Atmospheric deposition of nitrogen emitted in the Metropolitan Area of Buenos Aires to coastal waters of de la Plata River

Atmospheric Environment 43 (2009) 1339–1348

Contents lists available at ScienceDirect

Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Atmospheric deposition of nitrogen emitted in the Metropolitan Area of Buenos Aires to coastal waters of de la Plata River Andrea L. Pineda Rojas a, b, c, *, Laura E. Venegas a, d a

National Scientific and Technological Research Council (CONICET), Buenos Aires, Argentina ´sfera (CIMA/CONICET-UBA), Buenos Aires, Argentina Centro de Investigaciones del Mar y la Atmo c ´n II, Piso 2. 1428 Buenos Aires, Argentina Department of Atmospheric and Oceanic Sciences, Faculty of Sciences, University of Buenos Aires, Ciudad Universitaria, Pabello d Department of Chemical Engineering, National Technical University, Av. Mitre 750, 1870 Avellaneda, Buenos Aires, Argentina b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 10 August 2008 Received in revised form 3 November 2008 Accepted 30 November 2008

The Metropolitan Area of Buenos Aires (MABA) is the third mega-city in Latin America. Atmospheric N emitted in the area deposits to coastal waters of de la Plata River. This study describes the parameterizations included in DAUMOD-RD (v.3) model to evaluate concentrations of nitrogen compounds (nitrogen dioxide, gaseous nitric acid and nitrate aerosol) and their total (dry and wet) deposition to a water surface. This model is applied to area sources and CALPUFF model to point sources of NOx in the MABA. The models are run for 3 years of hourly meteorological data, with a spatial resolution of 1 km2. Mean annual deposition is 69, 728 kg-N year1 over 2 339 km2 of river. Dry deposition contributions of N-NO2, N-HNO3 and N-NO 3 to this value are 44%, 22% and 20%, respectively. Wet deposition of N-HNO3 and N-NO 3 represents 3% and 11% of total annual value, respectively. This very low contribution results from the rare occurrence of rainy hours with wind blowing from the city to the river. Monthly dry deposition flux estimated for coastal waters of MABA varies between 7 and 13 kg-N km2 month1. These results are comparable to values reported for other coastal zones in the world. Ó 2008 Elsevier Ltd. All rights reserved.

Keywords: Atmospheric dispersion modelling Atmospheric deposition Oxidized nitrogen compounds Urban area Coastal waters

1. Introduction Air pollutants emitted to the atmosphere may reach the aquatic environment (rivers, lakes, estuaries) through dry and wet deposition processes and affect the water quality of this system. Increasing human activities at coastal urban zones have lead to an increase of nitrogen oxides (NOx) emissions from fossil fuel combustion sources with important consequences for the environment. Secondary atmospheric nitrogen (N) compounds generated from NOx can be transported and transferred to coastal surface waters, contributing to the increasing discharge of nitrogen to these systems. Reduced nitrogen compounds, greatly produced by agricultural activities, can also contribute to total N deposition. Several studies have shown that the atmospheric pathway may constitute an important source of nitrogen for aquatic environments (Poor et al., 2001; Hertel et al., 2002; Gao, 2002; Luo et al., 2002; Imboden et al., 2003; Whitall et al., 2003; Clark and Kremer, 2005; Ayars and Gao, 2007). Excessive inputs of N compounds to the water may have

* Corresponding author. Department of Atmospheric and Oceanic Sciences, Faculty of Sciences, University of Buenos Aires, Ciudad Universitaria, Pabello´n II, Piso 2. 1428 Buenos Aires, Argentina. E-mail address: [email protected] (A.L. Pineda Rojas). 1352-2310/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2008.11.038

important ecological impacts such as acidification of surface waters and eutrophication (Nixon, 1995). Hence, the determination of nitrogen deposition is necessary to assess and control the water quality of aquatic environments greatly influenced by human activities. The Metropolitan Area of Buenos Aires (MABA) is considered one of the ten greatest urban conglomerates in the world and the third mega-city in Latin America, following Mexico City (Mexico) and Sao Paulo (Brazil). It is conformed by the city of Buenos Aires and the Greater Buenos Aires. Due to its geographical location, significant amounts of NOx coming from the great number of sources existing in the urban zone can be transferred to coastal waters of de la Plata River. A previous paper (Pineda Rojas and Venegas, 2008) presented the application of a former version of the atmospheric dispersion-deposition model DAUMOD-RD to estimate the formation and deposition of nitrogen dioxide (NO2) and gaseous nitric acid (HNO3) obtained from area source emissions of NOx located only in the city of Buenos Aires. This paper describes the DAUMOD-RD (v.3) model including a chemical reaction scheme to estimate the NO2, HNO3 and ammonium nitrate (NH4NO3) aerosol concentrations from the oxidation of NOx and algorithms to evaluate dry and wet deposition fluxes of these species over a water surface. DAUMOD-RD (v.3) is applied to the NOx emitted from the area sources in the MABA considering three years of hourly

1340

A.L. Pineda Rojas, L.E. Venegas / Atmospheric Environment 43 (2009) 1339–1348

meteorological data. Moreover, the contribution of the emissions coming from the main point sources in the area is evaluated applying the CALPUFF model (Scire et al., 2000). The horizontal distributions of dry and wet N deposition to de la Plata River are analyzed and results are compared with nitrogen deposition values obtained in other coastal sites of the world.

2. The DAUMOD-RD (v.3) model 2.1. Key assumptions The DAUMOD-RD (v.3) model is an extension of the atmospheric dispersion model DAUMOD (Mazzeo and Venegas, 1991; Venegas and Mazzeo, 2002). The DAUMOD model has been originally developed to estimate the concentration of inert pollutants emitted to the atmosphere from multiple area sources in an urban area. The starting point of the model development is to consider a semiinfinite volume of air, bounded by the planes z ¼ 0 and x ¼ 0. Steady-state conditions are assumed, with the x-axis in the direction of the mean wind and the z-axis vertical. The model is based on the bi-dimensional advection-diffusion crosswind integrated equation. The lower boundary condition is given by the area source strength. The top boundary of the model coincides with the upper boundary of the plume of contaminants, taken as 1% of ground level concentration, considering a polynomial solution to the diffusion problem. Furthermore, wind speed and eddy diffusivity are expressed as functions of height as well as stability, differing from earlier simple area source models (e.g. Gifford and Hanna, 1970). The expression to estimate ground level concentration is obtained assuming mass continuity. Finally, the expression for a finite and continuous area source is derived. The polynomial form of pollutant concentration [C(x,z)] is given by (Mazzeo and Venegas, 1991):

Cðx; zÞ ¼ Cðx; 0Þ

6 X

Aj ðz=hÞj

(1)

j¼0

where C(x,0) is the ground level concentration and h is the vertical extension of the pollutant plume (m) which is estimated by:

h=z0 ¼ aðx=z0 Þb

(2)

z0 being the surface roughness length (m). Coefficients a, b and Aj depend on the atmospheric stability (Mazzeo and Venegas, 1991) and their expressions can be found in Pineda Rojas and Venegas (2008). The ground level air pollutant concentration [C(x,0)] due to a horizontal distribution of area sources with emission strengths Qi (i ¼ 1, 2,., N, N being the number of sources upwind the receptor), is given by:

" b

Cðx; 0Þ ¼ a Q0 x þ

N X

# b

ðQi  Qi1 Þðx  xi Þ

=ðjA1 jkz0 u* Þ

(3)

application of complex numerical tools not possible, and simple urban background pollution models become an acceptable alternative. The performance of the model in estimating air pollutant concentrations has been evaluated in previous papers (Mazzeo and Venegas, 1991; Venegas and Mazzeo, 2002, 2006). Results show that the ability of the DAUMOD model to estimate pollutant concentrations in short averaging time (hourly and daily) is good and it improves when estimating values in long averaging time (monthly and annual). In order to estimate the transfer of nitrogen from the NOx emitted in the urban area to coastal waters, parameterizations of formation of secondary oxidized nitrogen compounds (NO2, gaseous HNO3 and NH4NO3 aerosol) and their dry and wet deposition to a water surface, are included in the model. Nitric acid may also be combined with sea salt particles to form sodium nitrate. However, the main sources of sodium are the sea spray droplets greatly produced at the breaking zones, so this reaction might be important in marine environments (Pryor and Sørensen, 2000; de Leeuw et al., 2001). Despite MABA not being in a marine environment, a preliminary study (Bogo et al., 2003) revealed the presence of sodium in the atmosphere. That study recommended that this possible marine characteristic in the atmosphere of Buenos Aires should be investigated in a more systematic study. Since no further results are available at present, the formation of sodium nitrate is not included in the model. The model assumes that pollutants are dispersed in the domain before being removed from the atmosphere; therefore, deposition over land before reaching the shoreline is not evaluated.

2.2. Chemical reactions The scheme of chemical transformations included in the DAUMOD-RD (v.3) model is briefly illustrated in Fig. 1. The first conservative assumption is that NOx emitted to the atmosphere is all transformed to NO2. In presence of solar light, NO2 is oxidized to HNO3 according to the reaction:

NO2 þ OH þ M/HNO3 þ M

(4)

and organic nitrates (RNO3). The HNO3 can then react with gaseous ammonia (NH3) present in the atmosphere to form NH4NO3 aerosol (solid or aqueous) through a reversible process:

NH3 ðgÞ þ HNO3 ðgÞ4NH4 NO3

(5)

2.2.1. Gas phase In the model, the mechanisms through which NO2 is lost and HNO3 is formed are considered as pseudo-first-order reactions, and the concentrations of these species following the oxidation of NOx can therefore be calculated as (Scire et al., 2000):

i¼1

where k is the von Karman constant (¼0.41) and u* is the friction velocity (m s1). DAUMOD model is simpler and requires less computation than a mesoscale model. Sometimes, available input data make

NOx

NO2

O3, HO2, OH, ROG

½NO2  ¼ ½NOx expðk1 Dt=100Þ

(6)

½HNO3 i ¼ ½NOx ½1  expðk2 Dt=100Þ

(7)

HNO3

NH3

NH4NO3

RNO3 Fig. 1. Schematic illustration of the chemical transformations included in the DAUMOD-RD (v.3) model. NOx, nitrogen oxides; NO2, nitrogen dioxide; O3, ozone; HO2, hydroperoxyl radical; ROG, reactive organic gases; HNO3, gaseous nitric acid; NH3, gaseous ammonia; NH4NO3, ammonium nitrate aerosol; RNO3, organic nitrates.

A.L. Pineda Rojas, L.E. Venegas / Atmospheric Environment 43 (2009) 1339–1348

1341

where the subscript i denotes ‘‘initial’’ concentration of HNO3 [i.e., after reaction (4) and before reaction (5)], Dt is the model time step (¼1 h) and [NOx] is the concentration (ppm) of NOx (expressed as NO2) before reacting [estimated by Eqs. (1) and (3)]. The reaction constants for the loss of NO2 (k1) and the formation of HNO3 (k2), can be estimated by the expressions (Scire et al., 2000):

where L is the scavenging coefficient (s1), h is calculated from Eq. (2) and Cm is the vertically averaged pollutant concentration before the wet removal process, computed from Eq. (10). The scavenging coefficient can be parameterized in terms of the precipitation intensity (p0) by the following expression (Levine and Schwartz, 1982; Mircea et al., 2000; Scire et al., 2000; Sportisse and du Bois, 2002):

k1 ¼ 1206½O3 1:5 S1:41 ½NOx 0:329 m

L ¼ lðp0 =p1 Þ

(8)

(15) 1

k2 ¼ 1262½O3 1:45 S1:34 ½NOx 0:122 m

(9)

where [O3] is the ozone background concentration (ppm), S is an atmospheric stability index [that varies between 2–6 according to the Pasquill–Gifford–Turner classification (Gifford, 1976)], and ½NOx m is the vertically averaged NOx concentration (ppm) within the pollutant plume, which can be obtained using Eq. (1) as:

Cm ¼

1 h

Z

h

Cðx; zÞ dz ¼

0

¼ Cðx; 0Þ

1 h

6  X

Z

h

Cðx; 0Þ 0

 Aj =ðj þ 1Þ

6 X

Aj ðz=hÞj dz

C 0 ðx; zÞ ¼ Cðx; zÞexpð  LDtÞ

j¼0

(10)

j¼0

At night, due to the relatively low concentration of the OH radical, NOx oxidation rates are lower than typical diurnal rates and constant values of k1 ¼ k2 ¼ 2.0% h1 are considered (Scire et al., 2000). 2.2.2. Aerosol phase According to reaction (5), part of HNO3 formed from the oxidation of NOx [Eq. (7)] will be converted to NH4NO3 aerosol and the rest will remain as gaseous HNO3. The fraction (g) of ammonium nitrate aerosol in equilibrium with gaseous HNO3 and NH3, is calculated as:

g ¼ ½NH4 NO3 =½NO3 T

½HNO3  ¼ ð1  gÞ½HNO3 i

(12)

½NH4 NO3  ¼ g½HNO3 i

(13)

2.3. Depositions module 2.3.1. Wet deposition Considering that the species is irreversibly soluble and that the size of drops does not vary with height, the wet deposition flux (Fw) can be estimated according to (Seinfeld and Pandis, 1998):

(14)

(16)

where C represents the species concentration before the wet removal [given by Eq. (12) for gaseous HNO3 and Eq. (13) for NH4NO3 aerosol]. 2.3.2. Dry deposition over water The dry deposition flux (Fd) is estimated by:

Fd ¼ vd C 0 ðx; 0Þ

(17) 1

where vd is the deposition velocity (cm s ) of the species and C0 (x,0) is given by Eq. (16) [note that in absence of precipitation, Eq. (16) gives C0 (x,0) ¼ C(x,0)]. The deposition velocities of gaseous species (vdg) and aerosol (vdp) are parameterized applying the resistance method. Based on an assumption of steady-state deposition flux conditions, these deposition velocities can be expressed as (Seinfeld and Pandis, 1998):

(11)

where [NH4NO3] is the ammonium nitrate equilibrium concentration and [NO3]T is the total nitrate concentration. In DAUMOD-RD (v.3) model, [NO3]T ¼ [HNO3]i is considered. Based on equilibrium considerations and assuming conservation of total nitrate and total ammonia concentrations, [NH4NO3] is obtained as a function of these parameters and the equilibrium constant. It is considered that total NH3 concentration is given by its background value. If no observations of hourly background ammonia concentration are available, monthly mean values can be used (Scire et al., 2000). The equilibrium constant is a nonlinear function of temperature and relative humidity and is estimated through a double linear interpolation algorithm on these variables following the relationships obtained by Stelson and Seinfeld (1982). Finally, gaseous HNO3 and NH4NO3 aerosol concentrations are estimated as:

Fw ¼ LhCm

where p1 is a reference value (¼1 mm h ) and l is a washout coefficient (s1) that depends on the species. Due to the low solubility of nitrogen dioxide in water, the removal of NO2 by precipitation can be assumed negligible (Lee and Schwartz, 1981; Seinfeld and Pandis, 1998). In DAUMOD-RD (v.3), l ¼ 0 for nitrogen dioxide, 6.0E-05 s1 for gaseous nitric acid and 1.0E-04 s1 for nitrate aerosol (Scire et al., 2000) are considered. The air concentration of each species remaining after the rain scavenging (C0 ) is estimated by the expression:

vdg ¼

vdp ¼



ra þ rdg þ rw



1

ra þ rdp þ ra rdp vs

(18) 1

(19)

þvs

where ra is the aerodynamic resistance (s cm1), rdg and rdp are the quasi-laminar layer resistances (s cm1) for gaseous and aerosol species, respectively, rw is the water surface resistance (s cm1) and vs is the aerosol gravitational settling velocity (cm s1). 2.3.2.1. Aerodynamic resistance (ra). This resistance represents the effect of turbulent transport of species through the atmospheric surface layer and does not depend on the type of species (gas or aerosol) but on the atmospheric conditions. It is estimated following the Monin–Obukhov theory as (Seinfeld and Pandis, 1998):

ra ¼ ½lnðzr =z0 Þ  4H ðzr =LÞ=ðku* Þ

(20)

where zr is a reference level (zr ¼ 1 m), L is the Monin–Obukhov length (m) and 4H is a stability correction term (Wieringa, 1980; Gryning et al., 1987):

4H ðzr =LÞ ¼



9:2zr =L 2 ln½ðhr þ 1Þ=ðh0 þ 1Þ

zr =L  0 zr =L < 0

(21)

with hr ¼ (1–13zr/L)1/2 and h0 ¼ (1–13z0/L)1/2. The water surface roughness length (z0) (m) is evaluated considering the wind speed (u) (m s1) at 10 m height (Hosker, 1974):

1342

A.L. Pineda Rojas, L.E. Venegas / Atmospheric Environment 43 (2009) 1339–1348

z0 ¼ 2:0E  06u2:5

(22)

2.3.2.2. Quasi-laminar layer resistance for gases (rdg). This resistance includes the effects of molecular diffusion of species through this thin layer and is calculated in terms of the Schmidt number (Sc ¼ na/n, where na is the air kinematic viscosity and n is the pollutant molecular diffusivity in air) by:

rdg ¼ d1 Scd2 =ðku* Þ

2.3.2.3. Quasi-laminar layer resistance for aerosol particles (rdp). In the case of aerosols, the quasi-laminar layer resistance accounts for Brownian diffusion, inertial impaction and interception processes. This resistance can be parameterized as a function of the Schmidt number and the Stokes number (St) as:

 i1 h ¼ Sc2=3 þ 103=St u*

(24)

The Stokes number is a measure of the likelihood of impaction of the particle and is given by (Seinfeld and Pandis, 1998):

St ¼ vs u2* =ðg na Þ

(25)

where g is the acceleration due to gravity (9.8 m s

2

).

2.3.2.4. Gravitational settling velocity (vs). The gravitational settling velocity of aerosol particles is given by the Stokes equation:

vs ¼ rp d2p gCc =ð18ma Þ

(26)

where rp is the particle density (g cm3), ma is the air dynamic viscosity (1.81E-04 g cm1 s1), dp is the particle diameter (mm) and Cc is the Cunningham correction factor for small particles, given by:

    Cc ¼ 1 þ 2c=dp a1 þ a2 exp a3 dp =c

h i1 vdpe ðjÞ ¼ ra þ rdp ðjÞ þ ra rdp ðjÞvs ðjÞ þ vs ðjÞ

(30)

and the ‘‘total’’ deposition velocity (vdp) is therefore calculated as the weighted average of the effective deposition velocities:

(23)

d1 and d2 are empirical parameters assumed to be 2 and 2/3, respectively (Scire et al., 2000).

rdp

of the interval mean diameter (Nint ¼ 9 is considered). Then, an ‘‘effective’’ deposition velocity (vdpe) for each interval j of the distribution is obtained:

vdp ¼

Nint X

(31)

vdpe ðjÞfp ðjÞ

j¼1

where fp(j) is the fraction of aerosol mass with diameters within the interval j.

3. NOx emission data The Metropolitan Area of Buenos Aires (MABA) has 11. 460. 575 inhabitants within an extension of 3 827 km2 (see Fig. 2). NOx emission data belong to a recently developed (Pineda Rojas et al., 2007) high spatial resolution (1 km2) emission inventory. Emission sources are classified into area and point sources. Area sources include road transport (cars, tracks and buses), residential, commercial and small industry activities and aircrafts at the Domestic and International airports. Moreover, the stacks of four thermal power plants and a large oil company (shown in Fig. 2) are considered as the main point sources located near the coast. Table 1 includes the annual NOx emission for each source category considered in the MABA. Area sources account for 67% (66, 823 ton-NOx year1) of NOx annual emissions in the MABA. The main contribution to this value comes from vehicles (81%). Residential, commercial and small industry activities represent a contribution of 18% and aircrafts account for around 1% of total area source NOx emission.

(27)

c being the mean free path of particles (¼6.53E-06 cm) and a1 ¼ 1.257, a2 ¼ 0.40 and a3 ¼ 0.55. The particle diffusivity in air (n) included in the Schmidt number is a function of the particle size and can be estimated as: 

n ¼ d3 Cc = 3pma dp



(28)

de la Plata River

where d3 is a constant (¼4.045E-14). 2.3.2.5. Water surface resistance (rw). Finally, the water surface resistance for gaseous species is calculated from (Slinn et al., 1978):

rw ¼ H=ða* d4 u* Þ

(29)

CBA GBA

where H is the Henry’s Law constant (i.e., ratio of gas to liquid phase pollutant concentration), a* is a factor related to the pollutant dissociation in the aqueous phase and d4 is a constant (¼4.8E-04). 2.4. Inclusion of particle size According to Eqs. (24)–(28), the quasi-laminar layer resistance for aerosols (rdp) and the gravitational settling velocity (vs) depend on the particle size (dp). To include these dependences in the model, a log-normal distribution with typical parameters for nitrate aerosol [geometric mean diameter: dp ¼ 0.48 mm, geometric standard deviation: sp ¼ 2.0 mm (Scire et al., 2000)] is considered. The distribution is divided into Nint particle size intervals, for which values of rdp and vs are evaluated as a function

10 km Fig. 2. Area of study: Metropolitan Area of Buenos Aires (MABA) composed by the city of Buenos Aires (CBA) and 24 districts from the Greater Buenos Aires (GBA); Jorge Newbery Domestic Airport (downward aeroplane symbol) and Ministro Pistarini (Ezeiza) International Airport (upward aeroplane symbol); thermal power plants (:); oil company (6) and surface of de la Plata River in front of MABA considered for calculations.

A.L. Pineda Rojas, L.E. Venegas / Atmospheric Environment 43 (2009) 1339–1348

1343

Table 1 Annual NOx emission (expressed as NO2) coming from area sources of the MABA and the main point sources located on the coast.

kg-N km-2 year-1 130 120

Annual emission (ton-NOx year1)

110

Area sources Road traffic Residential activities Commercial activities Small industry activities Aircrafts

53883 7521 702 3839 879

Point sources

33278

100 90 80 70 60

Total

50 40

100101

30 20

4. Results

10 10 km

4.1. Model set-up The DAUMOD-RD (v.3) model was applied to estimate the deposition of atmospheric N coming from area source NOx emissions, to coastal waters of de la Plata River. On the other hand, the CALPUFF model (Scire et al., 2000) was applied to evaluate the contribution of point sources. CALPUFF is a Gaussian, Lagrangian, non-steady-state puff dispersion model that simulates the effects of meteorological conditions on pollutant transport, dispersion, chemical transformations and deposition. In the case of nitrogen compounds, the parameterizations of chemical reactions and deposition processes included in both models are similar. Given that the considered point sources are located near the coast and that only pollutant transport over the river is considered, the CALPUFF model was run in screening mode (Scire et al., 2000). Both models were applied over a domain covering 2 339 km2 of river surface, considering a spatial resolution of 1 km2 and a temporal step of 1 h. Three years (1999–2001) of hourly surface meteorological information measured at a coastal site and sounding data from the station located at the international airport were used in calculations.

0

Fig. 3. Annual mean (1999–2001) (dry þ wet) deposition flux of total nitrogen (¼NNO2 þ N-HNO3 þ N-NO 3 ).

months to 60 ppb in summer are considered. Moreover, to estimate the ammonium nitrate aerosol formation rate (g), both models require ammonia background concentration values in the area. Due to the lack of measurements of NH3 concentration in the MABA, values observed in different cities of the world are considered (see Table 2). Monthly and annual mean concentrations of ammonia in urban areas vary between w1–9 ppb. In this study, a constant background concentration of 5 ppb for the MABA is considered. 4.3. Deposition to coastal waters Annual mean total deposition nitrogen (¼N-NO2 þ N2 HNO3 þ N-NO 3 ) to 2 339 km of coastal waters is 69, 728 kg1 N year . The greatest contribution of atmospheric N to de la Plata River is given by dry deposition (86%). The relatively low wet contribution (14%) results from the very low annual frequency of

4.2. Background concentrations

% Both atmospheric dispersion models require ozone background concentration values to evaluate diurnal reaction constants given by Eqs. (8) and (9). Several campaigns (Bogo et al., 1999; Mazzeo et al., 2005) reveal that ozone concentrations in the area are usually low. Monthly mean values varying between 30 ppb in winter

Place

NH3 (ppb)

Averaging time

Year

Reference

Bronx, USA Manhattan, USA Philadelphia, USA Chicago, USA Morehead City, USA Deurne, The Netherlands Munster, Germany Rome, Italy Athens, Greece

3.1 5.0 1.2–5.7 2.4 0.8 4.5

Annual Annual Annual Annual Annual Annual

1999–2000 1999–2000 1992–1993 1990–1991 2000 1996

Bari et al. (2003) Bari et al. (2003) Suh et al. (1995) Lee et al. (1993) Walker et al. (2004) Hoek et al. (1996)

5.2 6.2 9.1

3 months Monthly Annual

3.4

Annual

2004 Vogt et al. (2005) 1986 Allegrini et al. (1987) 1989–1990 Kirkitsos and Sikiotis (1991) 2002–2003 Anatolaki and Tsitouridou (2007) 1995–1996 Danalatos and Glavas (1999) 1996–1997 Lee et al. (1999)

2.9–6.3

Monthly

Seoul, Korea

6.3

Annual

NNE

20

NW

NE

15 WNW

Table 2 Ammonia concentration values in urban areas.

Thessaloniki, Greece Patras, Greece

NNW

N

25

10

ENE

5 W

E

0

WSW

ESE

SW

SE SSW

SSE S

Fig. 4. Annual mean (1999–2001) wind rose (black line, all hours; grey line, hours with precipitation).

1344

A.L. Pineda Rojas, L.E. Venegas / Atmospheric Environment 43 (2009) 1339–1348

HNO3 þ N-NO 3 ) wet deposition flux (Fig. 6) reaches a maximum of 15 kg-N km2 year1 in front of the city. Greater values obtained to the North of the city result from a greater relative contribution of winds from the South during rainy hours (Fig. 4). Comparing the spatial distributions of dry and wet deposition of nitrogen, the last one shows a relatively lower reduction with distance to the coast. This can be seen in Fig. 7. The reason for this behaviour is that rain removes species along the entire air column below the cloud while dry deposition involves the transfer of pollutants that are only near the surface. As a result, the relative contribution of wet deposition to total N deposition increases with distance to the coast. As shown in Fig. 8, this contribution varies from 9% in coastal zones to 23% at 30 km from the shoreline approximately.

4.4. Spatial variation of annual deposition flux 4.4.1. Total N flux The annual mean (1999–2001) horizontal distribution of total N deposition flux is shown in Fig. 3. The greatest nitrogen deposition flux is 137 kg-N km2 year1 and occurs in the first square kilometre in front of Buenos Aires city. Deposition of nitrogen reduces significantly with distance to the coast, reaching half of the maximum coastal values at 5 km from the coast and values near 30 kg-N km2 year1 at a distance of 20 km. According to the annual wind rose shown in Fig. 4, pollutants are transported from the MABA to the river during 41% of the time. Among these situations, winds blow from the SE–SW sector during 64% of the time. For this reason, greater total N deposition values can be found in front of the city and towards the North of it. 4.4.2. Dry and wet deposition fluxes Similar to total (dry þ wet) deposition of nitrogen, N dry deposition (Fig. 5) shows a strong coastal gradient, with values up to 124 kg-N km2 year1 near the shoreline. The N (¼N-

14 12 10 8 6 4

Dry Wet

120 100 80 60 40 20 0

0

2

4

6

8

10

12

14

16

18

20

22

24

26

0

2

4

6

8

10

12

14

16

18

20

22

24

26

0

2

4

6

8

10

12

14

16

18

20

22

24

26

b 140 120 100 80 60 40 20 0

c 140 N deposition (kg-N km-2 year-1)

kg-N km-2 year-1

N deposition (kg-N km-2 year-1)

rainy hours during offshore wind conditions (5%). Relative contributions are: N-NO2 (44%), gaseous N-HNO3 (22% dry, 3% wet) and N-NO 3 aerosol (20% dry, 11% wet).

a 140

N deposition (kg-N km-2 year-1)

Fig. 5. Annual mean (1999–2001) dry deposition flux of total N (¼N-NO2 þ NHNO3 þ N-NO 3 ).

120 100 80 60 40 20 0

2 10 km

distance to the coast (km)

0

Fig. 6. Annual mean (1999–2001) wet deposition flux of total N (¼N-HNO3 þ

N-NO 3 ).

Fig. 7. Variation of annual mean (1999–2001) dry and wet deposition fluxes with distance to the coast, in the North (a), Northeast (b) and East (c) directions, from the position of the maximum annual total N deposition flux in Fig. 3.

A.L. Pineda Rojas, L.E. Venegas / Atmospheric Environment 43 (2009) 1339–1348

1345

kg-N km-2 month-1

a

%

10 22

9 8

20

7 18

6 5

16

4 3

14

2 12

1 10 km

10 10 km

8

0

kg-N km-2 month-1

b

1.2

Fig. 8. Relative contribution (%) of annual mean (1999–2001) wet deposition to total N (¼N-NO2 þ N-HNO3 þ N-NO 3 ) deposition.

1.1

4.5. Spatial variation of monthly deposition flux

0.9

1 0.8

Monthly horizontal distributions of N dry deposition present a gradual and marked reduction of deposition with distance to the coast, with a maximum coastal value varying during the year between 7 and 13 kg-N km2 month1. The greatest coastal values of monthly wet deposition vary between 1 and 3 kg-N km2 month1. The very low frequency of situations with offshore winds and precipitation leads to a significant monthly variation of the spatial distribution of the wet deposition flux. Two examples are discussed. Mean horizontal distributions of N dry and wet deposition fluxes estimated for August are shown in Fig. 9. Greater dry deposition values (Fig. 9a) are obtained in coastal waters along the coast. In turn, wet deposition (Fig. 9b) is considerably greater N and NNW of the city. These results are clearly associated with the wind roses for August (1999–2001) shown in Fig. 10. In December, the spatial distribution of N dry deposition (Fig. 11a) is similar to that obtained for August, but regions of the river having greater wet deposition values are quite different in both months. The N wet deposition flux in December (Fig. 11b) presents much greater values to the NE/E of the city as a result of a greater frequency of winds from the SW/W sector during rainy hours (see Fig. 12).

0.7 0.6 0.5 0.4 0.3 0.2 0.1 10 km

Fig. 9. Mean (1999–2001) dry (a) and wet (b) deposition of total N (¼N-NO2 þ NHNO3 þ N-NO 3 ) in August.

NNW

% N 25

NNE

20

NW

NE

15

5. Errors and sensitivities Some of the factors that may introduce uncertain sources of error in estimated deposition are: the uncertainty of estimated NOx emission rates, the observed meteorological variables, the background ammonia concentration assumed for the area and the implicit approximations in the methodologies employed to estimate deposition velocities and scavenging coefficients of the species. Moreover, in this application, surface meteorological data measured at a coastal site have been considered as representative of the area of study, which can lead to considerable error in the case of precipitation. On the other hand, since the DAUMOD-RD (v.3) model assumes that the species is dispersed in the domain before being removed from the atmosphere, modelled N deposition values would be somewhat overestimated far from sources. However, this overestimation would be compensated by the fact that the model evaluates the deposition at each hour independently of the previous one, leading to a possible underestimation. The sensitivity of modelled N deposition values to background NH3 concentration was studied running the DAUMOD-RD (v.3)

0

WNW

10

ENE

5 W

E

0

WSW

ESE

SW

SE SSW

SSE S

Fig. 10. Mean (1999–2001) wind rose in August (black line, all hours; grey line, hours with precipitation).

1346

A.L. Pineda Rojas, L.E. Venegas / Atmospheric Environment 43 (2009) 1339–1348

kg-N km-2 month-1

a

Table 3 Deposition velocity of nitrogen compounds over aquatic surfaces.

11 10

Species

vd (cm s1)

Reference

NO2

0.2 0.006 0.02 0–0.25 <0.06

Kelly (1987) Duyzer et al. (1993) Hauglustaine et al. (1994) Holloway et al. (2002) This study

Gaseous HNO3

1 1 0.64 0.3–1.5 1.85 0.4–1.5 0.07–1.5

Kelly (1987) Hauglustaine et al. (1994) Tarnay et al. (2001) Holloway et al. (2002) Luo et al. (2002) Sickles II and Shadwick (2002) This study

NO 3 aerosol

0.17–0.51 0.05–2 0.5–2.5 0.24–2.0

Gao (2002) Clark and Kremer (2005) Zhang and Chen (2007) This study

9 8 7 6 5 4 3 2 1 10 km

0

kg-N km-2 month-1

b

1.6

model, assuming [NH3]T ¼ 2.5 ppb and 10 ppb for the same input data considered in Section 4.1. The results showed that, even when there would be a considerable redistribution of the relative contributions of nitrogen species to monthly total N deposition, the latter would vary between (1–3)%, with lower and greater values for the 2.5 ppb and 10 ppb cases, respectively.

1.4 1.2 1 0.8

6. Discussion

0.6 0.4 0.2 10 km

0

Fig. 11. Mean (1999–2001) dry (a) and wet (b) deposition of total N (¼N-NO2 þ NHNO3 þ N-NO 3 ) in December.

NNW

% N 25

Table 4 Oxidized N deposition to coastal waters at other sites of the world.

NNE

20

NW

Place

NE Tampa Bay, USA

10

ENE

5 W

N deposition (kg- Year N km2 month1) Dry

15 WNW

Given that it is not possible to evaluate the magnitude of all possible error sources, the obtained deposition velocities and N deposition values are compared with values reported in the literature. Table 3 shows that hourly deposition velocity values obtained in this work are similar to those reported by other authors. Table 4 presents dry and wet deposition values of oxidized N species obtained for coastal waters at other sites of the world. These values not only consider different compounds but have also been obtained for water bodies with different environmental and emission (e.g., emission rates, distance to emission sources)

Wet

2–9s2 s3

North Sea, in front of the 10–19 coasts of different countries Barnegat Bay, USA 1–7s1 Long Island Sound, USA 14–33s2

E

0

Greenwood Lake, USA

WSW

ESE

SW

SE

7–132

SSE S

Fig. 12. Mean (1999–2001) wind rose in December (black line, all hours; grey line, hours with precipitation).

Poor et al. (2001)

1–104s1 1999 0–85 1991– 1994 1997– 1999 2001

Gao (2002) Luo et al. (2002)

10–22s2

19–35s1

Kattegat Strait, Denmark

15–20

55–75 s2

13–66

s1

SSW

s3

1–92s1 1996– 1999 29–55s3 1999

Neuse River, USA

Waquoit Bay, USA

Reference

Mullica River-Great Bay Estuary, 5–9 USA de la Plata River 7–13

37 25–70s1 1–3

1996– 2000 1989– 1999 1992 2004– 2005 1999– 2001

Species: s1NO 3 aerosol (or dissolved in rain water); aerosol; s3NO2, gaseous HNO3 and NO 3 aerosol.

s2

Hertel et al. (2002)

Luo et al. (2002) Imboden et al. (2003) Whitall et al. (2003) Carstensen et al. (2005) Clark and Kremer (2005) Ayars and Gao (2007) This study

gaseous HNO3 and NO 3

A.L. Pineda Rojas, L.E. Venegas / Atmospheric Environment 43 (2009) 1339–1348

conditions. However, maximum coastal values of monthly N dry deposition estimated in this study (7–13 kg-N km2 month1) are consistent with other reported data. On the other hand, Table 4 shows that monthly wet deposition can vary significantly from near zero to 100 kg-N km2 month1. The estimated monthly wet deposition of oxidized nitrogen to de la Plata River results too small due to the very few cases of favourable situations for wet deposition registered in the area. It is worth noting that reduced nitrogen compounds (NHx) could also contribute significantly to total N deposition. While NHx may be considered to be N returning to the water body (Hertel et al., 2002; Clark and Kremer, 2005), local sources could still make a substantial contribution. Unfortunately, at present, no ammonia emission inventory for the MABA is available so as to include the anthropogenic contribution of NHx on calculations. 7. Conclusions The DAUMOD-RD (v.3) model was developed in order to evaluate air concentrations of nitrogen compounds generated from NOx emissions and total deposition of these species over a water surface, when emissions come from a great number of area sources in a coastal city. This model was applied to area sources in the Metropolitan Area of Buenos Aires and the CALPUFF model to main point sources located near the coast, to estimate the N deposition to coastal waters of de la Plata River. Both models were run considering a spatial resolution of 1 km2, 3 years of hourly meteorological information and high resolution NOx emission data. Annual mean deposition of total N (¼N-NO2 þ N-HNO3 þ N2 1 NO 3 ) to waters (2 339 km ) of the river is 69, 728 kg-N year . Dry  deposition contributions of N-NO2, N-HNO3 and N-NO3 to this value are 44%, 22% and 20%, respectively. Wet deposition of N-HNO3 and N-NO 3 represents 3% and 11%, respectively. Maximum monthly dry deposition flux obtained for de la Plata River varies between 7– 13 kg-N km2 month1. These results are comparable to those obtained by other authors for coastal waters at different parts of the world. However, estimated maximum wet deposition flux (1–3 kgN km2 month1) is near one order of magnitude lower than values reported for other coastal zones. The low contribution of wet deposition is the result of the very low frequency (5%) of hours with precipitation during offshore wind conditions. Acknowledgements This work has been partially supported by Projects UBACyT X060 and CONICET-PIP 6169. The authors wish to thank the National Meteorological Service of Argentina for providing meteorological data. The authors are grateful to unknown reviewers for their helpful comments and suggestions. References Allegrini, I., DeSantis, F., DiPalo, V., Febo, A., Perrino, C., Possanzini, M., 1987. Annular denuder method for sampling reactive gases and aerosols in the atmosphere. Science of the Total Environment 67, 1–16. Anatolaki, Ch, Tsitouridou, R., 2007. Atmospheric deposition of nitrogen, sulfur and chloride in Thessaloniki, Greece. Atmospheric Research 85 (3–4), 413–428. Ayars, J., Gao, Y., 2007. Atmospheric nitrogen deposition to the Mullica River-Great Bay Estuary. Marine Environmental Research 64, 590–600. Bari, A., Ferraro, V., Wilson, L.R., Luttinger, D., Husain, L., 2003. Measurements of gaseous HONO, HNO3, SO2, HCl, NH3, particulate sulfate and PM2.5 in New York, NY. Atmospheric Environment 37, 2825–2835. Bogo, H., Negri, R.M., San Roma´n, E., 1999. Continuous measurement of gaseous pollutants in Buenos Aires City. Atmospheric Environment 33, 2587–2598. Bogo, H., Otero, M., Castro, P., Azafra´n, M.J., Kreiner, A., Calvo, E., Negri, R.M., 2003. Study of atmospheric particulate matter in Buenos Aires city. Atmospheric Environment 37, 1135–1147.

1347

Carstensen, J., Frohn, L.M., Hasager, C.B., Gustafsson, B.G., 2005. Summer algal blooms in a coastal ecosystem: the role of atmospheric deposition versus entrainment fluxes. Estuarine. Coastal and Shelf Science 62, 595–608. Clark, H., Kremer, J.N., 2005. Estimating direct and episodic atmospheric nitrogen deposition to a coastal waterbody. Marine Environmental Research 59, 349–366. Danalatos, D., Glavas, S., 1999. Gas phase nitric acid, ammonia and related particulate matter at a Mediterranean coastal site, Patras, Greece. Atmospheric Environment 33, 3417–3425. Duyzer, J.H., Verhagen, H., Westrate, J., 1993. Dry deposition of ozone, nitrogen dioxide and ammonia to seawater. IMW-R 93/131, TNO Institute of Environmental Sciences, Delft, The Netherlands. Gao, Y., 2002. Atmospheric nitrogen deposition to Barnegat Bay. Atmospheric Environment 36, 5783–5794. Gifford, F.A., 1976. Turbulent diffusion typing schemes: a review. Nuclear Safety 17 (1), 25–43. Gifford, F.A., Hanna, S.R., 1970. Urban Air Pollution Modeling. Meeting Int. Union of Air Pollut. Prev. Assoc. Washington. Gryning, S.E., Footslog, A.A.M., Irwin, J.S., Silvertsen, B., 1987. Applied dispersion modelling based on meteorological scaling parameters. Atmospheric Environment 21, 79–89. Hauglustaine, D.A., Granier, C., Brasseur, G.P., Megie, G., 1994. The importance of atmospheric chemistry in the calculation of radiative forcing on the climate system. Journal of Geophysical Research 99, 1173–1186. Hertel, O., Ambelas Skjøth, C., Frohn, L.M., Vignati, E., Frydendall, J., de Leeuw, G., Schwarz, U., Reis, S., 2002. Assessment of the atmospheric nitrogen and sulphur inputs into the North Sea using a Lagrangian model. Physics and Chemistry of the Earth 27, 1507–1515. Hoek, G., Mennen, M.G., Allen, G.A., Hofschreuder, P., Meulen, T.V., 1996. Concentrations of acidic air pollutants in the Netherlands. Atmospheric Environment 30, 3141–3150. Holloway, T., Levy, H., Carmichael, G., 2002. Transfer of reactive nitrogen in Asia: development and evaluation of a source-receptor model. Atmospheric Environment 36, 4251–4264. Hosker, R.P., 1974. A comparison of estimation procedures for overwater plume dispersion. Proceedings of the Symposium on Atmospheric Diffusion and Air Pollution. American Meteorological Society, Boston, MA. 281–288. Imboden, A., Christoforou, S., Salmon, L.G., 2003. Determination of atmospheric nitrogen input to Lake Greenwood, South Carolina: Part 2dgaseous measurements and modeling. Journal of Air and Waste Management Association 53, 1499–1508. Kelly, N.A., 1987. The photochemical formation and fate of nitric acid in the Metropolitan Detroit Area: ambient, captive-air irradiation and modeling results. Atmospheric Environment 21, 2163–2177. Kirkitsos, F.D., Sikiotis, D., 1991. Nitric acid, ammonia and particulate nitrates, sulfates and ammonium in the atmosphere of Athens. Ecistics 348–349, 156–163. Lee, Y., Schwartz, S.E., 1981. Evaluation of the rate of uptake of nitrogen dioxide by atmospheric and surface liquid water. Journal of Geophysical Research 86 (C12), 11971–11983. Lee, H.S., Wadden, R.A., Scheff, P.A., 1993. Measurement and evaluation of acid air pollutants in Chicago using an annular denuder system. Atmospheric Environment 27A, 543–553. Lee, H.S., Kang, C.-M., Kang, B.-W., Kim, H.-K., 1999. Seasonal variations of acidic air pollutants in Seoul, South Korea. Atmospheric Environment 33, 3143–3152. de Leeuw, G., Cohen, L., Frohn, L.M., Geernaert, G., Hertel, O., Jensen, B., Jickells, T., Klein, L., Kunz, G., Lund, S., Moerman, M., Mu¨ller, F., Pedersen, B., Salzen, K., Schlu¨enzen, H., Schulz, M., Skjøth, C.A., Sørensen, L.L., Spokes, L., Tamm, S., Vignati, E., 2001. Atmospheric input of nitrogen in the North Sea: ANICE Project Overview. Continental Shelf Research 21, 2073–2094. Levine, S.Z., Schwartz, S.E., 1982. In-cloud and below-cloud scavenging of nitric acid vapor. Atmospheric Environment 16 (7), 1725–1734. Luo, Y., Yang, X., Carley, R.J., Perkins, C., 2002. Atmospheric deposition of nitrogen along the Connecticut coastline of Long Island Sound: a decade of measurements. Atmospheric Environment 36, 4517–4528. Mazzeo, N.A., Venegas, L.E., 1991. Air pollution model for an urban area. Atmospheric Research 26, 165–179. Mazzeo, N.A., Venegas, L.E., Choren, H., 2005. Analysis of NO, NO2, O3 and NOx concentrations measured at a green area of Buenos Aires City during wintertime. Atmospheric Environment 39, 3055–3068. Mircea, M., Stefan, S., Fuzzi, S., 2000. Precipitation scavenging coefficient: influence of measured aerosol and raindrop size distributions. Atmospheric Environment 34, 5169–5174. Nixon, S.W., 1995. Coastal marine eutrophication: A definition, social causes, and future concerns. Ophelia 41, 199–219. Pineda Rojas, A.L., Venegas, L.E., 2008. Dry and wet deposition of nitrogen emitted in Buenos Aires city to waters of de la Plata River. Water, Air, & Soil Pollution 193, 175–188. Pineda Rojas, A.L., Venegas, L.E., Mazzeo, N.A., 2007. Emission inventory of carbon monoxide and nitrogen oxides for area sources at Buenos Aires Metropolitan Area (Argentina). Proceedings of the 6th International Conference on Urban Air Quality, Emission measurements and modelling sessions, Cyprus, CD-ROM, 35–38. Poor, N., Pribble, R., Greening, H., 2001. Direct wet and dry deposition of ammonia, nitric acid, ammonium and nitrate to the Tampa Bay Estuary, FL, USA. Atmospheric Environment 35, 3947–3955.

1348

A.L. Pineda Rojas, L.E. Venegas / Atmospheric Environment 43 (2009) 1339–1348

Pryor, S.C., Sørensen, L.L., 2000. Nitric Acid-sea salt reactions: implications for nitrogen deposition to water surfaces. Journal of Applied Meteorology 39, 725–731. Scire, J.S., Strimaitis, D.G., Yamartino, R.J., 2000. A User’s Guide for the CALPUFF Dispersion Model, Earth Tech, Inc, 521 pp. Seinfeld, J.H., Pandis, S.N., 1998. Atmospheric Chemistry and Physics of Air Pollution. J. Wiley & Sons, 1326 pp. Sickles II, J.E., Shadwick, D.S., 2002. Precision of atmospheric dry deposition data from the Clean Air Status and Trends Network. Atmospheric Environment 36, 5671–5686. Slinn, W.G.N., Hasse, L., Hicks, B.B., Hogan, A.W., Lal, D., Liss, P.S., Munnich, K.O., Sehmel, G.A., Vittori, O., 1978. Some aspects of the transfer of atmospheric trace constituents past the air-sea interface. Atmospheric Environment 12, 2055–2087. Sportisse, B., du Bois, L., 2002. Numerical and theoretical investigation of a simplified model for the parameterization of below-cloud scavenging by falling raindrops. Atmospheric Environment 36, 5719–5727. Stelson, A.E., Seinfeld, J.H., 1982. Relative humidity and temperature dependence of the ammonium nitrate dissociation constant. Atmospheric Environment 16 (5), 983–992. Suh, H.H., Allen, G.A., Koutrakis, P., Burton, R.M., 1995. Spatial variation in acidic sulfate and ammonia concentrations within metropolitan Philadelphia. Journal of Air and Waste Management Association 45, 442–452.

Tarnay, L., Gertler, A.W., Blank, R.R., Taylor Jr., G.E., 2001. Preliminary measurements of summer nitric acid and ammonia concentrations in the Lake Tahoe Basin airshed: implications for dry deposition of the atmospheric nitrogen. Environmental Pollution 113, 145–153. Venegas, L.E., Mazzeo, N.A., 2002. An evaluation of DAUMOD model in estimating urban background concentrations. International Journal of Water, Air and Soil Pollution: Focus 2 (5–6), 433–443. Venegas, L.E., Mazzeo, N.A., 2006. Modelling of urban background pollution in Buenos Aires city (Argentina). Environmental Modelling & Software 21, 577–586. Vogt, E., Held, A., Klemm, O., 2005. Sources and concentrations of gaseous and particulate reduced nitrogen in the city of Mu¨nster (Germany). Atmospheric Environment 39, 7393–7402. Walker, J.T., Whitall, D.R., Robarge, W., Paerl, H.W., 2004. Ambient ammonia and ammonium aerosol across a region of variable ammonia emission density. Atmospheric Environment 38, 1235–1246. Whitall, D., Hendrickson, B., Paerl, H., 2003. Importance of atmospherically deposited nitrogen to the annual nitrogen budget of the Neuse River estuary, North Carolina. Environment International 29, 393–399. Wieringa, J., 1980. A revaluation of the Kansas mast influence on measurements of stress and cup anemometer overspeeding. Boundary Layer Meteorology 18, 411–430. Zhang, Y., Chen, L.M., 2007. Atmospheric dry deposition into the East China Sea. Proceedings of the SOLAS Open Science Conference, Xiamen, China. Abstract No 1095.