In this study, the processes behind observed new particle formation
(NPF) events and subsequent organic-dominated particle growth at the Pallas
Atmosphere–Ecosystem Supersite in Northern Finland are explored with the
one-dimensional column trajectory model ADCHEM. The modeled sub-micron
particle mass is up to
Atmospheric particles affect climate by scattering and absorbing solar radiation and by influencing cloud formation and cloud optical properties. Their climate effect depends on both the size and composition of the particles and remains one of the largest uncertainties in global climate predictions (IPCC, 2013). Small-scale, process-based models are important tools for studying different mechanisms behind aerosol formation and growth. It is crucial to understand these processes in order to improve the predictability of next-generation climate and weather forecast models.
In this study, the growth of biogenic secondary organic aerosols (BSOA) over
the boreal forest in northern Europe is modeled and the results are compared
to particle number size distribution measurements. New particle formation
(NPF) events in boreal forests are frequent (Asmi et al., 2011; Kulmala et
al., 2001; Tunved et al., 2003) and the newly formed particles can grow by
condensation to the climate-relevant cloud condensation nuclei (CCN) size
range, which starts at a diameter of
The different ways to model the formation of BSOA found in the literature
reflect the uncertainties of the formation mechanisms and also the often
unknown properties of the condensable vapors. In many studies (e.g.,
Bergström et al., 2012; Farina et al., 2010; Fountoukis et al., 2014;
Hodzic et al., 2009; Lane et al., 2008; Murphy et al., 2012) the vapors are
assumed to be semi-volatile and in equilibrium with the (liquid, well-mixed)
particles, making it possible to model formation of BSOA by simple
gas-particle equilibrium partitioning (Pankow, 1994). In other studies (e.g.,
Scott et al., 2015; Spracklen et al., 2008; Tunved et al., 2010; Westervelt
et al., 2013) the vapors are assumed to be nonvolatile and the irreversible
particle growth is only limited by the collision rate between the vapor
molecules and the particles. Recently, large-scale model studies (Jokinen et
al., 2015; Langmann et al., 2014; Li et al., 2015; Riipinen et al., 2011; Yu,
2011) have included both mechanisms to be able to treat semi-volatile and
nonvolatile vapors, which have yielded a better agreement between model
results and observations. This hybrid SOA formation mechanism is an important
step forward. However, in order to explicitly simulate the size-resolved
condensational growth, models need to take into account how the chemical
composition and curvature (Kelvin) effect vary with the size of the
particles. Smog-chamber studies have often focused on the SOA formation from
semi-volatile organic compounds (SVOCs). Recently the presence of highly
oxidized multifunctional organic molecules (HOMs) in the gas phase has been
shown in both lab and field studies (e.g., Ehn et al., 2014; Jokinen et
al., 2015). Many HOMs can be low-volatility organic compounds (LVOCs;
In this study, we assume the organic vapors to condense dynamically on the
Fuchs-corrected surface area of the particles. The two extremes of particle-phase state are tested; either the particles are assumed to be well-mixed
liquid droplets or they are assumed to be solid-like without diffusion in the
particle phase and with the gas-particle partitioning being controlled by the
composition at the surface. Based on Ehn et al. (2014), a formation pathway
of HOMs by the oxidation of
The aim is to evaluate the potential contribution of HOMs to the activation and growth of new particles over the boreal forest region. The model approach is described in Sect. 2, followed by results and discussion in Sect. 3 and conclusions in Sect. 4.
Mean HYSPLIT trajectories of each new particle formation event, all ending at Pallas. The trajectories start 7 days backward in time before they reach the measurement station.
ADCHEM was used to model the concentrations of gases and particles along
air-mass trajectories ending at the Pallas Atmosphere–Ecosystem Supersite
(67.97
Based on the particle number size distribution data measured at Pallas
between 2005 and 2010, days with NPF events suitable for modeling SOA
formation were selected for detailed analysis. This included days with strong
new particle formation and subsequent growth of the new particle mode for at
least 12
Information on land use along the trajectories was retrieved from the Global
Land Cover Map for the Year 2000, GLC2000 database, European Commission Joint
Research Centre (
All emissions were added at each model time step to the model layer closest to surface, where they were assumed to be instantaneously well mixed within this layer.
Anthropogenic emissions of
Biogenic emissions (
LPJ-GUESS was run with the same meteorological data as used for determining the air-mass trajectories (GDAS; Rolph, 2016) using 3-hourly data for 2005–2010, preceded by a spinup of 500 years to establish the vegetation and soil pools. Photosynthesis production and emissions of isoprene and monoterpenes were computed at the 3-hourly resolution of the GDAS data using air temperature and radiation, resulting in diurnal variations of the plants' transpirational demand and water stress. The maximum photosynthetic capacity along with water and leaf nitrogen content varied daily, following the daily averages of GDAS data. Land use was prescribed at the level of 2005 following Ahlström et al. (2012).
Primary particle emissions of wind-generated marine aerosol as well as from ship and road traffic were included.
The primary marine aerosol production was estimated when the air-mass trajectories passed over ocean (determined by the land-use map from GLC2000) based on a parameterization from Mårtensson et al. (2003), with the use of wind-speed data from GDAS. The particles were assumed to be composed of NaCl and organic material based on the measurements and analysis of marine aerosol particles from Mace Head in Ireland during high biological activity (O'Dowd et al., 2004).
The emission of particles from ship and road traffic were estimated based on
the
ADCHEM can be used as a two-, one- or zero-dimensional model to simulate the
aging of an air mass along a trajectory (Hermansson et al., 2014; Roldin et
al., 2011a, b). This section will focus on the modifications done to the
model; for a detailed description of the model the reader is referred to
Roldin et al. (2011a). In this study ADCHEM was used as a one-dimensional
column model that solves the atmospheric diffusion equation in the vertical
direction. The model included 20 vertical grid cells with a linear grid
resolution of 100
The gas-phase chemistry was solved using the Kinetic PreProcessor (KPP)
(Damian et al., 2002) with selected organic and inorganic reactions from the
Master Chemical Mechanism (MCM) version 3.3 (Jenkin et al., 1997; Saunders et
al., 2003) and with spectral irradiance modeled with the radiative transfer
model described in Roldin et al. (2011a). Table S1 lists the gas-phase
precursors included in the chemistry module. The two monoterpenes
For
The aerosol dynamics module in ADCHEM considers new particle formation,
Brownian coagulation, dry and wet deposition and condensation/evaporation.
The changes in the particle number size distribution due to condensation,
evaporation or coagulation were modeled using a full-stationary size grid
(Jacobson, 2005) consisting of 100 size bins between 1.5
The nucleation rate (
This value of
As an alternative to Eq. (2) the model was also run with kinetic
Organic compounds with a pure liquid saturation vapor pressure (
The HOMs are probably very reactive in the particle phase and could therefore
possibly be considered to be effectively nonvolatile despite their
surprisingly high pure liquid saturation vapor pressures (Kurtén et
al., 2016; Zhang et al., 2015). We evaluated the potential impact of
irreversible reactive uptake of HOMs by performing simulations where the
ADCHEM includes a detailed particle-phase chemistry module, adopted from the
Aerosol Dynamics gas- and particle-phase chemistry model for laboratory
CHAMber studies (ADCHAM) (Roldin et al., 2014). This module is used to
calculate the particle equilibrium water content, the particle acidity,
nitric acid and hydrochloric acid equilibrium vapor pressures for each
particle size bin and the non-ideal interactions between organic compounds,
water and inorganic ions using the activity coefficient model AIOMFAC (Zuend
et al., 2008, 2011). In this work, we did not simulate the specific
interactions between the organic and inorganic compounds but assumed a
complete phase separation of the inorganic and organic particle phase.
Topping et al. (2013a) concluded that the uncertainties in modeled SOA
formation are far greater because of uncertainties in the organic compound
pure liquid saturation vapor pressures than the omission of phase separation
between organic and inorganic compounds. In line with this, we have
previously shown that while the modeled SOA formation during
ADCHEM can be combined with a kinetic multilayer model for particles (Roldin
et al., 2014) where each particle consists of a surface bulk layer and
several bulk layers. In this study, the particles were either treated as
liquid-like with no mass-transport limitations between the layers or as
solid-like with no diffusion in the particle-phase. In the base-case
simulations all particulate material except the core of the particles formed
from soot particles were treated as liquid-like. The solid-like particles
were represented with three layers (a monolayer thick surface layer of
0.7
The initial particle size distribution was assumed to be a typical distribution found in clean maritime air (Seinfeld and Pandis, 2006) where 90 % of the dry particle molar volume had the same chemical composition as the primary marine aerosols in Sect. 2.2.2 and the remaining dry volume consisted of ammonium sulfate.
The initial gas concentrations of
Sensitivity tests were done to investigate the impact of the selected NPF mechanism (Eqs. 2 or 3) and how the growth of the particles was affected by the volatility of the HOMs and the SOA phase state of the particles. Table 1 lists the properties of the base-case simulation together with those of the sensitivity tests.
This section presents the median characteristics of the modeled particle number concentration compared to the measured concentrations at Pallas. The results from the sensitivity tests of the model mentioned in Sect. 2.5 will also be presented. First, however, model results from a typical day of observed new particle formation event are discussed.
Figure 2 shows the modeled (base-case simulation) and measured particle
number size distribution at Pallas on 5 July 2006. At the beginning of the
new particle formation event, around 09:00 UTC (11:00 local standard time),
almost 90 % of the modeled particle volume in the nucleation mode
consists of HOMs, the remaining volume largely consists of organic oxidation
products from the MCMv3.3 chemistry scheme and sulfate (Fig. S4a).
Nine hours later that day (Fig. S4b) the particles originating from the NPF
event form a new Aitken mode with a geometric mean diameter of
Different assumptions for the different model scenarios tested in this study.
The modeled particles are assumed to be liquid and the vapor
pressures of the HOMs are estimated with SIMPOL. Measured (red lines) and
modeled (blue lines) median number size distributions at
In Fig. 3a–d the observed and modeled (base-case scenario) median particle
number size distributions for all chosen trajectories are presented together
with their respective 25 and 75 percentiles. The newly formed particles reach
the DMPS detection limit size of 7
While the median GMD and the concentration of the growing particles the day
after the NPF events are underestimated, the model overpredicts the total
number of particles larger than 7
Median number of particles above 7
This might be caused by a too-fast initial growth of the newly formed
particles (1.5–7
The concentration of particles larger than 50
Median number of particles above 50
Modeled median vertical profiles of the particle number
concentrations of particles larger than
Mean mass fractions of each compound type that contributes to the growth of the particles during all chosen new particle formation events from the base-case simulations (from 06:00 UTC the morning of the event to 06:00 UTC the following day).
Figure 6 shows the modeled median vertical concentration profiles of
Figure 7 shows the mean mass fraction of each compound type that contributes
to the growth during all chosen NPF events, from roughly the start time of
the events (06:00 UTC) until the morning the next day (06:00 UTC). The
growth of the particles is dominated by HOMs; the base-case simulation
(Fig. 7) and the simulation with nonvolatile HOMs (Fig. S8b) both give HOM
mass fractions of
Due to the dominance of HOMs, the
Tröstl et al. (2016) showed that in order to explain the observed growth
rates of particles in the full size range between
Modeled mean volatility distribution of SOA components at Pallas
at 00:00 UTC. The gray bars are the sum of all oxidized organic compounds in
the gas phase with
We also evaluated the impact of the SOA phase by running the model as the
base-case model run but with solid-like SOA particles instead of liquid. The
differences between the base-case model runs and these simulations are minor
(Fig. S11). One of the reasons for this is that the main fraction of the SOA
is formed by condensation of LVOCs (Fig. 8). If a dominating fraction of the
SOA instead were SVOCs, the SOA phase state would most likely have a
larger impact on the model results (see, e.g., Zaveri et al., 2014). The most
notable difference in our model results is that the fraction of nitrate is
higher for particle sizes around 500
Finally, to test the influence of the nucleation rate on particle growth, a
sensitivity test was done where kinetic
During recent years the HOM formation from endocyclic
monoterpenes has been studied in laboratory and field environments (e.g., Ehn
et al., 2014; Jokinen et al., 2015). In this study we evaluated the
importance of HOM formation from monoterpene autoxidation in a boreal
environment. The modeled HOM formation rate is high enough to give sufficient
condensable vapors to explain or even slightly overestimate the growth of the
newly formed particles between 1.5 and
The modeled SOA mass formation was dominated by condensation of HOMs. However, the estimation of the vapor pressures of HOMs is very uncertain. A recent study by Kurtén et al. (2016) suggests that the vapor pressures might be higher than previously thought and that the contribution of HOMs in the particle phase might be due to rapid reactions in the particle phase. We performed a sensitivity test where the vapor pressures of the HOMs were in line with values in Kurtén et al. (2016) and found that the model then seemed to explain the initial growth of the particles better than in the simulation with lower vapor pressures.
The growth of the particles was found to be independent on the phase state of the particles; the phase state might, however, be of importance when the fraction of semi-volatile particulate matter is higher. In these cases, enrichment of low-volatility organic compounds at the particle surface might act as a protective shield against evaporation of SVOCs, ammonia and nitric acid.
Supplementary data are available at
The authors declare that they have no conflict of interest.
This work was carried out with the support by Nordic Center of Excellence programs CRAICC (Cryosphere–Atmosphere Interactions in a Changing Arctic Climate) and eSTICC (eScience tools for investigating Climate Change in Northern High Latitudes), the European Union's Horizon 2020 research and innovation programme under grant agreement no. 654109, the European Research Council (grant 638703-COALA), the Swedish Strategic Research Program MERGE, Modeling the Regional and Global Earth System, the Lund Centre for studies of Carbon Cycle and Climate Interaction (LUCCI), the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning FORMAS (project no. 2014-1445) and the Academy of Finland Center of Excellence program (project no. 272041).
The authors would like to thank Fredrik Söderberg at the Centre for Environmental and Climate Research at Lund University for providing help to set up the model at the high-performance computing cluster available at the Centre for Scientific and Technical Computing at Lund University (Lunarc) and Finnish Meteorological Institute for providing the measurement data at Pallas. Edited by: H. Grothe Reviewed by: two anonymous referees