4.1. Emission Production
The modeled lower emissions in the TRX scenario, compared to the UNW scenario, are due to a combination of lower canopy fuel consumption and running the model under the higher fuel moistures, typical of fall season prescribed fire. Focusing on the fuel aspect, the most obvious difference is the consumption of canopy fuel (Figure 5
). Canopy fuel loading was 64% lower and canopy consumption was 96% lower for the TRX, compared to UNW. The lower canopy fuel loading was due to the reduced tree density and increased canopy base heights [22
]. In addition to lower available fuel loading there was also significantly lower canopy consumption in the post-treatment (PTW) scenario. FFT does not calculate percent of the canopy burned. Rather, the user provides the model with that information. In our case, we used the MTBS fire severity geospatial layer to assign a wildfire severity for each stand and correlate the percent of canopy consumption to that stand. This correlation was performed by cross-walking severity to canopy mortality, according to Key and Benson [35
]. Since many of the stands were mapped under high fire severity, a higher percent consumption was assigned in the UNW scenario.
A higher ground fuel consumption in UNW also contributed to the higher emissions, relative to that of the post-treatment stands (TRX). This is important not only due to the change in quantity of fuel consumed, but also combustion efficiency. Combustion of fuels prone to smoldering, such as duff and coarse woody debris, is associated with higher emission factors [40
]. The higher combustion efficiency of flaming combustion, such as that typical of burning piles, often produces lower particulate emissions but a higher CO2
:CO ratio [14
]. We suspect this is the reason for the higher CO2
emission released in the COM modeling scenario. The post-treatment fuelbeds indicated a mean consumption reduction of 4%, relative to the wildfire scenario, even though the loading of these fuels was 36% higher due to treatment activity. These differences are likely due to the higher percent fuel moisture in the post-treatment prescribed burn scenario, 125 and 13 percent fuel moisture for duff and litter, respectively, compared to 35 and 4 percent fuel moisture under a wildfire scenario.
It is important to note the relatively high woody fuel loading in our TRX scenario (fuels resulting from mechanical thinning activities). These fuel loads exceed those of other mixed conifer stands where thinning activity fuel is not present [42
] and matches those of post logging fuels [45
]. The high woody fuel loading is due to the combination of material that would have been left on the ground from treatment activities, as well as the wood piles that would also be present. For simplicity, we reported the burning of these under the one TRX scenario, however in practice the forest would burn the piles at one point in time and return to conduct a broadcast burn to remove the rest of the fuels later. During the time these fuels are left on site, before they could be burned, they would increase the overall fire hazard [23
]. If such fuels had not been removed, either by controlled burning or removal from the site, they would have the potential to continue to increase the fire and smoke hazard. The increase in hazard when activity fuels are left on site has been recognized in research by Bernau et al. [46
]. PTW shows a similar pattern in consumption to that of UNW. However the overall fuel loading is greatly reduced in the PTW fuelbeds, thus there is far less fuel to consume. It is important to note that fire exclusion had allowed for several decades of fuel accumulation in our study area.
We found total mean emissions from the TRX scenario to be approximately 5% lower than modeled UNW emissions. This difference is less than that reported by Huff [17
] or Ottmar [15
]. Huff’s work, however, only evaluated PM10
and Ottmar focused on fuelbeds with similar fuel loading, where burn conditions (wild and prescribed) varied, considering the influence of fuel moisture and canopy fuels, but not the addition of activity fuels generated in thinning treatments, as is the case in our scenario. This is a different approach than our study, which evaluated emissions from untreated fuel loading under wildfire conditions, emissions in stands where the surface fuel loading was increased because of thinning and burned under prescribed conditions, and finally emissions from a wildfire in the new post-thinned and post prescribed burn landscape. The impact of treatments (TRX) represented for our study can be summarized as a shift of fuel from the overstory to the surface as stands are thinned, followed by the removal of that fuel by burning, to achieve a more resilient stand in the event that future wildfires occur (Figure 6
The overall reduction in emissions following a treatment is broadly in agreement with results from Stevens et al. [18
], who modeled emissions from treatments using Consume in Sierra Nevada’s mixed conifer forest, as well as Johnson et al. [19
], who evaluated salvage logging and pile burning. Our study supports the findings of previous research, while providing a detailed example of southern Idaho mixed conifer emissions, when fuel from mechanical thinning is left and burned under prescribed conditions, and emissions generated in the event of a post-treatment wildfire. There is a need for future studies that account for both differences in fuel and smoke dispersion mechanisms to inform the comparison between wildfire and prescribed fire emissions.
Another key aspect of emission production is the emission factors employed by a model. In addition to fuel loading, consumption, and moisture, emission factors also contribute to differences in emission characteristics between scenarios. Of the emissions modeled herein, CO2
makes up the greatest percentage of the emissions, exceeding the mass of fuel consumed. The CO2
emission factor has remained similar in past versions of Consume and represents such a large proportion of overall emissions due to the nature of wood combustion. If this study were repeated with future planned versions of emissions modeling software, we would expect the relative relationship between treatments and emissions quantities to be similar. However, the quantity of emissions, specifically particulate matter, may likely be different. Recent emission factor research indicates a PM2.5
range of 12.7–25 g/kg [41
], roughly double those in the version used here. However, the version of FFT available at the time of this writing employs PM2.5
emission factors of 4.8–11.8 g/kg for a mixed conifer forest, depending upon the combustion efficiency (flaming or smoldering combustion) [29
4.2. Emissions from Prescribed Fire and Wildfire
We quantified differences in modeled smoke emissions between wildfire and prescribed fire scenarios. For all pollutants, the untreated wildfire (UNW) resulted in higher mean emissions compared to post mechanical treatment fuels burned with prescribed fire (TRX) or a wildfire occurring post treatment (PTW) (Table 5
). Additionally, emissions generated in the prescribed fire phase are conducted under planned conditions. Before treatments are implemented, significant attention is given to ensure land management objectives are met. This planning process helps ensure that values, such as wildlife habitat, recreation, water quality, soil, and others, are not negatively impacted. Additionally, the planning process provides opportunity to avoid air quality impacts by varying the burn ignition, timing, and selecting days with acceptable meteorological conditions to disperse smoke. None of these opportunities are generally afforded in a wildfire scenario, a distinction noted by other researchers [50
The scope of our study focused on the overall emissions produced by fire per hectare. However, another important aspect of smoke impact is the dispersion of smoke and the trajectory it travels as it leaves the burn site. In the case of prescribed fire, ignition can be chosen to coincide with meteorological conditions that minimize smoke exposure to populated areas [14
] and thereby minimize health hazards to humans.
The timing of fire events is important to consider when comparing emissions from wildfire and prescribed fire. It is notable that our modeled prescribed fire emissions, and simulated post-prescription wildfire, are two events that would occur at different points in time. Each produced lower emissions than the untreated wildfire scenario, which would have occurred at a single point in time. In our case, most of the project area burned by the actual 2016 Pioneer fire was consumed within a seven-day span. Conversely, the treatments proposed for the area by the Boise National Forest were planned to occur over multiple years. Thus, when such a temporal scale is considered, the annual smoke emission impact of the treatment scenario would be even lower.
The fact that this area burned in a wildfire before mechanical and prescribed fire treatments could commence underscores the pressing need for fuel treatments, an issue not unique to Idaho. Nationally, Vaillant and Reinhardt [51
] compared historical disturbance regimes on National Forest lands to current levels of hazardous fuel treatments and concluded that approximately 45% of forest lands that would have historically experienced fuel reducing disturbances currently do not. Thus, 45% of forest lands could benefit from actions to reduce fuel loadings or the restoration of natural processes that would reduce fuel loading.
4.3. Study Limitations and Future Challenges
As a case study, our conclusion is limited to our representative area, the mixed conifer Becker Project Area. The project area we focused on was located within the greater Pioneer fire perimeter. Thus, we could not consider the impact of the greater area burned in the Pioneer fire on overall smoke impacts, but rather we focused on the per-hectare impact comparison. Additionally, our study area occurred in the western US, as did other similar studies [18
]. Further, our study focused on emissions where fuel accumulation from thinning treatments was removed by pile and broadcast burning. Future case studies could address other treatment variants or fuel treatments in different ecosystems, such as fuel treatments in broadleaf or longleaf pine (Pinus palustris
) forests of the southeast, many of which experience prescribed fire frequently and would, thus, be likely have much lower fuel loading when burned. There is also opportunity to compare the influences of targeted grazing or juniper (Juniperus
spp.) removal performed in xeric systems, which are different in vegetation structure than either western mixed conifer or southeastern forests.
A greater need, especially from a national policy standpoint, is to better understand the emission implications of wildfire and prescribed fire on a landscape and a national scale. Recent work by Urbanski and others [52
] developed an emissions inventory for the United States, but this focuses on overall emissions from fire and was not intended as a comparison between wild and prescribed fire. EPA’s National Emission Inventory, conducted every three years [53
], provides cumulative representations of smoke sources and is therefore difficult to use for comparisons of individual events. The challenges for researchers who pursue smoke assessments also include the difficulty in quantifying and mapping fuels [54
]. Quantifying the area burned by wild and prescribed fire, for equivalent vegetation types, is also a challenge given the difference in fire reporting data methods and sources. Verification of results can also be difficult. While we referred to other published data to infer the realism of our results, the entire project area burned in 2016, thus our treatment scenarios may be modeled but could not actually be implemented in the field. Recent broad scale efforts are currently underway by other researchers to record large detailed datasets to build and verify operational fire and smoke models [55
], which will be quite valuable.
Modeling frameworks are often employed to analyze emissions from different smoke data sources. Research on modeling frameworks to project emissions from fire is ongoing [56
]. Currently, BlueSky [58
] is one of the most complete online modeling frameworks for characterizing fuels and estimating emissions and their transport. The BlueSky framework uses FCCS to quantify fuels, Consume to estimate emissions, and Fire Emission Production Simulator (FEPS) to represent the timing of emission release, the same underlying models that compose FFT [29
], the desktop application we used in this study. There are several other online systems designed to address various aspects of wildland fire. Smartfire 2 [59
] provides a fire activity tracking database for emissions inventory purposes. Broadening into the planning and operations fields, the Interagency Fire Decision Support System (IFTDSS) currently provides fire behavior and comparison capabilities geospatially, but fire effects have not yet been built into the system [57
]. These frameworks have made fire effects representation much more efficient, relative to the previous broad spectrum of independent fire modeling and effects software applications that were, and still are, available to be run locally on individual computers. Perhaps, in the future, integration between these or similar systems will make modeling scenarios for emissions more efficient and uniform across the country. Enabling outputs from one framework to import into another would help facilitate emissions quantification for users. Furthermore, offering such tools in an online environment aids in the ability to store, back up, and share data and results.