Tropospheric deliquesced particles are characterised by concentrated
non-ideal solutions (“aerosol liquid water” or ALW) that can affect the
occurring multiphase chemistry. However, such non-ideal solution effects
have generally not yet been considered in and investigated by current
complex multiphase chemistry models in an adequate way. Therefore, the
present study aims at accessing the impact of non-ideality on multiphase
chemical processing in concentrated aqueous aerosols. Simulations with the
multiphase chemistry model (SPACCIM-SpactMod) are performed under different
environmental and microphysical conditions with and without a treatment of
non-ideal solutions in order to assess its impact on aqueous-phase chemical
processing.
The present study shows that activity coefficients of inorganic ions are
often below unity under 90 % RH-deliquesced aerosol conditions and that
most uncharged organic compounds exhibit activity coefficient values of
around or even above unity. Due to this behaviour, model studies have
revealed that the inclusion of non-ideality considerably affects the
multiphase chemical processing of transition metal ions (TMIs), oxidants,
and related chemical subsystems such as organic chemistry. In detail, both
the chemical formation and oxidation rates of Fe(II) are substantially
lowered by a factor of 2.8 in the non-ideal base case compared to the ideal
case. The reduced Fe(II) processing in the non-ideal base case, including
lowered chemical rates of the Fenton reaction (-70 %), leads to a reduced
processing of HOx/HOy under deliquesced aerosol conditions.
Consequently, higher multiphase H2O2 concentrations (larger by a
factor of 3.1) and lower aqueous-phase OH concentrations (lower by a factor
of ≈4) are modelled during non-cloud periods. For H2O2,
a comparison of the chemical reaction rates reveals that the most important
sink, the reaction with HSO3-, contributes with a 40 % higher
rate in the non-ideal base case than in the ideal case, leading to more
efficient sulfate formation. On the other hand, the chemical formation rates
of the OH radical are about 50 % lower in the non-ideal base case than in
the ideal case, leading to lower degradation rates of organic aerosol
components. Thus, considering non-ideality influences the chemical
processing and the concentrations of organic compounds under deliquesced
particle conditions in a compound-specific manner. For example, the reduced
oxidation budget under deliquesced particle conditions leads to both
increased and decreased concentration levels, e.g. of important
C2/C3 carboxylic acids. For oxalic acid, the present study
demonstrates that the non-ideality treatment enables more realistic
predictions of high oxalate concentrations than observed under ambient
highly polluted conditions. Furthermore, the simulations imply that
lower humidity conditions, i.e. more concentrated solutions, might promote
higher oxalic acid concentration levels in aqueous aerosols due to
differently affected formation and degradation processes.
Introduction
Aerosol particles represent a more or less omnipresent multiphase
compartment within the troposphere, which generally comprises complex
mixtures containing different organic and inorganic compounds including
water (see e.g. Saxena and Hildemann, 1996; Pöschl, 2005; Hallquist
et al., 2009, and references therein). Aerosol properties and their impacts
largely depend on chemical composition, size, and phase. Due to the various
important impacts of aerosols on atmospheric chemistry (Andreae and
Crutzen, 1997; Ravishankara, 1997), air pollution (Akimoto, 2003; von
Schneidemesser et al., 2015), biosphere (Adriano and Johnson, 1989),
climate (Charlson et al., 1992; Lohmann and Feichter, 2005; Boucher et
al., 2013; Myhre et al., 2013), and public health (Holgate, 1999;
Brunekreef and Holgate, 2002; Lelieveld et al., 2015), it is a key challenge
to understand how aerosol particles are physically and particularly
chemically processed in the tropospheric multiphase system in order to
finally clarify their global importance and impacts.
Tropospheric aerosol particles are a complex multiphase and multi-component
environment, in which a variety of physical and chemical processes can alter
the physical and chemical composition of the troposphere, potentially on a
global scale. With regard to aerosol particles, chemical reactions can occur
heterogeneously at the surface and homogenously in a bulk of organic and
aqueous aerosol particles (Ravishankara, 1997; George et al., 2015;
Herrmann et al., 2015). Through interaction with ambient water vapour,
aerosol particles can be deliquesced, forming an aqueous aerosol phase
characterised by highly concentrated solutions with rather low ALW (“aerosol liquid water”) contents
between 3×10-7 and 1×10-3 g m-3 (Herrmann et al., 2015). Chemical
bulk processes in the deliquesced aerosol phase are expected to be of
importance (Ervens et al., 2011; Tilgner et al., 2013; Herrmann et al.,
2015). Aqueous-phase processes are known to be very efficient in occurring
on short timescales and in producing secondary compounds, i.e. contributing
to secondary aerosol mass, e.g. aqSOA (Tilgner and Herrmann, 2010; Ervens
et al., 2011; McNeill et al., 2012a; Tilgner et al., 2013; Ervens, 2015;
Herrmann et al., 2015). Additionally, chemical processes in aqueous aerosols
can alter the composition of the surrounding gas phase (Tilgner
et al., 2013). However, compared to processes in well-diluted environments
such as the tropospheric gas and cloud phase, chemical processes in highly
concentrated non-ideal solutions of deliquesced aerosol particles are much
less investigated.
To assess the role of multiphase chemical processes related to aerosol
particles and cloud droplets, a variety of complex multiphase chemistry
mechanisms and multiphase models were developed and applied in the past (see
e.g. Sander and Crutzen, 1996; Vogt et al., 1996, 1999;
von Glasow et al., 2002a, b; Deguillaume et al., 2004; Ervens et al., 2004, 2011; Lim et al., 2005; Barth, 2006; Pechtl et al., 2007; Tilgner and
Herrmann, 2010; Ervens, 2015; Hoffmann et al., 2016, 2018; Mouchel-Vallon et al., 2017; Rose et al.,
2018). Many studies focused on the role of chemical aqueous-phase
processes mainly involving cloud droplets and partly deliquesced particles.
Model studies dealing with chemical processes in deliquesced particles were
often focused on marine particles (see e.g. Vogt et al., 1996, 1999; von Glasow and Sander, 2001; von Glasow et al., 2002a, b, 2004; Pechtl et al., 2007; Bräuer et al., 2013;
Hoffmann et al., 2016) and to a minor extent on continental particles
(Tilgner and Herrmann, 2010; McNeill et al., 2012b; Tilgner et al., 2013;
Guo et al., 2014). Such studies often revealed the potential role of
deliquesced particles to act as a reactive aqueous chemical environment in
the troposphere (see e.g. Tilgner et al., 2013). However, the
treatment of particle-phase chemistry in complex chemistry box models is
mostly approximated by dilute electrolyte solution, neglecting non-ideal
solution effects. However, because of their very low ALW contents,
deliquesced aerosol solutions are often characterised by quite high ionic
strengths of 2–45 mol L-1 (see e.g.
Herrmann et al., 2015, and references therein).
Therefore, highly concentrated solutions of typical deliquesced particles
cannot be treated as ideal solutions anymore, where intermolecular
interaction forces between the non-solvent molecules are almost unimportant.
Laboratory experiments (see e.g. Cappa et al., 2008) have
also confirmed that mixtures of aerosol components which are solids as pure
substances become liquid-like and exhibit non-ideal behaviour.
In highly concentrated solutions, dissolved electrolytes and polar
non-electrolytes are located very close to each other, causing electrostatic
forces or other physical interactions. These intermolecular forces are able
to influence both the phase transfer processes of a compound and its
affinity to undergo chemical reactions. Hence, an adequate treatment of
non-ideality is definitely needed for simulating chemical processes in
deliquesced particles. Thermodynamic non-ideality in a specific
multicomponent mixture, caused by all-molecular interactions, can be
represented by activity coefficients of the different components.
Consequently, suitable chemistry models have to apply activities instead of
concentrations in multiphase chemistry models. For the sake of completeness,
it is noted that besides the thermodynamic non-ideality, ionic strength
effects of reactions also play a role in highly concentrated solutions (see
Herrmann et al., 2015, for further details).
In the past, several approaches have been developed for the computation of
required activity coefficients (see e.g. Pitzer, 1991; Li et al., 1994;
Prausnitz et al., 1998; Yan et al., 1999; Ming and Russell, 2002;
Raatikainen and Laaksonen, 2005; Zaveri et al., 2005a, b; Erdakos et al., 2006a, b; Clegg et al.,
2008; Zuend et al., 2008, 2011). At the same time, several
authors attempted to develop these activity coefficient models to simulate
aerosol thermodynamic equilibrium at variable complexity (e.g. AIM,
Clegg et al., 1998a, b; GFEMIN, Ansari and Pandis, 1999;
ISORROPIA and ISORROPIA II; Nenes et al., 1998; Fountoukis and Nenes,
2007, EQUISOLV II; Jacobson et al., 1996;
Jacobson, 1997; MESA, Zaveri et al., 2005a;
UHAERO, Amundson et al., 2006, 2007). However, complex
multiphase chemistry models dealing with deliquesced particles usually
neglect or only roughly estimate the effect of solution non-ideality on
chemical processing (see e.g. Vogt et al., 1999; von Glasow et al., 2002a, b; Tilgner and Herrmann, 2010; Bräuer et al., 2013; Mao
et al., 2013; Tilgner et al., 2013; Guo et al., 2014). Therefore,
detailed studies characterising the effect of non-ideal solutions on
multiphase chemistry, e.g. in tropospheric deliquesced particles, are still
lacking. For this reason, during the last years considerable effort has been
devoted to developing kinetic model frameworks for the modelling of
processes in multicomponent atmospheric particles, which include both a
detailed description of organic and inorganic multiphase chemistry as well
as detailed thermodynamic description of its non-ideal behaviour (see
Shrivastava et al., 2011; Rusumdar et al., 2016).
In this context, the SPACCIM model framework (Wolke et al., 2005)
was advanced by implementing a complex activity coefficient calculation
module. The extended model approach of SPACCIM, including the treatment of
non-ideality, is described in the companion paper (see
Rusumdar et al., 2016). In the past, SPACCIM has been
successfully applied in several chemical process model studies using the
complex multiphase mechanism CAPRAM (Herrmann et al., 2005; Tilgner and
Herrmann, 2010; Bräuer et al., 2013; Tilgner et al., 2013), focusing on
both chemical and microphysical processes in cloud droplets and deliquesced
particles, assuming ideal solution conditions. In the companion paper, the
considered module, SpactMod (Rusumdar et al., 2016), was
tested against other activity coefficient modules and compared to the
measurement data, and showed its applicability within the model
framework. Consequently, the present follow-up study aims at investigating
and, finally, assessing the impact of non-ideality on aqueous-phase chemical
processing in tropospheric deliquesced particles. Overall, the treatment
described in Rusumdar et al. (2016) and applied here
allows the application of CAPRAM for ALW chemistry
with its full scope of detailed chemistry.
This paper is split into four sections. Section 2 outlines the multiphase model framework SPACCIM-SpactMod and the applied multiphase chemical
mechanism along with performed simulations. In Sect. 3, the modelled
activity coefficients of inorganic and organic compounds under different
environmental conditions are discussed separately. Subsequently, the
modelled results are described and discussed, including the differences
between the simulations, considering multiphase chemistry as ideal and
non-ideal solutions for key chemical organic and inorganic subsystems.
Finally, the main findings of the study are summarised in Sect. 4 and an
outlook of future model developments and investigations is given.
Model and mechanism descriptionMultiphase chemistry model SPACCIM-SpactMod
In the present study, the extended version of the multiphase chemistry model SPACCIM
(Wolke et al., 2005), including the considered activity
coefficient module (SpactMod, Rusumdar et al., 2016)
entitled SPACCIM-SpactMod in the following, has been applied to
investigate the influence of the treatment of non-ideality on multiphase
chemistry.
To put it briefly, SPACCIM is an air parcel model combining a complex
size-resolved multiphase chemistry model and a cloud microphysical model.
The interaction between both models is implemented by a coupling scheme,
enabling both models to run separately as far as possible. Changes in the
chemical aerosol composition due to gas scavenging and chemical reactions
have a feedback on the microphysical processes and properties. The
multiphase chemical model uses a high-order implicit time integration
scheme, which exploits the special sparse structure of the model equations
(Wolke and Knoth, 2002). The microphysical model applied in the SPACCIM
model framework is based on the work of Simmel and Wurzler (2006) and
Simmel et al. (2005). In the microphysical model, the growth and shrinking
processes of aerosols by water vapour diffusion as well as nucleation and
growth/evaporation of cloud droplets and other microphysical processes, such
as impaction of aerosol particles and collision/coalescence of droplets, are
described explicitly. Based on the Köhler theory (Köhler,
1936), the dynamic growth rate in the condensation/evaporation process and
the droplet activation is implemented. Moreover, cloud droplet formation,
evolution, and evaporation are implemented using one-dimensional sectional
microphysics, considering deliquesced particles and droplets, respectively.
Due to the focus of present model studies on the effects of non-ideality on
multiphase chemistry, microphysical processes, such as impaction of aerosol
particles, collision/coalescence of droplets, and precipitation, are not
covered in the model of the present study. Overall, the complex model
framework enables detailed investigations of multiphase chemical processing
of gases, deliquesced particles, and cloud droplets. Further details about
the SPACCIM model are given elsewhere in the literature (see
Sehili et al., 2005; Wolke et al., 2005, and
references therein).
In its latest version, SPACCIM-SpactMod additionally considers the treatment
of non-ideality. In the incorporated activity coefficient module SpactMod
(Rusumdar et al., 2016), non-ideality is treated with
the approach by Zuend et al. (2008, 2011)
applied in the AIOMFAC model (Aerosol Inorganic–Organic Mixtures Functional
groups Activity Coefficients, http://www.aiomfac.caltech.edu/index.html, last access: 6 May 2019).
This model is a thermodynamic group-contribution model specifically
developed to describe typical tropospheric aerosol compositions. The
group-contribution concept treats organic molecules as structures composed
of different functional groups. This approach allows for the representation
of thousands of different organic compounds using a relatively small number
of functional groups. The SpactMod module is based on AIOMFAC and combines a
Pitzer-like model approach with a UNIFAC model to predict activity
coefficients of mixed organic–inorganic systems. The activity coefficient
model approach considers both ion interactions and interactions of organic
compounds.
The non-ideality of a thermodynamic system is characterised by excess Gibbs
energy, which is expressed as the sum of long-range (LR), middle-range (MR),
and short-range (SR) contributions. The LR interactions are described by an
extended Debye–Hückel term. The MR part represents the
effects of interactions involving ions and permanent or induced dipoles, and
it contains most of the adjustable parameters to describe concentrated
aqueous electrolyte solutions and organic–inorganic mixtures. The SR term
is calculated through a modified UNIFAC approach (Fredenslund
et al., 1975) using the revised parameter set of
Hansen et al. (1991). Modifications of the
UNIFAC model part within SpactMod further the introduction of inorganic ions
in order to account for their effects on the entropy and enthalpy of mixing
apart from their charge-related interactions. The original SpactMod
(Rusumdar et al., 2016) mainly applies the interaction
parameter data set of Zuend et al. (2008). In order to treat
various aerosol constituents, additional parameters were included from the
modified LIFAC approach by Kiepe et al. (2006),
which can be rewritten in the AIOMFAC formalism. For the present study, the
module has been updated and extended based on Zuend et al. (2011).
Overall, SpactMod ensures a reliable calculation of activity coefficients
for organic-electrolyte mixtures from diluted aqueous solutions to mixtures
of high ionic strength composed of various ions and organic compounds with
several functional groups at each time step. A detailed description of
SpactMod and its integration into SPACCIM is given in a companion paper
(Rusumdar et al., 2016). Lastly, it has to be noted that
the current version of SpactMod does not consider the latest development
stage of the AIOMFAC model (Ganbavale et al., 2015; Gervasi et al.,
2020). Nevertheless, it has also been observed that SPACCIM-SpactMod allows
for model simulations with and without consideration of non-ideality by
treating molarities as concentrations and activities, respectively.
Therefore, the present implementation of SPACCIM-SpactMod allows for
detailed studies investigating the importance of non-ideality effects on the
multiphase chemistry in deliquesced particles for the first time.
Multiphase chemistry mechanism
For current model applications, the existing aqueous-phase mechanism CAPRAM
3.0i (Herrmann et al., 2005; Tilgner and Herrmann, 2010; Tilgner et al.,
2013) and the latest CAPRAM4.0 (Bräuer
et al., 2019) were rather complex. Hence, a condensed chemical mechanism
that still reflects the main chemical processes in tropospheric cloud
droplets and deliquesced particles was extracted from the CAPRAM3.0i
mechanism. The current employed mechanism consists of the inorganic core of
the CAPRAM3.0i (Ervens et al., 2003; Herrmann et al., 2005; Tilgner et
al., 2013) combined with the organic extension of the CAPRAM 3.0red
(Deguillaume et al., 2009) along with a condensed oxidation scheme
of malonic acid and succinic acid based on the CAPRAM 3.0red
(Deguillaume et al., 2009). Overall, it is worth noting that
most of the kinetic reaction parameters considered in CAPRAM are determined
in the laboratory under dilute and closer to ideal conditions rather than
concentrated aqueous aerosol conditions. Thus, effects of important
concentrated solution parameters such as ionic strength have not been
investigated by such studies and are therefore not considered in the current
version of CAPRAM. Once more ionic-strength-dependent reaction rate
constants become available, they have to be considered in future mechanisms.
A schematic illustration of current multiphase mechanisms used in this model
study and the number of considered processes can be found in Fig. S3 in the Supplement.
Briefly, the applied aqueous-phase mechanism with 395 reactions is coupled
with the RACM-MIM2ext gas-phase mechanism (see Tilgner and
Herrmann, 2010, for further details) with about 277 reactions. The uptake
processes of 42 soluble species are included in the mechanism specified
according to the Schwartz approach (Schwartz, 1986) considering
Henry's law solubility, gas-phase diffusion, and mass accommodation
coefficient.
Model simulations
A meteorological scenario has been created for the present simulations, in
which an air parcel moves along a predefined trajectory including four cloud
passages (two daytime (noon) and two nighttime (midnight) clouds) of about 2 h within 58 h of modelling time and a non-cloud deliquesced aerosol
period at 90 % relative humidity (RH) in the base case. The applied
meteorological scenario is depicted schematically in Fig. 1. Additionally, a
sensitivity run considering a lower RH level has been performed at 70 %
RH after the second cloud passage to investigate non-ideality effects under
lower ALW conditions. To access the role of a
non-ideality treatment for modelling multiphase chemistry in concentrated
aqueous aerosols, simulations have been performed both with and without a
treatment of non-ideality. Moreover, model simulations were carried out for
two different environmental scenarios (urban: anthropogenic polluted case,
remote: continental background case) considering summer conditions beginning
at 00:00 on 19 June (45∘ N). The two scenarios use
different initial gas compositions based on Ervens et al. (2003) (see
https://capram.tropos.de/capram24.pdf, last access: 30 August 2019, for details). Applied
physical and chemical aerosol initialisation data of the two environmental
scenarios are provided in the Supplement (see Tables S1 and S2 in the Supplement). For the
simulations, a mono-disperse particle population is assumed, with a dry
radius of 0.2 µm. For the sake of clarity, the acronyms “NIDU / NIDR”
(Non-IDeal-Urban / Remote) are used for simulations considering the treatment
of non-ideality for urban (remote) environmental scenarios and the acronym
“IDU / IDR” (IDeal-Urban / Remote) for the ideal solutions for urban (remote)
environmental scenarios. An overview of the performed model runs including
the acronyms used in this study can be found in Table 1.
List of the microphysical model scenarios and their acronyms used
in this study.
Depiction of the supersaturation along the trajectories of the two
different model scenarios with non-cloud periods at 90 % and 70 %
relative humidity.
Model results and discussions
In this section, SPACCIM-SpactMod simulation results are presented, focusing
on (i) the modelled activity coefficients of key compounds under different
microphysical conditions, (ii) the impact of a non-ideality treatment on
particle acidity and ionic strength, and (iii) the non-ideality impacts on
the multiphase processing of key inorganic and organic compounds.
Modelled activity coefficients
According to the treatment of activities instead of concentrations in the
NID runs, the chemical rates calculated by the model are affected by the
predicted activity coefficients. A comprehensive knowledge of the predicted
activity coefficients is, therefore, indispensable in order to understand
potential differences between model runs with and without a non-ideality
treatment. Thus, the predicted activity coefficients are discussed in the
beginning for the most important inorganic and organic compounds, and later
subsections focus on the non-ideal solution effects on multiphase chemical
processing.
The first model test applications using SPACCIM-SpactMod in the companion
publication (see Rusumdar et al., 2016) have already
briefly addressed the modelled activity coefficients of a few selected
compounds. However, the current paper aims at a more comprehensive
investigation of the predicted activity coefficients. Moreover, for the sake
of clarity, predicted activity coefficients of key inorganic ions and
organic compounds are presented separately in the next two subsections.
Inorganic ions
The predicted activity coefficients of key inorganic ions and water under
deliquesced particle conditions (at 29 h of modelling time) are
tabulated in Table 2 for urban and remote environmental conditions for the
two different RH cases of 70 % and 90 % (70 %-NIDU / 90 %-NIDR),
respectively (see Fig. 1 for details). Additionally, the respective ionic
strength and the water activity of the highly concentrated solutions are
given. As enumerated in Table 2, the predicted activity coefficients are
presented separately for inorganic anions and cations. Table 2 shows that
activity coefficients of inorganic ions are often less than unity, mainly due
to long-range electrostatic forces in highly concentrated solutions.
However, in some cases, activity coefficients of inorganic ions exceed
unity, particularly under lower humidity conditions. Furthermore, Table 2
shows that the 70 %-NIDU / 70 %-NIDR cases are characterised by higher
ionic strengths (21.5 M-1/ 25.8 M-1) compared to the
90 %-NIDU / 90 %-NIDR cases (5.2 M-1/ 8.5 M-1).
Predicted activity coefficients of selected ions and water, as well
as the ionic strength in deliquesced particles for the 90 % / 70 %-NIDR
and 90 % / 70 %-NIDU simulations at 29 h. Δγ
represents the ratio of the predicted activity coefficients of the 90 %
case and the 70 % case, respectively.
In detail, singly charged anions show activity coefficients within the range
of 0.43–1.03 (0.29–0.66), whereas for the double anion SO42- a
substantially lower value of 0.02 (0.02) is predicted in the base case
(90 %-NIDU (90 %-NIDR)). A similar behaviour including a strong
deviation of the predicted activity coefficients from the ion charge state
is also observed for cations. For singly charged cations, the predicted
activity coefficients are in the range of 0.25–0.38 (0.13–0.30), whereas for
doubly and triply charged cations substantially lower values are calculated,
with 0.07–0.19 (0.02–0.21) and 0.001 (0.001) under urban (remote) conditions
(90 %-NIDU / 90 %-NIDR case), respectively.
Comparison of activity coefficients applied in multiphase aerosol
chemistry models. The data of the present study are based on Table 1.
Cations/Mao et al. (2013)Guo et al. (2014)Sander andPresentAnionsCrutzen (1996)studyX+0.60.8 (based on Na+)0.74/4.3 (NH4+/H+)0.29–1.03Y-0.60.6 (based on HSO42-)0.46–1.9b0.13–0.38X2+≈0.067 (based on Cu2+)0.45 (based on Fe2+)–0.02–0.21Y2-–0.02 (based on SO42-)0.05 (SO42-)0.02X3+0.01a0.3 (based on Fe3+)–0.001
Remark: a In the study of Mao et al. (2013), an activity coefficient γ of 0.01 was applied for Fe3+ based on the lowest estimate from Millero and Woosley (2009). However, the lowest estimate in Millero and Woosley (2009) is ln(γ)=-2; i.e. the γ should be 0.135. The decadic logarithm was used by Mao et al. (2013) instead of the natural logarithm. b Other singly charged ions not listed in the paper are estimated by an activity coefficient of γ=0.44 obtained from Debye–Hückel theory.
A comparison of the predicted ion activity coefficients with a small number
of values applied in former chemical model studies (Sander and Crutzen,
1996; Mao et al., 2013; Guo et al., 2014) shows reasonable agreements for
singly charged anions and cations but larger differences for doubly and
triply charged cations (see Table 3). The above-mentioned studies have
considered constant activity coefficients for some aerosol constituents or
have applied an activity coefficient of a representative compound, i.e.
calculated activity coefficients of a selected ion assuming specific ionic
strength, aerosol liquid water (i.e. RH), etc., conditions. Thus, the studies
applied one single time-constant activity coefficient representative for
singly, doubly, and triply charged anions and cations, ignoring individual
ion interaction properties of single ions and RH dependencies. Table 3 shows
that the considered activity coefficients in former model studies lie in the
range of the coefficients predicted in the present work. For doubly and
triply charged ions, larger deviations are obtainable. Nevertheless, it can
be observed that the activity coefficients span a range of values, and an
approximation with a single representative value can introduce errors into
models. The low value of 0.01 for triply charged ions applied by
Mao et al. (2013) is caused by an incorrect implementation
of the lower limit estimate by Millero and Woosley (2009) (see
remarks in Table 3 for details). The correct value should be 0.13, which is
also much higher than the value predicted in the present study. This
deviation is caused by the missing middle-range interaction parameters of
iron and manganese in the current model implementation. In contrast to these
two transition metal ions, the Cu2+ cation shows a more complex
behaviour when lowering RH and increasing ionic strength. The predicted
Cu2+ cation activity coefficient at 90 % RH is about 0.19 (0.21) in
the urban (remote) case, which is similar to the activity coefficient of
Fe2+ and Mn2+. However, under lower RH conditions, the predicted
Cu2+ cation activity coefficient increases to about 3.7 and 1.75 under
urban and remote conditions, respectively. This increase cannot be observed
for Fe2+ and Mn2+. The behaviour of activity coefficients of metal
ions to be lower than unity with increasing ionic strength down to a certain
minimum followed by an increase to values partly above unity with further
increasing ionic strength is known for many metal ions (see
Millero and Woosley, 2009, for details). However, due to the
missing middle-range interaction parameters of iron and manganese ions in
the current model, only the ion–ion interactions are considered, leading to
an activity coefficient below unity only. The known behaviour with further
increasing ionic strength can only be obtained for Cu2+ and Mg2+.
This limitation needs to be kept in mind because of the potential impacts on
the multiphase chemistry discussed later in Sect. 3.3 and 3.4.
The comparison of the two sensitivity cases (70 %-NIDU / NIDR) reveals that
the predicted activity coefficients show a considerable variation between
the different RH cases (see Table 2). The predicted activity coefficients do not show a linear dependency on relative humidity (RH), water activity coefficient and ionic strength. The water activity coefficient increases by
about 0.04 under urban conditions and decreases by about 0.01 while
decreasing RH under remote conditions. In contrast, it can be seen that the
activity coefficients of both anions and cations are often lowered while
decreasing RH (see Table 2). The highest percentage decreases can be
observed for the triply charged ions Fe3+ and Mn3+, which are
lowered by a factor of about 3/2 in the 70 %-NIDU / NIDR cases compared to
the base cases (90 %-NIDU / NIDR). However, absolute differences are very
low for both of these triply charged ions under urban (remote) conditions.
Still, it has to be kept in mind that, due to the missing middle-range
interaction parameters of iron and manganese ions in the current model, only
the ion–ion interactions are considered, which leads to an overestimation of
this effect. Interestingly, the smallest percentage decrease/increase in the
activity coefficients of the considered singly charged ions has been found
for OH- and H+, showing changes of only 12 % (8 %) and
7 % (13 %) between the 70 %-NIDU(NIDR) and 90 %-NIDU(NIDR) runs.
Furthermore, a comparison of these model runs shows that the activity
coefficients of singly charged halogen ions (except I-) are higher under
lower RH conditions.
In total, activity coefficients of inorganic ions are often considerably
lower than unity, and notable differences exist between urban and remote
aerosols. Thus, it can be expected that the multiphase processing of
inorganic ions is mostly decreased in comparison to model runs assuming
ideal conditions. However, for some ions, the activity coefficients tend to
increase again under more concentrated conditions (lower RH conditions).
Consequently, a RH decrease leads to both lowered and increased activity
coefficients, implying an even more reduced or partly increased chemical
processing where inorganic ions are involved. The obtained differences in
water activity will subsequently affect microphysical parameters, i.e.
radius and saturation ratio. Finally, it needs to be mentioned that due to
the missing salt formation processes in the present chemical mechanism, the
observed values, in particular those for lower RH conditions, might be
slightly biased. Consequently, the present studies have not treated aerosol
conditions below 70 %, where insoluble salt and complex formation
processes will play an increasing role.
Predicted activity coefficients of selected organic compounds in
deliquesced particles for the 90 % / 70 %-NIDR and 90 % / 70 %-NIDU
simulations at 29 h. Δγ represents the ratio of the
predicted activity coefficients of the 90 % case and the 70 % case,
respectively.
Similarly to inorganic ions, the predicted activity coefficient values of key
organic compounds are tabulated in Table 4 under deliquesced particle
conditions (at 29 h of modelling time) for both urban and remote
environmental conditions and the two different RH cases. As can be seen, the
predicted activity coefficients of organic compounds show a quite uneven
pattern overall, with values both below and above unity, depending on the
functional subgroups of the corresponding compound and the modelled
environmental as well as RH conditions. The predicted activity coefficients
were observed to be quite variable even though they are chemically very
similar and differ only within the functional subgroups (e.g. CH2, OH,
and/or C(O)OH).
For organic compounds with one or more alcohol functionalities (i.e. without
other functionalities), the calculated activity coefficients are usually
larger than unity under remote conditions. Exceptions are methanol and
formaldehyde, which partly show values slightly below unity.
Organic compounds such as hydrated glycolaldehyde and glyoxal, including
more than three OH functionalities, show activity coefficients of about
1.1–3.1 and 1.1–2.9, respectively, in the two RH cases
(90 %-NIDU / 70 %-NIDU). The higher activity coefficients are calculated
in the 70 % RH runs. Table 4 shows that activity coefficients for mono
alcohols and gem-diols are mostly lower under urban conditions than under
remote conditions. Moreover, activity coefficients for mono alcohols and
gem-diols generally increase while decreasing RH under urban and remote
conditions, respectively.
For carbonyl compounds, the behaviour of the activity coefficients of the
different compounds is more even. For aldehydes (without other substituents,
except HCHO) and substituted carbonyl compounds (see Table 4), activity
coefficients are modelled above unity, whereas activity coefficients
increase with an increasing carbon chain length. Similarly to alcohols,
activity coefficients modelled for aldehydes increase with a decreasing RH.
Activity coefficients of monocarboxylic acids, which are often present in
deliquesced particles, are less than unity for dissociated acid anions and
in most cases around unity for undissociated acids. Only carboxylic acids
with further substituents and a longer non-polar carbon chain (e.g. pyruvic
and butyric acid) show higher activity coefficients, particularly with
decreasing RH. Non-unity activity coefficients for aerosol components have
previously been inferred from other measurements (Jang et
al., 1997; Mukherji et al., 1997). Interestingly, the predicted activity
coefficients of smaller protonated and deprotonated mono acids are partly
quite close. The activity coefficients of dissociated acids are expectedly
lower, but not significantly so. In Table 4, it can further be seen that the
predicted activity coefficients of organic acid anions are largely in the
same range as those of inorganic anions. A similar behaviour can be observed
for dicarboxylic acid anions and their corresponding iron complexes (see
Table 3). Mono anions are characterised by higher activity coefficients than
dianions, whereas undissociated diacids show activity coefficients partly
above unity, particularly when they do not contain many additional
substituents. The values of predicted activity coefficients (see Table 3)
that are less than unity are somehow unexpected, especially because this
will lead to an increased partitioning of these compounds in the particle
phase. As argued by Cappa et al. (2008), the vapour
pressures of individual components show strong, identity-dependent
deviations from ideality (i.e. Raoult's law), with the vapour pressures of
the smaller, more volatile compounds decreasing significantly in the
mixtures. In addition, in their experimental investigations, they found that
the activity coefficients for some of the organic compounds are also less
than unity, as the present model results show. Furthermore, based on the
obtained numerical values, it can be expected that the non-ideal behaviour
of these compounds can modify their gas-particle partitioning.
Overall, the present study shows that activity coefficients of organic
compounds are quite variable and compound-specific. Thus, a quite uneven
pattern is predicted, with values both below and above unity, leading to
both increased and decreased multiphase processing and partitioning of
organic compounds due to the treatment of non-ideal conditions. Resulting
chemical effects of a non-ideal treatment on key organic subsystems are
discussed in detail in the following section.
Other compounds
For the sake of completeness, it should be noted that the non-ideality of
some non-electrolyte compounds, including key tropospheric oxidants such as
OH, NO3, O3, and H2O2, have not yet been considered in
SPACCIM-SpactMod (see Rusumdar et al., 2016, for
details). They are considered with a constant activity coefficient equal to
unity. This is a slight limitation of the current implementation which needs
to be addressed in the future. For highly concentrated salt solutions, it is
known that non-electrolyte compounds can also influence the thermodynamic
equilibrium, although the corresponding interactions will generally be much
weaker than charge interactions. Accordingly, inorganic non-electrolytes can
be treated in principle in the same way as organic non-electrolytes in the
current SPACCIM-SpactMod implementation (Walther, 1997; Rusumdar et al.,
2016). However, this approach requires specific short-range interaction
parameters. When such parameters are available, the SPACCIM-SpactMod
implementation will be improved. However, for many of the considered
non-electrolyte compounds, e.g. radical compounds, such short-range
interaction parameters are not available yet. Thus, other simpler estimation
methods need to be applied. From the literature (see Marini, 2007,
and references therein), it is well known that the logarithm of the activity
coefficient of neutral solutes is a linear function of the effective ionic
strength and the Setchenow (also: Setschenow, Sechenov) coefficient
(Setchenow, 1892). This relation is typically applied to calculate
the activity coefficients of neutral solutes and determine their
salting-in/-out behaviour when the Setchenow coefficient and effective ionic
strength are known (Oelkers and Helgeson, 1991). Unfortunately,
Setchenow parameters are unknown for many chemical compounds. In this case,
available empirical or theoretical prediction methods should be applied to
calculate the Setchenow parameters (see Johnson, 2010; Yu and Yu, 2013,
and references therein).
Moreover, it should be mentioned that radical anions such as
SO4- and Cl2- are also not yet treated by the current
SPACCIM-SpactMod, and so their activity coefficients are set to unity. In the
future, radical anions such as SO4- might be treated similarly to
their comparable non-radical mono anion (HSO4-) in case of missing
interaction parameters. Finally, all of the above-mentioned model
improvements will be part of upcoming SPACCIM-SpactMod studies.
Particle acidity and ionic strength (I)Particle acidity
Particle acidity and ionic strength (I) are important factors for
physico-chemical multiphase chemistry. Thus, the evolution of acidity and
ionic strength throughout the simulation was investigated for cloud and
deliquesced particle conditions. The H+ activity was initialised by
means of a charge balance in SPACCIM. Afterwards, the time evolution of pH
is computed dynamically throughout the simulation time (see
Sehili et al., 2005, for details). Both particle acidity and
ionic strength can be affected by changes in chemical processing and
microphysical conditions, mainly microphysical parameters such as the ALW
content pattern pH and ionic strength. The modelled evolution of pH and
ionic strength of the 90 %-IDU / 90 %-NIDU and 70 %-IDU / 70 %-NIDU
model run throughout the whole simulation time is shown in Fig. 2. The
corresponding remote plot is given in the Supplement (see Fig. S9).
Modelled pH value (a) and ionic strength (I, b) as a
function of simulation time for the urban scenario for the different
simulation cases (90 %-IDU / 90 %-NIDU and 70 %-IDU / 70 %-NIDU).
As shown in Fig. 2, the predicted pH value is initially at around 3 in the
performed base case simulations (90 %-IDU / NIDU). During the first cloud
formation period (daytime cloud), the pH increases from about 2.5 under
deliquesced particle conditions (at 90 % RH) up to 3.8 under in-cloud
conditions (≈100 % RH); i.e. the solution becomes less acidic
under diluted cloud conditions. During the first daytime cloud, the
aqueous-phase oxidation of acid precursors such as SO2 leads to an
acidification of the cloud solution and a pH value of about 3.1 at the end
of the first daytime cloud. Together with cloud evaporation and the
substantial decrease in the ALW, the aerosol pH of the processed aerosol
drops down to about 1.0/0.5 (90 %-IDU / NIDU), which is about 1.5/2.0 lower
than the value before cloud processing. Subsequently, the pH slightly
decreases further in both cases (90 %-IDU / 90 %-NIDU), and the solution
becomes more acidic during the aqueous deliquesced particle periods on the
first day. Afterwards, the value of pH slightly decreases during the
following cloud and deliquesced periods. The simulation results show
substantially lower pH values for the NIDU cases. The predicted pH at the
end of the urban case simulation is about 0.15 and 0.3 for the 90 %-NIDU
and 90 %-IDU model runs, respectively.
A comparison of the two different RH cases (see Fig. 2) shows larger
differences in the predicted pH between the ideal and non-ideal model runs
for the lower RH case. The predicted pHs at the end of the 70 %-NIDU and
70 %-IDU model runs are about 0.05 and 0.9, respectively. This tendency
of increasing deviations from the ideal solution with decreasing relative
humidity has also been observed in experimental and model studies, e.g.
Chameides and Stelson (1992), Fridlind and Jacobson (2000), and von Glasow
and Sander (2001). Nevertheless, experimental investigation of pH in
particles is rather difficult, since the ALW contents are usually too small
for direct pH measurements (Craig et al., 2018).
However, model studies that varied relative humidity were performed mainly
for marine environmental conditions (see Fridlind and
Jacobson, 2000). Similar studies for remote and urban environmental
conditions to compare the present results to are still scarce.
Chameides and Stelson (1992) observed a decrease in sea salt
aerosol pH when decreasing relative humidity in box model simulations.
von Glasow and Sander (2001) argued that the results and the
explanation given by Chameides and Stelson (1992) were shown
to be insufficient by means of effects of activity coefficients, since
microphysical variables also have a certain influence on particle acidity
(see von Glasow and Sander, 2001). Moreover,
Fridlind and Jacobson (2000) applied the EQUISOLV II equilibrium
model in order to analyse the pH of sea salt aerosol for the
data obtained through the Aerosol Characterisation Experiment (ACE1)
campaign. Their results show that the aqueous-phase aerosol particle pH is
less acidic in decreasing relative humidity. Although these results
explained the behaviour of the particle pH of marine aerosol particles,
similar results were achieved in the sensitivity studies for all the
simulations on urban environmental conditions using SPACCIM-SpactMod. During
the last simulation period, the 70 %-NIDU case shows slightly lower pH
values than the 90 %-NIDU case. In the review of
Herrmann et al. (2015), the compiled pH data for
measured urban aerosols show values between -2 and 4. Thus, the predicted
pH values of urban conditions of the present study fit into this data range.
From the examination of the impact of the non-ideality treatment on pH, it
can finally be concluded that the resulting effects on occurring multiphase
chemistry (e.g. due to the impact on dissociation equilibriums) should be
higher with decreasing relative humidity and, vice versa, that the
non-ideality treatment leads to differences in multiphase chemistry, which
has feedbacks on acidity.
Ionic strength
Besides particle acidity, ionic strength (I) is a key parameter in
influencing multiphase chemistry in highly concentrated aqueous solutions.
In Fig. 2, the evolution of ionic strength is illustrated for the
simulations 90 %-IDU / 90 %-NIDU and 70 %-IDU / 70 %-NIDU,
respectively, throughout the whole simulation time. The corresponding plot
for the remote simulations is given in the Supplement (see Fig. S9).
Figure 2 shows that the ionic strength predicted for the 90 %-IDU case
is rather stable, with values from 5 at the beginning to around 3 mol L-1 throughout the simulation time during deliquesced aerosol periods.
Just during the well-diluted cloud periods, ionic strength drops down to
about 2×10-3–8×10-3 mol L-1. Due
to secondary aerosol mass formation (e.g. via in-cloud sulfur(VI)
production), the ionic strength of the in-cloud solution decreases slightly
from cloud to cloud. Interestingly, the ionic strength of the aerosol
solution stays rather constant throughout the simulation. This behaviour can
be explained by two issues. Firstly, connected to the formed soluble
secondary aerosol mass, the predicted ALW increases; i.e. the water fraction
stays almost constant throughout the simulation time. This compensation
mechanism leads to less increased molarities of important ions such as
sulfate. Secondly, ionic strength is buffered in aerosol solutions by the
acidity effect. The acidification, due to chemical processing (e.g. in-cloud
sulfur(VI) formation), affects the dissociations of acids and, thus, the
ratio between singly and doubly charged forms. In the case of sulfur(VI), a
shift towards the mono anion form (HSO4-) is found throughout
the modelling time. This shift lowers ionic strength because of the reduced
charge number. Overall, both issues buffer an ionic strength increase under
deliquesced aerosol conditions. Compared to the ideal base case
(90%-IDU), the 90 %-NIDU case shows slightly higher ionic strength
levels (approximately 2–3 mol L-1 higher during the non-cloud periods).
The higher ionic strength values result partly from higher sulfur(VI)
concentrations formed in the 90 %-NIDU case compared to the 90 %-IDU
case.
In contrast, for the 70 %-IDU / 70 %-NIDU cases, the ionic strength
increases to about 14/22 mol L-1 after the second cloud passage when
the air parcel is under lower relative humidity conditions (70 % RH). The
ideal case (70 %-IDU) always shows somewhat lower ionic strength values
than the 70 %-NIDU one. From the present simulations, it can be
determined that the ambient RH conditions and the ALW are most likely key
impact factors for the ionic strength of the aerosol solution. Finally, the
predicted ionic strengths for urban aerosols are in agreement with the data
range of literature values (7–45 mol L-1) compiled in the review of
Herrmann et al. (2015).
Multiphase processing of key inorganic compounds
In this section, the impact of a non-ideality treatment on key inorganic
compounds is discussed. As key inorganic chemical compounds, such as
sulfur(VI) and transition metals, are present in ionic form, their chemistry
can be strongly affected by non-ideal solution effects. Furthermore, this
leads to affected oxidant levels that are discussed secondly in this
section.
Sulfur chemistry
Since S(VI) is one of the main aerosol components substantially formed in
the atmospheric aqueous phase, the effects of the non-ideality treatment on
S(VI) formation have to be investigated. S(VI) is formed through
aqueous-phase oxidation of sulfur dioxide (SO2) through several
oxidants (see Tilgner et al., 2013, for details). Figure 3 shows
the modelled aqueous-phase S(VI) concentration in µg m(air)-3 as a function of the modelling time for the urban
scenario with and without consideration of non-ideality. As shown in Fig. 3,
S(VI) is mainly effectively produced in cloud droplets. Furthermore, the
production is higher in daytime clouds compared to nighttime clouds.
Additionally, it can be seen that there is a small difference in the formed
S(VI) of the ideal and non-ideal simulations (90 %-IDU / 90 %-NIDU). At
the end of the simulation, the concentration in the 90 %-NIDU case is
about 12 µg m(air)-3 higher than in the 90 %-IDU case
(approximately 20 % more S(VI) production). This result reveals that the
treatment of non-ideality plays a minor role in predicting the multiphase
processing of S(VI) under cloudy conditions, but might be important under
hazy conditions. The observed findings can be explained by a stronger
SO2 oxidation in the NIDU cases, where higher H2O2
concentrations are modelled, leading to higher S(IV) oxidations under daytime
aerosol conditions. However, it should be mentioned that the number of
available aerosol particles under real haze conditions in strongly polluted
areas can be much higher than in the present model study and, thus, the
available uptake interface cloud could be larger there. Consequently, S(VI)
formation would be less restricted by the uptake of SO2 into the acidic
aerosol phase. Due to quite acidic aerosol solution conditions (see Sect. 3.2.1) and a low aerosol surface/ALW volume, the uptake of the weak acid
SO2 is limited. Therefore, the aqueous-phase formation of S(VI) is more
restricted compared to cloud droplets in the present simulations, but would
be higher under hazy conditions (higher aerosol loadings and ALWs). From the
present study, it can be concluded that for simulating S(VI) formation in
polluted continental regions, the cloud phase still represents the most
important formation medium. However, considering non-ideal solution effects
in the chemistry model, higher S(VI) formation rates under deliquesced
aerosol conditions are modelled. This finding implies the possibility of
a stronger contribution under strongly polluted hazy conditions, which
should be investigated in upcoming studies.
Modelled sulfur(VI) aqueous-phase concentration in µg m-3 throughout the modelling time for the different urban simulation
cases (90 %-IDU / 90 %-NIDU and 70 %-IDU / 70 %-NIDU).
Processing of Fe(II)
The time-dependent chemical processing of iron in clouds and deliquesced
particles under non-ideal conditions is a quite important process, because
iron speciation and redox cycling is responsible for several chemical
interactions (Deguillaume et al., 2005; Tilgner et al., 2013).
Deguillaume et al. (2005) argued in their review that there is still a
large uncertainty of aqueous-phase TMI (transition metal ion) chemistry,
since iron speciation is an indicator of atmospheric oxidation and
reduction as well as reactivity of aqueous-phase radical chemistry. However,
uncertainty remains mainly because of the restricted knowledge about the
aqueous particle phase processing of TMIs, such as iron. Tilgner
et al. (2013) have shown that the chemical processing of iron in deliquesced
particles strongly affects other important chemical subsystems, such as HOx
chemistry. Therefore, the present study is aimed at reducing the
uncertainties of multiphase processing of Fe(II) by treating aqueous-phase
aerosol chemistry as non-ideal. In the following, only modelled results were
presented for the urban environmental scenario (IDU / NIDU) due to the higher
concentration levels and influence of iron under these conditions.
Modelled Fe(II) aqueous-phase concentration in ng m-3
throughout the modelling time (a) and corresponding time evolution of
the activity coefficient (b) for the different urban simulation cases
(90 %-IDU / 90 %-NIDU and 70 %-IDU / 70 %-NIDU).
Figure 4 illustrates the aqueous-phase concentration of Fe(II) throughout
the simulation time along with the temporal evolution of corresponding
predicted activity coefficients in the different urban simulations. As can
be seen in Fig. 4, the aqueous-phase concentration is on average a factor of
2.9 higher throughout the whole simulation time for the 90 %-NIDU
simulation compared to the 90 %-IDU model run. Especially under
deliquesced particle phase conditions, the concentrations of Fe(II) are
substantially higher, indicating a lower oxidation of Fe(II) to Fe(III) in
the non-ideal case. The activity coefficient values are less than unity for
the whole modelling time (below 0.1 in non-cloud periods), so the
non-ideality treatment leads to a reduced multiphase processing of Fe(II).
Moreover, Fig. 4 shows that the activity coefficient values tend to have
slightly lower values and higher aerosol concentrations of Fe(II) in the
70 %-NIDU case.
A comparison of the modelled total chemical sink and source rates of both
urban base case model runs (90 %-IDU / 90 %-NIDU) are presented in
Fig. 5. There, the modelled chemical sink and source rates of Fe(II) in the
aqueous phase plotted for the second model of the 90 %-IDU vs.
90 %-NIDU case can be seen. The corresponding plot for the remote cases
is presented in the Supplement (see Fig. S10). Figure 5 shows a
characteristic daytime profile of the redox-cycling rates, which is
interrupted by cloud periods. The comparison reveals both reduced formation
and oxidation rates for the 90 %-NIDU case compared to the 90 %-IDU
simulation. This finding shows that the Fe(II) oxidation is less efficient
when considering non-ideality. Whereas the chemical rates are quite similar
under cloud conditions of the 90 %-IDU / 90 %-NIDU cases, the chemical
rates under deliquesced aerosol conditions are substantially lowered.
Throughout the simulation time, the rates of the 90 %-NIDU case are
approximately a factor of 2.8 lower compared to the 90 %-IDU case.
Figure 5 reveals that Fe2+ interacts mainly with other ions of the
aerosol solution. The reaction of Fe3+ and Cu+ is, for example,
the main source of Fe2+ under deliquesced aerosol conditions. Due to
the calculated activity coefficient of ions (see Table 1), which are
predicted to be less than unity, the calculated reaction rates are lowered
under consideration of non-ideality. Hence, the contribution of this
reaction during the overall processing of Fe2+ is decreased in
aqueous particles. As a result, reduced formation rates, including the rates
of the most important sink processes, such as the Fenton reaction of
Fe2+ and H2O2, are modelled. This leads to a reduced
processing of HOx/HOy (see the following two subsections for
further details).
Modelled chemical sink and source mass rates of Fe(II) in the
aqueous phase in mol m-3 s-1 for the second day of the urban
cases (90 %-IDU / 90 %-NIDU). (a) Ideal solutions (90 %-IDU), (b) non-ideal solutions (90 %-NIDU), and (c) corresponding total rates (sum of
source and sink rates). Only sinks and sources with a contribution larger
than ±3 % are shown.
To summarise, the model simulations imply that the multiphase processing
of Fe(II) in aqueous particles is significantly affected by the treatment of
non-ideality, and the effect depends strongly on the ALW conditions. By
treating aqueous-phase chemistry as non-ideal solutions, considerably
smaller reaction rates of Fe(II) have been observed in deliquesced particles
compared to ideal solution runs. The impact of this lowered processing on
other chemical subsystems, e.g. the changed H2O2 processing in the
aerosol phase, is discussed in the following sections.
Non-ideality effect on the <inline-formula><mml:math id="M255" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">H</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> budget
Because of the substantially reduced Fe(II) cycling, the resulting effects
on the H2O2 budget have been studied. The time-concentration
profiles of gaseous and aqueous H2O2 are plotted in Fig. 6 for
both the 90 %-IDU / 90 %-NIDU and 70 %-IDU / 70 %-NIDU cases.
Figure 6 reveals that due to the consideration of a non-ideality, the
predicted H2O2 concentrations are significantly affected. Figure 6
shows that the predicted gaseous and aqueous concentrations of
H2O2 are higher for the NIDU simulation cases. The comparison of
the two base cases (90 %-IDU / 90 %-NIDU) shows that the modelled
aqueous concentrations of H2O2 are on average a factor of 3.1
larger during the non-cloud periods in the 90 %-NIDU case. A similar
pattern can be observed for the gas-phase concentrations of H2O2.
This finding is interesting because it demonstrates that the treatment of
non-ideality has a potential impact on the multiphase (gas- and aqueous-phase) budget of H2O2. Using an ideal solution treatment, the
gaseous budget of H2O2 would be underpredicted and less
H2O2 would be available for other processes such as aqueous-phase
S(VI) oxidation. The higher H2O2 concentrations can be explained
by chemical rates (see Fig. S6 in the Supplement).
The analysis of the chemical sink and source mass rates of aqueous
H2O2 reveals higher aqueous-phase formation rates (without
considering phase transfer as a source) during the non-cloud periods in the
90 %-NIDU case. The uptake of H2O2 from the gas phase is an
important source of aqueous H2O2 in the 90 %-IDU case, with a
contribution of about 48 %. However, the overall uptake rate of
H2O2 from the gas phase is about 22 % lower in the non-ideal
simulation (90 %-NIDU). This fact is consistent with the 23 % larger
aqueous-phase formation rates in the 90 %-NIDU simulation. Besides the
lower uptake rate from the gas phase, rate analysis shows an increased
contribution of the reaction of HO2 with Cu+ to aqueous
H2O2 formation. The formation rate of H2O2 by reaction
of HO2 with Cu+ is about 32 % larger in the 90 %-NIDU case
than in the ideal simulation and is the most important source, with about
53 %. Moreover, the analysis of the chemical sink rates of aqueous-phase
H2O2 shows that the most important H2O2 sink, the
reaction with HSO3-, contributes more in the 90 %-NIDU case
(40 % higher rates). That is the reason for the higher sulfate formation
in the non-ideal case. On the other hand, the overall rate of the other key
sinks of H2O2, the Fenton reaction with Fe2+, is about
70 % lower in the 90 %-NIDU case than in the 90 %-IDU one. The
reaction rate analysis implies that the contribution of reaction pathways
containing doubly or triply charged ions (e.g. the Fenton reaction of
Fe2+) is reduced and, in contrast, the contribution of reaction
pathways containing singly charged ions (e.g. the reaction of HO2 with
Cu+ and HSO3- oxidation) increases when non-ideality is
considered in the simulation. Overall, it can be concluded that the
treatment of non-ideality generally leads to higher multiphase
H2O2 concentrations and to changes in the rates of different
reaction pathways. Finally, similar results are modelled for the remote
cases (see Fig. S11 in the Supplement), but with smaller differences than in
the urban case.
Modelled gas- and aqueous-phase concentrations of H2O2
throughout the simulation time for the different urban simulation cases
(90 %-IDU / 90 %-NIDU and 70 %-IDU / 70 %-NIDU).
Aqueous-phase processing of OH radicalsAqueous-phase OH concentrations
In Fig. 7, the aqueous-phase concentrations of OH in mol L-1 are
plotted for the urban simulations at 90 % RH (90 %-IDU / 90 %-NIDU)
and 70 % RH (70 %-IDU / 70 %-NIDU). The corresponding plots for the
remote scenario are presented in the Supplement (see Fig. S12). Due to the
strong dependency on both microphysical conditions (e.g. aerosol/cloud water
content) and different chemical sink/source rates, the aqueous-phase OH
radical concentrations show a diurnal profile interrupted by cloud periods.
The modelled OH concentrations in the deliquesced particles are higher than
the concentrations under in-cloud conditions. Comparing the concentration
levels in the deliquesced particles before and after a cloud passage, Fig. 7
shows a reduction after cloud evaporation. This reflects effective in-cloud
oxidations, which affects OH formation pathways after cloud passages. The
reduction after cloud evaporation is more distinct in the non-ideal cases.
Modelled OH aqueous-phase concentrations in mol L-1 throughout
the simulation time for the different urban simulation cases
(90 %-IDU / 90 %-NIDU and 70 %-IDU / 70 %-NIDU).
A comparison of the ideal and non-ideal base case simulations
(90 %-IDU / 90 %-NIDU) shows substantially higher OH concentrations for
the ideal case (90 %-IDU). The ideal base model run (90 %-IDU) is
characterised by higher concentrations, on average a factor of 4 higher,
than the concentrations in the 90 %-NIDU case under non-cloud conditions.
At the end of the 90 %-IDU / 90 %-NIDU simulations, deliquesced
particle-phase concentrations of about 3.0×10-13/4.5×10-14 mol L-1 are modelled, showing a
factor of about 7 higher OH levels under aerosol conditions. On the other
hand, the cloud concentrations in both cases are almost similar. This
behaviour can be explained by the importance of different sources under
deliquesced aerosol and cloud conditions. The in situ sources are strongly
affected by the non-ideality treatment under non-cloud conditions, whereas
its impact under cloud conditions is minor.
Interestingly, OH concentrations of the 70 %-NIDU simulation are partly
higher than the ones modelled in the 90 %-NIDU case. This change in the
concentration pattern can be explained by changes in the reaction rates (see
below). Moreover, the 70 %-NIDU case also shows higher concentrations
during the first half of the second night period than the 90 %-NIDU case.
The modelled concentration is almost 1 order of magnitude higher in the
70 %-NIDU case before the second nighttime cloud (≈47 h),
indicating higher OH formation rates in this model case. This also enables
higher OH oxidations during this night period (see below for details). At
the end of the simulation, the daytime aerosol concentrations of the
70 %-NIDU case are about a factor of 4 higher than in the 90 %-NIDU
case, but still lower than in the two ideal simulations.
Aqueous-phase OH formation and oxidation rates
The significant differences obtained in the ideal and non-ideal
concentration patterns suggest that the activity coefficients of the
reaction partners of OH have a strong influence on the modelled chemical
sinks and sources and, finally, the aqueous-phase concentrations of OH. Even
though the activity coefficients for neutral radicals such as OH are
considered unity in the present model, the influence of non-ideality has
been considered for the computation of reaction rates when the radicals
react with other organic/inorganic compounds. As tabulated in Tables 2 and
4, the activity coefficients of key inorganic and organic OH reaction
partners strongly vary. Subsequently, the chemical mass rates of sink and
source reactions for the OH radical can be increased or decreased depending
on individual activity coefficients. The differences in the aqueous-phase
reaction rates are depicted in Fig. 8.
Modelled chemical sink and source mass rates of OH in the aqueous
phase in mol m-3 s-1 for the second day of modelling time for the
urban scenario for the simulations 90 %-IDU vs. 90 %-NIDU vs.
70 %-NIDU. (a) Ideal solutions (90 %-IDU), (b) non-ideal solutions
(90 %-NIDU), and (c) non-ideal solutions (70 %-NIDU).
In detail, Fig. 8 shows the chemical mass rates of different OH reaction
pathways (sinks/sources) for the simulations 90 %-IDU, 90 %-NIDU, and
70 %-NIDU for the urban scenario throughout the second model day.
Figure 8 shows only minor differences in the chemical rates under cloud
conditions where non-ideality effects are minor and are therefore not
further discussed. Larger differences can be obtained during the deliquesced
aerosol periods. As illustrated in Fig. 8, both the total source and sink
rates are lowered in the deliquesced particle phase when non-ideality is
considered. Further, the peak in the source and sink rates is slightly
delayed towards the later afternoon for the 70%/90%-NIDU simulations
compared to the 90 %-IDU one. Due to the treatment of non-ideality, the
total OH formation rate of the 90 %-NIDU case is reduced by about 52 %
throughout the whole simulation time compared to the 90 %-IDU case and
by a factor of ≈4 in the afternoon peak on the second day (see
Fig. 8). Interestingly, a rate comparison of the 70 %-NIDU and
90 %-NIDU simulations reveals a slightly higher afternoon peak on the
second day and markedly higher rates in the first half of the second
nighttime period in the 70 %-NIDU case. This finding implies higher OH
oxidation rates in aqueous aerosols at nighttime in the more concentrated
case (70 %-NIDU). Overall, the total OH rate in the deliquesced particle
periods of the 70 %-NIDU case is about 8 % higher than in the
90 %-NIDU one. This finding implies that the chemical processing in
deliquesced particles might be more favourable under more concentrated
solutions rather than higher humidity aerosol conditions.
A more detailed look at the specific reaction rates reveals that, in all
simulation cases, the OH production under aqueous particle conditions is
strongly related to the Fenton reaction of Fe2+. This means that
in situ OH production strongly depends on both Fe2+ and H2O2 concentrations. This finding is in agreement with former CAPRAM mechanism
studies (see Tilgner et al., 2013). Additionally, a comparison
of the formation rate pattern of the two non-ideal cases reveals that the
Fenton-like reaction of copper contributes noticeably more to the
aqueous-phase formation of OH in deliquesced particles. This additional
source leads to the slightly higher OH formation rates in the 70 %-NIDU
case than in the 90 %-NIDU one. The stronger contribution of this OH
source can be explained by both the substantially higher activity
coefficient of Cu+ under 70 % RH conditions (see Table 2), leading
to higher chemical rates and the larger H2O2 budget in the
70 %-NIDU case. The latter additionally causes higher formation rates in
the 70 %-NIDU case until the second nighttime cloud. After its
evaporation, the formation rates are lowered as a consequence of the
substantially lowered H2O2 budget due to efficient in-cloud sulfur
oxidation.
As shown in Fig. 8, the OH sinks are also affected in the deliquesced
particle periods due to the treatment of non-ideality. Figure 8 shows a
change in the sink rate pattern between the 90 %-IDU and 70%/90%-NIDU
cases, respectively. In aqueous particles, the relative OH radical loss by
reactions with organic compounds and the gaseous OH uptake is increased and,
on the other hand, the fraction of Cl- interaction with OH is lowered
under 90 %-NIDU conditions compared to the ideal base case. Throughout
the whole simulation, the Cl- interactions with OH contribute about
36 %, 26 %, and 14 % to the OH sink rates during the non-cloud
periods in the 90 %-IDU, 90 %-NIDU, and 70 %-NIDU cases,
respectively. Accordingly, the relative contributions of OH oxidation
reactions of organic compounds, particularly substituted carbonyl compounds
and undissociated organic acids, often increase in the non-ideal cases.
Moreover, a comparison of the two non-ideal simulations, 90 %-NIDU and
70 %-NIDU, shows differences (see Fig. 8) in the sink reactions under
deliquesced particle conditions. In the 70 %-NIDU case, OH reactions of
substituted carbonyl compounds and undissociated organic acids are more
important than in the 90 %-NIDU one. For example, the contribution of the
OH oxidation of pyruvic acid / glycolic acid (undissociated) to the total sink
rates (over 58 h) is increased from 0.7%/2.8%, 0.8%/3.6% to
2.6%/4.7% in the 90 %-IDU, 90 %-NIDU, and 70 %-NIDU cases,
respectively. The raised contribution of substituted organic compounds can
be partly explained by the higher activity coefficients of such compounds
under more concentrated conditions (see Table 4 and Sect. 3.1.2). The raised
degradations of some organic undissociated acids and other substituted
organic compounds leads to an affected concentration pattern of those
compounds (see the following subsection for details).
Overall, the present model investigations reveal that the processing of OH
radicals (formation and loss processes) in aqueous particles is reduced,
considering the treatment of non-ideality of concentrated solutions, mainly
because of the lowered in situ formation of OH. Furthermore, the treatment
of non-ideality leads to differences in the chemical sink and source
contributions of different chemical pathways. However, the results here
strongly depend on the non-ideality effects of transition metal ions and
microphysical conditions (ALW conditions). As discussed above, missing
middle-range interaction parameters of iron and manganese ions in the
current model lead to activity coefficients below unity only for iron and
manganese ions. This limitation may lead to a strong under-prediction of the
Fenton reaction rates and thus to undervalued OH formations and
aqueous-phase oxidation rates. Additionally, performed sensitivity studies,
which have applied the MR interaction parameters of Cu2+ for
Fe2+/Mn2+ (see the Supplement for further details), show a more
active TMI-HOx cycling (see Figs. S15–S17) under deliquesced aerosol
conditions and clearly demonstrate that MR interaction parameters of key
TMIs represent crucial parameters that need to be determined in future
laboratory experiments to improve current model implementations.
Multiphase processing of organic compounds
The aqueous-phase chemical processing of organic compounds is expected to
not only be limited to in-cloud conditions, but also to proceed in aqueous
particles with significant chemical rates (Tilgner and
Herrmann, 2010; Tilgner et al., 2013). In contrast to former studies that
assumed aqueous-phase chemistry as ideal solutions, the present
SPACCIM-SpactMod study is aimed at investigating the effects of a
non-ideality treatment on the organic processing of important organic
C2 and C3 oxidation pathways. The results of the C2 chemistry
are presented in the following and the C3 chemistry results are
presented in the Supplement for the sake of clarity.
As shown in many former model studies (Ervens et al., 2003; Herrmann et
al., 2005; Tilgner and Herrmann, 2010; Ervens et al., 2011), in-cloud
oxidations of C2 carbonyl compounds such as glycolaldehyde and glyoxal
lead to the formation of substituted organic acids (e.g. glyoxylic and
glycolic acid), which can be further oxidised into oxalic acid, contributing
to aqSOA under both cloud and aerosol conditions. However, available studies
within the literature have not yet considered the effects of non-ideality
and therefore have to be deemed incomplete.
Figure 9 shows the modelled aqueous-phase mass concentrations of oxalic acid
and its precursors, glyoxylic and glycolic acid, along with corresponding
activity coefficients vs. simulated time under urban environmental
conditions for the 90 %-IDU / 90 %-NIDU and 70 %-IDU / 70 %-NIDU
simulations, respectively. For the sake of clarity, the sums of dissociated
and undissociated aqueous-phase concentrations of the carboxylic acids are
plotted in Fig. 9.
Modelled aqueous-phase concentrations in ng m(air)-3 and
corresponding activity coefficients for the important C2 oxidation
products, (i) glycolic acid (a, b), (ii) glyoxylic acid (c, d), and (iii) oxalic acid (e, f). The plotted concentrations represent the sum of the
dissociated and undissociated forms of the acids.
As shown in Table 4 and Fig. 9, the activity coefficients of dissociated and
undissociated forms of the above-mentioned acids are different. The
predicted activity coefficients of the undissociated acids are substantially
higher than those of the dissociated forms. These differences in the
activity coefficients of dissociated and undissociated forms of the organic
acids, together with the different oxidant budget, affect the chemical
processing of those acids in deliquesced particles along with the molarity
of the compound under different RH conditions.
The predicted concentration-time profiles presented in Fig. 9 show that the
oxalic acid precursors, glyoxylic and glycolic acid, are effectively
produced under cloud conditions. Furthermore, it can be seen that their
degradation proceeds almost in deliquesced particles. Both findings are in
agreement with former studies (see e.g. Tilgner et al., 2013).
Glycolic and glyoxylic acid
For glycolic and glyoxylic acid, the modelled in-cloud productions in the
90 %-NIDU and 90 %-IDU cases are similar (see Figs. S7 and S8 in the
Supplement). However, due to the incorporation of activity coefficients,
lower degradations in the 90 %-NIDU case than in the 90 %-IDU case are
modelled in aqueous aerosols. Furthermore, the production in the aqueous
particle phase is also not similar. Throughout the whole simulation period,
the deviation increases with the simulation time for 90 %-IDU and
90 %-NIDU.
The modelled concentrations of glycolic acid in the 90 %-NIDU case are
substantially higher, at about 135 ng m-3, at the end of the
simulation time compared to the 90 %-IDU case, at about 40 ng m-3.
A comparison of the two non-ideal cases (90 %-NIDU / 70 %-NIDU) reveals
a faster and longer ongoing degradation of glycolic acid in the deliquesced
particle phase under the lower humidity conditions of the 70 %-NIDU case.
At the end of the simulation, the modelled glycolic acid is, surprisingly,
closer to the modelled 90 %-IDU case than to the 90 %-NIDU one, at
about 50 ng m-3. The modelled glycolic acid concentration pattern
reveals substantially higher degradation levels in the afternoon of the
second day and the first half of the second night period (37–47 h) until
nighttime cloud formation. Therefore, the final glycolic acid concentration
is much lower in the 70 %-NIDU case than in the 90 %-NIDU one. Due to
the considered non-ideality treatment, both formation and degradation rates
are decreased in the non-ideal 90 %-NIDU case. The formation and
degradation rates of glycolic acid in the ideal case are 28 % and 74 %
larger, respectively, throughout the whole simulation time. Moreover, the
comparison of the time-resolved overall sink rates shows 13 % and 71 %
higher formation rates in the 90 %-IDU case under in-cloud and aqueous
aerosol conditions, respectively. Furthermore, the sink rates under aerosol
conditions are about 2 times larger than in the non-ideal base case.
Consequently, the larger sink rates in the ideal case (90%-IDU) reflect
that the resulting glycolic acid concentrations are higher in the
90 %-NIDU case than in the 90 %-IDU one. The lower oxidation of
glycolic acid in the 90 %-NIDU case, particularly in the afternoon, also
leads to a shift in the contributions of oxidants to the overall oxidation
rate. Whereas the overall oxidation rate of glycolic acid via OH is lowered
by 50 % due to the strongly lowered OH budget, the NO3 oxidation
rate of glycolic acid is increased instead, particularly during the
nighttime cloud phase. During that phase, glycolic acid is more present in
its dissociated form, electron transfer reactions are favoured more, and the
concentration budget is higher because of the lowered daytime decay. A
similar picture can be seen for glyoxylic acid. The modelled urban
concentration profiles of glyoxylic acid show the same trend as glycolic
acid. Thus, a discussion of the glyoxylic acid concentration pattern and
rates is omitted in the present study for the sake of clarity.
Oxalic acid
Besides glyoxylic acid and glycolic acid, Fig. 9 also depicts the
concentrations of its key oxidation product, oxalic acid. From the reduced
degradation rate of the substituted mono carboxylic acids, one could expect
lower oxalic acid concentrations under non-ideal conditions (90 %-NIDU).
However, the lowered formation rates of oxalic acid do not lead directly to
lower predicted concentrations of oxalic acid. The treatment of non-ideality
significantly affects the chemical sinks of oxalic acid and the predicted
degradation rates are lower in cases treating non-ideality effects. Thus,
the resulting oxalic acid levels at the end of the simulation are
substantially higher in the 90 %-NIDU case and particularly in the
70 %-NIDU case. In the 90 %-NIDU case, the final concentration is a
factor of about 1.8 higher than in the 90 %-IDU case. The difference in
the predicted oxalic acid mass is even higher in the 70 % cases
(70 %-NIDU / 70 %-IDU). There, the predicted oxalic acid concentration
at the end of the simulation is a factor of 10 higher compared to the model
without non-ideality treatment (70 %-IDU). A similar tendency with higher
predicted oxalic acid concentrations in the non-ideal cases is also observed
in the remote simulations, but at lower concentration levels (see Fig. S13).
The higher Fe(II) concentrations, the reduced chemical processing of iron,
and the modelled activity coefficients of the different oxalic acid anions
and iron–oxalate complex ions in the simulations with non-ideality treatment
lead to a smaller complexation of the diacid. This results in substantially
lower rates of the photochemical decompositions of the iron–oxalate
complexes and thus to higher oxalic acid concentrations in both the
90 %-NIDU and 70 %-NIDU cases. A comparison of the modelled reaction
rates (90 %-IDU / 90 %-NIDU; see Fig. 10) shows, firstly, a distinct
reduction of the formation rates and, secondly, a drastic change in the
decomposition rate pattern.
Modelled chemical sink and source mass rates of oxalic
acid / oxalate in the aqueous phase in mol m-3 s-1 for the second
day of modelling time for the urban scenario for simulations 90 %-IDU
vs. 90 %-NIDU. (a) Ideal solutions (90 %-IDU), (b) non-ideal solutions
(90 %-NIDU), and (c) corresponding total rates. Only sinks and sources with a
contribution larger than ±3 % are presented.
As discussed in Tilgner and Herrmann (2010), the formation of
oxalic acid mostly takes place in the aqueous phase of deliquesced
particles. As shown in Fig. 10, oxalic acid is effectively produced by the
oxidation of glyoxylic acid, predominantly during the day, especially in
deliquesced particles. On the other hand, oxalic acid is also substantially
decomposed via the fast photo-catalytic decay of iron–oxalate complexes.
Consequently, quite low oxalic acid concentrations are predicted in the
ideal base case (90 %-IDU). A comparison of the modelled rate pattern
(90 %-IDU / 90 %-NIDU) in Fig. 10 reveals that the computed overall
formation and degradation reaction mass rates were decreased by factors of 2.5 and 2.9, respectively, for the 90 %-NIDU simulation compared to the
90 %-IDU one. This stronger reduction leads to higher oxalic acid
concentrations. Moreover, the sink rate pattern shows high values during
cloud periods in the non-ideal case (90 %-NIDU) and only small photolytic
decay rates during non-cloud periods. This change in sink pattern is caused
by the non-ideality treatment that lowers the formation of
iron–oxalate complexes. The interaction of the doubly charged oxalate and
the triply charged Fe3+ ion is reduced due to the modelled activity
coefficients of such ions that strongly depend on the charge number. Due to
the high charge numbers of the complexing agents (high LR interactions), the
iron complex formation is less efficient than in the ideal case.
Consequently, efficient photolysis is hampered and less oxalate is
decomposed. In total, the present studies demonstrate that the treatment of
non-ideality can significantly affect multiphase oxidation and the resulting
concentration budget of important C2 carboxylic acids. The non-ideality
treatment also enables more realistic predictions of high oxalate
concentrations, as observed in field campaigns under highly polluted
conditions (van Pinxteren et al., 2014; Kawamura and Bikkina, 2016; Zhu
et al., 2018).
Overall, the present investigations of organic chemistry have demonstrated
that the processing of organic compounds and, subsequently, their
concentrations in aqueous particles can be affected substantially by
considering the treatment of non-ideality of concentrated solutions. The
effects are mainly caused by the changed oxidant budget and the different
activity behaviours of the different organic compounds. Moreover, the
non-ideality treatment leads to substantial changes in the chemical sink and
source pattern compared to the ideal simulations.
Conclusions
For the first time, detailed simulations with the
SPACCIM-SpactMod advanced parcel model have been carried out for the different microphysical
conditions with and without a treatment of non-ideality for aqueous-phase
aerosol chemistry. Special emphasis was put on the different chemical
subsystems, including key inorganic compounds, radical and non-radical
oxidants, and organic compounds, in order to examine the effects of a
non-ideality solution treatment. The simulation results highlight that a
treatment of activities instead of concentrations strongly affects the
chemical multiphase processing in deliquesced particles.
The investigations of the predicted activity coefficients have revealed
substantial differences between different charged and uncharged aerosol
compounds. For inorganic ions, activity coefficients are often considerably
lower than unity under 90 % RH conditions and strongly decrease with the
increasing charge state of the respective ion because of the impact of
long-range electrostatic forces in highly concentrated solutions. In detail,
the predicted activity coefficients of singly, doubly, and triply charged
inorganic ions are in the range of 0.25–1.03 (0.13–0.66), 0.02–0.19
(0.02–0.21), and 0.001 (0.001), respectively, under urban (remote) 90 % RH
conditions. Interestingly however, the activity coefficients of some
inorganic ions exceed unity, particularly under lower humidity conditions
when the aerosol solution is even more concentrated. Consequently, an RH
decrease can cause both lowered and raised activity coefficients, leading to
more reduced or increased chemical processing for pathways involving
inorganic ions. However, once more, increasing activity coefficients with
values larger than unity under lower humidity conditions have only been
modelled for some metal ions, such as Cu2+ and Mg2+.
Unfortunately, the current model cannot model such an increase for other
chemically important transition metal ions, e.g. Fe2+ and Mn2+.
The behaviour of the activity coefficients of metal ions to be lower than
unity with increasing ionic strength down to a certain minimum, followed by
an increase to values partly above unity with further increasing
non-ideality, is strongly related to the MR interaction forces. However, due
to missing MR interaction parameters in the current model, e.g. of iron and
manganese ions, only ion–ion interactions are considered, leading to
activity coefficients below unity only. As demonstrated by additional
sensitivity studies, this limitation have potential impacts on the
multiphase chemistry in the current model, leading to an underestimation of
certain chemical processes under lower RH conditions. Therefore, the present
model studies imply that there is a demand for further improvements of
the current model implementation by MR interaction parameters from future
laboratory studies.
The predicted activity coefficients of organic acid anions are in the same
range of those of inorganic anions. However, the behaviour of the activity
coefficients of uncharged organic compounds is partly different because of
their dependence on the nature of intermolecular interaction forces. Thus,
the predicted activity coefficients of organic compounds are quite variable
and compound-specific. This uneven pattern of organic compounds with values
of predicted activity coefficients of both below and above unity implies an
increased or decreased aqueous-phase processing due to the treatment of
solution non-ideality. In general, the activity coefficients for alcohols,
gem-diols, aldehydes, dialdehydes, as well as undissociated mono and
dicarboxylic acids with longer carbon chains are observed as larger than
unity, whereas smaller carboxylic acids and particularly carboxylate ions
are observed as below unity. The activity coefficients strongly depend on
functional and CHx groups contained in organic molecules. Moreover, the
present study demonstrates that activity coefficients of many uncharged
organic compounds generally strongly increase while decreasing the RH under
both urban and remote conditions.
As a consequence of the non-ideal solution treatment, the present model
investigations show that the chemical multiphase processing of inorganic
compounds is often strongly affected. The model simulations reveal that the
multiphase processing of Fe(II) in aqueous particles is significantly
reduced by the treatment of non-ideality, and the effect depends strongly on
the ALW conditions. The decreased Fe(II) processing has a substantial effect
on other chemical subsystems, leading to, for example, changed H2O2
and OH processing in aqueous aerosols.
In the case of H2O2, the model simulations illustrated that due to
the consideration of a non-ideality treatment, the predicted H2O2
concentrations are significantly higher in both the gas and aqueous
phases. This effect is caused by the significantly lowered degradation rate
of H2O2, mainly the Fenton reaction, and increased formation
rates, e.g. via the reaction of HO2 with Cu+. On the other hand,
higher chemical rates of the sulfur oxidation, H2O2 reaction with
HSO3-, have been modelled. Thus, this study implies that the
treatment of non-ideality has a potential impact on H2O2,
resulting in higher multiphase concentrations of this oxidant. This higher
oxidant level is, consequently, available for other processes, such as a
more aqueous-phase S(VI) oxidation leading to a higher aerosol mass. The
present study has shown approximately 20 % more S(VI) production compared
to an ideal urban model run.
The smaller importance of the Fenton reaction under aqueous aerosol
conditions is shown to also affect other oxidants besides H2O2.
The present model investigations reveal that the processing of OH radicals
(formation and loss processes) in aqueous particles is substantially lowered
considering the treatment of non-ideality of concentrated solutions, mainly
as a consequence of the reduced in situ formation of OH by the Fenton
reaction. Additionally, the non-ideality treatment produces chemical
differences in the sink and source patterns of different chemical pathways.
However, this finding strongly depends on the non-ideality effects of
transition metal ions and the microphysical conditions (ALW). Therefore, the
missing MR interaction parameters of, for example, iron and manganese ions
introduce uncertainties into the current model. This may cause a too strong
underprediction of the Fenton reaction and may thus lead to an undervalued
OH formation and OH-initiated aqueous-phase oxidation rates. Thus, this
issue needs further investigations in upcoming model studies.
Due to the affected concentration budget and the rates of oxidants,
significant effects on multiphase oxidations and the resulting
concentrations of important organic aerosol components have been observed by
comparing ideal and non-ideal solution simulations. Interestingly, the
reduced aqueous-phase OH oxidation budget leads to lowered oxidations in
deliquesced particles and thus to higher concentration levels of oxidised
compounds mainly produced in the cloud phase. On the other hand, the present
simulations have demonstrated that production and degradation pathways can
be asymmetrically influenced, resulting, for example, in higher oxalate
concentrations. Although the oxalate precursors are less further oxidised in
deliquesced aerosols and the oxalate formation rates are therefore reduced,
the simulations with a non-ideality treatment revealed higher oxalate
concentrations because of the even stronger reduced photolytic decays of
iron–oxalate complexes. Thus, non-ideal simulations enable more realistic
predictions of high oxalate concentrations as observed in field campaigns
under highly polluted conditions. Moreover, this also makes artificial
disregard of the photolytic decays of iron–oxalate complexes in some other
multiphase chemical mechanisms (e.g. Ervens et al., 2004) unnecessary.
Furthermore, the present study implies that lower humidity conditions,
characterised by more concentrated solutions, might promote the formation of
higher oxalic acid concentration levels in aqueous aerosols. In conclusion,
the present investigations of organic chemistry have shown that the
processing of organic compounds and subsequently their concentrations in
aqueous particles can be affected substantially by considering the treatment
of non-ideality of concentrated solutions. The effects are mainly caused by
the changed oxidants budget and the different activity coefficient behaviour
of the different organic compounds. Moreover, the non-ideality treatment
leads to substantial changes in the chemical sink and source pattern
compared to the ideal simulations. Nevertheless, it should be kept in mind
that the present results are strongly related to the non-ideality effects of
transition metal ions, oxidants, and ALW (microphysical) conditions.
Therefore, the present results have to be considered an initial step for
further studies with more advanced model versions in the future.
Overall, the model studies have implied the importance of the
consideration of the treatment of non-ideality for tropospheric aerosol
constituents for the first time, especially in the deliquesced particle
phase. From the current SPACCIM-SpactMod model studies along with detailed
reaction rate investigations, it can be concluded that the treatment of
non-ideality is highly necessary in multiphase models to gain an
understanding of physico-chemical multiphase processing in aqueous aerosols.
Likewise, the current model studies reveal the need for further detailed
analysis in order to understand the effect of non-ideality and unsolved
issues of multiphase processing of aqueous aerosol particles by adopting
more complex chemistry with a high number of organic compounds. Further
studies also need to be performed for different meteorological conditions,
i.e. marine environmental conditions. Additionally, adequate consideration
of liquid–liquid separations as well as salt formation (crystallisation) in
deliquesced aerosol particles is also necessary, which can potentially alter
the ionic strength and acidity and thus influence non-ideality. Moreover,
model advancements are necessary by extending the database with new organic
and inorganic AIOMFAC interaction parameters (Ganbavale et al., 2015;
Gervasi et al., 2020).
Data availability
The model initialisation data are provided in the Appendix. Further
SPACCIM-SpactMod datasets and model simulation data of this study can be
accessed by contacting the corresponding authors
(Hartmut Herrmann: herrmann@tropos.de, Ralf Wolke: wolke@tropos.de).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-10351-2020-supplement.
Author contributions
AJR, AT and RW designed and performed the SPACCIM-SpactMod model
simulations. AJR, AT, RW and HH analysed model results. AT, RW, AJR and
HH wrote the paper.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Simulation chambers as tools in atmospheric research (AMT/ACP/GMD inter-journal SI)”. It is not associated with a conference.
Acknowledgements
Part of this work was performed within the ACoMa project funded by the
German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) under
project number TI 925/1-1. This work has received funding from the European
Union's Horizon 2020 research and innovation programme through the
EUROCHAMP-2020 Infrastructure Activity under grant agreement no. 730997.
Financial support
This research has been supported by the Deutsche Forschungsgemeinschaft
(grant no. TI 925/1-1) and the European Commission (EUROCHAMP-2020 (grant no. 730997)).
Review statement
This paper was edited by Christian George and reviewed by two anonymous referees.
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