Wildland fire is a major producer of aerosols from combustion of vegetation and soils, but little is known about the abundance and composition of smoke’s biological content. Bioaerosols, or aerosols derived from biological sources, may be a significant component of the aerosol load vectored in wildland fire smoke. If bioaerosols are injected into the upper troposphere via high-intensity wildland fires and transported across continents, there may be consequences for the ecosystems they reach. Such transport would also alter the concept of a wildfire’s perimeter and the disturbance domain of its impact. Recent research has revealed that viable microorganisms are directly aerosolized during biomass combustion, but sampling systems and methodology for quantifying this phenomenon are poorly developed. Using a series of prescribed fires in frequently burned forest ecosystems, we report the results of employing a small rotary-wing unmanned aircraft system (UAS) to concurrently sample aerosolized bacteria and fungi, particulate matter, and micrometeorology in smoke plumes versus background conditions. Airborne impaction-based bioaerosol sampling indicated that microbial composition differed between background air and smoke, with seven unique organisms in smoke vs. three in background air. The air temperature was negatively correlated with the number of fungal colony-forming units detected. Our results demonstrate the utility of a UAS-based sampling platform for active sampling of viable aerosolized microbes in smoke arising from wildland fires. This methodology can be extended to sample viable microbes in a wide variety of emissions sampling pursuits, especially those in hazardous and inaccessible environments.
Weather is an important factor that determines smoke development, which is essential information for planning smoke field measurements. This study identifies the synoptic systems that would favor to produce the desired smoke plumes for the Fire and Smoke Model Evaluation Experiment (FASMEE). Daysmoke and PB-Piedmont (PB-P) models are used to simulate smoke plume evolution during the day time and smoke drainage and fog formation during the nighttime for hypothetical prescribed burns on February 5-8, 2011 at the Stewart Army Base in the southeastern United States. Daysmoke simulation is evaluated using the measured smoke plume heights of two historical prescribed burns at the Eglin Air Force Base. The simulation results of the hypothetical prescribed burns show that the smoke plume is not fully developed with low plume height during the daytime on February 5th when the burn site is under the warm, moist, and windy conditions connected to a shallow cyclonic system and a cold front. However, smoke drainage and fog are formed during the nighttime. Well-developed smoke plumes, which rise mainly vertically, extend to a majority portion of the planetary boundary layer, and have steady clear boundaries, appear on both February 6th and 7th when the air is cool but dry and calm during a transition between two low-pressure systems. The plume rises higher on the second day, mainly due to lighter winds. The smoke on February 8th shows a loose structure of large horizontal dispersion and low height after passage of a deep low-pressure system with strong cool and dry winds. Smoke drainage and fog formation are rare for the nights during February 5-8th. It is concluded that prescribed burns conducted during a period between two low-pressure systems would likely generate the desired plumes for FASMEE measurement during daytime. Meanwhile, as the fire smolders into the night, the burns would likely lead to fog formation when the burn site is located in the warm and moist section of a low-pressure system or a cold front.
The Joint Fire Science Program (JFSP) and the Environmental Security Technology Certification Program (ESTCP) initiated the Fire and Smoke Model Experiment (FASMEE) (https://fasmee.net) by funding JFSP Project 15-S-01-01. This nationwide, multiagency effort identifies and collects critical measurements that will be used to advance fire and smoke science and modeling capabilities, allowing managers to 1) increase the use of managed fire, 2) improve firefighting strategies, 3) enhance smoke forecasts, 4) better assess carbon stores and fire-climate interactions and improve our understanding of other fire effects such as vegetation response. FASMEE also provides unparalleled opportunities to introduce new technology and the next generation of fire researchers in the largest coordinated fire project to date. The core leadership portioned FASMEE into three phases including analysis and planning (Phase 1), data collection (Phase 2), and future improvements (Phase 3). Phase 1 is complete, with the study plan as the main deliverable and a final report submitted and accepted by the JFSP in 2020. The plan includes science questions, data measurements and specifications, and burn recommendations that serve to guide planning. The plan has been published in the scientific literature.
The Joint Fire Science Program (JFSP) and the Environmental Security Technology Certification Program (ESTCP) initiated the Fire and Smoke Model Experiment (FASMEE) by funding Project 15-S-01-01 to identify and collect a set of critical measurements that will be used to advance wildland fire science knowledge and fire and smoke modeling capabilities. The project provided core leadership that developed a robust study plan and costing for a field campaign that would gather a novel set of observations, evaluate a selected set of models and use this information to advance operationally used fire and smoke modeling systems. FASMEE, with the support of the JFSP, leveraged several agency resources including the US Forest Service, National Science Foundation (NFS), National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA) to successfully initiate the western wildfire campaign, the first of three data collection campaigns identified in the FASMEE study plan.
This document presents the study plan for the Fire and Smoke Model Evaluation Experiment (FASMEE). FASMEE is a large-scale interagency effort to (1) identify the critical measurements necessary to improve operational wildland fire and smoke prediction systems, (2) collect observations through a coordinated field campaign, and (3) use these measures and observations to advance science and modeling capabilities. FASMEE is aimed at operational modeling systems in use today as well as the next generation of modeling systems expected to become operationally useful in the next 5 to 10 years.
Composition of pyrolysis gases for wildland fuels is often determined using ground samples heated in non-oxidising environments. Results are applied to wildland fires where fuels change spatially and temporally, resulting in variable fire behaviour with variable heating. Though historically used, applicability of traditional pyrolysis results to the wildland fire setting is unknown. Pyrolytic and flaming combustion gases measured in wind tunnel fires and prescribed burns were compared using compositional data techniques. CO2 was dominant in both. Other dominant gases included CO, H2 and CH4. Relative amounts of CO, CO2 and CH4 were similar between fire phases (pyrolysis, flaming combustion); relatively more H2 was observed in pyrolysis samples. All gas log-ratios with CO2 in pyrolysis samples were larger than in flaming combustion samples. Presence of live plants significantly affected gas composition. A logistic regression model correctly classified 76% of the wind tunnel samples as pyrolysis or flaming combustion based on gas composition. The model predicted 60% of the field samples originated from pyrolysis. Fire location (wind tunnel, field) and fire phase affected gas composition. The compositional approach enabled analysis and modelling of gas compositions, producing results consistent with the basic characteristics of the data.
Smoke emissions from wildland fires contribute to concentrations of atmospheric particulate matter and greenhouse gases, influencing public health and climate. Prediction of emissions is critical for smoke management to mitigate the effects on visibility and air quality. Models that predict emissions require estimates of the amount of combustible biomass. When measurements are unavailable, fuel maps may be used to define the inputs for models. Mapped products are based on averages that poorly represent the inherent variability of wildland fuels, but that variability is an important source of uncertainty in predicting emissions. We evaluated the sensitivity of emissions estimates to wildland fuel biomass variability using two models commonly used to predict emissions: Consume and the First Order Fire Effects Model (FOFEM). Flaming emissions were consistently most sensitive to litter loading (Sobol index 0.426–0.742). Smouldering emissions were most often sensitive to duff loading (Sobol 0.655–0.704) under the extreme environmental scenario. Under the moderate environmental scenario, FOFEM-predicted smouldering emissions were similarly sensitive to sound and rotten coarse woody debris (CWD) and duff fuel components (Sobol 0.193–0.376). High variability in loading propagated to wide prediction intervals for emissions. Direct measurements of litter, duff and coarse wood should be prioritised to reduce overall uncertainty.
Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8–20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke.
Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health.
