Exposure to wildfire smoke causes adverse health outcomes, suggesting the importance of accurately estimating smoke concentrations. Geostatistical methods can combine observed, modeled, and satellite-derived concentrations to produce accurate estimates. Here, we estimate daily average ground-level PM2.5 concentrations at a 1 km resolution during the October 2017 California wildfires, using the Constant Air Quality Model Performance (CAMP) and Bayesian Maximum Entropy (BME) methods to bias-correct and fuse three concentration datasets: permanent and temporary monitoring stations, a chemical transport model (CTM), and satellite-derived estimates. Four BME space/time kriging and data fusion methods were evaluated. All BME methods produce more accurate estimates than the standalone CTM and satellite products. Adding temporary station data increases the R2 by 36%. The data fusion of observations with the CAMP-corrected CTM and satellite-derived concentrations provides the best estimate (R2 = 0.713) in fire-impacted regions, emphasizing the importance of combining multiple datasets. We estimate that approximately 65,000 people were exposed to very unhealthy air (daily average PM2.5 ≥ 150.5 μg/m3).
Reactive nitrogen (Nr) within smoke plumes plays important roles in the production of ozone, the formation of secondary aerosols, and deposition of fixed N to ecosystems. The Western Wildfire Experiment for Cloud Chemistry, Aerosol Absorption, and Nitrogen (WE-CAN) field campaign sampled smoke from 23 wildfires throughout the western U.S. during summer 2018 using the NSF/NCAR C-130 research aircraft. We empirically estimate Nr normalized excess mixing ratios and emission factors from fires sampled within 80 min of estimated emission and explore variability in the dominant forms of Nr between these fires. We find that reduced N compounds comprise a majority (39%–80%; median = 66%) of total measured reactive nitrogen (ΣNr) emissions. The smoke plumes sampled during WE-CAN feature rapid chemical transformations after emission. As a result, within minutes after emission total measured oxidized nitrogen (ΣNOy) and measured total ΣNHx (NH3 + pNH4) are more robustly correlated with modified combustion efficiency (MCE) than NOx and NH3 by themselves. The ratio of ΣNHx/ΣNOy displays a negative relationship with MCE, consistent with previous studies. A positive relationship with total measured ΣNr suggests that both burn conditions and fuel N content/volatilization differences contribute to the observed variability in the distribution of reduced and oxidized Nr. Additionally, we compare our in situ field estimates of Nr EFs to previous lab and field studies. For similar fuel types, we find ΣNHx EFs are of the same magnitude or larger than lab-based NH3 EF estimates, and ΣNOy EFs are smaller than lab NOx EFs.
Emissions from a stand replacement prescribed burn were sampled using an unmanned aircraft system (UAS, or “drone”) in Fishlake National Forest, Utah, U.S.A. Sixteen flights over three days in June 2019 provided emission factors for a broad range of compounds including carbon monoxide (CO), carbon dioxide (CO2), nitric oxide (NO), nitrogen dioxide (NO2), particulate matter < 2.5 μm in diameter (PM2.5), volatile organic compounds (VOCs) including carbonyls, black carbon, and elemental/organic carbon. To our knowledge, this is the first UAS-based emission sampling for a fire of this magnitude, including both slash pile and crown fires resulting in wildfire-like conditions. The burns consisted of drip torch ignitions as well as ground-mobile and aerial helicopter ignitions of large stands comprising over 1000 ha, allowing for comparison of same-species emission factors burned under different conditions. The use of a UAS for emission sampling minimizes risk to personnel and equipment, allowing flexibility in sampling location and ensuring capture of representative, fresh smoke constituents. PM2.5 emission factors varied 5-fold and, like most pollutants, varied inversely with combustion efficiency resulting in lower emission factors from the slash piles than the crown fires.
The summer of 2018 saw intense smoke impacts on the eastern side of the Sierra Nevada in California, which have been anecdotally ascribed to the closest wildfire, the Lions Fire. We examined the role of the Lions Fire and four other, simultaneous large wildfires on smoke impacts across the Eastern Sierra. Our approach combined GOES-16 satellite data with fire activity, fuel loading, and fuel type, to allocate emissions diurnally per hour for each fire. To apportion smoke impacts at key monitoring sites, dispersion was modeled via the BlueSky framework, and daily averaged PM2.5 concentrations were estimated from 23 July to 29 August 2018. To estimate the relative impact of each contributing wildfire at six Eastern Sierra monitoring sites, we layered the multiple modeled impacts, calculated their proportion from each fire and at each site, and used that proportion to apportion smoke from each fire’s monitored impact. The combined smoke concentration due to multiple large, concurrent, but more distant fires was on many days substantially higher than the concentration attributable to the Lions Fire, which was much closer to the air quality monitoring sites. These daily apportionments provide an objective basis for understanding the extent to which local versus regional fire affected Eastern Sierra Nevada air quality. The results corroborate previous case studies showing that slower-growing fires, when and where managed for resource objectives, can create more transient and manageable air quality impacts relative to larger fires where such management strategies are not used or feasible.
Fine particulate matter, PM2.5, has been documented to have adverse health effects, and wildland fires are a major contributor to PM2.5 air pollution in the USA. Forecasters use numerical models to predict PM2.5 concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce bias and quantify uncertainty. Typical model calibration techniques do not allow for errors due to misalignment of geographic locations. We propose a spatiotemporal downscaling methodology that uses image registration techniques to identify the spatial misalignment and accounts for and corrects the bias produced by such warping. Our model is fitted in a Bayesian framework to provide uncertainty quantification of the misalignment and other sources of error. We apply this method to different simulated data sets and show enhanced performance of the method in presence of spatial misalignment. Finally, we apply the method to a large fire in Washington state and show that the proposed method provides more realistic uncertainty quantification than standard methods.
Wildland firefighters are exposed to wood smoke, which contains hazardous air pollutants, by suppressing thousands of wildfires across the U. S. each year. We estimated the relative risk of lung cancer and cardiovascular disease mortality from existing PM2.5 exposure-response relationships using measured PM4 concentrations from smoke and breathing rates from wildland firefighter field studies across different exposure scenarios. To estimate the relative risk of lung cancer (LC) and cardiovascular disease (CVD) mortality from exposure to PM2.5 from smoke, we used an existing exposure-response (ER) relationship. We estimated the daily dose of wildfire smoke PM2.5 from measured concentrations of PM4, estimated wildland firefighter breathing rates, daily shift duration (hours per day) and frequency of exposure (fire days per year and career duration). Firefighters who worked 49 days per year were exposed to a daily dose of PM4 that ranged from 0.15 mg to 0.74 mg for a 5- and 25-year career, respectively. The daily dose for firefighters working 98 days per year of PM4 ranged from 0.30 mg to 1.49 mg. Across all exposure scenarios (49 and 98 fire days per year) and career durations (5–25 years), we estimated that wildland firefighters were at an increased risk of LC (8 percent to 43 percent) and CVD (16 percent to 30 percent) mortality. This unique approach assessed long term health risks for wildland firefighters and demonstrated that wildland firefighters have an increased risk of lung cancer and cardiovascular disease mortality.
Fire plays an important role in wildland ecosystems, critical to sustaining biodiversity, wildlife habitat and ecosystem health. By area, 70% of US prescribed burns take place in the Southeast, where treatment objectives range widely and accomplishing them depends on finding specific weather conditions for the effective and controlled application of fire. The climatological variation of the preferred weather window is examined here using two weather model reanalyses, with focus on conditions critical to smoke dispersion and erratic fire behaviour. Large spatial gradients were evident in some months (e.g. 3× change across the Appalachian Mountains in winter). Over most of the Southeast, availability of preferred conditions in summer was several (up to 8) times less than in autumn or winter. We offer explanation for this variability in terms of the mean seasonal changes of key weather conditions (especially mixing height and transport wind). We also examine the interannual variability of the preferred weather window for linkage to the tropical Pacific (1979–2010). Associations with the subset of El Niño events identified by outgoing-longwave-radiation suggest skilful seasonal fire weather forecasts are feasible. Together, these findings offer a predictive tool to prioritise allocation of scarce prescribed fire resources and maximise annual area treated across this landscape.
