Introduction
New particles are introduced in the atmosphere by (i) direct emissions from a
variety of (primary) sources and (ii) nucleation (in situ formation).
Nucleation and subsequent growth of new particles are often observed in most
areas of the globe (Kulmala et al., 2004) and represent an important source
of ambient aerosol number concentration. Fresh particles formed by nucleation
can either grow through condensation of vapors (e.g., sulfuric acid and
ammonia, organics) to larger sizes or can be lost by coagulation with
pre-existing larger particles. The newly formed particles that manage to
survive coagulation can grow to larger sizes and become cloud condensation
nuclei (CCN), affecting the cloud droplet number concentration (Adams and
Seinfeld, 2002). Nucleation and subsequent growth by condensation can be an
important source of CCN (Lihavainen et al., 2003; Kerminen et al., 2005;
Laaksonen et al., 2005; Merikanto et al., 2009; Makkonen et al., 2009; Pierce
and Adams, 2009b; Wang and Penner, 2009; Yu and Luo, 2009; Spracklen et al.,
2010). Changes in CCN concentration affect cloud optical properties and cloud
lifetime, perturbing the energy balance of the planet (Twomey 1974, 1977,
1991; Albrecht, 1989). An increase in the number concentration of particles
that can act as CCN results in higher cloud droplet number concentrations and
brighter clouds with longer lifetimes. Measurements of CCN at a non-urban
site in Germany suggested that CCN concentrations are mainly determined by
the aerosol number size distribution, while the composition of aerosol plays
a secondary role (Dusek et al., 2006). Furthermore, nanoparticles can affect
human health by deposition in human lungs or the neurological system. For
small particles, the damage can be greater due to the larger surface area per
unit mass (Peters et al., 1997; Donaldson et al., 1998, 2002). The effects of
aerosol composition on human health are still uncertain (Godleski et al.,
2000).
Several mechanisms have been proposed to explain in situ particle formation.
These include sulfuric acid–water binary nucleation (Nilsson and Kulmala,
1998; Vehkamaki et al., 2002), ternary nucleation (Coffman and Hegg, 1995;
Korhonen et al., 1999; Kulmala et al., 2002; Napari et al., 2002),
nucleation of organic vapors (Marti et al., 1997; Zhang et al., 2004),
ion-induced nucleation (Laakso et al., 2002), and halogen oxide nucleation
(Hoffmann et al., 2001). The binary nucleation mechanism has been the most
commonly used in atmospheric models with the critical cluster assumed to be
composed of H2SO4 and H2O. Ternary nucleation theory usually
includes ammonia (NH3) as a third component. It is possible that other
compounds (e.g., organics, amines) may play a similar role under certain
conditions (Bonn et al., 2008; Kurtén et al., 2008; Metzger et al., 2010;
Smith et al., 2010; Berndt et al., 2010; Zhao et al., 2011; Kirkby et al.,
2011; Almeida et al., 2013; Riccobono et al., 2014). A strong correlation
has been found between measured aerosol nucleation rate and the gas-phase
sulfuric acid concentration (Weber et al., 1996; Sihto et al., 2006;
Riipinen et al., 2007; Kuang et al., 2008; Nieminen et al., 2009; Paasonen
et al., 2009, 2010) in various sites in Europe and the United States of America.
Nucleation events observed in sulfur-rich regions like the northeastern USA
appear to be initiated by the formation of gas-phase H2SO4 (via
SO2 oxidation) but terminated by the exhaustion of gas-phase NH3
or other bases (Jung et al., 2008). Ambient measurements and some laboratory
studies (Sipila et al., 2010) have revealed a linear or squared correlation
between new particle formation rate and concentration of sulfuric acid.
Significant uncertainties arise also from the incomplete understanding of
the identity of the species involved in the growth of these nuclei (Kulmala
et al., 2004). Field measurements (Eisele and McMurry, 1997; Weber et al.,
1998, 1999; Janson et al., 2001) and model simulations (Kerminen et al.,
2001; Kulmala et al., 2000; Pirjola and Kulmala, 2001; Anttila and Kerminen,
2003) have indicated that the condensation of sulfuric acid alone is often not
sufficient to explain the observed growth rates of fresh particles (Riipinen
et al., 2011). The growth of fresh nuclei could be due to the condensation
of organic species (Kerminen et al., 2000; Anttila and Kerminen, 2003),
heterogeneous reactions (Zhang and Wexler, 2002), or ion-enhanced
condensation (Laakso et al., 2002).
Jung et al. (2010) developed a three-dimensional regional chemical transport
model (CTM), PMCAMx-UF, with detailed aerosol microphysics (Gaydos et al.,
2007; Karydis et al., 2007) that has been used for simulations over the USA
and Europe (Fountoukis et al., 2012). In Europe the model predictions were
compared against size distribution measurements from seven sites (Fountoukis
et al., 2012). The model was found to reproduce more than 70 % of the
hourly number concentrations of particles larger than 10 nm (N10)
within a factor of 2. For particles larger than 100 nm (N100, a proxy
for the number of particles that can act as CCN) a systematic underprediction
was seen. The growth rates were also underpredicted (with smaller errors in
sites where the sulfate to organics mass ratio is high, e.g., Melpitz),
possibly because of insufficient organic vapor condensation (Fountoukis et
al., 2012) as the model did not explicitly include secondary organic aerosol
(SOA) condensation on ultrafine particles. Yu (2011) and Riipinen et
al. (2011) studied the condensation of organics on ultrafine particles using
global CTMs. Yu (2011) estimated that the concentration of low-volatility
organics is a factor of 2–20 higher than the H2SO4 concentration
in many continental locations and can significantly enhance the growth rate
of freshly nucleated particles. He compared predicted particle size
distributions with field measurements in a boreal forest site (Hyytiälä,
Finland) showing that the condensation of low-volatility organics can bring
the simulation results closer to the observations. Riipinen et al. (2011)
estimated that roughly half of the condensed organic mass needs to be
distributed proportionally to the aerosol surface area to explain the
observed aerosol growth. These organic compounds need to have both high
yields and very low volatility, which is inconsistent with laboratory
observations of the first-generation yields of SOA from biogenic precursors
(Presto and Donahue, 2006;
Pathak et al., 2007; Pandis et al., 2013). Pierce et
al. (2011) estimated that the average effective saturation concentration
(C∗) of condensing organics needs to be
10-3-10-2 µg m-3 or less to enhance the growth of
freshly nucleated particles.
SOA accounts for a significant mass fraction
(20–90 %) of sub-micrometer particulate matter at many locations around
the globe and is one of the most dominant particle components in the
atmosphere (Jimenez et al., 2009). The sources and the chemical composition
of OA (organic aerosol) are still uncertain due to the large number (tens of thousands) of
different atmospheric organic compounds (Goldstein and Galbally, 2007). OA
has been the subject of numerous studies during the last decade (Hallquist et
al., 2009) but remains the least understood component of atmospheric
aerosols. The organic aerosol composition continuously evolves with time due
to various chemical reactions (Kanakidou et al., 2005). Gas-phase oxidation
of volatile organic compounds (VOCs) produces semi-volatile products that can
then condense to the particulate phase. Products with high vapor pressures
can be oxidized to species with lower vapor pressures that can then condense
on preexisting particles. The chemical aging (further oxidation) of
semivolatile organic compounds is an important source of OA mass (Donahue et
al., 2006). Until recently, most CTMs described SOA formation using
two surrogate species per VOC (Odum et al., 1996). This approach is
computationally expensive due to the large number of products, while the use
of only two products per VOC limits the concentration range and the accuracy
of this approach (Murphy et al., 2009). SOA vapors may undergo further
gas-phase oxidation, and simulation of this aging process would require
introduction of even more species (Ng et al., 2006). The volatility basis set
(VBS) framework (Donahue et al., 2006) was proposed to address these
problems, describing the complete volatility range of OA compounds using
logarithmically spaced bins characterized by an effective saturation
concentration, C∗ (in µg m-3). This framework has been
shown to work well for simulations of aerosol mass distributions in 3-D CTMs
(Murphy et al., 2009; Tsimpidi et al., 2010; Fountoukis et al., 2011).
The overall objective of this work is to examine the contribution of organic
vapor condensation to the growth of fresh particles formed by nucleation and
whether this condensation can explain the observed growth rate of new
particles. We extend the Dynamic Model for Aerosol Nucleation (DMAN) of Jung
et al. (2006), which originally assumed that particles can grow only by
condensation (of sulfuric acid and ammonia) and coagulation. In this work, we
develop an updated version of DMAN (DMANx) which includes the condensation of
organic vapors on particles and the most recent version of the VBS framework.
We estimate for the first time the effect of the chemical aging gas-phase
reactions of SOA components on ultrafine particle growth. We examine the
effects of condensation of organics, the gas-phase chemical aging reactions,
and the Kelvin effect on the predicted particle number concentrations. We
focus on the composition of fresh particles during nucleation events as well
as during their subsequent growth, in two remote continental locations,
Hyytiälä, Finland, and Finokalia, Greece, where there are sufficient
measurements available to constrain the new model. This is the first step
towards improving the predictions of the 3-D CTM, PMCAMx-UF.
Model description
DMAN simulates nucleation, coagulation, and condensation/evaporation for a
multi-component atmospheric aerosol (Jung at el., 2006). It uses the
Two-Moment Aerosol Sectional (TOMAS) algorithm of Adams and Seinfeld (2002),
which is based on the sectional approach for the description of the aerosol
size composition distribution. TOMAS is an adaptation of cloud microphysics
algorithms (Tzivion et al., 1987, 1989) to aerosol processes, is
computationally efficient, and tracks both mass and number concentrations
simultaneously. The aerosol size distribution is described with 41 size
sections, with the lowest size bin corresponding to a
3.7 × 10-25 kg dry aerosol mass per particle. That
corresponds to 0.8 nm dry diameter assuming a density of 1.4 g cm-3.
Each successive section has double the mass of the previous one. The largest
bin corresponds to a diameter of 10 µm.
Nucleation
DMAN has the option of using a number of nucleation parameterizations. In
this work, the rate of nucleation is calculated using a scaled ternary
nucleation parameterization based on the original expressions of Napari et
al. (2002) if the NH3 concentration exceeds 0.01 ppt, and the binary
parameterization of Vehkamaki et al. (2002) if it is less than this threshold
value. The original NH3–H2SO4–H2O parameterization has
been successful in predicting the presence or lack of nucleation events
(Gaydos et al., 2005) in sulfur-rich environments. However, it overpredicts
ultrafine number concentrations during nucleation events (Gaydos et al.,
2005; Yu, 2006a, b; Jung et al., 2006, 2008; Merikanto et al., 2007; Zhang et
al., 2010) and thus a scaling factor of 10-5 is applied to the
nucleation rate following Jung et al. (2010). The critical nucleus is
predicted to consist of roughly two molecules of sulfuric acid and two
molecules of ammonia (Napari et al., 2002), so it is assumed here that the
newly formed particles consist of ammonium bisulfate and their diameter is
1 nm.
Gas-phase chemistry
In this work, the simulation of gas-phase chemistry in DMAN is updated using
the SAPRC99 chemical mechanism (Carter, 2000; Environ, 2003), which includes
211 reactions of 56 gases and 18 free radicals. It includes five lumped
alkanes, two lumped olefins, two lumped aromatics, isoprene, a lumped
monoterpene, and a lumped sesquiterpene species. Only the two highest
molecular weight alkane species are considered as SOA precursors because the
other three contain smaller hydrocarbons (Pandis et al., 1991). OLE1 contains
all the terminal alkenes, while OLE2 consists of all the internal and cyclic
alkenes. The major compounds for each VOC class used in SAPRC99 are listed in
Table S1 (Tsimpidi et al., 2010). The nine lumped VOCs are considered as
volatile SOA precursors, with three of them being biogenic and the rest
anthropogenic.
Coagulation
Coagulation of particles in the atmosphere is an important sink of aerosol
number but is also a mechanism by which freshly nucleated particles grow to
larger sizes. The TOMAS algorithm is used for the simulation of coagulation.
Following Adams and Seinfeld (2002), TOMAS assumes that the aerosol particles
coagulate via Brownian diffusion and the effects of gravitational settling
and turbulence are negligible. The calculation of the coagulation
coefficients is based on the wet diameters of the particles. These wet
diameters are calculated following the approach of Gaydos et al. (2005). For
small particles (< 100 nm), we use the expression of Dahneke et
al. (1983) in order to correct for non-continuum effects. The coagulation
algorithm uses an adaptive time step. The time step is limited so that the
aerosol number or mass concentration in any size category does not increase
by more than an order of magnitude or decrease by more than 25 %.
Condensation
Condensation of gas-phase species to existing aerosol particles is an
important source of aerosol mass and a means by which small particles grow
to CCN sizes. The TOMAS algorithm is used for the simulation of
condensation/evaporation of sulfuric acid, ammonia, and organic vapors, using
the wet diameters of the particles (Gaydos et al., 2005). The driving force
for condensation of a vapor to an aerosol particle is the difference between
its ambient vapor partial pressure and the equilibrium vapor pressure over
the particles, or
Δpi=pi-pi∗xi(Dp)exp4σMiRTρDp,
where Δpi is the condensational driving force of the organic vapor
i (the difference between the partial pressure of condensing vapor and its
equilibrium vapor pressure), pi is the ambient partial pressure, xi
is the mole fraction of i, pi∗ is the effective saturation
pressure over a flat surface, σ is the surface tension, Mi is
the molecular weight of i, R is the ideal gas constant, T is the
temperature, ρ is the liquid-phase density, and Dp is the
diameter of the particle. The exponential term is known as the Kelvin effect
due to the curvature of the particles. The exponential term is large for
small particles and “prevents” the condensation of organic vapors on these.
As a result, the Kelvin effect is important for the growth of newly formed
particles. We use TOMAS with an adaptive time step to efficiently solve the
equations for condensation. The time step is chosen so that individual
particles in any size bin do not grow by more than 10 %, the partial
pressure of the organic vapor does not fall below 25 % of its original
value, and the time step is never longer than 15 min.
DMANx uses the pseudo-steady-state approximation (PSSA), in which the
sulfuric acid concentration is calculated by assuming that its production
rate (oxidation of sulfur dioxide) is equal to its consumption rate
(condensation and nucleation). Pierce and Adams (2009a) showed that the PSSA
for sulfuric acid increases the computational speed with a small loss in
accuracy. The PSSA was tested for a variety of conditions ranging from highly
polluted to extremely clean conditions. Its predictions for the sulfuric acid
vapor concentration and the number of new particles formed during typical
atmospheric nucleation events agreed well with the “benchmark model”
(Pierce and Adams, 2009a). Jung et al. (2010) evaluated the performance of
PSSA for sulfuric acid in DMAN against a fourth-order Runge–Kutta algorithm and
showed that PSSA is accurate and computationally efficient.
Condensation of ammonia is simulated following the approach described by Jung
et al. (2006). Ammonia condensation on the ultrafine particles ends when
sulfate is fully neutralized to ammonium sulfate. The equilibrium ammonia
vapor pressure is assumed to be zero when aerosols are acidic, i.e., when the
molar ratio of NH4+ to SO42- is < 2. If the amount of
condensed ammonia exceeds the amount needed to neutralize particles during a
time step, we limit the maximum amount of ammonia that can condense to avoid
numerical problems.
Secondary organic aerosol formation
Gas-phase oxidation of volatile organic compounds (VOCs) produces
semi-volatile products that can then condense to the particle phase. The VBS
framework used in DMANx (Donahue et al., 2006) describes the complete
volatility range of OA compounds using logarithmically spaced bins,
characterized by an effective saturation concentration, C∗ (in
µg m-3). SOA components partition between the aerosol and
gas phases, and can be formed from anthropogenic SOA (aSOA) and biogenic SOA
(bSOA) precursors. Each of these types is simulated here with 12 volatility
bins (10-5–106µg m-3). We assume an average
molecular weight of 200 g mol-1 for both aSOA and bSOA, while the
effective enthalpies of vaporization are 30 kJ mol-1 (Pathak et al.,
2007; Stanier et al., 2007). The SOA yields used in the updated version of
DMAN are based on the NOx-dependent stoichiometric yields of Murphy et
al. (2009). The partitioning of OA between the gas and particulate phases is
approximated using vapor–liquid equilibrium theory (Eq. 1).
Semi-volatile and intermediate-volatility organics can be oxidized to species
with lower volatility (Donahue et al., 2006) leading to SOA production. The
gas-phase chemical aging of SOA precursors is modeled using a second-order
gas-phase reaction with the hydroxyl radical. We assume that each chemical
aging step reduces the volatility of the corresponding organic vapor by 1
order of magnitude (i.e., shifting organic material from a saturation
concentration of, for example, 103 to 102 µg m-3), with a
small net increase in mass (7.5 %) to account for the added oxygen. The
chemical aging reactions for aSOA precursors are modeled with a rate constant
k (298 K) = 1×10-11 cm3 molec s-1
(Murphy et al., 2009).
In the base case, the gas-phase chemical aging of bSOA precursors is assumed
to have a negligible effect on OA concentration (Lane et al., 2008). The
oxidation of biogenic VOCs produces semi-volatile organics with saturation
concentrations of 1, 10, 102, and 103 µg m-3. An
alternative hypothesis is that the condensation of very low volatility
organics may explain the observed growth. We assume that a small fraction of
organics, which are produced from the oxidation of biogenic VOCs, further
reacts to form very low volatility organics with a saturation concentration
of C∗=10-3 µg m-3 (Pierce et al., 2011). The
sensitivity of the model results to this assumption will be tested assuming a
reaction converting the gas-phase surrogate species with C∗=1 µg m-3 to extremely low volatility SOA with C∗=10-3 µg m-3 with a reasonable rate constant equal to
1×10-11 cm3 molec s-1. The importance of
extremely low volatility organic material for the growth of newly formed
particles is explored in a subsequent section. This pathway is consistent
with the recent observations of extremely low volatility organic compounds by
Ehn et al. (2014), during ozonolysis reactions as well as reactions with the
hydroxyl radical.
Model application
We simulated a “typical” spring day with nucleation at both Hyytiälä
and Finokalia. First, we identified the days with observed particle formation
and growth and then averaged the corresponding measurements during these
days, generating in this way the meteorological and chemical characteristics of an
“average” nucleation day for the specific periods in the two locations
(April 2007 in Hyytiälä and May 2008 in Finokalia). For the parameters
for which measurements were not available, but were needed for the model
input (e.g., OH concentration), we followed the same process using the
predicted values from the 3-D chemical transport model PMCAMx (Fountoukis et
al., 2011). Results are compared to the corresponding average values observed
during the specific periods.
The extended DMAN (DMANx) is first tested in Hyytiälä (Finland), an
environment dominated by biogenic VOCs. Meteorological data, gas-phase
concentrations, and aerosol number distributions are available from ground
measurements at the SMEAR II station in Hyytiälä and used here as inputs
for DMANx. SMEAR II (Station for Measuring Forest Ecosystem–Atmosphere
Relations) is located in a rather homogenous Scots pine (Pinus sylvestris) stand on flat terrain at the Hyytiälä Forestry Field Station
of the University of Helsinki (61∘51′ N, 24∘17′ E;
181 m a.s.l.). The biggest city near SMEAR II is Tampere, which has
approximately 200 000 inhabitants and is located 60 km from the measurement
site. Hari and Kulmala (2005) have described the station and its operation in
detail. The main inputs of our simulations (Table 1) are meteorological data
(temperature and relative humidity); SO2, NH3, OH, O3, and VOC
concentrations; and the initial aerosol number distribution. The SO2 and
O3 concentrations, T, and RH were measured continuously, while the OH
concentration was based on the predictions of the 3-D CTM PMCAMx-2008
(Fountoukis et al., 2011). The concentrations of the lumped VOCs, TERP, ISOP,
and ARO1 were estimated based on proton transfer reaction mass spectrometer
(PTRMS) measurements. The rest of the lumped VOCs were taken from
PMCAMx-2008. The concentration of NH3 was based on the measurements
during the QUEST IV campaign in Hyytiälä (Riipinen et al., 2007). The
initial aerosol number distributions are available from DMPS (differential
mobility particle sizer) measurements of ambient dry size distributions
(Aalto et al., 2001).
Main inputs of DMANx simulations.
Inputs
Hyytiälä (Finland)
Finokalia (Greece)
Temperature
Measurements
Measurements
RH
Measurements
Measurements
O3
Measurements
Measurements
OH
Model PMCAMx
Model PMCAMx
SO2
Measurements
Measurements
NH3
Measurements (PTR-MS)
Measurements
TERP
Measurements (PTR-MS)
Model PMCAMx
ISOP
Measurements (PTR-MS)
Model PMCAMx
ARO1
Measurements (PTR-MS)
Model PMCAMx
ARO2
Model PMCAMx
Model PMCAMx
ALK4
Model PMCAMx
Model PMCAMx
ALK5
Model PMCAMx
Model PMCAMx
Initial number distributions
Measurements (DMPS)
Measurements (SMPS)
DMANx was also tested in Finokalia, a remote area in the eastern
Mediterranean region with high sulfate levels and relatively low VOC
concentrations. Finokalia (35∘24′ N, 25∘60′ E) is a
remote coastal station located in the southeast of the Mediterranean Sea on
the island of Crete in Greece. The nearest large urban center is Heraklion,
with 150 000 inhabitants, located 50 km west of Finokalia. The Finokalia
station is located at the top of a hill at an elevation of 230 m, facing the
sea. There is no notable human activity at a range of approximately 15 km
(Kouvarakis et al., 2000). There are very few trees and little vegetation in
the surrounding area. Most of the aerosol at the site is transported from the
surrounding regions, such as Greece, Turkey, and northern Africa (Pikridas et
al., 2010). The concentrations of NH3 and SO2 and aerosol number
distributions were based on the measurements during the Finokalia Aerosol
Measurement Experiment 2008 and 2009 (FAME-08 and FAME-09) (Pikridas et
al., 2010, 2012). The other inputs (Table 1) were based on the predictions of
PMCAMx.
The model simulates a full day, beginning at midnight. In each simulation, we
assumed for the initial distribution that each size bin contains half
organics and half ammonium sulfate. Our results are not especially sensitive
to this assumed initial composition; the initial particle size distribution
is a lot more critical.
Results
Simulation without condensation of organic vapors
In the simulation neglecting the organic contribution to ultrafine particle
growth in Hyytiälä, the new particles reach a diameter of 9 nm and the
growth rate is only 1 nm h-1 (Fig. 1a). The typical observed growth
rates in Hyytiälä are between 1 and 4.5 nm h-1 and the final
diameters between 14 and 45 nm (Pierce et al., 2011; Yli-Juuti et al., 2011). A
significant advantage of DMANx is that it can track the composition of fresh
particles formed by nucleation. The growing nucleation mode consists of
sulfate and ammonium without any organics (Fig. 1b).
(a) Predicted aerosol dry size distribution for a typical
spring nucleation event at Hyytiälä without condensation of organics.
Particle number concentration is plotted against local time of day (x axis)
and particle diameter (y axis). (b) Predicted composition of new particles.
In the Finokalia nucleation simulation, the model predicted that the fresh
particles grew to 32 nm with a rate of 3 nm h-1 (Fig. S1a in the
Supplement), which is less than the 5 nm h-1 reported by Pikridas et
al. (2010, 2012). The particles reached a diameter of 10 nm at 10:00 LST.
These new particles consisted of sulfate and ammonium (Fig. S1b). At the
start of the nucleation event, fresh particles consist of 93 % sulfate, which
drops to 72 % by the end of the day.
Organic condensation with σ=0.025 N m-1 (no chemical aging of biogenic
SOA precursors)
In the simulation with organic vapor condensation and σ=0.025 N m-1 (Pierce et al., 2011) without gas-phase chemical aging
of biogenic SOA (bSOA) precursors, the predicted growth rate in Hyytiälä
remains low at 1.2 nm h-1 and the diameter of new particles at the end
of the day is 12 nm (Fig. 2a). These results are low compared to typical
measurements of growth rate in this area. The simulation showed that using a
reasonable surface tension value practically prevents the condensation of
organics on fresh particles until they can grow above a diameter of 5 nm.
Surface tension has a major effect, as expected, on the composition of
the new particles. At the beginning of the nucleation event (Fig. 2b), the
new particles do not contain any organics and their initial growth is due to
the condensation of sulfuric acid and ammonia. At 12:00 LST, when the new
particles have reached a diameter of 5 nm, the effect of surface tension has
decreased and organics start slowly condensing, contributing to growth. The
mass fraction of organics in the new particles gradually increases, reaching
60 % at the end of the day, when these particles reach 10 nm (Fig. 2b).
Simulation with surface tension σ=0.025 N m-1 at Hyytiälä: (a) predicted particle size distribution
with number concentration plotted against time of day (x axis) and particle
diameter (y axis) and (b) the composition of new particles.
The organic composition of the fresh particles in Hyytiälä is shown in
Fig. S2. Components with lower volatility (C∗=10-2 and
10-3 µg m-3) contribute around 30 % of the organic
mass in the initial stages of the growth (Fig. S2). Another 55 % is due to
the C∗=0.1 and 1 µg m-3 components. As the day goes
on, the contribution of the more volatile components increases, and at the end
of the day 65 % of the new particle organic mass is semi-volatile material
(C∗ of 1 and 10 µg m-3). We estimate that the
fraction of the condensing organic mass that has gone to the ultrafine
particles (diameter < 100 nm) reached a maximum value of 3.5×10-5 at 12:00 LST.
In the case of σ=0.025 N m-1 at Finokalia (without chemical
aging of bSOA precursors), the predicted growth rate is 3.8 nm h-1,
which is still lower than the measured 5 nm h-1 rate. The mode
diameter of the new particles reaches 42 nm (Fig. S3a). At 09:30 the newly
formed particles consist of 90 % sulfate, 6 % ammonium, and 4 % organics
(Fig. S3b). The organics condensation starts accelerating later, when these
particles become larger than 3 nm (after 10:00 LST). The predicted SOA mass
fraction increases to 35 % by the end of the day. The surrogate OA species with
C∗=1 and 10 µg m-3 were the major components of new
particles in this case too, representing around 70 % of the OA during the
day (Fig. S4). In this simulation a maximum of 3 % of condensing organic
mass has gone to the ultrafine particles (at 12:00).
Contribution of gas-phase chemical aging of biogenic SOA precursors
Comparison of (a) measured on 10 April 2007 and (b) predicted (with aging of bSOA precursors and σ=0.025 N m-1)
dry size distribution as a function of local time at Hyytiälä for a
typical nucleation event day.
The simulations described in the previous section (with condensation of
organics and σ=0.025 N m-1) showed that the addition of
condensation of semi-volatile organics did not close the gap between
predictions and field measurements of particle growth. Adding gas-phase
chemical aging of bSOA precursors in the Hyytiälä simulation results in a
growth rate of 2.2 nm h-1 and a final diameter of 23 nm. These are
very similar to the values of growth rate (2.1 nm h-1) and final
diameter (21 nm) observed in Hyytiälä on 10 April 2007 (Pierce et al.,
2011) (Fig. 3). In the beginning of the simulation, the new particles consist
mainly of ammonium sulfate and a small amount of organics (Fig. 4a). This
small fraction of organics is the extremely low volatility organics (C∗= 10-3 µg m-3) produced from the aging reaction of
bSOA precursors (Fig. 4b). During the day the new particles grow and the mass
of organics increase. The low-volatility material dominates the growth during
the first few hours, when the diameter is less than 5 nm, while the
semi-volatile (C∗=1, 10 and 100 µg m-3) dominates
the growth during the later stages. At 18:00 LST the new particles consist
mostly (90 %) of organics (40 % of low volatility and 60 %
semi-volatile SOA) (Fig. 4). The semi-volatile SOA contributes to growth when
the particles pass the size of ∼ 10 nm where the Kelvin effect is
small. The aging reaction affects new particles at the beginning of growth
when the particles are very small. The maximum mass of condensing organics
that has gone to the ultrafine particles is about 0.9 % at noon, which is
larger than in the simulation without aging.
(a) Mass fraction of fresh particles and (b) mass fraction
of SOA for the different volatility bins as a function of local time at
Hyytiälä. Simulation includes bSOA aging and σ=0.025 N m-1.
The assumed gas-phase aging reaction of bSOA precursors helps new particles
to grow to larger sizes in Finokalia too, and predictions are now consistent
with field measurements (Fig. 5). The predicted growth rate is
4.7 nm h-1, while the measured growth rate in Finokalia is around
5 nm h-1 (Pikridas et al., 2012). The revised model can reproduce the
observed growth rate and the final particle size encouragingly well. At the
start of the growth, the new particles consist of 85 %
(NH4)2SO4, while at the end of the day this drops to 55 %
(Fig. 6a). The organic components initially comprise 12 % of the nucleated
particle mass, and are mostly of low volatility (75 % is from the C∗=10-3 µg m-3 volatility bin). During the day, the
organics mass fraction increases, reaching a maximum of 45 % of the
nucleated particle mass and consisting of 30 % low-volatility and 70 %
semi-volatile organics (Fig. 6b). Four percent of the condensing organics at noon
has gone to the ultrafine particles.
Comparison of (a) measured on 19 March 2009 (Pikridas et
al., 2012) and (b) predicted dry size aerosol distribution (with bSOA aging
and σ=0.025 N m-1) as a function of time at Finokalia.
(a) Mass fraction of fresh particles and (b) mass fraction
of SOA for the different volatility bins as a function of local time at
Finokalia. Simulation includes bSOA aging and σ=0.025 N m-1.
Sensitivity analysis
Organic condensation neglecting the Kelvin effect without aging
of bSOA precursors
Condensation of organics neglecting the Kelvin effect (assuming σ = 0 N m-1) helps the newly formed particles to reach a diameter
of 28 nm in Hyytiälä, while the average growth rate is 1.8 nm h-1
(Fig. S5a). At 08:00 LST the nucleation mode particles have a size of
∼ 2 nm and consist of 55 % sulfate, 11 % ammonium, and 34 %
organics. During the day, the new particles continue to grow, and the fraction
of organics increases to 85 % at the end of the day (Fig. S5b). The size of
the new particles increases due to the condensation of organics. The
surrogate OA components with effective saturation concentration of C∗=1 and 10 µg m-3 are predicted to be the major components
of the fresh particles, contributing 80 % of the SOA, initially, and 70 %
in the end (Fig. S6).
For the Finokalia case, the predicted growth rate of new particles is
3.8 nm h-1 and the diameter of the new fresh particles reaches 42 nm
(Fig. S7a). The condensation of organics helps the particles to grow faster
and to reach larger sizes compared to the case described in Sect. 4.1. At
08:00, the particles consist of 35 % organics, 5 % ammonium, and 60 %
sulfate (Fig. S7b). After 11:00, the new particles composition is relatively
stable: 40 % organics, 42 % sulfate, and 18 % ammonium. For this case
of zero surface tension, organics condense immediately on the newly formed
particles, resulting in fast growth. In this case, about 6.5 % of
condensing organics has gone to the ultrafines at 12:00. The SOA composition
is similar to that of the σ=0.025 N m-1 case (Fig. S8).
Sensitivity of particle number concentration to surface tension
So far the model has shown that organic condensation can cause an increase in
the size of the ultrafine particles. However, large particles also grow by
organic condensation, resulting in increased coagulation probability for the
newly formed particles (Kuang et al., 2009; Westervelt et al., 2013, 2014).
This causes a significant reduction in the number concentration of small
particles compared to the case without condensation of organics. The increase
in surface tension leads to organic vapor condensation mostly on large
particles and to a smaller extent on freshly nucleated particles, due to the
increased Kelvin effect, which prevents the condensation of organics on new
particles.
Predicted concentrations of (a) N3 and (b) N100 at Hyytiälä for the four simulated cases. Black line represents
no condensation of organics, red is with condensation of organics with
σ=0.0 N m-1, blue is with condensation of organics with σ
=0.025 N m-1, and green is condensation of organics with aging
reactions of bSOA precursors and σ=0.025 N m-1.
The predicted daily mean number concentration of particles above 3 nm
(N3) in Hyytiälä is 3100 cm-3 for the simulation without
organics, 5400 cm-3 for the zero surface tension case, 2500 cm-3
when the organic surface tension is 0.025 N m-1, and 3000 cm-3
with the aging of bSOA precursors (Fig. 7a). The maximum value of N3
is predicted at 13:00 in all cases. For the zero surface tension simulation,
N3,max is 13 500 cm-3, while in the no-organics case
N3,max=6300 cm-3. When the organic surface tension is
0.025 N m-1, N3,max=4600 cm-3 and increases to 6000 cm-3 with the addition of aging of bSOA precursors (Fig. 7a).
At Finokalia the average predicted daily concentration of N3 is
3600 cm-3 without the condensation of organics and is predicted to be
the same for the bSOA aging case. A similar N3 is predicted for the
σ=0.025 N m-1 case (3550 cm-3) (Fig. S9a), while N3
increases to 4300 cm-3 for zero surface tension. The maximum N3 is
predicted to be the largest for the zero surface tension case (Fig. S9a).
Neglecting the Kelvin effect in both locations allows rapid condensation of
organics on the fresh particles, resulting in an increase in N3. The
Kelvin effect suppresses the condensation of organics on new particles,
resulting in a decrease in N3. The bSOA aging and the σ=0.025 N m-1 simulations do not significantly affect N3 compared
to the simulation without organics.
The daily mean number concentration of particles above 100 nm (N100)
in Hyytiälä is 380 cm-3 for the no-organics case and 440 cm-3
for all the other simulations (Fig. 7b). In all the Hyytiälä simulations
of condensing organics, N100 increases after 08:00
due to organics, while in the simulation without organics a smaller increase
is predicted after 12:00 due to sulfuric acid (Fig. 7b). Condensation of
organics increased N100 by 25 % at the end of the day.
A similar behavior is predicted at Finokalia. N100 starts increasing
after 08:00 for all the cases with organics condensation and after 09:00 for
the no-organics case. The reason for the shorter delay before the onset of
N100 increase in the no-organics case compared to Hyytiälä is that
the condensation of sulfuric acid–ammonium has a predominant role in
Finokalia at increasing N100, while at Hyytiälä simulations organics
dominate this growth process. Furthermore, at Finokalia the concentration of
sulfuric acid is higher and the photochemistry is faster (much higher OH
levels) than at Hyytiälä. The daily mean number concentration of
particles that can act as CCN (N100) is predicted to be 1000 cm-3
without organics and 1100 cm-3 for the other simulations (Fig. S9b).
N100 reaches a maximum (1070 cm-3) at 13:00 for the no-organics
case and at 18:00 in the other simulations (1210 cm-3) (Fig. S9b). At
the end of the day N100 has increased by 13 % due to condensation of
organics. The increase in surface tension and the addition of aging of bSOA
precursors do not change N100.
Conclusions
We developed an updated version of DMAN (DMANx) which includes the
condensation of organic vapors on ultrafine particles, using the volatility
basis set framework. Simulations were performed for two locations with
different organic sources, Hyytiälä and Finokalia, during a typical
springtime day with aerosol nucleation and growth.
Using realistic values of surface tension we estimate that the semi-volatile
organics condensation is not enough to grow the new particles to sizes
comparable to those observed. Assuming that biogenic SOA precursors
chemically age and produce extremely low volatility organics (i.e., with an
effective saturation concentration of 10-3 µg m-3)
results in predicted growth rates similar to those measured. In the biogenic
VOC-dominated environment of Hyytiälä, the very low volatility organics
condense onto particles smaller than 3 nm. After this first-stage of growth
for the new particles, the Kelvin effect becomes small and the semi-volatile
organics are the major components controlling the subsequent growth of the
nucleated particles. In an environment with more sulfuric acid and fewer
biogenic VOCs (Finokalia), the condensation of organics plays a complementary
role in the growth of nucleated particles, contributing 45 % of the total
mass of new particles during a day representative of springtime nucleation.
The gas-phase chemical aging of biogenic SOA precursors contributes to the
growth of the nucleated particles because of the extra mass added from the
aging reactions. Laboratory experiments and direct field measurements (Ehn
et al., 2014) support the importance of extremely low volatility VOCs
produced in the gas phase from the oxidation of monoterpenes and other VOCs.
The condensation of organics with zero surface tension resulted in a
predicted growth rate similar to the field measurements, but the zero value
of surface tension is unrealistic. The condensation of organics with zero
surface energy also affects the number concentration of particles. Increasing
surface tension inhibits the growth of new fresh particles and thus results
in a decrease of total particle number concentrations in both locations.
Interestingly, when including aging of bSOA precursors, the new model predicts
daily mean number concentrations similar to those for the no-organics
simulation. The number concentration of particles that can act as CCN
(N100) increases (by 13 % at Finokalia and 25 % at Hyytiälä)
during a typical spring day with nucleation compared to the case in which
the condensation of organics is neglected. The increase in surface tension
and the aging of bSOA precursors do not significantly affect N100
compared to the zero surface energy case.