Using GECKO-A to derive mechanistic understanding of SOA formation from the ubiquitous but understudied camphene

Abstract

Abstract. Camphene, a dominant monoterpene emitted from both biogenic and pyrogenic sources, has been significantly understudied, particularly in regard to secondary organic aerosol (SOA) formation. When camphene represents a significant fraction of emissions, the lack of model parameterizations for camphene can result in inadequate representation of gas-phase chemistry and underprediction 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; and to predict SOA mass formation and composition based on gas/particle partitioning theory. The model simulations represented observed trends in published gas-phase reaction pathways and SOA yields well under chamber-relevant photooxidation and dark ozonolysis conditions. For photooxidation conditions, 70 % of the simulated α-pinene oxidation products remained in the gas phase compared to 50 % for limonene; supporting model predictions and observations of limonene having higher SOA yields than α-pinene under equivalent conditions. The top 10 simulated particle-phase products in the α-pinene and limonene simulations represented 37–50 % of the SOA mass formed and 6–27 % of the hydrocarbon mass reacted. To facilitate comparison of camphene with α-pinene and limonene, model simulations were run under idealized atmospheric conditions, wherein the gas-phase oxidant levels were controlled. Metrics for comparison included: gas-phase reactivity profiles, time-evolution of SOA mass and yields, and physicochemical property distributions of gas- and particle-phase products. 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 condense at low mass loadings; and (2) the final simulated SOA yield for camphene (46 %) was relatively high, in between α-pinene (25 %) and limonene (74 %). A 50 / 50 (α-pinene / 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 provides an opportunity for future measurement-model comparisons and lays the foundation for developing chemical mechanism and SOA parameterizations for camphene that are suitable for air quality modeling.

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

“…At both NO X levels for the photooxidation simulations, the average predicted SOA mass yield was higher than the average of the observed SOA mass yield (factor of 1.4 for low NO X and 1.5 for high NO X ) although this increase was much more variable for the high NO X conditions. Model predictions of SOA O:C at both initial VOC concentrations and NO X levels (0.29-0.37) seemed to agree with historical observations for -pinene SOA (0.27-0.55) 19,38,[90][91][92][93][94][95] 85 and those for volatility distributions can be found in Morino et al 86 In Figure 2(c,d), we compare model predictions of the SOA volatility distribution from the end of the experiment to the average volatility distribution measured in earlier work. The average measured volatility distribution was based on measurements of the chemical composition of SOA and the response of SOA to heating.…”

“…In Figure 2(a,b), we compare model predictions of the SOA mass yields from simpleSOM to historical data gathered from 13 photooxidation and ozonolysis experiments performed on -pinene. 75,[87][88][89] The end-of-experiment observational data were recently summarized in Afreh et al 85 The model predictions were from simpleSOM simulations performed at two initial VOC concentrations (40 and ppbv) and across 24 hours of photochemical aging at a constant OH concentration of 1.5×10 6 molecules cm -3 . All other model inputs were the same as those used in Figure 1 (e.g., initial seed surface area, vapor wall loss rate, temperature).…”

Secondary Organic Aerosol Formation from Camphene Oxidation: Measurements and Modeling

“…The SAPRC model 140 was chosen because it has been designed to evaluate gas-phase chemistry in the UCR chamber. The GECKO-A model was chosen because of the ability to predict both gas and particle phase composition, and the prior work of Afreh et al (2020), in which GECKO-A was used to study SOA formation from camphene.…”

“…Detailed descriptions of GECKO-A, including mechanism generation and SOA formation, are provided by Aumont et al (2005) and Camredon et al (2007). GECKO-A has been used to predict SOA in a number of studies (e.g., Aumont et al, 2012;Lannuque et al, 2018;McVay et al, 2016), including camphene (Afreh et al, 2020). Details of the camphene mechanism and SOA box modeling were described in Afreh et al (2020).…”

“…GECKO-A has been used to predict SOA in a number of studies (e.g., Aumont et al, 2012;Lannuque et al, 2018;McVay et al, 2016), including camphene (Afreh et al, 2020). Details of the camphene mechanism and SOA box modeling were described in Afreh et al (2020). 170…”