3.1.1 Fires from Nuclear Detonations

Fire has long been recognized as a key outcome of the intense discharge of radiation, light, and heat produced by the detonation of a nuclear weapon. In the moments after the detonation, a thermal pulse would vaporize materials nearby and start fires at farther distances wherever it impinges on combustible materials with enough energy to ignite them. In addition, an expanding sphere of superheated air produced by the initial fireball would create a blast wave that breaks apart structures, creating rubble and igniting a second set of fires from damaged structures, vehicles, equipment, and other sources of fuel. Chapter 2 provides greater detail on the expected extent of the thermal and blast effects. With sufficiently high fuel loading, favorable atmospheric conditions, and widespread ignition, individual fires can merge into a large fire with a strong upwardly buoyant convective column, known as a firestorm. In areas with lower fuel loading, fires can occur in a line-like fire front, whose movement is often driven by ambient winds and interactions with local topography. Fires may produce environmental impacts on regional and

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¹ There is a diversity of technical terminology used to identify combustion products across multiple disciplines. The definitions used in this report are sufficiently broad to be applicable across different chapters, however, the committee acknowledged that practitioners in specific research communities may use more precise definitions. For example, in the field of soot formation, soot is, “carbonaceous particles formed during the incomplete combustion or pyrolysis of hydrocarbons, including incipient soot particles, mature soot particles, and all of the intermediate particulate stages between inception, maturity, and complete oxidation to gas-phase species” (Michelsen et al., 2020).

global scales if the emissions have sufficient mass, energy, and atmospheric lifetime, as can occur if these emissions reach the stratosphere where removal mechanisms are inefficient.

3.1.2 Estimating Emissions: Process and Components

There are three categories of material emissions that may result from a nuclear blast:

1. Particulate matter in the nuclear cloud formed by either vaporized and subsequently cooled material or entrained environmental material (i.e., debris and dirt swept up into the nuclear cloud). This material mostly becomes radioactive fallout and only has the potential to be injected into the stratosphere by larger-yield weapons (i.e., 100 kt to 1 Mt in scenarios described in Chapter 2).
2. Gases from the detonation, including oxides of nitrogen (NO_x) formed after heating of the air (by the thermal pulse and shock wave). When larger-yield weapons are detonated, the nuclear cloud can extend high enough to inject this gas into the stratosphere where it can produce chemical and physical effects (see Chapters 2 and 4).
3. Emissions of aerosols and trace gases (including NO_x) from fires after a detonation. These have the potential to occur in large quantities (several Tg) and have a long lifetime if transported to the stratosphere.

This chapter focuses on the emissions from fires after detonation (Category 3). Categories 1 and 2 were previously discussed in Chapter 2 and are less likely to be a concern in the detonation scenarios described in Chapter 2 due to employment of weapons 1 Mt in yield and below, which have less potential to directly inject material in the stratosphere when compared to weapons with yields greater than 1 Mt. The quantity of particulates in the nuclear cloud (Category 1) is likely small relative to that of the potential emissions from the subsequent fires (see Chapter 2, Box 2-3).

Emissions from fires, such as those started by a nuclear blast as explained above, can be estimated using a methodology previously developed to estimate pollutant emissions from biomass burning. The approach taken by Seiler and Crutzen (1980), which estimates biomass burning emissions as the product of several independent terms (Equation 3-1), may be useful for identifying the causes of diverging estimates from different studies and for drawing contrasts with the broader literature modeling aerosol and gas-phase emissions from wildfires. In this framework, the flux of a specific aerosol or gaseous constituent (E, with units of mass per unit time) is equal to the product of the area burned per unit time (A, with units of area per unit time), the fuel load (F_l, with units of mass per area), combustion completeness (CC; a unitless fuel consumption fraction), and an emission factor (Ef; with units of mass constituent per unit of mass dry fuel consumed) that describes the production of a specific constituent of interest under characteristic environmental conditions (e.g., moisture content and three-dimensional structure) of the fire under investigation.

This framework can also be used to predict the emissions of aerosol and gases in the context of a nuclear detonation, with A representing the area of fires ignited by the thermal pulse and air blast ; F_l representing the combined density of fuels in buildings, vehicles, structures, roads, and vegetation; CC being the fraction of these fuels in the environment that is consumed by the flash-ignited and subsequent fires (i.e., the combustion completeness fraction); and E_f being the production of pollutants, including particulate black carbon (BC), particulate organic carbon (OC), particulate matter less than 2.5 microns (PM_2.5), and reactive gases, such as carbon monoxide (CO) and nitrogen oxides (NO_x) per fuel that is consumed. The next sections of this chapter explore each of these terms separately, to understand the differences in and among several recent studies and to identify best practices for future work.

NOTE: ^a Fuel load (FL) in kg/m² is obtained by dividing its FLD value by 18.6 MJ/kg and S.D. values are provided for 1 standard deviation,

3.2.1.3 Fuels in the Wildlands

Wildland fire fuels consist of the various vegetation, litter, fine and coarse woody debris, and duff components of the ecosystem that are potentially combustible, including ground, surface, and crown fuels. A comprehensive analysis by van Leeuwen et al. (2014) synthesized field measurements of fuel loadings, combustion completeness, and fuel consumption related to wildland fires. The compilation of global measurements resulted in average fuel loadings for savannas ranging from 0.76 to 1.1 kg/m², and forests from 6.9 to 28.5 kg/m², with significant variability. The mass fraction of carbon across biomass fuels does not vary significantly and is typically assumed to be 0.45 (e.g., Andreae, 2019). The chemical composition of woody biomass is well characterized, with less than 1% by mass of nitrogen, sulfur, chlorine, and other trace inorganic elements (NASEM, 2022).

TABLE 3-4 Summarized Fuel Loadings and Combustion Completeness in Wildland Fires

SOURCE: van Leeuwen et al., 2014.

conditions. Under normal atmospheric conditions, combustible materials require a certain level of surface temperature or heat flux level to ignite.

Table 3-6 lists some typical temperatures and the expected response from different materials or fire conditions in a building. Table 3-7 lists the representative heat flux levels and examples of physical phenomena. Some cellulosic materials will undergo autoignition when their surface temperature reaches 200 °C. The minimum flux for ignition of plastics or wood-based materials can be as low as 10 kW/m². After a nuclear blast, ignition may occur wherever a combustible material’s surface has one of these ignition conditions. In fire modeling, average fuel temperature values have been used as the target ignition temperatures, such as 1000 K for thin fuels and 600 K for thick fuels (Reisner et al., 2018).

TABLE 3-6 Temperature as a Measure of the Degree of Molecular Activity of a Material Compared to a Reference Point

SOURCE: NIST, 2010.

TABLE 3-7 Examples of Heat Flux Rates of Heat Energy Transferred per Surface Unit Area

SOURCE: NIST, 2010.

Table 3-8 shows the typically expected damage to buildings and structures at specific overpressure values. Most buildings will be completely destroyed with 34 to 83 kPa (5 to 10 psi) of overpressure from the air blast. Rubblization from the nuclear denotation may reduce initial fire activities up to 30–40% in some cases (Glasstone and Dolan, 1977). However, it is not clear how rubblization will affect the secondary ignitions and fire propagation at later stages.

uncertainty and probability into fire modeling to assess likelihood and variability of fire behavior and its associated impacts. Deterministic fire modeling provides specific single-valued predictions of fire behavior and its effects without explicitly considering uncertainty or variability in input parameters. Deterministic fire models include empirical models using simple mathematical equations derived from field observations and experimental data (Quintiere and Wade, 2016), zone or grid models where the space is divided into zones or grid of cells with each zone or cell representing a specific state such as hot or cool and burned or unburned (Walton et al., 2016), and physics-based models using sophisticated computational simulations considering the physical process involved such as heat transfer, fluid dynamics, and combustion chemistry (McGrattan and Miles, 2016). Modern computational fire models can consider many factors including fuel characteristics, weather conditions, topography, presence of structures or other obstacles, and even interaction with the surrounding environment.

Different deterministic fire models have been developed for different purposes. Compartment fire models focus on prediction of the behavior and effects of fires within enclosed spaces such as buildings, considering factors such as compartment size and geometry, fuel characteristics, ventilation conditions, heat transfer mechanisms, and fire products (e.g., smoke and toxic gases). Compartment fire modeling has been widely used in fire protection engineering, building design, and firefighting to understand fire behavior and develop effective strategies for fire protection and suppression. Open-space fire modeling predicts or simulates the behavior and effects of fire in outdoor or unconfined environments such as wildlands, forests, grasslands, and WUI areas (Morvan et al., 2022). Open-space fire models can consider factors such as fuel characteristics, terrain conditions, wind conditions (e.g., speed and direction) and other atmospheric conditions. Open-space fire models are used for a variety of purposes, such as fire management, fire risk assessment, fire prevention and planning, fire emergency response, etc.

Fire modeling methods have been used for the study of fire behavior and fire impacts due to nuclear wars, such as the fire model HIGRAD-FIRETEC used in Reisner et. al. (2018). The Weather Research and Forecasting model coupled with a fire model (WRF-Fire) has been used to model fire spread and plume rise (Redfern et al., 2021). When implemented properly, fire modeling can be useful in predicting ignition, fire spread, burned area, and fire products after the initial nuclear blast.

3.3.1.4 Combustion Completeness

The combustion completeness (CC) is the fraction of fuel that is burned in a fire. For all fires, combustion completeness is controlled by the fire characteristics, which are dependent on fuel loading, composition, and density, as well as meteorological parameters. Of note, combustibility is often delimited to specific fire-exposure conditions. For example, building materials are considered combustible if they are capable of undergoing combustion in air at a pressure and temperature that might occur during a fire in a building. Similarly, some materials that are not combustible under such conditions may be combustible when exposed to a higher temperature or pressure or to an oxygen-enriched environment. Materials that are not combustible in bulk form may be combustible in finely divided form.

Characterization of fuel density and fuel thickness is important for understanding combustion completeness. In wildfires, thin fuels, such as leaves and tree limbs, usually burn first, while thick fuels, such as tree trunks, may not burn. Urban fires may burn a higher percentage of thick fuels than wildfires, as buildings can retain the heat of combustion within the structure (Brown et al., 2023), and in a firestorm, nearly all combustible material may burn. Fuel moisture content also determines the extent of combustion. In wildland fires, moisture content of the vegetation can control the combustion completeness. This may be different from urban areas, where wood-based building materials may have less than 5% moisture content (Hedayati et al., 2018) and significant amounts of synthetic material may be present.

Combustion completeness for wildland biomass burning is included in Table 3-4, where values range from 0.47 to 0.81 (van Leeuwen et al., 2014), with values dependent on the ecosystem, among other things, such as fuel condition and weather. Values for combustion completeness within the nuclear winter literature are presented by Brown et al. (2023) and are included here in Table 3-9. Note that these values

are estimates and highly uncertain, as there is a lack of data on combustion completeness in urban fires. Many studies report the fuel consumed or fire parameters, such as the heat flux, without quantifying an assumption for combustion completeness. These past studies have accounted for the possibility of incomplete combustion. In urban areas, it is also possible that noncombustible materials that are rubblized in the blast may cover combustible materials and prevent them from burning, a factor that could reduce the combustion completeness significantly in areas that primarily contain concrete structures.

TABLE 3-9 Combustion Completeness Assumptions for Studies Focusing on Urban Scenarios

SOURCE: Brown et al. 2023.

3.3.2 Key Uncertainties and Data Gaps

There are numerous uncertainties related to fire ignition, spread, burned area, and fuel consumption. Fuel characteristics (e.g., moisture content, albedo) are not well characterized or constrained, leading to uncertainty in the conditions required for ignition and fire spread. Additionally, there is a lack of understanding regarding the importance of ignition from thermal output of the weapon relative to ignition from blast-damaged buildings and infrastructure (see Chapter 2 for additional discussion) (Glasstone and Dolan, 1977). In the Hiroshima firestorm, secondary ignitions were reported to have played a large role in the initial fire starts, reminiscent of the fires following the San Francisco earthquake in 1906 (Scawthorn, 1986; Wilton et al., 1981).

Fire-spread models often do not predict the observed behavior well, due to the lack of available fuel data, parameterization of physical processes, and limited validation, particularly those in the WUI and in urban areas. Few models include chemistry and there are remaining questions about chemistry near the interior of the fire area, including oxygen-limited zones that may impact fuel combustion and fire spread. The amount of flaming versus smoldering combustion is important for the determination of fuel consumption and resulting emissions, and calculating plume height; however, current models do not constrain these well.

It might be expected that the urban fuels are rubblized in the initial stages of the blasts. However, it is not well understood how rubblized fuels, versus standing fuels, would burn in a fire or whether in certain scenarios subsequent detonations closely spaced in time could serve to quench fires from earlier detonations. It is possible that ruptured fuel lines and smoldering debris from a previous detonation could restart fires and increase fuel consumption. Fire spread will also be impacted by the ambient atmospheric conditions—including weather and seasonality—and by fire weather and winds, and the turbulence associated with a fire, which are not well constrained. These interactions may result in negative feedbacks to fire spread. Many studies of the climate effects from nuclear detonations assume that every detonation will become a mass fire; however, this outcome is unlikely. Improved fuel characterization and fire modeling tools will enable improved predictions of fire behavior, and previously developed criteria for firestorm formation can be applied to determine the likelihood of a mass fire for given fuel and atmospheric conditions.

The efficacy of firefighting as a means to constrain fire spread and burn area after a detonation is unknown. In Hiroshima and Nagasaki, most of the firefighting equipment was destroyed, and firefighting personnel were killed or unable to approach the fire (U.S. Strategic Bombing Survey, 1946). Damage was

sustained to the water distribution system resulting in a drop in water pressure, which also limited firefighting abilities (Maurer et al., 2009). The degree to which modern fire-resistive construction and firefighting will limit fire spread after a nuclear detonation is not well understood.

Overall, the type and amount of fuel that is burned, which is important in the determination of the emissions, is very important, and not well constrained, particularly when it is a combination of urban and vegetative fuels and overestimating the fire effects can lead to predicting more emissions to the atmosphere and more climatic and environmental impacts.

3.4 EMISSION FACTORS

3.4.1 State of Knowledge

The emissions of aerosols and gases from fires can be parameterized by emission factors, expressed as the mass of emitted pollutant per quantity of fuel burned. The type of fuel that is burned can control what is emitted and the quantity. Emission factors from wildland fires have been characterized through laboratory studies (e.g., (Hatch et al., 2019; Stockwell et al., 2014) and in the field (e.g., Karl et al., 2007; Kaufman et al., 1998; Permar et al., 2021; Warneke et al., 2023). There have been several efforts to compile the results of these many studies to produce consistent and summarized data for use in modeling applications (e.g., (Akagi et al., 2011; Andreae, 2019; Holder et al., 2023; May et al., 2014).

3.4.1.1 Emissions from Wildland Fires

Many hundreds of gas-phase species have been measured from wildland fire burning, as well as bulk particulate species and chemical components of particulate matter. Andreae (2019) compiled wildland fire emission factors from recent studies (some of which are included in Table 3-10). This compilation includes factors representing a complete wildland fire (rather than emissions calculated from different fuel components, such as the duff, crown, or trunk), although it is recognized that emissions are also dependent on the fire stage and the fuel conditions.

TABLE 3-10 Emission Factors (g/kg) for Key Gas-Phase and Aerosol Species Emitted from Various Types of Biomass Burning

NOTE: BC = black carbon; EC = elemental carbon; OC = particulate organic carbon; NMOG = non-methane organic gases; PAHs = polycyclic aromatic hydrocarbons.

SOURCE: Adapted from Andreae, 2019.

In forest biomes, wildfires oxidize most of the carbon in fuels to carbon dioxide and carbon monoxide (>99%), with the production of methane, non-methane organic gases (NMOGs), and OC accounting for most of the remainder (Andreae, 2019). BC typically is only 5% to 11% of primary OC production. Emission factors for BC are remarkably similar across tropical, temperate, and boreal forest ecosystems, with the mean values from the Andreae (2019) synthesis varying between 0.44 and 0.55 g/kg.

These estimates are relatively well constrained by observations from 26 separate field campaigns and standard deviations across different biomes varying between 0.21 and 0.36 g/kg (Table 3-10).

The physical and optical properties of aerosols emitted from biomass burning have also been characterized in numerous studies. Kaufman et al. (1998) and Remer and Kaufman (1998) found that the single-scattering albedo, or ratio of scattering efficiency to total extinction efficiency of a particle, of biomass burning aerosol in Brazil ranged from 0.82 to 0.94). The mean diameter of particles released by biomass burning are correlated with the combustion efficiency of the fire (Adler et al., 2011; Eck et al., 2001). The Fire Laboratory at Missoula Experiments study measured the geometric mean diameter of fresh biomass burning emissions of 0.20 – 0.57 µm and aerosol effective refractive indexes with values ranging from 1.41 to 1.61 (Levin et al., 2010).

3.4.1.2 Emissions from Urban Fires

Analogs, although imperfect, for emissions from urban fires ignited from a nuclear exchange may come from emission factors derived from the burning of other human-based materials, such as garbage and the burning of buildings and vehicles in the WUI. Garbage-burning measurements are sparse in the Andreae (2019) synthesis, with only three available studies. The BC emission factor for this class of burning (1.4 g/kg with a standard deviation of 5.1 g/kg), which presumably contains a broader range of combustible materials, including plastics, is nearly a factor of 3 higher than the mean of emission factors measured for forests. Gullett et al. (2017) estimated the emissions of BC, OC, polycyclic aromatic hydrocarbons (PAHs) and other pollutants of the Deepwater Horizon surface oil burns and derived a BC emission factor of 53 g/kg and OC of 4.1 g/kg. Middlebrook et al. (2012) estimated a BC emission factor of 39 g/kg from their in-situ measurements around the same event.

Brown et al. (2023) suggests a 2.5% smoke yield for a mix of fuels representative of urban areas and notes the importance of categorizing thick and thin fuels, which is not typically done in urban inventories, so that fire spread models can later use this information when calculating the rate and completeness with which the fuels will burn. Emissions from the combustion of urban fuels are described at length in the National Academies report on Chemistry of Fires at the Wildland-Urban Interface (NASEM, 2022). Current understanding of urban fire emissions is derived primarily from experimental fires, typically enclosure fires, with limited full-scale fire simulations. The results were highly variable as experiments were performed with differing fuel types, fuel loading, and combustion conditions (e.g., temperature and ventilation) (NASEM, 2022). Further, Holder et al. (2023) synthesize emission factors from the burning of urban fuels. For structures, they recommend an BC emission factor of 7.68 g/kg and an EC/OC ratio of 4.4 (much higher than the average waste burning emission factor from Andreae 2019). They report the emission factors of other trace gases, toxic compounds and metals that are emitted when urban fuels are burned. Table 3-11 shows a sample of the compiled emission factors from Holder et al. (2023).

TABLE 3-11 Key Emission Factors (g/kg) from Urban Fuels

SOURCE: Adapted from Holder et al., 2023.

resulting fires all determine the fire behavior and emissions. To predict the emissions of a given scenario of a nuclear detonation(s), the fuel loading on the surface—including the structure density and composition as well as the vegetation—needs to be well constrained. The type of blast will determine the extent to which the resulting fires and blast will impact those fuels. And based on the type of fuel and the extent to which the fuels are burned, the emissions can be estimated. These factors also relate to the heat flux and energy of the fires and lofting of emissions, the longevity of emissions in the atmosphere, and associated impacts as will be discussed in the following chapters. These are further complicated depending on the spatiotemporal relationship of subsequent blasts and the scenario, with questions as to whether these would have additive or somehow otherwise interacting characteristics.

3.6 FINDINGS AND RECOMMENDATIONS

FINDING 3-1: Fuel composition and loading—which are important in the estimation of fire spread and fuel consumption, the assignment of emission factors, and the prediction of the resulting plume height—of urban, suburban, and industrial areas are not well-characterized and vary significantly across the world. These also differ significantly from those of rural and wildland regions, for which more is known. Accurately characterizing fuel loading for a given nuclear exchange scenario is important in determining downstream environmental effects.

RECOMMENDATION 3-1: The National Nuclear Security Administration should coordinate with other federal agencies (e.g., the U.S. Departments of Agriculture, Defense, and Justice, the Federal Emergency Management Agency, the National Institute of Standards and Technology, and the National Science Foundation) to fund research by national laboratories and independent researchers with requisite capabilities to develop an ecosystem of modeling of urban fuels and buildings that would reduce uncertainties in the estimate of fire spread and emissions. These efforts should include:

• Integration of vegetation with the determination and mapping of urban and wildland–urban interface fuels in different regions;
• Improving available data and consistency of information on construction materials and building contents for improved accuracy of fuel load modeling;
• Validation and verification of fire load data and fire load modeling used in existing studies, model input sensitivity studies, models that account for uncertainties; and
• Clearly defined assumptions and modeling approaches for improved model intercomparison.

FINDING 3-2: Physical and environmental conditions, including rubblization, humidity, and topography, will have significant impact on the ignition and spread of fires following the detonation of a nuclear weapon.

FINDING 3-3: Existing ignition and spread models lack the detail needed to evaluate fire behavior in urban and wildland–urban interface areas that may result from a nuclear detonation, both in understanding the physics of fire dynamics and spread, and at the level needed to understand the resultant chemistry.

RECOMMENDATION 3-2: The National Nuclear Security Administration should coordinate with other federal agencies (e.g., U.S. Departments of Agriculture, Defense, and Justice, the Federal Emergency Management Agency, the National Institute of Standards and Technology, and the National Science Foundation) to fund research by national laboratories and independent researchers with requisite capabilities to support model development and experimental studies to improve understanding of fire ignition, spread,

and fuel consumption, and validate the models and assumptions particularly for urban and wildland–urban interface areas. These studies should consider the conditions that will produce likely upper and lower bounds with respect to fire ignition, spread, and fuel consumption, including dampening effects such as shielding, rubblization, and atmospheric humidity.

FINDING 3-4: The quantities of emitted black carbon and organic carbon, as well as optical and physical properties of emitted particulate matter, are important for determining plume behavior and potential downstream environmental effects from fires resulting from a nuclear detonation, but highly uncertain.

RECOMMENDATION 3-3: When estimating the emissions from a nuclear explosion and resulting fires, appropriate ranges of all emissions—including black carbon and organic carbon, trace gases, and other pollutants—should be determined and sensitivity analyses performed.

RECOMMENDATION 3-4: EENW researchers should improve on estimates of the quantity and physical and optical properties of black carbon and organic carbon including size upon emission to evaluate the environmental impacts of a nuclear explosion and resulting fires.

FINDING 3-5: Emissions of other trace gases and toxins during a nuclear blast have not been studied but can have important impacts on potential EENW. These emissions, particularly from burning of urban and wildland–urban interface fuels, are highly uncertain.

RECOMMENDATION 3-5: Researchers and firefighting agencies (e.g., the Federal Emergency Management Agency) should work together to measure aerosol and trace gas composition of fires in urban and suburban environments. This recommendation is directly related to the research agenda in the NASEM (2022) WUI report, and the information from these campaigns would also advance many goals related to better understanding fire effects on human health.

3.7 REFERENCES

E. Kuligowski, M. Puchovsky, J. Torero, J. M. Watts and C. Wieczorek, eds. New York, NY: Springer New York.

Michelsen, H.A., M.B. Colket, P.E. Bengtsson, A. D’anna, P. Desgroux, B.S. Haynes, J.H. Miller, G.J. Nathan, H. Pitsch, and H. Wang. 2020. “A review of terminology used to describe soot formation and evolution under combustion and pyrolytic conditions.” ACS nano, 14(10): 12470-12490. https://doi.org/10.1021/acsnano.0c06226.

Middlebrook, A. M., D. M. Murphy, R. Ahmadov, E. L. Atlas, R. Bahreini, D. R. Blake, J. Brioude, J. A. de Gouw, F. C. Fehsenfeld, G. J. Frost, J. S. Holloway, D. A. Lack, J. M. Langridge, R. A. Lueb, S. A. McKeen, J. F. Meagher, S. Meinardi, J. A. Neuman, J. B. Nowak, D. D. Parrish, J. Peischl, A. E. Perring, I. B. Pollack, J. M. Roberts, T. B. Ryerson, J. P. Schwarz, J. R. Spackman, C. Warneke, and A. R. Ravishankara. 2012. Air quality implications of the <i>Deepwater Horizon</i> oil spill. Proceedings of the National Academy of Sciences 109(50):20280-20285. https://doi.org/10.1073/pnas.1110052108.

Mills, M. J., O. B. Toon, J. Lee-Taylor, and A. Robock. 2014. Multidecadal global cooling and unprecedented ozone loss following a regional nuclear conflict. Earth’s Future 2(4):161-176. https://doi.org/10.1002/2013EF000205.

Morvan, D., G. Accary, S. Meradji, and N. Frangieh. 2022. Fifty years of progress in wildland fire modelling: from empirical to fully physical CFD models. Comptes Rendus. Mécanique 350:1-9. https://doi.org/10.5802/crmeca.133.

NASEM (National Academies of Sciences, Engineering, and Medicine). 2022. The Chemistry of Fires at the Wildland-Urban Interface.978-0-309-27705-1. Washington, DC: The National Academies Press.

NFPA (National Fire Protection Association). 2018. NFPA 1971: Standard on Protective Ensembles for Structural Fire Fighting and Proximity Fire Fighting.

NFPA. 2021. NFPA 921: Guide for Fire and Explosion Investigations.

NFPA. 2023. NFPA 557: Standard for Determination of Fire Loads for Use in Structural Fire Protection Design.

NIST (National Institute of Standards and Technology). 2010. Fire Dynamics. https://www.nist.gov/el/fire-research-division-73300/firegov-fire-service/fire-dynamics.

NRC (National Research Council). 1985. The Effects on the Atmosphere of a Major Nuclear Exchange. Washington, DC.

Noman, M., H. H. Ahmad, A. Hamza, M. Yaqub, M. S. Ali, and A. Khattak. 2023. Assessment of Fire Load and Probabilistic Temperature for Office Buildings in Pakistan. Civil Engineering Infrastructures Journal 56(1):193-204. https://doi.org/10.22059/ceij.2022.341199.1827.

Pausata, F. S. R., J. Lindvall, A. M. L. Ekman, and G. Svensson. 2016. Climate effects of a hypothetical regional nuclear war: Sensitivity to emission duration and particle composition. Earth’s Future 4(11):498-511. https://doi.org/10.1002/2016EF000415.

Permar, W., Q. Wang, V. Selimovic, C. Wielgasz, R. J. Yokelson, R. S. Hornbrook, A. J. Hills, E. C. Apel, I.-T. Ku, Y. Zhou, B. C. Sive, A. P. Sullivan, J. L. Collett Jr, T. L. Campos, B. B. Palm, Q. Peng, J. A. Thornton, L. A. Garofalo, D. K. Farmer, S. M. Kreidenweis, E. J. T. Levin, P. J. DeMott, F. Flocke, E. V. Fischer, and L. Hu. 2021. Emissions of Trace Organic Gases From Western U.S. Wildfires Based on WE-CAN Aircraft Measurements. Journal of Geophysical Research: Atmospheres 126(11):e2020JD033838. https://doi.org/10.1029/2020JD033838.

Quintiere, J. G., and C. A. Wade. 2016. Compartment Fire Modeling. In SFPE Handbook of Fire Protection Engineering. Hurley, M. J., D. Gottuk, J. R. Hall, K. Harada, E. Kuligowski, M. Puchovsky, J. Torero, J. M. Watts and C. Wieczorek, eds. New York, NY: Springer New York.

Redfern, S., J. K. Lundquist, O. B. Toon, D. Muñoz-Esparza, C. G. Bardeen, and B. Kosović. 2021. Upper Troposphere Smoke Injection From Large Areal Fires. Journal of Geophysical Research: Atmospheres 126(23):e2020JD034332. https://doi.org/10.1029/2020JD034332.

Reisner, J., E. Koo, E. Hunke, and M. Dubey. 2019. Reply to Comment by Robock et al. on “Climate Impact of a Regional Nuclear Weapon Exchange: An Improved Assessment Based on Detailed

Source Calculations”. Journal of Geophysical Research: Atmospheres 124(23):12959-12962. https://doi.org/10.1029/2019JD031281.

Reisner, J., G. D’Angelo, E. Koo, W. Even, M. Hecht, E. Hunke, D. Comeau, R. Bos, and J. Cooley. 2018. Climate Impact of a Regional Nuclear Weapons Exchange: An Improved Assessment Based On Detailed Source Calculations. Journal of Geophysical Research: Atmospheres 123(5):2752-2772. https://doi.org/10.1002/2017JD027331.

Reitter, T. A., D. B. McCallen, and S. W. Kang. 1982. Literature survey of blast and fire effects of nuclear weapons on urban areas. United States.

Remer, L. A., and Y. J. Kaufman. 1998. Dynamic aerosol model: Urban/industrial aerosol. Journal of Geophysical Research: Atmospheres 103(D12):13859-13871. https://doi.org/10.1029/98JD00994.

Rodden, R. M., F. I. John, and R. Laurino. 1965. Exploratory Analysis of Fire Storms. San Franciso, California: Stanford Research Institute.

Scawthorn, C. 1986. Fire Following Earthquake. Fire Safety Science (1):971-979. https://doi.org/10.3801/IAFSS.FSS.1-971.

Schubert, R. 1969. Examination of the Building Density and Fuel Loading in the Districts Eimsbuttel and Hammerbrook in the City of Hamburg as of July 1943 San Francisco, California: Stanford Research Institute.

Seiler, W. and P.J. Crutzen. 1980. “Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning.” Climatic Change 2(3): 207–247. https://doi.org/10.1007/BF00137988.

Small, R. D. 1989. Atmospheric Smoke Loading from a Nuclear Attack on the United States. Ambio 18(7):377-383.

Stockwell, C. E., R. J. Yokelson, S. M. Kreidenweis, A. L. Robinson, P. J. DeMott, R. C. Sullivan, J. Reardon, K. C. Ryan, D. W. T. Griffith, and L. Stevens. 2014. Trace gas emissions from combustion of peat, crop residue, domestic biofuels, grasses, and other fuels: configuration and Fourier transform infrared (FTIR) component of the fourth Fire Lab at Missoula Experiment (FLAME-4). Atmos. Chem. Phys. 14(18):9727-9754. https://doi.org/10.5194/acp-14-9727-2014.

Toon, O. B., R. P. Turco, A. Robock, C. Bardeen, L. Oman, and G. L. Stenchikov. 2007. Atmospheric effects and societal consequences of regional scale nuclear conflicts and acts of individual nuclear terrorism. Atmospheric Chemistry and Physics 7(8):1973-2002. https://doi.org/10.5194/acp-7-1973-2007.

Toon, O. B., C. G. Bardeen, A. Robock, L. Xia, H. M. Kristensen, M. G. McKinzie, R. J. Peterson, C. S. Harrison, C. S. Harrison, N. S. Lovenduski, and R. P. Turco. 2019. Rapidly expanding nuclear arsenals in Pakistan and India portend regional and global catastrophe. Science Advances 5(10):eaay5478. https://doi.org/10.1126/sciadv.aay5478.

Turco, R. P., O. B. Toon, T. P. Ackerman, J. B. Pollack, and C. Sagan. 1983. Nuclear Winter: Global Consequences of Multiple Nuclear Explosions. Science 222(4630):1283-1292. https://doi.org/10.1126/science.222.4630.1283.

U.S. Strategic Bombing Survey. 1946. The Effects of the Atomic Bombings of Hiroshima and Nagasaki.

USFS (United States Forest Service)- National Research Station. 2021. N.R.S. Website. https://web.archive.org/web/20210427041223/http:/www.nrs.fs.fed.us/fia/maps/nfr/Final_PDF/northern_100dpi/xlivebio_northern_100.pdf.

van der Werf, G. R., J. T. Randerson, L. Giglio, T. T. van Leeuwen, Y. Chen, B. M. Rogers, M. Mu, M. J. E. van Marle, D. C. Morton, G. J. Collatz, R. J. Yokelson, and P. S. Kasibhatla. 2017. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9(2):697-720. https://doi.org/10.5194/essd-9-697-2017.

van Leeuwen, T. T., G. R. van der Werf, A. A. Hoffmann, R. G. Detmers, G. Rücker, N. H. F. French, S. Archibald, J. A. Carvalho Jr, G. D. Cook, W. J. de Groot, C. Hély, E. S. Kasischke, S. Kloster, J. L. McCarty, M. L. Pettinari, P. Savadogo, E. C. Alvarado, L. Boschetti, S. Manuri, C. P. Meyer, F. Siegert, L. A. Trollope, and W. S. W. Trollope. 2014. Biomass burning fuel consumption rates: a

field measurement database. Biogeosciences 11(24):7305-7329. https://doi.org/10.5194/bg-11-7305-2014.

Veregin, H. 1993. The role of uncertainty in nuclear winter modelling. Global Environmental Change 3(3):228-255. https://doi.org/10.1016/0959-3780(93)90042-J.

Wagman, B. M., K. A. Lundquist, Q. Tang, L. G. Glascoe, and D. C. Bader. 2020. Examining the Climate Effects of a Regional Nuclear Weapons Exchange Using a Multiscale Atmospheric Modeling Approach. Journal of Geophysical Research: Atmospheres 125(24):e2020JD033056. https://doi.org/10.1029/2020JD033056.

Walton, W. D., D. J. Carpenter, and C. B. Wood. 2016. Zone Computer Fire Models for Enclosures. In SFPE Handbook of Fire Protection Engineering. Hurley, M. J., D. Gottuk, J. R. Hall, K. Harada, E. Kuligowski, M. Puchovsky, J. Torero, J. M. Watts and C. Wieczorek, eds. New York, NY: Springer New York.

Wang, Y., and J. Zhou. 2019. Study on Residential Fire Load in the Yangtze River Delta. IOP Conference Series: Materials Science and Engineering 592. https://doi.org/10.1088/1757-899X/592/1/012166.

Warneke, C., J. P. Schwarz, J. Dibb, O. Kalashnikova, G. Frost, J. Al-Saad, S. S. Brown, W. A. Brewer, A. Soja, F. C. Seidel, R. A. Washenfelder, E. B. Wiggins, R. H. Moore, B. E. Anderson, C. Jordan, T. I. Yacovitch, S. C. Herndon, S. Liu, T. Kuwayama, D. Jaffe, N. Johnston, V. Selimovic, R. Yokelson, D. M. Giles, B. N. Holben, P. Goloub, I. Popovici, M. Trainer, A. Kumar, R. B. Pierce, D. Fahey, J. Roberts, E. M. Gargulinski, D. A. Peterson, X. Ye, L. H. Thapa, P. E. Saide, C. H. Fite, C. D. Holmes, S. Wang, M. M. Coggon, Z. C. J. Decker, C. E. Stockwell, L. Xu, G. Gkatzelis, K. Aikin, B. Lefer, J. Kaspari, D. Griffin, L. Zeng, R. Weber, M. Hastings, J. Chai, G. M. Wolfe, T. F. Hanisco, J. Liao, P. Campuzano Jost, H. Guo, J. L. Jimenez, J. Crawford, and T. F.-A. S. Team. 2023. Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ). Journal of Geophysical Research: Atmospheres 128(2):e2022JD037758. https://doi.org/10.1029/2022JD037758.

Wilton, C., D. J. Myronuk, and J. V. Zaccor. 1981. Secondary Fire Analysis. SSI-8048-6. Redwood City, CA: Scientifc Service Inc.

Xu, L., Q. Zhu, W. J. Riley, Y. Chen, H. Wang, P.-L. Ma, and J. T. Randerson. 2021. The Influence of Fire Aerosols on Surface Climate and Gross Primary Production in the Energy Exascale Earth System Model (E3SM). Journal of Climate 34(17):7219-7238. https://doi.org/10.1175/JCLI-D-21-0193.1.