Fire Emissions Research Articles

Parameterizations of US wildfire and prescribed fire emission ratios and emission factors based on FIREX-AQ aircraft measurements

Abstract. Extensive airborne measurements of non-methane organic gases (NMOGs), methane, nitrogen oxides, reduced nitrogen species, and aerosol emissions from US wild and prescribed fires were conducted during the 2019 NOAA/NASA Fire Influence on Regional to Global Environments and Air Quality campaign (FIREX-AQ). Here, we report the atmospheric enhancement ratios (ERs) and inferred emission factors (EFs) for compounds measured on board the NASA DC-8 research aircraft for nine wildfires and one prescribed fire, which encompass a range of vegetation types. We use photochemical proxies to identify young smoke and reduce the effects of chemical degradation on our emissions calculations. ERs and EFs calculated from FIREX-AQ observations agree within a factor of 2, with values reported from previous laboratory and field studies for more than 80 % of the carbon- and nitrogen-containing species. Wildfire emissions are parameterized based on correlations of the sum of NMOGs with reactive nitrogen oxides (NOy) to modified combustion efficiency (MCE) as well as other chemical signatures indicative of flaming/smoldering combustion, including carbon monoxide (CO), nitrogen dioxide (NO2), and black carbon aerosol. The sum of primary NMOG EFs correlates to MCE with an R2 of 0.68 and a slope of −296 ± 51 g kg−1, consistent with previous studies. The sum of the NMOG mixing ratios correlates well with CO with an R2 of 0.98 and a slope of 137 ± 4 ppbv of NMOGs per parts per million by volume (ppmv) of CO, demonstrating that primary NMOG emissions can be estimated from CO. Individual nitrogen-containing species correlate better with NO2, NOy, and black carbon than with CO. More than half of the NOy in fresh plumes is NO2 with an R2 of 0.95 and a ratio of NO2 to NOy of 0.55 ± 0.05 ppbv ppbv−1, highlighting that fast photochemistry had already occurred in the sampled fire plumes. The ratio of NOy to the sum of NMOGs follows trends observed in laboratory experiments and increases exponentially with MCE, due to increased emission of key nitrogen species and reduced emission of NMOGs at higher MCE during flaming combustion. These parameterizations will provide more accurate boundary conditions for modeling and satellite studies of fire plume chemistry and evolution to predict the downwind formation of secondary pollutants, including ozone and secondary organic aerosol.

Wintertime Heavy Haze Episodes in Northeast China Driven by Agricultural Fire Emissions

Wintertime Heavy Haze Episodes in Northeast China Driven by Agricultural Fire Emissions

Quantifying effects of long-range transport of NO2 over Delhi using back trajectories and satellite data

Abstract. Exposure to air pollution is a leading public health risk factor in India, especially over densely populated Delhi and the surrounding Indo-Gangetic Plain. During the post-monsoon seasons, the prevailing north-westerly winds are known to influence aerosol pollution events in Delhi by advecting pollutants from agricultural fires as well as from local sources. Here we investigate the year-round impact of meteorology on gaseous nitrogen oxides (NOx=NO+NO2). We use bottom-up NOx emission inventories (anthropogenic and fire) and high-resolution satellite measurement based tropospheric column NO2 (TCNO2) data, from S5P aboard TROPOMI, alongside a back-trajectory model (ROTRAJ) to investigate the balance of local and external sources influencing air pollution changes in Delhi, with a focus on different emissions sectors. Our analysis shows that accumulated emissions (i.e. integrated along the trajectory path, allowing for chemical loss) are highest under westerly, north-westerly and northerly flow during pre-monsoon (February–May) and post-monsoon (October–February) seasons. According to this analysis, during the pre-monsoon season, the highest accumulated satellite TCNO2 trajectories come from the east and north-west of Delhi. TCNO2 is elevated within Delhi and the Indo-Gangetic Plain (IGP) to the east of city. The accumulated NOx emission trajectories indicate that the transport and industry sectors together account for more than 80 % of the total accumulated emissions, which are dominated by local sources (>70 %) under easterly winds and north-westerly winds. The high accumulated emissions estimated during the pre-monsoon season under north-westerly wind directions are likely to be driven by high NOx emissions locally and in nearby regions (since NOx lifetime is reduced and the boundary layer is relatively deeper in this season). During the post-monsoon season the highest accumulated satellite TCNO2 trajectories are advected from Punjab and Haryana, where satellite TCNO2 is elevated, indicating the potential for the long-range transport of agricultural burning emissions to Delhi. However, accumulated NOx emissions indicate local (70 %) emissions from the transport sector are the largest contributor to the total accumulated emissions. High local emissions, coupled with a relatively long NOx atmospheric lifetime and shallow boundary layer, aid the build-up of emissions locally and along the trajectory path. This indicates the possibility that fire emissions datasets may not capture emissions from agricultural waste burning in the north-west sufficiently to accurately quantify their influence on Delhi air quality (AQ). Analysis of daily ground-based NO2 observations indicates that high-pollution episodes (>90th percentile) occur predominantly in the post-monsoon season, and more than 75 % of high-pollution events are primarily caused by local sources. But there is also a considerable influence from non-local (30 %) emissions from the transport sector during the post-monsoon season. Overall, we find that in the post-monsoon season, there is substantial accumulation of high local NOx emissions from the transport sector (70 % of total emissions, 70 % local), alongside the import of NOx pollution into Delhi (30 % non-local). This work indicates that both high local NOx emissions from the transport sector and the advection of highly polluted air originating from outside Delhi are of concern for the population. As a result, air quality mitigation strategies need to be adopted not only in Delhi but in the surrounding regions to successfully control this issue. In addition, our analysis suggests that the largest benefits to Delhi NOx air quality would be seen with targeted reductions in emissions from the transport and agricultural waste burning sectors, particularly during the post-monsoon season.

Future fire-PM2.5 mortality varies depending on climate and socioeconomic changes

Fire emits hazardous air pollutants, the most dominant of which is fine particulate matter of diameter ⩽2.5 μm (PM2.5). PM2.5 is a great concern due to its adverse effects on public health. Previous studies have examined the health burden from fire-related PM2.5 for the historical period; however, future global mortality associated with fire-PM2.5, particularly under the coexisting impacts of climate and socioeconomic changes, is yet to be projected. Here, we estimated the mortality associated to fire-PM2.5 (fire-PM2.5 mortality) for the present period (2010s) and then projected the mortality for the 2050s and 2090s under 10 scenarios created by combining two Representative Concentration Pathways (RCP2.6 or 6.0) with five Shared Socioeconomic Pathways (SSP1–5). We used the Li-Park fire model in combination with a chemical transport model and health risk assessment to calculate fire-PM2.5 mortality. For the present period, the estimated fire-PM2.5 mortality was 135 180 (105 403–163 738), which is 1.7 deaths per 100 000 population globally. When countries were grouped by income, the fire-PM2.5 mortality rate was the highest in the high-income country group and lowest in the lowest-income country group. For the 2050s and 2090s, the fire-PM2.5 mortality rate was projected to decrease under most scenarios because of decreases in fire emissions and baseline mortality rate. However, a scenario of high population growth and low technical development (SSP3) together with severe global warming (RCP6.0) would lead to an increase in the fire-PM2.5 mortality rate in the 2090s, particularly in the highest-income countries, due to increased fire under drier and warmer weather conditions. Stratification of countries by gross domestic product indicated the need for adaptation efforts in the highest-income countries to avoid future increases of mortality associated with fire-PM2.5.

Generating fuel consumption maps on prescribed fire experiments from airborne laser scanning

Background Characterisation of fuel consumption provides critical insights into fire behaviour, effects, and emissions. Stand-replacing prescribed fire experiments in central Utah offered an opportunity to generate consumption estimates in coordination with other research efforts. Aims We sought to generate fuel consumption maps using pre- and post-fire airborne laser scanning (ALS) and ground measurements and to test the spatial transferability of the ALS-derived fuel models. Methods Using random forest (RF), we empirically modelled fuel load and estimated consumption from pre- and post-fire differences. We used cross-validation to assess RF model performance and test spatial transferability. Key results Consumption estimates for overstory fuels were more precise and accurate than for subcanopy fuels. Transferring RF models to provide consumption estimates in areas without ground training data resulted in loss of precision and accuracy. Conclusions Fuel consumption maps were produced and are available for researchers who collected coincident fire behaviour, effects, and emissions data. The precision and accuracy of these data vary by fuel type. Transferability of the models to novel areas depends on the user’s tolerance for error. Implications This study fills a critical need in the broader set of research efforts linking fire behaviour, effects, and emissions.

Contrasting trends of carbon emission from savanna and boreal forest fires during 1999–2022

AbstractBiomass burning (BB) as an important atmospheric carbon source has significant environmental and climatic influence. The frequent extreme BB cases in recent years have raised extensive concerns, yet the latest changes in BB emission on a global scale are not fully understood. Here, we systematically quantify the changes in BB carbon emission for 1999–2022 by fire types and on different scales based on the Global Fire Emissions Database with small fires (GFED4s) dataset. We find contrasting trends of savanna and boreal forest fires persistent over the study period, shaping the variation of global total BB carbon emission. The receding savanna fire drives a declining global BB carbon emission at −8 Tg C year−1 (−0.4% year−1) for 1999–2022, while an upturn of global carbon emission (5 Tg C year−1, 0.3% year−1) occurs in the recent decadal period (2008–2022) due to intensified boreal forest fires. The burned area decouples from carbon emission in terms of the changing tendency, as exhibited by the decreasing global burned area after 2008. Regionally, the fire carbon emission enhancement over the past 15 years (2008–2022) mainly comes from the boreal forests in northwestern North America, northeastern Siberia, and parts of the savanna area, all of which coincide with local climate change toward higher fire proneness. This study reveals a climate‐driven aggravation of the BB carbon emission, especially in high‐latitude boreal forests, and calls for attention to its potential impacts and effective fire management strategies.

Enhancing Refuge Space Safety: Tape Application to Reduce Door Leakage during Fires

As global climate change intensifies, the frequency and severity of wildfires affecting communities worldwide are on the rise. Therefore, the issue of residential safety for people is becoming increasingly crucial. During a fire, smoke particles and toxic emissions could infiltrate the refuge space through gaps in doors, making it a primary risk factor for casualties. It is worth conducting in‐depth research and investigation on how to effectively utilize available tools and facilities to enhance the probability of survival while waiting for rescue behind closed doors. This study uses iron, aluminum, and wooden doors as experimental samples. Thirty participants conducted a total of 1,620 experiments using six different qualities of tape to investigate the impact of applying different layers of tape on improving door leakage and the time required for tape application. This study proposes that in situations when people are waiting for rescue in refuge spaces, the most efficient course of action would involve the fastest available tape. The rationale behind this is that while different types of tape may vary in their smoke‐shielding effectiveness, using three layers of tape can meet the leakage effectiveness standards, specifically less than 25 m3/hr at a differential pressure of 25 Pa.

Developing customized fuel models for shrub and bracken communities in Galicia (NW Spain)

Geospatial fire behaviour and fire hazard simulators, fire effects models and smoke emission software commonly use standard fuel models in order to simplify data collection and the inclusion of complex fuel scenarios. These fuel models are often mapped using remotely sensed data. However, given the great complexity of fuelbeds, with properties that vary widely in both time and space, the use of these standard fuel models can greatly limit accurate fuel mapping. This affects fuel hazard assessment, fuel reduction treatment plans, fire management decision-making and evaluation of the environmental impact of wildfire. In this study, we developed unique customized fire behaviour fuel models for shrub and bracken communities, by using k-medoids clustering analysis based on both fuel structural characteristics and potential fire behaviour. We used an original database of 722 destructive sample plots in nine different shrub and bracken communities covering the entire distribution area in Galicia (NW Spain), one of the regions in Europe most affected by forest fires. Measurements of cover, height and fuel fractions loads differentiated by size and vegetative state (live or dead) were used to estimate the potential rate of fire spread with five different models including fireline intensity, heat per unit area and the flame length for each sampling site and considering extreme environmental conditions. The optimal number of clusters was established by combining practical knowledge about the shrubland communities under study and their associated fire behaviour, with maximization of the mean value of the silhouette variable and minimization of the within-cluster sum of squares. The structural characteristics of the medoids derived from the analysis were associated with each of the proposed customized fuel models. Finally, a simple dichotomous classification based only on shrub height was developed to enable construction of spatially explicit fuel model maps based on remotely sensed data. Thus, the methodology applied allows generation of a more realistic representation of fuel distribution in the landscape, based on fuel structure measurements of natural regional ecosystems rather than on the use of standard models. We believe that the proposed methodology is generally applicable to communities composed of other shrub and fern species in different biogeographical regions.

Development of a source-specific biomass burning emissions inventory for Washington, Oregon, and California

While wildfires are the dominant source of biomass burning emissions across the U.S., prescribed and agricultural burns also contribute to emissions and ambient air quality. Understanding the differential impacts that these different sources of biomass burning have on air quality and public health is important in the development of effective exposure reduction strategies. We leverage the high-resolution Fire INventory from NCAR (FINN) to develop a source-specific biomass burning emission inventory that distinguishes between wildfire, prescribed burn, and agricultural burn sources across Washington, Oregon, and California. We describe the methodology for reclassifying FINN using multiple national and state-level fire and fuel treatment inventories and present the results of the emissions reclassification process for these three states from 2012 to 2020. We matched 72%, 78% and 81% of FINN emissions event locations to fire locations from the fire type input datasets in Washington, Oregon, and California, respectively. When compared to county-level fire type-specific PM2.5 emissions estimates from the 2014, 2017, and 2020 EPA National Emissions Inventory, we found greater agreement among wildfire-specific estimates across years and states (R2 range: 0.59–0.98), relative to prescribed (R2 range: 0.12–0.73) and agricultural burns (R2 range: 0.43–0.95). This fire type-specific biomass burning emissions inventory can be used in air pollution transport models to better understand the impacts of each of these sources on air quality across the region along with downstream public health impacts.

Assessing local and transboundary fine particulate matter pollution and sectoral contributions in Southeast Asia during haze months of 2015–2019

While previous studies have investigated haze events over Southeast Asia (SEA), local and transboundary contributions of various emission sources to haze months over the entire SEA have yet to be assessed comprehensively and systematically. We utilized the Particle Source Apportionment Technique (PSAT) to quantify the spatial local, transboundary, and sectoral contributions to PM2.5 over SEA during the haze months of 2015–2019. Results show that local emission contributions accounted for 56.1 % ~ 94.2 % of PM2.5 in Indonesia, Philippines, Vietnam, and Thailand. Transboundary contributions (23.1 % ~ 57.6 %) from Indonesia notably influenced maritime SEA. Vietnam (15.6 % ~ 39.1 %) and super-regional (17.0 % ~ 34.3 %) contributions outside the SEA exerted remarkable impacts on mainland SEA. Among different sectors, fire emissions contributed the most to PM2.5 over maritime SEA (23.0 % ~ 68.6 %) during the studied haze months, whereas residential and other emissions were the main contributors to mainland SEA (27.2 % ~ 36.7 %). Regarding the source species, primary PM2.5 accounted for the majority of PM2.5. VOC and SO2 composed most of the secondary PM2.5 due to massive VOC emissions in the region and the priority reaction of NH3 with sulfuric acid (H2SO4) to form ammonium sulfate. Besides, the intensified haze months in Oct 2015 and Sep 2019 were characterized by more intensive fire emissions in the region and the climatic variability-induced meteorological effects that provided favorable condition for transboundary air pollution (56.9 % and 44.9 %, respectively, for maritime SEA, as well as 46.0 % and 37.7 %, respectively, for mainland SEA in the two studied haze months). The haze months can be attributed to the notable drought conditions amidst global climatic phenomena such as El Niño and positive Indian Ocean Dipole (IOD) in Oct 2015 and Sep 2019, respectively.

Startup process, safety and risk assessment of biomass gasification for off-grid rural electrification

Biomass gasification has significantly advanced in terms of performance and is increasingly used in rural off-grid electricity applications. The downdraft gasifier is primarily used in biomass gasification applications, in which it functions as a reactor into which biomass and gasifying air are introduced to generate producer gas that is then used in an engine generator to produce electricity. However, the safety and stability of biomass gasification remain challenging and depend on several factors, such as the startup heating process, which can affect risks of fire, explosion, and toxic gas emissions. As the biomass gasification is associated with high temperatures and demands safety measures, its startup process should follow a rigorous procedure that ensures reliable operation and minimizes the risk of hazard issues. This study presents a gasifier startup heating process based on a proposed safety protocols hazard analysis. The study indicates that the heating temperature in startup processes has been identified as a critical factor due to its role in impacting safety. The findings indicate that the biomass gasification process has significant risks, including the potential for fire, explosion, and release of environmental emissions via multiple pathways. The methods proposed here could lead to reduced risk from the abovementioned issues.

Long-term mortality burden trends attributed to black carbon and PM2·5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study

Long-term improvements in air quality and public health in the continental USA were disrupted over the past decade by increased fire emissions that potentially offset the decrease in anthropogenic emissions. This study aims to estimate trends in black carbon and PM2·5 concentrations and their attributable mortality burden across the USA. In this study, we derived daily concentrations of PM2·5 and its highly toxic black carbon component at a 1-km resolution in the USA from 2000 to 2020 via deep learning that integrated big data from satellites, models, and surface observations. We estimated the annual PM2·5-attributable and black carbon-attributable mortality burden at each 1-km2 grid using concentration-response functions collected from a national cohort study and a meta-analysis study, respectively. We investigated the spatiotemporal linear-regressed trends in PM2·5 and black carbon pollution and their associated premature deaths from 2000 to 2020, and the impact of wildfires on air quality and public health. Our results showed that PM2·5 and black carbon estimates are reliable, with sample-based cross-validated coefficients of determination of 0·82 and 0·80, respectively, for daily estimates (0·97 and 0·95 for monthly estimates). Both PM2·5 and black carbon in the USA showed significantly decreasing trends overall during 2000 to 2020 (22% decrease for PM2·5 and 11% decrease for black carbon), leading to a reduction of around 4200 premature deaths per year (95% CI 2960-5050). However, since 2010, the decreasing trends of fine particles and premature deaths have reversed to increase in the western USA (55% increase in PM2·5, 86% increase in black carbon, and increase of 670 premature deaths [460-810]), while remaining mostly unchanged in the eastern USA. The western USA showed large interannual fluctuations that were attributable to the increasing incidence of wildfires. Furthermore, the black carbon-to-PM2·5 mass ratio increased annually by 2·4% across the USA, mainly due to increasing wildfire emissions in the western USA and more rapid reductions of other components in the eastern USA, suggesting a potential increase in the relative toxicity of PM2·5. 100% of populated areas in the USA have experienced at least one day of PM2·5 pollution exceeding the daily air quality guideline level of 15 μg/m3 during 2000-2020, with 99% experiencing at least 7 days and 85% experiencing at least 30 days. The recent widespread wildfires have greatly increased the daily exposure risks in the western USA, and have also impacted the midwestern USA due to the long-range transport of smoke. Wildfires have become increasingly intensive and frequent in the western USA, resulting in a significant increase in smoke-related emissions in populated areas. This increase is likely to have contributed to a decline in air quality and an increase in attributable mortality. Reducing fire risk via effective policies besides mitigation of climate warming, such as wildfire prevention and management, forest restoration, and new revenue generation, could substantially improve air quality and public health in the coming decades. National Aeronautics and Space Administration (NASA) Applied Science programme, NASA MODIS maintenance programme, NASA MAIA satellite mission programme, NASA GMAO core fund, National Oceanic and Atmospheric Administration (NOAA) GEO-XO project, NOAA Atmospheric Chemistry, Carbon Cycle, and Climate (AC4) programme, and NOAA Educational Partnership Program with Minority Serving Institutions.

Multi-decadal trends and variability in burned area from the fifth version of the Global Fire Emissions Database (GFED5)

Abstract. Long-term records of burned area are needed to understand wildfire dynamics, assess fire impacts on ecosystems and air quality, and improve fire forecasts. Here, we fuse multiple streams of remote sensing data to create a 24 year (1997–2020) dataset of monthly burned area as a component of the fifth version of the Global Fire Emissions Database (GFED5). During 2001–2020, we use the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64A1 burned area product and adjust for the errors of commission and omission. Adjustment factors are estimated based on region, land cover, and tree cover fraction, using spatiotemporally aligned burned area from Landsat or Sentinel-2. Burned area in croplands, peatlands, and deforestation regions is estimated from MODIS active fire detections. Along-Track Scanning Radiometer (ATSR) and Visible and Infrared Scanner (VIRS) active fire data are used to extend the time series back to 1997. The global annual burned area during 2001–2020 is estimated to be 774 ± 63 Mha yr−1 or 5.9 ± 0.5 % of ice-free land. Burned area declined by 1.21 ± 0.66 % yr−1, a cumulative decrease of 24.2 ± 13.2 % over 20 years. The global reduction is primarily driven by a decrease in fires in savannas, grasslands, and croplands. Forest, peat, and deforestation fires did not exhibit significant long-term trends. The GFED5 global burned area is 93 % higher than MCD64A1, 61 % higher than GFED4s, and in closer agreement with products from higher-resolution satellite sensors. These data may reduce discrepancies between fire emission estimates from activity-based and atmospheric-based approaches, and improve our understanding of global fire impacts on the carbon cycle and climate system. The GFED5 global burned area product is freely accessible at https://doi.org/10.5281/zenodo.7668423 (Chen et al., 2023).

Multi-Source Satellite and WRF-Chem Analyses of Atmospheric Pollution from Fires in Peninsular Southeast Asia

Atmospheric pollutant gases emitted from straw burning and forest fires can lead to air quality and human health problems. This work explored the evolutionary trends of atmospheric CO2 and other pollutant gases in five countries of Peninsular Southeast Asia (PSEA) over a long time series using various satellite remote sensing data. The research results indicate that a considerable number of fires occur in the region every spring, which negatively affects air quality. The concentration of CO2 increased every year, indicating a correlation coefficient of 0.57 with the number of fire points. The concentration of CO and NO2, respectively, showed a correlation coefficient of 0.87 and 0.95 with the number of fire points as well. Additionally, the AOD reflects the relationship between fire points and air quality. The study also used the meteorological and air quality Weather Research and Forecasting with Chemistry (WRF-Chem) to simulate the fire season in March 2016. In this sensitivity study, we examined the impact of air pollutant gases on air quality in PSEA under a hypothetical scenario with and without fire emissions. The simulation results were also compared with satellite observations, which showed that the WRF-Chem model and the FINN (Fire INventory from NCAR) inventory could effectively simulate the spatial distribution and spatial–temporal variability characteristics of CO concentration in the fire, but the simulation result of NO2 was not satisfactory. This study suggests that spring wildfires affect not only air quality, but also short-term weather in the region.

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

Extratropical forests increasingly at risk due to lightning fires

Fires can be ignited by people or by natural causes, which are almost exclusively lightning strikes. Discriminating between lightning and anthropogenic fires is paramount when estimating impacts of changing socioeconomic and climatological conditions on fire activity. Here we use reference data of fire ignition locations, cause and burned area from seven world regions in a machine-learning approach to obtain a global attribution of lightning and anthropogenic ignitions as dominant fire ignition sources. We show that 77% (uncertainty expressed as one standard deviation = 8%) of the burned area in extratropical intact forests currently stems from lightning and that these areas will probably experience 11 to 31% more lightning per degree warming. Extratropical forests are of global importance for carbon storage. They currently experience high fire-related forest losses and have, per unit area, among the largest fire emissions on Earth. Future increases in lightning in intact forest may therefore compound the positive feedback loop between climate change and extratropical wildfires.

Health and economic burden of wildland fires PM2.5-related pollution in Portugal – A longitudinal study

Portugal has been affected by wildland fires that destroy thousands of hectares of forest, causing damage to the environment and to the exposed populations. This study aims to assess the influence of wildland fire emissions on air quality, its effect on population health and the related costs, between 2015 and 2018 in Portugal.The cause-specific mortality due to PM2.5 was calculated considering the exposure for five endpoints in adults, twelve age groups for adults and considering children under five years old. The contribution of wildfire emissions to PM2.5 concentrations in Portugal was assessed through EMEP-MSC/W model.Results showed that the average annual fire emissions of PM2.5, CO, CH4, CO2 and NO2 a significant and continuous increase was observed during the first three years (2015, 2016 and 2017) for all pollutants, followed by a decrease in 2018, with values lower than those observed in 2015.Regarding the long-term exposure to PM2.5 emitted by fires a total of 32, 93, 189 and 31 deaths, corresponding to a cost of 59, 174, 360 and 60 million EUR in 2015, 2016, 2017 and 2018, respectively, were estimated.On the other hand, in the first three years an increase in years of life lost (YLL) values of 496, 1608 and 3092 was observed, corresponding to a cost of 16, 54 and 105 million EUR, respectively, followed by a decrease in 2018 with a YLL of 480, corresponding to a cost of 17 M€.

Air quality impacts of a large waste fire in Stockholm, Sweden

Fires in waste facilities are a common occurrence. Since many waste facilities are located adjacent to densely populated areas, these fires could potentially expose large populations to the emitted pollutants. However, at the moment there are only few field studies investigating the impact of waste fire emissions on air quality since the unpredictable nature of these events makes them challenging to capture.This study investigated the impact of a large and persistent un-prescribed fire in a waste storage facility in Stockholm county, Sweden, on the local air quality of two residential areas in close proximity to the fire. In-situ measurements of particulate matter, black carbon and nitrogen oxide concentrations were conducted both during open burning and after the fire was fully covered. In addition, filter samples were collected for offline analysis of organic composition, metal content and toxicity.Strongly increased concentrations of PM10, PM2.5 and black carbon were found during the open burning period, especially when the wind was coming from the direction of the fire. In addition, elevated concentrations of particulate heavy metals and polycyclic aromatic hydrocarbons were observed in the air during the open burning period. These results show that waste fires can have a strong impact on the air quality of nearby residential areas.

Dynamic savanna burning emission factors based on satellite data using a machine learning approach

Abstract. Landscape fires, predominantly found in the frequently burning global savannas, are a substantial source of greenhouse gases and aerosols. The impact of these fires on atmospheric composition is partially determined by the chemical breakup of the constituents of the fuel into individual emitted chemical species, which is described by emission factors (EFs). These EFs are known to be dependent on, amongst other things, the type of fuel consumed, the moisture content of the fuel, and the meteorological conditions during the fire, indicating that savanna EFs are temporally and spatially dynamic. Global emission inventories, however, rely on static biome-averaged EFs, which makes them ill-suited for the estimation of regional biomass burning (BB) emissions and for capturing the effects of shifts in fire regimes. In this study we explore the main drivers of EF variability within the savanna biome and assess which geospatial proxies can be used to estimate dynamic EFs for global emission inventories. We made over 4500 bag measurements of CO2, CO, CH4, and N2O EFs using a UAS and also measured fuel parameters and fire-severity proxies during 129 individual fires. The measurements cover a variety of savanna ecosystems under different seasonal conditions sampled over the course of six fire seasons between 2017 and 2022. We complemented our own data with EFs from 85 fires with locations and dates provided in the literature. Based on the locations, dates, and times of the fires we retrieved a variety of fuel, weather, and fire-severity proxies (i.e. possible predictors) using globally available satellite and reanalysis data. We then trained random forest (RF) regressors to estimate EFs for CO2, CO, CH4, and N2O at a spatial resolution of 0.25∘ and a monthly time step. Using these modelled EFs, we calculated their spatiotemporal impact on BB emission estimates over the 2002–2016 period using the Global Fire Emissions Database version 4 with small fires (GFED4s). We found that the most important field indicators for the EFs of CO2, CO, and CH4 were tree cover density, fuel moisture content, and the grass-to-litter ratio. The grass-to-litter ratio and the nitrogen-to-carbon ratio were important indicators for N2O EFs. RF models using satellite observations performed well for the prediction of EF variability in the measured fires with out-of-sample correlation coefficients between 0.80 and 0.99, reducing the error between measured and modelled EFs by 60 %–85 % compared to using the static biome average. Using dynamic EFs, total global savanna emission estimates for 2002–2016 were 1.8 % higher for CO, while CO2, CH4, and N2O emissions were, respectively, 0.2 %, 5 %, and 18 % lower compared to GFED4s. On a regional scale we found a spatial redistribution compared to GFED4s with higher CO, CH4, and N2O EFs in mesic regions and lower ones in xeric regions. Over the course of the fire season, drying resulted in gradually lower EFs of these species. Relatively speaking, the trend was stronger in open savannas than in woodlands, where towards the end of the fire season they increased again. Contrary to the minor impact on annual average savanna fire emissions, the model predicts localized deviations from static averages of the EFs of CO, CH4, and N2O exceeding 60 % under seasonal conditions.

Air quality simulation with WRF-Chem over southeastern Brazil, part I: Model description and evaluation using ground-based and satellite data

A comprehensive Weather Research and Forecasting with Chemistry (WRF-Chem) model evaluation is conducted using ground-based and total column observational data from air quality stations and satellite retrievals. Fine particles (PM2.5; ≤ 2.5 μm in aerodynamic diameter), nitrogen oxides (NOx, NO + NO2), carbon monoxide (CO), tropospheric ozone (O3) concentrations and AOD values over southeastern Brazil were analyzed to assess the model's capability in reproducing atmospheric observation. The model simulations were performed over simple one domain at grid resolution of 10 km over southeastern Brazil. This spatial resolution was chosen due to a previous evaluation between five MODIS AOD products with AERONET estimates, resulting in Dark Target at 10 km of spatial resolution the best product to represent the AOD values over our study domain. Model input emissions comprise vehicular emissions derived from a bottom-up emission model, as well as on-line calculations of biogenic and fire emission rates. Given that the atmospheric state affects air pollution dispersion, a model evaluation on the meteorological conditions was carried out to better evaluate the model performance in reproducing the pollutant concentrations. Good agreement between model simulations and observations for air temperature and relative humidity at 2 m height was found, with correlation coefficients higher than 0.85 in most periods. Expected benchmarks for wind speed and direction at 10 m height were also found in this analysis, though with larger uncertainties. Underestimation occurred for daily accumulated precipitation due to the limitations of the cloud microphysics scheme or cumulus parameterization. Model simulations of PM2.5, NOx, CO and O3 agreed well with ground-based observations in terms of temporal variations and trends, with model-observation discrepancies due to uncertainties in the emission inventories. O3 was the better simulated pollutant in terms of temporal variability, with the characteristic large and small amplitudes observed over urban and rural areas being well represented by the model. High O3 concentrations were observed at the Botucatu station, due to transport of pollutants generated in the Metropolitan Area of São Paulo, and were also represented by the model, indicating the need of more active air quality monitoring stations over inland regions in southeastern Brazil. Moderate and high correlation coefficients (ranging 0.46–0.81) were found for tropospheric NO2 VCD and CO column, and AOD at 550 nm due to uncertainties in the emission inventories and aerosol model simplifications. Both the model and satellite captured higher values in similar regions over our study domain. This work represents a first effort, in southeastern Brazil, that combines numerical modeling, remote sensing and ground-based stations to analyze and understand the impact exerted by the emissions of urban pollution over surrounding areas. A more in-depth analysis of the impact of emissions transport to inland regions from urban areas in southeastern Brazil will be discussed in the second part of this work.