Particle‐resolved simulation of aerosol size, composition, mixing state, and the associated optical and cloud condensation nuclei activation properties in an evolving urban plume

Abstract

[1] The recently developed particle-resolved aerosol box model PartMC-MOSAIC (Particle Monte Carlo model-Model for Simulating Aerosol Interactions and Chemistry) was used to rigorously simulate the evolution of aerosol mixing state and the associated optical and cloud condensation nuclei (CCN) activation properties in an idealized urban plume. The model explicitly resolved the size and composition of individual particles from a number of sources and tracked their evolution due to condensation, evaporation, coagulation, emission, and dilution. The ensemble black carbon (BC)-specific absorption cross section increased by 40% over the course of 2 days due to BC aging by condensation and coagulation. Threefold and fourfold enhancements in CCN/CN ratios were predicted to occur within 6 h for 0.2% and 0.5% supersaturations (S), respectively. The particle-resolved results were used to evaluate the errors in the optical and CCN activation properties that would be predicted by a conventional sectional framework that assumes monodisperse, internally mixed particles within each bin. This assumption artificially increased the ensemble BC-specific absorption by 14-30% and decreased the single scattering albedo (SSA) by 0.03-0.07, while the bin resolution had a negligible effect. In contrast, the errors in CCN/CN ratios were sensitive to the bin resolution for a chosen supersaturation. For S = 0.2%, the CCN/CN ratio predicted using 100 internally mixed bins was up to 25% higher than the particle-resolved results, while it was up to 125% higher using 10 internally mixed bins. Neglecting coagulation overpredicted aerosol water content and number concentrations (<0.2 mm), causing errors in SSA from −0.02 to 0.035 and overprediction of CCN concentrations by 25-80% at S = 0.5%.Citation: Zaveri, R. A., J. C. Barnard, R. C. Easter, N. Riemer, and M. West (2010), Particle-resolved simulation of aerosol size, composition, mixing state, and the associated optical and cloud condensation nuclei activation properties in an evolving urban plume,

Elemental Mixing State of Aerosol Particles Collected in Central Amazonia during GoAmazon2014/15

“…and, because this takes the form of an expected value E(x) = ∑ f x x, the error in H α can be found with Equation (15) and then propagated with Equation (16) to determine the error in D α .…”

“…Many atmospheric models assume one of these extremes throughout their simulation [12][13][14]. Some models include a specific aspect of aerosol mixing such as the mixing state of black carbon [4,15], while other, nascent, models will account for a more complete mixing state [16]. Mixing state values for coated black carbon (BC) have been determined using a single-particle soot photometer (SP2) based on the time delay between light scattering and soot incandescence but thermodynamic properties of organic coatings must be assumed to infer coating thicknesses, making the technique qualitative [17,18].…”

Sensitivity of remote aerosol distributions to representation of cloud–aerosol interactions in a global climate model

“…Condensation of soluble material (e.g., sulfuric acid gas and condensable organics), coagulation with soluble particles and oxidation of organic material can be involved in aging, but uncertainties remain (Pöschl et al, 2001;Riemer et al, 2004). When BC aging is represented in global models, it is either simply parameterized by prescribing a fixed aging timescale (e.g., Collins et al, 2001) or represented more explicitly by treating condensation/coagulation with some simplifications (e.g., Vignati et al, 2004;Liu et al, 2012) because it is computationally impractical to explicitly treat the most detailed representations of the aging process (Riemer et al, 2009;Zaveri et al, 2010) in global models. Koch et al (2009b) compared global BC predictions from 17 AeroCom models, including an older version of CAM, and evaluated model results against surface and aircraft measurements.…”

Modal Bin Hybrid Model: A surface area consistent, triple‐moment sectional method for use in process‐oriented modeling of atmospheric aerosols

“…On the other hand, because secondary aerosols vary significantly from the nucleation mode (1 nm) to the submicron particles (~0.1-1 μm) in time and space, by 2 to 3 and 6 to 9 orders of magnitude in size and volume, respectively, those should be discretized into an adequate number of modes, depending on the accuracy required. An additional dimension can be added to MBHM to represent mixing state, in order to simulate changes in hygroscopicity and light absorption of aged black carbon more accurately, as in Oshima et al [2009], Zaveri et al [2010], and Matsui et al [2013], or a dimension can be added to represent photochemical age for tracking changes in particle viscosity, hygroscopicity, and thus uptake coefficients of organic aerosols, as proposed by Abbatt et al [2012]. Processes such as nucleation, condensation, and coagulation are consistently calculated over all sectional and categorical modes.…”

“…Aerosol modeling approaches now range from the one-dimensional size-resolving modal or sectional approach (conventional internal mixture assumption in regional chemical transport models), the category approach (conventional external mixture assumption in global climate models), and the category approach with a few mixing state categories [Wilson et al, 2001;Vignati et al, 2004;Stier et al, 2005;Wang et al, 2009;Aquila et al, 2011;Kajino and Kondo, 2011;Liu et al, 2012;Kajino et al, 2012aKajino et al, , 2012b to the computationally expensive two-dimensional (size and mixing state) mixing state resolving approach [Russell and Seinfeld, 1998;Jacobson, 2001;Oshima et al, 2009;Matsui et al, 2013], the multiple category with mixing state resolving approach [Jacobson, 2001;Bauer et al, 2008;Bergman et al, 2012], and ultimately to the particleresolving approach [Riemer et al, 2009;Zaveri et al, 2010].…”