MODELLING THE GROWTH OF NANOSIZED PARTICLES BASED ON AMBIENT ORGANIC
VAPOUR CONCENTRATIONS
A. HEITTO1, C. MOHR2, F. LOPEZ-HILFIKER3, A. LUTZ4, T. JOKINEN5, M.P. RISSANEN5,
R.L. MAULDIN III5,6, U. MAKKONEN6, M. SIPILÄ5, M. EHN5, M. HALLQUIST4, T.
PETÄJÄ5, J. A. THORNTON3 and T. YLI-JUUTI1
1
Department of Applied Physics, University of Eastern Finland, 70211 Kuopio, Finland
2
Institute of Meteorology and Climate, Atmospheric Aerosol Research, Karlsruhe Institute of Technology
(KIT), Karlsruhe, Germany
3
Department of Atmospheric Sciences, University of Washington, WA 98195, Seattle, USA
4
Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
5
Department of Physics, University of Helsinki, 00014, Helsinki, Finland
6
Institute for Arctic and Alpine Research, University of Colorado, CO 80309, Boulder, USA
7
Finnish Meteorological Institute, 00880 Helsinki, Finland
Keywords: NANOPARTICLE, GROWTH, ORGANIC AEROSOL, SOA
INTRODUCTION
Under certain conditions new particles can be formed in the atmosphere from ambient gases. These
newly formed particles are very small in the beginning, only nanometers in diameter, but later on, in
favorable conditions, they can grow to climatic relevant sizes. However, the mechanisms related to this
growth are still inadequately known.
We studied the growth of newly formed nanosized particles in the atmosphere based on measurements
and by using the particle growth model MABNAG. Particle growth was modelled using measurements of
organic vapours at Hyytiälä measurement station in spring 2014 during new particle formation (NPF)
events and the model results were compared to the measured evolution of particle size distribution.
METHODS
MABNAG (Model for acid base chemistry in nanoparticle growth) is a monodisperse particle population
growth model, in which the condensation dynamics is combined with particle’s internal acid-base
chemistry (Yli-Juuti et al., 2013). The gas phase vapor concentrations and equilibrium vapor
concentrations were used to determine the mass flux between gas and particle phase.
The condensing compounds in the model were water, ammonia, sulfuric acid and a number of organic
compounds. The inputs for the growth model were the ambient gas phase concentrations of the
condensing compounds. Sulfuric acid concentration was measured with HOxROx-CI-APiTOF (Mauldin
et al., 2017 (in preparation)), ammonia concentrations with MARGA (Makkonen et al., 2012) and
concentrations of the organic compounds with FIGAERO-HRToF-CIMS (from now on CIMS) (LopezHilfiker et al., 2014; Mohr et al., 2017, submitted). In addition, the meteorological data (relative
humidity, temperature and pressure) were used as input for the model, and simulated growth was
compared to particle number size distributions measured with Differential Mobility Particle Sizer
(DMPS).
The organic compounds were presented in the model with either five or nine model compounds,
depending on the simulation, by grouping the compounds measured by CIMS. One group was composed
of the compounds identified as dimers (Mohr et al., 2017, submitted) and the rest of the organics were
grouped based on their saturation vapor concentrations (C*) using the volatility basis set (VBS) approach
by Donahue et al. (2006). The saturation vapor concentrations (C*) for each of the compounds were
calculated using the method introduced by Donahue et al. (2011) with temperature dependence presented
by Epstein et al. (2010). We used four VBS bins with C* of 10-4, 10-3, 10-2 and 10-1 µg m-3. In each group
we included compounds from range 0.5*10i < C*5*10i µg m-3 (where i = -4, -3, -2, or -1). In addition, all
compounds with lower C* than 10-4 were included in C* = 10-4 µg m-3 bin. The compounds with C*
higher than 10-1 µg m-3 were neglected, since their contribution to the growth was insignificant. The
simulations were performed by either excluding or including the organic compounds that contained
nitrogen. In case nitrogen containing compounds were included these compounds were grouped in a
separate 4-bin VBS and the total number of organic model compounds was nine.
In this study, we made separate simulations using time-dependent or -averaged vapor concentrations. In
both cases, the properties of organic model compounds (molar mass, molar volume, diffusion coefficient)
were calculated using concentration weighted averages over each group. For the case with time-averaged
concentrations, the gas phase concentrations and ambient conditions were averaged over the duration of
each NPF-event. In time-dependent case the input for the model was taken from the measured
concentrations, RH and temperature at each time step. For some NPF events, we did not have data for
ammonia or sulfuric acid concentrations. In these cases we used day-time (8am to 6pm) averages over the
whole measurement period. Sulfuric acid and ammonia accounted for rather small fraction of the growth
according to the model, and, therefore, this assumption is expected to cause relatively small uncertainty
in the results.
The model simulations were initialized with a particle that contained 40 sulfuric acid molecules and
corresponding water and ammonia according to their gas-particle equilibrium constrained by ambient gas
phase observations.
RESULTS
Figure 1 shows the modelled growth of a particle for –average and time-dependent cases together with
the measured particle size distribution for one NPF event. As can be seen from the figure, for 29.4.2014,
both simulation cases overestimated the growth somewhat, as was the case for most of the NPF events in
spring 2014. Most of the particle growth was due to the organic compound with lowest C*, with its mass
fraction varying between the NPF events from 70 to 80% and 50 to 60% in simulations without and with
nitrogen containing compounds, respectively. We performed the model simulations assuming the
organics to be either strong or weak acids, or non-reacting compounds, and the changes between these
simulations were insignificant. Also assumptions on whether the organics were di- or monoacids had an
insignificant effect on the simulated particle growth.
The sensitivity of simulated particle growth to various factors was also tested. The sensitivity of the
simulated particle growth to uncertainties in concentrations of ammonia or sulfuric acid was low, as their
contribution to particle mass increase in general was quite small. The uncertainty in the assumed particle
and compound properties, such as particle density and saturation vapor concentration of the organics, did
have some impact on the predicted particle growth and could impact the quantitative results, although the
qualitative results seem reliable.
Figure 1. Modelled evolution of particle size for time-dependent (green line) and –averaged (blue line)
cases and the measured particle number size distribution on 29.4.2014.
CONCLUSIONS
Applying the vapor concentrations measured by CIMS in a particle growth model and grouping the
organic compounds by VBS approach reproduced the observed growth of particles quite well for spring
2014 in Hyytiälä. The growth was usually somewhat overestimated suggesting that the detected gas
phase organics are abundant enough to explain the particle growth. According to the model, the majority
of the particle mass increase was due to the low-volatile compounds and, consequently, the predicted
growth was not particularly sensitive to uncertainties in the estimated organic vapor pressures. The nitrate
containing compounds were predicted to account for a significant (approximately 27-40% by mass)
fraction of the particle growth.
ACKNOWLEDGEMENTS
This work was supported by the Academy of Finland (no. 272041, 299544).
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