A

C. Dry Deposition

By A.T. Vermeulen (ECN) with small adaptions by M. Schaap (TNO)

Several articles have reviewed the state of the science in evaluating dry deposition (Baldocchi, 1993; Erisman et al., 1994b; Erisman & Draaijers, 1995; Ruijgrok et al., 1995; Wesely & Hicks, 2000). Wesely and Hicks (2000) indicated that although models have been improving and can perform well at specific sites under certain conditions, there remain many problems and more research is needed. In spite of these problems, given the necessary meteorological and surface/vegetative data, there are a number of models for estimating deposition velocity (V_d) that have been shown to produce reasonable results using currently available information.

Dry deposition processes for gaseous species are generally understood better than for particles. Several dry deposition model formulations have been reported in the literature. These include big-leaf models (Hicks et al., 1987; Baldocchi et al., 1987), multi-layer models (Baldocchi, 1988; Meyers et al., 1998) and general dry deposition models (Erisman et al., 1996). Some of these models have been developed for estimating V_d at specific sites and are used within the framework of monitoring networks (Clarke et al., 1997; Meyers et al., 1991).

Computation of the dry deposition rate of a chemical species requires that the concentration c of the substance of interest is known through model computations or measurement. In most modelling schemes, the mass flux density F is found as

(5.1)

where c(z) is the concentration at height z and V_d is the dry deposition velocity. Estimates of deposition velocities V_d constitute the primary output of dry deposition models, both for large-scale models and site-specific methods of inferring dry deposition from local observations of concentrations, meteorological conditions, and surface conditions (Chang et al., 1987; Venkatram et al., 1988; Meyers et al., 1991; Ganzeveld and Lelieveld, 1995). z is the reference height above the surface. If the surface is covered with vegetation, a zero-plane displacement is included: z=z-d. d is usually taken as 0.6-0.8 times the vegetation height (Thom, 1975). The absorbing surface is often assumed to have zero surface concentration and the flux is therefore viewed as being linearly dependent on atmospheric concentration. This holds only for depositing gases and not for gases that might be also emitted, such as NH₃ and NO. For these gases a nonzero surface concentration, a compensation point c_p, might exist, which can be higher than the ambient concentration, in which case the gas is emitted. For these gases the flux is estimated as

(5.2)

V_d provides a measure of conductivity of the atmosphere-surface combination for the gas and it is widely used to parameterise gas uptake at the ground surface (Wesely & ., 1977; Hicks et al., 1989; Fowler et al., 1989). To describe the exchange of a range of gases and particles with very different chemical and physical properties, a common framework is provided, the resistance analogy (Thom, 1975; Garland, 1977; Wesely & Hicks, 1977; Fowler, 1978; Baldocchi et al., 1987). In this framework, V_d is calculated as the inverse of three resistances:

(5.3)

The three resistances represent bulk properties of the lower atmosphere or surface. R_a, R_b and R_c must be described by parameterisations. Although this approach is practical, it can lead to oversimplification of the physical, chemical, and biological properties of the atmosphere or surface that affect deposition.

The term R_a represents the aerodynamic resistance above the surface for the turbulent layer. R_a is governed by micrometeorological parameters and has the same value for all substances. R_a depends mainly on the local atmospheric turbulence intensities. Turbulence may be generated through mechanical forces of friction with the underlying surface (forced convection) or through surface heating (buoyancy or free convection). Unless wind speed is very low, free convection is small compared to mechanical turbulence.

The term R_b represents the quasi-laminar resistance to transport through the thin layer of air in contact with surface elements, and is governed by diffusivity of the gaseous species and air viscosity. For surfaces with bluff roughness elements, values of R_b are considerably larger than for relatively permeable, uniform vegetative cover, and the appropriate formulations should be used (Tuovinen et al., 1998).

Considerable variation from model to model is associated with the methods used to evaluate the surface or canopy resistance R_c for the receptor itself. R_c represents the capacity for a surface to act as a sink for a particular pollutant, and depends on the primary pathways for uptake such as diffusion through leaf stomata, uptake by the leaf cuticular membrane, and deposition to the soil surface. This makes R_c complicated, because it depends on the nature of the surface and how the sink capacities for specific surfaces vary as a function of the local microclimate.

The resistance analogy is not used for particles. For sub-micron particles, the transport through the boundary layer is more or less the same as for gases. However, transport of particles through the quasi-laminar layer can differ. Whereas gases are transported primarily through molecular diffusion, particle transport and deposition basically take place through sedimentation, interception, impaction and/or Brownian diffusion. Sedimentation under the influence of gravity is especially significant for receptor surfaces with horizontally oriented components. Interception occurs if particles moving in the mean air motion pass sufficiently close to an obstacle to collide with it. Like interception, impaction occurs when there are changes in the direction of airflow, but unlike interception a particle subject to impaction leaves the air streamline and crosses the quasi-laminar boundary layer with inertial energy imparted from the mean airflow. The driving force for Brownian diffusion transport is the random thermal energy of molecules. Transport is a function of atmospheric conditions, characteristics of the depositing contaminant and the magnitude of the concentration gradient over the quasi-laminar layer (Davidson and Wu, 1990).

Which type of transport process dominates is largely controlled by the size distribution of the particles (Sehmel, 1980; Slinn, 1982). For particles with a diameter <0.1mm, deposition is controlled by diffusion, whereas deposition of particles with a diameter >10mm is more controlled by sedimentation. Deposition of particles with a diameter between 0.1 and 1mm is determined by the rates of impaction and interception and depends heavily on the turbulence intensity. To describe particle dry deposition, the terms (R_b+R_c)^-1 on the right-hand side of Equation (5.3) must be replaced with a surface deposition velocity or conductance, and gravitational settings must be handled properly.

Dry deposition models or modules require several types of inputs from observations or from simulations of atmospheric chemistry, meteorology, and surface conditions. To compute fluxes, the concentrations of the substances must be known. Inputs required from meteorological models are values of friction velocity u^*, atmospheric stability via the Monin-Obukhov length scale L, aerodynamic surface roughness z₀, and aerodynamic displacement height d. Most dry deposition models also need solar radiation or, preferably, photosynthetically active radiation; ambient air temperature at a specified height; and measures of surface wetness caused by rain and dewfall. All models require a description of surface conditions, but the level of detail depends on the model chosen. Descriptions could include broad land use categories, plant species, leaf area index (LAI), greenness as indicated by the normalised difference vegetation index, various measures of plant structure, amount of bare soil exposed, and soil pH.

1.1.1 Land-use database

From a 1.1 x 1.1 km² resolution land use database (PELINDA; see Ch. 9) the fraction of surface in each grid cell covered by the land use classes used in DEPAC have been calculated (Nijenhuis and Groten, 1999). For each cell the deposition velocity is calculated weighting the surface fractions of every landuse class. Surface wetness and snow cover have a large effect on the deposition velocities for a number of species, especially SO₂. Surface wetness is determined as function of the relative humidity at the surface.

1.1.2 Aerodynamic resistance

The atmospheric resistance to transport of gases across the constant flux layer is assumed to be similar to that of heat (e.g., Hicks et al., 1989). The method to estimate the aerodynamic resistance in LOTOS-EUROS is described in the chapter on meteorology. Under the same meteorological conditions, the aerodynamic resistance is the same for all gases and in fact also for aerosols. Only for aerosols with a radius > 5mm does the additional contribution of gravitational settling become significant. When the wind speed increases, the turbulence usually increases as well and consequently R_a becomes smaller.

1.1.3 Quasi laminar layer resistance

The second atmospheric resistance component R_b is associated with transfer through the quasi-laminar layer in contact with the surface. The transport through the quasi-laminar boundary layer takes place for gases by molecular diffusion and for particles by several processes: Brownian diffusion, interception, impaction and by transport under influence of gravitation. None of the processes for particles are as efficient as the molecular diffusion of gas molecules. This is because molecules are much smaller than aerosols and therefore have much higher velocities. For particles with radii <0.1mm Brownian diffusion is the most efficient process, whereas impaction and interception are relatively important for those with radii >1mm. For particles with radii between 0.1 and 1mm the transport through the quasi-laminar boundary layer is slowest (R_b is largest). The quasi- laminar boundary layer resistance is for most surface types more or less constant (forest, at sea for a wind speed < 3m/s) or decreases with wind speed (low vegetation).

R_b quantifies the way in which pollutant or heat transfer differs from momentum transfer in the immediate vicinity of the surface. The quasi-laminar layer resistance R_b can be approximated by the procedure presented by Hicks et al. (1987):

(5.4)

where Sc and Pr are the Schmidt and Prandtl number, respectively. Pr is 0.72 and Sc is defined as , with u being the kinematic viscosity of air (0.15 cm² s^-1) and D_i the molecular diffusivity of pollutant i and thus component specific. The Schmidt and Prandtl number correction in the equation for R_b is listed in Table 5.4 for different gases. Molecular and Brownian diffusivities for a selected range of pollutants, and the deduced values of Schmidt number are listed in Table 5.5. Usually R_b values are smaller than R_a and R_c. Over very rough surfaces such as forest canopies, however, R_a may approach small values and the accuracy of the R_b estimate becomes important. This is especially the case for trace gases with a small or zero surface resistance.

Table 5.4: Schmidt and Prandtl number correction in equation for R_b (Hicks et al., 1987) for different gaseous species, and the diffusion coefficient ratio of water to the pollutant i (Perry, 1950).

Component		(Sc/Pr)^2/3
SO2 NO NO2 NH3 HNO2 HNO3 HCl PAN H2O O3	1.9 1.5 1.6 1 1.7 1.9 1.5 2.8 1 1.5	1.34 1.14 1.19 0.87 1.24 1.34 1.14 1.73 0.87 1.14

^*

Table 5.5: Molecular (for gases) and Brownian (for particles) diffusivities (D; cm² s^-1) for a range of pollutants, and the deduced values of Schmidt number (Sc). The viscosity of air is taken to be 0.15 cm² s^-1. From Hicks et al. (1987).

Component	D	Sc
Gaseous species H2 H2O O2 CO2 NO2 O3 HNO3 SO2 Particles (unit density) 0.001 mm radius 0.01 0.1 1 10	0.67 0.22 0.17 0.14 0.14 0.14 0.12 0.12 1.28 10^-2 1.35 10^-4 2.21 10^-6 1.27 10^-7 1.38 10^-8	0.22 0.68 0.88 1.07 1.07 1.07 1.25 1.25 1.17 10^1 1.11 10^3 6.79 10^4 1.18 10^6 10^7

1.1.4 Surface resistance

The surface or canopy resistance R_c is the most difficult of the three resistances to describe, and is often the controlling resistance of deposition flux. The analytical description of R_c has been difficult since it involves physical, chemical and biological interaction of the pollutant with the deposition surface. Over a given area of land, numerous plant, soil, water, and other material surfaces are present, each with a characteristic resistance to uptake of a given pollutant.

R_c values presented in the literature are primarily based on measurements of V_d and on chamber studies. By determining R_a and R_b from the meteorological measurements, R_c can be calculated as the residual resistance. Values of R_c can then be related to surface conditions, time of day, etc., yielding parameterisations. However, measurements using existing techniques are still neither accurate nor complete enough to obtain R_c values under most conditions. Furthermore, R_c is specific for a given combination of pollutants, type of vegetation and surface conditions, and measurements are available only for a limited number of combinations.

The surface resistance of gases consists of other resistances (Figure 5.3), either determined by the actual state of the receptor, or by a memory effect. R_c is a function of the canopy stomatal resistance R_stom and mesophyll resistance R_m; the canopy cuticle or external leaf resistance R_ext; the soil resistance R_soil and in-canopy resistance R_inc, and the resistance to surface waters or moorland pools R_wat. In turn, these resistances are affected by leaf area, stomatal physiology, soil and external leaf surface pH, and presence and chemistry of liquid drops and films. Based on values from the literature for the stomatal resistance (Wesely, 1989), and on estimated values for wet (due to rain and to an increase in relative humidity) and snow-covered surfaces, the following parameterisation (with the stomatal resistance, external leaf surface resistance and soil resistance acting in parallel) can be applied for routinely measured components (Erisman et al., 1994b):

vegetative surface:

(5.5)

water surfaces:

R_c=R_wat (5.6)

bare soil:

R_c=R_soil (5.7)

snow cover:

R_c=R_snow (5.8)

Figure 5.3: Resistance analogy approach in dry deposition models.

Table 5.6 shows some surface resistance values for soil surfaces (R_soil), snow-covered surfaces (R_snow) and water surfaces (R_wat).

Table 5.6: Surface resistance values (s m^-1) for soil surfaces (R_soil), snow-covered surfaces (R_snow) and water surfaces (R_wat). From Erisman et al. (1994b).

Gas	Soil surfaces, Rsoil		Water surfaces,	Soil or water	Snow-covered surfaces
	Wet	Dry	Rwat	pH	Rsnow	Temperature (^oC)
SO2 and HNO2 NH3 NO NO2 and PAN HNO3 and HCl O3	0 500 250 0 emission: 1000 2000 0 500	1000 Rext Emission: 500 50 emission: 1000 1000 0 100	0 500 500 0 2000 2000 0 2000	>4 <4 >8 <8 ---- ---- >2 ----	70 (2-T) 500 70 (2-T) 500 2000 2000 0 100 2000	-1<T<1 T<-1 -1<T<1 T<-1 ---- ---- T>-5 T<-5 ----

It is not clear whether R_m is relevant at ambient concentrations (Erisman et al., 1994b). Therefore, they consider the sum of R_stom and R_m to be a new resistance R_st, a stomatally controlled resistance which would equal the true stomatal resistance R_stom if R_m=0. Similarly, they defined a new resistance R_fs=R_inc+R_soil, a non-stomatal resistance to express that the uptake could be either direct foliage uptake or soil uptake. Thus, Equation (5.6) reduces to

(5.9)

Combining equations (5.3) and (5.10) yields

(5.10)

for daytime situations. During the night, when stomata are closed, R_st=¥ is assumed and Equation (5.11) can be reduced to

(5.11)

R_cut denotes local leaf cuticular resistance. In Brook et al. (1999):

R_cut(SO₂) = R_cut(LUC, season); (5.12)

R_cut(HNO₃) = 20 sm^-1. (5.13)

LUC denotes land use class. Under wet surface conditions after rainfall or dew R_cut is replaced by R_wcut, which denotes wet cuticle resistance. For SO₂, under wet/dew conditions it is assumed a constant value of 50 sm^-1 for both dew-covered and rainfall conditions:

R_wcut(SO₂) = 50 sm^-1 (5.14)

HNO₃ uptake is rapid regardless of wetness.

R_g denotes ground surface resistance, which varies depending upon whether the surface is soil, water or snow/ice and whether it is wet or dry.

R_g(SO₂) = 100 sm^-1 (5.15)

R_g(HNO₃) = 20 sm^-1 (5.16)

For all surface conditions (dry, wet or snow) a small value of 20 sm^-1 is used for the ground resistance of HNO₃. For wet soil, a constant value of 100 sm^-1 is used for SO₂. There is little information available for resistance over snow or ice surfaces. From the limited amount of data available (see Brook et al., 1999) a value of 200 s m^-1 is set for R_g(SO₂) for snow covered surfaces:

R_g(SO₂) = 200 sm-1 (5.17)

5.1.9.1 Stomatal (R_stom) and mesophyll (R_m) resistances

Most gases enter plants through stomata. As gas molecules enter the leaf, deposition occurs as molecules react with the moist cells in the sub-stomatal cavity and the mesophyll. Stomatal resistance decreases hyperbolically with increasing light and increases linearly with increasing vapour pressure deficits (Jarvis, 1976). Soil water deficits cause stomata to close after some threshold deficit level is exceeded. Low and high temperatures cause stomatal closure; stomatal opening is optimal at a vegetation-specific temperature. Leaf age, nutrition and adaptation are other factors affecting stomatal resistance (Jarvis, 1976). Elevated exposure to SO₂ causes stomata to close, whereas exposure to both O₃ and NH₃ may increase stomatal opening. Stomatal resistance is different for different types of vegetation.

The stomatal resistance for water vapour, R_stom, is a function of the photosynthetically active radiation (PAR), air temperature (T), leaf water potential (y), vapour pressure deficit (VPD), and can be calculated using a scheme described by Baldocchi et al. (1987). This scheme is based on a model presented by Jarvis (1976) for the computation of the stomatal resistance to water vapour transfer of a leaf that is biologically and physically realistic. It is a multiplicative model which is expressed in terms of stomatal conductance (g_s), the inverse of R_stom. In this scheme the bulk leaf stomatal conductance is written as:

(5.18)

Values of the functions f(T), f(y) and f(VPD) range from 0 to 1. f(PAR) is the influence of photosynthetically active radiation on the stomatal conductance, and depends on the LUC-dependent parameters of the minimum stomatal resistance, R_s(min); the light response constant, b_rs, equal to the PAR flux density at twice the minimum stomatal resistance; the leaf area index, LAI; and variations in PAR (table 5.7). The response of stomatal resistance to PAR is estimated using a rectangular hyperbola relationship (Turner and Begg, 1974):

(5.19)

PAR is estimated as a fraction of the short-wave incoming radiation, Q:

(5.20)

Stomatal conductance increases with increasing temperature until a threshold temperature, after which it decreases. This dependence on temperature is the result of energy balance feedbacks between humidity and transpiration of the leaf (Schulze and Hall, 1982) and the influence of temperature on enzymes associated with stomatal operation (Jarvis and Morison, 1981). The response of stomatal conductance to temperature (T) is computed using the relationship presented by Jarvis (1976):

(5.21)

where, according to Jarvis (1976), and Erisman et al. (1994b)

(5.22)

However, according to Baldocchi et al. (1987), and Brook et al. (1999)

(5.23)

T_min(i), T_max(i) indicates minimum and maximum temperatures at which stomatal closure occurs, and the optimum temperature T_opt(i) indicates the temperature of maximum stomatal opening (Table 5.7).

The influence of vapour pressure deficit on stomatal conductance f(VPD) is represented by

(5.24)

b_vpd is a constant (Table 5.7), while VPD, vapour pressure deficit, is estimated from relative humidity rh(%) by (Beljaars and Holtslag, 1990)

(5.25)

es is the saturated water vapour pressure (mbar):

(5.26)

According to Monteith (1975), the saturated water vapour pressure es (in kPa) at temperature t (^oC) can be calculated using:

(5.27)

The bulk stomatal resistance is approximated with

(5.28)

which will lead to an overestimation of R_stom caused by partial shading of leaves (Baldocchi et al., 1987).

Modelling the stomatal resistance in a detailed manner is only possible if enough information is available. This might be a problem for the water potential and for the leaf area index LAI. For those regions where such data are not available the parameterisation for the stomatal resistance given by Wesely (1989) may be used. This parameterisation is derived from the method by Baldocchi et al. (1987) and only needs data for global radiation Q (W m^-2) and surface temperature T_s (^oC):

(5.29)

Values for R_i can be obtained from a look-up table for different land use categories and seasons, as listed in Table 5.8 (from Wesely, 1989).

Table 5.7: Constants used in Erisman et al. (1994b) to compute R_stom for several vegetation types (adopted from Baldocchi et al., 1987).

Variable	Units	Spruce	Oak	Corn	Soybean
Rs (min) brs(PAR) Tmin Tmax Topt bvpd yo	s m^-1 W m^-2 ^oC ^oC ^oC k Pa^-1 M Pa	232 25 -5 35 9 -0.0026 -2.1	145 22 10 45 24-32 0 -2.0	242 66 5 45 22-25 0 -0.8	65 10 5 45 25 0 -1.1

Table 5.8: Internal resistance (R_i) used in Erisman et al. (1994b) to compute the stomatal resistance for different seasons and land use types. Entities of -999 indicate that there is no air-surface exchange via that resistance pathway (adopted from Wesely, 1989).

Seasonal Category	1	2	4	5	6	7	9	10
Midsummer with lush vegetation Autumn with unharvested cropland Late autumn after frost, no snow Winter, snow on ground and subfreezing Transitional spring with partially green short annuals	-999 -999 -999 -999 -999	60 -999 -999 -999 120	70 -999 -999 -999 140	130 250 250 400 250	100 500 500 800 190	-999 -999 -999 -999 -999	80 -999 -999 -999 160	100 -999 -999 -999 200

(1) Urban land, (2) agricultural land, (4) deciduous forest, (5) coniferous forest, (6) mixed forest including wetland, (7) water, both salt and fresh, (9) non-forested wetland, (10) mixed agricultural and range land

After the passage through the stomatal opening, transfer of pollutant must take place between the gas phase of the stomatal cavity and the apoplast fluids. Parameterisations for R_m usually include a dependency on the Henry constant of the compound (e.g., Wesely, 1989). It was considered independent of land use class and season, and Baldocchi et al. (1987) estimated that R_m should be between 10 and 50 s m^-1. However, many water soluble compounds, such as HNO₃ and SO₂ are assumed to dissolve easily into the apoplast fluid due to a high or moderate (respectively) Henry coefficient and/or efficient conversion and transport after dissolution. Therefore R_m for HNO₃ and SO₂ (also for O₃) is generally assumed to be negligible (Voldner et al., 1986; Wesely, 1989, Erisman et al., 1994b; NOAA, 1997). For NH₃, R_m is usually also set to zero. This approximation may be well acceptable for unfertilised vegetation. However, it may be far from realistic if fertilisation causes a high ammonium content in the apoplast, leading to frequent and significant emissions. In that case, it may be necessary to account for R_m, unless the concentration in the stomata is estimated or calculated directly as a compensation point. In general, the mesophyll resistances R_m for all the gases are assumed to be zero, because of insufficient knowledge.

This general framework for the water vapour stomatal resistance can be used to describe stomatal uptake for each gas by correcting the R_stom using the ratio of the diffusion coefficient of the gas involved to that of water vapour (; Table 5.4) and adding the mesophyll resistance:

(5.30)

5.1.9.2 External leaf uptake (R_ext)

Many studies have shown that the external leaf surface can act as an effective sink, especially for soluble gases at wet surfaces (Hicks et al., 1989; Fowler et al., 1991; Erisman et al., 1993a, 1994a). Under some conditions the external leaf sink can be much larger than the stomatal uptake. When R_ext is negligible, R_c also becomes negligible, dominating the other resistances.

1.1.4.1.1 SO₂

SO₂ dry deposition is enhanced over wet surfaces (Garland & Branson, 1977; Fowler & Unsworth, 1979; Fowler, 1985; Vermetten et al., 1992; Erisman et al., 1993b; Erisman & Wyers, 1993). Erisman et al. (1994b) derived an R_ext parameterisation for wet surfaces (due to precipitation and an increase in relative humidity) of heather plants:

during or just after precipitation:

R_ext = 1 s m^-1 (5.31)

in all other cases:

(5.32)

where rh is the relative humidity. The previous equation is applied to air temperatures above ‑1^oC. Below this temperature it is assumed that surface uptake decreases and R_ext is set at 200 (‑1>T>‑5^oC), or 500 (T<‑5^oC) s m^-1. R_ext will be zero for some hours after precipitation has stopped. This time limit varies with season and depends on environmental conditions. Drying of vegetation is approximated to take 2h during daytime in summer and 4h in winter. During night-time, vegetation is expected to be dry after 4h in summer and after 8h in winter (Erisman et al., 1993a).

1.1.4.1.2 NH₃

While most other gaseous pollutants have a consistently downward flux, NH₃ is both emitted from and deposited to land and water surfaces. For semi-natural vegetation, fluxes are usually directed to the surface, whereas fluxes are directed away from the surface over agricultural grassland treated with manure. For arable cropland fluxes may be bi-directional depending on atmospheric conditions and the stage in the cropping cycle (Sutton, 1990). Nitrogen metabolism has been shown to produce NH₃ and as a result there is a compensation point (Farquhar et al., 1980) at which deposition might change into emission when ambient concentrations fall below the compensation concentration and vice versa.

To describe NH₃ exchange it is necessary to consider natural and managed vegetation separately. For managed vegetation the compensation point approach seems to be most promising for use in models. However, the current state of knowledge is insufficient to define canopy resistance terms or compensation points reliable over different surface types and under different environmental conditions relevant for model parameterisation (Lövblad et al., 1993). Furthermore, the compensation point is expected to be a function of many (undefined) factors and not a constant value.

Ammonia generally deposits rapidly to semi-natural (unfertilised) ecosystems and forests. Results show R_c values mostly in the range of 0-50 s m^-1 (Duyzer et al., 1987, 1992; Sutton et al. 1992; Erisman et al., 1993b). There is a clear effect of canopy wetness and relative humidity on R_c values (Erisman & Wyers, 1993). Under very dry, warm conditions (rh<60%, T>15^oC) deposition to the leaf surface may saturate, so that exchange is limited to uptake through stomata, even allowing for the possibility of emission at low ambient concentrations. In this context a larger R_c may be appropriate (~50 s m^-1). Table 5.9 shows some values for R_ext for NH₃, for different land use categories.

Table 5.9: R_ext for NH₃ (s m^-1) over different vegetation categories in Europe. Negative values for R_ext denote emission for estimating a net upward flux. From Erisman and Draaijers (1995).

Land use category	Day		Night
	Dry	Wet	Dry	Wet
Pasture during grazing: summer winter Crops and ungrazed pasture: summer winter Semi-natural ecosystems and forests	-1000 50 -Rstom -Rstom -500	-1000 20 50 100 0	1000 100 200 300 1000	1000 20 50 100 0

Winter conditions: T>-1 ^oC, otherwise R_ext=200 s m^-1 (-1>T>-5 ^oC) or R_ext=500 s m^-1 (T<-5 ^oC)

1.1.4.1.3 NO_X

A very small stomatal uptake might be observed for NO at ambient concentrations. Fluxes are, however, very low and uptake is therefore neglected (Wesely et al., 1989; Lövblad & Erisman, 1992). Uptake of NO₂ seems to be under stomatal control with no internal resistance. In Eugster and Hesterberg (1996) it is addressed that, for deposition of NO₂, R_ext is assumed to be very large (Fowler et al., 1991) and can be set to infinity. R_ext is set at 9999 s m^-1.

1.1.4.1.4 HNO₃

The difficulty of measuring nitric acid (HNO₃) concentrations at ambient levels has limited the number of flux measurements of these gases. Recent investigations, however, consistently show that for vegetative surfaces these gases deposit rapidly, with negligible surface resistances. Deposition of HNO₃ seems to be limited by the aerodynamic resistance only. For this gas the external surface resistance is found to be negligible: R_ext is set at 1 s m^-1.

5.1.9.3 In-canopy transport (R_inc)

Deposition to canopies includes vegetation and soil. Early studies assumed that deposition to soils under vegetation was relatively small (5-10% of the total flux; Fowler, 1978). Recent work shows that a substantial amount of material can be deposited to the soil below vegetation. This substantial transfer occurs because large-scale intermittent eddies are able to penetrate through the vegetation and transport material to the soil.

The in-canopy aerodynamic resistance R_inc for vegetation is modelled according to data from van Pul and Jacobs (1993):

(5.33)

where LAI is the one-sided leaf area index (set to one for a deciduous forest in winter), h the vegetation height and b an empirical constant taken as 14 m^-1. The previous equation is only applied to tall vegetation. For low vegetation R_inc is assumed to be negligible. The resistance to uptake at the soil under the canopy R_soil is modelled similarly to the soil resistance to bare soils. This will probably underestimate uptake to surfaces under forests (partly) covered with vegetation. Parameters used for the calculation of R_inc are summarised in Table 5.10.

Table 5.10: Parameters for the calculation of R_inc, for simple vegetation classes by Wilson and Henderson-Sellers (1985) to translate Olson et al. (1985).

Vegetation type	LAI	b	h
Desert Tundra Grassland Grassland + shrub cover Grassland + tree cover Deciduous forest Coniferous forest Rain forest Ice Cultivation Bog or marsh Semi-desert Bare soil Water Urban	-9999 6 6 6 6 5 5 -9999 -9999 5 -9999 -9999 -9999 -9999 -9999	-9999 -9999 -9999 -9999 -9999 14 14 -9999 -9999 14 -9999 -9999 -9999 -9999 -9999	-9999 -9999 -9999 -9999 -9999 20 20 -9999 -9999 1 -9999 -9999 -9999 -9999 -9999

5.1.9.4 Deposition to soil (R_soil) and water surfaces (R_wat)

1.1.4.1.5 SO₂

Deposition of SO₂ to soil decreases at a soil pH below 4 and increases with relative humidity (Garland, 1977). In Spranger et al. (1994) R_soil dependence on pH and relative humidity is calculated as

(5.34)

When surface temperatures fall below zero or the surface is covered with snow, R_c values increase up to 200-500 s m^-1. The deposition of SO₂ to snow-covered surfaces depends on pH, snow temperature and probably the amount of SO₂ already scavenged by the snow pack. Erisman et al. (1994b) found the following relations for snow-covered surfaces:

R_snow=500 s m^-1 at T<-1^oC

R_snow=70(2-T) s m^-1 at -1<T<1^oC (5.35)

1.1.4.1.6 NH₃

Deposition of NH₃ to soil, snow and water surfaces is similar to that of SO₂, only the pH dependence is different. Resistances to unfertilised moist soils will be very small provided that the soil pH is below 7. Fertilised soils, or soils with a high ammonium content, will show emission fluxes, depending on the ambient concentration of NH₃. Resistances to water surfaces will be negligible if the water pH is below 7. Resistances to snow will be similar to that of SO₂ at pH<7. Resistances will increase rapidly above a pH of 7.

1.1.4.1.7 NO_X

For NO at ambient concentrations, emission from soils is observed more frequently than deposition. This emission, the result of microbial activity in the soil, is dependent on soil temperature, water content and ambient concentrations of NO (Hicks et al., 1989). Emissions are to be expected at locations with low ambient NO and NO₂ concentrations (<5ppb).

The surface resistance for NO₂ to soil surfaces is found to be about 1000-2000 sm^-1 (Wesely, 1989). If the soil is covered by snow, the resistance will become even higher. Resistances of NO₂ to water surfaces are also expected to be high due to the low solubility of this gas.

1.1.4.1.8 HNO₃

Resistances to water surfaces (pH>2) and soils for HNO₃ are assumed to be negligible. A surface resistance for HNO₃ to snow surfaces at temperatures below –5^oC is expected. Resistances for HNO₂ are assumed to follow those of SO₂.

R_soil, R_snow and R_wat values for different gases are summarised in Table 5.6.

1.1.5 Aerosol dry deposition

The process of dry deposition of particles differs from that of gases in two respects:

Deposition depends on particle size since transfer to the surface involves Brownian diffusion, inertial impaction/interception and sedimentation (all of which are a strong function of particle size).

Presumably the surface resistance for particles less than 10mm diameter (Hicks & Garland, 1983) is negligible small to all surfaces.

For submicron particles, the transport through the boundary layer is more or less the same as for gases. However, transport of particles through the quasi-laminar layer can differ. For particles with a diameter <0.1mm, deposition is controlled by diffusion, whereas deposition of particles with a diameter >10mm is more controlled by sedimentation. Deposition of particles with a diameter between 0.1 and 1mm is determined by the rates of impaction and interception and depends heavily on the turbulence density.

Ruijgrok et al. (1997) proposed another parameterisation derived from measurements over a coniferous forest. In this approach, which is simplified from Slinn’s (1982) model, V_d is not only a function of u_*, but also of relative humidity (rh) and surface wetness. Inclusion of rh allows to account for particle growth under humid conditions and for reduced particle bounce when the canopy is wet. Dry deposition velocity is expressed as:

(5.36)

where R_a is the aerodynamic resistance, which is the same as for gaseous species, and V_ds is the surface deposition velocity.

For tall canopies V_ds is parameterised by Ruijgrok et al. (1997) as

(5.37)

where u_h is the wind speed at the top of the canopy, which is obtained by extrapolating the logarithmic wind profile from Z_R to the canopy height h. u_h can be expressed as:

(5.38)

E is the total efficiency for canopy capture of particles, and is parameterised separately for dry and wet surfaces (Ruijgrok et al., 1997).

For dry surfaces, for SO₄^2- particles (Brook et al., 1999):

(5.39)

For wet surfaces, for SO₄^2- particles (Brook et al., 1999):

(5.40)

rh (relative humidity) is taken at the reference height.

Erisman and Draaijers (1995) used the following general form for the calculation of V_d:

(5.41)

where V_s is the deposition velocity due to sedimentation, to represent deposition of large particles, and V_ds can be estimated from Equation (5.38). Relations for E for different components and conditions are given in Table 5.11. These were derived from model calculations and multiple regression analysis (Erisman & Draaijers, 1995).

Table 5.11: Parameterisations of E values for different components and conditions.
From Erisman and Draaijers (1995).

	Wet surface		Dry surface
Compound	rh £ 80%	rh > 80%	rh £ 80%	rh > 80%
NH4^+
SO4^2-
NO3^-
Na^+, Ca^2+, Mg^2+

For the large particles (Na⁺, Ca²⁺, Mg²⁺) and for low vegetation (for all particles), the sedimentation velocity has to be added:

(5.42)

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