Fire behavior and smoke modeling: Model improvement and measurement needs for next-generation smoke research and forecasting systems

Uncertainty quantification in coupled wildfire-atmosphere simulations at scale

Uncertainties in wildfire simulations pose a major challenge for making decisions about fire management, mitigation, and evacuations. However, ensemble calculations to quantify uncertainties are prohibitively expensive with high-fidelity models that are needed to capture today's ever-more intense and severe wildfires. This work shows that surrogate models trained on related data enable scaling multifidelity uncertainty quantification to high-fidelity wildfire simulations of unprecedented scale with billions of degrees of freedom. The key insight is that correlation is all that matters while bias is irrelevant for speeding up uncertainty quantification when surrogate models are combined with high-fidelity models in multifidelity approaches. This allows the surrogate models to be trained on abundantly available or cheaply generated related data samples that can be strongly biased as long as they are correlated to predictions of high-fidelity simulations. Numerical results with scenarios of the Tubbs 2017 wildfire demonstrate that surrogate models trained on related data make multifidelity uncertainty quantification in large-scale wildfire simulations practical by reducing the training time by several orders of magnitude from 3 months to under 3 h and predicting the burned area at least twice as accurately compared with using high-fidelity simulations alone for a fixed computational budget. More generally, the results suggest that leveraging related data can greatly extend the scope of surrogate modeling, potentially benefiting other fields that require uncertainty quantification in computationally expensive high-fidelity simulations.

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.

Assessing the 2023 Canadian wildfire smoke impact in Northeastern US: Air quality, exposure and environmental justice

The Canadian wildfires in June 2023 significantly impacted the northeastern United States, particularly in terms of worsened air pollution and environmental justice concerns. While advancements have been made in low-cost sensor deployments and satellite observations of atmospheric composition, integrating dynamic human mobility with wildfire PM2.5 exposure to fully understand the environmental justice implications remains underinvestigated. This study aims to enhance the accuracy of estimating ground-level fine particulate matter (PM2.5) concentrations by fusing chemical transport model outputs with empirical observations, estimating exposures using human mobility data, and evaluating the impact of environmental justice. Employing a novel data fusion technique, the study combines the Weather Research and Forecasting model with Chemistry (WRF-Chem) outputs and surface PM2.5 measurements, providing a more accurate estimation of PM2.5 distribution. The study addresses the gap in traditional exposure assessments by incorporating human mobility data and further investigates the spatial correlation of PM2.5 levels with various environmental and demographic factors from the US Environmental Protection Agency (EPA) Environmental Justice Screening and Mapping Tool (EJScreen). Results reveal that despite reduced mobility during high PM2.5 levels from wildfire smoke, exposure for both residents and individuals on the move remains high. Regions already burdened with high environmental pollution levels face amplified PM2.5 effects from wildfire smoke. Furthermore, we observed mixed correlations between PM2.5 concentrations and various demographic and socioeconomic factors, indicating complex exposure patterns across communities. Urban areas, in particular, experience persistent high exposure, while significant correlations in rural areas with EJScreen factors highlight the unique vulnerabilities of these populations to smoke exposure. These results advocate for a comprehensive approach to environmental health that leverages advanced models, integrates human mobility data, and addresses socio-demographic disparities, contributing to the development of equitable strategies against the growing threat of wildfires.

Air quality impacts of observationally constrained biomass burning heat flux inputs

Biomass burning is a major contributor to ambient air pollution worldwide, and the accurate characterization of biomass burning plume behavior is an important consideration for air quality models that attempt to reproduce these emissions. Smoke plume injection height, or the vertical level into which the combustion emissions are released, is an important consideration for determining plume behavior, transport, and eventual impacts. This injection height is dependent on several fire properties, each with estimates and uncertainties in terms of historical fire emissions inventories. One such property is the fire heat flux, a fire property metric sometimes used to predict and parameterize plume injection heights in current chemical transport models. Although important for plume behavior, fire heat flux is difficult to predict and parameterize efficiently, and is therefore often held to fixed, constant values in these models, leading to potential model biases relative to real world conditions. In this study we collect observed heat flux estimates from satellite data products for three wildfire events over northern California and use these estimates in a regional chemical transport model to investigate and quantify the impacts of observationally constrained heat fluxes on the modeled injection height and downwind air quality. We find large differences between these observationally derived heat flux estimates and fixed model assumptions, with important implications for modeled behavior of plume dynamics and surface air quality impacts. Overall, we find that using observationally constrained heat flux estimates tends to reduce modeled injection heights for our chosen fires, resulting in large increases in surface particulate matter concentrations. While local wind conditions contribute to variability and additional uncertainties in the impacts of modified plume injection heights, we find observationally constrained heat fluxes to be an impactful and potentially useful tool towards the improvement of emissions inventory assumptions and parameterizations.

Bacterial Emission Factors: A Foundation for the Terrestrial-Atmospheric Modeling of Bacteria Aerosolized by Wildland Fires

Wildland fire is a major global driver in the exchange of aerosols between terrestrial environments and the atmosphere. This exchange is commonly quantified using emission factors or the mass of a pollutant emitted per mass of fuel burned. However, emission factors for microbes aerosolized by fire have yet to be determined. Using bacterial cell concentrations collected on unmanned aircraft systems over forest fires in Utah, USA, we determine bacterial emission factors (BEFs) for the first time. We estimate that 1.39 × 1010 and 7.68 × 1011 microbes are emitted for each Mg of biomass consumed in fires burning thinning residues and intact forests, respectively. These emissions exceed estimates of background bacterial emissions in other studies by 3-4 orders of magnitude. For the ∼2631 ha of similar forests in the Fishlake National Forest that burn each year on average, an estimated 1.35 × 1017 cells or 8.1 kg of bacterial biomass were emitted. BEFs were then used to parametrize a computationally scalable particle transport model that predicted over 99% of the emitted cells were transported beyond the 17.25 x 17.25 km model domain. BEFs can be used to expand understanding of global wildfire microbial emissions and their potential consequences to ecosystems, the atmosphere, and humans.

Predicting hourly PM2.5 concentrations in wildfire-prone areas using a SpatioTemporal Transformer model

Globally, wildfires are becoming more frequent and destructive, generating a significant amount of smoke that can transport thousands of miles. Therefore, improving air pollution forecasts from wildfires is essential and informing citizens of more frequent, accurate, and interpretable updates related to localized air pollution events. This research proposes a multi-head attention-based deep learning architecture, SpatioTemporal (ST)-Transformer, to improve spatiotemporal predictions of PM_2.5 concentrations in wildfire-prone areas. The ST-Transformer model employed a sparse attention mechanism that concentrates on the most useful contextual information across spatial, temporal, and variable-wise dimensions. The model includes critical driving factors of PM_2.5 concentrations as predicting factors, including wildfire perimeter and intensity, meteorological factors, road traffic, PM_2.5, and temporal indicators from the past 24 h. The model is trained to conduct time series forecasting on PM_2.5 concentrations at EPA's air quality stations in the greater Los Angeles area. Prediction results were compared with other existing time series forecasting methods and exhibited better performance, especially in capturing abrupt changes or spikes in PM_2.5 concentrations during wildfire situations. The attention matrix learned by the proposed model enabled interpretation of the complex spatial, temporal, and variable-wise dependencies, indicating that the model can differentiate between wildfires and non-wildfires. The ST-Transformer model's accurate predictability and interpretation capacity can help effectively monitor and predict the impacts of wildfire smoke and be applicable to other complex spatiotemporal prediction problems.

Categorizing and Harmonizing Natural, Technological, and Socio-Economic Perils Following the Catastrophe Modeling Paradigm

The literature on probabilistic hazard and risk assessment shows a rich and wide variety of modeling strategies tailored to specific perils. On one hand, catastrophe (CAT) modeling, a recent professional and scientific discipline, provides a general structure for the quantification of natural (e.g., geological, hydrological, meteorological) and man-made (e.g., terrorist, cyber) catastrophes. On the other hand, peril characteristics and related processes have yet to be categorized and harmonized to enable adequate comparison, limit silo effects, and simplify the implementation of emerging risks. We reviewed the literature for more than 20 perils from the natural, technological, and socio-economic systems to categorize them by following the CAT modeling hazard pipeline: (1) event source → (2) size distribution → (3) intensity footprint. We defined the following categorizations, which are applicable to any type of peril, specifically: (1) point/line/area/track/diffuse source, (2) discrete event/continuous flow, and (3) spatial diffusion (static)/threshold (passive)/sustained propagation (dynamic). We then harmonized the various hazard processes using energy as the common metric, noting that the hazard pipeline's underlying physical process consists of some energy being transferred from an energy stock (the source), via an event, to the environment (the footprint).

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.

An evaluation of empirical and statistically based smoke plume injection height parametrisations used within air quality models

Air quality models are used to assess the impact of smoke from wildland fires, both prescribed and natural, on ambient air quality and human health. However, the accuracy of these models is limited by uncertainties in the parametrisation of smoke plume injection height (PIH) and its vertical distribution. We compared PIH estimates from the plume rise method (Briggs) in the Community Multiscale Air Quality (CMAQ) modelling system with observations from the 2013 California Rim Fire and 2017 prescribed burns in Kansas. We also examined PIHs estimated using alternative plume rise algorithms, model grid resolutions and temporal burn profiles. For the Rim Fire, the Briggs method performed as well or better than the alternatives evaluated (mean bias of less than ±5-20% and root mean square error lower than 1000 m compared with the alternatives). PIH estimates for the Kansas prescribed burns improved when the burn window was reduced from the standard default of 12 h to 3 h. This analysis suggests that meteorological inputs, temporal allocation and heat release are the primary drivers for accurately modelling PIH.

Uncertainty in Health Impact Assessments of Smoke From a Wildfire Event

Wildfires cause elevated air pollution that can be detrimental to human health. However, health impact assessments associated with emissions from wildfire events are subject to uncertainty arising from different sources. Here, we quantify and compare major uncertainties in mortality and morbidity outcomes of exposure to fine particulate matter (PM2.5) pollution estimated for a series of wildfires in the Southeastern U.S. We present an approach to compare uncertainty in estimated health impacts specifically due to two driving factors, wildfire-related smoke PM2.5 fields and variability in concentration-response parameters from epidemiologic studies of ambient and smoke PM2.5. This analysis, focused on the 2016 Southeastern wildfires, suggests that emissions from these fires had public health consequences in North Carolina. Using several methods based on publicly available monitor data and atmospheric models to represent wildfire-attributable PM2.5, we estimate impacts on several health outcomes and quantify associated uncertainty. Multiple concentration-response parameters derived from studies of ambient and wildfire-specific PM2.5 are used to assess health-related uncertainty. Results show large variability and uncertainty in wildfire impact estimates, with comparable uncertainties due to the smoke pollution fields and health response parameters for some outcomes, but substantially larger health-related uncertainty for several outcomes. Consideration of these uncertainties can support efforts to improve estimates of wildfire impacts and inform fire-related decision-making.

Short-term health effects from outdoor exposure to biomass burning emissions: A review

Biomass burning (BB) including forest, bush, prescribed fires, agricultural fires, residential wood combustion, and power generation has long been known to affect climate, air quality and human health. With this work we supply a systematic review on the health effects of BB emissions in the framework of the WHO activities on air pollution. We performed a literature search of online databases (PubMed, ISI, and Scopus) from year 1980 up to 2020. A total of 81 papers were considered as relevant for mortality and morbidity effects. High risk of bias was related with poor estimation of BB exposure and lack of adjustment for important confounders. PM10 and PM2.5 concentrations originating from BB were associated with all-cause mortality: the meta-analytical estimate was equal to 1.31% (95% CI 0.71, 1.71) and 1.92% (95% CI -1.19, 5.03) increased mortality per each 10 μg m^-3 increase of PM10 and PM2.5, respectively. Regarding cardiovascular mortality 8 studies reported quantitative estimates. For smoky days and for each 10 μg m^-3 increase in PM2.5 concentrations, the risk of cardiovascular mortality increased by 4.45% (95% CI 0.96, 7.95) and by 3.30% (95% CI -1.97, 8.57), respectively. Fourteen studies evaluated whether respiratory morbidity was adversely related to PM2.5 (9 studies) or PM10 (5 studies) originating from BB. All found positive associations. The pooled effect estimates were 4.10% (95% CI 2.86, 5.34) and 4.83% (95% CI 0.06, 9.60) increased risk of total respiratory admissions/emergency visits, per 10 μg m^-3 increases in PM2.5 and PM10, respectively. Regarding cardiovascular morbidity, sixteen studies evaluated whether this was adversely related to PM2.5 (10 studies) or PM10 (6 studies) originating from BB. They found both positive and negative results, with summary estimates equal to 3.68% (95% CI -1.73, 9.09) and 0.93% (95% CI -0.18, 2.05) increased risk of total cardiovascular admissions/emergency visits, per 10 μg m^-3 increases in PM2.5 and PM10, respectively. To conclude, a significant number of studies indicate that BB exposure is associated with all-cause and cardiovascular mortality and respiratory morbidity.

The Wildland Fire Heat Budget-Using Bi-Directional Probes to Measure Sensible Heat Flux and Energy in Surface Fires

Sensible energy is the primary mode of heat dissipation from combustion in wildland surface fires. However, despite its importance to fire dynamics, smoke transport, and in determining ecological effects, it is not routinely measured. McCaffrey and Heskestad (A robust bidirectional low-velocity probe for flame and fire application. Combustion and Flame 26:125–127, 1976) describe measurements of flame velocity from a bi-directional probe which, when combined with gas temperature measurements, can be used to estimate sensible heat fluxes. In this first field application of bi-directional probes, we describe vertical and horizontal sensible heat fluxes during the RxCADRE experimental surface fires in longleaf pine savanna and open ranges at Eglin Air Force Base, Florida. Flame-front sensible energy is the time-integral of heat flux over a residence time, here defined by the rise in gas temperatures above ambient. Horizontal flow velocities and energies were larger than vertical velocities and energies. Sensible heat flux and energy measurements were coordinated with overhead radiometer measurements from which we estimated fire energy (total energy generated by combustion) under the assumption that 17% of fire energy is radiated. In approximation, horizontal, vertical, and resultant sensible energies averaged 75%, 54%, and 64%, respectively, of fire energy. While promising, measurement challenges remain, including obtaining accurate gas and velocity measurements and capturing three-dimensional flow in the field.

Wildland Fire Emission Sampling at Fishlake National Forest, Utah Using an Unmanned Aircraft System

Emissions from a stand replacement prescribed burn were sampled using an unmanned aircraft system (UAS, or "drone") in Fishlake National Forest, Utah, U.S.A. Sixteen flights over three days in June 2019 provided emission factors for a broad range of compounds including carbon monoxide (CO), carbon dioxide (CO₂), nitric oxide (NO), nitrogen oxide (NO₂), particulate matter < 2.5 microns in diameter (PM_2.5), volatile organic compounds (VOCs) including carbonyls, black carbon, and elemental/organic carbon. To our knowledge, this is the first UAS-based emission sampling for a fire of this magnitude, including both slash pile and crown fires resulting in wildfire-like conditions. The burns consisted of drip torch ignitions as well as ground-mobile and aerial helicopter ignitions of large stands comprising over 1,000 ha, allowing for comparison of same-species emission factors burned under different conditions. The use of a UAS for emission sampling minimizes risk to personnel and equipment, allowing flexibility in sampling location and ensuring capture of representative, fresh smoke constituents. PM_2.5 emission factors varied 5-fold and, like most pollutants, varied inversely with combustion efficiency resulting in lower emission factors from the slash piles than the crown fires.

Wildfire Smoke Transport and Air Quality Impacts in Different Regions of China

The air quality and human health impacts of wildfires depend on fire, meteorology, and demography. These properties vary substantially from one region to another in China. This study compared smoke from more than a dozen wildfires in Northeast, North, and Southwest China to understand the regional differences in smoke transport and the air quality and human health impacts. Smoke was simulated using the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) with fire emissions obtained from the Global Fire Emission Database (GFED). Although the simulated PM2.5 concentrations reached unhealthy or more severe levels at regional scale for some largest fires in Northeast China, smoke from only one fire was transported to densely populated areas (population density greater than 100 people/km2). In comparison, the PM2.5 concentrations reached unhealthy level in local densely populated areas for a few fires in North and Southwest China, though they were very low at regional scale. Thus, individual fires with very large sizes in Northeast China had a large amount of emissions but with a small chance to affect air quality in densely populated areas, while those in North and Southwest China had a small amount of emissions but with a certain chance to affect local densely populated areas. The results suggest that the fire and air quality management should focus on the regional air quality and human health impacts of very large fires under southward/southeastward winds toward densely populated areas in Northeast China and local air pollution near fire sites in North and Southwest China.

Criteria-Based Identification of Important Fuels for Wildland Fire Emission Research

Studies of the emissions from wildland fires are important for understanding the role of these events in the production, transport, and fate of emitted gases and particulate matter, and, consequently, their impact on atmospheric and ecological processes, and on human health and wellbeing. Wildland fire emission research provides the quantitative information needed for the understanding and management of wildland fire emissions impacts based on human needs. Recent work to characterize emissions from specific fuel types, or those from specific areas, has implicitly been driven by the recognition of the importance of those fuel types in the context of wildland fire science; however, the importance of specific fuels in driving investigations of biomass-burning emissions has not been made explicit thus far. Here, we make a first attempt to discuss the development and application of criteria to answer the question, “What are the most important fuels for biomass-burning emissions investigations to inform wildland fire science and management?” Four criteria for fuel selection are proposed: “(1) total emissions, (2) impacts, (3) availability and uncertainty, and (4) potential for future importance.” Attempting to develop and apply these criteria, we propose a list of several such fuels, based on prior investigations and the body of wildland-fire emission research.

Wildfire and prescribed burning impacts on air quality in the United States

Air quality impacts from wildfires have been dramatic in recent years, with millions of people exposed to elevated and sometimes hazardous fine particulate matter (PM _2.5 ) concentrations for extended periods. Fires emit particulate matter (PM) and gaseous compounds that can negatively impact human health and reduce visibility. While the overall trend in U.S. air quality has been improving for decades, largely due to implementation of the Clean Air Act, seasonal wildfires threaten to undo this in some regions of the United States. Our understanding of the health effects of smoke is growing with regard to respiratory and cardiovascular consequences and mortality. The costs of these health outcomes can exceed the billions already spent on wildfire suppression. In this critical review, we examine each of the processes that influence wildland fires and the effects of fires, including the natural role of wildland fire, forest management, ignitions, emissions, transport, chemistry, and human health impacts. We highlight key data gaps and examine the complexity and scope and scale of fire occurrence, estimated emissions, and resulting effects on regional air quality across the United States. The goal is to clarify which areas are well understood and which need more study. We conclude with a set of recommendations for future research.

IMPLICATIONS

In the recent decade the area of wildfires in the United States has increased dramatically and the resulting smoke has exposed millions of people to unhealthy air quality. In this critical review we examine the key factors and impacts from fires including natural role of wildland fire, forest management, ignitions, emissions, transport, chemistry and human health.

Predicting the influence of climate on grassland area burned in Xilingol, China with dynamic simulations of autoregressive distributed lag models

The influence of climate change on wildland fire has received considerable attention, but few studies have examined the potential effects of climate variability on grassland area burned within the extensive steppe land of Eurasia. We used a novel statistical approach borrowed from the social science literature—dynamic simulations of autoregressive distributed lag (ARDL) models—to explore the relationship between temperature, relative humidity, precipitation, wind speed, sunlight, and carbon emissions on grassland area burned in Xilingol, a large grassland-dominated landscape of Inner Mongolia in northern China. We used an ARDL model to describe the influence of these variables on observed area burned between 2001 and 2018 and used dynamic simulations of the model to project the influence of climate on area burned over the next twenty years. Our analysis demonstrates that area burned was most sensitive to wind speed and temperature. A 1% increase in wind speed was associated with a 20.8% and 22.8% increase in observed and predicted area burned respectively, while a 1% increase in maximum temperature was associated with an 8.7% and 9.7% increase in observed and predicted future area burned. Dynamic simulations of ARDL models provide insights into the variability of area burned across Inner Mongolia grasslands in the context of anthropogenic climate change.

The Fire and Smoke Model Evaluation Experiment-A Plan for Integrated, Large Fire-Atmosphere Field Campaigns

The Fire and Smoke Model Evaluation Experiment (FASMEE) is designed to collect integrated observations from large wildland fires and provide evaluation datasets for new models and operational systems. Wildland fire, smoke dispersion, and atmospheric chemistry models have become more sophisticated, and next-generation operational models will require evaluation datasets that are coordinated and comprehensive for their evaluation and advancement. Integrated measurements are required, including ground-based observations of fuels and fire behavior, estimates of fire-emitted heat and emissions fluxes, and observations of near-source micrometeorology, plume properties, smoke dispersion, and atmospheric chemistry. To address these requirements the FASMEE campaign design includes a study plan to guide the suite of required measurements in forested sites representative of many prescribed burning programs in the southeastern United States and increasingly common high-intensity fires in the western United States. Here we provide an overview of the proposed experiment and recommendations for key measurements. The FASMEE study provides a template for additional large-scale experimental campaigns to advance fire science and operational fire and smoke models.