A computationally efficient model to represent the chemistry, thermodynamics, and microphysics of secondary organic aerosols (simpleSOM): model development and application to α-pinene SOA

Abstract

Secondary organic aerosol (SOA) is an important fraction of the fine-mode atmospheric aerosol mass. Frameworks used to develop SOA parameters from laboratory experiments and subsequently used to simulate SOA formation...

Process-Level Modeling Can Simultaneously Explain Secondary Organic Aerosol Evolution in Chambers and Flow Reactors

“…Condensation of SOA to a liquidlike background aerosol (PA1) resulted in an SOA mass yield that varied between 0.18 and 0.3. The use of a semisolid aerosol (PA2) produced a lower SOA mass yield over the first few hours of photochemical aging, but the predictions ultimately converged with those for the liquid-like aerosol after about 8 h of aging, consistent with the study of Jathar et al 61 When heterogeneous oxidation was turned on (PA3), the SOA mass yield was similar to the PA2 case for 1 day of photochemical aging but resulted in a rapid decrease thereafter. For an absorbing seed aerosol in the Aitken mode (instead of the accumulation mode assumed in PA1 through PA3), the effect of heterogeneous oxidation on the SOA mass yield and O:C was even more pronounced.…”

“…For instance, for a 30 nm particle (representative of the number mode in the final particle size distribution measured in this work), the particle mixing timescale changes from 1 μs to 4 min as D b changes from 10 –10 to 4 × 10 –19 m 2 s –1 , respectively. Because the mixing timescales for a semisolid aerosol are comparable to typical OFR residence times, SOA mass yield measurements in OFRs are potentially more sensitive to the SOA phase state than those in ECs, where similar changes in D b (from 10 –10 to 4 × 10 –19 m 2 s –1 ) have been shown to have a negligible effect. , Moreover, in the OFR simulations, the longer particle mixing timescales in the semisolid SOA kept the condensable oxidation products in the gas phase for long enough that they were oxidized to form fragmented, more volatile products; the average O:C of the gas-phase products was 0.4 for the lowest OH exposure, for which the probability of fragmentation was 95%, based on the fitted SOM m frag parameter (see Section S1 and Figure S1). This additional oxidation presumably tended to further reduce SOA mass yields.…”

The driving effects of common atmospheric molecules for formation of prenucleation clusters: the case of sulfuric acid, formic acid, nitric acid, ammonia, and dimethyl amine

“…3,4 The precise way that gaseous vapor comes together to form prenucleation complexes, which eventually build to aerosols, and thus form CCN, is a very active area of research. 3,5–74 Much of this activity is driven by the uncertainty in how much aerosols and clouds will impact global warming. 75,76 While it is thought that in general more aerosols will lead to a cooling effect, the uncertainty in our knowledge exceeds the actual size of the predicted cooling.…”

Vapors Are Lost to Walls, Not to Particles on the Wall: Artifact-Corrected Parameters from Chamber Experiments and Implications for Global Secondary Organic Aerosol

“…We added these two extra configurations to demonstrate the errors incurred by incorrectly assuming that there is no chamber mixing limitation for vapors to condense onto wall-deposited particles. Similar to our earlier work, 19,36 the fitting procedure included using different initial guesses for the parameters before arriving at a final set of SOM-TOMAS parameters that has a high likelihood of being unique. Typically, SOA measurements in chambers report a minimum uncertainty of 30% and hence model predictions based on these SOM-TOMAS parameters have a minimum uncertainty of 30%.…”

“…Similar to our earlier work, , the fitting procedure included using different initial guesses for the parameters before arriving at a final set of SOM-TOMAS parameters that has a high likelihood of being unique. Typically, SOA measurements in chambers report a minimum uncertainty of 30% and hence model predictions based on these SOM-TOMAS parameters have a minimum uncertainty of 30%.…”