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A numerical simulation of annual acid deposition amount in Korea
AE International – Asia Atmospheric Environment 37 (2003) 1703–1713
A numerical simulation of annual acid deposition amount in Korea Jaehee Kim, Boeun Han, SeogYeon Cho* Department of Environmental Engineering, Inha University, Inchon, South Korea Received 26 August 2002; accepted 12 December 2002
Abstract A comprehensive acid deposition model was used to calculate an annual amount of acid deposition in North East Asia as well as to derive a source–receptor relation. The model results for gaseous SO2 and O3 agree very well with measurements, but the model under-estimated NO2 concentrations. Similarly, the model estimates the sulfate in rain waters quite well but under-estimates nitrate in the rain waters. The correlation coefficients for the spatial distributions of the calculated and measured annual mean concentrations were 0.97, 0.63 and 0.90 for the gaseous SO2, O3, and NO2. One-third of SO2 emitted in North East Asia was calculated to be advected out mostly in the form of sulfate. The amount removed by dry deposition is comparable to that removed by wet deposition for sulfur but it is 40% larger than that by wet deposition for nitrogen. The source–receptor relations derived by the counter-species method show that the wet deposition is more influenced by the long-range transport than the dry deposition. Furthermore, the long-range transport contribution was calculated to be the lowest in the summer and the highest in the winter. r 2003 Elsevier Science Ltd. All rights reserved. Keywords: Annual acid deposition; Eulerian model; Advection; Source–receptor relation; Counter species
1. Introduction Recently, a rapid economic growth in Korea and China has increased the public concerns on acid deposition in North East Asia. As a result, various national and international monitoring programs have been implemented to assess the significance of acid deposition in East Asia (Otoshi et al., 2001). On the other hand, an extensive effort has been made to construct the emission database for SO2, NOx and VOCs (Klimont and Streets, 2002). In the present study, a comprehensive acid deposition model was employed by utilizing these recent monitoring results and emission databases to access the current status of acid deposition and the trans-boundary characteristics of acid precursors in Korea.
*Corresponding author. E-mail address: [email protected] (S.Y. Cho).
Korea has distinctive four seasons characterized by temperatures, precipitations, principal wind directions to require at least one year long model simulation to access the acid deposition as well as the trans-boundary problems. Arndt et al. (1998) used ATMOS, a Lagrangian trajectory model, to assess the acid deposition in East Asia for the year of 1990. Only the sulfur related species were included in the study mainly because SO2 and sulfate were regarded as the most important acidifying species in 1980s. On the other hand, ATMOS was not able to simulate ozone and nitrogen compounds because of highly simplified chemistry. However, an aggressive sulfur reduction policy implemented in Korea has lowered the ambient SO2 concentration several times in major cities in 1990s. For example, the annual mean SO2 concentration in Seoul, the capital city in Korea, was reduced from 43 ppb in 1990 to 6 ppb in 2000. China is now also making a similar effort to control the ever-increasing sulfur emission. On the other hand, the nitrogen oxide
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emission in North East Asia has steadily increased over the years due to a rapid increase of automobiles. As a result, the relative importance of nitrogen containing acids has increased substantially over sulfur containing acids in Korea in the recent years. Although the nitrogen containing acid may be modeled by a Lagrangian trajectory model with a simplified chemistry (Holloway et al., 2002), it can be more accurately addressed by a comprehensive acid deposition model. Comprehensive acid deposition models such as Sulfur transport Eulerian Model (STEM, Carmichael et al., 1991) and Regional Acid Deposition Model (RADM, NCAR, 1986) incorporate complex multi-phase transport, chemistry and deposition processes to simulate various atmospheric processes leading to acid deposition. However, the application of comprehensive acid deposition models was limited to the episodic simulation rather than a long term simulation due to a high computational cost as well as a limited availability of acid deposition related data (Kitada and Lee, 1993; Uno and Murano, 1996; Park and Cho, 1998; Kim and Cho, 1999). This limitation of the modeling period for a comprehensive acid deposition model has become a less problem due to the recent advances in computers as well as in monitoring networks (Han et al., 2001). In the present paper, multi-scale STEM (Kim and Cho, 1999) coupled with MM5(Mesoscale model version 5, Grell et al., 1994) was employed to investigate the acid deposition by sulfate and nitric acid in North East Asia for the year of 1996. Multi-scale STEM was run continuously over the simulation period to test out its convergence and accuracy for the long term simulation. The model simulation results were compared with the extensive field monitoring data for the gas and rain phase chemical species. The comprehensive acid deposition model usually takes a Eulerian approach, which fails to explicitly provide a source–receptor relation unlikely to a Lagrangian trajectory model. In the present study, the counter species analysis was implemented to derive a source–receptor relation from a Eulerian model. The counter species analysis can be applied not only to chemical species with the linear chemistry but also to chemical species with the nonlinear chemistry such that the source–receptor relations for ozone and nitrogen compounds can be also easily derived.
tures, humidities, wind velocities, wind directions, and cloud/rain related parameters. Because MM5 is well known and extensively documented elsewhere (Grell et al., 1994), only the multi-scale STEM was described briefly here. Multi-scale STEM is a comprehensive acid deposition model solving the multi-phase atmospheric transport/chemistry/deposition equations numerically (Kim and Cho, 1999). The gas phase chemical species satisfy
2. Model description
SO2;c þ OH- Sulfatec þ HO2
2.1. Mathematical formulation of multi-scale STEM
The subscript c denotes for the counter species. Although the counter species such as SO2,c and Sulfatec are destroyed or produced by the above reaction, the other species such OH and HO2 are assumed not to be affected by the above reaction. Therefore, the chemical
A meteorological model named Mesoscale model version 5 (MM5) is coupled with multi-scale STEM to provide necessary meteorological fields such as tempera-
qðCi Þ þ r ðvCi Þ ¼ r KrCi þ Ri þ Ei þ Gi ; qt where Ci is the gas phase concentration of the ith chemical species, v is the wind velocity vector, K is the eddy diffusivity tensor, Ri is the reaction rate of the ith chemical species, Ei is the emission rate of the ith chemical species, and Gi is the mass transfer rate of the ith species between the gas and condensed phases. The condensed phase chemical species are described by c qðsp Ci;p Þ c þ r ðv vsp Þsp Ci;p qt c c ¼ rKpc rsp Ci;p þ Rci;p þ Gi;p ;
where the subscript p denotes the phase (e.g. cloud, water, rain and snow) and superscript c denotes the condensed phase. And sp is the water content of phase p and vsp is the settling velocity vector of the hydrometers of phase p. 2.2. Source–receptor analysis The source–receptor relations in the Eulerian model can be derived either by sensitivity analysis or by counter species analysis. The counter species analysis is selected here mainly due to its simplicity. The counter species analysis is designed to evaluate the importance of particular physical/chemical pathways by counting the number of moles passing through. Leone and Seinfeld (1984) assessed the role of particular reaction steps to evaluate the various photo-chemical mechanism using the counter species method. It is extended here to address a source–receptor relation. The counter species undergoes the same atmospheric processes as the corresponding species except setting the emissions other than the source region equal to zero. For example, the counter species SO2 is emitted from the source region of interest and produces the counter species sulfate as follows:
J. Kim et al. / Atmospheric Environment 37 (2003) 1703–1713
reaction rate of the counter species takes the same form as the reaction rate of the corresponding chemical species except that the chemical species concentration is replaced by the corresponding counter species concentration. Since the counter species is assumed not to influence any other chemical species concentrations, only the counter chemical species equations formulated below are required to be solved qðCi;c Þ þ r ðvCi;c Þ ¼ r KrCi;c þ Ri;c þ Ei;c di þ Gi;c ; qt c qðsp Ci;p;c Þ c þ r ðv vsp Þsp Ci;p;c qt c c ¼ rKpc rsp Ci;p;c þ Rci;p;c þ Gi;p;c ;
where the subscript c denotes the counter species and Ri;c denotes the chemical reaction rate of the counter specie. Kroneck delta function (di ) is set equal to one in the source region and zero in the receptor region. Therefore, the counter species concentrations based on the above equations represent the contribution from the source region.
3. Description of model simulation The year 1996 was chosen as a modeling period mainly because the field monitoring data for the chemical compositions in the gas and rain phase were readily available. Furthermore, it was not
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affected by the economic crisis initiated in 1997 in the region. 3.1. Grid system As shown in Fig. 1, the multi-grid system chosen for the present study consists of three domains. Domain I is a regional scale domain with the grid size of 80 km, including most part of China, Korean Peninsular and Japan. Domain II is a mesoscale domain with the grid size of 26.7 km, including Korean Peninsula and the eastern part of China and the southern part of Japan. In addition, the entire China including the neighboring ocean was chosen as a source region and the Republic of Korea including the neighboring ocean as a receptor region for the source–receptor analysis. The receptor region is named here as domain III. 3.2. Input data preparation Japan Grid Point value data (Nakakita et al., 1996) with a horizontal resolution of 1.25 and a temporal resolution of 6 h were used to generate initial and boundary conditions for MM5, which calculated meteorological fields necessary to run multi-scale STEM. Initial chemical species concentrations were derived by applying inverse r square interpolation to the available air monitoring data. The SO2 emission was estimated from the amount of fuel use and sulfur content for each source as described in Park and Cho (1998) and Kim and Cho (1999). The NO2 and VOC emissions in China and Japan were taken
Fig. 1. The model domain and grid structure.
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Meteorological parameters as well as chemical species concentrations were calculated by the multi-scale STEM coupled with MM5. Although the model domain covers China, Japan and Korea, the model results were compared to the measurements only in Korea due to the lack of measurement data in the other region.
in the summer. For the summer rainy season, the model predicted very well the amount of precipitation in the southern part of Korea including Kwangju, Pusan, Chinju but it underpredicted the other regions. The precipitation front was first formed in the southern sea of Korea in June and gradually moved northward to trigger the precipitation. However, the model slightly underpredicted this northward movement of the precipitation front to cause the underprediction of precipitation amounts at Seoul, Tonghae and Chunchon. In addition, the underpredictions in June at Tonghae and in August at Kwangju were caused by the localized heavy rain and by the rain accompanying Typhoon, respectively, which the model was not able to handle properly.
4.1. Meteorological parameters
4.2. Gas phase concentrations
The major meteorological parameters calculated by MM5 are winds, temperatures and precipitations. Because the calculated temperatures and winds agree quite well with measurement, only the performance of the model on the precipitation is discussed here. As shown in Fig. 2, MM5 reproduced a yearly variation of precipitation quite well; a low precipitation occurred in the fall and winter, and a high precipitation
There are over hundred air stations in Korea monitoring the criteria air pollutants including NO2, SO2, and O3, allowing a direct comparison with measurements. Fig. 3 compares the measurements with the model results during the first three days for every three month starting from January. The measured air concentrations were derived by averaging over 20 stations surrounding the grid point
from China Map Project (Aardenne et al., 1999; Streets and Waldhoff, 2000; Klimont and Streets, 2002), whereas those in Korea were taken from the recent local governmental documents (Inchon, 2000).
4. Comparison of model simulation results with field measurements
Pred.
Meas 600
500
500
400
400
mm
mm
Seoul 600
300
Chunchon
300
200
200
100
100 0
0
1
2
3
4
5
6 7 month
8
9
1
10 11
2
3
4
5
250
400
200
300
150
mm
mm
Taejon 500
200
8
9
10 11
8
9
10 11
Tonghae
100 50
100
0
0
1
2
3
4
5
6 7 month
8
9
1
10 11
300
300
200 100
mm
300
mm
400
200
5
6
7
month
6
7
8
9
10 11
200
0
0
4
5
100
100
0
3
4
Pusan
400
2
3
Chinju
400
1
2
month
Kwangju
mm
6 7 month
1
2
3
4
5
6 month
7
8
9
10 11
1
2
3
4
5
6
7
8
9
10 11
month
Fig. 2. Comparison between the calculated and measured monthly precipitation amounts at the six meteorological observatories located in Korea.
J. Kim et al. / Atmospheric Environment 37 (2003) 1703–1713
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Fig. 3. Comparison between the calculated and measured SO2, O3, and NO2 concentrations at Seoul, Korea.
representing the Seoul Metropolitan area. The Seoul Metropolitan area is one of the typical Asian mega-cities with the population over twenty millions accommodating numerous industrial complexes. The SO2 concentration on 3–5 April and 1–3 July exhibits the typical diurnal behavior of SO2; it increases during nighttime as the air stabilizes and it starts to decrease in the morning as the earth surface is heated to promote a vertical mixing. In contrast, the SO2 concentration in January does not exhibit the typical diurnal SO2 variation. The winter storm accompanied by a strong north-west wind with the speed of 7–10 m s1 interfered a diurnal variation of vertical mixing and at the same time it brought a polluted air mass from China to maintain the SO2 concentration high until it started to die out on the afternoon of 3 January. A strong northeast wind also suppressed the diurnal variations of SO2 on 1 October, but it brought a clean air from the ocean to keep the SO2 concentrations low. The model successfully generated the absence of diurnal variations on January and October and the presence of them on April and July. Similarly, the typical diurnal variation of O3, high during the daytime and low during the nighttime, appeared on 3–5 April and 1–3 July. Furthermore, the strong wind observed on 1–2 January and 1 October interfered the diurnal variations of O3. The diurnal patterns produced by the model are again consistent with those of measurements.
The model underpredicted NO2 concentration probably due to the following three reasons. Firstly, the mesoscale grid system is not sufficient enough to resolve the NOx emission in big cities such as Seoul. Secondly, the NOx emission may be under-estimated. Differently from SO2, the accurate estimation of NOx emission requires the knowledge of combustion conditions such as flame modes and temperatures in addition to the amount of fuel consumption. However, these combustion related parameters has not been well investigated yet in this region so that the NOx emission is much more uncertain than the SO2 emission. Finally, all the NO2 measuring devices are equipped with molybdenum converter and therefore respond not only to NO2 but also to HNO3 and other organic nitrates. This inaccurate NO2 measurement appears to be significant considering the calculated nighttime PAN and HNO3 concentrations. The ratio of the calculated hourly concentrations with respect to measured hourly concentrations is used as a measure for the appropriateness as well as the bias of calculated concentrations in Table 1. The six major cities selected for the analysis are Seoul, Taejon, Kwangju, Pusan, Changwon and Kangnung. Taejon and Kwangju are the fifth and sixth largest cities in Korea, respectively, with the populations over one million. There are no big industrial complexes in or near these two cities. On the other hand, Pusan, the second largest city in Korea, located in one of the mostly industrialized region. Changwon represents one of the largest
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Table 1 the calculated concentrations The percent occurrences for the given ranges of ratio r ¼ the measured concentrations % occurrence rp0:25
0:25orp0:5
0:5orp1
1orp2
2orp3
3or
SO2
Seoul Taejeon Kangnung Kwangju Changwon Pusan
0 2.4 8.1 1.1 7.8 2.2
7.48 17.3 12.8 7.5 25.5 15.6
38.9 41.5 31.7 39.7 30.6 29.9
41.6 28.4 30.2 40.5 25.7 42.5
9.48 8.3 10.0 8.3 7.15 8.4
2.5 2.2 7.1 3.0 3.3 1.4
O3
Seoul Taejeon Kangnung Kwangju Changwon Pusan
0.3 3.5 8.8 0.0 0.5 0.0
14.0 11.6 12.8 5.1 6.6 3.9
32.4 34.9 39.2 52.4 44.4 26.3
40.2 40.8 35.4 41.4 38.7 54.3
11.0 7.6 2.5 1.1 6.8 10.7
2.2 1.6 1.4 0.0 3.0 4.8
NO2
Seoul Taejeon Kangnung Kwangju Changwon Pusan
0.0 0.0 8.2 0.0 0.0 0.0
0.0 0.0 0.8 0.0 0.3 0.0
0.5 0.3 0.3 0.3 0.3 0.3
16.8 3.2 2.2 4.6 8.7 3.6
30.9 7.7 3.5 8.2 12.8 17.1
51.8 88.8 85.0 86.9 77.9 79.0
industrial complexes located in the southern central. Finally, Kangnug is known for tourism, located in the northeastern coast of Korea near by Tonghae in Fig. 2. These six cities were selected here not only because they represent the northern, centered, southwestern, southeastern, south central and northeastern Korea as shown in Fig. 2, but also because they have the sufficient number of air stations to provide a regionally averaged concentrations. For the SO2 in Seoul, the percent occurrences of 0:5oro1 is 38.9, implying that the 38.9 percentage of the calculated hourly concentrations ranges from 50% to 100% of the measured ones. On the other hand, the percent occurrences of 1oro2 is 41.6, comparable those for 0:5oro1: This indicates that the bias of the calculated hourly SO2 concentrations in Seoul is not significant. In contrary, the percent occurrence of 0:5oro1 exceeds that of 1oro2 by 12 for the SO2 in Taejeon. This underprediction of SO2 concentration in Taejeon is mainly because more than half of air stations are located in the urban site to cause the measured concentration higher than the calculated one. On the other hand, the overprediction of SO2 concentration in Pusan is probably because some of sites are located near the ocean far from the city. The percent occurrences of 0:5oro2 for all the six cities range 56.3–80.5, indicating the calculated SO2 concentrations compare well with the measurements. The percent occurrences of 0:5oro2 for O3 appears to be larger than those for SO2, indicating
the better performance of the model, In contrasts, the percent occurrences of r > 3 for NO2 ranges from 51.8 to 88.8, indicating the severe overprediction of the model as discussed previously. The capability of the model in simulating a spatial distribution can be examined by deriving the correlation coefficient. The calculated correlation coefficients for annual mean concentrations of the above-mentioned six cities are found to be 0.97 and 0.90 for SO2, and NO2, respectively. These reasonably high values for the correlation coefficients indicate that the model reproduces the spatial distribution quite well. The annual mean concentration has little meaning for O3 due to its strong diurnal and seasonal variation, and therefore the 90percentile concentration is used instead. The calculated correction coefficient is only 0.63 because the differences of 90-percentile O3 concentration among the six cities are too small to characterize the spatial distributions. 4.3. Rain water compositions An extensive field monitoring was carried out to chemically analyze rain compositions in 1996 (Lee et al., 2000). The selected sampling sites are Seoul, Chunchon, Jinyang and Changwon. The locations of Seoul and Chunchon are shown in Fig. 2, Jinyang is located near by Chinju as shown in Fig. 2 and Changwon is located almost middle between Chinju and Pusan as shown in Fig. 2.
J. Kim et al. / Atmospheric Environment 37 (2003) 1703–1713
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(a) Seoul Meas.
Pred.
-2
NO 3
-
3
6 5 4 3 2 1 0
conc. mg l -1
conc. mg l -1
SO4
2 1 0
5- 10
10- 20
20- 40
5- 10
more than 40
10- 20
20- 40
more than 40
precipitation mm
precipitation mm
(b) Chunchon SO 4
-2
NO 3
-
3 conc. mg l -1
conc. mg l -1
4 3 2 1 0
2 1 0
5- 10
10- 20
20- 40
more than 40
5- 10
10- 20
20- 40
more than 40
precipitation mm
precipitation mm
(c) Jinyang NO3
5
4
4
3
conc. mg l -1
conc. mg l -1
S O4-2
3 2 1 0
-
2 1 0
5- 10
10- 20
20- 40
5- 10
more than 40
10- 20
20- 40
more than 40
precipitation mm
precipitation mm
(d) Changwon -2
NO3
-
2
6 5 4 3 2 1 0
conc. mg l -1
conc. mg l -1
SO4
1
0 5- 10
10- 20
20- 40
precipitation mm
more than 40
5- 10
20- 40
precipitation mm
Fig. 4. Comparison between the calculated and measured concentration of
The field monitoring had been maintained for the whole year of 1996 to cover over 50% of precipitation at each site. Fig. 4 compares the calculated and measured sulfate and nitrate concentrations in the rain waters according to the amount of precipitation. The rain sampling sites in Seoul and Chunchon are classified as urban sites and the other two sites, Jinyang and Changwon, are as rural sites. The model under-
10- 20
SO2 4 ,
more than 40
NO 3 ions according to precipitation rates.
estimated the surface concentration in these urban sites, whereas the model performed quite well for the rural sites. It may be noted here that the model underestimated the sulfate concentrations especially for the amount of the precipitation ranging from 5 to 10 mm. This is probably either because of evaporation effects of sampled water or because of inaccurate modeling of initially present sulfate aerosols. As the amount of
J. Kim et al. / Atmospheric Environment 37 (2003) 1703–1713
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precipitation increases, the importance of evaporation of the sampled water as well as the significance of the initially present sulfate aerosols diminishes to enhance the model accuracy. Despite of the good performance in calculating sulfate concentrations, the model considerably under-estimated the nitrate concentrations. The main reason of the under-estimation is probably because the model assumes the nitrate in air to exist mostly in the gas phase by using the simplified aerosol model. The gaseous HNO3 has a higher dry deposition velocity than the aerosol nitrate to lower the nitrate concentration in the gas phase. In addition, the under-estimation of nitrate concentration in the rain water may be also associated with the underestimation of gaseous NO2 as discussed in the previous section. 4.4. Wet and dry deposition amounts There are no direct field measurements for the yearly wet and dry deposition amount of sulfur and nitrogen in Korea. Instead, Park et al. (2000) estimated the yearly amount of dry depositions of SO2 by using gaseous concentrations measured over hundred air stations with estimated dry deposition velocities as shown in Fig. 5. Park et al. also estimated the yearly amount of wet depositions by interpolating the measured sulfate concentrations in rain waters with weighting precipitation rates. The accuracy of Park et al.’s semi-empirical approach in estimating the amounts of wet and dry depositions heavily relies on the accuracy of SO2 monitoring data. However, the most of air stations are located in the
urban area to severely over-estimate the amount of depositions. As a result, the amount of wet and dry depositions calculated in the present modeling study is smaller than by Park et al. by a factor of two or so despite the fact that the present model successfully reproduced the SO2 concentrations in the vicinity of the monitoring stations. The urban oriented air monitoring station siting caused the dry deposition flux in the winter to be the largest in Park et al.’s study because the air tends to be more stable in the winter to confine the pollutant in the urban region. 4.5. Mass balance A mass balance was derived from the model results and shown in Fig. 6 to identify the individual contributions of advection, and wet/dry deposition of sulfuric oxides and nitrogen oxides. The negative value of advection amount implies that the more amount is advected out rather than into the model domain. Fig. 6a shows that approximately 30% of the SO2 emitted in the model domain I is advected out of the region mostly to the pacific ocean. More than two-thirds of the sulfur advection occurred in the form of sulfate because the model domain is large enough to provide the sufficient residence time for the conversion of SO2 to sulfate. The comparisons of advection amounts between model domains I and II and between model domains II and III show that as the model domain gets smaller, the advection becomes more important. And the advection of SO2, primary pollutant, surpasses that of sulfate, secondary pollutant, for model domains II and III unlikely to model domain I due to the short residence
Dry deposition of sulfur Present study
Park et al.
kg km
-2
2000 1500 1000 500 0
Spring
Summer
Fall
Winter
Wet deposition of sulfur Present study
Park et al.
kg km
-2
2000 1500 1000 500 0 Spring
Summer
Fall
Winter
Fig. 5. The Comparison of wet and dry deposition amounts of the present study with those by Park et al. (2000).
J. Kim et al. / Atmospheric Environment 37 (2003) 1703–1713
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Fig. 6. The mass balance of sulfuric oxides and nitrogen oxides in model domains I, II and III.
time. The mass balances of all the three domains show that the SO2 is deposited mainly by the dry deposition whereas the sulfate is deposited mainly by the wet deposition. In addition, the dry deposition amount of SO2 is as large as the wet deposition amount of sulfate. The mass balance of nitrogen oxides shown in Fig. 6b includes only NO, NO2 and HNO3 while neglecting PAN, HNO4, HONO for simplicity. Therefore, the amount of NO and NO2 emission exceeds the amount of advection and dry/wet deposition of NO, NO2 and HNO3 by 18%. These behaviors associated with the advection of nitrogen oxides are similar to those of sulfuric oxides. As also shown in Fig. 6b, the nitrogen oxides are mainly deposited onto the earth surface in the form of HNO3. Furthermore, the amount of the dry deposition of HNO3 is 40% larger than that of the wet deposition. However it may be noted here that the aerosol model employed here is a highly simplified one, which may overpredict the gaseous HNO3 concentration, resulting the higher dry deposition flux. Fig. 7 shows the seasonal variation of sulfur and nitrogen amounts advected out of model domain I representing North East Asia. During the spring and summer season, the amounts of SO2 and NOx advection,
primary pollutants, have negative values, indicating that the amount advected into the region surpasses the amount advected out of the region. These negative net advection amounts are arisen because the SO2 and NOx emitted in the region are removed before they are transported out of the domain. For the SO2, the high washout rates associated with a high precipitation rates in the spring and summer are the one reason. For both the SO2 and NOx, the fast photochemical reaction converting these primary pollutants to the secondary pollutants is the other reason. The advection amounts of sulfate and nitrate show little seasonal variation despite of their strong dependency on solar radiation. This is because the summer season provides the favorable conditions for wash-out, compensating the increased chemical production.
5. Source–receptor relations for sulfur and nitrogen containing species In the present study, all the sulfur and nitrogen containing species including radicals as well as stable chemical species have the corresponding counter species
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SO2
Sulfate
NOx
25
Fall
20
Spring
Summer
10 5 0 -5
Winter the amount of advection -105 ton of N
the amount of advection -105 ton of S
Winter
30
15
HNO3
2.5
35
2
Fall
1.5 1
Spring Summer
0.5 0 -0.5
Fig. 7. The seasonal advection amount from domain I representing North-East Asia.
to derive source–receptor relations of SO2, sulfate, NO, NO2 and HNO3. The source region was set to China and neighboring ocean and the receptor region was set to the Republic of Korea and neighboring ocean. In order to quantify the importance of transport from China, the transport contributions to the total mass and deposition were calculated as follows: transport contribution to total mass ð%Þ RRR Ci;c ðx; y; z; tÞ dx dy dz ¼ RRR 100; Ci ðx; y; z; tÞ dx dy dz transport contribution to deposition ð%Þ RRRR vd Ci;c ðx; y; z; tÞ dx dy dz dt R ¼ RRR 100; vd;c Ci ðx; y; z; tÞ dx dy dz dt where Ci is the concentration of chemical species i and Ci;c is the concentration of the corresponding counter species and vd;c is a wet or dry deposition velocity. The transport contributions for primary pollutants such as NO, NO2, and SO2 are smaller than those for secondary pollutants such as HNO3 and sulfate. This is not only because the primary pollutants are converted to the secondary pollutants during the transport, but also because the emissions of the primary pollutants in the receptor region lessen their own transport contributions. As shown in Fig. 8, the seasonal transport contributions to the total nitrogen and sulfur were calculated from those for individual chemical species. The summer is a rainy season in the North East Asia, such that the precipitation washes out SO2, sulfate, HNO3 to reduce the amount of transport from China to Korea. Furthermore, the wind direction is more favorable to transport from China to Korea in the winter. Therefore for all the quantities calculated here, the transport contribution is the lowest at the summer and the highest at the winter. The dry deposition, which is directly affected by chemical species in the surface layer, receives the smallest transport contribution. On the other hand, the wet deposition is affected by the chemical species concentrations from the cloud top to the surface to have
Fig. 8. The seasonal transport contributions from China to Korea for sulfur and nitrogen.
the higher transport contribution than the dry deposition. Especially the transport contribution of the dry deposition in the summer is less than half of that in winter. The annual average of transport contributions of sulfur calculated here is slightly larger than that calculated by Lagrangian model (Arndt and Carmichael, 1995, 1998) probably because the simulation period taken here is more favorable to the long-range transport. The transport contributions of nitrogen oxides was calculated here for the first time and therefore there are no previous studies to compare.
6. Concluding remarks A long term simulation of the comprehensive acid deposition model allows not only a comprehensive
J. Kim et al. / Atmospheric Environment 37 (2003) 1703–1713
evaluation of acid deposition but also an accurate estimation of the continental acid outflow. However, a one year long simulation has been rarely made, because of high computational costs as well as because of lacking field measurements. The present work demonstrates that yearly simulation of such a model is quite feasible with readily available computer resources. All the simulations of MM5 and multi-scale STEM were carried out by DEC 500, which is a widely available workstation. The counter species method was successfully extended to probe a source–receptor relation in North East Asia not only for sulfur containing species but also for nitrogen containing species. Although the model provides a detail source–receptor relation for nitrogen containing species for the first time, these results should be interpreted carefully because of higher uncertainties associated with emission data of nitrogen containing species compared to those of sulfur containing species. Despite of these uncertainty problems, the present results indicate the nitrogen containing species should be considered to assess the current status and future trend of acid deposition in North East Asia. It may be noted here that many researches are being performed to estimate the emissions of NOx and hydrocarbons in East Asia (Aardenne et al., 1999; Streets and Waldhoff, 2000; Klimont and Streets, 2002). A comprehensive Eulerian model coupled with the counter species method is a powerful tool in assessing the current status and the future trend of acid deposition by incorporating various complex atmospheric processes accurately. Although the present study focuses only on acid precursors and acidifying chemical species, it can be easily extended to transport of oxidants such as ozone.
Acknowledgements The authors would like to acknowledge, G-7 project and BK-21 (Brain Korea 21) project for support of the present.
References Arndt, R.L., Carmichael, G.R., 1995. Long-range transport and deposition of sulfur in Asia. Water, Air and Soil pollution 85, 2283–2288. Arndt, R.L., Carmichael, G.R., Roorda, J.M., 1998. Seasonal source–receptor relationships in Asia. Atmospheric Environment 32, 1397–1406. Aardenne, J.A., Carmichael, G.R., Levy II, H., Streets, D., Hordijk, L., 1999. Anthropogenic NOx emissions in Asia in the period 1990–2020. Atmospheric Environment 33, 633–646. Carmichael, G.R., Peters, L.K., Saylor, R.D., 1991. The STEM II regional scale acid deposition and photochemical model. Atmospheric Environment 25A, 2077.
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Grell, G.A., Dudhia, J., Stauffer, D.R., 1994. Description of the Fifth Generation Penn State/NCAR Meso-scale Model (MM5), NCAR Technical Note, NCAR/Tn398+STR. Han, B., Kim, J., Cho, S.Y., 2001. A numerical simulation of acid deposition in East Asia. Water, Air and Soil Pollution 130, 487–492. Holloway, T., Levy II, H., Carmichael, G.R., 2002. Transfer of reactive nitrogen in Asia: development and evaluation of a source–receptor model. Atmospheric Environment 36, 4251–4264. Inchon, 2000. Report for Implementation of SIP on Seoul metropolitan area. Kitada, T., Lee, P.C.S., 1993. Numerical modeling of longrange transport of acidic species in association with meso-bconvective cloud across the Japan sea resulting in acid snow over coastal Japan-II. Results and discussion. Atmospheric Environment 27A (7), 1077–1090. Kim, J.T., Cho, S.Y., 1999. Application of the nested grid STEM to an early summer acid rain in South Korea. Atmospheric Environment 33, 3167–3181. Klimont, Z., Streets, D.G., Gupta, S., Cofala, F., Lixin, F., Ichikawa, Y., 2002. Anthropogenic emissions of nonmethane volatile organic compounds in china. Atmospheric Environment 36, 1309–1322. Lee, B.K., Hong, S.H., Lee, D.S., 2000. Chemical composition of precipitation and wet deposition of major on the Korean peninsula. Atmospheric Environment 34, 563–575. Leone, J.A., Seinfeld, J.H., 1984. Analysis of the characteristics of complex chemical reaction mechanisms-application to photochemical smog chemistry. Environmental Science and Technology 18, 280–287. Nakakita, E., Ikebuchi, S., Nakamura, T., Kanmuri, M., Okuda, M., Yamaji, A., Takasao, T., 1996. Short-term rainfall prediction method using a volume scanning radar and grid point value data from numerical weather prediction. Journal of Geographical Research 101 (D21), 26181–26197. NCAR, 1986. Preliminary Evaluation Studies with the Regional Acid Deposition Model, National Center for Atmospheric Research, ADMP-68-4. Otoshi, T., Fukuzaki, N., Li, H., Hoshino, H., Sase, H., Suzuki, K., 2001. Quality control and its constraints during the preparatory-phase activities of the acid deposition monitoring network in East Asia (EANET). Water, Air, and Soil Pollution 130, 1613–1618. Park, J.K., Cho, S.Y., 1998. Transport of SO2 and sulfate between Korean and East China. Atmospheric Environment 32, 2745–2756. Park, S.U., In, H.J., Kim, S.W., Lee, Y.H., 2000. Estimation of sulfur deposition in South Korea. Atmospheric Environment 34, 3259–3269. Streets, D.G., Waldhoff, S.T., 2000. Present and future emissions of air pollutants in China: SO2, NOx, and CO. Atmospheric Environment 34, 363–374. Uno, I., Murano, K., 1996. Simulated acidic aerosol long-range transport and deposition over East Asia—role of synoptic scale weather system. Proceeding of the International Symposium on Acidic Deposition and its Impacts, December 10–12, Tsukuba, Japan, pp. 53–60.
A numerical simulation of annual acid deposition amount in Korea Jaehee Kim, Boeun Han, SeogYeon Cho* Department of Environmental Engineering, Inha University, Inchon, South Korea Received 26 August 2002; accepted 12 December 2002
Abstract A comprehensive acid deposition model was used to calculate an annual amount of acid deposition in North East Asia as well as to derive a source–receptor relation. The model results for gaseous SO2 and O3 agree very well with measurements, but the model under-estimated NO2 concentrations. Similarly, the model estimates the sulfate in rain waters quite well but under-estimates nitrate in the rain waters. The correlation coefficients for the spatial distributions of the calculated and measured annual mean concentrations were 0.97, 0.63 and 0.90 for the gaseous SO2, O3, and NO2. One-third of SO2 emitted in North East Asia was calculated to be advected out mostly in the form of sulfate. The amount removed by dry deposition is comparable to that removed by wet deposition for sulfur but it is 40% larger than that by wet deposition for nitrogen. The source–receptor relations derived by the counter-species method show that the wet deposition is more influenced by the long-range transport than the dry deposition. Furthermore, the long-range transport contribution was calculated to be the lowest in the summer and the highest in the winter. r 2003 Elsevier Science Ltd. All rights reserved. Keywords: Annual acid deposition; Eulerian model; Advection; Source–receptor relation; Counter species
1. Introduction Recently, a rapid economic growth in Korea and China has increased the public concerns on acid deposition in North East Asia. As a result, various national and international monitoring programs have been implemented to assess the significance of acid deposition in East Asia (Otoshi et al., 2001). On the other hand, an extensive effort has been made to construct the emission database for SO2, NOx and VOCs (Klimont and Streets, 2002). In the present study, a comprehensive acid deposition model was employed by utilizing these recent monitoring results and emission databases to access the current status of acid deposition and the trans-boundary characteristics of acid precursors in Korea.
*Corresponding author. E-mail address: [email protected] (S.Y. Cho).
Korea has distinctive four seasons characterized by temperatures, precipitations, principal wind directions to require at least one year long model simulation to access the acid deposition as well as the trans-boundary problems. Arndt et al. (1998) used ATMOS, a Lagrangian trajectory model, to assess the acid deposition in East Asia for the year of 1990. Only the sulfur related species were included in the study mainly because SO2 and sulfate were regarded as the most important acidifying species in 1980s. On the other hand, ATMOS was not able to simulate ozone and nitrogen compounds because of highly simplified chemistry. However, an aggressive sulfur reduction policy implemented in Korea has lowered the ambient SO2 concentration several times in major cities in 1990s. For example, the annual mean SO2 concentration in Seoul, the capital city in Korea, was reduced from 43 ppb in 1990 to 6 ppb in 2000. China is now also making a similar effort to control the ever-increasing sulfur emission. On the other hand, the nitrogen oxide
1352-2310/03/$ - see front matter r 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S1352-2310(03)00002-5
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emission in North East Asia has steadily increased over the years due to a rapid increase of automobiles. As a result, the relative importance of nitrogen containing acids has increased substantially over sulfur containing acids in Korea in the recent years. Although the nitrogen containing acid may be modeled by a Lagrangian trajectory model with a simplified chemistry (Holloway et al., 2002), it can be more accurately addressed by a comprehensive acid deposition model. Comprehensive acid deposition models such as Sulfur transport Eulerian Model (STEM, Carmichael et al., 1991) and Regional Acid Deposition Model (RADM, NCAR, 1986) incorporate complex multi-phase transport, chemistry and deposition processes to simulate various atmospheric processes leading to acid deposition. However, the application of comprehensive acid deposition models was limited to the episodic simulation rather than a long term simulation due to a high computational cost as well as a limited availability of acid deposition related data (Kitada and Lee, 1993; Uno and Murano, 1996; Park and Cho, 1998; Kim and Cho, 1999). This limitation of the modeling period for a comprehensive acid deposition model has become a less problem due to the recent advances in computers as well as in monitoring networks (Han et al., 2001). In the present paper, multi-scale STEM (Kim and Cho, 1999) coupled with MM5(Mesoscale model version 5, Grell et al., 1994) was employed to investigate the acid deposition by sulfate and nitric acid in North East Asia for the year of 1996. Multi-scale STEM was run continuously over the simulation period to test out its convergence and accuracy for the long term simulation. The model simulation results were compared with the extensive field monitoring data for the gas and rain phase chemical species. The comprehensive acid deposition model usually takes a Eulerian approach, which fails to explicitly provide a source–receptor relation unlikely to a Lagrangian trajectory model. In the present study, the counter species analysis was implemented to derive a source–receptor relation from a Eulerian model. The counter species analysis can be applied not only to chemical species with the linear chemistry but also to chemical species with the nonlinear chemistry such that the source–receptor relations for ozone and nitrogen compounds can be also easily derived.
tures, humidities, wind velocities, wind directions, and cloud/rain related parameters. Because MM5 is well known and extensively documented elsewhere (Grell et al., 1994), only the multi-scale STEM was described briefly here. Multi-scale STEM is a comprehensive acid deposition model solving the multi-phase atmospheric transport/chemistry/deposition equations numerically (Kim and Cho, 1999). The gas phase chemical species satisfy
2. Model description
SO2;c þ OH- Sulfatec þ HO2
2.1. Mathematical formulation of multi-scale STEM
The subscript c denotes for the counter species. Although the counter species such as SO2,c and Sulfatec are destroyed or produced by the above reaction, the other species such OH and HO2 are assumed not to be affected by the above reaction. Therefore, the chemical
A meteorological model named Mesoscale model version 5 (MM5) is coupled with multi-scale STEM to provide necessary meteorological fields such as tempera-
qðCi Þ þ r ðvCi Þ ¼ r KrCi þ Ri þ Ei þ Gi ; qt where Ci is the gas phase concentration of the ith chemical species, v is the wind velocity vector, K is the eddy diffusivity tensor, Ri is the reaction rate of the ith chemical species, Ei is the emission rate of the ith chemical species, and Gi is the mass transfer rate of the ith species between the gas and condensed phases. The condensed phase chemical species are described by c qðsp Ci;p Þ c þ r ðv vsp Þsp Ci;p qt c c ¼ rKpc rsp Ci;p þ Rci;p þ Gi;p ;
where the subscript p denotes the phase (e.g. cloud, water, rain and snow) and superscript c denotes the condensed phase. And sp is the water content of phase p and vsp is the settling velocity vector of the hydrometers of phase p. 2.2. Source–receptor analysis The source–receptor relations in the Eulerian model can be derived either by sensitivity analysis or by counter species analysis. The counter species analysis is selected here mainly due to its simplicity. The counter species analysis is designed to evaluate the importance of particular physical/chemical pathways by counting the number of moles passing through. Leone and Seinfeld (1984) assessed the role of particular reaction steps to evaluate the various photo-chemical mechanism using the counter species method. It is extended here to address a source–receptor relation. The counter species undergoes the same atmospheric processes as the corresponding species except setting the emissions other than the source region equal to zero. For example, the counter species SO2 is emitted from the source region of interest and produces the counter species sulfate as follows:
J. Kim et al. / Atmospheric Environment 37 (2003) 1703–1713
reaction rate of the counter species takes the same form as the reaction rate of the corresponding chemical species except that the chemical species concentration is replaced by the corresponding counter species concentration. Since the counter species is assumed not to influence any other chemical species concentrations, only the counter chemical species equations formulated below are required to be solved qðCi;c Þ þ r ðvCi;c Þ ¼ r KrCi;c þ Ri;c þ Ei;c di þ Gi;c ; qt c qðsp Ci;p;c Þ c þ r ðv vsp Þsp Ci;p;c qt c c ¼ rKpc rsp Ci;p;c þ Rci;p;c þ Gi;p;c ;
where the subscript c denotes the counter species and Ri;c denotes the chemical reaction rate of the counter specie. Kroneck delta function (di ) is set equal to one in the source region and zero in the receptor region. Therefore, the counter species concentrations based on the above equations represent the contribution from the source region.
3. Description of model simulation The year 1996 was chosen as a modeling period mainly because the field monitoring data for the chemical compositions in the gas and rain phase were readily available. Furthermore, it was not
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affected by the economic crisis initiated in 1997 in the region. 3.1. Grid system As shown in Fig. 1, the multi-grid system chosen for the present study consists of three domains. Domain I is a regional scale domain with the grid size of 80 km, including most part of China, Korean Peninsular and Japan. Domain II is a mesoscale domain with the grid size of 26.7 km, including Korean Peninsula and the eastern part of China and the southern part of Japan. In addition, the entire China including the neighboring ocean was chosen as a source region and the Republic of Korea including the neighboring ocean as a receptor region for the source–receptor analysis. The receptor region is named here as domain III. 3.2. Input data preparation Japan Grid Point value data (Nakakita et al., 1996) with a horizontal resolution of 1.25 and a temporal resolution of 6 h were used to generate initial and boundary conditions for MM5, which calculated meteorological fields necessary to run multi-scale STEM. Initial chemical species concentrations were derived by applying inverse r square interpolation to the available air monitoring data. The SO2 emission was estimated from the amount of fuel use and sulfur content for each source as described in Park and Cho (1998) and Kim and Cho (1999). The NO2 and VOC emissions in China and Japan were taken
Fig. 1. The model domain and grid structure.
J. Kim et al. / Atmospheric Environment 37 (2003) 1703–1713
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Meteorological parameters as well as chemical species concentrations were calculated by the multi-scale STEM coupled with MM5. Although the model domain covers China, Japan and Korea, the model results were compared to the measurements only in Korea due to the lack of measurement data in the other region.
in the summer. For the summer rainy season, the model predicted very well the amount of precipitation in the southern part of Korea including Kwangju, Pusan, Chinju but it underpredicted the other regions. The precipitation front was first formed in the southern sea of Korea in June and gradually moved northward to trigger the precipitation. However, the model slightly underpredicted this northward movement of the precipitation front to cause the underprediction of precipitation amounts at Seoul, Tonghae and Chunchon. In addition, the underpredictions in June at Tonghae and in August at Kwangju were caused by the localized heavy rain and by the rain accompanying Typhoon, respectively, which the model was not able to handle properly.
4.1. Meteorological parameters
4.2. Gas phase concentrations
The major meteorological parameters calculated by MM5 are winds, temperatures and precipitations. Because the calculated temperatures and winds agree quite well with measurement, only the performance of the model on the precipitation is discussed here. As shown in Fig. 2, MM5 reproduced a yearly variation of precipitation quite well; a low precipitation occurred in the fall and winter, and a high precipitation
There are over hundred air stations in Korea monitoring the criteria air pollutants including NO2, SO2, and O3, allowing a direct comparison with measurements. Fig. 3 compares the measurements with the model results during the first three days for every three month starting from January. The measured air concentrations were derived by averaging over 20 stations surrounding the grid point
from China Map Project (Aardenne et al., 1999; Streets and Waldhoff, 2000; Klimont and Streets, 2002), whereas those in Korea were taken from the recent local governmental documents (Inchon, 2000).
4. Comparison of model simulation results with field measurements
Pred.
Meas 600
500
500
400
400
mm
mm
Seoul 600
300
Chunchon
300
200
200
100
100 0
0
1
2
3
4
5
6 7 month
8
9
1
10 11
2
3
4
5
250
400
200
300
150
mm
mm
Taejon 500
200
8
9
10 11
8
9
10 11
Tonghae
100 50
100
0
0
1
2
3
4
5
6 7 month
8
9
1
10 11
300
300
200 100
mm
300
mm
400
200
5
6
7
month
6
7
8
9
10 11
200
0
0
4
5
100
100
0
3
4
Pusan
400
2
3
Chinju
400
1
2
month
Kwangju
mm
6 7 month
1
2
3
4
5
6 month
7
8
9
10 11
1
2
3
4
5
6
7
8
9
10 11
month
Fig. 2. Comparison between the calculated and measured monthly precipitation amounts at the six meteorological observatories located in Korea.
J. Kim et al. / Atmospheric Environment 37 (2003) 1703–1713
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Fig. 3. Comparison between the calculated and measured SO2, O3, and NO2 concentrations at Seoul, Korea.
representing the Seoul Metropolitan area. The Seoul Metropolitan area is one of the typical Asian mega-cities with the population over twenty millions accommodating numerous industrial complexes. The SO2 concentration on 3–5 April and 1–3 July exhibits the typical diurnal behavior of SO2; it increases during nighttime as the air stabilizes and it starts to decrease in the morning as the earth surface is heated to promote a vertical mixing. In contrast, the SO2 concentration in January does not exhibit the typical diurnal SO2 variation. The winter storm accompanied by a strong north-west wind with the speed of 7–10 m s1 interfered a diurnal variation of vertical mixing and at the same time it brought a polluted air mass from China to maintain the SO2 concentration high until it started to die out on the afternoon of 3 January. A strong northeast wind also suppressed the diurnal variations of SO2 on 1 October, but it brought a clean air from the ocean to keep the SO2 concentrations low. The model successfully generated the absence of diurnal variations on January and October and the presence of them on April and July. Similarly, the typical diurnal variation of O3, high during the daytime and low during the nighttime, appeared on 3–5 April and 1–3 July. Furthermore, the strong wind observed on 1–2 January and 1 October interfered the diurnal variations of O3. The diurnal patterns produced by the model are again consistent with those of measurements.
The model underpredicted NO2 concentration probably due to the following three reasons. Firstly, the mesoscale grid system is not sufficient enough to resolve the NOx emission in big cities such as Seoul. Secondly, the NOx emission may be under-estimated. Differently from SO2, the accurate estimation of NOx emission requires the knowledge of combustion conditions such as flame modes and temperatures in addition to the amount of fuel consumption. However, these combustion related parameters has not been well investigated yet in this region so that the NOx emission is much more uncertain than the SO2 emission. Finally, all the NO2 measuring devices are equipped with molybdenum converter and therefore respond not only to NO2 but also to HNO3 and other organic nitrates. This inaccurate NO2 measurement appears to be significant considering the calculated nighttime PAN and HNO3 concentrations. The ratio of the calculated hourly concentrations with respect to measured hourly concentrations is used as a measure for the appropriateness as well as the bias of calculated concentrations in Table 1. The six major cities selected for the analysis are Seoul, Taejon, Kwangju, Pusan, Changwon and Kangnung. Taejon and Kwangju are the fifth and sixth largest cities in Korea, respectively, with the populations over one million. There are no big industrial complexes in or near these two cities. On the other hand, Pusan, the second largest city in Korea, located in one of the mostly industrialized region. Changwon represents one of the largest
J. Kim et al. / Atmospheric Environment 37 (2003) 1703–1713
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Table 1 the calculated concentrations The percent occurrences for the given ranges of ratio r ¼ the measured concentrations % occurrence rp0:25
0:25orp0:5
0:5orp1
1orp2
2orp3
3or
SO2
Seoul Taejeon Kangnung Kwangju Changwon Pusan
0 2.4 8.1 1.1 7.8 2.2
7.48 17.3 12.8 7.5 25.5 15.6
38.9 41.5 31.7 39.7 30.6 29.9
41.6 28.4 30.2 40.5 25.7 42.5
9.48 8.3 10.0 8.3 7.15 8.4
2.5 2.2 7.1 3.0 3.3 1.4
O3
Seoul Taejeon Kangnung Kwangju Changwon Pusan
0.3 3.5 8.8 0.0 0.5 0.0
14.0 11.6 12.8 5.1 6.6 3.9
32.4 34.9 39.2 52.4 44.4 26.3
40.2 40.8 35.4 41.4 38.7 54.3
11.0 7.6 2.5 1.1 6.8 10.7
2.2 1.6 1.4 0.0 3.0 4.8
NO2
Seoul Taejeon Kangnung Kwangju Changwon Pusan
0.0 0.0 8.2 0.0 0.0 0.0
0.0 0.0 0.8 0.0 0.3 0.0
0.5 0.3 0.3 0.3 0.3 0.3
16.8 3.2 2.2 4.6 8.7 3.6
30.9 7.7 3.5 8.2 12.8 17.1
51.8 88.8 85.0 86.9 77.9 79.0
industrial complexes located in the southern central. Finally, Kangnug is known for tourism, located in the northeastern coast of Korea near by Tonghae in Fig. 2. These six cities were selected here not only because they represent the northern, centered, southwestern, southeastern, south central and northeastern Korea as shown in Fig. 2, but also because they have the sufficient number of air stations to provide a regionally averaged concentrations. For the SO2 in Seoul, the percent occurrences of 0:5oro1 is 38.9, implying that the 38.9 percentage of the calculated hourly concentrations ranges from 50% to 100% of the measured ones. On the other hand, the percent occurrences of 1oro2 is 41.6, comparable those for 0:5oro1: This indicates that the bias of the calculated hourly SO2 concentrations in Seoul is not significant. In contrary, the percent occurrence of 0:5oro1 exceeds that of 1oro2 by 12 for the SO2 in Taejeon. This underprediction of SO2 concentration in Taejeon is mainly because more than half of air stations are located in the urban site to cause the measured concentration higher than the calculated one. On the other hand, the overprediction of SO2 concentration in Pusan is probably because some of sites are located near the ocean far from the city. The percent occurrences of 0:5oro2 for all the six cities range 56.3–80.5, indicating the calculated SO2 concentrations compare well with the measurements. The percent occurrences of 0:5oro2 for O3 appears to be larger than those for SO2, indicating
the better performance of the model, In contrasts, the percent occurrences of r > 3 for NO2 ranges from 51.8 to 88.8, indicating the severe overprediction of the model as discussed previously. The capability of the model in simulating a spatial distribution can be examined by deriving the correlation coefficient. The calculated correlation coefficients for annual mean concentrations of the above-mentioned six cities are found to be 0.97 and 0.90 for SO2, and NO2, respectively. These reasonably high values for the correlation coefficients indicate that the model reproduces the spatial distribution quite well. The annual mean concentration has little meaning for O3 due to its strong diurnal and seasonal variation, and therefore the 90percentile concentration is used instead. The calculated correction coefficient is only 0.63 because the differences of 90-percentile O3 concentration among the six cities are too small to characterize the spatial distributions. 4.3. Rain water compositions An extensive field monitoring was carried out to chemically analyze rain compositions in 1996 (Lee et al., 2000). The selected sampling sites are Seoul, Chunchon, Jinyang and Changwon. The locations of Seoul and Chunchon are shown in Fig. 2, Jinyang is located near by Chinju as shown in Fig. 2 and Changwon is located almost middle between Chinju and Pusan as shown in Fig. 2.
J. Kim et al. / Atmospheric Environment 37 (2003) 1703–1713
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(a) Seoul Meas.
Pred.
-2
NO 3
-
3
6 5 4 3 2 1 0
conc. mg l -1
conc. mg l -1
SO4
2 1 0
5- 10
10- 20
20- 40
5- 10
more than 40
10- 20
20- 40
more than 40
precipitation mm
precipitation mm
(b) Chunchon SO 4
-2
NO 3
-
3 conc. mg l -1
conc. mg l -1
4 3 2 1 0
2 1 0
5- 10
10- 20
20- 40
more than 40
5- 10
10- 20
20- 40
more than 40
precipitation mm
precipitation mm
(c) Jinyang NO3
5
4
4
3
conc. mg l -1
conc. mg l -1
S O4-2
3 2 1 0
-
2 1 0
5- 10
10- 20
20- 40
5- 10
more than 40
10- 20
20- 40
more than 40
precipitation mm
precipitation mm
(d) Changwon -2
NO3
-
2
6 5 4 3 2 1 0
conc. mg l -1
conc. mg l -1
SO4
1
0 5- 10
10- 20
20- 40
precipitation mm
more than 40
5- 10
20- 40
precipitation mm
Fig. 4. Comparison between the calculated and measured concentration of
The field monitoring had been maintained for the whole year of 1996 to cover over 50% of precipitation at each site. Fig. 4 compares the calculated and measured sulfate and nitrate concentrations in the rain waters according to the amount of precipitation. The rain sampling sites in Seoul and Chunchon are classified as urban sites and the other two sites, Jinyang and Changwon, are as rural sites. The model under-
10- 20
SO2 4 ,
more than 40
NO 3 ions according to precipitation rates.
estimated the surface concentration in these urban sites, whereas the model performed quite well for the rural sites. It may be noted here that the model underestimated the sulfate concentrations especially for the amount of the precipitation ranging from 5 to 10 mm. This is probably either because of evaporation effects of sampled water or because of inaccurate modeling of initially present sulfate aerosols. As the amount of
J. Kim et al. / Atmospheric Environment 37 (2003) 1703–1713
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precipitation increases, the importance of evaporation of the sampled water as well as the significance of the initially present sulfate aerosols diminishes to enhance the model accuracy. Despite of the good performance in calculating sulfate concentrations, the model considerably under-estimated the nitrate concentrations. The main reason of the under-estimation is probably because the model assumes the nitrate in air to exist mostly in the gas phase by using the simplified aerosol model. The gaseous HNO3 has a higher dry deposition velocity than the aerosol nitrate to lower the nitrate concentration in the gas phase. In addition, the under-estimation of nitrate concentration in the rain water may be also associated with the underestimation of gaseous NO2 as discussed in the previous section. 4.4. Wet and dry deposition amounts There are no direct field measurements for the yearly wet and dry deposition amount of sulfur and nitrogen in Korea. Instead, Park et al. (2000) estimated the yearly amount of dry depositions of SO2 by using gaseous concentrations measured over hundred air stations with estimated dry deposition velocities as shown in Fig. 5. Park et al. also estimated the yearly amount of wet depositions by interpolating the measured sulfate concentrations in rain waters with weighting precipitation rates. The accuracy of Park et al.’s semi-empirical approach in estimating the amounts of wet and dry depositions heavily relies on the accuracy of SO2 monitoring data. However, the most of air stations are located in the
urban area to severely over-estimate the amount of depositions. As a result, the amount of wet and dry depositions calculated in the present modeling study is smaller than by Park et al. by a factor of two or so despite the fact that the present model successfully reproduced the SO2 concentrations in the vicinity of the monitoring stations. The urban oriented air monitoring station siting caused the dry deposition flux in the winter to be the largest in Park et al.’s study because the air tends to be more stable in the winter to confine the pollutant in the urban region. 4.5. Mass balance A mass balance was derived from the model results and shown in Fig. 6 to identify the individual contributions of advection, and wet/dry deposition of sulfuric oxides and nitrogen oxides. The negative value of advection amount implies that the more amount is advected out rather than into the model domain. Fig. 6a shows that approximately 30% of the SO2 emitted in the model domain I is advected out of the region mostly to the pacific ocean. More than two-thirds of the sulfur advection occurred in the form of sulfate because the model domain is large enough to provide the sufficient residence time for the conversion of SO2 to sulfate. The comparisons of advection amounts between model domains I and II and between model domains II and III show that as the model domain gets smaller, the advection becomes more important. And the advection of SO2, primary pollutant, surpasses that of sulfate, secondary pollutant, for model domains II and III unlikely to model domain I due to the short residence
Dry deposition of sulfur Present study
Park et al.
kg km
-2
2000 1500 1000 500 0
Spring
Summer
Fall
Winter
Wet deposition of sulfur Present study
Park et al.
kg km
-2
2000 1500 1000 500 0 Spring
Summer
Fall
Winter
Fig. 5. The Comparison of wet and dry deposition amounts of the present study with those by Park et al. (2000).
J. Kim et al. / Atmospheric Environment 37 (2003) 1703–1713
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Fig. 6. The mass balance of sulfuric oxides and nitrogen oxides in model domains I, II and III.
time. The mass balances of all the three domains show that the SO2 is deposited mainly by the dry deposition whereas the sulfate is deposited mainly by the wet deposition. In addition, the dry deposition amount of SO2 is as large as the wet deposition amount of sulfate. The mass balance of nitrogen oxides shown in Fig. 6b includes only NO, NO2 and HNO3 while neglecting PAN, HNO4, HONO for simplicity. Therefore, the amount of NO and NO2 emission exceeds the amount of advection and dry/wet deposition of NO, NO2 and HNO3 by 18%. These behaviors associated with the advection of nitrogen oxides are similar to those of sulfuric oxides. As also shown in Fig. 6b, the nitrogen oxides are mainly deposited onto the earth surface in the form of HNO3. Furthermore, the amount of the dry deposition of HNO3 is 40% larger than that of the wet deposition. However it may be noted here that the aerosol model employed here is a highly simplified one, which may overpredict the gaseous HNO3 concentration, resulting the higher dry deposition flux. Fig. 7 shows the seasonal variation of sulfur and nitrogen amounts advected out of model domain I representing North East Asia. During the spring and summer season, the amounts of SO2 and NOx advection,
primary pollutants, have negative values, indicating that the amount advected into the region surpasses the amount advected out of the region. These negative net advection amounts are arisen because the SO2 and NOx emitted in the region are removed before they are transported out of the domain. For the SO2, the high washout rates associated with a high precipitation rates in the spring and summer are the one reason. For both the SO2 and NOx, the fast photochemical reaction converting these primary pollutants to the secondary pollutants is the other reason. The advection amounts of sulfate and nitrate show little seasonal variation despite of their strong dependency on solar radiation. This is because the summer season provides the favorable conditions for wash-out, compensating the increased chemical production.
5. Source–receptor relations for sulfur and nitrogen containing species In the present study, all the sulfur and nitrogen containing species including radicals as well as stable chemical species have the corresponding counter species
J. Kim et al. / Atmospheric Environment 37 (2003) 1703–1713
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SO2
Sulfate
NOx
25
Fall
20
Spring
Summer
10 5 0 -5
Winter the amount of advection -105 ton of N
the amount of advection -105 ton of S
Winter
30
15
HNO3
2.5
35
2
Fall
1.5 1
Spring Summer
0.5 0 -0.5
Fig. 7. The seasonal advection amount from domain I representing North-East Asia.
to derive source–receptor relations of SO2, sulfate, NO, NO2 and HNO3. The source region was set to China and neighboring ocean and the receptor region was set to the Republic of Korea and neighboring ocean. In order to quantify the importance of transport from China, the transport contributions to the total mass and deposition were calculated as follows: transport contribution to total mass ð%Þ RRR Ci;c ðx; y; z; tÞ dx dy dz ¼ RRR 100; Ci ðx; y; z; tÞ dx dy dz transport contribution to deposition ð%Þ RRRR vd Ci;c ðx; y; z; tÞ dx dy dz dt R ¼ RRR 100; vd;c Ci ðx; y; z; tÞ dx dy dz dt where Ci is the concentration of chemical species i and Ci;c is the concentration of the corresponding counter species and vd;c is a wet or dry deposition velocity. The transport contributions for primary pollutants such as NO, NO2, and SO2 are smaller than those for secondary pollutants such as HNO3 and sulfate. This is not only because the primary pollutants are converted to the secondary pollutants during the transport, but also because the emissions of the primary pollutants in the receptor region lessen their own transport contributions. As shown in Fig. 8, the seasonal transport contributions to the total nitrogen and sulfur were calculated from those for individual chemical species. The summer is a rainy season in the North East Asia, such that the precipitation washes out SO2, sulfate, HNO3 to reduce the amount of transport from China to Korea. Furthermore, the wind direction is more favorable to transport from China to Korea in the winter. Therefore for all the quantities calculated here, the transport contribution is the lowest at the summer and the highest at the winter. The dry deposition, which is directly affected by chemical species in the surface layer, receives the smallest transport contribution. On the other hand, the wet deposition is affected by the chemical species concentrations from the cloud top to the surface to have
Fig. 8. The seasonal transport contributions from China to Korea for sulfur and nitrogen.
the higher transport contribution than the dry deposition. Especially the transport contribution of the dry deposition in the summer is less than half of that in winter. The annual average of transport contributions of sulfur calculated here is slightly larger than that calculated by Lagrangian model (Arndt and Carmichael, 1995, 1998) probably because the simulation period taken here is more favorable to the long-range transport. The transport contributions of nitrogen oxides was calculated here for the first time and therefore there are no previous studies to compare.
6. Concluding remarks A long term simulation of the comprehensive acid deposition model allows not only a comprehensive
J. Kim et al. / Atmospheric Environment 37 (2003) 1703–1713
evaluation of acid deposition but also an accurate estimation of the continental acid outflow. However, a one year long simulation has been rarely made, because of high computational costs as well as because of lacking field measurements. The present work demonstrates that yearly simulation of such a model is quite feasible with readily available computer resources. All the simulations of MM5 and multi-scale STEM were carried out by DEC 500, which is a widely available workstation. The counter species method was successfully extended to probe a source–receptor relation in North East Asia not only for sulfur containing species but also for nitrogen containing species. Although the model provides a detail source–receptor relation for nitrogen containing species for the first time, these results should be interpreted carefully because of higher uncertainties associated with emission data of nitrogen containing species compared to those of sulfur containing species. Despite of these uncertainty problems, the present results indicate the nitrogen containing species should be considered to assess the current status and future trend of acid deposition in North East Asia. It may be noted here that many researches are being performed to estimate the emissions of NOx and hydrocarbons in East Asia (Aardenne et al., 1999; Streets and Waldhoff, 2000; Klimont and Streets, 2002). A comprehensive Eulerian model coupled with the counter species method is a powerful tool in assessing the current status and the future trend of acid deposition by incorporating various complex atmospheric processes accurately. Although the present study focuses only on acid precursors and acidifying chemical species, it can be easily extended to transport of oxidants such as ozone.
Acknowledgements The authors would like to acknowledge, G-7 project and BK-21 (Brain Korea 21) project for support of the present.
References Arndt, R.L., Carmichael, G.R., 1995. Long-range transport and deposition of sulfur in Asia. Water, Air and Soil pollution 85, 2283–2288. Arndt, R.L., Carmichael, G.R., Roorda, J.M., 1998. Seasonal source–receptor relationships in Asia. Atmospheric Environment 32, 1397–1406. Aardenne, J.A., Carmichael, G.R., Levy II, H., Streets, D., Hordijk, L., 1999. Anthropogenic NOx emissions in Asia in the period 1990–2020. Atmospheric Environment 33, 633–646. Carmichael, G.R., Peters, L.K., Saylor, R.D., 1991. The STEM II regional scale acid deposition and photochemical model. Atmospheric Environment 25A, 2077.
1713
Grell, G.A., Dudhia, J., Stauffer, D.R., 1994. Description of the Fifth Generation Penn State/NCAR Meso-scale Model (MM5), NCAR Technical Note, NCAR/Tn398+STR. Han, B., Kim, J., Cho, S.Y., 2001. A numerical simulation of acid deposition in East Asia. Water, Air and Soil Pollution 130, 487–492. Holloway, T., Levy II, H., Carmichael, G.R., 2002. Transfer of reactive nitrogen in Asia: development and evaluation of a source–receptor model. Atmospheric Environment 36, 4251–4264. Inchon, 2000. Report for Implementation of SIP on Seoul metropolitan area. Kitada, T., Lee, P.C.S., 1993. Numerical modeling of longrange transport of acidic species in association with meso-bconvective cloud across the Japan sea resulting in acid snow over coastal Japan-II. Results and discussion. Atmospheric Environment 27A (7), 1077–1090. Kim, J.T., Cho, S.Y., 1999. Application of the nested grid STEM to an early summer acid rain in South Korea. Atmospheric Environment 33, 3167–3181. Klimont, Z., Streets, D.G., Gupta, S., Cofala, F., Lixin, F., Ichikawa, Y., 2002. Anthropogenic emissions of nonmethane volatile organic compounds in china. Atmospheric Environment 36, 1309–1322. Lee, B.K., Hong, S.H., Lee, D.S., 2000. Chemical composition of precipitation and wet deposition of major on the Korean peninsula. Atmospheric Environment 34, 563–575. Leone, J.A., Seinfeld, J.H., 1984. Analysis of the characteristics of complex chemical reaction mechanisms-application to photochemical smog chemistry. Environmental Science and Technology 18, 280–287. Nakakita, E., Ikebuchi, S., Nakamura, T., Kanmuri, M., Okuda, M., Yamaji, A., Takasao, T., 1996. Short-term rainfall prediction method using a volume scanning radar and grid point value data from numerical weather prediction. Journal of Geographical Research 101 (D21), 26181–26197. NCAR, 1986. Preliminary Evaluation Studies with the Regional Acid Deposition Model, National Center for Atmospheric Research, ADMP-68-4. Otoshi, T., Fukuzaki, N., Li, H., Hoshino, H., Sase, H., Suzuki, K., 2001. Quality control and its constraints during the preparatory-phase activities of the acid deposition monitoring network in East Asia (EANET). Water, Air, and Soil Pollution 130, 1613–1618. Park, J.K., Cho, S.Y., 1998. Transport of SO2 and sulfate between Korean and East China. Atmospheric Environment 32, 2745–2756. Park, S.U., In, H.J., Kim, S.W., Lee, Y.H., 2000. Estimation of sulfur deposition in South Korea. Atmospheric Environment 34, 3259–3269. Streets, D.G., Waldhoff, S.T., 2000. Present and future emissions of air pollutants in China: SO2, NOx, and CO. Atmospheric Environment 34, 363–374. Uno, I., Murano, K., 1996. Simulated acidic aerosol long-range transport and deposition over East Asia—role of synoptic scale weather system. Proceeding of the International Symposium on Acidic Deposition and its Impacts, December 10–12, Tsukuba, Japan, pp. 53–60.