The influence of losses of organic vapors to chamber walls during secondary
organic aerosol (SOA) formation experiments has recently been established.
Here, the influence of such losses on simulated ambient SOA concentrations
and properties is assessed in the University of California at Davis / California Institute of Technology (UCD/CIT) regional air quality model using
the statistical oxidation model (SOM) for SOA. The SOM was fit to laboratory
chamber data both with and without accounting for vapor wall losses
following the approach of Zhang et al. (2014). Two
vapor wall-loss scenarios are considered when fitting of SOM to chamber data
to determine best-fit SOM parameters, one with “low” and one with “high”
vapor wall-loss rates to approximately account for the current range of
uncertainty in this process. Simulations were run using these different
parameterizations (scenarios) for both the southern California/South Coast
Air Basin (SoCAB) and the eastern United States (US). Accounting for vapor
wall losses leads to substantial increases in the simulated SOA
concentrations from volatile organic compounds (VOCs) in both domains, by factors of
Particulate organic matter, or organic aerosol (OA), is derived from primary
emissions or from secondary chemical production in the atmosphere from the
oxidation of volatile organic compounds (VOCs). OA makes up a substantial
fraction of atmospheric submicron particulate matter (Zhang et al.,
2007), influencing the atmospheric fate and impact of PM on regional and
global scales. Gas-phase oxidation of VOCs leads to the formation of
oxygenated product species that can condense onto existing particles or
nucleate with other species to form new particles (e.g.
Ziemann and Atkinson, 2012). Much of the understanding regarding the
formation of secondary organic aerosol (SOA) via condensation has been
derived from experiments conducted in laboratory chambers. In a typical
experiment, a precursor VOC is added to the chamber and exposed to an
oxidant (e.g OH, O
Recent observations have demonstrated that organic vapors can be lost to
Teflon chamber walls, and that the extent of loss is related to the compound
vapor pressures with lower vapor pressure compounds partitioning more
strongly to the walls than higher vapor pressure compounds (Matsunaga and
Ziemann, 2010; Kokkola et al., 2014; Krechmer et al., 2015; Yeh and Ziemann,
2015; Zhang et al., 2015). These results suggest that vapor wall losses
during SOA formation experiments could potentially bias observed SOA
concentrations. Indeed, Zhang et al. (2014) observed
that SOA yields from toluene
Although the exact value of
In this study, the SOM SOA model (Cappa and Wilson,
2012) is utilized to examine the influence of vapor wall losses on simulated
SOA concentrations and O : C atomic ratios in a 3-D regional air quality model,
specifically the University of California at Davis / California Institute of Technology (UCD/CIT) (Kleeman and Cass, 2001). What
distinguishes the present approach is that the potential influence of vapor
wall losses is inherently accounted for during the development of the SOM
SOA parameterization (Zhang et al., 2014). This can
be contrasted with a simple scaling of an existing parameterization. The
current approach allows for more detailed characterization of different
precursor species, reaction conditions (e.g. NO
Regional air quality simulations were performed using the UCD/CIT chemical-transport model (Kleeman and Cass, 2001) for two geographical
domains: (i) the Southern California Air Basin (SoCAB) and (ii) the eastern
United States (US). Details regarding the general model configuration and emissions
inventory used have been previously discussed (Jathar et
al., 2015a), and the reader is referred to that work for further
information. Details specific to the current work are provided in the
following sections. Model simulations were run for SoCAB from 20 July to
2 August 2005 and for the eastern US from 20 August to 2 September 2006.
Model spatial resolution was higher in SoCAB (8 km
SOA formation from six VOC classes was simulated using the statistical
oxidation model (Cappa and Wilson, 2012; Cappa et al., 2013), which was
recently implemented in the UCD/CIT model (Jathar et al.,
2015a). The VOC classes considered are long alkanes, benzene, high-yield
aromatics (i.e. toluene), low-yield aromatics (i.e. m-xylene), isoprene and
terpenes (including both mono- and sesquiterpenes). SOM is a parameterizable
model that simulates the multi-generational oxidation of the product species
formed from reaction of the SOA precursor VOCs. In SOM, a “species” is
defined as a molecule with a specific number of carbon and oxygen atoms
(
Vapor wall losses have been accounted for using SOM, as detailed in
Zhang et al. (2014). Vapor wall loss is treated as a
reversible, absorptive process with vapor uptake specified using a
first-order rate coefficient (
An important aspect of vapor wall loss is that the impact it has on SOA
concentrations is dependent upon the timescale associated with
vapor-particle equilibration (
SOM was fit to time-dependent SOA formation experiments conducted in the
California Institute of Technology chamber, following the methodologies
described in Cappa et al. (2013) and
Zhang et al. (2014). Observed suspended particle
concentrations have been corrected only for physical deposition on chamber
walls, which is appropriate since vapor wall losses are accounted for
separately by SOM. Best-fit values for the SOM parameters for the base case
(SOM-no) are given in Jathar et al. (2015a) and values for
SOM-low and SOM-high determined here are given in Table S1, along with the
sources of the experimental data. Parameters have been separately determined
for experiments conducted under low-NO
Ideally, SOA levels from the SOM-based simulations can be compared with
similar results based on the commonly used two-product model. To do so
involves determining new parameters for the two-product model in which vapor
wall losses are explicitly accounted for. Therefore, vapor wall-loss-corrected SOA yield curves (i.e. [SOA] vs. [
Primary organic aerosol (POA) derived from anthropogenic (e.g. vehicular
activities, food cooking) or pyrogenic (e.g. wood combustion) sources are
simulated assuming that the POA is non-volatile. This is the standard
assumption in the CMAQ model framework (Simon and Bhave,
2011), and thus is adopted here. It is known that some POA is semi-volatile,
not non-volatile as assumed here. Had POA been treated within a
semi-volatile framework (Robinson et al., 2007),
such that some fraction of the POA can evaporate (i.e. SVOCs) and react
within the gas-phase and be converted to SOA (sometimes improperly referred
to as “oxidized POA”), then the amount of POA would likely decrease (due
to evaporation) and the amount of simulated SOA would increase (due to
condensation of oxidized SVOC vapors); the total OA concentration (POA
Six individual model simulations have been carried out to determine the
spatial distribution of SOA concentrations. Each simulation used one of the
SOM parameterizations, i.e. SOM-no, SOM-low or SOM-high with either the low-
or high-NO
14-day averaged SOA concentrations, in
As noted above, unique sets of SOM parameters were fit to experiments
conducted under either low- or high-NO
The spatial distribution of the SOM-no model SOA concentrations is shown for
SoCAB and the eastern US using the average from the simulations carried out
using the low- and high-NO
The influence of vapor wall losses on the simulated ambient SOA
concentrations is illustrated in Fig. 1c–f as the
ratio between the SOA from the SOM-low and SOM-high simulations to the
SOM-no (no wall losses) simulation. This ratio will be referred to generally
as the wall loss impact (
Variation of the ratio between simulated SOA concentrations from
SOM-low (red) and SOM-high (blue) simulations to SOM-no simulations for
Regional air quality models have historically overestimated the
urban-to-regional gradient in total OA concentrations.
Robinson et al. (2007) showed that the simulated
urban-to-regional gradient could be reduced and made more consistent with
observations by treating POA as semi-volatile and adding SVOCs and IVOCs as
SOA-forming species. The current results suggest a complementary
explanation, namely that the urban-to-regional gradient, can be reduced when
vapor wall losses are accounted for since
The simulated fraction of total OA that is SOA (
14-day averaged
For the eastern US, the predicted
The simulated total OA concentrations are compared to ambient OA
measurements made at the STN (Speciated Trends Network) and IMPROVE
(Interagency Monitoring of Protected Visual Environments; The Visibility Information Exchange Web System (VIEWS 2.0),
2015) air quality monitoring sites in SoCAB and the eastern US; the regional
differences in
Table 1 lists statistical metrics of fractional
bias, normalized mean square error (NMSE) and the concordance correlation
coefficients that capture model performance for OA for all simulations for
both domains across the STN and IMPROVE monitoring networks. Fractional bias
is calculated as:
Model performance metrics determined for the three simulation
groupings (SOM-no, SOM-low and SOM-high) for the low-NO
The simulations can also be compared with observations of the OA-to-
Bar charts showing the fractional contribution from the various
VOC precursor classes to the total simulated SOA for two locations in SoCAB
(central Los Angeles and Riverside) and two in the eastern US (Atlanta and
the Smoky Mountains). Results are shown for (top) average, (middle)
high-NO
Simulated and observed diurnal profiles for the OA
Accounting for vapor wall losses leads to regionally specific changes in the
simulated contributions from the different VOC classes (e.g. TRP1, ARO1) to
the SOA burden, as illustrated in Fig. 4 for two
sites in SoCAB (central Los Angeles and Riverside) and two in the eastern US
(Atlanta and the Smoky Mountains). Focusing first on contributions from the
biogenic VOCs, at all locations accounting for vapor wall losses leads to an
increase in the fractional contribution of isoprene SOA, typically at the
expense of terpene and sesquiterpene SOA. This is true for both the low- and
high-NO
In SoCAB, the predicted average isoprene SOA fraction in central LA is
relatively large for the SOM-low (36 %) and SOM-high (47 %) simulations,
compared to the SOM-no simulations (12 %). There is a large difference in
SoCAB between the simulations that use the low-NO
While the predicted isoprene SOA fraction increased, the predicted terpene and sesquiterpene SOA fractions decreased in the simulations that accounted for vapor wall losses. Additionally, the terpene SOA / sesquiterpene SOA ratio increased at all locations for the SOM-low and SOM-high simulations, in large part because the sesquiterpene yield is already large and thus accounting for vapor wall losses has a limited influence on the simulated sesquiterpene SOA concentrations.
There are some changes in the anthropogenic fraction of SOA when vapor wall
losses are accounted for. The anthropogenic fraction of SOA is defined here
as the sum of the SOA from long alkanes and aromatics, which are emitted
from combustion of fossil fuels, divided by the sum of the total SOA, which
additionally includes SOA from isoprene, monoterpenes and sesquiterpenes
emitted by trees, plants and other natural sources. The
14-day averaged O : C atomic ratios for SOA for
The SoCAB
The O : C atomic ratios of the SOA have been calculated from the simulated
distributions of compounds in
The (O : C)
14-day averaged O : C atomic ratios for total OA (POA
The simulated O : C for the total OA also differs substantially between
simulations (Fig. 7), especially in regions where
the simulated increase in
The simulated results at Riverside can be compared with bulk, campaign
average (O : C)
The simulated (O : C)
The above simulations included SOA only from VOCs, neglecting contributions
from S/IVOCs including oxidation of semi-volatile POA vapors. S/IVOCs and
semi-volatile POA vapors are likely
Simulated and observed diurnal profiles for the total OA O : C
The influence of chamber vapor wall losses on simulated SOA concentrations
and properties has been assessed. The statistical oxidation model was used
to parameterize SOA formation from laboratory chamber experiments both with
and without accounting for vapor wall losses using data from experiments
conducted under both high-NO
This increase in simulated SOA when vapor wall losses are accounted for
leads to a substantial increase in the simulated SOA fraction of total OA.
This is especially seen in SoCAB where
Overall, the generally improved model performance when vapor wall losses are accounted for – in terms of both absolute and relative concentrations and in terms of SOA properties – suggests that accounting for this chamber effect in atmospheric simulations of SOA is important, although certainly requiring further examination. Our results qualitatively agree with other recent efforts to assess the influence of vapor wall losses on ambient SOA concentrations (Baker et al., 2015; Hayes et al., 2015), but as our accounting for vapor wall loss is inherent in the SOA parameterization the simulations here serve to provide a more robust assessment. The results presented here additionally suggest that there may be no need to invoke ad hoc “ageing” schemes for aromatics (Tsimpidi et al., 2010) to achieve increases in simulated SOA concentrations in urban environments. Further, these results suggest that the contribution of S/IVOCs to urban SOA might be somewhat limited, albeit still important, although this issue certainly requires further investigation.
The manuscript was written through contributions of all authors. Christopher D. Cappa, Shantanu H. Jathar, Michael J. Kleeman, John H. Seinfeld and Anthony S. Wexler designed the project. Shantanu H. Jathar and Michael J. Kleeman carried out the simulations. Christopher D. Cappa determined model parameters using laboratory data collected by John H. Seinfeld. Kenneth S. Docherty and Jose L. Jimenez collected and processed the SOAR data. All authors have given approval to the final version of the manuscript.
The authors thank Pedro Campuzano-Jost for the SEAC4RS data. This study was funded by the California Air Resources Board, contract 12-312 and NOAA grant NA13OAR4310058. Jose L. Jimenez was supported by CARB 11-305 and EPA STAR 83587701-0. This manuscript has not been reviewed by the funding agencies and no endorsement should be inferred. Edited by: K. Lehtinen