Rapid evolution of aerosol particles and their optical properties downwind of wildfires in the western US

Abstract

Abstract. During the first phase of the Biomass Burn Operational Project (BBOP) field campaign, conducted in the Pacific Northwest, the DOE G-1 aircraft was used to follow the time evolution of wildfire smoke from near the point of emission to locations 2–3.5 h downwind. In nine flights we made repeated transects of wildfire plumes at varying downwind distances and could thereby follow the plume's time evolution. On average there was little change in dilution-normalized aerosol mass concentration as a function of downwind distance. This consistency hides a dynamic system in which primary aerosol particles are evaporating and secondary ones condensing. Organic aerosol is oxidized as a result. On all transects more than 90 % of aerosol is organic. In freshly emitted smoke aerosol, NH4+ is approximately equivalent to NO3. After 2 h of daytime aging, NH4+ increased and is approximately equivalent to the sum of Cl, SO42, and NO3. Particle size increased with downwind distance, causing particles to be more efficient scatters. Averaged over nine flights, mass scattering efficiency (MSE) increased in ∼ 2 h by 56 % and doubled in one flight. Mechanisms for redistributing mass from small to large particles are discussed. Coagulation is effective at moving aerosol from the Aitken to accumulation modes but yields only a minor increase in MSE. As absorption remained nearly constant with age, the time evolution of single scatter albedo was controlled by age-dependent scattering. Near-fire aerosol had a single scatter albedo (SSA) of 0.8–0.9. After 1 to 2 h of aging SSAs were typically 0.9 and greater. Assuming global-average surface and atmospheric conditions, the observed age dependence in SSA would change the direct radiative effect of a wildfire plume from near zero near the fire to a cooling effect downwind.

Comment on acp-2021-49

“…Compared to previous field measurements of BB, this study reports similar chemical and size evolution after emission, regardless of the combustion phase and emission conditions. There are enhanced compositions of some inorganic species and OA oxidation state (Capes et al, 2008;Pratt et al, 2011;Akagi et al, 2012;Kleinman et al, 2020), decreasing proportions of levoglucosan-like species (Cubison et al, 2011;Forrister et al, 2015;Kleinman et al, 2020) and growing bulk aerosol size (Vakkari et al, 2018). This study demonstrates increasing coatings on BC with transport time, consistent with previous field measurements of higher BC coating thickness as they experienced a longer transport period (e.g.…”

“…The chemical and size evolution of BB aerosols have been studied comprehensively in field and laboratory measurements under various BB conditions (e.g. Capes et al, 2008;Yokelson et al, 2009;Cubison et al, 2011;Pratt et al, 2011;Akagi et al, 2012;Ortega et al, 2013;Vakkari et al, 2018;Kleinman et al, 2020). However, studies of optical evolution are limited, especially in field observations.…”

“…The normalized excess aerosol scattering with respect to a BB tracer (carbon monoxide, CO)is shown to increase during atmospheric ageing due to aerosol growth caused by condensation and coagulation, resulting in an increasing SSA downwind from source (Abel et al, 2003;Yokelson et al, 2009;Akagi et al, 2012;Vakkari et al, 2014;Kleinman et al, 2020). The accurate characterization and application of these optical properties and their evolution with time under ambient conditions are key issues in modelling the climate effects of BB aerosols.…”

Insights into the aging of biomass burning aerosol from satellite observations and 3D atmospheric modeling: evolution of the aerosol optical properties in Siberian wildfire plumes

“…The optimal values of the weight coefficients are found by minimizing the root mean square difference between the individual values of EnR abs or EnR ext and the corresponding values of y (t e ). Following Konovalov et al (2010), the optimization of the weight coefficients is achieved using the Nelder-Mead simplex algorithm (Press et al, 1992). Although the number of neurons (N) used for the trend approximation can also be optimized in a regular (albeit more complicated) way (Konovalov et al, 2010), here we simply tried the fits with N = 1, N = 2, and N = 3.…”

“…Following Konovalov et al (2010), the optimization of the weight coefficients is achieved using the Nelder-Mead simplex algorithm (Press et al, 1992). Although the number of neurons (N) used for the trend approximation can also be optimized in a regular (albeit more complicated) way (Konovalov et al, 2010), here we simply tried the fits with N = 1, N = 2, and N = 3. We found that while the fits with N = 2 manifested statistically significant features that are missing in the approximations with N = 1, the use of three neurons instead of two did not reveal any new statistically significant features of the analyzed trends.…”