Introduction
Acid deposition is becoming increasingly important in Asia. The development of industries and rapid consumption of natural resources have led to a large increase in emissions. These emissions are causing severe environmental problems to our ecosystems. Also, the transport of these pollutants between countries is becoming an important political issue that has stimulated the interest in modeling long-range transport in Asia (Foell and Green 1991; Carmichael and Arndt 1995; Kotamarthi and Carmichael 1990). Modeling dry deposition is one of the major tasks in the study of acid deposition. Dry deposition, which is the vertical downward flux of a species, is represented in terms of an empirical parameter called the deposition velocity, vd, multiplied by the concentration of the material at some reference height above the surface. Thus, the simulation of dry deposition is turned into the simulation of dry deposition velocity.
The calculation of dry deposition velocity for gases is based on three resistance models (Wesely and Hicks 1977). The dry deposition process can be viewed as three distinct steps. The first step is controlled by the turbulent diffusion in the surface layer and is sometimes referred to as the aerodynamic component of the transfer. The second step involves the diffusion of the material through the laminar sublayer adjacent to the surface to the ultimate absorbing substrate. This step is called the surface component of the transport. The solubility or absorptivity of species at the surface determines how much of the species that diffuses through the laminar sublayer actually is removed, and this final step is called the transfer component. The resistances of each layer are referred to as aerodynamic resistance, ra, surface layer resistance, rb, and transfer resistance, rc, respectively.
Many detailed studies of dry deposition have been performed in the last few decades. The research on surface roughness length, transfer resistance of SO2, and the surface resistance of sulfate particles was performed over various land use types in the study over North America (Voldner et al. 1986). Due to the complexity of the transfer resistance, Wesely (1989) created a new computational method by using a detailed deposition mechanism. Other useful information about dry deposition of sulfur dioxide can be found in the study over forest and snow-covered surface by Pardo (1993) and over sea surfaces (Joffre 1988). Sorteberg’s recent paper (Sorteberg and Hov 1996) has new ideas about how the biological activity influences the dry deposition velocity.
For sulfate aerosol deposition, there is another approach, which is different from the one for sulfate particle deposition; it accounts for rb and rc together according to atmosphere stability, as discussed in the study over the eastern United States (Walcek et al. 1985).
Although many studies have been performed in the last few decades, little work on dry deposition has been done in East Asia, and only recently have estimates been taken of dry deposition velocity of SO2 over Japan (Fujita et al. 1996). Most modeling studies in this region assume that dry deposition is a simple function of land use and season. However, preliminary modeling results show that about 50% of removed sulfur is due to dry deposition during the whole year (Carmichael and Arndt 1995). The improvement of modeling dry deposition in this region thus is crucial in acid deposition studies. In this paper we present a more detailed analysis of dry deposition velocities in East Asia. Results are presented for the months of February, May, August, and December.
Simulation procedure
Estimation dry deposition velocity for sulfur dioxide
The roughness length z0 is generally a function of surface roughness, even though it may be affected by the wind speed (when the roughness elements bend with the wind) and by the wind direction when different terrain features surround the region. The roughness length can be developed as a function of season and surface type. In this study, nine land types are used based on GISS (Goddard Institute of Space Science) vegetation data (Matthews 1983, 1984). They are coniferous forest, deciduous forest, cultivation, grassland, swamp, snow, ocean, desert, and forb formations. The values of the surface roughness z0 (cm) for each surface type are presented in Table 1 (Voldner et al. 1986).
For sulfur dioxide deposition to snow-covered surface (i.e., frozen water bodies, swamp, grassland, and cultivated land in winter), an empirical relationship based on the experimental evidence (Barry 1965; Barry and Munn 1967) is used, where rb is 2 cm−1 s.
In the current version of the model, values for rc of SO2 are taken from the estimates for each surface during each season based on the work of Voldner et al. (1986) and summarized in Table 2. The rc for deserts is estimated by Sorteberg and Hov (1996).
Estimation dry deposition velocity for sulfate
The particle deposition differs from that of gases in two important respects: 1) deposition depends on particle size since transfer to the surface involves Brownian diffusion, inertial impaction/interception, and sedimentation (all of which are strong functions of particle size);and 2) presumably rc for particles less than 10 μm in diameter (Hicks and Garland 1983) is negligibly small to all surfaces (i.e., bounce off does not occur). Voldner et al. (1986) provide estimates of the resistance of particles that varies with day/night during each season.
Meteorology
The Monin–Obukhov length L is a parameter that characterizes the “stability” of the surface layer and is calculated from ground-level measurements. It can be computed by using power-law function such as 1/L =
The stability classes were determined by the temperature gradient. The relationship between these two is listed on Table 4.
Input data
The input meteorological data is the upper-air sonding data for 1990 provided by the National Meteorological Center [NMC (now known as the National Centers for Environmental Prediction)] of the National Oceanic and Atmospheric Administration. These data files contain rawinsonde and pibal vertical observations of wind speed and temperature from the surface to 500 mb. The data is provided at 6-h intervals. However, it is missing part of the data over the ocean. The wind speed thus are taken from ECMWF (European Centre For Medium-Range Weather Forecasts) data. ECMWF data contains 2.5° × 2.5° wind speed every 6 h. The surface layer data was used in the calculation. The surface layer height is defined as 20 m in height. For the need of 1° × 1° wind speed data, a two-dimensional distance weighting interpolation was applied.
The land use data was prepared from the GISS vegetation data combined with cultivation intensity data. The vegetation data has 32 vegetation types. All tropical and subtropical broad- and needle-leaved evergreens were classified into coniferous forest, category 1. All tropical and subtropical broad- and needle-leaved deciduous forest, woodland, or shrub land were classified as deciduous forest, category 2. The lands with cultivation greater than 75% were classified as cultivation, category 3. The tall, medium, and short grasslands were classified as grassland, category 4; ocean, category 5; swamp, category 6; desert, category 7; and forb formations, category 8.
In summary, land cover types in DDM are 1, coniferous forest; 2, deciduous forest; 3, cultivation; 4, grassland; 5, ocean/sea; 6, swamp; 7, desert; and 8, forb formations. The 1° × 1° Asian land cover is presented in Fig. 1.
Results and discussion
The averaged dry deposition velocity of four seasons
To examine dry deposition velocity for the four seasons, DDM was run for four months: February, May, August, and December.
The dry deposition velocity of SO2 has been calculated over the study domain at each grid point every 6 h. Then we got monthly averaged four-season nighttime and daytime velocities for each category of land cover. The velocity of SO2 demonstrates the strong diurnal and seasonal variability, as shown by Figs. 2a, nighttime; and 2b, daytime; SO2 velocities for each season.
The daytime velocity of SO2 is relatively higher than nighttime values. For example, in the summertime over forests, the velocity is 0.43 cm s−1 in the daytime and 0.13 cm s−1 at night. A brief analysis of these three resistances indicates that rc is 86% of the total resistance in the daytime and 91% at night. For forests the leaves’ absorption and stomata resistance is very important. This is also true for crops (cultivation), and rc is about 80% of the total resistance. But for small plants, such as grass, rc is 50% in the day and 60% at night. It is understandable that for grassland the percentage of rc of the total resistance is less than forests and cultivation. For swamps, rc is even less, 21.5% during the day and 27.5% at night.
If we take a look at another season, such as spring, we found that the largest velocity of SO2 appears over a cultivation surface with a daytime value of 0.9 cm s−1 and a nighttime value 0.6 cm s−1, when crops grow most rapidly, and rc is around zero in both day- and nighttime. It means that diurnal variability is controlled by two other resistances, ra and rb, over a cultivation surface in spring.
Minimum values all appear in winter with values around 0.1 cm s−1 in both day and night due to the assumption that these surfaces are covered by snow.
Through resistance analysis, we found that the seasonality of deposition velocity of SO2 over forests and cultivation is caused by the plants’ stomata and leaf absorption resistance. But for other surfaces, rc is not the dominant factor. The diurnal variability of deposition velocity of SO2 can be related to rc or the change of ra and rb with time of the day.
The summer and winter averaged dry deposition velocities of SO2 were compared to Fujita et al.’s recent results (Fujita et al. 1996), which are averaged values for Japan. As shown in Fig. 3, the results are very similar over forests. The wintertime values over cultivation, grassland, and swamp surfaces are also close. The most different estimate is over the ocean. Their result is the averaged value over sea along the coast, but DDM is the averaged value over open ocean.
Figure 4 illustrated the averaged deposition velocity of sulfate for four seasons. The averaged value does not have strong seasonal and diurnal variability, but the minimum values all appear in the wintertime.
The analysis of averaged values for each land cover and each season only gives us a general idea about the variation behavior of velocities of SO2 and sulfate. A more detailed geographic distribution of velocity for each region and country will be shown and discussed in the next section.
Dry deposition velocities in summer
The summertime dry deposition velocity of SO2 during day and night are shown by Figs. 5 and 6 with a resolution of 1° × 1° (116 km × 116 km). The largest dry deposition velocity of SO2 is about 0.5–0.6, distributed at the central part of China and its two neighboring countries, Mongolia and Nepal. It also appears in the central region of Afghanistan and Iran. These areas are all covered by grass. Besides the low stomata resistance of grass in summer, relatively higher wind speed reduces resistance ra, leading to large deposition velocity. Generally, the ocean has larger deposition velocity of SO2 than land. This also makes two islands covered by forests, Taiwan and Sri Lanka, to have as large a deposition velocity of SO2 as ocean in summer. The deposition velocity of SO2 over the ocean is close to 0.3–1.3 cm s−1 by Walcek et al. (1985).
Most parts of East Asia, covered by forests, have deposition velocities of SO2 around 0.45, such as Japan, the eastern part of China and its eastern neighboring countries and equator-region countries. Large cultivation areas are located in the western part of India and northeast China, where the velocity of SO2 is not too large, 0.25, because the crops in summer are already mature.
A nighttime velocity map shows a rapid decrease on dry deposition velocity of SO2 for all land surfaces. The SO2 velocity over grassland, forests, and cultivation is 0.25, 0.10, and 0.15, respectively. Generally, the daytime values are two or three times larger than those during nighttime. The largest decrease over forest reflects the closing of the tree’s stomata during the night. The nighttime lower deposition velocity of SO2 over the ocean, 0.35, may be caused by the more stable condition of the atmosphere.
The geographic distribution of sulfate deposition velocity is shown by Fig. 7, given by 24-h monthly averaged values. Over coniferous and deciduous forests, the deposition velocity is about 0.10–0.15; over cultivation and grassland surfaces it is about 0.05–0.075. Generally, sulfate deposition velocity is less than 0.15. The uneven distribution of the velocity implies that it is closely related to local meteorological conditions and atmospheric stability. This can be further proved by the four deposition velocity contours in the Indian and Pacific Oceans. The sulfate deposition velocity over oceans are estimated to be about 0.05–0.15, which is close to the conclusion made by Slinn and Slinn (1980). Their research showed that for the ammonium sulfate particle, whose diameter is about 0.2–0.3 μm, for a wind speed at 15 m s−1, the deposition velocity is between 0.08 and 0.12.
Dry deposition velocities in winter
In winter, many factors that affect the dry deposition velocity are changed in relation to the summer condition. These include roughness over cultivation, grassland, and swamp; wind speed; temperature; and resistance rc. The East Asian region was divided into a higher latitude region and a tropical region at 25°N to reflect the different climate impact in the simulation.
From Table 2 note that rc in winter is much larger than that in summer. Meanwhile, the decreased roughness and the increased wind speed reduce the aerodynamic resistance. Thus, it can be anticipated that wintertime deposition velocities are much smaller than those in summer in the higher latitude regions, as shown in Fig. 8. The deposition velocities of SO2 over land are 0.1–0.15. The homogeneous distribution of the velocity of SO2 is because most of the surfaces in winter are covered by snow. The resistance rc of snow is 7 s cm−1, showing the smoothness of the ice surface leads to a small amount of deposition of SO2. No strong diurnal variability shows in winter. This implies that rc is still an important factor among the three resistances for ice-covered surfaces.
In tropical and subtropical regions, from 20°S to 25°N, where forests and agriculture still grow in winter, the roughness length and resistance rc have been assumed to be the same in summer, and thus the deposition velocity of SO2 is maintained at 0.25–0.35. See the summer velocity map in Fig. 5; we found the velocity reduces from 0.45 to 0.35. This means that the wintertime deposition velocity of SO2 is smaller than the summer daytime value. The decrease of the velocity in winter reflects its close relation to meteorological conditions. In our model, we simply related it to latitude. The definition of the regions where winter affects resistance rc is rather coarse, and results show a distinct difference between the two regions. This can be refined with additional information, for example, satellite-derived“greenness,” which would eliminate this artificial boundary.
The resistance rc over the coniferous forest is increased in winter because plant stomata are closed during the nighttime and during the dormant periods of late fall to early spring (Garland and Branson 1977; Hallgren et al. 1982; Hicks and Wesely 1980; Fowler and Cape 1983; Johansson et al. 1983). The resistance rc over the deciduous forest is increased substantially in winter to reflect the absence of leaves. As a result, in winter over forest in higher latitude regions, the SO2 deposition velocity can be smaller than sulfate. This occurs for two reasons: 1) the high resistance for SO2 over the forest in winter reduces the velocity of SO2 and 2) the high wind speeds in winter reduce the rb and rc of sulfate and thus the velocity of sulfate increases. It also can be caused by the higher estimates of the surface resistance of the coniferous forest. The surface resistance for the coniferous forest is the same as that for the deciduous forest in the model. The coniferous forests grow very slowly in winter; however, it can still uptake SO2, and rc can be estimated as a larger value than the rc of the deciduous forest, which stops growing in winter.
Figure 9 is the sulfate dry deposition velocity map in winter. The averaged deposition velocities over cultivation and grassland are 0.05, and over forest are 0.075–0.15, showing little diurnal variability. Comparing the winter map, Fig. 9, to the summer map, Fig. 7, it can be seen that sulfate deposition velocities generally decrease with some exceptions. A typical example is in Japan, where the sulfate deposition velocity increases in the winter. This may be caused by the variation of wind speed and the distinct geographic location of Japan, which is an island.
The applications of DDM results
Comparison of predicted concentrations to observations
The sulfur oxidation reaction is treated as a pseudo-first-order reaction; the rate constant and washout ratio for SO2 are adapted from the EMEP (Iverson et al. 1991). In the equator region, from 20°S to 20°N, k1 is 8.0 × 10−5 in summer; 4.0 × 10−5 in winter; and W(SO2) is 4.0 × 105. In the middle latitude region, from 20°N to 30°N, k1 is 8.0 [3 × 10−6 + 7 × 10−6 sin(π time of year/365)] in summer; 4.0 [3 × 10−6 + 7 × 10−6 sin(π time of year/365)] in winter; and W(SO2) is 1.3 × 105 + 2.7 × 105 sin(π time of year/365). Here k1 is 8.0 (3 × 10−6 + 6 × 10−6 sin(π time of year/365) in summer, in the region from 30°N to 50°N; 4.0 (3 × 10−6 + 6 × 10−6 sin(π time of year/365) in winter;and W(SO2) remains the same as it is in the middle latitude region. The unit of k1 is inverse seconds.
The meteorological data used in the model is upper-air sonding data from the NMC, and the precipitation data is from the National Center of Atmospheric Research. Two-dimension linear interpolation has been performed when the precipitation at puff’s center is not available. The 1990 annual emission data was estimated by the RAINS–ASIA emission module (Carmichael and Arndt 1995).
The acid deposition model (ADM) runs for August and February 1990, using more detailed dry deposition velocity over nine land covers by DDM. The comparison of predicted concentrations with observations for these two months are presented in Figs. 10 and 11. Figure 10 is the result of model calibration, and Fig. 11 is the result of model verification. The observation data used were collected by the Central Research Institute of Electric Power Industry in 1992. From these two figures, we can see that the model is basically able to capture the variability of spatial distribution of concentrations, and predictions are within an error factor of 2.
Dry deposition in East Asia
The monthly averaged dry deposition velocities provided by DDM were used in ADM to calculate sulfur deposition in East Asia.
The ADM is a modified version of the branching atmospheric trajectory model (Heffter 1983). This model is a three-layer model that reflects plume spread from vertical wind shear at day/night transitions. The model incorporates the following transport features.
Three lower tropospheric layers identified as surface, boundary, and upper layers;
a series of puffs starting every 3 h, each of 7 days duration;
puff transport forward or backward in time;
transport using the average of observed wind in a layer and inverse distance squared wind weighting with the modified Euler advection technique;
branching puff trajectories at day/night transitions;
vertical puff mixing through a layer and horizontal puff diffusion.
The deposition mechanism inside ADM will be introduced by Xu (1998). In this paper, we focus on the analysis of part of the ADM result: dry deposition of SO2 and sulfate.
Figures 12 and 13 are the dry deposition of SO2 and sulfate in August 1990. The average dry deposition of SO2 in the summertime is 5 mgS m−2 over India and western China and 25 mgS m−2 over eastern China, Japan, Korea, and some of the equatorial countries. The deposition of SO2 due to ship emission is also around 25 mgS m−2. Some regions with large point sources have more than 25 mgS m−2 due to dry deposition of SO2. In comparing these two figures, we can see most of the sulfur dry deposition is removed in the form of SO2. The removal fraction by SO2 is high over most parts of the region. The average dry deposition of sulfate in summer is 5 mg m−2, except at regions around large point sources. This phenomena can be explained by the fact that the dry deposition velocity of SO2 is much higher than the velocity of sulfate over land. For example, the velocity of SO2 is four times larger than that of sulfate over forests. The other reason is that most of the sulfate in the atmosphere is removed by precipitation in the summer season.
The dry deposition pattern of SO2 in winter is similar to summer (see Fig. 14). However, in the eastern part of India the dry deposition of SO2 increases, from 5 to 25 mg m−2 as the dry deposition velocity of SO2 in the equator region does not change in winter, and this deposition velocity combined with a higher concentration in winter causes the increase of dry deposition in this region. Wintertime sulfate dry deposition, shown in Fig. 15, has increased one level, from 5 mgS m−2 to 25 mgS m−2, in a large part of eastern China. This is due to less precipitation in winter; the higher sulfate aerosol concentration in winter leads to higher dry deposition.
From the land use graph, note that those areas with higher sulfur dry removal processes fall in the cultivation category. How the sulfur dry deposition processes affect the agriculture in East Asia is a very interesting subject to study since agriculture must support a large population and is the main economic source for most of the countries in Asia.
Summary
A dry deposition model (DDM) has been set up to estimate the dry deposition velocities of sulfur dioxide and sulfate over eight typical land covers in Asia. Using this model, 1° × 1° every 6 h deposition velocities of SO2 and sulfate were generated over eight typical land use types in East Asia. Four month-long SO2 and sulfate dry deposition velocities were calculated and compared. They are February, May, August, and December 1990 and each represents one season. The monthly averaged values were shown in Figs. 2 and 4.
The simulation of dry deposition velocities of SO2 is based on the three resistance theories. Deposition velocities were found to have strong diurnal variability over land in summer for sulfur dioxide. The daytime values, 0.4–0.6 cm s−1, are about three times larger than nighttime values for the same category. The resistance rc is important over forest, but there is no dominant resistance over other land cover surfaces. The wintertime averaged values of SO2 are between 0.1 and 0.2 cm s−1. Little diurnal variability has been found in winter. The summer deposition velocities are larger than winter deposition velocities for SO2 over land.
The deposition velocities of sulfate were estimated from eddy-correlation measurements by Wesely et al. (1985). Model results do not show strong diurnal and seasonal variability. Generally, the averaged deposition velocities of sulfate are less than 0.1 cm s−1 over land and are smaller than those of SO2 in summer.
The deposition velocity over ocean for sulfur dioxide is much larger than those over land, ranging from 0.3 to 1.2 cm s−1. For sulfate, the model estimation is about 0.05 cm s−1.
In DDM, the resistance rc of sulfur dioxide was estimated by the best constant value in day and night for different seasons. The resistance analysis suggests that in large plant cover surfaces, the resistance rc is the dominant factor, thus the detailed modeling resistance rc may lead to better results.
The deposition velocities given out by DDM have been applied to the acid deposition model to calculate the sulfur deposition in East Asia in August and February 1990. In southern China, Japan, South Korea, Taiwan, and Thailand, the dry deposition was found to be relatively higher. In some other countries, such as the Philippines and part of Indonesia, the ocean with ship emissions also have a certain amount of sulfur dry deposition. Most of these areas with notable sulfur deposition are cultivation and forests. The impact of sulfur deposition to agriculture of these countries and how to reduce the deposition in these areas are very interesting subjects for future study.
Acknowledgments
The work performed as a part of RAINS–ASIA project funded by The World Bank and The Asia Development Bank. We are grateful to The World Bank and The Asia Development Bank for financial support. We also thank CGRER for supplying data.
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Asian land use.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Asian land use.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Asian land use.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
The dry deposition velocity of SO2 at (a) night and (b) day over various land covers.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
The dry deposition velocity of SO2 at (a) night and (b) day over various land covers.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
The dry deposition velocity of SO2 at (a) night and (b) day over various land covers.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Comparison of dry deposition velocity of SO2 in (a) August and (b) February.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Comparison of dry deposition velocity of SO2 in (a) August and (b) February.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Comparison of dry deposition velocity of SO2 in (a) August and (b) February.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
The dry deposition velocity of sulfate over various land covers.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
The dry deposition velocity of sulfate over various land covers.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
The dry deposition velocity of sulfate over various land covers.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
The dry deposition velocity of SO2 in August (day).
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
The dry deposition velocity of SO2 in August (day).
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
The dry deposition velocity of SO2 in August (day).
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
The dry deposition velocity of SO2 in August (night).
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
The dry deposition velocity of SO2 in August (night).
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
The dry deposition velocity of SO2 in August (night).
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Sulfate dry deposition velocity in August.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Sulfate dry deposition velocity in August.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Sulfate dry deposition velocity in August.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Dry deposition velocity of SO2 in February.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Dry deposition velocity of SO2 in February.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Dry deposition velocity of SO2 in February.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Sulfate dry deposition velocity in February.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Sulfate dry deposition velocity in February.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Sulfate dry deposition velocity in February.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Error analysis of predicted concentrations in August for (a) SO2 and (b) sulfate.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Error analysis of predicted concentrations in August for (a) SO2 and (b) sulfate.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Error analysis of predicted concentrations in August for (a) SO2 and (b) sulfate.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Error analysis of predicted concentrations in February for (a) SO2 and (b) sulfate.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Error analysis of predicted concentrations in February for (a) SO2 and (b) sulfate.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Error analysis of predicted concentrations in February for (a) SO2 and (b) sulfate.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Dry deposition of SO2 in August.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Dry deposition of SO2 in August.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Dry deposition of SO2 in August.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Dry deposition of sulfate in August.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Dry deposition of sulfate in August.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Dry deposition of sulfate in August.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Dry deposition of SO2 in February.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Dry deposition of SO2 in February.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Dry deposition of SO2 in February.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Dry deposition of sulfate in February.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Dry deposition of sulfate in February.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
Dry deposition of sulfate in February.
Citation: Journal of Applied Meteorology 37, 10; 10.1175/1520-0450(1998)037<1084:MTDDVO>2.0.CO;2
The surface roughness z0 (cm) for each surface type (Voldner et al. 1985).
Values of the resistance rc (s cm−1) for SO2 (g) (Voldner et al. 1985).
Coefficients a and b for equation (1/L) = az0b (Zannetti 1990).
Relationship between the Pasquill–Gifford stability classes and temperature stratification (Seinfeld 1986).
