Multi-generational oxidation of volatile organic compound (VOC) oxidation
products can significantly alter the mass, chemical composition and
properties of secondary organic aerosol (SOA) compared to calculations that
consider only the first few generations of oxidation reactions. However, the
most commonly used state-of-the-science schemes in 3-D regional or global
models that account for multi-generational oxidation (1) consider only
functionalization reactions but do not consider fragmentation reactions,
(2) have not been constrained to experimental data and (3) are added on top of
existing parameterizations. The incomplete description of multi-generational
oxidation in these models has the potential to bias source apportionment and
control calculations for SOA. In this work, we used the statistical oxidation model (SOM) of Cappa and Wilson (2012), constrained by
experimental laboratory chamber data, to evaluate the regional implications
of multi-generational oxidation considering both functionalization and
fragmentation reactions. SOM was implemented into the regional University of California at Davis / California Institute of Technology (UCD/CIT) air quality model and applied to air quality episodes in California and the
eastern USA. The mass, composition and properties of SOA predicted using SOM
were compared to SOA predictions generated by a traditional
Results show that SOA mass concentrations predicted by the UCD/CIT-SOM model
are very similar to those predicted by a two-product model when both models
use parameters that are derived from the same chamber data. Since the
two-product model does not explicitly resolve multi-generational oxidation
reactions, this finding suggests that the chamber data used to parameterize
the models captures the majority of the SOA mass formation from
multi-generational oxidation under the conditions tested. Consequently, the
use of low and high NO
Organic aerosol (OA) is generally the dominant component of submicrometer-sized atmospheric particulate matter (Jimenez et al., 2009), which plays an important role in the energy budget of the earth (Pachauri et al., 2014) and the health effects of air pollution (Bernstein et al., 2004). Despite its prominence, OA is the least understood component of atmospheric aerosol. Large-scale chemical transport models are the essential tool to simulate concentration distributions, which are needed to form strategies to mitigate, the climate and health impacts of atmospheric aerosols.
OA is a complex mixture of thousands of different compounds that have a wide
range of properties (Goldstein and Galbally, 2007). OA
can be directly emitted to the atmosphere in particulate form (so-called
primary organic aerosol; POA) or it can be formed in situ by the oxidation of
volatile organic compounds (VOCs) to yield lower volatility products that
condense into the aerosol phase, so-called secondary organic aerosol (SOA).
This latter route is generally the predominant one to form OA. Continuous
oxidation of VOCs and their oxidation products yields a broad range of
products, including those that have intermediate and low volatility. The
importance of such
Traditionally, models of SOA formation in chamber experiments have
represented SOA formation from VOCs using two to four surrogate products per
VOC, the yields for which have been parameterized to reproduce observed
levels of SOA (Odum et al., 1996). These models generally assume that the
surrogate products are non-reactive (i.e., do not undergo multi-generational
oxidation). These models, whether implemented in
In this work, we use the statistical oxidation model (SOM) of Cappa and Wilson (2012) to model the multi-generational oxidation reactions inherent in SOA formation. The SOM provides an efficient framework to track the experimentally constrained chemical evolution and gas–particle partitioning of SOA using a carbon and oxygen grid. In Jathar et al. (2015), we detailed the coupling of the SOM with the gas-phase chemical mechanism SAPRC-11 (Carter and Heo, 2013) within the UCD/CIT regional air quality model and used the new model to make predictions over the South Coast Air Basin (SoCAB) in California and the eastern United States. Here, we use the UCD/CIT-SOM model to investigate the influence of constrained multi-generational oxidation on the mass concentrations and properties of SOA and contrast those results against predictions from a traditional two-product model and an unconstrained multi-generational oxidation model.
The UCD/CIT air quality model is a regional chemical transport model (CTM)
(Kleeman and Cass, 2001) used here to simulate SOA formation
for two geographically distinct domains and time periods: (1) the state of
California simulated at a grid resolution of 24 km followed by a nested
simulation over the SoCAB at a grid resolution of 8 km from 20 July to 2 August 2005, and (2) the eastern half of the USA simulated at a grid
resolution of 36 km from 20 August to 2 September 2006.
Details about the latest version of the UCD/CIT model are provided in
Jathar et al. (2015) and
summarized in Table S1 in the Supplement. Briefly, anthropogenic emissions for California
were based on the California Regional PM10/PM2.5 Air Quality Study (CRPAQS)
inventory of 2000 but scaled to match conditions in 2005. FINN (Fire
Inventory for National Center for Atmospheric Research) (Wiedinmyer et al., 2011) and MEGAN (Model of Emissions of Gases and
Aerosols from Nature) (Guenther et al., 2006) were
used to calculate wildfire and biogenic emissions in California.
Anthropogenic and wildfire emissions for the eastern USA were based on the
2005 National Emissions Inventory (NEI), and biogenic emissions were
estimated using BEIS (Biogenic Emissions Inventory System) version 3. Hourly
meteorological fields were generated using the Weather Research and
Forecasting (WRF) v3.4 model (
Four types of SOA models are compared in this work: (1) a
The Base model simulated SOA formation as per the pathways and parameters in
the
CMAQ model version 4.7 (Carlton et al.,
2010) from the following gas-phase precursors: long alkanes (ALK5), benzene
(BENZENE), low-yield aromatics (ARO1), high-yield aromatics (ARO2),
isoprene, monoterpenes (TRP1) and sesquiterpenes (SESQ). The species in
parentheses are the model species representing those compounds in SAPRC-11
(the gas-phase chemical mechanism used here). The pathways considered
include (1) oxidation of the above-mentioned precursors to form
non-reactive semi-volatile products that partition into the particle-phase
(Odum et al., 1996) (the so-called two-product
model, where model parameters were previously determined from fitting
chamber data); (2) acid enhancement of isoprene SOA (Surratt et al., 2007). SOA formation from aromatics is NO
The
The SOM parameterizes multi-generational oxidation using a two-dimensional
carbon-oxygen grid to track the evolution of gas- and particle-phase organic
products arising from the oxidation of SOA precursors (Cappa
and Wilson, 2012; Cappa et al., 2013; Zhang et al.,
2014). This evolution through the SOM grid is VOC-specific and defined by
six parameters: (P1–P4) yields of the four products that add 1, 2, 3 and 4
oxygen atoms, without fragmentation; (P5) the probability of
fragmentation; and (P6) the decrease in vapor pressure (or volatility) of
the species per addition of oxygen atom. Details of the implementation and
parameterization of the SOM model in the UCD-CIT are presented in
Jathar et al. (2015).
Briefly, six SOM grids with precursor-specific parameter sets were used to
represent SOA formation from the same precursor classes in the Base model.
Parameter sets were separately determined from high NO
The SOM model parameters used in the present study were determined without
accounting for losses of vapors to chamber walls, which can lead to a
substantial underestimation of the actual SOA formation potential of a given
precursor (Matsunaga and Ziemann, 2010; Zhang
et al., 2014). A companion paper evaluates vapor wall-loss effects on the
SOM results (Cappa et al., 2015). The SOM
parameter fits were derived using dynamic gas-particle partitioning assuming
an accommodation coefficient of unity, which tends to minimize the influence
of vapor wall loss (McVay et al., 2014), and thus
represents a conservative lower bound of SOA formation. The SOM model was
additionally extended to consider the influence of oligomerization reactions
by allowing irreversible conversion of particle-phase SOM species into a
single non-volatile species using the same
Additional simulations were performed using a contemporary
multi-generational oxidation scheme, the cascading oxidation model (COM).
The COM builds on the two-product Base model but allows for additional
reaction of the semi-volatile products using the scheme of Baek et al. (2011). Briefly, the two semi-volatile products from a given precursor react
with OH, with the highest volatility product converted into the lowest
volatility product and the lowest volatility product converted to a
non-volatile product (see Supplement section on Cascading Oxidation Model). Like
most other schemes that have thus far been used to represent
multi-generational oxidation of SOA from traditional VOCs in 3-D models
(Lane et al., 2008), COM does not consider
fragmentation reactions, is not fit or constrained to experimental data and
adds these ageing reactions on top of an existing parameterization. The COM
model will be referred to as
Simulations performed in this work.
Table 1 lists the simulations performed in this work. We performed two simulations with the Base model (with and without oligomerization), two with the BaseM model (low and high yield), four with the SOM model (low and high yield and with oligomerization accounted for) and one with the COM model. These nine simulations were performed for both domains: SoCAB and the eastern USA. Simulations were performed for 19 days with the first 5 days used for spin-up. For the SoCAB, each simulated day using the SOM required approximately 4 h of elapsed time (on 40 Intel i5-3570 processor cores) so a 19-day episode was simulated in less than 4 days. For the eastern USA, each simulated day required approximately 9 h of elapsed time so a 19-day episode was simulated in about 8 days. The SOM simulations were approximately 4 times slower than the BaseM simulations on account of the large number of model species.
Although the main focus of the present study is on understanding the role of
multi-generational oxidation in SOA models, it is useful to begin by
considering differences between the predictions from Base and BaseM
(two-product parameters fit to more recent data sets). The 14-day averaged,
precursor-resolved SOA concentrations at two sites in the SoCAB (Los
Angeles: urban; Riverside: urban outflow) and at two sites in the eastern US
(Atlanta: urban; Smoky Mountains: remote) from Base and BaseM are compared
in Fig. 1. Base model predictions of total semi-volatile SOA
concentrations (i.e., SOA exclusive of oligomers) at all four sites are
similar to the BaseM (low-yield) model predictions that were parameterized
using high-NO
14-day averaged SOA concentrations at Los Angeles
Predictions from BaseM and SOM, which were parameterized using the same data, were used to investigate the influence of multi-generational oxidation. Domain-wide, 14-day averaged SOA concentrations from BaseM and SOM for the SoCAB and for the eastern US, along with the ratio of the SOA concentrations between SOM and BaseM, are shown in Fig. 2. The SOA concentrations presented are averages of the low-yield and high-yield simulations. Consideration of either the low-yield or high-yield simulations individually affects the details, but not the general conclusions about multi-generational oxidation below, even though the SOA mass concentrations from the high-yield simulations are typically 2–4 times larger than from the low-yield simulations (see Fig. S2). In both the SoCAB and the eastern USA, the predicted spatial distribution of SOA is generally similar between BaseM and SOM, with only minor differences evident in some locations. For the SoCAB, the SOA concentrations in SOM are somewhat lower everywhere compared to BaseM, by 10–20 % in the Los Angeles metropolitan area (marked by a red box) and by about 20–30 % in regions dominated by biogenic SOA (e.g., Los Padres National Forest located in the northwest corner of the simulated domain). Similarly, the SOM predictions for SOA concentrations in the eastern USA are 0–20 % lower than BaseM predictions over most of the domain. The urban vs. biogenic difference was not evident, probably owing to a coarser grid resolution (36 km for the eastern USA vs. 8 km for the SoCAB). It appears that multi-generational oxidation does not dramatically increase (from additional functionalization reactions) or decrease (from additional fragmentation reactions) the total SOA concentrations formed from the precursor compounds considered in either region.
In Fig. 1, at all sites, the SOM SOA concentrations are roughly the same
or slightly higher than the BaseM SOA concentrations for the low-yield
simulations but consistently lower for the high-yield simulations, by
18–25 %. When averaged, the SOM SOA concentrations are slightly lower than
the BaseM simulations, largely due to the lower predictions of SOA from
mono-terpene and sesquiterpenes in the SOM high-yield simulations. The low-
vs. high-yield distinction suggests that the SOM-predicted SOA is
probably similar to BaseM-predicted SOA in urban areas (low yield or high
NO
The seemingly limited influence of multi-generational oxidation on total SOA
concentrations runs counter to the findings from previous work that suggests
multi-generational oxidation is an important source of SOA (Robinson et al., 2007; Murphy and Pandis, 2009; Baek et al., 2011; Fast et al., 2014; Dzepina et
al., 2009). However, these previous efforts accounted for
multi-generational VOC oxidation by adding ageing reactions for
semi-volatile products on top of an existing parameterization, similar to
the COM model, and thus may suffer from
Fractional bias and fractional error at STN and IMPROVE sites for
the SoCAB and the eastern USA for the Base, BaseM (average of low- and
high-yield), COM and SOM (average of low- and high-yield) simulations.
Bold, italics, and bold-italics represent
The behavior of SOM vs. BaseM predictions is similar in the SoCAB and the
eastern USA, with minor differences likely related to the size of the domain
and the average atmospheric lifetime of the simulated SOA, differences in
the evolution of SOA from the various precursors, and the dominance of
certain precursors in different domains. These precursor-specific SOA
concentrations are visualized in Fig. 1 and listed as domain-wide averages
in Table S4. These results indicate that SOM typically produced more SOA
from alkanes (although very little overall) but less from terpenes and
isoprene in both the SoCAB and the eastern USA, compared to BaseM. For
aromatics and sesquiterpenes the concentrations are generally similar
between the two models, although slightly greater for sesquiterpenes for the
eastern USA SOM simulations. The use of the SOM model that inherently
accounts for multi-generational oxidation leads to more SOA mass for some
compounds (due to enhanced functionalization) but less SOA mass for others
(due to fragmentation) compared to a static representation of the
semi-volatile products. SOA concentrations in chamber photooxidation
experiments have been observed to decrease at longer times for some VOCs,
notably isoprene (Chhabra et al., 2011) and
The simulated total OA concentrations (POA
The effective volatility of the SOA was characterized for the Base, BaseM
and SOM simulations. SOA volatility influences the sensitivity of the SOA to
dilution and temperature changes. Since Base, BaseM and SOM use model
species that have very different volatilities, as characterized by the
species saturation concentration,
Volatility distributions of the 14-day averaged gas
It is not possible to compare the simulated volatility distributions to
ambient observations since direct measurement of volatility distributions
has not been demonstrated for such low
The Base-OLIG model includes an oligomerization pathway in which
semi-volatile, condensed-phase material is converted to a non-volatile, yet
absorptive material on a fixed timescale. This effectively
14-day averaged SOA concentrations at
The 14-day averaged SOA concentrations from the COM, Base and SOM simulations for the SoCAB and the eastern USA are compared in Fig. 5. Recall that COM allows for conversion of the semi-volatile products in the Base model to lower-volatility products on top of the original 2-product parameterization. The COM simulations predict a factor of 4 to 8 increase in SOA concentrations over the Base and SOM simulations, attributable to the production of low-volatility and non-volatile SOA from the added oxidation reactions. Because COM, like many ad hoc ageing schemes (Simon and Bhave, 2011; Robinson et al., 2007; Pye and Seinfeld, 2010; Baek et al., 2011), lacks fragmentation and adds ageing reactions on top of an existing parameterization, and with sufficient oxidation all semi-volatile products will be converted into non-volatile SOA. This means that the ultimate SOA mass yield is equal to the sum of the mass yields of the individual products, independent of their vapor pressures. Given that SOM inherently accounts for multi-generational oxidation as part of the model parameterization, this comparison clearly suggests that the unconstrained schemes used in the COM simulations form too much SOA and that such schemes are not truly representative of multi-generational oxidation in the atmosphere.
14-day averaged SOA concentrations in SoCAB
Some previous studies have defended the use of a COM-type model because its implementation improved model performance (Lane et al., 2008; Murphy and Pandis, 2009; Shrivastava et al., 2008), as was also observed here (Table 2). However, given that COM-type models remain generally unconstrained and have been inconsistently applied to different VOC precursor types (e.g., ageing of anthropogenics but not biogenics) (Farina et al., 2010; Lane et al., 2008; Murphy and Pandis, 2009), and since recent testing of a COM-type scheme in the laboratory demonstrated that such schemes do, indeed, lead to the over-prediction of SOA mass concentration (Zhao et al., 2015), we suggest that this apparently improved agreement is more likely fortuitous than a true indication of improved representation of atmospheric chemistry. It should be noted that the current study specifically assesses the performance of a COM-type model on the SOA production from traditional VOCs only, exclusive of potential contributions of IVOCs and semi-volatile POA vapors to the SOA burden. Previous studies that have examined the influence of multi-generational oxidation of traditional VOCs using COM-type models have typically combined the effects of VOC ageing and IVOC and POA vapor oxidation (e.g., Murphy and Pandis, 2009; Jathar et al., 2011) together and have not investigated the role of each process separately. Consequently, our results, which isolate the influence of using a COM-type oxidation scheme, suggest COM-type models may be inappropriate for use in regional air quality models even though they can lead to improved model–measurement comparison (Table 2). They also imply that models that employed COM-like schemes have potentially underplayed the role of other important OA formation pathways such as aqueous (aerosol, fog, cloud) processing of water-soluble organics (Ervens et al., 2011) and particle-surface reactions (Liggio et al., 2005; Shiraiwa et al., 2013). Future work to integrate semi-volatile POA treatments with constrained multi-generational ageing schemes like SOM is needed.
When constrained using the same chamber data, the BaseM (traditional two-product model that does not resolve multi-generational oxidation) and SOM models predict roughly the same SOA mass concentrations and spatial distribution for regional air pollution episodes in the SoCAB and the eastern USA. This suggests that the chamber data used to constrain the BaseM and SOM parameterizations presumably already includes a majority of the SOA mass that would be attributable to multi-generational oxidation. The extent to which multi-generational oxidation influences the production of SOA in a given chamber experiment depends on both the volatility and reactivity of the first-generation products and the timescale of the experiment (Wilson et al., 2012). If SOA formation is dominated by first-generation products, then explicit accounting for multi-generational ageing will not be important. Alternatively, if most SOA is formed from second-generation products with little direct contribution from first-generation products, then a static representation (such as with the 2-product model) might be sufficient even when multi-generational ageing is, in fact, dominant. But if SOA formation is balanced between contributions from first, second and later generation products, then the extent to which a static representation will capture the influence of multi-generational ageing may be highly variable and sensitive to the experimental conditions and number of oxidation lifetimes. Consequently, the appropriateness of extrapolating static model parameterizations to longer (global atmospheric) timescales remains unclear. The results presented here indicate that the 2-product model does capture the influence of multi-generational ageing as part of the parameterization in terms of mass concentration, at least for the regional episodes considered, but it is also apparent that the simulated SOA properties (e.g., volatility) and the explicit contributions of various SOA types are not fully captured by such simple models.
The BaseM and SOM simulations show that the SOA concentrations in the SoCAB
and eastern USA vary by a factor of 2 when using parameterizations
developed from low vs. high NO
SOM predicts a modestly different composition of SOA than BaseM despite similar total mass concentrations of SOA. The composition predicted by SOM has a slightly higher contribution from alkanes, aromatics (anthropogenic) and sesquiterpenes and a lower contribution from isoprene and monoterpenes. These modest differences in the predicted composition of SOA have implications for understanding the sources of ambient aerosol and eventually the regulation of these sources to achieve compliance with National Ambient Air Quality Standards (NAAQS). These more accurate SOA predictions resolved by chemical families should be tested in epidemiological studies to determine if they are associated with adverse health effects. Additionally, SOM predicted a much lower-volatility SOA than BaseM, and SOM predictions are in better qualitative agreement with ambient thermodenuder measurements of OA volatility. Since the SOA has a much lower volatility, there is very little enhancement (10–15 %) with the inclusion of oligomerization reactions, implying that while oligomerization might affect composition, it may not be a source of additional SOA formation as the Base model suggests.
In this work, we consider POA as non-volatile and non-reactive and do not
consider SOA contributions from IVOCs or semi-volatile POA vapors. Oxidation
of IVOCs and semi-volatile POA vapors (i.e., SVOCs) can lead to the
production of new SOA mass, but evaporation of POA leads to a decrease in
the total OA mass. To some extent, these effects are offsetting (especially
for SVOCs, which do not contribute new carbon mass to a model). To the
extent that the loss of POA is balanced exactly by the formation of SOA from
IVOCs and
Finally, the comparison between the constrained SOM and the unconstrained COM (commonly used in large-scale models) suggests that COM may be double counting SOA formation. These simple ageing schemes should be refit to chamber data where all parameters can be matched to observed trends in a self-consistent manner.
This work was supported by the California Air Resources Board (CARB) under contracts 11-755 and 12-312. Although this work was funded by the CARB, the statements and conclusions are those of the authors and not necessarily those of the CARB. Edited by: M. Shiraiwa