An extensive program of experiments focused on biomass burning emissions began with a laboratory phase in which vegetative fuels commonly consumed in prescribed fires were collected in the southeastern and southwestern US and burned in a series of 71 fires at the US Forest Service Fire Sciences Laboratory in Missoula, Montana. The particulate matter (PM_2.5) emissions were measured by gravimetric filter sampling with subsequent analysis for elemental carbon (EC), organic carbon (OC), and 38 elements. The trace gas emissions were measured by an open-path Fourier transform infrared (OP-FTIR) spectrometer, proton-transfer-reaction mass spectrometry (PTR-MS), proton-transfer ion-trap mass spectrometry (PIT-MS), negative-ion proton-transfer chemical-ionization mass spectrometry (NI-PT-CIMS), and gas chromatography with MS detection (GC-MS). 204 trace gas species (mostly non-methane organic compounds (NMOC)) were identified and quantified with the above instruments. Many of the 182 species quantified by the GC-MS have rarely, if ever, been measured in smoke before. An additional 153 significant peaks in the unit mass resolution mass spectra were quantified, but either could not be identified or most of the signal at that molecular mass was unaccounted for by identifiable species. In a second, "field" phase of this program, airborne and ground-based measurements were made of the emissions from prescribed fires that were mostly located in the same land management units where the fuels for the lab fires were collected. A broad variety, but smaller number of species (21 trace gas species and PM_2.5) was measured on 14 fires in chaparral and oak savanna in the southwestern US, as well as pine forest understory in the southeastern US and Sierra Nevada mountains of California. The field measurements of emission factors (EF) are useful both for modeling and to examine the representativeness of our lab fire EF. The lab EF/field EF ratio for the pine understory fuels was not statistically different from one, on average. However, our lab EF for "smoldering compounds" emitted from the semiarid shrubland fuels should likely be increased by a factor of ~2.7 to better represent field fires. Based on the lab/field comparison, we present emission factors for 357 pyrogenic species (including unidentified species) for 4 broad fuel types: pine understory, semiarid shrublands, coniferous canopy, and organic soil. To our knowledge this is the most comprehensive measurement of biomass burning emissions to date and it should enable improved representation of smoke composition in atmospheric models. The results support a recent estimate of global NMOC emissions from biomass burning that is much higher than widely used estimates and they provide important insights into the nature of smoke. 31–72% of the mass of gas-phase NMOC species was attributed to species that we could not identify. These unidentified species are not represented in most models, but some provision should be made for the fact that they will react in the atmosphere. In addition, the total mass of gas-phase NMOC divided by the mass of co-emitted PM_2.5 averaged about three (range ~2.0–8.7). About 35–64% of the NMOC were likely semivolatile or of intermediate volatility. Thus, the gas-phase NMOC represent a large reservoir of potential precursors for secondary formation of ozone and organic aerosol. For the single lab fire in organic soil about 28% of the emitted carbon was present as gas-phase NMOC and ~72% of the mass of these NMOC was unidentified, highlighting the need to learn more about the emissions from smoldering organic soils. The mass ratio of total NMOC to "NO_x as NO" ranged from 11 to 267, indicating that NO_x-limited O₃ production would be common in evolving biomass burning plumes. The fuel consumption per unit area was 7.0 ± 2.3 Mg ha⁻¹ and 7.7 ± 3.7 Mg ha⁻¹ for pine-understory and semiarid shrubland prescribed fires, respectively.

In October–November 2011 we measured trace gas emission factors from seven prescribed fires in South Carolina (SC), US, using two Fourier transform infrared spectrometer (FTIR) systems and whole air sampling (WAS) into canisters followed by gas-chromatographic analysis. A total of 97 trace gas species were quantified from both airborne and ground-based sampling platforms, making this one of the most detailed field studies of fire emissions to date. The measurements include the first emission factors for a suite of monoterpenes produced by heating vegetative fuels during field fires. The first quantitative FTIR observations of limonene in smoke are reported along with an expanded suite of monoterpenes measured by WAS including α-pinene, β-pinene, limonene, camphene, 4-carene, and myrcene. The known chemistry of the monoterpenes and their measured abundance of 0.4–27.9% of non-methane organic compounds (NMOCs) and ~ 21% of organic aerosol (mass basis) suggests that they impacted secondary formation of ozone (O₃), aerosols, and small organic trace gases such as methanol and formaldehyde in the sampled plumes in the first few hours after emission. The variability in the initial terpene emissions in the SC fire plumes was high and, in general, the speciation of the initially emitted gas-phase NMOCs was 13–195% different from that observed in a similar study in nominally similar pine forests in North Carolina ~ 20 months earlier. It is likely that differences in stand structure and environmental conditions contributed to the high variability observed within and between these studies. Similar factors may explain much of the variability in initial emissions in the literature. The ΔHCN/ΔCO emission ratio, however, was found to be fairly consistent with previous airborne fire measurements in other coniferous-dominated ecosystems, with the mean for these studies being 0.90 ± 0.06%, further confirming the value of HCN as a biomass burning tracer. The SC results also support an earlier finding that C₃-C₄ alkynes may be of use as biomass burning indicators on the time-scale of hours to a day. It was possible to measure the downwind chemical evolution of the plume on four of the fires and significant O₃ formation (ΔO₃/ΔCO from 10–90%) occurred in all of these plumes within two hours. The slowest O₃ production was observed on a cloudy day with low co-emission of NO_x. The fastest O₃ production was observed on a sunny day when the downwind plume almost certainly incorporated significant additional NO_x by passing over the Columbia, SC metropolitan area. Due to rapid plume dilution, it was only possible to acquire high-quality downwind data for two other trace gas species (formaldehyde and methanol) during two of the fires. In all four of these cases, significant increases in formaldehyde and methanol were observed in <2 h. This is likely the first direct observation of post-emission methanol production in biomass burning plumes. Post-emission production of methanol does not always happen in young biomass burning plumes, and its occurrence in this study could have involved terpene precursors to a significant extent.

In the US, wildfires and prescribed burning present significant challenges to air regulatory agencies attempting to achieve and maintain compliance with air quality regulations. Fire emission factors (EF) are essential input for the emission models used to develop wildland fire emission inventories. Most previous studies quantifying wildland fire EF of temperate ecosystems have focused on emissions from prescribed burning conducted outside of the wildfire season. Little information is available on EF for wildfires in temperate forests of the conterminous US. The goal of this work is to provide information on emissions from wildfire-season forest fires in the northern Rocky Mountains, US. In August 2011, we deployed airborne chemistry instruments and sampled emissions over eight days from three wildfires and a prescribed fire that occurred in mixed conifer forests of the northern Rocky Mountains. We measured the combustion efficiency, quantified as the modified combustion efficiency (MCE), and EF for CO₂, CO, and CH₄. Our study average values for MCE, EFCO₂, EFCO, and EFCH₄ were 0.883, 1596 g kg⁻¹, 135 g kg⁻¹, 7.30 g kg⁻¹, respectively. Compared with previous field studies of prescribed fires in temperate forests, the fires sampled in our study had significantly lower MCE and EFCO₂ and significantly higher EFCO and EFCH₄. The fires sampled in this study burned in areas reported to have moderate to heavy components of standing dead trees and down dead wood due to insect activity and previous fire, but fuel consumption data was not available. However, an analysis of MCE and fuel consumption data from 18 prescribed fires reported in the literature indicates that the availability of coarse fuels and conditions favorable for the combustion of these fuels favors low MCE fires. This analysis suggests that fuel composition was an important factor contributing to the low MCE of the fires measured in this study. This study only measured EF for CO₂, CO, and CH₄; however, we used our study average MCE to provide rough estimates of wildfire-season EF for PM_2.5 and four non-methane organic compounds (NMOC) using MCE and EF data reported in the literature. This analysis suggests the EFPM_2.5 for wildfires that occur in forests of the northern Rocky Mountains may be significantly larger than those reported for temperate forests in the literature and that used in a recent national emission inventory. If the MCE of the fires sampled in this work are representative of the combustion characteristics of wildfire-season fires in similar forest types across the western US then the use of EF based on prescribed fires may result in an underestimate of wildfire PM_2.5 and NMOC emissions. Given the magnitude of biomass consumed by western US wildfires, this may have important implications for the forecasting and management of regional air quality.

Biomass burning emission inventories serve as critical input for atmospheric chemical transport models that are used to understand the role of biomass fires in the chemical composition of the atmosphere, air quality, and the climate system. Significant progress has been achieved in the development of regional and global biomass burning emission inventories over the past decade using satellite remote sensing technology for fire detection and burned area mapping. However, agreement among biomass burning emission inventories is frequently poor. Furthermore, the uncertainties of the emission estimates are typically not well characterized, particularly at the spatio-temporal scales pertinent to regional air quality modeling. We present the Wildland Fire Emission Inventory (WFEI), a high resolution model for non-agricultural open biomass burning (hereafter referred to as wildland fires, WF) in the contiguous United States (CONUS). The model combines observations from the MODerate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua satellites, meteorological analyses, fuel loading maps, an emission factor database, and fuel condition and fuel consumption models to estimate emissions from WF. WFEI was used to estimate emissions of CO (ECO) and PM_2.5 (EPM_2.5) for the western United States from 2003–2008. The uncertainties in the inventory estimates of ECO and EPM_2.5 (u_ECO and uEPM_2.5, respectively) have been explored across spatial and temporal scales relevant to regional and global modeling applications. In order to evaluate the uncertainty in our emission estimates across multiple scales we used a figure of merit, the half mass uncertainty, ũ_EX (where X = CO or PM_2.5), defined such that for a given aggregation level 50% of total emissions occurred from elements with u_EX ũ_EX. The sensitivity of the WFEI estimates of ECO and EPM_2.5 to uncertainties in mapped fuel loading, fuel consumption, burned area and emission factors have also been examined. The estimated annual, domain wide ECO ranged from 436 Gg yr⁻¹ in 2004 to 3107 Gg yr⁻¹ in 2007. The extremes in estimated annual, domain wide EPM_2.5 were 65 Gg yr⁻¹ in 2004 and 454 Gg yr⁻¹ in 2007. Annual WF emissions were a significant share of total emissions from non-WF sources (agriculture, dust, non-WF fire, fuel combustion, industrial processes, transportation, solvent, and miscellaneous) in the western United States as estimated in a national emission inventory. In the peak fire year of 2007, WF emissions were ~20% of total (WF + non-WF) CO emissions and ~39% of total PM_2.5 emissions. During the months with the greatest fire activity, WF accounted for the majority of total CO and PM_2.5 emitted across the study region. Uncertainties in annual, domain wide emissions was 28% to 51% for CO and 40% to 65% for PM_2.5. Sensitivity of ũ_ECO and ũEPM_2.5 to the emission model components depended on scale. At scales relevant to regional modeling applications (Δx = 10 km, Δt = 1 day) WFEI estimates 50% of total ECO with an uncertainty <133% and half of total EPM_2.5 with an uncertainty <146%. ũ_ECO and ũEPM_2.5 are reduced by more than half at the scale of global modeling applications (Δ x = 100 km, Δ t = 30 day) where 50% of total emissions are estimated with an uncertainty <50% for CO and <64% for PM_2.5. Uncertainties in the estimates of burned area drives the emission uncertainties at regional scales. At global scales ũ_ECO is most sensitive to uncertainties in the fuel load consumed while the uncertainty in the emission factor for PM_2.5 plays the dominant role in ũEPM_2.5. Our analysis indicates that the large scale aggregate uncertainties (e.g. the uncertainty in annual CO emitted for CONUS) typically reported for biomass burning emission inventories may not be appropriate for evaluating and interpreting results of regional scale modeling applications that employ the emission estimates. When feasible, biomass burning emission inventories should be evaluated and reported across the scales for which they are intended to be used.

Particularly in rural settings, there has been little research regarding the health impacts of fine particulate matter (PM_2.5) during the wildfire season smoke exposure period on respiratory diseases, such as influenza, and their associated outbreaks months later. We examined the delayed effects of PM_2.5 concentrations for the short-lag (1-4 weeks prior) and the long-lag (during the prior wildfire season months) on the following winter influenza season in Montana, a mountainous state in the western United States. We created gridded maps of surface PM_2.5 for the state of Montana from 2009 to 2018 using spatial regression models fit with station observations and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical thickness data. We used a seasonal quasi-Poisson model with generalized estimating equations to estimate weekly, county-specific, influenza counts for Montana, associated with delayed PM_2.5 concentration periods (short-lag and long-lag effects), adjusted for temperature and seasonal trend. We did not detect an acute, short-lag PM_2.5 effect nor short-lag temperature effect on influenza in Montana. Higher daily average PM_2.5 concentrations during the wildfire season was positively associated with increased influenza in the following winter influenza season (expected 16% or 22% increase in influenza rate per 1 μg/m³ increase in average daily summer PM_2.5 based on two analyses, p = 0.04 or 0.008). This is one of the first observations of a relationship between PM_2.5 during wildfire season and influenza months later.