Robert J. Yokelson

The age of smoke, meaning the time elapsed since it was produced in a fire, is an important parameter for interpreting measurements of evolving smoke composition. This study describes the smoke age estimates developed for large plumes sampled in the 2019 NASA-NOAA FIREX-AQ field experiment. Smoke ages are computed using two methods and applied to observations from two aircraft: the NASA DC-8 and NOAA Chemistry Twin Otter. The first method uses measurements of mean horizontal wind speed, as observed by the sampling aircraft, and distance to the fire to provide a single age estimate for each plume-crossing performed by the aircraft. While this “mean-wind method” uses accurate wind measurements, it can be systematically biased by assumptions that plume rise time is negligible and that winds are homogeneous horizontally and in time during the plume transport. Wind inhomogeneities due to terrain effects and day-to-night transition, among other factors, affected some plumes during FIREX-AQ. The mean-wind method therefore performs best for short-range transport over level terrain with steady winds. The second method relies on upwind air parcel trajectories and plume rise computed with multiple high-resolution meteorological datasets. This “trajectory-based method” quantifies age uncertainty from the meteorological ensemble, plume rise speed, wind speed errors, and fire location. The second method also resolves age differences from the center to edge of a transect. Still, it is susceptible to errors in the meteorological model. With careful comparison of the simulated trajectories to smoke transport observed from geostationary satellite imagery described here, we filter out many trajectory errors and improve the smoke age estimates. Across the two aircraft, the dataset provides ages for over 43 h (815 transects) of in situ measurements in smoke plumes from 115 different fires, with smoke ages ranging from 1 min to 40 h. The two age methods are strongly correlated ( R = 0.89) for the periods during FIREX-AQ when both ages are available. The mean-wind age is systematically 12 % younger than the trajectory-based age and the median absolute difference between them is 15 % (21 % for mean). The favorable agreement between the two age methods reflects that the mean-wind method was selectively applied to plumes with little wind variability. Trajectory-based ages are available for more of the FIREX-AQ smoke observations than the mean-wind ages. The median trajectory-based age uncertainty during FIREX-AQ is 24 % and the mean uncertainty is 37 %, due to a long-tailed distribution. The main source of age uncertainty is spread within the meteorological ensemble, followed by discrepancy between measured and modeled wind speed, then other factors like plume rise. The age uncertainty variable enables the user to identify periods with high or low confidence in the age estimate, thereby informing studies of smoke aging. Smoke ages data are available at https://doi.org/10.5067/ASDC/FIREXAQ_MetNav_ AircraftInSitu_DC8_Data_1 (Bennett et al., 2020) for the NASA DC-8 and https://doi.org/10.5067/ASDC/SUBORBITAL/ FIREXAQ_Analysis_N48_Data_1 (Holmes, 2021a) for the NOAA Chemistry Twin Otter.

We report hourly surface observations of PM _2.5 , CO, NO _x , O ₃ , and 75 speciated VOCs in Missoula, Montana, during a strong smoke event in 2020. This study tests our current understanding of wildfire emissions, chemistry, and health effects as implemented in the GEOS-Chem chemical transport model. Three-or-more-day-old smoke transported from California and the Pacific Northwest increased CO, PM _2.5 , and total measured VOCs by factors of 2–8, with hourly maxima of 800 ppb, 120 µg m ⁻³ , and 85 ppb, respectively. In contrast, NO _x levels were not elevated compared to the urban background. O ₃ showed a non-monotonic response to wildfire smoke: MDA8 O ₃ increased under light smoke but flattened or declined when PM _2.5 exceeded ∼ 30–40 µg m ⁻³ , a feature that GEOS-Chem failed to reproduce. A 2020-style wildfire season recurring annually would yield an excess lifetime cancer risk of 100-in-1 million or approximately 7 times the non-smoke baseline. The chronic non-cancer hazard index (HI) would reach 3.0, indicating appreciable potential for chronic non-cancer effects. About 90 % of cancer risks are from PM _2.5 whereas non-cancer risks are dominated by formaldehyde, benzene, acrolein, and acetaldehyde. GEOS-Chem captured major smoke intrusions but underestimated CO, PM _2.5 , and VOCs by 30 %–90 %. These model biases propagate to health metrics, with GEOS-Chem underestimating smoke-attributable cancer risk by ∼ 40 % and chronic HI by ∼ 10 times. We attribute the model errors to underpredicted fire emissions and unrepresented VOC chemistry, which together led to an overestimation of OH and insufficient secondary production.

Benzene, toluene, ethylbenzene, and xylene (BTEX) are hazardous air pollutants with high toxicity and a strong potential for secondary pollutant formation. However, their occurrence and behavior in the Arctic remain poorly understood. During the Alaskan Layered Pollution and Chemical Analysis (ALPACA) field campaign in Fairbanks, Alaska in January-February 2022. Surface observations in downtown Fairbanks revealed two major pollution periods, with extremely cold (down to -35°C) and warmer temperatures (around 0°C), respectively. BTEX concentrations reached 4–12 times higher than those reported in the US and European countries under dark, cold Arctic winter conditions at breathing level, posing a significant health risk. We simulated BTEX atmospheric distributions in the Fairbanks region using the FLEXible PARTicle-Weather Research and Forecasting (FLEXPART-WRF) Lagrangian particle dispersion model and anthropogenic emissions at the surface and aloft. Due to limited photochemical loss in to the dark polar winter conditions, we treat BTEX as an unreactive tracer in the model. The control run with the emission inventory developed by Alaska Department of Environmental Conservation (ADEC) substantially underestimates BTEX concentrations compared to observations during both polluted periods, indicating deficiencies in winter emissions and near-surface mixing. Enhancing cold-start gasoline vehicle emissions by a factor of 2 during very low-temperatures substantially improved model results during the cold polluted period, while introducing a relative humidity dependence for mobile emissions improved simulated BTEX during the warm, humid pollution period. Addition of emissions of residential heating oil aromatics, not taken into account in the ADEC inventory, also reduced normalized mean biases by 5-10%.The improved model simulation was used to investigate contributing source sectors. While mobile traffic emissions were identified as the dominant source of BTEX across the Fairbanks North Star Borough, residential heating and non-point sources contributed substantially in downtown Fairbanks. Replacing residential wood burning in the inventory with oil heating during severe pollution periods, in line with air quality control guidelines, was found to effectively reduce BTEX concentrations, particularly benzene by up to 30%. While persistent surface-based temperature inversions largely confined BTEX below ~20 m, upward transport, induced by wind shear, during severe episodes, sometimes lofted near-surface pollutants to higher altitudes, potentially contributing to regional pollution and background Arctic haze.The findings of this study emphasise the need to accurately account for temperature and humidity dependent vehicle emissions, residential oil heating emissions, and winter boundary-layer dynamics for improved simulations of air quality in cold wintertime environments, not only in the Arctic but also in mid-latitudes.

We report hourly surface observations of PM_2.5, CO, NO_x, O₃, and 75 speciated VOCs in Missoula, Montana, during a strong smoke event in 2020. This study tests our current understanding of wildfire emissions, chemistry, and health effects as implemented in the GEOS-Chem chemical transport model. Three-or-more-day-old smoke transported from California and the Pacific Northwest increased CO, PM_2.5, and total measured VOCs by factors of 2–8, with hourly maxima of 800 ppb, 120 µg m^-3, and 85 ppb, respectively. In contrast, NO_x levels were not elevated compared to the urban background. O₃ showed a non-monotonic response to wildfire smoke: MDA8 O₃ increased under light smoke but flattened or declined when PM_2.5 exceeded ~30–40 µg m^-3, a feature that GEOS-Chem failed to reproduce. A 2020-style wildfire season recurring annually would yield an excess lifetime cancer risk of 100-in-1 million or approximately 7 times the non-smoke baseline. The noncancer hazard index (HI) would reach 3.0, meaning substantially elevated acute risks during high-smoke periods. About 90 % of cancer risks are from PM_2.5, whereas non-cancer risks are dominated by formaldehyde, benzene, acrolein, and acetaldehyde. GEOS-Chem captured major smoke intrusions but underestimated CO, PM_2.5, and VOCs by 30–90 %. These model biases propagate to health metrics, with GEOS-Chem underestimating smoke-attributable cancer risk by ~40 % and chronic HI by ~10 times. We attribute the model errors to underpredicted fire emissions and unrepresented VOC chemistry, which together led to an overestimation of OH and insufficient secondary production.

We report hourly surface observations of PM_2.5, CO, NO_x, O₃, and 75 speciated VOCs in Missoula, Montana, during a strong smoke event in 2020. This study tests our current understanding of wildfire emissions, chemistry, and health effects as implemented in the GEOS-Chem chemical transport model. Three-or-more-day-old smoke transported from California and the Pacific Northwest increased CO, PM_2.5, and total measured VOCs by factors of 2–8, with hourly maxima of 800 ppb, 120 µg m^-3, and 85 ppb, respectively. In contrast, NO_x levels were not elevated compared to the urban background. O₃ showed a non-monotonic response to wildfire smoke: MDA8 O₃ increased under light smoke but flattened or declined when PM_2.5 exceeded ~30–40 µg m^-3, a feature that GEOS-Chem failed to reproduce. A 2020-style wildfire season recurring annually would yield an excess lifetime cancer risk of 100-in-1 million or approximately 7 times the non-smoke baseline. The noncancer hazard index (HI) would reach 3.0, meaning substantially elevated acute risks during high-smoke periods. About 90 % of cancer risks are from PM_2.5, whereas non-cancer risks are dominated by formaldehyde, benzene, acrolein, and acetaldehyde. GEOS-Chem captured major smoke intrusions but underestimated CO, PM_2.5, and VOCs by 30–90 %. These model biases propagate to health metrics, with GEOS-Chem underestimating smoke-attributable cancer risk by ~40 % and chronic HI by ~10 times. We attribute the model errors to underpredicted fire emissions and unrepresented VOC chemistry, which together led to an overestimation of OH and insufficient secondary production.

The first 5 hours of aging in biomass burning plumes can strongly affect ozone photochemistry. We examine how volatile organic compounds (VOCs), nitrogen oxides, and nitrous acid influence hydroxyl radical, ozone, and peroxyacetyl nitrate (PAN) based on three aircraft campaigns over the United States. Our analyses reveal variable, highly elevated hydroxyl radical concentrations in the first 2 hours, resulting in evident fire-to-fire variability in VOCs oxidation and in ozone and PAN production. About 40 to 70% of the variability is explained by chemical aging. Ozone production in the plumes is usually VOC-limited for the first 2 hours and then nitrogen oxide limited downwind. Box model results for hydroxyl radical, ozone, and most VOCs, using the full, explicit Master Chemical Mechanism (MCM) mechanism, suggest no major gaps in the current best knowledge of gas-phase chemistry. However, the MCM sometimes overestimates PAN due to underestimated nitrogen oxide sinks. GEOS-Chem, a widely used chemical transport model with a reduced mechanism, generally underperforms because of incomplete VOC representation. We identify these critical pathways to guide future model development.

We report hourly surface observations of PM_2.5, CO, NO_x, O₃, and 75 speciated VOCs in Missoula, Montana, during a strong smoke event in 2020. This study tests our current understanding of wildfire emissions, chemistry, and health effects as implemented in the GEOS-Chem chemical transport model. Three-or-more-day-old smoke transported from California and the Pacific Northwest increased CO, PM_2.5, and total measured VOCs by factors of 2–8, with hourly maxima of 800 ppb, 120 µg m^-3, and 85 ppb, respectively. In contrast, NO_x levels were not elevated compared to the urban background. O₃ showed a non-monotonic response to wildfire smoke: MDA8 O₃ increased under light smoke but flattened or declined when PM_2.5 exceeded ~30–40 µg m^-3, a feature that GEOS-Chem failed to reproduce. A 2020-style wildfire season recurring annually would yield an excess lifetime cancer risk of 100-in-1 million or approximately 7 times the non-smoke baseline. The noncancer hazard index (HI) would reach 3.0, meaning substantially elevated acute risks during high-smoke periods. About 90 % of cancer risks are from PM_2.5, whereas non-cancer risks are dominated by formaldehyde, benzene, acrolein, and acetaldehyde. GEOS-Chem captured major smoke intrusions but underestimated CO, PM_2.5, and VOCs by 30–90 %. These model biases propagate to health metrics, with GEOS-Chem underestimating smoke-attributable cancer risk by ~40 % and chronic HI by ~10 times. We attribute the model errors to underpredicted fire emissions and unrepresented VOC chemistry, which together led to an overestimation of OH and insufficient secondary production.

Prescribed wildland and agricultural fires are common in the eastern United States (U.S.), but their small size and short durations have led polar‐orbiting satellite data sets to underestimate their emissions due to omission. The higher temporal resolution of the GOES‐16 Advanced Baseline Imager (ABI) improves fire detection, enabling development of a new biomass burning emissions inventory: GOES Eastern U.S. Fire Emissions (GEUFE, pronounced “goofy”). GEUFE is based on geostationary fire radiative power (FRP) observations, incorporates improved crop type classification for agricultural fires, and includes a new emission factor (EF) compilation for 16 land cover types in the eastern U.S., including forest, crop, and grassland types that differ markedly from globally reported EF values. GEUFE provides daily estimates of dry matter (DM) consumption and emissions of CO ₂ , CO, NO _x , NH ₃ , SO ₂ , organic carbon (OC), black carbon (BC), and fine particulate matter (PM _2.5 ) at 2 km resolution. An intercomparison of 10 biomass burning emissions inventories (GEUFE, GBBEPx, QFED, NEI‐2017, RAVE, FEER, VFEI, GFAS, FINN, GFED) showed that OC estimates in the eastern U.S. during August 2019–July 2020 ranged from 0.05 to 1.32 Tg yr ⁻¹ , with both EFs and DM consumption driving the large spread in aerosol estimates. GEUFE, at 0.64 Tg yr ⁻¹ , is at the high end of this range due both to region‐specific EFs and continuous GOES‐16 monitoring, reducing missed short‐lived fires. GEUFE is a process‐based inventory that eliminates reliance on empirical scaling factors, such as QFED's aerosol optical depth‐based adjustments, while explaining previously inferred “missing emissions” from small fires.

Fairbanks, Alaska experiences severe winter pollution under cold, dark conditions, making it a unique location for studying black carbon and brown carbon optical properties. The 2022 Alaskan Layered Pollution and Chemical Analysis (ALPACA) campaign deployed multiple instruments, including filter-based (AE33, MAAP) and in-situ (PAAS-4λ, PAX) instruments during January–February to characterize aerosol absorption. Black carbon contributed 2–5%, and organic aerosol 40–75% of total PM2.5 mass during campaign, with brown carbon representing only a small, weakly absorbing fraction of the organic component. Instrument inter-comparisons revealed significant discrepancies: the AE33 with default multiple scattering parameter (CAE33= 1.57), reported ~2.5 times higher absorption than the PAAS-4λ and PAX across 405–880 nm. A revised multiple scattering parameter (C*AE33= 3.64) improved agreement to within 10–20%. In contrast, MAAP (637 nm) which does not require multiple-scattering correction, exhibited ~1.9 times higher absorption than corrected AE33 (referred as AE33*) at 660 nm. Despite elevated organic concentrations, brown carbon contributed only ~20% to total absorption at 370 nm, suggesting a dominant role of black carbon to light absorption, with limited variability in absorption Ångström exponent (1.2–1.8). EPA thermal-optical elemental carbon concentrations were ~2 times higher than corrected AE33* black carbon during elevated pollution, highlighting systematic biases. These findings demonstrate the critical need for context-specific instrument calibrations and multi-method validation to accurately quantify black and brown carbon absorption in Arctic wintertime conditions.