Introduction
Gaseous amines may play an important role in new particle formation
and growth based on chamber experiments, theoretical calculations, and
field observations (Kurtén et al., 2008; Almeida et al., 2013;
Zhao et al., 2011; Erupe et al., 2011; Chen et al., 2012; Yu et al.,
2012; You et al., 2014; Chen et al., 2016;
Jen et al., 2016; Olenius et al., 2017). CLOUD (Cosmics Leaving
OUtdoor Droplets) chamber experiments (Almeida et al., 2013)
demonstrate that dimethylamine (DMA) of above 3 pptv can
enhance nucleation rate by more than 1000-fold. Lehtipalo
et al. (2016) reported that the growth rate of sub-3 nm
particles at a given H2SO4 monomer concentration was enhanced
by a factor of 10 with addition of >5 pptv DMA, compared to
a factor of 2–3 enhancement when NH3 of >100 pptv was added. As ubiquitous atmospheric organic bases,
amines can form ammonium salts by acid–base reactions (Murphy et al.,
2007; Kürten et al., 2014; Lehtipalo et al.,
2016; Tao et al., 2016). In addition to dry and wet deposition, the
concentrations of amines in the air decrease through oxidization
reactions with OH, NOx, and ozone (Carl and Crowley, 1998;
Murphy et al., 2007; Nielsen et al., 2012) and uptake by particles
(Qiu et al., 2011; Zhang et al., 2012; Qiu and Zhang,
2013). There are about 150 gaseous amines identified in the
atmosphere, but little is known about their thermodynamic and kinetic
properties and their importance in the atmosphere (Ge et al.,
2011). While measurements of amines in different environments (e.g.,
rural, urban, marine, and forest) have been reported (Sellegri et al.,
2005; Hanson et al., 2011; VandenBoer et al., 2011; Yu and Lee,
2012; Freshour et al., 2014; You et al., 2014; Zheng
et al., 2015; Yao et al., 2016), they are very limited, especially in
China. Zheng et al. (2015) measured C1-, C2-, and C3- amines at
a suburban site of Nanjing, China, during the summer of 2012 and they
reported an average total amines value of 7.4±4.7 pptv. Similar measurements of amines were conducted at
Fudan University, an urban site in Shanghai, China, during the summer
of 2015 and the observed mean concentrations of gaseous C1–C6 amines
were 15.7±5.9, 40.0±14.3, 1.1±0.6, 15.4±7.9,
3.4±3.7, and 3.5±2.2 pptv, respectively (Yao
et al., 2016). The results in both Nanjing and Shanghai suggest that
amines-enhanced particle formation and growth may be important in the
Yangtze River Delta, one of the highly polluted regions in China.
It is necessary and important to know the concentrations of key amines
and their variations in order to understand the role of amines in
particle nucleation and growth. In this regard, numerical models can
be useful in simulating the distributions of amines on regional or
global scales. To our knowledge, only three modeling studies of amines
have been reported in the literature, all on a global scale
(Myriokefalitakis et al., 2010; Yu and Luo, 2014; Bergman et al.,
2015). Myriokefalitakis et al. (2010) investigated the potential
contribution of amines emitted from oceans to secondary organic
formation (SOA) formation, assuming total amine emissions to be
one-tenth of the oceanic ammonia emissions. They did not consider
amines from continental sources and also did not report any simulated
concentrations of gaseous amines over oceans. Yu and Luo (2014)
studied the global distributions of the most common and abundant
amines in the air: monomethylamine (MMA), dimethylamine (DMA), and
trimethylamine (TMA). They used the ratios of MMA, DMA, and TMA to
ammonia fluxes given in Schade and Crutzen (1995), but approximate the spatial distributions and seasonal
variations of amine emissions following those of ammonia. Bergman
et al. (2015) added one single (unified) alkylamine species that has
the physical and chemical properties of TMA into a global
aerosol–climate model, and assumed an amine-to-ammonia ratio of
0.0057 kg (amine (N)) kg-1 (ammonia (N)). Due to
the lack of information regarding the emission of amines from
different sources, these three previous studies (Myriokefalitakis
et al., 2010; Yu and Luo, 2014; Bergman et al., 2015) used fixed
amines to ammonia ratios to estimate amine emissions. While such an
approximation provides a first order of magnitude estimation of amine
emission, it may lead to large uncertainties in the model-predicted
concentrations of amines, especially their spatial distributions at
regional and urban scales. In fact, Yu and Luo (2014) showed that the
predicted concentrations of amines based on a global model, with
amines to ammonia ratios as reported in the literature, are
significantly lower than those observed. One possible reason for the
model underprediction is the uncertainty in amine emissions near the
sites of measurements.
Amines are emitted into the atmosphere from both natural and
anthropogenic sources, including animal husbandry, chemical
facilities, industry, carbon sequestration, combustion, fish
processing, automobiles, sewage, composting operations, vegetation,
soil, biomass burning, and the oceans (Ge et al., 2011). In many
situations, amines are co-emitted with ammonia, but the ratios of
amines to ammonia from various sources may differ significantly and
there may also exist stand-alone sources of amines (Kuhn et al., 2011;
Zheng et al., 2015). For example, measurements have indicated that
industrial amine emission may be important sources in Nanjing (Zheng
et al., 2015). Kuhn et al. (2011) concluded that amines in
agricultural regions are mainly released from animal housing and
grazing animals, in contrast to ammonia, which is mostly emitted into
the atmosphere from agricultural fertilizers. Bergman et al. (2015)
also pointed out that the direct calculation of amine emissions based
on ammonia can skew the spatial extent of the amine emission and
emphasized a clear need for improved estimates of amine emissions from
different emission sectors.
Apparently, there is a clear need to better understand emissions of
amines from various source types and to improve the model simulations
of concentrations of amines and their spatial distributions. The main
objective of this study is to estimate amine emissions from five
different source types (chemical industry, other industry,
agriculture, residential, and transportation) and simulate spatial
distributions of gaseous amines over the Yangtze River Delta region in
China, using recently available amines measurements and year 2014
emission inventories in the region for various emission sectors with
4km×4km horizontal resolution. The
observational data used to constrain model simulations includes
continuous measurements of amines during a 1-month period (summer of
2015) at an urban site in Shanghai, China (Yao et al., 2016), and
a 1-week period (summer of 2012) at a suburban site in Nanjing,
China (Zheng et al., 2015).
Methods
Emission inventory for anthropogenic sources
Anthropogenic particulate and gas emissions for Asia and China are
based on INTEX-B (Zhang et al., 2009) and
Multiple-resolution emission inventory for China (MEIC) developed by Tsinghua
University (http://www.meicmodel.org, last access: 4 June 2018), respectively. The emissions for
SO2, NOx, CO, VOC, PM10, PM2.5, and
primary black carbon and organic carbon are included in the INTEX-B database
with 0.5∘ × 0.5∘ horizontal resolution, and (with
NH3 emissions as well) in the MEIC database with
0.25∘ × 0.25∘ horizontal resolution.
To improve the emission accuracy and spatial resolution for the
Yangtze River Delta region, we employ a refined bottom-up emission
inventory (4km×4km resolution) for the year
2014 developed by the Shanghai Academy of Environmental Sciences
(SAES). The SAES 2014 inventory includes anthropogenic emissions from
various chemical, industrial, vehicular, shipping, agricultural, and
residential sources. The SAES 2014 inventory is updated from the
previous work (Huang et al., 2011; Li et al., 2011), which consists of
large point sources, industrial, mobile, residential, and agricultural
sources. Point sources in this inventory consist of power plants and
large industrial combustion and processing sources. The point sources
data are obtained from a national environmental statistical
database. Mobile sources consist of on-road vehicle, non-road vehicle,
and ship emissions. The vehicle volume data, residential fuel
combustion, and the activity data of agriculture sources including the
amount of livestock and fertilizer consumption are obtained from the
statistical yearbooks of the 41 cities in the Yangtze River Delta. The
detailed information about estimation of ship emissions is given in
Fan et al. (2016).
For the temporal variations of emissions, we used profiles derived
from local investigation for different emissions sources (Tan et al.,
2015). For the spatial distributions of various emissions, ArcGIS was
used to distribute area and stack sources in the emission
inventory. Stack sources were allocated into grid cells based on their
geographical positions. The height of stack emissions ranges from 20
to 250 m were based on NOx and PM10
emission flux (Tan et al., 2015). Mobile, residential, and
agricultural emissions were treated as area sources and distributed
into corresponding grid cells.
Amine emissions
It is presently impossible to develop either a global or regional
bottom-up emission inventory of amines due to insufficient direct
measurements. The fixed amines to ammonia ratio assumed in two
previous global studies (Yu and Luo, 2014; Bergman et al., 2015)
resulted in higher concentrations of amines in agricultural areas than
in other areas because agriculture dominates NH3
emissions. However, very high concentrations of amines at an urban
site have been reported (Yao et al., 2016), indicating strong amines
sources not associated with agricultural activities. A refined amine
emission inventory is apparently needed.
Low-molecular-weight amines are the most common among about
150 amines that have been identified so far. The present study
focuses on C1-amine (CH3NH2), C2-amines
(C2H7N), and C3-amines (C3H9N). In contrast
to previous modeling studies assuming a fixed ratio (FR) of amines to
total ammonia emission, we take into account the dependence of C1-,
C2-, and C3-amines-to-ammonia ratios on five different source types
(chemical industry, other industry, agriculture, residential,
transportation). Agriculture includes livestock, biomass burning,
soil, and fertilizer usage. Ammonia is emitted from fertilizer plants
by volatilization, which is similar to ammonia volatilization in
soil. Hence, we group fertilizer plants into agricultural sources. The
chemical–industrial source type includes emissions from
petrochemicals, pharmaceuticals, agrochemicals excluding fertilizer
plants, paints, fine chemicals, and solvent use industries, while
other industrial type includes power plants, iron and steel mills,
cement, carbon sequestration, food industry (e.g., fish processing),
and other industry boilers. Residential source type includes cooking,
human waste, and gas (water) disposal, while transportation includes
automobiles and ships.
Zheng et al. (2015) simultaneously measured NH3, C1-, C2-, and
C3-amines, NOx and SO2 using an aerodyne HR-ToF-CIMS
with high time resolution at Nanjing University of Information Science
and Technology (NUIST), a suburban site in Nanjing, China, from
26 August to 8 September 2012. The high-time-resolution HR-ToF-CIMS
data resolve individual plumes. Zheng et al. (2015) analyzed these
data in detail and identified the possible source types of plumes
based on the differences in the concentrations of SO2 and
NOx along with wind directions. Table 1 gives the ratios of
concentrations of C1-, C2-, and C3-amines to ammonia for individual
plumes with different origins as identified by the authors. The ratios
were derived from the peak values of plumes simultaneously measured
concentrations of ammonia, C1-, C2-, and C3-amines shown in Fig. 6 of
Zheng et al. (2015). It is noteworthy that the ratios derived from
peak values are comparable with those based on orthogonal distance
regression analyses (Zheng et al., 2015) and the differences are well
within the uncertainty. Table 1 summarizes the ratios of C1-, C2-,
and C3-amines to ammonia in four source types: other industry,
agriculture, transportation, and residential based on the plumes. For
the chemical industry, Zheng et al. (2015) reported the presence of
relatively high concentrations of amines (2.6 % of MA, 0.7 %
of C2-amines, and 0.04 % of C3-amines) in the ammonia water
solution from a local chemical supplier that has been used as
absorbent during flue gas treatment. With the above information, the
estimated amines to ammonia emission ratios are 0.026, 0.0015, 0.0011,
0.0011, and 0.0011 for C1-amine, 0.007, 0.0018, 0.0015, 0.01, and
0.0009 for C2-amines, and 0.0004, 0.0005, 0.00043, 0.0006, and 0.0004
for C3-amines for chemical–industrial, other industrial, agricultural,
residential, and transportational source types, respectively. We
acknowledge that the above estimation of amine emissions from
different sources is subject to a large uncertainty, mainly due to
very limited measurements available to constrain the estimation.
Nevertheless, the above approach represents the first attempt to
derive source-type dependent amines to ammonia ratios, which, as we
show below, improves the skill of the model in simulating
concentrations of amines in polluted regions. We derive regional C1-,
C2-, and C3-amine emissions based on SDR ratios and ammonia emissions
from five different source types. In the present study, the temporal
and spatial distributions of C1-, C2-, and C3-amines follow those of
ammonia for the five different sources (agriculture, residential,
transportation, chemical industry, and other industry), whose emission
frequency is hourly with a daily cycle.
The ratios of C1-, C2-, and C3-amines to that of ammonia from the
peak values of individual plumes with different origins, derived from
measurements taken at a suburban site of Nanjing as reported in Zheng et al. (2015).
Plume no.
Time
[C1-amine]/[NH3]
[C2-amine]/[NH3]
[C3-amine]/[NH3]
Source type identified
1
∼19:30 8/26
0.0010
0.0018
0.0002
other industry except for chemistry
2
∼21:00 8/29
0.0009
0.0012
0.0004
3
∼09:00 8/30
0.0009
0.0024
0.0008
4
∼16:00 8/30
0.0013
0.0018
0.0006
5
∼15:30 8/31
0.0032
0.0018
0.0005
6
∼09:00 8/28
0.0010
0.0015
0.0003
agriculture
7
∼14:00 8/28
0.0012
0.0014
0.0006
8
∼08:00 8/29
0.0011
0.0009
0.0004
transportation
9
∼21:00 8/27
0.0011
0.0100
0.0006
residential
Model setup and configurations
We employ WRF-Chem (version 3.7.1), a regional multi-scale meteorology
model coupled with online chemistry (Grell et al., 2005). Ammonia is
simulated in the standard version of WRF-Chem, but amines are not
considered in previous studies. To simulate gaseous amines, we add
three new tracers (C1-amine, C2-amines, and C3-amines) in
WRF-Chem. The model configurations include Morrison2-mom microphysics
(Morrison et al., 2009), RRTMG longwave and shortwave radiation
(Clough et al., 2005), Noah land surface, Grell-3
cumulus (Grell and Freitas, 2014), and YSU PBL scheme (Hong et al.,
2006). For gas-phase chemistry, we use the CB05 scheme (Yarwood et al.,
2005). The surface areas of pre-existing particles, important for the
uptake of amines in the atmosphere, are calculated from particle size
distributions predicted by an advanced particle microphysics (APM)
model embedded into WRF-Chem (Luo and Yu, 2011). The initial and boundary conditions for meteorology are
generated from the National Centers for Environmental Prediction
(NCEP) Final (FNL) with horizontal resolution at 1∘×1∘ and time intervals at 6 h. The detailed
anthropogenic emissions are described in Sect. 2.1, and the biogenic
emissions are calculated online using MEGAN (Guenther et al.,
2006). After emissions, gaseous amines are removed by dry and wet
deposition, gas-phase reaction, and aerosol uptake (Yu and Luo, 2014;
Bergman et al., 2015). Yu and Luo (2014) showed that gas-phase
oxidation and aerosol uptake dominate removal of amines. In the
present study, the oxidation of C1-, C2-, and C3-amines by OH is
considered, with the reaction coefficients of
1.79 × 10-11, 6.49 × 10-11, and
3.58 × 10-11 cm3 molecule-1 s-1
for C1-, C2-, and C3-amines (Carl and Crowley, 1998),
respectively. The reactions of amines with NOx and
O3 are quite small (Ge et al., 2011) and thus are not
considered (Yu and Luo, 2014). The uptake of amines by particles is
calculated from particle surface areas derived from simulated particle
size distributions and uptake coefficient (γ), a main
uncertainty of gas-to-particle partitioning. Based on laboratory
measurements, the uptake coefficient was found to range from ∼4.4× 10-2 to 2.3 × 10-4 (Wang et al., 2010; Qiu
et al., 2011). In the numerical modeling, Yu and Luo (2014) carried
out sensitivity study with γ values ranging from 0 to 0.03,
while Bergman et al. (2015) assumed γ to be 0.002. We assumed
γ to be 0.001 in our baseline case simulations with γ=0.01 and 0.03 for sensitivity tests. The dry and wet deposition of
amines is treated in a way similar to that of ammonia.
WRF-Chem/APM is used for four nested domains simulations with
horizontal resolutions of 81, 27, 9, and 3 km
(Fig. 1) and vertical resolution of 22 layers
(from surface to ∼11.8 km) with 8 levels below
1.5 km. Domain 1 covers East Asia and part of southeast
Asia. Nested domains 2, 3, and 4 cover a large part of East China, the
Yangtze River Delta (including Nanjing and Shanghai), and Shanghai
with the complex underlying surface, respectively.
Four nested domains in the present study. Domain 1 covers
East Asia and part of southeast Asia. Nested domains 2, 3, and 4
cover a large part of East China, the Yangtze River Delta
(including Nanjing, Shanghai), and Shanghai, respectively.
Our simulations focus on two periods during which continuous
measurements of amines are available: (1) 26 to 31 August 2012, and
(2) 25 July to 25 August 2015. The model spin-up time is 3 days for
each case. For each period, two separate simulations were carried out:
one assumes a fixed ratio (FR) of amines to ammonia emissions used in
all previous studies (Myriokefalitakis et al., 2010; Yu and Luo, 2014;
Bergman et al., 2015), and the other one employs source-dependent
ratios (SDR) as described in Sect. 2.2. Table 2 summarizes the four
simulation cases: FR2012, SDR2012, FR2015, and SDR2015. For the two FR
cases, the ratios of amines to ammonia emissions for C1-, C2-, and
C3-amines for all source types, estimated from the global emission
budgets given in Schade and Crutzen (1995), are 0.0017, 0.0007, and
0.0034, respectively. For the two SDR cases, we also carry out
a sensitivity study by halving and doubling the ratios given in
Table 1. For the SDR2015 case, a sensitivity study for different
uptake coefficients (γ=0.01, 0.03) has been carried out and
the results are shown in Table 2.
Results
Contribution of methylamine emissions from various source
types
Ammonia, C1-, C2-, and C3-amine emission rates based on SDR and
ammonia emissions in the Yangtze River Delta for residential,
agricultural, other industrial, chemical–industrial, and
transportational sources are summarized in Table 3. Ammonia emission
rate in the Yangtze River Delta region is
919.61 GgNyr-1, and total C1-, C2-, and C3-amine
emission rates based on SDR (FR) are estimated as 551.88 (1563.34),
849.11 (643.73), and 117.78 (3126.67) MgNyr-1,
respectively. The significant difference in the estimated emission
rates of amines in the region can be clearly seen, especially for C1-
and C3-amines. Based on SDR, the contributions of agricultural,
residential, transportational, other industrial, and
chemical–industrial sources to domain-averaged methylamines
(C1-amine + C2-amines + C3-amines) are 66.04, 30.81, 1.61,
0.81, and 0.73 %, respectively. Agricultural source type is
the largest contributor for C1-, C2-, and C3-amines, while residential
is another main contributor, especially for C2-amines (∼46 %).
The horizontal emission flux distributions for C1-amine:
(a) residential; (b) agriculture;
(c) other industry; (d) chemical industry;
(e) transportation; (f) total.
Same as Fig. 2 but for C2-amines.
Same as Fig. 2 but for C3-amines.
The horizontal distributions of C1-amine, C2-amines and C3-amines from
different sources and total emission fluxes are presented in
Figs. 2–4. The emission fluxes for C1-amine, C2-amines, and
C3-amines, respectively, from five sources are mainly in the range of
0.1–10, 1–100, and 0.05–6 MgNkm-2yr-1 from
residential sources (Figs. 2a–4a), 0.1–50, 0.5–60, and
0.1–8 MgNkm-2yr-1 from agriculture
(Figs. 2b–4b), 0.01–1, 0.01–3, and
0.01–0.6 MgNkm-2yr-1 from other industry
(Figs. 2c–4c), 0.01–20, 0.01–10, and
0.01–0.03 MgNkm-2yr-1 from chemical industry
(Figs. 2d–4d), and 0.01–0.8, 0.01–0.6, and
0.01–0.3 MgNkm-2yr-1 from transportation
(Figs. 2e–4e). Total emission flux of C2-amines is in the range of
0.1–100 MgNkm-2yr-1 over land in the
Yangtze River Delta and below 0.01 MgNkm-2yr-1
over ocean near Yangtze River Delta (Fig. 3f). For C1-amine and
C3-amines, the total emission fluxes are 0.1–50 and
0.1–6 MgNkm-2yr-1 and less than
0.01 MgNkm-2yr-1 over oceanic area (see Figs. 2f
and 4f). As mentioned earlier, we assumed that the spatial
distributions of methylamines from five sources (agriculture,
residential, transportation, other industry, and chemical industry) to
be the same as those of ammonia. As can be seen from Figs. 2f–4f, the
horizontal distributions of total C1-, C2-, and C3-amine emission
fluxes are different from those of ammonia (not shown), especially
over agricultural areas for C2-amines. To assess the effect of amine
emission assumptions, comparisons of simulated C1-, C2-, and C3-amines
based on the SDR approach in the present study with the FR method used in
previous studies (e.g., Yu and Luo, 2014) with those observed at
a suburban site (NUIST site, Nanjing, China) and an urban site (Fudan
site, Shanghai, China) are given in the next section.
Simulation cases in the study.
Case
Methods of calculating methylamine emissions
Simulation periods
FR2012
Fixed ratios (Schade and Crutzen, 1995)
26–31 Aug 2012
SDR2012
Source-dependent ratios (this study)
FR2015
Fixed ratios (Schade and Crutzen, 1995)
25 Jul–25 Aug 2015
SDR2015
Source-dependent ratios (this study)
0.5 SDR2012
Source-dependent ratios (this study)
26–31 Aug 2012
2 SDR2012
0.5 SDR2015
25–31 Jul 2015
2 SDR2015
SDR2015(γ=0.01)
Source-dependent ratios (this study)
25–31 Jul 2015
SDR2015(γ=0.03)
Emission rates of ammonia, C1, C2, C3-amines from different sources
based on SDR for domain 3.
Ammonia
C1-amine
C2-amines
C3-amines
Agriculture
785.20
460.73
444.94
97.28
Residential
103.09
62.19
389.47
16.34
Transportation
23.19
13.48
7.88
3.01
Other industry
7.47
6.15
5.08
1.08
Chemical industry
0.65
9.32
1.73
0.08
Total
919.61
551.88
849.11
117.78
Notes: ammonia is in units of Gg(N)yr-1 and C1-, C2-, and
C3-amines in units of Mg(N)yr-1.
Comparisons of simulated and observed wind direction at
10 m (a), wind speed at 10 m (b),
C1-amine (c), C2-amines (d), and
C3-amines (e) concentrations at the NUIST site in Nanjing,
China, from 26 to 31 August 2012. In (c–e), black, red, and
green lines represent observations, simulated values in domain 3
based on SDR, and simulated values in domain 3 based on FR,
respectively.
Comparisons of simulated and observed wind
direction (a), wind speed (b),
C1-amine (c), C2-amines (d), and
C3-amines (e) concentrations at the Fudan site in Shanghai,
China from 25 July to 25 August 2015. In (c–e), black, blue,
red, and green lines present observations, simulated values in
domain 4 based on SDR, domain 3 based on SDR, and domain 3 based on FR,
respectively.
Comparisons of simulations with observations
Figures 5 and 6 compare wind fields and concentrations of C1-, C2-,
and C3-amines simulated using FR and SDR with measurements at the NUIST
site in Nanjing, China (FR2012 and SDR2012, Fig. 5), and the Fudan site in
Shanghai, China (FR2015 and SDR2015, Fig. 6). Simulated wind direction
at the NUIST site (Fig. 5a) is overall consistent with observations, and
so is wind speed at 10 m (Fig. 5b), except that the model
overpredicted for 28 August to midday of 29 August. For the Fudan
site, model simulations (Fig. 6a) generally reproduce observed wind
direction, although there exist large differences during some
periods. The simulated wind speeds at 10 m (Fig. 6b) are in
agreement with observations, except during the periods of 7–14 and
23–25 August. These deviations may be caused by local underlying
surface or other physical parameters in the complex urban environment.
Mean values and normalized mean biases (NMBs) are given in Table 4 to
summarize the statistical performance of model-calculated C1-, C2-, and
C3-amines for different cases. Previous global simulations (Yu and
Luo, 2014) show general underprediction of the model (NMB values of
-61.4 % for C1-amine, -79.9 % for C2-amines, and
-60.9 % for C3-amines), while this study indicates that
concentrations of amines based on the model with high spatial
resolution can also be overpredicted. Overall, simulations based on
SDR are in much better agreement with measurements than those based on
FR, especially for C2- and C3-amines. Replacement of FR with SDR
improves NMB for C2-amines from -71.5 to 49.12 % at the NUIST
site and from -96.13 to -37.43 % at the Fudan site, while NMB
improves for C3-amine from 359.02 to -41.26 % at the NUIST site
and from 494.28 to 21.34 % at the Fudan site. The different
performance of the model in the NUIST and Fudan sites is probably due
to, but not limited to, uncertainties in meteorology fields, amine
emissions and loss processes, and model resolutions. For C1-amine,
both FR and SDR overpredict the concentrations by a factor of 2–3 at
the NUIST site, while they underpredict by a factor of 3–4 at the Fudan
site. A comparison of simulated C1-, C2-, and C3-amines in domain 4 at a
resolution of 3km×3km (blue lines in
Fig. 6c–e) with those in domain 3 (9km×9km
horizontal resolution) (red lines in Fig. 6c–e) shows that the
concentrations in domain 4 are generally higher, especially for peak
values. As can be seen from the NMB values, domain 4 values are in
better agreement with observations, highlighting the importance of
high-resolution modeling in resolving the spatial variations in urban
environments. It should be noted that the model-predicted C1- and
C2-amines at the Fudan site for the period of 7–19 August are much
lower than the observed values (Fig. 6c–d), at least partially due to
the large deviation of the simulated wind directions and speeds during
the period (Fig. 6a–b).
Comparisons of simulated concentrations of C1-, C2-,
C3-amines in SDR2012, 0.5 SDR2012, and 2 SDR2012 (domain 3) at the
NUIST site with measurements from 26 to 31 August 2012. Black, red,
green, and blue lines present observations, simulated values in
0.5 SDR2012, SDR2012, and 2 SDR2012, respectively.
Same as Fig. 7 but for 0.5 SDR2015, SDR2015, and 2 SDR2015
(domain 4) at the Fudan site from 25 to 31 July 2015.
Statistical performance of methylamine simulation at the NUIST site
(cases: FR2012 and SDR2012) in domain 3 and the Fudan site (cases: FR2015 and
SDR2015) in both domain 3 and domain 4 (values given in parentheses). The
details of each case are given in Table 2.
Case
Variable
No. samples
Obs. ave
Sim. ave (domain 4)
NMB (domain 4)
NUIST FR2012
C1-amine
61
4.35
8.97
106.72
C2-amines
61
7.08
1.99
-71.50
C3-amines
61
1.91
8.64
359.02
NUIST SDR2012
C1-amine
61
4.35
6.39
45.60
C2-amines
61
7.08
10.56
49.12
C3-amines
61
1.91
1.12
-41.26
Fudan FR2015
C1-amine
719
15.71
6.79 (9.26)
-56.75 (-41.03)
C2-amines
719
40.20
1.56 (2.15)
-96.13 (-94.67)
C3-amines
719
1.13
6.71 (9.24)
494.28 (718.61)
Fudan SDR2015
C1-amine
719
15.71
4.97 (6.61)
-68.37 (-57.95)
C2-amines
719
40.20
16.33 (25.15)
-59.37 (-37.43)
C3-amines
719
1.13
1.01 (1.37)
-10.84 (21.34)
Notes: Obs. ave and Sim. ave are in units of pptv and NMB is in units of %.
As mentioned in Sect. 2.2, there exist large uncertainties in
methylamine emissions because of the very limited observations
available. To evaluate the effect of uncertainties in emissions on
simulated concentrations of amines, we also carry out a sensitivity
study for the two SDR cases by halving and doubling the five source
ratios simultaneously, defined as 0.5 SDR2012, 2 SDR2012, 0.5 SDR2015,
and 2 SDR2015, respectively. In this sensitivity study, only emission
ratios were changed, with other processes including deposition,
oxidation, and uptake for amines unchanged. For 0.5 SDR2012 and
2 SDR2012, simulations focus on the period from 26 to 31 August 2012
(the same as the SDR2012 case), while for 0.5 SDR2015 and 2 SDR2015
the simulated period is from 25 July to 31 July 2015 when the model
reproduced relatively well the wind fields (Fig. 6). Comparisons of
simulated concentrations of C1-, C2-, and C3-amines in SDR2012,
0.5 SDR2012, and 2 SDR2012 at the NUIST site (domain 3) and SDR2015,
0.5 SDR2015, and 2 SDR2015 at the Fudan site (domain 4) with
measurements are shown in Figs. 7 and 8, with corresponding NMB values
summarized in Table 5. As expected, simulated concentrations of amines
are sensitive to the assumed emission ratios and it is clear that the
uncertainties in emission ratios can account for a large fraction of
difference in the simulated and observed concentrations. It should be
noted that, as a result of variations in human activities and/or
operation conditions of facilities associated with amine emissions in
the real atmosphere, the amines to ammonia emission ratios from
a given source sector may vary with time, which may lead to the spikes
in the observed concentrations of amines that are missed by the model
simulations.
Variations in normalized mean biases (NMBs) of methylamine
simulations when amine emission rates are halved or doubled, at the NUIST site
(SDR2012, 0.5 SDR2012, 2 SDR2012) in domain 3 and the Fudan site
(SDR2015, 0.5 SDR2015, 2 SDR2015) in domain 3 and domain 4 (values
given in parentheses).
Sensitivity case
C1-amine
C2-amines
C3-amines
SDR2012
45.60
49.12
-41.26
0.5 SDR2012
-27.96
-25.73
-74.31
2 SDR2012
193.13
199.01
88.97
SDR2015
-74.95 (-51.23)
-58.07 (-9.73)
-17.13 (69.62)
0.5 SDR2015
-81.33 (-75.99)
-69.82 (-55.10)
-42.07 (-28.51)
2 SDR2015
-24.23 (-2.00)
21.19 (80.34)
388.59 (513.09)
Time series of observed and simulated concentrations of C1-,
C2-, and C3-amines with γ=0.001, 0.01, and 0.03 from 25 to
31 July 2015 at the Fudan site.
To explore the effect of uncertainty in uptake coefficients on
simulated concentrations of amines, we have carried out sensitivity
studies using two different uptake coefficients (γ=0.01,
0.03) for SDR2015 from 25 to 31 July 2015. Figure 9 shows time series
of observed and simulated concentrations of amines' different γ
values (0.001 0.01, 0.03) at the Fudan site from 25 to 31 July 2015, while
Table 6 gives the corresponding NMBs. It can be seen from Fig. 9 that
the effect of uptake coefficients is larger during the night time when
the oxidation sink is small. Simulated C1- and C2-amines are closer to
observations with γ=0.001 (NBM =-51.23, -9.37 %)
compared to γ=0.01 (NBM =-57.17, -14.98 %) and
γ=0.03 (NBM =-64.33, -23.02 %), while C3-amines are
in better agreement with measurements when γ=0.03 with NMB of
69.62, 54.79 %, and 31.84 % for γ=0.001, 0.01 and
0.03, respectively. The uncertainty in the uptake coefficient may
explain some of the differences between the simulated and observed
concentrations.
Variations in normalized mean biases (NMBs) of methylamines
simulations when aerosol uptake coefficients is 0.001, 0.01, and 0.03 at
the Fudan site in domain 4 during period from 25 and 31 July 2015.
Uptake coefficients (γ)
C1-amine
C2-amines
C3-amines
0.001
-51.23
-9.73
69.62
0.01
-57.17
-14.98
54.79
0.03
-64.33
-23.02
31.84
Comparisons of simulated and observed C2-amines at the Fudan
site for different wind direction zones: (a) wind
directions between 175 and 240∘ when the Fudan site is
downwind of high residential emissions; (b) other wind
directions. NMBd3_SDR,
NMBd4_SDR, and NMBd3_FR
represent normalized mean bias in domain 3 and domain 4 based on SDR
and domain 3 based on FR, respectively.
Simulated concentrations of C2-amines at 18:00 on
26 August 2012 and 02:00 on 29 July 2015 using SDR (a, c)
and FR (b, d) in domain 3 (horizontal resolution at
9km×9km).
The performance of simulations with different wind directions are of
varying quality at the Fudan site. Periods with difference in
simulated and observed wind directions within -30 to 30∘ are
selected for further comparisons. Figure 10 shows a close comparison
of simulated and observed concentrations of C2-amines with wind
direction between 175 and 240∘ (Fig. 10a) and other wind
directions (Fig. 10b) at the Fudan site. Figure 10 shows that simulated
concentrations of C2-amines with wind direction between 175 and
240∘ where the air mass was coming from residential areas (see
Fig. 3a) are more consistent with measurements
(NMBd3_SDR=-26.07 %,
NMBd4_SDR=7.03 %) than those from other wind
directions (NMBd3_SDR=-63.52 %,
NMBd4_SDR=-41.34 %), indicating that the
SDR-based residential emissions for C2-amines may be reasonable. It
can also be seen from Fig. 10 that FR assumption underpredicts
C2-amines by 1–2 orders of magnitude with NMB of -93.04 and
-96.29 % for wind direction between 175 and 240∘ and
other wind directions, respectively, highlighting the necessity to use
SDR in polluted urban areas such as Shanghai, China.
To illustrate further the difference in simulated amines for SDR and
FR cases and the effect of wind directions, we present in Fig. 11 the
horizontal distributions of simulated concentrations of C2-amines in
domain 3 (horizontal resolution 9km×9km) at
18:00 on 26 August and at 02:00 on 29 July 2015. As shown in Fig. 5d,
the observed C2-amines concentration at the NUIST site at 18:00 on
26 August is ∼19 pptv, and the corresponding simulated
value is slightly lower based on SDR (13.4 pptv), while it is
significantly lower (by a factor of ∼8) based on FR
(2.5 pptv). A similar difference can also be seen for the Fudan
site at 02:00 on 29 July 2015 (Fig. 6d). It can be seen from Fig. 11b
and d that the significantly lower predicted concentrations of
C2-amines are not limited to the NUIST and Fudan sites but to the
whole region. It is noteworthy that concentrations of C2-amines
downwind of heavy industrial zones (northeast of the NUIST site)
(Fig. 11a) is reproduced well, indicating the contribution of
industrial sources to concentrations of C2-amines observed at the
NUIST site. For the Fudan site, residential contribution from the
highly populated urban center is essential to maintain the relatively
higher concentrations of C2-amines.
As we show in this section, the results based on SDR are overall in
much better agreement with measurements than those based on FR assumed
in previous studies. Nevertheless, there still exist large differences
between SDR simulations and observations (Figs. 5–10). The
differences can be caused by many factors, including, but not limited
to, uncertainties in emission inventories (both ammonia and the
derived amines to ammonia ratios), meteorology, oxidation and aerosol
uptake of amines, and measurements. Further research is needed to
reduce these uncertainties.
Simulated horizontal distributions of mean concentrations of
C1-, C2-, and C3-amines in domain 3 and domain 4 during the period
of 25 July to 25 August 2015 (SDR2015 case).
Simulated vertical distributions of mean concentrations of
C1-, C2-, and C3-amines in domain 3 during the period of 25 July to
25 August 2015 (SDR2015 case).
Spatial distribution of methylamines over Yangtze River
Delta
Figure 12 presents simulated mean (25 July–25 August) surface layer
horizontal distributions of mean C1-, C2-, and C3-amines for the
SDR2015 case in the Yangtze River Delta region (left panels) and the
Shanghai area (right panels). It can be clearly seen that high
concentrations of methylamines are typically confined to source
regions, with very low concentrations over oceans. Figure 12a, c,
and e show that averaged concentrations of C1-, C2-, and C3-amines in
the surface layer in Yangtze River Delta region (based on domain
9km×9km resolution results) are,
respectively, in the range of 2–20, 5–50, and 0.5–4 pptv,
with spatial pattern similar to that of emissions (Figs. 2–4f).
Concentrations of C2-amines in urban areas are higher than those in
agricultural areas, while concentrations of C1-amine and C3-amines
show high values in agricultural areas such as in Zhejiang Province,
except for areas of high urbanization. Further measurements in these
regimes of high concentrations are needed to constrain the model
simulations. Considering the complex underlying surface in urban
Shanghai, we apply four-domain-nested simulations to further study the
Shanghai urban area. As shown in Sect. 3.2, simulations with higher
spatial resolution are in better agreement with
measurements. Figure 12b, d, and f, which were based on domain 4
simulations (3km×3km resolution), show that
the Shanghai urban area are hotspots for C1-, C2-, and C3-amines,
with concentrations in Shanghai downtown up to ∼15, 50, and
4 pptv, respectively. It can be seen clearly that the Fudan site
is on the edge of the central area such that concentrations of
methylamines are affected easily downwind of the city center,
especially for C2-amines. Vertically, the concentrations of C1-, C2,
and C3-amines decrease quickly with altitude (Fig. 13), dropping by
a factor of ∼10 from the surface to ∼900 hPa. The
horizontal and vertical distributions of methylamines for the SDR2012
case are similar to that for the SDR2015 case and are not shown. The
fact that the high concentrations of methylamines are confined to
source regions and the boundary layer are as a result of their short
lifetime (Yu and Luo, 2014; Bergman et al., 2015), again highlighting
the necessity to better quantify the emissions of amines from
different sources and to model with high spatial resolutions to study
their spatial distributions and potential impacts.
Summary and discussion
A few pptv of gaseous amines have been observed to be able to
significantly enhance new particle formation in the atmosphere
(Almeida et al., 2013; Chen et al., 2014; Jen et al., 2016; Lehtipalo
et al., 2016). Recent field measurements (Zheng et al., 2015; Yao
et al., 2016) indicate that gaseous amines in the Yangtze River Delta
region, China, can reach a few tens of pptv with large temporal
variations. To understand the processes controlling the concentrations
of amines and their spatio-temporal distribution in the atmosphere, we
improve a previous method in estimating amine emissions by
distinguishing amine emissions from five different source types and
simulating concentrations of amines over the Yangtze River Delta with
a regional model (WRF-Chem).
The present study calculates methylamine emissions from five source
types, including chemical industry, other industry, agriculture,
residential, and transportation. The temporal and spatial variations
of methylamine emissions are assumed to follow that of ammonia for
different sources. The amines-to-ammonia mass emission ratios, derived
from previous measurements reported in Zheng et al. (2015), are 0.026,
0.0015, 0.0011, 0.0011, and 0.0011 for C1-amine, 0.007, 0.0018,
0.0015, 0.01, and 0.0009 for C2-amines, and 0.0004, 0.0005, 0.00043,
0.0006, and 0.0004 for C3-amines for chemical–industrial, other
industrial, agricultural, residential, and transportational sources,
respectively. Ammonia, C1-, C2-, and C3-amine emission flux in
Yangtze River Delta are 919.61 GgNyr-1, 551.88, 849.11,
and 117.78 MgNyr-1, respectively. The contributions of
chemical–industrial, other industrial, agricultural, residential, and
transportational sources to domain-average methylamines
(C1-amine + C2-amines + C3-amines) are 0.73, 0.81, 66.04,
30.81 and 1.61 %, respectively, which shows that agricultural and
residential source types dominate methylamine emissions over the
Yangtze River Delta.
Three tracers representing C1-, C2-, and C3-amines have been added
into WRF-Chem and simulations with multiple nested domains have been
carried out. The simulated concentrations of C1-, C2-, and C3-amines,
based on fixed ratios (FR) of amines to ammonia assumed in previous
studies and source-dependent ratios (SDR) derived in the present
study, have been compared with field measurements at a suburban site
in Nanjing, China, and at an urban site in Shanghai, China. We show
that SDR substantially improves the ability of the model in capturing
the observed concentrations of methylamines. Concentrations of C1-,
C2-, and C3-amines are in the range of 2–20, 5–50, and
0.5–4 pptv in the surface layer in the Yangtze River Delta
region. Vertically, the concentrations of C1-, C2-, and C3-amines
decrease by a factor of ∼10 from the surface to ∼900 hPa. High concentrations of methylamines are generally
confined to source regions and the boundary layer as a result of their
short lifetime. For the urban Fudan site, simulated concentrations
downwind of areas of high residential activities are closer to site
measurements than for other wind directions, suggesting that
residential sources are important in an urban area and that the
present estimation of residential emissions may be reasonable.
It should be pointed out that the uncertainties in emissions (of both
ammonia and amines to ammonia ratios), meteorology, aerosol uptake,
and chemical processes can all impact the simulated values of amines
in this study. The uptake of different amines under different
atmospheric conditions is not clearly understood yet. To advance the
accuracy of amine emissions, more field observations as well as more
accurate source apportionment of amines are needed. This study focuses
on the summer season due to limited measurements, but the model
approach developed here can be applied to study the seasonal
characteristics of methylamines and subsequently the impact of amines
on new particle formation and growth in the future.