Introduction
In recent years, gaseous amines have attracted increasing attention due to
theoretical, laboratory, and field measurements indicating that amines may
considerably enhance particle formation and growth (Kurtén et al., 2008;
Nadykto et al., 2011, 2014; Almeida et al., 2013; Berndt et al., 2010; Zhao
et al., 2011; Erupe et al., 2011; Chen et al., 2012; Wang et al., 2010a; Yu
et al., 2012) and affect secondary organic aerosol (SOA) formation (De Haan
et al., 2009, Myriokefalitakis et al., 2010; Williams et al., 2010). Amines
are organic compounds and derivatives of ammonia wherein one or more
hydrogen atoms are replaced by a substituent such as an alkyl or aryl group.
About 150 amines have been identified in the atmosphere; the most common and
abundant amines being the low-molecular-weight methylamines such as
monomethylamine (MMA), dimethylamine (DMA), and trimethylamine (TMA) (Ge et
al., 2011a). Concentrations of amines can exceed several
parts-per-billion by volume (ppbv) near their sources (Ge et al., 2011a; Schade
and Crutzen, 1995) but are expected to be low farther away as a result of
their short lifetime due to oxidation by OH (Carl and Crowley, 1998) and
uptake by particles (Qiu and Zhang, 2013).
While amines are stronger bases than ammonia and ternary
H2SO4-H2O-amine clusters are more stable (Kurtén et al.,
2008; Nadykto et al., 2011, 2014; Almeida et al., 2013), the relative role
of amines versus ammonia in enhancing particle formation in the atmosphere
is yet to be determined (Zollner et al., 2012). This is because the
concentration of amines in the atmosphere is generally much lower than that
of ammonia (by 2–3 orders of magnitude or more) (Ge et al., 2011a; Hanson et
al., 2011). Recent measurements taken during the CLOUD (Cosmics Leaving
Outdoor Droplets) chamber experiments at CERN (European Council for Nuclear Research; Almeida et al., 2013)
indicate that a [DMA] of above ∼5 parts-per-trillion by volume
(pptv) enhances nucleation substantially, but enhancement drops
significantly as [DMA] decreases below that level.
In order to determine the contribution of ternary nucleation involving
amines to atmospheric particle production, it is critical to know the
concentrations of key amines and their variations in the atmosphere. Due to
their high reactivity and low concentrations, measurements of gaseous amines
in the background atmosphere are very limited (Ge et al., 2011a). Several
studies show [DMA] is below 1 pptv in urban areas (Grönberg et al.,
1992a, b) while a couple of other studies observed [DMA] around a few pptv
in rural and coastal areas (Hanson et al., 2011; VandenBoer et al., 2011,
2012; Van Neste et al., 1987; Gibb et al., 1999). Although TMA is generally
more abundant (Ge et al., 2011a), the concentration of TMA needed to
substantially enhance nucleation remains to be studied.
In addition to in situ measurements, numerical modeling is also needed to
integrate the various processes controlling amine concentrations and
ultimately assess the impact of amines on global nucleation, aerosol
properties, and climate. While limited measurements of amines are available,
modeling of global amines is basically nonexistent. Myriokefalitakis et
al. (2010) explored the potential contribution of amines emitted from oceans to
SOA formation, assuming total amine emissions to be one-tenth of the
oceanic ammonia emissions. Myriokefalitakis et al. (2010) neither considered
amines from continental sources nor presented concentrations of gaseous
amines over oceans. In the present work, we aim to simulate the global
distributions of gaseous amines in the air with a global chemistry transport
model. The key processes controlling amine concentrations (including
emission, transport, oxidation, deposition, and aerosol uptake) are
considered and the simulated results are compared to the limited
measurements available.
The methods of the present study (including sources, sinks, and model
representation) are described in Sect. 2. The modeling results,
comparisons with measurements, and sensitivity studies are given in Sect. 3.
Section 4 contains the summary and discussion.
Methods
Sources and fluxes
Amines are ubiquitous atmospheric organic bases, and are emitted from a wide
range of sources including animal husbandry, biomass burning, motor
vehicles, industry, meat cooking, fish processing, sewage treatment and
waste incineration, protein degradation, vegetation, soils, and ocean
organisms (Ge et al., 2011a). On a global scale, little is known about the
flux of most amines, especially various aromatic amines (Ge et al., 2011a).
Among about 150 amines identified in the atmosphere, methylamines (MMA, DMA,
and TMA) are most common and abundant. Schade and Crutzen (1995) estimated
the global emission fluxes of MMA, DMA, and TMA to be 83 ± 26,
33 ± 19, and 169 ± 33 Gg N yr-1, respectively. The total
methylamine flux of 285 ± 78 Gg N yr-1 is more than 2 orders of
magnitude smaller than the estimated global ammonia flux of
50 000 ± 30 000 Gg N yr-1 (Schade and Crutzen, 1995).
Calculated global annual mean emissions, sinks, and burdens of
ammonia, MMA, DMA, and TMA. Sinks and burdens under four different uptake
coefficients (γ = 0.03, 0.01, 0.001, and 0) are given.
Emission
Oxidation
Uptake
Dry and wet deposition
Burden
γ
(Gg N yr-1)
(Gg N yr-1)
(Gg N yr-1)
(Gg N yr-1)
(Gg N)
Ammonia
5.8 × 104
-4.9 × 102
-3.8 × 104
-1.9 × 104
79.9
MMA
0.03
96.2
-17.2
-65.8
-13.2
0.07
MMA
0.01
96.2
-28.4
-48.1
-19.8
0.12
MMA
0.001
96.2
-51.7
-14.2
-30.4
0.22
MMA
0
96.2
-61.8
0.0
-34.4
0.27
DMA
0.03
38.3
-12.2
-21.9
-4.2
0.03
DMA
0.01
38.3
-17.3
-15.0
-6.0
0.05
DMA
0.001
38.3
-25.9
-3.8
-8.6
0.08
DMA
0
38.3
-28.9
0.0
-9.3
0.08
TMA
0.03
196.0
-49.8
-122.0
-23.9
0.24
TMA
0.01
196.0
-75.4
-85.7
-34.7
0.38
TMA
0.001
196.0
-122.0
-23.0
-50.9
0.63
TMA
0
196.0
-140.0
0.0
-56.2
0.72
Sinks
The main sinks of amines emitted into the atmosphere include dry and wet
deposition, gas phase reactions, and heterogeneous uptake. Since most of the
amines are highly soluble, wet deposition is an important process to bring
amines in the air to the surface. As organic compounds, gaseous amines
undergo oxidation reactions with OH, NOx, or O3 (Nielsen et al.,
2012; Lee and Wexler, 2013). The lifetimes of amines with respect to OH
oxidation are typically a couple of hours, much shorter than those by
reactions with O3 and NOx. The gaseous methylamines, which are
strong bases, may also undergo rapid acid–base reactions to form salt
particles in the presence of inorganic acids (HCl, HNO3,
H2SO4) (Murphy et al., 2007). In addition, amines may react with
organic acids to form amides (Barsanti and Pankow, 2006). A detailed
discussion of the chemistry of amines in the atmosphere can be found in
several recent review articles (Nielsen et al., 2012; Lee and Wexler, 2013).
Owing to their high aqueous solubility and strong basicity, gaseous amines
can efficiently enter into a particulate phase via direct dissolution and
acid–base reactions. The importance of amines with regard to gas/particle
partitioning has been supported by the reactive uptake of TMA into ammonium
nitrate particles (Lloyd et al., 2009) and amine exchange into ammonium
bisulfate and nitrate nuclei (Bzdek et al., 2011). Laboratory studies show
that heterogeneous reactions of gaseous alkylamines on H2SO4
nanoparticles resulted in the formation of alkyl ammonium sulfates and
particle growth (Wang et al., 2010a, b). It has also been observed that
methylamine could react with glyoxal in drying cloud droplets to form SOA
(De Haan et al., 2009) and stable aminium salts could be formed by amine and
organic acids in the aerosols (Williams et al., 2010). The thermodynamic
properties of amines that control their partitioning between the gas and the
particle phase in the atmosphere are examined in a review paper (Ge et al.,
2011b). An overview of laboratory progress in the multiphase chemistry of
amines can be found in Qiu and Zhang (2013).
Model representation
A numerical model is needed to integrate the various processes influencing
the concentrations of amines in the atmosphere. In the present study we
employ GEOS-Chem, a global 3-D model of atmospheric composition driven by
assimilated meteorological data from the NASA Goddard Earth Observing
System 5 (GEOS-5) (e.g., Bey et al., 2001). The GEOS-Chem model has been developed
and used by many research groups and contains a number of state-of-the-art
modules treating various chemical and aerosol processes with up-to-date key
emission inventories (for details, see the model web page
http://geos-chem.org/). Global ammonia emissions are based on the inventory
developed by the Global Emission Inventory Activity (GEIA) (Bouwman et al.,
1997) and national emission estimates are used for the US (NEI05), Canada
(CAC), Europe (EMEP), and eastern Asia (Streets2000). While ammonia is
simulated in detail in GEOS-Chem, amines are not considered prior to this
study. Here, to represent gas phase methylamines, we add three tracers (MMA,
DMA, and TMA) in GEOS-Chem v8.3.2 with an advanced particle microphysics
(APM) model incorporated (Yu and Luo, 2009).
There exist large uncertainties in the estimated emission fluxes of amines,
and detailed emission inventories of amines from various sources are
currently not available. In the present study, we use the ratios of
methylamines to ammonia fluxes given in Schade and Crutzen (1995) but
approximate the spatial distribution and seasonal variations of amine
emissions following those of ammonia. Such a first-order approximation
enables us to simulate the typical concentrations of amines in the global
atmosphere. The dry and wet deposition, as well as horizontal and vertical
transport of amines, is also considered in GEOS-Chem, following the
approaches for ammonia.
In the present study, we only take into account the oxidation of
methylamines by OH as the oxidation of amines by NO3 and O3 is
much smaller. There have been limited measurements of the kinetics of OH
reactions with simple alkyl amines (Ge et al., 2011a; Nielsen et al., 2012;
Lee and Wexler, 2013). In this study we use the reaction coefficients
reported by Carl and Crowley (1998): 1.79 × 10-11,
6.49 × 10-11, and 3.58 × 10-11 cm3 molecule-1 s-1,
for MMA, DMA, and TMA, respectively. For
comparison, the reaction coefficient of NH3 with OH is
1.6 × 10-13 cm3 molecule-1 s-1 (Atkinson et al., 1997), more
than 2 orders of magnitude smaller. The uptake of amines by particles is
considered, using the particle surface areas calculated from particle size
distributions predicted by GEOS-Chem APM. One key uncertainty about the
heterogeneous uptake is the uptake coefficient (γ), defined as the
ratio of gas surface collisions that result in loss of the amines onto the
surface to the total gas surface collisions. Lloyd et al. (2009) reported a
reactive uptake coefficient of 2 × 10-3 for the uptake of TMA
by ammonium nitrate aerosols at 20 % RH (relative humidity). Wang et al. (2010b) studied the
uptake of alkylamines (MMA, DMA and TMA) on sulfuric acid surfaces and found
uptake coefficients in the range of (2.0–4.4) × 10-2. In a
laboratory study of the heterogeneous reactions between alkylamines (MMA,
DMA and TMA) and ammonium salts (ammonium sulfate and ammonium bisulfate),
Qiu et al. (2011) found that, for the three alkylamines, the initial uptake
coefficients (γ0) range from 2 × 10-2 to
3.4 × 10-2 and the steady-state uptake coefficients
(γss) range from 6.0 × 10-3 to 2.3 × 10-4 and
decrease as the number of methyl groups on the alkylamine increases. It is
clear from these laboratory studies that the values of γ depend on
the particle compositions. The secondary components of particles in the
atmosphere (sulfate, nitrate, SOA, and ammonium), which are likely to play
an important role in the uptake of amines, are generally internally mixed.
The uptake coefficients of amines by these mixed particles, under different
atmospheric conditions (especially RH), are not yet known. In the present
study, the sensitivity of predicted amine concentrations to γ values
ranging from 0 (no uptake) to 0.03 is studied. We assume no uptake of amines
by pure dust, black carbon, and primary organic carbon. We do not consider
the uptake of amines by sea salt particles due to lack of information with
regard to the uptake coefficients. The gaseous phase reactions of amines
with HNO3, HCl, and organic acids are not considered, since oxidation
and aerosol uptake likely dominate the loss of amines. In the present study,
we also do not consider the re-evaporation of amines after uptake by
secondary particles as laboratory studies indicate that amines can react
with various acids to form stable aminium salts (e.g., Qiu and Zhang, 2013).
For example, recent laboratory measurements show that sulfate particles act
as an almost perfect sink (negligible evaporation) for amines (Almeida et
al., 2013).
Results
The results presented below are based on a 1 yr simulation
(October 2005–December 2006, with the first 3 months as spin up) using GEOS-Chem
v8.3.2 + APM, with the kinetic condensation of low, volatile secondary
organic gases from successive oxidation aging taken into account (Yu, 2011).
The horizontal resolution (latitude by longitude) is 2∘ × 2.5∘
and there are 47 vertical layers in the model (surface to 0.01 hpa).
Available measurements of MMA, DMA, and TMA concentrations (in
pptv) and site information.
Site information (latitude, longitude)
Site type
Observation period
[MMA]
[DMA]
[TMA]
References
A. Gothenburg, Sweden (57.73, 11.97)
Urban
24–26 August 1991
3.6 ± 0.9
0.7 ± 0.5
1.3 ± 0.6
Grönberg et al. (1992a)
B. Lund, Sweden (55.71, 13.19)
Urban
July 1991
16 ± 5
0.5 ± 0.3
5.2 ± 2
Grönberg et al. (1992b)
C. Atlanta, GA (33.85, -84.41)
Urban
23 June–25 August 2009
<0.2
0.5–2
4–15
Hanson et al. (2011)
D. Vallby, Sweden(59.55, 17.13)
Rural
July 1991
10 ± 3
1.8 ± 0.6
41 ± 14
Grönberg et al. (1992b)
E. Toronto, ON (43.67, -79.39)
Rural
27 June–5 July 2009
0.2–2.5
VandenBoer et al. (2011)
F. Egbert, ON (44.23, -79.79)
Agricultural and semiforested
15 October–2 November 2010
6.5 ± 2.1
1.0–10
VandenBoer et al. (2012)
G. Coastal Sweden(Malmö) (55.62, 13.00)
Coast
13–15 August 1991
4.4 ± 1.1
1.1 ± 0.4
8.7 ± 3.1
Grönberg et al. (1992a)
H. Oahu, Hawaii (21.48, -158.00)
Coast
July–August 1985
0.2 ± 0.1
2.0 ± 1.1
0.7 ± 0.4
Van Neste et al. (1987)
I. Narragansett, RhodeIsland (41.45, -71.45)
Coast
1.2 ± 0.3
5.3 ± 0.9
2.2 ± 0.9
Van Neste et al. (1987)
J. Arabian Sea (14, 63)
Arabian Sea
27 August–4 October 1994
2.5
0.9
0.02
Gibb et al. (1999)
K. Arabian Sea (14, 63)
Arabian Sea
16 November–19 December 1994
3.2
4.4
0.2
Gibb et al. (1999)
L. NW Atlantic (13.2, -66.1)
Marine
28 February 1986
0.33
Mopper and Zika (1987)
Table 1 shows global annual mean emissions, sinks (due to oxidation, uptake,
and dry/wet deposition), and burdens for ammonia, MMA, DMA, and TMA. Sinks
and burdens of methylamines under four different uptake coefficients
(γ = 0.03, 0.01, 0.001, and 0) are given. The global ammonia emission
flux for 2006 based on GEOS-Chem is 5.8 × 104 Gg N yr-1,
about 15 % higher than the estimation of Schade and Crutzen (1995). The
MMA, DMA, and TMA emissions fluxes assumed in the present study (96.2, 38.3,
and 196.0 Gg N yr-1, respectively) are also 15 % higher, as the same
ratios of methylamines to ammonia emission fluxes given in Schade and
Crutzen (1995) are used. The 15 % difference is within the estimated
methylamine emission uncertainty of ∼30 % (Schade and
Crutzen, 1995).
Horizontal distributions of annual mean DMA emissions assumed in
the present study.
Simulated horizontal distributions of annual mean DMA oxidation
and uptake lifetime and concentration ([DMA]) in the model surface layer (0–150 m
above surface) under two aerosol uptake coefficients:
(a, b) γ = 0 (i.e., oxidation only) and
(c, d) γ = 0.03 (uptake by sulfuric
acid particles).
Same as Fig. 2 but for zonally averaged values. Vertical axis is
the ratio of pressure (P) at the model layer to the pressure at the surface
(Psurf).
As an example for the spatial distribution of emission fluxes, Fig. 1
presents the horizontal distributions of DMA emissions assumed in the
present study. As mentioned earlier, we approximate the spatial distribution
and seasonal variations of methylamine emissions following those of
ammonia. Again, this should be considered as a first-order approximation, as
the emission rates of amines from various sources may be quite different
from those of ammonia. With the understanding of this limitation, we can see
in Fig. 1 that DMA emission rates are in the range of ∼0.2–10 kg N km-2 yr-1 over major continents and below
0.2 kg N km-2 yr-1 over oceans. For MMA and TMA, the absolute emission
fluxes are a factor of 2.5 and 5.1 higher (Table 1). In Fig. 1 we also
marked the locations of sites where some kind of methylamine measurements
are available, as summarized in Table 2. It should be noted that sites A, B,
D, and G are close to each other and overlap in Fig. 1. Similarly, sites E
and F overlap in Fig. 1. Sites J and K are the same location but
measurements were taken during different time periods. A comparison of
simulated and observed methylamine concentrations is discussed later.
Horizontal distributions of [MMA] in the surface layer (a, c) and
its zonally averaged values (b, d) under two different uptake coefficients
(γ = 0.03, and 0).
Same as Fig. 4 except for [TMA].
A comparison of simulated and measured [MMA], [DMA], and [TMA] at
the sites listed in Table 2 and marked in Fig. 1 by letters. Model results
correspond to the months of the observations, and vertical bars define the
simulated ranges of monthly mean values.
It can be seen from Table 1 that gas phase oxidation and aerosol uptakes are
dominant sinks for methylamines (Table 1). As expected, the uptake sinks are
sensitive to γ when γ > ∼0.001
and the oxidation becomes more important when γ is smaller.
The change of γ from 0.03 to 0.001 increases the
modeled global burdens of methylamines by a factor of ∼2.7.
Further decrease of γ from 0.001 to 0 has relatively small effects
on the predicted burdens. Dry and wet deposition accounts for 11–14 % and
25–35 % of the sinks when γ = 0.03 and γ = 0,
respectively. The global burdens of MMA, DMA, and TMA are respectively from
0.07 to 0.27 Gg N, 0.03 to 0.08 Gg N, and 0.24 to 0.72 Gg N as γ
changes from 0.03 to 0. The ratios of ammonia burden to that of methylamines
(MMA + DMA + TMA) range from 74 (γ = 0) to 236 (γ = 0.03).
The burdens are roughly but not strictly proportional to emission fluxes
because of the difference in the oxidation rates and deposition velocities
(which also depend on molecular weights).
Figure 2 shows the simulated horizontal distributions of the annual mean DMA
oxidation and uptake lifetime (τ, calculated as the ratio of the
burden in each gird box to the corresponding sinks associated with oxidation
and uptake) and concentration ([DMA]) in the model surface layer (0–150 m
above surface) under two aerosol uptake coefficients: (a, b) γ = 0
(i.e., oxidation only) and (c, d) γ = 0.03 (uptake by sulfuric acid
particles). The corresponding zonally averaged vertical distributions of
τ and [DMA] are given in Fig. 3. The oxidation only condition (i.e.,
no aerosol uptake) leads to a DMA lifetime of 5–10 h in most parts of
lower and middle latitude regions, from the surface to the upper
troposphere. The oxidation lifetime is relatively long (from 10 to
>200 h) over the high latitude regions due to low OH
concentrations there. The aerosol uptake with γ = 0.03 (upper limit,
corresponding to the uptake by sulfuric acid particles) shortens the
lifetime of DMA by ∼30 % over oceans and much more over the
major continents, resulting in a DMA lifetime of less than 1–2 h over
central Europe, eastern Asia, and the eastern US (Fig. 2c). Our sensitivity
study indicates that τ values decrease with increasing γ when
γ > 0.001 but become relatively insensitive to γ
when γ < 0.001, as oxidation dominates the lifetime under
this condition.
As a result of a short lifetime, high values of [DMA] are generally confined
to the source regions (Figs. 1, 2b, d). Depending on the uptake
coefficients, [DMA] in the surface layer over major continents is in the
range of 0.1–2 ppt when γ = 0.03 (Fig. 2d) and 0.2–10 ppt
when γ = 0 (Fig. 2b). [DMA] decreases quickly with altitudes, with
zonally averaged values dropping below 0.1 ppt a few hundred meters above
the surface (Fig. 3b, d). [DMA] over oceans are below 0.05 ppt and these
DMA are emitted from marine organisms (Fig. 1) rather than transported from
continents. [DMA] over polar regions is below 0.01 ppt (Figs. 2, 3) due
to the lack of emissions there (Fig. 1).
The annual mean horizontal and vertical distributions of MMA and TMA
concentrations ([MMA], [TMA]) under two γ values (0.03, and 0) are
shown in Figs. 4 and 5. As a result of identical emission spatial distributions
(assumed) and short lifetimes, [MMA] and [TMA] have similar spatial
distributions as those of [DMA]. [MMA] is generally a factor of
∼2.5 higher than [DMA], reaching 0.2–5 ppt when γ = 0.03
(Fig. 4c) and 0.5–20 ppt when γ = 0 (Fig. 4a) in the
surface layer over major continents. While the oxidation rate of MMA is
smaller than that of DMA, its deposition velocity is larger. As a result,
the [MMA] to [DMA] ratio is close to the ratio of the corresponding global
emission fluxes. In contrast, both the oxidation and deposition velocities of TMA
are smaller than those of DMA, leading to a larger [TMA] to [DMA] ratio
(∼8) than the corresponding ratio of emission fluxes
(∼5). [TMA] in the surface layer over major continents
reaches 1–10 ppt when γ = 0.03 (Fig. 5c) and 2–50 ppt when
γ = 0 (Fig. 5a). Similar to [DMA], [MMA] and [TMA] decrease
quickly with altitudes, down to <0.1 ppt above ∼800 mb (Figs. 4b, d, 5b, d).
Figure 6 compares the simulated [MMA], [DMA], and [TMA] with measurements at
the sites listed in Table 2 and marked in Fig. 1. The modeling results under
four γ values (0.03, 0.01, 0.001, and 0) are given. It should be
noted that the model results in Figs. 2–5 are annual mean values, while most
of the methylamine data are from various field measurements that lasted
everywhere from less than 1 day to a few months (Table 2). Owing to large seasonal
variations, the model results corresponding to the months of the observations
are used for comparisons with observations in Fig. 6. The vertical bars in
Fig. 6 (for γ = 0.03 and 0 cases only) define the simulated ranges
of monthly mean concentrations of methylamines.
Based on very limited measurements currently available (Table 2), [DMA]
in urban areas is smaller than those in rural and coastal areas while
[MMA] and [TMA] in these regions do not show a systematic difference.
Over the Arabian Sea, measurements of two periods
differ by a factor of 5 for [DMA] and by a factor of 10 for [TMA],
indicating a large temporal variation in [DMA] and [TMA] concentrations at
some locations. It is clear in Fig. 6 that the model predictions of
methylamines are substantially lower than the limited observed values
available, with normalized mean bias (NMB) ranging from -57
(γ = 0) to -88 % (γ = 0.03) for MMA and TMA, and from
-78 (γ = 0) to -93 % (γ = 0.03) for DMA. [MMA] and [TMA] are
relatively closer to observed values, especially when
γ < ∼0.001. It appears that the simulated [DMA] values are close to the
measured values for the three urban sites (A, B, and C) (Fig. 6b).
It is unclear how much the underestimation is associated with the spatial
(2∘ × 2.5∘ model grid box with a depth of ∼150 m
versus measurements at given sites near the surface) and temporal (model monthly
mean versus measurements of a few days to a few weeks) average. The seasonal
variations of simulated concentrations of methylamines are generally within
a factor of 2–5. As we can see in Figs. 2–5 and Table 1, concentrations
of methylamines are roughly proportional to the emission fluxes.
Methylamine emissions in certain regions could be much larger, while in
other regions much lower than those shown in Fig. 1. Due to the short
lifetime of these amines, long-range transport is not important, and thus the
observed amine concentrations (together with their lifetime) can be used to
estimate the emission strength in the region. If the measurements are
representative and reflect the real methylamine concentrations, the
underprediction of methylamines by 1–2 orders of magnitude in some
sites (Fig. 6) may indicate that the methylamine emissions in these regions
are 1–2 orders of magnitude larger than those assumed in this study
(Fig. 1, Table 1), at least around the sites of the measurements.
Apparently, long-term measurements of amines at more locations are needed to
evaluate the potential importance of amines.
Summary and discussion
As a result of the substitution by one or more organic functional groups,
amines have a stronger basicity than ammonia and may participate in new
particle formation in the atmosphere. To integrate the various processes
controlling amine concentrations and understand the concentrations of key
amines and their spatiotemporal variations in the atmosphere, we simulate
the global distributions of amines in the air with a global chemistry
transport model (GEOS-Chem), focusing on methylamines (MMA, DMA, and TMA) in
this study.
We showed that gas phase oxidation and aerosol uptakes are dominant sinks
for methylamines. The uptake sinks are sensitive to γ when γ > ∼0.001 and the
oxidation becomes more important when γ is smaller. The oxidation
only (i.e., no aerosol uptake) leads to a methylamine lifetime of 5–10 h
in most part of low and middle latitude regions, from the surface to
the upper troposphere. The oxidation lifetime is relatively longer
(>10–50 h) over the high latitude regions due to low OH
concentrations there. The aerosol uptake with γ
of 0.03 reduces the lifetime of methylamines by ∼30 % over
oceans and much more over the major continents, resulting in methylamine
lifetimes as short as 1–2 h over central Europe, eastern Asia, and eastern
US. Depending on γ values, [DMA] in the surface layer over major
continents is in the range of 0.1–2 ppt when γ = 0.03 and
0.2–10 ppt when γ = 0, and much smaller than over oceans are polar regions
(<0.01–0.05 ppt). Compared to [DMA], [MMA] is generally a factor
of ∼2.5 higher while [TMA] is a factor of ∼8
higher. Concentrations of methylamines decrease quickly with altitudes, with
zonally averaged values dropping below 0.1 ppt above the boundary layer.
The simulated concentrations of methylamines are substantially lower than
the limited observed values available, with normalized mean bias (NMB)
ranging from -57 (γ = 0) to -88 % (γ = 0.03) for
MMA and TMA, and from -78 (γ = 0) to -93 % (γ = 0.03)
for DMA. The underestimation cannot be explained by the possible uncertainty
in the uptake coefficients and long-range transport. The concentrations of
methylamines are roughly proportional to their emission fluxes, and thus the
model underprediction by 1–2 orders of magnitude at some sites may
indicate that the methylamine emissions in these regions are 1–2
orders of magnitude higher than those assumed in this study. It should be
noted that methylamine measurements are very limited and subject to large
uncertainty as well because of their low concentrations and short lifetime.
Amines have been suggested to be the most likely compound to sequester
carbon dioxide and there exists concern about the potential impacts of
substantial increases in future amine emissions (Nielsen et al., 2012). Our
study indicates that the impact of amine emissions from carbon sequestration
is likely to be local rather than global as a result of their short
lifetime. The low concentrations of amines away from source regions
(<0.1–1 ppt) suggest that the impact of amines on global new
particle formation may be quite limited. Nevertheless, amines can exceed a
few ppt over the main source regions and thus may substantially enhance new
particle formation. It should be noted that about 150 amines have been
identified in the atmosphere and amines of different kinds are likely to
have different abilities in stabilizing prenucleation clusters. It is
important to identify those amines with abundant concentrations in the
atmosphere and study their ability in enhancing new particle formation. We
would like to emphasize that the present global simulations of methylamines
are subject to uncertainties associated with emissions, uptake coefficients,
and chemistry. Further laboratory study, field measurement, and numerical
modeling are needed to advance our understanding of spatiotemporal
distributions of key amines and to evaluate their contributions to new
particle formation and growth in the global atmosphere.