A new detailed aqueous phase mechanism named the Cloud Explicit
Physico-chemical Scheme (CLEPS 1.0) is proposed to describe the oxidation of
water soluble organic compounds resulting from isoprene oxidation. It is
based on structure activity relationships (SARs) which provide global rate
constants together with branching ratios for HO
The simulation of permanent cloud under low-NO
Clouds favor chemical reactions that would not occur in the gas phase or at a
rate much slower than in the aqueous phase (Epstein and Nizkorodov, 2012;
Herrmann, 2003; Herrmann et al., 2015). Reactivity in clouds is due to
(1) highly enhanced photochemical processes in cloud droplets; (2) faster
aqueous phase reactions than in clear sky, some of which do not occur in the
gas phase, especially those involving ions and hydration; and (3) possible
interactions between the aqueous phase and particulate phase. Clouds can also
be responsible for secondary organic aerosol (SOA) formation and ageing.
However, aqueous phase processes suffer from large uncertainties. Blando and
Turpin (2000) first proposed clouds as a source of SOAs. Recent field
measurements (Kaul et al., 2011; Lee et al., 2012, 2011), experimental work
(Brégonzio-Rozier et al., 2016) and modeling studies (Ervens, 2015;
Ervens et al., 2011) have shown that aqueous phase processes could lead to
SOA formation on the same order of magnitude as gas phase processes. The
contribution of cloud and fog processes to SOA formation is firstly indirect,
through the effects of cloud chemistry on the oxidant budget. Gas phase
reactivity of volatile organic compounds (VOCs) is controlled by daytime
HO
Competition between fragmentation and functionalization processes has been
identified as a major factor in the production of SOA in the gas phase
(Donahue et al., 2012; Jimenez et al., 2009). To better represent these
processes in clouds, detailed multiphase mechanisms are needed. A new
mechanism, the Cloud Explicit Physicochemical Scheme (CLEPS 1.0), including
organic compounds up to C4, has been developed under low-NO
The aqueous phase oxidation mechanism originally relied on inorganic
chemistry (see Deguillaume et al., 2004; Leriche et al., 2007) and on the
oxidation of several organic C
In the present study, the CLEPS mechanism is extended to the oxidation of
C
For each species and its oxidation products, the CLEPS mechanism describes
the oxidation of HO
Scatterplots of the estimated log(
Moreover, recent developments in empirical estimates of kinetic and
thermodynamic parameters (e.g., rate constants, Henry's law constants) for
aqueous phase chemistry (Doussin and Monod, 2013; Minakata et al., 2009;
Monod and Doussin, 2008; Raventos-Duran et al., 2010) are included in the
CLEPS mechanism. These structure activity relationships (SARs) are based on
experimental data and rely on robust hypotheses about the rate constants
(Sect. 3.1.3.2) and equilibrium constants (Sects. 3.1.1 and 3.1.2) of species
that are not documented in the literature. For instance, SARs can provide
estimations of the branching ratios between the different oxidation pathways
with HO
The mechanism currently includes 850 aqueous reactions and 465 equilibria.
Inorganic reactivity is described for 67 chemical species (e.g., TMIs,
H
Carbonyls, i.e., aldehydes and less likely ketones, may undergo hydration
leading to the formation of a gem-diol form:
There are 30 carbonyl species in the mechanism. Most of the C
To the best of our knowledge, there is only one SAR available to estimate
hydration constants (Raventos-Duran et al., 2010); it is provided by the
GROMHE (GROup contribution Method for Henry's law Estimate) SAR for Henry's
law constants. This SAR is based on five descriptors and is optimized on a
dataset comprising 61 species. Raventos-Duran et al. (2010) defined a global
descriptor, “tdescriptor”, to represent functional group interactions
with the sum of the so-called sigma Taft values (
Experimental hydration constants for carboxylate species.
In the present study, the SAR is extended to anionic species. The descriptors
have been optimized to include the Taft and Hammett sigma values for the
carboxylate moieties (
Hydration data are not available in the literature for the peroxyl
(RO
To represent acid dissociation, the acidity constant
The acidity constant
In general, the
Perrin et al. (1981) showed that the pK
Following the hydration constant treatment, the acidity constants for peroxyl
radicals are initially taken from their parent species when experimental data
are not documented. This assumption can be questioned, but very few
measurements suggest that peroxyl radicals are more acidic than their parent
species. Schuchmann et al. (1989) showed that the acetic acid peroxyl radical
(CH
For aliphatic organic compounds, HO
When rate constants of organic compound reactions with HO
In the present study, the SAR was modified to account for the electron
transfer on carboxylate compounds (Reaction R5). The relevance of this
process was discussed by Doussin and Monod (2013). They found an electron
transfer rate constant for
For all unsaturated species in the mechanism (i.e., methylvinylketone – MVK, methacrolein – MACR, hydroxymethylvinylketone – MVKOH and hydroxymethacrolein – MACROH), the addition reaction rates have been evaluated following the literature and similarity criteria. For further developments involving unknown unsaturated compounds, the SAR from Minakata et al. (2009) should be used because it is the only method that can estimate partial addition rate constants on double bonds.
Branching ratios are required to identify the most probable oxidation
products. In previous mechanisms, the most labile H-atom was empirically
identified (e.g., using bond dissociation energy estimations), and the
reaction was assumed to proceed exclusively
Furthermore, for simplicity, a reduction hypothesis was considered in the
mechanism for each stable species because explicitly writing all possible
reactions would yield a huge number of chemical species. For example, Aumont
et al. (2005) showed that the number of species formed in the gas phase for
such explicit schemes increases exponentially with the size of the carbon
skeleton of the parent species. One can assume based on Aumont et al. (2005)
that, starting from a C
Examples of the reduction scheme applied to estimate HO
Table 2 shows that the Doussin and Monod (2013) SAR estimates often lead to a
significant abstraction of the hydrogen atom bonded to the oxygen atom in the
hydroxyl moiety. This mechanism has never been addressed in an atmospheric
chemical scheme. This reactivity of the alcohol function towards the
HO
NO
The mechanism of NO
In this version of the mechanism, the addition of an NO
Data concerning NO
For most C
The branching ratios for NO
Reaction rates with radicals other than HO
The reactivity of selected oxygenated organic species with H
Most of the species considered in the mechanism are oxygenated and are likely
to bear chromophore functional groups. To calculate the photolysis rate, the
polychromatic absorption cross sections and quantum yields must
be known. Again, the literature data concerning these subjects are scarce.
Photolysis data are available for a few chromophore species: H
Because the hydroperoxide (
In dilute aqueous solution, alkyl radicals react with dissolved O
In general, a peroxyl radical reacts with itself or another peroxyl radical to form a tetroxide, which quickly decomposes (von Sonntag and Schuchmann, 1997). These reactions could be introduced to the mechanism by having each peroxyl radical react with every other peroxyl radical. With 363 peroxyl radicals in the mechanism, this would require more than 66 000 reactions to be written to account for these cross-reactions. As a first approach, we restrict the mechanism to self-reactions. There are available methods to simplify the description of cross-reactions (Madronich and Calvert, 1990). These methods could be adapted for future versions of the mechanism.
The decomposition of tetroxide follows different pathways, depending on the
nature of the initial peroxyl radical. Piesiak et al. (1984) proposed a
mechanism for the evolution of the tetroxide formed after dimerization of
For
The four pathways retained in this work are the most important identified by Schuchmann et al. (1985). The sum of these pathways contributes 87 % of the tetroxide decomposition, and each individual contribution is scaled to reach 100 % overall.
The evolution of
The three pathways are reported to contribute 90 % of the degradation of the tetroxide (Piesiak et al., 1984). The mechanism is restricted to these major pathways, and their individual contributions are scaled to reach 100 % overall in our mechanism.
Except for the
The rate constant was measured by Herrmann et al. (1999). Schuchmann and von Sonntag (1984) estimated that the first pathway (aldehyde pathway) contributes 20 % of the tetroxide decomposition. Studying the ethylperoxyl radical derived from the photooxidation of ethylhydroperoxyde, Monod et al. (2007) found that the second pathway (alkoxyl pathway) is more likely than the aldehyde pathway, in agreement with previous studies (Henon et al., 1997; von Sonntag and Schuchmann, 1997). Therefore, we attributed the remaining degradation of the tetroxide to the alkoxyl pathway.
When a hydroxyl moiety is in the alpha position of the peroxyl function, the
peroxyl radical likely undergoes HO
Generalization of HO
In the case of
As shown in Table 3, HO
Acylperoxyl radicals (
Alkoxyl radicals (RO
Both pathways are non-limiting steps that are in competition with each other.
Schuchmann et al. (1985) studied the fate of acetate peroxyl radicals and
showed that the produced alkoxyl radical
(CH
Because of their very short lifetimes, alkoxyl radicals are not explicitly
considered in the mechanism. Instead, electron transfer and fragmentation
products are directly included in the global reaction. For example, for the
The last reaction is the overall budget reaction, which is taken into account in the model.
The CLEPS mechanism is coupled to the gas phase Master Chemical Mechanism,
MCM v3.3.1 (Jenkin et al., 2015; Saunders et al., 2003) provided at
All gases are dissolved in CLEPS even if they are not further oxidized in the aqueous phase. Conversely, some aqueous species described in CLEPS can be outgassed even if there is no corresponding gas species in MCM. Among the 87 chemical species included in CLEPS, 33 do not have a counterpart in MCM. These are mostly highly oxygenated and highly soluble species. Conversely, 267 gas phase species from MCM have no corresponding aqueous species in CLEPS. We made sure that all species have an equivalent in the respective other phase, even if this species in that phase is not reactive. The mass transfer parameters are estimated as described below (Sect. 4.2).
Schematic diagram of the DSMACC version of the Kinetic PreProcessor. The developments related to aqueous phase reactivity are shown in blue.
Mass transfer is described following the kinetic parameterization from
Schwartz (1986). For a given species A,
When unavailable, the temperature dependencies (enthalpy of dissolution) are
set to 50 kJ mol
The mass transfer parameterization in our cloud chemistry model has been used
for a long time (Jacob, 1986). Most cloud chemistry models use experimentally
measured Henry's law constants. Ervens et al. (2003) proposed estimating the
accommodation coefficient based on using a SAR to empirically estimate
The mechanism resulting from the coupling of CLEPS with MCM v3.3.1 is integrated into a model based on the Dynamically Simple Model for Atmospheric Chemical Complexity (DSMACC; Emmerson and Evans, 2009) using the Kinetic PreProcessor (KPP: see Damian et al., 2002), which has been modified to account for an aqueous phase, as described in the following. The changes are summarized in blue in Fig. 2.
Aqueous phase reactions are implemented as a new reaction type. Rate
constants in units of M
Mass transfer is also implemented as a new reaction type. The mass transfer coefficients are calculated following Schwartz (1986) and depend on the Henry's law constants, gas diffusion coefficients, mean molecular speeds and accommodation coefficients (see Sect. 4.2).
The TUV version (TUV 4.5, Emmerson and Evans, 2009) included in DSMACC to calculate the photolysis rates in the gas phase has been modified to include aqueous phase photolysis reactions (Fig. 2). To calculate the photolysis coefficients inside the droplets, the clear-sky actinic flux values are multiplied by a factor of 1.6 (Ruggaber et al., 1997), and the cross sections and quantum yields are provided from available experimental data (Deguillaume et al., 2004; Long et al., 2013).
Differential equations are solved with a Rosenbrock solver, which has been shown to be a reliable numerical method for stiff ODE systems involved in modeling multiphase chemistry (Djouad et al., 2002, 2003).
The Cloud Explicit Physicochemical Scheme (CLEPS 1.0) has been developed in the most explicit way to take into account the most probable oxidation pathways of organic compounds. The protocol that is applied to develop CLEPS is in the same spirit as CAPRAM 3.0 (Chemical Aqueous Phase Radical Mechanism; Herrmann et al., 2005; Tilgner and Herrmann, 2010; Whalley et al., 2015). In this section, it is important to compare the main stages of the building of both aqueous phase mechanisms (CLEPS vs. CAPRAM).
CLEPS and CAPRAM present similarities. They are both developed on the hypothesis in the choice of chemical pathways and rate constants that are carefully calibrated against experimental data when available. For instance, inorganic chemistry, acidity constant estimates, and photolysis rate calculations are similar in both aqueous mechanisms. These two mechanisms were built upon their own set of recommended data (e.g., Ervens et al., 2004, for CAPRAM; Leriche et al., 2000, 2003, 2007; Deguillaume et al., 2004, for CLEPS). However, some differences exist and are listed below. Those differences are justified with the way both mechanisms will be applied for coupling with a regional/global model, interpreting laboratory and/or observational data from field experiments, introducing biodegradation processes, etc.
First of all, the two mechanisms are coupled to two quite contrasted gas phase mechanisms since CAPRAM is based upon RACM and CLEPS upon MCM. The fact that RACM (Stockwell et al., 1997) includes lumped species while MCM is fully explicit leads to different developments in the aqueous phase. In CAPRAM, the lumped gaseous species are split into several fractions that are then transferred to the corresponding species in the aqueous phase, whereas in CLEPS, individual gas species are directly transferred to the corresponding aqueous phase species. As an example, the “Ald” model species in RACM represents all gaseous aldehydes and is considered to be the source of dissolved acetaldehyde, propionaldehyde and butyraldehyde (Herrmann et al., 2005).
Secondly, CAPRAM only represents one oxidation pathway for each non-radical
aqueous species when, usually, in the laboratory, several first-generation
oxidation products are detected (Perri et al., 2009). In CLEPS however, the
various possible oxidation pathways of organic compounds are considered. In
this regard, CLEPS is more likely to take into account the variety of
oxidation products. For instance, in Table 2 the hydrated glycolaldehyde
final reactivity in CLEPS is equally distributed between three HO
Then, an important difference between CLEPS and CAPRAM lies in the hypotheses
that are made when rate constants, branching ratios, solubility and hydration
constants are missing. In CLEPS, the recent SAR from Doussin and Monod (2013)
is systematically applied to estimate rate constants and branching ratios for
the HO
There is one exception for the estimation of NO
In CLEPS, even solubility and hydration constants are estimated using SAR (GROMHE). In this way, all species identified in gas phase mechanism MCM are dissolved in CLEPS, whereas in CAPRAM only some organic compounds are transferred in the aqueous phase when their solubility is documented or estimated based on similarity criteria.
Some attention should be paid when comparing the hypotheses made to develop
CLEPS and CAPRAM since some of them are related to deliquescent particles
and/or cloud droplets. CAPRAM, in contrast to CLEPS, explicitly treats the
O
To summarize, CLEPS is based upon one of the most updated gas chemical mechanisms (MCM) that uses the very efficient preprocessor KPP and Rosenbrock solver. This is a good basis to develop an explicit aqueous phase chemistry model that is suitable for interpreting laboratory data and for describing the phase separation observed in long-term measurement stations (from the WMO and/or ACTRIS networks).
The model is run with the initial and environmental conditions adapted from
the low-NO
At noon on the 31st day of the simulation, relative humidity is increased to
100 % and aqueous phase conditions are activated assuming a constant
liquid water content of 3
The cloud scenario is initialized with 1
Time evolution of the gas phase mixing ratios without the cloud (dashed lines) and during the cloud (continuous line). The cloud simulations are depicted with (red lines) and without (blue lines) DOC. Please note that for most plots, the red line is hidden by the blue line.
Figure 3a and b show the time evolution of the targeted gases during the
31st day of the gas phase simulation (dashed lines). The NO
Figure 3a and b show the time evolution of targeted gases during the cloud
scenario (full lines) compared to the gas phase scenario (dashed lines).
Previous modeling studies have shown that gas phase HO
This trend in HO
Time evolution of the dissolved species concentrations during the
simulated cloud with (red lines) and without (blue lines) DOC. The vertical
scale is in
Glyoxal, glycolaldehyde, pyruvic acid, glyoxylic acid and glycolic acid are
readily soluble species that react in the aqueous phase (Herrmann et al.,
2015), explaining the sharp decrease in their gas phase mixing ratios. Cloud
dissolution and oxidation act as significant sinks for these species. For
instance, the glyoxal mixing ratio is reduced by 67 % at the start of the
cloud, and the glycolaldehyde mixing ratio is significantly reduced until
sunset (18:45). For all secondary organic species, daytime gas phase
oxidation is increased due to the higher HO
Figure 4 shows the time evolution of the main organic aqueous species
together with the H
The sensitivity test including the additional DOC sink shows that the reduced
concentration of HO
A detailed budget of aqueous HO
Contribution of the 10 most important species (in terms of concentrations) in the aqueous phase (colors). The solid line depicts the total concentration of dissolved organic compounds. The dashed line depicts the total concentration of reactive dissolved species.
Figure 5 depicts the contributions in terms of concentrations of the major
species in the aqueous phase. The total concentration of organic matter
(continuous line) reaches a maximum of 0.76 mM after 12 h of cloud
simulation, which corresponds to approximately 30 mgC L
The presence of acids as main contributors to the aqueous phase organic composition shows the potential for cloud reactivity to be a source of acids (Chameides, 1984). The total amount of organic acids (including formic and acetic acids) in both phases is almost doubled in less than 1 h by the aqueous phase sources, from approximately 0.48 ppbv of gaseous organic acids before the cloud to a total of 0.98 ppbv of organic acids in both phases (see Supplement SM7).
Time evolution of the mean O
Figure 6 depicts the time evolution of the O
At the beginning of the cloud, many oxygenated and large compounds are
dissolved, leading to an increase in the O
In this paper we described a new protocol with an explicit chemical scheme
for aqueous phase oxidation. This protocol provides an up-to-date method to
describe the dissolution of soluble VOCs, their hydration and/or acid
dissociation equilibria (as well as iron-oxalate complexation), and their
reactivity with HO
Under the simulated cloudy conditions, aqueous phase reactivity is shown to
impact the O
As long as the mechanism is used to simulate organic chemistry in cloud
droplets, the hypotheses it is built on remain valid. However, modifications
should be performed before applying the model to less dilute atmospheric
aqueous phases, such as deliquescent aerosols. First, the non-ideality of
such aqueous solutions should be taken into account. Second, H-abstraction
and O
This protocol is a powerful tool to explore and propose new reaction mechanisms as a basis for understanding experimental studies of scarcely investigated compounds. The mechanisms generated by our protocol can be used for different purposes in the study of atmospheric aqueous phase processes. They can be evaluated and adapted to laboratory experiments involving a small number of precursors that react only in the aqueous phase. The mechanisms are more likely to be useful for experiments involving multiphases in environmental cloud chambers (see for example Brégonzio-Rozier et al., 2016). They are also of interest for the modeling studies of field campaigns such as HCCT (Whalley et al., 2015) or SOAS (Nguyen et al., 2014). The SOA and the cloud chemistry communities are currently interested in studying the respective contributions of oxidation and accretion processes to the transformations of organic matter in the aqueous phase and to the oxidative capacity of clouds. Most recent modeling studies have focused on implementing newly identified accretion processes to evaluate their potential impacts on SOA formation (Ervens et al., 2015; McNeill, 2015; McNeill et al., 2012; Woo and McNeill, 2015). In this work, guidelines are developed to update oxidation mechanisms that will be compared in the future to descriptions of the formation of accretion products, such as oligomers, organonitrates and organosulfates.
The mechanism used in this paper is available in KPP format
upon request to l.deguillaume@opgc.univ-bpclermont.fr. Any suggestions and
corrections to the mechanism (e.g., a new experimental rate constant we may
have missed, typos) are also welcomed at the same address. The modified
version of DSMACC (originally downloaded at
The authors declare that they have no conflict of interest.
The authors acknowledge French National Agency for Research (ANR) project CUMULUS ANR-2010-BLAN-617-01 for providing financial support. The authors are very grateful to the Agence Nationale de la Recherche (ANR) for its financial support through the BIOCAP project (ANR-13-BS06-0004). Part of this work was also supported by CEA/CNRS through contract CEA 12-27-C-DSPG/CAJ – CNRS 77265. Edited by: S. Bekki Reviewed by: two anonymous referees