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Emission factors (EFs) are crucial in understanding the effects of wildfire emissions on air quality. We examined the variability of EFs of three wildfires (Nethker, Castle, and 204 Cow) during the 2019 Western US wildfire season using the Aerodyne Mobile Laboratory (AML) and compared them to previous studies. The AML sampling captured the high degree of variability present in wildfires, and we report results for a range of combustion conditions that is more extensive than previous field and laboratory studies. For instance, we captured emissions from freshly started flaming fuels and we report rare EF measurements at very high modified combustion efficiencies (MCEs); MCEs >0.9. Differences in emissions between AML‐observed wildfires were attributed to burning state/MCE rather than fuel type. A comparison of EFs versus MCE was made and linear fits were compared to previous observations to reveal important differences that incorporate these high MCEs. For some species, there remains an EF dependence on MCE at these high values, while others reach a minimum value and exhibit either no or a weak dependence above it. EF differences were found for many of the studied compounds when comparing ground‐based and airborne observations, with generally greater airborne EFs possibly due to photochemical oxidation. The largest differences were from monoterpenes and acetaldehyde. Comparisons were made between AML‐observed wildfires, aircraft observations, and the values in literature for EFs and emission ratios, with mixed agreement due to the high degree of variability caused by differences in MCE. Differences in MCE drove the diurnal EF differences.

 

Structural diversity is a key feature of forest ecosystems that influences ecosystem functions from local to macroscales. The ability to measure structural diversity in forests with varying ecological composition and management history can improve the understanding of linkages between forest structure and ecosystem functioning. Terrestrial LiDAR has often been used to provide a detailed characterization of structural diversity at local scales, but it is largely unknown whether these same structural features are detectable using aerial LiDAR data that are available across larger spatial scales. We used univariate and multivariate analyses to quantify cross-compatibility of structural diversity metrics from terrestrial versus aerial LiDAR in seven National Ecological Observatory Network sites across the eastern USA. We found strong univariate agreement between terrestrial and aerial LiDAR metrics of canopy height, openness, internal heterogeneity, and leaf area, but found marginal agreement between metrics that described heterogeneity of the outermost layer of the canopy. Terrestrial and aerial LiDAR both demonstrated the ability to distinguish forest sites from structural diversity metrics in multivariate space, but terrestrial LiDAR was able to resolve finer-scale detail within sites. Our findings indicated that aerial LiDAR could be of use in quantifying broad-scale variation in structural diversity across macroscales.