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The influence of biomass burning (BB)-derived organic aerosol (OA) emissions on solar radiationviaabsorption and scattering is related to their physicochemical properties and can change upon atmospheric aging. 

Abstract. Secondary inorganic aerosols (sulfate, nitrate, and ammonium, SNA) are major contributors to fine particulate matter. Predicting concentrations of these species is complicated by the cascade of processes that control their abundance, including emissions, chemistry, thermodynamic partitioning, and removal. In this study, we use 11 flight campaigns to evaluate the GEOS-Chem model performance for SNA. Across all the campaigns, the model performance is best for sulfate (R2 = 0.51; normalized mean bias (NMB) = 0.11) and worst for nitrate (R2=0.22; NMB = 1.76), indicating substantive model deficiencies in the nitrate simulation. Thermodynamic partitioning reproduces the total particulate nitrate well (R2=0.79; NMB = 0.09), but actual partitioning (i.e., ε(NO3-)= NO3- / TNO3) is challenging to assess given the limited sets of full gas- and particle-phase observations needed for ISORROPIA II. In particular, ammonia observations are not often included in aircraft campaigns, and more routine measurements would help constrain sources of SNA model bias. Model performance is sensitive to changes in emissions and dry and wet deposition, with modest improvements associated with the inclusion of different chemical loss and production pathways (i.e., acid uptake on dust, N2O5 uptake, and NO3- photolysis). However, these sensitivity tests show only modest reduction in the nitrate bias, with no improvement to the model skill (i.e., R2), implying that more work is needed to improve the description of loss and production of nitrate and SNA as a whole. 

The fractal dimension is a key parameter in quantifying the morphology of aerosol aggregates, which is necessary to understand their radiative impact. Here we used Transmission Electron Microscopy (TEM) images to determine 2D fractal dimensions using the nested square and box-grid method and used two different empirical equations to obtain the 3D fractal dimensions. The values ranged from 1.70 ± 0.05 for pine to 1.82 ± 0.07 for Eucalyptus, with both methods giving nearly identical results using one of the empirical equations and the other overestimated the 3D values significantly when compared to other values in the literature. The values we obtained are comparable to the fractal dimensions of fresh aerosols in the literature and were dependent on fuel type and combustion condition. Although these methods accurately calculated the fractal dimension, they have shortcomings if the images are not of the highest quality. While there are many ways of determining the fractal dimension of linear features, we conclude that the application of every method requires careful consideration of a range of methodological concerns. 

Abstract 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.