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Abstract. Camphene, a dominant monoterpene emitted from both biogenic and pyrogenicsources, has been significantly understudied, particularly in regard tosecondary organic aerosol (SOA) formation. When camphene represents asignificant fraction of emissions, the lack of model parameterizations forcamphene can result in inadequate representation of gas-phase chemistry andunderprediction of SOA formation. In this work, the first mechanistic study of SOA formation from camphene was performed using the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A). GECKO-A was used to generate gas-phase chemical mechanisms for camphene and two well-studied monoterpenes, α-pinene and limonene, as well as to predict SOAmass formation and composition based on gas/particle partitioning theory. Themodel simulations represented observed trends in published gas-phase reactionpathways and SOA yields well under chamber-relevant photooxidation and darkozonolysis conditions. For photooxidation conditions, 70 % of thesimulated α-pinene oxidation products remained in the gas phasecompared to 50 % for limonene, supporting model predictions andobservations of limonene having higher SOA yields than α-pinene underequivalent conditions. The top 10 simulated particle-phase products in theα-pinene and limonene simulations represented 37 %–50 % ofthe SOA mass formed and 6 %–27 % of the hydrocarbon mass reacted. Tofacilitate comparison of camphene with α-pinene and limonene, modelsimulations were run under idealized atmospheric conditions, wherein thegas-phase oxidant levels were controlled, and peroxy radicals reacted equallywith HO2 and NO. Metrics for comparison included gas-phasereactivity profiles, time-evolution of SOA mass and yields, andphysicochemical property distributions of gas- and particle-phaseproducts. The controlled-reactivity simulations demonstrated that (1)in the early stages of oxidation, camphene is predicted to form very low-volatility products, lower than α-pinene and limonene, which condenseat low mass loadings; and (2) the final simulated SOA yield for camphene(46 %) was relatively high, in between α-pinene (25 %) andlimonene (74 %). A 50 % α-pinene + 50 % limonene mixture was then used as a surrogate to represent SOA formation from camphene; while simulated SOA mass and yield were well represented, the volatility distribution of the particle-phase products was not. To demonstrate the potential importance of including a parameterized representation of SOA formation by camphene in air quality models, SOA mass and yield were predicted for three wildland fire fuels based on measured monoterpene distributions and published SOA parameterizations for α-pinene and limonene. Using the 50/50 surrogate mixture to represent camphene increased predicted SOA mass by 43 %–50 % for black spruce and by 56 %–108 % for Douglas fir. This first detailed modeling study of the gas-phase oxidation of camphene and subsequent SOA formation highlights opportunities for future measurement–model comparisons and lays a foundation for developing chemical mechanisms and SOA parameterizations for camphene that are suitable for air quality modeling. 

Abstract. Secondary organic aerosols (SOA) are major components of atmospheric fineparticulate matter, affecting climate and air quality. Mounting evidenceexists that SOA can adopt glassy and viscous semisolid states, impactingformation and partitioning of SOA. In this study, we apply the GECKO-A(Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere)model to conduct explicit chemical modeling of isoprene photooxidation andα-pinene ozonolysis and their subsequent SOA formation. The detailedgas-phase chemical schemes from GECKO-A are implemented into a box model andcoupled to our recently developed glass transition temperatureparameterizations, allowing us to predict SOA viscosity. The effects ofchemical composition, relative humidity, mass loadings and mass accommodation on particle viscosity are investigated in comparison withmeasurements of SOA viscosity. The simulated viscosity of isoprene SOAagrees well with viscosity measurements as a function of relative humidity,while the model underestimates viscosity of α-pinene SOA by a feworders of magnitude. This difference may be due to missing processes in themodel, including autoxidation and particle-phase reactions, leading to theformation of high-molar-mass compounds that would increase particleviscosity. Additional simulations imply that kinetic limitations of bulkdiffusion and reduction in mass accommodation coefficient may play a role inenhancing particle viscosity by suppressing condensation of semi-volatilecompounds. The developed model is a useful tool for analysis andinvestigation of the interplay among gas-phase reactions, particle chemicalcomposition and SOA phase state. 

Abstract. The GoAmazon 2014/5 field campaign took place in Manaus, Brazil, and allowed the investigation of the interaction between background-level biogenic air masses and anthropogenic plumes.We present in this work a box model built to simulate the impact of urban chemistry on biogenic secondary organic aerosol (SOA) formation and composition.An organic chemistry mechanism is generated with the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) to simulate the explicit oxidation of biogenic and anthropogenic compounds.A parameterization is also included to account for the reactive uptake of isoprene oxidation products on aqueous particles.The biogenic emissions estimated from existing emission inventories had to be reduced to match measurements.The model is able to reproduce ozone and NOx for clean and polluted situations.The explicit model is able to reproduce background case SOA mass concentrations but does not capture the enhancement observed in the urban plume.The oxidation of biogenic compounds is the major contributor to SOA mass.A volatility basis set (VBS) parameterization applied to the same cases obtains better results than GECKO-A for predicting SOA mass in the box model.The explicit mechanism may be missing SOA-formation processes related to the oxidation of monoterpenes that could be implicitly accounted for in the VBS parameterization. 

Chemical mechanisms play an important role in simulating the atmospheric chemistry of volatile organic compound oxidation. Comparison of mechanism simulations with laboratory chamber data tests our level of understanding of the prevailing chemistry as well as the dynamic processes occurring in the chamber itself. α-Pinene photooxidation is a well-studied system experimentally, for which detailed chemical mechanisms have been formulated. Here, we present the results of simulating low-NO α-pinene photooxidation experiments conducted in the Caltech chamber with the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) under varying concentrations of seed particles and OH levels. Unexpectedly, experiments conducted at low and high OH levels yield the same secondary organic aerosol (SOA) growth, whereas GECKO-A predicts greater SOA growth under high OH levels. SOA formation in the chamber is a result of a competition among the rates of gas-phase oxidation to low-volatility products, wall deposition of these products, and condensation into the aerosol phase. Various processes – such as photolysis of condensed-phase products, particle-phase dimerization, and peroxy radical autoxidation – are explored to rationalize the observations. In order to explain the observed similar SOA growth at different OH levels, we conclude that vapor wall loss in the Caltech chamber is likely of order 10⁻⁵ s⁻¹, consistent with previous experimental measurements in that chamber. We find that GECKO-A tends to overpredict the contribution to SOA of later-generation oxidation products under high-OH conditions. Moreover, we propose that autoxidation may alternatively resolve some or all of the measurement–model discrepancy, but this hypothesis cannot be confirmed until more explicit mechanisms are established for α-pinene autoxidation. The key role of the interplay among oxidation rate, product volatility, and vapor–wall deposition in chamber experiments is illustrated.