Investigation of particle and vapor wall-loss effects on controlled wood-smoke smog-chamber experiments

Abstract

Abstract. Smog chambers are extensively used to study processes that drive gas and particle evolution in the atmosphere. A limitation of these experiments is that particles and gas-phase species may be lost to chamber walls on shorter timescales than the timescales of the atmospheric processes being studied in the chamber experiments. These particle and vapor wall losses have been investigated in recent studies of secondary organic aerosol (SOA) formation, but they have not been systematically investigated in experiments of primary emissions from combustion. The semi-volatile nature of combustion emissions (e.g. from wood smoke) may complicate the behavior of particle and vapor wall deposition in the chamber over the course of the experiments due to the competition between gas/particle and gas/wall partitioning. Losses of vapors to the walls may impact particle evaporation in these experiments, and potential precursors for SOA formation from combustion may be lost to the walls, causing underestimations of aerosol yields. Here, we conduct simulations to determine how particle and gas-phase wall losses contributed to the observed evolution of the aerosol during experiments in the third Fire Lab At Missoula Experiment (FLAME III). We use the TwO-Moment Aerosol Sectional (TOMAS) microphysics algorithm coupled with the organic volatility basis set (VBS) and wall-loss formulations to examine the predicted extent of particle and vapor wall losses. We limit the scope of our study to the dark periods in the chamber before photo-oxidation to simplify the aerosol system for this initial study. Our model simulations suggest that over one-third of the initial particle-phase organic mass (41 %) was lost during the experiments, and over half of this particle-organic mass loss was from direct particle wall loss (65 % of the loss) with the remainder from evaporation of the particles driven by vapor losses to the walls (35 % of the loss). We perform a series of sensitivity tests to understand uncertainties in our simulations. Uncertainty in the initial wood-smoke volatility distribution contributes 18 % uncertainty to the final particle-organic mass remaining in the chamber (relative to base-assumption simulation). We show that the total mass loss may depend on the effective saturation concentration of vapor with respect to the walls as these values currently vary widely in the literature. The details of smoke dilution during the filling of smog chambers may influence the mass loss to the walls, and a dilution of ~ 25:1 during the experiments increased particle-organic mass loss by 33 % compared to a simulation where we assume the particles and vapors are initially in equilibrium in the chamber. Finally, we discuss how our findings may influence interpretations of emission factors and SOA production in wood-smoke smog-chamber experiments.

Particle Wall-loss Correction Methods in Smog Chamber Experiments

“…It then stayed practically constant until OH was introduced into the chamber at t=0.9 h. The second-310 generation of SOA formation led to an increase of the ratio to 1.6 at t=1.1 h. The ratio decreased gradually to 1.5 until the second introduction of HONO. This decrease could be explained as a loss of SOA due to photodegradation or other chemical processes such as SOA evaporation driven by organic-vapor uptake by the walls (Bian et al, 2015). Another explanation for the decreasing trend of OA/Sulfate during this period is the size dependence of the particle wall-loss rates.…”

Effects of gas–wall partitioning in Teflon tubing and instrumentation on time-resolved measurements of gas-phase organic compounds

“…Values of C w reported by Matsunaga and Ziemann (2010), Yeh and Ziemann (2015), and Krechmer et al (2016) ber, a range indicating that a significant fraction of organic products formed from oxidation reactions regularly studied in environmental chambers (and even some less-volatile precursors) will be absorbed into the walls at equilibrium. Using typical values for C w and the timescale for reaching gas-wall partitioning equilibrium, one can incorporate the effect into box models to estimate the effect of partitioning on chamber measurements, as has been done in several studies of secondary organic aerosol yields (Matsunaga and Ziemann, 2010;Shiraiwa et al, 2013;McVay et al, 2014;Bian et al, 2015;Krechmer et al, 2015;La et al, 2016).…”

Construction and Characterization of an Indoor Smog Chamber for the Measurement of the Optical and Physicochemical Properties of Aging Biomass Burning Aerosols Native to sub-Saharan Africa

“…The main limitation of chamber experiments is that particles and gas-phase species may be lost to chamber walls on timescales that are shorter than the timescales of the atmospheric processes being studied (Bian et al, 2015). Therefore, before use, chambers must be characterized to reduce uncertainties caused by the loss of gasses 235 and particles in the chamber walls, as well as "background reactivity" which includes possibly surface chemistry which can generate gases, including reactive radical species (Carter et al, 2005;Weitkamp et al, 2007).…”

“…Particle wall losses have been used to quantitatively correct 340 observed aerosol concentrations to deduce SOA formation in smog chambers (Hennigan et al, 2011) by assuming that semi-volatile compounds are in equilibrium with the particles deposited in the walls instead of the walls themselves. The magnitude of vapor wall loss has not been considered in most smog chamber studies, since the values are not constrained (Bian et al, 2015). Recently, Zhang et al (2014) showed that the vapor wall loss can underestimate SOA formation.…”

“…Smog chambers (both indoor and outdoor) have been used, beginning in the 1960s, to study atmospheric processes in controlled environments, such as biomass burning emissions (Kamens et al, 1984;Hennigan et al, 2011;Bian et al, 2015), SOA production (Carter et al, 2005;Babar et al, 2016), diesel exhaust (Weitkamp et al, 85 2007), and cigarette smoke (Schick et al, 2012). As opposed to in situ measurements, the aging process can be studied over longer times in a chamber with greater temporal accuracy and sampling frequency.…”