Introduction
Ammonia (NH3) is the most important and abundant alkaline constituent
in the atmosphere, with a wide range of impacts. It plays a key role in
atmospheric chemistry and ambient particle formation. NH3 partitions
to sulfate (SO42-), and nitrate (NO3-) aerosol, adding to
the concentration of secondary inorganic aerosol (SIA), including sulfate,
nitrate, and ammonium. Field measurements indicate that SIA is a major
contributing factor during haze days in China (He et al., 2014; Wang et al.,
2012; K. Huang et al., 2012). Ye et al. (2011) observed a strong correlation
between peak levels of fine particles and large increases
in NH3 concentrations. High aerosol concentrations also have a
significant effect on visibility range, climate forcing, and human health
(Cheng et al., 2013; Ding et al., 2013; Pope III et al., 2011). In addition, the
deposition of ammonium particles (NH4+) and gaseous ammonia can
cause soil acidification, water eutrophication, loss of biodiversity, and
perturbation of ecosystems (Lepori and Keck, 2012; Stevens et al., 2004; Zhu
et al., 2013). As one of the largest agricultural and meat producers in the
world (FAO, 2013), China is a significant source of NH3 emissions.
Previous studies have indicated that China's ammonia emissions contribute
23 % of the global NH3 budget (EDGARv4.1 2015; http://edgar.jrc.ec.europa.eu/datasets_list.php?v=41) and present a continuously increasing trend
(Dong et al., 2010).
Nitrogen fertilizer use is one of the largest sources of NH3 emissions
in China, accounting for 35–55 % of the national total (X. Huang et al.,
2012; Zhao et al., 2013). There are many studies focusing on NH3
emissions from agricultural fertilizer in China, but they are mostly based
on traditional “emission factors” (EFs) methods. Some of them (Klimont,
2001; Streets et al., 2003; Dong et al., 2010; Zhao et al., 2013) use
nationally averaged EFs for the whole of China. However, ammonia
volatilization from nitrogen fertilizer application depends strongly on
localized environmental parameters, such as ambient temperature and soil
acidity (Roelle and Aneja, 2002; Corstanje et al., 2008). In addition,
fertilizer application dates and application amounts vary by geographical
regions and crop types. Therefore, these estimates are subject to high
uncertainties, especially in their temporal and spatial distributions. Zhang
et al. (2011) and X. Huang et al. (2012) use some relative correction factors
to introduce the impacts of temperature, soil properties, and fertilization
method, which somewhat reduce temporal and spatial uncertainties. In recent
years, some scientists from outside China have begun to focus on estimating
NH3 emissions based on a bi-directional surface flux model (Cooter et
al., 2010; Wichink Kruit et al., 2012). For example, a group at the U.S.
Environmental Protection Agency (US EPA) (Cooter et al., 2012; Bash et al.,
2013; Pleim et al., 2013) has modified the Community Multi-scale Air Quality
(CMAQ) model to include a bi-directional NH3 exchange module. It is
coupled to the Fertilizer Emission Scenario Tool for CMAQ (FEST-C) system
(Ran et al., 2010; CMAS, 2014), which contains the Environmental Policy
Integrated Climate (EPIC) model (Williams et al., 1984). This system includes
the influences of meteorology, air–surface exchange, and human agricultural
activity. It has been used to simulate the bi-directional exchange of
NH3 in the United States. Compared with a traditional emission
inventory, the model performances for NO3- concentration and N
deposition in the USA are improved (Bash et al., 2013). However,
until now this method has not yet been used to estimate the agricultural
fertilizer NH3 emission in China.
For the first time in this study, we estimate China's NH3 emission from
agricultural fertilizer use in 2011, based on the CMAQ model with a
bi-directional NH3 exchange module coupled to the FEST-C system with
the EPIC agro-ecosystem model. The structure of this modeling system and
input data processing are described in detail in the next section. The
results of the fertilizer use and NH3 emissions simulation, along with
a comparison to other studies, are discussed in Sect. 3. The results of
CMAQ modeling are also discussed and compared with field measurements.
Finally, the uncertainties of this method are discussed in detail at the end
of the section.
Methodology and inputs
General description of the modeling system
The modeling system of agricultural fertilizer
NH3 emission for China.
![]()
Figure 1 shows the structure of the modeling system, which contains
three main components: (1) the FEST-C system containing the EPIC model, (2) the
mesoscale meteorology Weather Research and Forecasting (WRF) model, and
(3) the CMAQ air quality model with bi-directional ammonia fluxes. A detailed
description of the bi-directional module can be found in Bash et al. (2013).
Soil NH4+ content and agricultural activity data were simulated by
the EPIC model in the FEST-C system. In order to run the EPIC model for this
study, we collected and processed local Chinese agricultural information,
such as crop distribution, soil characteristics, climate patterns, and
fertilizer use characteristics. The details regarding these data sources and
processing methods are described in Sect. 2.2. In addition to agricultural
activity and soil information, this system also considers the influence of
WRF-simulated weather on NH3 emissions. The tools in the FEST-C system
can be used to process the EPIC input data and also extract the EPIC daily
output data required for CMAQ (CMAS, 2014).
The modeling domain. The black points represent the
locations of the nitrate observations.
![]()
The CMAQ simulation domain, as shown in Fig. 2, is based on a
Lambert projection with two true latitudes of 25 and
40∘ N and covers most of East Asia with a grid resolution of 36 km × 36 km. EPIC data and micrometeorological parameters are estimated for each
modeled CMAQ grid cell.
EPIC modeling in the FEST-C system
The EPIC model is a semi-empirical agro-ecosystem model which is designed to
simulate agricultural fields that are characterized by soil, landscape,
weather, and crop management (Williams et al., 1984). A wide range of
vegetative systems, tillage systems, and other crop management practices can
be simulated in this model (Gassman et al., 2005). Additionally, soil
nitrogen (N), carbon (C), and phosphorus (P) biogeochemical process models
are incorporated into EPIC. Therefore, it is well suited for simulation of
fertilizer management and soil nitrogen content in agricultural systems. The
input information required by EPIC includes crop site information, soil
characteristics, weather, and crop management, which are described in detail
in the next section. All data are processed to a 36 km × 36 km grid for
integration with the air quality model, CMAQ.
Crops
Fourteen crop types are modeled in this study: early rice, middle rice, late
rice, winter wheat, spring wheat, corn, sorghum, barley, soybean, potato,
peanuts, canola, cotton and other crops. The “other crops” category
represents all remaining crops. Data on the cropland area for each crop grown in the
2710 counties studied was collected and processed based on province-level or
city-level statistical yearbooks. The Moderate Resolution Imaging Spectroradiometer (MODIS; https://lpdaac.usgs.gov/products/modis_products_table/mcd12q1) was used to provide finer-level land
use information. The MODIS land use product provides annual 500 m pixel-scale
information for 20 land use categories. MODIS classes 12 (cropland) and 14
(cropland/natural vegetation mosaic) are of particular interest in this
study. In addition, irrigation is an important factor for crop growth and
soil characteristics. Here, we used the global irrigated area map (GIAM) at
1 km resolution (Thenkabail et al., 2008) to divide each crop into irrigated
and non-irrigated classes. The BELD4 tool in FEST-C system was used to
process these data into 36 km × 36 km grid cell (CMAS, 2014).
Soil information
The dominant soil type in each grid was taken from the Harmonized World Soil
Database (HWSD; http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/),
which gives soil distribution with 30 arc-second resolution (about 1 km × 1 km
maximally) in China. We matched the soil in each grid with a specific soil
profile in a US database (Cooter et al., 2012) based on soil type,
ecological region, and latitude. Soil characteristics of the matched soil
were extracted as soil input for the corresponding grid, including layer
depth, soil texture, soil carbon content, carbonate content, bulk density,
cation exchange capacity, pH, etc. The assumption taken is that the
characteristics of same soil types in similar eco-regions and latitudes
between China and the US are similar. These soil characteristics were used
as initial input data for EPIC because they were for general soil, not
specially for agriculture soil. A spin-up run allowed the soil
characteristics to adjust to agriculture management. For example, the EPIC
model applied lime to maintain the soil pH at levels that reduce crop stress
due to low pH. Besides, the soil characteristics are also updated with CMAQ
running.
Weather
The weather parameters required by EPIC for this simulation included maximum
and minimum temperature, radiation, precipitation, relative humidity, and
10 m wind speed. For the spin-up run, these variables were extracted from
the NASA Modern Era Reanalysis for Research and Applications (MERRA; http://disc.sci.gsfc.nasa.gov/mdisc/overview/index.shtml)
data,
which provides weather information from 1979 to the present with
0.5∘ × 0.667∘ grid resolution (approximately 55 km × 75 km
maximally). The climatological characteristics of the closest grid cell in
MERRA to each EPIC model grid cell were selected as the weather input for
the EPIC spin-up simulation run in each grid. For the year-specific EPIC
run, the output of the WRF was processed
to generate the gridded weather conditions on the CMAQ 36 km × 36 km grid
using the WRF/CMAQ to EPIC tool in the FEST-C system (CMAS, 2014).
Crop management
In the EPIC model, the timing of crop management can be prescribed or
scheduled based on a heat-unit (HU) method, as described in Cooter et
al. (2012). In this study, a combination of prescribed and HU scheduled
timing was used. The HU scheduled timing allowed for adaptation to
inter-annual and interregional temperature variability and more
realistically represents a farmer's dynamic decision-making. At the same
time, the timing was also limited to a fixed range based on available
information from the Chinese planting information network (http://www.zzys.moa.gov.cn/) and unpublished research about crop management
from the Chinese Academy of Agriculture Sciences. This allowed the timing to be adjusted to
Chinese agriculture.
The 2011 national-average fertilizer application rate for major
crops in China (kg N ha-1).
Total
Urea
ABCa
DAPb
NPKc
Others
Early rice
183.48
125.03
20.03
4.00
21.87
12.55
Middle rice
185.62
117.38
33.15
4.04
18.69
12.36
Late rice
181.14
124.20
19.13
4.02
21.63
12.17
Wheat
196.22
123.90
19.05
16.14
29.98
7.16
Corn
186.75
123.45
19.05
12.63
18.85
12.77
Soybean
45.92
19.50
1.65
10.48
11.51
2.77
Peanuts
95.14
36.30
11.70
3.43
29.03
14.68
Canola
128.14
75.90
30.90
2.35
11.02
7.97
Cotton
228.11
152.40
9.45
24.34
27.45
14.46
a ammonium bicarbonate (ABC); b diammonium phosphate (DAP);
c N–P–K compound fertilizer (NPK)
Nitrogen fertilizer application information is necessary to accurately
estimate NH3 emissions in this study. The application rates for
specific fertilizer type, crop, and province were extracted from Chinese
statistical material (National Bureau of Statistics of China or NBSC,
2012b). The fertilizer types included urea, ammonium bicarbonate (ABC),
diammonium phosphate (DAP), N–P–K compound fertilizer (NPK), and others
(e.g.,
ammonium nitrate and ammonium sulfate). Table 1 shows the national
average application rates for some major crops. We can see that the nitrogen
fertilizer application rates for different crops are varied. The largest
nitrogen amount is required for cotton and wheat, which are 228.11 and
196.22 kg N ha-1, respectively. However, nitrogen-fixing crops (e.g., soybean
and peanuts) require much less nitrogen input. Among all the fertilizer
types, urea and ammonium bicarbonate are dominant.
The nine agriculture regions in China. The thin black line
represents the county boundary and the small insert represents the South
China Sea and its islands.
![]()
Besides application rates, the ratio of basal and topdressing fertilizer is
also important for ammonia volatilization. Basal fertilizer is used before
crops are planted and topdressing fertilizer is used during crop growth.
Figure 3 presents the Chinese agriculture regions used to
characterize these management practices. Each region is a geographic area
where crop management practices are assumed to be similar. Based on the
results of previous field investigations (Wang et al., 2008; Zhang, 2008),
the ratios of basal and topdressing fertilizer for different crops in each
agriculture region are identified. Table 2 shows the fertilizer
ratios used on three major crops in China and a clear geographical
divergence can be observed. For example, the ratio of fertilizer used on
wheat in the middle and lower Yangtze River region is 1.39, but only 0.33 in
the southwest region. In general, the ratio of fertilizer used on corn is
the highest of the three major crops. A greater amount of fertilizer is
applied to corn just prior to or at planting than is applied to the crop
later in the growing season. The information in Tables 1 and 2 was
combined for this study to determine the amount of fertilizer applied to
each crop in each grid cell during basal and topdressing activities.
Ratio of basal and topdressing fertilizer for major crops in each
agriculture region.
Region
Wheat
Corn
Rice
basal
topdressing
basal
topdressing
basal
topdressing
The northeast region
1.00
0.80
1.00
1.23
1.00
0.88
The Gan-Xin region
1.00
0.44
1.00
3.50
1.00
1.00
The southern China region
1.00
1.00
1.00
2.98
1.00
2.91
The Huang-Huai-Hai region
1.00
0.80
1.00
2.07
1.00
1.29
The Loess Plateau region
1.00
0.44
1.00
3.50
1.00
1.00
The Inner Mongolia and along the Great Wall region
1.00
0.44
1.00
3.50
1.00
1.00
The Tibetan Plateau region
1.00
0.44
1.00
3.50
1.00
1.00
The southwest region
1.00
0.33
1.00
2.33
1.00
1.88
The middle and lower Yangtze River region
1.00
1.39
1.00
1.66
1.00
1.29
The bi-directional CMAQ model system
Direct flux measurements have shown that the air–surface flux of NH3
is bi-directional, and vegetation and soil can be either a sink or a source
of atmospheric NH3 (Fowler et al., 2009; Sutton et al., 1995). The
direction and magnitude of the flux depend on the concentration gradient
between canopy or soil and the atmosphere. Bash et al. (2013) has
implemented a bi-directional ammonia flux module in CMAQv5.0.1 to represent
this process. This module is based on the two-layer (soil and vegetation
canopy) resistance model described by Pleim et al. (2013), which is similar
to the model presented by Nemitz et al. (2001). The NH3 air–surface
flux (Ft) is calculated by the following formula
Ft=1Ra+0.5RincCc-Ca,
where the aerodynamic resistance (Ra) and the in-canopy aerodynamic
resistance (Rinc) are calculated following Pleim et al. (2013).
Ca is the atmospheric NH3 concentration. Cc is a function of
Ca, the soil compensation point (Cg) and the stomatal compensation
point (Cst).
Cc=CaRa+0.5Rinc+CstRb+Rst+Cg0.5Rinc+Rbg+RsoilRa+0.5Rinc-1+Rb+Rst-1+Rb+Rw-1+0.5Rinc+Rbg+Rsoil-1,
where the quasi-laminar boundary-layer resistance of leaf surface
(Rb), the stomatal resistance (Rst) and the quasi-laminar boundary-layer resistance of ground surface (Rbg) are calculated following Pleim
et al. (2013). The cuticular resistance (Rw) is a function of Cc
similar to Jones et al. (2007). Cst and Cg are calculated as
follows:
Cst=Mn/Vm161 500Tce-10 380TcΓs,Cg=Mn/Vm161 500Tse-10 380TsΓg,
where Mn is the molar mass of NH3, Vm is the conversion factor
of L to m3, and Ts and Tc are the soil and canopy temperature
in K. The apoplast gamma (Γs) is modeled with a function
similar to Zhang et al. (2010). The soil gamma (Γg) is defined
as soil [NH4+] / [H+], and the soil NH4+ budget in
CMAQ is parameterized following the method in EPIC (Williams et al., 1984).
The soil NH4+ would increase due to N deposition, and decrease
due to NH3 evasion and soil nitrification. When fertilizer is used,
Γg is calculated by the following function:
Γg=Napp/θsMNds10-pH,
where Napp is the fertilizer application rate (g N m-2), θs is the soil volumetric water content (m3 m-3), MN is
the molar mass of nitrogen (14 g mol-1), ds is the depth of soil layer
(m), and pH is soil pH. The initial soil NH4+, θs and
pH are taken from the EPIC output and then calculated in CMAQ hourly.
In addition to the inputs of soil condition and fertilizer use, other input
data used were the same as those in the traditional CMAQ model. WRF version
3.5.1 was used to generate the meteorological input. The configuration
options used in WRF and CMAQ were the same as those described by Fu et al. (2014).
In order to evaluate the performance of this method, two simulations – a
base case and a bi-directional case (bidi case) – were conducted in this
study using different methods to estimate ammonia emissions from fertilizer
use. For the base case, the emission inventory from Zhao et al. (2013) was
used, which is estimated by the traditional “emission factors” method. This
case did not include the bi-directional flux algorithm in CMAQ. For the
bidi case, NH3 emissions were estimated online using the
bi-directional module in CMAQ. The emissions of ammonia from other sectors
and the emissions of other pollutants were taken from Zhao et al. (2013) for
both cases.
Results and discussion
Nitrogen fertilizer application
Nitrogen fertilizer application was a key aspect in this study, explored
through a comparison of the EPIC results to existing statistical data. The N
use in each grid cell per day is calculated by the following formula
USEi=∑j=1cropNij×fij×129 600,
where USEi (kg) is the N application in grid cell i; Nij (kg ha-1)
is the N application rate in the grid cell i for crop j; fij is the
fraction of cell used for crop j in grid cell i; and 129 600 ha grid-1 is a
conversion factor accounting for the area of the grid cell.
Comparison of annual N fertilizer use at province level
between existing statistical data (a) and EPIC output (b). The small insert
represents the South China Sea and its islands.
![]()
Figure 4a and b show the patterns of annual fertilizer use at
province-level between the statistical data from NBSC (2012a) and the EPIC
output. We can see that EPIC results captured the general pattern,
especially for the provinces with the largest fertilizer use (> 1750 million kg), such as Henan, Shandong, Jiangsu, and Hebei provinces,
where the biases were -9.7, -5.1, -1, and -0.6 %, respectively.
At the same time, relatively large biases existed for some provinces, such
as Hunan province (-20.6 %) and Heilongjiang province (19.2 %). This may
be due to uncertainty in the statistical data. Additionally, the 36 km grid
is relatively coarse and uncertainty exists for the gridded crop areas
calculated according to the county-level statistical crop data and MODIS
crop data. Because the provinces with a larger bias applied relatively small
amounts of fertilizer, these modeled biases are not expected to lead to
large biases in the simulations.
Comparison of the fraction of N fertilizer use by each
month between statistics and EPIC output.
![]()
Figure 5 shows a comparison of the fraction of N fertilizer use
each month between existing statistics and EPIC output. The statistical data
is derived from the field investigation from Zhang et al. (2008) for 2004
and the model results capture the temporal characteristics. The fertilizer
amounts used from March to July and in October dominated the model, which
closely relates to the timing of crop fertilization in China. For example,
the North China Plain is the most important agricultural production region
in the country, where the major crop planting system is the winter
wheat–summer corn rotation. Winter wheat is usually planted in October with
an application of basal fertilizer, followed by the topdressing fertilizer
in March and April of the next year. Basal fertilizer for summer corn is
usually applied in June and topdressing fertilizer in July. The Northeast Plain, another major agricultural region, rice is the dominant crop. Due to
temperature limitations, rice is usually seeded in April and May and the
topdressing fertilizer is applied in June and July.
NH<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula> emissions
Spatial and temporal distribution
Spatial distribution of NH3 emissions
from N fertilizer use in 36 km × 36 km grid cell (kg yr-1). The small insert
represents the South China Sea and its islands.
![]()
The NH3 emissions from N fertilizer application in 2011 estimated in
this study were approximately 3.0 Tg. The spatial distribution of annual
NH3 emission in a 36 km × 36 km grid is presented in Fig. 6
and shows that NH3 volatilization was concentrated in Henan, Shandong,
Hebei, Jiangsu, and Anhui provinces, accounting for 11.1, 9.9,
8.8, 6.7, and 7.1 % of total emissions, respectively. The highest
NH3 emissions in this region were above 386 kg ha-1. The crop production
here is the most intense in China and the total crop area in these five
provinces accounts for about 31.4 % of China's total. These five provinces
consumed approximately 37.3 % of the nitrogen fertilizer for the whole
country in 2011 (NBSC, 2012b). Elevated emissions were also due to the high
fertilizer application rate. For example, the rate of N fertilizer use for
rice in Jiangsu province was above 300 kg ha-1, which is twice the national
average. The smaller contributors to NH3 emission were primarily
located in western China, in Tibet, Qinghai, and Gansu province, where the
amount of arable land and N fertilizer use was small.
(a) The variation of monthly precipitation (green) and
temperature (blue) in 31 provinces. In the box-and-whisker plots, the boxes
and whiskers indicate the 100th (max), 75th, 50th (median), 25th and 0th
(min) percentiles, respectively. The point represents the average value. (b)
Monthly NH3 emissions from N fertilizer use.
![]()
Figure 7b shows the monthly distribution of ammonia emissions,
which were dominant from March to July, and in October, accounting for
88.7 % of the annual total. This agrees with the pattern of N fertilizer
usage described in Sect. 3.1. Besides N fertilizer use, weather
parameters, like temperature and precipitation, also affected the temporal
and spatial distribution of emissions. For example, the emissions in March
were much smaller than April and May due to lower temperatures (as shown in
Fig. 7a), even though the amount of consumed fertilizer was
nearly equivalent. Similarly, the emissions in June were slightly less than
in April and May. A possible reason is that precipitation in June is greater
than that in the previous 2 months. Based on the statistical data of major
Chinese cities (NBSC, 2012a), the total precipitation in June 2011 was
165.1 mm, while in April and May, it was 28.5 and 67.4 mm, respectively (as
shown in Fig. 7a). Figure S1 in the Supplement presents the spatial
distribution for each month. Some differences for the months with larger
emissions can be seen. For example, in the North China Plain, like Hebei,
Henan, and Shandong provinces, NH3 emissions were relatively small in
May due to lower amounts of fertilizer application. In northeast China,
including Liaoning, Jilin, and Heilongjiang provinces, the NH3 emissions
in May, June and July were dominant. In November, major NH3 emissions
occurred in Jiangsu, Hubei and Anhui provinces, when winter canola basal
fertilizer was applied.
Comparison with other studies
The ammonia emissions from N fertilizer use in China were estimated for
different base years by different methods. The results of comparisons
between this study and some previous studies are listed in Table 3.
In order to make the inventories comparable, we updated the emissions from
different years to 2011 based on changes in fertilizer use, temperature, and
precipitation, as described in the supplementary materials. As presented,
the results of this study are generally equivalent and comparable to the
research of Zhang et al. (2011) and X. Huang et al. (2012), which is 60–70 %
lower compared with other studies. The discrepancies are mostly caused by
the various estimating methods and EFs employed. Streets et al. (2003), Dong
et al. (2010) and Zhao et al. (2013) used averaged emission factors for all
agriculture in China and did not consider the impacts of environmental
parameters, e.g., soil pH, or precipitation. For example, the EFs for urea
used by Streets et al. (2003), Dong et al. (2010), and Zhao et al. (2013) are
15–20 % (temperate and tropical ozone). However, the basic emission
factors for urea used by X. Huang et al. (2012) are 8.8 % for acid soil and
30.1 % for alkaline soil. The agricultural regions in China are dominated
by acidic soil (http://www.soil.csdb.cn/), so this value is nearly
50 % lower compared with averaged EFs. In addition to soil pH,
precipitation can also decrease NH3 emissions, because precipitation
can increase the water content in soil and fertilizer N can be leached to a
deeper soil layer by water (Wang et al., 2004). Zhang et al. (2011) adjusted
the EFs by 0.75, 0.80, 0.85, 0.90, 0.95, and 1.0 for significant rainfall
events (> 5 mm in 24 h) within 24, 24–48, 48–72, 72–96, 96–120, and > 120 h of fertilizer application. In this study, the impacts
of soil pH and precipitation on NH3 emissions were considered by
impacting soil gamma and resistances, as shown in Sect. 2.3. In addition,
our study and Zhang et al. (2011) included the impacts of irrigation. The
experiments of Wang et al. (2004) in Beijing for the winter wheat–summer
maize cycle show that NH3 volatilization is reduced after irrigation
and reveal a low EF value of 2.1–9.5 %.
Figures S4 and S5 represent the comparisons of provincial
distributions and seasonal variations of these different NH3 emission
inventories. The provincial distributions are similar, and the emissions
from Henan, Shandong, Jiangsu, Hebei, and Anhui provinces dominate the
country's annual total emissions. At the same time, some discrepancy also
exists for the specific provinces among the different studies, which may be
caused by distinct fertilizer consumptions and emission rates employed. For
example, for Henan province, the estimation of X. Huang et al. (2012) is the
highest among these studies. A possible reason for this difference is that
alkaline soil is dominant in Henan province and X. Huang et al. (2012) set a
uniform high emission factor for alkaline soil, which is twice as high as
that in Dong et al. (2010). Compared with provincial distributions, the
difference of seasonal variations among these studies is larger. The
seasonal profile in Zhao et al. (2013) is based on temperature variations.
In addition to temperature, others also considered the impacts of fertilizer
application timing. It is indeed difficult to capture the exact date of
fertilization for all of China, which may have created this large
discrepancy amongst studies. For example, X. Huang et al. (2012) states that
the basal fertilizer and topdressing fertilizer of winter wheat are
conducted in September and November. However, basal fertilizer was applied
in October in our study and in the Zhang et al. (2011), and the topdressing
fertilizer is mainly used in March of the next year. The diversity of
seasonal fertilization among different studies reflects that the large
uncertainties still exist for the temporal distribution of NH3
emissions and shows that continued local research is needed.
Comparison of NH3 emissions from fertilizer use in our study
with other published results.
Reference
Year
Original NH3
Revised to 2011
Emission (Tg yr-1)
(Tg yr-1)
Streets et al. (2003)
2000
6.7
7.0
Zhang et al. (2011)
2005
3.6
3.8
X. Huang et al. (2012)
2006
3.2
3.2
Dong et al. (2010)
2006
8.7
8.9
Zhao et al. (2013)
2010
9.8
9.8
This study
2011
3
3
Evaluation of the CMAQ results by ground observations
NH3 is the most important and abundant alkaline constituent in the
atmosphere, and NH3 emission estimates can affect the simulation of the
inorganic gas-particle system (Schiferl et al., 2014). As the dominant
positive ion in the atmosphere, NH4+ preferentially partitions
to SO42- and then partitions to NO3-. In NH3-rich
regions, the NO3- concentration is sensitive to NH3 changes,
but NH3 changes do not lead to large differences in SO42-
concentration (Wang et al., 2011). In order to evaluate the reliability of
this NH3 emissions estimate, we compared the CMAQ-modeled
NO3- concentrations using different NH3 emissions against
actual observations. In China, observation data on chemical components of
fine particulates is very limited and not publicly available. For this
study, we collected the observation data at three monitoring sites: Shanghai
station (121.5∘ E, 31.2∘ N), Suzhou station (120.6∘ E, 31.3∘ N), and Nanjing station
(118.7∘ E, 32.1∘ N). Ion chromatography (Dionex-3000, Dionex Corp,CA, USA) was
used to measure daily NO3- concentration in PM2.5 particles
(Cheng et al., 2014). Some statistical indices, including mean observation
(mean obs.), mean prediction (mean pred.), bias, normalized mean bias (NMB),
normalized mean error (NME) and correlation coefficient (R) were calculated
for the base case and bidi case in June, August, and November, as shown in
Table 4. For the base case, the emission inventory from Zhao et al. (2013) was used. For the bidi case, the NH3 emission from fertilizer
use was calculated online using CMAQ, while other emissions were also from
Zhao et al. (2013). The model performance from the bidi case is comparable
to or better in general than the base case. For August and November, the
NMBs and NMEs were improved by 3.29–66.85 % and 0.22–46.32 %,
respectively. The correlation coefficients for the bidi case were also
comparable or better than the base case. Though the bias for the bidi case
is a little larger in June, other statistical indices were acceptable. For
example, the NME decreased from 57.3 to 45.1 % and the correlation
coefficient increased from 0.83 to 0.91 % at Shanghai station. The
correlation coefficient at Suzhou station and the NME at Nanjing station
were comparable for these two cases.
The performance statistics of CMAQ-modeled daily NO3-
concentrations for base case and bidi case, compared to the observations at
three monitoring stations.
Shanghai station
Suzhou station
Nanjing station
June (1–30 Jun 2011)
mean obs. (µg m-3)
7.27
13.43
12.81
base case
mean pred. (µg m-3)
8.41
9.32
13.44
bias (µg m-3)
1.14
-4.10
0.63
NMB (%)
15.65
-30.56
4.90
NME (%)
57.34
40.71
59.93
R
0.83
0.81
0.24
bidi case
mean pred. (µg m-3)
8.60
7.16
7.59
bias (µg m-3)
1.32
-6.26
-5.23
NMB (%)
18.21
-46.63
-40.81
NME (%)
45.07
50.63
60.40
R
0.91
0.83
0.14
August (20 Jul–20 Aug 2011)
mean obs. (µg m-3)
2.99
7.04
6.24
base case
mean pred. (µg m-3)
6.42
14.51
12.02
bias (µg m-3)
3.43
7.46
5.78
NMB (%)
114.84
105.95
92.68
NME (%)
142.48
115.89
97.18
R
0.62
0.28
0.87
bidi case
mean pred. (µg m-3)
4.42
10.36
8.85
bias (µg m-3)
1.43
3.31
2.62
NMB (%)
47.99
47.01
41.92
NME (%)
96.16
79.43
62.64
R
0.64
0.24
0.90
November (1–30 Nov 2011)
mean obs. (µg m-3)
9.42
11.59
14.57
base case
mean pred. (µg m-3)
12.59
16.72
22.62
bias (µg m-3)
3.17
5.14
8.05
NMB (%)
33.68
44.32
55.24
NME (%)
83.85
53.68
74.81
R
0.71
0.72
0.68
bidi case
mean pred. (µg m-3)
12.28
12.41
12.88
bias (µg m-3)
2.86
0.82
-1.68
NMB (%)
30.39
7.05
-11.56
NME (%)
65.33
53.46
43.35
R
0.78
0.72
0.79
Uncertainty analysis
This is a pilot study to apply this model system to estimate NH3
emissions in China and therefore, large uncertainties still exist in some
aspects of this method. The quality of input data, mathematical algorithm,
and parameters applied in EPIC and the bi-directional model may be
associated with uncertainties in the model output.
Fertilizer application rates for each crop are important input data for the
estimation of NH3 emissions from agricultural fertilizers and were
obtained from agricultural statistics. These statistical data have some
level of uncertainty, because the number of samples in the census are
limited. Beusen et al. (2008) has employed an uncertainty of ±10 %
for the statistical data of fertilizer use based on expert judgments when
estimating the global NH3 emission. A June 2006 sensitivity run of this
bi-directional model in the USA shows that a 50 % increase of crop fertilizer
use would result in a 31 % increase in NH3 emissions (Dennis et al.,
2013). In addition, the spatial distribution of NH3 emissions from
agricultural fertilizer is strongly related to cropland area and its
distribution, which are achieved from the MODIS data. Friedl et al. (2010)
mentions that the producer's and user's accuracies are 83.3 and 92.8 %
for MODIS class 12 (cropland) and 60.5 and 27.5 % for class 14
(cropland/natural vegetation mosaic) in MODIS collection 5 product. This
leads to the uncertainties in spatial distribution. Additionally, due to the
limited data available, the initial characteristics of the dominant soil in
each grid were acquired from a US data set. Although we have matched the
soil based on soil type, eco-region, and latitude, uncertainties still
existed due to different long-term agriculture management.
Based on the algorithm described in Sect. 2.3, the EPIC outputs, including
soil NH4+ concentration, soil volumetric water content (θs) and soil pH, are important inputs of the bi-directional module.
EPIC has been used and evaluated world wide to simulate the nitrogen cycle
and soil water content. Some validation studies have found favorable results
for soil nitrogen and/or crop nitrogen uptake levels (Cavero et al., 1998,
1999; Wang et al., 2013). However, less accurate simulation results have
also been reported (Chung et al., 2002). Li et al. (2004) found that the
EPIC model could catch the variation of soil volumetric water content in
different years accurately, with a relative bias of 11.7 %. The research
conducted by Huang et al. (2006) also showed that the EPIC-simulated
long-term average θs values were not significantly different
from the measured values in the Loess Plateau of China. For soil pH, the
normal growth pH range of three dominant crops (rice, corn, and wheat) is
6.0–7.0. The
95 % confidence interval of EPIC-simulated values is 6.3–7.6, which is
reasonable and acceptable although uncertainties still exist.
The bi-directional ammonia flux module in CMAQ is the core of this model
system. The uncertainties of the bi-directional exchange parameterization
would bring uncertainties to NH3 emission estimates. Pleim et al. (2013) has compared the simulated NH3 flux from the box model of this
ammonia bi-directional flux algorithm with observations in three periods.
The results showed that the model generally reproduced the observed series
and significantly correlated with the observations (p<0.001). The
mean normalized biases were 78.6, -49, and 1 % for soybeans (18 June–24 August 2002),
corn (21–29 June 2007), and corn (11–19 July 2007),
respectively. The soil gamma (Γg) and apoplast gamma (Γs) are two important parameters in this ammonia bi-directional flux
algorithm (Bash et al., 2013) and their parameterization remains uncertain
(Massad et al., 2010). The field measurements of Γg and Γs are limited, and measured values are scattered, owing to complex
impact factors (Massad et al., 2010, and reference therein). Dennis et
al. (2013) assessed the effects of these uncertainties. A 50 % increase of
Γg would result in a 42.3 % increase in NH3 emission.
Two different parameterization methods of Bash et al. (2013) and Massad et
al. (2010) could lead to a 17 % change in NH3 emissions.
In order to reduce the uncertainty in emission estimates, work is needed to
improve the quality of input data and record additional local measurements
of soil and vegetation chemistry. Ambient NH3 concentration and flux
data are also needed to enhance and evaluate the parameterizations of EPIC
model and bi-directional module.
Conclusions
This study provides the first estimates of 2011 NH3 emissions from N
fertilizer use in China using the bi-directional CMAQ model rather than the
traditional “emission factors” method. Hourly NH3 emissions can be
calculated online with CMAQ. Compared with previous researches, this method
considers more influencing factors, such as meteorological fields, soil and
fertilizer application, and provides improved spatial and temporal
resolution. The higher resolution of NH3 emissions is beneficial for
modeling and exploring the impacts of NH3 emission on air quality. In
addition, the results can be used for a better comparison of novel and
traditional methods of emission estimation. This is an important
contribution to scientific literature on this topic.
China's NH3 emissions from N fertilizer application were approximately
3.0 Tg in 2011. The major contributors were Henan, Shandong, Hebei, Jiangsu,
and Anhui provinces, accounting for 11.1, 9.9, 8.8, 6.7, and
7.1 % of total emissions, respectively. The monthly distribution of these
ammonia emissions is in line with the pattern of N fertilizer consumption.
The emissions are dominant from March to July and in October, accounting for
88.7 % of the whole year. Compared to other NH3 sources, nitrogen
fertilizer application is the second largest contributor to NH3
emissions in China. It is important to reduce the use of N fertilizer to
control ammonia emissions.
This is a pilot study to apply this model system to estimate NH3
emissions in China and gaps still exist for this method due to the
uncertainties of model parameterization and input data. Much work is still
needed to improve this model system when applied to China in the future. For
example, it is important to build the initial soil input file for EPIC based
on Chinese soil profile data instead of US data. In addition, Chinese
farmers' logic of agriculture management must be explored and an automatic
management algorithm in the EPIC model for China shall be designed. This
model system can be improved with additional local measurements of soil and
vegetation chemistry, ambient NH3 concentration and flux data to
enhance and evaluate the parameterizations of the EPIC model and
bi-directional module.
Although uncertainties still exist in the NH3 emission estimation, the
CMAQ-EPIC modeling system allows for some interesting future research. This
system is a combination of air quality and agro-ecosystem models and couples
the processes and impacts that human activity has on air quality through
food production. The model could be applied at finer grid resolutions for
China in order to more accurately capture spatial gradients in NH3
emissions and the resulting impacts on air quality. Secondly, this system
reflects the impacts of weather and climate on NH3 emissions.
Therefore, it can be coupled with climate models to explore the interaction
of climate change and NH3 emission. When it is linked to a water quality
and transport model, the impacts of atmospheric nitrogen deposition from
CMAQ and nutrient run off from EPIC on water eutrophication are
estimated. This study is the first attempt to apply this model system to
China, and it is also the foundation for future scientific research.