Daily fire progression information is crucial for public health studies that examine the relationship between population-level smoke exposures and subsequent health events. Issues with remote sensing used in fire emissions inventories (FEI) lead to the possibility of missed exposures that impact the results of acute health effects studies.

Rural communities are increasingly impacted by smoke produced by wildfires and forest management activties. Understanding local influences on smoke adaptation and mitigation is critical to social adaptation as fire risk continues to rise.

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.

Emission measurements are available in the literature for a wide variety of field burns and laboratory experiments, although previous studies do not always isolate the effect of individual features such as fuel moisture content (FMC). This study explores the effect of FMC on gaseous and particulate emissions from flaming and smouldering combustion of four different wildland fuels found across the United States. A custom linear tube-heater apparatus was built to steadily produce emissions in different combustion modes over a wide range of FMC. Results showed that when compared with flaming combustion, smouldering combustion showed increased emissions of CO, particulate matter and unburned hydrocarbons, corroborating trends in the literature. CO and particulate matter emissions in the flaming mode were also significantly correlated with FMC, which had little influence on emissions for smouldering mode combustion, when taking into account the dry mass of fuel burned. These variations occurred for some vegetative fuel species but not others, indicating that the type of fuel plays an important role. This may be due to the chemical makeup of moist and recently live fuels, which is discussed and compared with previous measurements in the literature.

Fires in boreal forests of Alaska are changing, threatening human health and ecosystems. Given expected increases in fire activity with climate warming, insight into the controls on fire size from the time of ignition is necessary. Such insight may be increasingly useful for fire management, especially in cases where many ignitions occur in a short time period. Here we investigated the controls and predictability of final fire size at the time of ignition. Using decision trees, we show that ignitions can be classified as leading to small, medium or large fires with 50.4 ± 5.2% accuracy. This was accomplished using two variables: vapour pressure deficit and the fraction of spruce cover near the ignition point. The model predicted that 40% of ignitions would lead to large fires, and those ultimately accounted for 75% of the total burned area. Other machine learning classification algorithms, including random forests and multi-layer perceptrons, were tested but did not outperform the simpler decision tree model. Applying the model to areas with intensive human management resulted in overprediction of large fires, as expected. This type of simple classification system could offer insight into optimal resource allocation, helping to maintain a historical fire regime and protect Alaskan ecosystems.

Santa Ana winds have been implicated as a major driver of large wildfires in southern California. While numerous anecdotal reports exist, there is little quantitative analysis in peer-reviewed literature on how this weather phenomenon influences fire progression rates. We analysed fire progression within 158 fire events in southern California as a function of meteorologically defined Santa Ana conditions between 2001 and 2009. Our results show quantitatively that burned area per day is 3.5-4.5 times larger on Santa Ana days than on non-Santa Ana days. Santa Ana definition parameters (relative humidity, wind speed) along with other predictor variables (air temperature, fuel temperature, 10-h fuel moisture, population density, slope, fuel loading, previous-day burn perimeter) were tested individually and in combination for correlation with subsets of daily burned area. Relative humidity had the most consistently strong correlation with burned area per day. Gust and peak wind speed had a strong positive correlation with burned area per day particularly within subsets of burned area representing only the first day of a fire, >500 ha burned areas, and on Santa Ana days. The suite of variables comprising the best-fit generalised linear model for predicting burned area ( = 0.41) included relative humidity, peak wind speed, previous-day burn perimeter and two binary indicators for first and last day of a fire event.

Research shows that some categories of human-ignited wildfires might be forecastable, due to their temporal clustering, with the possibility that resources could be pre-deployed to help reduce the incidence of such wildfires. We estimated several kinds of incendiary and other human-ignited wildfire forecast models at the weekly time step for tribal land units in the United States, evaluating their forecast skill out of sample. Analyses show that an Autoregressive Conditional Poisson (ACP) model of both incendiary and non-incendiary human-ignited wildfires is more accurate out of sample compared to alternatives, and the simplest of the ACP models performed the best. Additionally, an ensemble of these and simpler, less analytically intensive approaches performed even better. Wildfire hotspot forecast models using all model types were evaluated in a simulation mode to assess the net benefits of forecasts in the context of law enforcement resource reallocations. Our analyses show that such hotspot tools could yield large positive net benefits for the tribes in terms of suppression expenditures averted for incendiary wildfires but that the hotspot tools were less likely to be beneficial for addressing outbreaks of non-incendiary human-ignited wildfires.

Wildland fire emissions are routinely estimated in the US Environmental Protection Agency's National Emissions Inventory, specifically for fine particulate matter (PM) and precursors to ozone (O); however, there is a large amount of uncertainty in this sector. We employ a brute-force zero-out sensitivity method to estimate the impact of wildland fire emissions on air quality across the contiguous US using the Community Multiscale Air Quality (CMAQ) modelling system. These simulations are designed to assess the importance of wildland fire emissions on CMAQ model performance and are not intended for regulatory assessments. CMAQ ver. 5.0.1 estimated that fires contributed 11% to the mean PM and less than 1% to the mean O concentrations during 2008-2012. Adding fires to CMAQ increases the number of 'grid-cell days' with PM above 35 μg m by a factor of 4 and the number of grid-cell days with maximum daily 8-h average O above 70 ppb by 14%. Although CMAQ simulations of specific fires have improved with the latest model version (e.g. for the 2008 California wildfire episode, the correlation = 0.82 with CMAQ ver. 5.0.1 = 0.68 for CMAQ ver. 4.7.1), the model still exhibits a low bias at higher observed concentrations and a high bias at lower observed concentrations. Given the large impact of wildland fire emissions on simulated concentrations of elevated PM and O, improvements are recommended on how these emissions are characterised and distributed vertically in the model.

There is an urgent need for next-generation smoke research and forecasting (SRF) systems to meet the challenges of the growing air quality, health, and safety concerns associated with wildland fire emissions. This review paper presents simulations and experiments of hypothetical prescribed burns with a suite of selected fire behavior and smoke models and identifies major issues for model improvement and the most critical observational needs. The results are used to understand the new and improved capability required for the next-generation SRF systems and to support the design of the Fire and Smoke Model Evaluation Experiment (FASMEE) and other field campaigns. The next-generation SRF systems should have more coupling of fire, smoke, and atmospheric processes to better simulate and forecast vertical smoke distributions and multiple sub-plumes, dynamical and high-resolution fire processes, and local and regional smoke chemistry during day and night. The development of the coupling capability requires comprehensive and spatially and temporally integrated measurements across the various disciplines to characterize flame and energy structure (e.g., individual cells, vertical heat profile and the height of well mixing flaming gases), smoke structure (vertical distributions and multiple sub-plumes), ambient air processes (smoke eddy, entrainment and radiative effects of smoke aerosols), fire emissions (for different fuel types and combustion conditions from flaming to residual smoldering), as well as night-time processes (smoke drainage and super-fog formation).