GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus GmbHGöttingen, Germany10.5194/gmd-8-1821-2015simpleGAMMA v1.0 – a reduced model of secondary organic aerosol
formation in the aqueous aerosol phase (aaSOA)WooJ. L.McNeillV. F.vfm2103@columbia.eduDepartment of Chemical Engineering, Columbia University, New York, NY 10027,
USAV. F. McNeill (vfm2103@columbia.edu)22June2015861821182905December201422January201519May201501June2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://gmd.copernicus.org/articles/8/1821/2015/gmd-8-1821-2015.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/8/1821/2015/gmd-8-1821-2015.pdf
There is increasing evidence that the uptake and aqueous processing of
water-soluble volatile organic compounds (VOCs) by wet aerosols or cloud
droplets is an important source of secondary organic aerosol (SOA). We
recently developed GAMMA (Gas–Aerosol Model for Mechanism Analysis), a
zero-dimensional kinetic model that couples gas-phase and detailed
aqueous-phase atmospheric chemistry for speciated prediction of SOA and
organosulfate formation in cloud water or aqueous aerosols. Results from GAMMA
simulations of SOA formation in aerosol water (aaSOA) (McNeill et al., 2012)
indicate that it is dominated by two pathways: isoprene epoxydiol (IEPOX)
uptake followed by ring-opening chemistry (under low-NOx conditions) and
glyoxal uptake. This suggested that it is possible to model the majority of
aaSOA mass using a highly simplified reaction scheme. We have therefore
developed a reduced version of GAMMA, simpleGAMMA. Close agreement in
predicted aaSOA mass is observed between simpleGAMMA and GAMMA under all
conditions tested (between pH 1–4 and RH 40–80 %) after 12 h of
simulation. simpleGAMMA is computationally efficient and suitable for
coupling with larger-scale atmospheric chemistry models or analyzing ambient
measurement data.
Introduction
Quantifying the sources of tropospheric aerosol material is important for
accurate modeling of air quality and climate. In situ processes leading to
the formation of new organic aerosol material, collectively known as
secondary organic aerosol (SOA) formation, are poorly constrained in
atmospheric chemistry models (Kanakidou et al., 2005; Hallquist et al., 2009;
Hodzic et al., 2010). Disagreement between model results and observations of
the quantity, degree of oxidation, and location of organic aerosols in the
atmosphere has suggested an incomplete representation of SOA formation
pathways in models (Heald et al., 2005; Jimenez et al., 2009). In the past
decade, the uptake of water-soluble volatile organic compounds (VOCs) into
cloud droplets or aerosol water, followed by aqueous-phase chemical
processing, has received increased attention as a possibly important source
of SOA (Blando and Turpin, 2000; Ervens et al., 2011). It is thought to be
especially significant in the case of isoprene-derived SOA formation. This is
because most of the gas-phase oxidation products of isoprene, are, like
isoprene itself, highly volatile; however, some, like glyoxal (GLYX),
isoprene-derived epoxydiols (IEPOX) (Paulot et al.,
2009; Surratt et al., 2010) and methacrylic acid epoxide (MAE) (Lin et al.,
2013), are water-soluble. These species also undergo reactive processing in
the aqueous phase of cloud droplets or aerosols, increasing their uptake from
the gas phase.
Despite mounting evidence that aqueous atmospheric chemistry is a significant
source of SOA, aqueous aerosol and
cloud water SOA formation is not yet widely represented in 3-D atmospheric
chemistry and air quality models. This is due, in part, to the challenges of
bridging scales between the detailed information generated by laboratory
experiments and simplified representations suitable for implementation in 3-D
models, which can afford to carry relatively few chemical tracers. Including
cloud-water organic chemistry in large-scale atmospheric chemistry models has
improved agreement with observations (Carlton et al., 2008; Myriokefalitakis
et al., 2011; Liu et al., 2012), but aqueous aerosol processes are just
beginning to be represented (Pye et al., 2013; Knote et al., 2014; Lin et
al., 2014).
Previously, we developed GAMMA (Gas–Aerosol Model for Mechanism Analysis), a
zero-dimensional kinetic model that couples gas- and detailed aqueous
aerosol-phase chemistry for speciated prediction of SOA and organosulfate
formation in the aqueous aerosol phase under ambient or laboratory conditions
(McNeill et al., 2012; Woo et al., 2013). GAMMA represents aaSOA (SOA
formation in aerosol water formation in terms of
aqueous uptake followed by aqueous-phase reaction (Schwartz, 1986). GAMMA
includes IEPOX chemistry following Eddingsaas et al. (2010) and uses
effective Henry's law constant, H*, constrained by aerosol chamber studies
(Sumner et al., 2014) to describe glyoxal uptake and dark reactions, as well
as detailed photochemical organosulfate formation and brown carbon formation
from glyoxal, methylglyoxal, and acetaldehyde (Woo et al., 2013). For more
information regarding other specific mechanisms included in GAMMA, as well as
rate constants for these reactions and other physical parameters, the reader
is referred to McNeill et al. (2012) (including the Supplement) and Woo et
al. (2013).
Simulations using GAMMA indicate that the IEPOX pathway dominates aaSOA
formation, leading to up to ∼ 0.9 µg m-3 of SOA mass
under conditions typical of the rural SE USA (McNeill et al., 2012). Pye et
al. (2013) predicted similar mean concentrations
(0.6–1.0 µg m-3) of IEPOX-derived SOA mass for the SE USA in
summer 2006, using CMAQ (Community Multiscale Air Quality model) with a
surface reactive uptake formulation of IEPOX aaSOA formation. In urban
(high-NOx) environments, aaSOA is primarily formed via glyoxal uptake
(McNeill et al., 2012).
This predominance of two aaSOA formation pathways involving relatively few
species, compared to the total number of aqueous compounds tracked by GAMMA,
suggests that it is possible to model the majority of aqueous aerosol-phase
SOA mass using a highly simplified reaction scheme, which is computationally
efficient and suitable for coupling with larger-scale atmospheric chemistry
models. GAMMA has therefore been used as a guide to develop a reduced
mechanism for aaSOA formation, simpleGAMMA. simpleGAMMA reduces the total
number of tracked aqueous species from 140 to 4 (glyoxal, IEPOX,
2-methyltetrol, and IEPOX organosulfate), with 2 species partitioning
between the gas and aqueous aerosol phases (glyoxal and IEPOX), and a single
aqueous-phase chemical process (reactive uptake of IEPOX), compared to 118
in GAMMA.
simpleGAMMA: model description
As in GAMMA, the time evolution of the aqueous aerosol-phase concentration
(Ci, in mol L-1) of a given chemical species i is described in
simpleGAMMA by the following differential equation (Schwartz, 1986):
dCidt=kmt,iRTPi-kmt,iHi∗RTCi+∑krik,aq.
Here, Pi is the gas-phase partial pressure of species i, Hi* is
effective Henry's law constant, R is the universal gas constant, and
T is temperature. The rates rik,aq represent chemical reactions
in the aerosol phase that can act as sources or sinks for a given species.
kmt,i is the gas–aerosol mass transfer coefficient for species
i, given by
kmt,i=1R23Dg,i+4R3ωiαi,
where R is the aerosol particle radius, Dg,i is the gas-phase
diffusion coefficient, ωi is the thermal velocity, and αi
is the accommodation coefficient. A suitable gas-phase chemical mechanism
should be employed, and the loss or gain of species to/from the aerosol phase
should be accounted for following, for example,
dPidt=-kmt,iaLPi+kmt,iaLHi∗Ci+∑jrij,gas+Ei-Di,
where aL is the aerosol aqueous liquid volume fraction
(cm3 cm-3 of air), rij,gas is the rate of gas-phase
reaction j that species i participates in, and Ei and Di are
the emission and deposition rates of species i, respectively.
Note that simpleGAMMA is a reduced version of the aqueous-phase mechanism of GAMMA (McNeill et al., 2012). The gas-phase mechanism of GAMMA
was not changed because it is intended that simpleGAMMA take gas-phase
concentration fields as inputs from an external source, i.e., from field
measurements or from existing models of atmospheric chemistry, which have
gas-phase chemical mechanisms but lack representations of aqueous aerosol-phase SOA formation. For tests reported here, we ran simpleGAMMA with the
full gas-phase mechanism of GAMMA, following Eqs. (1)–(3). A full
description of the gas- and aqueous-phase mechanisms of GAMMA, the simulation
conditions, and results can be found in McNeill et al. (2012).
The processes leading to aaSOA formation in simpleGAMMA are a subset of those
represented in GAMMA and they were selected with the
goal of minimizing the number of aqueous-phase tracers and species being
exchanged between the gas and aerosol phases, while maximizing the aaSOA mass
captured compared to that as predicted by GAMMA after 12 h of simulated
chemistry, assuming no initial aerosol-phase organic mass. The detailed
comparison of GAMMA and simpleGAMMA output under a range of typical
environmentally relevant conditions can be found in the following section.
The aqueous-phase species tracked in simpleGAMMA are IEPOX, glyoxal,
2-methyltetrol, and IEPOX organosulfate. Mass transfer between the gas and
aerosol phases only occurs for IEPOX and glyoxal. The effective Henry's law
constants (H*) and accommodation coefficients used to describe uptake for
these species are given in Table 1. These H* values have been updated
based on advances in the literature since McNeill et al. (2012), and they
represent our best understanding of the valid parameters for deliquesced
aerosols. They are not valid for non-aqueous aerosols. The values, especially
H* for IEPOX (Budisulistiorini et al., 2015), have significant uncertainty
associated with them, largely because relatively few experimental studies of
H* for uptake of these species to deliquesced aerosols are available in
the literature (Kampf et al., 2013; Gaston et al., 2014; Nguyen et al.,
2014).
Gas–aerosol mass transfer parameters in simpleGAMMA.
SpeciesEffective Henry's law constant, H* (M atm-1)Accommodation coefficient, αReferencesIEPOX3 × 1070.02McNeill et al. (2012),Nguyen et al. (2014)GLYX2.7 × 1070.023Herrmann et al. (2005),Sumner et al. (2014)
We note that, subsequent to the publication of McNeill et al. (2012), the
gas- and aqueous-phase chemistry of MAE was introduced
to the full version of GAMMA following Lin et al. (2013). The predicted
contribution of this pathway to aaSOA was minor compared to IEPOX and
glyoxal, consistent with the findings of Pye et al. (2013). Therefore, it is
not included in simpleGAMMA.
Reversible hydration and oligomerization chemistry of glyoxal in the aqueous
phase (Whipple, 1970) is captured using the effective Henry's
law constant, H*
(Schwartz, 1986). Therefore, those processes are not represented explicitly
in either GAMMA or simpleGAMMA, in order to avoid double counting. The
aqueous processing of IEPOX to form 2-methyltetrols (tetrol) and IEPOX
organosulfate (IEPOXOS) is represented as one reactive process following a
simplified version of the mechanism of Eddingsaas et al. (2010):
IEPOX(aq)→(1-β)tetrol+βIEPOXOS.
Here, we apply a value for the branching ratio, β, of 0.4, which is an
estimate based on the measurements of Eddingsaas and coworkers for the most
concentrated bulk solution they studied. The rate constant for the reaction,
k1, is a function of proton activity and nucleophile concentrations,
again following Eddingsaas et al. (2010). We have modified the formula to
include the possible protonation of IEPOX (aq) by ammonium as observed by
Nguyen et al. (2014).
Comparison of simpleGAMMA and GAMMA under low-NOx conditions,
pH 1, RH 45 %.
k1=H2O55.1kH+aH++kSO42-SO42-aH++kHSO4-HSO4-+kNH4+NH4+
Here, aH+ is the H+ activity, kH+=5×10-2 s-1, kSO42-=2×10-4 M-1 s-1, and kHSO4-=7.3×10-4 M-1 s-1. The ammonium rate
constant, kNH4+,
was calculated using GAMMA and the results of the chamber study of Nguyen et
al. (2014) to be 1.7×10-5 M-1 s-1. The rate constant
term kH+ from Eddingsaas et al. (2010) has been scaled to
account for variable water concentrations within the seed aerosol at
different pH and RH (relative humidity) conditions, consistent with recent
literature (Piletic et al., 2013; Pye et al., 2013). The architecture of the
simpleGAMMA program is similar to that of GAMMA (McNeill et al., 2012).
simpleGAMMA and GAMMA were originally written in MATLAB (MathWorks, Inc.),
utilizing the stiff initial value ordinary differential equation solver
ode15s.m, but simpleGAMMA is also available in Fortran. Required input
parameters for simpleGAMMA are gas-phase concentration fields for IEPOX and
glyoxal, aerosol pH, aerosol size distribution or volume-weighted average
aerosol diameter, aerosol liquid water content, and aerosol sulfate and
bisulfate concentrations. The test simulations in this study were for the
same conditions as the high-NOx and low-NOx scenarios in McNeill et
al. (2012), with one exception: for the low-NOx simulation, the initial
gas-phase mixing ratio of IEPOX is assumed to be 780 ppt, instead of zero.
This was the steady state value after three simulated day–night cycles in
GAMMA, in the absence of aerosol uptake. In all other simulations, the seed
aerosols were assumed to be initially composed solely of deliquesced ammonium
sulfate, following the size distribution of Whitby (1978), with aerosol
loadings of 4.0 µg m-3 (rural conditions, following Tanner et
al., 2009) or 20 µg m-3 (urban conditions, following Jimenez
et al., 2003). Initial inorganic aerosol composition was determined by E-AIM
(Extended-AIM) outputs for the defined initial pH and RH values.
ResultsLow-NOx (rural) conditions
Similar to what was observed in McNeill et al. (2012), under simulated rural
(low-NOx) environments, both GAMMA and simpleGAMMA predict that aaSOA is
dominated by IEPOX and its aerosol-phase reaction products. The evolution of
aaSOA mass as predicted over 12 h of dawn-to-dusk simulation under
low-NOx conditions using GAMMA and simpleGAMMA is shown in Fig. 1 (for
aerosol pH = 1 and 45 % RH). The pie charts compare the aaSOA
composition predicted by both models at 3, 6, 9, and 12 h of simulation.
Close agreement in predicted aaSOA mass and its composition can be seen
between the two models. Small differences arise due to the fact that,
although identical gas-phase mechanisms and initial conditions were used in
this model intercomparison, the gas-phase chemistry, especially gas-phase OH,
is perturbed by the differences in gas–aerosol mass transfer between the two
models. Specifically, VOCs which may partition into the particle phase in
GAMMA but not simpleGAMMA are present in the gas phase in higher
concentrations in simpleGAMMA, creating an increased sink for OH.
Figure 2 shows the total aaSOA mass predicted by GAMMA and simpleGAMMA for
12 h of simulation under low-NOx conditions, with varying aerosol pH and
RH. Like GAMMA, simpleGAMMA predicts maximum aaSOA formation under low-NOx
conditions when aerosol pH is low and RH is low (but not so low as to cause
aerosol efflorescence). This is because in-particle processing of IEPOX is
initiated by protonation, so conditions which maximize the in-particle proton
concentration yield the highest IEPOX processing. Close agreement (to within
30 %) exists between aaSOA mass predicted by GAMMA and by simpleGAMMA for
pH 1, and within 0.02 µg m-3 for aerosol pH ≥ 2.0.
The highly efficient in-particle IEPOX chemistry at low pH leads to larger
discrepancies between the two models.
Comparison of predicted aaSOA after 12 h of simulated
time with respect to RH at pH 1 (left) and with respect to pH at 45 % and
65 % RH (right, dotted and solid lines respectively), low-NOx conditions.
Comparison of high-NOx simpleGAMMA and the full high-NOx
GAMMA, pH 1, RH 45 %.
High-NOx (urban) conditions
aaSOA mass as predicted by GAMMA is dominated by “dark” uptake of glyoxal
under high-NOx conditions (McNeill et al., 2012). Gas-phase IEPOX
formation is expected to be minor in this regime (Paulot et al., 2009). A
comparison of evolved aaSOA mass and composition under high-NOx conditions as
predicted by GAMMA and simpleGAMMA can be seen in Fig. 3. Figure 4 shows
total aaSOA mass predicted by the two models after 12 h of simulation under
high-NOx conditions, with varying aerosol pH and RH. Close agreement (within
0.01 µg m-3) in predicted total aaSOA mass exists between
simpleGAMMA and GAMMA for all relative humidity and pH values tested. Like
GAMMA, under high-NOx conditions simpleGAMMA predicts increasing aaSOA
formation with increasing RH (and therefore increasing aerosol liquid water
content), and no pH dependence, consistent with glyoxal dark uptake being the
dominant aaSOA formation mechanism (Kroll et al., 2005; Galloway et al.,
2009; Volkamer et al., 2009). The increased uptake at higher RH amplifies
small differences in gas-phase chemistry between simpleGAMMA and GAMMA, due
to differing gas-phase OH sinks in the two models, as described above. GAMMA
predicts some contribution to aaSOA mass by photochemical production of
succinic acid (see Fig. 3), which is not included in simpleGAMMA. However,
since glyoxal is the dominant precursor for succinic acid formation and the
molecular weight of succinic acid (118 g mol-1) is comparable to the
molecular weight of the two glyoxal molecules that comprise it
(116 g mol-1), the predicted overall aaSOA mass is therefore very
similar for the two models.
Computational performance
The goal of simpleGAMMA is to faithfully represent aaSOA formation with a low
number of tracers, in order to simplify the implementation of aqueous aerosol
SOA formation in 3-D models (by coupling the gas-phase schemes of those
models with simpleGAMMA). However, simpleGAMMA is also computationally faster
than GAMMA when run as a box model, as described in this study, due to the
reduced number of tracers and reactions in the aqueous phase (recall that the
gas-phase mechanisms of GAMMA and simpleGAMMA were identical for the purposes of
this study). In 10 simulations with starting aerosol pH 1 and 65 %
ambient RH, computational run-time for simpleGAMMA under low-NOx
conditions spanned between 10 and 12 s for 12 h of simulation, compared to
33–42 s for GAMMA. These runs were performed on an Intel Core i7-3520M CPU
in a 2.90 GHz PC, using MATLAB R2014b with the solver ode15s.m Time steps to
completion between simpleGAMMA and GAMMA were comparable (∼ 11 000
and ∼ 14 000 respectively) (indicating similar stiffness in the two
models) but simpleGAMMA necessarily utilized less memory cache due to the
smaller number of aqueous-phase species and reactions.
Comparison of predicted aaSOA after 12 h of simulated time with
respect to RH at pH 1 (left) and with respect to pH at 45 % and 65 %
RH (right, dotted and solid lines respectively), high-NOx conditions.
Discussion and outlook
The agreement between GAMMA and simpleGAMMA indicate that this reduced
framework can be useful to represent aaSOA mass formation over a variety of
relevant ambient conditions. Coupling of simpleGAMMA with regional and global
scale 3-D atmospheric chemistry models (Jathar et al., 2014) and its
application to analysis of ambient measurement data (Budisulistiorini et al.,
2015) is currently underway.
While we have demonstrated good agreement between simpleGAMMA and GAMMA, the
limitations of GAMMA also apply to simpleGAMMA; for example, neither model
includes a treatment of oxidative aging of aaSOA at this time due to a lack
of kinetic and mechanistic data. As a result, overprediction of total aaSOA
mass is likely (Budisulistiorini et al., 2015). The only sources of
aqueous-phase OH in GAMMA are HOOH photolysis or Henry's law transfer of OH
from the gas phase. Therefore, we, like others (Waxman et al., 2013; Ervens
et al., 2014), have observed OH-limited chemistry in the aqueous aerosol
phase using GAMMA, and this informed the simpleGAMMA formulation. While
transition metal ion chemistry, a possible source of OH (Herrmann et al.,
2015), was not included in the first version of GAMMA (McNeill et al., 2012)
due to the focus on ammonium sulfate aerosols in that study, these mechanisms
may be active in ambient aerosols. Preliminary calculations in GAMMA show
that including transition metal ion (Fe+3, Cu+2, Mn+3)
chemistry following CAPRAM 3.0 (Chemical Aqueous Phase Radical Mechanism;
Herrmann et al., 2005) does not perturb the predicted aaSOA yield or product
distribution. Aqueous-phase diffusion is not accounted for in GAMMA or
simpleGAMMA, that is, Henry's law equilibration is assumed to occur
instantaneously and no spatial concentration gradients within the particle
are considered. This likely leads to an overestimate of OH chemistry when
this highly reactive species is taken up from the gas phase. However, since
we have found that aqueous-phase photochemistry does not dominate aaSOA
formation, inclusion of aqueous-phase diffusion limitations in this
calculation would not change our results or the formulation of simpleGAMMA.
Aqueous-phase diffusion may also be important for relatively large droplets
such as those encountered in marine aerosols.
simpleGAMMA is not recommended for the treatment of aqueous SOA formation in
cloud water, which is dominated by aqueous-phase photochemistry. The role of
UV light in aaSOA formation by glyoxal is unresolved (Galloway et al., 2009,
2011; Volkamer et al., 2009; Kampf et al., 2013). A recent data analysis
study using GAMMA (Sumner et al., 2014) suggested a possible role for
photo-enhanced chemistry in aaSOA formation by glyoxal involving organic
photosensitizers such as fulvic acid (Monge et al., 2012). This chemistry can
be represented in simpleGAMMA by including irreversible glyoxal uptake with
γ∼ 10-3 during sunlit hours, consistent with Fu et
al. (2008), who based their representation on the experiments of Liggio et
al. (2005), and with Waxman et al. (2013). A reactive uptake formulation was
also used by Pye et al. (2013) to represent aaSOA formation by IEPOX. While
reactive uptake may be the best alternative for representing unknown
processes such as glyoxal surface photochemistry, potential issues with
reactive uptake formulations stem from the fact that they generally represent
two or more physical processes (e.g., reversible uptake of VOCs followed by
an aqueous-phase reaction) as one irreversible reactive uptake step. Lin et
al. (2014) and Knote et al. (2014) found that a surface reactive uptake
formulation for glyoxal led to significantly higher predicted SOA mass than a
reversible multiphase representation of the chemistry.
We previously predicted, using GAMMA, that glyoxal is the main contributor to
aqueous aerosol-phase “brown carbon” formation by carbonyl-containing VOC
precursors (Woo et al., 2013). Following that work, it is straightforward to
track the formation of light-absorbing glyoxal derivatives in simpleGAMMA,
with concentration-dependent aerosol light absorption calculated in
post-processing. However, we note that fast photobleaching of aerosol brown
carbon formed via this pathway has been demonstrated, limiting its potential
impact on atmospheric chemistry and climate (Sareen et al., 2013; Woo et al.,
2013; Lee et al., 2014).
Code availability
For more information and to access the simpleGAMMA program, please visit
mcneill-lab.org/gamma or contact V. Faye McNeill (vfm2103@columbia.edu).
simpleGAMMA was originally written in MATLAB (MathWorks, Inc.) and is also
available in Fortran.
Acknowledgements
The authors acknowledge Columbia University for financial support and
Shantanu Jathar and Havala Pye for helpful discussions. The authors are also
grateful to the anonymous reviewers for their constructive comments and
suggestions. Edited by: A. Archibald
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