Emissions from burned structures in wildfires as significant yet unaccounted sources of US air pollution

Domain: physics
Published: 2025-12-12
Editorial outcome: major_revision

Abstract
Structure fires in the wildland–urban interface (WUI) are becoming more frequent and destructive, yet their emissions of air pollutants remain poorly quantified and are not included in national inventories. Here we present a conterminous-scale inventory of WUI-related structure fire emissions in the United States from 2000 to 2020. A small number of highly destructive events dominate structure fire emissions—the 20 most destructive fires account for 68% of total carbon monoxide emissions. Structure fire emissions are more spatially concentrated than vegetation fire emissions, and in several states emissions of specific hazardous air pollutants such as hydrochloric acid exceed those from all anthropogenic sources combined. We show that structure burning in wildfires is strongly influenced by fire-conducive weather, and destructive structure fires are more likely to occur in forested and urbanized landscapes. These results reveal structure fires as a major source of toxic air pollution, with important implications for air quality, public health, and fire management.

Introduction
The wildland-urban interface (WUI) is the geographic area where wildland vegetation and land developed by human activity come into contact or intermingle1. Fires in WUI have long posed a critical and growing challenge, particularly across the United States (U.S.), as they often involve not only natural vegetation but also the combustion of buildings, infrastructure, vehicles, and other urban materials. This mix of structural and vegetative fuels can produce smoke with distinct chemical composition and toxicity2. Since 2005, WUI fires have destroyed more than 100,000 homes across the U.S3. Recent studies have shown that WUI area, WUI housing, and the fractions of fire counts and burned area within WUI regions are rising4, thus highlighting the increasing prevalence of WUI fires. Moreover, wildfires are growing 250% faster across the West since 20055, challenging fire suppression efforts that can reduce home ignitions. In recent years, several destructive (defined by the number of houses destroyed in fires) WUI fires have highlighted the increasing significance of these WUI fires, including the 2018 Camp Fire in Butte County, California, which burned more than 15,000 structures6; the 2021 Marshall Fire in Boulder County, Colorado, which destroyed more than 1000 structures7; the 2023 Lahaina Fire in Maui, Hawaii, which destroyed more than 2000 structures8; and most recently, the 2025 fires near Los Angeles, California, which are estimated to have destroyed at least 16,000 structures9.

WUI fires differ from wildland fires in several important ways. First, WUI fires occur where human development and wildland areas overlap, and they are often initiated when vegetation fires spread into communities and ignite structures—typically through embers—rather than originating in urban settings. Because of this overlap, human-caused ignitions are more dominant, leading to a higher likelihood and frequency of ignitions compared to those in remote wildlands10,11. Additionally, because WUI fires often occur close to population centers, their emissions can more directly degrade local air quality, resulting in extensive public health impacts10,12–14. Fire behavior, environmental drivers, suppression strategies, and management approaches also differ significantly between WUI and wildland fires7,15–20. Perhaps most importantly for this study, WUI fires often involve the burning of structures, a key distinction from purely wildland vegetation fires12,14,21.

From the perspectives of atmospheric chemistry, air quality, and public health, structure burning is particularly important to quantify due to the toxic emissions produced from building materials22,23. Compared with vegetation fires, structure fires emit a broader and more complex mix of hazardous pollutants due to the combustion of materials, such as plastics, treated wood, wiring, and household contents. This mixture includes not only criteria pollutants, such as carbon monoxide (CO) and particulate matter (PM), but also volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), acid gases (e.g., hydrochloric acid, HCl), and toxic metals (e.g., lead, Pb)2,14. These pollutants not only degrade outdoor air quality but also infiltrate homes that are adjacent to the burn area, posing health risks to residents in nearby WUI neighborhoods13,23. In this study, we study emissions from burned structures in wildfires and use HCl and Pb as illustrative hazardous pollutants.

Quantifying emissions from burned structures in wildfires is essential for understanding their impacts on atmospheric composition and chemistry, air quality and human health. However, unlike vegetation fire emissions, which have been quantified through extensive modeling and field measurements24–27, large-scale information on structure fire emissions remains scarce. Quantifying fire emissions requires three key pieces of information: fire occurrence and extent, the mass of fuel burned (can be estimated from fuel load and percent combustion), and emission factors (EFs) for each chemical species (i.e., mass of pollutant emitted per mass of fuel burned)28. Obtaining these data at large scales has historically been challenging and subject to significant uncertainties, even for vegetation fires29. For structure fire emissions, it has only recently become feasible, thanks to the availability of critical datasets including the all-hazards incident records mined from the U.S. National Incident Management System (NIMS)3, structure fuel load data30,31, and recently developed EFs for structure burning14.

Despite remaining uncertainties, we present here a comprehensive inventory of structure burning emissions in wildfires across the conterminous U.S. for the period 2000–2020—named as the Fire Inventory from NCAR—Wildland Urban Interface (FINN-WUI) version32. We further analyze the spatial characteristics, temporal trends, and drivers of structure burning emissions, providing a foundation for future work on atmospheric and health impacts of WUI fires. Note that we do not use formally defined WUI layers (for example, ref. 33) in this analysis. Instead, we characterize events as WUI-related or structure fire emissions based on the co-occurrence of structure loss and wildfire activity. Our estimates focus on structure-impacted wildfires rather than relying on spatially delineated WUI boundaries.

Results
We compare structure burning emissions across the conterminous U.S. from 2000 to 2020 with emissions from vegetation burning. Here, we use CO as an illustration. CO is a major atmospheric criteria pollutant and a widely used tracer for fire emissions, with its EFs being relatively similar between structure and vegetation fires—approximately 70 g/kg of fuel for structure burning and 100 g/kg for biomass burning. The spatial pattern of CO emissions from structure and vegetation fires across the conterminous U.S. reveals both similarities and key distinctions (Fig. 1). From 2000 to 2020, structure fires occur nationwide, with the largest accumulation of burned structural fuel in California and the Southeast (Fig. 1A). In 2020, several structure fire events produce CO emissions exceeding 10⁹ grams (g), with notable hotspots in California (Fig. 1B). While structure fire emissions are episodic and locally intense, their spatial footprint in 2020 closely aligns with vegetation fire emissions within the WUI regions (Fig. 1C) and often across all landscapes (Fig. 1D). This resemblance suggests that both structure and vegetation fire emissions may be driven by common factors, such as regional fire weather and broader climatic conditions, which are explored in a later section. The co-occurrence of emission hotspots in areas such as California and the Southeast suggests that fire-conducive environments can simultaneously increase risks across various fuel types and land-use categories. Despite these similar regional patterns, structure fires tend to occur nearer to population centers, often amplifying their impact on air quality and public health10.

Despite their overall similar regional patterns, CO emissions from structure fires exhibit stronger spatial and event-level concentration across the conterminous U.S., in contrast to the broader distribution of emissions from vegetation fires. As shown in Fig. 2A, average annual CO emissions from states that experienced vegetation burning (2002–2020) are highest in California, Oregon, and several southeastern states, but are distributed across many parts of the country. These emissions include prescribed fire emissions, which are distributed across the southeast. In contrast, structure fire CO emissions (Fig. 2B) are more geographically concentrated, with California alone contributing nearly 5×10⁹ g/year (~69%). Other high-emission states include Tennessee, Texas, and Oregon, where large WUI fires have occurred. To complement our state-level emissions analysis, we normalize structure fire CO emissions by total burned area in each state (Fig. 2C). This reveals where structure burning emissions are disproportionately high relative to overall fire activity. California and Tennessee stand out, highlighting the intensity of structural loss in these states. Other states, such as Colorado, New York, and Michigan, also exhibit elevated structure emission density relative to their total burned area. These results suggest that structure fire emissions are not solely a function of fire size, but also depend on land use, structure density, and fire-urban interactions.

Figure 2D illustrates the dominant impact of a few extreme events. The 20 wildfires with the highest estimated structure fuel burned between 2000 and 2020 account for a disproportionate share of national burned structure emissions in wildfires. The 2018 Camp Fire in California stands out, with CO emissions from burned structure exceeding 5×10⁵ g and nearly 15,000 structures destroyed. These findings demonstrate how a small number of high-impact fires can significantly contribute to national emission totals, reflecting the episodic yet intense nature of structure fire pollution. Emissions of structure burning in wildfires are largely driven by extreme events, which appear to have become more frequent in recent years. Of the top 20 events in Fig. 2C, only 5 occurred during 2000–2010, while 15 occurred between 2010 and 2020. Notably, 12 of them took place in the most recent five years (2016–2020), suggesting a significant upward trend in the frequency of large, high-emission structure burning events (bootstrap test, p-value (p) < 0.01). The implications of this trend and the associated year-to-year variability will be analyzed in the next section.

Structure fire emissions (blue line in Fig. 3A) exhibit substantial year-to-year variability, with prominent peaks in 2003, 2007, 2016, 2020, and especially 2018—the year of the Camp Fire, which result in nearly 6×10¹⁰ g of CO emissions, accounting for 26% of the total emissions from structure burning in wildfires during the 2000–2020 period. The early 2000s are marked by relatively low fire activity and smaller events, while the post-2010 period has a clear increase in both the frequency and scale of wildfires that involve structure burning (Figs. S1 and S2). Vegetation fire emissions in the conterminous U.S. (orange line) are generally an order of magnitude higher and have smaller interannual variability (coefficient of variation for the time series of vegetation fire CO emissions and structure burning CO emissions in wildfires are 0.3 and 1.5, respectively), reflecting the broader spatial extent and frequency of wildland fires. These year-by-year variations show the episodic nature of structure fire pollution and the growing prevalence of large, high-emission events in recent years.

Despite the difference in absolute magnitude, the structure and vegetation fire emissions share similar interannual behavior in earlier years. Between 2002 and 2015, structure and vegetation fire emissions were moderately correlated (r = 0.51), suggesting common climatic or environmental drivers. From 2016 to 2020, this correlation weakens (r = 0.33), although the shorter time window limits the statistical confidence of this comparison. The reduced correspondence in recent years may indicate the emergence of factors influencing structure fire emissions that are less aligned with general vegetation fire variation. Long-term averages further emphasize a shifting structure fire regime. Between 2011 and 2020, average annual CO emissions from structure burning are approximately three times greater than those from 2000 to 2010, while emissions from vegetation burning increase by only 14% over the same periods. This disproportionate growth points to an increasing frequency and severity of high-impact structure fires. Notably, the top 20 most destructive events (‘destructive’ refers specifically to destruction of structures in this study) account for 68% of total structure fire CO emissions over the full period, highlighting the extent to which annual totals are shaped by a small number of extreme events. However, these events are becoming less rare. Recent fires—including the 2021 Marshall Fire (Colorado), the 2023 Lahaina Fire (Maui, Hawaii), and the 2025 fires near Los Angeles (California)—signal an ongoing trend toward more frequent, high-emission structure fires.

The monthly distribution of fire incidents highlights important differences between total fire activity, structure-involved fires, and the most destructive events (Fig. 3B). Both total fire incidents and those involving structure burning exhibit similar seasonal patterns, with peak activity in July and August. In contrast, the 20 most destructive structure fires are heavily concentrated in the late summer and early autumn (August to October), a period when overall fire counts begin to decline, but aligns with high wind conditions and often human-related ignitions, particularly in California34. This seasonal asymmetry suggests that the most damaging structure fires tend to occur under different conditions than typical wildland fires. The seasonality of the 20 most destructive structure fires is largely driven by wildfires in the western U.S., with 18 of the 20 events occurring in the West and only 2 in the East. Regional differences in seasonal patterns are further illustrated in Fig. S3. The seasonality of the most destructive structure fires also shifted from a peak in October (2000–2010) to August–September (2011–2020), indicating a trend toward earlier occurrence of most destructive events (Fig. S4). This seasonal asymmetry is consistent with the possibility that the largest structure-loss incidents occur later in the fire season—when fuels are drier and episodic offshore wind events (e.g., Santa Ana/Diablo) could promote rapid WUI fire spread—conditions that may differ from those governing typical summer wildland-fire activity35,36.

Structure fires contribute substantially to atmospheric pollutant burdens across the conterminous U.S. Averaged over 2000–2020, structure burning emits a broad suite of pollutants (Fig. 4A), including CO (up to 8×10⁹ g/year), total hydrocarbons, particulate elemental carbon, and a diverse range of hazardous air pollutants (e.g., PAHs, VOCs, and trace metals). These emissions, which are largely unaccounted for in current national inventories, can rival or exceed known anthropogenic sources in specific states. To illustrate this, we compare structure burning emissions of HCl and lead (Pb) in 2016–2020 with anthropogenic estimates from the U.S. Environmental Protection Agency (EPA) 2020 National Emissions Inventory (NEI). HCl and lead (Pb) are critical pollutants due to their significant health risks. HCl is classified as a respiratory irritant with both acute and chronic health risks upon inhalation by NIOSH37,38. Pb exposure can affect almost every organ and system in the body, especially for children38–40. In California and Tennessee, average annual HCl emissions from structure burning in wildfires during 2016–2020 exceed those from anthropogenic sources (Fig. 4B). Moreover, in California, Tennessee, Colorado, and Oregon, structure fire HCl emissions surpass anthropogenic sources in at least one year within that period (Table S1). Notably, in 2018, HCl emissions from structure burning in California were approximately 23 times higher than anthropogenic emissions. Pb emissions from structure burning are also substantial in several states (Fig. 4C). In Tennessee, average annual Pb emissions from structure burning in wildfires during 2016–2020 are roughly three times greater than those from anthropogenic sources. In 2016, Pb emissions from structure burning in Tennessee reached nearly 14 times the anthropogenic total (Table S2). In California, 2018 structure fire Pb emissions are equivalent to 54% of the state’s anthropogenic Pb emissions. In Washington in 2020, Pb emissions from structure burning are equivalent to 28% of anthropogenic emissions, while in Oregon in 2020, they represent 21% (Fig. 4C). More recent wind-driven WUI fire events, such as the 2021 Marshall fire in Colorado, 2023 Lahaina fire on Maui, Hawaii, and the 2025 Los Angeles fire, may have produced even greater emissions, highlighting the growing importance of structure fires as a pollution source, particularly for Pb and HCl.

We analyze the land cover type for wildfires involving structure burning (Fig. 5). Fires are first grouped into five categories based on the number of structures impacted. The number of structures impacted is defined as the number of structures destroyed plus 30% of the number of structures damaged, to approximate the partial contribution of damaged structures to overall fuel loss (uncertainties discussed in the Methods section). For each fire in each category, we then overlay the fire perimeter with the Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover type data (MCD12Q1, 500 m resolution) and count the pixels in each land-cover class. Summing the pixel counts across all fires within a category yield the overall fractional land-cover composition shown in Fig. 5. Across all groups, the majority of burned area occurs in shrublands, grasslands, and savannas, regardless of the number of structures impacted. This can be partially explained in that fire can propagate much faster in these types of ecosystems5. However, the fraction of forested land cover tends to increase with the number of structures impacted. For fires that affect more than 100 structures, forests account for 34.5% of the burned area—substantially higher than the 8.4% to 20.1% range observed in fires with 100 or fewer impacted structures. Based on data from 2000–2020, fires occurring in forested landscapes are more frequently associated with higher numbers of structures impacted, which is consistent with a previous study41. However, recent events such as the 2021 Marshall Fire and the 2025 Los Angeles fires—primarily grassland and shrubland fires—highlight that extensive structural losses can also occur in non-forested areas. Another notable pattern is the shift in cropland and urban land fractions. Fires that impact more than 100 structures tend to occur in areas with less cropland and cropland/natural vegetation mosaics (0.24%) and more urban and built-up lands (0.49%) compared to those that impact fewer structures (0.45–2.25% for croplands; 0.11–0.28% for urban areas). Fire size is an important driver of structure loss, as larger fires—often more difficult to suppress—have a greater potential to intersect developed areas and affect more structures than smaller, more easily contained fires. At the same time, the disproportionately high emissions from certain states (e.g., Colorado, New York, Michigan) indicate that factors beyond fire size, such as land use patterns and structure density also influence structure fire emissions.

We also analyze the influence of fire weather conditions—represented by the Fire Weather Index (FWI), calculated from the Canadian Forest Fire Danger Rating System (CFFDRS)42—on structure-involved fire activity and emissions. The FWI is a widely used proxy for potential fire intensity that combines the influence of temperature, relative humidity, precipitation, and wind speed on fuel availability for combustion across different fuel classes, along with a proxy for fire spread rate (see the Methods section for details). Fire weather conditions show a significant impact on both the burned area and emissions associated with structure fires over the western and eastern U.S. (western and eastern U.S. is separated by -104° longitude). Correlations are notably higher in the West (Spearman rank correlation coefficient (ρ) = 0.75 for burned area; ρ = 0.67 for emissions) compared to the eastern U.S. (ρ = 0.22 for burned area; ρ = 0.18 for emissions) (Fig. 6), indicating that structure fires in the West are more strongly influenced by fire-conducive weather conditions. In contrast, the weaker correlations in the East suggest that other factors—such as land use, local fuel structure, land management, or human activities—may play a more prominent role in regional-scale WUI fire hazard than fire weather. The correlation between FWI and the burned area of wildfires involving structure burning is consistently stronger than that between FWI and emissions. This is partially because fire weather directly influences fire spread, which governs burned area, whereas emissions are additionally affected by the amount and type of structure fuel burned and fire suppression practices. These findings reinforce the role of fire weather as a key driver of structure fire behavior in the western U.S., while also pointing to the need for broader consideration of landscape and human factors in the eastern U.S. Structure-involved fires also exhibit a strong seasonal pattern. For the majority of events—excluding a few highly destructive outliers shown in Fig. 1C—larger burned areas and higher emissions tend to occur in late summer (August–September), coinciding with peak fire weather conditions as indicated by FWI. FWI is a function of the Initial Spread Index (ISI) that represents the potential fire spread rate based on daily weather fluctuations, and a buildup index that represents antecedent conditions from prior months. To assess the sensitivity of our results, we repeat the analysis using ISI alone. While specific correlation values differ slightly, the overall patterns and conclusions remain consistent for FWI and ISI (Fig. S6). This similarity suggests that structure burning in wildfires is primarily influenced by daily weather fluctuations rather than longer-term antecedent conditions.

Methods
This study combines geospatial estimates of combustible material (Combustible mass of the building stock in the conterminous United States (COMBUST)31 and high-resolution building count data to reconstruct structure fuel loads over two decades. Fire incident data from the all-hazards dataset mined from the US NIMS (ICS-209-PLUS), paired with fire perimeters from the Monitoring Trends in Burn Severity (MTBS) database of large fires, enable event-level attribution of structure loss (including structures destroyed and damaged). These structural parameters are linked with EFs compiled by ref. 14 to estimate pollutant release. A flowchart for estimating emissions from structure burning in WUI fires using the ICS-209-PLUS, COMBUST, MTBS, built-up property locations (BUPL), and Microsoft Building Footprints (MSBF) datasets can be found in Fig. S7. For comparison and validation, we leverage the FINN v2.5 dataset to assess vegetation fire emissions and the EPA NEI to benchmark structure fire emissions against traditional anthropogenic sources. Together, this integrated framework allows us to generate a national structure fire emissions inventory for the 2000–2020 time period, analyze spatial and temporal patterns, and examine potential key environmental drivers. MODIS land cover data and FWI data are used to assist analysis.

The structure fuel in a WUI fire is all the combustible material that is consumed in the fire, which can be wooden structures, furnishings, personal items, etc. The COMBUST dataset31 estimates the structure fuel load, by estimating the combustible mass of the building stock, enumerated in grid cells of 250 × 250 m, including building material, building contents, gas stations, and refineries. The combustible mass of building material is derived from remote-sensing-based data on building volume and building mass, disaggregated by building material components, available for the conterminous U.S. at 10 m spatial resolution for the year 201849. For the production of COMBUST, non-flammable materials from the ref. 49 data were excluded, and mass estimates were resampled to a 250 m grid. The combustible mass of building contents in COMBUST was derived using a method proposed by ref. 50, estimating the mass of flammable building contents based on building indoor area and building function derived from the Zillow Transaction and Assessment Dataset (ZTRAX), a property dataset commonly used in environmental research51. More details are provided in the supplement (Text S1).

The MSBF building counts dataset is a derived product from Microsoft’s US Building Footprints geospatial vector dataset52. While US Building Footprints vector data have been extracted from overhead imagery using deep learning, the MSBF dataset represents an aggregated version of the US Building Footprints dataset, aggregated into grid cells of 250 × 250 m, aligned to the COMBUST grid, reporting the number of buildings per grid cell across the conterminous United States. The MSBF building counts are available as part of the COMBUST Plus dataset, accompanying the COMBUST combustible mass estimates, reflecting the approximate U.S. building stock in the year 2020.

HISDAC-US is a collection of gridded datasets measuring different components (building density, building indoor area, settlement age, and building function) over long time periods (i.e., 1810–2020), using large amounts of geocoded building construction year information and other property-level attributes from the ZTRAX dataset53,54. One component of HISDAC-US is the BUPL dataset55, providing semi-decadal, gridded estimates of building densities across the conterminous U.S. from 1810 to 2020 at 250-m grid resolution. HISDAC-US BUPL data captures urbanization processes such as land development, densification and expansion of the built environment56 and is enumerated in the same 250-m grid as the COMBUST dataset.

The ICS-209-PLUS dataset3 is based on publicly available ICS-209 forms from the U.S. NIMS, comprising daily reports for 34,478 wildland fire incidents from 1999 to 2020. ICS-209 reports are completed for significant incidents and include fields for structures threatened, damaged, and destroyed57. Although only ~1–2% of wildfires become large incidents, they account for ≈95% of the nation’s burned area each year3; consequently, ICS-209-PLUS covers the events responsible for most area burned and underpins NICC’s published ‘structures destroyed’ statistics58. ICS-209-PLUS provides various information on fires, including the number of structures destroyed and the number of structures damaged during a specific fire. In addition, ICS-209-PLUS also provides fire ID, fire source ignition type, and fire point of origin and discovery date. Note that the ICS-209-PLUS dataset only includes burned structures in wildfires and does not include municipal fires.

For fire perimeters, we use data from the MTBS Burned Areas Boundaries for 1984–2024 dataset from the USDA Forest Service59. Fire shape for a fire is matched with fires from the ICS-209-PLUS dataset using the fire identifier. MTBS provides consistent assessments of fire size and severity across the conterminous U.S., Alaska, Hawaii, and Puerto Rico. The dataset includes wildfires and prescribed burns that exceed 1,000 acres in the western U.S. and 500 acres in the eastern U.S., covering all land ownership types.

We estimate pollutant emissions by multiplying burned structure fuel with median EFs from a previous study14. We use EFs in units of g kg⁻¹ of structure fuel burned. ref. 14. compiled twenty-eight (28) of the 92 references reported EFs for a total of 346 test conditions covering a range of chemical species that were included in the urban fuel emission factor compilation. We use the median values of the structure fire EFs in our estimation. A concise summary of the EFs used in this study is provided in Table S3.

FINN provides daily global estimates of pollutant emissions from open fires with a high spatial and temporal resolution for use in air quality, atmospheric composition, and climate modeling applications26. FINN v2.5 uses active fire detections from both MODIS at 1 km resolution and the Visible Infrared Imaging Radiometer Suite (VIIRS) at 375-m spatial resolution for the calculation of fire emissions. We use gridded FINN v2.5 data during 2002–202060 with resolution of 0.1 degree × 0.1 degree for analysis. In addition, specifically for the year 2020, we also use processed data from our previous study10 which separate FINN vegetation fire emissions to WUI vegetation fire emissions and wildland vegetation fire emissions.

The U.S. EPA NEI is the most comprehensive and widely used dataset for characterizing anthropogenic emissions in the U.S61. It provides detailed estimates of a broad range of pollutants, including greenhouse gases, criteria air pollutants, and hazardous air pollutants, from major human-related sources such as transportation, industrial processes, power generation, and residential fuel use. The NEI is updated every three years and integrates reported emissions data, activity-based estimates, and standardized modeling techniques. Emissions are typically reported at the county level, enabling spatially resolved analyses. Owing to its consistency and scope, the NEI serves as a critical resource for air quality modeling, regulatory planning, and tracking emissions trends. In this study, we compare structure burning emissions of selected pollutants—specifically HCl and Pb—to NEI anthropogenic emissions for 2020 from the 2020 Air Toxics Screening Assessment (AirToxScreen)62, highlighting the magnitude and relevance of structure burning as a previously unaccounted source of pollution.

FWI from CFFDRS is used in this study due to its widespread use in both operational and research communities, its representation of potential fire hazard as a function of fuel aridity and fire weather—including sensitivities to temperature, precipitation, humidity, and wind speed—and its demonstrated empirical relationships with burned area and extreme fire events across diverse global regions42,63–66. The FWI integrates the effects of fuel availability through a buildup index that combines three different classes of fuel moisture with the potential rate of fire spread (i.e., ISI). The buildup index incorporates information (e.g., from antecedent precipitation and temperature) on timescales of a couple months to the previous day, and ISI represents daily fluctuations in weather. We calculated daily FWI using the 1/24° gridMET dataset on its native grid67, following the methodology of previous studies65,66. Inputs included daily total precipitation, maximum temperature, minimum relative humidity, and mean wind speed, in place of traditional local noon values, as in prior studies. Calculated FWI is then spatially averaged for regional scale comparisons with WUI fire burn area and CO emissions. We additionally considered ISI from the CFFDRS that is a proxy for fire spread rate based on wind speed and fine-fuel moisture that responds more quickly to meteorological variability.

Land cover type yearly data from 2001 to 2020 from MODIS/Terra + Aqua (MCD12Q1 v6.1)68 are used for land cover type analysis (Fig. 5) across the conterminous U.S. The original resolution of these data is approximately 500 m.

The fuel load dataset COMBUST provides coverage for all years; however, MSBF and the derived gridded MSBF data are only available for 2020, approximately. To estimate historical building counts for the years 2000, 2005, 2010, and 2015, we used the 2020 MSBF dataset as a baseline and applied scaling factors derived from historical HISDAC-US BUPL datasets. For each U.S. state, we identified grid cells within state boundaries and computed year-specific scaling factors as the ratio of total BUPL values in the target year to those in 2020. These factors were then used to scale the 2020 MSBF building counts and generate proxy estimates for earlier years. This method assumes that temporal changes in BUPL are proportional to changes in building density, enabling reconstruction of historical building distributions where direct data are lacking. For intermediate years (2001–2004, 2006–2009, 2011–2014, and 2016–2019), we performed linear interpolation at the pixel level using the nearest available years. For example, the estimated structure count for 2002 was derived by interpolating between the 2000 and 2005 values.

Structure burning emissions are calculated individually for each fire event recorded in the ICS-209-PLUS dataset. We first filter the dataset to identify fires that involved structural damage or destruction. For each fire, the number of structures destroyed and/or damaged is extracted directly from ICS-209-PLUS. We assume that 100% of the structural fuel is consumed for destroyed structures, and 30% is consumed for damaged structures—an empirically chosen value, with associated uncertainties discussed later.

The total structure fuel burned in each fire is calculated as the number of structures affected multiplied by the fuel load per structure within the fire perimeter and the corresponding burn fraction (100% or 30%). Fuel load per structure is estimated by dividing the total structural fuel load from the COMBUST dataset within the fire boundary by the total number of structures from the MSBF dataset within the same area.

For fires with available fire perimeters from the MTBS dataset—primarily large-scale fires—we use the provided fire shapes (see Figure S8). For smaller fires or those without MTBS coverage, we assume a circular fire footprint centered on the point of origin reported in ICS-209-PLUS (also shown in Figure S8). The area of the circle equals to the reported burned area of the fire reported in ICS-209-PLUS.

In cases where there is a mismatch among ICS-209-PLUS, COMBUST, and MTBS—such as when ICS-209-PLUS reports structure involvement but no structural fuel or building count is found in COMBUST or MSBF—we estimate an average fuel load per structure using an expanding search window centered on the fire’s point of origin. We first attempt to calculate this ratio at a 0.01 degree × 0.01 degree resolution. If either the fuel load or structure count is zero at that scale, we expand to 0.1 degree × 0.1 degree, and if still unavailable, to 1° × 1°. This adaptive method ensures that a fuel load per structure can be estimated for all structure-involved fires in the ICS-209-PLUS dataset. This approach was applied in approximately 10% of fire events, though these account for only less than 1% of the total number of structures impacted.

The estimation of structure burning emissions and the interpretation of the potential impacts of those emissions involves inherent uncertainties. Health risks from structure burning emissions depend on exposure concentrations, which depend on atmospheric dispersion varying for each fire. We address and quantify uncertainties of the emissions where possible at each step of the estimation process. One potential source of uncertainty arises from the reliance on the ICS-209-PLUS dataset, which serves as the basis for identifying structure fires. While this dataset is expected to capture most wildfires involving structural losses, some events—particularly smaller ones—may be missing. However, ICS-209-PLUS is considered comprehensive for large-scale wildfires, which, as discussed earlier, are the primary drivers of emissions. Therefore, any possible missing small fires are unlikely to contribute significantly to total emissions, and we do not consider this a major source of uncertainty.

Structure fuel load is another potential source of uncertainty. The COMBUST dataset provides lower and upper bounds for the combustible mass of the building stock, which we use to estimate the range of possible emissions. Using 2020 as an example, the central estimate of structure fuel burned is 3.10 × 10⁵ metric tons, resulting in 2.14 × 10¹⁰ g of CO emissions. When applying the lower bound of combustible mass, the estimated fuel burned is 2.63 × 10⁵ metric tons with 1.82 × 10¹⁰ g of CO emissions. Using the upper bound, the estimates increase to 3.78 × 10⁵ metric tons and 2.61 × 10¹⁰ g of CO emissions (Figure S9). These bounds represent a decrease of approximately 15% and an increase of 22% relative to the central estimate. Figure S9 also illustrates the sensitivity of estimates to the method used for calculating fuel load per structure. When using a 0.1° × 0.1° (~10 km) box around each fire’s center instead of detailed fire perimeters, the estimated total structure fuel burned in 2020 increases from 3.10 × 10⁵ to 3.42 × 10⁵ metric tons—a 10% increase, which remains within the uncertainty range defined by the upper and lower bounds. However, applying coarser approaches such as a 1° × 1° box or state-level average fuel loads lead to much larger increases in estimated fuel burned—49% and 55%, respectively. The similarity between the 1° × 1° and state-average results suggests that the 1° resolution is too coarse to capture local variation. These findings indicate that structures burned in wildfires tend to have lower fuel loads than the overall average. A likely explanation is that the COMBUST dataset includes both commercial and residential buildings, while residential structures are more commonly affected by fires. According to ICS-209-PLUS data from 2000–2020, the number of residential structures destroyed is 20.5 times greater than that of commercial structures. When fire shape data or a 0.1° box is used, the selected burned structures more accurately represent real-world situation and their inherent spatial variation. In contrast, broader averaging approaches overrepresent commercial structures, leading to overestimation of fuel load. Using a conterminous U.S.-wide average fuel load per building yields a higher estimated structure fuel burned (5.68 × 10⁵ metric tons) compared to using state-level averages (4.79 × 10⁵ metric tons). This is because structure burning emissions are primarily driven by fires in a few key states. For instance, the average fuel load per building in California is 46 metric tons per structure, which is lower than the national average of 51 metric tons per structure.

We approximate 30% of the structure fuel is burned if a structure is reported to be damaged. We do not expect this assumption will significantly change the results because overall the number of damaged structures is not a large fraction. ICS-209-PLUS reported structures destroyed to be 7.7 times more than the number of structures damaged. To evaluate the sensitivity of this assumption on the emission estimates, we calculate structure burning CO emissions for 2020 assuming 10% and 50% of the structure fuel is burned instead of 30% if a structure is reported to be damaged. The resulting estimates are 2.08 × 10¹⁰ and 2.2 × 10¹⁰ g, which are within a range of ±3% of the annual total.

Another potential source of uncertainty arises from the hindcasting of structure fuel loads for years prior to 2020. While COMBUST provides historical fuel load estimates, the MSBF building count data are only available for 2020. As described earlier, we use the BUPL dataset to derive state-level scaling factors and interpolate building counts at five-year intervals. This step is essential for estimating historical structure fuel loads. Here, we assess the differences that would arise if 2020 COMBUST and MSBF data were used for all years, without applying hindcasting of structure fuel loads. While this does not directly quantify the uncertainty introduced by our hindcasting approach, it provides insight into how changes in structure fuel loads over time can affect emission estimates. As shown in Figure S10, using 2020 structure fuel loads for all the years results in a modest increase in total CO emissions from structure burning over 2000–2020—from 2.25 × 10⁶ to 2.37 × 10⁶ metric tons, a 5.5% increase. At the annual level, differences are generally under 20%, except for 2000, 2002, and 2005, which have relatively low total emissions and thus limited influence on the overall total. The largest relative difference occurs in 2000 (35%), with the discrepancy declining over time to 0.25% by 2019. Importantly, HISDAC-US BUPL data model historical building density distribution based on the age of the 2020 building stock suffers from a survivorship bias, as buildings that existed in the past and were destroyed and not rebuilt are not reflected in the data. Hence, structure emissions toward the year 2000 may have been slightly higher than our estimates, thus representing a lower boundary of historical emissions in this regard.

A final source of uncertainty lies in the EFs used to convert structure fuel burned into pollutant emissions. In this study, we adopt the median EF values reported by a previous study14, which represent the most comprehensive and up-to-date synthesis currently available. Among all uncertainty sources, EFs may contribute a substantial share to the overall uncertainty in emissions, as they reflect complex combustion processes that vary across structure types, materials, and burn conditions. Given the diversity of buildings and the impracticality of characterizing EFs at the level of individual structures, the use of representative median values is a necessary simplification for regional to national-scale estimation. While this introduces uncertainty, it reflects the best available approach at present. Further refinement of EFs will depend on the availability of more targeted field and laboratory measurements, especially for a wider range of air pollutants and structure types.

This study focuses exclusively on structure-burning emissions from wildfires; emissions from municipal fires are not included. Additionally, emissions from vehicles burned during wildfires are not accounted for, though they may also represent a significant source of hazardous air pollutants. Future research will aim to quantify emissions from burned vehicles to better capture the full spectrum of toxic releases associated with WUI fires.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study presents the first conterminous U.S. inventory of structure fire emissions from 2000 to 2020, highlighting their significant contribution to toxic air pollution as well as the influence of landscape and weather factors on fire risk. I fully understand that this is a very important topic, especially given the limited number of studies directly addressing it. However, I would like to suggest major revisions due to several significant issues and some decent number of mistakes throughout the manuscript.\nMajor revision\n1.Discussion:\nI see that some paragraphs in the results section are discussing the findings, but it does not seem sufficient. Could you write a separate discussion section? Maybe you can reorder and add some paragraphs when needed.\n2.Method\nFor each U.S. state, we identified grid cells within state boundaries and computed year-specific scaling factors as the ratio of total BUPL values in the target year to those in 2020\n  Did you use state-specific scaling factors? In other words, did you apply 48 separate scaling factors for each year, corresponding to the 48 states in the CONUS?\n  If yes, do you think this spatial resolution is sufficient? Is there a reason you chose to do this at the state level? I am also curious why you did not apply the scaling factors at the grid level. Your manuscript mentions that both datasets are at a 250 m resolution.\n3.Method\nI know you summarized this at the beginning, but the current version is not very clear. Could you provide the overall research objectives along with what data and methods you used right at the start of your Data and Methods section? You are using many datasets, but it currently takes time to figure out which data you used for each objective. Providing this summary upfront would greatly help readers understand your datasets and how they align with your objectives. Creating a diagram showing how each dataset was combined and used for each objective could also help make this clearer and easier to understand.\n4.Land cover\nYour land cover data is from 2020, but your analysis is based on the last 20 years. Do you think using a single year of land cover data accurately represents land cover conditions throughout the study period?\n5.Data aggregation\nHow did you change the resolution to 250 m, and what method did you use for resampling? When the spatial resolutions differed across datasets (e.g., 250 m vs. 500 m), how did you combine these datasets? Additionally, since each dataset likely had different projections, how did you handle projection differences before combining the data?\n6.Reference\n->The Chemistry of Fires at the Wildland-Urban Interface (2022)\n-> I am not sure if you had a chance to look at this before, but this provides lots of good information on WUI-related fires.\nMinor\n1.Line 156: wildland-urban interface (WUI)\n-> WUI\n2.Figure 1. Total emissions in 2020 or daily average? Do you have any specific reason for selecting and showing only 2020? You used 2000-2020 in Figure 1A and other figures are only showing 2020.\n3.Line 206: conterminous US\n-> CONUS\n4.Line 325: Specific reference using 30%. Curious how you empirically select this\n5.Figure5:\nPlease provide more details on how you performed the categorization. For example, did you divide the data into categories based on quantiles, such as the highest 20%, the next 20%, and so on? Or did you use arbitrary thresholds to define the categories?\n6.Line 451: Frantz et al. data were excluded\n-> year\n7.We use the COMBUST dataset (Combustible mass of the building stock in the conterminous United States; Uhl et al., 2025)\n-> We use the COMBUST dataset (Uhl et al., 2025)\n8.Line 449: conterminous United States -> This issue occurs repeatedly throughout the manuscript. Please check and revise consistently.\n-> CONUS\n9.Not clear\n-> and mass estimates were resampled to at 250m grid\nFrom what original spatial resolution was the data resampled, and why did you choose to resample it? What is the reported accuracy of this dataset? Also, what specific data sources were used for the remote sensing-based data?\n10.MSBF, BUPL\n-> Line 430: What does this stand for? You should spell out all acronyms the first time you use them. Please check the entire manuscript for consistency, as I see many issues with undefined acronyms like CONUS, BUPL, and others.\n11.the MSBF datasets represents\n-> represent\n12. spatial resolution\nThe historical settlement compilation for the U.S. (HISDAC-US)\n13.Vague\nThese fires account for the vast majority of fires that involved structure burning in the United States.\n-> Any reference and what percent is vast majority ?", "reviewer_2": "The presented manuscript “Emissions from Burned Structures in Wildfires across the Conterminous United States: A Significant Yet Unaccounted Source of Air Pollution” quantifies the emission of air pollutants from structure fires in the Conterminous United States. The authors used a set of existing data sources, e.g., regarding housing, combustible materials, fire weather index, or fire perimeters, to show, among other results, that that structure fires are a highly relevant source of pollution and that regionally, air pollutants from structure fires can exceed emissions from all other anthropogenic sources.\nThe study is very well conducted and well motivated, the manuscript is well written, understandable, and informative. The study provides highly relevant data and underlines the importance of understanding large-scale and extreme structure fires that increasingly threaten people. Despite the rather simple methodological approaches, I expect some impact in the scientific community.\nI recommend this manuscript for publication but would require the authors to address some comments and questions.\n- l. 113, \"we characterize events as WUI-related or structure fire emissions based on the co-occurrence of structure loss and wildfire activity\": Please provide more detail. I have not understood on what grounds fires have been characterised as WUI-related. Please let me know if I missed the information. Also, I wonder why the WUI context is important here.\n- The study uses a large amount of different datasets. I suggest some kind of a data and processing flowchart, e.g., as part of the SI. To clarify: How were emissions calculated - based on a multiplication of median emissions factors and fuel load from COMBUST? This would also increase reproducibility.\n- Unfortunately, the COMBUST dataset (Uhl et al. 2025) is not accessible. The link produces a \"DOI not found\" message. Please make this dataset available to the reviewers.\n- The abstract reads \" In several states, emissions of hazardous air pollutants such as hydrochloric acid from structure fires exceeded those from all anthropogenic sources combined.\": In Fig. 4 B, this only seems to be the case in California and Tennessee, while the numbers from most other states seem rather unimpressive. While I agree with you that toxic pollutants from structure fires are highly relevant, the results of this manuscript do not give me a sense or urgency. That is especially so as the numbers are often driven by single large fire events and in selected states only. What would the implications of these results be on a more regional level? Also, what is special about Tennessee compared to surrounding eastern states that structure fires are so destructive?\n- Why is Figure 4B using data from the 2016-2020 period?\n- Figure 1B, single events becomne hidden which makes the map very hard to read.\n- Figure 2D, is there a way to add states to some of the non-CA fires?\n- Could you please clarify what the data source is for figures s1 and s2?\n- l. 227: Does such a trend also exist for vegetation fires?\n- Figure S6: I dont understand how state mean and conus mean relate to \"with 1x1\" or \"with fire shape\"? Also, do the different bars refer to a scenario where an aggregation method is applied to ALL fires, or only the ones affected by, e.g., missing fire shapes?\n- l 234, \"This seasonal asymmetry suggests that the most damaging structure fires tend to occur under different conditions than typical wildland fires.\": There is probably reason to believe that the larger fires occurr towards the end of the fire season - so structures being affected is more likely and fire suppression is harder. Is this correct?\n- l 287 typo: \"can produce\"?\n- Figure 4: It would be interesting to see what the variation of land cover composition across all fires looks like, as you correctly point out the recent big fire events can substantially affect this statistic.\n- An would be helpful to understand the emission factors by Holder et al., as many readers might not be familiar with these.\nl 640: Is this sentence grammatically correct?\n- ll. 341 ff., \"Fires that impacted more than 100 structures tended to occur in areas with less cropland and cropland/natural vegetation mosaics (0.24%) and more urban and built-up lands (0.49%) compared to those that impacted fewer structures (0.45%–2.25% for croplands; 0.11%–0.28% for urban areas).\": I wonder if this is not a circular conclusion. When there is more urban land, fires can become more. Doesn't the overall small fraction of urban land across fires point to the fact that fire size is indeed the major driver of increasingly burning structures? Bigger fires that are hard to suppress have a higher chance of affecting a settlement than small fires that are easier to suppress. So far, the only reasoning that \"structure fire emissions are not solely a function of fire size\" that are highlighted in the manuscript are the comparatively high emissions from structure fires in Colorado, New York, and Wisconsin (shouldn't this be Michigan absed on Fig. 2c?), two of which are in the Eastern US where fire is less driven by weather and vegetation. The land cover/land use component of this work seems to end when it starts to be interesting.", "reviewer_3": "This is a very interesting and well-written study that focuses on emissions from burned structures, which have been less of a focus in the literature. This topic is particularly salient, considering the many recent destructive WUI fires, including in Los Angeles. The authors also provided very helpful regional context and implications throughout the paper.\nI appreciate the detailed Uncertainty section in the Methods, but I believe the authors could add a few sentences about limitations in the Implications section, or at least clearly refer readers to the Uncertainty section, as it is at the end of the paper.\nI believe the introduction section could benefit from a few sentences on the toxicity of air pollution from structure fires vs. typical vegetation fires, since that point is brought up a few times throughout the paper but not really explained. It should also state and explain the choice of pollutants the study will be more heavily focusing on so it is not surprising throughout the results.\nLine by line comments:\nLine 71 - why does only the Hawaii fire list the number of lives claimed?\nLine 77 - \"more dominant and leading to\" - could remove \"and\" and add a comma after dominant, if I am understanding the purpose of the sentence accurately.\nLine 80 - Can the authors explain the statement about fires in the WUI having a larger impact on air quality? If they just mean on public health impacts due to their location closer to population centers, I think the sentence could be rearranged to state that. Even \"air quality near population centers, resulting in more extensive public health impacts\" or something along those lines.\nLine 90 - rephrase to say \"not only degrade outdoor air quality but also...\"\nLine 102-103 - typo here \"has only recently has it\"\nLine 266 - When discussing Figure 4b, could caveat by explaining that in other states the structure fires come nowhere close to anthropogenic emissions so readers know what to expect when looking at the figure.\nLine 282 - this paragraph on potential health impacts feels important but slightly underdeveloped. I understand this is just the Results section, so perhaps more relevant for the Implications section, but it would be great for this discussion to be expanded with citations from the literature on 1) health impacts from these pollutants, 2) any existing studies that have evaluated those intense acute exposures from structure fires compared to vegetation fires, and 3) opportunities and challenges in conducting a traditional HIA/public health evaluations for these pollutants and types of fires.\nLine 369 - typo at the end of the line \"involve\"\nLine 401 - comma not needed\nLine 452 - typo \"to at\"\nLine 640 - is there an explanation or citation in the paper for how 30% was chosen?"}, "author_response": "Response: We thank the reviewer for the helpful comments. We have addressed each comment and concern point by point. Detailed responses and revisions are provided below.\nMajor revision\n1.Discussion:\nI see that some paragraphs in the results section are discussing the findings, but it does not seem sufficient. Could you write a separate discussion section? Maybe you can reorder and add some paragraphs when needed.\nResponse: We have created a dedicated Discussion section to provide a more comprehensive synthesis and interpretation of our findings. We moved interpretive content from the Results section to this new section, expanded the discussion of land cover context, regional contrasts, and extreme-event contributions, and added new paragraphs addressing public health implications and study limitations. The revised Discussion section is provided below for reference:\n“Discussion\nThe interannual variability and the seasonality together show that temporal patterns of structure and vegetation fires are becoming increasingly decoupled, even though they historically seem to respond to similar large-scale environmental conditions (Figure 3A; 2002–2015). This points to the possibly growing influence of distinct drivers behind high-impact structure fires. Unlike vegetation fires, which are often governed by broad-scale climatic patterns (Abolafia-Rosenzweig et al., 2022), destructive structure fires may be more sensitive to localized factors such as land use WUI expansion. Balch et al. (2024) found that fast fires that grew more than 1,620 hectares in 1 day accounted for 78% of structures destroyed and 61% of suppression costs. These differences highlight the need for targeted strategies in forecasting, emissions modeling, and risk mitigation that account for the unique dynamics of structure fire behavior.\nOverall, the results indicate that large destructive fires tend to be more likely to occur in forested zones with more urban and built-up lands from 2000-2020. This shift toward higher proportions of developed and forested land covers within the perimeters of more destructive fires highlights that landscape context plays a critical role in the level of destructiveness of structure fires, and that structure fire risk could be amplified in those land cover types and/or the ability to manage/suppress fires in forested regions may be challenged, even though savanna, shrubland, and grasslands are still the dominant land cover affected. These findings contribute additional knowledge to the conclusions of Higuera et al. (2023), who suggested annual structure number loss was well explained by area burned from human-ignited fires, while the decadal number of structure loss was explained by state-level structure abundance in flammable vegetation.\nIn the West, structure-involved fire activity aligns closely with fire conducive weather (higher FWI–burned area/emissions correlations), underscoring the role of meteorology and fast spreading events. In the East, weaker weather–fire relationships point to land use configuration, local fuel structure, and human activities as relatively stronger determinants of structure losses and emissions.\nFour states—California, Colorado, Oregon, and Tennessee—each have at least one year during 2016–2020 in which structure-fire HCl exceeded the state’s anthropogenic total (NEI). Elsewhere, structure-fire HCl is generally lower than anthropogenic totals. While most states remain below anthropogenic totals, structure-involved wildfires occur across most states of the country (Figure 1A), and severe events can occur outside the West (e.g., the 2016 Tennessee fires). Tennessee does not appear to have distinctly elevated baseline climate, land cover, or WUI extent relative to neighboring states (Beck et al., 2018; Radeloff et al., 2018). Its exceedance is dominated by a single late-season, wind-driven incident with extensive structure loss (Jiménez et al., 2018; Reilly et al., 2022), indicating that it is possible that similar fire events occur in other states given similar fuels, weather, and WUI exposure, even if such events were not observed during our analysis period. A small number of such rare, high-impact incidents contribute disproportionately to annual structure fire emissions, highlighting the importance of preparedness and rapid response capacity for tail-risk events.\nThis study reveals structure fires as a previously under-quantified but significant source of pollution, with important implications for regional air quality and public health assessments. Beyond the substantial air pollutant emissions they produce, structure fires may have disproportionately greater health impacts compared to other anthropogenic emissions due to the potential greater exposure to hazardous air pollutants. Because structure burning emissions are often driven by extreme events, they are intensely concentrated both in time and space and can produce exceptionally high local pollutant concentrations that, depending upon atmospheric dispersion and wind direction, may be more likely to directly impact urban areas. Furthermore, populated locations adjacent to the fire do not always evacuate if not at immediate risk of fire. This was exemplified in the Eaton fire where the concentrated smoke plume impacted parts of Pasadena that were not evacuated and less than two miles from active fire (Schollaert et al. 2025). In addition, NEI anthropogenic emissions are annual totals, which may be emitted at low concentrations throughout the year, while structure fire emissions are typically concentrated over much shorter timeframes—often lasting only hours to days. As a result, the pollutant concentrations during structure fires can be substantially higher, leading to more acute exposures in areas impacted by the smoke plume. The potentially greater short-term exposure may lead to increased health risks for nearby populations (Reid et al., 2024), although this is still an active area of research. While direct comparisons remain limited, existing evidence suggests that structural fire smoke contains higher proportions of certain toxicants, potentially increasing risk (Holder et al., 2023; Reid et al., 2024). These features of structure burning emissions further emphasize the importance of accounting for structure fires in both emissions inventories and public health evaluations.\nWhile our estimates provide the first nationwide assessment of structure fire emissions in wildfires, several limitations should be noted. These include uncertainties in structure fuel load estimates, the fraction of fuel burned in damaged structures, hindcasting of historical building counts, and emission factors for certain pollutants. Additional limitations, along with quantitative sensitivity analyses, are discussed in detail in the Uncertainty section.”\n2.Method\nFor each U.S. state, we identified grid cells within state boundaries and computed year-specific scaling factors as the ratio of total BUPL values in the target year to those in 2020\n Did you use state-specific scaling factors? In other words, did you apply 48 separate scaling factors for each year, corresponding to the 48 states in the CONUS?\n If yes, do you think this spatial resolution is sufficient? Is there a reason you chose to do this at the state level? I am also curious why you did not apply the scaling factors at the grid level. Your manuscript mentions that both datasets are at a 250 m resolution.\nResponse: Yes. We used state-specific scaling factors—one per state (48 in the conterminous U.S.) for each anchor year (2000, 2005, 2010, 2015). We initially tested pixel-level scaling but found some mismatches between 2020 MSBF and 2020 BUPL (e.g., zero in one dataset but non-zero in the other), which produced undefined pixelwise ratios. To avoid these issues while still capturing temporal change, we (i) computed state totals from BUPL to form robust scale factors and (ii) applied each state’s factor uniformly to all 2020 MSBF pixels in that state to obtain MSBF for the anchor years. We then linearly interpolated MSBF in anchor years at the pixel level to fill annual values (2001–2019). This approach preserves the observed fine-scale spatial pattern from MSBF while using BUPL to drive temporal trends. We added a flowchart to explain this process as part of Figure S7 (please also see below).\n3.Method\nI know you summarized this at the beginning, but the current version is not very clear. Could you provide the overall research objectives along with what data and methods you used right at the start of your Data and Methods section? You are using many datasets, but it currently takes time to figure out which data you used for each objective. Providing this summary upfront would greatly help readers understand your datasets and how they align with your objectives. Creating a diagram showing how each dataset was combined and used for each objective could also help make this clearer and easier to understand.\nResponse: We thank the reviewer for the suggestion. We added the flowchart below to SI.\nFigure S7. Data and processing flowchart for estimating emissions from structure burning in WUI fires. ICS-209-PLUS, COMBUST and MTBS data are available annually during 2000–2020. BUPL is available for 2000, 2005, 2010, 2015, and 2020. MSBF is available for 2020.\nWe also revised the first paragraph of the Data section accordingly:\n“This study combines geospatial estimates of combustible material (Combustible mass of the building stock in the conterminous United States (COMBUST); Uhl et al., 2025) and high-resolution building count data to reconstruct structure fuel loads over two decades. Fire incident data from the all-hazards dataset mined from the US National Incident Management System (ICS-209-PLUS), paired with fire perimeters from the Monitoring Trends in Burn Severity (MTBS) database of large fires, enable event-level attribution of structure loss (including structures destroyed and damaged). These structural parameters are linked with emission factors compiled by Holder et al. (2023) to estimate pollutant release. A flowchart for estimating emissions from structure burning in WUI fires using the ICS-209-PLUS, COMBUST, MTBS, built-up property locations (BUPL), and Microsoft Building Footprints (MSBF) datasets can be found in Figure S7. For comparison and validation, we leverage the FINNv2.5 dataset to assess vegetation fire emissions and the EPA NEI to benchmark structure fire emissions against traditional anthropogenic sources. Together, this integrated framework allows us to generate the first national structure fire emissions inventory for the 2000–2020 time period, analyze spatial and temporal patterns, and examine potential key environmental drivers. MODIS land cover data and FWI data are used to assist analysis.”\n4.Land cover\nYour land cover data is from 2020, but your analysis is based on the last 20 years. Do you think using a single year of land cover data accurately represents land cover conditions throughout the study period?\nResponse: Thank you for pointing this out—the 2020 reference was a typo. We did not use a single-year snapshot. Our analysis uses annual MODIS land cover (MCD12Q1 v6.1) matched to each fire year 2001–2020. Due to data availability, we analyzed fires during 2001-2020 instead of 2000-2020. We have corrected the text accordingly so that land-cover composition reflects conditions at the time of each fire:\n“Land cover type yearly data from 2001 to 2020 from MODIS/Terra+Aqua (MCD12Q1 v6.1) (Friedl and Sulla-Menashe, 2019) are used for land cover type analysis (Figure 5) across the conterminous U.S.”\n5.Data aggregation\nHow did you change the resolution to 250 m, and what method did you use for resampling? When the spatial resolutions differed across datasets (e.g., 250 m vs. 500 m), how did you combine these datasets? Additionally, since each dataset likely had different projections, how did you handle projection differences before combining the data?\nResponse: Our emissions workflow operates on a common 250 m analysis grid (same projection and same grid locations), so no raster regridding was required for the structure-burning calculations. MSBF (2020), BUPL (250 m), and COMBUST are used on this grid. As for MODIS land cover (500 m) data, we work per fire perimeter rather than resampling rasters: MODIS land cover data is summarized by counting 500 m pixels whose centers (latitude and longitude) fall inside each perimeter (no up- or down-sampling).\n6.Reference\n->The Chemistry of Fires at the Wildland-Urban Interface (2022)\n-> I am not sure if you had a chance to look at this before, but this provides lots of good information on WUI-related fires.\nResponse: We appreciate the reviewer drawing our attention to this important reference, which we had previously missed. We now cite The Chemistry of Fires at the Wildland–Urban Interface (2022) in the Introduction and use it to highlight the distinct chemistry and emissions of WUI fires:\n“Fires in WUI have long posed a critical and growing challenge, particularly across the United States (U.S.), as they often involve not only natural vegetation but also the combustion of buildings, infrastructure, vehicles, and other urban materials. This mix of structural and vegetative fuels can produce smoke with distinct chemical composition and toxicity (National Academies of Sciences, Engineering, and Medicine, 2022).”\nMinor\n1.Line 156: wildland-urban interface (WUI) -> WUI\nResponse: Changed.\n2.Figure 1. Total emissions in 2020 or daily average? Do you have any specific reason for selecting and showing only 2020? You used 2000-2020 in Figure 1A and other figures are only showing 2020.\nResponse: In Figure 1, only 2020 is shown because WUI vegetation fuel burning CO emissions (Panel C) is only available for 2020. CO emissions (g/m²) in 2020 from vegetation fires occurring in the wildland–urban interface (WUI) based on Schug et al. (2023). Schug et al. (2023) provides a global high-resolution WUI map (10 m resolution) and we use the WUI map to separate vegetation fuel burning CO emissions from FINNv2.5 to WUI fires and non-WUI fires following the methodology in our previous study. The Schug et al. (2023) WUI map is only available for 2020.\n3.Line 206: conterminous US -> CONUS\nResponse: Thank you for flagging this. In the revised manuscript, to maximize clarity for a broad readership, we avoid the acronym “CONUS” and use “conterminous U.S.” (short for “conterminous United States”) consistently throughout. We have revised the main text, SI, figure captions, and titles accordingly.\n4.Line 325: Specific reference using 30%. Curious how you empirically select this\nResponse: Using 30% to represent fuel burned fraction for house damaged came from a discussion with a colleague at U.S. Environmental Protection Agency (EPA) (personal communication). To assess robustness, we recomputed 2020 structure-burning CO emissions using 10% and 50% burn fractions for “damaged” structures. We have clarified this assumption and the sensitivity analysis in the Data and Methods section:\n“We approximate 30% of the structure fuel is burned if a structure is reported to be “damaged”. We do not expect this assumption will significantly change the results because overall the number of “damaged” structures is not a large fraction. ICS-209-PLUS reported structures destroyed to be 7.7 times more than the number of structures damaged. To evaluate the sensitivity of this assumption on the emission estimates, we calculate structure burning CO emissions for 2020 assuming 10% and 50% of the structure fuel is burned instead of 30% if a structure is reported to be “damaged”. The resulting estimates are 2.08 × 10¹⁰ and 2.2 × 10¹⁰ g, which are within a range of ±3% of the annual total.”\n5.Figure5:\nPlease provide more details on how you performed the categorization. For example, did you divide the data into categories based on quantiles, such as the highest 20%, the next 20%, and so on? Or did you use arbitrary thresholds to define the categories?\nResponse:\nWe used fixed thresholds (not quantiles). The variable is the number of impacted structures per incident (from ICS-209-PLUS). Incidents were binned as ≤5, 6–10, 11–20, 21–100, and >100. These cut points were chosen for interpretability (single digits, tens, ~hundreds) and to maintain adequate sample sizes per bin. As a robustness check, we also tested a four-bin scheme (≤5, 6–20, 21–100, >100); total burned area per bin was 285,727, 149,182, 124,446, and 179,768, respectively, and the results and conclusions were unchanged. We therefore report the five-bin version for clarity.\n6.Line 451: Frantz et al. data were excluded -> year\nResponse: We changed “Frantz et al.” to “Frantz et al. (2023)”.\n7.We use the COMBUST dataset (Combustible mass of the building stock in the conterminous United States; Uhl et al., 2025)\n-> We use the COMBUST dataset (Uhl et al., 2025)\nResponse: Changed.\n8.Line 449: conterminous United States -> This issue occurs repeatedly throughout the manuscript. Please check and revise consistently. -> CONUS\nResponse: Thank you for flagging this. In the revised manuscript, to maximize clarity for a broad readership, we avoid the acronym “CONUS” and use “conterminous U.S.” (short form “conterminous United States”) consistently throughout. We have revised the main text, SI, figure captions, and titles accordingly.\n9.Not clear\n-> and mass estimates were resampled to at 250m grid\nFrom what original spatial resolution was the data resampled, and why did you choose to resample it? What is the reported accuracy of this dataset? Also, what specific data sources were used for the remote sensing-based data?\nResponse: Thank you for this comment. We have added an expanded description of the COMBUST dataset to the Supplement (Text S1), where we now detail (i) the input data sources and their original resolutions, (ii) the rationale for producing COMBUST at 250 m resolution, and (iii) reported accuracy and uncertainties inherited from Frantz et al. (2023) and other source datasets. We also include the sensitivity of our results to the upper and lower bounds of combustible mass (see Fig. S9).\n“Text S1. Additional information on COMBUST\nInput data of COMBUST\nCOMBUST is a multi-source data integration effort, using data at various spatial resolutions, including the Frantz et al. (2023) building mass data (10 m spatial resolution) and multi-temporal settlement data from the historical settlement data compilation for the U.S. (HISDAC-US) used for backcasting, available at 250 m spatial resolution. The building mass estimates from Frantz et al. (2023) have been derived from joint processing of Sentinel-1 and Sentinel-2 data. Specifically, Frantz et al. (2023) computed spectral-temporal metrics and textural metrics from both Sentinel-1 backscattering and Sentinel-2 multispectral data collected over the years 2017-2018. Employing a gridded version of Microsoft’s USBuildingFootprints data as processing mask, they estimated the average building height per 10-m grid cell using a support vector regression framework, with building height reference data for training, and the spectral-temporal and textural metrics as predictor variables. Similarly, a supervised classification was used to estimate building type from these predictors, using building type information from OpenStreetMap as training labels. Based on the predominant building type per cell, Frantz et al. inferred the building material composition and computed the mass per cell and material using material densities and the total building volume (derived from footprint area and building height).\nChoice of spatial resolution and resampling rationale\nThe coarse resolution of HISDAC-US is due to the positional accuracy of its source data, i.e., property locations that may represent a building centroid, a cadastral parcel centroid, or an address point, causing heterogeneous levels of positional uncertainty which is partially mitigated by aggregating the data to a relatively coarse resolution of 250 m (REF HISDAC-US SciData). Hence, COMBUST has been produced at the lowest spatial resolution of input data sources, i.e., 250 m.\nUncertainty and accuracy\nCOMBUST inherits the uncertainty directly from the source data used from Frantz et al. (2023). Specifically, these data are affected by (a) the uncertainty of thematic estimates of building function (e.g., residential, industrial, etc.), (b) the uncertainty in the quantitative estimates of building mass and volume, and, potentially, (c) by positional uncertainty due to the quality of georeferencing of underlying remote sensing data. While the latter component has a minor effect due to the aggregation to 250m grid cells, the uncertainty in building function estimates affects the model-based inference of building material composition. According to Frantz et al. (2023), building types are predicted with an overall accuracy of 79.92% (Supplementary Table 7 in Frantz et al. 2023). Building mass in Frantz et al. (2023) is derived from estimated building volume and material densities. Hence, the uncertainty in building volume estimation (defined as building footprint area x building height) affects the quality of mass estimates. Building footprint area estimates were derived from Microsoft´s USBuildingFootprint data, estimated to be highly accurate (i.e., with precision being 98.5% for polygon evaluation metrics; Microsoft, 2020). According to Frantz et al. (2023), their building height estimation based on Sentinel-1 and Sentinel-2 data achieves an average MAE of 2.99 m (internal validation) and 3.21 m (external validation). Based on building type and volume, and building-type specific material compositions and their densities, the total mass is estimated. This overall mass across the United States is estimated as 390 t/capita, which is in line with other studies, ranging from 300 t/capita to 430 t/capita (Frantz et al., 2023, Supplementary Figure 9). While these reported accuracy metrics are promising and the plausibility of the mass estimates is high, Frantz et al. (2023) provide both upper and lower bounds as well as a mean estimate for each grid cell. COMBUST is available under these three scenarios, and the sensitivity of the results to these uncertainties is shown in Figure S9 (for 2020, the lower to upper bound corresponds to approximately −15% to +22% relative to the central estimate). Finally, the historical built-up surface density estimates from the HISDAC-US BUPL dataset (used herein for backcasting emissions to the year 2000) have been validated in Uhl et al. (2021), reporting high levels of association with historical, census- and cadastre-based housing and building densities, yielding correlation coefficients of approximately 0.95 and 0.88, respectively, for the year 2000. Additional sources of uncertainty in COMBUST are not discussed here; see Uhl et al. (2025) for further details on the dataset.”\n10.MSBF, BUPL -> Line 430: What does this stand for? You should spell out all acronyms the first time you use them. Please check the entire manuscript for consistency, as I see many issues with undefined acronyms like CONUS, BUPL, and others.\nResponse: Thank you for flagging this. We now define each acronym at first use and use the same form consistently throughout the manuscript. In particular, we define Microsoft Building Footprints (MSBF) and Built-Up Property Locations (BUPL) from the Historical Settlement Data Compilation for the United States (HISDAC-US). We also checked the manuscript for other undefined acronyms and corrected them for consistency.\n11.the MSBF datasets represents-> represent\nResponse: We changed “the MSBF datasets represents” to “the MSBF dataset represents”.\n12. spatial resolution\nThe historical settlement compilation for the U.S. (HISDAC-US)\nResponse: The resolution for gridded BUPL from HISDAC-US is 250 m x 250 m. We have added this information to the Data description:\n“One component of HISDAC-US is the BUPL dataset (Uhl et al., 2020), providing semi-decadal, gridded estimates of building densities across the conterminous U.S. from 1810 to 2020 at 250-m grid resolution.”\n13.Vague\nThese fires account for the vast majority of fires that involved structure burning in the United States.\n-> Any reference and what percent is vast majority ?\nResponse: We removed the statement “These fires account for the vast majority of fires that involved structure burning in the U.S.”. Instead, we added quantitative description of ICS-209 coverage:\n“ICS-209 reports are completed for significant incidents and include fields for structures threatened, damaged, and destroyed (NIFC, 2023). Although only ~1–2% of wildfires become large incidents, they account for up to 95% of the nation’s burned area each year (St. Denis et al., 2023); consequently, ICS-209-PLUS covers the events responsible for most area burned and underpins NICC’s published ‘structures destroyed’ statistics (NICC, 2024).”\nReferences:\n NIFC (2023). ICS-209 Program User’s Guide. National Interagency Fire Center.\n St. Denis, L. A., Short, K. C., McConnell, K., Cook, M. C., Mietkiewicz, N. P., Buckland, M., … Balch, J. K. (2023). All-hazards dataset mined from the US National Incident Management System 1999–2020. Scientific Data, 10, 112.\n NICC (2024). Wildland Fire Summary and Statistics Annual Report 2024. National Interagency Coordination Center.\nReviewer #2 (Remarks to the Author):\nThe presented manuscript “Emissions from Burned Structures in Wildfires across the Conterminous United States: A Significant Yet Unaccounted Source of Air Pollution” quantifies the emission of air pollutants from structure fires in the Conterminous United States. The authors used a set of existing data sources, e.g., regarding housing, combustible materials, fire weather index, or fire perimeters, to show, among other results, that that structure fires are a highly relevant source of pollution and that regionally, air pollutants from structure fires can exceed emissions from all other anthropogenic sources.\nThe study is very well conducted and well motivated, the manuscript is well written, understandable, and informative. The study provides highly relevant data and underlines the importance of understanding large-scale and extreme structure fires that increasingly threaten people. Despite the rather simple methodological approaches, I expect some impact in the scientific community.\nI recommend this manuscript for publication but would require the authors to address some comments and questions.\nResponse: We thank the reviewer for the helpful comments. We have addressed each comment and concern point by point. Detailed responses and revisions are provided below.\n- l. 113, \"we characterize events as WUI-related or structure fire emissions based on the co-occurrence of structure loss and wildfire activity\": Please provide more detail. I have not understood on what grounds fires have been characterised as WUI-related. Please let me know if I missed the information. Also, I wonder why the WUI context is important here.\nResponse: WUI-related structure burning refers to wildfire incidents with documented structural involvement. We classify an incident as WUI-related if (i) it is a wildfire incident in ICS-209-PLUS/MTBS and (ii) the ICS-209 report lists ≥1 structure destroyed or damaged. In other words, these are fires that involve both vegetation burning and structural fuel burning, occurring in the wildland–urban interface (WUI). We also distinguish this from the EPA NEI ‘structure burning’ category, which represents municipal/non-wildland structure fires (e.g., urban building fires, some construction/demolition burns). Those municipal/non-wild events are not reported in ICS-209 but are included in the EPA NEI anthropogenic emissions.\n- The study uses a large amount of different datasets. I suggest some kind of a data and processing flowchart, e.g., as part of the SI. To clarify: How were emissions calculated - based on a multiplication of median emissions factors and fuel load from COMBUST? This would also increase reproducibility.\nResponse: We thank the reviewer for the suggestion. We added the flowchart below to SI.\n- Unfortunately, the COMBUST dataset (Uhl et al. 2025) is not accessible. The link produces a \"DOI not found\" message. Please make this dataset available to the reviewers.\nResponse: Thank you for pointing this out. The COMBUST dataset is now publicly available at https://zenodo.org/records/15611964. We have updated the Data Availability section of the manuscript to include the correct link.\n- The abstract reads \" In several states, emissions of hazardous air pollutants such as hydrochloric acid from structure fires exceeded those from all anthropogenic sources combined.\": In Fig. 4 B, this only seems to be the case in California and Tennessee, while the numbers from most other states seem rather unimpressive. While I agree with you that toxic pollutants from structure fires are highly relevant, the results of this manuscript do not give me a sense or urgency. That is especially so as the numbers are often driven by single large fire events and in selected states only. What would the implications of these results be on a more regional level? Also, what is special about Tennessee compared to surrounding eastern states that structure fires are so destructive?\nResponse: Thank you. We agree that exceedances are concentrated rather than nationwide. In our data, four states—California, Colorado, Oregon, and Tennessee—each have ≥1 year during 2016–2020 in which structure-fire HCl exceeds all anthropogenic sources combined (NEI) at the state level. Elsewhere, structure-fire HCl is generally lower. Although these exceedances are event-driven, our record shows more frequent high-impact incidents in 2016–2020 than in 2000–2015 (Fig. 3), which raises concern even if the pattern is episodic. Importantly, structure-involved wildfires occur in most states (Fig. 1A), and severe events can happen outside the West (e.g., the 2016 Tennessee fires). Tennessee does not have uniquely different background conditions; its exceedance is dominated by a single late-season, wind-driven event with extensive structure loss, illustrating that other eastern states could experience similar outcomes under a similar fuels and weather conditions. Note also that NEI values are annual totals, whereas structure-burning emissions are short-lived and localized; thus even when annual totals are smaller, short-duration peaks can pose substantial public-health risk near the plume. We added the following statement to the main text:\n“Four states—California, Colorado, Oregon, and Tennessee—each have at least one year during 2016–2020 in which structure-fire HCl exceeded the state’s anthropogenic total (NEI). Elsewhere, structure-fire HCl is generally lower than anthropogenic totals. While most states remain below anthropogenic totals, structure-involved wildfires occur across most states of the country (Figure 1A), and severe events can occur outside the West (e.g., the 2016 Tennessee fires). Tennessee does not appear to have distinctly elevated baseline climate, land cover, or WUI extent relative to neighboring states (Beck et al., 2018; Radeloff et al., 2018). Its exceedance is dominated by a single late-season, wind-driven incident with extensive structure loss (Jiménez et al., 2018; Reilly et al., 2022), indicating that it is possible that similar fire events occur in other states given similar fuels, weather, and WUI exposure, even if such events were not observed during our analysis period.”\n- Why is Figure 4B using data from the 2016-2020 period?\nResponse: We use 2016–2020 for structure-burning emissions to reflect the higher activity in recent years (see Fig. 3) and to reduce year-to-year variability driven by extreme events. A single year (e.g., 2020) can be unrepresentative—for example, it would miss large 2018 events. We therefore plot the 2016–2020 mean and show the five-year range. For anthropogenic sources, we use NEI 2020 because interannual variability is comparatively small and a single contemporary year provides a stable benchmark.\n- Figure 1B, single events becomne hidden which makes the map very hard to read.\nResponse: We updated Figure 1B.\n- Figure 2D, is there a way to add states to some of the non-CA fires?\nResponse: We revised Figure 2D so that the state abbreviation is shown for every event and non-California fires are highlighted with a contrasting bar color (California bars retain the original color):\n- Could you please clarify what the data source is for figures s1 and s2?\nResponse: Figures S1 and S2 use outputs from FINN-WUI, the structure-burning emissions dataset developed in this study (not an external source). S1 shows total structural fuel burned computed within FINN-WUI; S2 shows CO emissions from FINN-WUI. We have added the information to the figure captions.\n- l. 227: Does such a trend also exist for vegetation fires?\nResponse: In the western U.S., previous studies showed increases in wildfire burned area and number of large wildfires since the mid-1980s, implying higher vegetation-fire emissions in many recent years. However, trends are not uniform nationally, and interannual variability remains large.\n- Figure S6: I dont understand how state mean and conus mean relate to \"with 1x1\" or \"with fire shape\"? Also, do the different bars refer to a scenario where an aggregation method is applied to ALL fires, or only the ones affected by, e.g., missing fire shapes?\nResponse: Response: The bars compare alternative ways of estimating fuel load per structure (FPS), and each bar reflects a full re-calculation for all 2020 incidents (not only those missing shapes). Specifically:\n with fire shape (baseline): FPS is computed by overlaying MTBS fire perimeters (circular fallback where needed) with COMBUST and MSBF/BUPL when MTBS is available.\n with 0.1°×0.1° and with 1°×1°: FPS is computed within a square window centered on the incident point of origin; COMBUST mass and MSBF/BUPL structure counts are taken from the same window.\n with state mean and with CONUS mean: a single mean FPS (state-level or national), is applied uniformly to every incident (no spatial window).\nIn all cases, the numbers of structures destroyed/damaged come from ICS-209-PLUS and are held fixed, only FPS changes. The error bar on the baseline shows the range obtained by propagating the COMBUST lower/upper combustible-mass bounds.\nWe revised the caption of Figure S9 (formerly S6) to make it clearer.\n- l 234, \"This seasonal asymmetry suggests that the most damaging structure fires tend to occur under different conditions than typical wildland fires.\": There is probably reason to believe that the larger fires occurr towards the end of the fire season - so structures being affected is more likely and fire suppression is harder. Is this correct?\nResponse: Yes, this is consistent with our data and could be a possible reason. As shown in Fig. 3, incidents with the largest structure loss in the western U.S. cluster in Sep–Nov. Late-season fuel dryness and episodic offshore wind events (e.g., Santa Ana/Diablo) can promote rapid spread into the WUI and complicate suppression, although the pattern likely varies by region.\nWe added the following statement to the manuscript:\n“This seasonal asymmetry is consistent with the possibility that the largest structure-loss incidents occur later in the fire season—when fuels are drier and episodic offshore wind events (e.g., Santa Ana/Diablo) could promote rapid WUI spread—conditions that may differ from those governing typical summer wildland-fire activity (Guzman-Morales et al., 2016; Rolinski et al., 2016).”\n- l. 287 typo: \"can produce\"?\nResponse: Corrected.\n- Figure 4: It would be interesting to see what the variation of land cover composition across all fires looks like, as you correctly point out the recent big fire events can substantially affect this statistic.\nResponse: To show how land-cover composition varies across fires and years (including the influence of large recent events), we added Fig. Sx. For each year (2001–2020), the figure stacks the total number of MODIS 500 m pixels of grouped land-cover classes (Forest, Shrub/Grass, Cropland, Developed) found inside all fire perimeters, binned by impacted-structure numbers (≤5, 6–10, 11–20, 21–100, >100). Because pixel size is fixed, these counts are directly proportional to area. This complements the percent compositions in the main text by making year-to-year variation—and years dominated by high-impact events—visually explicit.\n- An would be helpful to understand the emission factors by Holder et al., as many readers might not be familiar with these.\nResponse: We extended the sub-section “Emission factors for structure burning” in “Data and Methods”:\n“We estimate pollutant emissions by multiplying burned structural fuel by median emission factors (EFs) from Holder et al. (2023). We use emission factors in units of g kg⁻¹ of structural fuel burned. Holder et al. (2023) compiled twenty-eight (28) of the 92 references reported emission factors for a total of 346 test conditions covering a range of chemical species that were included in the urban fuel emission factor compilation. We use the median values of the structural emission factors in our estimation. A concise summary of the EFs used in this study is provided in Table S3.”\nWe also added Table S3 in the supplement.\n- l 640: Is this sentence grammatically correct?\nResponse: We corrected the sentence – “We assume 30% of the structure fuel is burned if a structure is reported to be “damaged”.\n- ll. 341 ff., \"Fires that impacted more than 100 structures tended to occur in areas with less cropland and cropland/natural vegetation mosaics (0.24%) and more urban and built-up lands (0.49%) compared to those that impacted fewer structures (0.45%–2.25% for croplands; 0.11%–0.28% for urban areas).\": I wonder if this is not a circular conclusion. When there is more urban land, fires can become more. Doesn't the overall small fraction of urban land across fires point to the fact that fire size is indeed the major driver of increasingly burning structures? Bigger fires that are hard to suppress have a higher chance of affecting a settlement than small fires that are easier to suppress. So far, the only reasoning that \"structure fire emissions are not solely a function of fire size\" that are highlighted in the manuscript are the comparatively high emissions from structure fires in Colorado, New York, and Wisconsin (shouldn't this be Michigan absed on Fig. 2c?), two of which are in the Eastern US where fire is less driven by weather and vegetation. The land cover/land use component of this work seems to end when it starts to be interesting.\nResponse: We thank the reviewer for these insightful observations. We acknowledge the potential circularity in the relationship between urban land fraction and large structure-loss incidents — larger fires, by virtue of their size, are more likely to intersect developed areas, regardless of whether urban land cover itself drives destructiveness.\nTo clarify this in the manuscript, we now note that fire size can be a major driver of structure loss, while also recognizing that other factors contribute. We added the following statement to the manuscript:\n“Fire size is an important driver of structure loss, as larger fires—often more difficult to suppress—have a greater potential to intersect developed areas and affect more structures than smaller, more easily contained fires. At the same time, the disproportionately high emissions from certain states (e.g., Colorado, New York, Michigan) indicate that factors beyond fire size, such as land use patterns and structure density also influence structure fire emissions.”\nReviewer #3 (Remarks to the Author):\nThis is a very interesting and well-written study that focuses on emissions from burned structures, which have been less of a focus in the literature. This topic is particularly salient, considering the many recent destructive WUI fires, including in Los Angeles. The authors also provided very helpful regional context and implications throughout the paper.\nResponse: We thank the reviewer for the positive feedback.\nI appreciate the detailed Uncertainty section in the Methods, but I believe the authors could add a few sentences about limitations in the Implications section, or at least clearly refer readers to the Uncertainty section, as it is at the end of the paper.\nResponse: We agree with the reviewer that limitations should be highlighted in the main text. We have added a brief paragraph (please also see below) at the end of the Discussion section summarizing the main limitations and referring readers to the detailed Uncertainty section in the Methods. This ensures that the limitations are clear even to readers who do not review the Methods in detail.\n“While our estimates provide the first nationwide assessment of structure fire emissions in wildfires, several limitations should be noted. These include uncertainties in structure fuel load estimates, the fraction of fuel burned in damaged structures, hindcasting of historical building counts, and emission factors for certain pollutants. Additional limitations, along with quantitative sensitivity analyses, are discussed in detail in the Uncertainty section.”\nI believe the introduction section could benefit from a few sentences on the toxicity of air pollution from structure fires vs. typical vegetation fires, since that point is brought up a few times throughout the paper but not really explained. It should also state and explain the choice of pollutants the study will be more heavily focusing on so it is not surprising throughout the results.\nResponse: We added a separate paragraph to the introduction:\n“From the perspectives of atmospheric chemistry, air quality, and public health, structure burning is particularly important to quantify due to the toxic emissions produced from building materials (Silberstein et al., 2023; Reid et al., 2024). Compared with vegetation fires, structure fires emit a broader and more complex mix of hazardous pollutants due to the combustion of materials such as plastics, treated wood, wiring, and household contents. This mixture includes not only criteria pollutants like carbon monoxide (CO) and particulate matter (PM), but also volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), acid gases (e.g., hydrochloric acid, HCl), and toxic metals (e.g., lead, Pb) (National Academies of Sciences, Engineering, and Medicine, 2022; Holder et al., 2023). These pollutants can not only degrade outdoor air quality but also infiltrate homes that are adjacent to the burn area but remain unburned, posing health risks to residents in nearby WUI neighborhoods (Reid et al., 2024; Dressler et al., 2025). In this study, we study emissions from burned structure in wildfires and use HCl and Pb as illustrative hazardous pollutants.”\nLine by line comments:\nLine 71 - why does only the Hawaii fire list the number of lives claimed?\nResponse: We thank the reviewer for pointing out this inconsistency. We originally included the number of fatalities only for the Lahaina Fire, as it was widely reported as the deadliest U.S. wildfire in over a century. For consistency, we have now removed the fatality count, as it is not directly pertinent to the focus of this paper.\n“In recent years, several destructive (defined by the number of houses destroyed in the fire) WUI fires have highlighted the increasing significance of these WUI fires including the 2018 Camp Fire in Butte County, California, which burned more than 15,000 structures (Troy et al., 2022); the 2021 Marshall Fire in Boulder County, Colorado, which destroyed more than 1,000 structures (Juliano et al., 2023); the 2023 Lahaina Fire in Maui, Hawaii, which destroyed more than 2,000 structures (Roy et al., 2024); and most recently, the 2025 fires near Los Angeles, California which are estimated to have destroyed at least 16,000 structures (Qiu et al., 2025).”\nLine 77 - \"more dominant and leading to\" - could remove \"and\" and add a comma after dominant, if I am understanding the purpose of the sentence accurately.\nResponse: Changed.\nLine 80 - Can the authors explain the statement about fires in the WUI having a larger impact on air quality? If they just mean on public health impacts due to their location closer to population centers, I think the sentence could be rearranged to state that. Even \"air quality near population centers, resulting in more extensive public health impacts\" or something along those lines.\nResponse: We have revised the sentence to clarify that the greater public health impacts are due to WUI fires occurring near population centers, which increases the likelihood of degraded local air quality affecting residents.\n“…because WUI fires often occur close to population centers, their emissions can more directly degrade local air quality, resulting extensive public health impacts.”\nLine 90 - rephrase to say \"not only degrade outdoor air quality but also...\"\nResponse: Rephrased.\nLine 102-103 - typo here \"has only recently has it\"\nResponse: Corrected.\nLine 266 - When discussing Figure 4b, could caveat by explaining that in other states the structure fires come nowhere close to anthropogenic emissions so readers know what to expect when looking at the figure.\nResponse: We added the following statement to the main text:\n“Four states—California, Colorado, Oregon, and Tennessee—each have at least one year during 2016–2020 in which structure-fire HCl exceeded the state’s anthropogenic total (NEI). Elsewhere, structure-fire HCl is generally lower than anthropogenic totals. While most states remain below anthropogenic totals, structure-involved wildfires occur across most states of the country (Figure 1A), and severe events can occur outside the West (e.g., the 2016 Tennessee fires). Tennessee does not appear to have distinctly elevated baseline climate, land cover, or WUI extent relative to neighboring states (Beck et al., 2018; Radeloff et al., 2018). Its exceedance is dominated by a single late-season, wind-driven incident with extensive structure loss (Jiménez et al., 2018; Reilly et al., 2022), indicating that it is possible that similar fire events occur in other states given similar fuels, weather, and WUI exposure, even if such events were not observed during our analysis period.”\nLine 282 - this paragraph on potential health impacts feels important but slightly underdeveloped. I understand this is just the Results section, so perhaps more relevant for the Implications section, but it would be great for this discussion to be expanded with citations from the literature on 1) health impacts from these pollutants, 2) any existing studies that have evaluated those intense acute exposures from structure fires compared to vegetation fires, and 3) opportunities and challenges in conducting a traditional HIA/public health evaluations for these pollutants and types of fires.\nResponse: We thank the reviewer for this valuable suggestion. In the revised manuscript, we moved this paragraph from the Results section into the new Discussion section and substantially expanded it to address the reviewer’s three points:\n1) Health impacts from pollutants – The description below is in the Structure burning emissions of toxics can exceed anthropogenic emissions Section:\n“HCl is classified by NIOSH as a respiratory irritant with both acute and chronic health risks upon inhalation (NIOSH, 2025; EPA, 2023). Pb exposure can affect almost every organ and system in the body, especially for children (World Health Organization (WHO), 2019; EPA 2023).”\n2) Comparisons with vegetation fire exposures – We added citations to recent studies showing that structural fire smoke often contains higher proportions of certain toxicants compared with vegetation fire smoke, potentially increasing health risks (Holder et al., 2023; Reid et al., 2024).\n3) Opportunities and challenges for HIA/public health evaluation – While traditional health impact assessments—based on fixed monitors and longer-term exposure data—have been noted in the literature as difficult to apply to short-lived, high-intensity events such as wildfires (Chowdhury et al., 2022; Delfino et al., 2009; NASEM, 2022), these challenges are likely to be even greater for structure burning emissions in wildfires due to their more complex pollutant mixtures. Given that none of the coauthors are health experts, we have limited our discussion to summarizing relevant literature rather than providing an in-depth HIA analysis.\nBelow is paragraph we added to the Discussion section:\n“This study reveals structure fires as a previously under-quantified but significant source of pollution, with important implications for regional air quality and public health assessments. Beyond the substantial air pollutant emissions they produce, structure fires may have disproportionately greater health impacts compared to other anthropogenic emissions due to the potential greater exposure to hazardous air pollutants. Because structure burning emissions are often driven by extreme events, they are intensely concentrated both in time and space and can produce exceptionally high local pollutant concentrations that, depending upon atmospheric dispersion and wind direction, may be more likely to directly impact urban areas. Furthermore, populated locations adjacent to the fire do not always evacuate if not at immediate risk of fire. This was exemplified in the Eaton fire where the concentrated smoke plume impacted parts of Pasadena that were not evacuated and less than two miles from active fire (Schollaert et al. 2025). In addition, NEI anthropogenic emissions are annual totals, which may be emitted at low concentrations throughout the year, while structure fire emissions are typically concentrated over much shorter timeframes—often lasting only hours to days. As a result, the pollutant concentrations during structure fires can be substantially higher, leading to more acute exposures in areas impacted by the smoke plume. The potentially greater short-term exposure may lead to increased health risks for nearby populations (Reid et al., 2024), although this is still an active area of research. While direct comparisons remain limited, existing evidence suggests that structural fire smoke contains higher proportions of certain toxicants, potentially increasing risk (Holder et al., 2023; Reid et al., 2024). These features of structure burning emissions further emphasize the importance of accounting for structure fires in both emissions inventories and public health evaluations.”\nLine 369 - typo at the end of the line \"involve\"\nResponse: We changed \"involve\" to “involving”.\nLine 401 - comma not needed\nResponse: We removed the comma.\nLine 452 - typo \"to at\"\nResponse: We removed “at”.\nLine 640 - is there an explanation or citation in the paper for how 30% was chosen?\nResponse: Using 30% to represent fuel burned fraction for house damaged came from a discussion with a colleague at U.S. Environmental Protection Agency (EPA) (personal communication). To assess robustness, we recomputed 2020 structure-burning CO emissions using 10% and 50% burn fractions for “damaged” structures. We have clarified this assumption and the sensitivity analysis in the Data and Methods section:\n“We approximate 30% of the structure fuel is burned is a structure is reported to be “damaged”. We do not expect this assumption will significantly change the results because overall the number of “damaged” structures is not a large fraction. ICS-209-PLUS reported structures destroyed to be 7.7 times more than the number of structures damaged. To evaluate the sensitivity of this assumption on the emission estimates, we calculate structure burning CO emissions for 2020 assuming 10% and 50% of the structure fuel is burned instead of 30% if a structure is reported to be “damaged”. The resulting estimates are 2.08 × 10¹⁰ and 2.2 × 10¹⁰ g, which are within a range of ±3% of the annual total.”"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The answers are all satisfactory. Thanks for working really hard on this.", "reviewer_2": "Thank you very much for submitting a revised version of your manuscript “Emissions from Burned Structures in Wildfires across the Conterminous United States: A Significant Yet Unaccounted Source of Air Pollution”.\nI appreciate that you responded to all previous remarks and concerns and substantially improved previously under-explained sections of the manuscript. I recommended the manuscript for publication.\n(Remarks on code availability)\nThe code provided is written in matlab, which I am not an expert in. That said, all provided code only relates to the creation of figures. Based on the manuscript, I have no reason to mistrust the presented figures.", "reviewer_3": "I appreciate the detailed responses to my comments and those of the other reviewers. The revisions greatly improve the readability of the manuscript. I just have a few minor comments - I'd recommend the new Discussion section start with a statement about the importance and novelty of the study. Also, the Balch reference in the first paragraph of the Discussion could use a bit more spatial and temporal specificity - is this an annual analysis? For all of the U.S.? Also, perhaps adding the hectare-acre conversion or something along those lines would be helpful for interpretation."}, "author_response": "REVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nThe answers are all satisfactory. Thanks for working really hard on this.\nResponse: We sincerely thank the reviewer for their positive feedback and appreciation of our efforts. We are grateful for the constructive comments provided during the review process, which helped us significantly improve the manuscript.\nReviewer #2 (Remarks to the Author):\nThank you very much for submitting a revised version of your manuscript “Emissions from Burned Structures in Wildfires across the Conterminous United States: A Significant Yet Unaccounted Source of Air Pollution”.\nI appreciate that you responded to all previous remarks and concerns and substantially improved previously under-explained sections of the manuscript. I recommended the manuscript for publication.\nResponse: We sincerely thank the reviewer for the positive feedback and recommendation for publication. We appreciate the constructive comments provided during the review process, which greatly improved the clarity and quality of our manuscript.\nReviewer #2 (Remarks on code availability):\nThe code provided is written in matlab, which I am not an expert in. That said, all provided code only relates to the creation of figures. Based on the manuscript, I have no reason to mistrust the presented figures\nResponse: We thank the reviewer for reviewing the provided code. The MATLAB scripts and processed data used to generate the figures are available at https://zenodo.org/records/16997919. The raw data are provided separately and are publicly accessible through the links listed in the Data Availability section of the manuscript.\nReviewer #3 (Remarks to the Author):\nI appreciate the detailed responses to my comments and those of the other reviewers. The revisions greatly improve the readability of the manuscript. I just have a few minor comments – I'd recommend the new Discussion section start with a statement about the importance and novelty of the study. Also, the Balch reference in the first paragraph of the Discussion could use a bit more spatial and temporal specificity - is this an annual analysis? For all of the U.S.? Also, perhaps adding the hectare-acre conversion or something along those lines would be helpful for interpretation.\nResponse: We sincerely thank the reviewer for their positive feedback and appreciation of our efforts. We are grateful for the constructive comments provided during the review process, which helped us further improve the manuscript. We have added the following statement to the beginning of the Discussion to emphasize the importance and novelty of the study:\n“This study presents the first comprehensive assessment of emissions from burned structures in wildfires across the conterminous United States, revealing a major but previously unaccounted source of hazardous air pollutants with implications for air quality and public health.”\nWe also revised the sentence referencing Balch et al. (2024) for greater spatial and temporal specificity and to include the hectare–acre conversion. The original text:\n“Balch et al. (2024) found that fast fires that grew more than 1,620 hectares in 1 day accounted for 78% of structures destroyed and 61% of suppression costs.”\nhas been revise to:\n“Balch et al. (2024) analyzed annual wildfire data across the contiguous U.S. for 2001–2020 and found that fast-spreading fires—those growing more than 1,620 ha (≈4,000 acres) within a single day—accounted for 78% of structures destroyed and 61% of suppression costs.”"}]

Decision letter