel-based emissions (CO, CO2, CH4, NOx, SO2, NH3, PM2.5, PM10, OC, and BC) from forest fires over the Himalaya (including India, Nepal, and Bhutan). The MODIS-based burned area (MCD64A1), Land Use Land Cover (LULC; MCD12A1), NDVI (MOD13A2), percentage tree cover (MOD44A6), gridded biomass, and species-wise emissions factors were used to estimate the monthly emissions from forest fires over the last two decades (2001–2020). A bottom-up approach was adopted to retrieve the emissions. A substantial inter-annual variation of forest burn area was found over the western, central (Nepal), and eastern Himalaya (including Bhutan). The eastern Himalaya exhibited the highest average annual CO2 emission, i.e., 20.37 Tg, followed by Nepal, 15.52 Tg, and the western Himalaya, 4.92 Tg. Spatially, the higher CO2 (0.01–0.02 Tg year−1/km2) and CO (0.007–0.002 Tg year−1/km2) emissions were detected along the south-eastern parts of the eastern Himalaya, southern regions of Nepal, and south-eastern parts of the western Himalaya. The trend of forest fire emissions in 2001–2010 was significantly positive, while in the next decade (2011–2020) a negative trend was recorded. The estimated pixel-based emission and Global Fire Emission Dataset (GFEDv4.1s) data demonstrated a promising association with a correlation coefficient (r) between 0.80 and 0.93. An inventory of forest fire emissions over long-term periods can be helpful for policymakers. In addition, it helps to set guidelines for air quality and atmospheric transport modelling and to better understand atmospheric pollution over the Himalayan and associated regions.

Hourly biomass burning emissions product from blended geostationary and polar-orbiting satellites for air quality forecasting applications

Biomass burning influences atmospheric composition and regional air quality. The hourly biomass-burning emissions are usually required by air quality models, yet most available emission inventories provide daily or monthly estimates in 0.1° or coarser grids, limiting the prediction accuracy. The Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellites – R Series (GOES-R) observes fires across the conterminous United States (CONUS) every 5 min at a spatial resolution of 2 km, which allows for characterizing fires and emissions on diurnal scale. In this study, we developed a new operational algorithm to generate regional hourly 3 km fire emission across the CONUS by fusing temporally resolved ABI fire radiative power (FRP) and fine spatial-resolution (375 m) FRP from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Joint Polar Satellite System (JPSS) satellites. To do this, ABI FRP was first calibrated against and fused with VIIRS FRP in 3 km grids. Then, FRP diurnal cycles at an interval of 5 min were reconstructed using the fused ABI-VIIRS FRP and the land cover-ecoregion-specific FRP diurnal climatologies. The reconstructed FRP diurnal cycles were applied to estimate hourly emissions of eight species (e.g., carbon monoxide (CO) and fine particulate matter with diameters <2.5 μm (PM2.5)). The accuracy was verified by comparing with CO observations from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite, and with PM2.5 emissions from eight other inventories. The results of the ABI-VIIRS estimates during one year from April 2020 to March 2021 indicate that fires burned 221 Tg dry matter and emit 2.25 Tg PM2.5 emissions across the CONUS. The seasonal and diurnal patterns of emissions vary with land cover types. The largest and smallest seasonal variations are shown in forest and agriculture fire emissions, respectively. The diurnal emission patterns of different land cover types share similar shapes but differ largely in magnitude. Moreover, the diurnal pattern of forest fire emissions suggests that emissions are dominated during daytime in the eastern U.S. but strong during both daytime and nighttime in the western U.S. The evaluation shows that the fused ABI-VIIRS based CO agrees well with the TROPOMI CO, with a difference of 11%. However, the agreement between fused ABI-VIIRS emissions and other inventories varies for different fire events.

Spatial and temporal variation of air pollutant emissions from forest fires in China

Warm and dry climate conditions favor the occurrence of forest fires. Forest burning leads to the discharge of large amounts of particles and trace gases that play an important role in air quality degradation and have impact on human health. To date, most studies on China's forest fire emissions have concentrated in certain regions, also with a relatively coarse temporal resolution. In this study, we used the INteractive Fire and Emission algoRithm for Natural envirOnments (INFERNO), as well as high-resolution land cover data to compile a forest fire emission inventory for China in 2020. The variations of forest combustion emissions were then analyzed at the provincial and seasonal level. The results show that forest fires were concentrated in southern China and northeastern China, which are in agreement with MODIS observations. Total CO2, CO, CH4, NOx, SO2, PM2.5, OC, and BC emissions were estimated to be 3.06 × 104, 1.87 × 103, 96.92, 34.95, 13.84, 208, 116.03, and 9.95 Gg, respectively. Provinces with higher emissions were found in Yunnan, Guangdong, Hunan, and Sichuan. Peak emission from forest fire occurred in spring and winter, mainly from January to April, during which contributed 70% of the total forest fire contaminants emissions. The algorithm used in this study can be easily coupled in the meteorological model and air quality model to estimate the occurrence of fire and calculate pollutant emission online. This study updated emissions information that may support future research and policy development on greenhouse gas reduction and air pollution control.

Pan-Arctic seasonal cycles and long-term trends of aerosol properties from 10 observatories

Abstract. Even though the Arctic is remote, aerosol properties observed there are strongly influenced by anthropogenic emissions from outside the Arctic. This is particularly true for the so-called Arctic haze season (January through April). In summer (June through September), when atmospheric transport patterns change, and precipitation is more frequent, local Arctic sources, i.e., natural sources of aerosols and precursors, play an important role. Over the last few decades, significant reductions in anthropogenic emissions have taken place. At the same time a large body of literature shows evidence that the Arctic is undergoing fundamental environmental changes due to climate forcing, leading to enhanced emissions by natural processes that may impact aerosol properties. In this study, we analyze 9 aerosol chemical species and 4 particle optical properties from 10 Arctic observatories (Alert, Kevo, Pallas, Summit, Thule, Tiksi, Barrow/Utqiaġvik, Villum, and Gruvebadet and Zeppelin Observatory – both at Ny-Ålesund Research Station) to understand changes in anthropogenic and natural aerosol contributions. Variables include equivalent black carbon, particulate sulfate, nitrate, ammonium, methanesulfonic acid, sodium, iron, calcium and potassium, as well as scattering and absorption coefficients, single scattering albedo and scattering Ångström exponent. First, annual cycles are investigated, which despite anthropogenic emission reductions still show the Arctic haze phenomenon. Second, long-term trends are studied using the Mann–Kendall Theil–Sen slope method. We find in total 41 significant trends over full station records, i.e., spanning more than a decade, compared to 26 significant decadal trends. The majority of significantly declining trends is from anthropogenic tracers and occurred during the haze period, driven by emission changes between 1990 and 2000. For the summer period, no uniform picture of trends has emerged. Twenty-six percent of trends, i.e., 19 out of 73, are significant, and of those 5 are positive and 14 are negative. Negative trends include not only anthropogenic tracers such as equivalent black carbon at Kevo, but also natural indicators such as methanesulfonic acid and non-sea-salt calcium at Alert. Positive trends are observed for sulfate at Gruvebadet. No clear evidence of a significant change in the natural aerosol contribution can be observed yet. However, testing the sensitivity of the Mann–Kendall Theil–Sen method, we find that monotonic changes of around 5 % yr−1 in an aerosol property are needed to detect a significant trend within one decade. This highlights that long-term efforts well beyond a decade are needed to capture smaller changes. It is particularly important to understand the ongoing natural changes in the Arctic, where interannual variability can be high, such as with forest fire emissions and their influence on the aerosol population. To investigate the climate-change-induced influence on the aerosol population and the resulting climate feedback, long-term observations of tracers more specific to natural sources are needed, as well as of particle microphysical properties such as size distributions, which can be used to identify changes in particle populations which are not well captured by mass-oriented methods such as bulk chemical composition.