Secondary aerosol formation from the oxidation of toluene by chlorine atoms
Introduction
Atmospheric particulate matter (PM) influences human health, climate, and visibility (Twomey et al., 1978, Charlson et al., 1992, Mazurek et al., 1997, Pope et al., 2002). Organic aerosol (OA), much of which is believed to be secondary (Zhang et al., 2007), is a ubiquitous component of PM (Murphy et al., 1998). Secondary OA (SOA) is formed from partitioning of secondary non- and semi-volatile organic compounds (SVOCs) into organic and/or aqueous aerosol phases (Pankow, 1994, Saxena and Hildemann, 1996). Oxidation of volatile organic compounds (VOCs) or the gas-phase portion of primary SVOCs (Robinson et al., 2007) results in products that form SOA. Due to the significant mass of OA in the troposphere, many studies have been attempted to understand the chemical and thermodynamic processes that lead to its formation (Seinfeld and Pankow, 2003).
Aromatic hydrocarbons are important in the formation of urban photochemical smog because of their large emission rates, reactivities, and ozone (O3) and SOA-forming capabilities (Odum et al., 1996, Odum et al., 1997, Lewis et al., 2000, Hurley et al., 2001, Karlsson et al., 2001, Bahreini et al., 2005, Song et al., 2005, Alfarra et al., 2006, Ng et al., 2007). Toluene is a major component of aromatic emissions (Olivier et al., 1999), and SOA formed upon its oxidation accounts for a significant fraction of simulated SOA from mobile sources in urban areas (Dechapanya et al., 2004). Toluene in the atmosphere is oxidized by the hydroxyl radical (OH) which adds to the ring or abstracts a hydrogen atom (H) from the methyl substituent. In contrast, oxidation by chlorine atom (Cl) is expected to proceed only via the abstraction pathway (Wang et al., 2005).
Efficient oxidation of VOCs by Cl affects net O3 formation and likely leads to SOA formation in some areas of the troposphere (Ganske et al., 1992, Keene et al., 1996, Canosa-Mas et al., 1999, Karlsson et al., 2001, Knipping and Dabdub, 2003, Cai and Griffin, 2006, Pszenny et al., 2007). This study quantifies Cl-initiated SOA formation from toluene through calculation of the dimensionless SOA yield, Y (Pandis et al., 1992) = ΔM0/ΔVOC where ΔM0 (μg m−3) is the SOA mass concentration formed after the consumption of ΔVOC (μg m−3) of the parent VOC by oxidation. This yield is an overall measure of the SOA-forming potential of a VOC. Based on absorptive partitioning (Pankow, 1994), Y is also expressed as follows (Odum et al., 1996)where Kom,i (m3 μg−1) is the distribution coefficient of product i between the gas-phase and aerosol organic material (om) and αi is its mass-based stoichiometric factor. Yield data obtained from chamber experiments have been fit to Eq. (1) assuming two products (Odum et al., 1996, Odum et al., 1997, Song et al., 2005, Cai and Griffin, 2006, Ng et al., 2007). The four fitted parameters for this model are not associated with absolute physical meanings, but differences in yield curves between conditions and laboratories may be represented through such fits.
Section snippets
Experiments
The system for this study (Cai and Griffin, 2006) includes a 6-m3 hemi-cylindrical chamber made of FEP Teflon® film and mounted in a frame 0.30 m above the floor. Pure air provided by a TEI (Franklin, MA) 111 zero air generator is dehumidified and stripped to remove nitrogen oxides (NOx) and VOCs. The number concentration measured in the zero air is less than 0.1 particles cm−3. Blank experiments are conducted to verify that concentrations of VOCs, NOx, and O3 are below detection limits (1.0 ppb).
Yields
SOA yields for toluene are described in Table 1, Table 2, and Fig. 1. Fig. 1 shows two yield curves for toluene depending on (Cl2/VOC)0. For THR experiments, the SOA yields range from 0.050 to 0.079 for generated aerosol mass ranging from 4.0 to 12.0 μg m−3. In TLR experiments, the SOA yields range from 0.030 to 0.064, corresponding to a generated aerosol mass range of 3.0–11.0 μg m−3. The yields associated with the two curves are similar, and the experimental error bars overlap. However, the
Acknowledgments
This work was funded by CAREER award grant ATM-0327643 from the National Science Foundation. We thank J. Allan, T. Onasch, and M. Canagaratna for continued availability of Q-AMS software. Support of LZ and provision of the Q-AMS by the UNH/NOAA AIRMAP Cooperative Institute is acknowledged gratefully.
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