Wildland fires produce smoke plumes that impact air quality and human health. To understand the effects of wildland fire smoke on humans, the amount and composition of the smoke plume must be quantified. Using a fire emissions inventory is one way to determine the emissions rate and composition of smoke plumes from individual fires. There are multiple fire emissions inventories, and each uses a different method to estimate emissions. This paper presents a comparison of four emissions inventories and their products: Fire INventory from NCAR (FINN version 1.5), Global Fire Emissions Database (GFED version 4s), Missoula Fire Labs Emissions Inventory (MFLEI (250 m) and MFLEI (10 km) products), and Wildland Fire Emissions Inventory System (WFEIS (MODIS) and WFEIS (MTBS) products). The outputs from these inventories are compared directly. Because there are no validation datasets for fire emissions, the outlying points from the Bayesian models developed for each inventory were compared with visible images and fire radiative power (FRP) data from satellite remote sensing. This comparison provides a framework to check fire emissions inventory data against additional data by providing a set of days to investigate closely. Results indicate that FINN and GFED likely underestimate emissions, while the MFLEI products likely overestimate emissions. No fire emissions inventory matched the temporal distribution of emissions from an external FRP dataset. A discussion of the differences impacting the emissions estimates from the four fire emissions inventories is provided, including a qualitative comparison of the methods and inputs used by each inventory and the associated strengths and limitations.

Natural resource management intertwines with cultural practices and health outcomes for Indigenous peoples. Indigenous communities have managed and contributed to knowledge on ecosystems and sustainability since time immemorial. However, Indigenous communities in California face significant institutional constraints when implementing practices such as cultural burning. Indigenous-led research projects, programs, and political action are crucial to overcoming such constraints. It is important for non-Indigenous researchers to support Indigenous research agendas. This article helps to meet this need by identifying research procedures that respect Indigenous sovereignty and by using methods informed by Indigenous knowledge systems. The authors, representing the Southwest Climate Adaptation Science Center and the Karuk Tribe Department of Natural Resources, present a collaborative approach that integrates Native American and Indigenous Studies scholarship, participatory research methods, and engagement in the sovereign research protocols established by the Karuk Tribe. We share a process of effective collaborative research that respects Karuk research sovereignty. This process resulted in the Intentional Fire podcast series, a co-produced data set that documents Karuk stories on fire suppression, social impacts of fire exclusion, and Karuk determinants of healthy, resilient homeland ecosystems. The authors did not analyze the data further because Indigenous people do not need outside academics to speak on their behalf. The process also developed relationships, amplified knowledge, and strengthened capacities. We share our process and lessons learned to provide a model that can inform other collaborations that aim to support Indigenous research sovereignty.

Wildland fire smoke is a significant source of PM_2.5 pollution in the western United States, with varying chemical composition and toxicity depending on fire characteristics. We analyzed emergency department and urgent care visits in the Reno/Sparks, Nevada region during fire seasons (July-September) from 2012 to 2019. Wildland fire smoke PM_2.5 concentrations were determined using a novel approach that combined EPA monitor data, atmospheric dispersion modeling, fire emissions inventories, and Bayesian time-series modeling to estimate daily fire-specific smoke PM_2.5 concentrations. Distributed lag models were used to estimate associations between total smoke PM_2.5 concentrations, as well as smoke PM_2.5 concentrations classified by fire intensity and fuel type, and daily counts of 11 cardiorespiratory visit outcome groups. We observed positive lag-specific associations between total smoke PM_2.5 concentrations (per 10 µg/m³ increase) and visits for asthma exacerbation (Lag 0 RR: 1.040; 95 %CI: 1.006, 1.075), cardiovascular disease (Lag 3 RR: 1.015; 95 % CI: 0.999, 1.032), and cardiac dysrhythmia (Lag 3 RR: 1.029; 95 % CI:1.012, 1.047). Elevated cumulative associations were also observed between total smoke PM_2.5 and asthma exacerbation (Lag 0-4 RR: 1.066; 95 % CI: 1.005, 1.131), unspecified respiratory disorder (Lag 0-6 RR: 1.046; 95 % CI 0.993, 1.103), and dysrhythmia (Lag 0-5 RR: 1.072; 95 % CI: 1.018, 1.129). Visits for asthma exacerbation and unspecified respiratory disorder were more strongly associated with smoke from high intensity fires, whereas visits for dysrhythmia were more associated with smoke from low intensity fires. By fuel type, forest fire smoke was most strongly associated with increased asthma exacerbation, and visits for dysrhythmia were most strongly associated with smoke from shrub/scrub fires. This study provides novel evidence that wildland fire intensity and fuel type may influence the relationship between smoke exposure and specific health outcomes, with implications for public health messaging and land management strategies.

Background: Daily fire progression information is crucial for public health studies that examine the relationship between population-level smoke exposures and subsequent health events. Issues with remote sensing used in fire emissions inventories (FEI) lead to the possibility of missed exposures that impact the results of acute health effects studies. Aims: This paper provides a method for improving an FEI dataset with readily available information to create a more robust dataset with daily fire progression. Methods: High temporal and spatial resolution burned area information from two FEI products are combined into a single dataset, and a linear regression model fills gaps in daily fire progression. Key results: The combined dataset provides up to 71% more PM2.5 emissions, 69% more burned area, and 367% more fire days per year than using a single source of burned area information. Conclusions: The FEI combination method results in improved FEI information with no gaps in daily fire emissions estimates. Implications: The combined dataset provides a functional improvement to FEI data that can be achieved with currently available data.

Fire smoke significantly affects human health and air quality. The HYSPLIT dispersion model estimates the area impacted by smoke downwind, but the results are sensitive to input data. This study investigates the impact of different fire emission inputs on dispersion modeling results, focusing on three versions of the Wildland Fire Emissions Inventory System (WFEIS) used to initialize HYSPLIT. The three input datasets include MODIS (FEI_BASE), a combination of MODIS and MTBS (FEI_COMBO), and a version incorporating a cloud cover regression (FEI_COMBO+CC). Dispersion modeling results are compared across the western U.S. for 2013, 2016, and 2018, showing a variation of up to 200% in results depending on the emissions input. Model results are evaluated with ground-based PM_2.5 data and visible satellite imagery. The cloud cover regression improves the identification of fire days missed by FEI_BASE potentially impacting health effect studies. Correlations between modeled PM_2.5 and EPA data improve with FEI_COMBO+CC, particularly in 2013 and 2016, making it a stronger candidate for use in research on health effects. Despite some variability in RMSE, the higher correlation observed with FEI_COMBO+CC supports its use as a more accurate representation of fire-related PM_2.5 transport.