α-Pinene photooxidation mechanism
Gas-phase oxidation
A near-explicit gas-phase oxidation mechanism for the oxidation of
α-pinene was generated using the Generator for Explicit Chemistry and
Kinetics of Organics in the Atmosphere (GECKO-A) . GECKO-A automatically assigns reactions and rate constants on
the basis of experimental data and structure–activity relationships (SARs),
producing chemical mechanisms far more detailed and explicit than can be
written manually . As described in , a
protocol is implemented in GECKO-A to reduce the number of species generated
to meet computational limits while maintaining as much chemical detail as
possible; for example, gas-phase chemistry is not generated for species with
vapor pressures below 10-13 atm, and isomer substitution is allowed for
position isomers for non-radical species formed with a maximum yield less
than 2 % after two or more generations. Vapor pressures are estimated
using the method. Four generations of oxidation of
α-pinene in GECKO-A (a generation encompassing reaction with an
oxidant such as OH up to the formation of a stable product) yield 9×105 reactions and 1.8×105 species. SOA predictions for the
base case are found not to be sensitive to the number of generations beyond
three (sensitivity tests not shown).
Overview of the α-pinene + OH oxidation mechanism in
GECKO-A. Reactions in black are those originally implemented in
. Additions to this mechanism are shown in blue. Species
are labeled to be consistent with . All subsequent
chemistry is generated in GECKO-A according to the standard protocols in
GECKO-A.
OH oxidation of α-pinene proceeds predominantly via OH addition, with
minor channels proceeding via hydrogen abstraction. An overview of the base
mechanism in GECKO-A for the OH oxidation of α-pinene is shown in
black in Fig. and discussed in more detail in
. Updates to this mechanism (shown in blue in
Fig. ) were added here based on recent literature.
proposed several non-traditional pathways in the OH
oxidation of α-pinene based on quantum and theoretical chemical
calculations. Species in Fig. are labeled to be
consistent with . Species R1 is formed with 22 %
yield by a prompt ring opening of the four-membered ring following initial OH
addition to the double bond in α-pinene . A
40 : 60 ratio of syn vs. anti stereochemistry is
estimated for species R1 . The anti conformer
is predicted to undergo a 1,6-hydrogen shift to form a peroxide, forming a
radical stabilized by allyl resonance, R7. This reaction is predicted to
dominate over all other peroxy reactions for the anti conformer even
in high-NO conditions. The position of oxygen addition to the alkyl radical
R7 is not well constrained. The branching ratio between addition at the
secondary and tertiary sites has conservatively been predicted to be
50 : 50 . calculate that the
syn conformer of R1 will not only participate in traditional peroxy chemistry
but will also undergo ring closure to form a six-membered ring, species R3.
calculate a rate constant for this reaction of
2.6 s-1 at 298 K. Species R2 in Fig. ,
formed from the reaction of the syn conformer of species R1 with NO
and RO2, is predicted to undergo a ring-closure reaction to form species
R10. This reaction is predicted to dominate over acetone elimination and
hydroxy formation, the previous pathways for R2 in GECKO-A
. R10 adds oxygen and then participates in the usual
peroxy reactions.
The HO2 chemistry has also been updated in GECKO-A. The main reaction
channel for reaction of alkyl peroxy radicals with HO2 is formation of
hydroperoxides. Recent evidence indicates that the reaction of some peroxy
radicals with HO2 can also lead to an alkoxy radical, regenerating OH
. This
reaction is promoted by the presence of neighboring polar functional groups,
which can stabilize the intermediate leading to the alkoxy through hydrogen
bonding . This pathway has been added to GECKO-A for
molecules with an oxygenated moiety in the α position to the peroxy
radical. Estimates of the yield of the alkoxy for small acyl peroxy radicals
range from 0.15 to 0.7 ; however, branching
ratios for the larger molecules in the α-pinene mechanism have not
been determined. In light of this uncertainty, the branching ratio between
the hydroperoxide and the alkoxy has been assigned to 80 : 20 in GECKO-A.
Ozonolysis chemistry in GECKO-A does not explicitly include the Criegee
biradicals formed from the addition of O3 to the α-pinene double
bond; rather the predicted products of the Criegee intermediates are directly
assigned to the α-pinene ozonolysis reaction. This simplification
overlooks potential reactions of the stabilized Criegee intermediates (SCIs)
with water, alcohols, acids, carbonyls, etc. .
Reaction of SCIs with water to form pinonaldehyde is thought to be
significant , and a direct route to pinonaldehyde has been
added in GECKO-A. The initial reaction step for ozonolysis is shown in
Fig. S1 in the Supplement. Furthermore, proposed several
later-generation intramolecular isomerization reactions to form
low-volatility acids. These reactions have been implemented in GECKO-A as a
sensitivity test. Virtually no change in predicted SOA concentrations is
observed when adding these reactions or when changing the branching ratios of
the SCI products. Separate (not shown), purely ozonolysis simulations showed
very little SOA formation, indicating potential missing pathways in the
ozonolysis mechanism forming low-volatility products. Updating the ozonolysis
mechanism should be the subject of future studies. The above updates
constitute the base mechanism. Sensitivity tests applied on this base
mechanism are addressed in the sections that follow.
Condensed-phase photolysis
Photolysis of gas-phase compounds containing carbonyl, peroxide, or nitrate
chromophores is included automatically within oxidation schemes generated by
GECKO-A . For the simulations presented here,
compound-specific photolysis rates are calculated using cross sections and
quantum yields described in and measured irradiance data
in the Caltech chamber. Photolysis of condensed-phase compounds, recently
shown to lead to rapid loss of α-pinene SOA , is included here as a possible reaction
route. Because GECKO-A does not generate chemical reactions within the
condensed phase, radical species produced by condensed-phase photolytic
reactions are assumed to be irreversibly lost and to not participate in
subsequent chemistry . This approximation likely represents
an upper limit to the actual physical process since it does not account for
recombination of fragments in the particle phase
. Two methods of calculating
condensed-phase photolysis rate constants were tested in :
(1) all photolabile compounds in the condensed phase are assigned the same
empirically derived rate constant, and (2) the condensed-phase photolytic
rate constant for each species is set to the corresponding gas-phase value.
showed that the second method generally results in a
greater loss of SOA. Therefore, assumption (2) is tested in the present
simulations to evaluate the maximum possible impact of condensed-phase
photolysis on overall SOA growth.
Particle-phase dimerization
The importance of aerosol-phase dimerization and oligomerization reactions
has been demonstrated . These include alcohol + carbonyl to
form hemiacetals and acetals, hydroperoxide + carbonyl to form
peroxyhemiacetals and peroxyacetals, carboxylic acid + alcohol to form
esters, and aldehyde self-reactions to form aldols .
modeled a generalized particle-phase reaction of an
SVOC + carbonyl with a rate constant of 12 M-1s-1 when
fitting the observed evolution of the particle size distribution of dodecane
SOA. In contrast, modeled the formation of
peroxyhemiacetals during α-pinene SOA formation using a rate constant
of 0.06 M-1s-1; they found that these reactions had a minor
impact on SOA yield, except during ozonolysis with large VOC / NOx
ratios that led to high hydroperoxide yields.
In the present work, particle-phase dimerization reactions have been added to
the GECKO-A α-pinene oxidation scheme for aldehydes, hydroperoxides,
alcohols, and carboxylic acids. Three reaction pairings are considered:
aldehyde + alcohol, aldehyde + hydroperoxide, and carboxylic
acid + alcohol. As a first approximation, reactions are assumed to be
irreversible and to form nonvolatile products. The mass of dimers becomes
part of the organic aerosol mass into which gas-phase species can partition.
The rate constant is set to be the same for each dimerization reaction, and
two values are tested: 12 M-1s-1 and
0.01 M-1s-1 . The rate constant is
converted from liters of solution to volume of chamber air based on the
amount of organic aerosol condensed at each time step.
Box model for SOA formation
Each α-pinene oxidation scheme generated for the different sensitivity
tests is coupled to a box model describing the chemical dynamics within the
chamber, including dynamic transport of vapors to the particle phase and to
the chamber walls.
Vapor–particle transport
In previous versions of GECKO-A, instantaneous equilibrium partitioning is
assumed to occur between the vapor and particle phases
. As a more general treatment of
vapor–particle transport, dynamic partitioning for mass transfer between the
gas and the particle phase is implemented here as described in
, with a few modifications. Mass transfer to and from the
particle is represented by
dGidtgp=-kgpGi+kpgPi,dPidtpg=kgpGi-kpgPi,
where kgp and kpg are first-order rate constants for
transport to and from the particle (s-1), and Gi and Pi
are concentrations in the gas and particle phase, respectively. Mass transfer
can be limited by gas-phase diffusion, interfacial accommodation, or
particle-phase diffusion e.g.,. Particle-phase diffusion
in GECKO-A is assumed to be sufficiently rapid that particles are well mixed.
Recent studies have shown that the assumption of rapid particle-phase
diffusion in SOA may not be accurate owing to semi-solid behavior
.
Computationally, the gas–particle accommodation coefficient,
αp, is used to approximate resistances to gas–particle
partitioning from surface accommodation and particle-phase diffusion
. Therefore, the overall rate constant of mass transfer
kgp can be approximated as
1kgp=1kdiff+1kint
with kdiff=4πDgrpCp and
kint=αpc¯πrp2Cp, where Dg is the species
gas-phase diffusivity, rp is the particle radius,
Cp is the number of particles per unit volume of air,
αp is the gas–particle accommodation coefficient, and
c¯ is the gas-phase mean velocity . In the
present work, the accommodation coefficient αp is treated
as a parameter that can be varied in order to produce the best fit to the
observed SOA growth. In the model, a single particle size bin is used, so
that all particles have the same radius. For all experiments except
nucleation, the initial inorganic seed radius is set at 50 nm, and the
initial number concentration is calculated from the measured initial seed
surface area. GECKO-A does not currently include a mechanism for nucleation;
therefore, for the nucleation experiment, an initial seed concentration of
104 cm-3 was assumed with an initial radius of 5 nm. The
number concentration of particles Cp in the model remains
unchanged over the course of the experiment as rp grows owing
to condensation of organic aerosol. Particle wall loss is not included in
GECKO-A. SOA growth in the α-pinene system is essentially independent
of seed surface area (discussed in the Results section); therefore, lack of
particle wall loss in GECKO-A will not substantially affect SOA predictions.
rp is calculated at each time step as
rp=34π43πrp03+MaerCaerNAρOACp13,
where rp0 is the initial inorganic seed radius,
Maer is the organic aerosol mean molecular weight,
Caer is the organic aerosol mass concentration
(moleccm-3), and ρOA is the organic aerosol
density (1.32 gcm-3 for α-pinene SOA; ).
Condensation to and evaporation from the particle occurs until equilibrium is
reached between the gas and particle phases (if ever). The gas-particle
partitioning at equilibrium is assumed to follow Raoult's law; therefore the
reverse rate constant for mass transfer from the particle to the gas phase is
calculated from the relationship
kgpkpg=RTPvapCaerMaerγaer,
where R is the ideal gas constant, T is the temperature, Pvap
is the species vapor pressure, and γaer is the activity
coefficient in the particle phase (assumed to be 1).
Vapor–wall transport
Chamber data are affected by wall deposition of semi and low-volatility
vapors
.
Partitioning of vapor species between the gas phase and the chamber wall is
based on the parameterization of and is
implemented as described in , with minor variations. Reported
values for the rate of transport from the gas to the wall, kgw,
span several orders of magnitude in different chambers
. Wall loss
rates directly measured in the Caltech chamber are of order
10-5–10-6 s-1 ,
although fit a wall loss rate of 10-4 s-1
during toluene SOA experiments. Gas–wall equilibration timescales,
τg,w, of order 10–100 min have consistently been measured in
the 8.2 m3 chamber used by . With
τg,w=1/(kgw+kwg) , these
timescales correspond to wall loss rates of
10-3–10-4 s-1. The chamber is
smaller than the Caltech chamber, 8.2 vs. 24 m3; however, the
differences in measured wall loss rates are greater than expected if wall
loss scales with the surface-to-volume ratio of the chamber. It has been
suggested that rapid initial vapor wall loss could be difficult to
distinguish from changes in concentration due to injection and mixing during
the long fill period in the Caltech chamber . Vapor
species could potentially rapidly equilibrate with the wall and be
subsequently lost to the wall with a much slower uptake owing to relaxation
of the Teflon polymer . Sensitivity tests in the
present work examine the extent to which these differences indicate
significantly slower vapor wall loss in the Caltech chamber or arise from the
procedure by which experimental measurements were carried out. Furthermore,
showed that kgw may depend on the species'
volatility; here we assume kgw to be the same for all species in
order to evaluate the order of magnitude of kgw needed to fit the
observations. The nominal value is set to 10-3 s-1. As an
additional sensitivity test, we implement the kgw
parameterization developed by in which kgw
increases as species' volatility decreases. Transport between the gas phase
and the wall is represented by the balance equations
dGidtgw=-kgwGi+kwgWi,dWidtwg=kgwGi-kwgWi,
where kgw and kwg are first-order rate constants for
transport to and from the wall, and Wi is the concentration in the wall
layer. The reverse rate is calculated assuming gas–wall partitioning follows
Raoult's law :
kgwkwg=RTPvapCwMwγw,
where Cw is the equivalent overall organic mass concentration
in the wall, Mw is the equivalent molar weight of the organic
concentration in the wall, and γw is the activity
coefficient in the Teflon film. Values of
Cw/Mwγw must be determined
experimentally from chamber observations
. In the present simulations, a nominal
Cw/Mwγw value of
120 µmolm-3, the value determined by
for 2-ketones, is used for all species except
for α-pinene, for which
Cw/Mwγw=20 µmolm-3, the value determined by
for alkenes.
Cw/Mwγw is varied as a sensitivity
test.
Initial conditions for photooxidation experiments.
Expt.
Desc.
T (K)
RH (%)
HC0
Initial seed surface
Duration
(ppb)
Area (µm2 cm-3)
(min)
141007
Nucleation
300
<5 %
51
0
300
141016
Low SA
300
<5 %
56
1.7×103
534
141028
Med. SA
300
<5 %
53
3.2×103
233
141118
High SA
298
<5 %
51
3.4×103
410
141113
10 % UV, low SA
297
<5 %
53
9.9×102
1070
141125
10 % UV, med. SA
297
<5 %
49
2.4×103
1160
Experimental
Six α-pinene photooxidation experiments were conducted in the Caltech
dual 24 m3 environmental chambers at ∼298 K and
<5 % RH (Table ). Prior to each
experiment, the Teflon chambers were flushed with purified, dry air for 24 h
until the particle number concentration was <10 cm-3 and volume
concentration was <0.01 µm3cm-3. Hydrogen peroxide
(H2O2) was used as the OH source by evaporating 113 µL of
50 wt % aqueous solution into the chamber with 5 L min-1 purified
air for 110 min, resulting in an approximate starting H2O2 mixing
ratio of 2 ppm. Experiments were conducted at low NO (<2 ppb). Heated
5 L min-1 of purified, dry air was flowed through a glass bulb
containing liquid α-pinene into the chamber for 30 min, introducing
∼50 ppb α-pinene into the chamber. Ammonium sulfate (AS) seed
aerosol was injected into the chamber by atomizing 0.015 or 0.1 M aqueous
(NH4)2SO4 solution into the chamber for 30 to 90 min, varying
the initial AS concentration in order to vary the seed surface area (SA)
available for condensation. After ∼1 h of mixing, photooxidation
was initiated. Four of the experiments were performed using the full set of
blacklights, for which the calculated jNO2=4×10-3 s-1, with varying amounts of inorganic seed particles
(Table ). The remaining two experiments
were performed using only 10 % of the available blacklights, for which
the calculated jNO2=3.7×10-4 s-1, again
with varying amounts of inorganic seed particles.
Relative humidity (RH) and temperature were monitored via a Vaisala HMM211
probe. O3 and NOx mixing ratios were measured by a Horiba O3
analyzer (APOA-360) and a Teledyne NOx analyzer (T200), respectively.
α-Pinene concentration was monitored by a gas chromatograph equipped
with a HP-5 column (15 m × 0.53 mm
ID × 1.5 µm thickness, Hewlett-Packard) coupled with flame
ionization detector (GC/FID, Agilent 6890N). The size distribution and
number concentration of seed particles and organic aerosols were
characterized using a custom-built Scanning Mobility Particle Sizer (SMPS)
consisting of a Differential Mobility Analyzer (DMA, TSI, 3081) coupled with
a Condensation Particle Counter (CPC, TSI, 3010).
Measured volume distributions must be corrected for particle wall loss; the
DMA measures the total volume of particles, which is a mixture of organic
aerosol and inorganic seed. By accounting for particle wall loss, the seed
volume can be subtracted and the mass of SOA calculated. Two limiting
assumptions have traditionally been made when correcting for particle wall
loss : in the lower bound, once deposited, particles are
assumed to no longer interact with the vapor. The mass of SOA present on a
particle at the moment of its deposition is added when calculating the total
SOA. In the upper bound, deposited particles are assumed to continue growing
at the same rate as suspended particles, and this SOA is similarly added to
the total SOA. The upper bound can be viewed as an early approximation of
vapor wall loss from a time when vapor wall loss was less well understood.
However, this approximation does not account for the differing mechanisms of
vapor–particle and vapor–wall transport, which is reflected in different
timescales. Moreover, the absorbing mass of the wall, represented by the
parameter Cw, is ∼3 orders of magnitude higher than the
mass of deposited particles . The upper bound is therefore
less appropriate to use when vapor wall loss is accounted for separately, and
thus the lower bound is used to correct the SOA data, using size-dependent
wall loss rates measured in the Caltech chamber. Figure S2 shows temporal
plots of the experimental non-particle-wall-loss-corrected total volume and
the total volume with both the upper and lower bound particle wall loss
correction. (Gaps in the volume data resulted when the DMA was taken off-line
briefly to clean the inlet.)
The initial concentrations of H2O2, NOx, and O3 in the
chemical model were optimized to match the α-pinene decay and O3
formation. The initial mixing ratio of H2O2 was optimized to be 2
ppm, yielding predicted OH levels of ∼2×106 moleccm-3 for the 100 % UV experiments and ∼2×105 moleccm-3 for the 10 % UV experiments.
Although experiments were conducted under low-NO conditions, background levels
of NOx were observed in the chamber. Due to voltage interference in the
NOx measurements at such low values, it was not possible to determine
the exact amount of NOx present in the chamber. Therefore, the initial
background concentration in the simulations was set to 0.1 ppb, split evenly
between NO and NO2. NO and NO2 then evolve throughout the
simulation according to the standard inorganic gas-phase reactions. Ozone
formation was observed during the experiments, giving evidence of the
presence of NO2 in the chamber. Because any NO2 initially present
in the chamber is quickly photolyzed, a NOx wall off-gassing rate of
2.5 ppt min-1 is needed to match the observed ozone formation
e.g.,. Predicted SOA formation is not sensitive to the
assumed NOx off-gassing rate. An initial O3 concentration was
measured in the chamber; however, this was attributed to interference by
H2O2 because α-pinene did not decay in the dark, as would
have occurred in the presence of O3. Therefore, the initial O3 was
set to zero in the model. Comparisons of measurements and model predictions
for the decay of α-pinene and the evolution of O3 are shown in
Figs. S3 and S4 in the Supplement.
Figure S5 shows the amount of α-pinene predicted to react with OH vs.
O3, using both the modeled and measured O3 concentration. For the
100 % UV experiments, ∼ 2–3 % of the total α-pinene is
predicted to react with O3, using either the modeled or measured O3
concentration. For the 10 % UV experiments, ∼ 20 % of the total
α-pinene is predicted to react with O3 using the modeled O3
concentration. For experiment 141113, roughly the same amount of
α-pinene is predicted to react with O3 based on the measured
O3 concentration. However, for experiment 141125, because the model
underpredicts the O3 concentration, much more α-pinene is
predicted to react with the measured O3 concentration, ∼ 49 %.
Results
Experimental particle-wall-loss-corrected SOA growth curves as a function of
reacted α-pinene are shown in Fig. for the six
photooxidation experiments. With the exception of the nucleation experiment,
SOA growth is observed immediately upon irradiation, similar to
. When presented as a function of reacted α-pinene, the
growth curves essentially overlap, regardless of the UV level or the initial
seed particle concentration. Overlap at two different UV intensities
indicates that SOA growth is not very sensitive to the oxidation rate for
these conditions. SOA yields (ratio of maximum mass of SOA formed to mass of
α-pinene reacted) for these experiments range from 17 to 26 %, at
the low end of the range of previously reported yields of 26–45 % for
this system .
Mass of SOA, MOA, as a function of reacted
α-pinene. Experimental data are shown with filled circles, with colors
corresponding to individual experiments (see
Table ), and have been corrected for
particle wall loss (see text). Predictions using the default GECKO-A are
shown as solid lines, with the colors corresponding to the different
experiments. In GECKO-A, the vapor–particle accommodation coefficient is set
to αp=1, and the vapor wall loss rate is set to
kgw=10-3 s-1.
Figure also shows predictions from the base GECKO-A
mechanism for the six experiments. In the GECKO-A model, the two main
parameters representing vapor–particle and vapor–wall transport,
αp and kgw, are set to nominal values of 1 and
10-3 s-1, respectively.
Cw/(Mwγw) is set to
120 µmolm-3 for all species except α-pinene. These
parameters give a good agreement for the 100 % UV experiments. Figure S6
in the Supplement shows the effect of varying the vapor–particle
accommodation coefficient αp for different values of the
vapor wall loss rate kgw. αp=1 or 0.1 yields
almost identical SOA predictions for all experiments except nucleation. In
the nucleation case, αp=0.1 results in substantially less
SOA predicted, which may result because nucleation is not treated explicitly
in GECKO-A but instead approximated by initializing the particle
concentration with particles of 5 nm radius. Lowering the value of
αp to 0.01 or 0.001 delays the onset of SOA formation for
the 100 % UV experiments, which is not consistent with experimental
observations. Lowering kgw results in substantial overprediction
of SOA for the 100 % UV experiments. For the 10 % UV experiments, all
combinations of parameters underpredict the SOA. Figure S7 in the Supplement
shows the effect of varying
Cw/(Mwγw) for different values of
the vapor wall loss rate kgw.
Cw/(Mwγw) controls partitioning
between the gas phase and the wall at equilibrium; therefore, variations in
Cw/(Mwγw) have more of an effect
on SOA predictions when using a faster wall loss rate because equilibrium is
approached sooner. The base
Cw/(Mwγw) and
kgw=10-3 s-1 give the best agreement for 100 %
UV experiments; however, SOA for the 10 % UV experiments is still
underpredicted. This discrepancy will be addressed subsequently. All
subsequent simulations are conducted with αp=1 and the
base Cw/(Mwγw).
The best-fit αp=1 (and 0.1 for all experiments except
nucleation) for the 100 % UV experiments suggests that there are no
substantial limitations to vapor–particle mass transfer . This
conclusion is consistent with the experimental observation that the SOA
growth in this system is virtually independent of the amount of seed surface
area present; dependence of SOA growth on seed surface area occurs only with
diffusion- or accommodation-limited vapor–particle mass transfer
. Optimal αp=1 or 0.1 is consistent with
, who determined accommodation coefficients of order 0.1 for
α-pinene ozonolysis SOA. This result differs from that of
, who observed toluene SOA formation to depend strongly on
the seed surface area and consequently to adhere to a low
αp=0.001. The different behavior observed for
α-pinene and toluene SOA may reflect differences in SOA formation
mechanisms; however, it is not possible to discern the reason for the
difference based solely on the data at hand. If α-pinene SOA behaves
as a semi-solid
,
a lower αp might be expected. However, if, once formed,
SOA is converted into a glassy state through hydrogen bonding or
oligomerization , SOA formation itself could still be
characterized by a high αp.
The observation that SOA growth is insensitive to oxidation rate (shown by
the overlap of the growth curves in Fig. for different UV
exposures) is not consistent with predictions regarding the interplay of
reaction rate and vapor wall loss: if the reaction rate increases relative to
the vapor wall loss rate, successive generations of low-volatility species
will be produced more quickly and will condense preferentially onto particles
before their precursors are lost to the chamber walls, leading to a higher
SOA yield . Such behavior was observed
experimentally by : higher SOA yields were observed in the
aromatic system using HONO as an OH precursor than when only
NO and NO2 were present initially, leading to much lower OH levels. This
“rate effect” was tentatively attributed by to loss of
semivolatile organics to the chamber walls, albeit at a time when the
nature of vapor wall deposition was less well understood. A dependence of SOA
growth on oxidation rate is also predicted by the base scenario in GECKO-A:
the SOA levels are predicted to be substantially lower at 10 % UV owing
to significant organic mass loss to the walls. A number of explanations can
be advanced for the overlap of the growth curves at high and low UV, and these
are systematically explored below.
Negligible or slow vapor wall loss
In the absence of vapor wall loss, the growth curves predicted by GECKO-A
overlap at high and low UV (Fig. S8 in the Supplement). Oxidation occurs more
slowly under low UV owing to the decreased rate of generation of OH, but if
condensable species or their precursors do not deposit on the walls, the same
amount of SOA eventually forms. However, in the absence of vapor wall loss,
GECKO-A overpredicts SOA substantially for the high-UV experiments and
slightly for the low-UV experiments.
At a low level of vapor wall loss, kgw=10-5s-1,
the growth curves predicted by GECKO-A still essentially overlap
(Fig. ). A rate effect, in which the SOA yield
depends on the rate of oxidation, is observed only if the rate of oxidation
is slower than or competitive with other loss processes. The rate constant
for the reaction of OH with α-pinene is 5.23×10-11 cm3molec-1s-1 . OH
concentrations predicted by GECKO-A of 2×106 and 2×105 moleccm-3 for the high- and low-UV experiments,
respectively, give overall reaction rates of 10-4 and
10-5 s-1. (OH reaction rate constants for oxidized products
will be slower than that for α-pinene, and the reaction rate slows in
later generations.) A vapor wall loss characterized by
kgw=10-3 s-1 exceeds substantially either of these
reaction rates, and thus at this wall deposition rate the effect of changing
oxidation rate is strong. At a much slower wall loss,
kgw=10-5 s-1, the rate effect is less pronounced,
leading to overlap of the growth curves predicted by GECKO-A. The absence of
a rate effect in the experimental observations is consistent with a slower
vapor wall loss rate, in accord with wall loss rates that have been measured
previously in the Caltech chamber .
Mass of SOA as a function of reacted α-pinene. Experimental
data are shown as filled circles. Solid lines correspond to GECKO-A
predictions using a vapor wall loss rate
kgw=10-5 s-1.
With a vapor wall loss rate of 10-5 s-1, SOA predictions
match the data fairly well at low UV, within 10 µgm-3 of the
final SOA concentration, but remain overpredicted by 80 to
200 µgm-3 at high UV. Thus, a slow wall loss rate alone is
not sufficient to reconcile the predictions at high and low UV. However, it
is instructive to examine the contribution of different generations of
reaction to SOA predictions with this slow vapor wall loss
(Fig. ). GECKO-A predicts that SOA will consist
of almost entirely second-generation products. At the end of the experiment,
simulations under high UV predict ∼ 100 µgm-3 of
second-generation products, as opposed to ∼ 50 µgm-3
for low UV. Thus, reducing the contribution of second-generation products in
high UV may result in closer model–data agreement for both high- and low-UV
conditions at a slow vapor wall loss rate.
Mass of SOA as a function of reacted α-pinene. Experimental
data are shown with filled circles. Solid lines correspond to GECKO-A
predictions from different generations of reaction using a vapor wall loss
rate kgw=10-5 s-1, where a generation corresponds
to reaction with an oxidation such as OH up to formation of a stable
product.
Overcontribution of second- and later-generation species
As a sensitivity test, all OH reaction rate constants, except that of
OH + α-pinene, are reduced by varying factors. Figure S9 in the
Supplement shows the impact of reducing the OH rate constants to 1, 10, and
50 % of the default values at a vapor wall loss rate of
10-5 s-1. A reduction to 10 % and a wall loss rate of
10-5 s-1 result in the best fit to both high- and low-UV
experiments (Fig. ), albeit with an overprediction up to
75 % for the high-UV experiments and a 20–40 % underprediction for
the low-UV experiments. While this result does not necessarily suggest that
reaction rate constants in GECKO-A are overpredicted by an order of
magnitude, it does suggest that second- and higher-generation compounds may be
primarily responsible for the model–measurement discrepancy at differing UV
levels. An excess contribution of later-generation compounds to SOA in
GECKO-A could be due to several factors: (1) overprediction of the OH
reaction rate constants by the SARs, (2) assumed reaction pathways in
GECKO-A, (3) underestimation of volatilities for later-generation species, or
(4) significantly faster wall loss rate for later-generation products. Each
of these possibilities is considered in turn.
Mass of SOA as a function of reacted α-pinene. Experimental
data are shown with filled circles. Solid lines display GECKO-A predictions
when using OH reaction rate constants that have been reduced to 10 % of
their default values (with the exception of α-pinene + OH) and
kgw=10-5 s-1.
OH reaction rate constants in GECKO-A are based on SARs from
and subsequent updates, which have been shown to predict
OH reaction rate constants within a factor of 2 for alkanes, alkenes, and
diols but may be more uncertain for the complex and highly functionalized
compounds in later generations of α-pinene oxidation
. For example, showed that OH rate
constants for 1,2-hydroxyaldehydes are overestimated by factors of 3–4 using
the established SARs. Although rate constants are likely not overpredicted by
an order of magnitude as the simulations suggest, it is possible that
uncertainties in the SARs for later-generation species may contribute to the
overprediction of these products by GECKO-A.
SARs used to predict reaction pathways and branching ratios in GECKO-A are
also prone to uncertainties. In the reaction of peroxy radicals with
HO2, the dominant pathway in GECKO-A is formation of the hydroperoxide
(assumed branching ratio 80 %). α-Pinene hydroxy dihydroperoxide
(C10H16O5) is a second-generation compound formed via two such
reactions with HO2 that is predicted by GECKO-A to contribute
significantly to SOA under the high-UV conditions. Figure S10 in the Supplement
shows that eliminating the mass of this compound from SOA predictions in the
absence of vapor wall loss results in a much more significant decrease in SOA
at high UV (∼100 µgm-3) than at low UV (∼15 µgm-3). α-Pinene hydroxy dihydroperoxide results
from two successive OH reactions and is therefore formed more rapidly and in
greater amounts at a higher OH level. α-Pinene hydroxy dihydroperoxide
has been observed in the gas phase with an estimated 3 % yield
. In the high-UV simulation, this compound is formed
with > 30 % yield; therefore, it is likely that reaction pathways
leading to this product are overrepresented in GECKO-A.
A second pathway for the reaction of peroxy radicals with HO2 is
formation of an alkoxy and regeneration of OH, added to GECKO-A with an
uncertain branching ratio of 20 % and only for peroxy radicals with an
oxygenated moiety in the α position. Simulations show that SOA
predictions are not sensitive to the second assumption but are very sensitive
to the assumed branching ratio for this reaction
(Fig. ). Increasing formation of the alkoxy leads to
decreasing SOA formation, with a greater effect at high UV than at low UV.
Uncertainty in this branching ratio in the α-pinene system could be a
major factor leading to the high–low-UV discrepancy.
Mass of SOA as a function of time for one 100 % UV and one
10 % UV experiment. Experimental data are shown with filled circles.
Lines show GECKO-A predictions when varying the branching ratio for the
RO2 + HO2 reaction with differing vapor wall loss rates. Solid
lines show predictions with kgw=10-3 s-1, and
dashed lines show predictions with kgw=10-5 s-1.
Pink lines show the base case predictions, in which the branching between
formation of the hydroperoxide and the alkoxy from RO2 + HO2 is
80 : 20. For the red lines, the branching ratio is 50 : 50; for the
green, it is 25 : 75.
Finally, recently identified a third HO2 reaction
channel for peroxy radicals produced from methyl vinyl ketone (MVK):
formation of a carbonyl and regeneration of OH and HO2. In place of a
hydroperoxide, which results from the standard HO2 reaction channel,
this reaction produces a ketone which has a higher vapor pressure and
therefore forms less SOA. If this reaction occurs in the α-pinene
system, the absence of this pathway in GECKO-A could contribute to SOA
overprediction.
Volatilities in this version of GECKO-A are estimated using the
method, which is based on the Clausius–Clapeyron
equation. Boiling points are estimated using the
method with some group contributions
taken from . Limited experimental
data exist for vapor pressures of semivolatile and low-volatility species, and
uncertainty in vapor pressure estimation increases as vapor pressure
decreases . Therefore, underprediction of vapor pressures
for later-generation species could lead to excess SOA. Indeed,
showed that significant uncertainties in SOA predictions
result when using different methods for vapor pressure estimation. While the
method generally led to the highest volatilites and the
lowest SOA predictions when compared to the
method and the SIMPOL-1 method from , each of these
methods estimates vapor pressures via a group contribution method (i.e.,
summing the contributions of all functional groups). If this approach is less
accurate for compounds with many functional groups, the volatilities may be
underpredicted and these species may be overpredicted in SOA.
A final possible explanation for the overprediction of higher-generation SOA
species is that significantly faster vapor wall loss exists for these more
functionalized species. showed that vapor wall loss rates
increase as compound vapor pressure decreases. They measured wall loss rates
for primarily first-generation oxidation products to be
10-5–10-6 s-1 in the Caltech chambers. Implementing the
kgw parameterization developed by in GECKO-A
yields similar SOA predictions to those based on assuming a fixed
kgw=10-5 s-1 (with the exception of the nucleation
experiment, in which the kgw parameterization predicted more SOA
than using a fixed kgw=10-5 s-1). However, it is
certainly possible that later generation, more functionalized compounds could
exhibit significantly faster wall loss than predicted by the parameterization
of . As one example, fit a wall loss
rate of 3×10-3 s-1 for a C5H10O5
isoprene oxidation product in the Caltech chambers. If later-generation
species in the α-pinene system exhibit markedly higher wall loss as
well, SOA overprediction in GECKO-A will be reduced.
In summary, if GECKO-A overpredicts the contribution of later-generation
species, the measurement–model discrepancy at differing UV levels could be
reduced. Reducing the contribution of second-generation species also requires
a slower vapor wall loss rate to fit the data, which is actually more
consistent with those that have been measured in the Caltech chamber.
Condensed-phase photolysis
If condensed-phase photolysis occurs in the chamber but is missing in
GECKO-A, both the high–low-UV measurement–model discrepancy and the
high wall loss rate needed to match the 100 % UV observations could
potentially be explained. If condensed-phase photolysis is an efficient loss
mechanism for SOA at 100 % UV, a lower vapor wall loss rate would be
required in order to continue to fit experimental observations. Because
condensed-phase photolysis would be less efficient at 10 % UV, at this
slower wall loss rate the low-UV SOA predictions will increase in the
direction of the observations. However, this effect is not seen in the
simulations. Figure shows the effect of
condensed-phase photolysis, with the radicals produced assumed to be lost
permanently. Two wall loss rates are shown: the default
kgw=10-3 s-1 and a slower
kgw=10-4 s-1. Only one high-UV experiment is shown,
but the results are similar for all high-UV experiments. Moreover,
condensed-phase photolysis has no effect on the low-UV predictions (not
shown).
Mass of SOA as a function of time for one 100 % UV experiment.
Experimental data are shown with filled circles. Solid lines show GECKO-A
predictions with and without condensed-phase photolysis with two different
vapor wall loss rates.
Under high-UV conditions, the assumed presence of condensed-phase photolysis
leads to a slight reduction in SOA predictions at the end of the experiment,
regardless of kgw. Note that the current implementation of
condensed-phase photolysis represents an upper limit: compounds in the
condensed phase are taken to photolyze at the same rate as in the gas phase,
and fragmentation products are lost permanently. The minor effect of this
photolysis upper limit can be explained by comparing the wavelength-dependent
photon flux of the chamber to absorption cross sections of species predicted
to constitute the SOA (Fig. S11 in the Supplement). found
that the main absorption region of SOA produced by α-pinene ozonolysis
corresponds to characteristic absorptions of carbonyl and peroxide functional
groups and that the SOA absorption is strongest between 240 and 400 nm.
Although products of α-pinene OH oxidation and ozonolysis differ,
peroxide and carbonyl functional groups are generated during both
. The photon flux in the Caltech chamber is less
intense than the solar flux in this region, particularly for the wavelength
region over which peroxides photolyze (Fig. S11). Because peroxides are
expected to be abundant in α-pinene SOA , the effect of condensed-phase photolysis may be
underrepresented in the Caltech chamber as compared to the atmosphere.
The minor effect of condensed-phase photolysis in these simulations is
consistent with the findings of that reduction in SOA
from both gas-phase and condensed-phase photolysis for various systems,
including α-pinene, is minor during the initial 10 h of a simulation
but becomes substantial over a week of atmospheric ageing. Moreover, this
result indicates that this process does not explain the high–low-UV
measurement–model discrepancy. Even in the presence of condensed-phase
photolysis, at a wall loss rate kgw=10-4 s-1 a
strong overprediction of SOA still exists at 100 % UV. Although
condensed-phase photolysis is potentially important in the atmosphere on
longer timescales , it does not explain the low-UV
measurement–model discrepancy of these experiments.
Autoxidation chemistry
Autoxidation has been demonstrated to be important in SOA formation
. Formation of extremely low-volatility organic compounds
(ELVOCs) via autoxidation has been observed from both the OH oxidation and
ozonolysis of α-pinene, with higher yields observed from ozonolysis
. In autoxidation, a peroxy radical undergoes an
intramolecular hydrogen abstraction to form a hydroperoxide, generating an
alkyl radical which then adds oxygen to reform a peroxy radical. This pathway
becomes important only when the rate of hydrogen abstraction is competitive
with bimolecular reactions of the peroxy radicals with NO, HO2, and
RO2 . Because lower UV intensities will lead to lower
HO2 and RO2 concentrations, intramolecular hydrogen abstraction
could potentially be favored under low UV. For the high-UV experiments, the
predicted lifetime with HO2 is ∼ 10 s; for the low-UV
experiments it is ∼50 s, indicating that autoxidation may potentially be more
important under low UV.
Moreover, during the low-UV experiments, 20–50 % of α-pinene
reacts with O3 instead of OH, compared to only 2–3 % for the high-UV
experiments. As discussed previously, the ozonolysis mechanism in GECKO-A
is likely incomplete and may be lacking pathways to SOA precursors, such as
autoxidation. Because autoxidation is much more efficient from the ozonolysis
pathway than from the OH pathway , more ELVOCs
will likely be produced under low UV and will increase the amount of SOA
formed. Furthermore, autoxidation could also explain the difference in SOA
yield observed between the two low-UV experiments. Experiment 141125 has a
higher yield than experiment 141113 and also has a higher observed O3
concentration. ELVOC production was likely higher during this experiment
owing to the increased fraction of α-pinene reacted via ozonolysis.
Autoxidation is observed to occur immediately upon oxidation
, indicating that it is likely a first-generation process.
Second-generation species are predominantly responsible for the discrepancy
in model predictions between high and low UV due to the difference in OH
levels (Fig. ). Unlike second-generation
products, autoxidation products will not depend on the OH concentration and
will not be produced more slowly under low UV. Therefore, adding autoxidation
pathways will likely increase the fraction of SOA composed of
first-generation products, which may lead to similar predictions for high and
low UV.
However, explicit mechanisms and rate constants are still lacking for
autoxidation in the α-pinene system, although they have been developed
for simpler cycloalkenes . A recent
computational study found that the cyclobutyl ring in α-pinene must
open in order for intramolecular hydrogen shifts to be competitive with the
peroxy bimolecular sink reactions . The currently accepted
α-pinene ozonolysis mechanism does not include rapid opening of this
ring. investigated several pathways to break this ring but
found none that could explain all of the characteristics of observed ELVOCs
from α-pinene. In the absence of explicit mechanisms, the effect of
autoxidation cannot be fully tested. To approximate the effect, a
C10H15O9 species (an ELVOC predicted by )
is added with a 7 % molar yield (based on the 6–8 % yield measured
by ) as a direct product from the
α-pinene + O3 reaction. This addition has no effect on SOA
concentrations for the high-UV experiments but increases the final SOA
concentrations for the low-UV experiments by
∼ 10 µgm-3 (not shown). However, adding this fixed
ELVOC yield does not increase SOA predictions for low UV at the beginning of
the experiment, when no O3 is present. Therefore, although autoxidation
via ozonolysis is likely important towards the end of the low-UV experiments,
other explanations are needed to reconcile the underprediction of SOA at the
start of the experiment. Autoxidation via OH oxidation could potentially
resolve part of this discrepancy, but no pathways or yields are yet
available. Overall, autoxidation is a likely process to explain the
measurement–model discrepancy at high and low UV but is in need of more
study. The presence of significant autoxidation could furthermore lead to a
different best-fit vapor wall loss rate.
Particle-phase dimerization
If dimerization is more competitive with gas-phase fragmentation at the lower
OH levels at 10 % lights, increased SOA growth could result.
Figure shows one 100 % UV and one 10 % UV experiment
with two different values for the dimerization rate constant:
0.01 M-1s-1 and
12 M-1s-1 using the same base
kgw=10-3 s-1. The 12 M-1s-1 rate
constant results in significant SOA overprediction for both the 100 % and
the 10 % UV experiments; the slower dimerization rate constant still
results in an overprediction for the 100 % UV experiment but an
underprediction at 10 % UV. For this rate constant, rapid vapor wall loss
combined with the slower chemistry at low UV prevents significant SOA
formation, despite dimerization. In the high-UV experiment, because gas-phase
chemistry occurs faster relative to vapor wall loss, even a slower
dimerization rate increases SOA predictions. While dimerization increases SOA
predictions for low UV, it has the same effect at high UV. Therefore,
dimerization in its current implementation in GECKO-A does not resolve the
low-UV discrepancy without causing an SOA overprediction for high UV.
Mass of SOA as a function of time for one 100 % UV experiment
and one 10 % experiment. Experimental data are shown with filled circles.
Solid lines show GECKO-A predictions for the base case and then with two
values of a condensed-phase dimerization rate constant, with all simulations using
kgw=10-3 s-1.
Enhanced wall loss at high UV
To recapitulate, using the base chemistry in GECKO-A, predictions agree with
the high-UV experiments when using a relatively fast vapor wall loss rate of
10-3 s-1, and predictions approximately match the low-UV
experiments when using a much slower wall loss rate of
10-5 s-1. This result prompts the hypothesis that vapor wall
loss could somehow be dependent on the UV level. The diffusion coefficient
and the eddy diffusivity could change based solely on the UV level if a
change in the UV level results in a temperature change in the chamber. The
temperature of the chamber was ∼3 K higher for the high-UV
experiments than for the low. Gas-phase diffusion coefficients are ∼T3/2 . A 3 K temperature increase will
result in an increase in the diffusion coefficient by only a factor of 1.015.
The eddy diffusivity characterizes the degree of turbulent mixing in the
chamber, which could be increased by temperature variations. When UV lights
are turned on, the temperature in the chamber increases by 2–3 K and
then gradually decreases to the original temperature from a well-controlled
recirculating chilled-water system. This small fluctuation is not expected to
cause a dramatic increase in turbulence in the chamber. Therefore, increase
of vapor wall loss for high-UV experiments is deemed incapable of explaining
the modeling discrepancy.
Atmospheric implications
From the systematic analysis of the experiments and mechanism predictions
(and in the absence of explicit autoxidation mechanisms), two hypotheses
could explain some or all of the inability of the base case in GECKO-A to
simulate both the high- and low-UV experiments. The first hypothesis is that
later-generation species overcontribute to the SOA in GECKO-A. To compensate
for this overcontribution necessitates the assumption of rapid vapor wall
loss in order to fit the high-UV data; when extended to the low-UV
experiments, this rapid wall loss results in significant SOA underprediction,
since later-generation species are produced more slowly at the lower OH
levels. This overcontribution could be the result of several possibilities,
each of which has different implications for atmospheric SOA formation. If
some reaction pathways are under- or misrepresented in GECKO-A, atmospheric
predictions from GECKO-A may then overestimate SOA. If instead all reaction
pathways in GECKO-A are realistic but the rates are simply overpredicted,
these compounds may still form in the atmosphere but will be affected by
competing processes such as dry and wet deposition and uptake into cloud
particles . If volatilities are too low
in GECKO-A, atmospheric predictions will then be biased high. Finally, if
higher-generation species simply exhibit a faster wall loss, these compounds
will be produced quickly in the atmosphere and will generate significant SOA.
This hypothesis implies that vapor wall loss in the Caltech chamber is likely
slower than that of the base case assumed in GECKO-A. This slower wall loss
is consistent with measurements in the Caltech chamber but is considerably slower than those measured in the
8 m3 chamber used by ,
10-3–10-4 s-1. Differences in the size and operation of
two chambers may contribute to the differences in measured wall loss rates.
The vapor wall loss implied by the present experiments results in an order of
magnitude slower rate than that fit by during toluene SOA
photooxidation. This difference could potentially indicate differences
between chemical systems or could also arise from the manner in which the
modeling was performed: used the Statistical Oxidation
Model of vs. the GECKO-A model used here. An
implication of this difference is that the wall loss bias (the ratio of the
yield predicted in the absence of vapor wall loss divided by the observed
yield) for the α-pinene system is likely lower than the 1.6 predicted
by . However, because we could not definitively fit here
both the high- and low-UV experiments, we cannot calculate a corresponding
wall loss bias.
The second hypothesis with the potential to explain the measurement–model
discrepancy is autoxidation. Autoxidation from both the OH and ozonolysis
pathway could be important during the low-UV experiments. Explicit mechanisms
and rate constants are needed in order to test this hypothesis. If
significant, the presence of autoxidation could change the vapor wall loss
rate implied by these simulations.
Although other processes do not reconcile the high–low-UV model
discrepancy, such processes may still be important. Condensed-phase
photolysis, though not significant over the short timescale of chamber
experiments, has been shown to potentially have a more significant effect in
the atmosphere . Particle-phase reactions have been shown
to generate significant amounts of high-molecular weight, low-volatility
species in the α-pinene system
, albeit more efficiently
in ozonolysis . Even if particle-phase dimerization
is significant in this system, the assumed irreversible formation of dimers
in GECKO-A may not accurately represent particle-phase reactions, which have
been suggested to be reversible .